Exported on 2025-09-26 00:20:01

Notebook Metadata¶

  • Notebook Name: 02_model.ipynb
  • Title: MoneyLion DS Assessment
  • Author: Khoon Ching Wong
  • Created: 2024-09-24
  • Last Modified: 2025-09-25
  • Description:
    Continuation of 01_eda.ipynb.
    This notebook focuses on model training, where Optuna is applied for hyperparameter optimization, model selection and performance reporting, with the goal of minimizing institutional financial losses.
  • Inputs:
    • temp/Loan-level/clean_df.parquet
  • Outputs:
    • temp/Loan-level/best_trials_ClassifierX.json
    • temp/Loan-level/all_metrics_ClassifierX.csv
    • temp/Loan-level/cm_final_ClassifierX.csv
    • temp/Loan-level/df_metrics_ClassifierX.csv
    • temp/Loan-level/exec_time_ClassifierX.csv
    • temp/Loan-level/lloss_auc_test_ClassifierX.csv
    • temp/Loan-level/lloss_auc_train_ClassifierX.csv
    • temp/Loan-level/cm_ClassifierX_all.pkl
    • temp/Loan-level/cm_labels_ClassifierX_all.pkl
  • Repository/Project Link: https://github.com/wongkhoon/DS-Assessment/tree/main/MoneyLion/notebooks

Import libraries¶

In [1]:
import gc
import os
import platform
from IPython.display import display, Markdown, HTML
import json
import pickle

import pandas as pd
import numpy as np
from collections import Counter

import matplotlib.pyplot as plt
import plotly.express as px
import plotly.graph_objects as go
import seaborn as sns

from tqdm import tqdm
import time
import logging
import optuna
import optunahub
from optuna.visualization import plot_pareto_front

import lightgbm as lgb

import sklearn.datasets
from sklearn.model_selection import GroupShuffleSplit, StratifiedGroupKFold
from sklearn.utils.class_weight import compute_sample_weight

from catboost import Pool
from catboost import CatBoostClassifier

import xgboost as xgb
from xgboost import XGBClassifier

from sklearn.ensemble import HistGradientBoostingClassifier

from sklearn.metrics import log_loss, average_precision_score, roc_curve, roc_auc_score, precision_recall_curve
from sklearn.metrics import accuracy_score, confusion_matrix, classification_report

import session_info

Display settings configuration¶

  • Configure display settings for enhanced output in Jupyter notebook
In [2]:
# Display full output in output cell, not only last result
from IPython.core.interactiveshell import InteractiveShell
InteractiveShell.ast_node_interactivity = "all"

# Maximum rows and columns of Pandas DataFrame
# pd.options.display.max_rows,
pd.options.display.max_columns

# Print all the contents of a Pandas DataFrame
#pd.set_option('display.max_rows', None) # Print unlimited number of rows by setting to None, default is 10
pd.set_option("display.max_columns", None) # Do not truncate cols to display all of them by setting to None
pd.set_option("display.width", None) # Auto-detect the width of df to display all columns in single line by setting to None
pd.set_option("display.max_colwidth", None) # Auto detect the max size of col and print contents of that col without truncation

Create Temporary Directory for Intermediate Files¶

  • Create a temp directory to store intermediate files (e.g. model metrics, Optuna trials).
  • These files will be reused for generating plots and tables, as well as for reloading intermediate data i.e. clean_df.parquet.
In [3]:
# Create the directory path if it doesn't exist and raise no errors if already exist
os.makedirs("temp/Loan-level", exist_ok = True)
temp_dir = "temp/Loan-level"

Function¶

In [4]:
def save_results(clf_name, best_trials,
                 exec_time, lloss_auc_train, lloss_auc_test,
                 df_metrics, cm_final, cm_all, cm_labels):
    """
    Save results for a given classifier.
    
    Parameters:
        clf_name (str): Name identifier for the algorithm.
        exec_time (list of dict): Execution time data.
        lloss_auc_train (list of dict): Training log loss/AUC metrics.
        lloss_auc_test (list of dict): Test log loss/AUC metrics.
        df_metrics (pd.DataFrame): Additional metrics in Pandas DataFrame form.
        cm_final (array-like): The final confusion matrix.
        cm_all (list): List of confusion matrices for trials.
        cm_labels (list): Corresponding labels for the confusion matrices.
    """
    
    # Save tabular data as CSV
    pd.DataFrame(exec_time).to_csv(f'{temp_dir}/exec_time_{clf_name}.csv', index = False)
    pd.DataFrame(lloss_auc_train).to_csv(f'{temp_dir}/lloss_auc_train_{clf_name}.csv', index = False)
    pd.DataFrame(lloss_auc_test).to_csv(f'{temp_dir}/lloss_auc_test_{clf_name}.csv', index = False)
    pd.DataFrame(all_metrics).to_csv(f'{temp_dir}/all_metrics_{clf_name}.csv', index = False)
    df_metrics.to_csv(f'{temp_dir}/df_metrics_{clf_name}.csv', index = False)
    pd.DataFrame(cm_final).to_csv(f'{temp_dir}/cm_final_{clf_name}.csv', index = False)

    # Saves all the parameters of the best trial(s) into a JSON file
    with open(f'{temp_dir}/best_trials_{clf_name}.json', "w") as f:
        json.dump(best_trials, f, indent = 4)

    # Pickle complex objects (like lists, matrices, etc)
    with open(f'{temp_dir}/cm_{clf_name}_all.pkl', "wb") as f:
        pickle.dump(cm_all, f)
        
    with open(f'{temp_dir}/cm_labels_{clf_name}_all.pkl', "wb") as f:
        pickle.dump(cm_labels, f)

Load Parquet Data File¶

  • Load the processed Parquet data file generated in 01_eda.ipynb
  • Drop columns not of interest
In [5]:
# Load aggregated loan level parquet data file
clean_df = pd.read_parquet(f'{temp_dir}/clean_df.parquet', engine = "pyarrow")

#clean_df.columns.tolist()

# Drop columns not of interest
cols_to_drop = ["underwritingid", "loanId", "clarityFraudId",
                "applicationDate", "originatedDate",
                "loanStatus", "fpymtDate", "fpymtAmt", "fpymtStatus", "yr_mth", "mth",
                "principal_tot", "fees_tot", "paymentAmount_tot"]

clean_df.drop(columns = cols_to_drop,  inplace = True)

clean_df.info(verbose = "all")
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 32312 entries, 0 to 32311
Data columns (total 270 columns):
 #    Column                                           Dtype   
---   ------                                           -----   
 0    cfinq.thirtydaysago                              Int32   
 1    cfinq.twentyfourhoursago                         Int32   
 2    cfinq.oneminuteago                               Int32   
 3    cfinq.onehourago                                 Int32   
 4    cfinq.ninetydaysago                              Int32   
 5    cfinq.sevendaysago                               Int32   
 6    cfinq.tenminutesago                              Int32   
 7    cfinq.fifteendaysago                             Int32   
 8    cfinq.threesixtyfivedaysago                      Int32   
 9    cfind.inquiryonfilecurrentaddressconflict        boolean 
 10   cfind.totalnumberoffraudindicators               Int32   
 11   cfind.telephonenumberinconsistentwithaddress     boolean 
 12   cfind.inquiryageyoungerthanssnissuedate          boolean 
 13   cfind.onfileaddresscautious                      boolean 
 14   cfind.inquiryaddressnonresidential               boolean 
 15   cfind.onfileaddresshighrisk                      boolean 
 16   cfind.ssnreportedmorefrequentlyforanother        boolean 
 17   cfind.currentaddressreportedbytradeopenlt90days  boolean 
 18   cfind.inputssninvalid                            boolean 
 19   cfind.inputssnissuedatecannotbeverified          boolean 
 20   cfind.inquiryaddresscautious                     boolean 
 21   cfind.morethan3inquiriesinthelast30days          boolean 
 22   cfind.onfileaddressnonresidential                boolean 
 23   cfind.creditestablishedpriortossnissuedate       boolean 
 24   cfind.driverlicenseformatinvalid                 boolean 
 25   cfind.inputssnrecordedasdeceased                 boolean 
 26   cfind.inquiryaddresshighrisk                     boolean 
 27   cfind.inquirycurrentaddressnotonfile             boolean 
 28   cfind.bestonfilessnissuedatecannotbeverified     boolean 
 29   cfind.highprobabilityssnbelongstoanother         boolean 
 30   cfind.maxnumberofssnswithanybankaccount          Int32   
 31   cfind.bestonfilessnrecordedasdeceased            boolean 
 32   cfind.currentaddressreportedbynewtradeonly       boolean 
 33   cfind.creditestablishedbeforeage18               boolean 
 34   cfind.telephonenumberinconsistentwithstate       boolean 
 35   cfind.driverlicenseinconsistentwithonfile        boolean 
 36   cfind.workphonepreviouslylistedascellphone       boolean 
 37   cfind.workphonepreviouslylistedashomephone       boolean 
 38   cfindvrfy.ssnnamematch                           category
 39   cfindvrfy.nameaddressmatch                       category
 40   cfindvrfy.phonematchtype                         category
 41   cfindvrfy.phonematchresult                       category
 42   cfindvrfy.overallmatchresult                     category
 43   cfindvrfy.phonetype                              category
 44   cfindvrfy.ssndobreasoncode                       category
 45   cfindvrfy.ssnnamereasoncode                      category
 46   cfindvrfy.nameaddressreasoncode                  category
 47   cfindvrfy.ssndobmatch                            category
 48   cfindvrfy.overallmatchreasoncode                 float64 
 49   clearfraudscore                                  float64 
 50   anon_ssn                                         object  
 51   payFrequency                                     category
 52   apr                                              float64 
 53   originated                                       boolean 
 54   nPaidOff                                         Int32   
 55   approved                                         boolean 
 56   isFunded                                         boolean 
 57   loanAmount                                       float64 
 58   originallyScheduledPaymentAmount                 float64 
 59   state                                            category
 60   leadType                                         category
 61   leadCost                                         float64 
 62   fpStatus                                         category
 63   hasCF                                            boolean 
 64   sum_days_btw_pymts                               float64 
 65   mean_days_btw_pymts                              float64 
 66   med_days_btw_pymts                               float64 
 67   std_days_btw_pymts                               float64 
 68   cnt_days_btw_pymts                               Int32   
 69   min_days_btw_pymts                               float64 
 70   max_days_btw_pymts                               float64 
 71   sum_fees_Cancelled                               float64 
 72   sum_fees_Checked                                 float64 
 73   sum_fees_Complete                                float64 
 74   sum_fees_None                                    float64 
 75   sum_fees_Pending                                 float64 
 76   sum_fees_Rejected                                float64 
 77   sum_fees_Rejected Awaiting Retry                 float64 
 78   sum_fees_Returned                                float64 
 79   sum_fees_Skipped                                 float64 
 80   sum_principal_Cancelled                          float64 
 81   sum_principal_Checked                            float64 
 82   sum_principal_Complete                           float64 
 83   sum_principal_None                               float64 
 84   sum_principal_Pending                            float64 
 85   sum_principal_Rejected                           float64 
 86   sum_principal_Rejected Awaiting Retry            float64 
 87   sum_principal_Returned                           float64 
 88   sum_principal_Skipped                            float64 
 89   sum_pymtAmt_Cancelled                            float64 
 90   sum_pymtAmt_Checked                              float64 
 91   sum_pymtAmt_Complete                             float64 
 92   sum_pymtAmt_None                                 float64 
 93   sum_pymtAmt_Pending                              float64 
 94   sum_pymtAmt_Rejected                             float64 
 95   sum_pymtAmt_Rejected Awaiting Retry              float64 
 96   sum_pymtAmt_Returned                             float64 
 97   sum_pymtAmt_Skipped                              float64 
 98   mean_fees_Cancelled                              float64 
 99   mean_fees_Checked                                float64 
 100  mean_fees_Complete                               float64 
 101  mean_fees_None                                   float64 
 102  mean_fees_Pending                                float64 
 103  mean_fees_Rejected                               float64 
 104  mean_fees_Rejected Awaiting Retry                float64 
 105  mean_fees_Returned                               float64 
 106  mean_fees_Skipped                                float64 
 107  mean_principal_Cancelled                         float64 
 108  mean_principal_Checked                           float64 
 109  mean_principal_Complete                          float64 
 110  mean_principal_None                              float64 
 111  mean_principal_Pending                           float64 
 112  mean_principal_Rejected                          float64 
 113  mean_principal_Rejected Awaiting Retry           float64 
 114  mean_principal_Returned                          float64 
 115  mean_principal_Skipped                           float64 
 116  mean_pymtAmt_Cancelled                           float64 
 117  mean_pymtAmt_Checked                             float64 
 118  mean_pymtAmt_Complete                            float64 
 119  mean_pymtAmt_None                                float64 
 120  mean_pymtAmt_Pending                             float64 
 121  mean_pymtAmt_Rejected                            float64 
 122  mean_pymtAmt_Rejected Awaiting Retry             float64 
 123  mean_pymtAmt_Returned                            float64 
 124  mean_pymtAmt_Skipped                             float64 
 125  med_fees_Cancelled                               float64 
 126  med_fees_Checked                                 float64 
 127  med_fees_Complete                                float64 
 128  med_fees_None                                    float64 
 129  med_fees_Pending                                 float64 
 130  med_fees_Rejected                                float64 
 131  med_fees_Rejected Awaiting Retry                 float64 
 132  med_fees_Returned                                float64 
 133  med_fees_Skipped                                 float64 
 134  med_principal_Cancelled                          float64 
 135  med_principal_Checked                            float64 
 136  med_principal_Complete                           float64 
 137  med_principal_None                               float64 
 138  med_principal_Pending                            float64 
 139  med_principal_Rejected                           float64 
 140  med_principal_Rejected Awaiting Retry            float64 
 141  med_principal_Returned                           float64 
 142  med_principal_Skipped                            float64 
 143  med_pymtAmt_Cancelled                            float64 
 144  med_pymtAmt_Checked                              float64 
 145  med_pymtAmt_Complete                             float64 
 146  med_pymtAmt_None                                 float64 
 147  med_pymtAmt_Pending                              float64 
 148  med_pymtAmt_Rejected                             float64 
 149  med_pymtAmt_Rejected Awaiting Retry              float64 
 150  med_pymtAmt_Returned                             float64 
 151  med_pymtAmt_Skipped                              float64 
 152  std_fees_Cancelled                               float64 
 153  std_fees_Checked                                 float64 
 154  std_fees_None                                    float64 
 155  std_fees_Pending                                 float64 
 156  std_fees_Rejected                                float64 
 157  std_fees_Rejected Awaiting Retry                 float64 
 158  std_fees_Skipped                                 float64 
 159  std_principal_Cancelled                          float64 
 160  std_principal_Checked                            float64 
 161  std_principal_None                               float64 
 162  std_principal_Pending                            float64 
 163  std_principal_Rejected                           float64 
 164  std_principal_Rejected Awaiting Retry            float64 
 165  std_principal_Skipped                            float64 
 166  std_pymtAmt_Cancelled                            float64 
 167  std_pymtAmt_Checked                              float64 
 168  std_pymtAmt_None                                 float64 
 169  std_pymtAmt_Pending                              float64 
 170  std_pymtAmt_Rejected                             float64 
 171  std_pymtAmt_Rejected Awaiting Retry              float64 
 172  std_pymtAmt_Skipped                              float64 
 173  min_fees_Cancelled                               float64 
 174  min_fees_Checked                                 float64 
 175  min_fees_Complete                                float64 
 176  min_fees_None                                    float64 
 177  min_fees_Pending                                 float64 
 178  min_fees_Rejected                                float64 
 179  min_fees_Rejected Awaiting Retry                 float64 
 180  min_fees_Returned                                float64 
 181  min_fees_Skipped                                 float64 
 182  min_principal_Cancelled                          float64 
 183  min_principal_Checked                            float64 
 184  min_principal_Complete                           float64 
 185  min_principal_None                               float64 
 186  min_principal_Pending                            float64 
 187  min_principal_Rejected                           float64 
 188  min_principal_Rejected Awaiting Retry            float64 
 189  min_principal_Returned                           float64 
 190  min_principal_Skipped                            float64 
 191  min_pymtAmt_Cancelled                            float64 
 192  min_pymtAmt_Checked                              float64 
 193  min_pymtAmt_Complete                             float64 
 194  min_pymtAmt_None                                 float64 
 195  min_pymtAmt_Pending                              float64 
 196  min_pymtAmt_Rejected                             float64 
 197  min_pymtAmt_Rejected Awaiting Retry              float64 
 198  min_pymtAmt_Returned                             float64 
 199  min_pymtAmt_Skipped                              float64 
 200  max_fees_Cancelled                               float64 
 201  max_fees_Checked                                 float64 
 202  max_fees_Complete                                float64 
 203  max_fees_None                                    float64 
 204  max_fees_Pending                                 float64 
 205  max_fees_Rejected                                float64 
 206  max_fees_Rejected Awaiting Retry                 float64 
 207  max_fees_Returned                                float64 
 208  max_fees_Skipped                                 float64 
 209  max_principal_Cancelled                          float64 
 210  max_principal_Checked                            float64 
 211  max_principal_Complete                           float64 
 212  max_principal_None                               float64 
 213  max_principal_Pending                            float64 
 214  max_principal_Rejected                           float64 
 215  max_principal_Rejected Awaiting Retry            float64 
 216  max_principal_Returned                           float64 
 217  max_principal_Skipped                            float64 
 218  max_pymtAmt_Cancelled                            float64 
 219  max_pymtAmt_Checked                              float64 
 220  max_pymtAmt_Complete                             float64 
 221  max_pymtAmt_None                                 float64 
 222  max_pymtAmt_Pending                              float64 
 223  max_pymtAmt_Rejected                             float64 
 224  max_pymtAmt_Rejected Awaiting Retry              float64 
 225  max_pymtAmt_Returned                             float64 
 226  max_pymtAmt_Skipped                              float64 
 227  cnt_custom                                       Int32   
 228  cnt_non custom                                   Int32   
 229  cnt_pymtStatus_Cancelled                         Int32   
 230  cnt_pymtStatus_Checked                           Int32   
 231  cnt_pymtStatus_Complete                          Int32   
 232  cnt_pymtStatus_None                              Int32   
 233  cnt_pymtStatus_Pending                           Int32   
 234  cnt_pymtStatus_Rejected                          Int32   
 235  cnt_pymtStatus_Rejected Awaiting Retry           Int32   
 236  cnt_pymtStatus_Returned                          Int32   
 237  cnt_pymtStatus_Skipped                           Int32   
 238  cnt_pymtRCode_C01                                Int32   
 239  cnt_pymtRCode_C02                                Int32   
 240  cnt_pymtRCode_C03                                Int32   
 241  cnt_pymtRCode_C05                                Int32   
 242  cnt_pymtRCode_C07                                Int32   
 243  cnt_pymtRCode_LPP01                              Int32   
 244  cnt_pymtRCode_MISSED                             Int32   
 245  cnt_pymtRCode_R01                                Int32   
 246  cnt_pymtRCode_R02                                Int32   
 247  cnt_pymtRCode_R03                                Int32   
 248  cnt_pymtRCode_R04                                Int32   
 249  cnt_pymtRCode_R06                                Int32   
 250  cnt_pymtRCode_R07                                Int32   
 251  cnt_pymtRCode_R08                                Int32   
 252  cnt_pymtRCode_R09                                Int32   
 253  cnt_pymtRCode_R10                                Int32   
 254  cnt_pymtRCode_R13                                Int32   
 255  cnt_pymtRCode_R15                                Int32   
 256  cnt_pymtRCode_R16                                Int32   
 257  cnt_pymtRCode_R19                                Int32   
 258  cnt_pymtRCode_R20                                Int32   
 259  cnt_pymtRCode_R29                                Int32   
 260  cnt_pymtRCode_R99                                Int32   
 261  cnt_pymtRCode_RAF                                Int32   
 262  cnt_pymtRCode_RBW                                Int32   
 263  cnt_pymtRCode_RFG                                Int32   
 264  cnt_pymtRCode_RIR                                Int32   
 265  cnt_pymtRCode_RUP                                Int32   
 266  cnt_pymtRCode_RWC                                Int32   
 267  cnt_pymtRCode_RXL                                Int32   
 268  cnt_pymtRCode_RXS                                Int32   
 269  target                                           Int8    
dtypes: Int32(55), Int8(1), boolean(31), category(14), float64(168), object(1)
memory usage: 52.5+ MB
In [6]:
"""
# Print counts of unique values for all columns
for col in clean_df.columns:
    print(f'Value counts for column: {col}')
    print(clean_df[col].value_counts(dropna = False))  # Count occurrences of each unique value
    print("-" * 40)
""";

Models¶

  • Identity and hashing assumption
    - Assume a consistent, deterministic hashing algorithm that yields a unique hash for every loan applicant (anon_ssn)

  • Data splitting
    - Train–test (80/20) with GroupShuffleSplit so that no anon_ssn appears in both train and test

  • Cross-validation on the training split
    - StratifiedGroupKFold (5 folds) to:

    • Keep all samples from the same anon_ssn in the same fold (prevent leakage)
    • Preserve class balance
    • Use shuffling and a fixed random_state for robustness and reproducibility

  • Models (tree-based gradient boosting)
    - LightGBM: Grows trees by expanding the single branch that improves accuracy the most
    - HistGradientBoostingClassifier: Grows trees step by step, testing options in groups (buckets) for speed
    - XGBoost: Grows trees evenly level by level, keeping the structure balanced
    - CatBoostClassifier: Grows trees in a perfectly symmetrical way, splitting all branches the same way at each step

       Advantages of these classifiers:
       - Require minimal preprocessing (e.g. no need for extensive encoding or scaling)
       - Less sensitive to outliers in the data
       - Less sensitive to multicollinearity
       - Capable of capturing non-linear relationships effectively
       - Support categorical features natively

  • Hyperparameter optimization
    - Optuna multi-objective hyperparameter optimization with stratified 5-fold CV over 100 sequential trials:

    • Calibration: Log-loss (minimize) — evaluates how closely the predicted probabilities match the true outcomes. Lower values indicate better probability calibration.
    • Discrimination: PR-AUC and ROC-AUC (maximize)
      - ROC-AUC: Measures how well the model separates positives from negatives across all decision thresholds.
      - PR-AUC: Focuses on precision–recall trade-offs, particularly valuable for imbalanced datasets where one class is much rarer.

  • Evaluation and reporting
    - Report objective metrics above (on CV and hold-out test)
    - Confusion matrix and classification report
    - Elapsed time

In [7]:
# Runs the objective() function 100 times, each with a different set of hyperparameters suggested by Optuna
n_trials = 100
In [8]:
# Unsure which sampler to use so load the AutoSampler plugin from OptunaHub to automatically select the most suitable built-in sampler for the search space during Optuna optimization
module = optunahub.load_module(package = "samplers/auto_sampler")
In [9]:
seed = 42

Optuna with LightGBM¶

In [10]:
# Aggregated loan level data
X_df = clean_df.drop(columns = ["target", "anon_ssn"])

cat_cols = X_df.select_dtypes(include = "category").columns
cat_indices = [X_df.columns.get_loc(col) for col in cat_cols]

X_df = X_df.apply(lambda x: x.map(lambda z: np.nan if pd.isna(z) else z)) 
feature_name = list(X_df.columns)

y_df = clean_df["target"]

# Extract the grouping variable, anon_ssn
anon_ssn = clean_df["anon_ssn"];
In [11]:
# Convert categorical columns to numerical codes for Optuna with LightGBM, with NaN as -1
for col in X_df.columns:
    if isinstance(X_df[col].dtype, pd.CategoricalDtype):
        #print(col+": ordered?", {X_df[col].cat.ordered})
        X_df[col] = X_df[col].cat.codes
In [12]:
X = X_df.to_numpy()
y = y_df.to_numpy()
In [13]:
# A single train-test split (80%-20%) using GroupShuffleSplit, ensuring no data leakage due anon_ssn (grouped by anon_ssn) appear in both sets
# and use a fixed random seed for reproducibility
gss = GroupShuffleSplit(n_splits = 1, test_size = 0.2, random_state = seed)

# Generate the train and test indices from X, y, while ensuring all samples from the same anon_ssn stay together in train or test sets
train_idx, test_idx = next(gss.split(X, y, groups = anon_ssn))

# Use the indices to slice out the training and testing subsets of our feature matrix
X_train, X_test = X[train_idx], X[test_idx]

# Likewise, slice out the corresponding labels for training and testing
y_train, y_test = y[train_idx], y[test_idx]

# Keep track of anon_ssn for cross-validation
anon_ssn_train = anon_ssn[train_idx]  
In [14]:
display(Markdown(f"<span style = 'font-size: 18px; font-weight: bold;'> Overview of training and test sets:</span>"))

print(f'Training set:\n- {X_train.shape[0]} rows and {X_train.shape[1]} features')
print(f'- Target proportion:\n{pd.Series(y_train).value_counts(normalize = True)}\n')

print(f'Test set:\n- {X_test.shape[0]} rows and {X_test.shape[1]} features')
print(f'- Target proportion:\n{pd.Series(y_test).value_counts(normalize = True)}\n')

print(f'Training set for anon_ssn: {anon_ssn_train.shape[0]} row entries with {anon_ssn_train.nunique()} unique anon_ssn values')

Overview of training and test sets:

Training set:
- 25838 rows and 268 features
- Target proportion:
0    0.507973
1    0.492027
Name: proportion, dtype: float64

Test set:
- 6474 rows and 268 features
- Target proportion:
0    0.505715
1    0.494285
Name: proportion, dtype: float64

Training set for anon_ssn: 25838 row entries with 24008 unique anon_ssn values

The target distribution between safe and risky loans does not suffer from severe class imbalance and is well balanced across both the training and test sets.

In [15]:
del X_df, y_df, X, y, gss, train_idx, test_idx;
In [16]:
# Define the Optuna objective function
def objective(trial):
    # https://lightgbm.readthedocs.io/en/latest/Parameters.html
    # https://lightgbm.readthedocs.io/en/stable/Parameters.html
    # https://lightgbm.readthedocs.io/en/latest/Parameters-Tuning.html
    # Define parameter search space
    param = {"objective": "binary",
             "metric": ["binary_logloss", "auc", "average_precision"],
             "device_type": "cpu",
             #"device_type": "gpu",  # Uses OpenCL backend on Windows
             #"gpu_platform_id": 0,
             #"gpu_device_id": 0,   
             "verbosity": 2,
             "boosting_type": trial.suggest_categorical("boosting_type", ["gbdt", "rf", "dart"]),
             "lambda_l1": trial.suggest_float("lambda_l1", 1e-8, 1e1, log = True),
             "lambda_l2": trial.suggest_float("lambda_l2", 1e-8, 1e1, log = True),
             "num_leaves": trial.suggest_int("num_leaves", 2, 256),
             "feature_fraction": trial.suggest_float("feature_fraction", 1e-1, 1e0),
             "bagging_fraction": trial.suggest_float("bagging_fraction", 1e-1, 1e0),
             "bagging_freq": trial.suggest_int("bagging_freq", 1, 10),
             "min_child_samples": trial.suggest_int("min_child_samples", 5, 200),
             "learning_rate": trial.suggest_float("learning_rate", 1e-2, 1e-1, log = True),
             "random_state": seed,
             "deterministic": True,
             "bagging_seed": seed,
             "feature_fraction_seed": seed,  
             "drop_seed": seed,
             "force_col_wise": True,
             "num_threads": -1, 
             "is_unbalance": trial.suggest_categorical("is_unbalance", [True, False]),
             "max_depth": -1, # <= 0, No tree depth limit
             "max_bin": trial.suggest_int("max_bin", 40, 255),
             "min_sum_hessian_in_leaf": trial.suggest_float("min_sum_hessian_in_leaf", 1e-10, 1e1, log = True),
             "max_delta_step": trial.suggest_float("max_delta_step", 1, 100),
             "feature_fraction_bynode": trial.suggest_float("feature_fraction_bynode", 1e-1, 1e0),
            }

    # Define how to split using StratifiedGroupKFold (5 folds, stratified, anon_ssn-safe)
    sgkf = StratifiedGroupKFold(n_splits = 5, shuffle = True, random_state = seed)

    # Lists to store the performance metrics from each fold
    lloss_scores, pr_auc_scores, roc_auc_scores = [], [], []

    # Iterate over each fold of the stratified, anon_ssn-aware split, numbering folds starting from 1
    for fold_idx, (train_index, valid_index) in enumerate(sgkf.split(X_train, y_train, groups = anon_ssn_train), start = 1):

        # Split into training and validation sets for this fold
        X_train_fold, X_valid_fold = X_train[train_index], X_train[valid_index]
        y_train_fold, y_valid_fold = y_train[train_index], y_train[valid_index]

        # Summarize the composition of classes in the train and validation sets
        train_0, train_1 = len(y_train_fold[y_train_fold == 0]), len(y_train_fold[y_train_fold == 1])
        valid_0, valid_1 = len(y_valid_fold[y_valid_fold == 0]), len(y_valid_fold[y_valid_fold == 1])
       
        print(f'Trial {trial.number}, Fold {fold_idx}: Train size = {len(train_index)} where 0 = {train_0}, 1 = {train_1}, 0/1 = {train_0/train_1}')
        print(f'Trial {trial.number}, Fold {fold_idx}: Validation size = {len(valid_index)} where  0 = {valid_0}, 1 = {valid_1}, 0/1 = {valid_0/valid_1}')

        # Create LightGBM datasets for efficient training
        dtrain_fold = lgb.Dataset(X_train_fold, label = y_train_fold, categorical_feature = cat_indices)
        dvalid_fold = lgb.Dataset(X_valid_fold, label = y_valid_fold)

        # Suggest the number of boosting rounds (trees) for the LightGBM model
        # This value is being tuned using Optuna's suggest_int method, which picks an integer between 50 and 500
        # The number of boosting rounds defines how many trees are built during training
        num_round = trial.suggest_int("num_boost_round", 5, 100)
      
        # https://lightgbm.readthedocs.io/en/stable/pythonapi/lightgbm.train.html
        # Train the LightGBM model using the provided parameters and training data
        # The num_boost_round parameter is specified separately rather than included in the param dictionary
        # This is because num_boost_round is not a model hyperparameter that affects the tree-building process,
        # but rather controls the number of boosting iterations during training.
        # Including it as a separate argument in lgb.train allows for more flexible and clear tuning using Optuna    
        start_fold = time.perf_counter()
        clf = lgb.train(params = param, train_set = dtrain_fold, 
                        num_boost_round = num_round,  # Get num_rounds from Optuna
                        valid_sets = [dvalid_fold]
                       )  
        end_fold = time.perf_counter()

        # Predict probabilities and convert to binary labels at 0.5 threshold
        y_prob_fold = clf.predict(X_valid_fold)
        y_pred_fold = np.rint(y_prob_fold) # Set y_pred_fold = 1 if y_prob_fold => 0.5 and 0 if y_prob_fold < 0.5
        
        print(f'Trial {trial.number}, Fold {fold_idx}: '
              f'Log loss = {log_loss(y_valid_fold, y_prob_fold)}, '
              f'Average precision = {average_precision_score(y_valid_fold, y_prob_fold)}, '
              f'ROC-AUC = {roc_auc_score(y_valid_fold, y_prob_fold)}, '
              f'Elapsed Time = {end_fold - start_fold} seconds')         
        
        # Calculate and store the evaluation metrics for this fold
        lloss_scores.append(log_loss(y_valid_fold, y_prob_fold))
        pr_auc_scores.append(average_precision_score(y_valid_fold, y_prob_fold))
        roc_auc_scores.append(roc_auc_score(y_valid_fold, y_prob_fold))

        del X_train_fold, X_valid_fold, y_train_fold, y_valid_fold, dtrain_fold, dvalid_fold, clf, start_fold, end_fold
        gc.collect()

    # Calculate average metrics across all folds for Optuna to optimize
    mean_lloss = np.mean(lloss_scores)
    mean_pr_auc = np.mean(pr_auc_scores)
    mean_roc_auc = np.mean(roc_auc_scores)

    del lloss_scores, pr_auc_scores, roc_auc_scores
    gc.collect()
    
    #  Return the metrics to Optuna for optimization
    return mean_lloss, mean_pr_auc, mean_roc_auc
In [17]:
# Initialize a progress bar for visual feedback during optimization
# https://tqdm.github.io/docs/tqdm/
trial_progress = tqdm(total = n_trials, desc = "Optimization Progress", leave = True,
                      ascii = True, # Plain text mode
                      dynamic_ncols = True # Auto-fit width
                     )

# Callback for Optuna to update the progress bar after each trial
def update_progress(study_lgbm, trial):
    trial_progress.update(1)

# Disable Optuna's stdout handler so notebook isn’t spammed
optuna.logging.disable_default_handler()

# Enable propagation to Python’s logging
optuna.logging.enable_propagation()
optuna.logging.set_verbosity(optuna.logging.DEBUG)

# Configure Python logging
logging.basicConfig(filename = "optuna_debug_LGBM.log", filemode = "w", level = logging.DEBUG, format = "%(asctime)s %(levelname)s %(message)s")

study_lgbm = optuna.create_study(study_name = "Optuna for LGBM",
                                 directions = ["minimize", "maximize", "maximize"],
                                 sampler = module.AutoSampler(seed = seed)
                                )

start_optuna = time.perf_counter()
study_lgbm.optimize(objective, n_trials = n_trials, n_jobs = 1, callbacks = [update_progress])
end_optuna = time.perf_counter()
print(f'Optuna Optimization Elapsed Time: {end_optuna -  start_optuna} seconds')

# Create a Pareto-front plot (3D scatter plot) to show trade-off between log-loss, PR_AUC and ROC-AUC for each trial
fig = plot_pareto_front(study_lgbm, target_names = ["Log loss", "PR-AUC", "ROC-AUC"])
fig.update_layout(width = 900, height = 400)
fig.show()

trial_progress.close()

# Plot optimization history plot for each objective
metrics = ["Log loss", "PR-AUC", "ROC-AUC"]
for i, obj in enumerate(metrics):
    optuna.visualization.plot_optimization_history(study_lgbm,
                                                   target = lambda t: t.values[i],  # Correctly target each objective
                                                   target_name = obj).show()

best_trials = study_lgbm.best_trials
best_trials_lgbm = {}

# Lists to store the performance metrics from best trial(s)
exec_time_lgbm, lloss_auc_train_lgbm, lloss_auc_test_lgbm, all_metrics = [], [], [], []

# List to store confusion matrices and their labels
cm_lgbm_all, cm_labels_lgbm_all = [], []

for i, trial in enumerate(best_trials):

    display(Markdown(f"<span style = 'font-size: 18px; font-weight: bold;'> Training with Best Trial {trial.number} </span>"))

    best_params = trial.params

    # Non-optimized and best Optuna optimized parameters
    full_params = {"objective": "binary",
                   "metric": ["binary_logloss", "auc", "average_precision"],
                   "device_type": "cpu",
                   "verbosity": 2,   
                   "random_state": seed,
                   "deterministic": True,
                   "bagging_seed": seed,
                   "feature_fraction_seed": seed,
                   "drop_seed": seed,
                   "force_col_wise": True,
                   "num_threads": -1,
                   "max_depth": -1, # <= 0, No tree depth limit
                   **best_params
                  }
    
    # Prepare the data - 80% training set and 20% test set
    dtrain_all = lgb.Dataset(X_train, label = y_train, categorical_feature = cat_indices, feature_name = feature_name) 
    dtest_all = lgb.Dataset(X_test, label = y_test, categorical_feature = cat_indices, 
                            feature_name = feature_name)
    
    #display(HTML(str(trial.params)))
    display(HTML(full_params.__repr__()))

    # To be able to store num_boost_round as well
    best_trials_lgbm[trial.number] = full_params.copy()
    
    # Extract `num_boost_round` separately (default to 100 if not found)
    num_boost_round = full_params.pop("num_boost_round", 100)  # Remove from dictionary and set default
    
    start_train = time.perf_counter()
    # https://lightgbm.readthedocs.io/en/stable/pythonapi/lightgbm.train.html
    final_lgbm = lgb.train(params = full_params, 
                           train_set = dtrain_all,  
                           num_boost_round = num_boost_round # Pass explicitly
                          )
    end_train = time.perf_counter()

    print(f'Training Elapsed Time: {end_train - start_train} seconds') 
  
    # Save trained final_lgbm from the best trial
    final_lgbm.save_model(f'{temp_dir}/booster_LGBM_best_trial_{trial.number}.txt')

    y_prob_all = final_lgbm.predict(X_test)
    y_pred_all = np.rint(y_prob_all)

    print(f'Log loss: (Train) {trial.values[0]} vs (Test) {log_loss(y_test, y_prob_all)}')
    print(f'PR-AUC:   (Train) {trial.values[1]} vs (Test) {average_precision_score(y_test, y_prob_all)}')
    print(f'ROC-AUC:  (Train) {trial.values[2]} vs (Test) {roc_auc_score(y_test, y_prob_all)}')

    exec_time_lgbm.append({"Classifier": "LGBM",
                           "Best Trial": trial.number,
                           "Optimization Elapsed Time (s)": end_optuna - start_optuna,
                           "Training Elapsed Time (s)": end_train - start_train})

    lloss_auc_train_lgbm.append({"Classifier": "LGBM",
                               "Best Trial": trial.number,
                               "Set": "Training",
                               "Log loss": trial.values[0], 
                               "PR-AUC": trial.values[1], 
                               "ROC-AUC": trial.values[2]})

    lloss_auc_test_lgbm.append({"Classifier": "LGBM",
                              "Best Trial": trial.number,
                              "Set": "Test",
                              "Log loss": log_loss(y_test, y_prob_all), 
                              "PR-AUC": average_precision_score(y_test, y_prob_all), 
                              "ROC-AUC": roc_auc_score(y_test, y_prob_all)})
    
    report = classification_report(y_test, y_pred_all, target_names = ["Safe", "Risky"], output_dict = True)
    all_metrics.append({"Classifier": "LGBM",
                        "Trial": trial.number,
                        "Accuracy": accuracy_score(y_test, y_pred_all),
                        "Precision (Safe)": report["Safe"]["precision"],
                        "Recall (Safe)": report["Safe"]["recall"],
                        "F1-score (Safe)": report["Safe"]["f1-score"],
                        "Precision (Risky)": report["Risky"]["precision"],
                        "Recall (Risky)": report["Risky"]["recall"],
                        "F1-score (Risky)": report["Risky"]["f1-score"],
                        "Precision (Macro avg)": report["macro avg"]["precision"],
                        "Recall (Macro avg)": report["macro avg"]["recall"],
                        "F1-score (Macro avg)": report["macro avg"]["f1-score"],
                        "Precision (Weighted avg)": report["weighted avg"]["precision"],
                        "Recall (Weighted avg)": report["weighted avg"]["recall"],
                        "F1-score (Weighted avg)": report["weighted avg"]["f1-score"]})

    # Store confusion matrix
    cm_final_lgbm = confusion_matrix(y_test, y_pred_all)
    cm_lgbm_all.append(cm_final_lgbm)
    cm_labels_lgbm_all.append(f'LGBM Confusion Matrix for Best Trial {trial.number}')  # Store label for subplots     

df_metrics_lgbm = pd.DataFrame(all_metrics)

gc.collect();
Optimization Progress:   0%|          | 0/100 [00:00<?, ?it/s]
Trial 0, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 0, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[LightGBM] [Info] Number of positive: 10130, number of negative: 10533
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795662
[LightGBM] [Info] Total Bins 11600
[LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 258
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012
[LightGBM] [Info] Start training from score -0.039012
[LightGBM] [Debug] Re-bagging, using 5235 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5180 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5201 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5031 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 15
[LightGBM] [Debug] Re-bagging, using 5244 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5196 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5130 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5247 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5155 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5020 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5047 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 8
[LightGBM] [Debug] Re-bagging, using 5095 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5097 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 8
[LightGBM] [Debug] Re-bagging, using 5188 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11
[LightGBM] [Debug] Re-bagging, using 5245 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5188 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 12
[LightGBM] [Debug] Re-bagging, using 5172 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5087 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 8
[LightGBM] [Debug] Re-bagging, using 5206 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5239 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5076 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 12
[LightGBM] [Debug] Re-bagging, using 5165 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5163 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5106 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5240 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5208 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5188 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5208 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 8
[LightGBM] [Debug] Re-bagging, using 5129 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5176 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5092 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5208 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5088 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11
[LightGBM] [Debug] Re-bagging, using 5221 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5138 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5141 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5169 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5239 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5075 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5242 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11
[LightGBM] [Debug] Re-bagging, using 5151 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5164 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 8
[LightGBM] [Debug] Re-bagging, using 5038 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5202 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5189 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5091 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5172 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5010 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5203 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10
Trial 0, Fold 1: Log loss = 0.32964674084575407, Average precision = 0.9635670908320297, ROC-AUC = 0.9569285821826472, Elapsed Time = 1.5621259999998074 seconds
Trial 0, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 0, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[LightGBM] [Info] Number of positive: 10230, number of negative: 10471
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795629
[LightGBM] [Info] Total Bins 11614
[LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 258
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285
[LightGBM] [Info] Start training from score -0.023285
[LightGBM] [Debug] Re-bagging, using 5245 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11
[LightGBM] [Debug] Re-bagging, using 5191 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5207 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5044 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5262 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5190 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8
[LightGBM] [Debug] Re-bagging, using 5139 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 12
[LightGBM] [Debug] Re-bagging, using 5257 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11
[LightGBM] [Debug] Re-bagging, using 5173 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 8
[LightGBM] [Debug] Re-bagging, using 5043 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11
[LightGBM] [Debug] Re-bagging, using 5046 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5091 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 8
[LightGBM] [Debug] Re-bagging, using 5108 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5196 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5249 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 8
[LightGBM] [Debug] Re-bagging, using 5205 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5183 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5106 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 12
[LightGBM] [Debug] Re-bagging, using 5224 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 8
[LightGBM] [Debug] Re-bagging, using 5242 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5086 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5173 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5179 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5120 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5248 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11
[LightGBM] [Debug] Re-bagging, using 5212 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5190 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5206 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 11
[LightGBM] [Debug] Re-bagging, using 5136 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11
[LightGBM] [Debug] Re-bagging, using 5181 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5105 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5221 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5102 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 8
[LightGBM] [Debug] Re-bagging, using 5237 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5155 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11
[LightGBM] [Debug] Re-bagging, using 5151 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5182 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 11
[LightGBM] [Debug] Re-bagging, using 5246 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5071 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5237 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5169 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5167 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5054 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5207 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5205 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5098 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 13
[LightGBM] [Debug] Re-bagging, using 5188 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5023 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5216 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
Trial 0, Fold 2: Log loss = 0.32852376972252656, Average precision = 0.9609631282739565, ROC-AUC = 0.9569558301650926, Elapsed Time = 1.8901175999999396 seconds
Trial 0, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 0, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[LightGBM] [Info] Number of positive: 10165, number of negative: 10517
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795455
[LightGBM] [Info] Total Bins 11619
[LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 258
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043
[LightGBM] [Info] Start training from score -0.034043
[LightGBM] [Debug] Re-bagging, using 5240 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11
[LightGBM] [Debug] Re-bagging, using 5183 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5204 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 11
[LightGBM] [Debug] Re-bagging, using 5041 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5254 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5191 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5138 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5246 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5161 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5036 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5055 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 8
[LightGBM] [Debug] Re-bagging, using 5082 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5104 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5197 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5238 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 12
[LightGBM] [Debug] Re-bagging, using 5200 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11
[LightGBM] [Debug] Re-bagging, using 5176 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 11
[LightGBM] [Debug] Re-bagging, using 5096 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 12
[LightGBM] [Debug] Re-bagging, using 5212 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 8
[LightGBM] [Debug] Re-bagging, using 5254 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5084 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5157 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 12
[LightGBM] [Debug] Re-bagging, using 5159 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5127 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5255 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 8
[LightGBM] [Debug] Re-bagging, using 5211 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 12
[LightGBM] [Debug] Re-bagging, using 5183 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5194 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5143 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 8
[LightGBM] [Debug] Re-bagging, using 5177 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5101 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 8
[LightGBM] [Debug] Re-bagging, using 5204 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5092 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5224 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5156 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5154 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5179 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5247 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5068 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5248 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5155 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5158 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5040 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5190 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5214 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11
[LightGBM] [Debug] Re-bagging, using 5091 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 8
[LightGBM] [Debug] Re-bagging, using 5186 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 8
[LightGBM] [Debug] Re-bagging, using 5023 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5210 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9
Trial 0, Fold 3: Log loss = 0.32251907251604217, Average precision = 0.9651567134011153, ROC-AUC = 0.9593725320472691, Elapsed Time = 1.500842399999783 seconds
Trial 0, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 0, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[LightGBM] [Info] Number of positive: 10177, number of negative: 10479
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795144
[LightGBM] [Info] Total Bins 11600
[LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 258
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243
[LightGBM] [Info] Start training from score -0.029243
[LightGBM] [Debug] Re-bagging, using 5234 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5176 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11
[LightGBM] [Debug] Re-bagging, using 5198 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5033 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11
[LightGBM] [Debug] Re-bagging, using 5240 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5195 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5132 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5241 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5149 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5024 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 8
[LightGBM] [Debug] Re-bagging, using 5040 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5094 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 8
[LightGBM] [Debug] Re-bagging, using 5099 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5185 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 8
[LightGBM] [Debug] Re-bagging, using 5239 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5192 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5166 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11
[LightGBM] [Debug] Re-bagging, using 5096 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5196 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 8
[LightGBM] [Debug] Re-bagging, using 5241 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5075 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5156 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5161 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5116 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 8
[LightGBM] [Debug] Re-bagging, using 5230 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5206 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5177 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5206 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5140 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11
[LightGBM] [Debug] Re-bagging, using 5173 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5097 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5197 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5090 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5219 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5140 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5142 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5157 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5239 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11
[LightGBM] [Debug] Re-bagging, using 5060 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5246 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5150 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5163 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5048 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5195 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5189 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5090 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 8
[LightGBM] [Debug] Re-bagging, using 5177 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11
[LightGBM] [Debug] Re-bagging, using 5015 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5201 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10
Trial 0, Fold 4: Log loss = 0.3300627228082963, Average precision = 0.9630914115390379, ROC-AUC = 0.9564496496109836, Elapsed Time = 1.4581862999993973 seconds
Trial 0, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 0, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[LightGBM] [Info] Number of positive: 10150, number of negative: 10500
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.796026
[LightGBM] [Info] Total Bins 11602
[LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 258
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902
[LightGBM] [Info] Start training from score -0.033902
[LightGBM] [Debug] Re-bagging, using 5232 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 12
[LightGBM] [Debug] Re-bagging, using 5176 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5198 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5028 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5239 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5191 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5136 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5236 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 8
[LightGBM] [Debug] Re-bagging, using 5150 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5023 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 11
[LightGBM] [Debug] Re-bagging, using 5035 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 8
[LightGBM] [Debug] Re-bagging, using 5083 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5107 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5191 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5233 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5197 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5166 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8
[LightGBM] [Debug] Re-bagging, using 5082 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 8
[LightGBM] [Debug] Re-bagging, using 5200 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5232 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 8
[LightGBM] [Debug] Re-bagging, using 5077 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5152 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5158 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 12
[LightGBM] [Debug] Re-bagging, using 5120 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5232 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5201 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5175 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5194 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 8
[LightGBM] [Debug] Re-bagging, using 5142 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5181 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5093 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5202 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5085 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5212 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5143 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5138 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 8
[LightGBM] [Debug] Re-bagging, using 5161 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5234 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5061 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5241 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5146 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5162 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 8
[LightGBM] [Debug] Re-bagging, using 5038 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5204 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 12
[LightGBM] [Debug] Re-bagging, using 5193 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5082 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5175 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5020 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5202 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 8
Trial 0, Fold 5: Log loss = 0.3261443116609128, Average precision = 0.9628277217413703, ROC-AUC = 0.956578860338517, Elapsed Time = 1.429886599999918 seconds
Optimization Progress:   1%|1         | 1/100 [00:15<25:26, 15.42s/it]
Trial 1, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 1, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[LightGBM] [Info] Number of positive: 10130, number of negative: 10533
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791640
[LightGBM] [Info] Total Bins 21036
[LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012
[LightGBM] [Info] Start training from score -0.039012
[LightGBM] [Debug] Re-bagging, using 15798 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 11
[LightGBM] [Debug] Re-bagging, using 15944 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 12
[LightGBM] [Debug] Re-bagging, using 15852 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 11
[LightGBM] [Debug] Re-bagging, using 15754 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 11
[LightGBM] [Debug] Re-bagging, using 15800 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 13
[LightGBM] [Debug] Re-bagging, using 15785 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 12
[LightGBM] [Debug] Re-bagging, using 15802 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 13
[LightGBM] [Debug] Re-bagging, using 15801 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 12
[LightGBM] [Debug] Re-bagging, using 15868 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 14
[LightGBM] [Debug] Re-bagging, using 15771 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 12
[LightGBM] [Debug] Re-bagging, using 15788 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 11
[LightGBM] [Debug] Re-bagging, using 15706 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 12
[LightGBM] [Debug] Re-bagging, using 15775 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 14
[LightGBM] [Debug] Re-bagging, using 15707 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 13
[LightGBM] [Debug] Re-bagging, using 15895 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 12
[LightGBM] [Debug] Re-bagging, using 15813 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 13
[LightGBM] [Debug] Re-bagging, using 15845 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 11
[LightGBM] [Debug] Re-bagging, using 15755 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 12
[LightGBM] [Debug] Re-bagging, using 15851 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 12
[LightGBM] [Debug] Re-bagging, using 15954 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 11
Trial 1, Fold 1: Log loss = 0.3089323124480566, Average precision = 0.9724404048219716, ROC-AUC = 0.9668692316332334, Elapsed Time = 1.6683110999993005 seconds
Trial 1, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 1, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[LightGBM] [Info] Number of positive: 10230, number of negative: 10471
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791600
[LightGBM] [Info] Total Bins 21050
[LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285
[LightGBM] [Info] Start training from score -0.023285
[LightGBM] [Debug] Re-bagging, using 15831 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 11
[LightGBM] [Debug] Re-bagging, using 15971 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 12
[LightGBM] [Debug] Re-bagging, using 15881 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 11
[LightGBM] [Debug] Re-bagging, using 15787 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 12
[LightGBM] [Debug] Re-bagging, using 15827 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 11
[LightGBM] [Debug] Re-bagging, using 15802 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 12
[LightGBM] [Debug] Re-bagging, using 15827 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 12
[LightGBM] [Debug] Re-bagging, using 15846 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 12
[LightGBM] [Debug] Re-bagging, using 15895 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 12
[LightGBM] [Debug] Re-bagging, using 15806 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 13
[LightGBM] [Debug] Re-bagging, using 15811 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 12
[LightGBM] [Debug] Re-bagging, using 15724 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 10
[LightGBM] [Debug] Re-bagging, using 15800 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 12
[LightGBM] [Debug] Re-bagging, using 15725 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 12
[LightGBM] [Debug] Re-bagging, using 15924 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 12
[LightGBM] [Debug] Re-bagging, using 15860 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 11
[LightGBM] [Debug] Re-bagging, using 15886 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 15
[LightGBM] [Debug] Re-bagging, using 15792 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 12
[LightGBM] [Debug] Re-bagging, using 15886 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 12
[LightGBM] [Debug] Re-bagging, using 15962 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 12
Trial 1, Fold 2: Log loss = 0.3117475916409317, Average precision = 0.970029552862639, ROC-AUC = 0.9663060734623168, Elapsed Time = 2.4145504999996774 seconds
Trial 1, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 1, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[LightGBM] [Info] Number of positive: 10165, number of negative: 10517
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791401
[LightGBM] [Info] Total Bins 21049
[LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043
[LightGBM] [Info] Start training from score -0.034043
[LightGBM] [Debug] Re-bagging, using 15814 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 13
[LightGBM] [Debug] Re-bagging, using 15958 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 12
[LightGBM] [Debug] Re-bagging, using 15865 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 12
[LightGBM] [Debug] Re-bagging, using 15773 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 13
[LightGBM] [Debug] Re-bagging, using 15811 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 13
[LightGBM] [Debug] Re-bagging, using 15795 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 10
[LightGBM] [Debug] Re-bagging, using 15810 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 11
[LightGBM] [Debug] Re-bagging, using 15828 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 11
[LightGBM] [Debug] Re-bagging, using 15875 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 10
[LightGBM] [Debug] Re-bagging, using 15797 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 12
[LightGBM] [Debug] Re-bagging, using 15800 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 12
[LightGBM] [Debug] Re-bagging, using 15715 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 10
[LightGBM] [Debug] Re-bagging, using 15788 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 10
[LightGBM] [Debug] Re-bagging, using 15713 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 10
[LightGBM] [Debug] Re-bagging, using 15906 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 11
[LightGBM] [Debug] Re-bagging, using 15828 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 12
[LightGBM] [Debug] Re-bagging, using 15873 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 10
[LightGBM] [Debug] Re-bagging, using 15779 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 13
[LightGBM] [Debug] Re-bagging, using 15865 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 11
[LightGBM] [Debug] Re-bagging, using 15972 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 13
Trial 1, Fold 3: Log loss = 0.31072578688090474, Average precision = 0.972312781336129, ROC-AUC = 0.9677071394862806, Elapsed Time = 2.2389018999992913 seconds
Trial 1, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 1, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[LightGBM] [Info] Number of positive: 10177, number of negative: 10479
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791090
[LightGBM] [Info] Total Bins 21033
[LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243
[LightGBM] [Info] Start training from score -0.029243
[LightGBM] [Debug] Re-bagging, using 15793 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 11
[LightGBM] [Debug] Re-bagging, using 15936 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 12
[LightGBM] [Debug] Re-bagging, using 15848 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 12
[LightGBM] [Debug] Re-bagging, using 15752 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 14
[LightGBM] [Debug] Re-bagging, using 15794 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 12
[LightGBM] [Debug] Re-bagging, using 15777 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 13
[LightGBM] [Debug] Re-bagging, using 15798 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 13
[LightGBM] [Debug] Re-bagging, using 15799 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 12
[LightGBM] [Debug] Re-bagging, using 15857 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 10
[LightGBM] [Debug] Re-bagging, using 15772 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 11
[LightGBM] [Debug] Re-bagging, using 15777 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 12
[LightGBM] [Debug] Re-bagging, using 15698 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 10
[LightGBM] [Debug] Re-bagging, using 15779 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 13
[LightGBM] [Debug] Re-bagging, using 15696 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 15
[LightGBM] [Debug] Re-bagging, using 15892 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 10
[LightGBM] [Debug] Re-bagging, using 15811 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 11
[LightGBM] [Debug] Re-bagging, using 15839 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 14
[LightGBM] [Debug] Re-bagging, using 15747 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 11
[LightGBM] [Debug] Re-bagging, using 15839 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 12
[LightGBM] [Debug] Re-bagging, using 15952 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 14
Trial 1, Fold 4: Log loss = 0.31275412815430426, Average precision = 0.9708694402491778, ROC-AUC = 0.9654651029707362, Elapsed Time = 2.2350790000000416 seconds
Trial 1, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 1, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[LightGBM] [Info] Number of positive: 10150, number of negative: 10500
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.792012
[LightGBM] [Info] Total Bins 21038
[LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902
[LightGBM] [Info] Start training from score -0.033902
[LightGBM] [Debug] Re-bagging, using 15787 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 10
[LightGBM] [Debug] Re-bagging, using 15935 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 12
[LightGBM] [Debug] Re-bagging, using 15845 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 11
[LightGBM] [Debug] Re-bagging, using 15744 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 12
[LightGBM] [Debug] Re-bagging, using 15788 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 12
[LightGBM] [Debug] Re-bagging, using 15772 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 13
[LightGBM] [Debug] Re-bagging, using 15800 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 10
[LightGBM] [Debug] Re-bagging, using 15790 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 14
[LightGBM] [Debug] Re-bagging, using 15852 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 11
[LightGBM] [Debug] Re-bagging, using 15769 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 12
[LightGBM] [Debug] Re-bagging, using 15773 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 11
[LightGBM] [Debug] Re-bagging, using 15689 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 10
[LightGBM] [Debug] Re-bagging, using 15772 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 12
[LightGBM] [Debug] Re-bagging, using 15699 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 15
[LightGBM] [Debug] Re-bagging, using 15889 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 13
[LightGBM] [Debug] Re-bagging, using 15809 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 11
[LightGBM] [Debug] Re-bagging, using 15834 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 14
[LightGBM] [Debug] Re-bagging, using 15737 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 11
[LightGBM] [Debug] Re-bagging, using 15831 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 11
[LightGBM] [Debug] Re-bagging, using 15945 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 11
Trial 1, Fold 5: Log loss = 0.3118220763524803, Average precision = 0.9697970675961709, ROC-AUC = 0.9645898593538078, Elapsed Time = 2.185679899999741 seconds
Optimization Progress:   2%|2         | 2/100 [00:33<27:54, 17.08s/it]
Trial 2, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 2, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[LightGBM] [Info] Number of positive: 10130, number of negative: 10533
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791640
[LightGBM] [Info] Total Bins 10006
[LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012
[LightGBM] [Info] Start training from score -0.039012
[LightGBM] [Debug] Re-bagging, using 15148 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 13
[LightGBM] [Debug] Re-bagging, using 15286 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 12
[LightGBM] [Debug] Re-bagging, using 15252 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11
[LightGBM] [Debug] Re-bagging, using 15126 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 12
[LightGBM] [Debug] Re-bagging, using 15207 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 12
[LightGBM] [Debug] Re-bagging, using 15173 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 12
[LightGBM] [Debug] Re-bagging, using 15175 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 9
[LightGBM] [Debug] Re-bagging, using 15184 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 12
[LightGBM] [Debug] Re-bagging, using 15259 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 13
[LightGBM] [Debug] Re-bagging, using 15110 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 12
[LightGBM] [Debug] Re-bagging, using 15164 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 11
[LightGBM] [Debug] Re-bagging, using 15065 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 14
[LightGBM] [Debug] Re-bagging, using 15136 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 12
[LightGBM] [Debug] Re-bagging, using 15053 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11
[LightGBM] [Debug] Re-bagging, using 15282 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10
[LightGBM] [Debug] Re-bagging, using 15208 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 12
[LightGBM] [Debug] Re-bagging, using 15240 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 12
[LightGBM] [Debug] Re-bagging, using 15171 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 11
[LightGBM] [Debug] Re-bagging, using 15191 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11
[LightGBM] [Debug] Re-bagging, using 15330 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 12
[LightGBM] [Debug] Re-bagging, using 15061 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 11
[LightGBM] [Debug] Re-bagging, using 15175 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 12
[LightGBM] [Debug] Re-bagging, using 15148 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 12
[LightGBM] [Debug] Re-bagging, using 15192 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9
[LightGBM] [Debug] Re-bagging, using 15329 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 11
[LightGBM] [Debug] Re-bagging, using 15118 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10
[LightGBM] [Debug] Re-bagging, using 15213 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 11
[LightGBM] [Debug] Re-bagging, using 15256 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 14
[LightGBM] [Debug] Re-bagging, using 15262 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 10
[LightGBM] [Debug] Re-bagging, using 15094 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Debug] Re-bagging, using 15149 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 12
[LightGBM] [Debug] Re-bagging, using 15200 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 10
Trial 2, Fold 1: Log loss = 0.31630301819399675, Average precision = 0.9665739919306886, ROC-AUC = 0.9600088780870172, Elapsed Time = 1.318768000000091 seconds
Trial 2, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 2, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[LightGBM] [Info] Number of positive: 10230, number of negative: 10471
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791600
[LightGBM] [Info] Total Bins 10022
[LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285
[LightGBM] [Info] Start training from score -0.023285
[LightGBM] [Debug] Re-bagging, using 15180 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10
[LightGBM] [Debug] Re-bagging, using 15312 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 14
[LightGBM] [Debug] Re-bagging, using 15279 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10
[LightGBM] [Debug] Re-bagging, using 15158 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 12
[LightGBM] [Debug] Re-bagging, using 15235 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 12
[LightGBM] [Debug] Re-bagging, using 15192 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 11
[LightGBM] [Debug] Re-bagging, using 15194 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10
[LightGBM] [Debug] Re-bagging, using 15225 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11
[LightGBM] [Debug] Re-bagging, using 15288 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9
[LightGBM] [Debug] Re-bagging, using 15142 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11
[LightGBM] [Debug] Re-bagging, using 15187 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 13
[LightGBM] [Debug] Re-bagging, using 15082 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 11
[LightGBM] [Debug] Re-bagging, using 15159 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10
[LightGBM] [Debug] Re-bagging, using 15068 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 11
[LightGBM] [Debug] Re-bagging, using 15315 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 11
[LightGBM] [Debug] Re-bagging, using 15254 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 10
[LightGBM] [Debug] Re-bagging, using 15279 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11
[LightGBM] [Debug] Re-bagging, using 15207 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9
[LightGBM] [Debug] Re-bagging, using 15228 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10
[LightGBM] [Debug] Re-bagging, using 15336 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 16
[LightGBM] [Debug] Re-bagging, using 15089 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 11
[LightGBM] [Debug] Re-bagging, using 15186 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10
[LightGBM] [Debug] Re-bagging, using 15171 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 10
[LightGBM] [Debug] Re-bagging, using 15235 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 10
[LightGBM] [Debug] Re-bagging, using 15358 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10
[LightGBM] [Debug] Re-bagging, using 15154 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10
[LightGBM] [Debug] Re-bagging, using 15231 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 10
[LightGBM] [Debug] Re-bagging, using 15290 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 14
[LightGBM] [Debug] Re-bagging, using 15293 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11
[LightGBM] [Debug] Re-bagging, using 15133 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 12
[LightGBM] [Debug] Re-bagging, using 15172 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 12
[LightGBM] [Debug] Re-bagging, using 15218 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11
Trial 2, Fold 2: Log loss = 0.31566260066916224, Average precision = 0.9642587196322059, ROC-AUC = 0.9608000568143709, Elapsed Time = 1.4685500999994474 seconds
Trial 2, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 2, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[LightGBM] [Info] Number of positive: 10165, number of negative: 10517
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791401
[LightGBM] [Info] Total Bins 10024
[LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043
[LightGBM] [Info] Start training from score -0.034043
[LightGBM] [Debug] Re-bagging, using 15163 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 10
[LightGBM] [Debug] Re-bagging, using 15300 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 14
[LightGBM] [Debug] Re-bagging, using 15264 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10
[LightGBM] [Debug] Re-bagging, using 15146 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 12
[LightGBM] [Debug] Re-bagging, using 15219 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 12
[LightGBM] [Debug] Re-bagging, using 15181 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 14
[LightGBM] [Debug] Re-bagging, using 15183 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9
[LightGBM] [Debug] Re-bagging, using 15207 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 11
[LightGBM] [Debug] Re-bagging, using 15267 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 13
[LightGBM] [Debug] Re-bagging, using 15133 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 12
[LightGBM] [Debug] Re-bagging, using 15177 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 10
[LightGBM] [Debug] Re-bagging, using 15074 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10
[LightGBM] [Debug] Re-bagging, using 15151 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Debug] Re-bagging, using 15053 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10
[LightGBM] [Debug] Re-bagging, using 15298 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 12
[LightGBM] [Debug] Re-bagging, using 15222 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 10
[LightGBM] [Debug] Re-bagging, using 15265 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 12
[LightGBM] [Debug] Re-bagging, using 15195 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 11
[LightGBM] [Debug] Re-bagging, using 15205 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 11
[LightGBM] [Debug] Re-bagging, using 15348 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 13
[LightGBM] [Debug] Re-bagging, using 15080 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11
[LightGBM] [Debug] Re-bagging, using 15163 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 12
[LightGBM] [Debug] Re-bagging, using 15156 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 12
[LightGBM] [Debug] Re-bagging, using 15206 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10
[LightGBM] [Debug] Re-bagging, using 15353 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 14
[LightGBM] [Debug] Re-bagging, using 15145 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10
[LightGBM] [Debug] Re-bagging, using 15209 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 10
[LightGBM] [Debug] Re-bagging, using 15276 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11
[LightGBM] [Debug] Re-bagging, using 15277 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 13
[LightGBM] [Debug] Re-bagging, using 15115 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 9
[LightGBM] [Debug] Re-bagging, using 15164 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11
[LightGBM] [Debug] Re-bagging, using 15216 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 11
Trial 2, Fold 3: Log loss = 0.31411749801159977, Average precision = 0.9676660962346362, ROC-AUC = 0.9623256481686586, Elapsed Time = 1.5098760999999286 seconds
Trial 2, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 2, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[LightGBM] [Info] Number of positive: 10177, number of negative: 10479
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791090
[LightGBM] [Info] Total Bins 10003
[LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243
[LightGBM] [Info] Start training from score -0.029243
[LightGBM] [Debug] Re-bagging, using 15143 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 10
[LightGBM] [Debug] Re-bagging, using 15279 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10
[LightGBM] [Debug] Re-bagging, using 15247 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 10
[LightGBM] [Debug] Re-bagging, using 15124 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 9
[LightGBM] [Debug] Re-bagging, using 15201 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 11
[LightGBM] [Debug] Re-bagging, using 15168 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 12
[LightGBM] [Debug] Re-bagging, using 15168 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 11
[LightGBM] [Debug] Re-bagging, using 15185 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 11
[LightGBM] [Debug] Re-bagging, using 15248 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 11
[LightGBM] [Debug] Re-bagging, using 15109 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11
[LightGBM] [Debug] Re-bagging, using 15157 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11
[LightGBM] [Debug] Re-bagging, using 15055 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 12
[LightGBM] [Debug] Re-bagging, using 15141 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11
[LightGBM] [Debug] Re-bagging, using 15040 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 11
[LightGBM] [Debug] Re-bagging, using 15284 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 12
[LightGBM] [Debug] Re-bagging, using 15201 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 12
[LightGBM] [Debug] Re-bagging, using 15236 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 14
[LightGBM] [Debug] Re-bagging, using 15163 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 13
[LightGBM] [Debug] Re-bagging, using 15178 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 14
[LightGBM] [Debug] Re-bagging, using 15329 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9
[LightGBM] [Debug] Re-bagging, using 15058 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11
[LightGBM] [Debug] Re-bagging, using 15161 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 13
[LightGBM] [Debug] Re-bagging, using 15147 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 12
[LightGBM] [Debug] Re-bagging, using 15200 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 13
[LightGBM] [Debug] Re-bagging, using 15321 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 12
[LightGBM] [Debug] Re-bagging, using 15113 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 12
[LightGBM] [Debug] Re-bagging, using 15193 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9
[LightGBM] [Debug] Re-bagging, using 15257 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 14
[LightGBM] [Debug] Re-bagging, using 15256 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 10
[LightGBM] [Debug] Re-bagging, using 15103 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 12
[LightGBM] [Debug] Re-bagging, using 15128 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11
[LightGBM] [Debug] Re-bagging, using 15199 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 9
Trial 2, Fold 4: Log loss = 0.315470578568722, Average precision = 0.9666878301659132, ROC-AUC = 0.9606021737471715, Elapsed Time = 1.6039383999996062 seconds
Trial 2, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 2, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[LightGBM] [Info] Number of positive: 10150, number of negative: 10500
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.792012
[LightGBM] [Info] Total Bins 10007
[LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902
[LightGBM] [Info] Start training from score -0.033902
[LightGBM] [Debug] Re-bagging, using 15138 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 12
[LightGBM] [Debug] Re-bagging, using 15277 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11
[LightGBM] [Debug] Re-bagging, using 15245 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11
[LightGBM] [Debug] Re-bagging, using 15118 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11
[LightGBM] [Debug] Re-bagging, using 15195 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11
[LightGBM] [Debug] Re-bagging, using 15160 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 12
[LightGBM] [Debug] Re-bagging, using 15171 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10
[LightGBM] [Debug] Re-bagging, using 15176 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 14
[LightGBM] [Debug] Re-bagging, using 15244 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 12
[LightGBM] [Debug] Re-bagging, using 15107 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 12
[LightGBM] [Debug] Re-bagging, using 15152 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 9
[LightGBM] [Debug] Re-bagging, using 15046 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 11
[LightGBM] [Debug] Re-bagging, using 15135 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 12
[LightGBM] [Debug] Re-bagging, using 15041 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10
[LightGBM] [Debug] Re-bagging, using 15282 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 13
[LightGBM] [Debug] Re-bagging, using 15199 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 11
[LightGBM] [Debug] Re-bagging, using 15231 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 12
[LightGBM] [Debug] Re-bagging, using 15154 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 11
[LightGBM] [Debug] Re-bagging, using 15173 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 11
[LightGBM] [Debug] Re-bagging, using 15318 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11
[LightGBM] [Debug] Re-bagging, using 15058 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 12
[LightGBM] [Debug] Re-bagging, using 15157 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 14
[LightGBM] [Debug] Re-bagging, using 15135 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 13
[LightGBM] [Debug] Re-bagging, using 15200 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 12
[LightGBM] [Debug] Re-bagging, using 15330 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 9
[LightGBM] [Debug] Re-bagging, using 15101 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 13
[LightGBM] [Debug] Re-bagging, using 15196 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 13
[LightGBM] [Debug] Re-bagging, using 15237 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 11
[LightGBM] [Debug] Re-bagging, using 15259 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 15
[LightGBM] [Debug] Re-bagging, using 15090 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11
[LightGBM] [Debug] Re-bagging, using 15145 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10
[LightGBM] [Debug] Re-bagging, using 15178 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 11
Trial 2, Fold 5: Log loss = 0.31903414895241994, Average precision = 0.964618303671335, ROC-AUC = 0.9587533210709176, Elapsed Time = 1.4919348000003083 seconds
Optimization Progress:   3%|3         | 3/100 [00:49<26:41, 16.51s/it]
Trial 3, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 3, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[LightGBM] [Info] Number of positive: 10130, number of negative: 10533
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.805817
[LightGBM] [Info] Total Bins 24071
[LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 267
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012
[LightGBM] [Info] Start training from score -0.039012
[LightGBM] [Debug] Re-bagging, using 5514 data to train
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 8
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 8
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Debug] Re-bagging, using 5491 data to train
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Debug] Re-bagging, using 5495 data to train
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 5
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7
Trial 3, Fold 1: Log loss = 0.5597832453424669, Average precision = 0.9667125671758896, ROC-AUC = 0.9600614535686803, Elapsed Time = 0.5946309999999357 seconds
Trial 3, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 3, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[LightGBM] [Info] Number of positive: 10230, number of negative: 10471
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.806987
[LightGBM] [Info] Total Bins 24079
[LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 267
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285
[LightGBM] [Info] Start training from score -0.023285
[LightGBM] [Debug] Re-bagging, using 5524 data to train
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7
[LightGBM] [Debug] Re-bagging, using 5503 data to train
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Debug] Re-bagging, using 5501 data to train
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7
Trial 3, Fold 2: Log loss = 0.5610194828577617, Average precision = 0.9630685411421096, ROC-AUC = 0.9596014769308462, Elapsed Time = 0.6716820000001462 seconds
Trial 3, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 3, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[LightGBM] [Info] Number of positive: 10165, number of negative: 10517
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.806893
[LightGBM] [Info] Total Bins 24073
[LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 267
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043
[LightGBM] [Info] Start training from score -0.034043
[LightGBM] [Debug] Re-bagging, using 5519 data to train
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 8
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 8
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Debug] Re-bagging, using 5494 data to train
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Debug] Re-bagging, using 5499 data to train
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7
Trial 3, Fold 3: Log loss = 0.5581836889306623, Average precision = 0.9675225486549472, ROC-AUC = 0.9629256315551232, Elapsed Time = 0.6689807000002475 seconds
Trial 3, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 3, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[LightGBM] [Info] Number of positive: 10177, number of negative: 10479
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.804155
[LightGBM] [Info] Total Bins 24060
[LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 265
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243
[LightGBM] [Info] Start training from score -0.029243
[LightGBM] [Debug] Re-bagging, using 5511 data to train
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 8
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Debug] Re-bagging, using 5489 data to train
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 8
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 8
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Debug] Re-bagging, using 5492 data to train
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7
Trial 3, Fold 4: Log loss = 0.5640538981012556, Average precision = 0.9650158233172024, ROC-AUC = 0.9589714609994016, Elapsed Time = 0.713144000000284 seconds
Trial 3, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 3, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[LightGBM] [Info] Number of positive: 10150, number of negative: 10500
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.807356
[LightGBM] [Info] Total Bins 24068
[LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 267
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902
[LightGBM] [Info] Start training from score -0.033902
[LightGBM] [Debug] Re-bagging, using 5509 data to train
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 8
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 8
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 5
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Debug] Re-bagging, using 5489 data to train
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 8
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 8
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 8
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5492 data to train
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 8
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7
Trial 3, Fold 5: Log loss = 0.5609692072882091, Average precision = 0.962104120896841, ROC-AUC = 0.956462776136596, Elapsed Time = 0.6953654999997525 seconds
Optimization Progress:   4%|4         | 4/100 [01:00<22:48, 14.26s/it]
Trial 4, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 4, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[LightGBM] [Info] Number of positive: 10130, number of negative: 10533
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791640
[LightGBM] [Info] Total Bins 8523
[LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012
[LightGBM] [Info] Start training from score -0.039012
[LightGBM] [Debug] Re-bagging, using 4037 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6
Trial 4, Fold 1: Log loss = 0.3602231587822408, Average precision = 0.9516449899508185, ROC-AUC = 0.9442643136748826, Elapsed Time = 0.46940489999997226 seconds
Trial 4, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 4, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[LightGBM] [Info] Number of positive: 10230, number of negative: 10471
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791600
[LightGBM] [Info] Total Bins 8527
[LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285
[LightGBM] [Info] Start training from score -0.023285
[LightGBM] [Debug] Re-bagging, using 4045 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5
Trial 4, Fold 2: Log loss = 0.3391353717235677, Average precision = 0.9522235072890253, ROC-AUC = 0.9490507569027792, Elapsed Time = 0.4991823000000295 seconds
Trial 4, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 4, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[LightGBM] [Info] Number of positive: 10165, number of negative: 10517
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791401
[LightGBM] [Info] Total Bins 8536
[LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043
[LightGBM] [Info] Start training from score -0.034043
[LightGBM] [Debug] Re-bagging, using 4040 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 5
Trial 4, Fold 3: Log loss = 0.3550606159355602, Average precision = 0.956825668397955, ROC-AUC = 0.9500223018655315, Elapsed Time = 0.5020086000004085 seconds
Trial 4, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 4, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[LightGBM] [Info] Number of positive: 10177, number of negative: 10479
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791090
[LightGBM] [Info] Total Bins 8509
[LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243
[LightGBM] [Info] Start training from score -0.029243
[LightGBM] [Debug] Re-bagging, using 4036 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 6
Trial 4, Fold 4: Log loss = 0.34068448193557793, Average precision = 0.9555064606052168, ROC-AUC = 0.9494628073802251, Elapsed Time = 0.5262147999992521 seconds
Trial 4, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 4, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[LightGBM] [Info] Number of positive: 10150, number of negative: 10500
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.792012
[LightGBM] [Info] Total Bins 8515
[LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902
[LightGBM] [Info] Start training from score -0.033902
[LightGBM] [Debug] Re-bagging, using 4034 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5
Trial 4, Fold 5: Log loss = 0.3415364396590086, Average precision = 0.9558100300661114, ROC-AUC = 0.947333147539156, Elapsed Time = 0.507986699999492 seconds
Optimization Progress:   5%|5         | 5/100 [01:10<20:19, 12.84s/it]
Trial 5, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 5, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[LightGBM] [Info] Number of positive: 10130, number of negative: 10533
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791640
[LightGBM] [Info] Total Bins 27687
[LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012
[LightGBM] [Info] Start training from score -0.039012
[LightGBM] [Debug] Re-bagging, using 8104 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 10
[LightGBM] [Debug] Re-bagging, using 8171 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 11
Trial 5, Fold 1: Log loss = 0.5627463031660861, Average precision = 0.961618792767833, ROC-AUC = 0.9552116641095865, Elapsed Time = 0.49536629999965953 seconds
Trial 5, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 5, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[LightGBM] [Info] Number of positive: 10230, number of negative: 10471
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791600
[LightGBM] [Info] Total Bins 27695
[LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285
[LightGBM] [Info] Start training from score -0.023285
[LightGBM] [Debug] Re-bagging, using 8121 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 11
[LightGBM] [Debug] Re-bagging, using 8186 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 10
Trial 5, Fold 2: Log loss = 0.5602560012330817, Average precision = 0.9623815050366534, ROC-AUC = 0.958124667482665, Elapsed Time = 0.5083058999998684 seconds
Trial 5, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 5, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[LightGBM] [Info] Number of positive: 10165, number of negative: 10517
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791401
[LightGBM] [Info] Total Bins 27691
[LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043
[LightGBM] [Info] Start training from score -0.034043
[LightGBM] [Debug] Re-bagging, using 8112 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Debug] Re-bagging, using 8176 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
Trial 5, Fold 3: Log loss = 0.5581777532188745, Average precision = 0.9660936011271237, ROC-AUC = 0.9598167635388275, Elapsed Time = 0.525013399999807 seconds
Trial 5, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 5, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[LightGBM] [Info] Number of positive: 10177, number of negative: 10479
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791090
[LightGBM] [Info] Total Bins 27682
[LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243
[LightGBM] [Info] Start training from score -0.029243
[LightGBM] [Debug] Re-bagging, using 8100 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Debug] Re-bagging, using 8169 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10
Trial 5, Fold 4: Log loss = 0.5550038444062219, Average precision = 0.9652116478276259, ROC-AUC = 0.9590724258508171, Elapsed Time = 0.5312815999996019 seconds
Trial 5, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 5, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[LightGBM] [Info] Number of positive: 10150, number of negative: 10500
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.792012
[LightGBM] [Info] Total Bins 27687
[LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902
[LightGBM] [Info] Start training from score -0.033902
[LightGBM] [Debug] Re-bagging, using 8098 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Debug] Re-bagging, using 8168 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
Trial 5, Fold 5: Log loss = 0.5596013303068881, Average precision = 0.9631739580524441, ROC-AUC = 0.9570591011277707, Elapsed Time = 0.5385897999994995 seconds
Optimization Progress:   6%|6         | 6/100 [01:21<19:00, 12.13s/it]
Trial 6, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 6, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[LightGBM] [Info] Number of positive: 10130, number of negative: 10533
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.792997
[LightGBM] [Info] Total Bins 20218
[LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 256
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012
[LightGBM] [Info] Start training from score -0.039012
[LightGBM] [Debug] Re-bagging, using 12618 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 14
[LightGBM] [Debug] Re-bagging, using 12655 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 101 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 94 and depth = 12
[LightGBM] [Debug] Re-bagging, using 12703 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 101 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 99 and depth = 15
[LightGBM] [Debug] Re-bagging, using 12488 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 95 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 97 and depth = 13
[LightGBM] [Debug] Re-bagging, using 12648 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 99 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 91 and depth = 12
[LightGBM] [Debug] Re-bagging, using 12502 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 98 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 99 and depth = 17
[LightGBM] [Debug] Re-bagging, using 12601 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 88 and depth = 14
[LightGBM] [Debug] Re-bagging, using 12648 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 95 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 102 and depth = 15
[LightGBM] [Debug] Re-bagging, using 12650 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 97 and depth = 17
Trial 6, Fold 1: Log loss = 0.5724751092579775, Average precision = 0.9547962934851068, ROC-AUC = 0.9497921326766179, Elapsed Time = 0.5775649000006524 seconds
Trial 6, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 6, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[LightGBM] [Info] Number of positive: 10230, number of negative: 10471
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.792956
[LightGBM] [Info] Total Bins 20183
[LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 256
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285
[LightGBM] [Info] Start training from score -0.023285
[LightGBM] [Debug] Re-bagging, using 12643 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 87 and depth = 14
[LightGBM] [Debug] Re-bagging, using 12674 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 90 and depth = 13
[LightGBM] [Debug] Re-bagging, using 12729 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 98 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 97 and depth = 18
[LightGBM] [Debug] Re-bagging, using 12521 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 90 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 88 and depth = 15
[LightGBM] [Debug] Re-bagging, using 12666 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 87 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 100 and depth = 15
[LightGBM] [Debug] Re-bagging, using 12511 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 97 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 99 and depth = 15
[LightGBM] [Debug] Re-bagging, using 12618 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 101 and depth = 17
[LightGBM] [Debug] Re-bagging, using 12681 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 17
[LightGBM] [Debug] Re-bagging, using 12672 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 95 and depth = 19
Trial 6, Fold 2: Log loss = 0.5658112154684879, Average precision = 0.960626113189713, ROC-AUC = 0.9565368545294133, Elapsed Time = 0.6053950999994413 seconds
Trial 6, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 6, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[LightGBM] [Info] Number of positive: 10165, number of negative: 10517
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.792768
[LightGBM] [Info] Total Bins 20183
[LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 256
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043
[LightGBM] [Info] Start training from score -0.034043
[LightGBM] [Debug] Re-bagging, using 12629 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 90 and depth = 14
[LightGBM] [Debug] Re-bagging, using 12664 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 95 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 97 and depth = 16
[LightGBM] [Debug] Re-bagging, using 12715 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 99 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 97 and depth = 14
[LightGBM] [Debug] Re-bagging, using 12511 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 99 and depth = 14
[LightGBM] [Debug] Re-bagging, using 12656 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 99 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 95 and depth = 15
[LightGBM] [Debug] Re-bagging, using 12502 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 91 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 95 and depth = 14
[LightGBM] [Debug] Re-bagging, using 12611 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 14
[LightGBM] [Debug] Re-bagging, using 12667 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 93 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 11
[LightGBM] [Debug] Re-bagging, using 12653 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 101 and depth = 17
Trial 6, Fold 3: Log loss = 0.5633294475258322, Average precision = 0.9644919097673798, ROC-AUC = 0.9585736075931082, Elapsed Time = 0.6343689000004815 seconds
Trial 6, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 6, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[LightGBM] [Info] Number of positive: 10177, number of negative: 10479
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.792454
[LightGBM] [Info] Total Bins 20218
[LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 256
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243
[LightGBM] [Info] Start training from score -0.029243
[LightGBM] [Debug] Re-bagging, using 12614 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 14
[LightGBM] [Debug] Re-bagging, using 12651 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 94 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 97 and depth = 17
[LightGBM] [Debug] Re-bagging, using 12698 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 101 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 93 and depth = 18
[LightGBM] [Debug] Re-bagging, using 12485 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 98 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 100 and depth = 13
[LightGBM] [Debug] Re-bagging, using 12640 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 101 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 98 and depth = 16
[LightGBM] [Debug] Re-bagging, using 12500 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 86 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 100 and depth = 17
[LightGBM] [Debug] Re-bagging, using 12598 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 93 and depth = 12
[LightGBM] [Debug] Re-bagging, using 12649 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 15
[LightGBM] [Debug] Re-bagging, using 12637 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 93 and depth = 17
Trial 6, Fold 4: Log loss = 0.5648543846673204, Average precision = 0.9643431122624986, ROC-AUC = 0.9583624678402732, Elapsed Time = 0.7327912000000651 seconds
Trial 6, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 6, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[LightGBM] [Info] Number of positive: 10150, number of negative: 10500
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.793364
[LightGBM] [Info] Total Bins 20213
[LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 256
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902
[LightGBM] [Info] Start training from score -0.033902
[LightGBM] [Debug] Re-bagging, using 12611 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 88 and depth = 15
[LightGBM] [Debug] Re-bagging, using 12647 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 99 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 95 and depth = 20
[LightGBM] [Debug] Re-bagging, using 12698 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 94 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 99 and depth = 15
[LightGBM] [Debug] Re-bagging, using 12478 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 98 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 100 and depth = 13
[LightGBM] [Debug] Re-bagging, using 12635 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 94 and depth = 19
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 98 and depth = 15
[LightGBM] [Debug] Re-bagging, using 12496 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 97 and depth = 19
[LightGBM] [Debug] Re-bagging, using 12599 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 101 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 91 and depth = 18
[LightGBM] [Debug] Re-bagging, using 12640 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 90 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 94 and depth = 16
[LightGBM] [Debug] Re-bagging, using 12636 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 17
Trial 6, Fold 5: Log loss = 0.5652957851302245, Average precision = 0.9619926890222837, ROC-AUC = 0.956429333184698, Elapsed Time = 0.6392338000005111 seconds
Optimization Progress:   7%|7         | 7/100 [01:31<17:55, 11.57s/it]
Trial 7, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 7, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[LightGBM] [Info] Number of positive: 10130, number of negative: 10533
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795662
[LightGBM] [Info] Total Bins 11147
[LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 258
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012
[LightGBM] [Info] Start training from score -0.039012
[LightGBM] [Debug] Re-bagging, using 9953 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 11
[LightGBM] [Debug] Re-bagging, using 9912 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 10
[LightGBM] [Debug] Re-bagging, using 9892 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 13
[LightGBM] [Debug] Re-bagging, using 9802 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 13
[LightGBM] [Debug] Re-bagging, using 9970 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 11
[LightGBM] [Debug] Re-bagging, using 9834 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 10
[LightGBM] [Debug] Re-bagging, using 9910 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 12
[LightGBM] [Debug] Re-bagging, using 9921 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 13
[LightGBM] [Debug] Re-bagging, using 9935 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 12
[LightGBM] [Debug] Re-bagging, using 9660 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 12
Trial 7, Fold 1: Log loss = 0.34242023144480144, Average precision = 0.9722076973965884, ROC-AUC = 0.966624128322412, Elapsed Time = 1.238731899999948 seconds
Trial 7, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 7, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[LightGBM] [Info] Number of positive: 10230, number of negative: 10471
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.794298
[LightGBM] [Info] Total Bins 11158
[LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 257
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285
[LightGBM] [Info] Start training from score -0.023285
[LightGBM] [Debug] Re-bagging, using 9972 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 11
[LightGBM] [Debug] Re-bagging, using 9932 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 11
[LightGBM] [Debug] Re-bagging, using 9906 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 15
[LightGBM] [Debug] Re-bagging, using 9828 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 12
[LightGBM] [Debug] Re-bagging, using 9997 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 12
[LightGBM] [Debug] Re-bagging, using 9832 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 14
[LightGBM] [Debug] Re-bagging, using 9918 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 12
[LightGBM] [Debug] Re-bagging, using 9936 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 12
[LightGBM] [Debug] Re-bagging, using 9963 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 13
[LightGBM] [Debug] Re-bagging, using 9683 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 11
Trial 7, Fold 2: Log loss = 0.3405479248799782, Average precision = 0.9708784536841033, ROC-AUC = 0.9671138269243669, Elapsed Time = 1.569465699999455 seconds
Trial 7, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 7, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[LightGBM] [Info] Number of positive: 10165, number of negative: 10517
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795455
[LightGBM] [Info] Total Bins 11164
[LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 258
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043
[LightGBM] [Info] Start training from score -0.034043
[LightGBM] [Debug] Re-bagging, using 9963 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 12
[LightGBM] [Debug] Re-bagging, using 9919 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 12
[LightGBM] [Debug] Re-bagging, using 9897 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 12
[LightGBM] [Debug] Re-bagging, using 9823 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 10
[LightGBM] [Debug] Re-bagging, using 9982 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 12
[LightGBM] [Debug] Re-bagging, using 9829 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 12
[LightGBM] [Debug] Re-bagging, using 9917 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 13
[LightGBM] [Debug] Re-bagging, using 9927 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 11
[LightGBM] [Debug] Re-bagging, using 9941 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 10
[LightGBM] [Debug] Re-bagging, using 9682 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 11
Trial 7, Fold 3: Log loss = 0.3415467230855122, Average precision = 0.9724346573249535, ROC-AUC = 0.9676446882433956, Elapsed Time = 1.8036743000002389 seconds
Trial 7, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 7, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[LightGBM] [Info] Number of positive: 10177, number of negative: 10479
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795144
[LightGBM] [Info] Total Bins 11145
[LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 258
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243
[LightGBM] [Info] Start training from score -0.029243
[LightGBM] [Debug] Re-bagging, using 9949 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 12
[LightGBM] [Debug] Re-bagging, using 9908 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 12
[LightGBM] [Debug] Re-bagging, using 9889 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 13
[LightGBM] [Debug] Re-bagging, using 9800 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 13
[LightGBM] [Debug] Re-bagging, using 9963 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 11
[LightGBM] [Debug] Re-bagging, using 9832 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 12
[LightGBM] [Debug] Re-bagging, using 9908 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 12
[LightGBM] [Debug] Re-bagging, using 9920 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 11
[LightGBM] [Debug] Re-bagging, using 9932 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 11
[LightGBM] [Debug] Re-bagging, using 9658 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 13
Trial 7, Fold 4: Log loss = 0.3424819866368217, Average precision = 0.9721901674147935, ROC-AUC = 0.9669495470813634, Elapsed Time = 1.786351100000502 seconds
Trial 7, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 7, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[LightGBM] [Info] Number of positive: 10150, number of negative: 10500
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.796026
[LightGBM] [Info] Total Bins 11147
[LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 258
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902
[LightGBM] [Info] Start training from score -0.033902
[LightGBM] [Debug] Re-bagging, using 9947 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 11
[LightGBM] [Debug] Re-bagging, using 9905 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 12
[LightGBM] [Debug] Re-bagging, using 9889 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 11
[LightGBM] [Debug] Re-bagging, using 9793 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 12
[LightGBM] [Debug] Re-bagging, using 9961 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 11
[LightGBM] [Debug] Re-bagging, using 9826 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 10
[LightGBM] [Debug] Re-bagging, using 9912 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 12
[LightGBM] [Debug] Re-bagging, using 9914 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 10
[LightGBM] [Debug] Re-bagging, using 9930 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 12
[LightGBM] [Debug] Re-bagging, using 9660 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 11
Trial 7, Fold 5: Log loss = 0.3435956901704336, Average precision = 0.9697871855943496, ROC-AUC = 0.9651655240324767, Elapsed Time = 1.7220666000002893 seconds
Optimization Progress:   8%|8         | 8/100 [01:47<19:41, 12.85s/it]
Trial 8, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 8, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[LightGBM] [Info] Number of positive: 10130, number of negative: 10533
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791640
[LightGBM] [Info] Total Bins 7358
[LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012
[LightGBM] [Info] Start training from score -0.039012
[LightGBM] [Debug] Re-bagging, using 9522 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 6
[LightGBM] [Debug] Re-bagging, using 9517 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8
[LightGBM] [Debug] Re-bagging, using 9466 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8
[LightGBM] [Debug] Re-bagging, using 9408 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8
[LightGBM] [Debug] Re-bagging, using 9546 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 7
[LightGBM] [Debug] Re-bagging, using 9414 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8
[LightGBM] [Debug] Re-bagging, using 9518 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7
[LightGBM] [Debug] Re-bagging, using 9510 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8
[LightGBM] [Debug] Re-bagging, using 9540 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7
[LightGBM] [Debug] Re-bagging, using 9245 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 8
[LightGBM] [Debug] Re-bagging, using 9337 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7
[LightGBM] [Debug] Re-bagging, using 9365 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 8
[LightGBM] [Debug] Re-bagging, using 9368 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 6
[LightGBM] [Debug] Re-bagging, using 9405 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7
Trial 8, Fold 1: Log loss = 0.31846836571832476, Average precision = 0.9630925930307719, ROC-AUC = 0.9555496706863013, Elapsed Time = 1.1809659999998985 seconds
Trial 8, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 8, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[LightGBM] [Info] Number of positive: 10230, number of negative: 10471
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791600
[LightGBM] [Info] Total Bins 7374
[LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285
[LightGBM] [Info] Start training from score -0.023285
[LightGBM] [Debug] Re-bagging, using 9540 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8
[LightGBM] [Debug] Re-bagging, using 9536 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7
[LightGBM] [Debug] Re-bagging, using 9481 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8
[LightGBM] [Debug] Re-bagging, using 9433 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7
[LightGBM] [Debug] Re-bagging, using 9573 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 9
[LightGBM] [Debug] Re-bagging, using 9412 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7
[LightGBM] [Debug] Re-bagging, using 9528 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7
[LightGBM] [Debug] Re-bagging, using 9523 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 8
[LightGBM] [Debug] Re-bagging, using 9568 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8
[LightGBM] [Debug] Re-bagging, using 9264 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7
[LightGBM] [Debug] Re-bagging, using 9347 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 6
[LightGBM] [Debug] Re-bagging, using 9373 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8
[LightGBM] [Debug] Re-bagging, using 9373 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7
[LightGBM] [Debug] Re-bagging, using 9431 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7
Trial 8, Fold 2: Log loss = 0.3197585532648229, Average precision = 0.960242214402566, ROC-AUC = 0.9550711226695712, Elapsed Time = 1.3920828999998776 seconds
Trial 8, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 8, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[LightGBM] [Info] Number of positive: 10165, number of negative: 10517
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.790045
[LightGBM] [Info] Total Bins 7369
[LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 254
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043
[LightGBM] [Info] Start training from score -0.034043
[LightGBM] [Debug] Re-bagging, using 9531 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7
[LightGBM] [Debug] Re-bagging, using 9525 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8
[LightGBM] [Debug] Re-bagging, using 9470 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 6
[LightGBM] [Debug] Re-bagging, using 9428 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7
[LightGBM] [Debug] Re-bagging, using 9559 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 8
[LightGBM] [Debug] Re-bagging, using 9408 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8
[LightGBM] [Debug] Re-bagging, using 9527 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 9
[LightGBM] [Debug] Re-bagging, using 9516 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7
[LightGBM] [Debug] Re-bagging, using 9545 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 6
[LightGBM] [Debug] Re-bagging, using 9267 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 9
[LightGBM] [Debug] Re-bagging, using 9339 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7
[LightGBM] [Debug] Re-bagging, using 9371 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8
[LightGBM] [Debug] Re-bagging, using 9365 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 6
[LightGBM] [Debug] Re-bagging, using 9410 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 8
Trial 8, Fold 3: Log loss = 0.31408900076580654, Average precision = 0.9649005536183861, ROC-AUC = 0.9582289971203205, Elapsed Time = 1.5456070000000182 seconds
Trial 8, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 8, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[LightGBM] [Info] Number of positive: 10177, number of negative: 10479
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791090
[LightGBM] [Info] Total Bins 7355
[LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243
[LightGBM] [Info] Start training from score -0.029243
[LightGBM] [Debug] Re-bagging, using 9518 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7
[LightGBM] [Debug] Re-bagging, using 9513 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 7
[LightGBM] [Debug] Re-bagging, using 9464 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 9
[LightGBM] [Debug] Re-bagging, using 9406 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8
[LightGBM] [Debug] Re-bagging, using 9539 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8
[LightGBM] [Debug] Re-bagging, using 9411 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 6
[LightGBM] [Debug] Re-bagging, using 9516 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 8
[LightGBM] [Debug] Re-bagging, using 9509 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8
[LightGBM] [Debug] Re-bagging, using 9538 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 8
[LightGBM] [Debug] Re-bagging, using 9242 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 7
[LightGBM] [Debug] Re-bagging, using 9333 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7
[LightGBM] [Debug] Re-bagging, using 9364 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8
[LightGBM] [Debug] Re-bagging, using 9360 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7
[LightGBM] [Debug] Re-bagging, using 9405 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7
Trial 8, Fold 4: Log loss = 0.3132359088595296, Average precision = 0.9633989236643054, ROC-AUC = 0.9558619671142203, Elapsed Time = 1.369531599999391 seconds
Trial 8, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 8, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[LightGBM] [Info] Number of positive: 10150, number of negative: 10500
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.792012
[LightGBM] [Info] Total Bins 7363
[LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902
[LightGBM] [Info] Start training from score -0.033902
[LightGBM] [Debug] Re-bagging, using 9516 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7
[LightGBM] [Debug] Re-bagging, using 9510 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 9
[LightGBM] [Debug] Re-bagging, using 9464 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7
[LightGBM] [Debug] Re-bagging, using 9399 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8
[LightGBM] [Debug] Re-bagging, using 9537 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7
[LightGBM] [Debug] Re-bagging, using 9405 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 6
[LightGBM] [Debug] Re-bagging, using 9520 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7
[LightGBM] [Debug] Re-bagging, using 9503 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 6
[LightGBM] [Debug] Re-bagging, using 9536 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 8
[LightGBM] [Debug] Re-bagging, using 9245 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 8
[LightGBM] [Debug] Re-bagging, using 9324 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7
[LightGBM] [Debug] Re-bagging, using 9357 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8
[LightGBM] [Debug] Re-bagging, using 9358 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7
[LightGBM] [Debug] Re-bagging, using 9416 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 8
Trial 8, Fold 5: Log loss = 0.3175104373823987, Average precision = 0.9616862841710087, ROC-AUC = 0.954297456477714, Elapsed Time = 1.3472390000006271 seconds
Optimization Progress:   9%|9         | 9/100 [02:02<20:23, 13.45s/it]
Trial 9, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 9, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[LightGBM] [Info] Number of positive: 10130, number of negative: 10533
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791640
[LightGBM] [Info] Total Bins 17568
[LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012
[LightGBM] [Info] Start training from score -0.039012
[LightGBM] [Debug] Re-bagging, using 3223 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 5
[LightGBM] [Debug] Re-bagging, using 3216 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6
[LightGBM] [Debug] Re-bagging, using 3121 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 8
[LightGBM] [Debug] Re-bagging, using 3107 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 6
[LightGBM] [Debug] Re-bagging, using 3216 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Debug] Re-bagging, using 3204 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 6
[LightGBM] [Debug] Re-bagging, using 3121 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 7
Trial 9, Fold 1: Log loss = 0.5713470071668255, Average precision = 0.9584267499871053, ROC-AUC = 0.9509777695329864, Elapsed Time = 0.5892558999994435 seconds
Trial 9, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 9, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[LightGBM] [Info] Number of positive: 10230, number of negative: 10471
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791600
[LightGBM] [Info] Total Bins 17582
[LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285
[LightGBM] [Info] Start training from score -0.023285
[LightGBM] [Debug] Re-bagging, using 3229 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7
[LightGBM] [Debug] Re-bagging, using 3226 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Debug] Re-bagging, using 3119 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 5
[LightGBM] [Debug] Re-bagging, using 3119 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6
[LightGBM] [Debug] Re-bagging, using 3226 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 6
[LightGBM] [Debug] Re-bagging, using 3199 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6
[LightGBM] [Debug] Re-bagging, using 3127 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5
Trial 9, Fold 2: Log loss = 0.5698247483669396, Average precision = 0.9531280055612432, ROC-AUC = 0.9486416448731555, Elapsed Time = 0.7269937999999456 seconds
Trial 9, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 9, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[LightGBM] [Info] Number of positive: 10165, number of negative: 10517
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791401
[LightGBM] [Info] Total Bins 17585
[LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043
[LightGBM] [Info] Start training from score -0.034043
[LightGBM] [Debug] Re-bagging, using 3225 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Debug] Re-bagging, using 3220 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 7
[LightGBM] [Debug] Re-bagging, using 3122 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 7
[LightGBM] [Debug] Re-bagging, using 3113 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Debug] Re-bagging, using 3221 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 6
[LightGBM] [Debug] Re-bagging, using 3201 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 7
[LightGBM] [Debug] Re-bagging, using 3129 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6
Trial 9, Fold 3: Log loss = 0.5710888801937607, Average precision = 0.9603293675144042, ROC-AUC = 0.9536839010025906, Elapsed Time = 0.6494276999992508 seconds
Trial 9, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 9, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[LightGBM] [Info] Number of positive: 10177, number of negative: 10479
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.792454
[LightGBM] [Info] Total Bins 17572
[LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 256
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243
[LightGBM] [Info] Start training from score -0.029243
[LightGBM] [Debug] Re-bagging, using 3223 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6
[LightGBM] [Debug] Re-bagging, using 3214 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 7
[LightGBM] [Debug] Re-bagging, using 3117 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5
[LightGBM] [Debug] Re-bagging, using 3109 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Debug] Re-bagging, using 3214 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7
[LightGBM] [Debug] Re-bagging, using 3204 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Debug] Re-bagging, using 3122 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 5
Trial 9, Fold 4: Log loss = 0.5738447768195227, Average precision = 0.958603345988887, ROC-AUC = 0.9520692653156602, Elapsed Time = 0.6557638000003863 seconds
Trial 9, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 9, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[LightGBM] [Info] Number of positive: 10150, number of negative: 10500
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.792012
[LightGBM] [Info] Total Bins 17567
[LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902
[LightGBM] [Info] Start training from score -0.033902
[LightGBM] [Debug] Re-bagging, using 3222 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6
[LightGBM] [Debug] Re-bagging, using 3214 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Debug] Re-bagging, using 3117 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5
[LightGBM] [Debug] Re-bagging, using 3108 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 7
[LightGBM] [Debug] Re-bagging, using 3210 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5
[LightGBM] [Debug] Re-bagging, using 3201 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6
[LightGBM] [Debug] Re-bagging, using 3126 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5
Trial 9, Fold 5: Log loss = 0.5738391657501708, Average precision = 0.9533676394410786, ROC-AUC = 0.9467562937777529, Elapsed Time = 0.6461589999998978 seconds
Optimization Progress:  10%|#         | 10/100 [02:13<19:19, 12.88s/it]
Trial 10, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 10, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[LightGBM] [Info] Number of positive: 10130, number of negative: 10533
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.796981
[LightGBM] [Info] Total Bins 9564
[LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 259
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012
[LightGBM] [Info] Start training from score -0.039012
[LightGBM] [Debug] Re-bagging, using 6184 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 12
[LightGBM] [Debug] Re-bagging, using 6149 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 12
[LightGBM] [Debug] Re-bagging, using 6141 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 13
[LightGBM] [Debug] Re-bagging, using 6075 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 13
[LightGBM] [Debug] Re-bagging, using 6239 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 15
[LightGBM] [Debug] Re-bagging, using 6209 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 87 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 16
[LightGBM] [Debug] Re-bagging, using 6111 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 13
[LightGBM] [Debug] Re-bagging, using 6258 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 12
[LightGBM] [Debug] Re-bagging, using 6113 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 13
[LightGBM] [Debug] Re-bagging, using 5987 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 13
Trial 10, Fold 1: Log loss = 0.30683541454586777, Average precision = 0.9681071884451107, ROC-AUC = 0.962354461507578, Elapsed Time = 1.1848104000000603 seconds
Trial 10, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 10, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[LightGBM] [Info] Number of positive: 10230, number of negative: 10471
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.796949
[LightGBM] [Info] Total Bins 9556
[LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 259
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285
[LightGBM] [Info] Start training from score -0.023285
[LightGBM] [Debug] Re-bagging, using 6196 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 11
[LightGBM] [Debug] Re-bagging, using 6165 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 12
[LightGBM] [Debug] Re-bagging, using 6143 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 12
[LightGBM] [Debug] Re-bagging, using 6093 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 10
[LightGBM] [Debug] Re-bagging, using 6263 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 13
[LightGBM] [Debug] Re-bagging, using 6203 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 12
[LightGBM] [Debug] Re-bagging, using 6117 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 14
[LightGBM] [Debug] Re-bagging, using 6268 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 14
[LightGBM] [Debug] Re-bagging, using 6136 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 13
[LightGBM] [Debug] Re-bagging, using 6011 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 16
Trial 10, Fold 2: Log loss = 0.3178055408882424, Average precision = 0.9640378864927284, ROC-AUC = 0.9600655975327024, Elapsed Time = 1.279888199999732 seconds
Trial 10, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 10, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[LightGBM] [Info] Number of positive: 10165, number of negative: 10517
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.796784
[LightGBM] [Info] Total Bins 9563
[LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 259
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043
[LightGBM] [Info] Start training from score -0.034043
[LightGBM] [Debug] Re-bagging, using 6189 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 15
[LightGBM] [Debug] Re-bagging, using 6157 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 13
[LightGBM] [Debug] Re-bagging, using 6141 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 14
[LightGBM] [Debug] Re-bagging, using 6086 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 11
[LightGBM] [Debug] Re-bagging, using 6254 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 14
[LightGBM] [Debug] Re-bagging, using 6202 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 10
[LightGBM] [Debug] Re-bagging, using 6119 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 11
[LightGBM] [Debug] Re-bagging, using 6257 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 12
[LightGBM] [Debug] Re-bagging, using 6119 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 12
[LightGBM] [Debug] Re-bagging, using 6009 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 11
Trial 10, Fold 3: Log loss = 0.31589710878430993, Average precision = 0.9666835495238092, ROC-AUC = 0.9617884169949245, Elapsed Time = 1.5711670000000595 seconds
Trial 10, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 10, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[LightGBM] [Info] Number of positive: 10177, number of negative: 10479
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.796474
[LightGBM] [Info] Total Bins 9562
[LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 259
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243
[LightGBM] [Info] Start training from score -0.029243
[LightGBM] [Debug] Re-bagging, using 6181 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 12
[LightGBM] [Debug] Re-bagging, using 6146 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 10
[LightGBM] [Debug] Re-bagging, using 6138 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 12
[LightGBM] [Debug] Re-bagging, using 6077 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 13
[LightGBM] [Debug] Re-bagging, using 6235 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 12
[LightGBM] [Debug] Re-bagging, using 6206 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 11
[LightGBM] [Debug] Re-bagging, using 6111 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 12
[LightGBM] [Debug] Re-bagging, using 6254 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 94 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 12
[LightGBM] [Debug] Re-bagging, using 6109 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 11
[LightGBM] [Debug] Re-bagging, using 5990 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 15
Trial 10, Fold 4: Log loss = 0.3124373915207655, Average precision = 0.967054943875468, ROC-AUC = 0.9605874947244933, Elapsed Time = 1.385622499999954 seconds
Trial 10, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 10, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[LightGBM] [Info] Number of positive: 10150, number of negative: 10500
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.797340
[LightGBM] [Info] Total Bins 9544
[LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 259
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902
[LightGBM] [Info] Start training from score -0.033902
[LightGBM] [Debug] Re-bagging, using 6179 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 15
[LightGBM] [Debug] Re-bagging, using 6146 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 12
[LightGBM] [Debug] Re-bagging, using 6137 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 15
[LightGBM] [Debug] Re-bagging, using 6071 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 12
[LightGBM] [Debug] Re-bagging, using 6234 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 13
[LightGBM] [Debug] Re-bagging, using 6202 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 13
[LightGBM] [Debug] Re-bagging, using 6116 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 14
[LightGBM] [Debug] Re-bagging, using 6248 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 14
[LightGBM] [Debug] Re-bagging, using 6110 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 11
[LightGBM] [Debug] Re-bagging, using 5989 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 14
Trial 10, Fold 5: Log loss = 0.31701231614868497, Average precision = 0.9634314803611199, ROC-AUC = 0.9594070936216859, Elapsed Time = 1.3575648000005458 seconds
Optimization Progress:  11%|#1        | 11/100 [02:27<19:42, 13.29s/it]
Trial 11, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 11, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[LightGBM] [Info] Number of positive: 10130, number of negative: 10533
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791640
[LightGBM] [Info] Total Bins 19014
[LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012
[LightGBM] [Info] Start training from score -0.039012
[LightGBM] [Debug] Re-bagging, using 10167 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 12
[LightGBM] [Debug] Re-bagging, using 10127 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10114 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10000 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9
[LightGBM] [Debug] Re-bagging, using 10178 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10041 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
Trial 11, Fold 1: Log loss = 0.27839808071739647, Average precision = 0.9699116494901384, ROC-AUC = 0.9638715330054535, Elapsed Time = 0.9067294999995283 seconds
Trial 11, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 11, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[LightGBM] [Info] Number of positive: 10230, number of negative: 10471
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791600
[LightGBM] [Info] Total Bins 19026
[LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285
[LightGBM] [Info] Start training from score -0.023285
[LightGBM] [Debug] Re-bagging, using 10188 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10146 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10128 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10026 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10205 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10039 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10
Trial 11, Fold 2: Log loss = 0.2773252453732645, Average precision = 0.9679454703507686, ROC-AUC = 0.9644505774154984, Elapsed Time = 0.9466196999992462 seconds
Trial 11, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 11, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[LightGBM] [Info] Number of positive: 10165, number of negative: 10517
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791401
[LightGBM] [Info] Total Bins 19029
[LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043
[LightGBM] [Info] Start training from score -0.034043
[LightGBM] [Debug] Re-bagging, using 10177 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
[LightGBM] [Debug] Re-bagging, using 10134 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10120 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10021 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10189 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10037 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11
Trial 11, Fold 3: Log loss = 0.2747655322336973, Average precision = 0.9708889291563291, ROC-AUC = 0.9660925867515482, Elapsed Time = 0.9953179000003729 seconds
Trial 11, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 11, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[LightGBM] [Info] Number of positive: 10177, number of negative: 10479
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791090
[LightGBM] [Info] Total Bins 19010
[LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243
[LightGBM] [Info] Start training from score -0.029243
[LightGBM] [Debug] Re-bagging, using 10163 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 12
[LightGBM] [Debug] Re-bagging, using 10123 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10110 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10
[LightGBM] [Debug] Re-bagging, using 9999 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9
[LightGBM] [Debug] Re-bagging, using 10171 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10039 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
Trial 11, Fold 4: Log loss = 0.27565143365103895, Average precision = 0.9698695678849122, ROC-AUC = 0.9640579286393842, Elapsed Time = 1.3377421000004688 seconds
Trial 11, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 11, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[LightGBM] [Info] Number of positive: 10150, number of negative: 10500
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.792012
[LightGBM] [Info] Total Bins 19008
[LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902
[LightGBM] [Info] Start training from score -0.033902
[LightGBM] [Debug] Re-bagging, using 10160 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10120 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 12
[LightGBM] [Debug] Re-bagging, using 10111 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 13
[LightGBM] [Debug] Re-bagging, using 9992 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 13
[LightGBM] [Debug] Re-bagging, using 10169 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 12
[LightGBM] [Debug] Re-bagging, using 10032 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 12
Trial 11, Fold 5: Log loss = 0.28328383290878856, Average precision = 0.9680872451642151, ROC-AUC = 0.9623242851569032, Elapsed Time = 1.3138933999998699 seconds
Optimization Progress:  12%|#2        | 12/100 [02:40<19:21, 13.20s/it]
Trial 12, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 12, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[LightGBM] [Info] Number of positive: 10130, number of negative: 10533
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795662
[LightGBM] [Info] Total Bins 8297
[LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 258
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012
[LightGBM] [Info] Start training from score -0.039012
[LightGBM] [Debug] Re-bagging, using 6273 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10
[LightGBM] [Debug] Re-bagging, using 6246 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 10
[LightGBM] [Debug] Re-bagging, using 6230 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 10
[LightGBM] [Debug] Re-bagging, using 6158 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 12
[LightGBM] [Debug] Re-bagging, using 6329 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 12
[LightGBM] [Debug] Re-bagging, using 6300 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 15
[LightGBM] [Debug] Re-bagging, using 6199 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 14
[LightGBM] [Debug] Re-bagging, using 6347 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 13
[LightGBM] [Debug] Re-bagging, using 6209 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 12
[LightGBM] [Debug] Re-bagging, using 6080 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 14
[LightGBM] [Debug] Re-bagging, using 6116 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 15
[LightGBM] [Debug] Re-bagging, using 6158 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 15
[LightGBM] [Debug] Re-bagging, using 6159 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 15
[LightGBM] [Debug] Re-bagging, using 6254 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 14
[LightGBM] [Debug] Re-bagging, using 6371 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 13
[LightGBM] [Debug] Re-bagging, using 6253 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 16
[LightGBM] [Debug] Re-bagging, using 6209 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 13
[LightGBM] [Debug] Re-bagging, using 6193 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 13
[LightGBM] [Debug] Re-bagging, using 6305 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 16
[LightGBM] [Debug] Re-bagging, using 6296 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 14
[LightGBM] [Debug] Re-bagging, using 6141 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 13
[LightGBM] [Debug] Re-bagging, using 6221 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 13
[LightGBM] [Debug] Re-bagging, using 6269 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 12
[LightGBM] [Debug] Re-bagging, using 6198 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 12
[LightGBM] [Debug] Re-bagging, using 6366 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 14
[LightGBM] [Debug] Re-bagging, using 6274 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 14
[LightGBM] [Debug] Re-bagging, using 6255 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 12
[LightGBM] [Debug] Re-bagging, using 6214 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 13
[LightGBM] [Debug] Re-bagging, using 6198 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 12
Trial 12, Fold 1: Log loss = 0.2795864886375474, Average precision = 0.9725727838068232, ROC-AUC = 0.967581689154634, Elapsed Time = 0.7844225999997434 seconds
Trial 12, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 12, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[LightGBM] [Info] Number of positive: 10230, number of negative: 10471
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795629
[LightGBM] [Info] Total Bins 8313
[LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 258
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285
[LightGBM] [Info] Start training from score -0.023285
[LightGBM] [Debug] Re-bagging, using 6285 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10
[LightGBM] [Debug] Re-bagging, using 6262 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 11
[LightGBM] [Debug] Re-bagging, using 6232 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 11
[LightGBM] [Debug] Re-bagging, using 6177 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 12
[LightGBM] [Debug] Re-bagging, using 6352 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 13
[LightGBM] [Debug] Re-bagging, using 6295 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 10
[LightGBM] [Debug] Re-bagging, using 6207 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 11
[LightGBM] [Debug] Re-bagging, using 6357 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 14
[LightGBM] [Debug] Re-bagging, using 6231 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 15
[LightGBM] [Debug] Re-bagging, using 6102 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 14
[LightGBM] [Debug] Re-bagging, using 6113 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 14
[LightGBM] [Debug] Re-bagging, using 6159 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 14
[LightGBM] [Debug] Re-bagging, using 6169 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 16
[LightGBM] [Debug] Re-bagging, using 6263 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 12
[LightGBM] [Debug] Re-bagging, using 6381 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 15
[LightGBM] [Debug] Re-bagging, using 6273 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 13
[LightGBM] [Debug] Re-bagging, using 6221 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 15
[LightGBM] [Debug] Re-bagging, using 6212 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 14
[LightGBM] [Debug] Re-bagging, using 6322 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 13
[LightGBM] [Debug] Re-bagging, using 6302 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 14
[LightGBM] [Debug] Re-bagging, using 6157 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 12
[LightGBM] [Debug] Re-bagging, using 6228 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 11
[LightGBM] [Debug] Re-bagging, using 6288 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 13
[LightGBM] [Debug] Re-bagging, using 6214 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 12
[LightGBM] [Debug] Re-bagging, using 6370 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 13
[LightGBM] [Debug] Re-bagging, using 6282 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 14
[LightGBM] [Debug] Re-bagging, using 6264 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 12
[LightGBM] [Debug] Re-bagging, using 6211 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 13
[LightGBM] [Debug] Re-bagging, using 6207 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 12
Trial 12, Fold 2: Log loss = 0.2794407935119923, Average precision = 0.9694707487668018, ROC-AUC = 0.9668603322487413, Elapsed Time = 0.7739711000003808 seconds
Trial 12, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 12, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[LightGBM] [Info] Number of positive: 10165, number of negative: 10517
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795455
[LightGBM] [Info] Total Bins 8313
[LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 258
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043
[LightGBM] [Info] Start training from score -0.034043
[LightGBM] [Debug] Re-bagging, using 6278 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11
[LightGBM] [Debug] Re-bagging, using 6254 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 11
[LightGBM] [Debug] Re-bagging, using 6230 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 10
[LightGBM] [Debug] Re-bagging, using 6170 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 11
[LightGBM] [Debug] Re-bagging, using 6343 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 12
[LightGBM] [Debug] Re-bagging, using 6294 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 13
[LightGBM] [Debug] Re-bagging, using 6208 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 13
[LightGBM] [Debug] Re-bagging, using 6345 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 11
[LightGBM] [Debug] Re-bagging, using 6216 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 15
[LightGBM] [Debug] Re-bagging, using 6100 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 12
[LightGBM] [Debug] Re-bagging, using 6118 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 13
[LightGBM] [Debug] Re-bagging, using 6150 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 14
[LightGBM] [Debug] Re-bagging, using 6163 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 13
[LightGBM] [Debug] Re-bagging, using 6264 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 17
[LightGBM] [Debug] Re-bagging, using 6368 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 13
[LightGBM] [Debug] Re-bagging, using 6263 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 14
[LightGBM] [Debug] Re-bagging, using 6220 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 12
[LightGBM] [Debug] Re-bagging, using 6202 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 13
[LightGBM] [Debug] Re-bagging, using 6304 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 13
[LightGBM] [Debug] Re-bagging, using 6315 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 15
[LightGBM] [Debug] Re-bagging, using 6157 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 13
[LightGBM] [Debug] Re-bagging, using 6210 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 12
[LightGBM] [Debug] Re-bagging, using 6266 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 12
[LightGBM] [Debug] Re-bagging, using 6217 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 12
[LightGBM] [Debug] Re-bagging, using 6383 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 11
[LightGBM] [Debug] Re-bagging, using 6279 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 16
[LightGBM] [Debug] Re-bagging, using 6248 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 11
[LightGBM] [Debug] Re-bagging, using 6200 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 13
[LightGBM] [Debug] Re-bagging, using 6215 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 17
Trial 12, Fold 3: Log loss = 0.2764566261179014, Average precision = 0.9718120509134004, ROC-AUC = 0.9675455939218537, Elapsed Time = 0.7659664000002522 seconds
Trial 12, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 12, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[LightGBM] [Info] Number of positive: 10177, number of negative: 10479
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795144
[LightGBM] [Info] Total Bins 8295
[LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 258
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243
[LightGBM] [Info] Start training from score -0.029243
[LightGBM] [Debug] Re-bagging, using 6270 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11
[LightGBM] [Debug] Re-bagging, using 6243 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 9
[LightGBM] [Debug] Re-bagging, using 6227 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 10
[LightGBM] [Debug] Re-bagging, using 6160 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 13
[LightGBM] [Debug] Re-bagging, using 6325 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 13
[LightGBM] [Debug] Re-bagging, using 6297 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 14
[LightGBM] [Debug] Re-bagging, using 6199 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 13
[LightGBM] [Debug] Re-bagging, using 6343 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 12
[LightGBM] [Debug] Re-bagging, using 6205 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 14
[LightGBM] [Debug] Re-bagging, using 6083 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 13
[LightGBM] [Debug] Re-bagging, using 6105 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 14
[LightGBM] [Debug] Re-bagging, using 6160 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 13
[LightGBM] [Debug] Re-bagging, using 6160 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 13
[LightGBM] [Debug] Re-bagging, using 6250 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 16
[LightGBM] [Debug] Re-bagging, using 6366 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 12
[LightGBM] [Debug] Re-bagging, using 6256 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 13
[LightGBM] [Debug] Re-bagging, using 6206 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 13
[LightGBM] [Debug] Re-bagging, using 6198 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 15
[LightGBM] [Debug] Re-bagging, using 6295 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 15
[LightGBM] [Debug] Re-bagging, using 6298 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 13
[LightGBM] [Debug] Re-bagging, using 6142 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 13
[LightGBM] [Debug] Re-bagging, using 6208 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 15
[LightGBM] [Debug] Re-bagging, using 6270 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 11
[LightGBM] [Debug] Re-bagging, using 6207 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 13
[LightGBM] [Debug] Re-bagging, using 6353 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 17
[LightGBM] [Debug] Re-bagging, using 6274 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 13
[LightGBM] [Debug] Re-bagging, using 6241 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 11
[LightGBM] [Debug] Re-bagging, using 6213 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 12
[LightGBM] [Debug] Re-bagging, using 6209 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 15
Trial 12, Fold 4: Log loss = 0.2785140783863654, Average precision = 0.9718978332825114, ROC-AUC = 0.9670789013116639, Elapsed Time = 0.7898058999999193 seconds
Trial 12, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 12, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[LightGBM] [Info] Number of positive: 10150, number of negative: 10500
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.796026
[LightGBM] [Info] Total Bins 8302
[LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 258
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902
[LightGBM] [Info] Start training from score -0.033902
[LightGBM] [Debug] Re-bagging, using 6268 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 12
[LightGBM] [Debug] Re-bagging, using 6243 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11
[LightGBM] [Debug] Re-bagging, using 6225 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 10
[LightGBM] [Debug] Re-bagging, using 6155 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10
[LightGBM] [Debug] Re-bagging, using 6324 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 11
[LightGBM] [Debug] Re-bagging, using 6293 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 12
[LightGBM] [Debug] Re-bagging, using 6204 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 12
[LightGBM] [Debug] Re-bagging, using 6336 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 14
[LightGBM] [Debug] Re-bagging, using 6206 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 12
[LightGBM] [Debug] Re-bagging, using 6083 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 13
[LightGBM] [Debug] Re-bagging, using 6099 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 13
[LightGBM] [Debug] Re-bagging, using 6151 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 13
[LightGBM] [Debug] Re-bagging, using 6163 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 14
[LightGBM] [Debug] Re-bagging, using 6260 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 14
[LightGBM] [Debug] Re-bagging, using 6361 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 13
[LightGBM] [Debug] Re-bagging, using 6257 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 14
[LightGBM] [Debug] Re-bagging, using 6205 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 14
[LightGBM] [Debug] Re-bagging, using 6184 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 15
[LightGBM] [Debug] Re-bagging, using 6295 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 15
[LightGBM] [Debug] Re-bagging, using 6293 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 14
[LightGBM] [Debug] Re-bagging, using 6147 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 13
[LightGBM] [Debug] Re-bagging, using 6204 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 12
[LightGBM] [Debug] Re-bagging, using 6261 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 11
[LightGBM] [Debug] Re-bagging, using 6212 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 14
[LightGBM] [Debug] Re-bagging, using 6359 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 11
[LightGBM] [Debug] Re-bagging, using 6265 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 14
[LightGBM] [Debug] Re-bagging, using 6240 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 12
[LightGBM] [Debug] Re-bagging, using 6200 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 12
[LightGBM] [Debug] Re-bagging, using 6212 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 13
Trial 12, Fold 5: Log loss = 0.281669785331137, Average precision = 0.9701642707511918, ROC-AUC = 0.9660935287888076, Elapsed Time = 0.8207968000006076 seconds
Optimization Progress:  13%|#3        | 13/100 [02:52<18:29, 12.76s/it]
Trial 13, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 13, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[LightGBM] [Info] Number of positive: 10130, number of negative: 10533
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.804600
[LightGBM] [Info] Total Bins 29002
[LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 265
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012
[LightGBM] [Info] Start training from score -0.039012
[LightGBM] [Debug] Re-bagging, using 19497 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 20
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19
[LightGBM] [Debug] Re-bagging, using 19452 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 20
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18
[LightGBM] [Debug] Re-bagging, using 19514 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 20
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 14
[LightGBM] [Debug] Re-bagging, using 19469 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 23
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 16
[LightGBM] [Debug] Re-bagging, using 19477 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21
[LightGBM] [Debug] Re-bagging, using 19483 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 33
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19
[LightGBM] [Debug] Re-bagging, using 19435 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 20
[LightGBM] [Debug] Re-bagging, using 19467 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 27
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21
[LightGBM] [Debug] Re-bagging, using 19496 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21
[LightGBM] [Debug] Re-bagging, using 19436 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 23
[LightGBM] [Debug] Re-bagging, using 19473 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19
[LightGBM] [Debug] Re-bagging, using 19432 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 25
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 23
[LightGBM] [Debug] Re-bagging, using 19449 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 22
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 25
[LightGBM] [Debug] Re-bagging, using 19491 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19
[LightGBM] [Debug] Re-bagging, using 19458 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 22
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21
[LightGBM] [Debug] Re-bagging, using 19471 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 23
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19
[LightGBM] [Debug] Re-bagging, using 19429 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19
[LightGBM] [Debug] Re-bagging, using 19529 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 24
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19
[LightGBM] [Debug] Re-bagging, using 19504 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21
[LightGBM] [Debug] Re-bagging, using 19529 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 25
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18
[LightGBM] [Debug] Re-bagging, using 19402 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21
[LightGBM] [Debug] Re-bagging, using 19436 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 23
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 23
[LightGBM] [Debug] Re-bagging, using 19464 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 20
[LightGBM] [Debug] Re-bagging, using 19493 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 22
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18
[LightGBM] [Debug] Re-bagging, using 19521 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 22
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 16
Trial 13, Fold 1: Log loss = 0.3439567228683248, Average precision = 0.9642014557062354, ROC-AUC = 0.9580364013516679, Elapsed Time = 1.7321501999995235 seconds
Trial 13, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 13, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[LightGBM] [Info] Number of positive: 10230, number of negative: 10471
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.804566
[LightGBM] [Info] Total Bins 29006
[LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 265
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285
[LightGBM] [Info] Start training from score -0.023285
[LightGBM] [Debug] Re-bagging, using 19534 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 23
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 20
[LightGBM] [Debug] Re-bagging, using 19485 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 22
[LightGBM] [Debug] Re-bagging, using 19553 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 23
[LightGBM] [Debug] Re-bagging, using 19503 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 22
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21
[LightGBM] [Debug] Re-bagging, using 19517 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 20
[LightGBM] [Debug] Re-bagging, using 19506 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 20
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 22
[LightGBM] [Debug] Re-bagging, using 19475 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 23
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 15
[LightGBM] [Debug] Re-bagging, using 19514 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 16
[LightGBM] [Debug] Re-bagging, using 19532 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 22
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18
[LightGBM] [Debug] Re-bagging, using 19470 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 15
[LightGBM] [Debug] Re-bagging, using 19505 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 23
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 20
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 20
[LightGBM] [Debug] Re-bagging, using 19468 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 20
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 20
[LightGBM] [Debug] Re-bagging, using 19487 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 15
[LightGBM] [Debug] Re-bagging, using 19526 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19
[LightGBM] [Debug] Re-bagging, using 19493 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18
[LightGBM] [Debug] Re-bagging, using 19506 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 24
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 23
[LightGBM] [Debug] Re-bagging, using 19465 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 24
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 20
[LightGBM] [Debug] Re-bagging, using 19569 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 20
[LightGBM] [Debug] Re-bagging, using 19540 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18
[LightGBM] [Debug] Re-bagging, using 19560 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 25
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18
[LightGBM] [Debug] Re-bagging, using 19439 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 20
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18
[LightGBM] [Debug] Re-bagging, using 19469 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19
[LightGBM] [Debug] Re-bagging, using 19504 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 23
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18
[LightGBM] [Debug] Re-bagging, using 19535 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21
[LightGBM] [Debug] Re-bagging, using 19554 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 23
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 20
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 22
Trial 13, Fold 2: Log loss = 0.3371119253550399, Average precision = 0.9651042928311725, ROC-AUC = 0.9614018885315396, Elapsed Time = 2.2189558999998553 seconds
Trial 13, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 13, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[LightGBM] [Info] Number of positive: 10165, number of negative: 10517
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.804456
[LightGBM] [Info] Total Bins 29001
[LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 265
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043
[LightGBM] [Info] Start training from score -0.034043
[LightGBM] [Debug] Re-bagging, using 19516 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19
[LightGBM] [Debug] Re-bagging, using 19468 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18
[LightGBM] [Debug] Re-bagging, using 19533 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 22
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 20
[LightGBM] [Debug] Re-bagging, using 19490 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 25
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21
[LightGBM] [Debug] Re-bagging, using 19491 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 23
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21
[LightGBM] [Debug] Re-bagging, using 19497 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 24
[LightGBM] [Debug] Re-bagging, using 19453 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 26
[LightGBM] [Debug] Re-bagging, using 19492 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 24
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 26
[LightGBM] [Debug] Re-bagging, using 19513 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 23
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19
[LightGBM] [Debug] Re-bagging, using 19457 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 22
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17
[LightGBM] [Debug] Re-bagging, using 19487 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 22
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 23
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 23
[LightGBM] [Debug] Re-bagging, using 19451 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 26
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21
[LightGBM] [Debug] Re-bagging, using 19466 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 20
[LightGBM] [Debug] Re-bagging, using 19507 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21
[LightGBM] [Debug] Re-bagging, using 19480 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 20
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 25
[LightGBM] [Debug] Re-bagging, using 19483 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 16
[LightGBM] [Debug] Re-bagging, using 19449 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 16
[LightGBM] [Debug] Re-bagging, using 19550 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 20
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19
[LightGBM] [Debug] Re-bagging, using 19521 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19
[LightGBM] [Debug] Re-bagging, using 19549 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 20
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21
[LightGBM] [Debug] Re-bagging, using 19419 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 30
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 14
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 23
[LightGBM] [Debug] Re-bagging, using 19447 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 20
[LightGBM] [Debug] Re-bagging, using 19485 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17
[LightGBM] [Debug] Re-bagging, using 19513 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 24
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 20
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18
[LightGBM] [Debug] Re-bagging, using 19543 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21
Trial 13, Fold 3: Log loss = 0.3337444841372523, Average precision = 0.9682855889749199, ROC-AUC = 0.963323212720671, Elapsed Time = 2.7470613999994384 seconds
Trial 13, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 13, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[LightGBM] [Info] Number of positive: 10177, number of negative: 10479
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.804155
[LightGBM] [Info] Total Bins 28992
[LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 265
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243
[LightGBM] [Info] Start training from score -0.029243
[LightGBM] [Debug] Re-bagging, using 19490 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18
[LightGBM] [Debug] Re-bagging, using 19445 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21
[LightGBM] [Debug] Re-bagging, using 19509 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 20
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 22
[LightGBM] [Debug] Re-bagging, using 19465 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 20
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 20
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 20
[LightGBM] [Debug] Re-bagging, using 19468 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 23
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19
[LightGBM] [Debug] Re-bagging, using 19474 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 23
[LightGBM] [Debug] Re-bagging, using 19433 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 13
[LightGBM] [Debug] Re-bagging, using 19462 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 22
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17
[LightGBM] [Debug] Re-bagging, using 19487 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17
[LightGBM] [Debug] Re-bagging, using 19428 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 20
[LightGBM] [Debug] Re-bagging, using 19465 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 24
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17
[LightGBM] [Debug] Re-bagging, using 19426 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19
[LightGBM] [Debug] Re-bagging, using 19444 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 16
[LightGBM] [Debug] Re-bagging, using 19483 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 15
[LightGBM] [Debug] Re-bagging, using 19451 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 22
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21
[LightGBM] [Debug] Re-bagging, using 19463 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 20
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18
[LightGBM] [Debug] Re-bagging, using 19422 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 23
[LightGBM] [Debug] Re-bagging, using 19523 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21
[LightGBM] [Debug] Re-bagging, using 19498 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17
[LightGBM] [Debug] Re-bagging, using 19521 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 20
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18
[LightGBM] [Debug] Re-bagging, using 19399 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 24
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 16
[LightGBM] [Debug] Re-bagging, using 19422 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 14
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 16
[LightGBM] [Debug] Re-bagging, using 19464 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19
[LightGBM] [Debug] Re-bagging, using 19488 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 22
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 23
[LightGBM] [Debug] Re-bagging, using 19512 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 22
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 22
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19
Trial 13, Fold 4: Log loss = 0.3345920525712018, Average precision = 0.9672589026802049, ROC-AUC = 0.961008492075414, Elapsed Time = 2.6501938000001246 seconds
Trial 13, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 13, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[LightGBM] [Info] Number of positive: 10150, number of negative: 10500
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.804939
[LightGBM] [Info] Total Bins 28998
[LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 265
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902
[LightGBM] [Info] Start training from score -0.033902
[LightGBM] [Debug] Re-bagging, using 19484 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 24
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 23
[LightGBM] [Debug] Re-bagging, using 19440 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17
[LightGBM] [Debug] Re-bagging, using 19503 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 26
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17
[LightGBM] [Debug] Re-bagging, using 19460 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 20
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19
[LightGBM] [Debug] Re-bagging, using 19463 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21
[LightGBM] [Debug] Re-bagging, using 19465 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 20
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 16
[LightGBM] [Debug] Re-bagging, using 19433 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19
[LightGBM] [Debug] Re-bagging, using 19454 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 23
[LightGBM] [Debug] Re-bagging, using 19478 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18
[LightGBM] [Debug] Re-bagging, using 19423 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 16
[LightGBM] [Debug] Re-bagging, using 19459 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 24
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 25
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 20
[LightGBM] [Debug] Re-bagging, using 19422 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 22
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 16
[LightGBM] [Debug] Re-bagging, using 19437 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 20
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19
[LightGBM] [Debug] Re-bagging, using 19477 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 27
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18
[LightGBM] [Debug] Re-bagging, using 19449 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 20
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21
[LightGBM] [Debug] Re-bagging, using 19453 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 24
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 22
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 20
[LightGBM] [Debug] Re-bagging, using 19419 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19
[LightGBM] [Debug] Re-bagging, using 19518 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 16
[LightGBM] [Debug] Re-bagging, using 19489 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 24
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19
[LightGBM] [Debug] Re-bagging, using 19518 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 20
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 26
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19
[LightGBM] [Debug] Re-bagging, using 19393 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 22
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19
[LightGBM] [Debug] Re-bagging, using 19419 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 25
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 36
[LightGBM] [Debug] Re-bagging, using 19452 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 20
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 15
[LightGBM] [Debug] Re-bagging, using 19487 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 22
[LightGBM] [Debug] Re-bagging, using 19507 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 20
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 16
Trial 13, Fold 5: Log loss = 0.34868921272817555, Average precision = 0.9628221406956591, ROC-AUC = 0.9572423685041711, Elapsed Time = 2.0537867000002734 seconds
Optimization Progress:  14%|#4        | 14/100 [03:11<21:02, 14.68s/it]
Trial 14, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 14, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[LightGBM] [Info] Number of positive: 10130, number of negative: 10533
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791640
[LightGBM] [Info] Total Bins 12063
[LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012
[LightGBM] [Info] Start training from score -0.039012
[LightGBM] [Debug] Re-bagging, using 18369 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 10
[LightGBM] [Debug] Re-bagging, using 18393 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 10
[LightGBM] [Debug] Re-bagging, using 18421 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9
[LightGBM] [Debug] Re-bagging, using 18353 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 11
Trial 14, Fold 1: Log loss = 0.4712521550359508, Average precision = 0.9693039444042607, ROC-AUC = 0.9631592995272987, Elapsed Time = 0.7223698000007062 seconds
Trial 14, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 14, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[LightGBM] [Info] Number of positive: 10230, number of negative: 10471
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791600
[LightGBM] [Info] Total Bins 12084
[LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285
[LightGBM] [Info] Start training from score -0.023285
[LightGBM] [Debug] Re-bagging, using 18405 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Debug] Re-bagging, using 18423 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 10
[LightGBM] [Debug] Re-bagging, using 18454 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 11
[LightGBM] [Debug] Re-bagging, using 18390 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10
Trial 14, Fold 2: Log loss = 0.46625701603010106, Average precision = 0.9680161782461034, ROC-AUC = 0.9641126350972598, Elapsed Time = 0.6809782000000268 seconds
Trial 14, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 14, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[LightGBM] [Info] Number of positive: 10165, number of negative: 10517
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791401
[LightGBM] [Info] Total Bins 12088
[LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043
[LightGBM] [Info] Start training from score -0.034043
[LightGBM] [Debug] Re-bagging, using 18388 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9
[LightGBM] [Debug] Re-bagging, using 18406 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 11
[LightGBM] [Debug] Re-bagging, using 18438 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Debug] Re-bagging, using 18374 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9
Trial 14, Fold 3: Log loss = 0.4677295498601943, Average precision = 0.970775523157016, ROC-AUC = 0.9657632504984061, Elapsed Time = 0.7300248999999894 seconds
Trial 14, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 14, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[LightGBM] [Info] Number of positive: 10177, number of negative: 10479
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791090
[LightGBM] [Info] Total Bins 12066
[LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243
[LightGBM] [Info] Start training from score -0.029243
[LightGBM] [Debug] Re-bagging, using 18362 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Debug] Re-bagging, using 18386 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9
[LightGBM] [Debug] Re-bagging, using 18416 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10
[LightGBM] [Debug] Re-bagging, using 18350 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8
Trial 14, Fold 4: Log loss = 0.4673188628179886, Average precision = 0.969554405860509, ROC-AUC = 0.9634035571817232, Elapsed Time = 0.6793779999998151 seconds
Trial 14, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 14, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[LightGBM] [Info] Number of positive: 10150, number of negative: 10500
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.792012
[LightGBM] [Info] Total Bins 12068
[LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902
[LightGBM] [Info] Start training from score -0.033902
[LightGBM] [Debug] Re-bagging, using 18356 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9
[LightGBM] [Debug] Re-bagging, using 18382 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 10
[LightGBM] [Debug] Re-bagging, using 18411 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11
[LightGBM] [Debug] Re-bagging, using 18345 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
Trial 14, Fold 5: Log loss = 0.46827598185049213, Average precision = 0.9680601591436406, ROC-AUC = 0.963749029225424, Elapsed Time = 0.6934823000001415 seconds
Optimization Progress:  15%|#5        | 15/100 [03:23<19:19, 13.65s/it]
Trial 15, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 15, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[LightGBM] [Info] Number of positive: 10130, number of negative: 10533
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791640
[LightGBM] [Info] Total Bins 19890
[LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012
[LightGBM] [Info] Start training from score -0.039012
[LightGBM] [Debug] Re-bagging, using 10808 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10809 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8
[LightGBM] [Debug] Re-bagging, using 10793 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8
[LightGBM] [Debug] Re-bagging, using 10614 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 7
[LightGBM] [Debug] Re-bagging, using 10811 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8
[LightGBM] [Debug] Re-bagging, using 10650 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 12
[LightGBM] [Debug] Re-bagging, using 10781 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 9
[LightGBM] [Debug] Re-bagging, using 10721 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6
Trial 15, Fold 1: Log loss = 0.3307535494949883, Average precision = 0.9624177756608816, ROC-AUC = 0.9556137470545782, Elapsed Time = 0.7917609000005541 seconds
Trial 15, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 15, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[LightGBM] [Info] Number of positive: 10230, number of negative: 10471
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791600
[LightGBM] [Info] Total Bins 19902
[LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285
[LightGBM] [Info] Start training from score -0.023285
[LightGBM] [Debug] Re-bagging, using 10830 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8
[LightGBM] [Debug] Re-bagging, using 10828 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 9
[LightGBM] [Debug] Re-bagging, using 10810 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8
[LightGBM] [Debug] Re-bagging, using 10638 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 8
[LightGBM] [Debug] Re-bagging, using 10840 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9
[LightGBM] [Debug] Re-bagging, using 10648 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7
[LightGBM] [Debug] Re-bagging, using 10790 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7
[LightGBM] [Debug] Re-bagging, using 10742 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 6
Trial 15, Fold 2: Log loss = 0.32437769588339677, Average precision = 0.9611297020298436, ROC-AUC = 0.9558363108777972, Elapsed Time = 0.8626070999998774 seconds
Trial 15, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 15, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[LightGBM] [Info] Number of positive: 10165, number of negative: 10517
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791401
[LightGBM] [Info] Total Bins 19855
[LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043
[LightGBM] [Info] Start training from score -0.034043
[LightGBM] [Debug] Re-bagging, using 10818 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 9
[LightGBM] [Debug] Re-bagging, using 10816 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10799 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7
[LightGBM] [Debug] Re-bagging, using 10635 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 9
[LightGBM] [Debug] Re-bagging, using 10822 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 7
[LightGBM] [Debug] Re-bagging, using 10649 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6
[LightGBM] [Debug] Re-bagging, using 10786 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 7
[LightGBM] [Debug] Re-bagging, using 10730 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 8
Trial 15, Fold 3: Log loss = 0.32260896629795294, Average precision = 0.9643834699614529, ROC-AUC = 0.958475641908486, Elapsed Time = 0.8613505000002988 seconds
Trial 15, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 15, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[LightGBM] [Info] Number of positive: 10177, number of negative: 10479
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791090
[LightGBM] [Info] Total Bins 19885
[LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243
[LightGBM] [Info] Start training from score -0.029243
[LightGBM] [Debug] Re-bagging, using 10804 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7
[LightGBM] [Debug] Re-bagging, using 10805 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8
[LightGBM] [Debug] Re-bagging, using 10788 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 8
[LightGBM] [Debug] Re-bagging, using 10612 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 7
[LightGBM] [Debug] Re-bagging, using 10806 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7
[LightGBM] [Debug] Re-bagging, using 10646 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 9
[LightGBM] [Debug] Re-bagging, using 10781 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7
[LightGBM] [Debug] Re-bagging, using 10719 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8
Trial 15, Fold 4: Log loss = 0.3249451621825596, Average precision = 0.963468797794335, ROC-AUC = 0.9561431039292688, Elapsed Time = 0.9371393000001262 seconds
Trial 15, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 15, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[LightGBM] [Info] Number of positive: 10150, number of negative: 10500
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.792012
[LightGBM] [Info] Total Bins 19883
[LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902
[LightGBM] [Info] Start training from score -0.033902
[LightGBM] [Debug] Re-bagging, using 10801 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10801 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8
[LightGBM] [Debug] Re-bagging, using 10790 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 9
[LightGBM] [Debug] Re-bagging, using 10605 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 9
[LightGBM] [Debug] Re-bagging, using 10802 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 9
[LightGBM] [Debug] Re-bagging, using 10639 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 9
[LightGBM] [Debug] Re-bagging, using 10787 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9
[LightGBM] [Debug] Re-bagging, using 10713 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8
Trial 15, Fold 5: Log loss = 0.3293120261072558, Average precision = 0.96140222102461, ROC-AUC = 0.9544851085966967, Elapsed Time = 0.9248790000001463 seconds
Optimization Progress:  16%|#6        | 16/100 [03:34<18:13, 13.02s/it]
Trial 16, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 16, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[LightGBM] [Info] Number of positive: 10130, number of negative: 10533
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795662
[LightGBM] [Info] Total Bins 24804
[LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 258
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012
[LightGBM] [Info] Start training from score -0.039012
[LightGBM] [Debug] Re-bagging, using 2947 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 8
[LightGBM] [Debug] Re-bagging, using 2947 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 7
[LightGBM] [Debug] Re-bagging, using 2840 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 9
[LightGBM] [Debug] Re-bagging, using 2873 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8
[LightGBM] [Debug] Re-bagging, using 2977 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10
[LightGBM] [Debug] Re-bagging, using 2925 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9
[LightGBM] [Debug] Re-bagging, using 2842 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 11
[LightGBM] [Debug] Re-bagging, using 2990 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10
[LightGBM] [Debug] Re-bagging, using 2890 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 14
[LightGBM] [Debug] Re-bagging, using 2874 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 14
[LightGBM] [Debug] Re-bagging, using 2773 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 11
[LightGBM] [Debug] Re-bagging, using 2860 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 12
[LightGBM] [Debug] Re-bagging, using 2898 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 11
[LightGBM] [Debug] Re-bagging, using 2860 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 13
[LightGBM] [Debug] Re-bagging, using 2913 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 12
[LightGBM] [Debug] Re-bagging, using 2894 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 15
[LightGBM] [Debug] Re-bagging, using 2917 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 12
[LightGBM] [Debug] Re-bagging, using 2794 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11
[LightGBM] [Debug] Re-bagging, using 2964 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 14
Trial 16, Fold 1: Log loss = 0.20747082145114132, Average precision = 0.972974517916921, ROC-AUC = 0.9679727192995033, Elapsed Time = 1.2898568999999043 seconds
Trial 16, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 16, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[LightGBM] [Info] Number of positive: 10230, number of negative: 10471
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795629
[LightGBM] [Info] Total Bins 24811
[LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 258
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285
[LightGBM] [Info] Start training from score -0.023285
[LightGBM] [Debug] Re-bagging, using 2952 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 8
[LightGBM] [Debug] Re-bagging, using 2957 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 8
[LightGBM] [Debug] Re-bagging, using 2838 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 8
[LightGBM] [Debug] Re-bagging, using 2883 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7
[LightGBM] [Debug] Re-bagging, using 2986 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9
[LightGBM] [Debug] Re-bagging, using 2921 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8
[LightGBM] [Debug] Re-bagging, using 2847 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 10
[LightGBM] [Debug] Re-bagging, using 2996 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8
[LightGBM] [Debug] Re-bagging, using 2907 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10
[LightGBM] [Debug] Re-bagging, using 2887 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10
[LightGBM] [Debug] Re-bagging, using 2771 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 11
[LightGBM] [Debug] Re-bagging, using 2857 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 12
[LightGBM] [Debug] Re-bagging, using 2906 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 16
[LightGBM] [Debug] Re-bagging, using 2857 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 13
[LightGBM] [Debug] Re-bagging, using 2916 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 13
[LightGBM] [Debug] Re-bagging, using 2904 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 11
[LightGBM] [Debug] Re-bagging, using 2925 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 12
[LightGBM] [Debug] Re-bagging, using 2807 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11
[LightGBM] [Debug] Re-bagging, using 2972 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 15
Trial 16, Fold 2: Log loss = 0.2027997548495244, Average precision = 0.9719972349009797, ROC-AUC = 0.9685757650895721, Elapsed Time = 1.3284340999998676 seconds
Trial 16, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 16, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[LightGBM] [Info] Number of positive: 10165, number of negative: 10517
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795455
[LightGBM] [Info] Total Bins 24804
[LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 258
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043
[LightGBM] [Info] Start training from score -0.034043
[LightGBM] [Debug] Re-bagging, using 2949 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7
[LightGBM] [Debug] Re-bagging, using 2951 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 6
[LightGBM] [Debug] Re-bagging, using 2841 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8
[LightGBM] [Debug] Re-bagging, using 2876 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8
[LightGBM] [Debug] Re-bagging, using 2982 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 9
[LightGBM] [Debug] Re-bagging, using 2925 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8
[LightGBM] [Debug] Re-bagging, using 2847 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8
[LightGBM] [Debug] Re-bagging, using 2986 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10
[LightGBM] [Debug] Re-bagging, using 2901 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 12
[LightGBM] [Debug] Re-bagging, using 2879 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 10
[LightGBM] [Debug] Re-bagging, using 2782 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Debug] Re-bagging, using 2848 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10
[LightGBM] [Debug] Re-bagging, using 2904 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 11
[LightGBM] [Debug] Re-bagging, using 2861 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 15
[LightGBM] [Debug] Re-bagging, using 2907 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11
[LightGBM] [Debug] Re-bagging, using 2906 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10
[LightGBM] [Debug] Re-bagging, using 2920 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 13
[LightGBM] [Debug] Re-bagging, using 2795 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11
[LightGBM] [Debug] Re-bagging, using 2971 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 14
Trial 16, Fold 3: Log loss = 0.20250456993786586, Average precision = 0.9731164233792264, ROC-AUC = 0.9689353974246613, Elapsed Time = 1.448365900000681 seconds
Trial 16, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 16, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[LightGBM] [Info] Number of positive: 10177, number of negative: 10479
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795144
[LightGBM] [Info] Total Bins 24794
[LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 258
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243
[LightGBM] [Info] Start training from score -0.029243
[LightGBM] [Debug] Re-bagging, using 2947 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8
[LightGBM] [Debug] Re-bagging, using 2945 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7
[LightGBM] [Debug] Re-bagging, using 2836 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8
[LightGBM] [Debug] Re-bagging, using 2875 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7
[LightGBM] [Debug] Re-bagging, using 2976 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10
[LightGBM] [Debug] Re-bagging, using 2924 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8
[LightGBM] [Debug] Re-bagging, using 2844 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Debug] Re-bagging, using 2986 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Debug] Re-bagging, using 2888 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 12
[LightGBM] [Debug] Re-bagging, using 2873 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Debug] Re-bagging, using 2773 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 13
[LightGBM] [Debug] Re-bagging, using 2860 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9
[LightGBM] [Debug] Re-bagging, using 2894 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 13
[LightGBM] [Debug] Re-bagging, using 2858 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 19
[LightGBM] [Debug] Re-bagging, using 2911 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 18
[LightGBM] [Debug] Re-bagging, using 2899 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 17
[LightGBM] [Debug] Re-bagging, using 2913 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 11
[LightGBM] [Debug] Re-bagging, using 2798 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 15
[LightGBM] [Debug] Re-bagging, using 2960 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 12
Trial 16, Fold 4: Log loss = 0.2061300286833578, Average precision = 0.9725030883078034, ROC-AUC = 0.9671212245851722, Elapsed Time = 1.462485399999423 seconds
Trial 16, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 16, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[LightGBM] [Info] Number of positive: 10150, number of negative: 10500
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.796026
[LightGBM] [Info] Total Bins 24798
[LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 258
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902
[LightGBM] [Info] Start training from score -0.033902
[LightGBM] [Debug] Re-bagging, using 2946 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8
[LightGBM] [Debug] Re-bagging, using 2945 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8
[LightGBM] [Debug] Re-bagging, using 2836 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7
[LightGBM] [Debug] Re-bagging, using 2874 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 6
[LightGBM] [Debug] Re-bagging, using 2972 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 10
[LightGBM] [Debug] Re-bagging, using 2922 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 9
[LightGBM] [Debug] Re-bagging, using 2848 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7
[LightGBM] [Debug] Re-bagging, using 2981 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9
[LightGBM] [Debug] Re-bagging, using 2890 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Debug] Re-bagging, using 2872 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 10
[LightGBM] [Debug] Re-bagging, using 2768 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 10
[LightGBM] [Debug] Re-bagging, using 2849 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 13
[LightGBM] [Debug] Re-bagging, using 2909 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 11
[LightGBM] [Debug] Re-bagging, using 2857 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 13
[LightGBM] [Debug] Re-bagging, using 2909 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 13
[LightGBM] [Debug] Re-bagging, using 2902 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 12
[LightGBM] [Debug] Re-bagging, using 2916 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 11
[LightGBM] [Debug] Re-bagging, using 2786 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 12
[LightGBM] [Debug] Re-bagging, using 2963 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 10
Trial 16, Fold 5: Log loss = 0.20904784892066716, Average precision = 0.9722563489089914, ROC-AUC = 0.9676975270795013, Elapsed Time = 1.4544285000001764 seconds
Optimization Progress:  17%|#7        | 17/100 [03:49<18:40, 13.51s/it]
Trial 17, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 17, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[LightGBM] [Info] Number of positive: 10130, number of negative: 10533
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.794337
[LightGBM] [Info] Total Bins 10352
[LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 257
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012
[LightGBM] [Info] Start training from score -0.039012
[LightGBM] [Debug] Re-bagging, using 3248 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Debug] Re-bagging, using 3240 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Debug] Re-bagging, using 3135 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 7
[LightGBM] [Debug] Re-bagging, using 3119 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Debug] Re-bagging, using 3232 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Debug] Re-bagging, using 3221 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6
[LightGBM] [Debug] Re-bagging, using 3145 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6
[LightGBM] [Debug] Re-bagging, using 3263 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 7
[LightGBM] [Debug] Re-bagging, using 3164 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 7
[LightGBM] [Debug] Re-bagging, using 3161 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6
[LightGBM] [Debug] Re-bagging, using 3027 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6
[LightGBM] [Debug] Re-bagging, using 3119 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 6
[LightGBM] [Debug] Re-bagging, using 3176 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7
[LightGBM] [Debug] Re-bagging, using 3172 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Debug] Re-bagging, using 3193 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6
[LightGBM] [Debug] Re-bagging, using 3164 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6
[LightGBM] [Debug] Re-bagging, using 3247 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7
[LightGBM] [Debug] Re-bagging, using 3090 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 5
Trial 17, Fold 1: Log loss = 0.30963063875852925, Average precision = 0.9650738928081896, ROC-AUC = 0.958796878808735, Elapsed Time = 1.334805999999844 seconds
Trial 17, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 17, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[LightGBM] [Info] Number of positive: 10230, number of negative: 10471
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.792956
[LightGBM] [Info] Total Bins 10364
[LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 256
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285
[LightGBM] [Info] Start training from score -0.023285
[LightGBM] [Debug] Re-bagging, using 3254 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Debug] Re-bagging, using 3250 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Debug] Re-bagging, using 3133 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Debug] Re-bagging, using 3132 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6
[LightGBM] [Debug] Re-bagging, using 3241 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7
[LightGBM] [Debug] Re-bagging, using 3216 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 5
[LightGBM] [Debug] Re-bagging, using 3151 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5
[LightGBM] [Debug] Re-bagging, using 3269 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Debug] Re-bagging, using 3180 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 6
[LightGBM] [Debug] Re-bagging, using 3176 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Debug] Re-bagging, using 3028 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Debug] Re-bagging, using 3111 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Debug] Re-bagging, using 3186 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 8
[LightGBM] [Debug] Re-bagging, using 3169 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 5
[LightGBM] [Debug] Re-bagging, using 3197 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 7
[LightGBM] [Debug] Re-bagging, using 3173 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Debug] Re-bagging, using 3256 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Debug] Re-bagging, using 3104 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
Trial 17, Fold 2: Log loss = 0.31070298288297354, Average precision = 0.9617001013894317, ROC-AUC = 0.9575550821699083, Elapsed Time = 1.725110099999256 seconds
Trial 17, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 17, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[LightGBM] [Info] Number of positive: 10165, number of negative: 10517
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.792768
[LightGBM] [Info] Total Bins 10368
[LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 256
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043
[LightGBM] [Info] Start training from score -0.034043
[LightGBM] [Debug] Re-bagging, using 3250 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Debug] Re-bagging, using 3244 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6
[LightGBM] [Debug] Re-bagging, using 3136 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Debug] Re-bagging, using 3126 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 5
[LightGBM] [Debug] Re-bagging, using 3236 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Debug] Re-bagging, using 3218 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6
[LightGBM] [Debug] Re-bagging, using 3153 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Debug] Re-bagging, using 3258 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Debug] Re-bagging, using 3176 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6
[LightGBM] [Debug] Re-bagging, using 3163 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6
[LightGBM] [Debug] Re-bagging, using 3041 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Debug] Re-bagging, using 3105 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7
[LightGBM] [Debug] Re-bagging, using 3180 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Debug] Re-bagging, using 3175 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Debug] Re-bagging, using 3187 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 7
[LightGBM] [Debug] Re-bagging, using 3175 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Debug] Re-bagging, using 3252 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6
[LightGBM] [Debug] Re-bagging, using 3090 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 8
Trial 17, Fold 3: Log loss = 0.3052696325563928, Average precision = 0.9660969774617566, ROC-AUC = 0.9609569877974786, Elapsed Time = 1.5482718000002933 seconds
Trial 17, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 17, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[LightGBM] [Info] Number of positive: 10177, number of negative: 10479
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.792454
[LightGBM] [Info] Total Bins 10347
[LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 256
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243
[LightGBM] [Info] Start training from score -0.029243
[LightGBM] [Debug] Re-bagging, using 3248 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Debug] Re-bagging, using 3238 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 5
[LightGBM] [Debug] Re-bagging, using 3131 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6
[LightGBM] [Debug] Re-bagging, using 3121 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 5
[LightGBM] [Debug] Re-bagging, using 3230 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 6
[LightGBM] [Debug] Re-bagging, using 3221 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 7
[LightGBM] [Debug] Re-bagging, using 3146 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 5
[LightGBM] [Debug] Re-bagging, using 3259 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 7
[LightGBM] [Debug] Re-bagging, using 3163 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 5
[LightGBM] [Debug] Re-bagging, using 3159 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Debug] Re-bagging, using 3028 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Debug] Re-bagging, using 3116 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Debug] Re-bagging, using 3174 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Debug] Re-bagging, using 3171 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Debug] Re-bagging, using 3189 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6
[LightGBM] [Debug] Re-bagging, using 3169 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6
[LightGBM] [Debug] Re-bagging, using 3245 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 5
[LightGBM] [Debug] Re-bagging, using 3092 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6
Trial 17, Fold 4: Log loss = 0.30706864130283307, Average precision = 0.9650277959863939, ROC-AUC = 0.9583006222117307, Elapsed Time = 1.5326796999997896 seconds
Trial 17, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 17, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[LightGBM] [Info] Number of positive: 10150, number of negative: 10500
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.793364
[LightGBM] [Info] Total Bins 10351
[LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 256
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902
[LightGBM] [Info] Start training from score -0.033902
[LightGBM] [Debug] Re-bagging, using 3247 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Debug] Re-bagging, using 3238 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 5
[LightGBM] [Debug] Re-bagging, using 3131 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Debug] Re-bagging, using 3120 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6
[LightGBM] [Debug] Re-bagging, using 3226 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Debug] Re-bagging, using 3218 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7
[LightGBM] [Debug] Re-bagging, using 3150 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6
[LightGBM] [Debug] Re-bagging, using 3254 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Debug] Re-bagging, using 3164 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Debug] Re-bagging, using 3158 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Debug] Re-bagging, using 3025 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6
[LightGBM] [Debug] Re-bagging, using 3103 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6
[LightGBM] [Debug] Re-bagging, using 3190 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6
[LightGBM] [Debug] Re-bagging, using 3170 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Debug] Re-bagging, using 3188 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6
[LightGBM] [Debug] Re-bagging, using 3170 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7
[LightGBM] [Debug] Re-bagging, using 3249 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7
[LightGBM] [Debug] Re-bagging, using 3081 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
Trial 17, Fold 5: Log loss = 0.31262231004306085, Average precision = 0.9626557154725396, ROC-AUC = 0.9569277074856475, Elapsed Time = 1.2901283999999578 seconds
Optimization Progress:  18%|#8        | 18/100 [04:05<19:29, 14.26s/it]
Trial 18, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 18, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[LightGBM] [Info] Number of positive: 10130, number of negative: 10533
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791640
[LightGBM] [Info] Total Bins 7940
[LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012
[LightGBM] [Info] Start training from score -0.039012
[LightGBM] [Debug] Re-bagging, using 6760 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8
[LightGBM] [Debug] Re-bagging, using 6778 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8
[LightGBM] [Debug] Re-bagging, using 6679 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Debug] Re-bagging, using 6628 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8
[LightGBM] [Debug] Re-bagging, using 6807 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9
[LightGBM] [Debug] Re-bagging, using 6755 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
[LightGBM] [Debug] Re-bagging, using 6700 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 8
[LightGBM] [Debug] Re-bagging, using 6818 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 10
[LightGBM] [Debug] Re-bagging, using 6697 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Debug] Re-bagging, using 6546 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 8
[LightGBM] [Debug] Re-bagging, using 6579 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 8
[LightGBM] [Debug] Re-bagging, using 6631 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
[LightGBM] [Debug] Re-bagging, using 6639 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9
[LightGBM] [Debug] Re-bagging, using 6717 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11
[LightGBM] [Debug] Re-bagging, using 6851 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 8
[LightGBM] [Debug] Re-bagging, using 6724 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 11
[LightGBM] [Debug] Re-bagging, using 6703 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9
[LightGBM] [Debug] Re-bagging, using 6680 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9
[LightGBM] [Debug] Re-bagging, using 6784 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 13
[LightGBM] [Debug] Re-bagging, using 6788 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 13
[LightGBM] [Debug] Re-bagging, using 6612 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 13
[LightGBM] [Debug] Re-bagging, using 6680 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 12
[LightGBM] [Debug] Re-bagging, using 6769 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 16
[LightGBM] [Debug] Re-bagging, using 6719 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 12
[LightGBM] [Debug] Re-bagging, using 6867 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 15
[LightGBM] [Debug] Re-bagging, using 6756 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 13
Trial 18, Fold 1: Log loss = 0.20574575048184507, Average precision = 0.9739334509548979, ROC-AUC = 0.9692472266433423, Elapsed Time = 0.9175384999998641 seconds
Trial 18, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 18, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[LightGBM] [Info] Number of positive: 10230, number of negative: 10471
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791600
[LightGBM] [Info] Total Bins 7956
[LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285
[LightGBM] [Info] Start training from score -0.023285
[LightGBM] [Debug] Re-bagging, using 6773 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8
[LightGBM] [Debug] Re-bagging, using 6795 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8
[LightGBM] [Debug] Re-bagging, using 6687 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 8
[LightGBM] [Debug] Re-bagging, using 6643 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 12
[LightGBM] [Debug] Re-bagging, using 6835 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8
[LightGBM] [Debug] Re-bagging, using 6744 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9
[LightGBM] [Debug] Re-bagging, using 6713 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 8
[LightGBM] [Debug] Re-bagging, using 6826 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Debug] Re-bagging, using 6724 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 11
[LightGBM] [Debug] Re-bagging, using 6566 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 8
[LightGBM] [Debug] Re-bagging, using 6573 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Debug] Re-bagging, using 6634 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9
[LightGBM] [Debug] Re-bagging, using 6648 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 8
[LightGBM] [Debug] Re-bagging, using 6729 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9
[LightGBM] [Debug] Re-bagging, using 6866 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9
[LightGBM] [Debug] Re-bagging, using 6745 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9
[LightGBM] [Debug] Re-bagging, using 6716 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10
[LightGBM] [Debug] Re-bagging, using 6697 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 13
[LightGBM] [Debug] Re-bagging, using 6800 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10
[LightGBM] [Debug] Re-bagging, using 6799 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 14
[LightGBM] [Debug] Re-bagging, using 6626 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11
[LightGBM] [Debug] Re-bagging, using 6693 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11
[LightGBM] [Debug] Re-bagging, using 6790 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10
[LightGBM] [Debug] Re-bagging, using 6734 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 14
[LightGBM] [Debug] Re-bagging, using 6876 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 10
[LightGBM] [Debug] Re-bagging, using 6764 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 14
Trial 18, Fold 2: Log loss = 0.2013883232521865, Average precision = 0.9735480108160133, ROC-AUC = 0.9702207110840527, Elapsed Time = 1.02073879999989 seconds
Trial 18, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 18, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[LightGBM] [Info] Number of positive: 10165, number of negative: 10517
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791401
[LightGBM] [Info] Total Bins 7963
[LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043
[LightGBM] [Info] Start training from score -0.034043
[LightGBM] [Debug] Re-bagging, using 6766 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8
[LightGBM] [Debug] Re-bagging, using 6785 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 8
[LightGBM] [Debug] Re-bagging, using 6681 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8
[LightGBM] [Debug] Re-bagging, using 6641 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 8
[LightGBM] [Debug] Re-bagging, using 6823 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 8
[LightGBM] [Debug] Re-bagging, using 6745 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 8
[LightGBM] [Debug] Re-bagging, using 6710 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 8
[LightGBM] [Debug] Re-bagging, using 6820 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
[LightGBM] [Debug] Re-bagging, using 6699 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9
[LightGBM] [Debug] Re-bagging, using 6574 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10
[LightGBM] [Debug] Re-bagging, using 6572 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9
[LightGBM] [Debug] Re-bagging, using 6629 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9
[LightGBM] [Debug] Re-bagging, using 6640 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 8
[LightGBM] [Debug] Re-bagging, using 6727 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11
[LightGBM] [Debug] Re-bagging, using 6851 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9
[LightGBM] [Debug] Re-bagging, using 6737 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
[LightGBM] [Debug] Re-bagging, using 6714 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11
[LightGBM] [Debug] Re-bagging, using 6687 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 11
[LightGBM] [Debug] Re-bagging, using 6785 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10
[LightGBM] [Debug] Re-bagging, using 6806 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10
[LightGBM] [Debug] Re-bagging, using 6627 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10
[LightGBM] [Debug] Re-bagging, using 6673 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 8
[LightGBM] [Debug] Re-bagging, using 6767 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 12
[LightGBM] [Debug] Re-bagging, using 6737 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 16
[LightGBM] [Debug] Re-bagging, using 6888 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 18
[LightGBM] [Debug] Re-bagging, using 6758 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 12
Trial 18, Fold 3: Log loss = 0.20044029078217634, Average precision = 0.9740716095881156, ROC-AUC = 0.9701093303059779, Elapsed Time = 1.2052254000000175 seconds
Trial 18, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 18, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[LightGBM] [Info] Number of positive: 10177, number of negative: 10479
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.792454
[LightGBM] [Info] Total Bins 7949
[LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 256
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243
[LightGBM] [Info] Start training from score -0.029243
[LightGBM] [Debug] Re-bagging, using 6757 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9
[LightGBM] [Debug] Re-bagging, using 6775 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8
[LightGBM] [Debug] Re-bagging, using 6676 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 8
[LightGBM] [Debug] Re-bagging, using 6630 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 8
[LightGBM] [Debug] Re-bagging, using 6802 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Debug] Re-bagging, using 6752 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9
[LightGBM] [Debug] Re-bagging, using 6701 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
[LightGBM] [Debug] Re-bagging, using 6812 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9
[LightGBM] [Debug] Re-bagging, using 6694 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 8
[LightGBM] [Debug] Re-bagging, using 6548 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9
[LightGBM] [Debug] Re-bagging, using 6569 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 10
[LightGBM] [Debug] Re-bagging, using 6634 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9
[LightGBM] [Debug] Re-bagging, using 6638 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 8
[LightGBM] [Debug] Re-bagging, using 6714 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 10
[LightGBM] [Debug] Re-bagging, using 6845 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10
[LightGBM] [Debug] Re-bagging, using 6725 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11
[LightGBM] [Debug] Re-bagging, using 6701 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 12
[LightGBM] [Debug] Re-bagging, using 6683 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 10
[LightGBM] [Debug] Re-bagging, using 6775 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11
[LightGBM] [Debug] Re-bagging, using 6791 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 13
[LightGBM] [Debug] Re-bagging, using 6611 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 12
[LightGBM] [Debug] Re-bagging, using 6670 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11
[LightGBM] [Debug] Re-bagging, using 6770 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 12
[LightGBM] [Debug] Re-bagging, using 6721 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 15
[LightGBM] [Debug] Re-bagging, using 6862 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 12
[LightGBM] [Debug] Re-bagging, using 6752 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11
Trial 18, Fold 4: Log loss = 0.20460782943851485, Average precision = 0.9734286703235141, ROC-AUC = 0.9687859896850433, Elapsed Time = 1.236685199999556 seconds
Trial 18, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 18, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[LightGBM] [Info] Number of positive: 10150, number of negative: 10500
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.792012
[LightGBM] [Info] Total Bins 7944
[LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902
[LightGBM] [Info] Start training from score -0.033902
[LightGBM] [Debug] Re-bagging, using 6755 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Debug] Re-bagging, using 6775 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8
[LightGBM] [Debug] Re-bagging, using 6674 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8
[LightGBM] [Debug] Re-bagging, using 6625 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10
[LightGBM] [Debug] Re-bagging, using 6801 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8
[LightGBM] [Debug] Re-bagging, using 6745 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8
[LightGBM] [Debug] Re-bagging, using 6709 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 8
[LightGBM] [Debug] Re-bagging, using 6805 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 10
[LightGBM] [Debug] Re-bagging, using 6692 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9
[LightGBM] [Debug] Re-bagging, using 6551 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 10
[LightGBM] [Debug] Re-bagging, using 6559 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9
[LightGBM] [Debug] Re-bagging, using 6629 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9
[LightGBM] [Debug] Re-bagging, using 6639 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10
[LightGBM] [Debug] Re-bagging, using 6724 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 8
[LightGBM] [Debug] Re-bagging, using 6842 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 10
[LightGBM] [Debug] Re-bagging, using 6726 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10
[LightGBM] [Debug] Re-bagging, using 6697 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 12
[LightGBM] [Debug] Re-bagging, using 6669 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 8
[LightGBM] [Debug] Re-bagging, using 6776 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 11
[LightGBM] [Debug] Re-bagging, using 6784 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Debug] Re-bagging, using 6619 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 13
[LightGBM] [Debug] Re-bagging, using 6662 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Debug] Re-bagging, using 6764 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11
[LightGBM] [Debug] Re-bagging, using 6726 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 14
[LightGBM] [Debug] Re-bagging, using 6863 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 15
[LightGBM] [Debug] Re-bagging, using 6749 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 13
Trial 18, Fold 5: Log loss = 0.209003953144028, Average precision = 0.971670356080389, ROC-AUC = 0.9682428701484496, Elapsed Time = 1.1931323999997403 seconds
Optimization Progress:  19%|#9        | 19/100 [04:18<18:56, 14.03s/it]
Trial 19, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 19, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[LightGBM] [Info] Number of positive: 10130, number of negative: 10533
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.796981
[LightGBM] [Info] Total Bins 21915
[LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 259
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012
[LightGBM] [Info] Start training from score -0.039012
[LightGBM] [Debug] Re-bagging, using 8731 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 88 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 14
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 14
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 16
[LightGBM] [Debug] Re-bagging, using 8796 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 90 and depth = 13
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 86 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 87 and depth = 11
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 14
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 15
[LightGBM] [Debug] Re-bagging, using 8715 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 88 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 87 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 88 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 87 and depth = 14
[LightGBM] [Debug] Re-bagging, using 8664 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 86 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 87 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 88 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 86 and depth = 18
[LightGBM] [Debug] Re-bagging, using 8776 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 91 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 87 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 86 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 90 and depth = 13
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 14
[LightGBM] [Debug] Re-bagging, using 8704 data to train
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 12
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 86 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 18
[LightGBM] [Debug] Re-bagging, using 8720 data to train
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 12
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 17
[LightGBM] [Debug] Re-bagging, using 8788 data to train
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 22
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 23
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 21
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 26
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 20
[LightGBM] [Debug] Re-bagging, using 8763 data to train
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 26
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 26
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 30
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 21
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 23
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 22
[LightGBM] [Debug] Re-bagging, using 8545 data to train
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 23
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 21
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 20
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 23
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 24
[LightGBM] [Debug] Re-bagging, using 8540 data to train
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 20
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 14
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 20
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 23
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 14
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 20
[LightGBM] [Debug] Re-bagging, using 8563 data to train
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 23
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 27
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 30
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 20
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 21
[LightGBM] [Debug] Re-bagging, using 8668 data to train
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 26
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 25
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 20
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 34
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 20
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 22
[LightGBM] [Debug] Re-bagging, using 8668 data to train
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 14
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 20
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 19
[LightGBM] [Debug] Re-bagging, using 8874 data to train
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 23
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 20
Trial 19, Fold 1: Log loss = 0.19924243631778799, Average precision = 0.9757228974576991, ROC-AUC = 0.9711200788154266, Elapsed Time = 2.895565999999235 seconds
Trial 19, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 19, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[LightGBM] [Info] Number of positive: 10230, number of negative: 10471
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.796949
[LightGBM] [Info] Total Bins 21927
[LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 259
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285
[LightGBM] [Info] Start training from score -0.023285
[LightGBM] [Debug] Re-bagging, using 8749 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 88 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 19
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 90 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 12
[LightGBM] [Debug] Re-bagging, using 8813 data to train
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 91 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 13
[LightGBM] [Debug] Re-bagging, using 8729 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 86 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 88 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 13
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 14
[LightGBM] [Debug] Re-bagging, using 8685 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 14
[LightGBM] [Debug] Re-bagging, using 8804 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 88 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 19
[LightGBM] [Debug] Re-bagging, using 8699 data to train
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 23
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 30
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 15
[LightGBM] [Debug] Re-bagging, using 8730 data to train
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 14
[LightGBM] [Debug] Re-bagging, using 8804 data to train
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 23
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 21
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 17
[LightGBM] [Debug] Re-bagging, using 8789 data to train
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 22
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 24
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 21
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 21
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 23
[LightGBM] [Debug] Re-bagging, using 8564 data to train
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 21
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 22
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 22
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 21
[LightGBM] [Debug] Re-bagging, using 8547 data to train
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 21
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 21
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 20
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 23
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 27
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 26
[LightGBM] [Debug] Re-bagging, using 8565 data to train
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 21
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 20
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 23
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 24
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 22
[LightGBM] [Debug] Re-bagging, using 8676 data to train
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 13
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 21
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 20
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 18
[LightGBM] [Debug] Re-bagging, using 8687 data to train
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 20
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 22
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 21
[LightGBM] [Debug] Re-bagging, using 8906 data to train
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 23
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 20
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 19
Trial 19, Fold 2: Log loss = 0.18703719630522814, Average precision = 0.9756550682922964, ROC-AUC = 0.9730873784993419, Elapsed Time = 3.661527400000523 seconds
Trial 19, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 19, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[LightGBM] [Info] Number of positive: 10165, number of negative: 10517
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.796784
[LightGBM] [Info] Total Bins 21870
[LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 259
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043
[LightGBM] [Info] Start training from score -0.034043
[LightGBM] [Debug] Re-bagging, using 8740 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 20
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 15
[LightGBM] [Debug] Re-bagging, using 8803 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 87 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 13
[LightGBM] [Debug] Re-bagging, using 8720 data to train
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 20
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 91 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 14
[LightGBM] [Debug] Re-bagging, using 8682 data to train
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 13
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 90 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 91 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 14
[LightGBM] [Debug] Re-bagging, using 8789 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 86 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 87 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 87 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 86 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 91 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 87 and depth = 14
[LightGBM] [Debug] Re-bagging, using 8696 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 87 and depth = 14
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 19
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 88 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 18
[LightGBM] [Debug] Re-bagging, using 8727 data to train
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 30
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 14
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 15
[LightGBM] [Debug] Re-bagging, using 8796 data to train
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 22
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 20
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 21
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 28
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 24
[LightGBM] [Debug] Re-bagging, using 8769 data to train
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 22
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 21
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 31
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 25
[LightGBM] [Debug] Re-bagging, using 8565 data to train
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 20
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 26
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 22
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 15
[LightGBM] [Debug] Re-bagging, using 8542 data to train
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 20
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 20
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 20
[LightGBM] [Debug] Re-bagging, using 8565 data to train
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 23
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 27
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 27
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 31
[LightGBM] [Debug] Re-bagging, using 8665 data to train
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 13
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 20
[LightGBM] [Debug] Re-bagging, using 8676 data to train
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 24
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 20
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 22
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 28
[LightGBM] [Debug] Re-bagging, using 8886 data to train
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 30
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 21
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 23
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 23
Trial 19, Fold 3: Log loss = 0.1889680747041436, Average precision = 0.9750371189788504, ROC-AUC = 0.9728410831062014, Elapsed Time = 3.0296307999997225 seconds
Trial 19, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 19, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[LightGBM] [Info] Number of positive: 10177, number of negative: 10479
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.796474
[LightGBM] [Info] Total Bins 21910
[LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 259
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243
[LightGBM] [Info] Start training from score -0.029243
[LightGBM] [Debug] Re-bagging, using 8727 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 14
[LightGBM] [Debug] Re-bagging, using 8794 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 13
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 87 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 15
[LightGBM] [Debug] Re-bagging, using 8711 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 86 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 86 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 86 and depth = 13
[LightGBM] [Debug] Re-bagging, using 8663 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 86 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 91 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 16
[LightGBM] [Debug] Re-bagging, using 8769 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 88 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 14
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 14
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 21
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 17
[LightGBM] [Debug] Re-bagging, using 8702 data to train
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 14
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 13
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 13
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 22
[LightGBM] [Debug] Re-bagging, using 8718 data to train
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 28
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 24
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 20
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 19
[LightGBM] [Debug] Re-bagging, using 8787 data to train
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 14
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 19
[LightGBM] [Debug] Re-bagging, using 8761 data to train
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 20
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 22
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 19
[LightGBM] [Debug] Re-bagging, using 8542 data to train
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 13
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 30
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 22
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 26
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 21
[LightGBM] [Debug] Re-bagging, using 8536 data to train
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 24
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 23
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 27
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 18
[LightGBM] [Debug] Re-bagging, using 8561 data to train
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 22
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 29
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 26
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 26
[LightGBM] [Debug] Re-bagging, using 8662 data to train
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 21
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 20
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 34
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 20
[LightGBM] [Debug] Re-bagging, using 8669 data to train
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 14
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 20
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 20
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 18
[LightGBM] [Debug] Re-bagging, using 8872 data to train
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 21
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 27
Trial 19, Fold 4: Log loss = 0.19223260795860275, Average precision = 0.9760122026562066, ROC-AUC = 0.9717124056071781, Elapsed Time = 3.7891062999997303 seconds
Trial 19, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 19, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[LightGBM] [Info] Number of positive: 10150, number of negative: 10500
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.797340
[LightGBM] [Info] Total Bins 21919
[LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 259
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902
[LightGBM] [Info] Start training from score -0.033902
[LightGBM] [Debug] Re-bagging, using 8725 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 14
[LightGBM] [Debug] Re-bagging, using 8792 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 12
[LightGBM] [Debug] Re-bagging, using 8710 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 88 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 87 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 15
[LightGBM] [Debug] Re-bagging, using 8656 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 87 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 86 and depth = 14
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 86 and depth = 15
[LightGBM] [Debug] Re-bagging, using 8769 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 19
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 90 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 87 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 17
[LightGBM] [Debug] Re-bagging, using 8694 data to train
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 19
[LightGBM] [Debug] Re-bagging, using 8725 data to train
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 14
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 17
[LightGBM] [Debug] Re-bagging, using 8781 data to train
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 20
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 87 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 20
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 15
[LightGBM] [Debug] Re-bagging, using 8758 data to train
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 23
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 18
[LightGBM] [Debug] Re-bagging, using 8544 data to train
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 20
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 22
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 20
[LightGBM] [Debug] Re-bagging, using 8526 data to train
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 13
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 20
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 19
[LightGBM] [Debug] Re-bagging, using 8554 data to train
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 27
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 26
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 22
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 20
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 21
[LightGBM] [Debug] Re-bagging, using 8664 data to train
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 23
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 22
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 21
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 31
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 25
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 29
[LightGBM] [Debug] Re-bagging, using 8678 data to train
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 25
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 20
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 25
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 21
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 24
[LightGBM] [Debug] Re-bagging, using 8870 data to train
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 27
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 23
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 36
Trial 19, Fold 5: Log loss = 0.2012878534321624, Average precision = 0.9731398997533733, ROC-AUC = 0.9698096352860301, Elapsed Time = 3.564209299999675 seconds
Optimization Progress:  20%|##        | 20/100 [04:44<23:14, 17.43s/it]
Trial 20, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 20, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[LightGBM] [Info] Number of positive: 10130, number of negative: 10533
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791640
[LightGBM] [Info] Total Bins 14105
[LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012
[LightGBM] [Info] Start training from score -0.039012
[LightGBM] [Debug] Re-bagging, using 9540 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10
[LightGBM] [Debug] Re-bagging, using 9527 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
[LightGBM] [Debug] Re-bagging, using 9482 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11
[LightGBM] [Debug] Re-bagging, using 9420 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10
[LightGBM] [Debug] Re-bagging, using 9558 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11
[LightGBM] [Debug] Re-bagging, using 9423 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 9
[LightGBM] [Debug] Re-bagging, using 9525 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11
[LightGBM] [Debug] Re-bagging, using 9521 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10
Trial 20, Fold 1: Log loss = 0.3012211491529579, Average precision = 0.9681789405533022, ROC-AUC = 0.9619023422377081, Elapsed Time = 1.2858525999999983 seconds
Trial 20, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 20, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[LightGBM] [Info] Number of positive: 10230, number of negative: 10471
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791600
[LightGBM] [Info] Total Bins 14121
[LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285
[LightGBM] [Info] Start training from score -0.023285
[LightGBM] [Debug] Re-bagging, using 9558 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9
[LightGBM] [Debug] Re-bagging, using 9546 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9
[LightGBM] [Debug] Re-bagging, using 9497 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10
[LightGBM] [Debug] Re-bagging, using 9445 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10
[LightGBM] [Debug] Re-bagging, using 9585 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9
[LightGBM] [Debug] Re-bagging, using 9421 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11
[LightGBM] [Debug] Re-bagging, using 9535 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 9
[LightGBM] [Debug] Re-bagging, using 9534 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10
Trial 20, Fold 2: Log loss = 0.3005685009027957, Average precision = 0.9655611009212376, ROC-AUC = 0.9629260432887872, Elapsed Time = 1.4934327999999368 seconds
Trial 20, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 20, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[LightGBM] [Info] Number of positive: 10165, number of negative: 10517
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791401
[LightGBM] [Info] Total Bins 14124
[LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043
[LightGBM] [Info] Start training from score -0.034043
[LightGBM] [Debug] Re-bagging, using 9549 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 12
[LightGBM] [Debug] Re-bagging, using 9535 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11
[LightGBM] [Debug] Re-bagging, using 9486 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 9
[LightGBM] [Debug] Re-bagging, using 9440 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10
[LightGBM] [Debug] Re-bagging, using 9571 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10
[LightGBM] [Debug] Re-bagging, using 9417 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10
[LightGBM] [Debug] Re-bagging, using 9534 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10
[LightGBM] [Debug] Re-bagging, using 9527 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11
Trial 20, Fold 3: Log loss = 0.2985064549274648, Average precision = 0.96914730997676, ROC-AUC = 0.9638280143935819, Elapsed Time = 1.4035541000002922 seconds
Trial 20, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 20, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[LightGBM] [Info] Number of positive: 10177, number of negative: 10479
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.792454
[LightGBM] [Info] Total Bins 14106
[LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 256
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243
[LightGBM] [Info] Start training from score -0.029243
[LightGBM] [Debug] Re-bagging, using 9536 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10
[LightGBM] [Debug] Re-bagging, using 9523 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Debug] Re-bagging, using 9480 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Debug] Re-bagging, using 9418 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9
[LightGBM] [Debug] Re-bagging, using 9551 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 9
[LightGBM] [Debug] Re-bagging, using 9420 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11
[LightGBM] [Debug] Re-bagging, using 9523 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10
[LightGBM] [Debug] Re-bagging, using 9520 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11
Trial 20, Fold 4: Log loss = 0.29806484641027425, Average precision = 0.9684156480487836, ROC-AUC = 0.962071715296704, Elapsed Time = 1.396743599999354 seconds
Trial 20, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 20, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[LightGBM] [Info] Number of positive: 10150, number of negative: 10500
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.792012
[LightGBM] [Info] Total Bins 14104
[LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902
[LightGBM] [Info] Start training from score -0.033902
[LightGBM] [Debug] Re-bagging, using 9534 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9
[LightGBM] [Debug] Re-bagging, using 9520 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9
[LightGBM] [Debug] Re-bagging, using 9480 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10
[LightGBM] [Debug] Re-bagging, using 9411 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9
[LightGBM] [Debug] Re-bagging, using 9549 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9
[LightGBM] [Debug] Re-bagging, using 9414 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9
[LightGBM] [Debug] Re-bagging, using 9527 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9
[LightGBM] [Debug] Re-bagging, using 9514 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
Trial 20, Fold 5: Log loss = 0.3018067238368054, Average precision = 0.9668616390071544, ROC-AUC = 0.9616186388718577, Elapsed Time = 1.3381429000000935 seconds
Optimization Progress:  21%|##1       | 21/100 [04:59<22:04, 16.77s/it]
Trial 21, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 21, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[LightGBM] [Info] Number of positive: 10130, number of negative: 10533
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791640
[LightGBM] [Info] Total Bins 15336
[LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012
[LightGBM] [Info] Start training from score -0.039012
[LightGBM] [Debug] Re-bagging, using 10556 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 14
[LightGBM] [Debug] Re-bagging, using 10532 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 16
[LightGBM] [Debug] Re-bagging, using 10535 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 17
Trial 21, Fold 1: Log loss = 0.4294722857380578, Average precision = 0.9663014875585593, ROC-AUC = 0.9613171412798784, Elapsed Time = 0.7071726000003764 seconds
Trial 21, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 21, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[LightGBM] [Info] Number of positive: 10230, number of negative: 10471
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791600
[LightGBM] [Info] Total Bins 15352
[LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285
[LightGBM] [Info] Start training from score -0.023285
[LightGBM] [Debug] Re-bagging, using 10578 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 15
[LightGBM] [Debug] Re-bagging, using 10550 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 17
[LightGBM] [Debug] Re-bagging, using 10551 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 17
Trial 21, Fold 2: Log loss = 0.4269684819092223, Average precision = 0.9661205485665564, ROC-AUC = 0.9627950090760351, Elapsed Time = 0.7049581999999646 seconds
Trial 21, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 21, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[LightGBM] [Info] Number of positive: 10165, number of negative: 10517
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791401
[LightGBM] [Info] Total Bins 15353
[LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043
[LightGBM] [Info] Start training from score -0.034043
[LightGBM] [Debug] Re-bagging, using 10566 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 13
[LightGBM] [Debug] Re-bagging, using 10539 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 17
[LightGBM] [Debug] Re-bagging, using 10541 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 17
Trial 21, Fold 3: Log loss = 0.4242086322595482, Average precision = 0.9697600374481333, ROC-AUC = 0.9651152624216275, Elapsed Time = 0.7312525000006644 seconds
Trial 21, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 21, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[LightGBM] [Info] Number of positive: 10177, number of negative: 10479
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791090
[LightGBM] [Info] Total Bins 15336
[LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243
[LightGBM] [Info] Start training from score -0.029243
[LightGBM] [Debug] Re-bagging, using 10552 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 16
[LightGBM] [Debug] Re-bagging, using 10528 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 12
[LightGBM] [Debug] Re-bagging, using 10530 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 17
Trial 21, Fold 4: Log loss = 0.42668125159574394, Average precision = 0.9685943640991859, ROC-AUC = 0.9628020898159473, Elapsed Time = 0.7318037000004551 seconds
Trial 21, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 21, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[LightGBM] [Info] Number of positive: 10150, number of negative: 10500
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.792012
[LightGBM] [Info] Total Bins 15335
[LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902
[LightGBM] [Info] Start training from score -0.033902
[LightGBM] [Debug] Re-bagging, using 10549 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 16
[LightGBM] [Debug] Re-bagging, using 10524 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 13
[LightGBM] [Debug] Re-bagging, using 10532 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 14
Trial 21, Fold 5: Log loss = 0.4274642890160897, Average precision = 0.9688491111364995, ROC-AUC = 0.9643741151552311, Elapsed Time = 0.7462249999998676 seconds
Optimization Progress:  22%|##2       | 22/100 [05:10<19:37, 15.10s/it]
Trial 22, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 22, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[LightGBM] [Info] Number of positive: 10130, number of negative: 10533
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.790295
[LightGBM] [Info] Total Bins 22226
[LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 254
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012
[LightGBM] [Info] Start training from score -0.039012
[LightGBM] [Debug] Re-bagging, using 14649 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 9
[LightGBM] [Debug] Re-bagging, using 14767 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 9
[LightGBM] [Debug] Re-bagging, using 14796 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 8
[LightGBM] [Debug] Re-bagging, using 14659 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 8
[LightGBM] [Debug] Re-bagging, using 14721 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6
[LightGBM] [Debug] Re-bagging, using 14652 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 6
[LightGBM] [Debug] Re-bagging, using 14702 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 8
Trial 22, Fold 1: Log loss = 0.5228777373379064, Average precision = 0.9647546789177879, ROC-AUC = 0.9574565027506537, Elapsed Time = 0.7402208000003156 seconds
Trial 22, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 22, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[LightGBM] [Info] Number of positive: 10230, number of negative: 10471
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791600
[LightGBM] [Info] Total Bins 22241
[LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285
[LightGBM] [Info] Start training from score -0.023285
[LightGBM] [Debug] Re-bagging, using 14676 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 8
[LightGBM] [Debug] Re-bagging, using 14793 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 5
[LightGBM] [Debug] Re-bagging, using 14827 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 8
[LightGBM] [Debug] Re-bagging, using 14688 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7
[LightGBM] [Debug] Re-bagging, using 14750 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6
[LightGBM] [Debug] Re-bagging, using 14664 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8
[LightGBM] [Debug] Re-bagging, using 14723 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 9
Trial 22, Fold 2: Log loss = 0.5212665422613845, Average precision = 0.9636074131965384, ROC-AUC = 0.9593215022666567, Elapsed Time = 0.7811039999996865 seconds
Trial 22, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 22, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[LightGBM] [Info] Number of positive: 10165, number of negative: 10517
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.790045
[LightGBM] [Info] Total Bins 22123
[LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 254
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043
[LightGBM] [Info] Start training from score -0.034043
[LightGBM] [Debug] Re-bagging, using 14660 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 8
[LightGBM] [Debug] Re-bagging, using 14780 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7
[LightGBM] [Debug] Re-bagging, using 14813 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 8
[LightGBM] [Debug] Re-bagging, using 14679 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 7
[LightGBM] [Debug] Re-bagging, using 14731 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 8
[LightGBM] [Debug] Re-bagging, using 14655 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 10
[LightGBM] [Debug] Re-bagging, using 14714 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6
Trial 22, Fold 3: Log loss = 0.5236276273082059, Average precision = 0.9654209196951052, ROC-AUC = 0.9595892303358342, Elapsed Time = 0.7161302000004071 seconds
Trial 22, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 22, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[LightGBM] [Info] Number of positive: 10177, number of negative: 10479
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791090
[LightGBM] [Info] Total Bins 22222
[LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243
[LightGBM] [Info] Start training from score -0.029243
[LightGBM] [Debug] Re-bagging, using 14644 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 9
[LightGBM] [Debug] Re-bagging, using 14760 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 8
[LightGBM] [Debug] Re-bagging, using 14791 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 7
[LightGBM] [Debug] Re-bagging, using 14658 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 7
[LightGBM] [Debug] Re-bagging, using 14714 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 8
[LightGBM] [Debug] Re-bagging, using 14648 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6
[LightGBM] [Debug] Re-bagging, using 14696 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 9
Trial 22, Fold 4: Log loss = 0.5223469997818746, Average precision = 0.9648356841262216, ROC-AUC = 0.9584982301718444, Elapsed Time = 0.688500400000521 seconds
Trial 22, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 22, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[LightGBM] [Info] Number of positive: 10150, number of negative: 10500
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.792012
[LightGBM] [Info] Total Bins 22235
[LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902
[LightGBM] [Info] Start training from score -0.033902
[LightGBM] [Debug] Re-bagging, using 14640 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 8
[LightGBM] [Debug] Re-bagging, using 14757 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 9
[LightGBM] [Debug] Re-bagging, using 14789 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7
[LightGBM] [Debug] Re-bagging, using 14652 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5
[LightGBM] [Debug] Re-bagging, using 14709 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 6
[LightGBM] [Debug] Re-bagging, using 14639 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 8
[LightGBM] [Debug] Re-bagging, using 14700 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7
Trial 22, Fold 5: Log loss = 0.5231191120911644, Average precision = 0.963700468976446, ROC-AUC = 0.9570614792932389, Elapsed Time = 0.7112767999997232 seconds
Optimization Progress:  23%|##3       | 23/100 [05:22<18:10, 14.16s/it]
Trial 23, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 23, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[LightGBM] [Info] Number of positive: 10130, number of negative: 10533
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791640
[LightGBM] [Info] Total Bins 16789
[LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012
[LightGBM] [Info] Start training from score -0.039012
[LightGBM] [Debug] Re-bagging, using 14527 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 14
[LightGBM] [Debug] Re-bagging, using 14650 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 13
[LightGBM] [Debug] Re-bagging, using 14677 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 13
[LightGBM] [Debug] Re-bagging, using 14538 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 13
[LightGBM] [Debug] Re-bagging, using 14598 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 14
Trial 23, Fold 1: Log loss = 0.5225291289612243, Average precision = 0.9675563224495939, ROC-AUC = 0.9608456049287123, Elapsed Time = 0.8706756999999925 seconds
Trial 23, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 23, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[LightGBM] [Info] Number of positive: 10230, number of negative: 10471
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791600
[LightGBM] [Info] Total Bins 16800
[LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285
[LightGBM] [Info] Start training from score -0.023285
[LightGBM] [Debug] Re-bagging, using 14554 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 12
[LightGBM] [Debug] Re-bagging, using 14675 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 14
[LightGBM] [Debug] Re-bagging, using 14708 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 13
[LightGBM] [Debug] Re-bagging, using 14568 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 13
[LightGBM] [Debug] Re-bagging, using 14625 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 15
Trial 23, Fold 2: Log loss = 0.5232042123614699, Average precision = 0.9658566296699482, ROC-AUC = 0.9612999140197047, Elapsed Time = 0.9562309000002642 seconds
Trial 23, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 23, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[LightGBM] [Info] Number of positive: 10165, number of negative: 10517
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791401
[LightGBM] [Info] Total Bins 16807
[LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043
[LightGBM] [Info] Start training from score -0.034043
[LightGBM] [Debug] Re-bagging, using 14538 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 11
[LightGBM] [Debug] Re-bagging, using 14662 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 15
[LightGBM] [Debug] Re-bagging, using 14695 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 13
[LightGBM] [Debug] Re-bagging, using 14558 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 14
[LightGBM] [Debug] Re-bagging, using 14607 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 12
Trial 23, Fold 3: Log loss = 0.5238859532622018, Average precision = 0.9673452313722839, ROC-AUC = 0.9617849558417042, Elapsed Time = 0.9833689000006416 seconds
Trial 23, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 23, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[LightGBM] [Info] Number of positive: 10177, number of negative: 10479
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791090
[LightGBM] [Info] Total Bins 16788
[LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243
[LightGBM] [Info] Start training from score -0.029243
[LightGBM] [Debug] Re-bagging, using 14522 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 12
[LightGBM] [Debug] Re-bagging, using 14644 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 13
[LightGBM] [Debug] Re-bagging, using 14671 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 15
[LightGBM] [Debug] Re-bagging, using 14537 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 14
[LightGBM] [Debug] Re-bagging, using 14591 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 12
Trial 23, Fold 4: Log loss = 0.5247934093888289, Average precision = 0.966912358126536, ROC-AUC = 0.9606433048157923, Elapsed Time = 1.0001123000001826 seconds
Trial 23, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 23, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[LightGBM] [Info] Number of positive: 10150, number of negative: 10500
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.792012
[LightGBM] [Info] Total Bins 16787
[LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902
[LightGBM] [Info] Start training from score -0.033902
[LightGBM] [Debug] Re-bagging, using 14518 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 11
[LightGBM] [Debug] Re-bagging, using 14641 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 13
[LightGBM] [Debug] Re-bagging, using 14669 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 13
[LightGBM] [Debug] Re-bagging, using 14531 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 14
[LightGBM] [Debug] Re-bagging, using 14586 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 17
Trial 23, Fold 5: Log loss = 0.5265657072592534, Average precision = 0.9651006232741035, ROC-AUC = 0.9590224253571893, Elapsed Time = 0.924916399999347 seconds
Optimization Progress:  24%|##4       | 24/100 [05:34<17:10, 13.55s/it]
Trial 24, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 24, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[LightGBM] [Info] Number of positive: 10130, number of negative: 10533
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791640
[LightGBM] [Info] Total Bins 27471
[LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012
[LightGBM] [Info] Start training from score -0.039012
[LightGBM] [Debug] Re-bagging, using 2806 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2796 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2677 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2739 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5
[LightGBM] [Debug] Re-bagging, using 2824 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5
[LightGBM] [Debug] Re-bagging, using 2769 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5
[LightGBM] [Debug] Re-bagging, using 2714 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5
Trial 24, Fold 1: Log loss = 0.3751901903993324, Average precision = 0.9602343850255711, ROC-AUC = 0.9531562913733195, Elapsed Time = 0.8463048000003255 seconds
Trial 24, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 24, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[LightGBM] [Info] Number of positive: 10230, number of negative: 10471
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791600
[LightGBM] [Info] Total Bins 27480
[LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285
[LightGBM] [Info] Start training from score -0.023285
[LightGBM] [Debug] Re-bagging, using 2811 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5
[LightGBM] [Debug] Re-bagging, using 2804 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5
[LightGBM] [Debug] Re-bagging, using 2676 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5
[LightGBM] [Debug] Re-bagging, using 2750 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2833 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5
[LightGBM] [Debug] Re-bagging, using 2763 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2718 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5
Trial 24, Fold 2: Log loss = 0.3735114996032227, Average precision = 0.9565893234197076, ROC-AUC = 0.9526023379477813, Elapsed Time = 0.9060810000000856 seconds
Trial 24, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 24, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[LightGBM] [Info] Number of positive: 10165, number of negative: 10517
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791401
[LightGBM] [Info] Total Bins 27477
[LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043
[LightGBM] [Info] Start training from score -0.034043
[LightGBM] [Debug] Re-bagging, using 2808 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5
[LightGBM] [Debug] Re-bagging, using 2798 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5
[LightGBM] [Debug] Re-bagging, using 2680 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2742 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 6
[LightGBM] [Debug] Re-bagging, using 2829 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5
[LightGBM] [Debug] Re-bagging, using 2768 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2718 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5
Trial 24, Fold 3: Log loss = 0.36761244261956777, Average precision = 0.9631109753164551, ROC-AUC = 0.9575659605512803, Elapsed Time = 0.9513429000007818 seconds
Trial 24, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 24, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[LightGBM] [Info] Number of positive: 10177, number of negative: 10479
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791090
[LightGBM] [Info] Total Bins 27467
[LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243
[LightGBM] [Info] Start training from score -0.029243
[LightGBM] [Debug] Re-bagging, using 2806 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5
[LightGBM] [Debug] Re-bagging, using 2794 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5
[LightGBM] [Debug] Re-bagging, using 2674 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5
[LightGBM] [Debug] Re-bagging, using 2740 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5
[LightGBM] [Debug] Re-bagging, using 2824 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5
[LightGBM] [Debug] Re-bagging, using 2767 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 6
[LightGBM] [Debug] Re-bagging, using 2716 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
Trial 24, Fold 4: Log loss = 0.37014187852829117, Average precision = 0.961520701652486, ROC-AUC = 0.9546189444933249, Elapsed Time = 0.9763474999999744 seconds
Trial 24, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 24, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[LightGBM] [Info] Number of positive: 10150, number of negative: 10500
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.792012
[LightGBM] [Info] Total Bins 27472
[LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902
[LightGBM] [Info] Start training from score -0.033902
[LightGBM] [Debug] Re-bagging, using 2805 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2794 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2674 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2739 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2820 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2765 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5
[LightGBM] [Debug] Re-bagging, using 2720 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5
Trial 24, Fold 5: Log loss = 0.37331798813142764, Average precision = 0.9577758761061415, ROC-AUC = 0.9510315664307081, Elapsed Time = 0.9897025999998732 seconds
Optimization Progress:  25%|##5       | 25/100 [05:46<16:26, 13.15s/it]
Trial 25, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 25, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[LightGBM] [Info] Number of positive: 10130, number of negative: 10533
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791640
[LightGBM] [Info] Total Bins 24366
[LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012
[LightGBM] [Info] Start training from score -0.039012
[LightGBM] [Debug] Re-bagging, using 10496 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10463 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10459 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10304 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10526 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9
[LightGBM] [Debug] Re-bagging, using 10349 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10424 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10397 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10455 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9
[LightGBM] [Debug] Re-bagging, using 10215 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10299 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9
[LightGBM] [Debug] Re-bagging, using 10288 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10280 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 12
[LightGBM] [Debug] Re-bagging, using 10332 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10471 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10339 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10405 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10406 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9
[LightGBM] [Debug] Re-bagging, using 10406 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10536 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10252 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10333 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 12
[LightGBM] [Debug] Re-bagging, using 10417 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 9
[LightGBM] [Debug] Re-bagging, using 10416 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 9
[LightGBM] [Debug] Re-bagging, using 10489 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10393 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 9
[LightGBM] [Debug] Re-bagging, using 10390 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10517 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10351 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 9
[LightGBM] [Debug] Re-bagging, using 10370 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10378 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10368 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 9
[LightGBM] [Debug] Re-bagging, using 10411 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10478 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10342 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10371 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10431 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10482 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10335 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 8
[LightGBM] [Debug] Re-bagging, using 10582 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10333 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10386 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10290 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10357 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10473 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12
[LightGBM] [Debug] Re-bagging, using 10338 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10276 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10190 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 9
Trial 25, Fold 1: Log loss = 0.3522762790360541, Average precision = 0.967843534468308, ROC-AUC = 0.9614237858648427, Elapsed Time = 1.9450667999999496 seconds
Trial 25, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 25, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[LightGBM] [Info] Number of positive: 10230, number of negative: 10471
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791600
[LightGBM] [Info] Total Bins 24373
[LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285
[LightGBM] [Info] Start training from score -0.023285
[LightGBM] [Debug] Re-bagging, using 10518 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 12
[LightGBM] [Debug] Re-bagging, using 10481 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10475 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10330 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10555 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10346 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 9
[LightGBM] [Debug] Re-bagging, using 10429 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9
[LightGBM] [Debug] Re-bagging, using 10416 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9
[LightGBM] [Debug] Re-bagging, using 10483 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9
[LightGBM] [Debug] Re-bagging, using 10237 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 9
[LightGBM] [Debug] Re-bagging, using 10313 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9
[LightGBM] [Debug] Re-bagging, using 10299 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10290 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10360 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10507 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10381 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 9
[LightGBM] [Debug] Re-bagging, using 10420 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10426 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10420 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10541 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10282 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10349 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 9
[LightGBM] [Debug] Re-bagging, using 10439 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 9
[LightGBM] [Debug] Re-bagging, using 10436 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10496 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10406 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10409 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10538 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 9
[LightGBM] [Debug] Re-bagging, using 10368 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10395 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12
[LightGBM] [Debug] Re-bagging, using 10404 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10382 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 9
[LightGBM] [Debug] Re-bagging, using 10416 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10498 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10362 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10387 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10443 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10509 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 13
[LightGBM] [Debug] Re-bagging, using 10338 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10598 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10360 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10409 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10317 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10391 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10486 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 12
[LightGBM] [Debug] Re-bagging, using 10357 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10306 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10202 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10
Trial 25, Fold 2: Log loss = 0.34525464550036206, Average precision = 0.9670754836092235, ROC-AUC = 0.962763066167194, Elapsed Time = 2.3343526999997266 seconds
Trial 25, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 25, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[LightGBM] [Info] Number of positive: 10165, number of negative: 10517
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791401
[LightGBM] [Info] Total Bins 24368
[LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043
[LightGBM] [Info] Start training from score -0.034043
[LightGBM] [Debug] Re-bagging, using 10506 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9
[LightGBM] [Debug] Re-bagging, using 10470 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10465 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10325 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10537 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9
[LightGBM] [Debug] Re-bagging, using 10347 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 9
[LightGBM] [Debug] Re-bagging, using 10429 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10403 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9
[LightGBM] [Debug] Re-bagging, using 10463 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 9
[LightGBM] [Debug] Re-bagging, using 10236 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10305 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10295 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9
[LightGBM] [Debug] Re-bagging, using 10280 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 9
[LightGBM] [Debug] Re-bagging, using 10340 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10491 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10356 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10422 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10424 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10400 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10548 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10271 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10329 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 9
[LightGBM] [Debug] Re-bagging, using 10425 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 12
[LightGBM] [Debug] Re-bagging, using 10434 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10505 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10397 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10380 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10515 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10373 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12
[LightGBM] [Debug] Re-bagging, using 10395 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10395 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10372 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 13
[LightGBM] [Debug] Re-bagging, using 10412 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 12
[LightGBM] [Debug] Re-bagging, using 10481 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10355 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 9
[LightGBM] [Debug] Re-bagging, using 10381 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 9
[LightGBM] [Debug] Re-bagging, using 10437 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 9
[LightGBM] [Debug] Re-bagging, using 10496 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10341 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10588 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10346 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10391 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10294 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 13
[LightGBM] [Debug] Re-bagging, using 10360 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10501 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10344 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10304 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 9
[LightGBM] [Debug] Re-bagging, using 10211 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 9
Trial 25, Fold 3: Log loss = 0.3457557444663702, Average precision = 0.9693219298702005, ROC-AUC = 0.9640517102310485, Elapsed Time = 1.9869880999995075 seconds
Trial 25, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 25, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[LightGBM] [Info] Number of positive: 10177, number of negative: 10479
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791090
[LightGBM] [Info] Total Bins 24356
[LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243
[LightGBM] [Info] Start training from score -0.029243
[LightGBM] [Debug] Re-bagging, using 10492 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10459 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9
[LightGBM] [Debug] Re-bagging, using 10454 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10303 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 9
[LightGBM] [Debug] Re-bagging, using 10520 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10345 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9
[LightGBM] [Debug] Re-bagging, using 10424 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10396 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
[LightGBM] [Debug] Re-bagging, using 10451 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 9
[LightGBM] [Debug] Re-bagging, using 10214 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10295 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10283 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10271 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10332 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 9
[LightGBM] [Debug] Re-bagging, using 10472 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10328 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10402 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 13
[LightGBM] [Debug] Re-bagging, using 10406 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 9
[LightGBM] [Debug] Re-bagging, using 10386 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10544 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10259 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10315 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10421 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10417 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10478 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10391 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10377 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10519 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10358 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10373 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10369 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9
[LightGBM] [Debug] Re-bagging, using 10355 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10409 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10473 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 9
[LightGBM] [Debug] Re-bagging, using 10347 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 9
[LightGBM] [Debug] Re-bagging, using 10372 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10413 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10489 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 9
[LightGBM] [Debug] Re-bagging, using 10327 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10578 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 12
[LightGBM] [Debug] Re-bagging, using 10329 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 12
[LightGBM] [Debug] Re-bagging, using 10382 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10296 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10353 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 12
[LightGBM] [Debug] Re-bagging, using 10472 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 12
[LightGBM] [Debug] Re-bagging, using 10326 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10289 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10181 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10
Trial 25, Fold 4: Log loss = 0.3457546230696314, Average precision = 0.9686800320577474, ROC-AUC = 0.962634808567661, Elapsed Time = 1.9513052000002062 seconds
Trial 25, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 25, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[LightGBM] [Info] Number of positive: 10150, number of negative: 10500
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.792012
[LightGBM] [Info] Total Bins 24360
[LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902
[LightGBM] [Info] Start training from score -0.033902
[LightGBM] [Debug] Re-bagging, using 10489 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12
[LightGBM] [Debug] Re-bagging, using 10456 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10455 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10296 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 9
[LightGBM] [Debug] Re-bagging, using 10516 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 8
[LightGBM] [Debug] Re-bagging, using 10338 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9
[LightGBM] [Debug] Re-bagging, using 10430 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10391 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10448 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9
[LightGBM] [Debug] Re-bagging, using 10216 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10285 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10277 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10271 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 9
[LightGBM] [Debug] Re-bagging, using 10341 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10466 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10325 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 12
[LightGBM] [Debug] Re-bagging, using 10393 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10391 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10391 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10528 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10269 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 12
[LightGBM] [Debug] Re-bagging, using 10315 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10407 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 12
[LightGBM] [Debug] Re-bagging, using 10423 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 13
[LightGBM] [Debug] Re-bagging, using 10479 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10381 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10377 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10506 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 9
[LightGBM] [Debug] Re-bagging, using 10363 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10374 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10376 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10349 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 12
[LightGBM] [Debug] Re-bagging, using 10393 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 12
[LightGBM] [Debug] Re-bagging, using 10467 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10348 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10365 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10427 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10477 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10323 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10580 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10325 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10380 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 9
[LightGBM] [Debug] Re-bagging, using 10282 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10357 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10474 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10328 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10268 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10193 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10
Trial 25, Fold 5: Log loss = 0.3514596909372222, Average precision = 0.9655090119026054, ROC-AUC = 0.9597230923582856, Elapsed Time = 1.796817899999951 seconds
Optimization Progress:  26%|##6       | 26/100 [06:06<18:29, 14.99s/it]
Trial 26, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 26, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[LightGBM] [Info] Number of positive: 10130, number of negative: 10533
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791640
[LightGBM] [Info] Total Bins 29191
[LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012
[LightGBM] [Info] Start training from score -0.039012
[LightGBM] [Debug] Re-bagging, using 16006 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 9
[LightGBM] [Debug] Re-bagging, using 16142 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 10
[LightGBM] [Debug] Re-bagging, using 16052 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10
[LightGBM] [Debug] Re-bagging, using 15949 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 12
[LightGBM] [Debug] Re-bagging, using 15995 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10
[LightGBM] [Debug] Re-bagging, using 15998 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 11
[LightGBM] [Debug] Re-bagging, using 15998 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 12
[LightGBM] [Debug] Re-bagging, using 16006 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11
[LightGBM] [Debug] Re-bagging, using 16060 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11
Trial 26, Fold 1: Log loss = 0.39860973853534337, Average precision = 0.9658180373732671, ROC-AUC = 0.9595059756814499, Elapsed Time = 1.4416620000001785 seconds
Trial 26, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 26, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[LightGBM] [Info] Number of positive: 10230, number of negative: 10471
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791600
[LightGBM] [Info] Total Bins 29195
[LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285
[LightGBM] [Info] Start training from score -0.023285
[LightGBM] [Debug] Re-bagging, using 16039 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 7
[LightGBM] [Debug] Re-bagging, using 16169 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10
[LightGBM] [Debug] Re-bagging, using 16081 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10
[LightGBM] [Debug] Re-bagging, using 15983 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 7
[LightGBM] [Debug] Re-bagging, using 16021 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9
[LightGBM] [Debug] Re-bagging, using 16021 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9
[LightGBM] [Debug] Re-bagging, using 16020 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 8
[LightGBM] [Debug] Re-bagging, using 16052 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11
[LightGBM] [Debug] Re-bagging, using 16087 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9
Trial 26, Fold 2: Log loss = 0.3992144562722906, Average precision = 0.9625837281038332, ROC-AUC = 0.9588862137440397, Elapsed Time = 1.9067242000000988 seconds
Trial 26, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 26, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[LightGBM] [Info] Number of positive: 10165, number of negative: 10517
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791401
[LightGBM] [Info] Total Bins 29190
[LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043
[LightGBM] [Info] Start training from score -0.034043
[LightGBM] [Debug] Re-bagging, using 16022 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11
[LightGBM] [Debug] Re-bagging, using 16156 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Re-bagging, using 16065 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9
[LightGBM] [Debug] Re-bagging, using 15969 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 7
[LightGBM] [Debug] Re-bagging, using 16005 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 10
[LightGBM] [Debug] Re-bagging, using 16010 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9
[LightGBM] [Debug] Re-bagging, using 16007 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10
[LightGBM] [Debug] Re-bagging, using 16032 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9
[LightGBM] [Debug] Re-bagging, using 16068 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9
Trial 26, Fold 3: Log loss = 0.3947824437055664, Average precision = 0.9669910235944826, ROC-AUC = 0.9615008403078079, Elapsed Time = 1.8139153000001897 seconds
Trial 26, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 26, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[LightGBM] [Info] Number of positive: 10177, number of negative: 10479
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791090
[LightGBM] [Info] Total Bins 29181
[LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243
[LightGBM] [Info] Start training from score -0.029243
[LightGBM] [Debug] Re-bagging, using 16001 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 9
[LightGBM] [Debug] Re-bagging, using 16134 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 9
[LightGBM] [Debug] Re-bagging, using 16048 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 11
[LightGBM] [Debug] Re-bagging, using 15947 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 8
[LightGBM] [Debug] Re-bagging, using 15988 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9
[LightGBM] [Debug] Re-bagging, using 15991 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 13
[LightGBM] [Debug] Re-bagging, using 15993 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11
[LightGBM] [Debug] Re-bagging, using 16005 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 10
[LightGBM] [Debug] Re-bagging, using 16048 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10
Trial 26, Fold 4: Log loss = 0.3947772666877869, Average precision = 0.9661043514929211, ROC-AUC = 0.9597404331518798, Elapsed Time = 1.62656380000044 seconds
Trial 26, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 26, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[LightGBM] [Info] Number of positive: 10150, number of negative: 10500
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.792012
[LightGBM] [Info] Total Bins 29189
[LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902
[LightGBM] [Info] Start training from score -0.033902
[LightGBM] [Debug] Re-bagging, using 15995 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10
[LightGBM] [Debug] Re-bagging, using 16133 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11
[LightGBM] [Debug] Re-bagging, using 16045 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 8
[LightGBM] [Debug] Re-bagging, using 15939 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11
[LightGBM] [Debug] Re-bagging, using 15982 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10
[LightGBM] [Debug] Re-bagging, using 15986 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Re-bagging, using 15995 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11
[LightGBM] [Debug] Re-bagging, using 15996 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8
[LightGBM] [Debug] Re-bagging, using 16041 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 13
Trial 26, Fold 5: Log loss = 0.4027633484140435, Average precision = 0.9629441663634337, ROC-AUC = 0.9568721178678261, Elapsed Time = 1.5187667000000147 seconds
Optimization Progress:  27%|##7       | 27/100 [06:21<18:31, 15.22s/it]
Trial 27, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 27, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[LightGBM] [Info] Number of positive: 10130, number of negative: 10533
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791640
[LightGBM] [Info] Total Bins 11253
[LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012
[LightGBM] [Info] Start training from score -0.039012
[LightGBM] [Debug] Re-bagging, using 7912 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8
[LightGBM] [Debug] Re-bagging, using 7981 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8
[LightGBM] [Debug] Re-bagging, using 7859 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 8
[LightGBM] [Debug] Re-bagging, using 7810 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 10
Trial 27, Fold 1: Log loss = 0.3770457259073332, Average precision = 0.9679646461912859, ROC-AUC = 0.9618513350587649, Elapsed Time = 0.6671121999997922 seconds
Trial 27, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 27, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[LightGBM] [Info] Number of positive: 10230, number of negative: 10471
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791600
[LightGBM] [Info] Total Bins 11266
[LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285
[LightGBM] [Info] Start training from score -0.023285
[LightGBM] [Debug] Re-bagging, using 7928 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7996 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10
[LightGBM] [Debug] Re-bagging, using 7875 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10
[LightGBM] [Debug] Re-bagging, using 7825 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 11
Trial 27, Fold 2: Log loss = 0.3768335963671184, Average precision = 0.9652852768593705, ROC-AUC = 0.9619789853596772, Elapsed Time = 0.766985599999316 seconds
Trial 27, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 27, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[LightGBM] [Info] Number of positive: 10165, number of negative: 10517
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791401
[LightGBM] [Info] Total Bins 11269
[LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043
[LightGBM] [Info] Start training from score -0.034043
[LightGBM] [Debug] Re-bagging, using 7919 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7987 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 8
[LightGBM] [Debug] Re-bagging, using 7866 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7825 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 10
Trial 27, Fold 3: Log loss = 0.3753375017459059, Average precision = 0.9697573184424777, ROC-AUC = 0.9644684029817684, Elapsed Time = 0.8073904000002585 seconds
Trial 27, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 27, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[LightGBM] [Info] Number of positive: 10177, number of negative: 10479
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791090
[LightGBM] [Info] Total Bins 11250
[LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243
[LightGBM] [Info] Start training from score -0.029243
[LightGBM] [Debug] Re-bagging, using 7908 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8
[LightGBM] [Debug] Re-bagging, using 7979 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10
[LightGBM] [Debug] Re-bagging, using 7855 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10
[LightGBM] [Debug] Re-bagging, using 7810 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 11
Trial 27, Fold 4: Log loss = 0.37479868622985846, Average precision = 0.9683889358539486, ROC-AUC = 0.9620702995534002, Elapsed Time = 0.7829996000000392 seconds
Trial 27, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 27, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[LightGBM] [Info] Number of positive: 10150, number of negative: 10500
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.792012
[LightGBM] [Info] Total Bins 11253
[LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902
[LightGBM] [Info] Start training from score -0.033902
[LightGBM] [Debug] Re-bagging, using 7906 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 8
[LightGBM] [Debug] Re-bagging, using 7978 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7853 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10
[LightGBM] [Debug] Re-bagging, using 7805 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10
Trial 27, Fold 5: Log loss = 0.3770404917952498, Average precision = 0.9671860255854553, ROC-AUC = 0.9620701930401501, Elapsed Time = 0.7725092999999106 seconds
Optimization Progress:  28%|##8       | 28/100 [06:33<17:04, 14.23s/it]
Trial 28, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 28, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[LightGBM] [Info] Number of positive: 10130, number of negative: 10533
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791640
[LightGBM] [Info] Total Bins 28226
[LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012
[LightGBM] [Info] Start training from score -0.039012
[LightGBM] [Debug] Re-bagging, using 3312 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 5
[LightGBM] [Debug] Re-bagging, using 3310 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Debug] Re-bagging, using 3203 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Debug] Re-bagging, using 3173 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 6
Trial 28, Fold 1: Log loss = 0.36599374427258163, Average precision = 0.9603317174096473, ROC-AUC = 0.9534624091280596, Elapsed Time = 0.48313599999983126 seconds
Trial 28, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 28, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[LightGBM] [Info] Number of positive: 10230, number of negative: 10471
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791600
[LightGBM] [Info] Total Bins 28232
[LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285
[LightGBM] [Info] Start training from score -0.023285
[LightGBM] [Debug] Re-bagging, using 3319 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 6
[LightGBM] [Debug] Re-bagging, using 3319 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Debug] Re-bagging, using 3201 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5
[LightGBM] [Debug] Re-bagging, using 3187 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 6
Trial 28, Fold 2: Log loss = 0.3654086901441953, Average precision = 0.9566215972223977, ROC-AUC = 0.9521490673125862, Elapsed Time = 0.4869757999995272 seconds
Trial 28, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 28, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[LightGBM] [Info] Number of positive: 10165, number of negative: 10517
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791401
[LightGBM] [Info] Total Bins 28228
[LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043
[LightGBM] [Info] Start training from score -0.034043
[LightGBM] [Debug] Re-bagging, using 3314 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5
[LightGBM] [Debug] Re-bagging, using 3314 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Debug] Re-bagging, using 3204 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 6
[LightGBM] [Debug] Re-bagging, using 3181 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 6
Trial 28, Fold 3: Log loss = 0.36509507073811576, Average precision = 0.9613520695863139, ROC-AUC = 0.9549487267771667, Elapsed Time = 0.5294050000002244 seconds
Trial 28, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 28, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[LightGBM] [Info] Number of positive: 10177, number of negative: 10479
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791090
[LightGBM] [Info] Total Bins 28219
[LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243
[LightGBM] [Info] Start training from score -0.029243
[LightGBM] [Debug] Re-bagging, using 3312 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Debug] Re-bagging, using 3308 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5
[LightGBM] [Debug] Re-bagging, using 3199 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 4
[LightGBM] [Debug] Re-bagging, using 3175 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5
Trial 28, Fold 4: Log loss = 0.3631950589967323, Average precision = 0.9604381240848454, ROC-AUC = 0.9526484533525994, Elapsed Time = 0.5496585000000778 seconds
Trial 28, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 28, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[LightGBM] [Info] Number of positive: 10150, number of negative: 10500
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.792012
[LightGBM] [Info] Total Bins 28223
[LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902
[LightGBM] [Info] Start training from score -0.033902
[LightGBM] [Debug] Re-bagging, using 3311 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 5
[LightGBM] [Debug] Re-bagging, using 3307 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Debug] Re-bagging, using 3200 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 6
[LightGBM] [Debug] Re-bagging, using 3174 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5
Trial 28, Fold 5: Log loss = 0.3715326470936202, Average precision = 0.9579557801661939, ROC-AUC = 0.9517082288241085, Elapsed Time = 0.5568551000005755 seconds
Optimization Progress:  29%|##9       | 29/100 [06:43<15:19, 12.95s/it]
Trial 29, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 29, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[LightGBM] [Info] Number of positive: 10130, number of negative: 10533
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791640
[LightGBM] [Info] Total Bins 23584
[LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012
[LightGBM] [Info] Start training from score -0.039012
[LightGBM] [Debug] Re-bagging, using 13595 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 14
[LightGBM] [Debug] Re-bagging, using 13670 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 12
[LightGBM] [Debug] Re-bagging, using 13744 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 13
[LightGBM] [Debug] Re-bagging, using 13528 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 14
[LightGBM] [Debug] Re-bagging, using 13669 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 14
[LightGBM] [Debug] Re-bagging, using 13563 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 87 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 14
[LightGBM] [Debug] Re-bagging, using 13639 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 86 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 13
[LightGBM] [Debug] Re-bagging, using 13649 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 12
Trial 29, Fold 1: Log loss = 0.29683447983407174, Average precision = 0.9730766746375938, ROC-AUC = 0.9680085662188191, Elapsed Time = 1.040899100000388 seconds
Trial 29, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 29, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[LightGBM] [Info] Number of positive: 10230, number of negative: 10471
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.792956
[LightGBM] [Info] Total Bins 23515
[LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 256
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285
[LightGBM] [Info] Start training from score -0.023285
[LightGBM] [Debug] Re-bagging, using 13622 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 12
[LightGBM] [Debug] Re-bagging, using 13691 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 13
[LightGBM] [Debug] Re-bagging, using 13772 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 14
[LightGBM] [Debug] Re-bagging, using 13560 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 15
[LightGBM] [Debug] Re-bagging, using 13694 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 14
[LightGBM] [Debug] Re-bagging, using 13572 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 11
[LightGBM] [Debug] Re-bagging, using 13655 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 13
[LightGBM] [Debug] Re-bagging, using 13686 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 14
Trial 29, Fold 2: Log loss = 0.2948225769523191, Average precision = 0.9701316593407077, ROC-AUC = 0.9676732451354972, Elapsed Time = 1.1777079000003141 seconds
Trial 29, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 29, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[LightGBM] [Info] Number of positive: 10165, number of negative: 10517
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791401
[LightGBM] [Info] Total Bins 23508
[LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043
[LightGBM] [Info] Start training from score -0.034043
[LightGBM] [Debug] Re-bagging, using 13606 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 13
[LightGBM] [Debug] Re-bagging, using 13680 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 12
[LightGBM] [Debug] Re-bagging, using 13760 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 88 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 13
[LightGBM] [Debug] Re-bagging, using 13550 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 12
[LightGBM] [Debug] Re-bagging, using 13676 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 87 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 14
[LightGBM] [Debug] Re-bagging, using 13566 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 14
[LightGBM] [Debug] Re-bagging, using 13646 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 15
[LightGBM] [Debug] Re-bagging, using 13672 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 15
Trial 29, Fold 3: Log loss = 0.29240503741367013, Average precision = 0.9738813449367276, ROC-AUC = 0.9697626130442739, Elapsed Time = 1.215357199999744 seconds
Trial 29, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 29, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[LightGBM] [Info] Number of positive: 10177, number of negative: 10479
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.792454
[LightGBM] [Info] Total Bins 23522
[LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 256
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243
[LightGBM] [Info] Start training from score -0.029243
[LightGBM] [Debug] Re-bagging, using 13590 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 13
[LightGBM] [Debug] Re-bagging, using 13665 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 15
[LightGBM] [Debug] Re-bagging, using 13741 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 17
[LightGBM] [Debug] Re-bagging, using 13524 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 13
[LightGBM] [Debug] Re-bagging, using 13661 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 17
[LightGBM] [Debug] Re-bagging, using 13559 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 13
[LightGBM] [Debug] Re-bagging, using 13634 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 14
[LightGBM] [Debug] Re-bagging, using 13651 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 87 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 12
Trial 29, Fold 4: Log loss = 0.2952701846010938, Average precision = 0.9724463513089439, ROC-AUC = 0.9672528141996373, Elapsed Time = 1.2341573000003336 seconds
Trial 29, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 29, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[LightGBM] [Info] Number of positive: 10150, number of negative: 10500
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.793364
[LightGBM] [Info] Total Bins 23533
[LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 256
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902
[LightGBM] [Info] Start training from score -0.033902
[LightGBM] [Debug] Re-bagging, using 13587 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 14
[LightGBM] [Debug] Re-bagging, using 13661 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 15
[LightGBM] [Debug] Re-bagging, using 13740 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 13
[LightGBM] [Debug] Re-bagging, using 13518 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 13
[LightGBM] [Debug] Re-bagging, using 13656 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 14
[LightGBM] [Debug] Re-bagging, using 13553 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 13
[LightGBM] [Debug] Re-bagging, using 13635 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 15
[LightGBM] [Debug] Re-bagging, using 13643 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 14
Trial 29, Fold 5: Log loss = 0.2996227692538168, Average precision = 0.9708768054126033, ROC-AUC = 0.9662428329896142, Elapsed Time = 1.2330916000000798 seconds
Optimization Progress:  30%|###       | 30/100 [06:57<15:15, 13.08s/it]
Trial 30, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 30, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[LightGBM] [Info] Number of positive: 10130, number of negative: 10533
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791640
[LightGBM] [Info] Total Bins 8631
[LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012
[LightGBM] [Info] Start training from score -0.039012
[LightGBM] [Debug] Re-bagging, using 7776 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 5
[LightGBM] [Debug] Re-bagging, using 7845 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Debug] Re-bagging, using 7722 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 5
[LightGBM] [Debug] Re-bagging, using 7689 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Debug] Re-bagging, using 7843 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4
[LightGBM] [Debug] Re-bagging, using 7783 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Debug] Re-bagging, using 7774 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 5
[LightGBM] [Debug] Re-bagging, using 7855 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4
[LightGBM] [Debug] Re-bagging, using 7806 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 5
[LightGBM] [Debug] Re-bagging, using 7584 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Debug] Re-bagging, using 7595 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Debug] Re-bagging, using 7645 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4
Trial 30, Fold 1: Log loss = 0.3174825538284151, Average precision = 0.9605865459743074, ROC-AUC = 0.9548121203213796, Elapsed Time = 0.9692813999999998 seconds
Trial 30, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 30, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[LightGBM] [Info] Number of positive: 10230, number of negative: 10471
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791600
[LightGBM] [Info] Total Bins 8643
[LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285
[LightGBM] [Info] Start training from score -0.023285
[LightGBM] [Debug] Re-bagging, using 7792 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Debug] Re-bagging, using 7860 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4
[LightGBM] [Debug] Re-bagging, using 7736 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4
[LightGBM] [Debug] Re-bagging, using 7705 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 6
[LightGBM] [Debug] Re-bagging, using 7875 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 5
[LightGBM] [Debug] Re-bagging, using 7775 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Debug] Re-bagging, using 7785 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4
[LightGBM] [Debug] Re-bagging, using 7866 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Debug] Re-bagging, using 7828 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 6
[LightGBM] [Debug] Re-bagging, using 7605 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4
[LightGBM] [Debug] Re-bagging, using 7598 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Debug] Re-bagging, using 7644 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4
Trial 30, Fold 2: Log loss = 0.31623480166398493, Average precision = 0.9587342409317563, ROC-AUC = 0.9555490523199048, Elapsed Time = 1.113342200000261 seconds
Trial 30, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 30, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[LightGBM] [Info] Number of positive: 10165, number of negative: 10517
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791401
[LightGBM] [Info] Total Bins 8652
[LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043
[LightGBM] [Info] Start training from score -0.034043
[LightGBM] [Debug] Re-bagging, using 7783 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 5
[LightGBM] [Debug] Re-bagging, using 7851 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Debug] Re-bagging, using 7728 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Debug] Re-bagging, using 7704 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 7
[LightGBM] [Debug] Re-bagging, using 7859 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 5
[LightGBM] [Debug] Re-bagging, using 7774 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 5
[LightGBM] [Debug] Re-bagging, using 7781 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4
[LightGBM] [Debug] Re-bagging, using 7862 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4
[LightGBM] [Debug] Re-bagging, using 7808 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4
[LightGBM] [Debug] Re-bagging, using 7606 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4
[LightGBM] [Debug] Re-bagging, using 7595 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4
[LightGBM] [Debug] Re-bagging, using 7645 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 6
Trial 30, Fold 3: Log loss = 0.3125646760074285, Average precision = 0.9619599029006753, ROC-AUC = 0.9580770073484797, Elapsed Time = 1.2622389000007388 seconds
Trial 30, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 30, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[LightGBM] [Info] Number of positive: 10177, number of negative: 10479
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791090
[LightGBM] [Info] Total Bins 8625
[LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243
[LightGBM] [Info] Start training from score -0.029243
[LightGBM] [Debug] Re-bagging, using 7772 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 5
[LightGBM] [Debug] Re-bagging, using 7843 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Debug] Re-bagging, using 7718 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4
[LightGBM] [Debug] Re-bagging, using 7690 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4
[LightGBM] [Debug] Re-bagging, using 7836 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Debug] Re-bagging, using 7781 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Debug] Re-bagging, using 7775 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Debug] Re-bagging, using 7850 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Debug] Re-bagging, using 7804 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4
[LightGBM] [Debug] Re-bagging, using 7584 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4
[LightGBM] [Debug] Re-bagging, using 7588 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4
[LightGBM] [Debug] Re-bagging, using 7645 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4
Trial 30, Fold 4: Log loss = 0.3106067431192909, Average precision = 0.9648280060412078, ROC-AUC = 0.9583181327210168, Elapsed Time = 1.1553637999995772 seconds
Trial 30, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 30, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[LightGBM] [Info] Number of positive: 10150, number of negative: 10500
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.792012
[LightGBM] [Info] Total Bins 8630
[LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902
[LightGBM] [Info] Start training from score -0.033902
[LightGBM] [Debug] Re-bagging, using 7770 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4
[LightGBM] [Debug] Re-bagging, using 7842 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4
[LightGBM] [Debug] Re-bagging, using 7717 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Debug] Re-bagging, using 7684 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4
[LightGBM] [Debug] Re-bagging, using 7835 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4
[LightGBM] [Debug] Re-bagging, using 7772 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4
[LightGBM] [Debug] Re-bagging, using 7782 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4
[LightGBM] [Debug] Re-bagging, using 7846 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4
[LightGBM] [Debug] Re-bagging, using 7801 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 5
[LightGBM] [Debug] Re-bagging, using 7585 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4
[LightGBM] [Debug] Re-bagging, using 7580 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 5
[LightGBM] [Debug] Re-bagging, using 7638 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4
Trial 30, Fold 5: Log loss = 0.3168421825858889, Average precision = 0.9579488880533888, ROC-AUC = 0.9524936922876838, Elapsed Time = 1.0095891000000847 seconds
Optimization Progress:  31%|###1      | 31/100 [07:10<14:57, 13.01s/it]
Trial 31, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 31, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[LightGBM] [Info] Number of positive: 10130, number of negative: 10533
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791640
[LightGBM] [Info] Total Bins 9440
[LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012
[LightGBM] [Info] Start training from score -0.039012
[LightGBM] [Debug] Re-bagging, using 16585 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 90 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 13
[LightGBM] [Debug] Re-bagging, using 16662 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 12
[LightGBM] [Debug] Re-bagging, using 16603 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 86 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 13
[LightGBM] [Debug] Re-bagging, using 16552 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 12
[LightGBM] [Debug] Re-bagging, using 16610 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 93 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16
[LightGBM] [Debug] Re-bagging, using 16601 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 94 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 15
[LightGBM] [Debug] Re-bagging, using 16528 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 15
[LightGBM] [Debug] Re-bagging, using 16562 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 94 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 13
[LightGBM] [Debug] Re-bagging, using 16634 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 86 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 106 and depth = 13
[LightGBM] [Debug] Re-bagging, using 16508 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 99 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 15
[LightGBM] [Debug] Re-bagging, using 16530 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 94 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 91 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 91 and depth = 13
[LightGBM] [Debug] Re-bagging, using 16556 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 14
[LightGBM] [Debug] Re-bagging, using 16561 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 12
[LightGBM] [Debug] Re-bagging, using 16519 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 101 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 87 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 14
[LightGBM] [Debug] Re-bagging, using 16625 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 110 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 93 and depth = 16
[LightGBM] [Debug] Re-bagging, using 16564 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16
[LightGBM] [Debug] Re-bagging, using 16613 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 13
[LightGBM] [Debug] Re-bagging, using 16517 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 100 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 105 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 13
[LightGBM] [Debug] Re-bagging, using 16631 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 19
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 110 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 90 and depth = 13
[LightGBM] [Debug] Re-bagging, using 16709 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 99 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 99 and depth = 12
[LightGBM] [Debug] Re-bagging, using 16487 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 105 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 91 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 16
[LightGBM] [Debug] Re-bagging, using 16525 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 15
[LightGBM] [Debug] Re-bagging, using 16573 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 13
[LightGBM] [Debug] Re-bagging, using 16625 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 101 and depth = 17
Trial 31, Fold 1: Log loss = 0.25510860932062523, Average precision = 0.9695418810095376, ROC-AUC = 0.9637145533712832, Elapsed Time = 1.2519100999998045 seconds
Trial 31, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 31, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[LightGBM] [Info] Number of positive: 10230, number of negative: 10471
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791600
[LightGBM] [Info] Total Bins 9431
[LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285
[LightGBM] [Info] Start training from score -0.023285
[LightGBM] [Debug] Re-bagging, using 16618 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 13
[LightGBM] [Debug] Re-bagging, using 16693 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 12
[LightGBM] [Debug] Re-bagging, using 16630 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 16
[LightGBM] [Debug] Re-bagging, using 16589 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 13
[LightGBM] [Debug] Re-bagging, using 16637 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 14
[LightGBM] [Debug] Re-bagging, using 16621 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 97 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 13
[LightGBM] [Debug] Re-bagging, using 16553 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 15
[LightGBM] [Debug] Re-bagging, using 16610 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 97 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 13
[LightGBM] [Debug] Re-bagging, using 16663 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 88 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 16
[LightGBM] [Debug] Re-bagging, using 16541 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 12
[LightGBM] [Debug] Re-bagging, using 16557 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 13
[LightGBM] [Debug] Re-bagging, using 16577 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 11
[LightGBM] [Debug] Re-bagging, using 16588 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 101 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 91 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 93 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 15
[LightGBM] [Debug] Re-bagging, using 16540 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 94 and depth = 13
[LightGBM] [Debug] Re-bagging, using 16657 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 100 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 100 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 11
[LightGBM] [Debug] Re-bagging, using 16606 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 93 and depth = 16
[LightGBM] [Debug] Re-bagging, using 16654 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 105 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 13
[LightGBM] [Debug] Re-bagging, using 16548 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 91 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 91 and depth = 19
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 97 and depth = 12
[LightGBM] [Debug] Re-bagging, using 16665 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 102 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 13
[LightGBM] [Debug] Re-bagging, using 16718 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 109 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 88 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 94 and depth = 16
[LightGBM] [Debug] Re-bagging, using 16524 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 87 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 87 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 102 and depth = 12
[LightGBM] [Debug] Re-bagging, using 16549 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 86 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 105 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 110 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 16
[LightGBM] [Debug] Re-bagging, using 16598 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 88 and depth = 13
[LightGBM] [Debug] Re-bagging, using 16674 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 14
Trial 31, Fold 2: Log loss = 0.2563946312758171, Average precision = 0.9671244359838015, ROC-AUC = 0.9639688540705282, Elapsed Time = 1.475114300000314 seconds
Trial 31, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 31, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[LightGBM] [Info] Number of positive: 10165, number of negative: 10517
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791401
[LightGBM] [Info] Total Bins 9438
[LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043
[LightGBM] [Info] Start training from score -0.034043
[LightGBM] [Debug] Re-bagging, using 16601 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15
[LightGBM] [Debug] Re-bagging, using 16677 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 13
[LightGBM] [Debug] Re-bagging, using 16617 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 13
[LightGBM] [Debug] Re-bagging, using 16574 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 10
[LightGBM] [Debug] Re-bagging, using 16618 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 99 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 13
[LightGBM] [Debug] Re-bagging, using 16613 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 98 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 12
[LightGBM] [Debug] Re-bagging, using 16538 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 15
[LightGBM] [Debug] Re-bagging, using 16588 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 93 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 11
[LightGBM] [Debug] Re-bagging, using 16646 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 95 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 12
[LightGBM] [Debug] Re-bagging, using 16529 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 90 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 11
[LightGBM] [Debug] Re-bagging, using 16544 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 14
[LightGBM] [Debug] Re-bagging, using 16570 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 91 and depth = 17
[LightGBM] [Debug] Re-bagging, using 16570 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 14
[LightGBM] [Debug] Re-bagging, using 16531 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 86 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 16
[LightGBM] [Debug] Re-bagging, using 16638 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 102 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 105 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 90 and depth = 15
[LightGBM] [Debug] Re-bagging, using 16579 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 103 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 95 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 12
[LightGBM] [Debug] Re-bagging, using 16638 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 105 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 105 and depth = 13
[LightGBM] [Debug] Re-bagging, using 16536 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 91 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 15
[LightGBM] [Debug] Re-bagging, using 16646 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 86 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 108 and depth = 16
[LightGBM] [Debug] Re-bagging, using 16725 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 93 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 14
[LightGBM] [Debug] Re-bagging, using 16503 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 97 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 110 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 91 and depth = 12
[LightGBM] [Debug] Re-bagging, using 16520 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 95 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 110 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 88 and depth = 15
[LightGBM] [Debug] Re-bagging, using 16592 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 93 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 18
[LightGBM] [Debug] Re-bagging, using 16648 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 102 and depth = 13
Trial 31, Fold 3: Log loss = 0.24453248742690947, Average precision = 0.9713019505932408, ROC-AUC = 0.9668311667517409, Elapsed Time = 1.4896720999995523 seconds
Trial 31, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 31, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[LightGBM] [Info] Number of positive: 10177, number of negative: 10479
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.792454
[LightGBM] [Info] Total Bins 9441
[LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 256
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243
[LightGBM] [Info] Start training from score -0.029243
[LightGBM] [Debug] Re-bagging, using 16579 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 12
[LightGBM] [Debug] Re-bagging, using 16655 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 17
[LightGBM] [Debug] Re-bagging, using 16599 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 87 and depth = 13
[LightGBM] [Debug] Re-bagging, using 16549 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 93 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 11
[LightGBM] [Debug] Re-bagging, using 16604 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 14
[LightGBM] [Debug] Re-bagging, using 16593 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 94 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 94 and depth = 15
[LightGBM] [Debug] Re-bagging, using 16523 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 13
[LightGBM] [Debug] Re-bagging, using 16561 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 93 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 87 and depth = 13
[LightGBM] [Debug] Re-bagging, using 16623 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 88 and depth = 13
[LightGBM] [Debug] Re-bagging, using 16505 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 99 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 90 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 14
[LightGBM] [Debug] Re-bagging, using 16522 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 97 and depth = 12
[LightGBM] [Debug] Re-bagging, using 16547 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 95 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 90 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 12
[LightGBM] [Debug] Re-bagging, using 16563 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 13
[LightGBM] [Debug] Re-bagging, using 16511 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 14
[LightGBM] [Debug] Re-bagging, using 16619 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 97 and depth = 17
[LightGBM] [Debug] Re-bagging, using 16563 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 13
[LightGBM] [Debug] Re-bagging, using 16608 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 13
[LightGBM] [Debug] Re-bagging, using 16503 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 87 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 103 and depth = 15
[LightGBM] [Debug] Re-bagging, using 16624 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 98 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 101 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 12
[LightGBM] [Debug] Re-bagging, using 16707 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 113 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 101 and depth = 19
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 111 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 99 and depth = 14
[LightGBM] [Debug] Re-bagging, using 16483 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 103 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 11
[LightGBM] [Debug] Re-bagging, using 16516 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 99 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 105 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 14
[LightGBM] [Debug] Re-bagging, using 16568 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 12
[LightGBM] [Debug] Re-bagging, using 16633 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 112 and depth = 12
Trial 31, Fold 4: Log loss = 0.25129239627683203, Average precision = 0.9696580915958111, ROC-AUC = 0.9637685954157338, Elapsed Time = 1.4521058000000266 seconds
Trial 31, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 31, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[LightGBM] [Info] Number of positive: 10150, number of negative: 10500
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.792012
[LightGBM] [Info] Total Bins 9420
[LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902
[LightGBM] [Info] Start training from score -0.033902
[LightGBM] [Debug] Re-bagging, using 16573 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 88 and depth = 11
[LightGBM] [Debug] Re-bagging, using 16654 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 86 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 103 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 10
[LightGBM] [Debug] Re-bagging, using 16595 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 95 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 12
[LightGBM] [Debug] Re-bagging, using 16542 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 88 and depth = 18
[LightGBM] [Debug] Re-bagging, using 16598 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 14
[LightGBM] [Debug] Re-bagging, using 16585 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 105 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11
[LightGBM] [Debug] Re-bagging, using 16527 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 93 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 15
[LightGBM] [Debug] Re-bagging, using 16553 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 88 and depth = 15
[LightGBM] [Debug] Re-bagging, using 16616 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 90 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 102 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 86 and depth = 14
[LightGBM] [Debug] Re-bagging, using 16500 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 99 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 11
[LightGBM] [Debug] Re-bagging, using 16519 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 12
[LightGBM] [Debug] Re-bagging, using 16541 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 93 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 94 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 110 and depth = 14
[LightGBM] [Debug] Re-bagging, using 16555 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 86 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 86 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 91 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 14
[LightGBM] [Debug] Re-bagging, using 16510 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 100 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 13
[LightGBM] [Debug] Re-bagging, using 16618 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 102 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 91 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16
[LightGBM] [Debug] Re-bagging, using 16555 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 102 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 14
[LightGBM] [Debug] Re-bagging, using 16604 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 88 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 15
[LightGBM] [Debug] Re-bagging, using 16499 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 100 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 94 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 88 and depth = 13
[LightGBM] [Debug] Re-bagging, using 16616 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 12
[LightGBM] [Debug] Re-bagging, using 16699 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 90 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 90 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 16
[LightGBM] [Debug] Re-bagging, using 16480 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 19
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 99 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 14
[LightGBM] [Debug] Re-bagging, using 16513 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 114 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 98 and depth = 14
[LightGBM] [Debug] Re-bagging, using 16560 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 90 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 13
[LightGBM] [Debug] Re-bagging, using 16629 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 90 and depth = 15
Trial 31, Fold 5: Log loss = 0.25288434539497634, Average precision = 0.9680446458583952, ROC-AUC = 0.96308552105977, Elapsed Time = 1.429005899999538 seconds
Optimization Progress:  32%|###2      | 32/100 [07:24<15:15, 13.47s/it]
Trial 32, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 32, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[LightGBM] [Info] Number of positive: 10130, number of negative: 10533
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791640
[LightGBM] [Info] Total Bins 24150
[LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012
[LightGBM] [Info] Start training from score -0.039012
[LightGBM] [Debug] Re-bagging, using 7527 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8
[LightGBM] [Debug] Re-bagging, using 7589 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8
[LightGBM] [Debug] Re-bagging, using 7461 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7455 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7595 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7532 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 7
[LightGBM] [Debug] Re-bagging, using 7534 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8
[LightGBM] [Debug] Re-bagging, using 7603 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 10
[LightGBM] [Debug] Re-bagging, using 7537 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8
[LightGBM] [Debug] Re-bagging, using 7337 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7348 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8
[LightGBM] [Debug] Re-bagging, using 7421 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10
Trial 32, Fold 1: Log loss = 0.21780981303912247, Average precision = 0.9729802326851044, ROC-AUC = 0.9680215607270712, Elapsed Time = 1.631260299999667 seconds
Trial 32, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 32, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[LightGBM] [Info] Number of positive: 10230, number of negative: 10471
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791600
[LightGBM] [Info] Total Bins 24158
[LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285
[LightGBM] [Info] Start training from score -0.023285
[LightGBM] [Debug] Re-bagging, using 7542 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8
[LightGBM] [Debug] Re-bagging, using 7605 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8
[LightGBM] [Debug] Re-bagging, using 7474 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 7
[LightGBM] [Debug] Re-bagging, using 7470 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7625 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8
[LightGBM] [Debug] Re-bagging, using 7523 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8
[LightGBM] [Debug] Re-bagging, using 7546 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 11
[LightGBM] [Debug] Re-bagging, using 7613 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 12
[LightGBM] [Debug] Re-bagging, using 7560 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10
[LightGBM] [Debug] Re-bagging, using 7361 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8
[LightGBM] [Debug] Re-bagging, using 7347 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 11
[LightGBM] [Debug] Re-bagging, using 7418 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
Trial 32, Fold 2: Log loss = 0.2169047217816407, Average precision = 0.9718437056956949, ROC-AUC = 0.9688737977402326, Elapsed Time = 1.995632499999374 seconds
Trial 32, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 32, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[LightGBM] [Info] Number of positive: 10165, number of negative: 10517
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791401
[LightGBM] [Info] Total Bins 24152
[LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043
[LightGBM] [Info] Start training from score -0.034043
[LightGBM] [Debug] Re-bagging, using 7534 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7595 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 7
[LightGBM] [Debug] Re-bagging, using 7467 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7468 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8
[LightGBM] [Debug] Re-bagging, using 7609 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7525 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 10
[LightGBM] [Debug] Re-bagging, using 7543 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8
[LightGBM] [Debug] Re-bagging, using 7606 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 11
[LightGBM] [Debug] Re-bagging, using 7540 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8
[LightGBM] [Debug] Re-bagging, using 7361 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7349 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 11
[LightGBM] [Debug] Re-bagging, using 7416 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 11
Trial 32, Fold 3: Log loss = 0.21615489727185902, Average precision = 0.9730476703738112, ROC-AUC = 0.9688875432192698, Elapsed Time = 1.7993200999999317 seconds
Trial 32, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 32, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[LightGBM] [Info] Number of positive: 10177, number of negative: 10479
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791090
[LightGBM] [Info] Total Bins 24142
[LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243
[LightGBM] [Info] Start training from score -0.029243
[LightGBM] [Debug] Re-bagging, using 7523 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7587 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7457 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8
[LightGBM] [Debug] Re-bagging, using 7457 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 11
[LightGBM] [Debug] Re-bagging, using 7587 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8
[LightGBM] [Debug] Re-bagging, using 7530 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7537 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 12
[LightGBM] [Debug] Re-bagging, using 7597 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
[LightGBM] [Debug] Re-bagging, using 7534 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7339 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8
[LightGBM] [Debug] Re-bagging, using 7339 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11
[LightGBM] [Debug] Re-bagging, using 7421 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10
Trial 32, Fold 4: Log loss = 0.21562484342864127, Average precision = 0.9726778588701402, ROC-AUC = 0.9675838000815469, Elapsed Time = 1.8426258999998026 seconds
Trial 32, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 32, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[LightGBM] [Info] Number of positive: 10150, number of negative: 10500
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.792012
[LightGBM] [Info] Total Bins 24146
[LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902
[LightGBM] [Info] Start training from score -0.033902
[LightGBM] [Debug] Re-bagging, using 7521 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7586 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7456 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8
[LightGBM] [Debug] Re-bagging, using 7451 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8
[LightGBM] [Debug] Re-bagging, using 7586 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8
[LightGBM] [Debug] Re-bagging, using 7521 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7546 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8
[LightGBM] [Debug] Re-bagging, using 7591 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7532 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 11
[LightGBM] [Debug] Re-bagging, using 7339 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10
[LightGBM] [Debug] Re-bagging, using 7332 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7413 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 12
Trial 32, Fold 5: Log loss = 0.22144917844599368, Average precision = 0.9705417710797805, ROC-AUC = 0.9670558683090873, Elapsed Time = 1.8122117000002618 seconds
Optimization Progress:  33%|###3      | 33/100 [07:41<16:15, 14.56s/it]
Trial 33, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 33, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[LightGBM] [Info] Number of positive: 10130, number of negative: 10533
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795662
[LightGBM] [Info] Total Bins 12184
[LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 258
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012
[LightGBM] [Info] Start training from score -0.039012
[LightGBM] [Debug] Re-bagging, using 5834 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5799 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 7
[LightGBM] [Debug] Re-bagging, using 5805 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 7
[LightGBM] [Debug] Re-bagging, using 5686 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 7
[LightGBM] [Debug] Re-bagging, using 5875 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5845 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 8
[LightGBM] [Debug] Re-bagging, using 5727 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 7
Trial 33, Fold 1: Log loss = 0.314628043679088, Average precision = 0.9658566787223346, ROC-AUC = 0.9591776776453832, Elapsed Time = 0.796326699999554 seconds
Trial 33, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 33, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[LightGBM] [Info] Number of positive: 10230, number of negative: 10471
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795629
[LightGBM] [Info] Total Bins 12205
[LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 258
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285
[LightGBM] [Info] Start training from score -0.023285
[LightGBM] [Debug] Re-bagging, using 5846 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5812 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 8
[LightGBM] [Debug] Re-bagging, using 5809 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7
[LightGBM] [Debug] Re-bagging, using 5704 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5896 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7
[LightGBM] [Debug] Re-bagging, using 5836 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 8
[LightGBM] [Debug] Re-bagging, using 5735 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8
Trial 33, Fold 2: Log loss = 0.3131834756667966, Average precision = 0.9634034482236395, ROC-AUC = 0.959467635384063, Elapsed Time = 0.8349615999995876 seconds
Trial 33, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 33, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[LightGBM] [Info] Number of positive: 10165, number of negative: 10517
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795455
[LightGBM] [Info] Total Bins 12209
[LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 258
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043
[LightGBM] [Info] Start training from score -0.034043
[LightGBM] [Debug] Re-bagging, using 5839 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8
[LightGBM] [Debug] Re-bagging, using 5805 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 8
[LightGBM] [Debug] Re-bagging, using 5807 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Debug] Re-bagging, using 5696 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5888 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 6
[LightGBM] [Debug] Re-bagging, using 5837 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5737 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6
Trial 33, Fold 3: Log loss = 0.31472151021701855, Average precision = 0.9662963901788351, ROC-AUC = 0.960705377006867, Elapsed Time = 0.8537070000002132 seconds
Trial 33, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 33, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[LightGBM] [Info] Number of positive: 10177, number of negative: 10479
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795144
[LightGBM] [Info] Total Bins 12187
[LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 258
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243
[LightGBM] [Info] Start training from score -0.029243
[LightGBM] [Debug] Re-bagging, using 5831 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5797 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 8
[LightGBM] [Debug] Re-bagging, using 5801 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5688 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Debug] Re-bagging, using 5872 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7
[LightGBM] [Debug] Re-bagging, using 5842 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5727 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
Trial 33, Fold 4: Log loss = 0.31352811928269, Average precision = 0.9659387169422231, ROC-AUC = 0.9593025213941166, Elapsed Time = 1.0476693999999043 seconds
Trial 33, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 33, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[LightGBM] [Info] Number of positive: 10150, number of negative: 10500
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.796026
[LightGBM] [Info] Total Bins 12189
[LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 258
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902
[LightGBM] [Info] Start training from score -0.033902
[LightGBM] [Debug] Re-bagging, using 5829 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5797 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 8
[LightGBM] [Debug] Re-bagging, using 5801 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7
[LightGBM] [Debug] Re-bagging, using 5681 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 8
[LightGBM] [Debug] Re-bagging, using 5871 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 6
[LightGBM] [Debug] Re-bagging, using 5838 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8
[LightGBM] [Debug] Re-bagging, using 5733 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 8
Trial 33, Fold 5: Log loss = 0.3174828222684896, Average precision = 0.9638476806395812, ROC-AUC = 0.957613362317225, Elapsed Time = 1.1022882999996 seconds
Optimization Progress:  34%|###4      | 34/100 [07:54<15:23, 13.99s/it]
Trial 34, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 34, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[LightGBM] [Info] Number of positive: 10130, number of negative: 10533
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791640
[LightGBM] [Info] Total Bins 15448
[LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012
[LightGBM] [Info] Start training from score -0.039012
[LightGBM] [Debug] Re-bagging, using 7530 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7
[LightGBM] [Debug] Re-bagging, using 7591 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8
[LightGBM] [Debug] Re-bagging, using 7465 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7
[LightGBM] [Debug] Re-bagging, using 7460 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7595 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7535 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 11
[LightGBM] [Debug] Re-bagging, using 7535 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 7
[LightGBM] [Debug] Re-bagging, using 7606 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 10
[LightGBM] [Debug] Re-bagging, using 7538 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8
[LightGBM] [Debug] Re-bagging, using 7339 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8
[LightGBM] [Debug] Re-bagging, using 7350 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8
[LightGBM] [Debug] Re-bagging, using 7424 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7407 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7461 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7637 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8
[LightGBM] [Debug] Re-bagging, using 7546 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7517 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7498 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Debug] Re-bagging, using 7590 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10
[LightGBM] [Debug] Re-bagging, using 7643 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10
[LightGBM] [Debug] Re-bagging, using 7390 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7479 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 11
[LightGBM] [Debug] Re-bagging, using 7559 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 11
Trial 34, Fold 1: Log loss = 0.2139692345325275, Average precision = 0.9721244320632372, ROC-AUC = 0.9677689893080589, Elapsed Time = 0.9208619000000908 seconds
Trial 34, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 34, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[LightGBM] [Info] Number of positive: 10230, number of negative: 10471
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791600
[LightGBM] [Info] Total Bins 15463
[LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285
[LightGBM] [Info] Start training from score -0.023285
[LightGBM] [Debug] Re-bagging, using 7545 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 6
[LightGBM] [Debug] Re-bagging, using 7607 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8
[LightGBM] [Debug] Re-bagging, using 7478 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7
[LightGBM] [Debug] Re-bagging, using 7475 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7625 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8
[LightGBM] [Debug] Re-bagging, using 7526 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8
[LightGBM] [Debug] Re-bagging, using 7547 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8
[LightGBM] [Debug] Re-bagging, using 7616 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8
[LightGBM] [Debug] Re-bagging, using 7561 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7363 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 10
[LightGBM] [Debug] Re-bagging, using 7349 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8
[LightGBM] [Debug] Re-bagging, using 7421 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8
[LightGBM] [Debug] Re-bagging, using 7419 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8
[LightGBM] [Debug] Re-bagging, using 7477 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7659 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10
[LightGBM] [Debug] Re-bagging, using 7571 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 11
[LightGBM] [Debug] Re-bagging, using 7529 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 12
[LightGBM] [Debug] Re-bagging, using 7517 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8
[LightGBM] [Debug] Re-bagging, using 7602 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10
[LightGBM] [Debug] Re-bagging, using 7655 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8
[LightGBM] [Debug] Re-bagging, using 7409 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 11
[LightGBM] [Debug] Re-bagging, using 7490 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 11
[LightGBM] [Debug] Re-bagging, using 7584 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 11
Trial 34, Fold 2: Log loss = 0.2093861632111387, Average precision = 0.971479734768855, ROC-AUC = 0.9680098975975593, Elapsed Time = 0.9724216999993587 seconds
Trial 34, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 34, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[LightGBM] [Info] Number of positive: 10165, number of negative: 10517
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791401
[LightGBM] [Info] Total Bins 15465
[LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043
[LightGBM] [Info] Start training from score -0.034043
[LightGBM] [Debug] Re-bagging, using 7537 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8
[LightGBM] [Debug] Re-bagging, using 7597 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8
[LightGBM] [Debug] Re-bagging, using 7471 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7
[LightGBM] [Debug] Re-bagging, using 7473 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7609 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7528 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 7
[LightGBM] [Debug] Re-bagging, using 7544 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7609 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7541 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8
[LightGBM] [Debug] Re-bagging, using 7363 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7351 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7419 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 7
[LightGBM] [Debug] Re-bagging, using 7405 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7472 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8
[LightGBM] [Debug] Re-bagging, using 7644 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10
[LightGBM] [Debug] Re-bagging, using 7562 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 10
[LightGBM] [Debug] Re-bagging, using 7525 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10
[LightGBM] [Debug] Re-bagging, using 7509 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8
[LightGBM] [Debug] Re-bagging, using 7589 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10
[LightGBM] [Debug] Re-bagging, using 7659 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 11
[LightGBM] [Debug] Re-bagging, using 7404 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10
[LightGBM] [Debug] Re-bagging, using 7477 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 11
[LightGBM] [Debug] Re-bagging, using 7563 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 12
Trial 34, Fold 3: Log loss = 0.20776298384101446, Average precision = 0.9728767115684074, ROC-AUC = 0.9690121447351948, Elapsed Time = 0.9004064999999173 seconds
Trial 34, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 34, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[LightGBM] [Info] Number of positive: 10177, number of negative: 10479
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791090
[LightGBM] [Info] Total Bins 15448
[LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243
[LightGBM] [Info] Start training from score -0.029243
[LightGBM] [Debug] Re-bagging, using 7526 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7589 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7461 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7462 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 6
[LightGBM] [Debug] Re-bagging, using 7587 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8
[LightGBM] [Debug] Re-bagging, using 7533 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8
[LightGBM] [Debug] Re-bagging, using 7538 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8
[LightGBM] [Debug] Re-bagging, using 7600 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7535 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8
[LightGBM] [Debug] Re-bagging, using 7341 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8
[LightGBM] [Debug] Re-bagging, using 7341 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 10
[LightGBM] [Debug] Re-bagging, using 7424 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7
[LightGBM] [Debug] Re-bagging, using 7405 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 8
[LightGBM] [Debug] Re-bagging, using 7460 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10
[LightGBM] [Debug] Re-bagging, using 7631 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7543 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 11
[LightGBM] [Debug] Re-bagging, using 7516 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10
[LightGBM] [Debug] Re-bagging, using 7501 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8
[LightGBM] [Debug] Re-bagging, using 7577 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 11
[LightGBM] [Debug] Re-bagging, using 7646 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 10
[LightGBM] [Debug] Re-bagging, using 7392 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 11
[LightGBM] [Debug] Re-bagging, using 7470 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11
[LightGBM] [Debug] Re-bagging, using 7561 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 11
Trial 34, Fold 4: Log loss = 0.21491292913763768, Average precision = 0.9721148252396932, ROC-AUC = 0.9669598298485185, Elapsed Time = 0.8922624000006181 seconds
Trial 34, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 34, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[LightGBM] [Info] Number of positive: 10150, number of negative: 10500
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.792012
[LightGBM] [Info] Total Bins 15447
[LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902
[LightGBM] [Info] Start training from score -0.033902
[LightGBM] [Debug] Re-bagging, using 7524 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7
[LightGBM] [Debug] Re-bagging, using 7588 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8
[LightGBM] [Debug] Re-bagging, using 7460 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7
[LightGBM] [Debug] Re-bagging, using 7456 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7
[LightGBM] [Debug] Re-bagging, using 7586 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8
[LightGBM] [Debug] Re-bagging, using 7524 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8
[LightGBM] [Debug] Re-bagging, using 7547 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7594 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8
[LightGBM] [Debug] Re-bagging, using 7533 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 7
[LightGBM] [Debug] Re-bagging, using 7341 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7334 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10
[LightGBM] [Debug] Re-bagging, using 7416 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7410 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 8
[LightGBM] [Debug] Re-bagging, using 7468 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10
[LightGBM] [Debug] Re-bagging, using 7629 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10
[LightGBM] [Debug] Re-bagging, using 7543 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10
[LightGBM] [Debug] Re-bagging, using 7509 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 12
[LightGBM] [Debug] Re-bagging, using 7485 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 13
[LightGBM] [Debug] Re-bagging, using 7583 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10
[LightGBM] [Debug] Re-bagging, using 7634 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7402 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 14
[LightGBM] [Debug] Re-bagging, using 7461 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7556 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 13
Trial 34, Fold 5: Log loss = 0.213919976562517, Average precision = 0.9716793669447494, ROC-AUC = 0.9675710384036564, Elapsed Time = 0.8812234000006356 seconds
Optimization Progress:  35%|###5      | 35/100 [08:07<14:52, 13.72s/it]
Trial 35, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 35, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[LightGBM] [Info] Number of positive: 10130, number of negative: 10533
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791640
[LightGBM] [Info] Total Bins 7358
[LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012
[LightGBM] [Info] Start training from score -0.039012
[LightGBM] [Debug] Re-bagging, using 2241 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2204 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2114 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2130 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2242 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2196 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2168 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2204 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2184 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5
[LightGBM] [Debug] Re-bagging, using 2161 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2066 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2161 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 5
[LightGBM] [Debug] Re-bagging, using 2196 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2177 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2165 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2183 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2189 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2139 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
Trial 35, Fold 1: Log loss = 0.4108622812425437, Average precision = 0.9573498878587887, ROC-AUC = 0.9509579790462808, Elapsed Time = 0.8165712999998505 seconds
Trial 35, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 35, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[LightGBM] [Info] Number of positive: 10230, number of negative: 10471
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791600
[LightGBM] [Info] Total Bins 7374
[LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285
[LightGBM] [Info] Start training from score -0.023285
[LightGBM] [Debug] Re-bagging, using 2245 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2209 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2110 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2142 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2251 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2192 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2168 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 5
[LightGBM] [Debug] Re-bagging, using 2210 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2202 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2169 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2066 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2158 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2201 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2173 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2172 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2187 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2192 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5
[LightGBM] [Debug] Re-bagging, using 2147 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
Trial 35, Fold 2: Log loss = 0.41126862578961465, Average precision = 0.9547552990346806, ROC-AUC = 0.950380902116305, Elapsed Time = 0.8838040999999066 seconds
Trial 35, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 35, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[LightGBM] [Info] Number of positive: 10165, number of negative: 10517
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791401
[LightGBM] [Info] Total Bins 7372
[LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043
[LightGBM] [Info] Start training from score -0.034043
[LightGBM] [Debug] Re-bagging, using 2243 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2205 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2115 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2135 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2246 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2195 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2169 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5
[LightGBM] [Debug] Re-bagging, using 2201 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2197 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2162 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2074 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2153 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2200 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2175 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2165 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5
[LightGBM] [Debug] Re-bagging, using 2190 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2191 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2137 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 5
Trial 35, Fold 3: Log loss = 0.40891116404980826, Average precision = 0.9584163722175101, ROC-AUC = 0.9530043863345244, Elapsed Time = 0.9321852000002764 seconds
Trial 35, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 35, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[LightGBM] [Info] Number of positive: 10177, number of negative: 10479
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791090
[LightGBM] [Info] Total Bins 7355
[LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243
[LightGBM] [Info] Start training from score -0.029243
[LightGBM] [Debug] Re-bagging, using 2241 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2202 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2111 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 5
[LightGBM] [Debug] Re-bagging, using 2132 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2243 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2193 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2170 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2201 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5
[LightGBM] [Debug] Re-bagging, using 2185 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2156 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2069 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2158 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2192 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5
[LightGBM] [Debug] Re-bagging, using 2178 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2165 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2184 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2187 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5
[LightGBM] [Debug] Re-bagging, using 2144 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
Trial 35, Fold 4: Log loss = 0.41120225290133705, Average precision = 0.955421450053424, ROC-AUC = 0.9491549949808173, Elapsed Time = 0.9162712999996074 seconds
Trial 35, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 35, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[LightGBM] [Info] Number of positive: 10150, number of negative: 10500
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.792012
[LightGBM] [Info] Total Bins 7363
[LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902
[LightGBM] [Info] Start training from score -0.033902
[LightGBM] [Debug] Re-bagging, using 2241 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5
[LightGBM] [Debug] Re-bagging, using 2201 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2111 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2131 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2240 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2193 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2171 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2199 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5
[LightGBM] [Debug] Re-bagging, using 2184 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2157 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2064 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2150 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2206 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2173 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 5
[LightGBM] [Debug] Re-bagging, using 2167 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2183 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2191 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5
[LightGBM] [Debug] Re-bagging, using 2133 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
Trial 35, Fold 5: Log loss = 0.413217515676604, Average precision = 0.954621797764589, ROC-AUC = 0.9490674247069096, Elapsed Time = 0.9273424000002706 seconds
Optimization Progress:  36%|###6      | 36/100 [08:19<14:00, 13.13s/it]
Trial 36, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 36, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[LightGBM] [Info] Number of positive: 10130, number of negative: 10533
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795662
[LightGBM] [Info] Total Bins 11033
[LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 258
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012
[LightGBM] [Info] Start training from score -0.039012
[LightGBM] [Debug] Re-bagging, using 11682 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 87 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 90 and depth = 13
[LightGBM] [Debug] Re-bagging, using 11729 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 87 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 95 and depth = 14
[LightGBM] [Debug] Re-bagging, using 11741 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 87 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 86 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 87 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 88 and depth = 14
[LightGBM] [Debug] Trained a tree with leaves = 98 and depth = 13
[LightGBM] [Debug] Re-bagging, using 11502 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 86 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 93 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 98 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 86 and depth = 14
[LightGBM] [Debug] Re-bagging, using 11687 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 90 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 93 and depth = 14
[LightGBM] [Debug] Re-bagging, using 11592 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 91 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 90 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 88 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 91 and depth = 15
[LightGBM] [Debug] Re-bagging, using 11705 data to train
[LightGBM] [Debug] Trained a tree with leaves = 98 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 91 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 88 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 13
Trial 36, Fold 1: Log loss = 0.5434716286160565, Average precision = 0.9693808772361909, ROC-AUC = 0.9630761794831353, Elapsed Time = 1.2474445999996533 seconds
Trial 36, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 36, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[LightGBM] [Info] Number of positive: 10230, number of negative: 10471
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.794298
[LightGBM] [Info] Total Bins 11045
[LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 257
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285
[LightGBM] [Info] Start training from score -0.023285
[LightGBM] [Debug] Re-bagging, using 11707 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 13
[LightGBM] [Debug] Re-bagging, using 11746 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 91 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 13
[LightGBM] [Debug] Re-bagging, using 11764 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 87 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 87 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 12
[LightGBM] [Debug] Trained a tree with leaves = 98 and depth = 12
[LightGBM] [Debug] Re-bagging, using 11530 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 86 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 13
[LightGBM] [Debug] Re-bagging, using 11709 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 90 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 97 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 87 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 12
[LightGBM] [Debug] Re-bagging, using 11597 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 93 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 88 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 94 and depth = 14
[LightGBM] [Debug] Re-bagging, using 11720 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 87 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 90 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 11
Trial 36, Fold 2: Log loss = 0.5449643510258457, Average precision = 0.9666909822952459, ROC-AUC = 0.9626037309924519, Elapsed Time = 1.2437085000001389 seconds
Trial 36, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 36, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[LightGBM] [Info] Number of positive: 10165, number of negative: 10517
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795455
[LightGBM] [Info] Total Bins 11051
[LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 258
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043
[LightGBM] [Info] Start training from score -0.034043
[LightGBM] [Debug] Re-bagging, using 11693 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 86 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 12
[LightGBM] [Debug] Re-bagging, using 11736 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 86 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 87 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 88 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 87 and depth = 14
[LightGBM] [Debug] Re-bagging, using 11752 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 90 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 86 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 86 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 86 and depth = 14
[LightGBM] [Debug] Re-bagging, using 11522 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 88 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 87 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 12
[LightGBM] [Debug] Trained a tree with leaves = 98 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 93 and depth = 13
[LightGBM] [Debug] Re-bagging, using 11696 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 88 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 87 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 86 and depth = 12
[LightGBM] [Debug] Re-bagging, using 11593 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 95 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 93 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 95 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 14
[LightGBM] [Debug] Re-bagging, using 11715 data to train
[LightGBM] [Debug] Trained a tree with leaves = 98 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 12
[LightGBM] [Debug] Trained a tree with leaves = 98 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 98 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 98 and depth = 15
Trial 36, Fold 3: Log loss = 0.5417627858781487, Average precision = 0.9701888548272047, ROC-AUC = 0.964835811920332, Elapsed Time = 1.0679775999997219 seconds
Trial 36, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 36, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[LightGBM] [Info] Number of positive: 10177, number of negative: 10479
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795144
[LightGBM] [Info] Total Bins 11032
[LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 258
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243
[LightGBM] [Info] Start training from score -0.029243
[LightGBM] [Debug] Re-bagging, using 11678 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 86 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 14
[LightGBM] [Debug] Re-bagging, using 11725 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 88 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 14
[LightGBM] [Debug] Trained a tree with leaves = 98 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 90 and depth = 14
[LightGBM] [Debug] Re-bagging, using 11736 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 88 and depth = 11
[LightGBM] [Debug] Re-bagging, using 11499 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 93 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 12
[LightGBM] [Debug] Re-bagging, using 11681 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 87 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 90 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 86 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 93 and depth = 14
[LightGBM] [Debug] Re-bagging, using 11589 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 91 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 91 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 93 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 95 and depth = 13
[LightGBM] [Debug] Re-bagging, using 11702 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 97 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 88 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 90 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 94 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 15
Trial 36, Fold 4: Log loss = 0.5420935465836577, Average precision = 0.9693905511387977, ROC-AUC = 0.963465030246238, Elapsed Time = 1.0401756999999634 seconds
Trial 36, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 36, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[LightGBM] [Info] Number of positive: 10150, number of negative: 10500
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.796026
[LightGBM] [Info] Total Bins 11035
[LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 258
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902
[LightGBM] [Info] Start training from score -0.033902
[LightGBM] [Debug] Re-bagging, using 11675 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 12
[LightGBM] [Debug] Re-bagging, using 11721 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 91 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 13
[LightGBM] [Debug] Re-bagging, using 11736 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 86 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 11
[LightGBM] [Debug] Re-bagging, using 11493 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 87 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 86 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 12
[LightGBM] [Debug] Re-bagging, using 11675 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 88 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 91 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 88 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 88 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 94 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 88 and depth = 13
[LightGBM] [Debug] Re-bagging, using 11585 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 91 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 87 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 87 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 88 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 12
[LightGBM] [Debug] Re-bagging, using 11707 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 86 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 93 and depth = 13
[LightGBM] [Debug] Trained a tree with leaves = 98 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 14
[LightGBM] [Debug] Trained a tree with leaves = 98 and depth = 12
[LightGBM] [Debug] Trained a tree with leaves = 98 and depth = 13
Trial 36, Fold 5: Log loss = 0.5418271649599954, Average precision = 0.9683225385236497, ROC-AUC = 0.9626778143172994, Elapsed Time = 1.0533833999998024 seconds
Optimization Progress:  37%|###7      | 37/100 [08:32<13:49, 13.16s/it]
Trial 37, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 37, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[LightGBM] [Info] Number of positive: 10130, number of negative: 10533
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791640
[LightGBM] [Info] Total Bins 23907
[LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012
[LightGBM] [Info] Start training from score -0.039012
[LightGBM] [Debug] Re-bagging, using 17461 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 12
[LightGBM] [Debug] Re-bagging, using 17476 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 91 and depth = 13
[LightGBM] [Debug] Re-bagging, using 17479 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 12
[LightGBM] [Debug] Re-bagging, using 17416 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 13
[LightGBM] [Debug] Re-bagging, using 17411 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 11
[LightGBM] [Debug] Re-bagging, using 17408 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 87 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 16
[LightGBM] [Debug] Re-bagging, using 17358 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 88 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 11
Trial 37, Fold 1: Log loss = 0.45498393800634146, Average precision = 0.9655085446117008, ROC-AUC = 0.9593522820148838, Elapsed Time = 0.8728379999993194 seconds
Trial 37, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 37, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[LightGBM] [Info] Number of positive: 10230, number of negative: 10471
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791600
[LightGBM] [Info] Total Bins 23835
[LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285
[LightGBM] [Info] Start training from score -0.023285
[LightGBM] [Debug] Re-bagging, using 17495 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 10
[LightGBM] [Debug] Re-bagging, using 17506 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 13
[LightGBM] [Debug] Re-bagging, using 17508 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 12
[LightGBM] [Debug] Re-bagging, using 17453 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 19
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 90 and depth = 13
[LightGBM] [Debug] Re-bagging, using 17444 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 13
[LightGBM] [Debug] Re-bagging, using 17425 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 11
[LightGBM] [Debug] Re-bagging, using 17392 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 14
Trial 37, Fold 2: Log loss = 0.4584689056532689, Average precision = 0.9614054215271424, ROC-AUC = 0.9579318719212271, Elapsed Time = 0.9410419999994701 seconds
Trial 37, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 37, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[LightGBM] [Info] Number of positive: 10165, number of negative: 10517
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791401
[LightGBM] [Info] Total Bins 23830
[LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043
[LightGBM] [Info] Start training from score -0.034043
[LightGBM] [Debug] Re-bagging, using 17478 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 13
[LightGBM] [Debug] Re-bagging, using 17490 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 13
[LightGBM] [Debug] Re-bagging, using 17494 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 12
[LightGBM] [Debug] Re-bagging, using 17437 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 87 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 13
[LightGBM] [Debug] Re-bagging, using 17426 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 13
[LightGBM] [Debug] Re-bagging, using 17414 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 13
[LightGBM] [Debug] Re-bagging, using 17376 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 13
Trial 37, Fold 3: Log loss = 0.45767112706573265, Average precision = 0.964278662255139, ROC-AUC = 0.9595124077828394, Elapsed Time = 0.9496364000005997 seconds
Trial 37, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 37, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[LightGBM] [Info] Number of positive: 10177, number of negative: 10479
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791090
[LightGBM] [Info] Total Bins 23819
[LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243
[LightGBM] [Info] Start training from score -0.029243
[LightGBM] [Debug] Re-bagging, using 17455 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 12
[LightGBM] [Debug] Re-bagging, using 17469 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 86 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 10
[LightGBM] [Debug] Re-bagging, using 17475 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 12
[LightGBM] [Debug] Re-bagging, using 17411 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 13
[LightGBM] [Debug] Re-bagging, using 17405 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 13
[LightGBM] [Debug] Re-bagging, using 17401 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 86 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 12
[LightGBM] [Debug] Re-bagging, using 17354 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 13
Trial 37, Fold 4: Log loss = 0.45301620955825905, Average precision = 0.9650677575600484, ROC-AUC = 0.9586500127565922, Elapsed Time = 1.0068817000001218 seconds
Trial 37, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 37, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[LightGBM] [Info] Number of positive: 10150, number of negative: 10500
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.792012
[LightGBM] [Info] Total Bins 23826
[LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902
[LightGBM] [Info] Start training from score -0.033902
[LightGBM] [Debug] Re-bagging, using 17449 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 11
[LightGBM] [Debug] Re-bagging, using 17467 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 13
[LightGBM] [Debug] Re-bagging, using 17470 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 14
[LightGBM] [Debug] Re-bagging, using 17405 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 14
[LightGBM] [Debug] Re-bagging, using 17398 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 12
[LightGBM] [Debug] Re-bagging, using 17395 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 13
[LightGBM] [Debug] Re-bagging, using 17356 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 14
Trial 37, Fold 5: Log loss = 0.4544535693194063, Average precision = 0.9638041944638919, ROC-AUC = 0.9578615833379782, Elapsed Time = 0.9900883999998769 seconds
Optimization Progress:  38%|###8      | 38/100 [08:44<13:16, 12.84s/it]
Trial 38, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 38, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[LightGBM] [Info] Number of positive: 10130, number of negative: 10533
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.798290
[LightGBM] [Info] Total Bins 17915
[LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 260
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012
[LightGBM] [Info] Start training from score -0.039012
[LightGBM] [Debug] Re-bagging, using 14894 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 13
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 12
[LightGBM] [Debug] Re-bagging, using 15018 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 14
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 14
[LightGBM] [Debug] Re-bagging, using 15035 data to train
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 12
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 12
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 14
Trial 38, Fold 1: Log loss = 0.28412838776738836, Average precision = 0.9727748937905127, ROC-AUC = 0.9688282657738395, Elapsed Time = 0.6541177000008247 seconds
Trial 38, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 38, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[LightGBM] [Info] Number of positive: 10230, number of negative: 10471
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.798259
[LightGBM] [Info] Total Bins 17930
[LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 260
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285
[LightGBM] [Info] Start training from score -0.023285
[LightGBM] [Debug] Re-bagging, using 14922 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 12
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 13
[LightGBM] [Debug] Re-bagging, using 15044 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 13
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 13
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 13
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 14
[LightGBM] [Debug] Re-bagging, using 15065 data to train
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 13
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 14
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 10
Trial 38, Fold 2: Log loss = 0.2830484800282979, Average precision = 0.9732954845031008, ROC-AUC = 0.9701862643367514, Elapsed Time = 0.719259399999828 seconds
Trial 38, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 38, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[LightGBM] [Info] Number of positive: 10165, number of negative: 10517
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.798104
[LightGBM] [Info] Total Bins 17934
[LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 260
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043
[LightGBM] [Info] Start training from score -0.034043
[LightGBM] [Debug] Re-bagging, using 14905 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 11
[LightGBM] [Debug] Re-bagging, using 15032 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 14
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 12
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 14
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 14
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 12
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 14
[LightGBM] [Debug] Re-bagging, using 15051 data to train
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 13
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 14
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 14
Trial 38, Fold 3: Log loss = 0.2825482226615492, Average precision = 0.9733412577409921, ROC-AUC = 0.9699044450838382, Elapsed Time = 0.7281477999995332 seconds
Trial 38, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 38, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[LightGBM] [Info] Number of positive: 10177, number of negative: 10479
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.797795
[LightGBM] [Info] Total Bins 17917
[LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 260
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243
[LightGBM] [Info] Start training from score -0.029243
[LightGBM] [Debug] Re-bagging, using 14889 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 13
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 11
[LightGBM] [Debug] Re-bagging, using 15011 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 14
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 11
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 14
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 14
[LightGBM] [Debug] Re-bagging, using 15030 data to train
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 14
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 14
Trial 38, Fold 4: Log loss = 0.28471134543479953, Average precision = 0.9735303764738737, ROC-AUC = 0.9686139396172069, Elapsed Time = 0.7371172000002844 seconds
Trial 38, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 38, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[LightGBM] [Info] Number of positive: 10150, number of negative: 10500
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.798646
[LightGBM] [Info] Total Bins 17916
[LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 260
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902
[LightGBM] [Info] Start training from score -0.033902
[LightGBM] [Debug] Re-bagging, using 14884 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 13
[LightGBM] [Debug] Re-bagging, using 15009 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 12
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 12
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 14
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 12
[LightGBM] [Debug] Re-bagging, using 15028 data to train
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 12
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 12
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 14
Trial 38, Fold 5: Log loss = 0.2870246817623127, Average precision = 0.9718168184861367, ROC-AUC = 0.9678533712353454, Elapsed Time = 0.7391860000007 seconds
Optimization Progress:  39%|###9      | 39/100 [08:55<12:27, 12.25s/it]
Trial 39, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 39, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[LightGBM] [Info] Number of positive: 10130, number of negative: 10533
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791640
[LightGBM] [Info] Total Bins 20107
[LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012
[LightGBM] [Info] Start training from score -0.039012
[LightGBM] [Debug] Re-bagging, using 6705 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10
[LightGBM] [Debug] Re-bagging, using 6698 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 11
[LightGBM] [Debug] Re-bagging, using 6634 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Debug] Re-bagging, using 6569 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9
[LightGBM] [Debug] Re-bagging, using 6766 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10
[LightGBM] [Debug] Re-bagging, using 6709 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10
[LightGBM] [Debug] Re-bagging, using 6651 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10
[LightGBM] [Debug] Re-bagging, using 6767 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Debug] Re-bagging, using 6639 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8
[LightGBM] [Debug] Re-bagging, using 6495 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9
[LightGBM] [Debug] Re-bagging, using 6526 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8
[LightGBM] [Debug] Re-bagging, using 6574 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 11
[LightGBM] [Debug] Re-bagging, using 6597 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10
[LightGBM] [Debug] Re-bagging, using 6667 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10
[LightGBM] [Debug] Re-bagging, using 6807 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11
[LightGBM] [Debug] Re-bagging, using 6676 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 11
[LightGBM] [Debug] Re-bagging, using 6647 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10
[LightGBM] [Debug] Re-bagging, using 6625 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10
[LightGBM] [Debug] Re-bagging, using 6735 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9
[LightGBM] [Debug] Re-bagging, using 6733 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 11
[LightGBM] [Debug] Re-bagging, using 6559 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9
[LightGBM] [Debug] Re-bagging, using 6620 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 12
[LightGBM] [Debug] Re-bagging, using 6721 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 11
[LightGBM] [Debug] Re-bagging, using 6658 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Debug] Re-bagging, using 6812 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 11
[LightGBM] [Debug] Re-bagging, using 6707 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10
[LightGBM] [Debug] Re-bagging, using 6689 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11
[LightGBM] [Debug] Re-bagging, using 6642 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9
[LightGBM] [Debug] Re-bagging, using 6614 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9
[LightGBM] [Debug] Re-bagging, using 6691 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10
[LightGBM] [Debug] Re-bagging, using 6584 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 12
[LightGBM] [Debug] Re-bagging, using 6663 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9
[LightGBM] [Debug] Re-bagging, using 6598 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10
[LightGBM] [Debug] Re-bagging, using 6714 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8
[LightGBM] [Debug] Re-bagging, using 6635 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9
[LightGBM] [Debug] Re-bagging, using 6680 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 7
[LightGBM] [Debug] Re-bagging, using 6674 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10
[LightGBM] [Debug] Re-bagging, using 6723 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Re-bagging, using 6645 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Debug] Re-bagging, using 6802 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10
[LightGBM] [Debug] Re-bagging, using 6616 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11
[LightGBM] [Debug] Re-bagging, using 6637 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Re-bagging, using 6520 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Debug] Re-bagging, using 6595 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10
Trial 39, Fold 1: Log loss = 0.4118523317409845, Average precision = 0.9640110933053028, ROC-AUC = 0.9570288041945675, Elapsed Time = 1.676568099999713 seconds
Trial 39, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 39, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[LightGBM] [Info] Number of positive: 10230, number of negative: 10471
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791600
[LightGBM] [Info] Total Bins 20073
[LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285
[LightGBM] [Info] Start training from score -0.023285
[LightGBM] [Debug] Re-bagging, using 6718 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Debug] Re-bagging, using 6715 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9
[LightGBM] [Debug] Re-bagging, using 6642 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 12
[LightGBM] [Debug] Re-bagging, using 6584 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Debug] Re-bagging, using 6794 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10
[LightGBM] [Debug] Re-bagging, using 6698 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10
[LightGBM] [Debug] Re-bagging, using 6662 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10
[LightGBM] [Debug] Re-bagging, using 6776 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Debug] Re-bagging, using 6666 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10
[LightGBM] [Debug] Re-bagging, using 6515 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10
[LightGBM] [Debug] Re-bagging, using 6521 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10
[LightGBM] [Debug] Re-bagging, using 6577 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 11
[LightGBM] [Debug] Re-bagging, using 6606 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 11
[LightGBM] [Debug] Re-bagging, using 6678 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 11
[LightGBM] [Debug] Re-bagging, using 6821 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10
[LightGBM] [Debug] Re-bagging, using 6696 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 12
[LightGBM] [Debug] Re-bagging, using 6660 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8
[LightGBM] [Debug] Re-bagging, using 6643 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10
[LightGBM] [Debug] Re-bagging, using 6751 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Debug] Re-bagging, using 6745 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 11
[LightGBM] [Debug] Re-bagging, using 6573 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Debug] Re-bagging, using 6634 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Debug] Re-bagging, using 6742 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9
[LightGBM] [Debug] Re-bagging, using 6671 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9
[LightGBM] [Debug] Re-bagging, using 6820 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 12
[LightGBM] [Debug] Re-bagging, using 6713 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10
[LightGBM] [Debug] Re-bagging, using 6696 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Debug] Re-bagging, using 6639 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 11
[LightGBM] [Debug] Re-bagging, using 6622 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 11
[LightGBM] [Debug] Re-bagging, using 6701 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 12
[LightGBM] [Debug] Re-bagging, using 6601 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Debug] Re-bagging, using 6669 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 11
[LightGBM] [Debug] Re-bagging, using 6610 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Debug] Re-bagging, using 6734 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8
[LightGBM] [Debug] Re-bagging, using 6649 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Debug] Re-bagging, using 6687 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10
[LightGBM] [Debug] Re-bagging, using 6690 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8
[LightGBM] [Debug] Re-bagging, using 6736 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 11
[LightGBM] [Debug] Re-bagging, using 6645 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Debug] Re-bagging, using 6808 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10
[LightGBM] [Debug] Re-bagging, using 6636 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 11
[LightGBM] [Debug] Re-bagging, using 6655 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Debug] Re-bagging, using 6538 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9
[LightGBM] [Debug] Re-bagging, using 6612 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
Trial 39, Fold 2: Log loss = 0.4136268071513568, Average precision = 0.9608336604594377, ROC-AUC = 0.9558583901805829, Elapsed Time = 1.9231171000001268 seconds
Trial 39, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 39, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[LightGBM] [Info] Number of positive: 10165, number of negative: 10517
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791401
[LightGBM] [Info] Total Bins 20072
[LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043
[LightGBM] [Info] Start training from score -0.034043
[LightGBM] [Debug] Re-bagging, using 6711 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Debug] Re-bagging, using 6705 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Debug] Re-bagging, using 6636 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10
[LightGBM] [Debug] Re-bagging, using 6582 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10
[LightGBM] [Debug] Re-bagging, using 6782 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10
[LightGBM] [Debug] Re-bagging, using 6699 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9
[LightGBM] [Debug] Re-bagging, using 6661 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 11
[LightGBM] [Debug] Re-bagging, using 6768 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10
[LightGBM] [Debug] Re-bagging, using 6642 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11
[LightGBM] [Debug] Re-bagging, using 6522 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10
[LightGBM] [Debug] Re-bagging, using 6520 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 11
[LightGBM] [Debug] Re-bagging, using 6572 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 11
[LightGBM] [Debug] Re-bagging, using 6598 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10
[LightGBM] [Debug] Re-bagging, using 6677 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 11
[LightGBM] [Debug] Re-bagging, using 6806 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Debug] Re-bagging, using 6689 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10
[LightGBM] [Debug] Re-bagging, using 6658 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10
[LightGBM] [Debug] Re-bagging, using 6632 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 12
[LightGBM] [Debug] Re-bagging, using 6736 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Debug] Re-bagging, using 6751 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 11
[LightGBM] [Debug] Re-bagging, using 6574 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10
[LightGBM] [Debug] Re-bagging, using 6614 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Debug] Re-bagging, using 6719 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10
[LightGBM] [Debug] Re-bagging, using 6676 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 12
[LightGBM] [Debug] Re-bagging, using 6833 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Debug] Re-bagging, using 6709 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Debug] Re-bagging, using 6680 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 11
[LightGBM] [Debug] Re-bagging, using 6631 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10
[LightGBM] [Debug] Re-bagging, using 6629 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 11
[LightGBM] [Debug] Re-bagging, using 6701 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10
[LightGBM] [Debug] Re-bagging, using 6588 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10
[LightGBM] [Debug] Re-bagging, using 6659 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Debug] Re-bagging, using 6604 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10
[LightGBM] [Debug] Re-bagging, using 6719 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10
[LightGBM] [Debug] Re-bagging, using 6647 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Debug] Re-bagging, using 6690 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 11
[LightGBM] [Debug] Re-bagging, using 6688 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 11
[LightGBM] [Debug] Re-bagging, using 6727 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8
[LightGBM] [Debug] Re-bagging, using 6642 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 11
[LightGBM] [Debug] Re-bagging, using 6810 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10
[LightGBM] [Debug] Re-bagging, using 6620 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9
[LightGBM] [Debug] Re-bagging, using 6641 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 11
[LightGBM] [Debug] Re-bagging, using 6521 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8
[LightGBM] [Debug] Re-bagging, using 6592 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8
Trial 39, Fold 3: Log loss = 0.4062084001497198, Average precision = 0.9655958077729394, ROC-AUC = 0.9596640213423737, Elapsed Time = 1.8035933000001023 seconds
Trial 39, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 39, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[LightGBM] [Info] Number of positive: 10177, number of negative: 10479
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791090
[LightGBM] [Info] Total Bins 20105
[LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243
[LightGBM] [Info] Start training from score -0.029243
[LightGBM] [Debug] Re-bagging, using 6702 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9
[LightGBM] [Debug] Re-bagging, using 6695 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Debug] Re-bagging, using 6631 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10
[LightGBM] [Debug] Re-bagging, using 6571 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Debug] Re-bagging, using 6761 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 11
[LightGBM] [Debug] Re-bagging, using 6706 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 11
[LightGBM] [Debug] Re-bagging, using 6652 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10
[LightGBM] [Debug] Re-bagging, using 6762 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Debug] Re-bagging, using 6635 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10
[LightGBM] [Debug] Re-bagging, using 6497 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10
[LightGBM] [Debug] Re-bagging, using 6516 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9
[LightGBM] [Debug] Re-bagging, using 6577 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10
[LightGBM] [Debug] Re-bagging, using 6596 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11
[LightGBM] [Debug] Re-bagging, using 6664 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10
[LightGBM] [Debug] Re-bagging, using 6801 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10
[LightGBM] [Debug] Re-bagging, using 6677 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10
[LightGBM] [Debug] Re-bagging, using 6646 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Debug] Re-bagging, using 6628 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Debug] Re-bagging, using 6726 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10
[LightGBM] [Debug] Re-bagging, using 6736 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10
[LightGBM] [Debug] Re-bagging, using 6557 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 11
[LightGBM] [Debug] Re-bagging, using 6611 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 11
[LightGBM] [Debug] Re-bagging, using 6721 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Debug] Re-bagging, using 6660 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 11
[LightGBM] [Debug] Re-bagging, using 6807 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Debug] Re-bagging, using 6703 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9
[LightGBM] [Debug] Re-bagging, using 6672 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10
[LightGBM] [Debug] Re-bagging, using 6646 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 11
[LightGBM] [Debug] Re-bagging, using 6623 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Debug] Re-bagging, using 6691 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10
[LightGBM] [Debug] Re-bagging, using 6586 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Debug] Re-bagging, using 6650 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10
[LightGBM] [Debug] Re-bagging, using 6599 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9
[LightGBM] [Debug] Re-bagging, using 6718 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9
[LightGBM] [Debug] Re-bagging, using 6633 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Debug] Re-bagging, using 6677 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Debug] Re-bagging, using 6666 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10
[LightGBM] [Debug] Re-bagging, using 6725 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 11
[LightGBM] [Debug] Re-bagging, using 6633 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9
[LightGBM] [Debug] Re-bagging, using 6801 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11
[LightGBM] [Debug] Re-bagging, using 6614 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10
[LightGBM] [Debug] Re-bagging, using 6640 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8
[LightGBM] [Debug] Re-bagging, using 6528 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10
[LightGBM] [Debug] Re-bagging, using 6585 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
Trial 39, Fold 4: Log loss = 0.4129772817691646, Average precision = 0.9637340592536143, ROC-AUC = 0.9571041700942555, Elapsed Time = 1.6821828000001915 seconds
Trial 39, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 39, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[LightGBM] [Info] Number of positive: 10150, number of negative: 10500
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.792012
[LightGBM] [Info] Total Bins 20102
[LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902
[LightGBM] [Info] Start training from score -0.033902
[LightGBM] [Debug] Re-bagging, using 6700 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10
[LightGBM] [Debug] Re-bagging, using 6695 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Debug] Re-bagging, using 6629 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10
[LightGBM] [Debug] Re-bagging, using 6566 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Debug] Re-bagging, using 6760 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 12
[LightGBM] [Debug] Re-bagging, using 6699 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 11
[LightGBM] [Debug] Re-bagging, using 6660 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10
[LightGBM] [Debug] Re-bagging, using 6755 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Debug] Re-bagging, using 6634 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 11
[LightGBM] [Debug] Re-bagging, using 6499 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Debug] Re-bagging, using 6507 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8
[LightGBM] [Debug] Re-bagging, using 6571 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10
[LightGBM] [Debug] Re-bagging, using 6597 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Debug] Re-bagging, using 6674 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 11
[LightGBM] [Debug] Re-bagging, using 6798 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 11
[LightGBM] [Debug] Re-bagging, using 6678 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Debug] Re-bagging, using 6642 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10
[LightGBM] [Debug] Re-bagging, using 6614 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9
[LightGBM] [Debug] Re-bagging, using 6728 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Debug] Re-bagging, using 6729 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8
[LightGBM] [Debug] Re-bagging, using 6565 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 11
[LightGBM] [Debug] Re-bagging, using 6602 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 11
[LightGBM] [Debug] Re-bagging, using 6716 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 12
[LightGBM] [Debug] Re-bagging, using 6664 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10
[LightGBM] [Debug] Re-bagging, using 6808 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10
[LightGBM] [Debug] Re-bagging, using 6700 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11
[LightGBM] [Debug] Re-bagging, using 6667 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8
[LightGBM] [Debug] Re-bagging, using 6630 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 11
[LightGBM] [Debug] Re-bagging, using 6631 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9
[LightGBM] [Debug] Re-bagging, using 6696 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 11
[LightGBM] [Debug] Re-bagging, using 6584 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 12
[LightGBM] [Debug] Re-bagging, using 6654 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Debug] Re-bagging, using 6589 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Debug] Re-bagging, using 6710 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10
[LightGBM] [Debug] Re-bagging, using 6645 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9
[LightGBM] [Debug] Re-bagging, using 6671 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 11
[LightGBM] [Debug] Re-bagging, using 6665 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 11
[LightGBM] [Debug] Re-bagging, using 6720 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Debug] Re-bagging, using 6634 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10
[LightGBM] [Debug] Re-bagging, using 6800 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 11
[LightGBM] [Debug] Re-bagging, using 6609 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Debug] Re-bagging, using 6638 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Debug] Re-bagging, using 6518 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10
[LightGBM] [Debug] Re-bagging, using 6596 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10
Trial 39, Fold 5: Log loss = 0.4146718211969893, Average precision = 0.9620377715252689, ROC-AUC = 0.9551693513925275, Elapsed Time = 1.58937909999986 seconds
Optimization Progress:  40%|####      | 40/100 [09:11<13:26, 13.44s/it]
Trial 40, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 40, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[LightGBM] [Info] Number of positive: 10130, number of negative: 10533
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.790295
[LightGBM] [Info] Total Bins 27148
[LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 254
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012
[LightGBM] [Info] Start training from score -0.039012
[LightGBM] [Debug] Re-bagging, using 18199 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 11
[LightGBM] [Debug] Re-bagging, using 18214 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 9
[LightGBM] [Debug] Re-bagging, using 18242 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 10
[LightGBM] [Debug] Re-bagging, using 18173 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 11
[LightGBM] [Debug] Re-bagging, using 18156 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 9
[LightGBM] [Debug] Re-bagging, using 18148 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 14
[LightGBM] [Debug] Re-bagging, using 18129 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 10
[LightGBM] [Debug] Re-bagging, using 18129 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 11
Trial 40, Fold 1: Log loss = 0.31388538222959717, Average precision = 0.9656748092626154, ROC-AUC = 0.9587817185491079, Elapsed Time = 1.5820659000000887 seconds
Trial 40, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 40, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[LightGBM] [Info] Number of positive: 10230, number of negative: 10471
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791600
[LightGBM] [Info] Total Bins 27157
[LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285
[LightGBM] [Info] Start training from score -0.023285
[LightGBM] [Debug] Re-bagging, using 18235 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10
[LightGBM] [Debug] Re-bagging, using 18242 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 10
[LightGBM] [Debug] Re-bagging, using 18276 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 10
[LightGBM] [Debug] Re-bagging, using 18210 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 12
[LightGBM] [Debug] Re-bagging, using 18191 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 10
[LightGBM] [Debug] Re-bagging, using 18168 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11
[LightGBM] [Debug] Re-bagging, using 18166 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 11
[LightGBM] [Debug] Re-bagging, using 18172 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9
Trial 40, Fold 2: Log loss = 0.31499227675900127, Average precision = 0.9634456488372517, ROC-AUC = 0.9585552518239325, Elapsed Time = 1.7733633999996528 seconds
Trial 40, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 40, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[LightGBM] [Info] Number of positive: 10165, number of negative: 10517
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.790045
[LightGBM] [Info] Total Bins 27151
[LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 254
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043
[LightGBM] [Info] Start training from score -0.034043
[LightGBM] [Debug] Re-bagging, using 18218 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 11
[LightGBM] [Debug] Re-bagging, using 18226 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10
[LightGBM] [Debug] Re-bagging, using 18259 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 10
[LightGBM] [Debug] Re-bagging, using 18194 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 10
[LightGBM] [Debug] Re-bagging, using 18171 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 12
[LightGBM] [Debug] Re-bagging, using 18158 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 9
[LightGBM] [Debug] Re-bagging, using 18148 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 10
[LightGBM] [Debug] Re-bagging, using 18150 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 10
Trial 40, Fold 3: Log loss = 0.311584660495534, Average precision = 0.9669552582564993, ROC-AUC = 0.9613503553851933, Elapsed Time = 1.7890182999999524 seconds
Trial 40, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 40, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[LightGBM] [Info] Number of positive: 10177, number of negative: 10479
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791090
[LightGBM] [Info] Total Bins 27145
[LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243
[LightGBM] [Info] Start training from score -0.029243
[LightGBM] [Debug] Re-bagging, using 18192 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 9
[LightGBM] [Debug] Re-bagging, using 18207 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 13
[LightGBM] [Debug] Re-bagging, using 18238 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 10
[LightGBM] [Debug] Re-bagging, using 18169 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 11
[LightGBM] [Debug] Re-bagging, using 18147 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 11
[LightGBM] [Debug] Re-bagging, using 18142 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 10
[LightGBM] [Debug] Re-bagging, using 18126 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 10
[LightGBM] [Debug] Re-bagging, using 18125 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 11
Trial 40, Fold 4: Log loss = 0.3075476142307136, Average precision = 0.9664963265197322, ROC-AUC = 0.9599498141352582, Elapsed Time = 1.762109899999814 seconds
Trial 40, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 40, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[LightGBM] [Info] Number of positive: 10150, number of negative: 10500
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.792012
[LightGBM] [Info] Total Bins 27151
[LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902
[LightGBM] [Info] Start training from score -0.033902
[LightGBM] [Debug] Re-bagging, using 18186 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 12
[LightGBM] [Debug] Re-bagging, using 18203 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 11
[LightGBM] [Debug] Re-bagging, using 18233 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 11
[LightGBM] [Debug] Re-bagging, using 18164 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 11
[LightGBM] [Debug] Re-bagging, using 18141 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 10
[LightGBM] [Debug] Re-bagging, using 18135 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 9
[LightGBM] [Debug] Re-bagging, using 18126 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 10
[LightGBM] [Debug] Re-bagging, using 18117 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 10
Trial 40, Fold 5: Log loss = 0.3169454622000901, Average precision = 0.9636389789724216, ROC-AUC = 0.9571915352172864, Elapsed Time = 1.74589079999987 seconds
Optimization Progress:  41%|####1     | 41/100 [09:28<14:13, 14.47s/it]
Trial 41, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 41, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[LightGBM] [Info] Number of positive: 10130, number of negative: 10533
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795662
[LightGBM] [Info] Total Bins 25876
[LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 258
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012
[LightGBM] [Info] Start training from score -0.039012
[LightGBM] [Debug] Re-bagging, using 5292 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5231 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5263 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 8
[LightGBM] [Debug] Re-bagging, using 5098 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11
[LightGBM] [Debug] Re-bagging, using 5302 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5265 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5186 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5301 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11
[LightGBM] [Debug] Re-bagging, using 5205 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11
[LightGBM] [Debug] Re-bagging, using 5062 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11
[LightGBM] [Debug] Re-bagging, using 5099 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5147 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11
[LightGBM] [Debug] Re-bagging, using 5146 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11
[LightGBM] [Debug] Re-bagging, using 5243 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11
[LightGBM] [Debug] Re-bagging, using 5302 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5234 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11
[LightGBM] [Debug] Re-bagging, using 5222 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11
[LightGBM] [Debug] Re-bagging, using 5146 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11
[LightGBM] [Debug] Re-bagging, using 5266 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11
[LightGBM] [Debug] Re-bagging, using 5297 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11
[LightGBM] [Debug] Re-bagging, using 5121 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11
[LightGBM] [Debug] Re-bagging, using 5209 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 13
[LightGBM] [Debug] Re-bagging, using 5228 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11
[LightGBM] [Debug] Re-bagging, using 5171 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 12
[LightGBM] [Debug] Re-bagging, using 5294 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5258 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5230 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5256 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11
[LightGBM] [Debug] Re-bagging, using 5197 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 12
[LightGBM] [Debug] Re-bagging, using 5243 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11
[LightGBM] [Debug] Re-bagging, using 5146 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5269 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11
[LightGBM] [Debug] Re-bagging, using 5145 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5268 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 12
[LightGBM] [Debug] Re-bagging, using 5185 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5203 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 12
[LightGBM] [Debug] Re-bagging, using 5223 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5303 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5137 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 14
[LightGBM] [Debug] Re-bagging, using 5300 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5217 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11
[LightGBM] [Debug] Re-bagging, using 5213 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5097 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11
Trial 41, Fold 1: Log loss = 0.27293726745619906, Average precision = 0.9709746855113583, ROC-AUC = 0.9652606011289389, Elapsed Time = 1.5060511000001497 seconds
Trial 41, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 41, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[LightGBM] [Info] Number of positive: 10230, number of negative: 10471
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.794298
[LightGBM] [Info] Total Bins 25878
[LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 257
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285
[LightGBM] [Info] Start training from score -0.023285
[LightGBM] [Debug] Re-bagging, using 5302 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5242 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5269 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5112 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5322 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11
[LightGBM] [Debug] Re-bagging, using 5259 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5194 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11
[LightGBM] [Debug] Re-bagging, using 5311 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5221 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11
[LightGBM] [Debug] Re-bagging, using 5085 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 12
[LightGBM] [Debug] Re-bagging, using 5099 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11
[LightGBM] [Debug] Re-bagging, using 5143 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5156 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 12
[LightGBM] [Debug] Re-bagging, using 5251 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11
[LightGBM] [Debug] Re-bagging, using 5307 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 13
[LightGBM] [Debug] Re-bagging, using 5251 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 13
[LightGBM] [Debug] Re-bagging, using 5234 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11
[LightGBM] [Debug] Re-bagging, using 5165 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11
[LightGBM] [Debug] Re-bagging, using 5286 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11
[LightGBM] [Debug] Re-bagging, using 5300 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 12
[LightGBM] [Debug] Re-bagging, using 5131 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5217 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 12
[LightGBM] [Debug] Re-bagging, using 5242 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5184 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 12
[LightGBM] [Debug] Re-bagging, using 5301 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 13
[LightGBM] [Debug] Re-bagging, using 5262 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11
[LightGBM] [Debug] Re-bagging, using 5233 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11
[LightGBM] [Debug] Re-bagging, using 5255 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 12
[LightGBM] [Debug] Re-bagging, using 5204 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 13
[LightGBM] [Debug] Re-bagging, using 5249 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5159 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 11
[LightGBM] [Debug] Re-bagging, using 5282 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 12
[LightGBM] [Debug] Re-bagging, using 5157 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 13
[LightGBM] [Debug] Re-bagging, using 5285 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11
[LightGBM] [Debug] Re-bagging, using 5203 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11
[LightGBM] [Debug] Re-bagging, using 5213 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11
[LightGBM] [Debug] Re-bagging, using 5237 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 13
[LightGBM] [Debug] Re-bagging, using 5310 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5133 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11
[LightGBM] [Debug] Re-bagging, using 5295 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 12
[LightGBM] [Debug] Re-bagging, using 5234 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11
[LightGBM] [Debug] Re-bagging, using 5216 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5114 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10
Trial 41, Fold 2: Log loss = 0.2702378761409877, Average precision = 0.9704559815636802, ROC-AUC = 0.96690608420606, Elapsed Time = 1.9325926999999865 seconds
Trial 41, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 41, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[LightGBM] [Info] Number of positive: 10165, number of negative: 10517
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795455
[LightGBM] [Info] Total Bins 25877
[LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 258
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043
[LightGBM] [Info] Start training from score -0.034043
[LightGBM] [Debug] Re-bagging, using 5297 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11
[LightGBM] [Debug] Re-bagging, using 5234 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5266 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5108 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5313 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5260 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 12
[LightGBM] [Debug] Re-bagging, using 5194 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5301 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 12
[LightGBM] [Debug] Re-bagging, using 5209 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 13
[LightGBM] [Debug] Re-bagging, using 5078 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12
[LightGBM] [Debug] Re-bagging, using 5107 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11
[LightGBM] [Debug] Re-bagging, using 5135 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 13
[LightGBM] [Debug] Re-bagging, using 5152 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 13
[LightGBM] [Debug] Re-bagging, using 5252 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 12
[LightGBM] [Debug] Re-bagging, using 5295 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 12
[LightGBM] [Debug] Re-bagging, using 5247 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5226 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5155 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5273 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 12
[LightGBM] [Debug] Re-bagging, using 5312 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11
[LightGBM] [Debug] Re-bagging, using 5129 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11
[LightGBM] [Debug] Re-bagging, using 5202 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 13
[LightGBM] [Debug] Re-bagging, using 5223 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 12
[LightGBM] [Debug] Re-bagging, using 5191 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11
[LightGBM] [Debug] Re-bagging, using 5308 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11
[LightGBM] [Debug] Re-bagging, using 5261 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11
[LightGBM] [Debug] Re-bagging, using 5225 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 12
[LightGBM] [Debug] Re-bagging, using 5242 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 13
[LightGBM] [Debug] Re-bagging, using 5211 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11
[LightGBM] [Debug] Re-bagging, using 5246 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11
[LightGBM] [Debug] Re-bagging, using 5155 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11
[LightGBM] [Debug] Re-bagging, using 5265 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 12
[LightGBM] [Debug] Re-bagging, using 5148 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 12
[LightGBM] [Debug] Re-bagging, using 5271 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 12
[LightGBM] [Debug] Re-bagging, using 5203 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11
[LightGBM] [Debug] Re-bagging, using 5215 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 11
[LightGBM] [Debug] Re-bagging, using 5233 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11
[LightGBM] [Debug] Re-bagging, using 5312 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5131 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 14
[LightGBM] [Debug] Re-bagging, using 5308 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 13
[LightGBM] [Debug] Re-bagging, using 5220 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 14
[LightGBM] [Debug] Re-bagging, using 5207 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 12
[LightGBM] [Debug] Re-bagging, using 5098 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 12
Trial 41, Fold 3: Log loss = 0.2688744270662882, Average precision = 0.9722850054993755, ROC-AUC = 0.967731668528667, Elapsed Time = 1.771677599999748 seconds
Trial 41, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 41, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[LightGBM] [Info] Number of positive: 10177, number of negative: 10479
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795144
[LightGBM] [Info] Total Bins 25866
[LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 258
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243
[LightGBM] [Info] Start training from score -0.029243
[LightGBM] [Debug] Re-bagging, using 5291 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5227 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5260 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5100 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5298 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5264 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11
[LightGBM] [Debug] Re-bagging, using 5186 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5297 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 13
[LightGBM] [Debug] Re-bagging, using 5199 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11
[LightGBM] [Debug] Re-bagging, using 5066 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5092 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11
[LightGBM] [Debug] Re-bagging, using 5146 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 12
[LightGBM] [Debug] Re-bagging, using 5147 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12
[LightGBM] [Debug] Re-bagging, using 5241 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 12
[LightGBM] [Debug] Re-bagging, using 5296 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11
[LightGBM] [Debug] Re-bagging, using 5238 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5216 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11
[LightGBM] [Debug] Re-bagging, using 5155 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11
[LightGBM] [Debug] Re-bagging, using 5256 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 12
[LightGBM] [Debug] Re-bagging, using 5298 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11
[LightGBM] [Debug] Re-bagging, using 5120 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5201 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5226 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11
[LightGBM] [Debug] Re-bagging, using 5180 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11
[LightGBM] [Debug] Re-bagging, using 5284 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 12
[LightGBM] [Debug] Re-bagging, using 5257 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 12
[LightGBM] [Debug] Re-bagging, using 5219 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 12
[LightGBM] [Debug] Re-bagging, using 5254 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 12
[LightGBM] [Debug] Re-bagging, using 5208 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5240 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 12
[LightGBM] [Debug] Re-bagging, using 5151 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5258 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 12
[LightGBM] [Debug] Re-bagging, using 5145 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5267 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5188 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5203 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5211 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5304 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5122 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 13
[LightGBM] [Debug] Re-bagging, using 5304 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11
[LightGBM] [Debug] Re-bagging, using 5215 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11
[LightGBM] [Debug] Re-bagging, using 5212 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12
[LightGBM] [Debug] Re-bagging, using 5107 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 13
Trial 41, Fold 4: Log loss = 0.2683222397204066, Average precision = 0.9710829752471616, ROC-AUC = 0.9657162111251791, Elapsed Time = 1.8824927999994543 seconds
Trial 41, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 41, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[LightGBM] [Info] Number of positive: 10150, number of negative: 10500
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.796026
[LightGBM] [Info] Total Bins 25873
[LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 258
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902
[LightGBM] [Info] Start training from score -0.033902
[LightGBM] [Debug] Re-bagging, using 5289 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5227 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 8
[LightGBM] [Debug] Re-bagging, using 5260 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5095 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5296 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 12
[LightGBM] [Debug] Re-bagging, using 5260 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5191 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5292 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5199 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5066 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11
[LightGBM] [Debug] Re-bagging, using 5087 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5135 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11
[LightGBM] [Debug] Re-bagging, using 5155 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5247 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 13
[LightGBM] [Debug] Re-bagging, using 5290 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 12
[LightGBM] [Debug] Re-bagging, using 5243 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 13
[LightGBM] [Debug] Re-bagging, using 5216 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11
[LightGBM] [Debug] Re-bagging, using 5140 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5261 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 13
[LightGBM] [Debug] Re-bagging, using 5289 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 12
[LightGBM] [Debug] Re-bagging, using 5122 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 12
[LightGBM] [Debug] Re-bagging, using 5196 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 12
[LightGBM] [Debug] Re-bagging, using 5222 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11
[LightGBM] [Debug] Re-bagging, using 5186 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11
[LightGBM] [Debug] Re-bagging, using 5285 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 14
[LightGBM] [Debug] Re-bagging, using 5252 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12
[LightGBM] [Debug] Re-bagging, using 5218 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5242 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12
[LightGBM] [Debug] Re-bagging, using 5210 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12
[LightGBM] [Debug] Re-bagging, using 5248 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11
[LightGBM] [Debug] Re-bagging, using 5147 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5263 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5140 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5259 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5191 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12
[LightGBM] [Debug] Re-bagging, using 5200 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12
[LightGBM] [Debug] Re-bagging, using 5215 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5298 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5124 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11
[LightGBM] [Debug] Re-bagging, using 5299 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 13
[LightGBM] [Debug] Re-bagging, using 5211 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 12
[LightGBM] [Debug] Re-bagging, using 5211 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11
[LightGBM] [Debug] Re-bagging, using 5096 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 12
Trial 41, Fold 5: Log loss = 0.276624281389057, Average precision = 0.9678688271291056, ROC-AUC = 0.9640171673819742, Elapsed Time = 1.8289052000000083 seconds
Optimization Progress:  42%|####2     | 42/100 [09:45<14:43, 15.23s/it]
Trial 42, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 42, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[LightGBM] [Info] Number of positive: 10130, number of negative: 10533
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.805817
[LightGBM] [Info] Total Bins 16928
[LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 267
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012
[LightGBM] [Info] Start training from score -0.039012
[LightGBM] [Debug] Re-bagging, using 5608 data to train
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 22
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 14
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 13
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 16
[LightGBM] [Debug] Re-bagging, using 5567 data to train
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 14
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 20
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 14
[LightGBM] [Debug] Re-bagging, using 5575 data to train
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 14
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 23
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 13
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 15
[LightGBM] [Debug] Re-bagging, using 5431 data to train
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 20
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 22
[LightGBM] [Debug] Re-bagging, using 5645 data to train
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 16
[LightGBM] [Debug] Re-bagging, using 5586 data to train
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 20
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 23
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 22
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 20
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 19
[LightGBM] [Debug] Re-bagging, using 5492 data to train
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 23
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 24
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 23
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 14
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 20
[LightGBM] [Debug] Re-bagging, using 5644 data to train
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 24
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 22
Trial 42, Fold 1: Log loss = 0.22486720866549162, Average precision = 0.9744573034457623, ROC-AUC = 0.9691979371292831, Elapsed Time = 1.8659290999994482 seconds
Trial 42, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 42, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[LightGBM] [Info] Number of positive: 10230, number of negative: 10471
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.806987
[LightGBM] [Info] Total Bins 16941
[LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 267
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285
[LightGBM] [Info] Start training from score -0.023285
[LightGBM] [Debug] Re-bagging, using 5618 data to train
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 12
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 16
[LightGBM] [Debug] Re-bagging, using 5580 data to train
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 14
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 14
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 13
[LightGBM] [Debug] Re-bagging, using 5580 data to train
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 16
[LightGBM] [Debug] Re-bagging, using 5448 data to train
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 21
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 19
[LightGBM] [Debug] Re-bagging, using 5664 data to train
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 14
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 14
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 15
[LightGBM] [Debug] Re-bagging, using 5580 data to train
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 17
[LightGBM] [Debug] Re-bagging, using 5499 data to train
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 21
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 14
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 19
[LightGBM] [Debug] Re-bagging, using 5655 data to train
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 20
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 21
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 22
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 22
Trial 42, Fold 2: Log loss = 0.22524210099664396, Average precision = 0.9739922954990727, ROC-AUC = 0.9709663238279531, Elapsed Time = 2.416602799999964 seconds
Trial 42, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 42, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[LightGBM] [Info] Number of positive: 10165, number of negative: 10517
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.806893
[LightGBM] [Info] Total Bins 16947
[LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 267
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043
[LightGBM] [Info] Start training from score -0.034043
[LightGBM] [Debug] Re-bagging, using 5613 data to train
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 13
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 13
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 19
[LightGBM] [Debug] Re-bagging, using 5571 data to train
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 20
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 19
[LightGBM] [Debug] Re-bagging, using 5578 data to train
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 14
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 24
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 18
[LightGBM] [Debug] Re-bagging, using 5442 data to train
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 22
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 23
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 24
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 20
[LightGBM] [Debug] Re-bagging, using 5655 data to train
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 14
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 13
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 14
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 15
[LightGBM] [Debug] Re-bagging, using 5580 data to train
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 20
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 22
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 20
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 20
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 22
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 20
[LightGBM] [Debug] Re-bagging, using 5501 data to train
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 20
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 22
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 20
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 20
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 20
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 17
[LightGBM] [Debug] Re-bagging, using 5646 data to train
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 26
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 22
Trial 42, Fold 3: Log loss = 0.22862562686622087, Average precision = 0.9740826150240826, ROC-AUC = 0.9698103920072041, Elapsed Time = 2.305730500000209 seconds
Trial 42, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 42, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[LightGBM] [Info] Number of positive: 10177, number of negative: 10479
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.805384
[LightGBM] [Info] Total Bins 16929
[LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 267
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243
[LightGBM] [Info] Start training from score -0.029243
[LightGBM] [Debug] Re-bagging, using 5605 data to train
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 13
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 12
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 18
[LightGBM] [Debug] Re-bagging, using 5565 data to train
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 19
[LightGBM] [Debug] Re-bagging, using 5572 data to train
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 21
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 21
[LightGBM] [Debug] Re-bagging, using 5432 data to train
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 26
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 21
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 20
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 18
[LightGBM] [Debug] Re-bagging, using 5642 data to train
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 21
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 23
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 28
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 21
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 16
[LightGBM] [Debug] Re-bagging, using 5583 data to train
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 15
[LightGBM] [Debug] Re-bagging, using 5492 data to train
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 22
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 29
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 21
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 20
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 21
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 17
[LightGBM] [Debug] Re-bagging, using 5642 data to train
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 14
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 13
Trial 42, Fold 4: Log loss = 0.2264667894282577, Average precision = 0.9738043014455474, ROC-AUC = 0.9688653458228716, Elapsed Time = 2.3237183999999615 seconds
Trial 42, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 42, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[LightGBM] [Info] Number of positive: 10150, number of negative: 10500
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.807356
[LightGBM] [Info] Total Bins 16928
[LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 267
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902
[LightGBM] [Info] Start training from score -0.033902
[LightGBM] [Debug] Re-bagging, using 5603 data to train
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 20
[LightGBM] [Debug] Re-bagging, using 5565 data to train
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 17
[LightGBM] [Debug] Re-bagging, using 5572 data to train
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 13
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 14
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 13
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 13
[LightGBM] [Debug] Re-bagging, using 5427 data to train
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 21
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 27
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 20
[LightGBM] [Debug] Re-bagging, using 5640 data to train
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 20
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 23
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 14
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 30
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 26
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 22
[LightGBM] [Debug] Re-bagging, using 5579 data to train
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 24
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 17
[LightGBM] [Debug] Re-bagging, using 5497 data to train
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 19
[LightGBM] [Debug] Re-bagging, using 5637 data to train
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 21
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 18
Trial 42, Fold 5: Log loss = 0.22583290083240043, Average precision = 0.9723513066476789, ROC-AUC = 0.9693319956152575, Elapsed Time = 1.9339842999997927 seconds
Optimization Progress:  43%|####3     | 43/100 [10:03<15:17, 16.10s/it]
Trial 43, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 43, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[LightGBM] [Info] Number of positive: 10130, number of negative: 10533
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.790295
[LightGBM] [Info] Total Bins 20104
[LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 254
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012
[LightGBM] [Info] Start training from score -0.039012
[LightGBM] [Debug] Re-bagging, using 15800 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8
[LightGBM] [Debug] Re-bagging, using 15945 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 12
[LightGBM] [Debug] Re-bagging, using 15858 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 11
[LightGBM] [Debug] Re-bagging, using 15755 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 8
[LightGBM] [Debug] Re-bagging, using 15802 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 8
[LightGBM] [Debug] Re-bagging, using 15790 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8
[LightGBM] [Debug] Re-bagging, using 15806 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7
[LightGBM] [Debug] Re-bagging, using 15802 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 6
[LightGBM] [Debug] Re-bagging, using 15872 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 5
[LightGBM] [Debug] Re-bagging, using 15771 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 9
[LightGBM] [Debug] Re-bagging, using 15792 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6
[LightGBM] [Debug] Re-bagging, using 15707 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7
[LightGBM] [Debug] Re-bagging, using 15781 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 8
[LightGBM] [Debug] Re-bagging, using 15709 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 6
[LightGBM] [Debug] Re-bagging, using 15898 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 5
[LightGBM] [Debug] Re-bagging, using 15815 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 5
Trial 43, Fold 1: Log loss = 0.5212313297273229, Average precision = 0.9627836292091663, ROC-AUC = 0.9549571509824445, Elapsed Time = 0.6328744000002189 seconds
Trial 43, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 43, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[LightGBM] [Info] Number of positive: 10230, number of negative: 10471
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791600
[LightGBM] [Info] Total Bins 20073
[LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285
[LightGBM] [Info] Start training from score -0.023285
[LightGBM] [Debug] Re-bagging, using 15833 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7
[LightGBM] [Debug] Re-bagging, using 15972 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 10
[LightGBM] [Debug] Re-bagging, using 15887 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 9
[LightGBM] [Debug] Re-bagging, using 15788 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7
[LightGBM] [Debug] Re-bagging, using 15829 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7
[LightGBM] [Debug] Re-bagging, using 15807 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 9
[LightGBM] [Debug] Re-bagging, using 15831 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 7
[LightGBM] [Debug] Re-bagging, using 15847 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 9
[LightGBM] [Debug] Re-bagging, using 15899 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 6
[LightGBM] [Debug] Re-bagging, using 15806 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8
[LightGBM] [Debug] Re-bagging, using 15815 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 10
[LightGBM] [Debug] Re-bagging, using 15725 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7
[LightGBM] [Debug] Re-bagging, using 15806 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8
[LightGBM] [Debug] Re-bagging, using 15727 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 9
[LightGBM] [Debug] Re-bagging, using 15927 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 7
[LightGBM] [Debug] Re-bagging, using 15862 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 10
Trial 43, Fold 2: Log loss = 0.5209099033618703, Average precision = 0.9621351418743129, ROC-AUC = 0.9574910446044405, Elapsed Time = 0.6737821000006079 seconds
Trial 43, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 43, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[LightGBM] [Info] Number of positive: 10165, number of negative: 10517
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.790045
[LightGBM] [Info] Total Bins 20069
[LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 254
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043
[LightGBM] [Info] Start training from score -0.034043
[LightGBM] [Debug] Re-bagging, using 15816 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 8
[LightGBM] [Debug] Re-bagging, using 15959 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 8
[LightGBM] [Debug] Re-bagging, using 15871 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 9
[LightGBM] [Debug] Re-bagging, using 15774 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 12
[LightGBM] [Debug] Re-bagging, using 15813 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 9
[LightGBM] [Debug] Re-bagging, using 15800 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7
[LightGBM] [Debug] Re-bagging, using 15814 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 11
[LightGBM] [Debug] Re-bagging, using 15829 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7
[LightGBM] [Debug] Re-bagging, using 15879 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 7
[LightGBM] [Debug] Re-bagging, using 15797 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 8
[LightGBM] [Debug] Re-bagging, using 15804 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Debug] Re-bagging, using 15716 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 9
[LightGBM] [Debug] Re-bagging, using 15794 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9
[LightGBM] [Debug] Re-bagging, using 15715 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 5
[LightGBM] [Debug] Re-bagging, using 15909 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 13
[LightGBM] [Debug] Re-bagging, using 15830 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7
Trial 43, Fold 3: Log loss = 0.5124468728151358, Average precision = 0.9650361000889902, ROC-AUC = 0.9592594426279242, Elapsed Time = 0.6866634000007252 seconds
Trial 43, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 43, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[LightGBM] [Info] Number of positive: 10177, number of negative: 10479
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791090
[LightGBM] [Info] Total Bins 20105
[LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243
[LightGBM] [Info] Start training from score -0.029243
[LightGBM] [Debug] Re-bagging, using 15795 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 10
[LightGBM] [Debug] Re-bagging, using 15937 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 8
[LightGBM] [Debug] Re-bagging, using 15854 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Debug] Re-bagging, using 15753 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 11
[LightGBM] [Debug] Re-bagging, using 15796 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 8
[LightGBM] [Debug] Re-bagging, using 15782 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7
[LightGBM] [Debug] Re-bagging, using 15802 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 8
[LightGBM] [Debug] Re-bagging, using 15800 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 10
[LightGBM] [Debug] Re-bagging, using 15861 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9
[LightGBM] [Debug] Re-bagging, using 15772 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 10
[LightGBM] [Debug] Re-bagging, using 15781 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 11
[LightGBM] [Debug] Re-bagging, using 15699 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Debug] Re-bagging, using 15785 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6
[LightGBM] [Debug] Re-bagging, using 15698 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 8
[LightGBM] [Debug] Re-bagging, using 15895 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 8
[LightGBM] [Debug] Re-bagging, using 15813 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8
Trial 43, Fold 4: Log loss = 0.521958522934716, Average precision = 0.9634605752827095, ROC-AUC = 0.9565819843535032, Elapsed Time = 0.690412699999797 seconds
Trial 43, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 43, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[LightGBM] [Info] Number of positive: 10150, number of negative: 10500
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.792012
[LightGBM] [Info] Total Bins 20102
[LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902
[LightGBM] [Info] Start training from score -0.033902
[LightGBM] [Debug] Re-bagging, using 15789 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 8
[LightGBM] [Debug] Re-bagging, using 15936 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6
[LightGBM] [Debug] Re-bagging, using 15851 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7
[LightGBM] [Debug] Re-bagging, using 15745 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 10
[LightGBM] [Debug] Re-bagging, using 15790 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7
[LightGBM] [Debug] Re-bagging, using 15777 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 11
[LightGBM] [Debug] Re-bagging, using 15804 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 13
[LightGBM] [Debug] Re-bagging, using 15791 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 6
[LightGBM] [Debug] Re-bagging, using 15856 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 6
[LightGBM] [Debug] Re-bagging, using 15769 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 6
[LightGBM] [Debug] Re-bagging, using 15777 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 9
[LightGBM] [Debug] Re-bagging, using 15690 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 8
[LightGBM] [Debug] Re-bagging, using 15778 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 8
[LightGBM] [Debug] Re-bagging, using 15701 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 6
[LightGBM] [Debug] Re-bagging, using 15892 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7
[LightGBM] [Debug] Re-bagging, using 15811 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 8
Trial 43, Fold 5: Log loss = 0.5191817461829943, Average precision = 0.9611701754237488, ROC-AUC = 0.9542330230570574, Elapsed Time = 0.7347669999999198 seconds
Optimization Progress:  44%|####4     | 44/100 [10:14<13:28, 14.45s/it]
Trial 44, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 44, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[LightGBM] [Info] Number of positive: 10130, number of negative: 10533
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791640
[LightGBM] [Info] Total Bins 12968
[LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012
[LightGBM] [Info] Start training from score -0.039012
[LightGBM] [Debug] Re-bagging, using 3425 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 3
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 3
[LightGBM] [Debug] Re-bagging, using 3435 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 5 and depth = 3
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4
[LightGBM] [Debug] Re-bagging, using 3339 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 3
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 4
[LightGBM] [Debug] Re-bagging, using 3305 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 3
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 3
[LightGBM] [Debug] Re-bagging, using 3412 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 3
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4
[LightGBM] [Debug] Re-bagging, using 3398 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 5 and depth = 3
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 3
[LightGBM] [Debug] Re-bagging, using 3350 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 5 and depth = 3
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 3
[LightGBM] [Debug] Re-bagging, using 3469 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 3
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4
[LightGBM] [Debug] Re-bagging, using 3367 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 3
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 3
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 5
[LightGBM] [Debug] Re-bagging, using 3346 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 3
[LightGBM] [Debug] Re-bagging, using 3221 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 5 and depth = 3
[LightGBM] [Debug] Re-bagging, using 3304 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 4
[LightGBM] [Debug] Re-bagging, using 3360 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 3
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 5 and depth = 3
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 3
[LightGBM] [Debug] Re-bagging, using 3388 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 3
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 3
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Debug] Re-bagging, using 3380 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 5 and depth = 3
[LightGBM] [Debug] Re-bagging, using 3349 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4
Trial 44, Fold 1: Log loss = 0.48721062203998294, Average precision = 0.9535683851934116, ROC-AUC = 0.9451276269817372, Elapsed Time = 0.7687642999999298 seconds
Trial 44, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 44, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[LightGBM] [Info] Number of positive: 10230, number of negative: 10471
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791600
[LightGBM] [Info] Total Bins 12982
[LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285
[LightGBM] [Info] Start training from score -0.023285
[LightGBM] [Debug] Re-bagging, using 3432 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 5 and depth = 3
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 3
[LightGBM] [Debug] Re-bagging, using 3444 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 3
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 3
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4
[LightGBM] [Debug] Re-bagging, using 3339 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 3
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 3
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 3
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 3
[LightGBM] [Debug] Re-bagging, using 3318 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 3
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 3
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 5 and depth = 3
[LightGBM] [Debug] Re-bagging, using 3421 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 3
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4
[LightGBM] [Debug] Re-bagging, using 3394 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 3
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 3
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 3
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4
[LightGBM] [Debug] Re-bagging, using 3356 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 3
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 3
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 3
[LightGBM] [Debug] Re-bagging, using 3478 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 3
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4
[LightGBM] [Debug] Re-bagging, using 3377 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 3
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4
[LightGBM] [Debug] Re-bagging, using 3365 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4
[LightGBM] [Debug] Re-bagging, using 3224 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 5 and depth = 3
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 3
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 3
[LightGBM] [Debug] Re-bagging, using 3297 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 3
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 5 and depth = 3
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 5 and depth = 3
[LightGBM] [Debug] Re-bagging, using 3370 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 3
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4
[LightGBM] [Debug] Re-bagging, using 3383 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 3
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 5
[LightGBM] [Debug] Re-bagging, using 3385 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 5 and depth = 3
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 3
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 5 and depth = 3
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 4
[LightGBM] [Debug] Re-bagging, using 3357 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4
Trial 44, Fold 2: Log loss = 0.4897891343912608, Average precision = 0.9531068724675854, ROC-AUC = 0.9482266905537914, Elapsed Time = 0.8361282999994728 seconds
Trial 44, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 44, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[LightGBM] [Info] Number of positive: 10165, number of negative: 10517
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791401
[LightGBM] [Info] Total Bins 12988
[LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043
[LightGBM] [Info] Start training from score -0.034043
[LightGBM] [Debug] Re-bagging, using 3427 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 5 and depth = 3
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 3
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 3
[LightGBM] [Debug] Re-bagging, using 3439 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 3
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 5 and depth = 3
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4
[LightGBM] [Debug] Re-bagging, using 3341 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 5 and depth = 3
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 3
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 3
[LightGBM] [Debug] Re-bagging, using 3312 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 3
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 5 and depth = 3
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 6
[LightGBM] [Debug] Re-bagging, using 3417 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4
[LightGBM] [Debug] Re-bagging, using 3396 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 3
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 4
[LightGBM] [Debug] Re-bagging, using 3356 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 3
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 3
[LightGBM] [Debug] Re-bagging, using 3467 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4
[LightGBM] [Debug] Re-bagging, using 3375 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 3
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4
[LightGBM] [Debug] Re-bagging, using 3348 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 5
[LightGBM] [Debug] Re-bagging, using 3239 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 3
[LightGBM] [Debug] Re-bagging, using 3288 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 3
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 5 and depth = 3
[LightGBM] [Debug] Re-bagging, using 3367 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 3
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 3
[LightGBM] [Debug] Re-bagging, using 3389 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 3
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4
[LightGBM] [Debug] Re-bagging, using 3373 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 3
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 3
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 3
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 3
[LightGBM] [Debug] Re-bagging, using 3360 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 3
Trial 44, Fold 3: Log loss = 0.48084034590108415, Average precision = 0.961348036882991, ROC-AUC = 0.9536307798249077, Elapsed Time = 0.8631216000003405 seconds
Trial 44, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 44, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[LightGBM] [Info] Number of positive: 10177, number of negative: 10479
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791090
[LightGBM] [Info] Total Bins 12968
[LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243
[LightGBM] [Info] Start training from score -0.029243
[LightGBM] [Debug] Re-bagging, using 3425 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 3
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 3
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 3
[LightGBM] [Debug] Re-bagging, using 3432 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 3
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 5 and depth = 3
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4
[LightGBM] [Debug] Re-bagging, using 3335 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 3
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 3
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4
[LightGBM] [Debug] Re-bagging, using 3308 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 5 and depth = 3
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 3
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 5
[LightGBM] [Debug] Re-bagging, using 3409 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 3
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 4
[LightGBM] [Debug] Re-bagging, using 3399 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 3
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 3
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 3
[LightGBM] [Debug] Re-bagging, using 3351 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 3
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4
[LightGBM] [Debug] Re-bagging, using 3463 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 3
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 3
[LightGBM] [Debug] Re-bagging, using 3363 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Debug] Re-bagging, using 3349 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 5 and depth = 3
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 3
[LightGBM] [Debug] Re-bagging, using 3222 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 3
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 3
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 3
[LightGBM] [Debug] Re-bagging, using 3298 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 5 and depth = 3
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 5 and depth = 3
[LightGBM] [Debug] Re-bagging, using 3360 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 3
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 4
[LightGBM] [Debug] Re-bagging, using 3385 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 3
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 5
[LightGBM] [Debug] Re-bagging, using 3378 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 3
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 5 and depth = 3
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4
[LightGBM] [Debug] Re-bagging, using 3354 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 3
Trial 44, Fold 4: Log loss = 0.4804119373286586, Average precision = 0.9578869844868307, ROC-AUC = 0.9496273316547088, Elapsed Time = 0.8647602999999435 seconds
Trial 44, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 44, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[LightGBM] [Info] Number of positive: 10150, number of negative: 10500
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.792012
[LightGBM] [Info] Total Bins 12970
[LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902
[LightGBM] [Info] Start training from score -0.033902
[LightGBM] [Debug] Re-bagging, using 3423 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4
[LightGBM] [Debug] Re-bagging, using 3432 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 3
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 5 and depth = 3
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4
[LightGBM] [Debug] Re-bagging, using 3336 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 3
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 3
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 3
[LightGBM] [Debug] Re-bagging, using 3307 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 3
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 5 and depth = 3
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 5
[LightGBM] [Debug] Re-bagging, using 3404 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 3
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 3
[LightGBM] [Debug] Re-bagging, using 3397 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 3
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 3
[LightGBM] [Debug] Re-bagging, using 3354 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 3
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 3
[LightGBM] [Debug] Re-bagging, using 3459 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 5 and depth = 3
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 3
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4
[LightGBM] [Debug] Re-bagging, using 3363 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 5 and depth = 3
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 5 and depth = 3
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 3
[LightGBM] [Debug] Re-bagging, using 3349 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 3
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 5 and depth = 3
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 3
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 3
[LightGBM] [Debug] Re-bagging, using 3219 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 5
[LightGBM] [Debug] Re-bagging, using 3284 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 3
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 3
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 3
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4
[LightGBM] [Debug] Re-bagging, using 3376 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 5 and depth = 3
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 5 and depth = 3
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4
[LightGBM] [Debug] Re-bagging, using 3384 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 5 and depth = 3
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 5
[LightGBM] [Debug] Re-bagging, using 3376 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 3
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 5
[LightGBM] [Debug] Re-bagging, using 3356 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 3
Trial 44, Fold 5: Log loss = 0.48515789446581037, Average precision = 0.9568248681429414, ROC-AUC = 0.9481767274213626, Elapsed Time = 0.8975406999998086 seconds
Optimization Progress:  45%|####5     | 45/100 [10:26<12:38, 13.78s/it]
Trial 45, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 45, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[LightGBM] [Info] Number of positive: 10130, number of negative: 10533
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.796981
[LightGBM] [Info] Total Bins 14564
[LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 259
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012
[LightGBM] [Info] Start training from score -0.039012
[LightGBM] [Debug] Re-bagging, using 9603 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9
[LightGBM] [Debug] Re-bagging, using 9576 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 9
[LightGBM] [Debug] Re-bagging, using 9541 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Debug] Re-bagging, using 9485 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Debug] Re-bagging, using 9620 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
[LightGBM] [Debug] Re-bagging, using 9498 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
Trial 45, Fold 1: Log loss = 0.3353843979777529, Average precision = 0.9716630024206406, ROC-AUC = 0.9657538696749401, Elapsed Time = 0.9208911999994598 seconds
Trial 45, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 45, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[LightGBM] [Info] Number of positive: 10230, number of negative: 10471
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795629
[LightGBM] [Info] Total Bins 14576
[LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 258
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285
[LightGBM] [Info] Start training from score -0.023285
[LightGBM] [Debug] Re-bagging, using 9621 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9
[LightGBM] [Debug] Re-bagging, using 9596 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8
[LightGBM] [Debug] Re-bagging, using 9555 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Debug] Re-bagging, using 9510 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 8
[LightGBM] [Debug] Re-bagging, using 9647 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 6
[LightGBM] [Debug] Re-bagging, using 9496 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
Trial 45, Fold 2: Log loss = 0.3353204673392179, Average precision = 0.9697656759149734, ROC-AUC = 0.9668807423258869, Elapsed Time = 0.9826597999999649 seconds
Trial 45, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 45, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[LightGBM] [Info] Number of positive: 10165, number of negative: 10517
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795455
[LightGBM] [Info] Total Bins 14580
[LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 258
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043
[LightGBM] [Info] Start training from score -0.034043
[LightGBM] [Debug] Re-bagging, using 9612 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 8
[LightGBM] [Debug] Re-bagging, using 9584 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 8
[LightGBM] [Debug] Re-bagging, using 9545 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8
[LightGBM] [Debug] Re-bagging, using 9505 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8
[LightGBM] [Debug] Re-bagging, using 9633 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 8
[LightGBM] [Debug] Re-bagging, using 9492 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
Trial 45, Fold 3: Log loss = 0.3302802993933815, Average precision = 0.9730979632333543, ROC-AUC = 0.9685356342277355, Elapsed Time = 1.0227054999995744 seconds
Trial 45, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 45, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[LightGBM] [Info] Number of positive: 10177, number of negative: 10479
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.796474
[LightGBM] [Info] Total Bins 14561
[LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 259
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243
[LightGBM] [Info] Start training from score -0.029243
[LightGBM] [Debug] Re-bagging, using 9599 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8
[LightGBM] [Debug] Re-bagging, using 9572 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8
[LightGBM] [Debug] Re-bagging, using 9538 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 8
[LightGBM] [Debug] Re-bagging, using 9484 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 7
[LightGBM] [Debug] Re-bagging, using 9613 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10
[LightGBM] [Debug] Re-bagging, using 9495 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
Trial 45, Fold 4: Log loss = 0.33213599249562337, Average precision = 0.9722569957339893, ROC-AUC = 0.9670891840788189, Elapsed Time = 1.1327862000007372 seconds
Trial 45, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 45, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[LightGBM] [Info] Number of positive: 10150, number of negative: 10500
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.797340
[LightGBM] [Info] Total Bins 14563
[LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 259
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902
[LightGBM] [Info] Start training from score -0.033902
[LightGBM] [Debug] Re-bagging, using 9597 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 9
[LightGBM] [Debug] Re-bagging, using 9569 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10
[LightGBM] [Debug] Re-bagging, using 9538 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9
[LightGBM] [Debug] Re-bagging, using 9477 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Debug] Re-bagging, using 9611 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 11
[LightGBM] [Debug] Re-bagging, using 9489 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 8
Trial 45, Fold 5: Log loss = 0.3349875746116728, Average precision = 0.9701929619761038, ROC-AUC = 0.9653170577634097, Elapsed Time = 1.2616589999997814 seconds
Optimization Progress:  46%|####6     | 46/100 [10:39<12:05, 13.44s/it]
Trial 46, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 46, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[LightGBM] [Info] Number of positive: 10130, number of negative: 10533
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.796981
[LightGBM] [Info] Total Bins 15571
[LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 259
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012
[LightGBM] [Info] Start training from score -0.039012
[LightGBM] [Debug] Re-bagging, using 17830 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 178 and depth = 19
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 201 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 206 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 186 and depth = 19
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 181 and depth = 19
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 185 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 192 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 160 and depth = 20
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 158 and depth = 20
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 197 and depth = 20
[LightGBM] [Debug] Re-bagging, using 17848 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 186 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 174 and depth = 21
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 164 and depth = 20
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 159 and depth = 18
Trial 46, Fold 1: Log loss = 0.5045576873189126, Average precision = 0.9703136294974727, ROC-AUC = 0.9661611056145836, Elapsed Time = 0.6460338999995656 seconds
Trial 46, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 46, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[LightGBM] [Info] Number of positive: 10230, number of negative: 10471
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.796949
[LightGBM] [Info] Total Bins 15586
[LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 259
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285
[LightGBM] [Info] Start training from score -0.023285
[LightGBM] [Debug] Re-bagging, using 17866 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 177 and depth = 19
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 204 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 157 and depth = 19
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 175 and depth = 20
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 195 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 206 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 206 and depth = 19
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 155 and depth = 19
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 177 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 159 and depth = 20
[LightGBM] [Debug] Re-bagging, using 17876 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 158 and depth = 22
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 193 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 183 and depth = 20
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 198 and depth = 19
Trial 46, Fold 2: Log loss = 0.501208742468372, Average precision = 0.9713255478845191, ROC-AUC = 0.9676740797483173, Elapsed Time = 0.6990193999999974 seconds
Trial 46, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 46, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[LightGBM] [Info] Number of positive: 10165, number of negative: 10517
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.796784
[LightGBM] [Info] Total Bins 15588
[LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 259
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043
[LightGBM] [Info] Start training from score -0.034043
[LightGBM] [Debug] Re-bagging, using 17849 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 192 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 198 and depth = 20
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 146 and depth = 19
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 195 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 201 and depth = 20
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 172 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 194 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 150 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 191 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 170 and depth = 20
[LightGBM] [Debug] Re-bagging, using 17860 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 169 and depth = 21
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 172 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 195 and depth = 22
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 193 and depth = 20
Trial 46, Fold 3: Log loss = 0.4985493422929144, Average precision = 0.9706569359296852, ROC-AUC = 0.9679967477198524, Elapsed Time = 0.7081786000007924 seconds
Trial 46, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 46, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[LightGBM] [Info] Number of positive: 10177, number of negative: 10479
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.796474
[LightGBM] [Info] Total Bins 15571
[LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 259
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243
[LightGBM] [Info] Start training from score -0.029243
[LightGBM] [Debug] Re-bagging, using 17823 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 187 and depth = 19
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 199 and depth = 19
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 160 and depth = 20
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 201 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 160 and depth = 20
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 187 and depth = 20
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 160 and depth = 23
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 203 and depth = 21
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 184 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 206 and depth = 18
[LightGBM] [Debug] Re-bagging, using 17842 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 162 and depth = 19
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 158 and depth = 21
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 159 and depth = 20
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 198 and depth = 19
Trial 46, Fold 4: Log loss = 0.5015279726399929, Average precision = 0.9721892005210573, ROC-AUC = 0.9666833128274094, Elapsed Time = 0.725632300000143 seconds
Trial 46, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 46, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[LightGBM] [Info] Number of positive: 10150, number of negative: 10500
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.798646
[LightGBM] [Info] Total Bins 15573
[LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 260
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902
[LightGBM] [Info] Start training from score -0.033902
[LightGBM] [Debug] Re-bagging, using 17817 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 187 and depth = 20
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 152 and depth = 23
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 196 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 158 and depth = 25
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 156 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 187 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 188 and depth = 21
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 192 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 196 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 195 and depth = 18
[LightGBM] [Debug] Re-bagging, using 17839 data to train
[LightGBM] [Debug] Trained a tree with leaves = 206 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 181 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 190 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 202 and depth = 18
Trial 46, Fold 5: Log loss = 0.5064286734003713, Average precision = 0.9674327831857938, ROC-AUC = 0.9628119577132452, Elapsed Time = 0.7206710000000385 seconds
Optimization Progress:  47%|####6     | 47/100 [10:50<11:18, 12.80s/it]
Trial 47, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 47, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[LightGBM] [Info] Number of positive: 10130, number of negative: 10533
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.794337
[LightGBM] [Info] Total Bins 22776
[LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 257
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012
[LightGBM] [Info] Start training from score -0.039012
[LightGBM] [Debug] Re-bagging, using 10427 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 13
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10372 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 11
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10366 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 13
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 13
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10223 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 10
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 15
[LightGBM] [Debug] Re-bagging, using 10432 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 12
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10261 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 12
[LightGBM] [Debug] Re-bagging, using 10353 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 12
[LightGBM] [Debug] Re-bagging, using 10317 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 11
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10
Trial 47, Fold 1: Log loss = 0.3166572862889005, Average precision = 0.9661894137719051, ROC-AUC = 0.959624942644929, Elapsed Time = 1.3378720000000612 seconds
Trial 47, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 47, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[LightGBM] [Info] Number of positive: 10230, number of negative: 10471
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.794298
[LightGBM] [Info] Total Bins 22787
[LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 257
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285
[LightGBM] [Info] Start training from score -0.023285
[LightGBM] [Debug] Re-bagging, using 10449 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 13
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 10
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10390 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10381 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 13
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10250 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10461 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10257 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 12
[LightGBM] [Debug] Re-bagging, using 10359 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 10
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 11
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 12
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 12
[LightGBM] [Debug] Re-bagging, using 10336 data to train
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 11
Trial 47, Fold 2: Log loss = 0.3114679041777061, Average precision = 0.9638589230571969, ROC-AUC = 0.9598386587195339, Elapsed Time = 1.3030173999995895 seconds
Trial 47, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 47, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[LightGBM] [Info] Number of positive: 10165, number of negative: 10517
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795455
[LightGBM] [Info] Total Bins 22678
[LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 258
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043
[LightGBM] [Info] Start training from score -0.034043
[LightGBM] [Debug] Re-bagging, using 10437 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 13
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 11
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10379 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 12
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10372 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 12
[LightGBM] [Debug] Re-bagging, using 10244 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 12
[LightGBM] [Debug] Re-bagging, using 10443 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 9
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 12
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10258 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 12
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 11
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 12
[LightGBM] [Debug] Re-bagging, using 10359 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10323 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 13
Trial 47, Fold 3: Log loss = 0.307874197146473, Average precision = 0.9678081239560853, ROC-AUC = 0.9624470895012085, Elapsed Time = 1.2034134999994421 seconds
Trial 47, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 47, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[LightGBM] [Info] Number of positive: 10177, number of negative: 10479
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795144
[LightGBM] [Info] Total Bins 22770
[LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 258
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243
[LightGBM] [Info] Start training from score -0.029243
[LightGBM] [Debug] Re-bagging, using 10423 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10368 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 10
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 10
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10361 data to train
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 11
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 14
[LightGBM] [Debug] Re-bagging, using 10223 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 10
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 13
[LightGBM] [Debug] Re-bagging, using 10425 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10257 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10353 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 12
[LightGBM] [Debug] Re-bagging, using 10316 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 9
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 9
Trial 47, Fold 4: Log loss = 0.31159670408281126, Average precision = 0.9665855502745488, ROC-AUC = 0.9600392295018252, Elapsed Time = 1.1818234000002121 seconds
Trial 47, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 47, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[LightGBM] [Info] Number of positive: 10150, number of negative: 10500
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.794703
[LightGBM] [Info] Total Bins 22726
[LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 257
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902
[LightGBM] [Info] Start training from score -0.033902
[LightGBM] [Debug] Re-bagging, using 10420 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 13
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10365 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10362 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 10
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 10
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 13
[LightGBM] [Debug] Re-bagging, using 10216 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 9
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10421 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 12
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 12
[LightGBM] [Debug] Re-bagging, using 10250 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 12
[LightGBM] [Debug] Re-bagging, using 10359 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 11
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 12
[LightGBM] [Debug] Re-bagging, using 10311 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 11
Trial 47, Fold 5: Log loss = 0.3116467143025362, Average precision = 0.9643136124715688, ROC-AUC = 0.9583216097207514, Elapsed Time = 1.2777191000004677 seconds
Optimization Progress:  48%|####8     | 48/100 [11:04<11:26, 13.20s/it]
Trial 48, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 48, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[LightGBM] [Info] Number of positive: 10130, number of negative: 10533
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795662
[LightGBM] [Info] Total Bins 25983
[LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 258
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012
[LightGBM] [Info] Start training from score -0.039012
[LightGBM] [Debug] Re-bagging, using 5347 data to train
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 7
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5294 data to train
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5327 data to train
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5160 data to train
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8
[LightGBM] [Debug] Re-bagging, using 5355 data to train
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 12
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5330 data to train
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 12
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
Trial 48, Fold 1: Log loss = 0.26771342615351423, Average precision = 0.9693245107568664, ROC-AUC = 0.9633420441347271, Elapsed Time = 0.9700815000005605 seconds
Trial 48, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 48, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[LightGBM] [Info] Number of positive: 10230, number of negative: 10471
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.794298
[LightGBM] [Info] Total Bins 25985
[LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 257
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285
[LightGBM] [Info] Start training from score -0.023285
[LightGBM] [Debug] Re-bagging, using 5357 data to train
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11
[LightGBM] [Debug] Re-bagging, using 5305 data to train
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5333 data to train
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 7
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5174 data to train
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 14
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11
[LightGBM] [Debug] Re-bagging, using 5376 data to train
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 12
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8
[LightGBM] [Debug] Re-bagging, using 5324 data to train
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 13
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
Trial 48, Fold 2: Log loss = 0.26531075075348565, Average precision = 0.966748909372579, ROC-AUC = 0.9628439477368487, Elapsed Time = 1.041997000000265 seconds
Trial 48, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 48, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[LightGBM] [Info] Number of positive: 10165, number of negative: 10517
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795455
[LightGBM] [Info] Total Bins 25984
[LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 258
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043
[LightGBM] [Info] Start training from score -0.034043
[LightGBM] [Debug] Re-bagging, using 5352 data to train
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5297 data to train
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5330 data to train
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 7
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 12
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8
[LightGBM] [Debug] Re-bagging, using 5170 data to train
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 13
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5366 data to train
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5325 data to train
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11
Trial 48, Fold 3: Log loss = 0.2653291047390906, Average precision = 0.9690770258886212, ROC-AUC = 0.9640110793019425, Elapsed Time = 0.9497467000001052 seconds
Trial 48, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 48, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[LightGBM] [Info] Number of positive: 10177, number of negative: 10479
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795144
[LightGBM] [Info] Total Bins 25973
[LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 258
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243
[LightGBM] [Info] Start training from score -0.029243
[LightGBM] [Debug] Re-bagging, using 5344 data to train
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 13
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5292 data to train
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8
[LightGBM] [Debug] Re-bagging, using 5324 data to train
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5162 data to train
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11
[LightGBM] [Debug] Re-bagging, using 5351 data to train
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5328 data to train
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 12
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8
Trial 48, Fold 4: Log loss = 0.2724567192845021, Average precision = 0.9676774602201403, ROC-AUC = 0.9611384424081586, Elapsed Time = 0.9449987999996665 seconds
Trial 48, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 48, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[LightGBM] [Info] Number of positive: 10150, number of negative: 10500
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.796026
[LightGBM] [Info] Total Bins 25979
[LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 258
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902
[LightGBM] [Info] Start training from score -0.033902
[LightGBM] [Debug] Re-bagging, using 5342 data to train
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 13
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5292 data to train
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5324 data to train
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 12
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5157 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 12
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8
[LightGBM] [Debug] Re-bagging, using 5349 data to train
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5324 data to train
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 14
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
Trial 48, Fold 5: Log loss = 0.28253181094529456, Average precision = 0.9636006730319187, ROC-AUC = 0.9580317708043029, Elapsed Time = 0.9368782000001374 seconds
Optimization Progress:  49%|####9     | 49/100 [11:17<11:16, 13.26s/it]
Trial 49, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 49, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[LightGBM] [Info] Number of positive: 10130, number of negative: 10533
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791640
[LightGBM] [Info] Total Bins 24796
[LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012
[LightGBM] [Info] Start training from score -0.039012
[LightGBM] [Debug] Re-bagging, using 12453 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9
[LightGBM] [Debug] Re-bagging, using 12477 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9
[LightGBM] [Debug] Re-bagging, using 12537 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8
[LightGBM] [Debug] Re-bagging, using 12317 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 9
Trial 49, Fold 1: Log loss = 0.3063735045663137, Average precision = 0.9679374832296199, ROC-AUC = 0.961229092284309, Elapsed Time = 0.7717340999997759 seconds
Trial 49, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 49, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[LightGBM] [Info] Number of positive: 10230, number of negative: 10471
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791600
[LightGBM] [Info] Total Bins 24803
[LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285
[LightGBM] [Info] Start training from score -0.023285
[LightGBM] [Debug] Re-bagging, using 12478 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9
[LightGBM] [Debug] Re-bagging, using 12495 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10
[LightGBM] [Debug] Re-bagging, using 12564 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7
[LightGBM] [Debug] Re-bagging, using 12346 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8
Trial 49, Fold 2: Log loss = 0.307872180921878, Average precision = 0.9646038770080863, ROC-AUC = 0.9606868529664113, Elapsed Time = 0.8407884999996895 seconds
Trial 49, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 49, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[LightGBM] [Info] Number of positive: 10165, number of negative: 10517
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791401
[LightGBM] [Info] Total Bins 24796
[LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043
[LightGBM] [Info] Start training from score -0.034043
[LightGBM] [Debug] Re-bagging, using 12464 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9
[LightGBM] [Debug] Re-bagging, using 12485 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 9
[LightGBM] [Debug] Re-bagging, using 12550 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 7
[LightGBM] [Debug] Re-bagging, using 12338 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8
Trial 49, Fold 3: Log loss = 0.29787310753822754, Average precision = 0.9689564130818498, ROC-AUC = 0.9635157581791565, Elapsed Time = 0.9720113000003039 seconds
Trial 49, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 49, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[LightGBM] [Info] Number of positive: 10177, number of negative: 10479
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791090
[LightGBM] [Info] Total Bins 24786
[LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243
[LightGBM] [Info] Start training from score -0.029243
[LightGBM] [Debug] Re-bagging, using 12449 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8
[LightGBM] [Debug] Re-bagging, using 12473 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9
[LightGBM] [Debug] Re-bagging, using 12532 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 10
[LightGBM] [Debug] Re-bagging, using 12314 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9
Trial 49, Fold 4: Log loss = 0.31610381071939087, Average precision = 0.9654936821814473, ROC-AUC = 0.9590535741110323, Elapsed Time = 0.9608648000003086 seconds
Trial 49, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 49, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[LightGBM] [Info] Number of positive: 10150, number of negative: 10500
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.792012
[LightGBM] [Info] Total Bins 24790
[LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902
[LightGBM] [Info] Start training from score -0.033902
[LightGBM] [Debug] Re-bagging, using 12446 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Debug] Re-bagging, using 12469 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 11
[LightGBM] [Debug] Re-bagging, using 12532 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8
[LightGBM] [Debug] Re-bagging, using 12308 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9
Trial 49, Fold 5: Log loss = 0.31440501143902616, Average precision = 0.9644052616094297, ROC-AUC = 0.958472028686621, Elapsed Time = 0.9044985000000452 seconds
Optimization Progress:  50%|#####     | 50/100 [11:32<11:18, 13.58s/it]
Trial 50, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 50, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[LightGBM] [Info] Number of positive: 10130, number of negative: 10533
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795662
[LightGBM] [Info] Total Bins 15120
[LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 258
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012
[LightGBM] [Info] Start training from score -0.039012
[LightGBM] [Debug] Re-bagging, using 14380 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 14
[LightGBM] [Debug] Re-bagging, using 14499 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11
[LightGBM] [Debug] Re-bagging, using 14519 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 15
Trial 50, Fold 1: Log loss = 0.2982694639889566, Average precision = 0.9688332355090242, ROC-AUC = 0.964010439817802, Elapsed Time = 0.5601753000000826 seconds
Trial 50, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 50, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[LightGBM] [Info] Number of positive: 10230, number of negative: 10471
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.794298
[LightGBM] [Info] Total Bins 15133
[LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 257
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285
[LightGBM] [Info] Start training from score -0.023285
[LightGBM] [Debug] Re-bagging, using 14407 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 12
[LightGBM] [Debug] Re-bagging, using 14523 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 12
[LightGBM] [Debug] Re-bagging, using 14550 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 11
Trial 50, Fold 2: Log loss = 0.3062531891195165, Average precision = 0.9664051336574846, ROC-AUC = 0.9624566873883326, Elapsed Time = 0.5621974999994563 seconds
Trial 50, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 50, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[LightGBM] [Info] Number of positive: 10165, number of negative: 10517
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795455
[LightGBM] [Info] Total Bins 15138
[LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 258
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043
[LightGBM] [Info] Start training from score -0.034043
[LightGBM] [Debug] Re-bagging, using 14391 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 11
[LightGBM] [Debug] Re-bagging, using 14510 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 10
[LightGBM] [Debug] Re-bagging, using 14537 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 14
Trial 50, Fold 3: Log loss = 0.3072467004888748, Average precision = 0.9687241074849424, ROC-AUC = 0.9633100452899424, Elapsed Time = 0.5728681999999026 seconds
Trial 50, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 50, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[LightGBM] [Info] Number of positive: 10177, number of negative: 10479
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795144
[LightGBM] [Info] Total Bins 15120
[LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 258
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243
[LightGBM] [Info] Start training from score -0.029243
[LightGBM] [Debug] Re-bagging, using 14375 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11
[LightGBM] [Debug] Re-bagging, using 14493 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 13
[LightGBM] [Debug] Re-bagging, using 14514 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 15
Trial 50, Fold 4: Log loss = 0.3113197561966934, Average precision = 0.9667452770254485, ROC-AUC = 0.9607278023371986, Elapsed Time = 0.5734934000001886 seconds
Trial 50, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 50, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[LightGBM] [Info] Number of positive: 10150, number of negative: 10500
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.796026
[LightGBM] [Info] Total Bins 15119
[LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 258
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902
[LightGBM] [Info] Start training from score -0.033902
[LightGBM] [Debug] Re-bagging, using 14371 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 12
[LightGBM] [Debug] Re-bagging, using 14490 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 13
[LightGBM] [Debug] Re-bagging, using 14512 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 13
Trial 50, Fold 5: Log loss = 0.32099173466049086, Average precision = 0.9641892476702298, ROC-AUC = 0.9583259201456626, Elapsed Time = 0.5571890000001076 seconds
Optimization Progress:  51%|#####1    | 51/100 [11:43<10:28, 12.83s/it]
Trial 51, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 51, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[LightGBM] [Info] Number of positive: 10130, number of negative: 10533
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791640
[LightGBM] [Info] Total Bins 24796
[LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012
[LightGBM] [Info] Start training from score -0.039012
[LightGBM] [Debug] Re-bagging, using 12453 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 13
[LightGBM] [Debug] Re-bagging, using 12477 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 13
[LightGBM] [Debug] Re-bagging, using 12537 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 12
[LightGBM] [Debug] Re-bagging, using 12317 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 13
[LightGBM] [Debug] Re-bagging, using 12486 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 14
[LightGBM] [Debug] Re-bagging, using 12352 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 12
[LightGBM] [Debug] Re-bagging, using 12428 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 15
[LightGBM] [Debug] Re-bagging, using 12478 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 14
[LightGBM] [Debug] Re-bagging, using 12471 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 13
[LightGBM] [Debug] Re-bagging, using 12346 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 12
[LightGBM] [Debug] Re-bagging, using 12410 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 19
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 18
Trial 51, Fold 1: Log loss = 0.40943127585042277, Average precision = 0.9668415201170047, ROC-AUC = 0.96053000267657, Elapsed Time = 1.668200600000091 seconds
Trial 51, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 51, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[LightGBM] [Info] Number of positive: 10230, number of negative: 10471
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791600
[LightGBM] [Info] Total Bins 24803
[LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285
[LightGBM] [Info] Start training from score -0.023285
[LightGBM] [Debug] Re-bagging, using 12478 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 15
[LightGBM] [Debug] Re-bagging, using 12495 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 15
[LightGBM] [Debug] Re-bagging, using 12564 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 12
[LightGBM] [Debug] Re-bagging, using 12346 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 14
[LightGBM] [Debug] Re-bagging, using 12508 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 19
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 18
[LightGBM] [Debug] Re-bagging, using 12361 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 19
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 18
[LightGBM] [Debug] Re-bagging, using 12443 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 13
[LightGBM] [Debug] Re-bagging, using 12507 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 15
[LightGBM] [Debug] Re-bagging, using 12497 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 20
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 20
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 15
[LightGBM] [Debug] Re-bagging, using 12372 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 16
[LightGBM] [Debug] Re-bagging, using 12421 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 15
Trial 51, Fold 2: Log loss = 0.40561261665555776, Average precision = 0.9647727106963009, ROC-AUC = 0.9609372368124345, Elapsed Time = 1.935546699999577 seconds
Trial 51, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 51, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[LightGBM] [Info] Number of positive: 10165, number of negative: 10517
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791401
[LightGBM] [Info] Total Bins 24796
[LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043
[LightGBM] [Info] Start training from score -0.034043
[LightGBM] [Debug] Re-bagging, using 12464 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 15
[LightGBM] [Debug] Re-bagging, using 12485 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 17
[LightGBM] [Debug] Re-bagging, using 12550 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 14
[LightGBM] [Debug] Re-bagging, using 12338 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 16
[LightGBM] [Debug] Re-bagging, using 12496 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 15
[LightGBM] [Debug] Re-bagging, using 12352 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 15
[LightGBM] [Debug] Re-bagging, using 12438 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 13
[LightGBM] [Debug] Re-bagging, using 12493 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 17
[LightGBM] [Debug] Re-bagging, using 12476 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 18
[LightGBM] [Debug] Re-bagging, using 12367 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 15
[LightGBM] [Debug] Re-bagging, using 12420 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 15
Trial 51, Fold 3: Log loss = 0.40089889042129667, Average precision = 0.9691804405867582, ROC-AUC = 0.9640356083443289, Elapsed Time = 1.9567003000001932 seconds
Trial 51, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 51, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[LightGBM] [Info] Number of positive: 10177, number of negative: 10479
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791090
[LightGBM] [Info] Total Bins 24786
[LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243
[LightGBM] [Info] Start training from score -0.029243
[LightGBM] [Debug] Re-bagging, using 12449 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 14
[LightGBM] [Debug] Re-bagging, using 12473 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 15
[LightGBM] [Debug] Re-bagging, using 12532 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 11
[LightGBM] [Debug] Re-bagging, using 12314 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 15
[LightGBM] [Debug] Re-bagging, using 12478 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 14
[LightGBM] [Debug] Re-bagging, using 12350 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 14
[LightGBM] [Debug] Re-bagging, using 12425 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 14
[LightGBM] [Debug] Re-bagging, using 12479 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 10
[LightGBM] [Debug] Re-bagging, using 12459 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 16
[LightGBM] [Debug] Re-bagging, using 12346 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 12
[LightGBM] [Debug] Re-bagging, using 12410 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 16
Trial 51, Fold 4: Log loss = 0.4101429593770919, Average precision = 0.9659798685954358, ROC-AUC = 0.9598668068699614, Elapsed Time = 1.9267745999995896 seconds
Trial 51, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 51, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[LightGBM] [Info] Number of positive: 10150, number of negative: 10500
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.792012
[LightGBM] [Info] Total Bins 24790
[LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902
[LightGBM] [Info] Start training from score -0.033902
[LightGBM] [Debug] Re-bagging, using 12446 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 16
[LightGBM] [Debug] Re-bagging, using 12469 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 17
[LightGBM] [Debug] Re-bagging, using 12532 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 13
[LightGBM] [Debug] Re-bagging, using 12308 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 17
[LightGBM] [Debug] Re-bagging, using 12472 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 15
[LightGBM] [Debug] Re-bagging, using 12346 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 16
[LightGBM] [Debug] Re-bagging, using 12426 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 13
[LightGBM] [Debug] Re-bagging, using 12470 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 15
[LightGBM] [Debug] Re-bagging, using 12459 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 15
[LightGBM] [Debug] Re-bagging, using 12346 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 17
[LightGBM] [Debug] Re-bagging, using 12402 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 19
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 13
Trial 51, Fold 5: Log loss = 0.4074947757547993, Average precision = 0.9650135359660748, ROC-AUC = 0.958990617394051, Elapsed Time = 1.938223399999515 seconds
Optimization Progress:  52%|#####2    | 52/100 [12:00<11:15, 14.07s/it]
Trial 52, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 52, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[LightGBM] [Info] Number of positive: 10130, number of negative: 10533
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795662
[LightGBM] [Info] Total Bins 25876
[LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 258
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012
[LightGBM] [Info] Start training from score -0.039012
[LightGBM] [Debug] Re-bagging, using 5347 data to train
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5294 data to train
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8
[LightGBM] [Debug] Re-bagging, using 5327 data to train
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5160 data to train
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5355 data to train
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5330 data to train
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5234 data to train
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11
[LightGBM] [Debug] Re-bagging, using 5370 data to train
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5261 data to train
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5126 data to train
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5160 data to train
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5207 data to train
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11
[LightGBM] [Debug] Re-bagging, using 5205 data to train
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5307 data to train
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11
[LightGBM] [Debug] Re-bagging, using 5363 data to train
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5283 data to train
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5288 data to train
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 14
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 12
[LightGBM] [Debug] Re-bagging, using 5206 data to train
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11
[LightGBM] [Debug] Re-bagging, using 5330 data to train
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5364 data to train
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 12
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 14
[LightGBM] [Debug] Re-bagging, using 5186 data to train
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 14
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 14
[LightGBM] [Debug] Re-bagging, using 5265 data to train
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 14
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11
[LightGBM] [Debug] Re-bagging, using 5286 data to train
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 12
[LightGBM] [Debug] Re-bagging, using 5234 data to train
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5350 data to train
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 12
Trial 52, Fold 1: Log loss = 0.21649941651454954, Average precision = 0.972431254470847, ROC-AUC = 0.966919716044603, Elapsed Time = 0.8327139999992141 seconds
Trial 52, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 52, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[LightGBM] [Info] Number of positive: 10230, number of negative: 10471
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.794298
[LightGBM] [Info] Total Bins 25878
[LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 257
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285
[LightGBM] [Info] Start training from score -0.023285
[LightGBM] [Debug] Re-bagging, using 5357 data to train
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8
[LightGBM] [Debug] Re-bagging, using 5305 data to train
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5333 data to train
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8
[LightGBM] [Debug] Re-bagging, using 5174 data to train
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 7
[LightGBM] [Debug] Re-bagging, using 5376 data to train
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5324 data to train
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 7
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5242 data to train
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11
[LightGBM] [Debug] Re-bagging, using 5379 data to train
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5277 data to train
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5149 data to train
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5160 data to train
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5203 data to train
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5215 data to train
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5315 data to train
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 12
[LightGBM] [Debug] Re-bagging, using 5368 data to train
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 12
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5300 data to train
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 14
[LightGBM] [Debug] Re-bagging, using 5300 data to train
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 13
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 12
[LightGBM] [Debug] Re-bagging, using 5226 data to train
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 12
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 12
[LightGBM] [Debug] Re-bagging, using 5353 data to train
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5367 data to train
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 17
[LightGBM] [Debug] Re-bagging, using 5197 data to train
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 13
[LightGBM] [Debug] Re-bagging, using 5271 data to train
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 13
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5298 data to train
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 13
[LightGBM] [Debug] Re-bagging, using 5247 data to train
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 15
[LightGBM] [Debug] Re-bagging, using 5356 data to train
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 14
Trial 52, Fold 2: Log loss = 0.21555834957559505, Average precision = 0.9703827467001086, ROC-AUC = 0.9668916681664406, Elapsed Time = 0.844282199999725 seconds
Trial 52, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 52, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[LightGBM] [Info] Number of positive: 10165, number of negative: 10517
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795455
[LightGBM] [Info] Total Bins 25877
[LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 258
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043
[LightGBM] [Info] Start training from score -0.034043
[LightGBM] [Debug] Re-bagging, using 5352 data to train
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5297 data to train
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5330 data to train
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5170 data to train
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5366 data to train
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5325 data to train
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5243 data to train
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5369 data to train
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5265 data to train
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 12
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5142 data to train
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11
[LightGBM] [Debug] Re-bagging, using 5168 data to train
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8
[LightGBM] [Debug] Re-bagging, using 5195 data to train
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5211 data to train
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 13
[LightGBM] [Debug] Re-bagging, using 5316 data to train
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5356 data to train
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 13
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11
[LightGBM] [Debug] Re-bagging, using 5296 data to train
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5292 data to train
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 13
[LightGBM] [Debug] Re-bagging, using 5215 data to train
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 13
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11
[LightGBM] [Debug] Re-bagging, using 5337 data to train
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 12
[LightGBM] [Debug] Re-bagging, using 5381 data to train
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11
[LightGBM] [Debug] Re-bagging, using 5196 data to train
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 16
[LightGBM] [Debug] Re-bagging, using 5256 data to train
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 15
[LightGBM] [Debug] Re-bagging, using 5280 data to train
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 13
[LightGBM] [Debug] Re-bagging, using 5254 data to train
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5363 data to train
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 15
Trial 52, Fold 3: Log loss = 0.21758157949179519, Average precision = 0.9725401558795804, ROC-AUC = 0.9687837086226657, Elapsed Time = 0.8842640000002575 seconds
Trial 52, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 52, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[LightGBM] [Info] Number of positive: 10177, number of negative: 10479
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795144
[LightGBM] [Info] Total Bins 25866
[LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 258
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243
[LightGBM] [Info] Start training from score -0.029243
[LightGBM] [Debug] Re-bagging, using 5344 data to train
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11
[LightGBM] [Debug] Re-bagging, using 5292 data to train
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8
[LightGBM] [Debug] Re-bagging, using 5324 data to train
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5162 data to train
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5351 data to train
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5328 data to train
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5235 data to train
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5366 data to train
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5255 data to train
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5130 data to train
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5153 data to train
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5206 data to train
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 12
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5206 data to train
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 12
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8
[LightGBM] [Debug] Re-bagging, using 5305 data to train
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 13
[LightGBM] [Debug] Re-bagging, using 5357 data to train
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11
[LightGBM] [Debug] Re-bagging, using 5287 data to train
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5282 data to train
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 13
[LightGBM] [Debug] Re-bagging, using 5215 data to train
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 12
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5320 data to train
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 12
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5365 data to train
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11
[LightGBM] [Debug] Re-bagging, using 5185 data to train
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 13
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5255 data to train
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5284 data to train
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 14
[LightGBM] [Debug] Re-bagging, using 5245 data to train
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 14
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 12
[LightGBM] [Debug] Re-bagging, using 5339 data to train
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 15
Trial 52, Fold 4: Log loss = 0.2224244821480468, Average precision = 0.9709196358797433, ROC-AUC = 0.9655868568948784, Elapsed Time = 1.0510160999992877 seconds
Trial 52, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 52, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[LightGBM] [Info] Number of positive: 10150, number of negative: 10500
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.796026
[LightGBM] [Info] Total Bins 25873
[LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 258
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902
[LightGBM] [Info] Start training from score -0.033902
[LightGBM] [Debug] Re-bagging, using 5342 data to train
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11
[LightGBM] [Debug] Re-bagging, using 5292 data to train
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5324 data to train
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8
[LightGBM] [Debug] Re-bagging, using 5157 data to train
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5349 data to train
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5324 data to train
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11
[LightGBM] [Debug] Re-bagging, using 5240 data to train
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8
[LightGBM] [Debug] Re-bagging, using 5361 data to train
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8
[LightGBM] [Debug] Re-bagging, using 5255 data to train
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5130 data to train
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 7
[LightGBM] [Debug] Re-bagging, using 5148 data to train
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5195 data to train
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5214 data to train
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11
[LightGBM] [Debug] Re-bagging, using 5311 data to train
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5351 data to train
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 12
[LightGBM] [Debug] Re-bagging, using 5292 data to train
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 13
[LightGBM] [Debug] Re-bagging, using 5282 data to train
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 14
[LightGBM] [Debug] Re-bagging, using 5200 data to train
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 14
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 13
[LightGBM] [Debug] Re-bagging, using 5325 data to train
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 12
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11
[LightGBM] [Debug] Re-bagging, using 5356 data to train
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 12
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11
[LightGBM] [Debug] Re-bagging, using 5187 data to train
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 12
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5250 data to train
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11
[LightGBM] [Debug] Re-bagging, using 5278 data to train
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 14
[LightGBM] [Debug] Re-bagging, using 5251 data to train
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11
[LightGBM] [Debug] Re-bagging, using 5342 data to train
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 13
Trial 52, Fold 5: Log loss = 0.22496569853134174, Average precision = 0.9695400144057988, ROC-AUC = 0.9648006242684355, Elapsed Time = 1.025408299999981 seconds
Optimization Progress:  53%|#####3    | 53/100 [12:12<10:34, 13.51s/it]
Trial 53, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 53, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[LightGBM] [Info] Number of positive: 10130, number of negative: 10533
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.805817
[LightGBM] [Info] Total Bins 16928
[LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 267
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012
[LightGBM] [Info] Start training from score -0.039012
[LightGBM] [Debug] Re-bagging, using 5608 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 145 and depth = 20
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 153 and depth = 20
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 159 and depth = 21
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 168 and depth = 23
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 168 and depth = 21
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 174 and depth = 20
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 174 and depth = 21
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 173 and depth = 22
[LightGBM] [Debug] Re-bagging, using 5567 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 194 and depth = 20
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 190 and depth = 21
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 193 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 190 and depth = 21
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 185 and depth = 21
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 203 and depth = 19
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 188 and depth = 22
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 198 and depth = 17
[LightGBM] [Debug] Re-bagging, using 5575 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 195 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 194 and depth = 19
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 198 and depth = 20
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 194 and depth = 19
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 200 and depth = 19
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 201 and depth = 19
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 197 and depth = 20
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 196 and depth = 20
[LightGBM] [Debug] Re-bagging, using 5431 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 195 and depth = 28
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 190 and depth = 29
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 194 and depth = 25
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 193 and depth = 27
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 193 and depth = 19
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 193 and depth = 21
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 195 and depth = 21
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 196 and depth = 23
[LightGBM] [Debug] Re-bagging, using 5645 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 208 and depth = 19
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 200 and depth = 19
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 210 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 202 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 198 and depth = 19
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 203 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 194 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 197 and depth = 19
[LightGBM] [Debug] Re-bagging, using 5586 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 198 and depth = 21
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 198 and depth = 26
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 196 and depth = 23
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 196 and depth = 22
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 194 and depth = 20
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 189 and depth = 23
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 183 and depth = 21
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 191 and depth = 23
[LightGBM] [Debug] Re-bagging, using 5492 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 187 and depth = 20
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 184 and depth = 19
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 179 and depth = 19
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 183 and depth = 19
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 178 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 179 and depth = 19
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 175 and depth = 20
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 181 and depth = 16
[LightGBM] [Debug] Re-bagging, using 5644 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 188 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 188 and depth = 20
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 187 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 185 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 187 and depth = 19
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 179 and depth = 17
Trial 53, Fold 1: Log loss = 0.3327160762671582, Average precision = 0.9716258069958176, ROC-AUC = 0.9672362891508104, Elapsed Time = 2.8530601000002207 seconds
Trial 53, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 53, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[LightGBM] [Info] Number of positive: 10230, number of negative: 10471
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.806987
[LightGBM] [Info] Total Bins 16941
[LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 267
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285
[LightGBM] [Info] Start training from score -0.023285
[LightGBM] [Debug] Re-bagging, using 5618 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 140 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 144 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 164 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 157 and depth = 20
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 170 and depth = 20
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 167 and depth = 19
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 182 and depth = 20
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 186 and depth = 19
[LightGBM] [Debug] Re-bagging, using 5580 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 191 and depth = 22
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 194 and depth = 19
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 198 and depth = 21
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 198 and depth = 19
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 192 and depth = 22
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 195 and depth = 21
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 193 and depth = 19
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 186 and depth = 21
[LightGBM] [Debug] Re-bagging, using 5580 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 201 and depth = 20
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 206 and depth = 19
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 200 and depth = 20
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 200 and depth = 19
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 203 and depth = 19
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 203 and depth = 25
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 204 and depth = 19
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 194 and depth = 21
[LightGBM] [Debug] Re-bagging, using 5448 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 193 and depth = 22
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 193 and depth = 21
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 192 and depth = 21
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 187 and depth = 21
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 188 and depth = 20
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 184 and depth = 24
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 180 and depth = 22
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 193 and depth = 21
[LightGBM] [Debug] Re-bagging, using 5664 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 189 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 197 and depth = 20
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 191 and depth = 20
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 196 and depth = 21
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 196 and depth = 19
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 195 and depth = 19
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 197 and depth = 20
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 189 and depth = 18
[LightGBM] [Debug] Re-bagging, using 5580 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 199 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 194 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 195 and depth = 21
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 194 and depth = 19
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 189 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 196 and depth = 19
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 193 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 192 and depth = 23
[LightGBM] [Debug] Re-bagging, using 5499 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 189 and depth = 20
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 190 and depth = 21
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 188 and depth = 21
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 185 and depth = 24
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 181 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 186 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 183 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 185 and depth = 21
[LightGBM] [Debug] Re-bagging, using 5655 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 191 and depth = 19
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 193 and depth = 19
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 189 and depth = 19
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 192 and depth = 19
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 183 and depth = 19
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 192 and depth = 18
Trial 53, Fold 2: Log loss = 0.32865200527720656, Average precision = 0.9737266722440454, ROC-AUC = 0.9695671333720391, Elapsed Time = 3.666922400000658 seconds
Trial 53, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 53, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[LightGBM] [Info] Number of positive: 10165, number of negative: 10517
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.806893
[LightGBM] [Info] Total Bins 16947
[LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 267
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043
[LightGBM] [Info] Start training from score -0.034043
[LightGBM] [Debug] Re-bagging, using 5613 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 147 and depth = 20
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 146 and depth = 19
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 166 and depth = 20
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 169 and depth = 20
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 178 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 181 and depth = 23
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 178 and depth = 21
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 188 and depth = 23
[LightGBM] [Debug] Re-bagging, using 5571 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 200 and depth = 19
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 198 and depth = 21
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 197 and depth = 19
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 197 and depth = 19
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 195 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 189 and depth = 21
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 190 and depth = 19
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 192 and depth = 20
[LightGBM] [Debug] Re-bagging, using 5578 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 197 and depth = 21
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 191 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 188 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 190 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 196 and depth = 19
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 189 and depth = 19
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 199 and depth = 19
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 195 and depth = 19
[LightGBM] [Debug] Re-bagging, using 5442 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 195 and depth = 21
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 198 and depth = 20
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 192 and depth = 21
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 198 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 188 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 194 and depth = 25
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 193 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 188 and depth = 17
[LightGBM] [Debug] Re-bagging, using 5655 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 197 and depth = 19
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 197 and depth = 20
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 194 and depth = 19
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 191 and depth = 21
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 191 and depth = 22
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 191 and depth = 19
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 189 and depth = 21
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 188 and depth = 19
[LightGBM] [Debug] Re-bagging, using 5580 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 185 and depth = 20
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 197 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 193 and depth = 21
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 176 and depth = 23
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 181 and depth = 19
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 180 and depth = 22
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 182 and depth = 19
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 178 and depth = 20
[LightGBM] [Debug] Re-bagging, using 5501 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 202 and depth = 20
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 197 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 186 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 184 and depth = 20
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 187 and depth = 19
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 179 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 174 and depth = 20
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 182 and depth = 19
[LightGBM] [Debug] Re-bagging, using 5646 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 184 and depth = 20
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 187 and depth = 20
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 186 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 182 and depth = 20
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 176 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 177 and depth = 19
Trial 53, Fold 3: Log loss = 0.32894213973091035, Average precision = 0.9721211712269816, ROC-AUC = 0.9693382455625006, Elapsed Time = 3.4834272999996756 seconds
Trial 53, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 53, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[LightGBM] [Info] Number of positive: 10177, number of negative: 10479
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.805384
[LightGBM] [Info] Total Bins 16929
[LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 267
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243
[LightGBM] [Info] Start training from score -0.029243
[LightGBM] [Debug] Re-bagging, using 5605 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 144 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 144 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 152 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 163 and depth = 21
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 171 and depth = 19
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 166 and depth = 20
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 172 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 174 and depth = 20
[LightGBM] [Debug] Re-bagging, using 5565 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 196 and depth = 19
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 196 and depth = 20
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 192 and depth = 19
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 193 and depth = 19
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 196 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 193 and depth = 21
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 200 and depth = 19
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 194 and depth = 25
[LightGBM] [Debug] Re-bagging, using 5572 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 204 and depth = 23
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 202 and depth = 21
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 199 and depth = 20
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 199 and depth = 20
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 200 and depth = 20
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 208 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 202 and depth = 20
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 201 and depth = 17
[LightGBM] [Debug] Re-bagging, using 5432 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 202 and depth = 21
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 196 and depth = 22
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 204 and depth = 21
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 205 and depth = 20
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 191 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 193 and depth = 20
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 190 and depth = 20
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 192 and depth = 21
[LightGBM] [Debug] Re-bagging, using 5642 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 205 and depth = 24
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 208 and depth = 22
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 20
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 207 and depth = 19
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 207 and depth = 19
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 201 and depth = 22
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 205 and depth = 20
[LightGBM] [Debug] Re-bagging, using 5583 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 208 and depth = 23
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 23
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 203 and depth = 19
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 204 and depth = 19
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 206 and depth = 25
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 202 and depth = 21
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 199 and depth = 21
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 198 and depth = 19
[LightGBM] [Debug] Re-bagging, using 5492 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 194 and depth = 24
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 193 and depth = 22
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 194 and depth = 24
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 190 and depth = 22
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 190 and depth = 22
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 190 and depth = 20
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 191 and depth = 19
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 187 and depth = 18
[LightGBM] [Debug] Re-bagging, using 5642 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 176 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 181 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 178 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 175 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 176 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 171 and depth = 16
Trial 53, Fold 4: Log loss = 0.32957811689794386, Average precision = 0.9740590844027639, ROC-AUC = 0.9695006420023321, Elapsed Time = 4.050504700000602 seconds
Trial 53, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 53, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[LightGBM] [Info] Number of positive: 10150, number of negative: 10500
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.807356
[LightGBM] [Info] Total Bins 16928
[LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 267
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902
[LightGBM] [Info] Start training from score -0.033902
[LightGBM] [Debug] Re-bagging, using 5603 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 141 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 150 and depth = 20
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 156 and depth = 21
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 156 and depth = 19
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 162 and depth = 21
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 184 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 186 and depth = 20
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 183 and depth = 19
[LightGBM] [Debug] Re-bagging, using 5565 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 200 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 198 and depth = 21
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 194 and depth = 19
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 197 and depth = 20
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 196 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 194 and depth = 19
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 196 and depth = 19
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 196 and depth = 19
[LightGBM] [Debug] Re-bagging, using 5572 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 201 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 199 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 194 and depth = 19
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 198 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 199 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 195 and depth = 21
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 192 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 193 and depth = 18
[LightGBM] [Debug] Re-bagging, using 5427 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 195 and depth = 20
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 193 and depth = 20
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 195 and depth = 22
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 196 and depth = 22
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 188 and depth = 20
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 192 and depth = 21
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 185 and depth = 19
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 187 and depth = 19
[LightGBM] [Debug] Re-bagging, using 5640 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 204 and depth = 26
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 200 and depth = 25
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 196 and depth = 24
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 196 and depth = 24
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 199 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 189 and depth = 21
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 191 and depth = 20
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 195 and depth = 20
[LightGBM] [Debug] Re-bagging, using 5579 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 206 and depth = 21
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 204 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 208 and depth = 19
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 207 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 206 and depth = 20
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 200 and depth = 19
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 203 and depth = 19
[LightGBM] [Debug] Re-bagging, using 5497 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 184 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 177 and depth = 19
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 174 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 181 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 182 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 178 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 169 and depth = 19
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 176 and depth = 18
[LightGBM] [Debug] Re-bagging, using 5637 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 180 and depth = 21
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 172 and depth = 24
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 176 and depth = 22
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 170 and depth = 21
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 171 and depth = 20
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 174 and depth = 23
Trial 53, Fold 5: Log loss = 0.3317355849042862, Average precision = 0.9704764770963913, ROC-AUC = 0.9674112554112553, Elapsed Time = 3.419964799999434 seconds
Optimization Progress:  54%|#####4    | 54/100 [12:38<13:16, 17.32s/it]
Trial 54, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 54, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[LightGBM] [Info] Number of positive: 10130, number of negative: 10533
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791640
[LightGBM] [Info] Total Bins 7940
[LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012
[LightGBM] [Info] Start training from score -0.039012
[LightGBM] [Debug] Re-bagging, using 5347 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5294 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7
[LightGBM] [Debug] Re-bagging, using 5327 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5160 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11
[LightGBM] [Debug] Re-bagging, using 5355 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 10
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 11
[LightGBM] [Debug] Re-bagging, using 5330 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8
Trial 54, Fold 1: Log loss = 0.2752874065988546, Average precision = 0.9677937124264382, ROC-AUC = 0.9611638956998035, Elapsed Time = 0.7653597999997146 seconds
Trial 54, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 54, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[LightGBM] [Info] Number of positive: 10230, number of negative: 10471
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791600
[LightGBM] [Info] Total Bins 7956
[LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285
[LightGBM] [Info] Start training from score -0.023285
[LightGBM] [Debug] Re-bagging, using 5357 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8
[LightGBM] [Debug] Re-bagging, using 5305 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5333 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8
[LightGBM] [Debug] Re-bagging, using 5174 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8
[LightGBM] [Debug] Re-bagging, using 5376 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5324 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8
Trial 54, Fold 2: Log loss = 0.27866620105733475, Average precision = 0.9643420683093281, ROC-AUC = 0.9599818327551237, Elapsed Time = 0.8094517999998061 seconds
Trial 54, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 54, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[LightGBM] [Info] Number of positive: 10165, number of negative: 10517
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791401
[LightGBM] [Info] Total Bins 7963
[LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043
[LightGBM] [Info] Start training from score -0.034043
[LightGBM] [Debug] Re-bagging, using 5352 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 10
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8
[LightGBM] [Debug] Re-bagging, using 5297 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 7
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5330 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5170 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5366 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8
[LightGBM] [Debug] Re-bagging, using 5325 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 11
Trial 54, Fold 3: Log loss = 0.2700365962158037, Average precision = 0.9682625609104769, ROC-AUC = 0.9626263170440428, Elapsed Time = 0.8366236999991088 seconds
Trial 54, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 54, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[LightGBM] [Info] Number of positive: 10177, number of negative: 10479
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.792454
[LightGBM] [Info] Total Bins 7949
[LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 256
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243
[LightGBM] [Info] Start training from score -0.029243
[LightGBM] [Debug] Re-bagging, using 5344 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8
[LightGBM] [Debug] Re-bagging, using 5292 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 7
[LightGBM] [Debug] Re-bagging, using 5324 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5162 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5351 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 10
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5328 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
Trial 54, Fold 4: Log loss = 0.27234685971359907, Average precision = 0.9677491230315155, ROC-AUC = 0.961371816514899, Elapsed Time = 0.8441063999998732 seconds
Trial 54, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 54, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[LightGBM] [Info] Number of positive: 10150, number of negative: 10500
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.792012
[LightGBM] [Info] Total Bins 7944
[LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902
[LightGBM] [Info] Start training from score -0.033902
[LightGBM] [Debug] Re-bagging, using 5342 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8
[LightGBM] [Debug] Re-bagging, using 5292 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8
[LightGBM] [Debug] Re-bagging, using 5324 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8
[LightGBM] [Debug] Re-bagging, using 5157 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5349 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8
[LightGBM] [Debug] Re-bagging, using 5324 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8
Trial 54, Fold 5: Log loss = 0.27751199575788493, Average precision = 0.9634017326562361, ROC-AUC = 0.9582249967486018, Elapsed Time = 0.8501927999996042 seconds
Optimization Progress:  55%|#####5    | 55/100 [12:50<11:43, 15.64s/it]
Trial 55, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 55, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[LightGBM] [Info] Number of positive: 10130, number of negative: 10533
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795662
[LightGBM] [Info] Total Bins 15120
[LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 258
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012
[LightGBM] [Info] Start training from score -0.039012
[LightGBM] [Debug] Re-bagging, using 5834 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 6
[LightGBM] [Debug] Re-bagging, using 5799 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5805 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8
Trial 55, Fold 1: Log loss = 0.319710227830236, Average precision = 0.9637692004130884, ROC-AUC = 0.9577537334566466, Elapsed Time = 0.46974260000024515 seconds
Trial 55, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 55, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[LightGBM] [Info] Number of positive: 10230, number of negative: 10471
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795629
[LightGBM] [Info] Total Bins 15136
[LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 258
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285
[LightGBM] [Info] Start training from score -0.023285
[LightGBM] [Debug] Re-bagging, using 5846 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7
[LightGBM] [Debug] Re-bagging, using 5812 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5809 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 7
Trial 55, Fold 2: Log loss = 0.31212313747011217, Average precision = 0.9633393334570692, ROC-AUC = 0.958773920382793, Elapsed Time = 0.5101971000003687 seconds
Trial 55, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 55, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[LightGBM] [Info] Number of positive: 10165, number of negative: 10517
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795455
[LightGBM] [Info] Total Bins 15138
[LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 258
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043
[LightGBM] [Info] Start training from score -0.034043
[LightGBM] [Debug] Re-bagging, using 5839 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5805 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 6
[LightGBM] [Debug] Re-bagging, using 5807 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8
Trial 55, Fold 3: Log loss = 0.31374128541596613, Average precision = 0.9651192620136533, ROC-AUC = 0.9601149494129884, Elapsed Time = 0.5250800000003437 seconds
Trial 55, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 55, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[LightGBM] [Info] Number of positive: 10177, number of negative: 10479
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795144
[LightGBM] [Info] Total Bins 15120
[LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 258
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243
[LightGBM] [Info] Start training from score -0.029243
[LightGBM] [Debug] Re-bagging, using 5831 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5797 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5801 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8
Trial 55, Fold 4: Log loss = 0.3170786943123466, Average precision = 0.9644478627146373, ROC-AUC = 0.9579533180254225, Elapsed Time = 0.5307753999995839 seconds
Trial 55, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 55, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[LightGBM] [Info] Number of positive: 10150, number of negative: 10500
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.796026
[LightGBM] [Info] Total Bins 15119
[LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 258
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902
[LightGBM] [Info] Start training from score -0.033902
[LightGBM] [Debug] Re-bagging, using 5829 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8
[LightGBM] [Debug] Re-bagging, using 5797 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 8
[LightGBM] [Debug] Re-bagging, using 5801 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6
Trial 55, Fold 5: Log loss = 0.31820035782452255, Average precision = 0.9625577655712161, ROC-AUC = 0.9560946807127064, Elapsed Time = 0.5251232000000527 seconds
Optimization Progress:  56%|#####6    | 56/100 [13:00<10:12, 13.92s/it]
Trial 56, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 56, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[LightGBM] [Info] Number of positive: 10130, number of negative: 10533
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791640
[LightGBM] [Info] Total Bins 7940
[LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012
[LightGBM] [Info] Start training from score -0.039012
[LightGBM] [Debug] Re-bagging, using 7530 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 6
[LightGBM] [Debug] Re-bagging, using 7591 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7
[LightGBM] [Debug] Re-bagging, using 7465 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8
[LightGBM] [Debug] Re-bagging, using 7460 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8
[LightGBM] [Debug] Re-bagging, using 7595 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7535 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8
[LightGBM] [Debug] Re-bagging, using 7535 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 6
[LightGBM] [Debug] Re-bagging, using 7606 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7538 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8
[LightGBM] [Debug] Re-bagging, using 7339 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8
[LightGBM] [Debug] Re-bagging, using 7350 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7424 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 7
[LightGBM] [Debug] Re-bagging, using 7407 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7461 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 7
[LightGBM] [Debug] Re-bagging, using 7637 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7546 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10
[LightGBM] [Debug] Re-bagging, using 7517 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7498 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7590 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 11
[LightGBM] [Debug] Re-bagging, using 7643 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 14
[LightGBM] [Debug] Re-bagging, using 7390 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10
[LightGBM] [Debug] Re-bagging, using 7479 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 12
[LightGBM] [Debug] Re-bagging, using 7559 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11
Trial 56, Fold 1: Log loss = 0.20832250070227745, Average precision = 0.9728527881784961, ROC-AUC = 0.9678535282927785, Elapsed Time = 0.7831826000001456 seconds
Trial 56, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 56, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[LightGBM] [Info] Number of positive: 10230, number of negative: 10471
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791600
[LightGBM] [Info] Total Bins 7956
[LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285
[LightGBM] [Info] Start training from score -0.023285
[LightGBM] [Debug] Re-bagging, using 7545 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 7
[LightGBM] [Debug] Re-bagging, using 7607 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7
[LightGBM] [Debug] Re-bagging, using 7478 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8
[LightGBM] [Debug] Re-bagging, using 7475 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7
[LightGBM] [Debug] Re-bagging, using 7625 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7
[LightGBM] [Debug] Re-bagging, using 7526 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 7
[LightGBM] [Debug] Re-bagging, using 7547 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7616 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7561 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8
[LightGBM] [Debug] Re-bagging, using 7363 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8
[LightGBM] [Debug] Re-bagging, using 7349 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8
[LightGBM] [Debug] Re-bagging, using 7421 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7419 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8
[LightGBM] [Debug] Re-bagging, using 7477 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8
[LightGBM] [Debug] Re-bagging, using 7659 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 10
[LightGBM] [Debug] Re-bagging, using 7571 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 13
[LightGBM] [Debug] Re-bagging, using 7529 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 12
[LightGBM] [Debug] Re-bagging, using 7517 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 14
[LightGBM] [Debug] Re-bagging, using 7602 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10
[LightGBM] [Debug] Re-bagging, using 7655 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 13
[LightGBM] [Debug] Re-bagging, using 7409 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 11
[LightGBM] [Debug] Re-bagging, using 7490 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Debug] Re-bagging, using 7584 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
Trial 56, Fold 2: Log loss = 0.20397070967147965, Average precision = 0.9727620209508783, ROC-AUC = 0.9690584747951481, Elapsed Time = 0.8534793999997419 seconds
Trial 56, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 56, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[LightGBM] [Info] Number of positive: 10165, number of negative: 10517
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791401
[LightGBM] [Info] Total Bins 7963
[LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043
[LightGBM] [Info] Start training from score -0.034043
[LightGBM] [Debug] Re-bagging, using 7537 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 6
[LightGBM] [Debug] Re-bagging, using 7597 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7
[LightGBM] [Debug] Re-bagging, using 7471 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7
[LightGBM] [Debug] Re-bagging, using 7473 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7
[LightGBM] [Debug] Re-bagging, using 7609 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 7
[LightGBM] [Debug] Re-bagging, using 7528 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7
[LightGBM] [Debug] Re-bagging, using 7544 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8
[LightGBM] [Debug] Re-bagging, using 7609 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 7
[LightGBM] [Debug] Re-bagging, using 7541 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8
[LightGBM] [Debug] Re-bagging, using 7363 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 7
[LightGBM] [Debug] Re-bagging, using 7351 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7
[LightGBM] [Debug] Re-bagging, using 7419 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8
[LightGBM] [Debug] Re-bagging, using 7405 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10
[LightGBM] [Debug] Re-bagging, using 7472 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8
[LightGBM] [Debug] Re-bagging, using 7644 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10
[LightGBM] [Debug] Re-bagging, using 7562 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11
[LightGBM] [Debug] Re-bagging, using 7525 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10
[LightGBM] [Debug] Re-bagging, using 7509 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10
[LightGBM] [Debug] Re-bagging, using 7589 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 11
[LightGBM] [Debug] Re-bagging, using 7659 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 11
[LightGBM] [Debug] Re-bagging, using 7404 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Debug] Re-bagging, using 7477 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 11
[LightGBM] [Debug] Re-bagging, using 7563 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 11
Trial 56, Fold 3: Log loss = 0.2031745484476355, Average precision = 0.9729414594836683, ROC-AUC = 0.9691502898941549, Elapsed Time = 0.8719132999995054 seconds
Trial 56, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 56, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[LightGBM] [Info] Number of positive: 10177, number of negative: 10479
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791090
[LightGBM] [Info] Total Bins 7946
[LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243
[LightGBM] [Info] Start training from score -0.029243
[LightGBM] [Debug] Re-bagging, using 7526 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7
[LightGBM] [Debug] Re-bagging, using 7589 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7
[LightGBM] [Debug] Re-bagging, using 7461 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8
[LightGBM] [Debug] Re-bagging, using 7462 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7
[LightGBM] [Debug] Re-bagging, using 7587 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8
[LightGBM] [Debug] Re-bagging, using 7533 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8
[LightGBM] [Debug] Re-bagging, using 7538 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8
[LightGBM] [Debug] Re-bagging, using 7600 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7
[LightGBM] [Debug] Re-bagging, using 7535 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8
[LightGBM] [Debug] Re-bagging, using 7341 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8
[LightGBM] [Debug] Re-bagging, using 7341 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8
[LightGBM] [Debug] Re-bagging, using 7424 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8
[LightGBM] [Debug] Re-bagging, using 7405 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7460 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7631 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7543 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10
[LightGBM] [Debug] Re-bagging, using 7516 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 11
[LightGBM] [Debug] Re-bagging, using 7501 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7577 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10
[LightGBM] [Debug] Re-bagging, using 7646 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 12
[LightGBM] [Debug] Re-bagging, using 7392 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10
[LightGBM] [Debug] Re-bagging, using 7470 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7561 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10
Trial 56, Fold 4: Log loss = 0.2059442149093668, Average precision = 0.9734037759849372, ROC-AUC = 0.9688341249573786, Elapsed Time = 0.9462315000000672 seconds
Trial 56, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 56, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[LightGBM] [Info] Number of positive: 10150, number of negative: 10500
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.792012
[LightGBM] [Info] Total Bins 7944
[LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902
[LightGBM] [Info] Start training from score -0.033902
[LightGBM] [Debug] Re-bagging, using 7524 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7
[LightGBM] [Debug] Re-bagging, using 7588 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8
[LightGBM] [Debug] Re-bagging, using 7460 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7
[LightGBM] [Debug] Re-bagging, using 7456 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8
[LightGBM] [Debug] Re-bagging, using 7586 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7
[LightGBM] [Debug] Re-bagging, using 7524 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8
[LightGBM] [Debug] Re-bagging, using 7547 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8
[LightGBM] [Debug] Re-bagging, using 7594 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8
[LightGBM] [Debug] Re-bagging, using 7533 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8
[LightGBM] [Debug] Re-bagging, using 7341 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7334 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 10
[LightGBM] [Debug] Re-bagging, using 7416 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7410 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7468 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 11
[LightGBM] [Debug] Re-bagging, using 7629 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7543 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 11
[LightGBM] [Debug] Re-bagging, using 7509 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 11
[LightGBM] [Debug] Re-bagging, using 7485 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7583 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10
[LightGBM] [Debug] Re-bagging, using 7634 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 11
[LightGBM] [Debug] Re-bagging, using 7402 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10
[LightGBM] [Debug] Re-bagging, using 7461 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8
[LightGBM] [Debug] Re-bagging, using 7556 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10
Trial 56, Fold 5: Log loss = 0.21099260244594162, Average precision = 0.9715680379175089, ROC-AUC = 0.9680349293053157, Elapsed Time = 0.9077146999998149 seconds
Optimization Progress:  57%|#####6    | 57/100 [13:12<09:31, 13.30s/it]
Trial 57, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 57, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[LightGBM] [Info] Number of positive: 10130, number of negative: 10533
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791640
[LightGBM] [Info] Total Bins 7358
[LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012
[LightGBM] [Info] Start training from score -0.039012
[LightGBM] [Debug] Re-bagging, using 2241 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2204 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2114 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2130 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2242 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2196 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2168 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2204 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2184 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5
[LightGBM] [Debug] Re-bagging, using 2161 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2066 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2161 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 5
[LightGBM] [Debug] Re-bagging, using 2196 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2177 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2165 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2183 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2189 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2139 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2249 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5
[LightGBM] [Debug] Re-bagging, using 2186 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2113 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 5
[LightGBM] [Debug] Re-bagging, using 2218 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5
[LightGBM] [Debug] Re-bagging, using 2215 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
Trial 57, Fold 1: Log loss = 0.37892899961134713, Average precision = 0.9575913146504536, ROC-AUC = 0.9511021882154448, Elapsed Time = 0.9399035000005824 seconds
Trial 57, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 57, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[LightGBM] [Info] Number of positive: 10230, number of negative: 10471
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791600
[LightGBM] [Info] Total Bins 7374
[LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285
[LightGBM] [Info] Start training from score -0.023285
[LightGBM] [Debug] Re-bagging, using 2245 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2209 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2110 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2142 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2251 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2192 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2168 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 5
[LightGBM] [Debug] Re-bagging, using 2210 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2202 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2169 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2066 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2158 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2201 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2173 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2172 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2187 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2192 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5
[LightGBM] [Debug] Re-bagging, using 2147 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2253 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2190 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2115 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2226 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2229 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
Trial 57, Fold 2: Log loss = 0.3760319081535994, Average precision = 0.9562582062183459, ROC-AUC = 0.9522469446342134, Elapsed Time = 1.015178399999968 seconds
Trial 57, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 57, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[LightGBM] [Info] Number of positive: 10165, number of negative: 10517
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791401
[LightGBM] [Info] Total Bins 7372
[LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043
[LightGBM] [Info] Start training from score -0.034043
[LightGBM] [Debug] Re-bagging, using 2243 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2205 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2115 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2135 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2246 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2195 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2169 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5
[LightGBM] [Debug] Re-bagging, using 2201 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2197 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2162 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2074 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2153 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2200 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2175 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2165 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5
[LightGBM] [Debug] Re-bagging, using 2190 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2191 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2137 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 5
[LightGBM] [Debug] Re-bagging, using 2253 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5
[LightGBM] [Debug] Re-bagging, using 2196 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5
[LightGBM] [Debug] Re-bagging, using 2109 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2216 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2216 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
Trial 57, Fold 3: Log loss = 0.3728635305151177, Average precision = 0.9598185775067866, ROC-AUC = 0.9544234591547803, Elapsed Time = 1.0134969999999157 seconds
Trial 57, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 57, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[LightGBM] [Info] Number of positive: 10177, number of negative: 10479
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791090
[LightGBM] [Info] Total Bins 7355
[LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243
[LightGBM] [Info] Start training from score -0.029243
[LightGBM] [Debug] Re-bagging, using 2241 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2202 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2111 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 5
[LightGBM] [Debug] Re-bagging, using 2132 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2243 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2193 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2170 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2201 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5
[LightGBM] [Debug] Re-bagging, using 2185 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2156 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2069 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2158 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2192 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5
[LightGBM] [Debug] Re-bagging, using 2178 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2165 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2184 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2187 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5
[LightGBM] [Debug] Re-bagging, using 2144 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2247 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2186 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2108 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2216 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5
[LightGBM] [Debug] Re-bagging, using 2218 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
Trial 57, Fold 4: Log loss = 0.3768339494526506, Average precision = 0.9570589877268126, ROC-AUC = 0.9501171788378864, Elapsed Time = 1.086921899999652 seconds
Trial 57, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 57, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[LightGBM] [Info] Number of positive: 10150, number of negative: 10500
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.792012
[LightGBM] [Info] Total Bins 7363
[LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902
[LightGBM] [Info] Start training from score -0.033902
[LightGBM] [Debug] Re-bagging, using 2241 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5
[LightGBM] [Debug] Re-bagging, using 2201 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2111 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2131 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2240 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2193 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2171 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2199 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5
[LightGBM] [Debug] Re-bagging, using 2184 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2157 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2064 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2150 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2206 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2173 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 5
[LightGBM] [Debug] Re-bagging, using 2167 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2183 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2191 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5
[LightGBM] [Debug] Re-bagging, using 2133 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5
[LightGBM] [Debug] Re-bagging, using 2250 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2185 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5
[LightGBM] [Debug] Re-bagging, using 2110 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2211 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2218 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 6
Trial 57, Fold 5: Log loss = 0.37952485796871727, Average precision = 0.9561166398751471, ROC-AUC = 0.9498930940304331, Elapsed Time = 1.0671511999998984 seconds
Optimization Progress:  58%|#####8    | 58/100 [13:24<09:08, 13.05s/it]
Trial 58, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 58, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[LightGBM] [Info] Number of positive: 10130, number of negative: 10533
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795662
[LightGBM] [Info] Total Bins 11147
[LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 258
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012
[LightGBM] [Info] Start training from score -0.039012
[LightGBM] [Debug] Re-bagging, using 5292 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5231 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5263 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5098 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11
[LightGBM] [Debug] Re-bagging, using 5302 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11
[LightGBM] [Debug] Re-bagging, using 5265 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11
[LightGBM] [Debug] Re-bagging, using 5186 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11
[LightGBM] [Debug] Re-bagging, using 5301 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5205 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11
[LightGBM] [Debug] Re-bagging, using 5062 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11
Trial 58, Fold 1: Log loss = 0.4291124910075804, Average precision = 0.9672170501464812, ROC-AUC = 0.9607728655549341, Elapsed Time = 1.0771875000000364 seconds
Trial 58, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 58, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[LightGBM] [Info] Number of positive: 10230, number of negative: 10471
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.794298
[LightGBM] [Info] Total Bins 11158
[LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 257
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285
[LightGBM] [Info] Start training from score -0.023285
[LightGBM] [Debug] Re-bagging, using 5302 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5242 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5269 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5112 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5322 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 12
[LightGBM] [Debug] Re-bagging, using 5259 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 12
[LightGBM] [Debug] Re-bagging, using 5194 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11
[LightGBM] [Debug] Re-bagging, using 5311 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 12
[LightGBM] [Debug] Re-bagging, using 5221 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 12
[LightGBM] [Debug] Re-bagging, using 5085 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11
Trial 58, Fold 2: Log loss = 0.43217372256804676, Average precision = 0.9640188943857604, ROC-AUC = 0.9597874438419383, Elapsed Time = 1.1487709999992148 seconds
Trial 58, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 58, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[LightGBM] [Info] Number of positive: 10165, number of negative: 10517
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795455
[LightGBM] [Info] Total Bins 11164
[LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 258
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043
[LightGBM] [Info] Start training from score -0.034043
[LightGBM] [Debug] Re-bagging, using 5297 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11
[LightGBM] [Debug] Re-bagging, using 5234 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5266 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11
[LightGBM] [Debug] Re-bagging, using 5108 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5313 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 12
[LightGBM] [Debug] Re-bagging, using 5260 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 12
[LightGBM] [Debug] Re-bagging, using 5194 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 12
[LightGBM] [Debug] Re-bagging, using 5301 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5209 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 12
[LightGBM] [Debug] Re-bagging, using 5078 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10
Trial 58, Fold 3: Log loss = 0.4258938337035939, Average precision = 0.9683770395765154, ROC-AUC = 0.9628013310090435, Elapsed Time = 1.3509405000004335 seconds
Trial 58, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 58, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[LightGBM] [Info] Number of positive: 10177, number of negative: 10479
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795144
[LightGBM] [Info] Total Bins 11145
[LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 258
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243
[LightGBM] [Info] Start training from score -0.029243
[LightGBM] [Debug] Re-bagging, using 5291 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5227 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5260 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5100 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11
[LightGBM] [Debug] Re-bagging, using 5298 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11
[LightGBM] [Debug] Re-bagging, using 5264 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11
[LightGBM] [Debug] Re-bagging, using 5186 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12
[LightGBM] [Debug] Re-bagging, using 5297 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5199 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 12
[LightGBM] [Debug] Re-bagging, using 5066 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 12
Trial 58, Fold 4: Log loss = 0.4284234880137364, Average precision = 0.96641092870218, ROC-AUC = 0.9596838034197204, Elapsed Time = 1.3038344999995388 seconds
Trial 58, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 58, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[LightGBM] [Info] Number of positive: 10150, number of negative: 10500
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.796026
[LightGBM] [Info] Total Bins 11147
[LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 258
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902
[LightGBM] [Info] Start training from score -0.033902
[LightGBM] [Debug] Re-bagging, using 5289 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 12
[LightGBM] [Debug] Re-bagging, using 5227 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11
[LightGBM] [Debug] Re-bagging, using 5260 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12
[LightGBM] [Debug] Re-bagging, using 5095 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 12
[LightGBM] [Debug] Re-bagging, using 5296 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11
[LightGBM] [Debug] Re-bagging, using 5260 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 13
[LightGBM] [Debug] Re-bagging, using 5191 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 14
[LightGBM] [Debug] Re-bagging, using 5292 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11
[LightGBM] [Debug] Re-bagging, using 5199 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5066 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
Trial 58, Fold 5: Log loss = 0.4294260241321941, Average precision = 0.9645658906559297, ROC-AUC = 0.9585099306987719, Elapsed Time = 1.2730691999995543 seconds
Optimization Progress:  59%|#####8    | 59/100 [13:38<09:03, 13.26s/it]
Trial 59, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 59, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[LightGBM] [Info] Number of positive: 10130, number of negative: 10533
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791640
[LightGBM] [Info] Total Bins 7940
[LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012
[LightGBM] [Info] Start training from score -0.039012
[LightGBM] [Debug] Re-bagging, using 6760 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8
[LightGBM] [Debug] Re-bagging, using 6778 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8
[LightGBM] [Debug] Re-bagging, using 6679 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8
[LightGBM] [Debug] Re-bagging, using 6628 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8
[LightGBM] [Debug] Re-bagging, using 6807 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8
[LightGBM] [Debug] Re-bagging, using 6755 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9
[LightGBM] [Debug] Re-bagging, using 6700 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Debug] Re-bagging, using 6818 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8
[LightGBM] [Debug] Re-bagging, using 6697 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 10
[LightGBM] [Debug] Re-bagging, using 6546 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9
[LightGBM] [Debug] Re-bagging, using 6579 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Debug] Re-bagging, using 6631 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 8
[LightGBM] [Debug] Re-bagging, using 6639 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Re-bagging, using 6717 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9
[LightGBM] [Debug] Re-bagging, using 6851 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 11
[LightGBM] [Debug] Re-bagging, using 6724 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Debug] Re-bagging, using 6703 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9
[LightGBM] [Debug] Re-bagging, using 6680 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10
[LightGBM] [Debug] Re-bagging, using 6784 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8
[LightGBM] [Debug] Re-bagging, using 6788 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11
[LightGBM] [Debug] Re-bagging, using 6612 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8
[LightGBM] [Debug] Re-bagging, using 6680 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9
[LightGBM] [Debug] Re-bagging, using 6769 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11
[LightGBM] [Debug] Re-bagging, using 6719 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 14
[LightGBM] [Debug] Re-bagging, using 6867 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11
[LightGBM] [Debug] Re-bagging, using 6756 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11
Trial 59, Fold 1: Log loss = 0.22675598811748662, Average precision = 0.9723914444623574, ROC-AUC = 0.9673419628817099, Elapsed Time = 0.8552437999996982 seconds
Trial 59, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 59, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[LightGBM] [Info] Number of positive: 10230, number of negative: 10471
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791600
[LightGBM] [Info] Total Bins 7956
[LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285
[LightGBM] [Info] Start training from score -0.023285
[LightGBM] [Debug] Re-bagging, using 6773 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8
[LightGBM] [Debug] Re-bagging, using 6795 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Debug] Re-bagging, using 6687 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 8
[LightGBM] [Debug] Re-bagging, using 6643 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Re-bagging, using 6835 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9
[LightGBM] [Debug] Re-bagging, using 6744 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 7
[LightGBM] [Debug] Re-bagging, using 6713 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9
[LightGBM] [Debug] Re-bagging, using 6826 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9
[LightGBM] [Debug] Re-bagging, using 6724 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 10
[LightGBM] [Debug] Re-bagging, using 6566 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 11
[LightGBM] [Debug] Re-bagging, using 6573 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8
[LightGBM] [Debug] Re-bagging, using 6634 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8
[LightGBM] [Debug] Re-bagging, using 6648 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9
[LightGBM] [Debug] Re-bagging, using 6729 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10
[LightGBM] [Debug] Re-bagging, using 6866 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 8
[LightGBM] [Debug] Re-bagging, using 6745 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9
[LightGBM] [Debug] Re-bagging, using 6716 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Re-bagging, using 6697 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9
[LightGBM] [Debug] Re-bagging, using 6800 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
[LightGBM] [Debug] Re-bagging, using 6799 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 8
[LightGBM] [Debug] Re-bagging, using 6626 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 8
[LightGBM] [Debug] Re-bagging, using 6693 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9
[LightGBM] [Debug] Re-bagging, using 6790 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
[LightGBM] [Debug] Re-bagging, using 6734 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 12
[LightGBM] [Debug] Re-bagging, using 6876 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 12
[LightGBM] [Debug] Re-bagging, using 6764 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 13
Trial 59, Fold 2: Log loss = 0.2252146869930471, Average precision = 0.9707824030321003, ROC-AUC = 0.9679105027980773, Elapsed Time = 0.9205776999997397 seconds
Trial 59, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 59, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[LightGBM] [Info] Number of positive: 10165, number of negative: 10517
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791401
[LightGBM] [Info] Total Bins 7963
[LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043
[LightGBM] [Info] Start training from score -0.034043
[LightGBM] [Debug] Re-bagging, using 6766 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8
[LightGBM] [Debug] Re-bagging, using 6785 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9
[LightGBM] [Debug] Re-bagging, using 6681 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Debug] Re-bagging, using 6641 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 11
[LightGBM] [Debug] Re-bagging, using 6823 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8
[LightGBM] [Debug] Re-bagging, using 6745 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 7
[LightGBM] [Debug] Re-bagging, using 6710 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9
[LightGBM] [Debug] Re-bagging, using 6820 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 8
[LightGBM] [Debug] Re-bagging, using 6699 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8
[LightGBM] [Debug] Re-bagging, using 6574 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9
[LightGBM] [Debug] Re-bagging, using 6572 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8
[LightGBM] [Debug] Re-bagging, using 6629 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10
[LightGBM] [Debug] Re-bagging, using 6640 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 8
[LightGBM] [Debug] Re-bagging, using 6727 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10
[LightGBM] [Debug] Re-bagging, using 6851 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 8
[LightGBM] [Debug] Re-bagging, using 6737 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10
[LightGBM] [Debug] Re-bagging, using 6714 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 11
[LightGBM] [Debug] Re-bagging, using 6687 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10
[LightGBM] [Debug] Re-bagging, using 6785 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9
[LightGBM] [Debug] Re-bagging, using 6806 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 11
[LightGBM] [Debug] Re-bagging, using 6627 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
[LightGBM] [Debug] Re-bagging, using 6673 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9
[LightGBM] [Debug] Re-bagging, using 6767 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 12
[LightGBM] [Debug] Re-bagging, using 6737 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 8
[LightGBM] [Debug] Re-bagging, using 6888 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 14
[LightGBM] [Debug] Re-bagging, using 6758 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10
Trial 59, Fold 3: Log loss = 0.2235571018816946, Average precision = 0.972709413979505, ROC-AUC = 0.9680875653706502, Elapsed Time = 0.9554589000008491 seconds
Trial 59, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 59, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[LightGBM] [Info] Number of positive: 10177, number of negative: 10479
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.792454
[LightGBM] [Info] Total Bins 7949
[LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 256
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243
[LightGBM] [Info] Start training from score -0.029243
[LightGBM] [Debug] Re-bagging, using 6757 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Debug] Re-bagging, using 6775 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8
[LightGBM] [Debug] Re-bagging, using 6676 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Debug] Re-bagging, using 6630 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8
[LightGBM] [Debug] Re-bagging, using 6802 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 8
[LightGBM] [Debug] Re-bagging, using 6752 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Re-bagging, using 6701 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Debug] Re-bagging, using 6812 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 10
[LightGBM] [Debug] Re-bagging, using 6694 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9
[LightGBM] [Debug] Re-bagging, using 6548 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 7
[LightGBM] [Debug] Re-bagging, using 6569 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9
[LightGBM] [Debug] Re-bagging, using 6634 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9
[LightGBM] [Debug] Re-bagging, using 6638 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9
[LightGBM] [Debug] Re-bagging, using 6714 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
[LightGBM] [Debug] Re-bagging, using 6845 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9
[LightGBM] [Debug] Re-bagging, using 6725 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 8
[LightGBM] [Debug] Re-bagging, using 6701 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9
[LightGBM] [Debug] Re-bagging, using 6683 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Debug] Re-bagging, using 6775 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 8
[LightGBM] [Debug] Re-bagging, using 6791 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9
[LightGBM] [Debug] Re-bagging, using 6611 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 8
[LightGBM] [Debug] Re-bagging, using 6670 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10
[LightGBM] [Debug] Re-bagging, using 6770 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Debug] Re-bagging, using 6721 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9
[LightGBM] [Debug] Re-bagging, using 6862 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10
[LightGBM] [Debug] Re-bagging, using 6752 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9
Trial 59, Fold 4: Log loss = 0.22644173638755147, Average precision = 0.9724764906199391, ROC-AUC = 0.9675457985507556, Elapsed Time = 0.9750905000000785 seconds
Trial 59, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 59, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[LightGBM] [Info] Number of positive: 10150, number of negative: 10500
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.792012
[LightGBM] [Info] Total Bins 7944
[LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902
[LightGBM] [Info] Start training from score -0.033902
[LightGBM] [Debug] Re-bagging, using 6755 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Debug] Re-bagging, using 6775 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8
[LightGBM] [Debug] Re-bagging, using 6674 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 7
[LightGBM] [Debug] Re-bagging, using 6625 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8
[LightGBM] [Debug] Re-bagging, using 6801 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8
[LightGBM] [Debug] Re-bagging, using 6745 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 10
[LightGBM] [Debug] Re-bagging, using 6709 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Debug] Re-bagging, using 6805 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Debug] Re-bagging, using 6692 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8
[LightGBM] [Debug] Re-bagging, using 6551 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 11
[LightGBM] [Debug] Re-bagging, using 6559 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Debug] Re-bagging, using 6629 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9
[LightGBM] [Debug] Re-bagging, using 6639 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
[LightGBM] [Debug] Re-bagging, using 6724 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Re-bagging, using 6842 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 8
[LightGBM] [Debug] Re-bagging, using 6726 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9
[LightGBM] [Debug] Re-bagging, using 6697 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9
[LightGBM] [Debug] Re-bagging, using 6669 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 11
[LightGBM] [Debug] Re-bagging, using 6776 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11
[LightGBM] [Debug] Re-bagging, using 6784 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9
[LightGBM] [Debug] Re-bagging, using 6619 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9
[LightGBM] [Debug] Re-bagging, using 6662 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9
[LightGBM] [Debug] Re-bagging, using 6764 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9
[LightGBM] [Debug] Re-bagging, using 6726 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9
[LightGBM] [Debug] Re-bagging, using 6863 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9
[LightGBM] [Debug] Re-bagging, using 6749 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11
Trial 59, Fold 5: Log loss = 0.2278463569606474, Average precision = 0.9716918799256338, ROC-AUC = 0.9674079854337365, Elapsed Time = 1.0714367000000493 seconds
Optimization Progress:  60%|######    | 60/100 [13:50<08:36, 12.91s/it]
Trial 60, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 60, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[LightGBM] [Info] Number of positive: 10130, number of negative: 10533
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795662
[LightGBM] [Info] Total Bins 24804
[LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 258
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012
[LightGBM] [Info] Start training from score -0.039012
[LightGBM] [Debug] Re-bagging, using 10496 data to train
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 14
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 9
[LightGBM] [Debug] Re-bagging, using 10463 data to train
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12
[LightGBM] [Debug] Re-bagging, using 10459 data to train
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12
[LightGBM] [Debug] Re-bagging, using 10304 data to train
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 13
[LightGBM] [Debug] Re-bagging, using 10526 data to train
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10349 data to train
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 13
[LightGBM] [Debug] Re-bagging, using 10424 data to train
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 9
[LightGBM] [Debug] Re-bagging, using 10397 data to train
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 14
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10455 data to train
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 16
[LightGBM] [Debug] Re-bagging, using 10215 data to train
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 15
[LightGBM] [Debug] Re-bagging, using 10299 data to train
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12
Trial 60, Fold 1: Log loss = 0.27400577030241996, Average precision = 0.9722423793246776, ROC-AUC = 0.966849291784364, Elapsed Time = 0.6557243000006565 seconds
Trial 60, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 60, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[LightGBM] [Info] Number of positive: 10230, number of negative: 10471
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795629
[LightGBM] [Info] Total Bins 24811
[LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 258
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285
[LightGBM] [Info] Start training from score -0.023285
[LightGBM] [Debug] Re-bagging, using 10518 data to train
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10481 data to train
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 9
[LightGBM] [Debug] Re-bagging, using 10475 data to train
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10330 data to train
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12
[LightGBM] [Debug] Re-bagging, using 10555 data to train
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10346 data to train
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12
[LightGBM] [Debug] Re-bagging, using 10429 data to train
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 13
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12
[LightGBM] [Debug] Re-bagging, using 10416 data to train
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 15
[LightGBM] [Debug] Re-bagging, using 10483 data to train
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 14
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 14
[LightGBM] [Debug] Re-bagging, using 10237 data to train
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12
[LightGBM] [Debug] Re-bagging, using 10313 data to train
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10
Trial 60, Fold 2: Log loss = 0.2655808748637191, Average precision = 0.9727684389468915, ROC-AUC = 0.9691335140750623, Elapsed Time = 0.7152415000000474 seconds
Trial 60, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 60, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[LightGBM] [Info] Number of positive: 10165, number of negative: 10517
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795455
[LightGBM] [Info] Total Bins 24804
[LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 258
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043
[LightGBM] [Info] Start training from score -0.034043
[LightGBM] [Debug] Re-bagging, using 10506 data to train
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10470 data to train
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 9
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10465 data to train
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 15
[LightGBM] [Debug] Re-bagging, using 10325 data to train
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 13
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10537 data to train
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 9
[LightGBM] [Debug] Re-bagging, using 10347 data to train
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10429 data to train
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 13
[LightGBM] [Debug] Re-bagging, using 10403 data to train
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 13
[LightGBM] [Debug] Re-bagging, using 10463 data to train
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10236 data to train
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 13
[LightGBM] [Debug] Re-bagging, using 10305 data to train
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10
Trial 60, Fold 3: Log loss = 0.270243769829823, Average precision = 0.9713635394785448, ROC-AUC = 0.9683922220964838, Elapsed Time = 0.7405485999997836 seconds
Trial 60, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 60, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[LightGBM] [Info] Number of positive: 10177, number of negative: 10479
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795144
[LightGBM] [Info] Total Bins 24794
[LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 258
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243
[LightGBM] [Info] Start training from score -0.029243
[LightGBM] [Debug] Re-bagging, using 10492 data to train
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 13
[LightGBM] [Debug] Re-bagging, using 10459 data to train
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 13
[LightGBM] [Debug] Re-bagging, using 10454 data to train
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 9
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10303 data to train
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10520 data to train
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12
[LightGBM] [Debug] Re-bagging, using 10345 data to train
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10424 data to train
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12
[LightGBM] [Debug] Re-bagging, using 10396 data to train
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 13
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12
[LightGBM] [Debug] Re-bagging, using 10451 data to train
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 14
[LightGBM] [Debug] Re-bagging, using 10214 data to train
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10295 data to train
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 13
Trial 60, Fold 4: Log loss = 0.27232331528172177, Average precision = 0.9720421759528667, ROC-AUC = 0.9666652062156794, Elapsed Time = 0.7559318000003259 seconds
Trial 60, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 60, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[LightGBM] [Info] Number of positive: 10150, number of negative: 10500
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.796026
[LightGBM] [Info] Total Bins 24798
[LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 258
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902
[LightGBM] [Info] Start training from score -0.033902
[LightGBM] [Debug] Re-bagging, using 10489 data to train
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12
[LightGBM] [Debug] Re-bagging, using 10456 data to train
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 9
[LightGBM] [Debug] Re-bagging, using 10455 data to train
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 13
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10296 data to train
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12
[LightGBM] [Debug] Re-bagging, using 10516 data to train
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10338 data to train
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 13
[LightGBM] [Debug] Re-bagging, using 10430 data to train
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10391 data to train
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 13
[LightGBM] [Debug] Re-bagging, using 10448 data to train
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10216 data to train
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 14
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 9
[LightGBM] [Debug] Re-bagging, using 10285 data to train
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12
Trial 60, Fold 5: Log loss = 0.2777135945673179, Average precision = 0.969383736130569, ROC-AUC = 0.9651062928487821, Elapsed Time = 0.7577618000004804 seconds
Optimization Progress:  61%|######1   | 61/100 [14:01<08:01, 12.34s/it]
Trial 61, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 61, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[LightGBM] [Info] Number of positive: 10130, number of negative: 10533
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791640
[LightGBM] [Info] Total Bins 12968
[LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012
[LightGBM] [Info] Start training from score -0.039012
[LightGBM] [Debug] Re-bagging, using 3425 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 3
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 5 and depth = 3
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 3
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 3
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4
[LightGBM] [Debug] Re-bagging, using 3435 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 5
[LightGBM] [Debug] Re-bagging, using 3339 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 6
Trial 61, Fold 1: Log loss = 0.48690144509883243, Average precision = 0.9569593258807947, ROC-AUC = 0.9484052303045075, Elapsed Time = 0.49105599999984406 seconds
Trial 61, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 61, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[LightGBM] [Info] Number of positive: 10230, number of negative: 10471
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791600
[LightGBM] [Info] Total Bins 12982
[LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285
[LightGBM] [Info] Start training from score -0.023285
[LightGBM] [Debug] Re-bagging, using 3432 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 5 and depth = 3
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 5 and depth = 3
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 4
[LightGBM] [Debug] Re-bagging, using 3444 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 3
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 3
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 5
[LightGBM] [Debug] Re-bagging, using 3339 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 5
Trial 61, Fold 2: Log loss = 0.4883409069805142, Average precision = 0.9495550603192829, ROC-AUC = 0.943807567419265, Elapsed Time = 0.4926711999996769 seconds
Trial 61, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 61, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[LightGBM] [Info] Number of positive: 10165, number of negative: 10517
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791401
[LightGBM] [Info] Total Bins 12988
[LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043
[LightGBM] [Info] Start training from score -0.034043
[LightGBM] [Debug] Re-bagging, using 3427 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 5 and depth = 3
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 3
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 3
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Debug] Re-bagging, using 3439 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 5
[LightGBM] [Debug] Re-bagging, using 3341 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 6
Trial 61, Fold 3: Log loss = 0.48699146362382356, Average precision = 0.9595972136306565, ROC-AUC = 0.9528731634820043, Elapsed Time = 0.5241814000000886 seconds
Trial 61, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 61, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[LightGBM] [Info] Number of positive: 10177, number of negative: 10479
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791090
[LightGBM] [Info] Total Bins 12968
[LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243
[LightGBM] [Info] Start training from score -0.029243
[LightGBM] [Debug] Re-bagging, using 3425 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 3
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 3
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 3
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 3
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4
[LightGBM] [Debug] Re-bagging, using 3432 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 5 and depth = 3
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 3
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 5 and depth = 3
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4
[LightGBM] [Debug] Re-bagging, using 3335 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4
Trial 61, Fold 4: Log loss = 0.48865133948204637, Average precision = 0.9537333610631538, ROC-AUC = 0.94517988583446, Elapsed Time = 0.545862599999964 seconds
Trial 61, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 61, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[LightGBM] [Info] Number of positive: 10150, number of negative: 10500
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.792012
[LightGBM] [Info] Total Bins 12970
[LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902
[LightGBM] [Info] Start training from score -0.033902
[LightGBM] [Debug] Re-bagging, using 3423 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 3
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4
[LightGBM] [Debug] Re-bagging, using 3432 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 9 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 3
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 7 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 8 and depth = 4
[LightGBM] [Debug] Re-bagging, using 3336 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 6 and depth = 4
Trial 61, Fold 5: Log loss = 0.480761489092147, Average precision = 0.9529118812446569, ROC-AUC = 0.9442202032588298, Elapsed Time = 0.5731816999996227 seconds
Optimization Progress:  62%|######2   | 62/100 [14:11<07:22, 11.66s/it]
Trial 62, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 62, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[LightGBM] [Info] Number of positive: 10130, number of negative: 10533
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791640
[LightGBM] [Info] Total Bins 23584
[LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012
[LightGBM] [Info] Start training from score -0.039012
[LightGBM] [Debug] Re-bagging, using 13595 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 12
[LightGBM] [Debug] Re-bagging, using 13670 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 12
[LightGBM] [Debug] Re-bagging, using 13744 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 86 and depth = 14
[LightGBM] [Debug] Re-bagging, using 13528 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 86 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 86 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 13
[LightGBM] [Debug] Re-bagging, using 13669 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 14
[LightGBM] [Debug] Re-bagging, using 13563 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 16
[LightGBM] [Debug] Re-bagging, using 13639 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 14
[LightGBM] [Debug] Re-bagging, using 13649 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 86 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 14
Trial 62, Fold 1: Log loss = 0.2946442850084093, Average precision = 0.9735029491011513, ROC-AUC = 0.9683388059630155, Elapsed Time = 1.019398600000386 seconds
Trial 62, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 62, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[LightGBM] [Info] Number of positive: 10230, number of negative: 10471
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.792956
[LightGBM] [Info] Total Bins 23515
[LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 256
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285
[LightGBM] [Info] Start training from score -0.023285
[LightGBM] [Debug] Re-bagging, using 13622 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 12
[LightGBM] [Debug] Re-bagging, using 13691 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 12
[LightGBM] [Debug] Re-bagging, using 13772 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 14
[LightGBM] [Debug] Re-bagging, using 13560 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 14
[LightGBM] [Debug] Re-bagging, using 13694 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 12
[LightGBM] [Debug] Re-bagging, using 13572 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 86 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 14
[LightGBM] [Debug] Re-bagging, using 13655 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 17
[LightGBM] [Debug] Re-bagging, using 13686 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 12
Trial 62, Fold 2: Log loss = 0.2933031130971272, Average precision = 0.972143220909115, ROC-AUC = 0.9683382798053137, Elapsed Time = 1.161121699999967 seconds
Trial 62, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 62, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[LightGBM] [Info] Number of positive: 10165, number of negative: 10517
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791401
[LightGBM] [Info] Total Bins 23508
[LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043
[LightGBM] [Info] Start training from score -0.034043
[LightGBM] [Debug] Re-bagging, using 13606 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 12
[LightGBM] [Debug] Re-bagging, using 13680 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 11
[LightGBM] [Debug] Re-bagging, using 13760 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 88 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 86 and depth = 14
[LightGBM] [Debug] Re-bagging, using 13550 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 12
[LightGBM] [Debug] Re-bagging, using 13676 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 13
[LightGBM] [Debug] Re-bagging, using 13566 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 14
[LightGBM] [Debug] Re-bagging, using 13646 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 13
[LightGBM] [Debug] Re-bagging, using 13672 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 13
Trial 62, Fold 3: Log loss = 0.29255958868463744, Average precision = 0.9741501584594792, ROC-AUC = 0.9695752593156188, Elapsed Time = 1.2140692000002673 seconds
Trial 62, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 62, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[LightGBM] [Info] Number of positive: 10177, number of negative: 10479
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.792454
[LightGBM] [Info] Total Bins 23522
[LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 256
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243
[LightGBM] [Info] Start training from score -0.029243
[LightGBM] [Debug] Re-bagging, using 13590 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 11
[LightGBM] [Debug] Re-bagging, using 13665 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 12
[LightGBM] [Debug] Re-bagging, using 13741 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 16
[LightGBM] [Debug] Re-bagging, using 13524 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 12
[LightGBM] [Debug] Re-bagging, using 13661 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 12
[LightGBM] [Debug] Re-bagging, using 13559 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 12
[LightGBM] [Debug] Re-bagging, using 13634 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 14
[LightGBM] [Debug] Re-bagging, using 13651 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 14
Trial 62, Fold 4: Log loss = 0.2928080487627608, Average precision = 0.9730172557150623, ROC-AUC = 0.968042724450453, Elapsed Time = 1.2142837999999756 seconds
Trial 62, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 62, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[LightGBM] [Info] Number of positive: 10150, number of negative: 10500
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.793364
[LightGBM] [Info] Total Bins 23533
[LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 256
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902
[LightGBM] [Info] Start training from score -0.033902
[LightGBM] [Debug] Re-bagging, using 13587 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 13
[LightGBM] [Debug] Re-bagging, using 13661 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 13
[LightGBM] [Debug] Re-bagging, using 13740 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 14
[LightGBM] [Debug] Re-bagging, using 13518 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 15
[LightGBM] [Debug] Re-bagging, using 13656 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 86 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 19
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 16
[LightGBM] [Debug] Re-bagging, using 13553 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 13
[LightGBM] [Debug] Re-bagging, using 13635 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 13
[LightGBM] [Debug] Re-bagging, using 13643 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 14
Trial 62, Fold 5: Log loss = 0.29784747770938497, Average precision = 0.9711117089106877, ROC-AUC = 0.9667426936439811, Elapsed Time = 1.2246425000003 seconds
Optimization Progress:  63%|######3   | 63/100 [14:24<07:29, 12.14s/it]
Trial 63, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 63, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[LightGBM] [Info] Number of positive: 10130, number of negative: 10533
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791640
[LightGBM] [Info] Total Bins 12063
[LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012
[LightGBM] [Info] Start training from score -0.039012
[LightGBM] [Debug] Re-bagging, using 18369 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
[LightGBM] [Debug] Re-bagging, using 18393 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9
[LightGBM] [Debug] Re-bagging, using 18421 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10
[LightGBM] [Debug] Re-bagging, using 18353 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11
Trial 63, Fold 1: Log loss = 0.3871478465053016, Average precision = 0.9701212026243069, ROC-AUC = 0.964097219234979, Elapsed Time = 0.6317716000003202 seconds
Trial 63, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 63, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[LightGBM] [Info] Number of positive: 10230, number of negative: 10471
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791600
[LightGBM] [Info] Total Bins 12084
[LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285
[LightGBM] [Info] Start training from score -0.023285
[LightGBM] [Debug] Re-bagging, using 18405 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11
[LightGBM] [Debug] Re-bagging, using 18423 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Debug] Re-bagging, using 18454 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 8
[LightGBM] [Debug] Re-bagging, using 18390 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
Trial 63, Fold 2: Log loss = 0.3901727806816126, Average precision = 0.9682952301589332, ROC-AUC = 0.9648423901975787, Elapsed Time = 0.8101253000004363 seconds
Trial 63, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 63, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[LightGBM] [Info] Number of positive: 10165, number of negative: 10517
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791401
[LightGBM] [Info] Total Bins 12088
[LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043
[LightGBM] [Info] Start training from score -0.034043
[LightGBM] [Debug] Re-bagging, using 18388 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11
[LightGBM] [Debug] Re-bagging, using 18406 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9
[LightGBM] [Debug] Re-bagging, using 18438 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 12
[LightGBM] [Debug] Re-bagging, using 18374 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 10
Trial 63, Fold 3: Log loss = 0.38395415345069356, Average precision = 0.9714498437181125, ROC-AUC = 0.967068707202088, Elapsed Time = 0.7840771999999561 seconds
Trial 63, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 63, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[LightGBM] [Info] Number of positive: 10177, number of negative: 10479
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791090
[LightGBM] [Info] Total Bins 12066
[LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243
[LightGBM] [Info] Start training from score -0.029243
[LightGBM] [Debug] Re-bagging, using 18362 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 12
[LightGBM] [Debug] Re-bagging, using 18386 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9
[LightGBM] [Debug] Re-bagging, using 18416 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 11
[LightGBM] [Debug] Re-bagging, using 18350 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10
Trial 63, Fold 4: Log loss = 0.3813460075386185, Average precision = 0.9717720706462006, ROC-AUC = 0.9665523193153882, Elapsed Time = 0.7806607000002259 seconds
Trial 63, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 63, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[LightGBM] [Info] Number of positive: 10150, number of negative: 10500
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.792012
[LightGBM] [Info] Total Bins 12068
[LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902
[LightGBM] [Info] Start training from score -0.033902
[LightGBM] [Debug] Re-bagging, using 18356 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10
[LightGBM] [Debug] Re-bagging, using 18382 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 11
[LightGBM] [Debug] Re-bagging, using 18411 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9
[LightGBM] [Debug] Re-bagging, using 18345 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11
Trial 63, Fold 5: Log loss = 0.39025751567850453, Average precision = 0.9689818062249139, ROC-AUC = 0.9639260539174703, Elapsed Time = 0.7991799999999785 seconds
Optimization Progress:  64%|######4   | 64/100 [14:36<07:06, 11.85s/it]
Trial 64, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 64, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[LightGBM] [Info] Number of positive: 10130, number of negative: 10533
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791640
[LightGBM] [Info] Total Bins 10006
[LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012
[LightGBM] [Info] Start training from score -0.039012
[LightGBM] [Debug] Re-bagging, using 15148 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 13
[LightGBM] [Debug] Re-bagging, using 15286 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 12
[LightGBM] [Debug] Re-bagging, using 15252 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11
[LightGBM] [Debug] Re-bagging, using 15126 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 12
[LightGBM] [Debug] Re-bagging, using 15207 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 12
[LightGBM] [Debug] Re-bagging, using 15173 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 12
[LightGBM] [Debug] Re-bagging, using 15175 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 9
[LightGBM] [Debug] Re-bagging, using 15184 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 12
[LightGBM] [Debug] Re-bagging, using 15259 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 13
[LightGBM] [Debug] Re-bagging, using 15110 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 12
[LightGBM] [Debug] Re-bagging, using 15164 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 11
[LightGBM] [Debug] Re-bagging, using 15065 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 14
[LightGBM] [Debug] Re-bagging, using 15136 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 12
[LightGBM] [Debug] Re-bagging, using 15053 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11
[LightGBM] [Debug] Re-bagging, using 15282 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10
[LightGBM] [Debug] Re-bagging, using 15208 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 12
[LightGBM] [Debug] Re-bagging, using 15240 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 12
[LightGBM] [Debug] Re-bagging, using 15171 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 11
[LightGBM] [Debug] Re-bagging, using 15191 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11
[LightGBM] [Debug] Re-bagging, using 15330 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 12
[LightGBM] [Debug] Re-bagging, using 15061 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 11
[LightGBM] [Debug] Re-bagging, using 15175 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 12
[LightGBM] [Debug] Re-bagging, using 15148 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 12
[LightGBM] [Debug] Re-bagging, using 15192 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9
[LightGBM] [Debug] Re-bagging, using 15329 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 11
[LightGBM] [Debug] Re-bagging, using 15118 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10
[LightGBM] [Debug] Re-bagging, using 15213 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 11
[LightGBM] [Debug] Re-bagging, using 15256 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 14
[LightGBM] [Debug] Re-bagging, using 15262 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 10
[LightGBM] [Debug] Re-bagging, using 15094 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Debug] Re-bagging, using 15149 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 12
[LightGBM] [Debug] Re-bagging, using 15200 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 10
Trial 64, Fold 1: Log loss = 0.31630301819399675, Average precision = 0.9665739919306886, ROC-AUC = 0.9600088780870172, Elapsed Time = 1.321477500000583 seconds
Trial 64, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 64, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[LightGBM] [Info] Number of positive: 10230, number of negative: 10471
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791600
[LightGBM] [Info] Total Bins 10022
[LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285
[LightGBM] [Info] Start training from score -0.023285
[LightGBM] [Debug] Re-bagging, using 15180 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10
[LightGBM] [Debug] Re-bagging, using 15312 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 14
[LightGBM] [Debug] Re-bagging, using 15279 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10
[LightGBM] [Debug] Re-bagging, using 15158 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 12
[LightGBM] [Debug] Re-bagging, using 15235 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 12
[LightGBM] [Debug] Re-bagging, using 15192 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 11
[LightGBM] [Debug] Re-bagging, using 15194 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10
[LightGBM] [Debug] Re-bagging, using 15225 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11
[LightGBM] [Debug] Re-bagging, using 15288 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9
[LightGBM] [Debug] Re-bagging, using 15142 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11
[LightGBM] [Debug] Re-bagging, using 15187 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 13
[LightGBM] [Debug] Re-bagging, using 15082 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 11
[LightGBM] [Debug] Re-bagging, using 15159 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10
[LightGBM] [Debug] Re-bagging, using 15068 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 11
[LightGBM] [Debug] Re-bagging, using 15315 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 11
[LightGBM] [Debug] Re-bagging, using 15254 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 10
[LightGBM] [Debug] Re-bagging, using 15279 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11
[LightGBM] [Debug] Re-bagging, using 15207 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9
[LightGBM] [Debug] Re-bagging, using 15228 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10
[LightGBM] [Debug] Re-bagging, using 15336 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 16
[LightGBM] [Debug] Re-bagging, using 15089 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 11
[LightGBM] [Debug] Re-bagging, using 15186 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10
[LightGBM] [Debug] Re-bagging, using 15171 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 10
[LightGBM] [Debug] Re-bagging, using 15235 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 10
[LightGBM] [Debug] Re-bagging, using 15358 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10
[LightGBM] [Debug] Re-bagging, using 15154 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10
[LightGBM] [Debug] Re-bagging, using 15231 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 10
[LightGBM] [Debug] Re-bagging, using 15290 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 14
[LightGBM] [Debug] Re-bagging, using 15293 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11
[LightGBM] [Debug] Re-bagging, using 15133 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 12
[LightGBM] [Debug] Re-bagging, using 15172 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 12
[LightGBM] [Debug] Re-bagging, using 15218 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11
Trial 64, Fold 2: Log loss = 0.31566260066916224, Average precision = 0.9642587196322059, ROC-AUC = 0.9608000568143709, Elapsed Time = 1.3881407999997464 seconds
Trial 64, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 64, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[LightGBM] [Info] Number of positive: 10165, number of negative: 10517
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791401
[LightGBM] [Info] Total Bins 10024
[LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043
[LightGBM] [Info] Start training from score -0.034043
[LightGBM] [Debug] Re-bagging, using 15163 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 10
[LightGBM] [Debug] Re-bagging, using 15300 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 14
[LightGBM] [Debug] Re-bagging, using 15264 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10
[LightGBM] [Debug] Re-bagging, using 15146 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 12
[LightGBM] [Debug] Re-bagging, using 15219 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 12
[LightGBM] [Debug] Re-bagging, using 15181 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 14
[LightGBM] [Debug] Re-bagging, using 15183 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9
[LightGBM] [Debug] Re-bagging, using 15207 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 11
[LightGBM] [Debug] Re-bagging, using 15267 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 13
[LightGBM] [Debug] Re-bagging, using 15133 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 12
[LightGBM] [Debug] Re-bagging, using 15177 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 10
[LightGBM] [Debug] Re-bagging, using 15074 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10
[LightGBM] [Debug] Re-bagging, using 15151 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Debug] Re-bagging, using 15053 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10
[LightGBM] [Debug] Re-bagging, using 15298 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 12
[LightGBM] [Debug] Re-bagging, using 15222 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 10
[LightGBM] [Debug] Re-bagging, using 15265 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 12
[LightGBM] [Debug] Re-bagging, using 15195 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 11
[LightGBM] [Debug] Re-bagging, using 15205 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 11
[LightGBM] [Debug] Re-bagging, using 15348 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 13
[LightGBM] [Debug] Re-bagging, using 15080 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11
[LightGBM] [Debug] Re-bagging, using 15163 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 12
[LightGBM] [Debug] Re-bagging, using 15156 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 12
[LightGBM] [Debug] Re-bagging, using 15206 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10
[LightGBM] [Debug] Re-bagging, using 15353 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 14
[LightGBM] [Debug] Re-bagging, using 15145 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10
[LightGBM] [Debug] Re-bagging, using 15209 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 10
[LightGBM] [Debug] Re-bagging, using 15276 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11
[LightGBM] [Debug] Re-bagging, using 15277 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 13
[LightGBM] [Debug] Re-bagging, using 15115 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 9
[LightGBM] [Debug] Re-bagging, using 15164 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11
[LightGBM] [Debug] Re-bagging, using 15216 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 11
Trial 64, Fold 3: Log loss = 0.31411749801159977, Average precision = 0.9676660962346362, ROC-AUC = 0.9623256481686586, Elapsed Time = 1.5844845999999961 seconds
Trial 64, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 64, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[LightGBM] [Info] Number of positive: 10177, number of negative: 10479
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791090
[LightGBM] [Info] Total Bins 10003
[LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243
[LightGBM] [Info] Start training from score -0.029243
[LightGBM] [Debug] Re-bagging, using 15143 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 10
[LightGBM] [Debug] Re-bagging, using 15279 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10
[LightGBM] [Debug] Re-bagging, using 15247 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 10
[LightGBM] [Debug] Re-bagging, using 15124 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 9
[LightGBM] [Debug] Re-bagging, using 15201 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 11
[LightGBM] [Debug] Re-bagging, using 15168 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 12
[LightGBM] [Debug] Re-bagging, using 15168 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 11
[LightGBM] [Debug] Re-bagging, using 15185 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 11
[LightGBM] [Debug] Re-bagging, using 15248 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 11
[LightGBM] [Debug] Re-bagging, using 15109 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11
[LightGBM] [Debug] Re-bagging, using 15157 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11
[LightGBM] [Debug] Re-bagging, using 15055 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 12
[LightGBM] [Debug] Re-bagging, using 15141 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11
[LightGBM] [Debug] Re-bagging, using 15040 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 11
[LightGBM] [Debug] Re-bagging, using 15284 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 12
[LightGBM] [Debug] Re-bagging, using 15201 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 12
[LightGBM] [Debug] Re-bagging, using 15236 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 14
[LightGBM] [Debug] Re-bagging, using 15163 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 13
[LightGBM] [Debug] Re-bagging, using 15178 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 14
[LightGBM] [Debug] Re-bagging, using 15329 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9
[LightGBM] [Debug] Re-bagging, using 15058 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11
[LightGBM] [Debug] Re-bagging, using 15161 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 13
[LightGBM] [Debug] Re-bagging, using 15147 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 12
[LightGBM] [Debug] Re-bagging, using 15200 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 13
[LightGBM] [Debug] Re-bagging, using 15321 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 12
[LightGBM] [Debug] Re-bagging, using 15113 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 12
[LightGBM] [Debug] Re-bagging, using 15193 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9
[LightGBM] [Debug] Re-bagging, using 15257 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 14
[LightGBM] [Debug] Re-bagging, using 15256 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 10
[LightGBM] [Debug] Re-bagging, using 15103 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 12
[LightGBM] [Debug] Re-bagging, using 15128 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11
[LightGBM] [Debug] Re-bagging, using 15199 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 9
Trial 64, Fold 4: Log loss = 0.315470578568722, Average precision = 0.9666878301659132, ROC-AUC = 0.9606021737471715, Elapsed Time = 1.5104277000000366 seconds
Trial 64, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 64, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[LightGBM] [Info] Number of positive: 10150, number of negative: 10500
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.792012
[LightGBM] [Info] Total Bins 10007
[LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902
[LightGBM] [Info] Start training from score -0.033902
[LightGBM] [Debug] Re-bagging, using 15138 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 12
[LightGBM] [Debug] Re-bagging, using 15277 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11
[LightGBM] [Debug] Re-bagging, using 15245 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11
[LightGBM] [Debug] Re-bagging, using 15118 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11
[LightGBM] [Debug] Re-bagging, using 15195 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11
[LightGBM] [Debug] Re-bagging, using 15160 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 12
[LightGBM] [Debug] Re-bagging, using 15171 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10
[LightGBM] [Debug] Re-bagging, using 15176 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 14
[LightGBM] [Debug] Re-bagging, using 15244 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 12
[LightGBM] [Debug] Re-bagging, using 15107 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 12
[LightGBM] [Debug] Re-bagging, using 15152 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 9
[LightGBM] [Debug] Re-bagging, using 15046 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 11
[LightGBM] [Debug] Re-bagging, using 15135 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 12
[LightGBM] [Debug] Re-bagging, using 15041 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10
[LightGBM] [Debug] Re-bagging, using 15282 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 13
[LightGBM] [Debug] Re-bagging, using 15199 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 11
[LightGBM] [Debug] Re-bagging, using 15231 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 12
[LightGBM] [Debug] Re-bagging, using 15154 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 11
[LightGBM] [Debug] Re-bagging, using 15173 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 11
[LightGBM] [Debug] Re-bagging, using 15318 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11
[LightGBM] [Debug] Re-bagging, using 15058 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 12
[LightGBM] [Debug] Re-bagging, using 15157 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 14
[LightGBM] [Debug] Re-bagging, using 15135 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 13
[LightGBM] [Debug] Re-bagging, using 15200 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 12
[LightGBM] [Debug] Re-bagging, using 15330 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 9
[LightGBM] [Debug] Re-bagging, using 15101 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 13
[LightGBM] [Debug] Re-bagging, using 15196 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 13
[LightGBM] [Debug] Re-bagging, using 15237 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 11
[LightGBM] [Debug] Re-bagging, using 15259 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 15
[LightGBM] [Debug] Re-bagging, using 15090 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11
[LightGBM] [Debug] Re-bagging, using 15145 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10
[LightGBM] [Debug] Re-bagging, using 15178 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 11
Trial 64, Fold 5: Log loss = 0.31903414895241994, Average precision = 0.964618303671335, ROC-AUC = 0.9587533210709176, Elapsed Time = 1.6158471999997346 seconds
Optimization Progress:  65%|######5   | 65/100 [14:51<07:30, 12.88s/it]
Trial 65, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 65, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[LightGBM] [Info] Number of positive: 10130, number of negative: 10533
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791640
[LightGBM] [Info] Total Bins 24150
[LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012
[LightGBM] [Info] Start training from score -0.039012
[LightGBM] [Debug] Re-bagging, using 7527 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8
[LightGBM] [Debug] Re-bagging, using 7589 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8
[LightGBM] [Debug] Re-bagging, using 7461 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8
[LightGBM] [Debug] Re-bagging, using 7455 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7
[LightGBM] [Debug] Re-bagging, using 7595 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 7
[LightGBM] [Debug] Re-bagging, using 7532 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7534 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8
[LightGBM] [Debug] Re-bagging, using 7603 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7537 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7337 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8
[LightGBM] [Debug] Re-bagging, using 7348 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 7
[LightGBM] [Debug] Re-bagging, using 7421 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9
Trial 65, Fold 1: Log loss = 0.387947778516032, Average precision = 0.9633361701294318, ROC-AUC = 0.9562298659803177, Elapsed Time = 1.4673411999992823 seconds
Trial 65, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 65, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[LightGBM] [Info] Number of positive: 10230, number of negative: 10471
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791600
[LightGBM] [Info] Total Bins 24158
[LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285
[LightGBM] [Info] Start training from score -0.023285
[LightGBM] [Debug] Re-bagging, using 7542 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7
[LightGBM] [Debug] Re-bagging, using 7605 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7474 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8
[LightGBM] [Debug] Re-bagging, using 7470 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8
[LightGBM] [Debug] Re-bagging, using 7625 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7523 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8
[LightGBM] [Debug] Re-bagging, using 7546 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8
[LightGBM] [Debug] Re-bagging, using 7613 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8
[LightGBM] [Debug] Re-bagging, using 7560 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8
[LightGBM] [Debug] Re-bagging, using 7361 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8
[LightGBM] [Debug] Re-bagging, using 7347 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8
[LightGBM] [Debug] Re-bagging, using 7418 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8
Trial 65, Fold 2: Log loss = 0.38556459991614683, Average precision = 0.9615695436743681, ROC-AUC = 0.9566611359657123, Elapsed Time = 1.68528249999963 seconds
Trial 65, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 65, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[LightGBM] [Info] Number of positive: 10165, number of negative: 10517
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791401
[LightGBM] [Info] Total Bins 24152
[LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043
[LightGBM] [Info] Start training from score -0.034043
[LightGBM] [Debug] Re-bagging, using 7534 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 7
[LightGBM] [Debug] Re-bagging, using 7595 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7
[LightGBM] [Debug] Re-bagging, using 7467 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7468 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 7
[LightGBM] [Debug] Re-bagging, using 7609 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8
[LightGBM] [Debug] Re-bagging, using 7525 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7543 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7606 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8
[LightGBM] [Debug] Re-bagging, using 7540 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8
[LightGBM] [Debug] Re-bagging, using 7361 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7349 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 10
[LightGBM] [Debug] Re-bagging, using 7416 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8
Trial 65, Fold 3: Log loss = 0.3833619629188997, Average precision = 0.9651936736622466, ROC-AUC = 0.9589117472142232, Elapsed Time = 1.7250509000004968 seconds
Trial 65, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 65, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[LightGBM] [Info] Number of positive: 10177, number of negative: 10479
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791090
[LightGBM] [Info] Total Bins 24142
[LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243
[LightGBM] [Info] Start training from score -0.029243
[LightGBM] [Debug] Re-bagging, using 7523 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 7
[LightGBM] [Debug] Re-bagging, using 7587 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8
[LightGBM] [Debug] Re-bagging, using 7457 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7457 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8
[LightGBM] [Debug] Re-bagging, using 7587 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 7
[LightGBM] [Debug] Re-bagging, using 7530 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8
[LightGBM] [Debug] Re-bagging, using 7537 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10
[LightGBM] [Debug] Re-bagging, using 7597 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 7
[LightGBM] [Debug] Re-bagging, using 7534 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7
[LightGBM] [Debug] Re-bagging, using 7339 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 7
[LightGBM] [Debug] Re-bagging, using 7339 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7421 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8
Trial 65, Fold 4: Log loss = 0.38938473344963875, Average precision = 0.9621895753988847, ROC-AUC = 0.9551340515175576, Elapsed Time = 1.6923073999996632 seconds
Trial 65, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 65, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[LightGBM] [Info] Number of positive: 10150, number of negative: 10500
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.792012
[LightGBM] [Info] Total Bins 24146
[LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902
[LightGBM] [Info] Start training from score -0.033902
[LightGBM] [Debug] Re-bagging, using 7521 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8
[LightGBM] [Debug] Re-bagging, using 7586 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7
[LightGBM] [Debug] Re-bagging, using 7456 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7
[LightGBM] [Debug] Re-bagging, using 7451 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8
[LightGBM] [Debug] Re-bagging, using 7586 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8
[LightGBM] [Debug] Re-bagging, using 7521 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8
[LightGBM] [Debug] Re-bagging, using 7546 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 7
[LightGBM] [Debug] Re-bagging, using 7591 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 7
[LightGBM] [Debug] Re-bagging, using 7532 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 7
[LightGBM] [Debug] Re-bagging, using 7339 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7332 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7413 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 7
Trial 65, Fold 5: Log loss = 0.39631969132039463, Average precision = 0.9600423777986307, ROC-AUC = 0.9534142652769263, Elapsed Time = 1.6810988000006546 seconds
Optimization Progress:  66%|######6   | 66/100 [15:07<07:48, 13.77s/it]
Trial 66, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 66, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[LightGBM] [Info] Number of positive: 10130, number of negative: 10533
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795662
[LightGBM] [Info] Total Bins 11147
[LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 258
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012
[LightGBM] [Info] Start training from score -0.039012
[LightGBM] [Debug] Re-bagging, using 14380 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 14
[LightGBM] [Debug] Re-bagging, using 14499 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 12
[LightGBM] [Debug] Re-bagging, using 14519 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 11
[LightGBM] [Debug] Re-bagging, using 14345 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 12
[LightGBM] [Debug] Re-bagging, using 14432 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 11
[LightGBM] [Debug] Re-bagging, using 14391 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 13
[LightGBM] [Debug] Re-bagging, using 14436 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 12
[LightGBM] [Debug] Re-bagging, using 14426 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 14
[LightGBM] [Debug] Re-bagging, using 14510 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 12
[LightGBM] [Debug] Re-bagging, using 14362 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 13
Trial 66, Fold 1: Log loss = 0.3087517333061513, Average precision = 0.9687092433911032, ROC-AUC = 0.9625075577254891, Elapsed Time = 1.0490066000002116 seconds
Trial 66, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 66, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[LightGBM] [Info] Number of positive: 10230, number of negative: 10471
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.794298
[LightGBM] [Info] Total Bins 11158
[LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 257
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285
[LightGBM] [Info] Start training from score -0.023285
[LightGBM] [Debug] Re-bagging, using 14407 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 12
[LightGBM] [Debug] Re-bagging, using 14523 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 12
[LightGBM] [Debug] Re-bagging, using 14550 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 13
[LightGBM] [Debug] Re-bagging, using 14375 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 12
[LightGBM] [Debug] Re-bagging, using 14458 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 11
[LightGBM] [Debug] Re-bagging, using 14405 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 10
[LightGBM] [Debug] Re-bagging, using 14455 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 13
[LightGBM] [Debug] Re-bagging, using 14463 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 14
[LightGBM] [Debug] Re-bagging, using 14542 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 13
[LightGBM] [Debug] Re-bagging, using 14392 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 12
Trial 66, Fold 2: Log loss = 0.30928391734393224, Average precision = 0.966970769585805, ROC-AUC = 0.9628082870072635, Elapsed Time = 1.1502757999996902 seconds
Trial 66, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 66, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[LightGBM] [Info] Number of positive: 10165, number of negative: 10517
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795455
[LightGBM] [Info] Total Bins 11164
[LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 258
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043
[LightGBM] [Info] Start training from score -0.034043
[LightGBM] [Debug] Re-bagging, using 14391 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 11
[LightGBM] [Debug] Re-bagging, using 14510 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 15
[LightGBM] [Debug] Re-bagging, using 14537 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 14
[LightGBM] [Debug] Re-bagging, using 14366 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 10
[LightGBM] [Debug] Re-bagging, using 14440 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 9
[LightGBM] [Debug] Re-bagging, using 14395 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 10
[LightGBM] [Debug] Re-bagging, using 14448 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 12
[LightGBM] [Debug] Re-bagging, using 14447 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 16
[LightGBM] [Debug] Re-bagging, using 14515 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 13
[LightGBM] [Debug] Re-bagging, using 14391 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 13
Trial 66, Fold 3: Log loss = 0.3057466631568812, Average precision = 0.9694848143497176, ROC-AUC = 0.9645859317063306, Elapsed Time = 1.2300295000004553 seconds
Trial 66, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 66, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[LightGBM] [Info] Number of positive: 10177, number of negative: 10479
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795144
[LightGBM] [Info] Total Bins 11145
[LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 258
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243
[LightGBM] [Info] Start training from score -0.029243
[LightGBM] [Debug] Re-bagging, using 14375 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 13
[LightGBM] [Debug] Re-bagging, using 14493 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11
[LightGBM] [Debug] Re-bagging, using 14514 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 12
[LightGBM] [Debug] Re-bagging, using 14343 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 11
[LightGBM] [Debug] Re-bagging, using 14425 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 12
[LightGBM] [Debug] Re-bagging, using 14387 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 14
[LightGBM] [Debug] Re-bagging, using 14430 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 12
[LightGBM] [Debug] Re-bagging, using 14429 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 13
[LightGBM] [Debug] Re-bagging, using 14498 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 15
[LightGBM] [Debug] Re-bagging, using 14360 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11
Trial 66, Fold 4: Log loss = 0.3149989869422477, Average precision = 0.9668836789137704, ROC-AUC = 0.9608261592404225, Elapsed Time = 1.2065956000005826 seconds
Trial 66, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 66, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[LightGBM] [Info] Number of positive: 10150, number of negative: 10500
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.796026
[LightGBM] [Info] Total Bins 11147
[LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 258
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902
[LightGBM] [Info] Start training from score -0.033902
[LightGBM] [Debug] Re-bagging, using 14371 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 11
[LightGBM] [Debug] Re-bagging, using 14490 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 14
[LightGBM] [Debug] Re-bagging, using 14512 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 11
[LightGBM] [Debug] Re-bagging, using 14337 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 11
[LightGBM] [Debug] Re-bagging, using 14420 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 11
[LightGBM] [Debug] Re-bagging, using 14380 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 14
[LightGBM] [Debug] Re-bagging, using 14433 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 15
[LightGBM] [Debug] Re-bagging, using 14420 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 19
[LightGBM] [Debug] Re-bagging, using 14493 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 12
[LightGBM] [Debug] Re-bagging, using 14359 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 11
Trial 66, Fold 5: Log loss = 0.3105819669547541, Average precision = 0.9669574604331346, ROC-AUC = 0.9615033721643164, Elapsed Time = 1.2555413000000044 seconds
Optimization Progress:  67%|######7   | 67/100 [15:20<07:29, 13.63s/it]
Trial 67, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 67, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[LightGBM] [Info] Number of positive: 10130, number of negative: 10533
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795662
[LightGBM] [Info] Total Bins 9448
[LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 258
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012
[LightGBM] [Info] Start training from score -0.039012
[LightGBM] [Debug] Re-bagging, using 16585 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 90 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 90 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 94 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 90 and depth = 13
[LightGBM] [Debug] Re-bagging, using 16662 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 87 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 97 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 88 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 95 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 88 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 14
[LightGBM] [Debug] Re-bagging, using 16603 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 94 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 88 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 94 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 100 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 15
[LightGBM] [Debug] Re-bagging, using 16552 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 99 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 102 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 99 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 14
[LightGBM] [Debug] Re-bagging, using 16610 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 93 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 90 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 102 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 100 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 101 and depth = 20
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 91 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 93 and depth = 16
[LightGBM] [Debug] Re-bagging, using 16601 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 100 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 95 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 103 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 98 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 102 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 103 and depth = 17
[LightGBM] [Debug] Re-bagging, using 16528 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 101 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 108 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 97 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 91 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 99 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 99 and depth = 14
[LightGBM] [Debug] Re-bagging, using 16562 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 94 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 103 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 97 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 99 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 103 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 106 and depth = 15
[LightGBM] [Debug] Re-bagging, using 16634 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 102 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 106 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 97 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 109 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 90 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 98 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 95 and depth = 11
[LightGBM] [Debug] Re-bagging, using 16508 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 91 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 93 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 97 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 99 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 97 and depth = 15
[LightGBM] [Debug] Re-bagging, using 16530 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 102 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 91 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 109 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 110 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 100 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 117 and depth = 13
[LightGBM] [Debug] Re-bagging, using 16556 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 99 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 100 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 94 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 101 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 98 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 99 and depth = 15
[LightGBM] [Debug] Re-bagging, using 16561 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 93 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 99 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 103 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 94 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 91 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 95 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 106 and depth = 14
[LightGBM] [Debug] Re-bagging, using 16519 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 97 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 107 and depth = 13
Trial 67, Fold 1: Log loss = 0.28741825917483693, Average precision = 0.9739333585759847, ROC-AUC = 0.9687488051026895, Elapsed Time = 1.8393102 seconds
Trial 67, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 67, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[LightGBM] [Info] Number of positive: 10230, number of negative: 10471
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.794298
[LightGBM] [Info] Total Bins 9436
[LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 257
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285
[LightGBM] [Info] Start training from score -0.023285
[LightGBM] [Debug] Re-bagging, using 16618 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 91 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 14
[LightGBM] [Debug] Re-bagging, using 16693 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 97 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 93 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 95 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 100 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 12
[LightGBM] [Debug] Re-bagging, using 16630 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 86 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 93 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 95 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 86 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 105 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 90 and depth = 12
[LightGBM] [Debug] Re-bagging, using 16589 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 101 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 86 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 101 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 93 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 13
[LightGBM] [Debug] Re-bagging, using 16637 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 101 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 94 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 108 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 87 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 105 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 94 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 91 and depth = 14
[LightGBM] [Debug] Re-bagging, using 16621 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 88 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 111 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 94 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 97 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 88 and depth = 15
[LightGBM] [Debug] Re-bagging, using 16553 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 105 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 93 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 111 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 107 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 101 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 107 and depth = 13
[LightGBM] [Debug] Re-bagging, using 16610 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 103 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 97 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 101 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 100 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 100 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 111 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 105 and depth = 13
[LightGBM] [Debug] Re-bagging, using 16663 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 91 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 97 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 100 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 110 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 99 and depth = 17
[LightGBM] [Debug] Re-bagging, using 16541 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 102 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 94 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 95 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 107 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 98 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 106 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 101 and depth = 14
[LightGBM] [Debug] Re-bagging, using 16557 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 110 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 99 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 103 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 108 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 93 and depth = 14
[LightGBM] [Debug] Re-bagging, using 16577 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 90 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 101 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 99 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 102 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 105 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 95 and depth = 14
[LightGBM] [Debug] Re-bagging, using 16588 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 93 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 103 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 95 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 100 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 105 and depth = 16
[LightGBM] [Debug] Re-bagging, using 16540 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 107 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 108 and depth = 16
Trial 67, Fold 2: Log loss = 0.28359683000303204, Average precision = 0.9717995056127583, ROC-AUC = 0.9694156890821415, Elapsed Time = 2.248905299999933 seconds
Trial 67, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 67, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[LightGBM] [Info] Number of positive: 10165, number of negative: 10517
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795455
[LightGBM] [Info] Total Bins 9446
[LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 258
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043
[LightGBM] [Info] Start training from score -0.034043
[LightGBM] [Debug] Re-bagging, using 16601 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 88 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 94 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 93 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 88 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 86 and depth = 14
[LightGBM] [Debug] Re-bagging, using 16677 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 93 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 95 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 88 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 95 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 103 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 15
[LightGBM] [Debug] Re-bagging, using 16617 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 107 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 86 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 87 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 94 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 90 and depth = 15
[LightGBM] [Debug] Re-bagging, using 16574 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 86 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 95 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 93 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 99 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 94 and depth = 14
[LightGBM] [Debug] Re-bagging, using 16618 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 105 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 95 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 93 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 100 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 102 and depth = 16
[LightGBM] [Debug] Re-bagging, using 16613 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 91 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 99 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 91 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 95 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 91 and depth = 12
[LightGBM] [Debug] Re-bagging, using 16538 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 102 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 103 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 99 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 98 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 103 and depth = 14
[LightGBM] [Debug] Re-bagging, using 16588 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 106 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 101 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 105 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 95 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 90 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 97 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 93 and depth = 14
[LightGBM] [Debug] Re-bagging, using 16646 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 90 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 86 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 100 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 107 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 105 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 99 and depth = 13
[LightGBM] [Debug] Re-bagging, using 16529 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 97 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 94 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 95 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 95 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 94 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 103 and depth = 13
[LightGBM] [Debug] Re-bagging, using 16544 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 95 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 94 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 90 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 95 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 90 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 101 and depth = 14
[LightGBM] [Debug] Re-bagging, using 16570 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 98 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 101 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 106 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 100 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 103 and depth = 13
[LightGBM] [Debug] Re-bagging, using 16570 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 100 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 97 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 106 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 101 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 90 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 97 and depth = 12
[LightGBM] [Debug] Re-bagging, using 16531 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 102 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 99 and depth = 17
Trial 67, Fold 3: Log loss = 0.2814484218320297, Average precision = 0.9743597628338133, ROC-AUC = 0.9699769035740771, Elapsed Time = 2.2512329000001046 seconds
Trial 67, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 67, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[LightGBM] [Info] Number of positive: 10177, number of negative: 10479
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795144
[LightGBM] [Info] Total Bins 9446
[LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 258
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243
[LightGBM] [Info] Start training from score -0.029243
[LightGBM] [Debug] Re-bagging, using 16579 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 91 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 93 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 95 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 13
[LightGBM] [Debug] Re-bagging, using 16655 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 101 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 94 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 90 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 94 and depth = 13
[LightGBM] [Debug] Re-bagging, using 16599 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 93 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 88 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 90 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 102 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 95 and depth = 14
[LightGBM] [Debug] Re-bagging, using 16549 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 97 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 88 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 88 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 88 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 91 and depth = 15
[LightGBM] [Debug] Re-bagging, using 16604 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 94 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 88 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 102 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 95 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 101 and depth = 13
[LightGBM] [Debug] Re-bagging, using 16593 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 100 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 93 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 91 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 94 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 105 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 100 and depth = 14
[LightGBM] [Debug] Re-bagging, using 16523 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 91 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 101 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 98 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 101 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 106 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 12
[LightGBM] [Debug] Re-bagging, using 16561 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 103 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 97 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 90 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 98 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 97 and depth = 12
[LightGBM] [Debug] Re-bagging, using 16623 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 94 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 94 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 93 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 106 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 103 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 105 and depth = 17
[LightGBM] [Debug] Re-bagging, using 16505 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 88 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 98 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 99 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 88 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 101 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 13
[LightGBM] [Debug] Re-bagging, using 16522 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 94 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 93 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 107 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 103 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 98 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 105 and depth = 15
[LightGBM] [Debug] Re-bagging, using 16547 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 87 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 100 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 100 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 95 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 93 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 90 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 116 and depth = 13
[LightGBM] [Debug] Re-bagging, using 16563 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 106 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 100 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 101 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 109 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 107 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 87 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 108 and depth = 15
[LightGBM] [Debug] Re-bagging, using 16511 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 99 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 105 and depth = 14
Trial 67, Fold 4: Log loss = 0.28250526105517854, Average precision = 0.973973155793459, ROC-AUC = 0.9691755426320545, Elapsed Time = 2.24522160000015 seconds
Trial 67, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 67, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[LightGBM] [Info] Number of positive: 10150, number of negative: 10500
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.796026
[LightGBM] [Info] Total Bins 9428
[LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 258
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902
[LightGBM] [Info] Start training from score -0.033902
[LightGBM] [Debug] Re-bagging, using 16573 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 93 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 86 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 103 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 86 and depth = 13
[LightGBM] [Debug] Re-bagging, using 16654 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 88 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 88 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 87 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 93 and depth = 14
[LightGBM] [Debug] Re-bagging, using 16595 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 98 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 99 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 87 and depth = 13
[LightGBM] [Debug] Re-bagging, using 16542 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 90 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 91 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 87 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 94 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 87 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 17
[LightGBM] [Debug] Re-bagging, using 16598 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 91 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 87 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 95 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 93 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 103 and depth = 13
[LightGBM] [Debug] Re-bagging, using 16585 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 90 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 100 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 95 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 91 and depth = 13
[LightGBM] [Debug] Re-bagging, using 16527 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 98 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 90 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 99 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 90 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 88 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 102 and depth = 13
[LightGBM] [Debug] Re-bagging, using 16553 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 98 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 103 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 95 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 98 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 99 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 94 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 99 and depth = 14
[LightGBM] [Debug] Re-bagging, using 16616 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 94 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 103 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 102 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 102 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 101 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 87 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 103 and depth = 15
[LightGBM] [Debug] Re-bagging, using 16500 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 95 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 99 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 99 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 99 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 100 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 100 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 95 and depth = 12
[LightGBM] [Debug] Re-bagging, using 16519 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 100 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 103 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 100 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 102 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 91 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 95 and depth = 15
[LightGBM] [Debug] Re-bagging, using 16541 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 93 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 101 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 95 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 100 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 95 and depth = 15
[LightGBM] [Debug] Re-bagging, using 16555 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 97 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 99 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 98 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 99 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 102 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 94 and depth = 18
[LightGBM] [Debug] Re-bagging, using 16510 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 98 and depth = 16
Trial 67, Fold 5: Log loss = 0.2847991314597355, Average precision = 0.9727540451579517, ROC-AUC = 0.9683353213310295, Elapsed Time = 2.201694000000316 seconds
Optimization Progress:  68%|######8   | 68/100 [15:38<08:00, 15.03s/it]
Trial 68, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 68, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[LightGBM] [Info] Number of positive: 10130, number of negative: 10533
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.798290
[LightGBM] [Info] Total Bins 21918
[LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 260
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012
[LightGBM] [Info] Start training from score -0.039012
[LightGBM] [Debug] Re-bagging, using 14894 data to train
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 14
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 14
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 17
[LightGBM] [Debug] Re-bagging, using 15018 data to train
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 14
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 13
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 14
[LightGBM] [Debug] Re-bagging, using 15035 data to train
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 14
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 13
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 12
[LightGBM] [Debug] Re-bagging, using 14902 data to train
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 14
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 15
[LightGBM] [Debug] Re-bagging, using 14960 data to train
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 14
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 14
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 22
[LightGBM] [Debug] Re-bagging, using 14907 data to train
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 20
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 21
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 14
[LightGBM] [Debug] Re-bagging, using 14960 data to train
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 13
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 20
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 21
[LightGBM] [Debug] Re-bagging, using 14950 data to train
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 21
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 13
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 20
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 15
[LightGBM] [Debug] Re-bagging, using 15011 data to train
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 24
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 23
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 20
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 24
[LightGBM] [Debug] Re-bagging, using 14870 data to train
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 22
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 28
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 20
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 24
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 26
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 14
[LightGBM] [Debug] Re-bagging, using 14919 data to train
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 22
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 30
[LightGBM] [Debug] Re-bagging, using 14818 data to train
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 21
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 25
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 27
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 17
[LightGBM] [Debug] Re-bagging, using 14902 data to train
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 21
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 20
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 21
[LightGBM] [Debug] Re-bagging, using 14795 data to train
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 20
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 22
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 23
[LightGBM] [Debug] Re-bagging, using 15006 data to train
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 25
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 28
Trial 68, Fold 1: Log loss = 0.19255620850863048, Average precision = 0.9771368172255197, ROC-AUC = 0.9729349784679504, Elapsed Time = 2.999589400000332 seconds
Trial 68, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 68, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[LightGBM] [Info] Number of positive: 10230, number of negative: 10471
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.798259
[LightGBM] [Info] Total Bins 21930
[LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 260
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285
[LightGBM] [Info] Start training from score -0.023285
[LightGBM] [Debug] Re-bagging, using 14922 data to train
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 14
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 13
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 14
[LightGBM] [Debug] Re-bagging, using 15044 data to train
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 13
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16
[LightGBM] [Debug] Re-bagging, using 15065 data to train
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 13
[LightGBM] [Debug] Re-bagging, using 14932 data to train
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 17
[LightGBM] [Debug] Re-bagging, using 14990 data to train
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 15
[LightGBM] [Debug] Re-bagging, using 14924 data to train
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 22
[LightGBM] [Debug] Re-bagging, using 14980 data to train
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 23
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 24
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 18
[LightGBM] [Debug] Re-bagging, using 14987 data to train
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 23
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 22
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 21
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 18
[LightGBM] [Debug] Re-bagging, using 15039 data to train
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 22
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 27
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 27
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 14
[LightGBM] [Debug] Re-bagging, using 14902 data to train
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 20
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 13
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 23
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16
[LightGBM] [Debug] Re-bagging, using 14939 data to train
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 14
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 26
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 20
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16
[LightGBM] [Debug] Re-bagging, using 14837 data to train
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 23
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 24
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 24
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 22
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 22
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 22
[LightGBM] [Debug] Re-bagging, using 14927 data to train
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 14
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 14
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 22
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 22
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 23
[LightGBM] [Debug] Re-bagging, using 14812 data to train
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 21
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 14
[LightGBM] [Debug] Re-bagging, using 15038 data to train
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 37
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 23
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 39
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 25
Trial 68, Fold 2: Log loss = 0.1810799720171419, Average precision = 0.9768380951427735, ROC-AUC = 0.9741089445911172, Elapsed Time = 3.4721553999997923 seconds
Trial 68, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 68, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[LightGBM] [Info] Number of positive: 10165, number of negative: 10517
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.798104
[LightGBM] [Info] Total Bins 21873
[LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 260
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043
[LightGBM] [Info] Start training from score -0.034043
[LightGBM] [Debug] Re-bagging, using 14905 data to train
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 14
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 12
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 20
[LightGBM] [Debug] Re-bagging, using 15032 data to train
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 20
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 13
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 22
[LightGBM] [Debug] Re-bagging, using 15051 data to train
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 13
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 14
[LightGBM] [Debug] Re-bagging, using 14922 data to train
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 22
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 24
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16
[LightGBM] [Debug] Re-bagging, using 14971 data to train
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 14
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 21
[LightGBM] [Debug] Re-bagging, using 14913 data to train
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 12
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 14
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 19
[LightGBM] [Debug] Re-bagging, using 14971 data to train
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 13
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 14
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 17
[LightGBM] [Debug] Re-bagging, using 14972 data to train
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 14
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16
[LightGBM] [Debug] Re-bagging, using 15016 data to train
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 29
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 14
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 22
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 23
[LightGBM] [Debug] Re-bagging, using 14894 data to train
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 24
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 24
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 18
[LightGBM] [Debug] Re-bagging, using 14931 data to train
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 20
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 22
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 22
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 22
[LightGBM] [Debug] Re-bagging, using 14826 data to train
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 21
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 20
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 25
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 25
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 26
[LightGBM] [Debug] Re-bagging, using 14917 data to train
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 12
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 14
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 20
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 18
[LightGBM] [Debug] Re-bagging, using 14798 data to train
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 21
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 30
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 26
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 21
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 31
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 23
[LightGBM] [Debug] Re-bagging, using 15021 data to train
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 24
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 18
Trial 68, Fold 3: Log loss = 0.18652658405551348, Average precision = 0.9771003102923845, ROC-AUC = 0.9738919193208194, Elapsed Time = 3.2772579000002224 seconds
Trial 68, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 68, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[LightGBM] [Info] Number of positive: 10177, number of negative: 10479
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.797795
[LightGBM] [Info] Total Bins 21913
[LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 260
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243
[LightGBM] [Info] Start training from score -0.029243
[LightGBM] [Debug] Re-bagging, using 14889 data to train
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 13
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16
[LightGBM] [Debug] Re-bagging, using 15011 data to train
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 13
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 18
[LightGBM] [Debug] Re-bagging, using 15030 data to train
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16
[LightGBM] [Debug] Re-bagging, using 14900 data to train
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 20
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 14
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 13
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 20
[LightGBM] [Debug] Re-bagging, using 14954 data to train
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 15
[LightGBM] [Debug] Re-bagging, using 14902 data to train
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 14
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 22
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 21
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16
[LightGBM] [Debug] Re-bagging, using 14953 data to train
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 18
[LightGBM] [Debug] Re-bagging, using 14953 data to train
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 24
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 21
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 25
[LightGBM] [Debug] Re-bagging, using 15001 data to train
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 21
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 21
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 27
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 22
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 20
[LightGBM] [Debug] Re-bagging, using 14867 data to train
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 20
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 17
[LightGBM] [Debug] Re-bagging, using 14913 data to train
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 20
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 20
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 20
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 23
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 23
[LightGBM] [Debug] Re-bagging, using 14810 data to train
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 26
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 21
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 13
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 18
[LightGBM] [Debug] Re-bagging, using 14904 data to train
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 25
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 24
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 36
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 25
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 25
[LightGBM] [Debug] Re-bagging, using 14781 data to train
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 20
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 21
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 23
[LightGBM] [Debug] Re-bagging, using 15008 data to train
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 23
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 17
Trial 68, Fold 4: Log loss = 0.18663521781285533, Average precision = 0.9769212559836613, ROC-AUC = 0.9733175604626709, Elapsed Time = 3.38944790000005 seconds
Trial 68, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 68, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[LightGBM] [Info] Number of positive: 10150, number of negative: 10500
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.798646
[LightGBM] [Info] Total Bins 21922
[LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 260
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902
[LightGBM] [Info] Start training from score -0.033902
[LightGBM] [Debug] Re-bagging, using 14884 data to train
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 14
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 11
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 20
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 18
[LightGBM] [Debug] Re-bagging, using 15009 data to train
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 21
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16
[LightGBM] [Debug] Re-bagging, using 15028 data to train
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 14
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 18
[LightGBM] [Debug] Re-bagging, using 14894 data to train
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 12
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 13
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 12
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 20
[LightGBM] [Debug] Re-bagging, using 14949 data to train
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 20
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 15
[LightGBM] [Debug] Re-bagging, using 14893 data to train
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16
[LightGBM] [Debug] Re-bagging, using 14956 data to train
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 12
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 22
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 20
[LightGBM] [Debug] Re-bagging, using 14944 data to train
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 20
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 23
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16
[LightGBM] [Debug] Re-bagging, using 14997 data to train
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 24
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 22
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 22
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 25
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 17
[LightGBM] [Debug] Re-bagging, using 14866 data to train
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 22
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16
[LightGBM] [Debug] Re-bagging, using 14907 data to train
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 20
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 14
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 19
[LightGBM] [Debug] Re-bagging, using 14801 data to train
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 22
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 24
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 23
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 23
[LightGBM] [Debug] Re-bagging, using 14900 data to train
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 22
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 25
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 25
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 21
[LightGBM] [Debug] Re-bagging, using 14784 data to train
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 28
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 25
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 31
[LightGBM] [Debug] Re-bagging, using 15003 data to train
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 24
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 22
Trial 68, Fold 5: Log loss = 0.1877665116315995, Average precision = 0.9769218532521622, ROC-AUC = 0.9735586645114542, Elapsed Time = 3.328153100000236 seconds
Optimization Progress:  69%|######9   | 69/100 [16:02<09:10, 17.77s/it]
Trial 69, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 69, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[LightGBM] [Info] Number of positive: 10130, number of negative: 10533
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791640
[LightGBM] [Info] Total Bins 7940
[LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012
[LightGBM] [Info] Start training from score -0.039012
[LightGBM] [Debug] Re-bagging, using 7776 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7845 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Debug] Re-bagging, using 7722 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7689 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 10
[LightGBM] [Debug] Re-bagging, using 7843 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Debug] Re-bagging, using 7783 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7774 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7855 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11
[LightGBM] [Debug] Re-bagging, using 7806 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7584 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7595 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 12
[LightGBM] [Debug] Re-bagging, using 7645 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7683 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10
[LightGBM] [Debug] Re-bagging, using 7725 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Debug] Re-bagging, using 7935 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 10
[LightGBM] [Debug] Re-bagging, using 7804 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7762 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10
[LightGBM] [Debug] Re-bagging, using 7741 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10
[LightGBM] [Debug] Re-bagging, using 7821 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10
[LightGBM] [Debug] Re-bagging, using 7913 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 13
[LightGBM] [Debug] Re-bagging, using 7649 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 18
[LightGBM] [Debug] Re-bagging, using 7722 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11
[LightGBM] [Debug] Re-bagging, using 7841 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 12
[LightGBM] [Debug] Re-bagging, using 7829 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 14
[LightGBM] [Debug] Re-bagging, using 7906 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 14
[LightGBM] [Debug] Re-bagging, using 7765 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 13
Trial 69, Fold 1: Log loss = 0.2022036418702006, Average precision = 0.9745308643163686, ROC-AUC = 0.970485438981374, Elapsed Time = 0.9094229000002088 seconds
Trial 69, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 69, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[LightGBM] [Info] Number of positive: 10230, number of negative: 10471
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791600
[LightGBM] [Info] Total Bins 7956
[LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285
[LightGBM] [Info] Start training from score -0.023285
[LightGBM] [Debug] Re-bagging, using 7792 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10
[LightGBM] [Debug] Re-bagging, using 7860 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7736 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7705 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7875 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7775 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 8
[LightGBM] [Debug] Re-bagging, using 7785 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11
[LightGBM] [Debug] Re-bagging, using 7866 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7828 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10
[LightGBM] [Debug] Re-bagging, using 7605 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10
[LightGBM] [Debug] Re-bagging, using 7598 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 8
[LightGBM] [Debug] Re-bagging, using 7644 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7693 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 12
[LightGBM] [Debug] Re-bagging, using 7740 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7959 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 12
[LightGBM] [Debug] Re-bagging, using 7830 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10
[LightGBM] [Debug] Re-bagging, using 7776 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 11
[LightGBM] [Debug] Re-bagging, using 7761 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 10
[LightGBM] [Debug] Re-bagging, using 7834 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 13
[LightGBM] [Debug] Re-bagging, using 7924 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 12
[LightGBM] [Debug] Re-bagging, using 7667 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 13
[LightGBM] [Debug] Re-bagging, using 7733 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 13
[LightGBM] [Debug] Re-bagging, using 7866 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 14
[LightGBM] [Debug] Re-bagging, using 7842 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 13
[LightGBM] [Debug] Re-bagging, using 7914 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 14
[LightGBM] [Debug] Re-bagging, using 7783 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 16
Trial 69, Fold 2: Log loss = 0.1989488805167814, Average precision = 0.9738621553061394, ROC-AUC = 0.9705636610792121, Elapsed Time = 0.955718700000034 seconds
Trial 69, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 69, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[LightGBM] [Info] Number of positive: 10165, number of negative: 10517
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791401
[LightGBM] [Info] Total Bins 7963
[LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043
[LightGBM] [Info] Start training from score -0.034043
[LightGBM] [Debug] Re-bagging, using 7783 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7851 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 10
[LightGBM] [Debug] Re-bagging, using 7728 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7704 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 8
[LightGBM] [Debug] Re-bagging, using 7859 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 8
[LightGBM] [Debug] Re-bagging, using 7774 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7781 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 12
[LightGBM] [Debug] Re-bagging, using 7862 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7808 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 10
[LightGBM] [Debug] Re-bagging, using 7606 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7595 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Debug] Re-bagging, using 7645 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 10
[LightGBM] [Debug] Re-bagging, using 7679 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11
[LightGBM] [Debug] Re-bagging, using 7736 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7942 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11
[LightGBM] [Debug] Re-bagging, using 7820 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11
[LightGBM] [Debug] Re-bagging, using 7769 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
[LightGBM] [Debug] Re-bagging, using 7756 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11
[LightGBM] [Debug] Re-bagging, using 7822 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7924 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 12
[LightGBM] [Debug] Re-bagging, using 7663 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 14
[LightGBM] [Debug] Re-bagging, using 7724 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 13
[LightGBM] [Debug] Re-bagging, using 7844 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 10
[LightGBM] [Debug] Re-bagging, using 7835 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 15
[LightGBM] [Debug] Re-bagging, using 7926 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 10
[LightGBM] [Debug] Re-bagging, using 7774 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 12
Trial 69, Fold 3: Log loss = 0.1985734875654708, Average precision = 0.9746141114328595, ROC-AUC = 0.9702187328447189, Elapsed Time = 1.015855300000112 seconds
Trial 69, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 69, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[LightGBM] [Info] Number of positive: 10177, number of negative: 10479
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.792454
[LightGBM] [Info] Total Bins 7949
[LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 256
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243
[LightGBM] [Info] Start training from score -0.029243
[LightGBM] [Debug] Re-bagging, using 7772 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7843 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7718 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 8
[LightGBM] [Debug] Re-bagging, using 7690 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 8
[LightGBM] [Debug] Re-bagging, using 7836 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11
[LightGBM] [Debug] Re-bagging, using 7781 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7775 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7850 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7804 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10
[LightGBM] [Debug] Re-bagging, using 7584 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7588 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 12
[LightGBM] [Debug] Re-bagging, using 7645 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11
[LightGBM] [Debug] Re-bagging, using 7678 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 10
[LightGBM] [Debug] Re-bagging, using 7727 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7929 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 12
[LightGBM] [Debug] Re-bagging, using 7801 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7759 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11
[LightGBM] [Debug] Re-bagging, using 7745 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11
[LightGBM] [Debug] Re-bagging, using 7808 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10
[LightGBM] [Debug] Re-bagging, using 7912 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 11
[LightGBM] [Debug] Re-bagging, using 7654 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 15
[LightGBM] [Debug] Re-bagging, using 7714 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 14
[LightGBM] [Debug] Re-bagging, using 7842 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 15
[LightGBM] [Debug] Re-bagging, using 7822 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11
[LightGBM] [Debug] Re-bagging, using 7906 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 12
[LightGBM] [Debug] Re-bagging, using 7765 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 11
Trial 69, Fold 4: Log loss = 0.20015244545998195, Average precision = 0.9746741557231141, ROC-AUC = 0.9703810107989919, Elapsed Time = 1.0393052999997963 seconds
Trial 69, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 69, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[LightGBM] [Info] Number of positive: 10150, number of negative: 10500
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.792012
[LightGBM] [Info] Total Bins 7944
[LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902
[LightGBM] [Info] Start training from score -0.033902
[LightGBM] [Debug] Re-bagging, using 7770 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10
[LightGBM] [Debug] Re-bagging, using 7842 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7717 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7684 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 11
[LightGBM] [Debug] Re-bagging, using 7835 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7772 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7782 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7846 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7801 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
[LightGBM] [Debug] Re-bagging, using 7585 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7580 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7638 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10
[LightGBM] [Debug] Re-bagging, using 7682 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 10
[LightGBM] [Debug] Re-bagging, using 7734 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 11
[LightGBM] [Debug] Re-bagging, using 7928 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11
[LightGBM] [Debug] Re-bagging, using 7800 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7753 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 12
[LightGBM] [Debug] Re-bagging, using 7728 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 13
[LightGBM] [Debug] Re-bagging, using 7815 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
[LightGBM] [Debug] Re-bagging, using 7900 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 16
[LightGBM] [Debug] Re-bagging, using 7661 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 13
[LightGBM] [Debug] Re-bagging, using 7707 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 11
[LightGBM] [Debug] Re-bagging, using 7838 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 14
[LightGBM] [Debug] Re-bagging, using 7827 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 14
[LightGBM] [Debug] Re-bagging, using 7900 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 12
[LightGBM] [Debug] Re-bagging, using 7765 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 12
Trial 69, Fold 5: Log loss = 0.20355979520113743, Average precision = 0.9736788859168499, ROC-AUC = 0.9702772420712333, Elapsed Time = 1.0688019000008353 seconds
Optimization Progress:  70%|#######   | 70/100 [16:15<08:03, 16.13s/it]
Trial 70, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 70, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[LightGBM] [Info] Number of positive: 10130, number of negative: 10533
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.790295
[LightGBM] [Info] Total Bins 20104
[LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 254
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012
[LightGBM] [Info] Start training from score -0.039012
[LightGBM] [Debug] Re-bagging, using 15800 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8
[LightGBM] [Debug] Re-bagging, using 15945 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 12
[LightGBM] [Debug] Re-bagging, using 15858 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 11
[LightGBM] [Debug] Re-bagging, using 15755 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 8
[LightGBM] [Debug] Re-bagging, using 15802 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 8
[LightGBM] [Debug] Re-bagging, using 15790 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8
[LightGBM] [Debug] Re-bagging, using 15806 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7
[LightGBM] [Debug] Re-bagging, using 15802 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 6
[LightGBM] [Debug] Re-bagging, using 15872 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 5
[LightGBM] [Debug] Re-bagging, using 15771 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 9
[LightGBM] [Debug] Re-bagging, using 15792 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6
[LightGBM] [Debug] Re-bagging, using 15707 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7
[LightGBM] [Debug] Re-bagging, using 15781 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 8
[LightGBM] [Debug] Re-bagging, using 15709 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 6
[LightGBM] [Debug] Re-bagging, using 15898 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 5
[LightGBM] [Debug] Re-bagging, using 15815 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 5
Trial 70, Fold 1: Log loss = 0.5212313297273229, Average precision = 0.9627836292091663, ROC-AUC = 0.9549571509824445, Elapsed Time = 0.6239304999999149 seconds
Trial 70, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 70, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[LightGBM] [Info] Number of positive: 10230, number of negative: 10471
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791600
[LightGBM] [Info] Total Bins 20073
[LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285
[LightGBM] [Info] Start training from score -0.023285
[LightGBM] [Debug] Re-bagging, using 15833 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7
[LightGBM] [Debug] Re-bagging, using 15972 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 10
[LightGBM] [Debug] Re-bagging, using 15887 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 9
[LightGBM] [Debug] Re-bagging, using 15788 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7
[LightGBM] [Debug] Re-bagging, using 15829 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7
[LightGBM] [Debug] Re-bagging, using 15807 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 9
[LightGBM] [Debug] Re-bagging, using 15831 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 7
[LightGBM] [Debug] Re-bagging, using 15847 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 9
[LightGBM] [Debug] Re-bagging, using 15899 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 6
[LightGBM] [Debug] Re-bagging, using 15806 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8
[LightGBM] [Debug] Re-bagging, using 15815 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 10
[LightGBM] [Debug] Re-bagging, using 15725 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7
[LightGBM] [Debug] Re-bagging, using 15806 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8
[LightGBM] [Debug] Re-bagging, using 15727 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 9
[LightGBM] [Debug] Re-bagging, using 15927 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 7
[LightGBM] [Debug] Re-bagging, using 15862 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 10
Trial 70, Fold 2: Log loss = 0.5209099033618703, Average precision = 0.9621351418743129, ROC-AUC = 0.9574910446044405, Elapsed Time = 0.6758497000000716 seconds
Trial 70, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 70, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[LightGBM] [Info] Number of positive: 10165, number of negative: 10517
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.790045
[LightGBM] [Info] Total Bins 20069
[LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 254
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043
[LightGBM] [Info] Start training from score -0.034043
[LightGBM] [Debug] Re-bagging, using 15816 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 8
[LightGBM] [Debug] Re-bagging, using 15959 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 8
[LightGBM] [Debug] Re-bagging, using 15871 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 9
[LightGBM] [Debug] Re-bagging, using 15774 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 12
[LightGBM] [Debug] Re-bagging, using 15813 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 9
[LightGBM] [Debug] Re-bagging, using 15800 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7
[LightGBM] [Debug] Re-bagging, using 15814 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 11
[LightGBM] [Debug] Re-bagging, using 15829 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7
[LightGBM] [Debug] Re-bagging, using 15879 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 7
[LightGBM] [Debug] Re-bagging, using 15797 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 8
[LightGBM] [Debug] Re-bagging, using 15804 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Debug] Re-bagging, using 15716 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 9
[LightGBM] [Debug] Re-bagging, using 15794 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9
[LightGBM] [Debug] Re-bagging, using 15715 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 5
[LightGBM] [Debug] Re-bagging, using 15909 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 13
[LightGBM] [Debug] Re-bagging, using 15830 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7
Trial 70, Fold 3: Log loss = 0.5124468728151358, Average precision = 0.9650361000889902, ROC-AUC = 0.9592594426279242, Elapsed Time = 0.7345519999998942 seconds
Trial 70, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 70, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[LightGBM] [Info] Number of positive: 10177, number of negative: 10479
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791090
[LightGBM] [Info] Total Bins 20105
[LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243
[LightGBM] [Info] Start training from score -0.029243
[LightGBM] [Debug] Re-bagging, using 15795 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 10
[LightGBM] [Debug] Re-bagging, using 15937 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 8
[LightGBM] [Debug] Re-bagging, using 15854 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Debug] Re-bagging, using 15753 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 11
[LightGBM] [Debug] Re-bagging, using 15796 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 8
[LightGBM] [Debug] Re-bagging, using 15782 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7
[LightGBM] [Debug] Re-bagging, using 15802 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 8
[LightGBM] [Debug] Re-bagging, using 15800 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 10
[LightGBM] [Debug] Re-bagging, using 15861 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9
[LightGBM] [Debug] Re-bagging, using 15772 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 10
[LightGBM] [Debug] Re-bagging, using 15781 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 11
[LightGBM] [Debug] Re-bagging, using 15699 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Debug] Re-bagging, using 15785 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6
[LightGBM] [Debug] Re-bagging, using 15698 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 8
[LightGBM] [Debug] Re-bagging, using 15895 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 8
[LightGBM] [Debug] Re-bagging, using 15813 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8
Trial 70, Fold 4: Log loss = 0.521958522934716, Average precision = 0.9634605752827095, ROC-AUC = 0.9565819843535032, Elapsed Time = 0.705152999999882 seconds
Trial 70, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 70, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[LightGBM] [Info] Number of positive: 10150, number of negative: 10500
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.792012
[LightGBM] [Info] Total Bins 20102
[LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902
[LightGBM] [Info] Start training from score -0.033902
[LightGBM] [Debug] Re-bagging, using 15789 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 8
[LightGBM] [Debug] Re-bagging, using 15936 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6
[LightGBM] [Debug] Re-bagging, using 15851 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7
[LightGBM] [Debug] Re-bagging, using 15745 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 10
[LightGBM] [Debug] Re-bagging, using 15790 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7
[LightGBM] [Debug] Re-bagging, using 15777 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 11
[LightGBM] [Debug] Re-bagging, using 15804 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 13
[LightGBM] [Debug] Re-bagging, using 15791 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 6
[LightGBM] [Debug] Re-bagging, using 15856 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 6
[LightGBM] [Debug] Re-bagging, using 15769 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 6
[LightGBM] [Debug] Re-bagging, using 15777 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 9
[LightGBM] [Debug] Re-bagging, using 15690 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 8
[LightGBM] [Debug] Re-bagging, using 15778 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 8
[LightGBM] [Debug] Re-bagging, using 15701 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 6
[LightGBM] [Debug] Re-bagging, using 15892 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7
[LightGBM] [Debug] Re-bagging, using 15811 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 8
Trial 70, Fold 5: Log loss = 0.5191817461829943, Average precision = 0.9611701754237488, ROC-AUC = 0.9542330230570574, Elapsed Time = 0.7133186000000933 seconds
Optimization Progress:  71%|#######1  | 71/100 [16:26<07:01, 14.54s/it]
Trial 71, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 71, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[LightGBM] [Info] Number of positive: 10130, number of negative: 10533
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795662
[LightGBM] [Info] Total Bins 11147
[LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 258
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012
[LightGBM] [Info] Start training from score -0.039012
[LightGBM] [Debug] Re-bagging, using 9953 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 11
[LightGBM] [Debug] Re-bagging, using 9912 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 11
[LightGBM] [Debug] Re-bagging, using 9892 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 14
[LightGBM] [Debug] Re-bagging, using 9802 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 14
[LightGBM] [Debug] Re-bagging, using 9970 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 14
[LightGBM] [Debug] Re-bagging, using 9834 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 15
[LightGBM] [Debug] Re-bagging, using 9910 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 11
[LightGBM] [Debug] Re-bagging, using 9921 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 13
[LightGBM] [Debug] Re-bagging, using 9935 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 13
Trial 71, Fold 1: Log loss = 0.33959284495604364, Average precision = 0.9724411600723212, ROC-AUC = 0.9668345796112281, Elapsed Time = 1.8166848999999274 seconds
Trial 71, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 71, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[LightGBM] [Info] Number of positive: 10230, number of negative: 10471
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.794298
[LightGBM] [Info] Total Bins 11158
[LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 257
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285
[LightGBM] [Info] Start training from score -0.023285
[LightGBM] [Debug] Re-bagging, using 9972 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 11
[LightGBM] [Debug] Re-bagging, using 9932 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 12
[LightGBM] [Debug] Re-bagging, using 9906 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 12
[LightGBM] [Debug] Re-bagging, using 9828 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 11
[LightGBM] [Debug] Re-bagging, using 9997 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 13
[LightGBM] [Debug] Re-bagging, using 9832 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 14
[LightGBM] [Debug] Re-bagging, using 9918 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 12
[LightGBM] [Debug] Re-bagging, using 9936 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 15
[LightGBM] [Debug] Re-bagging, using 9963 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 12
Trial 71, Fold 2: Log loss = 0.34029115995704734, Average precision = 0.9706272485357673, ROC-AUC = 0.9671575302865818, Elapsed Time = 2.0769625000002634 seconds
Trial 71, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 71, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[LightGBM] [Info] Number of positive: 10165, number of negative: 10517
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795455
[LightGBM] [Info] Total Bins 11164
[LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 258
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043
[LightGBM] [Info] Start training from score -0.034043
[LightGBM] [Debug] Re-bagging, using 9963 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 10
[LightGBM] [Debug] Re-bagging, using 9919 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 12
[LightGBM] [Debug] Re-bagging, using 9897 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 12
[LightGBM] [Debug] Re-bagging, using 9823 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 11
[LightGBM] [Debug] Re-bagging, using 9982 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 11
[LightGBM] [Debug] Re-bagging, using 9829 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 11
[LightGBM] [Debug] Re-bagging, using 9917 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 12
[LightGBM] [Debug] Re-bagging, using 9927 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 15
[LightGBM] [Debug] Re-bagging, using 9941 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 13
Trial 71, Fold 3: Log loss = 0.34119889364049394, Average precision = 0.9716527135534025, ROC-AUC = 0.9667977590989204, Elapsed Time = 2.086481299999832 seconds
Trial 71, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 71, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[LightGBM] [Info] Number of positive: 10177, number of negative: 10479
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795144
[LightGBM] [Info] Total Bins 11145
[LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 258
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243
[LightGBM] [Info] Start training from score -0.029243
[LightGBM] [Debug] Re-bagging, using 9949 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 12
[LightGBM] [Debug] Re-bagging, using 9908 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 11
[LightGBM] [Debug] Re-bagging, using 9889 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 12
[LightGBM] [Debug] Re-bagging, using 9800 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 14
[LightGBM] [Debug] Re-bagging, using 9963 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 13
[LightGBM] [Debug] Re-bagging, using 9832 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 88 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 12
[LightGBM] [Debug] Re-bagging, using 9908 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 14
[LightGBM] [Debug] Re-bagging, using 9920 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 14
[LightGBM] [Debug] Re-bagging, using 9932 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 12
Trial 71, Fold 4: Log loss = 0.33957611513155844, Average precision = 0.9720091270541313, ROC-AUC = 0.9665419620354276, Elapsed Time = 2.2675779999999577 seconds
Trial 71, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 71, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[LightGBM] [Warning] Met negative value in categorical features, will convert it to NaN
[LightGBM] [Warning] Met negative value in categorical features, will convert it to NaN
[LightGBM] [Warning] Met negative value in categorical features, will convert it to NaN
[LightGBM] [Warning] Met negative value in categorical features, will convert it to NaN
[LightGBM] [Warning] Met negative value in categorical features, will convert it to NaN
[LightGBM] [Warning] Met negative value in categorical features, will convert it to NaN
[LightGBM] [Warning] Met negative value in categorical features, will convert it to NaN
[LightGBM] [Warning] Met negative value in categorical features, will convert it to NaN
[LightGBM] [Warning] Met negative value in categorical features, will convert it to NaN
[LightGBM] [Warning] Met negative value in categorical features, will convert it to NaN
[LightGBM] [Warning] Met negative value in categorical features, will convert it to NaN
[LightGBM] [Info] Number of positive: 10150, number of negative: 10500
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.796026
[LightGBM] [Info] Total Bins 11147
[LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 258
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902
[LightGBM] [Info] Start training from score -0.033902
[LightGBM] [Debug] Re-bagging, using 9947 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 11
[LightGBM] [Debug] Re-bagging, using 9905 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 13
[LightGBM] [Debug] Re-bagging, using 9889 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 15
[LightGBM] [Debug] Re-bagging, using 9793 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 12
[LightGBM] [Debug] Re-bagging, using 9961 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 13
[LightGBM] [Debug] Re-bagging, using 9826 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 11
[LightGBM] [Debug] Re-bagging, using 9912 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 15
[LightGBM] [Debug] Re-bagging, using 9914 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 14
[LightGBM] [Debug] Re-bagging, using 9930 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 12
Trial 71, Fold 5: Log loss = 0.34555205055908644, Average precision = 0.969191136142906, ROC-AUC = 0.964253497575386, Elapsed Time = 1.7241317999996681 seconds
Optimization Progress:  72%|#######2  | 72/100 [16:43<07:12, 15.43s/it]
Trial 72, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 72, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[LightGBM] [Info] Number of positive: 10130, number of negative: 10533
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791640
[LightGBM] [Info] Total Bins 19014
[LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012
[LightGBM] [Info] Start training from score -0.039012
[LightGBM] [Debug] Re-bagging, using 10167 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 12
[LightGBM] [Debug] Re-bagging, using 10127 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10114 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10000 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9
[LightGBM] [Debug] Re-bagging, using 10178 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10041 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
Trial 72, Fold 1: Log loss = 0.27839808071739647, Average precision = 0.9699116494901384, ROC-AUC = 0.9638715330054535, Elapsed Time = 0.9093064999997296 seconds
Trial 72, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 72, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[LightGBM] [Info] Number of positive: 10230, number of negative: 10471
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791600
[LightGBM] [Info] Total Bins 19026
[LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285
[LightGBM] [Info] Start training from score -0.023285
[LightGBM] [Debug] Re-bagging, using 10188 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10146 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10128 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10026 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10205 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10039 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10
Trial 72, Fold 2: Log loss = 0.2773252453732645, Average precision = 0.9679454703507686, ROC-AUC = 0.9644505774154984, Elapsed Time = 0.9629867000003287 seconds
Trial 72, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 72, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[LightGBM] [Info] Number of positive: 10165, number of negative: 10517
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791401
[LightGBM] [Info] Total Bins 19029
[LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043
[LightGBM] [Info] Start training from score -0.034043
[LightGBM] [Debug] Re-bagging, using 10177 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
[LightGBM] [Debug] Re-bagging, using 10134 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10120 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10021 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10189 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10037 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11
Trial 72, Fold 3: Log loss = 0.2747655322336973, Average precision = 0.9708889291563291, ROC-AUC = 0.9660925867515482, Elapsed Time = 1.03429369999958 seconds
Trial 72, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 72, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[LightGBM] [Info] Number of positive: 10177, number of negative: 10479
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791090
[LightGBM] [Info] Total Bins 19010
[LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243
[LightGBM] [Info] Start training from score -0.029243
[LightGBM] [Debug] Re-bagging, using 10163 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 12
[LightGBM] [Debug] Re-bagging, using 10123 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10110 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10
[LightGBM] [Debug] Re-bagging, using 9999 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9
[LightGBM] [Debug] Re-bagging, using 10171 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10039 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
Trial 72, Fold 4: Log loss = 0.27565143365103895, Average precision = 0.9698695678849122, ROC-AUC = 0.9640579286393842, Elapsed Time = 1.0720799000000625 seconds
Trial 72, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 72, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[LightGBM] [Info] Number of positive: 10150, number of negative: 10500
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.792012
[LightGBM] [Info] Total Bins 19008
[LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902
[LightGBM] [Info] Start training from score -0.033902
[LightGBM] [Debug] Re-bagging, using 10160 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10120 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 12
[LightGBM] [Debug] Re-bagging, using 10111 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 13
[LightGBM] [Debug] Re-bagging, using 9992 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 13
[LightGBM] [Debug] Re-bagging, using 10169 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 12
[LightGBM] [Debug] Re-bagging, using 10032 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 12
Trial 72, Fold 5: Log loss = 0.28328383290878856, Average precision = 0.9680872451642151, ROC-AUC = 0.9623242851569032, Elapsed Time = 1.0523961999997482 seconds
Optimization Progress:  73%|#######3  | 73/100 [16:55<06:32, 14.54s/it]
Trial 73, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 73, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[LightGBM] [Info] Number of positive: 10130, number of negative: 10533
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.796981
[LightGBM] [Info] Total Bins 21915
[LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 259
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012
[LightGBM] [Info] Start training from score -0.039012
[LightGBM] [Debug] Re-bagging, using 8731 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 16
[LightGBM] [Debug] Re-bagging, using 8796 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16
[LightGBM] [Debug] Re-bagging, using 8715 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 87 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 15
[LightGBM] [Debug] Re-bagging, using 8664 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 19
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16
[LightGBM] [Debug] Re-bagging, using 8776 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 14
[LightGBM] [Debug] Re-bagging, using 8704 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 12
[LightGBM] [Debug] Re-bagging, using 8720 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 15
[LightGBM] [Debug] Re-bagging, using 8788 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 17
[LightGBM] [Debug] Re-bagging, using 8763 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 13
[LightGBM] [Debug] Re-bagging, using 8545 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15
[LightGBM] [Debug] Re-bagging, using 8540 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 87 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 13
[LightGBM] [Debug] Re-bagging, using 8563 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 86 and depth = 14
[LightGBM] [Debug] Re-bagging, using 8668 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 86 and depth = 15
[LightGBM] [Debug] Re-bagging, using 8668 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 14
[LightGBM] [Debug] Re-bagging, using 8874 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15
[LightGBM] [Debug] Re-bagging, using 8724 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 13
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 16
[LightGBM] [Debug] Re-bagging, using 8682 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 87 and depth = 19
[LightGBM] [Debug] Re-bagging, using 8682 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 13
[LightGBM] [Debug] Re-bagging, using 8762 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15
[LightGBM] [Debug] Re-bagging, using 8877 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 20
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 20
[LightGBM] [Debug] Re-bagging, using 8552 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 20
[LightGBM] [Debug] Re-bagging, using 8668 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 14
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 18
[LightGBM] [Debug] Re-bagging, using 8737 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 20
[LightGBM] [Debug] Re-bagging, using 8789 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 21
[LightGBM] [Debug] Re-bagging, using 8827 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 20
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 18
[LightGBM] [Debug] Re-bagging, using 8742 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 20
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 18
[LightGBM] [Debug] Re-bagging, using 8695 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 23
[LightGBM] [Debug] Re-bagging, using 8804 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 19
[LightGBM] [Debug] Re-bagging, using 8649 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 25
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 27
[LightGBM] [Debug] Re-bagging, using 8741 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 20
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 17
[LightGBM] [Debug] Re-bagging, using 8673 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 25
[LightGBM] [Debug] Re-bagging, using 8750 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 25
[LightGBM] [Debug] Re-bagging, using 8691 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 27
[LightGBM] [Debug] Re-bagging, using 8792 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 20
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 21
[LightGBM] [Debug] Re-bagging, using 8668 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 19
[LightGBM] [Debug] Re-bagging, using 8708 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 21
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 22
[LightGBM] [Debug] Re-bagging, using 8678 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 16
[LightGBM] [Debug] Re-bagging, using 8785 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 22
[LightGBM] [Debug] Re-bagging, using 8675 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 21
[LightGBM] [Debug] Re-bagging, using 8925 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 25
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 21
[LightGBM] [Debug] Re-bagging, using 8668 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 20
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15
[LightGBM] [Debug] Re-bagging, using 8689 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 14
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 19
[LightGBM] [Debug] Re-bagging, using 8586 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 24
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 23
[LightGBM] [Debug] Re-bagging, using 8618 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 23
Trial 73, Fold 1: Log loss = 0.19207670774348418, Average precision = 0.9764999102293743, ROC-AUC = 0.972403846613422, Elapsed Time = 2.5400171000001137 seconds
Trial 73, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 73, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[LightGBM] [Info] Number of positive: 10230, number of negative: 10471
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.796949
[LightGBM] [Info] Total Bins 21927
[LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 259
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285
[LightGBM] [Info] Start training from score -0.023285
[LightGBM] [Debug] Re-bagging, using 8749 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 12
[LightGBM] [Debug] Re-bagging, using 8813 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 88 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 12
[LightGBM] [Debug] Re-bagging, using 8729 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 11
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 13
[LightGBM] [Debug] Re-bagging, using 8685 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 14
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 14
[LightGBM] [Debug] Re-bagging, using 8804 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 14
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 12
[LightGBM] [Debug] Re-bagging, using 8699 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 88 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 16
[LightGBM] [Debug] Re-bagging, using 8730 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 15
[LightGBM] [Debug] Re-bagging, using 8804 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 88 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 15
[LightGBM] [Debug] Re-bagging, using 8789 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 14
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 17
[LightGBM] [Debug] Re-bagging, using 8564 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 13
[LightGBM] [Debug] Re-bagging, using 8547 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 87 and depth = 14
[LightGBM] [Debug] Re-bagging, using 8565 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 12
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 12
[LightGBM] [Debug] Re-bagging, using 8676 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 16
[LightGBM] [Debug] Re-bagging, using 8687 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 13
[LightGBM] [Debug] Re-bagging, using 8906 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 14
[LightGBM] [Debug] Re-bagging, using 8756 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 87 and depth = 13
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 16
[LightGBM] [Debug] Re-bagging, using 8699 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 14
[LightGBM] [Debug] Re-bagging, using 8701 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 17
[LightGBM] [Debug] Re-bagging, using 8769 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 14
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 16
[LightGBM] [Debug] Re-bagging, using 8883 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 18
[LightGBM] [Debug] Re-bagging, using 8574 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 13
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 16
[LightGBM] [Debug] Re-bagging, using 8682 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 19
[LightGBM] [Debug] Re-bagging, using 8763 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 19
[LightGBM] [Debug] Re-bagging, using 8804 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 19
[LightGBM] [Debug] Re-bagging, using 8835 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 16
[LightGBM] [Debug] Re-bagging, using 8757 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 20
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15
[LightGBM] [Debug] Re-bagging, using 8715 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 21
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15
[LightGBM] [Debug] Re-bagging, using 8813 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 26
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 20
[LightGBM] [Debug] Re-bagging, using 8660 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 26
[LightGBM] [Debug] Re-bagging, using 8759 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 20
[LightGBM] [Debug] Re-bagging, using 8696 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 20
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 21
[LightGBM] [Debug] Re-bagging, using 8763 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 21
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 16
[LightGBM] [Debug] Re-bagging, using 8689 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 25
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 18
[LightGBM] [Debug] Re-bagging, using 8818 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 14
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 22
[LightGBM] [Debug] Re-bagging, using 8681 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 25
[LightGBM] [Debug] Re-bagging, using 8715 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 28
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 31
[LightGBM] [Debug] Re-bagging, using 8686 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 21
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 19
[LightGBM] [Debug] Re-bagging, using 8804 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 21
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 20
[LightGBM] [Debug] Re-bagging, using 8671 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 18
[LightGBM] [Debug] Re-bagging, using 8944 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 24
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15
[LightGBM] [Debug] Re-bagging, using 8684 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 24
[LightGBM] [Debug] Re-bagging, using 8720 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 24
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 19
[LightGBM] [Debug] Re-bagging, using 8615 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 26
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 21
[LightGBM] [Debug] Re-bagging, using 8653 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 21
Trial 73, Fold 2: Log loss = 0.18436194570607786, Average precision = 0.9759969547269485, ROC-AUC = 0.9731626454009343, Elapsed Time = 3.1395682000002125 seconds
Trial 73, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 73, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[LightGBM] [Info] Number of positive: 10165, number of negative: 10517
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.796784
[LightGBM] [Info] Total Bins 21870
[LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 259
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043
[LightGBM] [Info] Start training from score -0.034043
[LightGBM] [Debug] Re-bagging, using 8740 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 16
[LightGBM] [Debug] Re-bagging, using 8803 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 15
[LightGBM] [Debug] Re-bagging, using 8720 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 13
[LightGBM] [Debug] Re-bagging, using 8682 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 14
[LightGBM] [Debug] Re-bagging, using 8789 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 14
[LightGBM] [Debug] Re-bagging, using 8696 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 13
[LightGBM] [Debug] Re-bagging, using 8727 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 16
[LightGBM] [Debug] Re-bagging, using 8796 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 14
[LightGBM] [Debug] Re-bagging, using 8769 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 21
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 86 and depth = 16
[LightGBM] [Debug] Re-bagging, using 8565 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 86 and depth = 16
[LightGBM] [Debug] Re-bagging, using 8542 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 15
[LightGBM] [Debug] Re-bagging, using 8565 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 13
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15
[LightGBM] [Debug] Re-bagging, using 8665 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 14
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15
[LightGBM] [Debug] Re-bagging, using 8676 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 14
[LightGBM] [Debug] Re-bagging, using 8886 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 17
[LightGBM] [Debug] Re-bagging, using 8740 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 21
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15
[LightGBM] [Debug] Re-bagging, using 8697 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 86 and depth = 14
[LightGBM] [Debug] Re-bagging, using 8699 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 88 and depth = 14
[LightGBM] [Debug] Re-bagging, using 8759 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 13
[LightGBM] [Debug] Re-bagging, using 8886 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 21
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 17
[LightGBM] [Debug] Re-bagging, using 8565 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 16
[LightGBM] [Debug] Re-bagging, using 8667 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 22
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 18
[LightGBM] [Debug] Re-bagging, using 8744 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 19
[LightGBM] [Debug] Re-bagging, using 8800 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 20
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 16
[LightGBM] [Debug] Re-bagging, using 8845 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 25
[LightGBM] [Debug] Re-bagging, using 8751 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 22
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 18
[LightGBM] [Debug] Re-bagging, using 8688 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 21
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 22
[LightGBM] [Debug] Re-bagging, using 8797 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 18
[LightGBM] [Debug] Re-bagging, using 8667 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 20
[LightGBM] [Debug] Re-bagging, using 8766 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 18
[LightGBM] [Debug] Re-bagging, using 8682 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 14
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15
[LightGBM] [Debug] Re-bagging, using 8751 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 26
[LightGBM] [Debug] Re-bagging, using 8686 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 21
[LightGBM] [Debug] Re-bagging, using 8800 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 20
[LightGBM] [Debug] Re-bagging, using 8683 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 17
[LightGBM] [Debug] Re-bagging, using 8717 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 22
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 20
[LightGBM] [Debug] Re-bagging, using 8690 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 19
[LightGBM] [Debug] Re-bagging, using 8790 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 17
[LightGBM] [Debug] Re-bagging, using 8667 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 24
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 18
[LightGBM] [Debug] Re-bagging, using 8930 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 16
[LightGBM] [Debug] Re-bagging, using 8670 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 24
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 20
[LightGBM] [Debug] Re-bagging, using 8697 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 19
[LightGBM] [Debug] Re-bagging, using 8597 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 17
[LightGBM] [Debug] Re-bagging, using 8623 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 20
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 17
Trial 73, Fold 3: Log loss = 0.19152865274959918, Average precision = 0.97518386567535, ROC-AUC = 0.9725112201558301, Elapsed Time = 3.040055299999949 seconds
Trial 73, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 73, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[LightGBM] [Info] Number of positive: 10177, number of negative: 10479
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.796474
[LightGBM] [Info] Total Bins 21910
[LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 259
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243
[LightGBM] [Info] Start training from score -0.029243
[LightGBM] [Debug] Re-bagging, using 8727 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 14
[LightGBM] [Debug] Re-bagging, using 8794 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 15
[LightGBM] [Debug] Re-bagging, using 8711 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 13
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 14
[LightGBM] [Debug] Re-bagging, using 8663 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 13
[LightGBM] [Debug] Re-bagging, using 8769 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 88 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 14
[LightGBM] [Debug] Re-bagging, using 8702 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 15
[LightGBM] [Debug] Re-bagging, using 8718 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 87 and depth = 12
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15
[LightGBM] [Debug] Re-bagging, using 8787 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 88 and depth = 16
[LightGBM] [Debug] Re-bagging, using 8761 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 87 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 13
[LightGBM] [Debug] Re-bagging, using 8542 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 86 and depth = 15
[LightGBM] [Debug] Re-bagging, using 8536 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 14
[LightGBM] [Debug] Re-bagging, using 8561 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 14
[LightGBM] [Debug] Re-bagging, using 8662 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 86 and depth = 16
[LightGBM] [Debug] Re-bagging, using 8669 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 13
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 14
[LightGBM] [Debug] Re-bagging, using 8872 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15
[LightGBM] [Debug] Re-bagging, using 8714 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 14
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 18
[LightGBM] [Debug] Re-bagging, using 8681 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 12
[LightGBM] [Debug] Re-bagging, using 8684 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15
[LightGBM] [Debug] Re-bagging, using 8747 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 12
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 18
[LightGBM] [Debug] Re-bagging, using 8879 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 14
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 18
[LightGBM] [Debug] Re-bagging, using 8559 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 21
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 22
[LightGBM] [Debug] Re-bagging, using 8656 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 18
[LightGBM] [Debug] Re-bagging, using 8742 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 21
[LightGBM] [Debug] Re-bagging, using 8782 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 19
[LightGBM] [Debug] Re-bagging, using 8823 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 20
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 16
[LightGBM] [Debug] Re-bagging, using 8743 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 19
[LightGBM] [Debug] Re-bagging, using 8679 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15
[LightGBM] [Debug] Re-bagging, using 8801 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 23
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 18
[LightGBM] [Debug] Re-bagging, using 8659 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 12
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 35
[LightGBM] [Debug] Re-bagging, using 8744 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 26
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 20
[LightGBM] [Debug] Re-bagging, using 8667 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 20
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 23
[LightGBM] [Debug] Re-bagging, using 8740 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 20
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 18
[LightGBM] [Debug] Re-bagging, using 8680 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 28
[LightGBM] [Debug] Re-bagging, using 8801 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 21
[LightGBM] [Debug] Re-bagging, using 8671 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 20
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 18
[LightGBM] [Debug] Re-bagging, using 8706 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 23
[LightGBM] [Debug] Re-bagging, using 8665 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 25
[LightGBM] [Debug] Re-bagging, using 8789 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 21
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 19
[LightGBM] [Debug] Re-bagging, using 8663 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 28
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 20
[LightGBM] [Debug] Re-bagging, using 8924 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 21
[LightGBM] [Debug] Re-bagging, using 8662 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 16
[LightGBM] [Debug] Re-bagging, using 8695 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 21
[LightGBM] [Debug] Re-bagging, using 8600 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 25
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 18
[LightGBM] [Debug] Re-bagging, using 8609 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 19
Trial 73, Fold 4: Log loss = 0.19165113364949743, Average precision = 0.9757806427128366, ROC-AUC = 0.9716902007911471, Elapsed Time = 3.2279449000006935 seconds
Trial 73, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 73, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[LightGBM] [Info] Number of positive: 10150, number of negative: 10500
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.797340
[LightGBM] [Info] Total Bins 21919
[LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 259
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902
[LightGBM] [Info] Start training from score -0.033902
[LightGBM] [Debug] Re-bagging, using 8725 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 86 and depth = 14
[LightGBM] [Debug] Re-bagging, using 8792 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 13
[LightGBM] [Debug] Re-bagging, using 8710 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 13
[LightGBM] [Debug] Re-bagging, using 8656 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 17
[LightGBM] [Debug] Re-bagging, using 8769 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 16
[LightGBM] [Debug] Re-bagging, using 8694 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 86 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 16
[LightGBM] [Debug] Re-bagging, using 8725 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 15
[LightGBM] [Debug] Re-bagging, using 8781 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 15
[LightGBM] [Debug] Re-bagging, using 8758 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 87 and depth = 12
[LightGBM] [Debug] Re-bagging, using 8544 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 16
[LightGBM] [Debug] Re-bagging, using 8526 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 15
[LightGBM] [Debug] Re-bagging, using 8554 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 17
[LightGBM] [Debug] Re-bagging, using 8664 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 12
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15
[LightGBM] [Debug] Re-bagging, using 8678 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 16
[LightGBM] [Debug] Re-bagging, using 8870 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 19
[LightGBM] [Debug] Re-bagging, using 8715 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 12
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 17
[LightGBM] [Debug] Re-bagging, using 8673 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 18
[LightGBM] [Debug] Re-bagging, using 8665 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 17
[LightGBM] [Debug] Re-bagging, using 8751 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15
[LightGBM] [Debug] Re-bagging, using 8867 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 23
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 19
[LightGBM] [Debug] Re-bagging, using 8567 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 14
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 17
[LightGBM] [Debug] Re-bagging, using 8655 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 18
[LightGBM] [Debug] Re-bagging, using 8731 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 12
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 18
[LightGBM] [Debug] Re-bagging, using 8790 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 21
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 21
[LightGBM] [Debug] Re-bagging, using 8818 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15
[LightGBM] [Debug] Re-bagging, using 8739 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 18
[LightGBM] [Debug] Re-bagging, using 8681 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 20
[LightGBM] [Debug] Re-bagging, using 8787 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 20
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 21
[LightGBM] [Debug] Re-bagging, using 8659 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 21
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 18
[LightGBM] [Debug] Re-bagging, using 8749 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 13
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 23
[LightGBM] [Debug] Re-bagging, using 8671 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 18
[LightGBM] [Debug] Re-bagging, using 8735 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 18
[LightGBM] [Debug] Re-bagging, using 8672 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 22
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 28
[LightGBM] [Debug] Re-bagging, using 8785 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 16
[LightGBM] [Debug] Re-bagging, using 8680 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 17
[LightGBM] [Debug] Re-bagging, using 8706 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 20
[LightGBM] [Debug] Re-bagging, using 8667 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 24
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 18
[LightGBM] [Debug] Re-bagging, using 8784 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 21
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 18
[LightGBM] [Debug] Re-bagging, using 8657 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 20
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 20
[LightGBM] [Debug] Re-bagging, using 8924 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 25
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 19
[LightGBM] [Debug] Re-bagging, using 8659 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 23
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 21
[LightGBM] [Debug] Re-bagging, using 8695 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 20
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 27
[LightGBM] [Debug] Re-bagging, using 8584 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 22
[LightGBM] [Debug] Re-bagging, using 8625 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 21
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 22
Trial 73, Fold 5: Log loss = 0.19210909317188127, Average precision = 0.9761174581789205, ROC-AUC = 0.9729034799249392, Elapsed Time = 3.01584129999992 seconds
Optimization Progress:  74%|#######4  | 74/100 [17:18<07:21, 16.97s/it]
Trial 74, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 74, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[LightGBM] [Info] Number of positive: 10130, number of negative: 10533
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795662
[LightGBM] [Info] Total Bins 19898
[LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 258
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012
[LightGBM] [Info] Start training from score -0.039012
[LightGBM] [Debug] Re-bagging, using 2947 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7
[LightGBM] [Debug] Re-bagging, using 2947 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7
[LightGBM] [Debug] Re-bagging, using 2840 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 10
[LightGBM] [Debug] Re-bagging, using 2873 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 7
[LightGBM] [Debug] Re-bagging, using 2977 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8
[LightGBM] [Debug] Re-bagging, using 2925 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 9
[LightGBM] [Debug] Re-bagging, using 2842 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 9
[LightGBM] [Debug] Re-bagging, using 2990 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 11
[LightGBM] [Debug] Re-bagging, using 2890 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10
[LightGBM] [Debug] Re-bagging, using 2874 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 11
[LightGBM] [Debug] Re-bagging, using 2773 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 12
[LightGBM] [Debug] Re-bagging, using 2860 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 12
[LightGBM] [Debug] Re-bagging, using 2898 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 11
[LightGBM] [Debug] Re-bagging, using 2860 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 14
[LightGBM] [Debug] Re-bagging, using 2913 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 14
[LightGBM] [Debug] Re-bagging, using 2894 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 12
[LightGBM] [Debug] Re-bagging, using 2917 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Debug] Re-bagging, using 2794 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 18
[LightGBM] [Debug] Re-bagging, using 2964 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 11
Trial 74, Fold 1: Log loss = 0.20789237807057712, Average precision = 0.9728302046346347, ROC-AUC = 0.9679757065427798, Elapsed Time = 1.160290700000587 seconds
Trial 74, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 74, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[LightGBM] [Info] Number of positive: 10230, number of negative: 10471
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795629
[LightGBM] [Info] Total Bins 19910
[LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 258
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285
[LightGBM] [Info] Start training from score -0.023285
[LightGBM] [Debug] Re-bagging, using 2952 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7
[LightGBM] [Debug] Re-bagging, using 2957 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8
[LightGBM] [Debug] Re-bagging, using 2838 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8
[LightGBM] [Debug] Re-bagging, using 2883 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 7
[LightGBM] [Debug] Re-bagging, using 2986 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8
[LightGBM] [Debug] Re-bagging, using 2921 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 12
[LightGBM] [Debug] Re-bagging, using 2847 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8
[LightGBM] [Debug] Re-bagging, using 2996 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 12
[LightGBM] [Debug] Re-bagging, using 2907 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 13
[LightGBM] [Debug] Re-bagging, using 2887 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 12
[LightGBM] [Debug] Re-bagging, using 2771 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10
[LightGBM] [Debug] Re-bagging, using 2857 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10
[LightGBM] [Debug] Re-bagging, using 2906 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 11
[LightGBM] [Debug] Re-bagging, using 2857 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 15
[LightGBM] [Debug] Re-bagging, using 2916 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 13
[LightGBM] [Debug] Re-bagging, using 2904 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 17
[LightGBM] [Debug] Re-bagging, using 2925 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 16
[LightGBM] [Debug] Re-bagging, using 2807 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 13
[LightGBM] [Debug] Re-bagging, using 2972 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 12
Trial 74, Fold 2: Log loss = 0.20502511626084324, Average precision = 0.9716575349201336, ROC-AUC = 0.9684218928351069, Elapsed Time = 1.2803405000004204 seconds
Trial 74, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 74, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[LightGBM] [Info] Number of positive: 10165, number of negative: 10517
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795455
[LightGBM] [Info] Total Bins 19863
[LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 258
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043
[LightGBM] [Info] Start training from score -0.034043
[LightGBM] [Debug] Re-bagging, using 2949 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 8
[LightGBM] [Debug] Re-bagging, using 2951 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 9
[LightGBM] [Debug] Re-bagging, using 2841 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8
[LightGBM] [Debug] Re-bagging, using 2876 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9
[LightGBM] [Debug] Re-bagging, using 2982 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 8
[LightGBM] [Debug] Re-bagging, using 2925 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9
[LightGBM] [Debug] Re-bagging, using 2847 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9
[LightGBM] [Debug] Re-bagging, using 2986 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8
[LightGBM] [Debug] Re-bagging, using 2901 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 17
[LightGBM] [Debug] Re-bagging, using 2879 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8
[LightGBM] [Debug] Re-bagging, using 2782 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 10
[LightGBM] [Debug] Re-bagging, using 2848 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 12
[LightGBM] [Debug] Re-bagging, using 2904 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Debug] Re-bagging, using 2861 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 20
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 12
[LightGBM] [Debug] Re-bagging, using 2907 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 13
[LightGBM] [Debug] Re-bagging, using 2906 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 12
[LightGBM] [Debug] Re-bagging, using 2920 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 13
[LightGBM] [Debug] Re-bagging, using 2795 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 13
[LightGBM] [Debug] Re-bagging, using 2971 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 13
Trial 74, Fold 3: Log loss = 0.20485285144190643, Average precision = 0.9725765501366499, ROC-AUC = 0.9683117879053462, Elapsed Time = 1.4039001000001008 seconds
Trial 74, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 74, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[LightGBM] [Info] Number of positive: 10177, number of negative: 10479
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795144
[LightGBM] [Info] Total Bins 19893
[LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 258
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243
[LightGBM] [Info] Start training from score -0.029243
[LightGBM] [Debug] Re-bagging, using 2947 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 6
[LightGBM] [Debug] Re-bagging, using 2945 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 9
[LightGBM] [Debug] Re-bagging, using 2836 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7
[LightGBM] [Debug] Re-bagging, using 2875 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8
[LightGBM] [Debug] Re-bagging, using 2976 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8
[LightGBM] [Debug] Re-bagging, using 2924 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7
[LightGBM] [Debug] Re-bagging, using 2844 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 11
[LightGBM] [Debug] Re-bagging, using 2986 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 12
[LightGBM] [Debug] Re-bagging, using 2888 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 11
[LightGBM] [Debug] Re-bagging, using 2873 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 11
[LightGBM] [Debug] Re-bagging, using 2773 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9
[LightGBM] [Debug] Re-bagging, using 2860 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10
[LightGBM] [Debug] Re-bagging, using 2894 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 14
[LightGBM] [Debug] Re-bagging, using 2858 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 14
[LightGBM] [Debug] Re-bagging, using 2911 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 14
[LightGBM] [Debug] Re-bagging, using 2899 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 11
[LightGBM] [Debug] Re-bagging, using 2913 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 20
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11
[LightGBM] [Debug] Re-bagging, using 2798 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10
[LightGBM] [Debug] Re-bagging, using 2960 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11
Trial 74, Fold 4: Log loss = 0.20620693439015805, Average precision = 0.9719124400290444, ROC-AUC = 0.9663950227830355, Elapsed Time = 1.486533999999665 seconds
Trial 74, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 74, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[LightGBM] [Info] Number of positive: 10150, number of negative: 10500
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.796026
[LightGBM] [Info] Total Bins 19891
[LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 258
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902
[LightGBM] [Info] Start training from score -0.033902
[LightGBM] [Debug] Re-bagging, using 2946 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 6
[LightGBM] [Debug] Re-bagging, using 2945 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7
[LightGBM] [Debug] Re-bagging, using 2836 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8
[LightGBM] [Debug] Re-bagging, using 2874 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 6
[LightGBM] [Debug] Re-bagging, using 2972 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8
[LightGBM] [Debug] Re-bagging, using 2922 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 10
[LightGBM] [Debug] Re-bagging, using 2848 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 9
[LightGBM] [Debug] Re-bagging, using 2981 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8
[LightGBM] [Debug] Re-bagging, using 2890 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8
[LightGBM] [Debug] Re-bagging, using 2872 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 7
[LightGBM] [Debug] Re-bagging, using 2768 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 11
[LightGBM] [Debug] Re-bagging, using 2849 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Debug] Re-bagging, using 2909 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 11
[LightGBM] [Debug] Re-bagging, using 2857 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 12
[LightGBM] [Debug] Re-bagging, using 2909 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11
[LightGBM] [Debug] Re-bagging, using 2902 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 14
[LightGBM] [Debug] Re-bagging, using 2916 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 11
[LightGBM] [Debug] Re-bagging, using 2786 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 16
[LightGBM] [Debug] Re-bagging, using 2963 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 14
Trial 74, Fold 5: Log loss = 0.21038025074049305, Average precision = 0.9709620645493768, ROC-AUC = 0.9671163628931869, Elapsed Time = 1.355631500000527 seconds
Optimization Progress:  75%|#######5  | 75/100 [17:32<06:43, 16.15s/it]
Trial 75, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 75, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[LightGBM] [Info] Number of positive: 10130, number of negative: 10533
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791640
[LightGBM] [Info] Total Bins 24366
[LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012
[LightGBM] [Info] Start training from score -0.039012
[LightGBM] [Debug] Re-bagging, using 10496 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10463 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10459 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10304 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10526 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9
[LightGBM] [Debug] Re-bagging, using 10349 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10424 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 8
[LightGBM] [Debug] Re-bagging, using 10397 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10455 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 8
[LightGBM] [Debug] Re-bagging, using 10215 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10299 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10288 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 9
[LightGBM] [Debug] Re-bagging, using 10280 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10332 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9
[LightGBM] [Debug] Re-bagging, using 10471 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10339 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9
[LightGBM] [Debug] Re-bagging, using 10405 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10406 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10406 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10536 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10252 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10333 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10417 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10416 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10489 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 9
[LightGBM] [Debug] Re-bagging, using 10393 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 9
[LightGBM] [Debug] Re-bagging, using 10390 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 12
[LightGBM] [Debug] Re-bagging, using 10517 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10351 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10370 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10378 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10368 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10411 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 9
[LightGBM] [Debug] Re-bagging, using 10478 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10342 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10371 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10431 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10482 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 9
[LightGBM] [Debug] Re-bagging, using 10335 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10582 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10333 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10386 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10290 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10357 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10473 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10338 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10276 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10190 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10
Trial 75, Fold 1: Log loss = 0.3386479276944911, Average precision = 0.9700048427156842, ROC-AUC = 0.9639866912337552, Elapsed Time = 1.6730855999994674 seconds
Trial 75, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 75, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[LightGBM] [Info] Number of positive: 10230, number of negative: 10471
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791600
[LightGBM] [Info] Total Bins 24373
[LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285
[LightGBM] [Info] Start training from score -0.023285
[LightGBM] [Debug] Re-bagging, using 10518 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10481 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9
[LightGBM] [Debug] Re-bagging, using 10475 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9
[LightGBM] [Debug] Re-bagging, using 10330 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9
[LightGBM] [Debug] Re-bagging, using 10555 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9
[LightGBM] [Debug] Re-bagging, using 10346 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10429 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10416 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10483 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10237 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10313 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 9
[LightGBM] [Debug] Re-bagging, using 10299 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9
[LightGBM] [Debug] Re-bagging, using 10290 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10360 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9
[LightGBM] [Debug] Re-bagging, using 10507 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12
[LightGBM] [Debug] Re-bagging, using 10381 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10420 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 13
[LightGBM] [Debug] Re-bagging, using 10426 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10420 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10541 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10282 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10349 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10439 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10436 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10496 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 9
[LightGBM] [Debug] Re-bagging, using 10406 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10409 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10538 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10368 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 9
[LightGBM] [Debug] Re-bagging, using 10395 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10404 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10382 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10416 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10498 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10362 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10387 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10443 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 9
[LightGBM] [Debug] Re-bagging, using 10509 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10338 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10598 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 9
[LightGBM] [Debug] Re-bagging, using 10360 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 9
[LightGBM] [Debug] Re-bagging, using 10409 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10317 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 13
[LightGBM] [Debug] Re-bagging, using 10391 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10486 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 9
[LightGBM] [Debug] Re-bagging, using 10357 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10306 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10202 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11
Trial 75, Fold 2: Log loss = 0.33831803482587663, Average precision = 0.968553079645746, ROC-AUC = 0.9648312367353467, Elapsed Time = 2.0940998000005493 seconds
Trial 75, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 75, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[LightGBM] [Info] Number of positive: 10165, number of negative: 10517
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791401
[LightGBM] [Info] Total Bins 24368
[LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043
[LightGBM] [Info] Start training from score -0.034043
[LightGBM] [Debug] Re-bagging, using 10506 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9
[LightGBM] [Debug] Re-bagging, using 10470 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9
[LightGBM] [Debug] Re-bagging, using 10465 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10325 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 8
[LightGBM] [Debug] Re-bagging, using 10537 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10347 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10429 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10403 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10463 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 9
[LightGBM] [Debug] Re-bagging, using 10236 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 9
[LightGBM] [Debug] Re-bagging, using 10305 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10295 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 8
[LightGBM] [Debug] Re-bagging, using 10280 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10340 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 13
[LightGBM] [Debug] Re-bagging, using 10491 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 9
[LightGBM] [Debug] Re-bagging, using 10356 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9
[LightGBM] [Debug] Re-bagging, using 10422 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10424 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 9
[LightGBM] [Debug] Re-bagging, using 10400 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10548 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10271 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10329 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10425 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10434 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10505 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10397 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10380 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 9
[LightGBM] [Debug] Re-bagging, using 10515 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10373 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12
[LightGBM] [Debug] Re-bagging, using 10395 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10395 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9
[LightGBM] [Debug] Re-bagging, using 10372 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10412 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 9
[LightGBM] [Debug] Re-bagging, using 10481 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 9
[LightGBM] [Debug] Re-bagging, using 10355 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10381 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10437 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10496 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10341 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10588 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 9
[LightGBM] [Debug] Re-bagging, using 10346 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10391 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10294 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10360 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 9
[LightGBM] [Debug] Re-bagging, using 10501 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10344 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10304 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10211 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 11
Trial 75, Fold 3: Log loss = 0.3374498500721996, Average precision = 0.9709400920837394, ROC-AUC = 0.9658439856593888, Elapsed Time = 2.0018864000003305 seconds
Trial 75, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 75, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[LightGBM] [Info] Number of positive: 10177, number of negative: 10479
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791090
[LightGBM] [Info] Total Bins 24356
[LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243
[LightGBM] [Info] Start training from score -0.029243
[LightGBM] [Debug] Re-bagging, using 10492 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10459 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9
[LightGBM] [Debug] Re-bagging, using 10454 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9
[LightGBM] [Debug] Re-bagging, using 10303 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9
[LightGBM] [Debug] Re-bagging, using 10520 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10345 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 9
[LightGBM] [Debug] Re-bagging, using 10424 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9
[LightGBM] [Debug] Re-bagging, using 10396 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9
[LightGBM] [Debug] Re-bagging, using 10451 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 8
[LightGBM] [Debug] Re-bagging, using 10214 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 12
[LightGBM] [Debug] Re-bagging, using 10295 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10283 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10271 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 9
[LightGBM] [Debug] Re-bagging, using 10332 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 8
[LightGBM] [Debug] Re-bagging, using 10472 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10328 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10402 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10406 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10386 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 9
[LightGBM] [Debug] Re-bagging, using 10544 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10259 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 9
[LightGBM] [Debug] Re-bagging, using 10315 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10421 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10417 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10478 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 9
[LightGBM] [Debug] Re-bagging, using 10391 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10377 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9
[LightGBM] [Debug] Re-bagging, using 10519 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 9
[LightGBM] [Debug] Re-bagging, using 10358 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 8
[LightGBM] [Debug] Re-bagging, using 10373 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 9
[LightGBM] [Debug] Re-bagging, using 10369 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 12
[LightGBM] [Debug] Re-bagging, using 10355 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10409 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10473 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10347 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 8
[LightGBM] [Debug] Re-bagging, using 10372 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10413 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10489 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10327 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10578 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 9
[LightGBM] [Debug] Re-bagging, using 10329 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10382 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 9
[LightGBM] [Debug] Re-bagging, using 10296 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10353 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10472 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10326 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 9
[LightGBM] [Debug] Re-bagging, using 10289 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 13
[LightGBM] [Debug] Re-bagging, using 10181 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9
Trial 75, Fold 4: Log loss = 0.33745229817959493, Average precision = 0.9701460653297643, ROC-AUC = 0.9644183470794558, Elapsed Time = 2.0010793999999805 seconds
Trial 75, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 75, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[LightGBM] [Info] Number of positive: 10150, number of negative: 10500
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.792012
[LightGBM] [Info] Total Bins 24360
[LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902
[LightGBM] [Info] Start training from score -0.033902
[LightGBM] [Debug] Re-bagging, using 10489 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10456 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9
[LightGBM] [Debug] Re-bagging, using 10455 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10296 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 8
[LightGBM] [Debug] Re-bagging, using 10516 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9
[LightGBM] [Debug] Re-bagging, using 10338 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10430 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9
[LightGBM] [Debug] Re-bagging, using 10391 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 9
[LightGBM] [Debug] Re-bagging, using 10448 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10216 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 8
[LightGBM] [Debug] Re-bagging, using 10285 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 9
[LightGBM] [Debug] Re-bagging, using 10277 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10271 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 9
[LightGBM] [Debug] Re-bagging, using 10341 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10466 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 8
[LightGBM] [Debug] Re-bagging, using 10325 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 12
[LightGBM] [Debug] Re-bagging, using 10393 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 9
[LightGBM] [Debug] Re-bagging, using 10391 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10391 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 9
[LightGBM] [Debug] Re-bagging, using 10528 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10269 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10315 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10407 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10423 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 9
[LightGBM] [Debug] Re-bagging, using 10479 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 9
[LightGBM] [Debug] Re-bagging, using 10381 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10377 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10506 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10363 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10374 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10376 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10349 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10393 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10467 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10348 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10365 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 12
[LightGBM] [Debug] Re-bagging, using 10427 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10477 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10323 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 12
[LightGBM] [Debug] Re-bagging, using 10580 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 12
[LightGBM] [Debug] Re-bagging, using 10325 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 9
[LightGBM] [Debug] Re-bagging, using 10380 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10282 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10357 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10474 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10328 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10268 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10193 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11
Trial 75, Fold 5: Log loss = 0.3406738747682316, Average precision = 0.9684612209351231, ROC-AUC = 0.96299306987719, Elapsed Time = 1.9582474000008006 seconds
Optimization Progress:  76%|#######6  | 76/100 [17:50<06:34, 16.45s/it]
Trial 76, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 76, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[LightGBM] [Info] Number of positive: 10130, number of negative: 10533
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791640
[LightGBM] [Info] Total Bins 15448
[LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012
[LightGBM] [Info] Start training from score -0.039012
[LightGBM] [Debug] Re-bagging, using 12453 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9
[LightGBM] [Debug] Re-bagging, using 12477 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8
[LightGBM] [Debug] Re-bagging, using 12537 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Re-bagging, using 12317 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8
[LightGBM] [Debug] Re-bagging, using 12486 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8
[LightGBM] [Debug] Re-bagging, using 12352 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9
Trial 76, Fold 1: Log loss = 0.3155715255143001, Average precision = 0.9662255319221411, ROC-AUC = 0.9594275605454466, Elapsed Time = 0.8203442000003633 seconds
Trial 76, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 76, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[LightGBM] [Info] Number of positive: 10230, number of negative: 10471
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791600
[LightGBM] [Info] Total Bins 15463
[LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285
[LightGBM] [Info] Start training from score -0.023285
[LightGBM] [Debug] Re-bagging, using 12478 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10
[LightGBM] [Debug] Re-bagging, using 12495 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9
[LightGBM] [Debug] Re-bagging, using 12564 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11
[LightGBM] [Debug] Re-bagging, using 12346 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7
[LightGBM] [Debug] Re-bagging, using 12508 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 7
[LightGBM] [Debug] Re-bagging, using 12361 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 7
Trial 76, Fold 2: Log loss = 0.3124389825131124, Average precision = 0.9638516831597628, ROC-AUC = 0.960129938593741, Elapsed Time = 0.881771999999728 seconds
Trial 76, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 76, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[LightGBM] [Info] Number of positive: 10165, number of negative: 10517
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791401
[LightGBM] [Info] Total Bins 15465
[LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043
[LightGBM] [Info] Start training from score -0.034043
[LightGBM] [Debug] Re-bagging, using 12464 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 11
[LightGBM] [Debug] Re-bagging, using 12485 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 10
[LightGBM] [Debug] Re-bagging, using 12550 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 9
[LightGBM] [Debug] Re-bagging, using 12338 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Debug] Re-bagging, using 12496 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11
[LightGBM] [Debug] Re-bagging, using 12352 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8
Trial 76, Fold 3: Log loss = 0.3054877716963616, Average precision = 0.9673556159887645, ROC-AUC = 0.9617291259354142, Elapsed Time = 0.9247225999997681 seconds
Trial 76, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 76, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[LightGBM] [Info] Number of positive: 10177, number of negative: 10479
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791090
[LightGBM] [Info] Total Bins 15448
[LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243
[LightGBM] [Info] Start training from score -0.029243
[LightGBM] [Debug] Re-bagging, using 12449 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 7
[LightGBM] [Debug] Re-bagging, using 12473 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8
[LightGBM] [Debug] Re-bagging, using 12532 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8
[LightGBM] [Debug] Re-bagging, using 12314 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 7
[LightGBM] [Debug] Re-bagging, using 12478 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9
[LightGBM] [Debug] Re-bagging, using 12350 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9
Trial 76, Fold 4: Log loss = 0.3103363416716246, Average precision = 0.9664082453702845, ROC-AUC = 0.9597652459161022, Elapsed Time = 0.9628636999996161 seconds
Trial 76, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 76, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[LightGBM] [Info] Number of positive: 10150, number of negative: 10500
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.792012
[LightGBM] [Info] Total Bins 15447
[LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902
[LightGBM] [Info] Start training from score -0.033902
[LightGBM] [Debug] Re-bagging, using 12446 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7
[LightGBM] [Debug] Re-bagging, using 12469 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 11
[LightGBM] [Debug] Re-bagging, using 12532 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11
[LightGBM] [Debug] Re-bagging, using 12308 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8
[LightGBM] [Debug] Re-bagging, using 12472 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8
[LightGBM] [Debug] Re-bagging, using 12346 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 8
Trial 76, Fold 5: Log loss = 0.3148505789874095, Average precision = 0.9643632385544947, ROC-AUC = 0.9577471341248165, Elapsed Time = 0.9563079999998081 seconds
Optimization Progress:  77%|#######7  | 77/100 [18:01<05:47, 15.10s/it]
Trial 77, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 77, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[LightGBM] [Info] Number of positive: 10130, number of negative: 10533
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795662
[LightGBM] [Info] Total Bins 8297
[LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 258
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012
[LightGBM] [Info] Start training from score -0.039012
[LightGBM] [Debug] Re-bagging, using 6273 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 13
[LightGBM] [Debug] Re-bagging, using 6246 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 12
[LightGBM] [Debug] Re-bagging, using 6230 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 16
[LightGBM] [Debug] Re-bagging, using 6158 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 17
[LightGBM] [Debug] Re-bagging, using 6329 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 14
[LightGBM] [Debug] Re-bagging, using 6300 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 19
[LightGBM] [Debug] Re-bagging, using 6199 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 17
[LightGBM] [Debug] Re-bagging, using 6347 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 17
[LightGBM] [Debug] Re-bagging, using 6209 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 16
[LightGBM] [Debug] Re-bagging, using 6080 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 16
[LightGBM] [Debug] Re-bagging, using 6116 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 16
[LightGBM] [Debug] Re-bagging, using 6158 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 18
[LightGBM] [Debug] Re-bagging, using 6159 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 15
[LightGBM] [Debug] Re-bagging, using 6254 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 20
[LightGBM] [Debug] Re-bagging, using 6371 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 18
[LightGBM] [Debug] Re-bagging, using 6253 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 17
[LightGBM] [Debug] Re-bagging, using 6209 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 20
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 19
[LightGBM] [Debug] Re-bagging, using 6193 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 15
[LightGBM] [Debug] Re-bagging, using 6305 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 18
[LightGBM] [Debug] Re-bagging, using 6296 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 19
[LightGBM] [Debug] Re-bagging, using 6141 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 17
[LightGBM] [Debug] Re-bagging, using 6221 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 18
[LightGBM] [Debug] Re-bagging, using 6269 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 21
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 19
[LightGBM] [Debug] Re-bagging, using 6198 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 16
[LightGBM] [Debug] Re-bagging, using 6366 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 16
[LightGBM] [Debug] Re-bagging, using 6274 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 18
Trial 77, Fold 1: Log loss = 0.22585042950322304, Average precision = 0.9734482911791483, ROC-AUC = 0.9681996004263393, Elapsed Time = 1.0046127000005072 seconds
Trial 77, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 77, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[LightGBM] [Info] Number of positive: 10230, number of negative: 10471
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795629
[LightGBM] [Info] Total Bins 8313
[LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 258
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285
[LightGBM] [Info] Start training from score -0.023285
[LightGBM] [Debug] Re-bagging, using 6285 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 13
[LightGBM] [Debug] Re-bagging, using 6262 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 16
[LightGBM] [Debug] Re-bagging, using 6232 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 19
[LightGBM] [Debug] Re-bagging, using 6177 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 18
[LightGBM] [Debug] Re-bagging, using 6352 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 17
[LightGBM] [Debug] Re-bagging, using 6295 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 19
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 15
[LightGBM] [Debug] Re-bagging, using 6207 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 16
[LightGBM] [Debug] Re-bagging, using 6357 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 18
[LightGBM] [Debug] Re-bagging, using 6231 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 15
[LightGBM] [Debug] Re-bagging, using 6102 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 18
[LightGBM] [Debug] Re-bagging, using 6113 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 17
[LightGBM] [Debug] Re-bagging, using 6159 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 19
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 17
[LightGBM] [Debug] Re-bagging, using 6169 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 16
[LightGBM] [Debug] Re-bagging, using 6263 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 16
[LightGBM] [Debug] Re-bagging, using 6381 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 20
[LightGBM] [Debug] Re-bagging, using 6273 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 18
[LightGBM] [Debug] Re-bagging, using 6221 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 20
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 16
[LightGBM] [Debug] Re-bagging, using 6212 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 15
[LightGBM] [Debug] Re-bagging, using 6322 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 16
[LightGBM] [Debug] Re-bagging, using 6302 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 18
[LightGBM] [Debug] Re-bagging, using 6157 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 14
[LightGBM] [Debug] Re-bagging, using 6228 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 16
[LightGBM] [Debug] Re-bagging, using 6288 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 20
[LightGBM] [Debug] Re-bagging, using 6214 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 18
[LightGBM] [Debug] Re-bagging, using 6370 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 16
[LightGBM] [Debug] Re-bagging, using 6282 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 19
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 18
Trial 77, Fold 2: Log loss = 0.22227296826877135, Average precision = 0.9713868294735187, ROC-AUC = 0.968683506017255, Elapsed Time = 1.0871165999997174 seconds
Trial 77, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 77, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[LightGBM] [Info] Number of positive: 10165, number of negative: 10517
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795455
[LightGBM] [Info] Total Bins 8313
[LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 258
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043
[LightGBM] [Info] Start training from score -0.034043
[LightGBM] [Debug] Re-bagging, using 6278 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 12
[LightGBM] [Debug] Re-bagging, using 6254 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 14
[LightGBM] [Debug] Re-bagging, using 6230 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 19
[LightGBM] [Debug] Re-bagging, using 6170 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 17
[LightGBM] [Debug] Re-bagging, using 6343 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 14
[LightGBM] [Debug] Re-bagging, using 6294 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 16
[LightGBM] [Debug] Re-bagging, using 6208 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 18
[LightGBM] [Debug] Re-bagging, using 6345 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 16
[LightGBM] [Debug] Re-bagging, using 6216 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 16
[LightGBM] [Debug] Re-bagging, using 6100 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 14
[LightGBM] [Debug] Re-bagging, using 6118 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 15
[LightGBM] [Debug] Re-bagging, using 6150 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 21
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 14
[LightGBM] [Debug] Re-bagging, using 6163 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 17
[LightGBM] [Debug] Re-bagging, using 6264 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 19
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 18
[LightGBM] [Debug] Re-bagging, using 6368 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 17
[LightGBM] [Debug] Re-bagging, using 6263 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 20
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 15
[LightGBM] [Debug] Re-bagging, using 6220 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 16
[LightGBM] [Debug] Re-bagging, using 6202 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 17
[LightGBM] [Debug] Re-bagging, using 6304 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 17
[LightGBM] [Debug] Re-bagging, using 6315 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 17
[LightGBM] [Debug] Re-bagging, using 6157 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 16
[LightGBM] [Debug] Re-bagging, using 6210 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 16
[LightGBM] [Debug] Re-bagging, using 6266 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 17
[LightGBM] [Debug] Re-bagging, using 6217 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 16
[LightGBM] [Debug] Re-bagging, using 6383 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 16
[LightGBM] [Debug] Re-bagging, using 6279 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 14
Trial 77, Fold 3: Log loss = 0.22081356715814243, Average precision = 0.9731649895583823, ROC-AUC = 0.9689281741483757, Elapsed Time = 1.1825113999993846 seconds
Trial 77, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 77, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[LightGBM] [Info] Number of positive: 10177, number of negative: 10479
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795144
[LightGBM] [Info] Total Bins 8295
[LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 258
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243
[LightGBM] [Info] Start training from score -0.029243
[LightGBM] [Debug] Re-bagging, using 6270 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 13
[LightGBM] [Debug] Re-bagging, using 6243 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 16
[LightGBM] [Debug] Re-bagging, using 6227 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 14
[LightGBM] [Debug] Re-bagging, using 6160 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 13
[LightGBM] [Debug] Re-bagging, using 6325 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 19
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 16
[LightGBM] [Debug] Re-bagging, using 6297 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 15
[LightGBM] [Debug] Re-bagging, using 6199 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 21
[LightGBM] [Debug] Re-bagging, using 6343 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 17
[LightGBM] [Debug] Re-bagging, using 6205 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 15
[LightGBM] [Debug] Re-bagging, using 6083 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 18
[LightGBM] [Debug] Re-bagging, using 6105 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 20
[LightGBM] [Debug] Re-bagging, using 6160 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 17
[LightGBM] [Debug] Re-bagging, using 6160 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 18
[LightGBM] [Debug] Re-bagging, using 6250 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 16
[LightGBM] [Debug] Re-bagging, using 6366 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 20
[LightGBM] [Debug] Re-bagging, using 6256 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 16
[LightGBM] [Debug] Re-bagging, using 6206 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 16
[LightGBM] [Debug] Re-bagging, using 6198 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 17
[LightGBM] [Debug] Re-bagging, using 6295 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 14
[LightGBM] [Debug] Re-bagging, using 6298 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 16
[LightGBM] [Debug] Re-bagging, using 6142 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 19
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 16
[LightGBM] [Debug] Re-bagging, using 6208 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 16
[LightGBM] [Debug] Re-bagging, using 6270 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 18
[LightGBM] [Debug] Re-bagging, using 6207 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 20
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 15
[LightGBM] [Debug] Re-bagging, using 6353 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 15
[LightGBM] [Debug] Re-bagging, using 6274 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 20
Trial 77, Fold 4: Log loss = 0.22251997590152925, Average precision = 0.9732956379599811, ROC-AUC = 0.9683390618778182, Elapsed Time = 1.1666599000000133 seconds
Trial 77, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 77, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[LightGBM] [Info] Number of positive: 10150, number of negative: 10500
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.796026
[LightGBM] [Info] Total Bins 8302
[LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 258
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902
[LightGBM] [Info] Start training from score -0.033902
[LightGBM] [Debug] Re-bagging, using 6268 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 12
[LightGBM] [Debug] Re-bagging, using 6243 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 13
[LightGBM] [Debug] Re-bagging, using 6225 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 16
[LightGBM] [Debug] Re-bagging, using 6155 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 18
[LightGBM] [Debug] Re-bagging, using 6324 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 18
[LightGBM] [Debug] Re-bagging, using 6293 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 19
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 16
[LightGBM] [Debug] Re-bagging, using 6204 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 17
[LightGBM] [Debug] Re-bagging, using 6336 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 21
[LightGBM] [Debug] Re-bagging, using 6206 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 15
[LightGBM] [Debug] Re-bagging, using 6083 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 19
[LightGBM] [Debug] Re-bagging, using 6099 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 15
[LightGBM] [Debug] Re-bagging, using 6151 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 21
[LightGBM] [Debug] Re-bagging, using 6163 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 19
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 21
[LightGBM] [Debug] Re-bagging, using 6260 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 20
[LightGBM] [Debug] Re-bagging, using 6361 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 17
[LightGBM] [Debug] Re-bagging, using 6257 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 20
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 21
[LightGBM] [Debug] Re-bagging, using 6205 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 17
[LightGBM] [Debug] Re-bagging, using 6184 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 20
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 20
[LightGBM] [Debug] Re-bagging, using 6295 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 15
[LightGBM] [Debug] Re-bagging, using 6293 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 16
[LightGBM] [Debug] Re-bagging, using 6147 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 17
[LightGBM] [Debug] Re-bagging, using 6204 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 17
[LightGBM] [Debug] Re-bagging, using 6261 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 18
[LightGBM] [Debug] Re-bagging, using 6212 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 20
[LightGBM] [Debug] Re-bagging, using 6359 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 18
[LightGBM] [Debug] Re-bagging, using 6265 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 19
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 20
Trial 77, Fold 5: Log loss = 0.2264348846158163, Average precision = 0.9721371360826228, ROC-AUC = 0.9677990450179292, Elapsed Time = 1.1781983000000764 seconds
Optimization Progress:  78%|#######8  | 78/100 [18:15<05:19, 14.51s/it]
Trial 78, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 78, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[LightGBM] [Info] Number of positive: 10130, number of negative: 10533
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795662
[LightGBM] [Info] Total Bins 24158
[LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 258
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012
[LightGBM] [Info] Start training from score -0.039012
[LightGBM] [Debug] Re-bagging, using 7527 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7589 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 10
[LightGBM] [Debug] Re-bagging, using 7461 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 12
[LightGBM] [Debug] Re-bagging, using 7455 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 11
[LightGBM] [Debug] Re-bagging, using 7595 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 12
[LightGBM] [Debug] Re-bagging, using 7532 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 13
[LightGBM] [Debug] Re-bagging, using 7534 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11
[LightGBM] [Debug] Re-bagging, using 7603 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 11
[LightGBM] [Debug] Re-bagging, using 7537 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 12
[LightGBM] [Debug] Re-bagging, using 7337 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 14
[LightGBM] [Debug] Re-bagging, using 7348 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 10
[LightGBM] [Debug] Re-bagging, using 7421 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 12
[LightGBM] [Debug] Re-bagging, using 7403 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 12
[LightGBM] [Debug] Re-bagging, using 7458 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 12
[LightGBM] [Debug] Re-bagging, using 7636 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 12
[LightGBM] [Debug] Re-bagging, using 7543 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 12
[LightGBM] [Debug] Re-bagging, using 7515 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 11
[LightGBM] [Debug] Re-bagging, using 7498 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 11
[LightGBM] [Debug] Re-bagging, using 7589 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7642 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 10
[LightGBM] [Debug] Re-bagging, using 7387 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 11
[LightGBM] [Debug] Re-bagging, using 7477 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7556 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 10
[LightGBM] [Debug] Re-bagging, using 7540 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 15
[LightGBM] [Debug] Re-bagging, using 7655 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 14
[LightGBM] [Debug] Re-bagging, using 7527 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 12
[LightGBM] [Debug] Re-bagging, using 7536 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7528 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11
[LightGBM] [Debug] Re-bagging, using 7485 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 13
[LightGBM] [Debug] Re-bagging, using 7553 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10
[LightGBM] [Debug] Re-bagging, using 7454 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 13
[LightGBM] [Debug] Re-bagging, using 7534 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11
[LightGBM] [Debug] Re-bagging, using 7428 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 11
[LightGBM] [Debug] Re-bagging, using 7560 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 12
[LightGBM] [Debug] Re-bagging, using 7522 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 13
[LightGBM] [Debug] Re-bagging, using 7476 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 12
[LightGBM] [Debug] Re-bagging, using 7516 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 13
[LightGBM] [Debug] Re-bagging, using 7561 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 10
[LightGBM] [Debug] Re-bagging, using 7460 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 17
[LightGBM] [Debug] Re-bagging, using 7705 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 11
[LightGBM] [Debug] Re-bagging, using 7475 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 12
[LightGBM] [Debug] Re-bagging, using 7488 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7376 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 12
[LightGBM] [Debug] Re-bagging, using 7411 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 19
[LightGBM] [Debug] Re-bagging, using 7577 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 18
[LightGBM] [Debug] Re-bagging, using 7486 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 11
Trial 78, Fold 1: Log loss = 0.20672061417487297, Average precision = 0.9748272179255015, ROC-AUC = 0.9703260695525826, Elapsed Time = 1.8581021999998484 seconds
Trial 78, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 78, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[LightGBM] [Info] Number of positive: 10230, number of negative: 10471
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.794298
[LightGBM] [Info] Total Bins 24163
[LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 257
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285
[LightGBM] [Info] Start training from score -0.023285
[LightGBM] [Debug] Re-bagging, using 7542 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 10
[LightGBM] [Debug] Re-bagging, using 7605 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7474 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 12
[LightGBM] [Debug] Re-bagging, using 7470 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 11
[LightGBM] [Debug] Re-bagging, using 7625 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 11
[LightGBM] [Debug] Re-bagging, using 7523 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 11
[LightGBM] [Debug] Re-bagging, using 7546 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7613 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 10
[LightGBM] [Debug] Re-bagging, using 7560 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11
[LightGBM] [Debug] Re-bagging, using 7361 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 11
[LightGBM] [Debug] Re-bagging, using 7347 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 10
[LightGBM] [Debug] Re-bagging, using 7418 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 12
[LightGBM] [Debug] Re-bagging, using 7415 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 10
[LightGBM] [Debug] Re-bagging, using 7474 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7658 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 10
[LightGBM] [Debug] Re-bagging, using 7568 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 11
[LightGBM] [Debug] Re-bagging, using 7527 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12
[LightGBM] [Debug] Re-bagging, using 7517 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 12
[LightGBM] [Debug] Re-bagging, using 7601 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 12
[LightGBM] [Debug] Re-bagging, using 7654 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 13
[LightGBM] [Debug] Re-bagging, using 7406 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 12
[LightGBM] [Debug] Re-bagging, using 7488 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 11
[LightGBM] [Debug] Re-bagging, using 7581 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 12
[LightGBM] [Debug] Re-bagging, using 7555 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 11
[LightGBM] [Debug] Re-bagging, using 7661 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 12
[LightGBM] [Debug] Re-bagging, using 7540 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 12
[LightGBM] [Debug] Re-bagging, using 7550 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 12
[LightGBM] [Debug] Re-bagging, using 7529 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 12
[LightGBM] [Debug] Re-bagging, using 7490 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7567 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 11
[LightGBM] [Debug] Re-bagging, using 7468 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 13
[LightGBM] [Debug] Re-bagging, using 7539 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 12
[LightGBM] [Debug] Re-bagging, using 7434 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 11
[LightGBM] [Debug] Re-bagging, using 7580 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 19
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 14
[LightGBM] [Debug] Re-bagging, using 7537 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 14
[LightGBM] [Debug] Re-bagging, using 7487 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 14
[LightGBM] [Debug] Re-bagging, using 7532 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 18
[LightGBM] [Debug] Re-bagging, using 7570 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 13
[LightGBM] [Debug] Re-bagging, using 7460 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 12
[LightGBM] [Debug] Re-bagging, using 7718 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 12
[LightGBM] [Debug] Re-bagging, using 7495 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 13
[LightGBM] [Debug] Re-bagging, using 7512 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 13
[LightGBM] [Debug] Re-bagging, using 7390 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 12
[LightGBM] [Debug] Re-bagging, using 7436 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 15
[LightGBM] [Debug] Re-bagging, using 7604 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 14
[LightGBM] [Debug] Re-bagging, using 7499 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 12
Trial 78, Fold 2: Log loss = 0.20328855586184658, Average precision = 0.9741812854286478, ROC-AUC = 0.9719177065689493, Elapsed Time = 2.229027600000336 seconds
Trial 78, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 78, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[LightGBM] [Info] Number of positive: 10165, number of negative: 10517
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795455
[LightGBM] [Info] Total Bins 24160
[LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 258
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043
[LightGBM] [Info] Start training from score -0.034043
[LightGBM] [Debug] Re-bagging, using 7534 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 11
[LightGBM] [Debug] Re-bagging, using 7595 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 11
[LightGBM] [Debug] Re-bagging, using 7467 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 12
[LightGBM] [Debug] Re-bagging, using 7468 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 12
[LightGBM] [Debug] Re-bagging, using 7609 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 10
[LightGBM] [Debug] Re-bagging, using 7525 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 11
[LightGBM] [Debug] Re-bagging, using 7543 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 12
[LightGBM] [Debug] Re-bagging, using 7606 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 11
[LightGBM] [Debug] Re-bagging, using 7540 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 12
[LightGBM] [Debug] Re-bagging, using 7361 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 10
[LightGBM] [Debug] Re-bagging, using 7349 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10
[LightGBM] [Debug] Re-bagging, using 7416 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 11
[LightGBM] [Debug] Re-bagging, using 7401 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 11
[LightGBM] [Debug] Re-bagging, using 7469 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 11
[LightGBM] [Debug] Re-bagging, using 7643 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 13
[LightGBM] [Debug] Re-bagging, using 7559 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 11
[LightGBM] [Debug] Re-bagging, using 7523 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 13
[LightGBM] [Debug] Re-bagging, using 7509 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 10
[LightGBM] [Debug] Re-bagging, using 7588 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 10
[LightGBM] [Debug] Re-bagging, using 7658 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 13
[LightGBM] [Debug] Re-bagging, using 7401 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10
[LightGBM] [Debug] Re-bagging, using 7475 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 10
[LightGBM] [Debug] Re-bagging, using 7560 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 11
[LightGBM] [Debug] Re-bagging, using 7550 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 11
[LightGBM] [Debug] Re-bagging, using 7675 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 12
[LightGBM] [Debug] Re-bagging, using 7534 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 11
[LightGBM] [Debug] Re-bagging, using 7530 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 10
[LightGBM] [Debug] Re-bagging, using 7519 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7500 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 14
[LightGBM] [Debug] Re-bagging, using 7568 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 12
[LightGBM] [Debug] Re-bagging, using 7457 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 12
[LightGBM] [Debug] Re-bagging, using 7531 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7431 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 11
[LightGBM] [Debug] Re-bagging, using 7563 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 11
[LightGBM] [Debug] Re-bagging, using 7534 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 15
[LightGBM] [Debug] Re-bagging, using 7484 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 12
[LightGBM] [Debug] Re-bagging, using 7531 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 12
[LightGBM] [Debug] Re-bagging, using 7564 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 12
[LightGBM] [Debug] Re-bagging, using 7462 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 13
[LightGBM] [Debug] Re-bagging, using 7713 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10
[LightGBM] [Debug] Re-bagging, using 7477 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 15
[LightGBM] [Debug] Re-bagging, using 7490 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 13
[LightGBM] [Debug] Re-bagging, using 7376 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 14
[LightGBM] [Debug] Re-bagging, using 7413 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11
[LightGBM] [Debug] Re-bagging, using 7608 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 16
[LightGBM] [Debug] Re-bagging, using 7497 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 12
Trial 78, Fold 3: Log loss = 0.20468469213154777, Average precision = 0.9756805906632632, ROC-AUC = 0.971607257225684, Elapsed Time = 2.315972100000181 seconds
Trial 78, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 78, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[LightGBM] [Info] Number of positive: 10177, number of negative: 10479
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795144
[LightGBM] [Info] Total Bins 24150
[LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 258
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243
[LightGBM] [Info] Start training from score -0.029243
[LightGBM] [Debug] Re-bagging, using 7523 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 12
[LightGBM] [Debug] Re-bagging, using 7587 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7457 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 11
[LightGBM] [Debug] Re-bagging, using 7457 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 10
[LightGBM] [Debug] Re-bagging, using 7587 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 12
[LightGBM] [Debug] Re-bagging, using 7530 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 10
[LightGBM] [Debug] Re-bagging, using 7537 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 11
[LightGBM] [Debug] Re-bagging, using 7597 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 10
[LightGBM] [Debug] Re-bagging, using 7534 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 11
[LightGBM] [Debug] Re-bagging, using 7339 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 11
[LightGBM] [Debug] Re-bagging, using 7339 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 12
[LightGBM] [Debug] Re-bagging, using 7421 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 13
[LightGBM] [Debug] Re-bagging, using 7401 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 13
[LightGBM] [Debug] Re-bagging, using 7457 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 10
[LightGBM] [Debug] Re-bagging, using 7630 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11
[LightGBM] [Debug] Re-bagging, using 7540 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 10
[LightGBM] [Debug] Re-bagging, using 7514 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 10
[LightGBM] [Debug] Re-bagging, using 7501 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 12
[LightGBM] [Debug] Re-bagging, using 7576 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 10
[LightGBM] [Debug] Re-bagging, using 7645 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 11
[LightGBM] [Debug] Re-bagging, using 7389 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 12
[LightGBM] [Debug] Re-bagging, using 7468 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 13
[LightGBM] [Debug] Re-bagging, using 7558 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 8
[LightGBM] [Debug] Re-bagging, using 7536 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 12
[LightGBM] [Debug] Re-bagging, using 7651 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 12
[LightGBM] [Debug] Re-bagging, using 7527 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 10
[LightGBM] [Debug] Re-bagging, using 7522 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 10
[LightGBM] [Debug] Re-bagging, using 7524 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10
[LightGBM] [Debug] Re-bagging, using 7494 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 11
[LightGBM] [Debug] Re-bagging, using 7558 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 10
[LightGBM] [Debug] Re-bagging, using 7447 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 14
[LightGBM] [Debug] Re-bagging, using 7524 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 14
[LightGBM] [Debug] Re-bagging, using 7430 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 13
[LightGBM] [Debug] Re-bagging, using 7562 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 13
[LightGBM] [Debug] Re-bagging, using 7525 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 13
[LightGBM] [Debug] Re-bagging, using 7471 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 17
[LightGBM] [Debug] Re-bagging, using 7509 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 13
[LightGBM] [Debug] Re-bagging, using 7562 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7450 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 14
[LightGBM] [Debug] Re-bagging, using 7705 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 13
[LightGBM] [Debug] Re-bagging, using 7470 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 11
[LightGBM] [Debug] Re-bagging, using 7492 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 15
[LightGBM] [Debug] Re-bagging, using 7380 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 16
[LightGBM] [Debug] Re-bagging, using 7404 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 16
[LightGBM] [Debug] Re-bagging, using 7578 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 15
[LightGBM] [Debug] Re-bagging, using 7487 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 14
Trial 78, Fold 4: Log loss = 0.20804533960468277, Average precision = 0.9746462747165743, ROC-AUC = 0.9700041250289111, Elapsed Time = 2.1942557000002125 seconds
Trial 78, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 78, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[LightGBM] [Info] Number of positive: 10150, number of negative: 10500
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.796026
[LightGBM] [Info] Total Bins 24154
[LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 258
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902
[LightGBM] [Info] Start training from score -0.033902
[LightGBM] [Debug] Re-bagging, using 7521 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11
[LightGBM] [Debug] Re-bagging, using 7586 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 11
[LightGBM] [Debug] Re-bagging, using 7456 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 10
[LightGBM] [Debug] Re-bagging, using 7451 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 11
[LightGBM] [Debug] Re-bagging, using 7586 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 12
[LightGBM] [Debug] Re-bagging, using 7521 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 11
[LightGBM] [Debug] Re-bagging, using 7546 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 10
[LightGBM] [Debug] Re-bagging, using 7591 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
[LightGBM] [Debug] Re-bagging, using 7532 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 10
[LightGBM] [Debug] Re-bagging, using 7339 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 11
[LightGBM] [Debug] Re-bagging, using 7332 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 12
[LightGBM] [Debug] Re-bagging, using 7413 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 11
[LightGBM] [Debug] Re-bagging, using 7406 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 11
[LightGBM] [Debug] Re-bagging, using 7465 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10
[LightGBM] [Debug] Re-bagging, using 7628 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 11
[LightGBM] [Debug] Re-bagging, using 7540 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 11
[LightGBM] [Debug] Re-bagging, using 7507 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 11
[LightGBM] [Debug] Re-bagging, using 7485 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10
[LightGBM] [Debug] Re-bagging, using 7582 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 11
[LightGBM] [Debug] Re-bagging, using 7633 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 10
[LightGBM] [Debug] Re-bagging, using 7399 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 10
[LightGBM] [Debug] Re-bagging, using 7459 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 12
[LightGBM] [Debug] Re-bagging, using 7553 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 12
[LightGBM] [Debug] Re-bagging, using 7542 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 10
[LightGBM] [Debug] Re-bagging, using 7648 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11
[LightGBM] [Debug] Re-bagging, using 7523 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11
[LightGBM] [Debug] Re-bagging, using 7521 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 11
[LightGBM] [Debug] Re-bagging, using 7511 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 13
[LightGBM] [Debug] Re-bagging, using 7495 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 10
[LightGBM] [Debug] Re-bagging, using 7563 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 12
[LightGBM] [Debug] Re-bagging, using 7451 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10
[LightGBM] [Debug] Re-bagging, using 7521 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 11
[LightGBM] [Debug] Re-bagging, using 7419 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 10
[LightGBM] [Debug] Re-bagging, using 7556 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 14
[LightGBM] [Debug] Re-bagging, using 7533 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 12
[LightGBM] [Debug] Re-bagging, using 7471 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 11
[LightGBM] [Debug] Re-bagging, using 7507 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 13
[LightGBM] [Debug] Re-bagging, using 7558 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 10
[LightGBM] [Debug] Re-bagging, using 7449 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 13
[LightGBM] [Debug] Re-bagging, using 7703 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 12
[LightGBM] [Debug] Re-bagging, using 7468 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 13
[LightGBM] [Debug] Re-bagging, using 7489 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 11
[LightGBM] [Debug] Re-bagging, using 7369 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 13
[LightGBM] [Debug] Re-bagging, using 7413 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 11
[LightGBM] [Debug] Re-bagging, using 7579 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 17
[LightGBM] [Debug] Re-bagging, using 7481 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 14
Trial 78, Fold 5: Log loss = 0.20993519748703102, Average precision = 0.9730666504093994, ROC-AUC = 0.9692958029095369, Elapsed Time = 2.297114899999542 seconds
Optimization Progress:  79%|#######9  | 79/100 [18:33<05:30, 15.71s/it]
Trial 79, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 79, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[LightGBM] [Info] Number of positive: 10130, number of negative: 10533
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795662
[LightGBM] [Info] Total Bins 9448
[LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 258
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012
[LightGBM] [Info] Start training from score -0.039012
[LightGBM] [Debug] Re-bagging, using 16585 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 12
[LightGBM] [Debug] Re-bagging, using 16662 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 13
[LightGBM] [Debug] Re-bagging, using 16603 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 14
Trial 79, Fold 1: Log loss = 0.35574942751144145, Average precision = 0.953768924563304, ROC-AUC = 0.9488460727310095, Elapsed Time = 0.5450129999999263 seconds
Trial 79, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 79, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[LightGBM] [Info] Number of positive: 10230, number of negative: 10471
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.794298
[LightGBM] [Info] Total Bins 9436
[LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 257
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285
[LightGBM] [Info] Start training from score -0.023285
[LightGBM] [Debug] Re-bagging, using 16618 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 10
[LightGBM] [Debug] Re-bagging, using 16693 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 10
[LightGBM] [Debug] Re-bagging, using 16630 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 11
Trial 79, Fold 2: Log loss = 0.36948599381691194, Average precision = 0.947260346271916, ROC-AUC = 0.9453780052510802, Elapsed Time = 0.5532783000007839 seconds
Trial 79, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 79, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[LightGBM] [Info] Number of positive: 10165, number of negative: 10517
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795455
[LightGBM] [Info] Total Bins 9446
[LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 258
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043
[LightGBM] [Info] Start training from score -0.034043
[LightGBM] [Debug] Re-bagging, using 16601 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 9
[LightGBM] [Debug] Re-bagging, using 16677 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 10
[LightGBM] [Debug] Re-bagging, using 16617 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 13
Trial 79, Fold 3: Log loss = 0.36693852889771394, Average precision = 0.9495115214204645, ROC-AUC = 0.9450564499041711, Elapsed Time = 0.5432987000003777 seconds
Trial 79, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 79, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[LightGBM] [Info] Number of positive: 10177, number of negative: 10479
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795144
[LightGBM] [Info] Total Bins 9446
[LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 258
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243
[LightGBM] [Info] Start training from score -0.029243
[LightGBM] [Debug] Re-bagging, using 16579 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 12
[LightGBM] [Debug] Re-bagging, using 16655 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 102 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 9
[LightGBM] [Debug] Re-bagging, using 16599 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10
Trial 79, Fold 4: Log loss = 0.35530964163414613, Average precision = 0.9599567042135402, ROC-AUC = 0.9534670510335225, Elapsed Time = 0.6347985000002154 seconds
Trial 79, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 79, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[LightGBM] [Info] Number of positive: 10150, number of negative: 10500
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.796026
[LightGBM] [Info] Total Bins 9428
[LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 258
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902
[LightGBM] [Info] Start training from score -0.033902
[LightGBM] [Debug] Re-bagging, using 16573 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 9
[LightGBM] [Debug] Re-bagging, using 16654 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 11
[LightGBM] [Debug] Re-bagging, using 16595 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 8
Trial 79, Fold 5: Log loss = 0.35161469182991933, Average precision = 0.9587201253380273, ROC-AUC = 0.9516898723594003, Elapsed Time = 0.5850366999993639 seconds
Optimization Progress:  80%|########  | 80/100 [18:45<04:48, 14.44s/it]
Trial 80, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 80, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[LightGBM] [Info] Number of positive: 10130, number of negative: 10533
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795662
[LightGBM] [Info] Total Bins 23592
[LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 258
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012
[LightGBM] [Info] Start training from score -0.039012
[LightGBM] [Debug] Re-bagging, using 9953 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 13
[LightGBM] [Debug] Re-bagging, using 9912 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 11
[LightGBM] [Debug] Re-bagging, using 9892 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 13
[LightGBM] [Debug] Re-bagging, using 9802 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 12
[LightGBM] [Debug] Re-bagging, using 9970 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 12
[LightGBM] [Debug] Re-bagging, using 9834 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 11
[LightGBM] [Debug] Re-bagging, using 9910 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 13
[LightGBM] [Debug] Re-bagging, using 9921 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 13
Trial 80, Fold 1: Log loss = 0.29717970856942566, Average precision = 0.9729448820054003, ROC-AUC = 0.9678465082710793, Elapsed Time = 0.96355179999955 seconds
Trial 80, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 80, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[LightGBM] [Info] Number of positive: 10230, number of negative: 10471
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.794298
[LightGBM] [Info] Total Bins 23517
[LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 257
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285
[LightGBM] [Info] Start training from score -0.023285
[LightGBM] [Debug] Re-bagging, using 9972 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 13
[LightGBM] [Debug] Re-bagging, using 9932 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 11
[LightGBM] [Debug] Re-bagging, using 9906 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 12
[LightGBM] [Debug] Re-bagging, using 9828 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 11
[LightGBM] [Debug] Re-bagging, using 9997 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 11
[LightGBM] [Debug] Re-bagging, using 9832 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 13
[LightGBM] [Debug] Re-bagging, using 9918 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 12
[LightGBM] [Debug] Re-bagging, using 9936 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 11
Trial 80, Fold 2: Log loss = 0.2975817993313782, Average precision = 0.971209615433894, ROC-AUC = 0.9677456288291657, Elapsed Time = 1.021224500000244 seconds
Trial 80, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 80, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[LightGBM] [Info] Number of positive: 10165, number of negative: 10517
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795455
[LightGBM] [Info] Total Bins 23516
[LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 258
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043
[LightGBM] [Info] Start training from score -0.034043
[LightGBM] [Debug] Re-bagging, using 9963 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 11
[LightGBM] [Debug] Re-bagging, using 9919 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 11
[LightGBM] [Debug] Re-bagging, using 9897 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 14
[LightGBM] [Debug] Re-bagging, using 9823 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 11
[LightGBM] [Debug] Re-bagging, using 9982 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 11
[LightGBM] [Debug] Re-bagging, using 9829 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 12
[LightGBM] [Debug] Re-bagging, using 9917 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 13
[LightGBM] [Debug] Re-bagging, using 9927 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 11
Trial 80, Fold 3: Log loss = 0.29619044182555126, Average precision = 0.9728419392402384, ROC-AUC = 0.9684816853829782, Elapsed Time = 1.0942439000000377 seconds
Trial 80, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 80, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[LightGBM] [Info] Number of positive: 10177, number of negative: 10479
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795144
[LightGBM] [Info] Total Bins 23527
[LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 258
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243
[LightGBM] [Info] Start training from score -0.029243
[LightGBM] [Debug] Re-bagging, using 9949 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 12
[LightGBM] [Debug] Re-bagging, using 9908 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 12
[LightGBM] [Debug] Re-bagging, using 9889 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 10
[LightGBM] [Debug] Re-bagging, using 9800 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 12
[LightGBM] [Debug] Re-bagging, using 9963 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 11
[LightGBM] [Debug] Re-bagging, using 9832 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 13
[LightGBM] [Debug] Re-bagging, using 9908 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 11
[LightGBM] [Debug] Re-bagging, using 9920 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 13
Trial 80, Fold 4: Log loss = 0.29494973302308586, Average precision = 0.9727109825081937, ROC-AUC = 0.9675677053155648, Elapsed Time = 1.1352965000005497 seconds
Trial 80, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 80, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[LightGBM] [Info] Number of positive: 10150, number of negative: 10500
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.796026
[LightGBM] [Info] Total Bins 23538
[LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 258
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902
[LightGBM] [Info] Start training from score -0.033902
[LightGBM] [Debug] Re-bagging, using 9947 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 11
[LightGBM] [Debug] Re-bagging, using 9905 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 11
[LightGBM] [Debug] Re-bagging, using 9889 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 12
[LightGBM] [Debug] Re-bagging, using 9793 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 12
[LightGBM] [Debug] Re-bagging, using 9961 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 13
[LightGBM] [Debug] Re-bagging, using 9826 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 15
[LightGBM] [Debug] Re-bagging, using 9912 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 11
[LightGBM] [Debug] Re-bagging, using 9914 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 14
Trial 80, Fold 5: Log loss = 0.29787818514134823, Average precision = 0.9707340038556295, ROC-AUC = 0.9663314196533079, Elapsed Time = 1.196688500000164 seconds
Optimization Progress:  81%|########1 | 81/100 [18:58<04:27, 14.09s/it]
Trial 81, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 81, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[LightGBM] [Info] Number of positive: 10130, number of negative: 10533
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.796981
[LightGBM] [Info] Total Bins 20226
[LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 259
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012
[LightGBM] [Info] Start training from score -0.039012
[LightGBM] [Debug] Re-bagging, using 13083 data to train
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 14
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 14
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 18
[LightGBM] [Debug] Re-bagging, using 13151 data to train
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 14
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 13
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 12
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 12
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 16
[LightGBM] [Debug] Re-bagging, using 13216 data to train
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 14
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 14
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 14
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 12
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 13
[LightGBM] [Debug] Re-bagging, using 12985 data to train
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 14
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 13
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 14
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 13
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 14
[LightGBM] [Debug] Re-bagging, using 13153 data to train
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 13
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 14
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 17
[LightGBM] [Debug] Re-bagging, using 13052 data to train
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 12
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 14
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 15
Trial 81, Fold 1: Log loss = 0.32300591972987625, Average precision = 0.9734513638765099, ROC-AUC = 0.9695915064309373, Elapsed Time = 1.536184399999911 seconds
Trial 81, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 81, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[LightGBM] [Info] Number of positive: 10230, number of negative: 10471
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795629
[LightGBM] [Info] Total Bins 20188
[LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 258
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285
[LightGBM] [Info] Start training from score -0.023285
[LightGBM] [Debug] Re-bagging, using 13110 data to train
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 12
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 13
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 14
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 13
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 13
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 12
[LightGBM] [Debug] Re-bagging, using 13171 data to train
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 14
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 14
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 14
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 14
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 13
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 12
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 13
[LightGBM] [Debug] Re-bagging, using 13241 data to train
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 12
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 14
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 14
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 12
[LightGBM] [Debug] Re-bagging, using 13018 data to train
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 13
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 13
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 13
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 18
[LightGBM] [Debug] Re-bagging, using 13176 data to train
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 13
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 13
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 14
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 15
[LightGBM] [Debug] Re-bagging, using 13058 data to train
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 14
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 13
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 13
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 13
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 15
Trial 81, Fold 2: Log loss = 0.32332806823816257, Average precision = 0.9720980957530481, ROC-AUC = 0.9681450290005192, Elapsed Time = 1.8439387999997052 seconds
Trial 81, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 81, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[LightGBM] [Info] Number of positive: 10165, number of negative: 10517
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795455
[LightGBM] [Info] Total Bins 20188
[LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 258
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043
[LightGBM] [Info] Start training from score -0.034043
[LightGBM] [Debug] Re-bagging, using 13094 data to train
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 14
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 14
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 13
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 12
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 14
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 14
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 15
[LightGBM] [Debug] Re-bagging, using 13161 data to train
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 13
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 14
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 14
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 14
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 13
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 15
[LightGBM] [Debug] Re-bagging, using 13228 data to train
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 13
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 12
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 13
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 16
[LightGBM] [Debug] Re-bagging, using 13008 data to train
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 13
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 12
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 13
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 14
[LightGBM] [Debug] Re-bagging, using 13161 data to train
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 14
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 12
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 14
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 13
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 15
[LightGBM] [Debug] Re-bagging, using 13053 data to train
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 14
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 13
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 15
Trial 81, Fold 3: Log loss = 0.3216995749037648, Average precision = 0.9744215142566041, ROC-AUC = 0.9700077529832132, Elapsed Time = 1.765900899999906 seconds
Trial 81, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 81, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[LightGBM] [Info] Number of positive: 10177, number of negative: 10479
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.796474
[LightGBM] [Info] Total Bins 20226
[LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 259
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243
[LightGBM] [Info] Start training from score -0.029243
[LightGBM] [Debug] Re-bagging, using 13078 data to train
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 14
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 12
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 14
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 17
[LightGBM] [Debug] Re-bagging, using 13147 data to train
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 14
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 14
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 14
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 15
[LightGBM] [Debug] Re-bagging, using 13212 data to train
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 13
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 14
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 18
[LightGBM] [Debug] Re-bagging, using 12981 data to train
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 13
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 13
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 14
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 13
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 13
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 12
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 14
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 15
[LightGBM] [Debug] Re-bagging, using 13145 data to train
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 14
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 13
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 17
[LightGBM] [Debug] Re-bagging, using 13050 data to train
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 14
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 14
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 15
Trial 81, Fold 4: Log loss = 0.32243371529582, Average precision = 0.9739904678441192, ROC-AUC = 0.9691439492025341, Elapsed Time = 1.831553900000472 seconds
Trial 81, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 81, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[LightGBM] [Info] Number of positive: 10150, number of negative: 10500
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.797340
[LightGBM] [Info] Total Bins 20221
[LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 259
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902
[LightGBM] [Info] Start training from score -0.033902
[LightGBM] [Debug] Re-bagging, using 13075 data to train
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 14
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 14
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 13
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 15
[LightGBM] [Debug] Re-bagging, using 13143 data to train
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 14
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 12
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 11
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 12
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 14
[LightGBM] [Debug] Re-bagging, using 13212 data to train
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 14
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 14
[LightGBM] [Debug] Re-bagging, using 12974 data to train
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 14
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 13
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 14
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 15
[LightGBM] [Debug] Re-bagging, using 13140 data to train
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 14
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 14
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 13
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 14
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 13
[LightGBM] [Debug] Re-bagging, using 13044 data to train
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 14
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 15
Trial 81, Fold 5: Log loss = 0.32431580591947057, Average precision = 0.9719594880006669, ROC-AUC = 0.9685382828902143, Elapsed Time = 1.7745714000002408 seconds
Optimization Progress:  82%|########2 | 82/100 [19:14<04:24, 14.70s/it]
Trial 82, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 82, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[LightGBM] [Info] Number of positive: 10130, number of negative: 10533
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795662
[LightGBM] [Info] Total Bins 24158
[LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 258
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012
[LightGBM] [Info] Start training from score -0.039012
[LightGBM] [Debug] Re-bagging, using 5834 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11
[LightGBM] [Debug] Re-bagging, using 5799 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5805 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 12
[LightGBM] [Debug] Re-bagging, using 5686 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5875 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 11
[LightGBM] [Debug] Re-bagging, using 5845 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5727 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5869 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5745 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11
[LightGBM] [Debug] Re-bagging, using 5623 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5645 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11
[LightGBM] [Debug] Re-bagging, using 5738 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
Trial 82, Fold 1: Log loss = 0.30882726610776134, Average precision = 0.9673871031756777, ROC-AUC = 0.961117444066857, Elapsed Time = 1.5058353999993415 seconds
Trial 82, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 82, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[LightGBM] [Info] Number of positive: 10230, number of negative: 10471
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795629
[LightGBM] [Info] Total Bins 24166
[LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 258
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285
[LightGBM] [Info] Start training from score -0.023285
[LightGBM] [Debug] Re-bagging, using 5846 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5812 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11
[LightGBM] [Debug] Re-bagging, using 5809 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5704 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5896 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5836 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11
[LightGBM] [Debug] Re-bagging, using 5735 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5880 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11
[LightGBM] [Debug] Re-bagging, using 5764 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5649 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5641 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5736 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11
Trial 82, Fold 2: Log loss = 0.3052057853384527, Average precision = 0.9662910981216197, ROC-AUC = 0.9624116941699411, Elapsed Time = 1.8040215000000899 seconds
Trial 82, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 82, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[LightGBM] [Info] Number of positive: 10165, number of negative: 10517
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795455
[LightGBM] [Info] Total Bins 24160
[LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 258
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043
[LightGBM] [Info] Start training from score -0.034043
[LightGBM] [Debug] Re-bagging, using 5839 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5805 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5807 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5696 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5888 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 13
[LightGBM] [Debug] Re-bagging, using 5837 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 12
[LightGBM] [Debug] Re-bagging, using 5737 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5869 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5749 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11
[LightGBM] [Debug] Re-bagging, using 5643 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5651 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11
[LightGBM] [Debug] Re-bagging, using 5725 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 9
Trial 82, Fold 3: Log loss = 0.3009154014630842, Average precision = 0.9692448578588674, ROC-AUC = 0.9639620212171702, Elapsed Time = 1.7570170999997572 seconds
Trial 82, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 82, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[LightGBM] [Info] Number of positive: 10177, number of negative: 10479
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795144
[LightGBM] [Info] Total Bins 24150
[LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 258
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243
[LightGBM] [Info] Start training from score -0.029243
[LightGBM] [Debug] Re-bagging, using 5831 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5797 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5801 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5688 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5872 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5842 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5727 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5867 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11
[LightGBM] [Debug] Re-bagging, using 5738 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5626 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11
[LightGBM] [Debug] Re-bagging, using 5637 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5737 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10
Trial 82, Fold 4: Log loss = 0.30238588560447804, Average precision = 0.9679750797460415, ROC-AUC = 0.9617191952140127, Elapsed Time = 1.7509574999994584 seconds
Trial 82, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 82, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[LightGBM] [Info] Number of positive: 10150, number of negative: 10500
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.796026
[LightGBM] [Info] Total Bins 24154
[LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 258
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902
[LightGBM] [Info] Start training from score -0.033902
[LightGBM] [Debug] Re-bagging, using 5829 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5797 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5801 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 13
[LightGBM] [Debug] Re-bagging, using 5681 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5871 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5838 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5733 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5860 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11
[LightGBM] [Debug] Re-bagging, using 5739 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11
[LightGBM] [Debug] Re-bagging, using 5626 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5631 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11
[LightGBM] [Debug] Re-bagging, using 5728 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10
Trial 82, Fold 5: Log loss = 0.30639183738309717, Average precision = 0.9655618446251829, ROC-AUC = 0.9598895639410661, Elapsed Time = 1.7526851000002353 seconds
Optimization Progress:  83%|########2 | 83/100 [19:30<04:16, 15.08s/it]
Trial 83, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 83, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[LightGBM] [Info] Number of positive: 10130, number of negative: 10533
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791640
[LightGBM] [Info] Total Bins 28977
[LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012
[LightGBM] [Info] Start training from score -0.039012
[LightGBM] [Debug] Re-bagging, using 2928 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5
[LightGBM] [Debug] Re-bagging, using 2933 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5
[LightGBM] [Debug] Re-bagging, using 2823 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 6
[LightGBM] [Debug] Re-bagging, using 2862 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5
[LightGBM] [Debug] Re-bagging, using 2958 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 6
[LightGBM] [Debug] Re-bagging, using 2907 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5
[LightGBM] [Debug] Re-bagging, using 2827 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5
[LightGBM] [Debug] Re-bagging, using 2965 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 6
[LightGBM] [Debug] Re-bagging, using 2868 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5
[LightGBM] [Debug] Re-bagging, using 2854 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5
[LightGBM] [Debug] Re-bagging, using 2747 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5
[LightGBM] [Debug] Re-bagging, using 2836 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2876 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 5
[LightGBM] [Debug] Re-bagging, using 2848 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5
[LightGBM] [Debug] Re-bagging, using 2889 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2868 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5
[LightGBM] [Debug] Re-bagging, using 2896 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5
[LightGBM] [Debug] Re-bagging, using 2779 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5
[LightGBM] [Debug] Re-bagging, using 2943 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5
[LightGBM] [Debug] Re-bagging, using 2912 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5
[LightGBM] [Debug] Re-bagging, using 2803 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5
[LightGBM] [Debug] Re-bagging, using 2921 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 5
[LightGBM] [Debug] Re-bagging, using 2919 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5
[LightGBM] [Debug] Re-bagging, using 2841 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5
[LightGBM] [Debug] Re-bagging, using 2995 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5
Trial 83, Fold 1: Log loss = 0.3518844215145265, Average precision = 0.9577284032215908, ROC-AUC = 0.9496131221232846, Elapsed Time = 1.1002770000004602 seconds
Trial 83, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 83, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[LightGBM] [Info] Number of positive: 10230, number of negative: 10471
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791600
[LightGBM] [Info] Total Bins 28981
[LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285
[LightGBM] [Info] Start training from score -0.023285
[LightGBM] [Debug] Re-bagging, using 2933 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5
[LightGBM] [Debug] Re-bagging, using 2943 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5
[LightGBM] [Debug] Re-bagging, using 2821 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 6
[LightGBM] [Debug] Re-bagging, using 2872 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 6
[LightGBM] [Debug] Re-bagging, using 2967 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5
[LightGBM] [Debug] Re-bagging, using 2903 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5
[LightGBM] [Debug] Re-bagging, using 2832 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5
[LightGBM] [Debug] Re-bagging, using 2971 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5
[LightGBM] [Debug] Re-bagging, using 2885 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2867 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5
[LightGBM] [Debug] Re-bagging, using 2745 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5
[LightGBM] [Debug] Re-bagging, using 2833 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5
[LightGBM] [Debug] Re-bagging, using 2884 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 5
[LightGBM] [Debug] Re-bagging, using 2844 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5
[LightGBM] [Debug] Re-bagging, using 2892 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5
[LightGBM] [Debug] Re-bagging, using 2877 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2905 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 5
[LightGBM] [Debug] Re-bagging, using 2792 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 6
[LightGBM] [Debug] Re-bagging, using 2951 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5
[LightGBM] [Debug] Re-bagging, using 2911 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5
[LightGBM] [Debug] Re-bagging, using 2806 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5
[LightGBM] [Debug] Re-bagging, using 2928 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5
[LightGBM] [Debug] Re-bagging, using 2931 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5
[LightGBM] [Debug] Re-bagging, using 2855 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5
[LightGBM] [Debug] Re-bagging, using 2998 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5
Trial 83, Fold 2: Log loss = 0.3468327660580311, Average precision = 0.9540629330455027, ROC-AUC = 0.9482948253094668, Elapsed Time = 1.2421316999998453 seconds
Trial 83, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 83, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[LightGBM] [Info] Number of positive: 10165, number of negative: 10517
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791401
[LightGBM] [Info] Total Bins 28976
[LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043
[LightGBM] [Info] Start training from score -0.034043
[LightGBM] [Debug] Re-bagging, using 2930 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5
[LightGBM] [Debug] Re-bagging, using 2937 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5
[LightGBM] [Debug] Re-bagging, using 2824 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 6
[LightGBM] [Debug] Re-bagging, using 2865 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 5
[LightGBM] [Debug] Re-bagging, using 2963 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 6
[LightGBM] [Debug] Re-bagging, using 2907 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5
[LightGBM] [Debug] Re-bagging, using 2832 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 6
[LightGBM] [Debug] Re-bagging, using 2961 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2879 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5
[LightGBM] [Debug] Re-bagging, using 2859 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5
[LightGBM] [Debug] Re-bagging, using 2756 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5
[LightGBM] [Debug] Re-bagging, using 2824 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 4
[LightGBM] [Debug] Re-bagging, using 2882 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 5
[LightGBM] [Debug] Re-bagging, using 2849 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 6
[LightGBM] [Debug] Re-bagging, using 2883 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 6
[LightGBM] [Debug] Re-bagging, using 2879 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5
[LightGBM] [Debug] Re-bagging, using 2900 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5
[LightGBM] [Debug] Re-bagging, using 2779 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5
[LightGBM] [Debug] Re-bagging, using 2951 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 5
[LightGBM] [Debug] Re-bagging, using 2924 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5
[LightGBM] [Debug] Re-bagging, using 2797 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5
[LightGBM] [Debug] Re-bagging, using 2912 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5
[LightGBM] [Debug] Re-bagging, using 2923 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5
[LightGBM] [Debug] Re-bagging, using 2862 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 6
[LightGBM] [Debug] Re-bagging, using 2998 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 5
Trial 83, Fold 3: Log loss = 0.33874433004893345, Average precision = 0.9606818061407815, ROC-AUC = 0.9534419212470264, Elapsed Time = 1.305384199999935 seconds
Trial 83, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 83, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[LightGBM] [Info] Number of positive: 10177, number of negative: 10479
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791090
[LightGBM] [Info] Total Bins 28967
[LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243
[LightGBM] [Info] Start training from score -0.029243
[LightGBM] [Debug] Re-bagging, using 2928 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5
[LightGBM] [Debug] Re-bagging, using 2931 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5
[LightGBM] [Debug] Re-bagging, using 2819 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 6
[LightGBM] [Debug] Re-bagging, using 2864 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5
[LightGBM] [Debug] Re-bagging, using 2957 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 5
[LightGBM] [Debug] Re-bagging, using 2906 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5
[LightGBM] [Debug] Re-bagging, using 2829 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 6
[LightGBM] [Debug] Re-bagging, using 2961 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5
[LightGBM] [Debug] Re-bagging, using 2866 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5
[LightGBM] [Debug] Re-bagging, using 2853 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5
[LightGBM] [Debug] Re-bagging, using 2747 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5
[LightGBM] [Debug] Re-bagging, using 2836 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 6
[LightGBM] [Debug] Re-bagging, using 2872 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5
[LightGBM] [Debug] Re-bagging, using 2846 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5
[LightGBM] [Debug] Re-bagging, using 2887 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5
[LightGBM] [Debug] Re-bagging, using 2873 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 5
[LightGBM] [Debug] Re-bagging, using 2893 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 6
[LightGBM] [Debug] Re-bagging, using 2782 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5
[LightGBM] [Debug] Re-bagging, using 2940 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5
[LightGBM] [Debug] Re-bagging, using 2911 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5
[LightGBM] [Debug] Re-bagging, using 2798 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5
[LightGBM] [Debug] Re-bagging, using 2918 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 5
[LightGBM] [Debug] Re-bagging, using 2924 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 6
[LightGBM] [Debug] Re-bagging, using 2846 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5
[LightGBM] [Debug] Re-bagging, using 2983 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5
Trial 83, Fold 4: Log loss = 0.34823218671688666, Average precision = 0.9580260734276069, ROC-AUC = 0.9501910210281099, Elapsed Time = 1.2381753999998182 seconds
Trial 83, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 83, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[LightGBM] [Info] Number of positive: 10150, number of negative: 10500
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.792012
[LightGBM] [Info] Total Bins 28973
[LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902
[LightGBM] [Info] Start training from score -0.033902
[LightGBM] [Debug] Re-bagging, using 2927 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5
[LightGBM] [Debug] Re-bagging, using 2931 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5
[LightGBM] [Debug] Re-bagging, using 2819 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5
[LightGBM] [Debug] Re-bagging, using 2863 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5
[LightGBM] [Debug] Re-bagging, using 2953 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5
[LightGBM] [Debug] Re-bagging, using 2904 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5
[LightGBM] [Debug] Re-bagging, using 2833 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5
[LightGBM] [Debug] Re-bagging, using 2956 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5
[LightGBM] [Debug] Re-bagging, using 2868 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5
[LightGBM] [Debug] Re-bagging, using 2852 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5
[LightGBM] [Debug] Re-bagging, using 2742 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5
[LightGBM] [Debug] Re-bagging, using 2825 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5
[LightGBM] [Debug] Re-bagging, using 2887 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5
[LightGBM] [Debug] Re-bagging, using 2845 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5
[LightGBM] [Debug] Re-bagging, using 2885 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5
[LightGBM] [Debug] Re-bagging, using 2876 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 5
[LightGBM] [Debug] Re-bagging, using 2896 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5
[LightGBM] [Debug] Re-bagging, using 2770 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 6
[LightGBM] [Debug] Re-bagging, using 2943 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 10 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5
[LightGBM] [Debug] Re-bagging, using 2908 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5
[LightGBM] [Debug] Re-bagging, using 2799 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 11 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 5
[LightGBM] [Debug] Re-bagging, using 2910 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 5
[LightGBM] [Debug] Re-bagging, using 2925 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 4
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 5
[LightGBM] [Debug] Re-bagging, using 2855 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 14 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 6
[LightGBM] [Debug] Re-bagging, using 2981 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 13 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 12 and depth = 4
Trial 83, Fold 5: Log loss = 0.35669165442933026, Average precision = 0.9562607558042574, ROC-AUC = 0.9485020901844936, Elapsed Time = 1.1870617000004131 seconds
Optimization Progress:  84%|########4 | 84/100 [19:44<03:55, 14.74s/it]
Trial 84, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 84, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[LightGBM] [Info] Number of positive: 10130, number of negative: 10533
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.790295
[LightGBM] [Info] Total Bins 11136
[LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 254
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012
[LightGBM] [Info] Start training from score -0.039012
[LightGBM] [Debug] Re-bagging, using 9953 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8
[LightGBM] [Debug] Re-bagging, using 9912 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9
[LightGBM] [Debug] Re-bagging, using 9892 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8
[LightGBM] [Debug] Re-bagging, using 9802 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8
[LightGBM] [Debug] Re-bagging, using 9970 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Debug] Re-bagging, using 9834 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10
[LightGBM] [Debug] Re-bagging, using 9910 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8
[LightGBM] [Debug] Re-bagging, using 9921 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8
Trial 84, Fold 1: Log loss = 0.33400758482481047, Average precision = 0.9622280943411928, ROC-AUC = 0.9545879277134923, Elapsed Time = 1.0007489999998143 seconds
Trial 84, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 84, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[LightGBM] [Info] Number of positive: 10230, number of negative: 10471
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791600
[LightGBM] [Info] Total Bins 11153
[LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285
[LightGBM] [Info] Start training from score -0.023285
[LightGBM] [Debug] Re-bagging, using 9972 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9
[LightGBM] [Debug] Re-bagging, using 9932 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8
[LightGBM] [Debug] Re-bagging, using 9906 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 8
[LightGBM] [Debug] Re-bagging, using 9828 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8
[LightGBM] [Debug] Re-bagging, using 9997 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8
[LightGBM] [Debug] Re-bagging, using 9832 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8
[LightGBM] [Debug] Re-bagging, using 9918 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9
[LightGBM] [Debug] Re-bagging, using 9936 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 7
Trial 84, Fold 2: Log loss = 0.332232484757396, Average precision = 0.9601227420006524, ROC-AUC = 0.9546932706837543, Elapsed Time = 1.0486750999998549 seconds
Trial 84, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 84, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[LightGBM] [Info] Number of positive: 10165, number of negative: 10517
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.790045
[LightGBM] [Info] Total Bins 11153
[LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 254
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043
[LightGBM] [Info] Start training from score -0.034043
[LightGBM] [Debug] Re-bagging, using 9963 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 7
[LightGBM] [Debug] Re-bagging, using 9919 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 7
[LightGBM] [Debug] Re-bagging, using 9897 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 7
[LightGBM] [Debug] Re-bagging, using 9823 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8
[LightGBM] [Debug] Re-bagging, using 9982 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8
[LightGBM] [Debug] Re-bagging, using 9829 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Debug] Re-bagging, using 9917 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8
[LightGBM] [Debug] Re-bagging, using 9927 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9
Trial 84, Fold 3: Log loss = 0.32143217731769164, Average precision = 0.9645005379665792, ROC-AUC = 0.957877012886325, Elapsed Time = 1.1556067000001349 seconds
Trial 84, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 84, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[LightGBM] [Info] Number of positive: 10177, number of negative: 10479
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791090
[LightGBM] [Info] Total Bins 11137
[LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243
[LightGBM] [Info] Start training from score -0.029243
[LightGBM] [Debug] Re-bagging, using 9949 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Debug] Re-bagging, using 9908 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Debug] Re-bagging, using 9889 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8
[LightGBM] [Debug] Re-bagging, using 9800 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8
[LightGBM] [Debug] Re-bagging, using 9963 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Debug] Re-bagging, using 9832 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Debug] Re-bagging, using 9908 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 9
[LightGBM] [Debug] Re-bagging, using 9920 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8
Trial 84, Fold 4: Log loss = 0.33049159274169043, Average precision = 0.9629257029960119, ROC-AUC = 0.9557678574409083, Elapsed Time = 1.2255662999996275 seconds
Trial 84, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 84, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[LightGBM] [Info] Number of positive: 10150, number of negative: 10500
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.792012
[LightGBM] [Info] Total Bins 11139
[LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902
[LightGBM] [Info] Start training from score -0.033902
[LightGBM] [Debug] Re-bagging, using 9947 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8
[LightGBM] [Debug] Re-bagging, using 9905 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8
[LightGBM] [Debug] Re-bagging, using 9889 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8
[LightGBM] [Debug] Re-bagging, using 9793 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8
[LightGBM] [Debug] Re-bagging, using 9961 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8
[LightGBM] [Debug] Re-bagging, using 9826 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9
[LightGBM] [Debug] Re-bagging, using 9912 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9
[LightGBM] [Debug] Re-bagging, using 9914 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9
Trial 84, Fold 5: Log loss = 0.33872359839674515, Average precision = 0.9608101868097333, ROC-AUC = 0.953562603348011, Elapsed Time = 1.1282185999998546 seconds
Optimization Progress:  85%|########5 | 85/100 [19:57<03:32, 14.19s/it]
Trial 85, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 85, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[LightGBM] [Info] Number of positive: 10130, number of negative: 10533
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795662
[LightGBM] [Info] Total Bins 25876
[LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 258
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012
[LightGBM] [Info] Start training from score -0.039012
[LightGBM] [Debug] Re-bagging, using 2806 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7
[LightGBM] [Debug] Re-bagging, using 2796 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7
[LightGBM] [Debug] Re-bagging, using 2677 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 8
[LightGBM] [Debug] Re-bagging, using 2739 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7
[LightGBM] [Debug] Re-bagging, using 2824 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7
[LightGBM] [Debug] Re-bagging, using 2769 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7
[LightGBM] [Debug] Re-bagging, using 2714 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 6
Trial 85, Fold 1: Log loss = 0.4490798698237985, Average precision = 0.9661487854658025, ROC-AUC = 0.9595509336927583, Elapsed Time = 0.9809285999999702 seconds
Trial 85, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 85, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[LightGBM] [Info] Number of positive: 10230, number of negative: 10471
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.794298
[LightGBM] [Info] Total Bins 25878
[LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 257
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285
[LightGBM] [Info] Start training from score -0.023285
[LightGBM] [Debug] Re-bagging, using 2811 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 7
[LightGBM] [Debug] Re-bagging, using 2804 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 8
[LightGBM] [Debug] Re-bagging, using 2676 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 6
[LightGBM] [Debug] Re-bagging, using 2750 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6
[LightGBM] [Debug] Re-bagging, using 2833 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 7
[LightGBM] [Debug] Re-bagging, using 2763 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7
[LightGBM] [Debug] Re-bagging, using 2718 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 6
Trial 85, Fold 2: Log loss = 0.4472839815272487, Average precision = 0.9633424876446511, ROC-AUC = 0.9583642013620273, Elapsed Time = 1.0804873999995834 seconds
Trial 85, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 85, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[LightGBM] [Info] Number of positive: 10165, number of negative: 10517
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795455
[LightGBM] [Info] Total Bins 25877
[LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 258
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043
[LightGBM] [Info] Start training from score -0.034043
[LightGBM] [Debug] Re-bagging, using 2808 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7
[LightGBM] [Debug] Re-bagging, using 2798 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 7
[LightGBM] [Debug] Re-bagging, using 2680 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 6
[LightGBM] [Debug] Re-bagging, using 2742 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7
[LightGBM] [Debug] Re-bagging, using 2829 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7
[LightGBM] [Debug] Re-bagging, using 2768 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 6
[LightGBM] [Debug] Re-bagging, using 2718 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7
Trial 85, Fold 3: Log loss = 0.4468608871653139, Average precision = 0.9668104377238605, ROC-AUC = 0.961073387885121, Elapsed Time = 1.1630433999998786 seconds
Trial 85, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 85, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[LightGBM] [Info] Number of positive: 10177, number of negative: 10479
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795144
[LightGBM] [Info] Total Bins 25866
[LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 258
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243
[LightGBM] [Info] Start training from score -0.029243
[LightGBM] [Debug] Re-bagging, using 2806 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7
[LightGBM] [Debug] Re-bagging, using 2794 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7
[LightGBM] [Debug] Re-bagging, using 2674 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 6
[LightGBM] [Debug] Re-bagging, using 2740 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7
[LightGBM] [Debug] Re-bagging, using 2824 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 6
[LightGBM] [Debug] Re-bagging, using 2767 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 25 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 7
[LightGBM] [Debug] Re-bagging, using 2716 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 6
Trial 85, Fold 4: Log loss = 0.44792467876332814, Average precision = 0.9659312944127066, ROC-AUC = 0.959395066298514, Elapsed Time = 1.1747064999999566 seconds
Trial 85, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 85, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[LightGBM] [Info] Number of positive: 10150, number of negative: 10500
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.796026
[LightGBM] [Info] Total Bins 25873
[LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 258
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902
[LightGBM] [Info] Start training from score -0.033902
[LightGBM] [Debug] Re-bagging, using 2805 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6
[LightGBM] [Debug] Re-bagging, using 2794 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 6
[LightGBM] [Debug] Re-bagging, using 2674 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 7
[LightGBM] [Debug] Re-bagging, using 2739 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7
[LightGBM] [Debug] Re-bagging, using 2820 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 6
[LightGBM] [Debug] Re-bagging, using 2765 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7
[LightGBM] [Debug] Re-bagging, using 2720 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7
Trial 85, Fold 5: Log loss = 0.44603286692157473, Average precision = 0.9641034079380562, ROC-AUC = 0.957529309031455, Elapsed Time = 1.116951500000141 seconds
Optimization Progress:  86%|########6 | 86/100 [20:10<03:13, 13.83s/it]
Trial 86, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 86, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[LightGBM] [Info] Number of positive: 10130, number of negative: 10533
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791640
[LightGBM] [Info] Total Bins 22771
[LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012
[LightGBM] [Info] Start training from score -0.039012
[LightGBM] [Debug] Re-bagging, using 16585 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 11
[LightGBM] [Debug] Re-bagging, using 16662 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 12
[LightGBM] [Debug] Re-bagging, using 16603 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 86 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 12
[LightGBM] [Debug] Re-bagging, using 16552 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 12
[LightGBM] [Debug] Re-bagging, using 16610 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 12
[LightGBM] [Debug] Re-bagging, using 16601 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 12
[LightGBM] [Debug] Re-bagging, using 16528 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 86 and depth = 14
[LightGBM] [Debug] Re-bagging, using 16562 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 12
[LightGBM] [Debug] Re-bagging, using 16634 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 88 and depth = 13
[LightGBM] [Debug] Re-bagging, using 16508 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 14
[LightGBM] [Debug] Re-bagging, using 16530 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 11
[LightGBM] [Debug] Re-bagging, using 16556 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 91 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 14
[LightGBM] [Debug] Re-bagging, using 16561 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 11
[LightGBM] [Debug] Re-bagging, using 16519 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 87 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 11
[LightGBM] [Debug] Re-bagging, using 16625 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 87 and depth = 14
[LightGBM] [Debug] Re-bagging, using 16564 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 13
[LightGBM] [Debug] Re-bagging, using 16613 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 13
[LightGBM] [Debug] Re-bagging, using 16517 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 13
[LightGBM] [Debug] Re-bagging, using 16631 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 12
[LightGBM] [Debug] Re-bagging, using 16709 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 12
[LightGBM] [Debug] Re-bagging, using 16487 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 10
[LightGBM] [Debug] Re-bagging, using 16525 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 13
[LightGBM] [Debug] Re-bagging, using 16573 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 86 and depth = 13
[LightGBM] [Debug] Re-bagging, using 16625 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 14
Trial 86, Fold 1: Log loss = 0.305143960093892, Average precision = 0.9682342423734314, ROC-AUC = 0.961671428332449, Elapsed Time = 2.3427920000003724 seconds
Trial 86, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 86, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[LightGBM] [Info] Number of positive: 10230, number of negative: 10471
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791600
[LightGBM] [Info] Total Bins 22782
[LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285
[LightGBM] [Info] Start training from score -0.023285
[LightGBM] [Debug] Re-bagging, using 16618 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 12
[LightGBM] [Debug] Re-bagging, using 16693 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 11
[LightGBM] [Debug] Re-bagging, using 16630 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 86 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 13
[LightGBM] [Debug] Re-bagging, using 16589 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 13
[LightGBM] [Debug] Re-bagging, using 16637 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 13
[LightGBM] [Debug] Re-bagging, using 16621 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 12
[LightGBM] [Debug] Re-bagging, using 16553 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 12
[LightGBM] [Debug] Re-bagging, using 16610 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 93 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 12
[LightGBM] [Debug] Re-bagging, using 16663 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 11
[LightGBM] [Debug] Re-bagging, using 16541 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 11
[LightGBM] [Debug] Re-bagging, using 16557 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 13
[LightGBM] [Debug] Re-bagging, using 16577 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 11
[LightGBM] [Debug] Re-bagging, using 16588 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 87 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 11
[LightGBM] [Debug] Re-bagging, using 16540 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 13
[LightGBM] [Debug] Re-bagging, using 16657 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 13
[LightGBM] [Debug] Re-bagging, using 16606 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 11
[LightGBM] [Debug] Re-bagging, using 16654 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 12
[LightGBM] [Debug] Re-bagging, using 16548 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 13
[LightGBM] [Debug] Re-bagging, using 16665 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 12
[LightGBM] [Debug] Re-bagging, using 16718 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 86 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 11
[LightGBM] [Debug] Re-bagging, using 16524 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 11
[LightGBM] [Debug] Re-bagging, using 16549 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 87 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 12
[LightGBM] [Debug] Re-bagging, using 16598 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 12
[LightGBM] [Debug] Re-bagging, using 16674 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 11
Trial 86, Fold 2: Log loss = 0.3031811940840515, Average precision = 0.9666190300590372, ROC-AUC = 0.9625416661481951, Elapsed Time = 2.989899200000764 seconds
Trial 86, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 86, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[LightGBM] [Info] Number of positive: 10165, number of negative: 10517
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791401
[LightGBM] [Info] Total Bins 22670
[LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043
[LightGBM] [Info] Start training from score -0.034043
[LightGBM] [Debug] Re-bagging, using 16601 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 11
[LightGBM] [Debug] Re-bagging, using 16677 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 87 and depth = 12
[LightGBM] [Debug] Re-bagging, using 16617 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 15
[LightGBM] [Debug] Re-bagging, using 16574 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 12
[LightGBM] [Debug] Re-bagging, using 16618 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 13
[LightGBM] [Debug] Re-bagging, using 16613 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 12
[LightGBM] [Debug] Re-bagging, using 16538 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 13
[LightGBM] [Debug] Re-bagging, using 16588 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 88 and depth = 11
[LightGBM] [Debug] Re-bagging, using 16646 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 11
[LightGBM] [Debug] Re-bagging, using 16529 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 11
[LightGBM] [Debug] Re-bagging, using 16544 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 86 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 13
[LightGBM] [Debug] Re-bagging, using 16570 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 87 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 14
[LightGBM] [Debug] Re-bagging, using 16570 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 15
[LightGBM] [Debug] Re-bagging, using 16531 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 13
[LightGBM] [Debug] Re-bagging, using 16638 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 11
[LightGBM] [Debug] Re-bagging, using 16579 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 13
[LightGBM] [Debug] Re-bagging, using 16638 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 13
[LightGBM] [Debug] Re-bagging, using 16536 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 12
[LightGBM] [Debug] Re-bagging, using 16646 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 12
[LightGBM] [Debug] Re-bagging, using 16725 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 11
[LightGBM] [Debug] Re-bagging, using 16503 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 14
[LightGBM] [Debug] Re-bagging, using 16520 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 14
[LightGBM] [Debug] Re-bagging, using 16592 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 86 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 12
[LightGBM] [Debug] Re-bagging, using 16648 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 12
Trial 86, Fold 3: Log loss = 0.30205762492099164, Average precision = 0.9691763190075562, ROC-AUC = 0.9639460698153729, Elapsed Time = 2.8637853000000177 seconds
Trial 86, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 86, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[LightGBM] [Info] Number of positive: 10177, number of negative: 10479
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.792454
[LightGBM] [Info] Total Bins 22765
[LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 256
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243
[LightGBM] [Info] Start training from score -0.029243
[LightGBM] [Debug] Re-bagging, using 16579 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 13
[LightGBM] [Debug] Re-bagging, using 16655 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 12
[LightGBM] [Debug] Re-bagging, using 16599 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 13
[LightGBM] [Debug] Re-bagging, using 16549 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 12
[LightGBM] [Debug] Re-bagging, using 16604 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 88 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 12
[LightGBM] [Debug] Re-bagging, using 16593 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 11
[LightGBM] [Debug] Re-bagging, using 16523 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 13
[LightGBM] [Debug] Re-bagging, using 16561 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 13
[LightGBM] [Debug] Re-bagging, using 16623 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 11
[LightGBM] [Debug] Re-bagging, using 16505 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 12
[LightGBM] [Debug] Re-bagging, using 16522 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 13
[LightGBM] [Debug] Re-bagging, using 16547 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 12
[LightGBM] [Debug] Re-bagging, using 16563 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 14
[LightGBM] [Debug] Re-bagging, using 16511 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 12
[LightGBM] [Debug] Re-bagging, using 16619 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 16
[LightGBM] [Debug] Re-bagging, using 16563 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 88 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 10
[LightGBM] [Debug] Re-bagging, using 16608 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 12
[LightGBM] [Debug] Re-bagging, using 16503 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 13
[LightGBM] [Debug] Re-bagging, using 16624 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 15
[LightGBM] [Debug] Re-bagging, using 16707 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 13
[LightGBM] [Debug] Re-bagging, using 16483 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 13
[LightGBM] [Debug] Re-bagging, using 16516 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 12
[LightGBM] [Debug] Re-bagging, using 16568 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 12
[LightGBM] [Debug] Re-bagging, using 16633 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 11
Trial 86, Fold 4: Log loss = 0.30270349940476843, Average precision = 0.9686390023398933, ROC-AUC = 0.9624188704574013, Elapsed Time = 2.87171219999982 seconds
Trial 86, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 86, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[LightGBM] [Info] Number of positive: 10150, number of negative: 10500
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.792012
[LightGBM] [Info] Total Bins 22721
[LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902
[LightGBM] [Info] Start training from score -0.033902
[LightGBM] [Debug] Re-bagging, using 16573 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 12
[LightGBM] [Debug] Re-bagging, using 16654 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 86 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 86 and depth = 12
[LightGBM] [Debug] Re-bagging, using 16595 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 90 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 13
[LightGBM] [Debug] Re-bagging, using 16542 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 11
[LightGBM] [Debug] Re-bagging, using 16598 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 13
[LightGBM] [Debug] Re-bagging, using 16585 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 12
[LightGBM] [Debug] Re-bagging, using 16527 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 12
[LightGBM] [Debug] Re-bagging, using 16553 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 13
[LightGBM] [Debug] Re-bagging, using 16616 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 13
[LightGBM] [Debug] Re-bagging, using 16500 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 11
[LightGBM] [Debug] Re-bagging, using 16519 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 13
[LightGBM] [Debug] Re-bagging, using 16541 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 11
[LightGBM] [Debug] Re-bagging, using 16555 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 14
[LightGBM] [Debug] Re-bagging, using 16510 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 13
[LightGBM] [Debug] Re-bagging, using 16618 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 14
[LightGBM] [Debug] Re-bagging, using 16555 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 14
[LightGBM] [Debug] Re-bagging, using 16604 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 12
[LightGBM] [Debug] Re-bagging, using 16499 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 11
[LightGBM] [Debug] Re-bagging, using 16616 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 11
[LightGBM] [Debug] Re-bagging, using 16699 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 87 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 12
[LightGBM] [Debug] Re-bagging, using 16480 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 13
[LightGBM] [Debug] Re-bagging, using 16513 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 12
[LightGBM] [Debug] Re-bagging, using 16560 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 12
[LightGBM] [Debug] Re-bagging, using 16629 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 13
Trial 86, Fold 5: Log loss = 0.3074469106939738, Average precision = 0.9656815610375317, ROC-AUC = 0.9594476710699886, Elapsed Time = 2.8436714999998003 seconds
Optimization Progress:  87%|########7 | 87/100 [20:31<03:30, 16.17s/it]
Trial 87, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 87, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[LightGBM] [Info] Number of positive: 10130, number of negative: 10533
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.798290
[LightGBM] [Info] Total Bins 17915
[LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 260
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012
[LightGBM] [Info] Start training from score -0.039012
[LightGBM] [Debug] Re-bagging, using 14894 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 10
[LightGBM] [Debug] Re-bagging, using 15018 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 12
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 14
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 13
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 10
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 11
[LightGBM] [Debug] Re-bagging, using 15035 data to train
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 14
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 11
Trial 87, Fold 1: Log loss = 0.3128623381671113, Average precision = 0.9699769960360858, ROC-AUC = 0.9640660025427417, Elapsed Time = 0.6332750999999917 seconds
Trial 87, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 87, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[LightGBM] [Info] Number of positive: 10230, number of negative: 10471
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.798259
[LightGBM] [Info] Total Bins 17930
[LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 260
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285
[LightGBM] [Info] Start training from score -0.023285
[LightGBM] [Debug] Re-bagging, using 14922 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 12
[LightGBM] [Debug] Re-bagging, using 15044 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 10
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 13
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 13
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 13
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16
[LightGBM] [Debug] Re-bagging, using 15065 data to train
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 10
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 12
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16
Trial 87, Fold 2: Log loss = 0.31324368228512456, Average precision = 0.9690407017541076, ROC-AUC = 0.9654967266485197, Elapsed Time = 0.7145328000005975 seconds
Trial 87, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 87, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[LightGBM] [Info] Number of positive: 10165, number of negative: 10517
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.798104
[LightGBM] [Info] Total Bins 17934
[LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 260
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043
[LightGBM] [Info] Start training from score -0.034043
[LightGBM] [Debug] Re-bagging, using 14905 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 9
[LightGBM] [Debug] Re-bagging, using 15032 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 14
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 10
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 12
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 13
[LightGBM] [Debug] Re-bagging, using 15051 data to train
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 12
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 14
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 17
Trial 87, Fold 3: Log loss = 0.301574581235144, Average precision = 0.9727812892251233, ROC-AUC = 0.9680483640483093, Elapsed Time = 0.7058385999998791 seconds
Trial 87, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 87, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[LightGBM] [Info] Number of positive: 10177, number of negative: 10479
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.797795
[LightGBM] [Info] Total Bins 17917
[LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 260
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243
[LightGBM] [Info] Start training from score -0.029243
[LightGBM] [Debug] Re-bagging, using 14889 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 13
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 10
[LightGBM] [Debug] Re-bagging, using 15011 data to train
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 12
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 13
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 13
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 14
[LightGBM] [Debug] Re-bagging, using 15030 data to train
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 12
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 14
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 12
Trial 87, Fold 4: Log loss = 0.31242890490674685, Average precision = 0.9695993487992027, ROC-AUC = 0.9642784120307781, Elapsed Time = 0.6826785000002928 seconds
Trial 87, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 87, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[LightGBM] [Info] Number of positive: 10150, number of negative: 10500
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.798646
[LightGBM] [Info] Total Bins 17916
[LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 260
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902
[LightGBM] [Info] Start training from score -0.033902
[LightGBM] [Debug] Re-bagging, using 14884 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 8
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 13
[LightGBM] [Debug] Re-bagging, using 15009 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 12
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 10
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 13
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 13
[LightGBM] [Debug] Re-bagging, using 15028 data to train
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 13
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 13
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 12
Trial 87, Fold 5: Log loss = 0.3150488481613542, Average precision = 0.9685165788771627, ROC-AUC = 0.9634251528157108, Elapsed Time = 0.7011179999999513 seconds
Optimization Progress:  88%|########8 | 88/100 [20:43<02:57, 14.83s/it]
Trial 88, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 88, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[LightGBM] [Info] Number of positive: 10130, number of negative: 10533
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791640
[LightGBM] [Info] Total Bins 22663
[LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012
[LightGBM] [Info] Start training from score -0.039012
[LightGBM] [Debug] Re-bagging, using 6273 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8
[LightGBM] [Debug] Re-bagging, using 6246 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8
[LightGBM] [Debug] Re-bagging, using 6230 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8
[LightGBM] [Debug] Re-bagging, using 6158 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8
Trial 88, Fold 1: Log loss = 0.3061306220134525, Average precision = 0.9662695164729078, ROC-AUC = 0.9598435341716732, Elapsed Time = 0.7078392000003078 seconds
Trial 88, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 88, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[LightGBM] [Info] Number of positive: 10230, number of negative: 10471
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791600
[LightGBM] [Info] Total Bins 22674
[LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285
[LightGBM] [Info] Start training from score -0.023285
[LightGBM] [Debug] Re-bagging, using 6285 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8
[LightGBM] [Debug] Re-bagging, using 6262 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 7
[LightGBM] [Debug] Re-bagging, using 6232 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9
[LightGBM] [Debug] Re-bagging, using 6177 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8
Trial 88, Fold 2: Log loss = 0.3087993840397771, Average precision = 0.9633442503351907, ROC-AUC = 0.9590662624914983, Elapsed Time = 0.7561420999991242 seconds
Trial 88, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 88, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[LightGBM] [Info] Number of positive: 10165, number of negative: 10517
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791401
[LightGBM] [Info] Total Bins 22561
[LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043
[LightGBM] [Info] Start training from score -0.034043
[LightGBM] [Debug] Re-bagging, using 6278 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8
[LightGBM] [Debug] Re-bagging, using 6254 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8
[LightGBM] [Debug] Re-bagging, using 6230 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8
[LightGBM] [Debug] Re-bagging, using 6170 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8
Trial 88, Fold 3: Log loss = 0.30574315365828897, Average precision = 0.9666086984224752, ROC-AUC = 0.9610126672188462, Elapsed Time = 0.7820138999995834 seconds
Trial 88, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 88, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[LightGBM] [Info] Number of positive: 10177, number of negative: 10479
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.792454
[LightGBM] [Info] Total Bins 22657
[LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 256
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243
[LightGBM] [Info] Start training from score -0.029243
[LightGBM] [Debug] Re-bagging, using 6270 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Debug] Re-bagging, using 6243 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Debug] Re-bagging, using 6227 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8
[LightGBM] [Debug] Re-bagging, using 6160 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 7
Trial 88, Fold 4: Log loss = 0.30470793975541044, Average precision = 0.9663651109636795, ROC-AUC = 0.9594859719211906, Elapsed Time = 0.8285013000004255 seconds
Trial 88, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 88, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[LightGBM] [Info] Number of positive: 10150, number of negative: 10500
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.792012
[LightGBM] [Info] Total Bins 22613
[LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902
[LightGBM] [Info] Start training from score -0.033902
[LightGBM] [Debug] Re-bagging, using 6268 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9
[LightGBM] [Debug] Re-bagging, using 6243 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8
[LightGBM] [Debug] Re-bagging, using 6225 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 7
[LightGBM] [Debug] Re-bagging, using 6155 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8
Trial 88, Fold 5: Log loss = 0.3070984501419724, Average precision = 0.9657053793675368, ROC-AUC = 0.9597007970570203, Elapsed Time = 0.8024282999995194 seconds
Optimization Progress:  89%|########9 | 89/100 [20:55<02:32, 13.84s/it]
Trial 89, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 89, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[LightGBM] [Info] Number of positive: 10130, number of negative: 10533
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.796981
[LightGBM] [Info] Total Bins 9564
[LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 259
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012
[LightGBM] [Info] Start training from score -0.039012
[LightGBM] [Debug] Re-bagging, using 6184 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 11
[LightGBM] [Debug] Re-bagging, using 6149 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 11
[LightGBM] [Debug] Re-bagging, using 6141 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 11
[LightGBM] [Debug] Re-bagging, using 6075 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 12
[LightGBM] [Debug] Re-bagging, using 6239 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 14
[LightGBM] [Debug] Re-bagging, using 6209 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 12
Trial 89, Fold 1: Log loss = 0.33416366488173527, Average precision = 0.9717220616218375, ROC-AUC = 0.9662786536375065, Elapsed Time = 1.0397594999994908 seconds
Trial 89, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 89, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[LightGBM] [Info] Number of positive: 10230, number of negative: 10471
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.796949
[LightGBM] [Info] Total Bins 9556
[LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 259
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285
[LightGBM] [Info] Start training from score -0.023285
[LightGBM] [Debug] Re-bagging, using 6196 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 13
[LightGBM] [Debug] Re-bagging, using 6165 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 11
[LightGBM] [Debug] Re-bagging, using 6143 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 14
[LightGBM] [Debug] Re-bagging, using 6093 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 12
[LightGBM] [Debug] Re-bagging, using 6263 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 12
[LightGBM] [Debug] Re-bagging, using 6203 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 11
Trial 89, Fold 2: Log loss = 0.33738411543798374, Average precision = 0.9680395626741372, ROC-AUC = 0.9650552164666985, Elapsed Time = 1.1089407999988907 seconds
Trial 89, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 89, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[LightGBM] [Info] Number of positive: 10165, number of negative: 10517
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.796784
[LightGBM] [Info] Total Bins 9563
[LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 259
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043
[LightGBM] [Info] Start training from score -0.034043
[LightGBM] [Debug] Re-bagging, using 6189 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 12
[LightGBM] [Debug] Re-bagging, using 6157 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 10
[LightGBM] [Debug] Re-bagging, using 6141 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 13
[LightGBM] [Debug] Re-bagging, using 6086 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 10
[LightGBM] [Debug] Re-bagging, using 6254 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 11
[LightGBM] [Debug] Re-bagging, using 6202 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 12
Trial 89, Fold 3: Log loss = 0.332700635870908, Average precision = 0.9727254923682255, ROC-AUC = 0.9683099820862748, Elapsed Time = 1.2187215000012657 seconds
Trial 89, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 89, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[LightGBM] [Info] Number of positive: 10177, number of negative: 10479
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.796474
[LightGBM] [Info] Total Bins 9562
[LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 259
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243
[LightGBM] [Info] Start training from score -0.029243
[LightGBM] [Debug] Re-bagging, using 6181 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 13
[LightGBM] [Debug] Re-bagging, using 6146 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 10
[LightGBM] [Debug] Re-bagging, using 6138 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 10
[LightGBM] [Debug] Re-bagging, using 6077 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 11
[LightGBM] [Debug] Re-bagging, using 6235 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 13
[LightGBM] [Debug] Re-bagging, using 6206 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 10
Trial 89, Fold 4: Log loss = 0.3365947795505942, Average precision = 0.9718392168322076, ROC-AUC = 0.9664804144581072, Elapsed Time = 1.2304527000014787 seconds
Trial 89, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 89, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[LightGBM] [Info] Number of positive: 10150, number of negative: 10500
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.797340
[LightGBM] [Info] Total Bins 9544
[LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 259
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902
[LightGBM] [Info] Start training from score -0.033902
[LightGBM] [Debug] Re-bagging, using 6179 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 13
[LightGBM] [Debug] Re-bagging, using 6146 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 12
[LightGBM] [Debug] Re-bagging, using 6137 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 11
[LightGBM] [Debug] Re-bagging, using 6071 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 12
[LightGBM] [Debug] Re-bagging, using 6234 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 13
[LightGBM] [Debug] Re-bagging, using 6202 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 11
Trial 89, Fold 5: Log loss = 0.338789382130623, Average precision = 0.9691199604516071, ROC-AUC = 0.9640932686769597, Elapsed Time = 1.192119699999239 seconds
Optimization Progress:  90%|######### | 90/100 [21:08<02:17, 13.73s/it]
Trial 90, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 90, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[LightGBM] [Info] Number of positive: 10130, number of negative: 10533
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791640
[LightGBM] [Info] Total Bins 12968
[LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012
[LightGBM] [Info] Start training from score -0.039012
[LightGBM] [Debug] Re-bagging, using 4096 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 6
[LightGBM] [Debug] Re-bagging, using 4091 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Debug] Re-bagging, using 4061 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Debug] Re-bagging, using 3955 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6
[LightGBM] [Debug] Re-bagging, using 4115 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 5
[LightGBM] [Debug] Re-bagging, using 4078 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7
[LightGBM] [Debug] Re-bagging, using 4013 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6
[LightGBM] [Debug] Re-bagging, using 4160 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 6
[LightGBM] [Debug] Re-bagging, using 4058 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 6
[LightGBM] [Debug] Re-bagging, using 3982 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 6
[LightGBM] [Debug] Re-bagging, using 3916 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6
[LightGBM] [Debug] Re-bagging, using 3992 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 6
[LightGBM] [Debug] Re-bagging, using 4001 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7
[LightGBM] [Debug] Re-bagging, using 4059 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Debug] Re-bagging, using 4043 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Debug] Re-bagging, using 4028 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Debug] Re-bagging, using 4100 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 7
[LightGBM] [Debug] Re-bagging, using 3968 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 7
[LightGBM] [Debug] Re-bagging, using 4111 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 7
[LightGBM] [Debug] Re-bagging, using 4090 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 6
Trial 90, Fold 1: Log loss = 0.331844758191932, Average precision = 0.9606197678651246, ROC-AUC = 0.9531092422917175, Elapsed Time = 1.0667802999996638 seconds
Trial 90, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 90, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[LightGBM] [Info] Number of positive: 10230, number of negative: 10471
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791600
[LightGBM] [Info] Total Bins 12982
[LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285
[LightGBM] [Info] Start training from score -0.023285
[LightGBM] [Debug] Re-bagging, using 4104 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Debug] Re-bagging, using 4100 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 7
[LightGBM] [Debug] Re-bagging, using 4065 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Debug] Re-bagging, using 3970 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Debug] Re-bagging, using 4128 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Debug] Re-bagging, using 4067 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 5
[LightGBM] [Debug] Re-bagging, using 4020 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Debug] Re-bagging, using 4168 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Debug] Re-bagging, using 4078 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Debug] Re-bagging, using 4005 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Debug] Re-bagging, using 3914 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7
[LightGBM] [Debug] Re-bagging, using 3983 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 5
[LightGBM] [Debug] Re-bagging, using 4007 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 7
[LightGBM] [Debug] Re-bagging, using 4059 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 6
[LightGBM] [Debug] Re-bagging, using 4051 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7
[LightGBM] [Debug] Re-bagging, using 4042 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6
[LightGBM] [Debug] Re-bagging, using 4111 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6
[LightGBM] [Debug] Re-bagging, using 3982 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7
[LightGBM] [Debug] Re-bagging, using 4126 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Debug] Re-bagging, using 4093 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6
Trial 90, Fold 2: Log loss = 0.33514325857746285, Average precision = 0.9569969912288875, ROC-AUC = 0.9518625674936214, Elapsed Time = 1.074133999998594 seconds
Trial 90, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 90, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[LightGBM] [Info] Number of positive: 10165, number of negative: 10517
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791401
[LightGBM] [Info] Total Bins 12988
[LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043
[LightGBM] [Info] Start training from score -0.034043
[LightGBM] [Debug] Re-bagging, using 4099 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Debug] Re-bagging, using 4095 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 7
[LightGBM] [Debug] Re-bagging, using 4063 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 7
[LightGBM] [Debug] Re-bagging, using 3964 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 5
[LightGBM] [Debug] Re-bagging, using 4124 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7
[LightGBM] [Debug] Re-bagging, using 4070 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 7
[LightGBM] [Debug] Re-bagging, using 4021 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 6
[LightGBM] [Debug] Re-bagging, using 4156 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 7
[LightGBM] [Debug] Re-bagging, using 4068 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Debug] Re-bagging, using 3994 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Debug] Re-bagging, using 3927 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 7
[LightGBM] [Debug] Re-bagging, using 3978 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 6
[LightGBM] [Debug] Re-bagging, using 4003 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 6
[LightGBM] [Debug] Re-bagging, using 4063 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6
[LightGBM] [Debug] Re-bagging, using 4035 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 5
[LightGBM] [Debug] Re-bagging, using 4043 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 6
[LightGBM] [Debug] Re-bagging, using 4108 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7
[LightGBM] [Debug] Re-bagging, using 3968 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7
[LightGBM] [Debug] Re-bagging, using 4117 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7
[LightGBM] [Debug] Re-bagging, using 4104 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6
Trial 90, Fold 3: Log loss = 0.32265590326638777, Average precision = 0.9635499999456428, ROC-AUC = 0.9568613901435987, Elapsed Time = 1.1633805000001303 seconds
Trial 90, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 90, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[LightGBM] [Info] Number of positive: 10177, number of negative: 10479
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791090
[LightGBM] [Info] Total Bins 12968
[LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243
[LightGBM] [Info] Start training from score -0.029243
[LightGBM] [Debug] Re-bagging, using 4095 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Debug] Re-bagging, using 4089 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 6
[LightGBM] [Debug] Re-bagging, using 4057 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Debug] Re-bagging, using 3958 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Debug] Re-bagging, using 4111 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 6
[LightGBM] [Debug] Re-bagging, using 4078 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 7
[LightGBM] [Debug] Re-bagging, using 4014 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Debug] Re-bagging, using 4154 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7
[LightGBM] [Debug] Re-bagging, using 4055 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Debug] Re-bagging, using 3985 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 7
[LightGBM] [Debug] Re-bagging, using 3910 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Debug] Re-bagging, using 3991 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 6
[LightGBM] [Debug] Re-bagging, using 4000 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 7
[LightGBM] [Debug] Re-bagging, using 4059 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 7
[LightGBM] [Debug] Re-bagging, using 4040 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 6
[LightGBM] [Debug] Re-bagging, using 4033 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6
[LightGBM] [Debug] Re-bagging, using 4095 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 6
[LightGBM] [Debug] Re-bagging, using 3970 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 6
[LightGBM] [Debug] Re-bagging, using 4107 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Debug] Re-bagging, using 4091 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 15 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 6
Trial 90, Fold 4: Log loss = 0.3344532381528901, Average precision = 0.9603714714322781, ROC-AUC = 0.9529044793522036, Elapsed Time = 1.1753873999987263 seconds
Trial 90, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 90, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[LightGBM] [Info] Number of positive: 10150, number of negative: 10500
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.792012
[LightGBM] [Info] Total Bins 12970
[LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902
[LightGBM] [Info] Start training from score -0.033902
[LightGBM] [Debug] Re-bagging, using 4093 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 22 and depth = 7
[LightGBM] [Debug] Re-bagging, using 4089 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 6
[LightGBM] [Debug] Re-bagging, using 4057 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 7
[LightGBM] [Debug] Re-bagging, using 3956 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Debug] Re-bagging, using 4107 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 7
[LightGBM] [Debug] Re-bagging, using 4075 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 7
[LightGBM] [Debug] Re-bagging, using 4019 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 6
[LightGBM] [Debug] Re-bagging, using 4148 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7
[LightGBM] [Debug] Re-bagging, using 4057 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 7
[LightGBM] [Debug] Re-bagging, using 3982 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7
[LightGBM] [Debug] Re-bagging, using 3908 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7
[LightGBM] [Debug] Re-bagging, using 3977 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6
[LightGBM] [Debug] Re-bagging, using 4014 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 8
[LightGBM] [Debug] Re-bagging, using 4062 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Debug] Re-bagging, using 4036 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 5
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 16 and depth = 5
[LightGBM] [Debug] Re-bagging, using 4036 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6
[LightGBM] [Debug] Re-bagging, using 4099 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Debug] Re-bagging, using 3955 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 23 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 6
[LightGBM] [Debug] Re-bagging, using 4109 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 17 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 19 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 18 and depth = 7
[LightGBM] [Debug] Re-bagging, using 4087 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 20 and depth = 6
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 21 and depth = 7
Trial 90, Fold 5: Log loss = 0.3372951464810977, Average precision = 0.9593237982203765, ROC-AUC = 0.9517935455102837, Elapsed Time = 1.1603880000002391 seconds
Optimization Progress:  91%|#########1| 91/100 [21:21<02:02, 13.58s/it]
Trial 91, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 91, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[LightGBM] [Info] Number of positive: 10130, number of negative: 10533
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791640
[LightGBM] [Info] Total Bins 23584
[LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012
[LightGBM] [Info] Start training from score -0.039012
[LightGBM] [Debug] Re-bagging, using 10496 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 12
[LightGBM] [Debug] Re-bagging, using 10463 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 13
[LightGBM] [Debug] Re-bagging, using 10459 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 15
[LightGBM] [Debug] Re-bagging, using 10304 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 16
[LightGBM] [Debug] Re-bagging, using 10526 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 17
[LightGBM] [Debug] Re-bagging, using 10349 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 19
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 17
[LightGBM] [Debug] Re-bagging, using 10424 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 16
[LightGBM] [Debug] Re-bagging, using 10397 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 16
[LightGBM] [Debug] Re-bagging, using 10455 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 20
[LightGBM] [Debug] Re-bagging, using 10215 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 14
[LightGBM] [Debug] Re-bagging, using 10299 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 18
[LightGBM] [Debug] Re-bagging, using 10288 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 19
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 16
[LightGBM] [Debug] Re-bagging, using 10280 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 17
[LightGBM] [Debug] Re-bagging, using 10332 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 18
[LightGBM] [Debug] Re-bagging, using 10471 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 19
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 17
[LightGBM] [Debug] Re-bagging, using 10339 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 18
[LightGBM] [Debug] Re-bagging, using 10405 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 15
[LightGBM] [Debug] Re-bagging, using 10406 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 14
[LightGBM] [Debug] Re-bagging, using 10406 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 18
Trial 91, Fold 1: Log loss = 0.3008881871994671, Average precision = 0.9718838331284857, ROC-AUC = 0.9666126274357981, Elapsed Time = 0.9970556000007491 seconds
Trial 91, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 91, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[LightGBM] [Info] Number of positive: 10230, number of negative: 10471
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.792956
[LightGBM] [Info] Total Bins 23515
[LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 256
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285
[LightGBM] [Info] Start training from score -0.023285
[LightGBM] [Debug] Re-bagging, using 10518 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10481 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 12
[LightGBM] [Debug] Re-bagging, using 10475 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 14
[LightGBM] [Debug] Re-bagging, using 10330 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 14
[LightGBM] [Debug] Re-bagging, using 10555 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 17
[LightGBM] [Debug] Re-bagging, using 10346 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 15
[LightGBM] [Debug] Re-bagging, using 10429 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 18
[LightGBM] [Debug] Re-bagging, using 10416 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 16
[LightGBM] [Debug] Re-bagging, using 10483 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 16
[LightGBM] [Debug] Re-bagging, using 10237 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 14
[LightGBM] [Debug] Re-bagging, using 10313 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 18
[LightGBM] [Debug] Re-bagging, using 10299 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 19
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 17
[LightGBM] [Debug] Re-bagging, using 10290 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 17
[LightGBM] [Debug] Re-bagging, using 10360 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 16
[LightGBM] [Debug] Re-bagging, using 10507 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 18
[LightGBM] [Debug] Re-bagging, using 10381 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 19
[LightGBM] [Debug] Re-bagging, using 10420 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 18
[LightGBM] [Debug] Re-bagging, using 10426 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 22
[LightGBM] [Debug] Re-bagging, using 10420 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 16
Trial 91, Fold 2: Log loss = 0.2991729032591439, Average precision = 0.9707289483964904, ROC-AUC = 0.9667573713763008, Elapsed Time = 1.1395652999999584 seconds
Trial 91, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 91, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[LightGBM] [Warning] Met negative value in categorical features, will convert it to NaN
[LightGBM] [Warning] Met negative value in categorical features, will convert it to NaN
[LightGBM] [Warning] Met negative value in categorical features, will convert it to NaN
[LightGBM] [Warning] Met negative value in categorical features, will convert it to NaN
[LightGBM] [Warning] Met negative value in categorical features, will convert it to NaN
[LightGBM] [Warning] Met negative value in categorical features, will convert it to NaN
[LightGBM] [Warning] Met negative value in categorical features, will convert it to NaN
[LightGBM] [Warning] Met negative value in categorical features, will convert it to NaN
[LightGBM] [Warning] Met negative value in categorical features, will convert it to NaN
[LightGBM] [Warning] Met negative value in categorical features, will convert it to NaN
[LightGBM] [Warning] Met negative value in categorical features, will convert it to NaN
[LightGBM] [Info] Number of positive: 10165, number of negative: 10517
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791401
[LightGBM] [Info] Total Bins 23508
[LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043
[LightGBM] [Info] Start training from score -0.034043
[LightGBM] [Debug] Re-bagging, using 10506 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10470 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 13
[LightGBM] [Debug] Re-bagging, using 10465 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 13
[LightGBM] [Debug] Re-bagging, using 10325 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 15
[LightGBM] [Debug] Re-bagging, using 10537 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 14
[LightGBM] [Debug] Re-bagging, using 10347 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 15
[LightGBM] [Debug] Re-bagging, using 10429 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 18
[LightGBM] [Debug] Re-bagging, using 10403 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 15
[LightGBM] [Debug] Re-bagging, using 10463 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 17
[LightGBM] [Debug] Re-bagging, using 10236 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 19
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 17
[LightGBM] [Debug] Re-bagging, using 10305 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 18
[LightGBM] [Debug] Re-bagging, using 10295 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 21
[LightGBM] [Debug] Re-bagging, using 10280 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 15
[LightGBM] [Debug] Re-bagging, using 10340 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 15
[LightGBM] [Debug] Re-bagging, using 10491 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 18
[LightGBM] [Debug] Re-bagging, using 10356 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 15
[LightGBM] [Debug] Re-bagging, using 10422 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 16
[LightGBM] [Debug] Re-bagging, using 10424 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 16
[LightGBM] [Debug] Re-bagging, using 10400 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 14
Trial 91, Fold 3: Log loss = 0.2955545562487179, Average precision = 0.9719937847754485, ROC-AUC = 0.9677634960897998, Elapsed Time = 1.16592270000001 seconds
Trial 91, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 91, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[LightGBM] [Info] Number of positive: 10177, number of negative: 10479
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.792454
[LightGBM] [Info] Total Bins 23522
[LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 256
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243
[LightGBM] [Info] Start training from score -0.029243
[LightGBM] [Debug] Re-bagging, using 10492 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 10
[LightGBM] [Debug] Re-bagging, using 10459 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 14
[LightGBM] [Debug] Re-bagging, using 10454 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 15
[LightGBM] [Debug] Re-bagging, using 10303 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 17
[LightGBM] [Debug] Re-bagging, using 10520 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 16
[LightGBM] [Debug] Re-bagging, using 10345 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 17
[LightGBM] [Debug] Re-bagging, using 10424 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 19
[LightGBM] [Debug] Re-bagging, using 10396 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 17
[LightGBM] [Debug] Re-bagging, using 10451 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 19
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 18
[LightGBM] [Debug] Re-bagging, using 10214 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 15
[LightGBM] [Debug] Re-bagging, using 10295 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 15
[LightGBM] [Debug] Re-bagging, using 10283 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 15
[LightGBM] [Debug] Re-bagging, using 10271 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 17
[LightGBM] [Debug] Re-bagging, using 10332 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 15
[LightGBM] [Debug] Re-bagging, using 10472 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 13
[LightGBM] [Debug] Re-bagging, using 10328 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 15
[LightGBM] [Debug] Re-bagging, using 10402 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 16
[LightGBM] [Debug] Re-bagging, using 10406 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 16
[LightGBM] [Debug] Re-bagging, using 10386 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 20
Trial 91, Fold 4: Log loss = 0.3017551683302678, Average precision = 0.9715725587706239, ROC-AUC = 0.9661564327799117, Elapsed Time = 1.1777863999996043 seconds
Trial 91, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 91, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[LightGBM] [Info] Number of positive: 10150, number of negative: 10500
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.793364
[LightGBM] [Info] Total Bins 23533
[LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 256
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902
[LightGBM] [Info] Start training from score -0.033902
[LightGBM] [Debug] Re-bagging, using 10489 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 12
[LightGBM] [Debug] Re-bagging, using 10456 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 14
[LightGBM] [Debug] Re-bagging, using 10455 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 14
[LightGBM] [Debug] Re-bagging, using 10296 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 16
[LightGBM] [Debug] Re-bagging, using 10516 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 17
[LightGBM] [Debug] Re-bagging, using 10338 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 14
[LightGBM] [Debug] Re-bagging, using 10430 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 19
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 18
[LightGBM] [Debug] Re-bagging, using 10391 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 18
[LightGBM] [Debug] Re-bagging, using 10448 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 17
[LightGBM] [Debug] Re-bagging, using 10216 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 15
[LightGBM] [Debug] Re-bagging, using 10285 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 18
[LightGBM] [Debug] Re-bagging, using 10277 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 18
[LightGBM] [Debug] Re-bagging, using 10271 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 18
[LightGBM] [Debug] Re-bagging, using 10341 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 77 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 20
[LightGBM] [Debug] Re-bagging, using 10466 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 15
[LightGBM] [Debug] Re-bagging, using 10325 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 14
[LightGBM] [Debug] Re-bagging, using 10393 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 75 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 14
[LightGBM] [Debug] Re-bagging, using 10391 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 19
[LightGBM] [Debug] Re-bagging, using 10391 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 16
Trial 91, Fold 5: Log loss = 0.3005683826155532, Average precision = 0.9699361551117898, ROC-AUC = 0.9658108986864351, Elapsed Time = 1.171874800000296 seconds
Optimization Progress:  92%|#########2| 92/100 [21:34<01:47, 13.41s/it]
Trial 92, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 92, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[LightGBM] [Info] Number of positive: 10130, number of negative: 10533
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795662
[LightGBM] [Info] Total Bins 24804
[LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 258
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012
[LightGBM] [Info] Start training from score -0.039012
[LightGBM] [Debug] Re-bagging, using 5347 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5294 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5327 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5160 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5355 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5330 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5234 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 12
[LightGBM] [Debug] Re-bagging, using 5370 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5261 data to train
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5126 data to train
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11
[LightGBM] [Debug] Re-bagging, using 5160 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5207 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5205 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9
Trial 92, Fold 1: Log loss = 0.24369748691301674, Average precision = 0.9719584055289463, ROC-AUC = 0.9668471260329887, Elapsed Time = 1.0470143999991706 seconds
Trial 92, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 92, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[LightGBM] [Info] Number of positive: 10230, number of negative: 10471
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.794298
[LightGBM] [Info] Total Bins 24808
[LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 257
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285
[LightGBM] [Info] Start training from score -0.023285
[LightGBM] [Debug] Re-bagging, using 5357 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5305 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5333 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 8
[LightGBM] [Debug] Re-bagging, using 5174 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5376 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5324 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5242 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5379 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5277 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5149 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5160 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5203 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5215 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10
Trial 92, Fold 2: Log loss = 0.24594562479299417, Average precision = 0.9695862442338552, ROC-AUC = 0.9657001445549405, Elapsed Time = 1.1108922000003076 seconds
Trial 92, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 92, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[LightGBM] [Info] Number of positive: 10165, number of negative: 10517
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795455
[LightGBM] [Info] Total Bins 24804
[LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 258
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043
[LightGBM] [Info] Start training from score -0.034043
[LightGBM] [Debug] Re-bagging, using 5352 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5297 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5330 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 8
[LightGBM] [Debug] Re-bagging, using 5170 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5366 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5325 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5243 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5369 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5265 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 12
[LightGBM] [Debug] Re-bagging, using 5142 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5168 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 8
[LightGBM] [Debug] Re-bagging, using 5195 data to train
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11
[LightGBM] [Debug] Re-bagging, using 5211 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 12
Trial 92, Fold 3: Log loss = 0.24196498044713488, Average precision = 0.9718823432725099, ROC-AUC = 0.9677592072695054, Elapsed Time = 1.1935706000003847 seconds
Trial 92, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 92, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[LightGBM] [Info] Number of positive: 10177, number of negative: 10479
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795144
[LightGBM] [Info] Total Bins 24794
[LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 258
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243
[LightGBM] [Info] Start training from score -0.029243
[LightGBM] [Debug] Re-bagging, using 5344 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5292 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 12
[LightGBM] [Debug] Re-bagging, using 5324 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11
[LightGBM] [Debug] Re-bagging, using 5162 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 12
[LightGBM] [Debug] Re-bagging, using 5351 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11
[LightGBM] [Debug] Re-bagging, using 5328 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 11
[LightGBM] [Debug] Re-bagging, using 5235 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5366 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5255 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 11
[LightGBM] [Debug] Re-bagging, using 5130 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5153 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 12
[LightGBM] [Debug] Re-bagging, using 5206 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5206 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11
Trial 92, Fold 4: Log loss = 0.24702565761415668, Average precision = 0.9708847749496918, ROC-AUC = 0.9653432745337883, Elapsed Time = 1.2001282000001083 seconds
Trial 92, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 92, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[LightGBM] [Info] Number of positive: 10150, number of negative: 10500
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.796026
[LightGBM] [Info] Total Bins 24798
[LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 258
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902
[LightGBM] [Info] Start training from score -0.033902
[LightGBM] [Debug] Re-bagging, using 5342 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 8
[LightGBM] [Debug] Re-bagging, using 5292 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5324 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5157 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5349 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 39 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5324 data to train
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5240 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5361 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5255 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Debug] Re-bagging, using 5130 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 38 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5148 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 9
[LightGBM] [Debug] Re-bagging, using 5195 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11
[LightGBM] [Debug] Re-bagging, using 5214 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11
Trial 92, Fold 5: Log loss = 0.24949129955282773, Average precision = 0.9692402742286004, ROC-AUC = 0.9643114653586757, Elapsed Time = 1.1896702000012738 seconds
Optimization Progress:  93%|#########3| 93/100 [21:48<01:33, 13.35s/it]
Trial 93, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 93, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[LightGBM] [Info] Number of positive: 10130, number of negative: 10533
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795662
[LightGBM] [Info] Total Bins 15120
[LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 258
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012
[LightGBM] [Info] Start training from score -0.039012
[LightGBM] [Debug] Re-bagging, using 14380 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 14
[LightGBM] [Debug] Re-bagging, using 14499 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 15
[LightGBM] [Debug] Re-bagging, using 14519 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 12
[LightGBM] [Debug] Re-bagging, using 14345 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12
[LightGBM] [Debug] Re-bagging, using 14432 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 41 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 11
[LightGBM] [Debug] Re-bagging, using 14391 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 14
[LightGBM] [Debug] Re-bagging, using 14436 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11
[LightGBM] [Debug] Re-bagging, using 14426 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 12
[LightGBM] [Debug] Re-bagging, using 14510 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 12
[LightGBM] [Debug] Re-bagging, using 14362 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 12
[LightGBM] [Debug] Re-bagging, using 14427 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 13
[LightGBM] [Debug] Re-bagging, using 14283 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 13
[LightGBM] [Debug] Re-bagging, using 14392 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10
[LightGBM] [Debug] Re-bagging, using 14301 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 13
[LightGBM] [Debug] Re-bagging, using 14539 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 14
[LightGBM] [Debug] Re-bagging, using 14436 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 13
[LightGBM] [Debug] Re-bagging, using 14439 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 14
[LightGBM] [Debug] Re-bagging, using 14454 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 14
Trial 93, Fold 1: Log loss = 0.28890787041309307, Average precision = 0.9721419694650522, ROC-AUC = 0.9666721482580787, Elapsed Time = 1.2925502000016422 seconds
Trial 93, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 93, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[LightGBM] [Info] Number of positive: 10230, number of negative: 10471
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.794298
[LightGBM] [Info] Total Bins 15133
[LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 257
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285
[LightGBM] [Info] Start training from score -0.023285
[LightGBM] [Debug] Re-bagging, using 14407 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 12
[LightGBM] [Debug] Re-bagging, using 14523 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 12
[LightGBM] [Debug] Re-bagging, using 14550 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 11
[LightGBM] [Debug] Re-bagging, using 14375 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 13
[LightGBM] [Debug] Re-bagging, using 14458 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 12
[LightGBM] [Debug] Re-bagging, using 14405 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 12
[LightGBM] [Debug] Re-bagging, using 14455 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 12
[LightGBM] [Debug] Re-bagging, using 14463 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 14
[LightGBM] [Debug] Re-bagging, using 14542 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 12
[LightGBM] [Debug] Re-bagging, using 14392 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10
[LightGBM] [Debug] Re-bagging, using 14441 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 11
[LightGBM] [Debug] Re-bagging, using 14305 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 13
[LightGBM] [Debug] Re-bagging, using 14416 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 12
[LightGBM] [Debug] Re-bagging, using 14319 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 12
[LightGBM] [Debug] Re-bagging, using 14567 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 14
[LightGBM] [Debug] Re-bagging, using 14483 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 10
[LightGBM] [Debug] Re-bagging, using 14475 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 11
[LightGBM] [Debug] Re-bagging, using 14496 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 12
Trial 93, Fold 2: Log loss = 0.2883054234552478, Average precision = 0.9700646790336668, ROC-AUC = 0.9665828614230118, Elapsed Time = 1.5125194000011106 seconds
Trial 93, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 93, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[LightGBM] [Info] Number of positive: 10165, number of negative: 10517
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795455
[LightGBM] [Info] Total Bins 15138
[LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 258
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043
[LightGBM] [Info] Start training from score -0.034043
[LightGBM] [Debug] Re-bagging, using 14391 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 14
[LightGBM] [Debug] Re-bagging, using 14510 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 12
[LightGBM] [Debug] Re-bagging, using 14537 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 13
[LightGBM] [Debug] Re-bagging, using 14366 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 15
[LightGBM] [Debug] Re-bagging, using 14440 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 14
[LightGBM] [Debug] Re-bagging, using 14395 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 12
[LightGBM] [Debug] Re-bagging, using 14448 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 15
[LightGBM] [Debug] Re-bagging, using 14447 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 12
[LightGBM] [Debug] Re-bagging, using 14515 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 12
[LightGBM] [Debug] Re-bagging, using 14391 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 14
[LightGBM] [Debug] Re-bagging, using 14431 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 12
[LightGBM] [Debug] Re-bagging, using 14294 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 13
[LightGBM] [Debug] Re-bagging, using 14406 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 11
[LightGBM] [Debug] Re-bagging, using 14302 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 14
[LightGBM] [Debug] Re-bagging, using 14557 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 13
[LightGBM] [Debug] Re-bagging, using 14450 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11
[LightGBM] [Debug] Re-bagging, using 14457 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 13
[LightGBM] [Debug] Re-bagging, using 14482 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 14
Trial 93, Fold 3: Log loss = 0.2847499990984428, Average precision = 0.9724436861937583, ROC-AUC = 0.9676233193843842, Elapsed Time = 1.528636799999731 seconds
Trial 93, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 93, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[LightGBM] [Info] Number of positive: 10177, number of negative: 10479
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.795144
[LightGBM] [Info] Total Bins 15120
[LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 258
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243
[LightGBM] [Info] Start training from score -0.029243
[LightGBM] [Debug] Re-bagging, using 14375 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 11
[LightGBM] [Debug] Re-bagging, using 14493 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 12
[LightGBM] [Debug] Re-bagging, using 14514 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 14
[LightGBM] [Debug] Re-bagging, using 14343 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 14
[LightGBM] [Debug] Re-bagging, using 14425 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 11
[LightGBM] [Debug] Re-bagging, using 14387 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 13
[LightGBM] [Debug] Re-bagging, using 14430 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 10
[LightGBM] [Debug] Re-bagging, using 14429 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 13
[LightGBM] [Debug] Re-bagging, using 14498 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 12
[LightGBM] [Debug] Re-bagging, using 14360 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 12
[LightGBM] [Debug] Re-bagging, using 14420 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 13
[LightGBM] [Debug] Re-bagging, using 14281 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 11
[LightGBM] [Debug] Re-bagging, using 14390 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 12
[LightGBM] [Debug] Re-bagging, using 14286 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11
[LightGBM] [Debug] Re-bagging, using 14542 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 12
[LightGBM] [Debug] Re-bagging, using 14431 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 12
[LightGBM] [Debug] Re-bagging, using 14433 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 14
[LightGBM] [Debug] Re-bagging, using 14451 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 14
Trial 93, Fold 4: Log loss = 0.28714861168790945, Average precision = 0.9713751042348534, ROC-AUC = 0.965691323848151, Elapsed Time = 1.502879200001189 seconds
Trial 93, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 93, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[LightGBM] [Info] Number of positive: 10150, number of negative: 10500
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.796026
[LightGBM] [Info] Total Bins 15119
[LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 258
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902
[LightGBM] [Info] Start training from score -0.033902
[LightGBM] [Debug] Re-bagging, using 14371 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 15
[LightGBM] [Debug] Re-bagging, using 14490 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 13
[LightGBM] [Debug] Re-bagging, using 14512 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 12
[LightGBM] [Debug] Re-bagging, using 14337 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 13
[LightGBM] [Debug] Re-bagging, using 14420 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 13
[LightGBM] [Debug] Re-bagging, using 14380 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 10
[LightGBM] [Debug] Re-bagging, using 14433 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 13
[LightGBM] [Debug] Re-bagging, using 14420 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 13
[LightGBM] [Debug] Re-bagging, using 14493 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 13
[LightGBM] [Debug] Re-bagging, using 14359 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 13
[LightGBM] [Debug] Re-bagging, using 14413 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 13
[LightGBM] [Debug] Re-bagging, using 14274 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 45 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 10
[LightGBM] [Debug] Re-bagging, using 14386 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 12
[LightGBM] [Debug] Re-bagging, using 14289 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11
[LightGBM] [Debug] Re-bagging, using 14540 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 14
[LightGBM] [Debug] Re-bagging, using 14425 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 11
[LightGBM] [Debug] Re-bagging, using 14429 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 10
[LightGBM] [Debug] Re-bagging, using 14443 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 14
Trial 93, Fold 5: Log loss = 0.2910606291415502, Average precision = 0.969880620876491, ROC-AUC = 0.9651118666740985, Elapsed Time = 1.4681914000011602 seconds
Optimization Progress:  94%|#########3| 94/100 [22:02<01:22, 13.75s/it]
Trial 94, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 94, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[LightGBM] [Info] Number of positive: 10130, number of negative: 10533
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791640
[LightGBM] [Info] Total Bins 19014
[LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012
[LightGBM] [Info] Start training from score -0.039012
[LightGBM] [Debug] Re-bagging, using 16585 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 103 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 90 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 88 and depth = 14
[LightGBM] [Debug] Re-bagging, using 16662 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 87 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 106 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 86 and depth = 14
[LightGBM] [Debug] Re-bagging, using 16603 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 111 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 101 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 102 and depth = 19
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 107 and depth = 15
[LightGBM] [Debug] Re-bagging, using 16552 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 100 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 95 and depth = 19
[LightGBM] [Debug] Re-bagging, using 16610 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 94 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 91 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 111 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 99 and depth = 18
[LightGBM] [Debug] Re-bagging, using 16601 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 113 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 98 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 91 and depth = 17
[LightGBM] [Debug] Re-bagging, using 16528 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 97 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 103 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 16
[LightGBM] [Debug] Re-bagging, using 16562 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 106 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 93 and depth = 19
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 95 and depth = 14
[LightGBM] [Debug] Re-bagging, using 16634 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 97 and depth = 19
[LightGBM] [Debug] Re-bagging, using 16508 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 100 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 91 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 86 and depth = 18
[LightGBM] [Debug] Re-bagging, using 16530 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 97 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 93 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 93 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 17
[LightGBM] [Debug] Re-bagging, using 16556 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 100 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 90 and depth = 16
[LightGBM] [Debug] Re-bagging, using 16561 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 87 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 90 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 90 and depth = 16
[LightGBM] [Debug] Re-bagging, using 16519 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 87 and depth = 21
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 93 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 98 and depth = 16
[LightGBM] [Debug] Re-bagging, using 16625 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 88 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 87 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 99 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 94 and depth = 15
[LightGBM] [Debug] Re-bagging, using 16564 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 95 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 108 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 86 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 109 and depth = 14
[LightGBM] [Debug] Re-bagging, using 16613 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 113 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 90 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 15
[LightGBM] [Debug] Re-bagging, using 16517 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 98 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 19
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 94 and depth = 14
[LightGBM] [Debug] Re-bagging, using 16631 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 87 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 98 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 87 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 102 and depth = 14
[LightGBM] [Debug] Re-bagging, using 16709 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 102 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 108 and depth = 13
[LightGBM] [Debug] Re-bagging, using 16487 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 110 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 93 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 95 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 110 and depth = 13
[LightGBM] [Debug] Re-bagging, using 16525 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 94 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 114 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 109 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 16
[LightGBM] [Debug] Re-bagging, using 16573 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 90 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 101 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 105 and depth = 15
[LightGBM] [Debug] Re-bagging, using 16625 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 21
Trial 94, Fold 1: Log loss = 0.24722053119581827, Average precision = 0.9700217835372049, ROC-AUC = 0.964115889505456, Elapsed Time = 1.3311601000004885 seconds
Trial 94, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 94, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[LightGBM] [Info] Number of positive: 10230, number of negative: 10471
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791600
[LightGBM] [Info] Total Bins 19026
[LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285
[LightGBM] [Info] Start training from score -0.023285
[LightGBM] [Debug] Re-bagging, using 16618 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 87 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 90 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 18
[LightGBM] [Debug] Re-bagging, using 16693 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 98 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 94 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 11
[LightGBM] [Debug] Re-bagging, using 16630 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 103 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 105 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 91 and depth = 20
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 90 and depth = 14
[LightGBM] [Debug] Re-bagging, using 16589 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 109 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 101 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 91 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 87 and depth = 18
[LightGBM] [Debug] Re-bagging, using 16637 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 109 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 100 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 14
[LightGBM] [Debug] Re-bagging, using 16621 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 88 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 97 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 106 and depth = 12
[LightGBM] [Debug] Re-bagging, using 16553 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 87 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 109 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 100 and depth = 19
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 93 and depth = 14
[LightGBM] [Debug] Re-bagging, using 16610 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 109 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 94 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 105 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 112 and depth = 16
[LightGBM] [Debug] Re-bagging, using 16663 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 88 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 99 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 105 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 93 and depth = 17
[LightGBM] [Debug] Re-bagging, using 16541 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 99 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 99 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 17
[LightGBM] [Debug] Re-bagging, using 16557 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 100 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 91 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 100 and depth = 18
[LightGBM] [Debug] Re-bagging, using 16577 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 88 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 14
[LightGBM] [Debug] Re-bagging, using 16588 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 114 and depth = 19
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 108 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 101 and depth = 19
[LightGBM] [Debug] Re-bagging, using 16540 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 94 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 95 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 91 and depth = 15
[LightGBM] [Debug] Re-bagging, using 16657 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 95 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 110 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 95 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 108 and depth = 16
[LightGBM] [Debug] Re-bagging, using 16606 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 115 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 93 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 90 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 94 and depth = 15
[LightGBM] [Debug] Re-bagging, using 16654 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 93 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 87 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 97 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 103 and depth = 17
[LightGBM] [Debug] Re-bagging, using 16548 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 19
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 90 and depth = 20
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 101 and depth = 20
[LightGBM] [Debug] Re-bagging, using 16665 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 100 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 21
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 101 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15
[LightGBM] [Debug] Re-bagging, using 16718 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 103 and depth = 19
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 99 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 86 and depth = 14
[LightGBM] [Debug] Re-bagging, using 16524 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 102 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 98 and depth = 19
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 87 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 95 and depth = 13
[LightGBM] [Debug] Re-bagging, using 16549 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 111 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 20
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 108 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 107 and depth = 14
[LightGBM] [Debug] Re-bagging, using 16598 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 87 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 88 and depth = 20
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 98 and depth = 14
[LightGBM] [Debug] Re-bagging, using 16674 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 97 and depth = 20
Trial 94, Fold 2: Log loss = 0.2518173165478632, Average precision = 0.9688081030783827, ROC-AUC = 0.9652927017509569, Elapsed Time = 1.426636199999848 seconds
Trial 94, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 94, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[LightGBM] [Info] Number of positive: 10165, number of negative: 10517
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791401
[LightGBM] [Info] Total Bins 19029
[LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043
[LightGBM] [Info] Start training from score -0.034043
[LightGBM] [Debug] Re-bagging, using 16601 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 94 and depth = 16
[LightGBM] [Debug] Re-bagging, using 16677 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 91 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 90 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 108 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 107 and depth = 17
[LightGBM] [Debug] Re-bagging, using 16617 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 103 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 101 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 91 and depth = 11
[LightGBM] [Debug] Re-bagging, using 16574 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 95 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 112 and depth = 23
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15
[LightGBM] [Debug] Re-bagging, using 16618 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 107 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 111 and depth = 14
[LightGBM] [Debug] Re-bagging, using 16613 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 103 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 97 and depth = 19
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 91 and depth = 13
[LightGBM] [Debug] Re-bagging, using 16538 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 107 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 118 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 94 and depth = 21
[LightGBM] [Debug] Re-bagging, using 16588 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 105 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 108 and depth = 14
[LightGBM] [Debug] Re-bagging, using 16646 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 109 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 102 and depth = 17
[LightGBM] [Debug] Re-bagging, using 16529 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 100 and depth = 16
[LightGBM] [Debug] Re-bagging, using 16544 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 98 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 112 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 14
[LightGBM] [Debug] Re-bagging, using 16570 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 97 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 93 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 106 and depth = 13
[LightGBM] [Debug] Re-bagging, using 16570 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 103 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 109 and depth = 18
[LightGBM] [Debug] Re-bagging, using 16531 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 103 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 93 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 93 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 108 and depth = 14
[LightGBM] [Debug] Re-bagging, using 16638 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 13
[LightGBM] [Debug] Re-bagging, using 16579 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 108 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 93 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 105 and depth = 17
[LightGBM] [Debug] Re-bagging, using 16638 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 112 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 94 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 91 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 99 and depth = 15
[LightGBM] [Debug] Re-bagging, using 16536 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 86 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 114 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 113 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 93 and depth = 16
[LightGBM] [Debug] Re-bagging, using 16646 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 116 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 101 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 86 and depth = 14
[LightGBM] [Debug] Re-bagging, using 16725 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 111 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 12
[LightGBM] [Debug] Re-bagging, using 16503 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 107 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 101 and depth = 14
[LightGBM] [Debug] Re-bagging, using 16520 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 90 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 90 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 101 and depth = 20
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 17
[LightGBM] [Debug] Re-bagging, using 16592 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 93 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 99 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 109 and depth = 14
[LightGBM] [Debug] Re-bagging, using 16648 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 100 and depth = 13
Trial 94, Fold 3: Log loss = 0.2580977406334601, Average precision = 0.9691910105800448, ROC-AUC = 0.964746649603683, Elapsed Time = 1.6245612000002438 seconds
Trial 94, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 94, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[LightGBM] [Info] Number of positive: 10177, number of negative: 10479
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.792454
[LightGBM] [Info] Total Bins 19013
[LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 256
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243
[LightGBM] [Info] Start training from score -0.029243
[LightGBM] [Debug] Re-bagging, using 16579 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 87 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 101 and depth = 15
[LightGBM] [Debug] Re-bagging, using 16655 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 111 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 82 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 93 and depth = 15
[LightGBM] [Debug] Re-bagging, using 16599 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 91 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 91 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 100 and depth = 14
[LightGBM] [Debug] Re-bagging, using 16549 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 108 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 105 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 91 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 91 and depth = 16
[LightGBM] [Debug] Re-bagging, using 16604 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 106 and depth = 19
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 99 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 90 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 13
[LightGBM] [Debug] Re-bagging, using 16593 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 88 and depth = 19
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 100 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 93 and depth = 13
[LightGBM] [Debug] Re-bagging, using 16523 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 91 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 113 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 12
[LightGBM] [Debug] Re-bagging, using 16561 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 113 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 76 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 16
[LightGBM] [Debug] Re-bagging, using 16623 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 100 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 107 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 16
[LightGBM] [Debug] Re-bagging, using 16505 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 98 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 90 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 88 and depth = 18
[LightGBM] [Debug] Re-bagging, using 16522 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 93 and depth = 19
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 80 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 105 and depth = 13
[LightGBM] [Debug] Re-bagging, using 16547 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 95 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 103 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 98 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 15
[LightGBM] [Debug] Re-bagging, using 16563 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 79 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 17
[LightGBM] [Debug] Re-bagging, using 16511 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 105 and depth = 19
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 105 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 105 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 90 and depth = 14
[LightGBM] [Debug] Re-bagging, using 16619 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 102 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 91 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 86 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 91 and depth = 12
[LightGBM] [Debug] Re-bagging, using 16563 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 99 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 102 and depth = 17
[LightGBM] [Debug] Re-bagging, using 16608 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 108 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 14
[LightGBM] [Debug] Re-bagging, using 16503 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 98 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 101 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 112 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 16
[LightGBM] [Debug] Re-bagging, using 16624 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 103 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 99 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 107 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 110 and depth = 13
[LightGBM] [Debug] Re-bagging, using 16707 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 90 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 90 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 99 and depth = 16
[LightGBM] [Debug] Re-bagging, using 16483 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 121 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 20
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 101 and depth = 17
[LightGBM] [Debug] Re-bagging, using 16516 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 110 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 103 and depth = 14
[LightGBM] [Debug] Re-bagging, using 16568 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 110 and depth = 21
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 109 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 113 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 93 and depth = 17
[LightGBM] [Debug] Re-bagging, using 16633 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 107 and depth = 17
Trial 94, Fold 4: Log loss = 0.24575069718207382, Average precision = 0.9701339402577004, ROC-AUC = 0.9641384769821002, Elapsed Time = 1.6441727000001265 seconds
Trial 94, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 94, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[LightGBM] [Info] Number of positive: 10150, number of negative: 10500
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.792012
[LightGBM] [Info] Total Bins 19008
[LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902
[LightGBM] [Info] Start training from score -0.033902
[LightGBM] [Debug] Re-bagging, using 16573 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 98 and depth = 22
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 93 and depth = 17
[LightGBM] [Debug] Re-bagging, using 16654 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 93 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 107 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 102 and depth = 12
[LightGBM] [Debug] Re-bagging, using 16595 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 101 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 101 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 99 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 97 and depth = 17
[LightGBM] [Debug] Re-bagging, using 16542 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 78 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 106 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 98 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 84 and depth = 12
[LightGBM] [Debug] Re-bagging, using 16598 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 102 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 94 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 105 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 90 and depth = 14
[LightGBM] [Debug] Re-bagging, using 16585 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 103 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 98 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 16
[LightGBM] [Debug] Re-bagging, using 16527 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 86 and depth = 21
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 91 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 87 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 11
[LightGBM] [Debug] Re-bagging, using 16553 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 91 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 90 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 94 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 95 and depth = 15
[LightGBM] [Debug] Re-bagging, using 16616 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 104 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 117 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 105 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 97 and depth = 20
[LightGBM] [Debug] Re-bagging, using 16500 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 97 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 106 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 98 and depth = 21
[LightGBM] [Debug] Re-bagging, using 16519 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 98 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 106 and depth = 19
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 99 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 94 and depth = 17
[LightGBM] [Debug] Re-bagging, using 16541 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 97 and depth = 19
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 99 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 100 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 95 and depth = 23
[LightGBM] [Debug] Re-bagging, using 16555 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 98 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 101 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 97 and depth = 16
[LightGBM] [Debug] Re-bagging, using 16510 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 98 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 107 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 83 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 102 and depth = 15
[LightGBM] [Debug] Re-bagging, using 16618 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 94 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 98 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 94 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 91 and depth = 14
[LightGBM] [Debug] Re-bagging, using 16555 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 105 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 96 and depth = 15
[LightGBM] [Debug] Re-bagging, using 16604 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 105 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 108 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 95 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 100 and depth = 14
[LightGBM] [Debug] Re-bagging, using 16499 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 98 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 110 and depth = 19
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 108 and depth = 18
[LightGBM] [Debug] Re-bagging, using 16616 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 97 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 108 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 113 and depth = 13
[LightGBM] [Debug] Re-bagging, using 16699 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 111 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 101 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 101 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 93 and depth = 19
[LightGBM] [Debug] Re-bagging, using 16480 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 103 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 103 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 93 and depth = 13
[LightGBM] [Debug] Re-bagging, using 16513 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 117 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 110 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 92 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 93 and depth = 14
[LightGBM] [Debug] Re-bagging, using 16560 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 20
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 85 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 90 and depth = 16
[LightGBM] [Debug] Re-bagging, using 16629 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 107 and depth = 18
Trial 94, Fold 5: Log loss = 0.24745709343345237, Average precision = 0.9671251946854702, ROC-AUC = 0.9629334671051409, Elapsed Time = 1.5948129999997036 seconds
Optimization Progress:  95%|#########5| 95/100 [22:18<01:11, 14.24s/it]
Trial 95, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 95, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[LightGBM] [Info] Number of positive: 10130, number of negative: 10533
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791640
[LightGBM] [Info] Total Bins 24150
[LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012
[LightGBM] [Info] Start training from score -0.039012
[LightGBM] [Debug] Re-bagging, using 7527 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 24 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10
[LightGBM] [Debug] Re-bagging, using 7589 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7461 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10
[LightGBM] [Debug] Re-bagging, using 7455 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10
[LightGBM] [Debug] Re-bagging, using 7595 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7532 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7534 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7603 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7537 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10
[LightGBM] [Debug] Re-bagging, using 7337 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7348 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10
[LightGBM] [Debug] Re-bagging, using 7421 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10
[LightGBM] [Debug] Re-bagging, using 7403 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8
[LightGBM] [Debug] Re-bagging, using 7458 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10
[LightGBM] [Debug] Re-bagging, using 7636 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Debug] Re-bagging, using 7543 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 11
Trial 95, Fold 1: Log loss = 0.2819267212222898, Average precision = 0.9687208579186044, ROC-AUC = 0.962326082696453, Elapsed Time = 1.3650629999992816 seconds
Trial 95, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 95, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[LightGBM] [Info] Number of positive: 10230, number of negative: 10471
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791600
[LightGBM] [Info] Total Bins 24158
[LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285
[LightGBM] [Info] Start training from score -0.023285
[LightGBM] [Debug] Re-bagging, using 7542 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8
[LightGBM] [Debug] Re-bagging, using 7605 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7474 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8
[LightGBM] [Debug] Re-bagging, using 7470 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8
[LightGBM] [Debug] Re-bagging, using 7625 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10
[LightGBM] [Debug] Re-bagging, using 7523 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7546 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10
[LightGBM] [Debug] Re-bagging, using 7613 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8
[LightGBM] [Debug] Re-bagging, using 7560 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 12
[LightGBM] [Debug] Re-bagging, using 7361 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7347 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10
[LightGBM] [Debug] Re-bagging, using 7418 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7415 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7474 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7658 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7568 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
Trial 95, Fold 2: Log loss = 0.27767917005180226, Average precision = 0.9672489021741485, ROC-AUC = 0.9627532025611384, Elapsed Time = 1.6671499000003678 seconds
Trial 95, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 95, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[LightGBM] [Info] Number of positive: 10165, number of negative: 10517
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791401
[LightGBM] [Info] Total Bins 24152
[LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043
[LightGBM] [Info] Start training from score -0.034043
[LightGBM] [Debug] Re-bagging, using 7534 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 27 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 28 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8
[LightGBM] [Debug] Re-bagging, using 7595 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8
[LightGBM] [Debug] Re-bagging, using 7467 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10
[LightGBM] [Debug] Re-bagging, using 7468 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8
[LightGBM] [Debug] Re-bagging, using 7609 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10
[LightGBM] [Debug] Re-bagging, using 7525 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10
[LightGBM] [Debug] Re-bagging, using 7543 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7606 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10
[LightGBM] [Debug] Re-bagging, using 7540 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 12
[LightGBM] [Debug] Re-bagging, using 7361 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8
[LightGBM] [Debug] Re-bagging, using 7349 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8
[LightGBM] [Debug] Re-bagging, using 7416 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10
[LightGBM] [Debug] Re-bagging, using 7401 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7469 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10
[LightGBM] [Debug] Re-bagging, using 7643 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 12
[LightGBM] [Debug] Re-bagging, using 7559 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 37 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
Trial 95, Fold 3: Log loss = 0.2742493392445868, Average precision = 0.9705207772076768, ROC-AUC = 0.9650506291473644, Elapsed Time = 1.686474399999497 seconds
Trial 95, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 95, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[LightGBM] [Info] Number of positive: 10177, number of negative: 10479
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791090
[LightGBM] [Info] Total Bins 24142
[LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243
[LightGBM] [Info] Start training from score -0.029243
[LightGBM] [Debug] Re-bagging, using 7523 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8
[LightGBM] [Debug] Re-bagging, using 7587 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10
[LightGBM] [Debug] Re-bagging, using 7457 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8
[LightGBM] [Debug] Re-bagging, using 7457 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7587 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7530 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7537 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10
[LightGBM] [Debug] Re-bagging, using 7597 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7534 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 8
[LightGBM] [Debug] Re-bagging, using 7339 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10
[LightGBM] [Debug] Re-bagging, using 7339 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10
[LightGBM] [Debug] Re-bagging, using 7421 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7401 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7457 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 7
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7630 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 11
[LightGBM] [Debug] Re-bagging, using 7540 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8
Trial 95, Fold 4: Log loss = 0.2786265672968445, Average precision = 0.9688874155910504, ROC-AUC = 0.9626656568691268, Elapsed Time = 1.6847445999992487 seconds
Trial 95, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 95, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[LightGBM] [Info] Number of positive: 10150, number of negative: 10500
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.792012
[LightGBM] [Info] Total Bins 24146
[LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902
[LightGBM] [Info] Start training from score -0.033902
[LightGBM] [Debug] Re-bagging, using 7521 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 26 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7586 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7456 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7451 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8
[LightGBM] [Debug] Re-bagging, using 7586 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7521 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7546 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7591 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 29 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7532 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7339 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 30 and depth = 8
[LightGBM] [Debug] Re-bagging, using 7332 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7413 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 36 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7406 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 35 and depth = 10
[LightGBM] [Debug] Re-bagging, using 7465 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7628 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 8
[LightGBM] [Debug] Re-bagging, using 7540 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 34 and depth = 9
Trial 95, Fold 5: Log loss = 0.2833765556609774, Average precision = 0.9667874212231111, ROC-AUC = 0.96182643107965, Elapsed Time = 1.6412778000012622 seconds
Optimization Progress:  96%|#########6| 96/100 [22:33<00:58, 14.65s/it]
Trial 96, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 96, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[LightGBM] [Info] Number of positive: 10130, number of negative: 10533
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791640
[LightGBM] [Info] Total Bins 25868
[LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012
[LightGBM] [Info] Start training from score -0.039012
[LightGBM] [Debug] Re-bagging, using 10167 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 47 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10127 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 13
[LightGBM] [Debug] Re-bagging, using 10114 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 14
[LightGBM] [Debug] Re-bagging, using 10000 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 14
[LightGBM] [Debug] Re-bagging, using 10178 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 14
[LightGBM] [Debug] Re-bagging, using 10041 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 20
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 14
[LightGBM] [Debug] Re-bagging, using 10119 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 19
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 15
[LightGBM] [Debug] Re-bagging, using 10107 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 19
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 14
[LightGBM] [Debug] Re-bagging, using 10157 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 19
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 19
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 22
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 18
[LightGBM] [Debug] Re-bagging, using 9877 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 19
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 20
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 24
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 21
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 15
[LightGBM] [Debug] Re-bagging, using 9954 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 20
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 19
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 20
Trial 96, Fold 1: Log loss = 0.19265856405157333, Average precision = 0.9761579744323005, ROC-AUC = 0.9717249955788799, Elapsed Time = 2.0507928999995784 seconds
Trial 96, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 96, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[LightGBM] [Info] Number of positive: 10230, number of negative: 10471
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791600
[LightGBM] [Info] Total Bins 25873
[LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285
[LightGBM] [Info] Start training from score -0.023285
[LightGBM] [Debug] Re-bagging, using 10188 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 43 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 12
[LightGBM] [Debug] Re-bagging, using 10146 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 13
[LightGBM] [Debug] Re-bagging, using 10128 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10026 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 12
[LightGBM] [Debug] Re-bagging, using 10205 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 18
[LightGBM] [Debug] Re-bagging, using 10039 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 20
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 14
[LightGBM] [Debug] Re-bagging, using 10126 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 20
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 72 and depth = 20
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 17
[LightGBM] [Debug] Re-bagging, using 10125 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 23
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 20
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 20
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 18
[LightGBM] [Debug] Re-bagging, using 10184 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 19
[LightGBM] [Debug] Re-bagging, using 9899 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 25
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 19
[LightGBM] [Debug] Re-bagging, using 9967 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 22
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 21
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 21
Trial 96, Fold 2: Log loss = 0.18734632777382243, Average precision = 0.9734495986173171, ROC-AUC = 0.9719588302188112, Elapsed Time = 2.1794813000014983 seconds
Trial 96, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 96, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[LightGBM] [Info] Number of positive: 10165, number of negative: 10517
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791401
[LightGBM] [Info] Total Bins 25869
[LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043
[LightGBM] [Info] Start training from score -0.034043
[LightGBM] [Debug] Re-bagging, using 10177 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 12
[LightGBM] [Debug] Re-bagging, using 10134 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10120 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 12
[LightGBM] [Debug] Re-bagging, using 10021 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10189 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 13
[LightGBM] [Debug] Re-bagging, using 10037 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 14
[LightGBM] [Debug] Re-bagging, using 10125 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 14
[LightGBM] [Debug] Re-bagging, using 10114 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 21
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 22
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 19
[LightGBM] [Debug] Re-bagging, using 10163 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 15
[LightGBM] [Debug] Re-bagging, using 9899 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 19
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 21
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 21
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 19
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 73 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 18
[LightGBM] [Debug] Re-bagging, using 9959 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 20
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 22
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 74 and depth = 19
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 19
Trial 96, Fold 3: Log loss = 0.1883741298901994, Average precision = 0.9752283440379164, ROC-AUC = 0.9723419246178886, Elapsed Time = 2.1102914000002784 seconds
Trial 96, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 96, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[LightGBM] [Info] Number of positive: 10177, number of negative: 10479
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791090
[LightGBM] [Info] Total Bins 25858
[LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243
[LightGBM] [Info] Start training from score -0.029243
[LightGBM] [Debug] Re-bagging, using 10163 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 42 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 14
[LightGBM] [Debug] Re-bagging, using 10123 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 12
[LightGBM] [Debug] Re-bagging, using 10110 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 13
[LightGBM] [Debug] Re-bagging, using 9999 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 12
[LightGBM] [Debug] Re-bagging, using 10171 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 12
[LightGBM] [Debug] Re-bagging, using 10039 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 15
[LightGBM] [Debug] Re-bagging, using 10117 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 21
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 12
[LightGBM] [Debug] Re-bagging, using 10106 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 19
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 20
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 16
[LightGBM] [Debug] Re-bagging, using 10153 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 18
[LightGBM] [Debug] Re-bagging, using 9876 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 23
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 20
[LightGBM] [Debug] Re-bagging, using 9950 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 17
Trial 96, Fold 4: Log loss = 0.19104105517645986, Average precision = 0.9752330648341077, ROC-AUC = 0.9707036512466887, Elapsed Time = 2.073414300000877 seconds
Trial 96, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 96, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[LightGBM] [Info] Number of positive: 10150, number of negative: 10500
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.792012
[LightGBM] [Info] Total Bins 25865
[LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902
[LightGBM] [Info] Start training from score -0.033902
[LightGBM] [Debug] Re-bagging, using 10160 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 46 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 14
[LightGBM] [Debug] Re-bagging, using 10120 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 53 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 14
[LightGBM] [Debug] Re-bagging, using 10111 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 59 and depth = 12
[LightGBM] [Debug] Re-bagging, using 9992 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 49 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 48 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 56 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 50 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 11
[LightGBM] [Debug] Re-bagging, using 10169 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 51 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 55 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 52 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 54 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 13
[LightGBM] [Debug] Re-bagging, using 10032 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 15
[LightGBM] [Debug] Re-bagging, using 10121 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 44 and depth = 10
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 60 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 17
[LightGBM] [Debug] Re-bagging, using 10101 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 62 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 70 and depth = 20
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 14
[LightGBM] [Debug] Re-bagging, using 10150 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 11
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 17
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 65 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 19
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 71 and depth = 19
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 16
[LightGBM] [Debug] Re-bagging, using 9879 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 68 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 69 and depth = 15
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 64 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 23
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 58 and depth = 14
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 57 and depth = 19
[LightGBM] [Debug] Re-bagging, using 9940 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 63 and depth = 13
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 61 and depth = 12
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 16
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 66 and depth = 18
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 67 and depth = 15
Trial 96, Fold 5: Log loss = 0.19329716201171468, Average precision = 0.9739603585368279, ROC-AUC = 0.9715274139308474, Elapsed Time = 2.0674993000011455 seconds
Optimization Progress:  97%|#########7| 97/100 [22:52<00:47, 15.98s/it]
Trial 97, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 97, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[LightGBM] [Info] Number of positive: 10130, number of negative: 10533
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.804600
[LightGBM] [Info] Total Bins 29002
[LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 265
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012
[LightGBM] [Info] Start training from score -0.039012
[LightGBM] [Debug] Re-bagging, using 19497 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 20
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19
[LightGBM] [Debug] Re-bagging, using 19452 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 20
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18
[LightGBM] [Debug] Re-bagging, using 19514 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 20
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 14
[LightGBM] [Debug] Re-bagging, using 19469 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 23
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 16
[LightGBM] [Debug] Re-bagging, using 19477 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21
[LightGBM] [Debug] Re-bagging, using 19483 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 33
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19
[LightGBM] [Debug] Re-bagging, using 19435 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 20
[LightGBM] [Debug] Re-bagging, using 19467 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 27
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21
[LightGBM] [Debug] Re-bagging, using 19496 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21
[LightGBM] [Debug] Re-bagging, using 19436 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 23
[LightGBM] [Debug] Re-bagging, using 19473 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19
[LightGBM] [Debug] Re-bagging, using 19432 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 25
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 23
[LightGBM] [Debug] Re-bagging, using 19449 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 22
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 25
[LightGBM] [Debug] Re-bagging, using 19491 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19
[LightGBM] [Debug] Re-bagging, using 19458 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 22
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21
[LightGBM] [Debug] Re-bagging, using 19471 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 23
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19
[LightGBM] [Debug] Re-bagging, using 19429 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19
[LightGBM] [Debug] Re-bagging, using 19529 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 24
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19
[LightGBM] [Debug] Re-bagging, using 19504 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21
[LightGBM] [Debug] Re-bagging, using 19529 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 25
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18
[LightGBM] [Debug] Re-bagging, using 19402 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21
[LightGBM] [Debug] Re-bagging, using 19436 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 23
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 23
[LightGBM] [Debug] Re-bagging, using 19464 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 20
[LightGBM] [Debug] Re-bagging, using 19493 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 22
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18
[LightGBM] [Debug] Re-bagging, using 19521 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 22
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 16
Trial 97, Fold 1: Log loss = 0.3439567228683248, Average precision = 0.9642014557062354, ROC-AUC = 0.9580364013516679, Elapsed Time = 1.6739467000006698 seconds
Trial 97, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 97, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[LightGBM] [Info] Number of positive: 10230, number of negative: 10471
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.804566
[LightGBM] [Info] Total Bins 29006
[LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 265
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285
[LightGBM] [Info] Start training from score -0.023285
[LightGBM] [Debug] Re-bagging, using 19534 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 23
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 20
[LightGBM] [Debug] Re-bagging, using 19485 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 22
[LightGBM] [Debug] Re-bagging, using 19553 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 23
[LightGBM] [Debug] Re-bagging, using 19503 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 22
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21
[LightGBM] [Debug] Re-bagging, using 19517 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 20
[LightGBM] [Debug] Re-bagging, using 19506 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 20
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 22
[LightGBM] [Debug] Re-bagging, using 19475 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 23
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 15
[LightGBM] [Debug] Re-bagging, using 19514 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 16
[LightGBM] [Debug] Re-bagging, using 19532 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 22
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18
[LightGBM] [Debug] Re-bagging, using 19470 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 15
[LightGBM] [Debug] Re-bagging, using 19505 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 23
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 20
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 20
[LightGBM] [Debug] Re-bagging, using 19468 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 20
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 20
[LightGBM] [Debug] Re-bagging, using 19487 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 15
[LightGBM] [Debug] Re-bagging, using 19526 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19
[LightGBM] [Debug] Re-bagging, using 19493 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18
[LightGBM] [Debug] Re-bagging, using 19506 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 24
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 23
[LightGBM] [Debug] Re-bagging, using 19465 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 24
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 20
[LightGBM] [Debug] Re-bagging, using 19569 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 20
[LightGBM] [Debug] Re-bagging, using 19540 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18
[LightGBM] [Debug] Re-bagging, using 19560 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 25
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18
[LightGBM] [Debug] Re-bagging, using 19439 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 20
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18
[LightGBM] [Debug] Re-bagging, using 19469 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19
[LightGBM] [Debug] Re-bagging, using 19504 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 23
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18
[LightGBM] [Debug] Re-bagging, using 19535 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21
[LightGBM] [Debug] Re-bagging, using 19554 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 23
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 20
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 22
Trial 97, Fold 2: Log loss = 0.3371119253550399, Average precision = 0.9651042928311725, ROC-AUC = 0.9614018885315396, Elapsed Time = 2.0464709000007133 seconds
Trial 97, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 97, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[LightGBM] [Info] Number of positive: 10165, number of negative: 10517
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.804456
[LightGBM] [Info] Total Bins 29001
[LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 265
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043
[LightGBM] [Info] Start training from score -0.034043
[LightGBM] [Debug] Re-bagging, using 19516 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19
[LightGBM] [Debug] Re-bagging, using 19468 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18
[LightGBM] [Debug] Re-bagging, using 19533 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 22
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 20
[LightGBM] [Debug] Re-bagging, using 19490 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 25
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21
[LightGBM] [Debug] Re-bagging, using 19491 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 23
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21
[LightGBM] [Debug] Re-bagging, using 19497 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 24
[LightGBM] [Debug] Re-bagging, using 19453 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 26
[LightGBM] [Debug] Re-bagging, using 19492 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 24
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 26
[LightGBM] [Debug] Re-bagging, using 19513 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 23
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19
[LightGBM] [Debug] Re-bagging, using 19457 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 22
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17
[LightGBM] [Debug] Re-bagging, using 19487 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 22
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 23
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 23
[LightGBM] [Debug] Re-bagging, using 19451 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 26
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21
[LightGBM] [Debug] Re-bagging, using 19466 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 20
[LightGBM] [Debug] Re-bagging, using 19507 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21
[LightGBM] [Debug] Re-bagging, using 19480 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 20
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 25
[LightGBM] [Debug] Re-bagging, using 19483 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 16
[LightGBM] [Debug] Re-bagging, using 19449 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 16
[LightGBM] [Debug] Re-bagging, using 19550 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 20
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19
[LightGBM] [Debug] Re-bagging, using 19521 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19
[LightGBM] [Debug] Re-bagging, using 19549 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 20
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21
[LightGBM] [Debug] Re-bagging, using 19419 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 30
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 14
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 23
[LightGBM] [Debug] Re-bagging, using 19447 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 20
[LightGBM] [Debug] Re-bagging, using 19485 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17
[LightGBM] [Debug] Re-bagging, using 19513 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 24
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 20
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18
[LightGBM] [Debug] Re-bagging, using 19543 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21
Trial 97, Fold 3: Log loss = 0.3337444841372523, Average precision = 0.9682855889749199, ROC-AUC = 0.963323212720671, Elapsed Time = 2.080849899999521 seconds
Trial 97, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 97, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[LightGBM] [Info] Number of positive: 10177, number of negative: 10479
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.804155
[LightGBM] [Info] Total Bins 28992
[LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 265
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243
[LightGBM] [Info] Start training from score -0.029243
[LightGBM] [Debug] Re-bagging, using 19490 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18
[LightGBM] [Debug] Re-bagging, using 19445 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21
[LightGBM] [Debug] Re-bagging, using 19509 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 20
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 22
[LightGBM] [Debug] Re-bagging, using 19465 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 20
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 20
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 20
[LightGBM] [Debug] Re-bagging, using 19468 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 23
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19
[LightGBM] [Debug] Re-bagging, using 19474 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 23
[LightGBM] [Debug] Re-bagging, using 19433 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 13
[LightGBM] [Debug] Re-bagging, using 19462 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 22
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17
[LightGBM] [Debug] Re-bagging, using 19487 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17
[LightGBM] [Debug] Re-bagging, using 19428 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 20
[LightGBM] [Debug] Re-bagging, using 19465 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 24
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17
[LightGBM] [Debug] Re-bagging, using 19426 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19
[LightGBM] [Debug] Re-bagging, using 19444 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 16
[LightGBM] [Debug] Re-bagging, using 19483 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 15
[LightGBM] [Debug] Re-bagging, using 19451 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 22
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21
[LightGBM] [Debug] Re-bagging, using 19463 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 20
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18
[LightGBM] [Debug] Re-bagging, using 19422 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 23
[LightGBM] [Debug] Re-bagging, using 19523 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21
[LightGBM] [Debug] Re-bagging, using 19498 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17
[LightGBM] [Debug] Re-bagging, using 19521 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 20
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18
[LightGBM] [Debug] Re-bagging, using 19399 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 24
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 16
[LightGBM] [Debug] Re-bagging, using 19422 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 14
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 16
[LightGBM] [Debug] Re-bagging, using 19464 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19
[LightGBM] [Debug] Re-bagging, using 19488 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 22
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 23
[LightGBM] [Debug] Re-bagging, using 19512 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 22
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 22
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19
Trial 97, Fold 4: Log loss = 0.3345920525712018, Average precision = 0.9672589026802049, ROC-AUC = 0.961008492075414, Elapsed Time = 2.0492997000001196 seconds
Trial 97, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 97, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[LightGBM] [Info] Number of positive: 10150, number of negative: 10500
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.804939
[LightGBM] [Info] Total Bins 28998
[LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 265
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902
[LightGBM] [Info] Start training from score -0.033902
[LightGBM] [Debug] Re-bagging, using 19484 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 24
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 23
[LightGBM] [Debug] Re-bagging, using 19440 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17
[LightGBM] [Debug] Re-bagging, using 19503 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 26
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17
[LightGBM] [Debug] Re-bagging, using 19460 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 20
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19
[LightGBM] [Debug] Re-bagging, using 19463 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21
[LightGBM] [Debug] Re-bagging, using 19465 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 20
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 16
[LightGBM] [Debug] Re-bagging, using 19433 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19
[LightGBM] [Debug] Re-bagging, using 19454 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 23
[LightGBM] [Debug] Re-bagging, using 19478 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18
[LightGBM] [Debug] Re-bagging, using 19423 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 16
[LightGBM] [Debug] Re-bagging, using 19459 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 24
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 25
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 20
[LightGBM] [Debug] Re-bagging, using 19422 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 22
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 16
[LightGBM] [Debug] Re-bagging, using 19437 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 20
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19
[LightGBM] [Debug] Re-bagging, using 19477 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 27
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18
[LightGBM] [Debug] Re-bagging, using 19449 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 20
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21
[LightGBM] [Debug] Re-bagging, using 19453 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 24
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 22
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 20
[LightGBM] [Debug] Re-bagging, using 19419 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19
[LightGBM] [Debug] Re-bagging, using 19518 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 16
[LightGBM] [Debug] Re-bagging, using 19489 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 24
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19
[LightGBM] [Debug] Re-bagging, using 19518 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 20
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 26
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19
[LightGBM] [Debug] Re-bagging, using 19393 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 22
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19
[LightGBM] [Debug] Re-bagging, using 19419 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 25
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 36
[LightGBM] [Debug] Re-bagging, using 19452 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 20
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 15
[LightGBM] [Debug] Re-bagging, using 19487 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 21
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 22
[LightGBM] [Debug] Re-bagging, using 19507 data to train
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 20
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 209 and depth = 16
Trial 97, Fold 5: Log loss = 0.34868921272817555, Average precision = 0.9628221406956591, ROC-AUC = 0.9572423685041711, Elapsed Time = 2.0245357000003423 seconds
Optimization Progress:  98%|#########8| 98/100 [23:10<00:32, 16.41s/it]
Trial 98, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 98, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[LightGBM] [Info] Number of positive: 10130, number of negative: 10533
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.805817
[LightGBM] [Info] Total Bins 7969
[LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 267
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012
[LightGBM] [Info] Start training from score -0.039012
[LightGBM] [Debug] Re-bagging, using 6760 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 13
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 12
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 14
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 17
[LightGBM] [Debug] Re-bagging, using 6778 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 13
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 14
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 12
[LightGBM] [Debug] Re-bagging, using 6679 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 14
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 13
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 13
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 14
[LightGBM] [Debug] Re-bagging, using 6628 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 20
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 25
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 21
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 18
[LightGBM] [Debug] Re-bagging, using 6807 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 14
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 14
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 14
[LightGBM] [Debug] Re-bagging, using 6755 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 25
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 21
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 21
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 21
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 18
[LightGBM] [Debug] Re-bagging, using 6700 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 20
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 17
Trial 98, Fold 1: Log loss = 0.2059870375568024, Average precision = 0.9736278666055719, ROC-AUC = 0.9687441748756112, Elapsed Time = 1.1208126999990782 seconds
Trial 98, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 98, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[LightGBM] [Info] Number of positive: 10230, number of negative: 10471
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.806987
[LightGBM] [Info] Total Bins 7985
[LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 267
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285
[LightGBM] [Info] Start training from score -0.023285
[LightGBM] [Debug] Re-bagging, using 6773 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 14
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 13
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 12
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15
[LightGBM] [Debug] Re-bagging, using 6795 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 13
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 12
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 13
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 16
[LightGBM] [Debug] Re-bagging, using 6687 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 20
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 14
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15
[LightGBM] [Debug] Re-bagging, using 6643 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 20
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 20
[LightGBM] [Debug] Re-bagging, using 6835 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 14
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 24
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 21
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 19
[LightGBM] [Debug] Re-bagging, using 6744 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 26
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 20
[LightGBM] [Debug] Re-bagging, using 6713 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 14
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 17
Trial 98, Fold 2: Log loss = 0.1950855705719745, Average precision = 0.9740338406308985, ROC-AUC = 0.9714351486111586, Elapsed Time = 1.53894020000007 seconds
Trial 98, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 98, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[LightGBM] [Info] Number of positive: 10165, number of negative: 10517
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.806893
[LightGBM] [Info] Total Bins 7992
[LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 267
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043
[LightGBM] [Info] Start training from score -0.034043
[LightGBM] [Debug] Re-bagging, using 6766 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 12
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 13
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 14
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 14
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 14
[LightGBM] [Debug] Re-bagging, using 6785 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 21
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 14
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 19
[LightGBM] [Debug] Re-bagging, using 6681 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 13
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 12
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 14
[LightGBM] [Debug] Re-bagging, using 6641 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 22
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 14
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 24
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 14
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 20
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 20
[LightGBM] [Debug] Re-bagging, using 6823 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 14
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 19
[LightGBM] [Debug] Re-bagging, using 6745 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 20
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 21
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 26
[LightGBM] [Debug] Re-bagging, using 6710 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 22
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 18
Trial 98, Fold 3: Log loss = 0.19211088925521713, Average precision = 0.974235235731083, ROC-AUC = 0.9724076865290712, Elapsed Time = 1.3739041999997426 seconds
Trial 98, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 98, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[LightGBM] [Info] Number of positive: 10177, number of negative: 10479
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.804155
[LightGBM] [Info] Total Bins 7971
[LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 265
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243
[LightGBM] [Info] Start training from score -0.029243
[LightGBM] [Debug] Re-bagging, using 6757 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 13
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 13
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 12
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 12
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 20
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 14
[LightGBM] [Debug] Re-bagging, using 6775 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 11
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 14
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 13
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 13
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 19
[LightGBM] [Debug] Re-bagging, using 6676 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 21
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 17
[LightGBM] [Debug] Re-bagging, using 6630 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 14
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 21
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 30
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 20
[LightGBM] [Debug] Re-bagging, using 6802 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 22
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 12
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 16
[LightGBM] [Debug] Re-bagging, using 6752 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 22
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15
[LightGBM] [Debug] Re-bagging, using 6701 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 24
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 21
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 21
Trial 98, Fold 4: Log loss = 0.20014693335544856, Average precision = 0.97502289346449, ROC-AUC = 0.970527950051384, Elapsed Time = 1.3837733000000298 seconds
Trial 98, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 98, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[LightGBM] [Info] Number of positive: 10150, number of negative: 10500
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.807356
[LightGBM] [Info] Total Bins 7973
[LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 267
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902
[LightGBM] [Info] Start training from score -0.033902
[LightGBM] [Debug] Re-bagging, using 6755 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 12
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 13
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 22
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 12
[LightGBM] [Debug] Re-bagging, using 6775 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 25
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 12
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 16
[LightGBM] [Debug] Re-bagging, using 6674 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 12
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 13
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 14
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 14
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 12
[LightGBM] [Debug] Re-bagging, using 6625 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 13
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 18
[LightGBM] [Debug] Re-bagging, using 6801 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 22
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 23
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 23
[LightGBM] [Debug] Re-bagging, using 6745 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 24
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 20
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 30
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 22
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 20
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 14
[LightGBM] [Debug] Re-bagging, using 6709 data to train
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 13
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 89 and depth = 18
Trial 98, Fold 5: Log loss = 0.20402423794622532, Average precision = 0.9736777729507122, ROC-AUC = 0.9695342139977332, Elapsed Time = 1.3356522999984008 seconds
Optimization Progress:  99%|#########9| 99/100 [23:24<00:15, 15.83s/it]
Trial 99, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 99, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[LightGBM] [Info] Number of positive: 10130, number of negative: 10533
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791640
[LightGBM] [Info] Total Bins 11253
[LightGBM] [Info] Number of data points in the train set: 20663, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.490248 -> initscore=-0.039012
[LightGBM] [Info] Start training from score -0.039012
[LightGBM] [Debug] Re-bagging, using 7912 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 9
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7981 data to train
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10
[LightGBM] [Debug] Re-bagging, using 7859 data to train
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 8
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7810 data to train
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7975 data to train
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 8
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 8
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7910 data to train
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7908 data to train
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 8
Trial 99, Fold 1: Log loss = 0.4228630313718012, Average precision = 0.9653912293403434, ROC-AUC = 0.9590646104873748, Elapsed Time = 0.658761600001526 seconds
Trial 99, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 99, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[LightGBM] [Info] Number of positive: 10230, number of negative: 10471
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791600
[LightGBM] [Info] Total Bins 11266
[LightGBM] [Info] Number of data points in the train set: 20701, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.494179 -> initscore=-0.023285
[LightGBM] [Info] Start training from score -0.023285
[LightGBM] [Debug] Re-bagging, using 7928 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 8
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 8
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10
[LightGBM] [Debug] Re-bagging, using 7996 data to train
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 12
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 12
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11
[LightGBM] [Debug] Re-bagging, using 7875 data to train
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 12
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10
[LightGBM] [Debug] Re-bagging, using 7825 data to train
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
[LightGBM] [Debug] Re-bagging, using 8007 data to train
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 12
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7902 data to train
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 8
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10
[LightGBM] [Debug] Re-bagging, using 7918 data to train
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
Trial 99, Fold 2: Log loss = 0.42258646154902085, Average precision = 0.9609599053042722, ROC-AUC = 0.9579277747310195, Elapsed Time = 0.7333526000002166 seconds
Trial 99, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 99, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[LightGBM] [Info] Number of positive: 10165, number of negative: 10517
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791401
[LightGBM] [Info] Total Bins 11269
[LightGBM] [Info] Number of data points in the train set: 20682, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491490 -> initscore=-0.034043
[LightGBM] [Info] Start training from score -0.034043
[LightGBM] [Debug] Re-bagging, using 7919 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 31 and depth = 9
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 8
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7987 data to train
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 8
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11
[LightGBM] [Debug] Re-bagging, using 7866 data to train
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11
[LightGBM] [Debug] Re-bagging, using 7825 data to train
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 8
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 8
[LightGBM] [Debug] Re-bagging, using 7990 data to train
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 8
[LightGBM] [Debug] Re-bagging, using 7901 data to train
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11
[LightGBM] [Debug] Re-bagging, using 7915 data to train
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 12
Trial 99, Fold 3: Log loss = 0.42016989402690114, Average precision = 0.9661421572245269, ROC-AUC = 0.9603194584228216, Elapsed Time = 0.7783467999997811 seconds
Trial 99, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 99, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[LightGBM] [Info] Number of positive: 10177, number of negative: 10479
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.791090
[LightGBM] [Info] Total Bins 11250
[LightGBM] [Info] Number of data points in the train set: 20656, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492690 -> initscore=-0.029243
[LightGBM] [Info] Start training from score -0.029243
[LightGBM] [Debug] Re-bagging, using 7908 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 32 and depth = 8
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 8
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10
[LightGBM] [Debug] Re-bagging, using 7979 data to train
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10
[LightGBM] [Debug] Re-bagging, using 7855 data to train
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7810 data to train
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 8
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 13
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10
[LightGBM] [Debug] Re-bagging, using 7969 data to train
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11
[LightGBM] [Debug] Re-bagging, using 7907 data to train
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7909 data to train
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
Trial 99, Fold 4: Log loss = 0.42064763482572876, Average precision = 0.9653052217148508, ROC-AUC = 0.9584859355589415, Elapsed Time = 0.8043866000007256 seconds
Trial 99, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 99, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[LightGBM] [Info] Number of positive: 10150, number of negative: 10500
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.792012
[LightGBM] [Info] Total Bins 11253
[LightGBM] [Info] Number of data points in the train set: 20650, number of used features: 255
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.491525 -> initscore=-0.033902
[LightGBM] [Info] Start training from score -0.033902
[LightGBM] [Debug] Re-bagging, using 7906 data to train
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Debug] Trained a tree with leaves = 33 and depth = 8
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10
[LightGBM] [Debug] Re-bagging, using 7978 data to train
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 8
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10
[LightGBM] [Debug] Re-bagging, using 7853 data to train
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 8
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 12
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11
[LightGBM] [Debug] Re-bagging, using 7805 data to train
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 8
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10
[LightGBM] [Debug] Re-bagging, using 7968 data to train
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 12
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 11
[LightGBM] [Debug] Re-bagging, using 7898 data to train
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 10
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
[LightGBM] [Debug] Re-bagging, using 7916 data to train
[LightGBM] [Debug] Trained a tree with leaves = 40 and depth = 9
Trial 99, Fold 5: Log loss = 0.42703576123191167, Average precision = 0.9626720951126174, ROC-AUC = 0.9566728721921856, Elapsed Time = 0.772207600000911 seconds
Optimization Progress: 100%|##########| 100/100 [23:36<00:00, 14.51s/it]
Optuna Optimization Elapsed Time: 1416.1865574000003 seconds
Optimization Progress: 100%|##########| 100/100 [23:36<00:00, 14.17s/it]

Training with Best Trial 68

{'objective': 'binary', 'metric': ['binary_logloss', 'auc', 'average_precision'], 'device_type': 'cpu', 'verbosity': 2, 'random_state': 42, 'deterministic': True, 'bagging_seed': 42, 'feature_fraction_seed': 42, 'drop_seed': 42, 'force_col_wise': True, 'num_threads': -1, 'max_depth': -1, 'boosting_type': 'gbdt', 'lambda_l1': 0.001799212849506679, 'lambda_l2': 1.1881208494212355e-08, 'num_leaves': 81, 'feature_fraction': 0.8529067830109069, 'bagging_fraction': 0.7231722623902207, 'bagging_freq': 6, 'min_child_samples': 28, 'learning_rate': 0.0749127622821807, 'is_unbalance': True, 'max_bin': 184, 'min_sum_hessian_in_leaf': 0.005200324540154612, 'max_delta_step': 40.70003905844247, 'feature_fraction_bynode': 0.9524479541474791, 'num_boost_round': 88}
[LightGBM] [Warning] Found whitespace in feature_names, replace with underlines
[LightGBM] [Info] Number of positive: 12713, number of negative: 13125
[LightGBM] [Debug] Dataset::GetMultiBinFromSparseFeatures: sparse rate 0.804279
[LightGBM] [Info] Total Bins 22087
[LightGBM] [Info] Number of data points in the train set: 25838, number of used features: 265
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.492027 -> initscore=-0.031894
[LightGBM] [Info] Start training from score -0.031894
[LightGBM] [Debug] Re-bagging, using 18633 data to train
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 14
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 13
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16
[LightGBM] [Debug] Re-bagging, using 18737 data to train
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 21
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 15
[LightGBM] [Debug] Re-bagging, using 18772 data to train
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 12
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 21
[LightGBM] [Debug] Re-bagging, using 18702 data to train
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 20
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 20
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 20
[LightGBM] [Debug] Re-bagging, using 18714 data to train
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 14
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 19
[LightGBM] [Debug] Re-bagging, using 18601 data to train
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 13
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 20
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 19
[LightGBM] [Debug] Re-bagging, using 18731 data to train
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 20
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 21
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16
[LightGBM] [Debug] Re-bagging, using 18744 data to train
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 13
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 22
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 15
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 23
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 19
[LightGBM] [Debug] Re-bagging, using 18737 data to train
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 27
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 20
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 21
[LightGBM] [Debug] Re-bagging, using 18635 data to train
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 21
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 22
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 23
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 21
[LightGBM] [Debug] Re-bagging, using 18640 data to train
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 22
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 23
[LightGBM] [Debug] Re-bagging, using 18506 data to train
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 20
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 14
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 20
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 24
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 19
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 24
[LightGBM] [Debug] Re-bagging, using 18695 data to train
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 21
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 20
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 20
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 17
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 26
[LightGBM] [Debug] Re-bagging, using 18539 data to train
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 21
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 20
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 20
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 25
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 16
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 20
[LightGBM] [Debug] Re-bagging, using 18768 data to train
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 21
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 18
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 22
[LightGBM] [Debug] Trained a tree with leaves = 81 and depth = 28
Training Elapsed Time: 3.8860796999997547 seconds
Log loss: (Train) 0.18691289880514814 vs (Test) 0.19337372842261577
PR-AUC:   (Train) 0.9769836663793002 vs (Test) 0.9751402347947055
ROC-AUC:  (Train) 0.9735624134708025 vs (Test) 0.9717662835980452
In [18]:
save_results(clf_name = "LGBM",
             best_trials = best_trials_lgbm,
             exec_time = exec_time_lgbm,
             lloss_auc_train = lloss_auc_train_lgbm,
             lloss_auc_test = lloss_auc_test_lgbm,
             df_metrics = df_metrics_lgbm,
             cm_final = cm_final_lgbm,
             cm_all = cm_lgbm_all,
             cm_labels = cm_labels_lgbm_all)

Optuna with HistGradientBoostingClassifier¶

In [19]:
gc.collect();
In [20]:
X_df = clean_df.drop(columns = ["target", "anon_ssn"])

y_df = clean_df.target

anon_ssn = clean_df.anon_ssn;
In [21]:
# A single train-test split (80%-20%) using GroupShuffleSplit, ensuring that no anon_ssn (grouped by anon_ssn) appear in both sets
gss = GroupShuffleSplit(n_splits = 1, test_size = 0.2, random_state = seed)
train_idx, test_idx = next(gss.split(X_df, y_df, groups = anon_ssn))

X_train, X_test = X_df.iloc[train_idx], X_df.iloc[test_idx]
y_train, y_test = y_df.iloc[train_idx], y_df.iloc[test_idx]

anon_ssn_train = anon_ssn[train_idx]  # Keeping track of anon_ssn for cross-validation
In [22]:
del X_df, y_df, gss, train_idx, test_idx;
In [23]:
def objective(trial):

    # https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.HistGradientBoostingClassifier.html
    # https://scikit-learn.org/stable/modules/ensemble.html#categorical-support-gbdt
    param = {"loss": "log_loss", 
             "verbose": 2, 
             "learning_rate": trial.suggest_float("learning_rate", 1e-2, 1e-1, log = True),
             "max_iter": trial.suggest_int("max_iter", 5, 100),
             "max_leaf_nodes": trial.suggest_int("max_leaf_nodes", 2, 256),
             "min_samples_leaf": trial.suggest_int("min_samples_leaf", 5, 200),
             "l2_regularization": trial.suggest_float("l2_regularization", 1e-8, 1e1, log = True),
             "class_weight": trial.suggest_categorical("class_weight", [None, "balanced"]), # classes are balance
             "random_state": seed,
             "categorical_features": "from_dtype",
             "max_depth": None,
             "max_features": trial.suggest_float("max_features", 1e-1, 1e0),
             "max_bins": trial.suggest_int("max_bins", 40, 255),
             #"monotonic_cst":  trial.suggest_categorical("monotonic_cst", [None, -1, 0, 1]),
             "interaction_cst": trial.suggest_categorical("interaction_cst", 
                                                          ["pairwise"
                                                           ,"no_interactions"]),
             "warm_start": trial.suggest_categorical("warm_start",[False, True])
            }   
    
    sgkf = StratifiedGroupKFold(n_splits = 5, shuffle = True, random_state = seed)
    lloss_scores, pr_auc_scores, roc_auc_scores = [], [], []
    
    for fold_idx, (train_index, valid_index) in enumerate(sgkf.split(X_train, y_train, groups = anon_ssn_train), start = 1):

        # Extract train and validation sets
        X_train_fold, X_valid_fold = X_train.iloc[train_index], X_train.iloc[valid_index]
        y_train_fold, y_valid_fold = y_train.iloc[train_index], y_train.iloc[valid_index]
        
        # Summarize the composition of classes in the train and validation sets
        train_0, train_1 = len(y_train_fold[y_train_fold == 0]), len(y_train_fold[y_train_fold == 1])
        valid_0, valid_1 = len(y_valid_fold[y_valid_fold == 0]), len(y_valid_fold[y_valid_fold == 1])

        print(f'Trial {trial.number}, Fold {fold_idx}: Train size = {len(train_index)} where 0 = {train_0}, 1 = {train_1}, 0/1 = {train_0/train_1}')
        print(f'Trial {trial.number}, Fold {fold_idx}: Validation size = {len(valid_index)} where  0 = {valid_0}, 1 = {valid_1}, 0/1 = {valid_0/valid_1}')

        clf = HistGradientBoostingClassifier(**param)
            
        start_fold = time.perf_counter()
        clf.fit(X_train_fold, y_train_fold)
        end_fold = time.perf_counter()

        # Predict probabilities
        y_prob_fold = clf.predict_proba(X_valid_fold)[:, 1]

        print(f'Trial {trial.number}, Fold {fold_idx}: '
              f'Log loss = {log_loss(y_valid_fold, y_prob_fold)}, '
              f'Average precision = {average_precision_score(y_valid_fold, y_prob_fold)}, '
              f'ROC-AUC = {roc_auc_score(y_valid_fold, y_prob_fold)}, '
              f'Elapsed Time = {end_fold - start_fold} seconds')          
        
        # Calculate and store the evaluation metrics for this fold
        lloss_scores.append(log_loss(y_valid_fold, y_prob_fold))
        pr_auc_scores.append(average_precision_score(y_valid_fold, y_prob_fold))
        roc_auc_scores.append(roc_auc_score(y_valid_fold, y_prob_fold))

        del X_train_fold, X_valid_fold, y_train_fold, y_valid_fold, clf, start_fold, end_fold
        gc.collect()

    # Calculate average metrics across all folds for Optuna to optimize
    mean_lloss = np.mean(lloss_scores)
    mean_pr_auc = np.mean(pr_auc_scores)
    mean_roc_auc = np.mean(roc_auc_scores)

    del lloss_scores, pr_auc_scores, roc_auc_scores
    gc.collect()
    
    #  Return the metrics to Optuna for optimization
    return mean_lloss, mean_pr_auc, mean_roc_auc  
In [24]:
trial_progress = tqdm(total = n_trials, desc = "Optimization Progress", leave = True,
                      ascii = True,  # Plain text mode
                      dynamic_ncols = True   # Auto-fit width
                     )

def update_progress(study_hgbc, trial):
    trial_progress.update(1)

optuna.logging.disable_default_handler()

optuna.logging.enable_propagation()
optuna.logging.set_verbosity(optuna.logging.DEBUG)

logging.basicConfig(filename = "optuna_debug_HistGradientBoostingClassifier.log", filemode = "w", level = logging.DEBUG, format="%(asctime)s %(levelname)s %(message)s")

study_hgbc = optuna.create_study(study_name = "Optuna with HistGradientBoostingClassifier",
                                 directions = ["minimize", "maximize", "maximize"],
                                 sampler = module.AutoSampler(seed = seed)                                     
                               )

start_optuna = time.perf_counter()
study_hgbc.optimize(objective, n_trials = n_trials, n_jobs = 1, callbacks = [update_progress]) 
end_optuna = time.perf_counter()

print(f'Optuna Optimization Elapsed Time: {end_optuna - start_optuna} seconds')

fig = plot_pareto_front(study_hgbc, target_names = ["Log loss", "PR-AUC", "ROC-AUC"])
fig.update_layout(width = 900, height = 400)  # Set desired width and height in pixels
fig.show()

trial_progress.close()

metrics = ["Log loss", "PR-AUC", "ROC-AUC"]
for i, obj in enumerate(metrics):
    optuna.visualization.plot_optimization_history(study_hgbc,
                                                   target = lambda t: t.values[i],  # Correctly target each objective
                                                   target_name = obj).show()

best_trials = study_hgbc.best_trials
best_trials_hgbc = {}

exec_time_hgbc, lloss_auc_train_hgbc, lloss_auc_test_hgbc, all_metrics = [], [], [], []
cm_hgbc_all, cm_labels_hgbc_all = [], []

for i, trial in enumerate(best_trials):

    display(Markdown(f'<span style = "font-size: 18px; font-weight: bold;"> Training with Best Trial {trial.number}: </span>'))

    best_params = trial.params
    
    # Non-optimized and best Optuna optimized parameters
    full_params = {"loss": "log_loss",
                   "verbose": 2, 
                   "random_state": seed,
                   "categorical_features": "from_dtype",
                   "max_depth": None,                     
                   **best_params
                  }      
    print("Full_params:", full_params)
    best_trials_hgbc[trial.number] = full_params

    final_hgbc = HistGradientBoostingClassifier(**full_params)

    start_train = time.perf_counter()
    final_hgbc.fit(X_train, y_train)
    end_train = time.perf_counter()

    print(f'Training Elapsed Time: {end_train - start_train} seconds') 

    exec_time_hgbc.append({"Classifier": "HistGradientBoostingClassifier",
                           "Best Trial": trial.number,
                           "Optimization Elapsed Time (s)": end_optuna - start_optuna,
                           "Training Elapsed Time (s)": end_train - start_train})
    
    y_prob_all = final_hgbc.predict_proba(X_test)[:, 1]  
    y_pred_all = final_hgbc.predict(X_test)

    print(f'Log loss: (Train) {trial.values[0]} vs (Test) {log_loss(y_test, y_prob_all)}')
    print(f'PR-AUC:   (Train) {trial.values[1]} vs (Test) {average_precision_score(y_test, y_prob_all)}')
    print(f'ROC-AUC:  (Train) {trial.values[2]} vs (Test) {roc_auc_score(y_test, y_prob_all)}')

    lloss_auc_train_hgbc.append({"Classifier": "HistGradientBoostingClassifier",
                                 "Best Trial": trial.number,
                                 "Set": "Training",
                                 "Log loss": trial.values[0], 
                                 "PR-AUC": trial.values[1], 
                                 "ROC-AUC": trial.values[2]})

    lloss_auc_test_hgbc.append({"Classifier": "HistGradientBoostingClassifier",
                                "Best Trial": trial.number,
                                "Set": "Test",
                                "Log loss": log_loss(y_test, y_prob_all), 
                                "PR-AUC": average_precision_score(y_test, y_prob_all),
                                "ROC-AUC": roc_auc_score(y_test, y_prob_all)})

    report = classification_report(y_test, y_pred_all, target_names = ["Safe", "Risky"], output_dict = True)
    all_metrics.append({"Classifier": "HistGradientBoostingClassifier",
                        "Trial": trial.number,
                        "Accuracy": accuracy_score(y_test, y_pred_all),
                        "Precision (Safe)": report["Safe"]["precision"],
                        "Recall (Safe)": report["Safe"]["recall"],
                        "F1-score (Safe)": report["Safe"]["f1-score"],
                        "Precision (Risky)": report["Risky"]["precision"],
                        "Recall (Risky)": report["Risky"]["recall"],
                        "F1-score (Risky)": report["Risky"]["f1-score"],
                        "Precision (Macro avg)": report["macro avg"]["precision"],
                        "Recall (Macro avg)": report["macro avg"]["recall"],
                        "F1-score (Macro avg)": report["macro avg"]["f1-score"],
                        "Precision (Weighted avg)": report["weighted avg"]["precision"],
                        "Recall (Weighted avg)": report["weighted avg"]["recall"],
                        "F1-score (Weighted avg)": report["weighted avg"]["f1-score"]})
  

    # Store confusion matrix
    cm_final_hgbc = confusion_matrix(y_test, y_pred_all)
    cm_hgbc_all.append(cm_final_hgbc)
    cm_labels_hgbc_all.append(f'HistGradientBoostingClassifier Confusion Matrix for Best Trial {trial.number}')  # Store label for subplots     

df_metrics_hgbc = pd.DataFrame(all_metrics)

gc.collect();
Optimization Progress:   0%|          | 0/100 [00:00<?, ?it/s]
Trial 0, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 0, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
Binning 0.040 GB of training data: 0.141 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 19 leaves, max depth = 9, train loss: 0.68006, val loss: 0.67964, in 0.016s
1 tree, 19 leaves, max depth = 9, train loss: 0.66763, val loss: 0.66685, in 0.016s
1 tree, 19 leaves, max depth = 9, train loss: 0.65585, val loss: 0.65476, in 0.000s
1 tree, 7 leaves, max depth = 5, train loss: 0.64478, val loss: 0.64325, in 0.000s
1 tree, 19 leaves, max depth = 9, train loss: 0.63425, val loss: 0.63238, in 0.016s
1 tree, 19 leaves, max depth = 9, train loss: 0.62427, val loss: 0.62208, in 0.000s
1 tree, 19 leaves, max depth = 8, train loss: 0.61494, val loss: 0.61244, in 0.000s
1 tree, 19 leaves, max depth = 8, train loss: 0.60610, val loss: 0.60325, in 0.000s
1 tree, 19 leaves, max depth = 9, train loss: 0.59758, val loss: 0.59443, in 0.000s
1 tree, 19 leaves, max depth = 8, train loss: 0.58933, val loss: 0.58588, in 0.016s
1 tree, 19 leaves, max depth = 9, train loss: 0.58173, val loss: 0.57794, in 0.000s
1 tree, 19 leaves, max depth = 8, train loss: 0.57455, val loss: 0.57044, in 0.016s
1 tree, 19 leaves, max depth = 9, train loss: 0.56761, val loss: 0.56323, in 0.000s
1 tree, 19 leaves, max depth = 9, train loss: 0.56109, val loss: 0.55642, in 0.016s
1 tree, 19 leaves, max depth = 9, train loss: 0.55480, val loss: 0.54987, in 0.000s
1 tree, 19 leaves, max depth = 8, train loss: 0.54838, val loss: 0.54389, in 0.016s
1 tree, 19 leaves, max depth = 9, train loss: 0.54251, val loss: 0.53780, in 0.000s
1 tree, 19 leaves, max depth = 9, train loss: 0.53693, val loss: 0.53200, in 0.016s
1 tree, 7 leaves, max depth = 5, train loss: 0.53161, val loss: 0.52637, in 0.000s
1 tree, 19 leaves, max depth = 8, train loss: 0.52641, val loss: 0.52093, in 0.016s
1 tree, 19 leaves, max depth = 8, train loss: 0.52146, val loss: 0.51575, in 0.000s
1 tree, 19 leaves, max depth = 8, train loss: 0.51675, val loss: 0.51081, in 0.016s
1 tree, 19 leaves, max depth = 8, train loss: 0.51226, val loss: 0.50610, in 0.000s
1 tree, 19 leaves, max depth = 8, train loss: 0.50798, val loss: 0.50161, in 0.000s
1 tree, 19 leaves, max depth = 9, train loss: 0.50401, val loss: 0.49747, in 0.016s
1 tree, 19 leaves, max depth = 9, train loss: 0.50023, val loss: 0.49352, in 0.000s
1 tree, 19 leaves, max depth = 10, train loss: 0.49667, val loss: 0.48973, in 0.016s
1 tree, 19 leaves, max depth = 10, train loss: 0.49329, val loss: 0.48618, in 0.000s
1 tree, 19 leaves, max depth = 10, train loss: 0.49009, val loss: 0.48278, in 0.016s
1 tree, 19 leaves, max depth = 7, train loss: 0.48493, val loss: 0.47801, in 0.000s
1 tree, 19 leaves, max depth = 6, train loss: 0.48142, val loss: 0.47476, in 0.016s
1 tree, 19 leaves, max depth = 8, train loss: 0.47846, val loss: 0.47159, in 0.000s
1 tree, 19 leaves, max depth = 9, train loss: 0.47550, val loss: 0.46843, in 0.016s
1 tree, 19 leaves, max depth = 7, train loss: 0.47081, val loss: 0.46413, in 0.000s
1 tree, 19 leaves, max depth = 10, train loss: 0.46805, val loss: 0.46118, in 0.016s
1 tree, 19 leaves, max depth = 9, train loss: 0.46553, val loss: 0.45850, in 0.000s
1 tree, 19 leaves, max depth = 9, train loss: 0.46311, val loss: 0.45595, in 0.000s
1 tree, 19 leaves, max depth = 7, train loss: 0.45874, val loss: 0.45193, in 0.016s
1 tree, 19 leaves, max depth = 9, train loss: 0.45649, val loss: 0.44955, in 0.000s
1 tree, 19 leaves, max depth = 6, train loss: 0.45361, val loss: 0.44691, in 0.016s
1 tree, 19 leaves, max depth = 9, train loss: 0.45150, val loss: 0.44468, in 0.000s
1 tree, 19 leaves, max depth = 9, train loss: 0.44940, val loss: 0.44241, in 0.016s
1 tree, 19 leaves, max depth = 9, train loss: 0.44749, val loss: 0.44037, in 0.000s
1 tree, 19 leaves, max depth = 7, train loss: 0.44353, val loss: 0.43677, in 0.000s
1 tree, 19 leaves, max depth = 7, train loss: 0.44097, val loss: 0.43443, in 0.000s
1 tree, 19 leaves, max depth = 7, train loss: 0.43853, val loss: 0.43222, in 0.000s
1 tree, 19 leaves, max depth = 7, train loss: 0.43496, val loss: 0.42897, in 0.016s
1 tree, 19 leaves, max depth = 7, train loss: 0.43316, val loss: 0.42704, in 0.000s
1 tree, 19 leaves, max depth = 6, train loss: 0.43096, val loss: 0.42505, in 0.016s
1 tree, 19 leaves, max depth = 7, train loss: 0.42767, val loss: 0.42206, in 0.000s
1 tree, 19 leaves, max depth = 8, train loss: 0.42607, val loss: 0.42044, in 0.016s
1 tree, 19 leaves, max depth = 7, train loss: 0.42298, val loss: 0.41764, in 0.000s
1 tree, 19 leaves, max depth = 7, train loss: 0.42004, val loss: 0.41498, in 0.016s
1 tree, 19 leaves, max depth = 6, train loss: 0.41825, val loss: 0.41338, in 0.000s
1 tree, 19 leaves, max depth = 8, train loss: 0.41668, val loss: 0.41169, in 0.016s
1 tree, 19 leaves, max depth = 7, train loss: 0.41396, val loss: 0.40923, in 0.000s
1 tree, 7 leaves, max depth = 4, train loss: 0.41249, val loss: 0.40760, in 0.000s
1 tree, 19 leaves, max depth = 8, train loss: 0.41109, val loss: 0.40610, in 0.016s
1 tree, 19 leaves, max depth = 7, train loss: 0.40850, val loss: 0.40379, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.40666, val loss: 0.40176, in 0.016s
1 tree, 19 leaves, max depth = 6, train loss: 0.40505, val loss: 0.40035, in 0.000s
1 tree, 19 leaves, max depth = 7, train loss: 0.40265, val loss: 0.39822, in 0.016s
1 tree, 19 leaves, max depth = 9, train loss: 0.40143, val loss: 0.39688, in 0.000s
1 tree, 19 leaves, max depth = 8, train loss: 0.40022, val loss: 0.39559, in 0.016s
1 tree, 19 leaves, max depth = 9, train loss: 0.39912, val loss: 0.39437, in 0.000s
1 tree, 19 leaves, max depth = 6, train loss: 0.39772, val loss: 0.39316, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.39601, val loss: 0.39126, in 0.000s
1 tree, 19 leaves, max depth = 8, train loss: 0.39480, val loss: 0.39007, in 0.016s
1 tree, 19 leaves, max depth = 8, train loss: 0.39263, val loss: 0.38815, in 0.000s
1 tree, 19 leaves, max depth = 7, train loss: 0.39055, val loss: 0.38632, in 0.016s
1 tree, 19 leaves, max depth = 8, train loss: 0.38947, val loss: 0.38513, in 0.000s
1 tree, 19 leaves, max depth = 6, train loss: 0.38818, val loss: 0.38403, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.38656, val loss: 0.38223, in 0.000s
1 tree, 19 leaves, max depth = 8, train loss: 0.38551, val loss: 0.38120, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.38397, val loss: 0.37949, in 0.000s
1 tree, 19 leaves, max depth = 8, train loss: 0.38299, val loss: 0.37846, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.38152, val loss: 0.37683, in 0.000s
1 tree, 19 leaves, max depth = 12, train loss: 0.38065, val loss: 0.37592, in 0.016s
1 tree, 19 leaves, max depth = 8, train loss: 0.37978, val loss: 0.37496, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.37839, val loss: 0.37341, in 0.016s
1 tree, 19 leaves, max depth = 7, train loss: 0.37752, val loss: 0.37247, in 0.000s
1 tree, 19 leaves, max depth = 8, train loss: 0.37562, val loss: 0.37082, in 0.016s
1 tree, 19 leaves, max depth = 9, train loss: 0.37463, val loss: 0.36988, in 0.000s
1 tree, 19 leaves, max depth = 8, train loss: 0.37284, val loss: 0.36834, in 0.016s
Fit 84 trees in 1.032 s, (1476 total leaves)
Time spent computing histograms: 0.355s
Time spent finding best splits:  0.037s
Time spent applying splits:      0.033s
Time spent predicting:           0.016s
Trial 0, Fold 1: Log loss = 0.3744440833225494, Average precision = 0.9491437057327736, ROC-AUC = 0.9445759578296841, Elapsed Time = 1.0311564000003273 seconds
Trial 0, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 0, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
Binning 0.040 GB of training data: 0.158 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 19 leaves, max depth = 10, train loss: 0.68016, val loss: 0.67951, in 0.016s
1 tree, 19 leaves, max depth = 10, train loss: 0.66784, val loss: 0.66660, in 0.000s
1 tree, 19 leaves, max depth = 7, train loss: 0.65608, val loss: 0.65428, in 0.016s
1 tree, 7 leaves, max depth = 5, train loss: 0.64497, val loss: 0.64262, in 0.000s
1 tree, 19 leaves, max depth = 11, train loss: 0.63454, val loss: 0.63165, in 0.016s
1 tree, 19 leaves, max depth = 9, train loss: 0.62463, val loss: 0.62126, in 0.000s
1 tree, 19 leaves, max depth = 11, train loss: 0.61526, val loss: 0.61139, in 0.016s
1 tree, 19 leaves, max depth = 11, train loss: 0.60638, val loss: 0.60202, in 0.000s
1 tree, 19 leaves, max depth = 7, train loss: 0.59786, val loss: 0.59302, in 0.016s
1 tree, 19 leaves, max depth = 7, train loss: 0.58966, val loss: 0.58432, in 0.000s
1 tree, 19 leaves, max depth = 8, train loss: 0.58205, val loss: 0.57630, in 0.000s
1 tree, 19 leaves, max depth = 11, train loss: 0.57483, val loss: 0.56866, in 0.000s
1 tree, 19 leaves, max depth = 10, train loss: 0.56797, val loss: 0.56137, in 0.000s
1 tree, 19 leaves, max depth = 9, train loss: 0.56146, val loss: 0.55444, in 0.016s
1 tree, 19 leaves, max depth = 11, train loss: 0.55526, val loss: 0.54786, in 0.016s
1 tree, 19 leaves, max depth = 8, train loss: 0.54889, val loss: 0.54169, in 0.000s
1 tree, 19 leaves, max depth = 7, train loss: 0.54303, val loss: 0.53546, in 0.000s
1 tree, 19 leaves, max depth = 7, train loss: 0.53745, val loss: 0.52952, in 0.000s
1 tree, 7 leaves, max depth = 5, train loss: 0.53212, val loss: 0.52385, in 0.000s
1 tree, 19 leaves, max depth = 7, train loss: 0.52696, val loss: 0.51831, in 0.016s
1 tree, 19 leaves, max depth = 7, train loss: 0.52205, val loss: 0.51304, in 0.016s
1 tree, 19 leaves, max depth = 7, train loss: 0.51737, val loss: 0.50801, in 0.000s
1 tree, 19 leaves, max depth = 7, train loss: 0.51291, val loss: 0.50321, in 0.016s
1 tree, 19 leaves, max depth = 7, train loss: 0.50867, val loss: 0.49863, in 0.000s
1 tree, 19 leaves, max depth = 7, train loss: 0.50470, val loss: 0.49438, in 0.016s
1 tree, 19 leaves, max depth = 7, train loss: 0.50093, val loss: 0.49032, in 0.000s
1 tree, 19 leaves, max depth = 8, train loss: 0.49737, val loss: 0.48652, in 0.016s
1 tree, 19 leaves, max depth = 12, train loss: 0.49398, val loss: 0.48286, in 0.000s
1 tree, 19 leaves, max depth = 11, train loss: 0.49076, val loss: 0.47936, in 0.000s
1 tree, 19 leaves, max depth = 8, train loss: 0.48563, val loss: 0.47443, in 0.016s
1 tree, 19 leaves, max depth = 12, train loss: 0.48263, val loss: 0.47118, in 0.000s
1 tree, 19 leaves, max depth = 8, train loss: 0.47976, val loss: 0.46810, in 0.016s
1 tree, 19 leaves, max depth = 7, train loss: 0.47690, val loss: 0.46498, in 0.000s
1 tree, 19 leaves, max depth = 7, train loss: 0.47213, val loss: 0.46041, in 0.016s
1 tree, 19 leaves, max depth = 7, train loss: 0.46947, val loss: 0.45750, in 0.000s
1 tree, 19 leaves, max depth = 7, train loss: 0.46702, val loss: 0.45481, in 0.016s
1 tree, 19 leaves, max depth = 8, train loss: 0.46467, val loss: 0.45226, in 0.000s
1 tree, 19 leaves, max depth = 7, train loss: 0.46022, val loss: 0.44801, in 0.016s
1 tree, 19 leaves, max depth = 8, train loss: 0.45804, val loss: 0.44562, in 0.000s
1 tree, 19 leaves, max depth = 6, train loss: 0.45509, val loss: 0.44287, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.45307, val loss: 0.44079, in 0.000s
1 tree, 19 leaves, max depth = 7, train loss: 0.45098, val loss: 0.43848, in 0.016s
1 tree, 19 leaves, max depth = 10, train loss: 0.44895, val loss: 0.43672, in 0.000s
1 tree, 19 leaves, max depth = 7, train loss: 0.44497, val loss: 0.43294, in 0.016s
1 tree, 19 leaves, max depth = 5, train loss: 0.44236, val loss: 0.43053, in 0.000s
1 tree, 19 leaves, max depth = 5, train loss: 0.43989, val loss: 0.42824, in 0.016s
1 tree, 19 leaves, max depth = 7, train loss: 0.43631, val loss: 0.42485, in 0.000s
1 tree, 19 leaves, max depth = 7, train loss: 0.43444, val loss: 0.42283, in 0.016s
1 tree, 19 leaves, max depth = 6, train loss: 0.43221, val loss: 0.42079, in 0.000s
1 tree, 19 leaves, max depth = 7, train loss: 0.42890, val loss: 0.41765, in 0.016s
1 tree, 19 leaves, max depth = 8, train loss: 0.42728, val loss: 0.41590, in 0.000s
1 tree, 19 leaves, max depth = 7, train loss: 0.42413, val loss: 0.41292, in 0.016s
1 tree, 19 leaves, max depth = 7, train loss: 0.42113, val loss: 0.41008, in 0.016s
1 tree, 19 leaves, max depth = 6, train loss: 0.41929, val loss: 0.40842, in 0.000s
1 tree, 19 leaves, max depth = 8, train loss: 0.41780, val loss: 0.40682, in 0.016s
1 tree, 19 leaves, max depth = 6, train loss: 0.41503, val loss: 0.40419, in 0.000s
1 tree, 7 leaves, max depth = 4, train loss: 0.41356, val loss: 0.40257, in 0.016s
1 tree, 19 leaves, max depth = 13, train loss: 0.41225, val loss: 0.40113, in 0.000s
1 tree, 19 leaves, max depth = 6, train loss: 0.40961, val loss: 0.39862, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.40789, val loss: 0.39685, in 0.000s
1 tree, 19 leaves, max depth = 8, train loss: 0.40653, val loss: 0.39571, in 0.016s
1 tree, 19 leaves, max depth = 6, train loss: 0.40406, val loss: 0.39338, in 0.000s
1 tree, 19 leaves, max depth = 6, train loss: 0.40258, val loss: 0.39209, in 0.000s
1 tree, 19 leaves, max depth = 8, train loss: 0.40131, val loss: 0.39070, in 0.016s
1 tree, 19 leaves, max depth = 11, train loss: 0.40018, val loss: 0.38943, in 0.000s
1 tree, 19 leaves, max depth = 6, train loss: 0.39879, val loss: 0.38822, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.39719, val loss: 0.38656, in 0.000s
1 tree, 19 leaves, max depth = 6, train loss: 0.39597, val loss: 0.38556, in 0.000s
1 tree, 19 leaves, max depth = 8, train loss: 0.39378, val loss: 0.38351, in 0.016s
1 tree, 19 leaves, max depth = 6, train loss: 0.39170, val loss: 0.38155, in 0.000s
1 tree, 19 leaves, max depth = 8, train loss: 0.39058, val loss: 0.38029, in 0.016s
1 tree, 19 leaves, max depth = 6, train loss: 0.38924, val loss: 0.37911, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.38773, val loss: 0.37754, in 0.000s
1 tree, 19 leaves, max depth = 9, train loss: 0.38667, val loss: 0.37669, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.38523, val loss: 0.37519, in 0.000s
1 tree, 19 leaves, max depth = 9, train loss: 0.38419, val loss: 0.37406, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.38282, val loss: 0.37263, in 0.000s
1 tree, 19 leaves, max depth = 7, train loss: 0.38159, val loss: 0.37161, in 0.016s
1 tree, 19 leaves, max depth = 9, train loss: 0.38065, val loss: 0.37060, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.37934, val loss: 0.36924, in 0.016s
1 tree, 19 leaves, max depth = 9, train loss: 0.37841, val loss: 0.36822, in 0.000s
1 tree, 19 leaves, max depth = 6, train loss: 0.37655, val loss: 0.36649, in 0.016s
1 tree, 19 leaves, max depth = 6, train loss: 0.37558, val loss: 0.36572, in 0.000s
1 tree, 19 leaves, max depth = 6, train loss: 0.37384, val loss: 0.36411, in 0.016s
Fit 84 trees in 0.940 s, (1462 total leaves)
Time spent computing histograms: 0.374s
Time spent finding best splits:  0.037s
Time spent applying splits:      0.034s
Time spent predicting:           0.000s
Trial 0, Fold 2: Log loss = 0.375627800803536, Average precision = 0.9455558130432133, ROC-AUC = 0.9450537961074265, Elapsed Time = 0.9430072000013752 seconds
Trial 0, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 0, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
Binning 0.040 GB of training data: 0.157 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 19 leaves, max depth = 10, train loss: 0.68016, val loss: 0.67973, in 0.000s
1 tree, 19 leaves, max depth = 7, train loss: 0.66781, val loss: 0.66692, in 0.016s
1 tree, 19 leaves, max depth = 8, train loss: 0.65615, val loss: 0.65478, in 0.000s
1 tree, 7 leaves, max depth = 4, train loss: 0.64511, val loss: 0.64333, in 0.000s
1 tree, 19 leaves, max depth = 7, train loss: 0.63465, val loss: 0.63246, in 0.016s
1 tree, 19 leaves, max depth = 7, train loss: 0.62475, val loss: 0.62216, in 0.000s
1 tree, 19 leaves, max depth = 9, train loss: 0.61545, val loss: 0.61249, in 0.016s
1 tree, 19 leaves, max depth = 12, train loss: 0.60665, val loss: 0.60333, in 0.000s
1 tree, 19 leaves, max depth = 9, train loss: 0.59820, val loss: 0.59451, in 0.016s
1 tree, 19 leaves, max depth = 7, train loss: 0.59006, val loss: 0.58607, in 0.000s
1 tree, 19 leaves, max depth = 8, train loss: 0.58248, val loss: 0.57825, in 0.016s
1 tree, 19 leaves, max depth = 12, train loss: 0.57533, val loss: 0.57078, in 0.000s
1 tree, 19 leaves, max depth = 7, train loss: 0.56844, val loss: 0.56358, in 0.016s
1 tree, 19 leaves, max depth = 7, train loss: 0.56198, val loss: 0.55683, in 0.000s
1 tree, 19 leaves, max depth = 7, train loss: 0.55574, val loss: 0.55029, in 0.000s
1 tree, 19 leaves, max depth = 7, train loss: 0.54935, val loss: 0.54430, in 0.016s
1 tree, 19 leaves, max depth = 8, train loss: 0.54356, val loss: 0.53819, in 0.000s
1 tree, 19 leaves, max depth = 8, train loss: 0.53806, val loss: 0.53237, in 0.016s
1 tree, 7 leaves, max depth = 5, train loss: 0.53275, val loss: 0.52682, in 0.000s
1 tree, 19 leaves, max depth = 7, train loss: 0.52764, val loss: 0.52150, in 0.016s
1 tree, 19 leaves, max depth = 7, train loss: 0.52278, val loss: 0.51642, in 0.000s
1 tree, 19 leaves, max depth = 7, train loss: 0.51815, val loss: 0.51158, in 0.016s
1 tree, 19 leaves, max depth = 7, train loss: 0.51375, val loss: 0.50696, in 0.000s
1 tree, 19 leaves, max depth = 7, train loss: 0.50955, val loss: 0.50256, in 0.000s
1 tree, 19 leaves, max depth = 7, train loss: 0.50563, val loss: 0.49841, in 0.000s
1 tree, 19 leaves, max depth = 9, train loss: 0.50191, val loss: 0.49443, in 0.016s
1 tree, 19 leaves, max depth = 9, train loss: 0.49836, val loss: 0.49075, in 0.000s
1 tree, 19 leaves, max depth = 10, train loss: 0.49500, val loss: 0.48718, in 0.016s
1 tree, 19 leaves, max depth = 12, train loss: 0.49181, val loss: 0.48378, in 0.000s
1 tree, 19 leaves, max depth = 8, train loss: 0.48668, val loss: 0.47902, in 0.016s
1 tree, 19 leaves, max depth = 7, train loss: 0.48316, val loss: 0.47580, in 0.000s
1 tree, 19 leaves, max depth = 10, train loss: 0.48022, val loss: 0.47274, in 0.000s
1 tree, 19 leaves, max depth = 7, train loss: 0.47732, val loss: 0.46969, in 0.016s
1 tree, 19 leaves, max depth = 7, train loss: 0.47266, val loss: 0.46538, in 0.000s
1 tree, 19 leaves, max depth = 7, train loss: 0.46996, val loss: 0.46253, in 0.016s
1 tree, 19 leaves, max depth = 8, train loss: 0.46747, val loss: 0.45984, in 0.000s
1 tree, 19 leaves, max depth = 7, train loss: 0.46498, val loss: 0.45715, in 0.016s
1 tree, 19 leaves, max depth = 7, train loss: 0.46065, val loss: 0.45316, in 0.000s
1 tree, 19 leaves, max depth = 7, train loss: 0.45843, val loss: 0.45076, in 0.016s
1 tree, 19 leaves, max depth = 7, train loss: 0.45556, val loss: 0.44816, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.45346, val loss: 0.44624, in 0.016s
1 tree, 19 leaves, max depth = 7, train loss: 0.45133, val loss: 0.44397, in 0.000s
1 tree, 19 leaves, max depth = 9, train loss: 0.44936, val loss: 0.44184, in 0.016s
1 tree, 19 leaves, max depth = 8, train loss: 0.44543, val loss: 0.43824, in 0.000s
1 tree, 19 leaves, max depth = 7, train loss: 0.44288, val loss: 0.43595, in 0.016s
1 tree, 19 leaves, max depth = 7, train loss: 0.44045, val loss: 0.43378, in 0.000s
1 tree, 19 leaves, max depth = 6, train loss: 0.43691, val loss: 0.43055, in 0.016s
1 tree, 19 leaves, max depth = 7, train loss: 0.43511, val loss: 0.42855, in 0.000s
1 tree, 19 leaves, max depth = 7, train loss: 0.43292, val loss: 0.42662, in 0.016s
1 tree, 19 leaves, max depth = 7, train loss: 0.42965, val loss: 0.42364, in 0.000s
1 tree, 19 leaves, max depth = 9, train loss: 0.42798, val loss: 0.42217, in 0.000s
1 tree, 19 leaves, max depth = 7, train loss: 0.42492, val loss: 0.41939, in 0.016s
1 tree, 19 leaves, max depth = 7, train loss: 0.42199, val loss: 0.41674, in 0.000s
1 tree, 19 leaves, max depth = 7, train loss: 0.42040, val loss: 0.41497, in 0.016s
1 tree, 19 leaves, max depth = 7, train loss: 0.41888, val loss: 0.41327, in 0.000s
1 tree, 19 leaves, max depth = 7, train loss: 0.41612, val loss: 0.41077, in 0.016s
1 tree, 7 leaves, max depth = 5, train loss: 0.41465, val loss: 0.40918, in 0.000s
1 tree, 19 leaves, max depth = 7, train loss: 0.41329, val loss: 0.40767, in 0.000s
1 tree, 19 leaves, max depth = 7, train loss: 0.41066, val loss: 0.40531, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.40887, val loss: 0.40365, in 0.000s
1 tree, 19 leaves, max depth = 7, train loss: 0.40721, val loss: 0.40213, in 0.016s
1 tree, 19 leaves, max depth = 8, train loss: 0.40478, val loss: 0.39997, in 0.000s
1 tree, 19 leaves, max depth = 7, train loss: 0.40331, val loss: 0.39874, in 0.016s
1 tree, 19 leaves, max depth = 7, train loss: 0.40210, val loss: 0.39738, in 0.000s
1 tree, 19 leaves, max depth = 11, train loss: 0.40098, val loss: 0.39616, in 0.016s
1 tree, 19 leaves, max depth = 7, train loss: 0.39960, val loss: 0.39502, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.39793, val loss: 0.39348, in 0.000s
1 tree, 19 leaves, max depth = 10, train loss: 0.39666, val loss: 0.39242, in 0.000s
1 tree, 19 leaves, max depth = 7, train loss: 0.39450, val loss: 0.39052, in 0.000s
1 tree, 19 leaves, max depth = 8, train loss: 0.39244, val loss: 0.38871, in 0.016s
1 tree, 19 leaves, max depth = 7, train loss: 0.39138, val loss: 0.38752, in 0.000s
1 tree, 19 leaves, max depth = 7, train loss: 0.39009, val loss: 0.38635, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.38852, val loss: 0.38489, in 0.000s
1 tree, 19 leaves, max depth = 10, train loss: 0.38739, val loss: 0.38398, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.38590, val loss: 0.38259, in 0.000s
1 tree, 19 leaves, max depth = 8, train loss: 0.38491, val loss: 0.38148, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.38349, val loss: 0.38016, in 0.000s
1 tree, 19 leaves, max depth = 14, train loss: 0.38257, val loss: 0.37915, in 0.016s
1 tree, 19 leaves, max depth = 8, train loss: 0.38166, val loss: 0.37821, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.38031, val loss: 0.37696, in 0.016s
1 tree, 19 leaves, max depth = 7, train loss: 0.37943, val loss: 0.37600, in 0.000s
1 tree, 19 leaves, max depth = 8, train loss: 0.37753, val loss: 0.37434, in 0.016s
1 tree, 19 leaves, max depth = 11, train loss: 0.37647, val loss: 0.37350, in 0.000s
1 tree, 19 leaves, max depth = 8, train loss: 0.37470, val loss: 0.37196, in 0.016s
Fit 84 trees in 0.923 s, (1462 total leaves)
Time spent computing histograms: 0.352s
Time spent finding best splits:  0.037s
Time spent applying splits:      0.033s
Time spent predicting:           0.000s
Trial 0, Fold 3: Log loss = 0.36979576733385733, Average precision = 0.9509140324614288, ROC-AUC = 0.9483565090146489, Elapsed Time = 0.9244606000011117 seconds
Trial 0, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 0, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
Binning 0.040 GB of training data: 0.142 s
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 19 leaves, max depth = 9, train loss: 0.68017, val loss: 0.67941, in 0.000s
1 tree, 19 leaves, max depth = 8, train loss: 0.66780, val loss: 0.66634, in 0.016s
1 tree, 19 leaves, max depth = 8, train loss: 0.65611, val loss: 0.65397, in 0.000s
1 tree, 6 leaves, max depth = 4, train loss: 0.64506, val loss: 0.64229, in 0.016s
1 tree, 19 leaves, max depth = 8, train loss: 0.63458, val loss: 0.63116, in 0.000s
1 tree, 19 leaves, max depth = 8, train loss: 0.62466, val loss: 0.62060, in 0.016s
1 tree, 19 leaves, max depth = 10, train loss: 0.61534, val loss: 0.61068, in 0.000s
1 tree, 19 leaves, max depth = 9, train loss: 0.60650, val loss: 0.60129, in 0.016s
1 tree, 19 leaves, max depth = 10, train loss: 0.59803, val loss: 0.59221, in 0.016s
1 tree, 19 leaves, max depth = 9, train loss: 0.58990, val loss: 0.58345, in 0.000s
1 tree, 19 leaves, max depth = 13, train loss: 0.58231, val loss: 0.57527, in 0.016s
1 tree, 19 leaves, max depth = 9, train loss: 0.57514, val loss: 0.56758, in 0.000s
1 tree, 19 leaves, max depth = 8, train loss: 0.56823, val loss: 0.56014, in 0.016s
1 tree, 19 leaves, max depth = 9, train loss: 0.56175, val loss: 0.55310, in 0.000s
1 tree, 19 leaves, max depth = 9, train loss: 0.55550, val loss: 0.54633, in 0.016s
1 tree, 19 leaves, max depth = 7, train loss: 0.54915, val loss: 0.54007, in 0.000s
1 tree, 19 leaves, max depth = 8, train loss: 0.54333, val loss: 0.53375, in 0.016s
1 tree, 19 leaves, max depth = 8, train loss: 0.53780, val loss: 0.52773, in 0.000s
1 tree, 7 leaves, max depth = 5, train loss: 0.53249, val loss: 0.52198, in 0.016s
1 tree, 19 leaves, max depth = 9, train loss: 0.52739, val loss: 0.51637, in 0.000s
1 tree, 19 leaves, max depth = 9, train loss: 0.52253, val loss: 0.51101, in 0.016s
1 tree, 19 leaves, max depth = 9, train loss: 0.51790, val loss: 0.50590, in 0.000s
1 tree, 19 leaves, max depth = 9, train loss: 0.51350, val loss: 0.50102, in 0.016s
1 tree, 19 leaves, max depth = 9, train loss: 0.50930, val loss: 0.49636, in 0.000s
1 tree, 19 leaves, max depth = 9, train loss: 0.50537, val loss: 0.49201, in 0.016s
1 tree, 19 leaves, max depth = 9, train loss: 0.50163, val loss: 0.48785, in 0.000s
1 tree, 19 leaves, max depth = 13, train loss: 0.49808, val loss: 0.48388, in 0.016s
1 tree, 19 leaves, max depth = 10, train loss: 0.49470, val loss: 0.48013, in 0.000s
1 tree, 19 leaves, max depth = 9, train loss: 0.49148, val loss: 0.47655, in 0.016s
1 tree, 19 leaves, max depth = 7, train loss: 0.48639, val loss: 0.47158, in 0.000s
1 tree, 19 leaves, max depth = 7, train loss: 0.48323, val loss: 0.46807, in 0.016s
1 tree, 19 leaves, max depth = 12, train loss: 0.48037, val loss: 0.46482, in 0.000s
1 tree, 19 leaves, max depth = 9, train loss: 0.47754, val loss: 0.46161, in 0.016s
1 tree, 19 leaves, max depth = 7, train loss: 0.47282, val loss: 0.45703, in 0.016s
1 tree, 19 leaves, max depth = 9, train loss: 0.47020, val loss: 0.45403, in 0.000s
1 tree, 19 leaves, max depth = 8, train loss: 0.46776, val loss: 0.45126, in 0.016s
1 tree, 19 leaves, max depth = 8, train loss: 0.46544, val loss: 0.44861, in 0.000s
1 tree, 19 leaves, max depth = 6, train loss: 0.46101, val loss: 0.44433, in 0.016s
1 tree, 19 leaves, max depth = 8, train loss: 0.45885, val loss: 0.44185, in 0.000s
1 tree, 19 leaves, max depth = 6, train loss: 0.45593, val loss: 0.43903, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.45393, val loss: 0.43689, in 0.000s
1 tree, 19 leaves, max depth = 9, train loss: 0.45187, val loss: 0.43450, in 0.016s
1 tree, 19 leaves, max depth = 10, train loss: 0.44987, val loss: 0.43245, in 0.000s
1 tree, 19 leaves, max depth = 7, train loss: 0.44592, val loss: 0.42865, in 0.016s
1 tree, 19 leaves, max depth = 6, train loss: 0.44334, val loss: 0.42618, in 0.000s
1 tree, 19 leaves, max depth = 6, train loss: 0.44090, val loss: 0.42382, in 0.016s
1 tree, 19 leaves, max depth = 6, train loss: 0.43733, val loss: 0.42040, in 0.016s
1 tree, 19 leaves, max depth = 7, train loss: 0.43548, val loss: 0.41828, in 0.000s
1 tree, 19 leaves, max depth = 6, train loss: 0.43328, val loss: 0.41616, in 0.016s
1 tree, 19 leaves, max depth = 6, train loss: 0.43000, val loss: 0.41302, in 0.016s
1 tree, 19 leaves, max depth = 13, train loss: 0.42836, val loss: 0.41112, in 0.016s
1 tree, 19 leaves, max depth = 6, train loss: 0.42524, val loss: 0.40813, in 0.016s
1 tree, 19 leaves, max depth = 6, train loss: 0.42226, val loss: 0.40528, in 0.000s
1 tree, 19 leaves, max depth = 6, train loss: 0.42044, val loss: 0.40352, in 0.047s
1 tree, 19 leaves, max depth = 7, train loss: 0.41889, val loss: 0.40173, in 0.016s
1 tree, 19 leaves, max depth = 8, train loss: 0.41613, val loss: 0.39910, in 0.016s
1 tree, 7 leaves, max depth = 5, train loss: 0.41465, val loss: 0.39742, in 0.016s
1 tree, 19 leaves, max depth = 8, train loss: 0.41326, val loss: 0.39579, in 0.000s
1 tree, 19 leaves, max depth = 8, train loss: 0.41064, val loss: 0.39331, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.40892, val loss: 0.39146, in 0.016s
1 tree, 19 leaves, max depth = 8, train loss: 0.40727, val loss: 0.38990, in 0.016s
1 tree, 19 leaves, max depth = 8, train loss: 0.40485, val loss: 0.38759, in 0.000s
1 tree, 19 leaves, max depth = 6, train loss: 0.40342, val loss: 0.38621, in 0.000s
1 tree, 19 leaves, max depth = 8, train loss: 0.40217, val loss: 0.38475, in 0.016s
1 tree, 19 leaves, max depth = 10, train loss: 0.40103, val loss: 0.38338, in 0.016s
1 tree, 19 leaves, max depth = 6, train loss: 0.39968, val loss: 0.38209, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.39809, val loss: 0.38037, in 0.016s
1 tree, 19 leaves, max depth = 6, train loss: 0.39686, val loss: 0.37911, in 0.000s
1 tree, 19 leaves, max depth = 8, train loss: 0.39472, val loss: 0.37709, in 0.016s
1 tree, 19 leaves, max depth = 7, train loss: 0.39267, val loss: 0.37517, in 0.016s
1 tree, 19 leaves, max depth = 9, train loss: 0.39158, val loss: 0.37386, in 0.000s
1 tree, 19 leaves, max depth = 8, train loss: 0.39029, val loss: 0.37265, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.38878, val loss: 0.37102, in 0.000s
1 tree, 19 leaves, max depth = 7, train loss: 0.38771, val loss: 0.36993, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.38627, val loss: 0.36837, in 0.016s
1 tree, 19 leaves, max depth = 7, train loss: 0.38525, val loss: 0.36718, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.38388, val loss: 0.36570, in 0.016s
1 tree, 19 leaves, max depth = 9, train loss: 0.38297, val loss: 0.36462, in 0.016s
1 tree, 19 leaves, max depth = 9, train loss: 0.38207, val loss: 0.36354, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.38077, val loss: 0.36214, in 0.016s
1 tree, 19 leaves, max depth = 7, train loss: 0.37990, val loss: 0.36111, in 0.000s
1 tree, 19 leaves, max depth = 8, train loss: 0.37802, val loss: 0.35936, in 0.016s
1 tree, 19 leaves, max depth = 9, train loss: 0.37701, val loss: 0.35835, in 0.016s
1 tree, 19 leaves, max depth = 8, train loss: 0.37526, val loss: 0.35672, in 0.000s
Fit 84 trees in 1.126 s, (1461 total leaves)
Time spent computing histograms: 0.462s
Time spent finding best splits:  0.064s
Time spent applying splits:      0.050s
Time spent predicting:           0.000s
Trial 0, Fold 4: Log loss = 0.37527621904651487, Average precision = 0.9487464960271949, ROC-AUC = 0.9447211104911646, Elapsed Time = 1.1381462000008469 seconds
Trial 0, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 0, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
Binning 0.040 GB of training data: 0.203 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 19 leaves, max depth = 10, train loss: 0.67998, val loss: 0.67905, in 0.016s
1 tree, 19 leaves, max depth = 8, train loss: 0.66756, val loss: 0.66584, in 0.000s
1 tree, 19 leaves, max depth = 9, train loss: 0.65575, val loss: 0.65320, in 0.016s
1 tree, 7 leaves, max depth = 5, train loss: 0.64456, val loss: 0.64124, in 0.000s
1 tree, 19 leaves, max depth = 8, train loss: 0.63405, val loss: 0.62998, in 0.016s
1 tree, 19 leaves, max depth = 7, train loss: 0.62408, val loss: 0.61925, in 0.000s
1 tree, 19 leaves, max depth = 9, train loss: 0.61465, val loss: 0.60911, in 0.000s
1 tree, 19 leaves, max depth = 10, train loss: 0.60571, val loss: 0.59947, in 0.016s
1 tree, 19 leaves, max depth = 7, train loss: 0.59715, val loss: 0.59025, in 0.000s
1 tree, 19 leaves, max depth = 8, train loss: 0.58892, val loss: 0.58133, in 0.016s
1 tree, 19 leaves, max depth = 7, train loss: 0.58125, val loss: 0.57300, in 0.016s
1 tree, 19 leaves, max depth = 9, train loss: 0.57398, val loss: 0.56510, in 0.000s
1 tree, 19 leaves, max depth = 9, train loss: 0.56707, val loss: 0.55758, in 0.000s
1 tree, 19 leaves, max depth = 9, train loss: 0.56052, val loss: 0.55037, in 0.016s
1 tree, 19 leaves, max depth = 9, train loss: 0.55426, val loss: 0.54354, in 0.000s
1 tree, 19 leaves, max depth = 9, train loss: 0.54799, val loss: 0.53751, in 0.016s
1 tree, 19 leaves, max depth = 9, train loss: 0.54209, val loss: 0.53104, in 0.000s
1 tree, 19 leaves, max depth = 9, train loss: 0.53648, val loss: 0.52487, in 0.016s
1 tree, 6 leaves, max depth = 4, train loss: 0.53111, val loss: 0.51897, in 0.016s
1 tree, 19 leaves, max depth = 7, train loss: 0.52593, val loss: 0.51324, in 0.000s
1 tree, 19 leaves, max depth = 8, train loss: 0.52100, val loss: 0.50778, in 0.016s
1 tree, 19 leaves, max depth = 8, train loss: 0.51631, val loss: 0.50257, in 0.000s
1 tree, 19 leaves, max depth = 8, train loss: 0.51183, val loss: 0.49759, in 0.016s
1 tree, 19 leaves, max depth = 8, train loss: 0.50757, val loss: 0.49283, in 0.000s
1 tree, 19 leaves, max depth = 9, train loss: 0.50358, val loss: 0.48837, in 0.016s
1 tree, 19 leaves, max depth = 9, train loss: 0.49978, val loss: 0.48410, in 0.000s
1 tree, 19 leaves, max depth = 7, train loss: 0.49620, val loss: 0.48005, in 0.016s
1 tree, 19 leaves, max depth = 10, train loss: 0.49278, val loss: 0.47621, in 0.016s
1 tree, 19 leaves, max depth = 10, train loss: 0.48953, val loss: 0.47254, in 0.000s
1 tree, 19 leaves, max depth = 7, train loss: 0.48449, val loss: 0.46783, in 0.016s
1 tree, 19 leaves, max depth = 11, train loss: 0.48146, val loss: 0.46439, in 0.000s
1 tree, 19 leaves, max depth = 7, train loss: 0.47856, val loss: 0.46108, in 0.016s
1 tree, 19 leaves, max depth = 8, train loss: 0.47569, val loss: 0.45781, in 0.000s
1 tree, 19 leaves, max depth = 7, train loss: 0.47101, val loss: 0.45347, in 0.016s
1 tree, 19 leaves, max depth = 8, train loss: 0.46834, val loss: 0.45041, in 0.000s
1 tree, 19 leaves, max depth = 8, train loss: 0.46589, val loss: 0.44760, in 0.016s
1 tree, 19 leaves, max depth = 8, train loss: 0.46352, val loss: 0.44486, in 0.000s
1 tree, 19 leaves, max depth = 7, train loss: 0.45915, val loss: 0.44084, in 0.016s
1 tree, 19 leaves, max depth = 8, train loss: 0.45695, val loss: 0.43828, in 0.016s
1 tree, 19 leaves, max depth = 7, train loss: 0.45404, val loss: 0.43575, in 0.000s
1 tree, 19 leaves, max depth = 8, train loss: 0.45199, val loss: 0.43335, in 0.016s
1 tree, 19 leaves, max depth = 7, train loss: 0.44995, val loss: 0.43097, in 0.000s
1 tree, 19 leaves, max depth = 7, train loss: 0.44796, val loss: 0.42898, in 0.016s
1 tree, 19 leaves, max depth = 6, train loss: 0.44404, val loss: 0.42539, in 0.000s
1 tree, 19 leaves, max depth = 7, train loss: 0.44149, val loss: 0.42320, in 0.016s
1 tree, 19 leaves, max depth = 7, train loss: 0.43905, val loss: 0.42112, in 0.000s
1 tree, 19 leaves, max depth = 6, train loss: 0.43555, val loss: 0.41794, in 0.016s
1 tree, 19 leaves, max depth = 7, train loss: 0.43375, val loss: 0.41583, in 0.000s
1 tree, 19 leaves, max depth = 7, train loss: 0.43156, val loss: 0.41399, in 0.016s
1 tree, 19 leaves, max depth = 6, train loss: 0.42831, val loss: 0.41103, in 0.000s
1 tree, 19 leaves, max depth = 7, train loss: 0.42673, val loss: 0.40915, in 0.016s
1 tree, 19 leaves, max depth = 6, train loss: 0.42363, val loss: 0.40634, in 0.016s
1 tree, 19 leaves, max depth = 7, train loss: 0.42070, val loss: 0.40372, in 0.000s
1 tree, 19 leaves, max depth = 7, train loss: 0.41889, val loss: 0.40224, in 0.016s
1 tree, 19 leaves, max depth = 7, train loss: 0.41744, val loss: 0.40050, in 0.000s
1 tree, 19 leaves, max depth = 6, train loss: 0.41470, val loss: 0.39805, in 0.016s
1 tree, 7 leaves, max depth = 5, train loss: 0.41327, val loss: 0.39635, in 0.000s
1 tree, 19 leaves, max depth = 11, train loss: 0.41199, val loss: 0.39482, in 0.016s
1 tree, 19 leaves, max depth = 6, train loss: 0.40938, val loss: 0.39249, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.40759, val loss: 0.39077, in 0.016s
1 tree, 19 leaves, max depth = 7, train loss: 0.40625, val loss: 0.38947, in 0.000s
1 tree, 19 leaves, max depth = 6, train loss: 0.40382, val loss: 0.38730, in 0.016s
1 tree, 19 leaves, max depth = 7, train loss: 0.40236, val loss: 0.38616, in 0.000s
1 tree, 19 leaves, max depth = 8, train loss: 0.40115, val loss: 0.38468, in 0.016s
1 tree, 19 leaves, max depth = 9, train loss: 0.40002, val loss: 0.38330, in 0.000s
1 tree, 19 leaves, max depth = 7, train loss: 0.39865, val loss: 0.38225, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.39698, val loss: 0.38065, in 0.000s
1 tree, 19 leaves, max depth = 8, train loss: 0.39579, val loss: 0.37948, in 0.000s
1 tree, 19 leaves, max depth = 6, train loss: 0.39363, val loss: 0.37758, in 0.016s
1 tree, 19 leaves, max depth = 7, train loss: 0.39157, val loss: 0.37576, in 0.000s
1 tree, 19 leaves, max depth = 7, train loss: 0.39049, val loss: 0.37445, in 0.016s
1 tree, 19 leaves, max depth = 6, train loss: 0.38919, val loss: 0.37320, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.38761, val loss: 0.37170, in 0.016s
1 tree, 19 leaves, max depth = 8, train loss: 0.38656, val loss: 0.37069, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.38506, val loss: 0.36925, in 0.016s
1 tree, 19 leaves, max depth = 8, train loss: 0.38407, val loss: 0.36804, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.38264, val loss: 0.36668, in 0.016s
1 tree, 19 leaves, max depth = 9, train loss: 0.38140, val loss: 0.36558, in 0.000s
1 tree, 19 leaves, max depth = 7, train loss: 0.38048, val loss: 0.36445, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.37913, val loss: 0.36316, in 0.000s
1 tree, 19 leaves, max depth = 8, train loss: 0.37824, val loss: 0.36206, in 0.016s
1 tree, 19 leaves, max depth = 7, train loss: 0.37640, val loss: 0.36047, in 0.016s
1 tree, 19 leaves, max depth = 8, train loss: 0.37544, val loss: 0.35956, in 0.000s
1 tree, 19 leaves, max depth = 6, train loss: 0.37372, val loss: 0.35808, in 0.016s
Fit 84 trees in 1.063 s, (1475 total leaves)
Time spent computing histograms: 0.406s
Time spent finding best splits:  0.041s
Time spent applying splits:      0.037s
Time spent predicting:           0.000s
Trial 0, Fold 5: Log loss = 0.3795476722528022, Average precision = 0.9488541635377791, ROC-AUC = 0.9447077271798302, Elapsed Time = 1.0641242000001512 seconds
Optimization Progress:   1%|1         | 1/100 [00:12<21:16, 12.90s/it]
Trial 1, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 1, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
0.143 s 0.040 GB of training data: 
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 64 leaves, max depth = 14, train loss: 0.67836, val loss: 0.67826, in 0.016s
1 tree, 101 leaves, max depth = 13, train loss: 0.66426, val loss: 0.66417, in 0.016s
1 tree, 97 leaves, max depth = 12, train loss: 0.65085, val loss: 0.65081, in 0.016s
1 tree, 91 leaves, max depth = 13, train loss: 0.63799, val loss: 0.63795, in 0.016s
1 tree, 93 leaves, max depth = 13, train loss: 0.62573, val loss: 0.62569, in 0.016s
1 tree, 101 leaves, max depth = 13, train loss: 0.61408, val loss: 0.61410, in 0.016s
1 tree, 93 leaves, max depth = 13, train loss: 0.60293, val loss: 0.60296, in 0.016s
1 tree, 93 leaves, max depth = 13, train loss: 0.59236, val loss: 0.59238, in 0.016s
1 tree, 118 leaves, max depth = 13, train loss: 0.58230, val loss: 0.58222, in 0.016s
1 tree, 136 leaves, max depth = 14, train loss: 0.57288, val loss: 0.57261, in 0.031s
1 tree, 122 leaves, max depth = 14, train loss: 0.56366, val loss: 0.56332, in 0.016s
1 tree, 67 leaves, max depth = 10, train loss: 0.55492, val loss: 0.55450, in 0.016s
1 tree, 67 leaves, max depth = 10, train loss: 0.54656, val loss: 0.54604, in 0.016s
1 tree, 96 leaves, max depth = 13, train loss: 0.53829, val loss: 0.53784, in 0.016s
1 tree, 101 leaves, max depth = 13, train loss: 0.53042, val loss: 0.52995, in 0.031s
1 tree, 100 leaves, max depth = 14, train loss: 0.52282, val loss: 0.52240, in 0.016s
1 tree, 100 leaves, max depth = 14, train loss: 0.51552, val loss: 0.51515, in 0.016s
1 tree, 124 leaves, max depth = 13, train loss: 0.50863, val loss: 0.50824, in 0.016s
1 tree, 99 leaves, max depth = 14, train loss: 0.50187, val loss: 0.50153, in 0.016s
1 tree, 116 leaves, max depth = 16, train loss: 0.49555, val loss: 0.49519, in 0.031s
1 tree, 100 leaves, max depth = 14, train loss: 0.48929, val loss: 0.48900, in 0.016s
1 tree, 87 leaves, max depth = 14, train loss: 0.48314, val loss: 0.48283, in 0.016s
1 tree, 100 leaves, max depth = 14, train loss: 0.47735, val loss: 0.47712, in 0.016s
1 tree, 99 leaves, max depth = 14, train loss: 0.47177, val loss: 0.47161, in 0.016s
1 tree, 104 leaves, max depth = 14, train loss: 0.46641, val loss: 0.46630, in 0.031s
1 tree, 123 leaves, max depth = 14, train loss: 0.46124, val loss: 0.46104, in 0.016s
1 tree, 102 leaves, max depth = 14, train loss: 0.45627, val loss: 0.45613, in 0.016s
1 tree, 103 leaves, max depth = 14, train loss: 0.45133, val loss: 0.45119, in 0.016s
1 tree, 103 leaves, max depth = 14, train loss: 0.44670, val loss: 0.44663, in 0.016s
1 tree, 107 leaves, max depth = 14, train loss: 0.44224, val loss: 0.44224, in 0.016s
1 tree, 95 leaves, max depth = 16, train loss: 0.43553, val loss: 0.43579, in 0.031s
1 tree, 122 leaves, max depth = 16, train loss: 0.42920, val loss: 0.42964, in 0.016s
1 tree, 128 leaves, max depth = 13, train loss: 0.42524, val loss: 0.42570, in 0.016s
1 tree, 109 leaves, max depth = 14, train loss: 0.42125, val loss: 0.42170, in 0.032s
1 tree, 95 leaves, max depth = 15, train loss: 0.41533, val loss: 0.41599, in 0.016s
1 tree, 128 leaves, max depth = 13, train loss: 0.41175, val loss: 0.41244, in 0.031s
1 tree, 95 leaves, max depth = 15, train loss: 0.40618, val loss: 0.40708, in 0.016s
1 tree, 96 leaves, max depth = 15, train loss: 0.40082, val loss: 0.40196, in 0.031s
1 tree, 105 leaves, max depth = 14, train loss: 0.39732, val loss: 0.39851, in 0.016s
1 tree, 106 leaves, max depth = 14, train loss: 0.39398, val loss: 0.39516, in 0.016s
1 tree, 95 leaves, max depth = 15, train loss: 0.38903, val loss: 0.39042, in 0.031s
1 tree, 96 leaves, max depth = 15, train loss: 0.38431, val loss: 0.38591, in 0.016s
1 tree, 106 leaves, max depth = 14, train loss: 0.38131, val loss: 0.38303, in 0.031s
1 tree, 99 leaves, max depth = 15, train loss: 0.37685, val loss: 0.37879, in 0.016s
1 tree, 96 leaves, max depth = 15, train loss: 0.37255, val loss: 0.37468, in 0.016s
1 tree, 108 leaves, max depth = 15, train loss: 0.36979, val loss: 0.37203, in 0.031s
1 tree, 101 leaves, max depth = 15, train loss: 0.36575, val loss: 0.36819, in 0.016s
1 tree, 107 leaves, max depth = 15, train loss: 0.36315, val loss: 0.36572, in 0.016s
1 tree, 136 leaves, max depth = 13, train loss: 0.36072, val loss: 0.36338, in 0.031s
1 tree, 124 leaves, max depth = 14, train loss: 0.35702, val loss: 0.35983, in 0.016s
1 tree, 108 leaves, max depth = 13, train loss: 0.35464, val loss: 0.35756, in 0.016s
1 tree, 52 leaves, max depth = 14, train loss: 0.35126, val loss: 0.35421, in 0.016s
1 tree, 50 leaves, max depth = 14, train loss: 0.34801, val loss: 0.35102, in 0.016s
1 tree, 52 leaves, max depth = 14, train loss: 0.34491, val loss: 0.34793, in 0.016s
1 tree, 51 leaves, max depth = 14, train loss: 0.34192, val loss: 0.34499, in 0.016s
1 tree, 119 leaves, max depth = 15, train loss: 0.33932, val loss: 0.34245, in 0.016s
1 tree, 52 leaves, max depth = 12, train loss: 0.33652, val loss: 0.33968, in 0.016s
1 tree, 120 leaves, max depth = 15, train loss: 0.33404, val loss: 0.33719, in 0.016s
1 tree, 82 leaves, max depth = 13, train loss: 0.33131, val loss: 0.33475, in 0.016s
1 tree, 53 leaves, max depth = 12, train loss: 0.32873, val loss: 0.33219, in 0.016s
1 tree, 121 leaves, max depth = 15, train loss: 0.32640, val loss: 0.32986, in 0.031s
1 tree, 109 leaves, max depth = 18, train loss: 0.32437, val loss: 0.32786, in 0.016s
1 tree, 83 leaves, max depth = 13, train loss: 0.32189, val loss: 0.32565, in 0.016s
1 tree, 82 leaves, max depth = 13, train loss: 0.31951, val loss: 0.32354, in 0.016s
1 tree, 55 leaves, max depth = 12, train loss: 0.31724, val loss: 0.32133, in 0.016s
1 tree, 84 leaves, max depth = 13, train loss: 0.31500, val loss: 0.31935, in 0.016s
1 tree, 119 leaves, max depth = 16, train loss: 0.31292, val loss: 0.31735, in 0.016s
1 tree, 115 leaves, max depth = 17, train loss: 0.31092, val loss: 0.31534, in 0.016s
1 tree, 53 leaves, max depth = 12, train loss: 0.30885, val loss: 0.31330, in 0.031s
1 tree, 82 leaves, max depth = 13, train loss: 0.30681, val loss: 0.31151, in 0.016s
1 tree, 52 leaves, max depth = 12, train loss: 0.30487, val loss: 0.30960, in 0.016s
1 tree, 53 leaves, max depth = 12, train loss: 0.30300, val loss: 0.30775, in 0.016s
1 tree, 86 leaves, max depth = 13, train loss: 0.30111, val loss: 0.30607, in 0.016s
1 tree, 109 leaves, max depth = 17, train loss: 0.29951, val loss: 0.30454, in 0.016s
Fit 74 trees in 1.690 s, (7096 total leaves)
Time spent computing histograms: 0.503s
Time spent finding best splits:  0.131s
Time spent applying splits:      0.114s
Time spent predicting:           0.000s
Trial 1, Fold 1: Log loss = 0.306563213010577, Average precision = 0.9577684804959341, ROC-AUC = 0.9521826741084871, Elapsed Time = 1.7062987999997858 seconds
Trial 1, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 1, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
Binning 0.040 GB of training data: 0.157 s
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 61 leaves, max depth = 11, train loss: 0.67837, val loss: 0.67810, in 0.016s
1 tree, 89 leaves, max depth = 11, train loss: 0.66391, val loss: 0.66349, in 0.016s
1 tree, 92 leaves, max depth = 13, train loss: 0.65038, val loss: 0.64974, in 0.016s
1 tree, 92 leaves, max depth = 13, train loss: 0.63750, val loss: 0.63666, in 0.031s
1 tree, 93 leaves, max depth = 11, train loss: 0.62502, val loss: 0.62406, in 0.016s
1 tree, 97 leaves, max depth = 11, train loss: 0.61313, val loss: 0.61206, in 0.016s
1 tree, 97 leaves, max depth = 11, train loss: 0.60177, val loss: 0.60059, in 0.031s
1 tree, 100 leaves, max depth = 12, train loss: 0.59093, val loss: 0.58965, in 0.016s
1 tree, 112 leaves, max depth = 11, train loss: 0.58067, val loss: 0.57935, in 0.031s
1 tree, 135 leaves, max depth = 17, train loss: 0.57143, val loss: 0.57008, in 0.016s
1 tree, 121 leaves, max depth = 11, train loss: 0.56202, val loss: 0.56064, in 0.016s
1 tree, 79 leaves, max depth = 11, train loss: 0.55309, val loss: 0.55160, in 0.016s
1 tree, 102 leaves, max depth = 12, train loss: 0.54433, val loss: 0.54274, in 0.016s
1 tree, 127 leaves, max depth = 12, train loss: 0.53606, val loss: 0.53444, in 0.016s
1 tree, 125 leaves, max depth = 15, train loss: 0.52835, val loss: 0.52674, in 0.031s
1 tree, 142 leaves, max depth = 13, train loss: 0.52118, val loss: 0.51955, in 0.031s
1 tree, 103 leaves, max depth = 12, train loss: 0.51370, val loss: 0.51204, in 0.016s
1 tree, 103 leaves, max depth = 12, train loss: 0.50653, val loss: 0.50479, in 0.016s
1 tree, 108 leaves, max depth = 12, train loss: 0.49963, val loss: 0.49787, in 0.031s
1 tree, 106 leaves, max depth = 12, train loss: 0.49301, val loss: 0.49117, in 0.031s
1 tree, 107 leaves, max depth = 15, train loss: 0.48666, val loss: 0.48475, in 0.016s
1 tree, 124 leaves, max depth = 17, train loss: 0.48090, val loss: 0.47893, in 0.016s
1 tree, 121 leaves, max depth = 14, train loss: 0.47323, val loss: 0.47138, in 0.031s
1 tree, 102 leaves, max depth = 16, train loss: 0.46751, val loss: 0.46558, in 0.016s
1 tree, 104 leaves, max depth = 16, train loss: 0.46199, val loss: 0.45998, in 0.016s
1 tree, 103 leaves, max depth = 16, train loss: 0.45669, val loss: 0.45460, in 0.031s
1 tree, 99 leaves, max depth = 16, train loss: 0.44973, val loss: 0.44770, in 0.016s
1 tree, 100 leaves, max depth = 16, train loss: 0.44307, val loss: 0.44109, in 0.031s
1 tree, 99 leaves, max depth = 16, train loss: 0.43669, val loss: 0.43476, in 0.016s
1 tree, 107 leaves, max depth = 16, train loss: 0.43200, val loss: 0.43003, in 0.016s
1 tree, 102 leaves, max depth = 16, train loss: 0.42749, val loss: 0.42546, in 0.016s
1 tree, 121 leaves, max depth = 16, train loss: 0.42341, val loss: 0.42136, in 0.016s
1 tree, 108 leaves, max depth = 16, train loss: 0.41919, val loss: 0.41710, in 0.016s
1 tree, 106 leaves, max depth = 16, train loss: 0.41512, val loss: 0.41301, in 0.031s
1 tree, 81 leaves, max depth = 13, train loss: 0.41138, val loss: 0.40924, in 0.016s
1 tree, 100 leaves, max depth = 16, train loss: 0.40595, val loss: 0.40389, in 0.016s
1 tree, 107 leaves, max depth = 16, train loss: 0.40226, val loss: 0.40018, in 0.031s
1 tree, 102 leaves, max depth = 16, train loss: 0.39873, val loss: 0.39665, in 0.016s
1 tree, 112 leaves, max depth = 16, train loss: 0.39533, val loss: 0.39331, in 0.016s
1 tree, 102 leaves, max depth = 16, train loss: 0.39040, val loss: 0.38848, in 0.016s
1 tree, 107 leaves, max depth = 16, train loss: 0.38715, val loss: 0.38524, in 0.016s
1 tree, 108 leaves, max depth = 16, train loss: 0.38400, val loss: 0.38210, in 0.031s
1 tree, 102 leaves, max depth = 16, train loss: 0.37939, val loss: 0.37756, in 0.016s
1 tree, 130 leaves, max depth = 12, train loss: 0.37649, val loss: 0.37472, in 0.016s
1 tree, 103 leaves, max depth = 16, train loss: 0.37218, val loss: 0.37051, in 0.031s
1 tree, 128 leaves, max depth = 13, train loss: 0.36806, val loss: 0.36654, in 0.016s
1 tree, 104 leaves, max depth = 16, train loss: 0.36532, val loss: 0.36378, in 0.016s
1 tree, 104 leaves, max depth = 17, train loss: 0.36140, val loss: 0.35996, in 0.031s
1 tree, 103 leaves, max depth = 15, train loss: 0.35760, val loss: 0.35622, in 0.016s
1 tree, 108 leaves, max depth = 14, train loss: 0.35508, val loss: 0.35374, in 0.016s
1 tree, 91 leaves, max depth = 12, train loss: 0.35274, val loss: 0.35142, in 0.016s
1 tree, 51 leaves, max depth = 14, train loss: 0.34944, val loss: 0.34818, in 0.016s
1 tree, 54 leaves, max depth = 14, train loss: 0.34626, val loss: 0.34508, in 0.016s
1 tree, 119 leaves, max depth = 18, train loss: 0.34360, val loss: 0.34250, in 0.016s
1 tree, 56 leaves, max depth = 14, train loss: 0.34061, val loss: 0.33958, in 0.016s
1 tree, 133 leaves, max depth = 13, train loss: 0.33851, val loss: 0.33762, in 0.016s
1 tree, 86 leaves, max depth = 13, train loss: 0.33558, val loss: 0.33494, in 0.031s
1 tree, 87 leaves, max depth = 13, train loss: 0.33276, val loss: 0.33236, in 0.016s
1 tree, 55 leaves, max depth = 15, train loss: 0.33011, val loss: 0.32978, in 0.016s
1 tree, 87 leaves, max depth = 15, train loss: 0.32748, val loss: 0.32735, in 0.016s
1 tree, 121 leaves, max depth = 20, train loss: 0.32513, val loss: 0.32513, in 0.016s
1 tree, 122 leaves, max depth = 20, train loss: 0.32286, val loss: 0.32298, in 0.031s
1 tree, 57 leaves, max depth = 15, train loss: 0.32043, val loss: 0.32064, in 0.016s
1 tree, 88 leaves, max depth = 13, train loss: 0.31803, val loss: 0.31845, in 0.016s
1 tree, 144 leaves, max depth = 17, train loss: 0.31582, val loss: 0.31635, in 0.016s
1 tree, 57 leaves, max depth = 15, train loss: 0.31359, val loss: 0.31419, in 0.016s
1 tree, 87 leaves, max depth = 13, train loss: 0.31137, val loss: 0.31217, in 0.016s
1 tree, 52 leaves, max depth = 16, train loss: 0.30929, val loss: 0.31012, in 0.016s
1 tree, 145 leaves, max depth = 18, train loss: 0.30721, val loss: 0.30817, in 0.031s
1 tree, 87 leaves, max depth = 13, train loss: 0.30517, val loss: 0.30632, in 0.016s
1 tree, 56 leaves, max depth = 15, train loss: 0.30324, val loss: 0.30445, in 0.016s
1 tree, 117 leaves, max depth = 18, train loss: 0.30137, val loss: 0.30268, in 0.031s
1 tree, 86 leaves, max depth = 15, train loss: 0.29946, val loss: 0.30096, in 0.016s
1 tree, 87 leaves, max depth = 15, train loss: 0.29763, val loss: 0.29931, in 0.016s
Fit 74 trees in 1.830 s, (7413 total leaves)
Time spent computing histograms: 0.529s
Time spent finding best splits:  0.143s
Time spent applying splits:      0.128s
Time spent predicting:           0.000s
Trial 1, Fold 2: Log loss = 0.30159064159157584, Average precision = 0.9600446233961784, ROC-AUC = 0.9570980178400766, Elapsed Time = 1.844349200000579 seconds
Trial 1, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 1, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
Binning 0.040 GB of training data: 0.159 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 66 leaves, max depth = 12, train loss: 0.67847, val loss: 0.67835, in 0.016s
1 tree, 99 leaves, max depth = 12, train loss: 0.66446, val loss: 0.66422, in 0.031s
1 tree, 95 leaves, max depth = 11, train loss: 0.65103, val loss: 0.65079, in 0.016s
1 tree, 97 leaves, max depth = 11, train loss: 0.63825, val loss: 0.63801, in 0.031s
1 tree, 96 leaves, max depth = 11, train loss: 0.62608, val loss: 0.62584, in 0.016s
1 tree, 85 leaves, max depth = 11, train loss: 0.61427, val loss: 0.61405, in 0.016s
1 tree, 98 leaves, max depth = 11, train loss: 0.60321, val loss: 0.60293, in 0.016s
1 tree, 98 leaves, max depth = 11, train loss: 0.59264, val loss: 0.59231, in 0.031s
1 tree, 106 leaves, max depth = 12, train loss: 0.58264, val loss: 0.58227, in 0.016s
1 tree, 101 leaves, max depth = 12, train loss: 0.57299, val loss: 0.57254, in 0.016s
1 tree, 114 leaves, max depth = 15, train loss: 0.56362, val loss: 0.56323, in 0.016s
1 tree, 117 leaves, max depth = 15, train loss: 0.55466, val loss: 0.55433, in 0.031s
1 tree, 119 leaves, max depth = 13, train loss: 0.54612, val loss: 0.54583, in 0.016s
1 tree, 122 leaves, max depth = 15, train loss: 0.53790, val loss: 0.53767, in 0.016s
1 tree, 120 leaves, max depth = 16, train loss: 0.53006, val loss: 0.53001, in 0.031s
1 tree, 140 leaves, max depth = 17, train loss: 0.52281, val loss: 0.52283, in 0.031s
1 tree, 104 leaves, max depth = 15, train loss: 0.51538, val loss: 0.51546, in 0.016s
1 tree, 105 leaves, max depth = 15, train loss: 0.50825, val loss: 0.50839, in 0.016s
1 tree, 105 leaves, max depth = 15, train loss: 0.50140, val loss: 0.50159, in 0.031s
1 tree, 131 leaves, max depth = 14, train loss: 0.49497, val loss: 0.49518, in 0.016s
1 tree, 105 leaves, max depth = 15, train loss: 0.48863, val loss: 0.48890, in 0.016s
1 tree, 104 leaves, max depth = 15, train loss: 0.48254, val loss: 0.48287, in 0.016s
1 tree, 100 leaves, max depth = 14, train loss: 0.47463, val loss: 0.47545, in 0.016s
1 tree, 106 leaves, max depth = 15, train loss: 0.46896, val loss: 0.46984, in 0.031s
1 tree, 106 leaves, max depth = 15, train loss: 0.46349, val loss: 0.46445, in 0.016s
1 tree, 105 leaves, max depth = 15, train loss: 0.45824, val loss: 0.45926, in 0.016s
1 tree, 99 leaves, max depth = 14, train loss: 0.45116, val loss: 0.45265, in 0.031s
1 tree, 99 leaves, max depth = 14, train loss: 0.44438, val loss: 0.44634, in 0.016s
1 tree, 102 leaves, max depth = 14, train loss: 0.43788, val loss: 0.44029, in 0.016s
1 tree, 108 leaves, max depth = 15, train loss: 0.43325, val loss: 0.43572, in 0.031s
1 tree, 111 leaves, max depth = 14, train loss: 0.42878, val loss: 0.43128, in 0.016s
1 tree, 108 leaves, max depth = 14, train loss: 0.42448, val loss: 0.42701, in 0.016s
1 tree, 106 leaves, max depth = 15, train loss: 0.42032, val loss: 0.42292, in 0.031s
1 tree, 101 leaves, max depth = 14, train loss: 0.41458, val loss: 0.41761, in 0.031s
1 tree, 107 leaves, max depth = 15, train loss: 0.41068, val loss: 0.41377, in 0.016s
1 tree, 106 leaves, max depth = 15, train loss: 0.40691, val loss: 0.41008, in 0.016s
1 tree, 99 leaves, max depth = 14, train loss: 0.40161, val loss: 0.40520, in 0.016s
1 tree, 109 leaves, max depth = 14, train loss: 0.39807, val loss: 0.40174, in 0.016s
1 tree, 111 leaves, max depth = 14, train loss: 0.39466, val loss: 0.39839, in 0.031s
1 tree, 107 leaves, max depth = 15, train loss: 0.39136, val loss: 0.39516, in 0.016s
1 tree, 100 leaves, max depth = 14, train loss: 0.38654, val loss: 0.39074, in 0.016s
1 tree, 111 leaves, max depth = 14, train loss: 0.38343, val loss: 0.38771, in 0.031s
1 tree, 100 leaves, max depth = 14, train loss: 0.37889, val loss: 0.38356, in 0.031s
1 tree, 110 leaves, max depth = 14, train loss: 0.37596, val loss: 0.38071, in 0.016s
1 tree, 99 leaves, max depth = 16, train loss: 0.37170, val loss: 0.37682, in 0.031s
1 tree, 111 leaves, max depth = 14, train loss: 0.36894, val loss: 0.37413, in 0.016s
1 tree, 99 leaves, max depth = 16, train loss: 0.36491, val loss: 0.37047, in 0.031s
1 tree, 110 leaves, max depth = 14, train loss: 0.36231, val loss: 0.36795, in 0.016s
1 tree, 99 leaves, max depth = 16, train loss: 0.35850, val loss: 0.36451, in 0.031s
1 tree, 132 leaves, max depth = 14, train loss: 0.35609, val loss: 0.36223, in 0.016s
1 tree, 52 leaves, max depth = 13, train loss: 0.35264, val loss: 0.35910, in 0.016s
1 tree, 52 leaves, max depth = 13, train loss: 0.34934, val loss: 0.35610, in 0.016s
1 tree, 52 leaves, max depth = 13, train loss: 0.34618, val loss: 0.35323, in 0.016s
1 tree, 102 leaves, max depth = 14, train loss: 0.34294, val loss: 0.35032, in 0.016s
1 tree, 118 leaves, max depth = 18, train loss: 0.34035, val loss: 0.34762, in 0.016s
1 tree, 115 leaves, max depth = 19, train loss: 0.33783, val loss: 0.34496, in 0.031s
1 tree, 53 leaves, max depth = 16, train loss: 0.33502, val loss: 0.34242, in 0.016s
1 tree, 112 leaves, max depth = 15, train loss: 0.33302, val loss: 0.34051, in 0.031s
1 tree, 53 leaves, max depth = 13, train loss: 0.33035, val loss: 0.33813, in 0.016s
1 tree, 85 leaves, max depth = 14, train loss: 0.32766, val loss: 0.33595, in 0.016s
1 tree, 84 leaves, max depth = 14, train loss: 0.32508, val loss: 0.33386, in 0.016s
1 tree, 55 leaves, max depth = 16, train loss: 0.32267, val loss: 0.33169, in 0.032s
1 tree, 110 leaves, max depth = 15, train loss: 0.32087, val loss: 0.32999, in 0.016s
1 tree, 120 leaves, max depth = 18, train loss: 0.31871, val loss: 0.32771, in 0.031s
1 tree, 53 leaves, max depth = 13, train loss: 0.31645, val loss: 0.32567, in 0.016s
1 tree, 85 leaves, max depth = 14, train loss: 0.31416, val loss: 0.32380, in 0.016s
1 tree, 121 leaves, max depth = 18, train loss: 0.31213, val loss: 0.32169, in 0.031s
1 tree, 53 leaves, max depth = 13, train loss: 0.31005, val loss: 0.31982, in 0.016s
1 tree, 53 leaves, max depth = 13, train loss: 0.30805, val loss: 0.31802, in 0.016s
1 tree, 121 leaves, max depth = 18, train loss: 0.30614, val loss: 0.31602, in 0.016s
1 tree, 86 leaves, max depth = 14, train loss: 0.30409, val loss: 0.31437, in 0.016s
1 tree, 86 leaves, max depth = 14, train loss: 0.30213, val loss: 0.31278, in 0.016s
1 tree, 86 leaves, max depth = 14, train loss: 0.30023, val loss: 0.31130, in 0.031s
1 tree, 85 leaves, max depth = 14, train loss: 0.29841, val loss: 0.30983, in 0.016s
Fit 74 trees in 1.956 s, (7280 total leaves)
Time spent computing histograms: 0.587s
Time spent finding best splits:  0.152s
Time spent applying splits:      0.135s
Time spent predicting:           0.000s
Trial 1, Fold 3: Log loss = 0.29955521394130014, Average precision = 0.9582165465175596, ROC-AUC = 0.9550424788839557, Elapsed Time = 1.9552509000004648 seconds
Trial 1, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 1, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
Binning 0.040 GB of training data: 0.159 s
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 72 leaves, max depth = 14, train loss: 0.67840, val loss: 0.67787, in 0.016s
1 tree, 96 leaves, max depth = 12, train loss: 0.66423, val loss: 0.66319, in 0.016s
1 tree, 100 leaves, max depth = 14, train loss: 0.65076, val loss: 0.64929, in 0.016s
1 tree, 100 leaves, max depth = 13, train loss: 0.63793, val loss: 0.63605, in 0.016s
1 tree, 103 leaves, max depth = 12, train loss: 0.62571, val loss: 0.62338, in 0.031s
1 tree, 124 leaves, max depth = 12, train loss: 0.61418, val loss: 0.61130, in 0.016s
1 tree, 100 leaves, max depth = 12, train loss: 0.60304, val loss: 0.59977, in 0.047s
1 tree, 101 leaves, max depth = 12, train loss: 0.59241, val loss: 0.58877, in 0.031s
1 tree, 105 leaves, max depth = 12, train loss: 0.58225, val loss: 0.57822, in 0.031s
1 tree, 104 leaves, max depth = 12, train loss: 0.57253, val loss: 0.56811, in 0.031s
1 tree, 99 leaves, max depth = 12, train loss: 0.56322, val loss: 0.55845, in 0.016s
1 tree, 113 leaves, max depth = 17, train loss: 0.55466, val loss: 0.54952, in 0.031s
1 tree, 102 leaves, max depth = 11, train loss: 0.54659, val loss: 0.54129, in 0.016s
1 tree, 76 leaves, max depth = 12, train loss: 0.53839, val loss: 0.53273, in 0.031s
1 tree, 99 leaves, max depth = 11, train loss: 0.53035, val loss: 0.52436, in 0.016s
1 tree, 131 leaves, max depth = 13, train loss: 0.52272, val loss: 0.51636, in 0.031s
1 tree, 100 leaves, max depth = 13, train loss: 0.51530, val loss: 0.50864, in 0.031s
1 tree, 99 leaves, max depth = 12, train loss: 0.50821, val loss: 0.50120, in 0.016s
1 tree, 102 leaves, max depth = 12, train loss: 0.50137, val loss: 0.49406, in 0.031s
1 tree, 133 leaves, max depth = 13, train loss: 0.49489, val loss: 0.48724, in 0.016s
1 tree, 101 leaves, max depth = 13, train loss: 0.48856, val loss: 0.48063, in 0.031s
1 tree, 102 leaves, max depth = 13, train loss: 0.48249, val loss: 0.47428, in 0.016s
1 tree, 134 leaves, max depth = 13, train loss: 0.47673, val loss: 0.46820, in 0.031s
1 tree, 137 leaves, max depth = 13, train loss: 0.47119, val loss: 0.46235, in 0.016s
1 tree, 133 leaves, max depth = 13, train loss: 0.46586, val loss: 0.45674, in 0.031s
1 tree, 125 leaves, max depth = 15, train loss: 0.45868, val loss: 0.44940, in 0.031s
1 tree, 97 leaves, max depth = 13, train loss: 0.45172, val loss: 0.44237, in 0.016s
1 tree, 107 leaves, max depth = 14, train loss: 0.44679, val loss: 0.43720, in 0.031s
1 tree, 95 leaves, max depth = 15, train loss: 0.44024, val loss: 0.43056, in 0.016s
1 tree, 139 leaves, max depth = 13, train loss: 0.43572, val loss: 0.42577, in 0.031s
1 tree, 107 leaves, max depth = 13, train loss: 0.43126, val loss: 0.42112, in 0.031s
1 tree, 111 leaves, max depth = 13, train loss: 0.42696, val loss: 0.41662, in 0.016s
1 tree, 105 leaves, max depth = 12, train loss: 0.42281, val loss: 0.41228, in 0.016s
1 tree, 96 leaves, max depth = 15, train loss: 0.41701, val loss: 0.40642, in 0.031s
1 tree, 136 leaves, max depth = 13, train loss: 0.41322, val loss: 0.40242, in 0.016s
1 tree, 116 leaves, max depth = 17, train loss: 0.40963, val loss: 0.39868, in 0.031s
1 tree, 97 leaves, max depth = 15, train loss: 0.40426, val loss: 0.39328, in 0.016s
1 tree, 113 leaves, max depth = 13, train loss: 0.40071, val loss: 0.38955, in 0.016s
1 tree, 112 leaves, max depth = 14, train loss: 0.39727, val loss: 0.38594, in 0.031s
1 tree, 110 leaves, max depth = 14, train loss: 0.39396, val loss: 0.38245, in 0.016s
1 tree, 130 leaves, max depth = 14, train loss: 0.38920, val loss: 0.37763, in 0.031s
1 tree, 99 leaves, max depth = 16, train loss: 0.38451, val loss: 0.37292, in 0.016s
1 tree, 111 leaves, max depth = 14, train loss: 0.38146, val loss: 0.36973, in 0.031s
1 tree, 132 leaves, max depth = 14, train loss: 0.37716, val loss: 0.36538, in 0.016s
1 tree, 138 leaves, max depth = 14, train loss: 0.37438, val loss: 0.36240, in 0.031s
1 tree, 108 leaves, max depth = 12, train loss: 0.37160, val loss: 0.35948, in 0.016s
1 tree, 102 leaves, max depth = 14, train loss: 0.36750, val loss: 0.35538, in 0.032s
1 tree, 110 leaves, max depth = 14, train loss: 0.36487, val loss: 0.35263, in 0.016s
1 tree, 98 leaves, max depth = 15, train loss: 0.36097, val loss: 0.34874, in 0.031s
1 tree, 136 leaves, max depth = 12, train loss: 0.35857, val loss: 0.34619, in 0.016s
1 tree, 52 leaves, max depth = 14, train loss: 0.35510, val loss: 0.34261, in 0.016s
1 tree, 52 leaves, max depth = 14, train loss: 0.35178, val loss: 0.33917, in 0.016s
1 tree, 126 leaves, max depth = 16, train loss: 0.34908, val loss: 0.33648, in 0.031s
1 tree, 56 leaves, max depth = 13, train loss: 0.34595, val loss: 0.33324, in 0.016s
1 tree, 150 leaves, max depth = 18, train loss: 0.34328, val loss: 0.33054, in 0.031s
1 tree, 86 leaves, max depth = 13, train loss: 0.34026, val loss: 0.32767, in 0.016s
1 tree, 53 leaves, max depth = 14, train loss: 0.33738, val loss: 0.32468, in 0.016s
1 tree, 86 leaves, max depth = 14, train loss: 0.33455, val loss: 0.32203, in 0.016s
1 tree, 126 leaves, max depth = 18, train loss: 0.33218, val loss: 0.31967, in 0.031s
1 tree, 53 leaves, max depth = 14, train loss: 0.32953, val loss: 0.31689, in 0.016s
1 tree, 86 leaves, max depth = 14, train loss: 0.32693, val loss: 0.31445, in 0.031s
1 tree, 56 leaves, max depth = 14, train loss: 0.32446, val loss: 0.31189, in 0.016s
1 tree, 124 leaves, max depth = 17, train loss: 0.32227, val loss: 0.30969, in 0.016s
1 tree, 53 leaves, max depth = 14, train loss: 0.31993, val loss: 0.30726, in 0.031s
1 tree, 86 leaves, max depth = 14, train loss: 0.31758, val loss: 0.30508, in 0.016s
1 tree, 126 leaves, max depth = 14, train loss: 0.31551, val loss: 0.30303, in 0.016s
1 tree, 86 leaves, max depth = 14, train loss: 0.31329, val loss: 0.30093, in 0.031s
1 tree, 126 leaves, max depth = 14, train loss: 0.31132, val loss: 0.29898, in 0.016s
1 tree, 86 leaves, max depth = 14, train loss: 0.30922, val loss: 0.29703, in 0.016s
1 tree, 53 leaves, max depth = 14, train loss: 0.30719, val loss: 0.29493, in 0.031s
1 tree, 85 leaves, max depth = 14, train loss: 0.30522, val loss: 0.29307, in 0.016s
1 tree, 108 leaves, max depth = 14, train loss: 0.30369, val loss: 0.29145, in 0.016s
1 tree, 51 leaves, max depth = 13, train loss: 0.30182, val loss: 0.28948, in 0.031s
1 tree, 149 leaves, max depth = 17, train loss: 0.29991, val loss: 0.28754, in 0.031s
Fit 74 trees in 2.096 s, (7611 total leaves)
Time spent computing histograms: 0.623s
Time spent finding best splits:  0.176s
Time spent applying splits:      0.163s
Time spent predicting:           0.016s
Trial 1, Fold 4: Log loss = 0.3012262393830042, Average precision = 0.9599601894426121, ROC-AUC = 0.956310683228777, Elapsed Time = 2.0986773000004177 seconds
Trial 1, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 1, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
Binning 0.040 GB of training data: 0.173 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 66 leaves, max depth = 11, train loss: 0.67817, val loss: 0.67751, in 0.016s
1 tree, 115 leaves, max depth = 15, train loss: 0.66462, val loss: 0.66343, in 0.031s
1 tree, 94 leaves, max depth = 15, train loss: 0.65091, val loss: 0.64928, in 0.016s
1 tree, 97 leaves, max depth = 15, train loss: 0.63782, val loss: 0.63568, in 0.016s
1 tree, 99 leaves, max depth = 15, train loss: 0.62536, val loss: 0.62275, in 0.031s
1 tree, 101 leaves, max depth = 15, train loss: 0.61347, val loss: 0.61038, in 0.016s
1 tree, 100 leaves, max depth = 15, train loss: 0.60215, val loss: 0.59870, in 0.016s
1 tree, 100 leaves, max depth = 15, train loss: 0.59131, val loss: 0.58742, in 0.016s
1 tree, 126 leaves, max depth = 13, train loss: 0.58106, val loss: 0.57674, in 0.016s
1 tree, 99 leaves, max depth = 15, train loss: 0.57115, val loss: 0.56643, in 0.031s
1 tree, 97 leaves, max depth = 15, train loss: 0.56168, val loss: 0.55664, in 0.016s
1 tree, 130 leaves, max depth = 12, train loss: 0.55271, val loss: 0.54727, in 0.016s
1 tree, 128 leaves, max depth = 14, train loss: 0.54411, val loss: 0.53830, in 0.031s
1 tree, 103 leaves, max depth = 15, train loss: 0.53575, val loss: 0.52959, in 0.016s
1 tree, 106 leaves, max depth = 15, train loss: 0.52774, val loss: 0.52126, in 0.016s
1 tree, 90 leaves, max depth = 13, train loss: 0.52066, val loss: 0.51381, in 0.016s
1 tree, 106 leaves, max depth = 15, train loss: 0.51326, val loss: 0.50609, in 0.016s
1 tree, 129 leaves, max depth = 15, train loss: 0.50625, val loss: 0.49876, in 0.016s
1 tree, 122 leaves, max depth = 15, train loss: 0.49976, val loss: 0.49204, in 0.031s
1 tree, 120 leaves, max depth = 15, train loss: 0.49354, val loss: 0.48558, in 0.016s
1 tree, 109 leaves, max depth = 15, train loss: 0.48717, val loss: 0.47899, in 0.016s
1 tree, 110 leaves, max depth = 15, train loss: 0.48104, val loss: 0.47262, in 0.016s
1 tree, 112 leaves, max depth = 15, train loss: 0.47514, val loss: 0.46647, in 0.031s
1 tree, 82 leaves, max depth = 13, train loss: 0.46768, val loss: 0.45885, in 0.016s
1 tree, 124 leaves, max depth = 15, train loss: 0.46251, val loss: 0.45347, in 0.031s
1 tree, 109 leaves, max depth = 15, train loss: 0.45718, val loss: 0.44794, in 0.016s
1 tree, 110 leaves, max depth = 15, train loss: 0.45205, val loss: 0.44260, in 0.016s
1 tree, 109 leaves, max depth = 15, train loss: 0.44712, val loss: 0.43748, in 0.031s
1 tree, 76 leaves, max depth = 15, train loss: 0.44255, val loss: 0.43264, in 0.016s
1 tree, 110 leaves, max depth = 15, train loss: 0.43794, val loss: 0.42791, in 0.016s
1 tree, 103 leaves, max depth = 15, train loss: 0.43148, val loss: 0.42148, in 0.031s
1 tree, 112 leaves, max depth = 15, train loss: 0.42717, val loss: 0.41702, in 0.016s
1 tree, 109 leaves, max depth = 15, train loss: 0.42303, val loss: 0.41272, in 0.031s
1 tree, 102 leaves, max depth = 15, train loss: 0.41708, val loss: 0.40681, in 0.016s
1 tree, 112 leaves, max depth = 15, train loss: 0.41318, val loss: 0.40279, in 0.031s
1 tree, 134 leaves, max depth = 12, train loss: 0.40948, val loss: 0.39895, in 0.016s
1 tree, 101 leaves, max depth = 15, train loss: 0.40398, val loss: 0.39350, in 0.031s
1 tree, 111 leaves, max depth = 15, train loss: 0.40044, val loss: 0.38985, in 0.016s
1 tree, 133 leaves, max depth = 11, train loss: 0.39709, val loss: 0.38640, in 0.031s
1 tree, 134 leaves, max depth = 11, train loss: 0.39385, val loss: 0.38307, in 0.016s
1 tree, 113 leaves, max depth = 15, train loss: 0.39066, val loss: 0.37977, in 0.031s
1 tree, 103 leaves, max depth = 15, train loss: 0.38572, val loss: 0.37491, in 0.031s
1 tree, 103 leaves, max depth = 15, train loss: 0.38099, val loss: 0.37025, in 0.016s
1 tree, 125 leaves, max depth = 14, train loss: 0.37653, val loss: 0.36585, in 0.031s
1 tree, 102 leaves, max depth = 14, train loss: 0.37217, val loss: 0.36155, in 0.031s
1 tree, 125 leaves, max depth = 14, train loss: 0.36806, val loss: 0.35752, in 0.016s
1 tree, 111 leaves, max depth = 14, train loss: 0.36531, val loss: 0.35470, in 0.016s
1 tree, 101 leaves, max depth = 14, train loss: 0.36135, val loss: 0.35081, in 0.031s
1 tree, 128 leaves, max depth = 13, train loss: 0.35764, val loss: 0.34715, in 0.016s
1 tree, 109 leaves, max depth = 14, train loss: 0.35507, val loss: 0.34461, in 0.031s
1 tree, 101 leaves, max depth = 15, train loss: 0.35147, val loss: 0.34109, in 0.016s
1 tree, 126 leaves, max depth = 17, train loss: 0.34864, val loss: 0.33830, in 0.016s
1 tree, 54 leaves, max depth = 13, train loss: 0.34542, val loss: 0.33503, in 0.016s
1 tree, 125 leaves, max depth = 17, train loss: 0.34273, val loss: 0.33238, in 0.016s
1 tree, 54 leaves, max depth = 13, train loss: 0.33969, val loss: 0.32929, in 0.016s
1 tree, 123 leaves, max depth = 17, train loss: 0.33713, val loss: 0.32678, in 0.016s
1 tree, 124 leaves, max depth = 17, train loss: 0.33467, val loss: 0.32436, in 0.031s
1 tree, 83 leaves, max depth = 13, train loss: 0.33178, val loss: 0.32175, in 0.016s
1 tree, 51 leaves, max depth = 12, train loss: 0.32903, val loss: 0.31896, in 0.016s
1 tree, 82 leaves, max depth = 13, train loss: 0.32632, val loss: 0.31653, in 0.016s
1 tree, 152 leaves, max depth = 17, train loss: 0.32393, val loss: 0.31411, in 0.031s
1 tree, 54 leaves, max depth = 16, train loss: 0.32139, val loss: 0.31155, in 0.016s
1 tree, 81 leaves, max depth = 13, train loss: 0.31890, val loss: 0.30931, in 0.016s
1 tree, 50 leaves, max depth = 12, train loss: 0.31654, val loss: 0.30691, in 0.016s
1 tree, 50 leaves, max depth = 12, train loss: 0.31427, val loss: 0.30459, in 0.016s
1 tree, 110 leaves, max depth = 14, train loss: 0.31253, val loss: 0.30285, in 0.031s
1 tree, 83 leaves, max depth = 12, train loss: 0.31027, val loss: 0.30080, in 0.016s
1 tree, 53 leaves, max depth = 14, train loss: 0.30817, val loss: 0.29867, in 0.016s
1 tree, 150 leaves, max depth = 17, train loss: 0.30609, val loss: 0.29656, in 0.031s
1 tree, 83 leaves, max depth = 12, train loss: 0.30400, val loss: 0.29471, in 0.016s
1 tree, 83 leaves, max depth = 12, train loss: 0.30199, val loss: 0.29294, in 0.016s
1 tree, 50 leaves, max depth = 14, train loss: 0.30011, val loss: 0.29106, in 0.016s
1 tree, 83 leaves, max depth = 12, train loss: 0.29822, val loss: 0.28940, in 0.016s
1 tree, 156 leaves, max depth = 18, train loss: 0.29629, val loss: 0.28745, in 0.031s
Fit 74 trees in 1.925 s, (7611 total leaves)
Time spent computing histograms: 0.567s
Time spent finding best splits:  0.152s
Time spent applying splits:      0.134s
Time spent predicting:           0.016s
Trial 1, Fold 5: Log loss = 0.3059257783920364, Average precision = 0.9571162576778489, ROC-AUC = 0.952768147446259, Elapsed Time = 1.9239307999996527 seconds
Optimization Progress:   2%|2         | 2/100 [00:29<24:13, 14.83s/it]
Trial 2, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 2, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
0.142 s 0.040 GB of training data: 
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 33 leaves, max depth = 8, train loss: 0.66988, val loss: 0.66936, in 0.016s
1 tree, 32 leaves, max depth = 7, train loss: 0.64735, val loss: 0.64677, in 0.016s
1 tree, 27 leaves, max depth = 8, train loss: 0.62702, val loss: 0.62621, in 0.016s
1 tree, 33 leaves, max depth = 7, train loss: 0.60789, val loss: 0.60700, in 0.016s
1 tree, 37 leaves, max depth = 7, train loss: 0.59157, val loss: 0.59044, in 0.031s
1 tree, 27 leaves, max depth = 7, train loss: 0.57534, val loss: 0.57389, in 0.000s
1 tree, 40 leaves, max depth = 9, train loss: 0.56066, val loss: 0.55895, in 0.016s
1 tree, 33 leaves, max depth = 10, train loss: 0.54615, val loss: 0.54432, in 0.016s
1 tree, 41 leaves, max depth = 10, train loss: 0.53325, val loss: 0.53123, in 0.016s
1 tree, 44 leaves, max depth = 11, train loss: 0.52143, val loss: 0.51907, in 0.016s
1 tree, 39 leaves, max depth = 8, train loss: 0.50972, val loss: 0.50712, in 0.016s
1 tree, 44 leaves, max depth = 11, train loss: 0.49942, val loss: 0.49652, in 0.016s
Fit 12 trees in 0.486 s, (430 total leaves)
Time spent computing histograms: 0.071s
Time spent finding best splits:  0.009s
Time spent applying splits:      0.008s
Time spent predicting:           0.016s
Trial 2, Fold 1: Log loss = 0.5002498264839964, Average precision = 0.9215519475609961, ROC-AUC = 0.9313925811215783, Elapsed Time = 0.48928890000024694 seconds
Trial 2, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 2, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
Binning 0.040 GB of training data: 0.142 s
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 26 leaves, max depth = 7, train loss: 0.66975, val loss: 0.66923, in 0.016s
1 tree, 34 leaves, max depth = 11, train loss: 0.64713, val loss: 0.64618, in 0.016s
1 tree, 34 leaves, max depth = 11, train loss: 0.62625, val loss: 0.62494, in 0.016s
1 tree, 22 leaves, max depth = 7, train loss: 0.60738, val loss: 0.60567, in 0.016s
1 tree, 46 leaves, max depth = 10, train loss: 0.59063, val loss: 0.58876, in 0.016s
1 tree, 37 leaves, max depth = 10, train loss: 0.57389, val loss: 0.57174, in 0.016s
1 tree, 33 leaves, max depth = 10, train loss: 0.56004, val loss: 0.55744, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.54544, val loss: 0.54262, in 0.016s
1 tree, 41 leaves, max depth = 11, train loss: 0.53273, val loss: 0.52961, in 0.031s
1 tree, 32 leaves, max depth = 9, train loss: 0.52105, val loss: 0.51779, in 0.016s
1 tree, 40 leaves, max depth = 11, train loss: 0.50992, val loss: 0.50640, in 0.016s
1 tree, 42 leaves, max depth = 11, train loss: 0.49882, val loss: 0.49509, in 0.031s
Fit 12 trees in 0.533 s, (422 total leaves)
Time spent computing histograms: 0.087s
Time spent finding best splits:  0.011s
Time spent applying splits:      0.010s
Time spent predicting:           0.000s
Trial 2, Fold 2: Log loss = 0.4999592546829724, Average precision = 0.9167748509612672, ROC-AUC = 0.9306778179032644, Elapsed Time = 0.5291692999999213 seconds
Trial 2, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 2, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
Binning 0.040 GB of training data: 0.127 s
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 31 leaves, max depth = 8, train loss: 0.66986, val loss: 0.66962, in 0.016s
1 tree, 37 leaves, max depth = 10, train loss: 0.64715, val loss: 0.64693, in 0.016s
1 tree, 24 leaves, max depth = 6, train loss: 0.62681, val loss: 0.62637, in 0.016s
1 tree, 36 leaves, max depth = 10, train loss: 0.60765, val loss: 0.60713, in 0.016s
1 tree, 41 leaves, max depth = 7, train loss: 0.59130, val loss: 0.59069, in 0.016s
1 tree, 30 leaves, max depth = 8, train loss: 0.57563, val loss: 0.57501, in 0.000s
1 tree, 39 leaves, max depth = 7, train loss: 0.56090, val loss: 0.56041, in 0.031s
1 tree, 36 leaves, max depth = 10, train loss: 0.54646, val loss: 0.54594, in 0.000s
1 tree, 51 leaves, max depth = 11, train loss: 0.53362, val loss: 0.53316, in 0.016s
1 tree, 31 leaves, max depth = 7, train loss: 0.52212, val loss: 0.52155, in 0.016s
1 tree, 41 leaves, max depth = 10, train loss: 0.51011, val loss: 0.50963, in 0.016s
1 tree, 46 leaves, max depth = 8, train loss: 0.49965, val loss: 0.49929, in 0.016s
Fit 12 trees in 0.502 s, (443 total leaves)
Time spent computing histograms: 0.082s
Time spent finding best splits:  0.008s
Time spent applying splits:      0.009s
Time spent predicting:           0.000s
Trial 2, Fold 3: Log loss = 0.4973438762829475, Average precision = 0.924954635794069, ROC-AUC = 0.9353176676522426, Elapsed Time = 0.5145811000002141 seconds
Trial 2, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 2, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
Binning 0.040 GB of training data: 0.142 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 33 leaves, max depth = 8, train loss: 0.66985, val loss: 0.66880, in 0.016s
1 tree, 34 leaves, max depth = 10, train loss: 0.64736, val loss: 0.64531, in 0.016s
1 tree, 29 leaves, max depth = 8, train loss: 0.62709, val loss: 0.62403, in 0.016s
1 tree, 33 leaves, max depth = 10, train loss: 0.60789, val loss: 0.60389, in 0.016s
1 tree, 40 leaves, max depth = 10, train loss: 0.59126, val loss: 0.58663, in 0.016s
1 tree, 30 leaves, max depth = 8, train loss: 0.57582, val loss: 0.57043, in 0.000s
1 tree, 38 leaves, max depth = 8, train loss: 0.56136, val loss: 0.55521, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.54675, val loss: 0.53984, in 0.031s
1 tree, 37 leaves, max depth = 10, train loss: 0.53403, val loss: 0.52653, in 0.016s
1 tree, 37 leaves, max depth = 10, train loss: 0.52218, val loss: 0.51412, in 0.016s
1 tree, 39 leaves, max depth = 10, train loss: 0.51036, val loss: 0.50163, in 0.016s
1 tree, 44 leaves, max depth = 9, train loss: 0.50014, val loss: 0.49096, in 0.016s
Fit 12 trees in 0.501 s, (429 total leaves)
Time spent computing histograms: 0.075s
Time spent finding best splits:  0.009s
Time spent applying splits:      0.009s
Time spent predicting:           0.000s
Trial 2, Fold 4: Log loss = 0.499519425604958, Average precision = 0.9219241863020324, ROC-AUC = 0.9324585380945227, Elapsed Time = 0.5062674999990122 seconds
Trial 2, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 2, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
Binning 0.040 GB of training data: 0.157 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 32 leaves, max depth = 8, train loss: 0.66967, val loss: 0.66820, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.64699, val loss: 0.64472, in 0.016s
1 tree, 28 leaves, max depth = 8, train loss: 0.62652, val loss: 0.62337, in 0.016s
1 tree, 34 leaves, max depth = 9, train loss: 0.60723, val loss: 0.60335, in 0.016s
1 tree, 26 leaves, max depth = 7, train loss: 0.58990, val loss: 0.58519, in 0.016s
1 tree, 32 leaves, max depth = 9, train loss: 0.57447, val loss: 0.56880, in 0.016s
1 tree, 41 leaves, max depth = 9, train loss: 0.55998, val loss: 0.55352, in 0.016s
1 tree, 36 leaves, max depth = 10, train loss: 0.54538, val loss: 0.53844, in 0.016s
1 tree, 47 leaves, max depth = 10, train loss: 0.53263, val loss: 0.52515, in 0.016s
1 tree, 36 leaves, max depth = 8, train loss: 0.52105, val loss: 0.51313, in 0.000s
1 tree, 38 leaves, max depth = 8, train loss: 0.50947, val loss: 0.50111, in 0.016s
1 tree, 40 leaves, max depth = 7, train loss: 0.49927, val loss: 0.49066, in 0.016s
Fit 12 trees in 0.485 s, (421 total leaves)
Time spent computing histograms: 0.075s
Time spent finding best splits:  0.008s
Time spent applying splits:      0.008s
Time spent predicting:           0.000s
Trial 2, Fold 5: Log loss = 0.5021190230330362, Average precision = 0.9164841006897811, ROC-AUC = 0.9291524441224014, Elapsed Time = 0.5015739999998914 seconds
Optimization Progress:   3%|3         | 3/100 [00:38<19:37, 12.14s/it]
Trial 3, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 3, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
0.141 s 0.040 GB of training data: 
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 56 leaves, max depth = 11, train loss: 0.68265, val loss: 0.68255, in 0.016s
1 tree, 76 leaves, max depth = 11, train loss: 0.67219, val loss: 0.67220, in 0.016s
1 tree, 101 leaves, max depth = 13, train loss: 0.66265, val loss: 0.66258, in 0.031s
1 tree, 93 leaves, max depth = 12, train loss: 0.65301, val loss: 0.65292, in 0.016s
1 tree, 73 leaves, max depth = 13, train loss: 0.64421, val loss: 0.64402, in 0.016s
1 tree, 82 leaves, max depth = 11, train loss: 0.63505, val loss: 0.63491, in 0.016s
1 tree, 81 leaves, max depth = 13, train loss: 0.62663, val loss: 0.62649, in 0.016s
1 tree, 79 leaves, max depth = 13, train loss: 0.61810, val loss: 0.61803, in 0.016s
1 tree, 78 leaves, max depth = 13, train loss: 0.60989, val loss: 0.60981, in 0.016s
1 tree, 82 leaves, max depth = 11, train loss: 0.60188, val loss: 0.60184, in 0.016s
1 tree, 81 leaves, max depth = 13, train loss: 0.59451, val loss: 0.59446, in 0.016s
1 tree, 102 leaves, max depth = 13, train loss: 0.58734, val loss: 0.58720, in 0.031s
1 tree, 85 leaves, max depth = 11, train loss: 0.58029, val loss: 0.58008, in 0.016s
1 tree, 74 leaves, max depth = 11, train loss: 0.57330, val loss: 0.57296, in 0.016s
1 tree, 82 leaves, max depth = 12, train loss: 0.56644, val loss: 0.56610, in 0.016s
1 tree, 97 leaves, max depth = 12, train loss: 0.55995, val loss: 0.55954, in 0.016s
1 tree, 72 leaves, max depth = 11, train loss: 0.55399, val loss: 0.55352, in 0.016s
1 tree, 82 leaves, max depth = 12, train loss: 0.54771, val loss: 0.54725, in 0.016s
1 tree, 102 leaves, max depth = 13, train loss: 0.54172, val loss: 0.54122, in 0.016s
1 tree, 81 leaves, max depth = 11, train loss: 0.53582, val loss: 0.53531, in 0.031s
1 tree, 101 leaves, max depth = 16, train loss: 0.53045, val loss: 0.52987, in 0.016s
1 tree, 84 leaves, max depth = 12, train loss: 0.52477, val loss: 0.52416, in 0.016s
1 tree, 59 leaves, max depth = 11, train loss: 0.51987, val loss: 0.51922, in 0.016s
1 tree, 98 leaves, max depth = 12, train loss: 0.51475, val loss: 0.51405, in 0.016s
1 tree, 103 leaves, max depth = 12, train loss: 0.50961, val loss: 0.50881, in 0.016s
1 tree, 102 leaves, max depth = 12, train loss: 0.50462, val loss: 0.50372, in 0.016s
1 tree, 83 leaves, max depth = 15, train loss: 0.49996, val loss: 0.49904, in 0.016s
1 tree, 101 leaves, max depth = 15, train loss: 0.49552, val loss: 0.49450, in 0.016s
1 tree, 74 leaves, max depth = 14, train loss: 0.48950, val loss: 0.48863, in 0.016s
1 tree, 84 leaves, max depth = 15, train loss: 0.48521, val loss: 0.48432, in 0.016s
1 tree, 81 leaves, max depth = 12, train loss: 0.48083, val loss: 0.47996, in 0.016s
1 tree, 82 leaves, max depth = 11, train loss: 0.47658, val loss: 0.47578, in 0.016s
1 tree, 84 leaves, max depth = 15, train loss: 0.47263, val loss: 0.47181, in 0.016s
1 tree, 87 leaves, max depth = 11, train loss: 0.46881, val loss: 0.46802, in 0.031s
1 tree, 62 leaves, max depth = 12, train loss: 0.46516, val loss: 0.46424, in 0.016s
1 tree, 85 leaves, max depth = 12, train loss: 0.46121, val loss: 0.46026, in 0.016s
1 tree, 86 leaves, max depth = 12, train loss: 0.45738, val loss: 0.45640, in 0.016s
1 tree, 61 leaves, max depth = 12, train loss: 0.45382, val loss: 0.45275, in 0.016s
1 tree, 61 leaves, max depth = 12, train loss: 0.45037, val loss: 0.44921, in 0.016s
1 tree, 105 leaves, max depth = 13, train loss: 0.44689, val loss: 0.44565, in 0.016s
1 tree, 84 leaves, max depth = 12, train loss: 0.44363, val loss: 0.44236, in 0.016s
1 tree, 85 leaves, max depth = 11, train loss: 0.44028, val loss: 0.43901, in 0.031s
1 tree, 102 leaves, max depth = 12, train loss: 0.43709, val loss: 0.43577, in 0.016s
1 tree, 104 leaves, max depth = 13, train loss: 0.43399, val loss: 0.43260, in 0.016s
1 tree, 74 leaves, max depth = 14, train loss: 0.42936, val loss: 0.42815, in 0.016s
1 tree, 75 leaves, max depth = 14, train loss: 0.42491, val loss: 0.42388, in 0.031s
1 tree, 85 leaves, max depth = 11, train loss: 0.42216, val loss: 0.42118, in 0.016s
1 tree, 83 leaves, max depth = 12, train loss: 0.41925, val loss: 0.41828, in 0.016s
1 tree, 96 leaves, max depth = 14, train loss: 0.41512, val loss: 0.41428, in 0.016s
1 tree, 61 leaves, max depth = 12, train loss: 0.41263, val loss: 0.41170, in 0.016s
1 tree, 71 leaves, max depth = 12, train loss: 0.41006, val loss: 0.40906, in 0.016s
1 tree, 95 leaves, max depth = 14, train loss: 0.40613, val loss: 0.40525, in 0.016s
1 tree, 82 leaves, max depth = 12, train loss: 0.40353, val loss: 0.40266, in 0.016s
1 tree, 82 leaves, max depth = 12, train loss: 0.40097, val loss: 0.40011, in 0.031s
1 tree, 86 leaves, max depth = 13, train loss: 0.39845, val loss: 0.39760, in 0.016s
1 tree, 83 leaves, max depth = 12, train loss: 0.39602, val loss: 0.39517, in 0.016s
1 tree, 84 leaves, max depth = 14, train loss: 0.39382, val loss: 0.39300, in 0.016s
1 tree, 75 leaves, max depth = 13, train loss: 0.39016, val loss: 0.38953, in 0.016s
1 tree, 83 leaves, max depth = 14, train loss: 0.38807, val loss: 0.38746, in 0.016s
Fit 59 trees in 1.344 s, (4932 total leaves)
Time spent computing histograms: 0.405s
Time spent finding best splits:  0.082s
Time spent applying splits:      0.077s
Time spent predicting:           0.000s
Trial 3, Fold 1: Log loss = 0.3930796249502737, Average precision = 0.9518336436663236, ROC-AUC = 0.9472694057297716, Elapsed Time = 1.3563878999993904 seconds
Trial 3, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 3, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
Binning 0.040 GB of training data: 0.141 s
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 61 leaves, max depth = 9, train loss: 0.68264, val loss: 0.68243, in 0.016s
1 tree, 75 leaves, max depth = 12, train loss: 0.67194, val loss: 0.67151, in 0.016s
1 tree, 100 leaves, max depth = 13, train loss: 0.66241, val loss: 0.66189, in 0.016s
1 tree, 80 leaves, max depth = 12, train loss: 0.65243, val loss: 0.65170, in 0.016s
1 tree, 70 leaves, max depth = 14, train loss: 0.64361, val loss: 0.64272, in 0.016s
1 tree, 80 leaves, max depth = 12, train loss: 0.63430, val loss: 0.63320, in 0.016s
1 tree, 81 leaves, max depth = 13, train loss: 0.62583, val loss: 0.62465, in 0.016s
1 tree, 82 leaves, max depth = 13, train loss: 0.61715, val loss: 0.61584, in 0.016s
1 tree, 83 leaves, max depth = 16, train loss: 0.60929, val loss: 0.60787, in 0.016s
1 tree, 100 leaves, max depth = 16, train loss: 0.60179, val loss: 0.60030, in 0.016s
1 tree, 102 leaves, max depth = 13, train loss: 0.59396, val loss: 0.59233, in 0.031s
1 tree, 83 leaves, max depth = 12, train loss: 0.58629, val loss: 0.58449, in 0.016s
1 tree, 81 leaves, max depth = 16, train loss: 0.57940, val loss: 0.57749, in 0.016s
1 tree, 73 leaves, max depth = 14, train loss: 0.57243, val loss: 0.57036, in 0.016s
1 tree, 82 leaves, max depth = 12, train loss: 0.56545, val loss: 0.56322, in 0.016s
1 tree, 84 leaves, max depth = 14, train loss: 0.55918, val loss: 0.55686, in 0.016s
1 tree, 101 leaves, max depth = 13, train loss: 0.55275, val loss: 0.55034, in 0.016s
1 tree, 100 leaves, max depth = 12, train loss: 0.54646, val loss: 0.54394, in 0.031s
1 tree, 84 leaves, max depth = 14, train loss: 0.54074, val loss: 0.53814, in 0.016s
1 tree, 84 leaves, max depth = 14, train loss: 0.53520, val loss: 0.53252, in 0.031s
1 tree, 85 leaves, max depth = 13, train loss: 0.52974, val loss: 0.52703, in 0.016s
1 tree, 83 leaves, max depth = 12, train loss: 0.52408, val loss: 0.52125, in 0.016s
1 tree, 101 leaves, max depth = 12, train loss: 0.51862, val loss: 0.51572, in 0.016s
1 tree, 84 leaves, max depth = 11, train loss: 0.51370, val loss: 0.51067, in 0.031s
1 tree, 83 leaves, max depth = 14, train loss: 0.50890, val loss: 0.50582, in 0.016s
1 tree, 82 leaves, max depth = 16, train loss: 0.50425, val loss: 0.50111, in 0.016s
1 tree, 99 leaves, max depth = 12, train loss: 0.49937, val loss: 0.49622, in 0.016s
1 tree, 84 leaves, max depth = 14, train loss: 0.49498, val loss: 0.49179, in 0.016s
1 tree, 82 leaves, max depth = 12, train loss: 0.49026, val loss: 0.48698, in 0.016s
1 tree, 83 leaves, max depth = 12, train loss: 0.48569, val loss: 0.48231, in 0.016s
1 tree, 72 leaves, max depth = 16, train loss: 0.48178, val loss: 0.47836, in 0.016s
1 tree, 84 leaves, max depth = 12, train loss: 0.47745, val loss: 0.47397, in 0.016s
1 tree, 71 leaves, max depth = 14, train loss: 0.47336, val loss: 0.46981, in 0.016s
1 tree, 99 leaves, max depth = 13, train loss: 0.46963, val loss: 0.46606, in 0.016s
1 tree, 83 leaves, max depth = 16, train loss: 0.46566, val loss: 0.46201, in 0.031s
1 tree, 76 leaves, max depth = 13, train loss: 0.46039, val loss: 0.45680, in 0.016s
1 tree, 76 leaves, max depth = 13, train loss: 0.45528, val loss: 0.45176, in 0.016s
1 tree, 96 leaves, max depth = 17, train loss: 0.45042, val loss: 0.44700, in 0.016s
1 tree, 62 leaves, max depth = 14, train loss: 0.44571, val loss: 0.44232, in 0.016s
1 tree, 61 leaves, max depth = 18, train loss: 0.44263, val loss: 0.43922, in 0.016s
1 tree, 103 leaves, max depth = 12, train loss: 0.43918, val loss: 0.43576, in 0.031s
1 tree, 83 leaves, max depth = 12, train loss: 0.43576, val loss: 0.43227, in 0.016s
1 tree, 102 leaves, max depth = 12, train loss: 0.43254, val loss: 0.42904, in 0.016s
1 tree, 72 leaves, max depth = 14, train loss: 0.42942, val loss: 0.42589, in 0.016s
1 tree, 98 leaves, max depth = 17, train loss: 0.42519, val loss: 0.42176, in 0.031s
1 tree, 82 leaves, max depth = 13, train loss: 0.42234, val loss: 0.41889, in 0.016s
1 tree, 73 leaves, max depth = 14, train loss: 0.41816, val loss: 0.41477, in 0.016s
1 tree, 85 leaves, max depth = 12, train loss: 0.41520, val loss: 0.41177, in 0.016s
1 tree, 96 leaves, max depth = 13, train loss: 0.41131, val loss: 0.40800, in 0.031s
1 tree, 69 leaves, max depth = 13, train loss: 0.40885, val loss: 0.40551, in 0.016s
1 tree, 74 leaves, max depth = 13, train loss: 0.40623, val loss: 0.40288, in 0.016s
1 tree, 81 leaves, max depth = 13, train loss: 0.40377, val loss: 0.40040, in 0.016s
1 tree, 73 leaves, max depth = 14, train loss: 0.40004, val loss: 0.39674, in 0.016s
1 tree, 102 leaves, max depth = 14, train loss: 0.39754, val loss: 0.39423, in 0.031s
1 tree, 82 leaves, max depth = 15, train loss: 0.39502, val loss: 0.39169, in 0.016s
1 tree, 82 leaves, max depth = 15, train loss: 0.39252, val loss: 0.38917, in 0.016s
1 tree, 83 leaves, max depth = 15, train loss: 0.39009, val loss: 0.38672, in 0.016s
1 tree, 81 leaves, max depth = 15, train loss: 0.38778, val loss: 0.38440, in 0.016s
1 tree, 62 leaves, max depth = 13, train loss: 0.38560, val loss: 0.38218, in 0.016s
Fit 59 trees in 1.407 s, (4915 total leaves)
Time spent computing histograms: 0.415s
Time spent finding best splits:  0.089s
Time spent applying splits:      0.083s
Time spent predicting:           0.016s
Trial 3, Fold 2: Log loss = 0.3883934943415345, Average precision = 0.9514920456568772, ROC-AUC = 0.949003108098142, Elapsed Time = 1.4139042999995581 seconds
Trial 3, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 3, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
Binning 0.040 GB of training data: 0.174 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 64 leaves, max depth = 11, train loss: 0.68269, val loss: 0.68256, in 0.016s
1 tree, 77 leaves, max depth = 12, train loss: 0.67223, val loss: 0.67205, in 0.016s
1 tree, 98 leaves, max depth = 13, train loss: 0.66272, val loss: 0.66240, in 0.016s
1 tree, 82 leaves, max depth = 13, train loss: 0.65294, val loss: 0.65264, in 0.016s
1 tree, 71 leaves, max depth = 14, train loss: 0.64410, val loss: 0.64378, in 0.016s
1 tree, 81 leaves, max depth = 13, train loss: 0.63497, val loss: 0.63465, in 0.016s
1 tree, 81 leaves, max depth = 14, train loss: 0.62656, val loss: 0.62628, in 0.031s
1 tree, 81 leaves, max depth = 12, train loss: 0.61799, val loss: 0.61769, in 0.016s
1 tree, 82 leaves, max depth = 14, train loss: 0.61000, val loss: 0.60975, in 0.016s
1 tree, 99 leaves, max depth = 14, train loss: 0.60247, val loss: 0.60226, in 0.031s
1 tree, 99 leaves, max depth = 12, train loss: 0.59478, val loss: 0.59456, in 0.016s
1 tree, 81 leaves, max depth = 12, train loss: 0.58723, val loss: 0.58703, in 0.016s
1 tree, 85 leaves, max depth = 14, train loss: 0.58026, val loss: 0.58009, in 0.031s
1 tree, 71 leaves, max depth = 15, train loss: 0.57323, val loss: 0.57307, in 0.016s
1 tree, 82 leaves, max depth = 15, train loss: 0.56628, val loss: 0.56618, in 0.016s
1 tree, 82 leaves, max depth = 14, train loss: 0.55994, val loss: 0.55989, in 0.016s
1 tree, 98 leaves, max depth = 14, train loss: 0.55347, val loss: 0.55346, in 0.016s
1 tree, 98 leaves, max depth = 14, train loss: 0.54720, val loss: 0.54724, in 0.031s
1 tree, 83 leaves, max depth = 14, train loss: 0.54142, val loss: 0.54150, in 0.016s
1 tree, 82 leaves, max depth = 14, train loss: 0.53581, val loss: 0.53593, in 0.016s
1 tree, 83 leaves, max depth = 14, train loss: 0.53026, val loss: 0.53046, in 0.031s
1 tree, 82 leaves, max depth = 14, train loss: 0.52460, val loss: 0.52478, in 0.016s
1 tree, 98 leaves, max depth = 13, train loss: 0.51918, val loss: 0.51935, in 0.016s
1 tree, 81 leaves, max depth = 12, train loss: 0.51421, val loss: 0.51444, in 0.016s
1 tree, 83 leaves, max depth = 14, train loss: 0.50936, val loss: 0.50963, in 0.031s
1 tree, 84 leaves, max depth = 14, train loss: 0.50466, val loss: 0.50497, in 0.016s
1 tree, 99 leaves, max depth = 15, train loss: 0.49979, val loss: 0.50017, in 0.062s
1 tree, 83 leaves, max depth = 14, train loss: 0.49535, val loss: 0.49576, in 0.047s
1 tree, 84 leaves, max depth = 14, train loss: 0.49066, val loss: 0.49113, in 0.031s
1 tree, 83 leaves, max depth = 14, train loss: 0.48611, val loss: 0.48662, in 0.016s
1 tree, 64 leaves, max depth = 15, train loss: 0.48043, val loss: 0.48129, in 0.016s
1 tree, 83 leaves, max depth = 14, train loss: 0.47611, val loss: 0.47697, in 0.031s
1 tree, 82 leaves, max depth = 14, train loss: 0.47224, val loss: 0.47318, in 0.016s
1 tree, 81 leaves, max depth = 14, train loss: 0.46846, val loss: 0.46943, in 0.016s
1 tree, 56 leaves, max depth = 15, train loss: 0.46324, val loss: 0.46457, in 0.031s
1 tree, 85 leaves, max depth = 14, train loss: 0.45930, val loss: 0.46070, in 0.016s
1 tree, 83 leaves, max depth = 14, train loss: 0.45549, val loss: 0.45689, in 0.016s
1 tree, 70 leaves, max depth = 14, train loss: 0.45188, val loss: 0.45330, in 0.016s
1 tree, 71 leaves, max depth = 14, train loss: 0.44837, val loss: 0.44982, in 0.031s
1 tree, 71 leaves, max depth = 14, train loss: 0.44496, val loss: 0.44644, in 0.016s
1 tree, 83 leaves, max depth = 14, train loss: 0.44181, val loss: 0.44333, in 0.016s
1 tree, 82 leaves, max depth = 14, train loss: 0.43846, val loss: 0.43999, in 0.016s
1 tree, 100 leaves, max depth = 13, train loss: 0.43526, val loss: 0.43682, in 0.016s
1 tree, 101 leaves, max depth = 12, train loss: 0.43214, val loss: 0.43378, in 0.016s
1 tree, 76 leaves, max depth = 14, train loss: 0.42758, val loss: 0.42960, in 0.016s
1 tree, 77 leaves, max depth = 15, train loss: 0.42320, val loss: 0.42559, in 0.016s
1 tree, 82 leaves, max depth = 14, train loss: 0.42020, val loss: 0.42266, in 0.016s
1 tree, 99 leaves, max depth = 13, train loss: 0.41737, val loss: 0.41986, in 0.016s
1 tree, 95 leaves, max depth = 15, train loss: 0.41327, val loss: 0.41615, in 0.016s
1 tree, 69 leaves, max depth = 11, train loss: 0.41062, val loss: 0.41350, in 0.016s
1 tree, 77 leaves, max depth = 15, train loss: 0.40666, val loss: 0.40989, in 0.016s
1 tree, 62 leaves, max depth = 14, train loss: 0.40414, val loss: 0.40739, in 0.016s
1 tree, 82 leaves, max depth = 14, train loss: 0.40175, val loss: 0.40505, in 0.016s
1 tree, 84 leaves, max depth = 15, train loss: 0.39948, val loss: 0.40281, in 0.031s
1 tree, 101 leaves, max depth = 15, train loss: 0.39719, val loss: 0.40063, in 0.016s
1 tree, 99 leaves, max depth = 14, train loss: 0.39484, val loss: 0.39835, in 0.016s
1 tree, 81 leaves, max depth = 15, train loss: 0.39269, val loss: 0.39628, in 0.016s
1 tree, 73 leaves, max depth = 16, train loss: 0.38911, val loss: 0.39305, in 0.031s
1 tree, 80 leaves, max depth = 15, train loss: 0.38706, val loss: 0.39108, in 0.016s
Fit 59 trees in 1.596 s, (4886 total leaves)
Time spent computing histograms: 0.492s
Time spent finding best splits:  0.115s
Time spent applying splits:      0.093s
Time spent predicting:           0.000s
Trial 3, Fold 3: Log loss = 0.38656042904283894, Average precision = 0.9553353227323066, ROC-AUC = 0.9516027697652917, Elapsed Time = 1.598971100000199 seconds
Trial 3, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 3, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
Binning 0.040 GB of training data: 0.157 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 58 leaves, max depth = 12, train loss: 0.68270, val loss: 0.68226, in 0.016s
1 tree, 80 leaves, max depth = 11, train loss: 0.67219, val loss: 0.67131, in 0.016s
1 tree, 105 leaves, max depth = 15, train loss: 0.66270, val loss: 0.66134, in 0.031s
1 tree, 82 leaves, max depth = 11, train loss: 0.65294, val loss: 0.65123, in 0.016s
1 tree, 71 leaves, max depth = 13, train loss: 0.64426, val loss: 0.64216, in 0.016s
1 tree, 84 leaves, max depth = 13, train loss: 0.63514, val loss: 0.63269, in 0.031s
1 tree, 82 leaves, max depth = 13, train loss: 0.62685, val loss: 0.62410, in 0.016s
1 tree, 84 leaves, max depth = 13, train loss: 0.61828, val loss: 0.61516, in 0.016s
1 tree, 83 leaves, max depth = 13, train loss: 0.61046, val loss: 0.60700, in 0.031s
1 tree, 103 leaves, max depth = 13, train loss: 0.60307, val loss: 0.59923, in 0.016s
1 tree, 103 leaves, max depth = 13, train loss: 0.59541, val loss: 0.59118, in 0.016s
1 tree, 79 leaves, max depth = 11, train loss: 0.58790, val loss: 0.58338, in 0.016s
1 tree, 84 leaves, max depth = 14, train loss: 0.58095, val loss: 0.57613, in 0.031s
1 tree, 72 leaves, max depth = 12, train loss: 0.57391, val loss: 0.56877, in 0.016s
1 tree, 82 leaves, max depth = 13, train loss: 0.56704, val loss: 0.56160, in 0.016s
1 tree, 85 leaves, max depth = 14, train loss: 0.56071, val loss: 0.55499, in 0.031s
1 tree, 106 leaves, max depth = 12, train loss: 0.55434, val loss: 0.54828, in 0.016s
1 tree, 105 leaves, max depth = 13, train loss: 0.54817, val loss: 0.54177, in 0.031s
1 tree, 73 leaves, max depth = 12, train loss: 0.54226, val loss: 0.53555, in 0.016s
1 tree, 82 leaves, max depth = 12, train loss: 0.53639, val loss: 0.52941, in 0.016s
1 tree, 82 leaves, max depth = 14, train loss: 0.53094, val loss: 0.52369, in 0.016s
1 tree, 81 leaves, max depth = 12, train loss: 0.52540, val loss: 0.51788, in 0.016s
1 tree, 104 leaves, max depth = 13, train loss: 0.52009, val loss: 0.51225, in 0.016s
1 tree, 84 leaves, max depth = 13, train loss: 0.51519, val loss: 0.50723, in 0.031s
1 tree, 82 leaves, max depth = 14, train loss: 0.51033, val loss: 0.50212, in 0.016s
1 tree, 84 leaves, max depth = 13, train loss: 0.50571, val loss: 0.49737, in 0.016s
1 tree, 107 leaves, max depth = 13, train loss: 0.50083, val loss: 0.49226, in 0.016s
1 tree, 81 leaves, max depth = 15, train loss: 0.49641, val loss: 0.48762, in 0.016s
1 tree, 85 leaves, max depth = 11, train loss: 0.49214, val loss: 0.48322, in 0.016s
1 tree, 84 leaves, max depth = 13, train loss: 0.48755, val loss: 0.47845, in 0.016s
1 tree, 72 leaves, max depth = 14, train loss: 0.48363, val loss: 0.47431, in 0.016s
1 tree, 82 leaves, max depth = 12, train loss: 0.47932, val loss: 0.46976, in 0.016s
1 tree, 84 leaves, max depth = 13, train loss: 0.47538, val loss: 0.46571, in 0.016s
1 tree, 83 leaves, max depth = 13, train loss: 0.47159, val loss: 0.46181, in 0.016s
1 tree, 66 leaves, max depth = 12, train loss: 0.46639, val loss: 0.45651, in 0.016s
1 tree, 86 leaves, max depth = 12, train loss: 0.46245, val loss: 0.45240, in 0.031s
1 tree, 84 leaves, max depth = 12, train loss: 0.45861, val loss: 0.44840, in 0.016s
1 tree, 73 leaves, max depth = 12, train loss: 0.45502, val loss: 0.44461, in 0.016s
1 tree, 105 leaves, max depth = 12, train loss: 0.45150, val loss: 0.44085, in 0.031s
1 tree, 63 leaves, max depth = 12, train loss: 0.44814, val loss: 0.43724, in 0.016s
1 tree, 86 leaves, max depth = 13, train loss: 0.44492, val loss: 0.43390, in 0.016s
1 tree, 82 leaves, max depth = 12, train loss: 0.44160, val loss: 0.43038, in 0.016s
1 tree, 103 leaves, max depth = 12, train loss: 0.43844, val loss: 0.42699, in 0.031s
1 tree, 107 leaves, max depth = 12, train loss: 0.43531, val loss: 0.42371, in 0.016s
1 tree, 74 leaves, max depth = 12, train loss: 0.43074, val loss: 0.41907, in 0.016s
1 tree, 76 leaves, max depth = 12, train loss: 0.42633, val loss: 0.41463, in 0.016s
1 tree, 86 leaves, max depth = 12, train loss: 0.42333, val loss: 0.41150, in 0.016s
1 tree, 103 leaves, max depth = 12, train loss: 0.42053, val loss: 0.40850, in 0.031s
1 tree, 95 leaves, max depth = 12, train loss: 0.41643, val loss: 0.40431, in 0.031s
1 tree, 69 leaves, max depth = 12, train loss: 0.41381, val loss: 0.40148, in 0.016s
1 tree, 78 leaves, max depth = 12, train loss: 0.40982, val loss: 0.39747, in 0.016s
1 tree, 72 leaves, max depth = 12, train loss: 0.40732, val loss: 0.39477, in 0.016s
1 tree, 84 leaves, max depth = 12, train loss: 0.40494, val loss: 0.39235, in 0.031s
1 tree, 86 leaves, max depth = 11, train loss: 0.40267, val loss: 0.39003, in 0.016s
1 tree, 106 leaves, max depth = 15, train loss: 0.40049, val loss: 0.38773, in 0.031s
1 tree, 72 leaves, max depth = 11, train loss: 0.39820, val loss: 0.38529, in 0.016s
1 tree, 74 leaves, max depth = 12, train loss: 0.39454, val loss: 0.38160, in 0.016s
1 tree, 74 leaves, max depth = 12, train loss: 0.39098, val loss: 0.37802, in 0.031s
1 tree, 84 leaves, max depth = 12, train loss: 0.38876, val loss: 0.37568, in 0.016s
Fit 59 trees in 1.533 s, (4986 total leaves)
Time spent computing histograms: 0.452s
Time spent finding best splits:  0.096s
Time spent applying splits:      0.090s
Time spent predicting:           0.000s
Trial 3, Fold 4: Log loss = 0.38953950043832297, Average precision = 0.9538868791853735, ROC-AUC = 0.9492232487106304, Elapsed Time = 1.5329940999999963 seconds
Trial 3, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 3, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
Binning 0.040 GB of training data: 0.173 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 61 leaves, max depth = 14, train loss: 0.68267, val loss: 0.68208, in 0.016s
1 tree, 81 leaves, max depth = 12, train loss: 0.67200, val loss: 0.67100, in 0.016s
1 tree, 103 leaves, max depth = 15, train loss: 0.66251, val loss: 0.66098, in 0.031s
1 tree, 77 leaves, max depth = 12, train loss: 0.65257, val loss: 0.65070, in 0.016s
1 tree, 82 leaves, max depth = 14, train loss: 0.64343, val loss: 0.64118, in 0.016s
1 tree, 82 leaves, max depth = 12, train loss: 0.63415, val loss: 0.63158, in 0.016s
1 tree, 83 leaves, max depth = 14, train loss: 0.62570, val loss: 0.62276, in 0.031s
1 tree, 79 leaves, max depth = 11, train loss: 0.61705, val loss: 0.61379, in 0.016s
1 tree, 85 leaves, max depth = 14, train loss: 0.60916, val loss: 0.60554, in 0.016s
1 tree, 104 leaves, max depth = 14, train loss: 0.60165, val loss: 0.59769, in 0.016s
1 tree, 80 leaves, max depth = 12, train loss: 0.59376, val loss: 0.58952, in 0.016s
1 tree, 82 leaves, max depth = 13, train loss: 0.58638, val loss: 0.58179, in 0.016s
1 tree, 83 leaves, max depth = 14, train loss: 0.57945, val loss: 0.57456, in 0.031s
1 tree, 73 leaves, max depth = 11, train loss: 0.57243, val loss: 0.56719, in 0.016s
1 tree, 85 leaves, max depth = 12, train loss: 0.56547, val loss: 0.55997, in 0.016s
1 tree, 102 leaves, max depth = 13, train loss: 0.55927, val loss: 0.55349, in 0.016s
1 tree, 102 leaves, max depth = 12, train loss: 0.55278, val loss: 0.54674, in 0.031s
1 tree, 102 leaves, max depth = 12, train loss: 0.54652, val loss: 0.54024, in 0.031s
1 tree, 81 leaves, max depth = 13, train loss: 0.54076, val loss: 0.53426, in 0.016s
1 tree, 80 leaves, max depth = 13, train loss: 0.53518, val loss: 0.52846, in 0.016s
1 tree, 82 leaves, max depth = 14, train loss: 0.52969, val loss: 0.52273, in 0.016s
1 tree, 81 leaves, max depth = 13, train loss: 0.52400, val loss: 0.51686, in 0.016s
1 tree, 101 leaves, max depth = 13, train loss: 0.51858, val loss: 0.51124, in 0.031s
1 tree, 85 leaves, max depth = 13, train loss: 0.51357, val loss: 0.50614, in 0.016s
1 tree, 85 leaves, max depth = 13, train loss: 0.50875, val loss: 0.50113, in 0.016s
1 tree, 81 leaves, max depth = 13, train loss: 0.50407, val loss: 0.49626, in 0.016s
1 tree, 100 leaves, max depth = 13, train loss: 0.49919, val loss: 0.49119, in 0.016s
1 tree, 82 leaves, max depth = 13, train loss: 0.49477, val loss: 0.48660, in 0.016s
1 tree, 83 leaves, max depth = 12, train loss: 0.49006, val loss: 0.48171, in 0.031s
1 tree, 83 leaves, max depth = 13, train loss: 0.48548, val loss: 0.47696, in 0.016s
1 tree, 72 leaves, max depth = 11, train loss: 0.48157, val loss: 0.47280, in 0.016s
1 tree, 80 leaves, max depth = 13, train loss: 0.47725, val loss: 0.46832, in 0.016s
1 tree, 86 leaves, max depth = 12, train loss: 0.47334, val loss: 0.46434, in 0.016s
1 tree, 82 leaves, max depth = 13, train loss: 0.46954, val loss: 0.46047, in 0.016s
1 tree, 57 leaves, max depth = 13, train loss: 0.46432, val loss: 0.45519, in 0.031s
1 tree, 82 leaves, max depth = 13, train loss: 0.46038, val loss: 0.45112, in 0.016s
1 tree, 85 leaves, max depth = 13, train loss: 0.45654, val loss: 0.44716, in 0.016s
1 tree, 64 leaves, max depth = 12, train loss: 0.45297, val loss: 0.44341, in 0.016s
1 tree, 64 leaves, max depth = 12, train loss: 0.44949, val loss: 0.43978, in 0.016s
1 tree, 101 leaves, max depth = 13, train loss: 0.44605, val loss: 0.43621, in 0.031s
1 tree, 84 leaves, max depth = 12, train loss: 0.44292, val loss: 0.43296, in 0.016s
1 tree, 84 leaves, max depth = 13, train loss: 0.43958, val loss: 0.42950, in 0.016s
1 tree, 103 leaves, max depth = 13, train loss: 0.43640, val loss: 0.42620, in 0.031s
1 tree, 103 leaves, max depth = 13, train loss: 0.43329, val loss: 0.42301, in 0.016s
1 tree, 75 leaves, max depth = 14, train loss: 0.42871, val loss: 0.41845, in 0.016s
1 tree, 73 leaves, max depth = 14, train loss: 0.42427, val loss: 0.41406, in 0.016s
1 tree, 82 leaves, max depth = 13, train loss: 0.42128, val loss: 0.41097, in 0.031s
1 tree, 82 leaves, max depth = 13, train loss: 0.41838, val loss: 0.40798, in 0.016s
1 tree, 82 leaves, max depth = 12, train loss: 0.41553, val loss: 0.40508, in 0.016s
1 tree, 79 leaves, max depth = 14, train loss: 0.41295, val loss: 0.40244, in 0.031s
1 tree, 74 leaves, max depth = 14, train loss: 0.40888, val loss: 0.39840, in 0.016s
1 tree, 83 leaves, max depth = 12, train loss: 0.40626, val loss: 0.39575, in 0.016s
1 tree, 77 leaves, max depth = 12, train loss: 0.40369, val loss: 0.39310, in 0.016s
1 tree, 84 leaves, max depth = 13, train loss: 0.40142, val loss: 0.39076, in 0.031s
1 tree, 102 leaves, max depth = 12, train loss: 0.39923, val loss: 0.38857, in 0.016s
1 tree, 91 leaves, max depth = 14, train loss: 0.39554, val loss: 0.38494, in 0.016s
1 tree, 84 leaves, max depth = 13, train loss: 0.39337, val loss: 0.38278, in 0.031s
1 tree, 80 leaves, max depth = 14, train loss: 0.38972, val loss: 0.37921, in 0.016s
1 tree, 65 leaves, max depth = 12, train loss: 0.38757, val loss: 0.37696, in 0.016s
Fit 59 trees in 1.502 s, (4930 total leaves)
Time spent computing histograms: 0.441s
Time spent finding best splits:  0.095s
Time spent applying splits:      0.089s
Time spent predicting:           0.000s
Trial 3, Fold 5: Log loss = 0.39399982523344507, Average precision = 0.9492866062937685, ROC-AUC = 0.9478948404956988, Elapsed Time = 1.5117119999995339 seconds
Optimization Progress:   4%|4         | 4/100 [00:51<20:32, 12.84s/it]
Trial 4, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 4, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
0.174 s 0.040 GB of training data: 
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 17 leaves, max depth = 7, train loss: 0.67412, val loss: 0.67382, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.65525, val loss: 0.65467, in 0.000s
1 tree, 17 leaves, max depth = 7, train loss: 0.63726, val loss: 0.63628, in 0.031s
1 tree, 17 leaves, max depth = 7, train loss: 0.62082, val loss: 0.61942, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.60647, val loss: 0.60492, in 0.016s
1 tree, 17 leaves, max depth = 9, train loss: 0.59321, val loss: 0.59147, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.57988, val loss: 0.57792, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.56780, val loss: 0.56557, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.55604, val loss: 0.55344, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.54339, val loss: 0.54088, in 0.016s
1 tree, 17 leaves, max depth = 8, train loss: 0.53386, val loss: 0.53113, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.52390, val loss: 0.52099, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.51381, val loss: 0.51081, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.50556, val loss: 0.50232, in 0.016s
1 tree, 17 leaves, max depth = 8, train loss: 0.49718, val loss: 0.49367, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.48948, val loss: 0.48578, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.48109, val loss: 0.47722, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.47319, val loss: 0.46920, in 0.016s
1 tree, 17 leaves, max depth = 8, train loss: 0.46678, val loss: 0.46247, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.46041, val loss: 0.45609, in 0.031s
1 tree, 17 leaves, max depth = 6, train loss: 0.45454, val loss: 0.44995, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.44794, val loss: 0.44327, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.44206, val loss: 0.43727, in 0.031s
1 tree, 17 leaves, max depth = 6, train loss: 0.43525, val loss: 0.43046, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.43039, val loss: 0.42546, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.42565, val loss: 0.42064, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.41986, val loss: 0.41492, in 0.031s
1 tree, 17 leaves, max depth = 6, train loss: 0.41536, val loss: 0.41028, in 0.016s
1 tree, 4 leaves, max depth = 2, train loss: 0.41087, val loss: 0.40555, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.40634, val loss: 0.40100, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.40269, val loss: 0.39725, in 0.031s
1 tree, 17 leaves, max depth = 9, train loss: 0.39968, val loss: 0.39406, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.39481, val loss: 0.38923, in 0.016s
1 tree, 17 leaves, max depth = 8, train loss: 0.39169, val loss: 0.38601, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.38506, val loss: 0.37956, in 0.016s
Fit 35 trees in 0.987 s, (570 total leaves)
Time spent computing histograms: 0.281s
Time spent finding best splits:  0.023s
Time spent applying splits:      0.018s
Time spent predicting:           0.000s
Trial 4, Fold 1: Log loss = 0.3857294285343602, Average precision = 0.9546373970653965, ROC-AUC = 0.9465977987601746, Elapsed Time = 1.0007836999993742 seconds
Trial 4, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 4, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
Binning 0.040 GB of training data: 0.205 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 17 leaves, max depth = 7, train loss: 0.67433, val loss: 0.67347, in 0.016s
1 tree, 17 leaves, max depth = 8, train loss: 0.65446, val loss: 0.65314, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.63657, val loss: 0.63500, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.62000, val loss: 0.61798, in 0.016s
1 tree, 17 leaves, max depth = 8, train loss: 0.60582, val loss: 0.60311, in 0.031s
1 tree, 17 leaves, max depth = 8, train loss: 0.59264, val loss: 0.58920, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.57918, val loss: 0.57533, in 0.031s
1 tree, 12 leaves, max depth = 6, train loss: 0.56709, val loss: 0.56271, in 0.016s
1 tree, 17 leaves, max depth = 8, train loss: 0.55458, val loss: 0.55005, in 0.016s
1 tree, 17 leaves, max depth = 9, train loss: 0.54171, val loss: 0.53693, in 0.016s
1 tree, 17 leaves, max depth = 9, train loss: 0.53217, val loss: 0.52690, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.52228, val loss: 0.51665, in 0.031s
1 tree, 17 leaves, max depth = 5, train loss: 0.51238, val loss: 0.50663, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.50429, val loss: 0.49824, in 0.031s
1 tree, 17 leaves, max depth = 7, train loss: 0.49574, val loss: 0.48940, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.48818, val loss: 0.48147, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.47964, val loss: 0.47268, in 0.031s
1 tree, 17 leaves, max depth = 7, train loss: 0.47165, val loss: 0.46456, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.46514, val loss: 0.45802, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.45833, val loss: 0.45112, in 0.031s
1 tree, 17 leaves, max depth = 6, train loss: 0.45239, val loss: 0.44500, in 0.016s
1 tree, 7 leaves, max depth = 4, train loss: 0.44376, val loss: 0.43639, in 0.031s
1 tree, 17 leaves, max depth = 6, train loss: 0.43665, val loss: 0.42944, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.43129, val loss: 0.42406, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.42487, val loss: 0.41774, in 0.031s
1 tree, 17 leaves, max depth = 6, train loss: 0.41908, val loss: 0.41184, in 0.031s
1 tree, 17 leaves, max depth = 7, train loss: 0.41473, val loss: 0.40730, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.41004, val loss: 0.40265, in 0.031s
1 tree, 17 leaves, max depth = 7, train loss: 0.40493, val loss: 0.39749, in 0.016s
1 tree, 17 leaves, max depth = 8, train loss: 0.40137, val loss: 0.39379, in 0.031s
1 tree, 17 leaves, max depth = 8, train loss: 0.39769, val loss: 0.39010, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.39364, val loss: 0.38605, in 0.016s
1 tree, 17 leaves, max depth = 8, train loss: 0.39037, val loss: 0.38272, in 0.031s
1 tree, 17 leaves, max depth = 7, train loss: 0.38742, val loss: 0.37971, in 0.016s
1 tree, 10 leaves, max depth = 6, train loss: 0.38473, val loss: 0.37692, in 0.031s
Fit 35 trees in 1.283 s, (573 total leaves)
Time spent computing histograms: 0.329s
Time spent finding best splits:  0.031s
Time spent applying splits:      0.023s
Time spent predicting:           0.000s
Trial 4, Fold 2: Log loss = 0.3869140975171336, Average precision = 0.950777794365889, ROC-AUC = 0.9465078433877876, Elapsed Time = 1.288409300001149 seconds
Trial 4, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 4, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
Binning 0.040 GB of training data: 0.173 s
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 17 leaves, max depth = 6, train loss: 0.67429, val loss: 0.67398, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.65557, val loss: 0.65486, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.63772, val loss: 0.63694, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.62128, val loss: 0.62002, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.60715, val loss: 0.60547, in 0.016s
1 tree, 17 leaves, max depth = 9, train loss: 0.59404, val loss: 0.59203, in 0.031s
1 tree, 17 leaves, max depth = 7, train loss: 0.58058, val loss: 0.57846, in 0.000s
1 tree, 17 leaves, max depth = 8, train loss: 0.56862, val loss: 0.56621, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.55634, val loss: 0.55353, in 0.016s
1 tree, 17 leaves, max depth = 8, train loss: 0.54372, val loss: 0.54080, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.53417, val loss: 0.53103, in 0.031s
1 tree, 17 leaves, max depth = 6, train loss: 0.52413, val loss: 0.52081, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.51363, val loss: 0.51032, in 0.016s
1 tree, 17 leaves, max depth = 8, train loss: 0.50477, val loss: 0.50132, in 0.016s
1 tree, 6 leaves, max depth = 3, train loss: 0.49335, val loss: 0.49047, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.48428, val loss: 0.48145, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.47658, val loss: 0.47356, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.46858, val loss: 0.46559, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.46269, val loss: 0.45952, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.45526, val loss: 0.45214, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.44841, val loss: 0.44531, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.44198, val loss: 0.43894, in 0.016s
1 tree, 17 leaves, max depth = 9, train loss: 0.43709, val loss: 0.43393, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.43167, val loss: 0.42869, in 0.031s
1 tree, 17 leaves, max depth = 6, train loss: 0.42544, val loss: 0.42215, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.41996, val loss: 0.41667, in 0.016s
1 tree, 17 leaves, max depth = 8, train loss: 0.41561, val loss: 0.41227, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.41079, val loss: 0.40763, in 0.016s
1 tree, 4 leaves, max depth = 2, train loss: 0.40642, val loss: 0.40342, in 0.031s
1 tree, 17 leaves, max depth = 6, train loss: 0.40199, val loss: 0.39868, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.39832, val loss: 0.39492, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.39498, val loss: 0.39150, in 0.016s
1 tree, 17 leaves, max depth = 5, train loss: 0.39030, val loss: 0.38667, in 0.016s
1 tree, 17 leaves, max depth = 9, train loss: 0.38721, val loss: 0.38377, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.38087, val loss: 0.37797, in 0.016s
Fit 35 trees in 1.002 s, (559 total leaves)
Time spent computing histograms: 0.279s
Time spent finding best splits:  0.022s
Time spent applying splits:      0.018s
Time spent predicting:           0.000s
Trial 4, Fold 3: Log loss = 0.3760721421921893, Average precision = 0.956812154332084, ROC-AUC = 0.9499740714478335, Elapsed Time = 1.0188118000005488 seconds
Trial 4, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 4, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
Binning 0.040 GB of training data: 0.172 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 17 leaves, max depth = 9, train loss: 0.67437, val loss: 0.67377, in 0.031s
1 tree, 17 leaves, max depth = 6, train loss: 0.65373, val loss: 0.65215, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.63749, val loss: 0.63533, in 0.000s
1 tree, 17 leaves, max depth = 6, train loss: 0.62145, val loss: 0.61868, in 0.000s
1 tree, 17 leaves, max depth = 7, train loss: 0.60728, val loss: 0.60401, in 0.031s
1 tree, 17 leaves, max depth = 8, train loss: 0.59418, val loss: 0.59029, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.58072, val loss: 0.57638, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.56861, val loss: 0.56359, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.55655, val loss: 0.55088, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.54381, val loss: 0.53749, in 0.016s
1 tree, 17 leaves, max depth = 8, train loss: 0.53429, val loss: 0.52760, in 0.016s
1 tree, 17 leaves, max depth = 5, train loss: 0.52443, val loss: 0.51726, in 0.016s
1 tree, 17 leaves, max depth = 9, train loss: 0.51608, val loss: 0.50850, in 0.016s
1 tree, 17 leaves, max depth = 5, train loss: 0.50802, val loss: 0.50002, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.49940, val loss: 0.49062, in 0.016s
1 tree, 17 leaves, max depth = 8, train loss: 0.49184, val loss: 0.48259, in 0.016s
1 tree, 17 leaves, max depth = 8, train loss: 0.48338, val loss: 0.47340, in 0.031s
1 tree, 17 leaves, max depth = 7, train loss: 0.47538, val loss: 0.46475, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.46876, val loss: 0.45746, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.46164, val loss: 0.44983, in 0.016s
1 tree, 17 leaves, max depth = 8, train loss: 0.45553, val loss: 0.44322, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.44877, val loss: 0.43603, in 0.031s
1 tree, 17 leaves, max depth = 7, train loss: 0.44179, val loss: 0.42898, in 0.016s
1 tree, 17 leaves, max depth = 8, train loss: 0.43637, val loss: 0.42311, in 0.016s
1 tree, 17 leaves, max depth = 8, train loss: 0.43005, val loss: 0.41659, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.42451, val loss: 0.41068, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.41982, val loss: 0.40562, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.41511, val loss: 0.40067, in 0.016s
1 tree, 4 leaves, max depth = 2, train loss: 0.41073, val loss: 0.39602, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.40627, val loss: 0.39127, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.39866, val loss: 0.38385, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.39524, val loss: 0.38019, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.39052, val loss: 0.37538, in 0.031s
1 tree, 17 leaves, max depth = 7, train loss: 0.38737, val loss: 0.37202, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.38404, val loss: 0.36837, in 0.016s
Fit 35 trees in 0.985 s, (582 total leaves)
Time spent computing histograms: 0.270s
Time spent finding best splits:  0.022s
Time spent applying splits:      0.018s
Time spent predicting:           0.016s
Trial 4, Fold 4: Log loss = 0.3838827584958714, Average precision = 0.9537464555030243, ROC-AUC = 0.9472256349087129, Elapsed Time = 0.9993679999988672 seconds
Trial 4, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 4, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
Binning 0.040 GB of training data: 0.174 s
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 17 leaves, max depth = 7, train loss: 0.67408, val loss: 0.67314, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.65417, val loss: 0.65205, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.63645, val loss: 0.63339, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.62000, val loss: 0.61584, in 0.016s
1 tree, 17 leaves, max depth = 9, train loss: 0.60572, val loss: 0.60077, in 0.016s
1 tree, 17 leaves, max depth = 9, train loss: 0.59242, val loss: 0.58659, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.57897, val loss: 0.57263, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.56682, val loss: 0.55988, in 0.031s
1 tree, 17 leaves, max depth = 7, train loss: 0.55445, val loss: 0.54666, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.54181, val loss: 0.53346, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.53230, val loss: 0.52316, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.52232, val loss: 0.51280, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.51193, val loss: 0.50179, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.50264, val loss: 0.49164, in 0.016s
1 tree, 8 leaves, max depth = 4, train loss: 0.49117, val loss: 0.47989, in 0.016s
1 tree, 17 leaves, max depth = 8, train loss: 0.48224, val loss: 0.47039, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.47453, val loss: 0.46197, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.46784, val loss: 0.45473, in 0.016s
1 tree, 6 leaves, max depth = 4, train loss: 0.46093, val loss: 0.44757, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.45322, val loss: 0.43958, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.44623, val loss: 0.43221, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.43958, val loss: 0.42519, in 0.016s
1 tree, 17 leaves, max depth = 5, train loss: 0.43457, val loss: 0.41991, in 0.016s
1 tree, 17 leaves, max depth = 8, train loss: 0.42919, val loss: 0.41417, in 0.016s
1 tree, 17 leaves, max depth = 8, train loss: 0.42288, val loss: 0.40770, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.41731, val loss: 0.40193, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.41294, val loss: 0.39738, in 0.031s
1 tree, 17 leaves, max depth = 6, train loss: 0.40817, val loss: 0.39247, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.40400, val loss: 0.38794, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.39875, val loss: 0.38266, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.39373, val loss: 0.37764, in 0.016s
1 tree, 17 leaves, max depth = 10, train loss: 0.38908, val loss: 0.37311, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.38617, val loss: 0.36985, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.38333, val loss: 0.36672, in 0.031s
1 tree, 5 leaves, max depth = 3, train loss: 0.37712, val loss: 0.36046, in 0.000s
Fit 35 trees in 0.987 s, (563 total leaves)
Time spent computing histograms: 0.277s
Time spent finding best splits:  0.021s
Time spent applying splits:      0.017s
Time spent predicting:           0.000s
Trial 4, Fold 5: Log loss = 0.3835427485060661, Average precision = 0.953093001420166, ROC-AUC = 0.9466856176727422, Elapsed Time = 1.0019355999993422 seconds
Optimization Progress:   5%|5         | 5/100 [01:05<20:30, 12.96s/it]
Trial 5, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 5, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
0.158 s 0.040 GB of training data: 
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 12 leaves, max depth = 7, train loss: 0.68648, val loss: 0.68626, in 0.016s
1 tree, 15 leaves, max depth = 9, train loss: 0.67994, val loss: 0.67954, in 0.000s
1 tree, 12 leaves, max depth = 8, train loss: 0.67354, val loss: 0.67299, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.66732, val loss: 0.66654, in 0.000s
1 tree, 19 leaves, max depth = 10, train loss: 0.66132, val loss: 0.66036, in 0.000s
1 tree, 15 leaves, max depth = 7, train loss: 0.65548, val loss: 0.65430, in 0.016s
1 tree, 19 leaves, max depth = 10, train loss: 0.64981, val loss: 0.64846, in 0.000s
1 tree, 15 leaves, max depth = 9, train loss: 0.64430, val loss: 0.64278, in 0.000s
1 tree, 15 leaves, max depth = 10, train loss: 0.63891, val loss: 0.63720, in 0.016s
1 tree, 10 leaves, max depth = 6, train loss: 0.63355, val loss: 0.63167, in 0.000s
1 tree, 15 leaves, max depth = 7, train loss: 0.62846, val loss: 0.62638, in 0.016s
1 tree, 21 leaves, max depth = 10, train loss: 0.62352, val loss: 0.62128, in 0.000s
1 tree, 18 leaves, max depth = 10, train loss: 0.61872, val loss: 0.61632, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.61406, val loss: 0.61148, in 0.000s
1 tree, 15 leaves, max depth = 8, train loss: 0.60951, val loss: 0.60677, in 0.016s
1 tree, 18 leaves, max depth = 8, train loss: 0.60508, val loss: 0.60217, in 0.000s
1 tree, 12 leaves, max depth = 8, train loss: 0.60070, val loss: 0.59766, in 0.016s
1 tree, 12 leaves, max depth = 8, train loss: 0.59644, val loss: 0.59328, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.59229, val loss: 0.58894, in 0.016s
1 tree, 12 leaves, max depth = 6, train loss: 0.58817, val loss: 0.58465, in 0.000s
Fit 20 trees in 0.408 s, (282 total leaves)
Time spent computing histograms: 0.082s
Time spent finding best splits:  0.006s
Time spent applying splits:      0.006s
Time spent predicting:           0.000s
Trial 5, Fold 1: Log loss = 0.5874254284962492, Average precision = 0.8367587948394197, ROC-AUC = 0.8674685622517603, Elapsed Time = 0.42420400000082736 seconds
Trial 5, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 5, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
Binning 0.040 GB of training data: 0.174 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 15 leaves, max depth = 8, train loss: 0.68647, val loss: 0.68613, in 0.016s
1 tree, 14 leaves, max depth = 6, train loss: 0.67988, val loss: 0.67921, in 0.000s
1 tree, 13 leaves, max depth = 7, train loss: 0.67348, val loss: 0.67249, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.66725, val loss: 0.66596, in 0.000s
1 tree, 14 leaves, max depth = 6, train loss: 0.66120, val loss: 0.65960, in 0.016s
1 tree, 14 leaves, max depth = 6, train loss: 0.65533, val loss: 0.65341, in 0.000s
1 tree, 19 leaves, max depth = 10, train loss: 0.64964, val loss: 0.64744, in 0.016s
1 tree, 12 leaves, max depth = 7, train loss: 0.64412, val loss: 0.64165, in 0.000s
1 tree, 14 leaves, max depth = 6, train loss: 0.63871, val loss: 0.63595, in 0.000s
1 tree, 11 leaves, max depth = 7, train loss: 0.63340, val loss: 0.63035, in 0.016s
1 tree, 18 leaves, max depth = 9, train loss: 0.62831, val loss: 0.62498, in 0.000s
1 tree, 13 leaves, max depth = 7, train loss: 0.62336, val loss: 0.61978, in 0.016s
1 tree, 14 leaves, max depth = 6, train loss: 0.61852, val loss: 0.61466, in 0.000s
1 tree, 19 leaves, max depth = 8, train loss: 0.61386, val loss: 0.60974, in 0.016s
1 tree, 14 leaves, max depth = 6, train loss: 0.60927, val loss: 0.60488, in 0.000s
1 tree, 19 leaves, max depth = 11, train loss: 0.60482, val loss: 0.60020, in 0.016s
1 tree, 13 leaves, max depth = 7, train loss: 0.60047, val loss: 0.59560, in 0.000s
1 tree, 18 leaves, max depth = 9, train loss: 0.59625, val loss: 0.59113, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.59209, val loss: 0.58675, in 0.000s
1 tree, 15 leaves, max depth = 7, train loss: 0.58804, val loss: 0.58246, in 0.000s
Fit 20 trees in 0.440 s, (277 total leaves)
Time spent computing histograms: 0.087s
Time spent finding best splits:  0.006s
Time spent applying splits:      0.007s
Time spent predicting:           0.000s
Trial 5, Fold 2: Log loss = 0.5878867246029013, Average precision = 0.8337268872759109, ROC-AUC = 0.869113741338616, Elapsed Time = 0.45685410000078264 seconds
Trial 5, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 5, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
Binning 0.040 GB of training data: 0.173 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 14 leaves, max depth = 9, train loss: 0.68650, val loss: 0.68627, in 0.016s
1 tree, 15 leaves, max depth = 11, train loss: 0.68001, val loss: 0.67955, in 0.000s
1 tree, 14 leaves, max depth = 7, train loss: 0.67368, val loss: 0.67297, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.66748, val loss: 0.66656, in 0.000s
1 tree, 13 leaves, max depth = 7, train loss: 0.66152, val loss: 0.66038, in 0.000s
1 tree, 14 leaves, max depth = 9, train loss: 0.65570, val loss: 0.65439, in 0.016s
1 tree, 22 leaves, max depth = 9, train loss: 0.65007, val loss: 0.64853, in 0.000s
1 tree, 15 leaves, max depth = 7, train loss: 0.64460, val loss: 0.64285, in 0.000s
1 tree, 12 leaves, max depth = 7, train loss: 0.63921, val loss: 0.63726, in 0.000s
1 tree, 12 leaves, max depth = 6, train loss: 0.63393, val loss: 0.63179, in 0.000s
1 tree, 22 leaves, max depth = 9, train loss: 0.62889, val loss: 0.62654, in 0.016s
1 tree, 20 leaves, max depth = 12, train loss: 0.62399, val loss: 0.62145, in 0.000s
1 tree, 22 leaves, max depth = 13, train loss: 0.61922, val loss: 0.61650, in 0.016s
1 tree, 18 leaves, max depth = 9, train loss: 0.61459, val loss: 0.61168, in 0.000s
1 tree, 20 leaves, max depth = 11, train loss: 0.61008, val loss: 0.60698, in 0.016s
1 tree, 22 leaves, max depth = 13, train loss: 0.60568, val loss: 0.60241, in 0.000s
1 tree, 16 leaves, max depth = 7, train loss: 0.60136, val loss: 0.59789, in 0.016s
1 tree, 16 leaves, max depth = 7, train loss: 0.59715, val loss: 0.59349, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.59301, val loss: 0.58920, in 0.000s
1 tree, 16 leaves, max depth = 6, train loss: 0.58895, val loss: 0.58499, in 0.016s
Fit 20 trees in 0.439 s, (313 total leaves)
Time spent computing histograms: 0.079s
Time spent finding best splits:  0.007s
Time spent applying splits:      0.007s
Time spent predicting:           0.000s
Trial 5, Fold 3: Log loss = 0.5860022740246483, Average precision = 0.8476111167806911, ROC-AUC = 0.8774352975026725, Elapsed Time = 0.4495236999991903 seconds
Trial 5, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 5, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
0.157 s 0.040 GB of training data: 
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 7 leaves, max depth = 5, train loss: 0.68650, val loss: 0.68611, in 0.000s
1 tree, 15 leaves, max depth = 9, train loss: 0.67998, val loss: 0.67924, in 0.016s
1 tree, 12 leaves, max depth = 7, train loss: 0.67362, val loss: 0.67253, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.66742, val loss: 0.66599, in 0.016s
1 tree, 19 leaves, max depth = 9, train loss: 0.66144, val loss: 0.65967, in 0.000s
1 tree, 19 leaves, max depth = 11, train loss: 0.65560, val loss: 0.65347, in 0.016s
1 tree, 19 leaves, max depth = 9, train loss: 0.64995, val loss: 0.64749, in 0.000s
1 tree, 19 leaves, max depth = 9, train loss: 0.64445, val loss: 0.64167, in 0.016s
1 tree, 19 leaves, max depth = 10, train loss: 0.63909, val loss: 0.63596, in 0.000s
1 tree, 10 leaves, max depth = 6, train loss: 0.63379, val loss: 0.63032, in 0.000s
1 tree, 19 leaves, max depth = 11, train loss: 0.62872, val loss: 0.62491, in 0.016s
1 tree, 20 leaves, max depth = 9, train loss: 0.62379, val loss: 0.61967, in 0.000s
1 tree, 19 leaves, max depth = 9, train loss: 0.61900, val loss: 0.61458, in 0.016s
1 tree, 12 leaves, max depth = 5, train loss: 0.61436, val loss: 0.60960, in 0.000s
1 tree, 15 leaves, max depth = 9, train loss: 0.60982, val loss: 0.60477, in 0.016s
1 tree, 17 leaves, max depth = 9, train loss: 0.60540, val loss: 0.60004, in 0.000s
1 tree, 13 leaves, max depth = 7, train loss: 0.60106, val loss: 0.59540, in 0.000s
1 tree, 16 leaves, max depth = 8, train loss: 0.59683, val loss: 0.59088, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.59269, val loss: 0.58647, in 0.000s
1 tree, 14 leaves, max depth = 7, train loss: 0.58862, val loss: 0.58209, in 0.016s
Fit 20 trees in 0.454 s, (294 total leaves)
Time spent computing histograms: 0.086s
Time spent finding best splits:  0.006s
Time spent applying splits:      0.007s
Time spent predicting:           0.000s
Trial 5, Fold 4: Log loss = 0.5879750852345283, Average precision = 0.8255683557170996, ROC-AUC = 0.8667012853160894, Elapsed Time = 0.45265290000133973 seconds
Trial 5, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 5, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
Binning 0.040 GB of training data: 0.173 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 17 leaves, max depth = 9, train loss: 0.68641, val loss: 0.68594, in 0.016s
1 tree, 16 leaves, max depth = 10, train loss: 0.67980, val loss: 0.67887, in 0.000s
1 tree, 11 leaves, max depth = 6, train loss: 0.67335, val loss: 0.67201, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.66708, val loss: 0.66532, in 0.000s
1 tree, 17 leaves, max depth = 8, train loss: 0.66102, val loss: 0.65881, in 0.000s
1 tree, 18 leaves, max depth = 8, train loss: 0.65511, val loss: 0.65247, in 0.016s
1 tree, 17 leaves, max depth = 8, train loss: 0.64939, val loss: 0.64636, in 0.000s
1 tree, 17 leaves, max depth = 8, train loss: 0.64383, val loss: 0.64037, in 0.016s
1 tree, 14 leaves, max depth = 8, train loss: 0.63838, val loss: 0.63455, in 0.000s
1 tree, 11 leaves, max depth = 7, train loss: 0.63305, val loss: 0.62880, in 0.016s
1 tree, 19 leaves, max depth = 8, train loss: 0.62791, val loss: 0.62326, in 0.000s
1 tree, 17 leaves, max depth = 8, train loss: 0.62292, val loss: 0.61787, in 0.016s
1 tree, 15 leaves, max depth = 7, train loss: 0.61808, val loss: 0.61263, in 0.000s
1 tree, 18 leaves, max depth = 8, train loss: 0.61339, val loss: 0.60755, in 0.016s
1 tree, 16 leaves, max depth = 10, train loss: 0.60878, val loss: 0.60257, in 0.000s
1 tree, 16 leaves, max depth = 7, train loss: 0.60431, val loss: 0.59775, in 0.000s
1 tree, 11 leaves, max depth = 7, train loss: 0.59994, val loss: 0.59301, in 0.016s
1 tree, 20 leaves, max depth = 8, train loss: 0.59568, val loss: 0.58838, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.59150, val loss: 0.58386, in 0.016s
1 tree, 12 leaves, max depth = 8, train loss: 0.58740, val loss: 0.57940, in 0.000s
Fit 20 trees in 0.439 s, (292 total leaves)
Time spent computing histograms: 0.083s
Time spent finding best splits:  0.006s
Time spent applying splits:      0.007s
Time spent predicting:           0.000s
Trial 5, Fold 5: Log loss = 0.5906025603832106, Average precision = 0.829664343872623, ROC-AUC = 0.8594015941140405, Elapsed Time = 0.4455651999996917 seconds
Optimization Progress:   6%|6         | 6/100 [01:14<18:16, 11.67s/it]
Trial 6, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 6, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
0.142 s 0.040 GB of training data: 
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 31 leaves, max depth = 7, train loss: 0.64682, val loss: 0.64584, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.60923, val loss: 0.60826, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.57663, val loss: 0.57493, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.54702, val loss: 0.54501, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.52327, val loss: 0.52099, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.50163, val loss: 0.49901, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.48332, val loss: 0.48045, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.46715, val loss: 0.46376, in 0.016s
1 tree, 26 leaves, max depth = 9, train loss: 0.45198, val loss: 0.44809, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.43849, val loss: 0.43484, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.42493, val loss: 0.42120, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.41304, val loss: 0.40976, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.40354, val loss: 0.40020, in 0.016s
1 tree, 24 leaves, max depth = 9, train loss: 0.38656, val loss: 0.38455, in 0.016s
1 tree, 6 leaves, max depth = 4, train loss: 0.37841, val loss: 0.37587, in 0.016s
1 tree, 31 leaves, max depth = 12, train loss: 0.37063, val loss: 0.36800, in 0.000s
1 tree, 31 leaves, max depth = 13, train loss: 0.36308, val loss: 0.36019, in 0.016s
1 tree, 24 leaves, max depth = 8, train loss: 0.35007, val loss: 0.34797, in 0.016s
Fit 18 trees in 0.564 s, (514 total leaves)
Time spent computing histograms: 0.110s
Time spent finding best splits:  0.014s
Time spent applying splits:      0.011s
Time spent predicting:           0.000s
Trial 6, Fold 1: Log loss = 0.351445931369262, Average precision = 0.9549264452505613, ROC-AUC = 0.9488231456388638, Elapsed Time = 0.5713984000012715 seconds
Trial 6, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 6, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
Binning 0.040 GB of training data: 0.157 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 31 leaves, max depth = 8, train loss: 0.64672, val loss: 0.64567, in 0.016s
1 tree, 31 leaves, max depth = 8, train loss: 0.60891, val loss: 0.60705, in 0.016s
1 tree, 31 leaves, max depth = 14, train loss: 0.57563, val loss: 0.57322, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.54689, val loss: 0.54408, in 0.016s
1 tree, 31 leaves, max depth = 15, train loss: 0.52281, val loss: 0.51955, in 0.016s
1 tree, 31 leaves, max depth = 7, train loss: 0.50099, val loss: 0.49712, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.48171, val loss: 0.47747, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.46327, val loss: 0.45859, in 0.016s
1 tree, 31 leaves, max depth = 8, train loss: 0.44737, val loss: 0.44247, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.43278, val loss: 0.42760, in 0.016s
1 tree, 16 leaves, max depth = 5, train loss: 0.41305, val loss: 0.40819, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.39614, val loss: 0.39161, in 0.016s
1 tree, 7 leaves, max depth = 4, train loss: 0.38659, val loss: 0.38186, in 0.016s
1 tree, 31 leaves, max depth = 8, train loss: 0.37596, val loss: 0.37114, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.36871, val loss: 0.36346, in 0.016s
1 tree, 31 leaves, max depth = 13, train loss: 0.36135, val loss: 0.35629, in 0.031s
1 tree, 31 leaves, max depth = 11, train loss: 0.35377, val loss: 0.34874, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.34666, val loss: 0.34195, in 0.016s
Fit 18 trees in 0.626 s, (479 total leaves)
Time spent computing histograms: 0.125s
Time spent finding best splits:  0.015s
Time spent applying splits:      0.011s
Time spent predicting:           0.000s
Trial 6, Fold 2: Log loss = 0.3485951215531161, Average precision = 0.9527528591451231, ROC-AUC = 0.9498124549119393, Elapsed Time = 0.6378046999998332 seconds
Trial 6, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 6, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
Binning 0.040 GB of training data: 0.173 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 31 leaves, max depth = 9, train loss: 0.64689, val loss: 0.64606, in 0.016s
1 tree, 31 leaves, max depth = 8, train loss: 0.60951, val loss: 0.60831, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.57613, val loss: 0.57510, in 0.031s
1 tree, 31 leaves, max depth = 12, train loss: 0.54743, val loss: 0.54646, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.52314, val loss: 0.52207, in 0.016s
1 tree, 31 leaves, max depth = 8, train loss: 0.50169, val loss: 0.50025, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.48170, val loss: 0.47987, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.46353, val loss: 0.46166, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.44786, val loss: 0.44578, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.43362, val loss: 0.43174, in 0.016s
1 tree, 13 leaves, max depth = 6, train loss: 0.41372, val loss: 0.41334, in 0.016s
1 tree, 18 leaves, max depth = 8, train loss: 0.39648, val loss: 0.39754, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.38458, val loss: 0.38508, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.37387, val loss: 0.37399, in 0.016s
1 tree, 7 leaves, max depth = 4, train loss: 0.36103, val loss: 0.36236, in 0.016s
1 tree, 9 leaves, max depth = 6, train loss: 0.34986, val loss: 0.35234, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.34225, val loss: 0.34499, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.33422, val loss: 0.33678, in 0.016s
Fit 18 trees in 0.642 s, (481 total leaves)
Time spent computing histograms: 0.125s
Time spent finding best splits:  0.016s
Time spent applying splits:      0.012s
Time spent predicting:           0.000s
Trial 6, Fold 3: Log loss = 0.33131520081959953, Average precision = 0.9602025195875663, ROC-AUC = 0.9562246884360162, Elapsed Time = 0.6537537000003795 seconds
Trial 6, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 6, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
Binning 0.040 GB of training data: 0.173 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 31 leaves, max depth = 9, train loss: 0.64797, val loss: 0.64605, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.61040, val loss: 0.60692, in 0.016s
1 tree, 31 leaves, max depth = 12, train loss: 0.57731, val loss: 0.57218, in 0.016s
1 tree, 31 leaves, max depth = 13, train loss: 0.54897, val loss: 0.54247, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.52528, val loss: 0.51725, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.50382, val loss: 0.49495, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.48282, val loss: 0.47276, in 0.031s
1 tree, 31 leaves, max depth = 9, train loss: 0.46470, val loss: 0.45353, in 0.000s
1 tree, 31 leaves, max depth = 10, train loss: 0.44889, val loss: 0.43673, in 0.016s
1 tree, 31 leaves, max depth = 12, train loss: 0.43514, val loss: 0.42176, in 0.031s
1 tree, 20 leaves, max depth = 9, train loss: 0.41537, val loss: 0.40175, in 0.016s
1 tree, 31 leaves, max depth = 8, train loss: 0.40489, val loss: 0.39095, in 0.016s
1 tree, 29 leaves, max depth = 13, train loss: 0.39317, val loss: 0.37912, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.38347, val loss: 0.36885, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.37073, val loss: 0.35523, in 0.016s
1 tree, 7 leaves, max depth = 4, train loss: 0.35966, val loss: 0.34315, in 0.016s
1 tree, 31 leaves, max depth = 12, train loss: 0.35111, val loss: 0.33455, in 0.016s
1 tree, 19 leaves, max depth = 7, train loss: 0.34035, val loss: 0.32368, in 0.016s
Fit 18 trees in 0.657 s, (483 total leaves)
Time spent computing histograms: 0.125s
Time spent finding best splits:  0.016s
Time spent applying splits:      0.012s
Time spent predicting:           0.000s
Trial 6, Fold 4: Log loss = 0.33944525800884284, Average precision = 0.9586036453769143, ROC-AUC = 0.9534304652460353, Elapsed Time = 0.6549568999998883 seconds
Trial 6, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 6, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
Binning 0.040 GB of training data: 0.172 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 31 leaves, max depth = 9, train loss: 0.64656, val loss: 0.64368, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.60851, val loss: 0.60445, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.57553, val loss: 0.56993, in 0.016s
1 tree, 31 leaves, max depth = 14, train loss: 0.54676, val loss: 0.53985, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.52258, val loss: 0.51387, in 0.016s
1 tree, 31 leaves, max depth = 8, train loss: 0.50078, val loss: 0.49148, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.48064, val loss: 0.47014, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.46273, val loss: 0.45134, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.44693, val loss: 0.43461, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.43251, val loss: 0.41911, in 0.016s
1 tree, 9 leaves, max depth = 5, train loss: 0.41342, val loss: 0.39988, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.39633, val loss: 0.38292, in 0.016s
1 tree, 7 leaves, max depth = 4, train loss: 0.38660, val loss: 0.37296, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.37632, val loss: 0.36253, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.36899, val loss: 0.35500, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.36171, val loss: 0.34769, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.35397, val loss: 0.33972, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.34685, val loss: 0.33242, in 0.016s
Fit 18 trees in 0.641 s, (472 total leaves)
Time spent computing histograms: 0.119s
Time spent finding best splits:  0.015s
Time spent applying splits:      0.011s
Time spent predicting:           0.000s
Trial 6, Fold 5: Log loss = 0.3542236616621092, Average precision = 0.9494919234787114, ROC-AUC = 0.9467516860821583, Elapsed Time = 0.6471075999997993 seconds
Optimization Progress:   7%|7         | 7/100 [01:24<17:07, 11.05s/it]
Trial 7, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 7, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
Binning 0.040 GB of training data: 0.220 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 50 leaves, max depth = 11, train loss: 0.66112, val loss: 0.66111, in 0.031s
1 tree, 49 leaves, max depth = 11, train loss: 0.63276, val loss: 0.63277, in 0.016s
1 tree, 50 leaves, max depth = 11, train loss: 0.60721, val loss: 0.60719, in 0.031s
1 tree, 52 leaves, max depth = 11, train loss: 0.58504, val loss: 0.58493, in 0.016s
1 tree, 49 leaves, max depth = 11, train loss: 0.56405, val loss: 0.56390, in 0.031s
1 tree, 52 leaves, max depth = 11, train loss: 0.54583, val loss: 0.54557, in 0.016s
1 tree, 47 leaves, max depth = 11, train loss: 0.52809, val loss: 0.52771, in 0.016s
1 tree, 48 leaves, max depth = 11, train loss: 0.51192, val loss: 0.51143, in 0.031s
[9/36] 1 tree, 47 leaves, max depth = 11, train loss: 0.49707, val loss: 0.49647, in 0.016s
1 tree, 46 leaves, max depth = 11, train loss: 0.48348, val loss: 0.48268, in 0.031s
1 tree, 47 leaves, max depth = 10, train loss: 0.47105, val loss: 0.47016, in 0.016s
1 tree, 50 leaves, max depth = 11, train loss: 0.45957, val loss: 0.45860, in 0.031s
1 tree, 56 leaves, max depth = 11, train loss: 0.44915, val loss: 0.44785, in 0.031s
1 tree, 45 leaves, max depth = 10, train loss: 0.43945, val loss: 0.43808, in 0.016s
1 tree, 51 leaves, max depth = 11, train loss: 0.42548, val loss: 0.42457, in 0.031s
1 tree, 48 leaves, max depth = 11, train loss: 0.41704, val loss: 0.41610, in 0.031s
1 tree, 51 leaves, max depth = 12, train loss: 0.40486, val loss: 0.40436, in 0.016s
1 tree, 47 leaves, max depth = 10, train loss: 0.39745, val loss: 0.39694, in 0.031s
1 tree, 52 leaves, max depth = 12, train loss: 0.38679, val loss: 0.38671, in 0.016s
1 tree, 49 leaves, max depth = 11, train loss: 0.38032, val loss: 0.38026, in 0.031s
1 tree, 53 leaves, max depth = 12, train loss: 0.37095, val loss: 0.37131, in 0.031s
1 tree, 49 leaves, max depth = 12, train loss: 0.36527, val loss: 0.36567, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.35741, val loss: 0.35787, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.35027, val loss: 0.35078, in 0.031s
1 tree, 51 leaves, max depth = 12, train loss: 0.34466, val loss: 0.34528, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.33837, val loss: 0.33904, in 0.031s
1 tree, 50 leaves, max depth = 11, train loss: 0.33325, val loss: 0.33402, in 0.016s
1 tree, 51 leaves, max depth = 13, train loss: 0.32750, val loss: 0.32895, in 0.031s
1 tree, 31 leaves, max depth = 11, train loss: 0.32226, val loss: 0.32375, in 0.016s
1 tree, 49 leaves, max depth = 13, train loss: 0.31730, val loss: 0.31944, in 0.016s
1 tree, 50 leaves, max depth = 11, train loss: 0.31299, val loss: 0.31518, in 0.031s
1 tree, 31 leaves, max depth = 10, train loss: 0.30861, val loss: 0.31083, in 0.016s
1 tree, 61 leaves, max depth = 11, train loss: 0.30462, val loss: 0.30681, in 0.031s
1 tree, 51 leaves, max depth = 13, train loss: 0.30056, val loss: 0.30334, in 0.031s
1 tree, 30 leaves, max depth = 9, train loss: 0.29687, val loss: 0.29966, in 0.016s
1 tree, 50 leaves, max depth = 13, train loss: 0.29335, val loss: 0.29669, in 0.016s
Fit 36 trees in 1.330 s, (1686 total leaves)
Time spent computing histograms: 0.323s
Time spent finding best splits:  0.057s
Time spent applying splits:      0.052s
Time spent predicting:           0.000s
Trial 7, Fold 1: Log loss = 0.2971298608703609, Average precision = 0.9601508407125532, ROC-AUC = 0.954229981287908, Elapsed Time = 1.3452409000001353 seconds
Trial 7, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 7, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
Binning 0.040 GB of training data: 0.236 s
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 50 leaves, max depth = 11, train loss: 0.66131, val loss: 0.66052, in 0.016s
1 tree, 47 leaves, max depth = 9, train loss: 0.63253, val loss: 0.63114, in 0.031s
1 tree, 49 leaves, max depth = 10, train loss: 0.60688, val loss: 0.60477, in 0.016s
1 tree, 51 leaves, max depth = 13, train loss: 0.58478, val loss: 0.58232, in 0.016s
1 tree, 48 leaves, max depth = 11, train loss: 0.56350, val loss: 0.56065, in 0.031s
1 tree, 51 leaves, max depth = 13, train loss: 0.54533, val loss: 0.54218, in 0.031s
1 tree, 53 leaves, max depth = 11, train loss: 0.52756, val loss: 0.52399, in 0.016s
1 tree, 50 leaves, max depth = 9, train loss: 0.51136, val loss: 0.50739, in 0.031s
1 tree, 49 leaves, max depth = 11, train loss: 0.49658, val loss: 0.49232, in 0.031s
1 tree, 49 leaves, max depth = 9, train loss: 0.48304, val loss: 0.47846, in 0.016s
1 tree, 48 leaves, max depth = 10, train loss: 0.47059, val loss: 0.46569, in 0.031s
1 tree, 46 leaves, max depth = 10, train loss: 0.45917, val loss: 0.45407, in 0.031s
1 tree, 49 leaves, max depth = 10, train loss: 0.44866, val loss: 0.44341, in 0.031s
1 tree, 46 leaves, max depth = 10, train loss: 0.43895, val loss: 0.43343, in 0.016s
1 tree, 54 leaves, max depth = 12, train loss: 0.42508, val loss: 0.41974, in 0.016s
1 tree, 49 leaves, max depth = 11, train loss: 0.41672, val loss: 0.41110, in 0.031s
1 tree, 53 leaves, max depth = 12, train loss: 0.40472, val loss: 0.39931, in 0.016s
1 tree, 50 leaves, max depth = 10, train loss: 0.39751, val loss: 0.39189, in 0.031s
1 tree, 53 leaves, max depth = 12, train loss: 0.38699, val loss: 0.38159, in 0.016s
1 tree, 51 leaves, max depth = 12, train loss: 0.37742, val loss: 0.37220, in 0.016s
1 tree, 51 leaves, max depth = 10, train loss: 0.37146, val loss: 0.36610, in 0.031s
1 tree, 50 leaves, max depth = 10, train loss: 0.36584, val loss: 0.36035, in 0.031s
1 tree, 31 leaves, max depth = 11, train loss: 0.35803, val loss: 0.35271, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.35094, val loss: 0.34576, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.34451, val loss: 0.33951, in 0.031s
1 tree, 50 leaves, max depth = 11, train loss: 0.33911, val loss: 0.33410, in 0.016s
1 tree, 49 leaves, max depth = 12, train loss: 0.33332, val loss: 0.32871, in 0.031s
1 tree, 31 leaves, max depth = 11, train loss: 0.32796, val loss: 0.32343, in 0.016s
1 tree, 51 leaves, max depth = 11, train loss: 0.32326, val loss: 0.31872, in 0.016s
1 tree, 51 leaves, max depth = 14, train loss: 0.31891, val loss: 0.31445, in 0.016s
1 tree, 49 leaves, max depth = 12, train loss: 0.31421, val loss: 0.31009, in 0.016s
1 tree, 62 leaves, max depth = 12, train loss: 0.31019, val loss: 0.30625, in 0.031s
1 tree, 31 leaves, max depth = 10, train loss: 0.30598, val loss: 0.30218, in 0.016s
1 tree, 50 leaves, max depth = 12, train loss: 0.30203, val loss: 0.29852, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.29839, val loss: 0.29499, in 0.016s
1 tree, 49 leaves, max depth = 12, train loss: 0.29495, val loss: 0.29180, in 0.016s
Fit 36 trees in 1.346 s, (1694 total leaves)
Time spent computing histograms: 0.313s
Time spent finding best splits:  0.055s
Time spent applying splits:      0.050s
Time spent predicting:           0.031s
Trial 7, Fold 2: Log loss = 0.29597727707207266, Average precision = 0.9594641087138922, ROC-AUC = 0.9563577921425602, Elapsed Time = 1.3618537000002107 seconds
Trial 7, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 7, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
Binning 0.040 GB of training data: 0.237 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 51 leaves, max depth = 11, train loss: 0.66100, val loss: 0.66112, in 0.016s
1 tree, 53 leaves, max depth = 10, train loss: 0.63240, val loss: 0.63261, in 0.031s
1 tree, 48 leaves, max depth = 11, train loss: 0.60692, val loss: 0.60694, in 0.016s
1 tree, 53 leaves, max depth = 11, train loss: 0.58480, val loss: 0.58485, in 0.031s
1 tree, 52 leaves, max depth = 11, train loss: 0.56367, val loss: 0.56370, in 0.016s
1 tree, 49 leaves, max depth = 11, train loss: 0.54464, val loss: 0.54451, in 0.016s
1 tree, 52 leaves, max depth = 11, train loss: 0.52803, val loss: 0.52794, in 0.016s
1 tree, 53 leaves, max depth = 11, train loss: 0.51196, val loss: 0.51182, in 0.031s
1 tree, 51 leaves, max depth = 11, train loss: 0.49740, val loss: 0.49713, in 0.016s
1 tree, 53 leaves, max depth = 11, train loss: 0.48393, val loss: 0.48361, in 0.016s
1 tree, 54 leaves, max depth = 12, train loss: 0.47159, val loss: 0.47124, in 0.031s
1 tree, 53 leaves, max depth = 10, train loss: 0.46022, val loss: 0.45986, in 0.016s
1 tree, 64 leaves, max depth = 10, train loss: 0.44987, val loss: 0.44958, in 0.031s
1 tree, 52 leaves, max depth = 12, train loss: 0.43527, val loss: 0.43601, in 0.016s
1 tree, 51 leaves, max depth = 11, train loss: 0.42617, val loss: 0.42690, in 0.031s
1 tree, 54 leaves, max depth = 11, train loss: 0.41773, val loss: 0.41836, in 0.016s
1 tree, 51 leaves, max depth = 11, train loss: 0.40551, val loss: 0.40712, in 0.031s
1 tree, 53 leaves, max depth = 11, train loss: 0.39816, val loss: 0.39972, in 0.016s
1 tree, 51 leaves, max depth = 11, train loss: 0.38746, val loss: 0.38993, in 0.031s
1 tree, 51 leaves, max depth = 11, train loss: 0.38115, val loss: 0.38360, in 0.016s
1 tree, 53 leaves, max depth = 12, train loss: 0.37173, val loss: 0.37505, in 0.016s
1 tree, 51 leaves, max depth = 10, train loss: 0.36608, val loss: 0.36932, in 0.031s
1 tree, 31 leaves, max depth = 10, train loss: 0.35813, val loss: 0.36208, in 0.016s
1 tree, 30 leaves, max depth = 10, train loss: 0.35091, val loss: 0.35552, in 0.016s
1 tree, 51 leaves, max depth = 12, train loss: 0.34535, val loss: 0.34960, in 0.031s
1 tree, 49 leaves, max depth = 13, train loss: 0.33903, val loss: 0.34420, in 0.016s
1 tree, 32 leaves, max depth = 10, train loss: 0.33303, val loss: 0.33877, in 0.016s
1 tree, 50 leaves, max depth = 13, train loss: 0.32756, val loss: 0.33420, in 0.016s
1 tree, 30 leaves, max depth = 10, train loss: 0.32242, val loss: 0.32949, in 0.016s
1 tree, 49 leaves, max depth = 13, train loss: 0.31769, val loss: 0.32553, in 0.016s
1 tree, 51 leaves, max depth = 12, train loss: 0.31329, val loss: 0.32079, in 0.031s
1 tree, 28 leaves, max depth = 10, train loss: 0.30897, val loss: 0.31685, in 0.016s
1 tree, 48 leaves, max depth = 12, train loss: 0.30501, val loss: 0.31263, in 0.016s
1 tree, 48 leaves, max depth = 13, train loss: 0.30119, val loss: 0.30945, in 0.016s
[35/36] 1 tree, 31 leaves, max depth = 10, train loss: 0.29756, val loss: 0.30616, in 0.016s
1 tree, 46 leaves, max depth = 14, train loss: 0.29408, val loss: 0.30241, in 0.016s
Fit 36 trees in 1.222 s, (1727 total leaves)
Time spent computing histograms: 0.293s
Time spent finding best splits:  0.047s
Time spent applying splits:      0.044s
Time spent predicting:           0.016s
Trial 7, Fold 3: Log loss = 0.2932838393195154, Average precision = 0.9595868858698278, ROC-AUC = 0.9563772801475475, Elapsed Time = 1.2324594000001525 seconds
Trial 7, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 7, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
0.173 s 0.040 GB of training data: 
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 46 leaves, max depth = 9, train loss: 0.66148, val loss: 0.66016, in 0.016s
1 tree, 46 leaves, max depth = 9, train loss: 0.63340, val loss: 0.63081, in 0.016s
1 tree, 46 leaves, max depth = 9, train loss: 0.60809, val loss: 0.60438, in 0.031s
1 tree, 53 leaves, max depth = 11, train loss: 0.58620, val loss: 0.58132, in 0.016s
1 tree, 51 leaves, max depth = 11, train loss: 0.56504, val loss: 0.55906, in 0.031s
1 tree, 54 leaves, max depth = 13, train loss: 0.54696, val loss: 0.54001, in 0.016s
1 tree, 52 leaves, max depth = 11, train loss: 0.52929, val loss: 0.52137, in 0.016s
1 tree, 51 leaves, max depth = 10, train loss: 0.51313, val loss: 0.50434, in 0.016s
1 tree, 53 leaves, max depth = 10, train loss: 0.49843, val loss: 0.48873, in 0.016s
1 tree, 52 leaves, max depth = 11, train loss: 0.48494, val loss: 0.47441, in 0.016s
1 tree, 52 leaves, max depth = 10, train loss: 0.47255, val loss: 0.46141, in 0.016s
1 tree, 52 leaves, max depth = 10, train loss: 0.46121, val loss: 0.44939, in 0.031s
1 tree, 49 leaves, max depth = 10, train loss: 0.45075, val loss: 0.43820, in 0.016s
1 tree, 49 leaves, max depth = 10, train loss: 0.44110, val loss: 0.42796, in 0.016s
1 tree, 53 leaves, max depth = 13, train loss: 0.42733, val loss: 0.41394, in 0.031s
1 tree, 51 leaves, max depth = 9, train loss: 0.41898, val loss: 0.40503, in 0.016s
1 tree, 52 leaves, max depth = 13, train loss: 0.40697, val loss: 0.39281, in 0.016s
1 tree, 49 leaves, max depth = 10, train loss: 0.39972, val loss: 0.38506, in 0.031s
1 tree, 54 leaves, max depth = 13, train loss: 0.38923, val loss: 0.37444, in 0.016s
1 tree, 49 leaves, max depth = 9, train loss: 0.38286, val loss: 0.36757, in 0.016s
1 tree, 53 leaves, max depth = 13, train loss: 0.37360, val loss: 0.35817, in 0.031s
1 tree, 48 leaves, max depth = 10, train loss: 0.36802, val loss: 0.35209, in 0.016s
1 tree, 31 leaves, max depth = 12, train loss: 0.36019, val loss: 0.34397, in 0.016s
1 tree, 54 leaves, max depth = 12, train loss: 0.35269, val loss: 0.33636, in 0.031s
1 tree, 31 leaves, max depth = 12, train loss: 0.34619, val loss: 0.32964, in 0.000s
1 tree, 51 leaves, max depth = 11, train loss: 0.34083, val loss: 0.32413, in 0.016s
1 tree, 49 leaves, max depth = 12, train loss: 0.33513, val loss: 0.31871, in 0.016s
1 tree, 52 leaves, max depth = 13, train loss: 0.33031, val loss: 0.31376, in 0.016s
1 tree, 31 leaves, max depth = 12, train loss: 0.32506, val loss: 0.30829, in 0.031s
1 tree, 49 leaves, max depth = 12, train loss: 0.32029, val loss: 0.30378, in 0.016s
1 tree, 31 leaves, max depth = 12, train loss: 0.31577, val loss: 0.29909, in 0.016s
1 tree, 59 leaves, max depth = 14, train loss: 0.31162, val loss: 0.29473, in 0.016s
1 tree, 49 leaves, max depth = 14, train loss: 0.30762, val loss: 0.29098, in 0.031s
1 tree, 31 leaves, max depth = 11, train loss: 0.30380, val loss: 0.28696, in 0.016s
1 tree, 49 leaves, max depth = 14, train loss: 0.30019, val loss: 0.28327, in 0.016s
1 tree, 49 leaves, max depth = 14, train loss: 0.29681, val loss: 0.28013, in 0.031s
Fit 36 trees in 1.111 s, (1731 total leaves)
Time spent computing histograms: 0.286s
Time spent finding best splits:  0.046s
Time spent applying splits:      0.043s
Time spent predicting:           0.016s
Trial 7, Fold 4: Log loss = 0.2960871708318758, Average precision = 0.9603063654296586, ROC-AUC = 0.9556672651535202, Elapsed Time = 1.122271999998702 seconds
Trial 7, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 7, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
Binning 0.040 GB of training data: 0.173 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 51 leaves, max depth = 10, train loss: 0.66088, val loss: 0.65953, in 0.016s
1 tree, 51 leaves, max depth = 10, train loss: 0.63219, val loss: 0.62956, in 0.016s
1 tree, 49 leaves, max depth = 9, train loss: 0.60645, val loss: 0.60265, in 0.016s
1 tree, 53 leaves, max depth = 11, train loss: 0.58433, val loss: 0.57944, in 0.031s
1 tree, 52 leaves, max depth = 10, train loss: 0.56314, val loss: 0.55732, in 0.016s
1 tree, 53 leaves, max depth = 11, train loss: 0.54495, val loss: 0.53821, in 0.016s
1 tree, 55 leaves, max depth = 10, train loss: 0.52727, val loss: 0.51973, in 0.031s
1 tree, 53 leaves, max depth = 10, train loss: 0.51118, val loss: 0.50295, in 0.016s
1 tree, 50 leaves, max depth = 9, train loss: 0.49644, val loss: 0.48754, in 0.016s
1 tree, 49 leaves, max depth = 11, train loss: 0.48294, val loss: 0.47345, in 0.016s
1 tree, 53 leaves, max depth = 10, train loss: 0.47053, val loss: 0.46045, in 0.031s
1 tree, 49 leaves, max depth = 11, train loss: 0.45914, val loss: 0.44850, in 0.016s
1 tree, 49 leaves, max depth = 11, train loss: 0.44865, val loss: 0.43752, in 0.031s
1 tree, 49 leaves, max depth = 9, train loss: 0.43897, val loss: 0.42744, in 0.016s
1 tree, 52 leaves, max depth = 11, train loss: 0.42513, val loss: 0.41360, in 0.016s
1 tree, 50 leaves, max depth = 9, train loss: 0.41675, val loss: 0.40490, in 0.016s
1 tree, 51 leaves, max depth = 11, train loss: 0.40467, val loss: 0.39284, in 0.016s
1 tree, 50 leaves, max depth = 11, train loss: 0.39734, val loss: 0.38523, in 0.031s
1 tree, 54 leaves, max depth = 9, train loss: 0.39059, val loss: 0.37825, in 0.016s
1 tree, 50 leaves, max depth = 11, train loss: 0.38430, val loss: 0.37173, in 0.016s
1 tree, 52 leaves, max depth = 12, train loss: 0.37444, val loss: 0.36192, in 0.031s
1 tree, 53 leaves, max depth = 11, train loss: 0.36544, val loss: 0.35301, in 0.016s
1 tree, 53 leaves, max depth = 11, train loss: 0.35723, val loss: 0.34485, in 0.031s
1 tree, 52 leaves, max depth = 11, train loss: 0.35224, val loss: 0.33979, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.34525, val loss: 0.33263, in 0.016s
1 tree, 32 leaves, max depth = 10, train loss: 0.33889, val loss: 0.32610, in 0.016s
1 tree, 32 leaves, max depth = 10, train loss: 0.33310, val loss: 0.32015, in 0.016s
1 tree, 51 leaves, max depth = 12, train loss: 0.32816, val loss: 0.31523, in 0.031s
1 tree, 50 leaves, max depth = 12, train loss: 0.32291, val loss: 0.31059, in 0.016s
1 tree, 32 leaves, max depth = 10, train loss: 0.31809, val loss: 0.30561, in 0.000s
1 tree, 52 leaves, max depth = 14, train loss: 0.31376, val loss: 0.30131, in 0.016s
1 tree, 32 leaves, max depth = 10, train loss: 0.30951, val loss: 0.29696, in 0.016s
1 tree, 61 leaves, max depth = 10, train loss: 0.30552, val loss: 0.29298, in 0.031s
1 tree, 48 leaves, max depth = 11, train loss: 0.30149, val loss: 0.28942, in 0.016s
1 tree, 48 leaves, max depth = 11, train loss: 0.29780, val loss: 0.28617, in 0.016s
1 tree, 32 leaves, max depth = 13, train loss: 0.29439, val loss: 0.28269, in 0.016s
Fit 36 trees in 1.095 s, (1734 total leaves)
Time spent computing histograms: 0.281s
Time spent finding best splits:  0.044s
Time spent applying splits:      0.042s
Time spent predicting:           0.000s
Trial 7, Fold 5: Log loss = 0.30208427335159127, Average precision = 0.9562072059339609, ROC-AUC = 0.9511322668747562, Elapsed Time = 1.1022063000000344 seconds
Optimization Progress:   8%|8         | 8/100 [01:38<18:50, 12.29s/it]
Trial 8, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 8, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
0.141 s 0.040 GB of training data: 
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 5 leaves, max depth = 3, train loss: 0.67012, val loss: 0.66942, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.64882, val loss: 0.64743, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.62956, val loss: 0.62755, in 0.000s
1 tree, 4 leaves, max depth = 3, train loss: 0.61219, val loss: 0.60943, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.59636, val loss: 0.59301, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.58199, val loss: 0.57806, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.56910, val loss: 0.56462, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.55737, val loss: 0.55235, in 0.016s
1 tree, 7 leaves, max depth = 4, train loss: 0.54646, val loss: 0.54095, in 0.000s
1 tree, 6 leaves, max depth = 3, train loss: 0.53625, val loss: 0.53025, in 0.000s
1 tree, 5 leaves, max depth = 4, train loss: 0.52727, val loss: 0.52078, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.51911, val loss: 0.51220, in 0.000s
1 tree, 6 leaves, max depth = 4, train loss: 0.51148, val loss: 0.50416, in 0.000s
1 tree, 7 leaves, max depth = 5, train loss: 0.50457, val loss: 0.49684, in 0.016s
1 tree, 6 leaves, max depth = 3, train loss: 0.49815, val loss: 0.49003, in 0.000s
1 tree, 19 leaves, max depth = 9, train loss: 0.48850, val loss: 0.48109, in 0.016s
1 tree, 4 leaves, max depth = 2, train loss: 0.48286, val loss: 0.47511, in 0.000s
1 tree, 4 leaves, max depth = 2, train loss: 0.47769, val loss: 0.46962, in 0.000s
1 tree, 3 leaves, max depth = 2, train loss: 0.47284, val loss: 0.46432, in 0.016s
1 tree, 9 leaves, max depth = 5, train loss: 0.46828, val loss: 0.45940, in 0.000s
1 tree, 29 leaves, max depth = 10, train loss: 0.46285, val loss: 0.45438, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.45729, val loss: 0.44932, in 0.000s
1 tree, 8 leaves, max depth = 5, train loss: 0.45363, val loss: 0.44533, in 0.000s
1 tree, 9 leaves, max depth = 5, train loss: 0.45002, val loss: 0.44139, in 0.016s
1 tree, 27 leaves, max depth = 11, train loss: 0.44541, val loss: 0.43721, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.44204, val loss: 0.43345, in 0.016s
1 tree, 16 leaves, max depth = 7, train loss: 0.43875, val loss: 0.43013, in 0.000s
1 tree, 23 leaves, max depth = 11, train loss: 0.43533, val loss: 0.42704, in 0.016s
1 tree, 18 leaves, max depth = 8, train loss: 0.43109, val loss: 0.42332, in 0.000s
1 tree, 16 leaves, max depth = 8, train loss: 0.42498, val loss: 0.41784, in 0.000s
1 tree, 18 leaves, max depth = 8, train loss: 0.42142, val loss: 0.41476, in 0.016s
1 tree, 19 leaves, max depth = 8, train loss: 0.41863, val loss: 0.41202, in 0.000s
1 tree, 9 leaves, max depth = 5, train loss: 0.41591, val loss: 0.40902, in 0.016s
1 tree, 22 leaves, max depth = 8, train loss: 0.41336, val loss: 0.40652, in 0.000s
1 tree, 10 leaves, max depth = 5, train loss: 0.41089, val loss: 0.40379, in 0.016s
1 tree, 22 leaves, max depth = 8, train loss: 0.40857, val loss: 0.40153, in 0.000s
1 tree, 14 leaves, max depth = 7, train loss: 0.40629, val loss: 0.39913, in 0.016s
1 tree, 18 leaves, max depth = 9, train loss: 0.40155, val loss: 0.39492, in 0.000s
1 tree, 14 leaves, max depth = 7, train loss: 0.39952, val loss: 0.39278, in 0.016s
1 tree, 24 leaves, max depth = 9, train loss: 0.39685, val loss: 0.39055, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.39408, val loss: 0.38745, in 0.000s
1 tree, 11 leaves, max depth = 5, train loss: 0.39227, val loss: 0.38544, in 0.016s
1 tree, 21 leaves, max depth = 8, train loss: 0.39033, val loss: 0.38357, in 0.000s
1 tree, 11 leaves, max depth = 5, train loss: 0.38634, val loss: 0.38010, in 0.016s
1 tree, 29 leaves, max depth = 10, train loss: 0.38400, val loss: 0.37818, in 0.000s
1 tree, 27 leaves, max depth = 9, train loss: 0.38193, val loss: 0.37652, in 0.016s
1 tree, 11 leaves, max depth = 5, train loss: 0.37860, val loss: 0.37367, in 0.000s
1 tree, 13 leaves, max depth = 7, train loss: 0.37707, val loss: 0.37201, in 0.016s
1 tree, 24 leaves, max depth = 9, train loss: 0.37533, val loss: 0.37067, in 0.000s
1 tree, 22 leaves, max depth = 9, train loss: 0.37236, val loss: 0.36811, in 0.016s
1 tree, 30 leaves, max depth = 9, train loss: 0.37099, val loss: 0.36685, in 0.000s
1 tree, 26 leaves, max depth = 9, train loss: 0.36835, val loss: 0.36461, in 0.016s
1 tree, 16 leaves, max depth = 8, train loss: 0.36698, val loss: 0.36318, in 0.000s
1 tree, 13 leaves, max depth = 7, train loss: 0.36572, val loss: 0.36183, in 0.000s
1 tree, 14 leaves, max depth = 7, train loss: 0.36458, val loss: 0.36060, in 0.016s
1 tree, 24 leaves, max depth = 10, train loss: 0.36212, val loss: 0.35854, in 0.016s
1 tree, 8 leaves, max depth = 5, train loss: 0.36107, val loss: 0.35719, in 0.000s
1 tree, 6 leaves, max depth = 4, train loss: 0.36006, val loss: 0.35610, in 0.000s
1 tree, 26 leaves, max depth = 9, train loss: 0.35773, val loss: 0.35399, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.35523, val loss: 0.35119, in 0.000s
1 tree, 53 leaves, max depth = 11, train loss: 0.35382, val loss: 0.35011, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.35174, val loss: 0.34827, in 0.000s
1 tree, 10 leaves, max depth = 5, train loss: 0.35077, val loss: 0.34707, in 0.016s
1 tree, 7 leaves, max depth = 5, train loss: 0.34990, val loss: 0.34613, in 0.000s
1 tree, 10 leaves, max depth = 4, train loss: 0.34906, val loss: 0.34510, in 0.016s
1 tree, 40 leaves, max depth = 11, train loss: 0.34775, val loss: 0.34396, in 0.000s
1 tree, 4 leaves, max depth = 2, train loss: 0.34544, val loss: 0.34136, in 0.016s
1 tree, 59 leaves, max depth = 12, train loss: 0.34421, val loss: 0.34047, in 0.000s
1 tree, 25 leaves, max depth = 7, train loss: 0.34241, val loss: 0.33890, in 0.016s
1 tree, 29 leaves, max depth = 9, train loss: 0.34075, val loss: 0.33745, in 0.000s
1 tree, 11 leaves, max depth = 5, train loss: 0.33989, val loss: 0.33641, in 0.016s
1 tree, 8 leaves, max depth = 5, train loss: 0.33912, val loss: 0.33549, in 0.000s
1 tree, 3 leaves, max depth = 2, train loss: 0.33696, val loss: 0.33304, in 0.000s
1 tree, 46 leaves, max depth = 13, train loss: 0.33604, val loss: 0.33229, in 0.016s
1 tree, 4 leaves, max depth = 2, train loss: 0.33403, val loss: 0.33002, in 0.000s
1 tree, 13 leaves, max depth = 5, train loss: 0.33329, val loss: 0.32912, in 0.016s
1 tree, 4 leaves, max depth = 2, train loss: 0.33144, val loss: 0.32701, in 0.000s
1 tree, 8 leaves, max depth = 4, train loss: 0.33079, val loss: 0.32635, in 0.016s
1 tree, 14 leaves, max depth = 12, train loss: 0.33011, val loss: 0.32554, in 0.000s
1 tree, 4 leaves, max depth = 2, train loss: 0.32840, val loss: 0.32357, in 0.016s
1 tree, 14 leaves, max depth = 5, train loss: 0.32779, val loss: 0.32288, in 0.000s
1 tree, 18 leaves, max depth = 7, train loss: 0.32613, val loss: 0.32148, in 0.016s
1 tree, 54 leaves, max depth = 12, train loss: 0.32505, val loss: 0.32075, in 0.000s
1 tree, 26 leaves, max depth = 7, train loss: 0.32356, val loss: 0.31948, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.32218, val loss: 0.31832, in 0.000s
1 tree, 4 leaves, max depth = 2, train loss: 0.32052, val loss: 0.31642, in 0.016s
1 tree, 16 leaves, max depth = 9, train loss: 0.31988, val loss: 0.31570, in 0.000s
1 tree, 6 leaves, max depth = 4, train loss: 0.31932, val loss: 0.31516, in 0.016s
1 tree, 40 leaves, max depth = 12, train loss: 0.31852, val loss: 0.31459, in 0.000s
1 tree, 4 leaves, max depth = 2, train loss: 0.31697, val loss: 0.31280, in 0.016s
1 tree, 52 leaves, max depth = 14, train loss: 0.31605, val loss: 0.31222, in 0.000s
1 tree, 30 leaves, max depth = 9, train loss: 0.31482, val loss: 0.31123, in 0.016s
1 tree, 7 leaves, max depth = 5, train loss: 0.31423, val loss: 0.31051, in 0.000s
1 tree, 54 leaves, max depth = 11, train loss: 0.31341, val loss: 0.30999, in 0.016s
1 tree, 3 leaves, max depth = 2, train loss: 0.31191, val loss: 0.30827, in 0.000s
Fit 95 trees in 0.923 s, (1545 total leaves)
Time spent computing histograms: 0.382s
Time spent finding best splits:  0.039s
Time spent applying splits:      0.032s
Time spent predicting:           0.000s
Trial 8, Fold 1: Log loss = 0.3164149077430296, Average precision = 0.9534281493802323, ROC-AUC = 0.9489805733595255, Elapsed Time = 0.9312449999997625 seconds
Trial 8, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 8, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
Binning 0.040 GB of training data: 0.142 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 5 leaves, max depth = 4, train loss: 0.67009, val loss: 0.66893, in 0.016s
1 tree, 4 leaves, max depth = 2, train loss: 0.64885, val loss: 0.64658, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.62968, val loss: 0.62634, in 0.000s
1 tree, 4 leaves, max depth = 3, train loss: 0.61225, val loss: 0.60801, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.59649, val loss: 0.59130, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.58217, val loss: 0.57609, in 0.016s
1 tree, 6 leaves, max depth = 4, train loss: 0.56921, val loss: 0.56239, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.55742, val loss: 0.54988, in 0.016s
1 tree, 6 leaves, max depth = 3, train loss: 0.54658, val loss: 0.53831, in 0.000s
1 tree, 6 leaves, max depth = 4, train loss: 0.53648, val loss: 0.52750, in 0.000s
1 tree, 5 leaves, max depth = 4, train loss: 0.52750, val loss: 0.51786, in 0.016s
1 tree, 6 leaves, max depth = 4, train loss: 0.51929, val loss: 0.50907, in 0.000s
1 tree, 6 leaves, max depth = 3, train loss: 0.51172, val loss: 0.50090, in 0.000s
1 tree, 6 leaves, max depth = 4, train loss: 0.50483, val loss: 0.49343, in 0.016s
1 tree, 6 leaves, max depth = 3, train loss: 0.49847, val loss: 0.48653, in 0.000s
1 tree, 13 leaves, max depth = 6, train loss: 0.48885, val loss: 0.47729, in 0.016s
1 tree, 6 leaves, max depth = 4, train loss: 0.48328, val loss: 0.47118, in 0.016s
1 tree, 6 leaves, max depth = 4, train loss: 0.47818, val loss: 0.46558, in 0.016s
1 tree, 3 leaves, max depth = 2, train loss: 0.47333, val loss: 0.46031, in 0.000s
1 tree, 10 leaves, max depth = 5, train loss: 0.46885, val loss: 0.45540, in 0.016s
1 tree, 21 leaves, max depth = 8, train loss: 0.46341, val loss: 0.45040, in 0.016s
1 tree, 22 leaves, max depth = 8, train loss: 0.45792, val loss: 0.44531, in 0.000s
1 tree, 9 leaves, max depth = 5, train loss: 0.45427, val loss: 0.44129, in 0.016s
1 tree, 11 leaves, max depth = 5, train loss: 0.45073, val loss: 0.43737, in 0.016s
1 tree, 19 leaves, max depth = 7, train loss: 0.44612, val loss: 0.43319, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.44280, val loss: 0.42977, in 0.016s
1 tree, 7 leaves, max depth = 4, train loss: 0.43989, val loss: 0.42653, in 0.016s
1 tree, 24 leaves, max depth = 10, train loss: 0.43644, val loss: 0.42327, in 0.000s
1 tree, 20 leaves, max depth = 8, train loss: 0.43209, val loss: 0.41927, in 0.016s
1 tree, 13 leaves, max depth = 6, train loss: 0.42582, val loss: 0.41336, in 0.016s
1 tree, 21 leaves, max depth = 8, train loss: 0.42217, val loss: 0.41006, in 0.000s
1 tree, 19 leaves, max depth = 8, train loss: 0.41920, val loss: 0.40756, in 0.016s
1 tree, 24 leaves, max depth = 8, train loss: 0.41646, val loss: 0.40524, in 0.016s
1 tree, 24 leaves, max depth = 8, train loss: 0.41394, val loss: 0.40315, in 0.000s
1 tree, 14 leaves, max depth = 6, train loss: 0.41150, val loss: 0.40041, in 0.016s
1 tree, 23 leaves, max depth = 8, train loss: 0.40920, val loss: 0.39852, in 0.000s
1 tree, 7 leaves, max depth = 5, train loss: 0.40711, val loss: 0.39617, in 0.016s
1 tree, 18 leaves, max depth = 7, train loss: 0.40233, val loss: 0.39169, in 0.000s
1 tree, 6 leaves, max depth = 4, train loss: 0.40047, val loss: 0.38959, in 0.016s
1 tree, 21 leaves, max depth = 8, train loss: 0.39775, val loss: 0.38717, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.39504, val loss: 0.38436, in 0.016s
1 tree, 11 leaves, max depth = 7, train loss: 0.39334, val loss: 0.38242, in 0.000s
1 tree, 20 leaves, max depth = 8, train loss: 0.39141, val loss: 0.38097, in 0.016s
1 tree, 16 leaves, max depth = 7, train loss: 0.38736, val loss: 0.37718, in 0.000s
1 tree, 33 leaves, max depth = 10, train loss: 0.38502, val loss: 0.37517, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.38289, val loss: 0.37339, in 0.016s
1 tree, 16 leaves, max depth = 6, train loss: 0.37949, val loss: 0.37024, in 0.000s
1 tree, 14 leaves, max depth = 7, train loss: 0.37795, val loss: 0.36856, in 0.016s
1 tree, 40 leaves, max depth = 10, train loss: 0.37615, val loss: 0.36714, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.37316, val loss: 0.36438, in 0.000s
1 tree, 32 leaves, max depth = 8, train loss: 0.37179, val loss: 0.36342, in 0.016s
1 tree, 24 leaves, max depth = 8, train loss: 0.36904, val loss: 0.36096, in 0.000s
1 tree, 4 leaves, max depth = 3, train loss: 0.36776, val loss: 0.35947, in 0.016s
1 tree, 36 leaves, max depth = 11, train loss: 0.36656, val loss: 0.35868, in 0.000s
1 tree, 9 leaves, max depth = 5, train loss: 0.36532, val loss: 0.35727, in 0.016s
1 tree, 20 leaves, max depth = 6, train loss: 0.36284, val loss: 0.35504, in 0.000s
1 tree, 4 leaves, max depth = 3, train loss: 0.36159, val loss: 0.35361, in 0.000s
1 tree, 7 leaves, max depth = 5, train loss: 0.36058, val loss: 0.35254, in 0.016s
1 tree, 14 leaves, max depth = 5, train loss: 0.35840, val loss: 0.35058, in 0.000s
1 tree, 3 leaves, max depth = 2, train loss: 0.35596, val loss: 0.34804, in 0.016s
1 tree, 46 leaves, max depth = 10, train loss: 0.35462, val loss: 0.34697, in 0.000s
1 tree, 20 leaves, max depth = 7, train loss: 0.35258, val loss: 0.34516, in 0.016s
1 tree, 10 leaves, max depth = 4, train loss: 0.35152, val loss: 0.34386, in 0.000s
1 tree, 8 leaves, max depth = 4, train loss: 0.35056, val loss: 0.34267, in 0.016s
1 tree, 12 leaves, max depth = 5, train loss: 0.34969, val loss: 0.34161, in 0.000s
1 tree, 35 leaves, max depth = 12, train loss: 0.34832, val loss: 0.34057, in 0.016s
1 tree, 3 leaves, max depth = 2, train loss: 0.34606, val loss: 0.33821, in 0.000s
1 tree, 53 leaves, max depth = 10, train loss: 0.34487, val loss: 0.33730, in 0.013s
1 tree, 26 leaves, max depth = 8, train loss: 0.34305, val loss: 0.33571, in 0.000s
1 tree, 17 leaves, max depth = 8, train loss: 0.34151, val loss: 0.33438, in 0.016s
1 tree, 12 leaves, max depth = 5, train loss: 0.34063, val loss: 0.33330, in 0.000s
1 tree, 6 leaves, max depth = 4, train loss: 0.33987, val loss: 0.33254, in 0.000s
1 tree, 3 leaves, max depth = 2, train loss: 0.33776, val loss: 0.33033, in 0.016s
1 tree, 9 leaves, max depth = 5, train loss: 0.33699, val loss: 0.32938, in 0.000s
1 tree, 3 leaves, max depth = 2, train loss: 0.33504, val loss: 0.32734, in 0.016s
1 tree, 8 leaves, max depth = 4, train loss: 0.33432, val loss: 0.32643, in 0.000s
1 tree, 4 leaves, max depth = 2, train loss: 0.33252, val loss: 0.32453, in 0.016s
1 tree, 33 leaves, max depth = 12, train loss: 0.33132, val loss: 0.32366, in 0.000s
1 tree, 13 leaves, max depth = 5, train loss: 0.33059, val loss: 0.32275, in 0.016s
1 tree, 4 leaves, max depth = 2, train loss: 0.32891, val loss: 0.32098, in 0.000s
1 tree, 11 leaves, max depth = 5, train loss: 0.32829, val loss: 0.32019, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.32665, val loss: 0.31876, in 0.000s
1 tree, 48 leaves, max depth = 11, train loss: 0.32563, val loss: 0.31802, in 0.016s
1 tree, 19 leaves, max depth = 6, train loss: 0.32415, val loss: 0.31672, in 0.000s
1 tree, 22 leaves, max depth = 8, train loss: 0.32278, val loss: 0.31555, in 0.016s
1 tree, 4 leaves, max depth = 2, train loss: 0.32115, val loss: 0.31383, in 0.000s
1 tree, 10 leaves, max depth = 6, train loss: 0.32049, val loss: 0.31301, in 0.000s
1 tree, 11 leaves, max depth = 7, train loss: 0.31990, val loss: 0.31230, in 0.000s
1 tree, 14 leaves, max depth = 6, train loss: 0.31931, val loss: 0.31156, in 0.000s
1 tree, 4 leaves, max depth = 2, train loss: 0.31779, val loss: 0.30995, in 0.016s
1 tree, 52 leaves, max depth = 13, train loss: 0.31687, val loss: 0.30931, in 0.016s
1 tree, 19 leaves, max depth = 6, train loss: 0.31558, val loss: 0.30819, in 0.000s
1 tree, 7 leaves, max depth = 4, train loss: 0.31501, val loss: 0.30755, in 0.016s
1 tree, 51 leaves, max depth = 12, train loss: 0.31420, val loss: 0.30700, in 0.000s
1 tree, 3 leaves, max depth = 2, train loss: 0.31274, val loss: 0.30545, in 0.016s
Fit 95 trees in 1.049 s, (1419 total leaves)
Time spent computing histograms: 0.435s
Time spent finding best splits:  0.048s
Time spent applying splits:      0.041s
Time spent predicting:           0.000s
Trial 8, Fold 2: Log loss = 0.31504257772127, Average precision = 0.9514261562429316, ROC-AUC = 0.9503154229468751, Elapsed Time = 1.0518565000002127 seconds
Trial 8, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 8, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
Binning 0.040 GB of training data: 0.205 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 4 leaves, max depth = 3, train loss: 0.67019, val loss: 0.66930, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.64909, val loss: 0.64730, in 0.000s
1 tree, 6 leaves, max depth = 4, train loss: 0.63001, val loss: 0.62740, in 0.000s
1 tree, 4 leaves, max depth = 3, train loss: 0.61268, val loss: 0.60942, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.59702, val loss: 0.59306, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.58285, val loss: 0.57835, in 0.000s
1 tree, 4 leaves, max depth = 2, train loss: 0.57002, val loss: 0.56486, in 0.016s
1 tree, 4 leaves, max depth = 2, train loss: 0.55835, val loss: 0.55256, in 0.000s
1 tree, 6 leaves, max depth = 4, train loss: 0.54756, val loss: 0.54123, in 0.016s
1 tree, 6 leaves, max depth = 3, train loss: 0.53749, val loss: 0.53072, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.52855, val loss: 0.52138, in 0.016s
1 tree, 4 leaves, max depth = 2, train loss: 0.52044, val loss: 0.51277, in 0.000s
1 tree, 6 leaves, max depth = 4, train loss: 0.51290, val loss: 0.50479, in 0.000s
1 tree, 6 leaves, max depth = 4, train loss: 0.50606, val loss: 0.49749, in 0.016s
1 tree, 6 leaves, max depth = 3, train loss: 0.49973, val loss: 0.49077, in 0.000s
1 tree, 18 leaves, max depth = 8, train loss: 0.49007, val loss: 0.48179, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.48451, val loss: 0.47582, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.47943, val loss: 0.47033, in 0.016s
1 tree, 5 leaves, max depth = 4, train loss: 0.47460, val loss: 0.46526, in 0.000s
1 tree, 9 leaves, max depth = 5, train loss: 0.47014, val loss: 0.46055, in 0.000s
1 tree, 32 leaves, max depth = 8, train loss: 0.46466, val loss: 0.45538, in 0.016s
1 tree, 19 leaves, max depth = 8, train loss: 0.45910, val loss: 0.45038, in 0.000s
1 tree, 8 leaves, max depth = 4, train loss: 0.45547, val loss: 0.44655, in 0.016s
1 tree, 10 leaves, max depth = 5, train loss: 0.45196, val loss: 0.44281, in 0.000s
1 tree, 34 leaves, max depth = 8, train loss: 0.44729, val loss: 0.43846, in 0.016s
1 tree, 4 leaves, max depth = 2, train loss: 0.44388, val loss: 0.43533, in 0.000s
1 tree, 12 leaves, max depth = 5, train loss: 0.44099, val loss: 0.43228, in 0.016s
1 tree, 23 leaves, max depth = 9, train loss: 0.43751, val loss: 0.42915, in 0.000s
1 tree, 17 leaves, max depth = 7, train loss: 0.43311, val loss: 0.42523, in 0.016s
1 tree, 13 leaves, max depth = 6, train loss: 0.42685, val loss: 0.41949, in 0.000s
1 tree, 17 leaves, max depth = 8, train loss: 0.42315, val loss: 0.41627, in 0.016s
1 tree, 27 leaves, max depth = 9, train loss: 0.42011, val loss: 0.41367, in 0.000s
1 tree, 27 leaves, max depth = 9, train loss: 0.41733, val loss: 0.41131, in 0.016s
1 tree, 28 leaves, max depth = 9, train loss: 0.41479, val loss: 0.40917, in 0.000s
1 tree, 9 leaves, max depth = 6, train loss: 0.41237, val loss: 0.40657, in 0.016s
1 tree, 35 leaves, max depth = 12, train loss: 0.41003, val loss: 0.40465, in 0.000s
1 tree, 13 leaves, max depth = 7, train loss: 0.40782, val loss: 0.40215, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.40308, val loss: 0.39793, in 0.000s
1 tree, 12 leaves, max depth = 7, train loss: 0.40112, val loss: 0.39570, in 0.016s
1 tree, 20 leaves, max depth = 8, train loss: 0.39841, val loss: 0.39344, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.39561, val loss: 0.39086, in 0.016s
1 tree, 18 leaves, max depth = 6, train loss: 0.39375, val loss: 0.38930, in 0.000s
1 tree, 41 leaves, max depth = 13, train loss: 0.39187, val loss: 0.38784, in 0.016s
1 tree, 22 leaves, max depth = 10, train loss: 0.38794, val loss: 0.38442, in 0.000s
1 tree, 41 leaves, max depth = 11, train loss: 0.38562, val loss: 0.38236, in 0.016s
1 tree, 21 leaves, max depth = 9, train loss: 0.38361, val loss: 0.38077, in 0.000s
1 tree, 19 leaves, max depth = 7, train loss: 0.38033, val loss: 0.37794, in 0.016s
1 tree, 14 leaves, max depth = 7, train loss: 0.37872, val loss: 0.37613, in 0.000s
1 tree, 15 leaves, max depth = 9, train loss: 0.37725, val loss: 0.37456, in 0.016s
1 tree, 16 leaves, max depth = 6, train loss: 0.37423, val loss: 0.37197, in 0.000s
1 tree, 40 leaves, max depth = 14, train loss: 0.37285, val loss: 0.37099, in 0.016s
1 tree, 29 leaves, max depth = 11, train loss: 0.37009, val loss: 0.36867, in 0.016s
1 tree, 13 leaves, max depth = 7, train loss: 0.36878, val loss: 0.36713, in 0.000s
1 tree, 45 leaves, max depth = 13, train loss: 0.36757, val loss: 0.36630, in 0.016s
1 tree, 14 leaves, max depth = 7, train loss: 0.36634, val loss: 0.36490, in 0.000s
1 tree, 25 leaves, max depth = 12, train loss: 0.36392, val loss: 0.36284, in 0.016s
1 tree, 6 leaves, max depth = 3, train loss: 0.36268, val loss: 0.36147, in 0.000s
1 tree, 8 leaves, max depth = 6, train loss: 0.36169, val loss: 0.36043, in 0.016s
1 tree, 36 leaves, max depth = 12, train loss: 0.35939, val loss: 0.35854, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.35689, val loss: 0.35622, in 0.016s
1 tree, 67 leaves, max depth = 15, train loss: 0.35547, val loss: 0.35506, in 0.000s
1 tree, 38 leaves, max depth = 10, train loss: 0.35336, val loss: 0.35334, in 0.016s
1 tree, 11 leaves, max depth = 4, train loss: 0.35237, val loss: 0.35202, in 0.016s
1 tree, 6 leaves, max depth = 4, train loss: 0.35152, val loss: 0.35114, in 0.000s
1 tree, 10 leaves, max depth = 4, train loss: 0.35066, val loss: 0.34992, in 0.000s
1 tree, 28 leaves, max depth = 9, train loss: 0.34962, val loss: 0.34925, in 0.016s
1 tree, 4 leaves, max depth = 2, train loss: 0.34730, val loss: 0.34711, in 0.000s
1 tree, 69 leaves, max depth = 16, train loss: 0.34606, val loss: 0.34613, in 0.016s
1 tree, 27 leaves, max depth = 14, train loss: 0.34436, val loss: 0.34475, in 0.016s
1 tree, 38 leaves, max depth = 15, train loss: 0.34264, val loss: 0.34340, in 0.000s
1 tree, 11 leaves, max depth = 4, train loss: 0.34180, val loss: 0.34220, in 0.016s
1 tree, 5 leaves, max depth = 4, train loss: 0.34105, val loss: 0.34108, in 0.000s
1 tree, 4 leaves, max depth = 2, train loss: 0.33888, val loss: 0.33907, in 0.016s
1 tree, 11 leaves, max depth = 5, train loss: 0.33815, val loss: 0.33820, in 0.000s
1 tree, 4 leaves, max depth = 2, train loss: 0.33616, val loss: 0.33635, in 0.016s
1 tree, 10 leaves, max depth = 5, train loss: 0.33547, val loss: 0.33542, in 0.000s
1 tree, 4 leaves, max depth = 2, train loss: 0.33363, val loss: 0.33371, in 0.000s
1 tree, 34 leaves, max depth = 13, train loss: 0.33240, val loss: 0.33277, in 0.016s
1 tree, 11 leaves, max depth = 7, train loss: 0.33170, val loss: 0.33185, in 0.000s
1 tree, 4 leaves, max depth = 2, train loss: 0.32998, val loss: 0.33025, in 0.016s
1 tree, 11 leaves, max depth = 6, train loss: 0.32938, val loss: 0.32946, in 0.000s
1 tree, 26 leaves, max depth = 8, train loss: 0.32769, val loss: 0.32816, in 0.016s
1 tree, 68 leaves, max depth = 13, train loss: 0.32662, val loss: 0.32737, in 0.016s
1 tree, 25 leaves, max depth = 8, train loss: 0.32514, val loss: 0.32623, in 0.000s
1 tree, 37 leaves, max depth = 13, train loss: 0.32376, val loss: 0.32521, in 0.016s
1 tree, 3 leaves, max depth = 2, train loss: 0.32210, val loss: 0.32366, in 0.000s
1 tree, 12 leaves, max depth = 5, train loss: 0.32147, val loss: 0.32280, in 0.016s
1 tree, 18 leaves, max depth = 11, train loss: 0.32084, val loss: 0.32218, in 0.000s
1 tree, 46 leaves, max depth = 14, train loss: 0.32004, val loss: 0.32181, in 0.016s
1 tree, 4 leaves, max depth = 2, train loss: 0.31848, val loss: 0.32035, in 0.000s
1 tree, 73 leaves, max depth = 17, train loss: 0.31756, val loss: 0.31971, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.31630, val loss: 0.31883, in 0.000s
1 tree, 12 leaves, max depth = 5, train loss: 0.31571, val loss: 0.31800, in 0.016s
1 tree, 74 leaves, max depth = 17, train loss: 0.31489, val loss: 0.31744, in 0.016s
1 tree, 3 leaves, max depth = 2, train loss: 0.31339, val loss: 0.31605, in 0.000s
Fit 95 trees in 1.080 s, (1767 total leaves)
Time spent computing histograms: 0.428s
Time spent finding best splits:  0.049s
Time spent applying splits:      0.040s
Time spent predicting:           0.000s
Trial 8, Fold 3: Log loss = 0.3097361123640661, Average precision = 0.956834791175193, ROC-AUC = 0.9536477846211633, Elapsed Time = 1.09967609999876 seconds
Trial 8, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 8, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
Binning 0.040 GB of training data: 0.189 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 5 leaves, max depth = 4, train loss: 0.67015, val loss: 0.66875, in 0.016s
1 tree, 6 leaves, max depth = 4, train loss: 0.64905, val loss: 0.64636, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.62994, val loss: 0.62599, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.61261, val loss: 0.60759, in 0.000s
1 tree, 8 leaves, max depth = 4, train loss: 0.59694, val loss: 0.59086, in 0.016s
1 tree, 6 leaves, max depth = 3, train loss: 0.58276, val loss: 0.57562, in 0.000s
1 tree, 4 leaves, max depth = 3, train loss: 0.56991, val loss: 0.56179, in 0.016s
1 tree, 4 leaves, max depth = 2, train loss: 0.55817, val loss: 0.54917, in 0.000s
1 tree, 7 leaves, max depth = 3, train loss: 0.54736, val loss: 0.53745, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.53729, val loss: 0.52640, in 0.000s
1 tree, 6 leaves, max depth = 3, train loss: 0.52834, val loss: 0.51658, in 0.016s
1 tree, 7 leaves, max depth = 4, train loss: 0.52016, val loss: 0.50764, in 0.016s
1 tree, 6 leaves, max depth = 3, train loss: 0.51261, val loss: 0.49931, in 0.000s
1 tree, 6 leaves, max depth = 4, train loss: 0.50573, val loss: 0.49160, in 0.016s
1 tree, 7 leaves, max depth = 4, train loss: 0.49938, val loss: 0.48452, in 0.000s
1 tree, 17 leaves, max depth = 7, train loss: 0.48977, val loss: 0.47517, in 0.016s
1 tree, 8 leaves, max depth = 4, train loss: 0.48419, val loss: 0.46893, in 0.000s
1 tree, 8 leaves, max depth = 4, train loss: 0.47907, val loss: 0.46318, in 0.016s
1 tree, 3 leaves, max depth = 2, train loss: 0.47424, val loss: 0.45776, in 0.000s
1 tree, 6 leaves, max depth = 4, train loss: 0.46977, val loss: 0.45262, in 0.016s
1 tree, 30 leaves, max depth = 10, train loss: 0.46438, val loss: 0.44729, in 0.016s
1 tree, 17 leaves, max depth = 8, train loss: 0.45894, val loss: 0.44208, in 0.000s
1 tree, 9 leaves, max depth = 4, train loss: 0.45530, val loss: 0.43787, in 0.016s
1 tree, 8 leaves, max depth = 5, train loss: 0.45178, val loss: 0.43377, in 0.000s
1 tree, 26 leaves, max depth = 8, train loss: 0.44719, val loss: 0.42925, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.44391, val loss: 0.42577, in 0.000s
1 tree, 9 leaves, max depth = 4, train loss: 0.44101, val loss: 0.42235, in 0.016s
1 tree, 24 leaves, max depth = 11, train loss: 0.43760, val loss: 0.41871, in 0.016s
1 tree, 19 leaves, max depth = 8, train loss: 0.43327, val loss: 0.41459, in 0.000s
1 tree, 13 leaves, max depth = 5, train loss: 0.42702, val loss: 0.40859, in 0.016s
1 tree, 21 leaves, max depth = 8, train loss: 0.42338, val loss: 0.40515, in 0.016s
1 tree, 22 leaves, max depth = 8, train loss: 0.42041, val loss: 0.40213, in 0.000s
1 tree, 22 leaves, max depth = 8, train loss: 0.41770, val loss: 0.39935, in 0.016s
1 tree, 21 leaves, max depth = 7, train loss: 0.41521, val loss: 0.39680, in 0.000s
1 tree, 9 leaves, max depth = 6, train loss: 0.41279, val loss: 0.39393, in 0.016s
1 tree, 27 leaves, max depth = 10, train loss: 0.41052, val loss: 0.39159, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.40825, val loss: 0.38892, in 0.000s
1 tree, 18 leaves, max depth = 9, train loss: 0.40355, val loss: 0.38449, in 0.016s
1 tree, 18 leaves, max depth = 8, train loss: 0.40154, val loss: 0.38210, in 0.000s
1 tree, 24 leaves, max depth = 8, train loss: 0.39887, val loss: 0.37972, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.39618, val loss: 0.37684, in 0.000s
1 tree, 11 leaves, max depth = 6, train loss: 0.39442, val loss: 0.37470, in 0.016s
1 tree, 26 leaves, max depth = 9, train loss: 0.39252, val loss: 0.37275, in 0.000s
1 tree, 16 leaves, max depth = 9, train loss: 0.38855, val loss: 0.36905, in 0.000s
1 tree, 32 leaves, max depth = 8, train loss: 0.38625, val loss: 0.36691, in 0.016s
1 tree, 23 leaves, max depth = 9, train loss: 0.38417, val loss: 0.36509, in 0.000s
1 tree, 12 leaves, max depth = 5, train loss: 0.38086, val loss: 0.36199, in 0.016s
1 tree, 15 leaves, max depth = 8, train loss: 0.37935, val loss: 0.36015, in 0.000s
1 tree, 24 leaves, max depth = 8, train loss: 0.37761, val loss: 0.35861, in 0.016s
1 tree, 22 leaves, max depth = 9, train loss: 0.37468, val loss: 0.35593, in 0.016s
1 tree, 27 leaves, max depth = 9, train loss: 0.37336, val loss: 0.35458, in 0.000s
1 tree, 22 leaves, max depth = 8, train loss: 0.37076, val loss: 0.35222, in 0.016s
1 tree, 15 leaves, max depth = 8, train loss: 0.36941, val loss: 0.35057, in 0.000s
1 tree, 11 leaves, max depth = 8, train loss: 0.36818, val loss: 0.34905, in 0.016s
1 tree, 16 leaves, max depth = 9, train loss: 0.36705, val loss: 0.34766, in 0.000s
1 tree, 17 leaves, max depth = 9, train loss: 0.36464, val loss: 0.34547, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.36346, val loss: 0.34408, in 0.000s
1 tree, 7 leaves, max depth = 5, train loss: 0.36247, val loss: 0.34302, in 0.016s
1 tree, 21 leaves, max depth = 9, train loss: 0.36025, val loss: 0.34099, in 0.000s
1 tree, 3 leaves, max depth = 2, train loss: 0.35783, val loss: 0.33837, in 0.016s
1 tree, 54 leaves, max depth = 16, train loss: 0.35643, val loss: 0.33721, in 0.016s
1 tree, 26 leaves, max depth = 9, train loss: 0.35447, val loss: 0.33550, in 0.031s
1 tree, 8 leaves, max depth = 4, train loss: 0.35348, val loss: 0.33439, in 0.016s
1 tree, 6 leaves, max depth = 4, train loss: 0.35264, val loss: 0.33352, in 0.000s
1 tree, 12 leaves, max depth = 6, train loss: 0.35178, val loss: 0.33252, in 0.016s
1 tree, 33 leaves, max depth = 11, train loss: 0.35038, val loss: 0.33118, in 0.031s
1 tree, 3 leaves, max depth = 2, train loss: 0.34814, val loss: 0.32875, in 0.000s
1 tree, 57 leaves, max depth = 16, train loss: 0.34692, val loss: 0.32779, in 0.031s
1 tree, 21 leaves, max depth = 8, train loss: 0.34520, val loss: 0.32619, in 0.000s
1 tree, 28 leaves, max depth = 9, train loss: 0.34362, val loss: 0.32479, in 0.016s
1 tree, 10 leaves, max depth = 4, train loss: 0.34274, val loss: 0.32380, in 0.016s
1 tree, 7 leaves, max depth = 4, train loss: 0.34195, val loss: 0.32292, in 0.000s
1 tree, 3 leaves, max depth = 2, train loss: 0.33986, val loss: 0.32064, in 0.016s
1 tree, 44 leaves, max depth = 16, train loss: 0.33895, val loss: 0.31979, in 0.016s
1 tree, 3 leaves, max depth = 2, train loss: 0.33701, val loss: 0.31768, in 0.000s
1 tree, 15 leaves, max depth = 7, train loss: 0.33626, val loss: 0.31685, in 0.016s
1 tree, 3 leaves, max depth = 2, train loss: 0.33447, val loss: 0.31490, in 0.016s
1 tree, 13 leaves, max depth = 7, train loss: 0.33382, val loss: 0.31422, in 0.000s
1 tree, 11 leaves, max depth = 6, train loss: 0.33315, val loss: 0.31351, in 0.016s
1 tree, 3 leaves, max depth = 2, train loss: 0.33149, val loss: 0.31170, in 0.000s
1 tree, 15 leaves, max depth = 7, train loss: 0.33091, val loss: 0.31108, in 0.016s
1 tree, 13 leaves, max depth = 6, train loss: 0.32933, val loss: 0.30964, in 0.000s
1 tree, 52 leaves, max depth = 11, train loss: 0.32823, val loss: 0.30883, in 0.016s
1 tree, 19 leaves, max depth = 9, train loss: 0.32685, val loss: 0.30759, in 0.016s
1 tree, 16 leaves, max depth = 9, train loss: 0.32561, val loss: 0.30654, in 0.000s
1 tree, 3 leaves, max depth = 2, train loss: 0.32401, val loss: 0.30478, in 0.016s
1 tree, 9 leaves, max depth = 5, train loss: 0.32340, val loss: 0.30414, in 0.016s
1 tree, 7 leaves, max depth = 5, train loss: 0.32282, val loss: 0.30359, in 0.000s
1 tree, 12 leaves, max depth = 5, train loss: 0.32226, val loss: 0.30301, in 0.016s
1 tree, 4 leaves, max depth = 2, train loss: 0.32076, val loss: 0.30136, in 0.000s
1 tree, 57 leaves, max depth = 14, train loss: 0.31977, val loss: 0.30067, in 0.016s
1 tree, 21 leaves, max depth = 9, train loss: 0.31859, val loss: 0.29964, in 0.016s
1 tree, 11 leaves, max depth = 5, train loss: 0.31806, val loss: 0.29909, in 0.016s
1 tree, 50 leaves, max depth = 13, train loss: 0.31719, val loss: 0.29848, in 0.000s
1 tree, 4 leaves, max depth = 2, train loss: 0.31575, val loss: 0.29690, in 0.016s
Fit 95 trees in 1.283 s, (1467 total leaves)
Time spent computing histograms: 0.535s
Time spent finding best splits:  0.066s
Time spent applying splits:      0.054s
Time spent predicting:           0.000s
Trial 8, Fold 4: Log loss = 0.31759633195988035, Average precision = 0.9537953575007956, ROC-AUC = 0.9493490263262684, Elapsed Time = 1.2855565000008937 seconds
Trial 8, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 8, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
Binning 0.040 GB of training data: 0.158 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 4 leaves, max depth = 3, train loss: 0.66986, val loss: 0.66819, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.64852, val loss: 0.64533, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.62922, val loss: 0.62458, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.61167, val loss: 0.60572, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.59591, val loss: 0.58863, in 0.016s
1 tree, 5 leaves, max depth = 4, train loss: 0.58158, val loss: 0.57303, in 0.000s
1 tree, 6 leaves, max depth = 4, train loss: 0.56856, val loss: 0.55885, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.55669, val loss: 0.54582, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.54576, val loss: 0.53380, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.53560, val loss: 0.52259, in 0.016s
1 tree, 5 leaves, max depth = 4, train loss: 0.52656, val loss: 0.51251, in 0.000s
1 tree, 6 leaves, max depth = 4, train loss: 0.51830, val loss: 0.50327, in 0.016s
1 tree, 7 leaves, max depth = 4, train loss: 0.51066, val loss: 0.49473, in 0.000s
1 tree, 6 leaves, max depth = 4, train loss: 0.50372, val loss: 0.48684, in 0.016s
1 tree, 7 leaves, max depth = 4, train loss: 0.49730, val loss: 0.47961, in 0.000s
1 tree, 17 leaves, max depth = 9, train loss: 0.48781, val loss: 0.47076, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.48217, val loss: 0.46431, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.47700, val loss: 0.45837, in 0.016s
1 tree, 3 leaves, max depth = 2, train loss: 0.47212, val loss: 0.45278, in 0.000s
1 tree, 10 leaves, max depth = 6, train loss: 0.46762, val loss: 0.44761, in 0.000s
1 tree, 24 leaves, max depth = 9, train loss: 0.46233, val loss: 0.44243, in 0.016s
1 tree, 13 leaves, max depth = 8, train loss: 0.45696, val loss: 0.43780, in 0.000s
1 tree, 8 leaves, max depth = 4, train loss: 0.45327, val loss: 0.43346, in 0.016s
1 tree, 9 leaves, max depth = 6, train loss: 0.44970, val loss: 0.42931, in 0.000s
1 tree, 28 leaves, max depth = 9, train loss: 0.44521, val loss: 0.42495, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.44191, val loss: 0.42174, in 0.000s
1 tree, 6 leaves, max depth = 4, train loss: 0.43898, val loss: 0.41824, in 0.016s
1 tree, 20 leaves, max depth = 7, train loss: 0.43562, val loss: 0.41489, in 0.000s
1 tree, 20 leaves, max depth = 8, train loss: 0.43134, val loss: 0.41136, in 0.016s
1 tree, 13 leaves, max depth = 6, train loss: 0.42517, val loss: 0.40587, in 0.000s
1 tree, 22 leaves, max depth = 9, train loss: 0.42158, val loss: 0.40297, in 0.016s
1 tree, 18 leaves, max depth = 7, train loss: 0.41864, val loss: 0.40007, in 0.000s
1 tree, 19 leaves, max depth = 7, train loss: 0.41594, val loss: 0.39742, in 0.016s
1 tree, 18 leaves, max depth = 7, train loss: 0.41347, val loss: 0.39499, in 0.000s
1 tree, 9 leaves, max depth = 5, train loss: 0.41099, val loss: 0.39206, in 0.000s
1 tree, 25 leaves, max depth = 9, train loss: 0.40871, val loss: 0.38981, in 0.016s
1 tree, 6 leaves, max depth = 4, train loss: 0.40659, val loss: 0.38728, in 0.000s
1 tree, 14 leaves, max depth = 6, train loss: 0.40195, val loss: 0.38325, in 0.016s
1 tree, 12 leaves, max depth = 7, train loss: 0.39993, val loss: 0.38079, in 0.000s
1 tree, 28 leaves, max depth = 9, train loss: 0.39729, val loss: 0.37880, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.39456, val loss: 0.37617, in 0.000s
1 tree, 10 leaves, max depth = 5, train loss: 0.39275, val loss: 0.37399, in 0.016s
1 tree, 25 leaves, max depth = 8, train loss: 0.39085, val loss: 0.37215, in 0.000s
1 tree, 15 leaves, max depth = 6, train loss: 0.38694, val loss: 0.36881, in 0.016s
1 tree, 35 leaves, max depth = 12, train loss: 0.38466, val loss: 0.36674, in 0.000s
1 tree, 33 leaves, max depth = 10, train loss: 0.38261, val loss: 0.36528, in 0.016s
1 tree, 15 leaves, max depth = 7, train loss: 0.37934, val loss: 0.36254, in 0.016s
1 tree, 11 leaves, max depth = 7, train loss: 0.37780, val loss: 0.36066, in 0.000s
1 tree, 30 leaves, max depth = 10, train loss: 0.37608, val loss: 0.35949, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.37320, val loss: 0.35705, in 0.000s
1 tree, 35 leaves, max depth = 15, train loss: 0.37184, val loss: 0.35574, in 0.016s
1 tree, 20 leaves, max depth = 7, train loss: 0.36925, val loss: 0.35349, in 0.000s
1 tree, 16 leaves, max depth = 8, train loss: 0.36788, val loss: 0.35177, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.36668, val loss: 0.35026, in 0.000s
1 tree, 17 leaves, max depth = 10, train loss: 0.36547, val loss: 0.34889, in 0.016s
1 tree, 21 leaves, max depth = 7, train loss: 0.36304, val loss: 0.34682, in 0.000s
1 tree, 4 leaves, max depth = 3, train loss: 0.36183, val loss: 0.34533, in 0.016s
1 tree, 8 leaves, max depth = 6, train loss: 0.36086, val loss: 0.34421, in 0.000s
1 tree, 22 leaves, max depth = 7, train loss: 0.35863, val loss: 0.34233, in 0.016s
1 tree, 3 leaves, max depth = 2, train loss: 0.35619, val loss: 0.33997, in 0.000s
1 tree, 49 leaves, max depth = 13, train loss: 0.35481, val loss: 0.33880, in 0.016s
1 tree, 23 leaves, max depth = 8, train loss: 0.35284, val loss: 0.33716, in 0.000s
1 tree, 8 leaves, max depth = 4, train loss: 0.35182, val loss: 0.33599, in 0.000s
1 tree, 12 leaves, max depth = 6, train loss: 0.35090, val loss: 0.33493, in 0.000s
1 tree, 13 leaves, max depth = 6, train loss: 0.35005, val loss: 0.33381, in 0.000s
1 tree, 36 leaves, max depth = 13, train loss: 0.34866, val loss: 0.33268, in 0.016s
1 tree, 3 leaves, max depth = 2, train loss: 0.34640, val loss: 0.33050, in 0.000s
1 tree, 47 leaves, max depth = 12, train loss: 0.34519, val loss: 0.32951, in 0.016s
1 tree, 22 leaves, max depth = 7, train loss: 0.34347, val loss: 0.32813, in 0.000s
1 tree, 19 leaves, max depth = 7, train loss: 0.34192, val loss: 0.32690, in 0.016s
1 tree, 10 leaves, max depth = 5, train loss: 0.34104, val loss: 0.32586, in 0.000s
1 tree, 11 leaves, max depth = 5, train loss: 0.34025, val loss: 0.32492, in 0.016s
1 tree, 3 leaves, max depth = 2, train loss: 0.33814, val loss: 0.32288, in 0.000s
1 tree, 31 leaves, max depth = 13, train loss: 0.33697, val loss: 0.32198, in 0.016s
1 tree, 3 leaves, max depth = 2, train loss: 0.33502, val loss: 0.32009, in 0.016s
1 tree, 12 leaves, max depth = 6, train loss: 0.33424, val loss: 0.31920, in 0.000s
1 tree, 3 leaves, max depth = 2, train loss: 0.33244, val loss: 0.31746, in 0.000s
1 tree, 12 leaves, max depth = 6, train loss: 0.33178, val loss: 0.31671, in 0.016s
1 tree, 14 leaves, max depth = 6, train loss: 0.33107, val loss: 0.31590, in 0.000s
1 tree, 3 leaves, max depth = 2, train loss: 0.32939, val loss: 0.31429, in 0.016s
1 tree, 12 leaves, max depth = 5, train loss: 0.32879, val loss: 0.31360, in 0.000s
1 tree, 19 leaves, max depth = 7, train loss: 0.32714, val loss: 0.31229, in 0.016s
1 tree, 49 leaves, max depth = 14, train loss: 0.32609, val loss: 0.31145, in 0.016s
1 tree, 23 leaves, max depth = 7, train loss: 0.32464, val loss: 0.31030, in 0.000s
1 tree, 26 leaves, max depth = 9, train loss: 0.32330, val loss: 0.30930, in 0.016s
1 tree, 3 leaves, max depth = 2, train loss: 0.32168, val loss: 0.30773, in 0.000s
1 tree, 10 leaves, max depth = 6, train loss: 0.32106, val loss: 0.30700, in 0.016s
1 tree, 7 leaves, max depth = 5, train loss: 0.32049, val loss: 0.30636, in 0.000s
1 tree, 43 leaves, max depth = 14, train loss: 0.31967, val loss: 0.30566, in 0.016s
1 tree, 3 leaves, max depth = 2, train loss: 0.31816, val loss: 0.30418, in 0.000s
1 tree, 52 leaves, max depth = 14, train loss: 0.31725, val loss: 0.30347, in 0.016s
1 tree, 24 leaves, max depth = 10, train loss: 0.31607, val loss: 0.30257, in 0.000s
1 tree, 12 leaves, max depth = 5, train loss: 0.31547, val loss: 0.30191, in 0.000s
1 tree, 54 leaves, max depth = 14, train loss: 0.31467, val loss: 0.30128, in 0.016s
1 tree, 3 leaves, max depth = 2, train loss: 0.31321, val loss: 0.29987, in 0.000s
Fit 95 trees in 1.033 s, (1455 total leaves)
Time spent computing histograms: 0.434s
Time spent finding best splits:  0.044s
Time spent applying splits:      0.036s
Time spent predicting:           0.000s
Trial 8, Fold 5: Log loss = 0.319615800834132, Average precision = 0.9529594062011224, ROC-AUC = 0.9482054883599947, Elapsed Time = 1.0457816000016464 seconds
Optimization Progress:   9%|9         | 9/100 [01:51<18:51, 12.44s/it]
Trial 9, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 9, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
0.159 s 0.040 GB of training data: 
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 77 leaves, max depth = 17, train loss: 0.64898, val loss: 0.64781, in 0.016s
1 tree, 75 leaves, max depth = 17, train loss: 0.61250, val loss: 0.61028, in 0.016s
1 tree, 76 leaves, max depth = 18, train loss: 0.58242, val loss: 0.57932, in 0.000s
1 tree, 77 leaves, max depth = 18, train loss: 0.55778, val loss: 0.55395, in 0.016s
1 tree, 127 leaves, max depth = 13, train loss: 0.53453, val loss: 0.53256, in 0.016s
1 tree, 76 leaves, max depth = 17, train loss: 0.51534, val loss: 0.51258, in 0.016s
1 tree, 127 leaves, max depth = 14, train loss: 0.49704, val loss: 0.49590, in 0.016s
1 tree, 77 leaves, max depth = 17, train loss: 0.48227, val loss: 0.48041, in 0.000s
1 tree, 76 leaves, max depth = 16, train loss: 0.46965, val loss: 0.46711, in 0.016s
1 tree, 76 leaves, max depth = 13, train loss: 0.45857, val loss: 0.45533, in 0.000s
1 tree, 127 leaves, max depth = 14, train loss: 0.44484, val loss: 0.44306, in 0.016s
1 tree, 77 leaves, max depth = 16, train loss: 0.43659, val loss: 0.43430, in 0.016s
1 tree, 76 leaves, max depth = 17, train loss: 0.42911, val loss: 0.42639, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.42246, val loss: 0.41905, in 0.000s
1 tree, 168 leaves, max depth = 16, train loss: 0.41469, val loss: 0.41243, in 0.016s
1 tree, 127 leaves, max depth = 15, train loss: 0.40459, val loss: 0.40373, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.39914, val loss: 0.39767, in 0.000s
1 tree, 76 leaves, max depth = 15, train loss: 0.39378, val loss: 0.39209, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.38923, val loss: 0.38700, in 0.000s
1 tree, 76 leaves, max depth = 13, train loss: 0.38458, val loss: 0.38209, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.38077, val loss: 0.37779, in 0.000s
1 tree, 168 leaves, max depth = 15, train loss: 0.37539, val loss: 0.37369, in 0.016s
1 tree, 127 leaves, max depth = 15, train loss: 0.36746, val loss: 0.36718, in 0.031s
1 tree, 76 leaves, max depth = 13, train loss: 0.36381, val loss: 0.36332, in 0.000s
1 tree, 153 leaves, max depth = 19, train loss: 0.35998, val loss: 0.35994, in 0.000s
1 tree, 127 leaves, max depth = 15, train loss: 0.35365, val loss: 0.35484, in 0.031s
1 tree, 127 leaves, max depth = 15, train loss: 0.34825, val loss: 0.35056, in 0.000s
1 tree, 46 leaves, max depth = 11, train loss: 0.34571, val loss: 0.34787, in 0.016s
1 tree, 127 leaves, max depth = 15, train loss: 0.34113, val loss: 0.34431, in 0.016s
1 tree, 127 leaves, max depth = 16, train loss: 0.33723, val loss: 0.34132, in 0.016s
1 tree, 97 leaves, max depth = 15, train loss: 0.33447, val loss: 0.33823, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.33099, val loss: 0.33425, in 0.000s
1 tree, 76 leaves, max depth = 13, train loss: 0.32862, val loss: 0.33170, in 0.016s
1 tree, 127 leaves, max depth = 15, train loss: 0.32508, val loss: 0.32909, in 0.016s
1 tree, 97 leaves, max depth = 15, train loss: 0.32291, val loss: 0.32668, in 0.016s
1 tree, 96 leaves, max depth = 14, train loss: 0.32095, val loss: 0.32436, in 0.000s
1 tree, 164 leaves, max depth = 17, train loss: 0.31834, val loss: 0.32270, in 0.016s
1 tree, 127 leaves, max depth = 15, train loss: 0.31546, val loss: 0.32062, in 0.016s
1 tree, 97 leaves, max depth = 14, train loss: 0.31375, val loss: 0.31863, in 0.016s
1 tree, 164 leaves, max depth = 17, train loss: 0.31169, val loss: 0.31743, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.30852, val loss: 0.31378, in 0.000s
1 tree, 96 leaves, max depth = 16, train loss: 0.30688, val loss: 0.31184, in 0.016s
1 tree, 153 leaves, max depth = 22, train loss: 0.30509, val loss: 0.31056, in 0.016s
1 tree, 127 leaves, max depth = 16, train loss: 0.30282, val loss: 0.30899, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.30000, val loss: 0.30573, in 0.000s
1 tree, 76 leaves, max depth = 15, train loss: 0.29847, val loss: 0.30409, in 0.016s
Fit 46 trees in 0.799 s, (4201 total leaves)
Time spent computing histograms: 0.215s
Time spent finding best splits:  0.084s
Time spent applying splits:      0.060s
Time spent predicting:           0.016s
Trial 9, Fold 1: Log loss = 0.30940654560773445, Average precision = 0.9546708274374668, ROC-AUC = 0.9510657438474738, Elapsed Time = 0.8121594000003824 seconds
Trial 9, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 9, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
Binning 0.040 GB of training data: 0.141 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 77 leaves, max depth = 20, train loss: 0.64881, val loss: 0.64702, in 0.015s
1 tree, 77 leaves, max depth = 15, train loss: 0.61244, val loss: 0.60872, in 0.016s
1 tree, 77 leaves, max depth = 13, train loss: 0.58246, val loss: 0.57713, in 0.016s
1 tree, 77 leaves, max depth = 17, train loss: 0.55774, val loss: 0.55117, in 0.000s
1 tree, 126 leaves, max depth = 19, train loss: 0.53504, val loss: 0.52940, in 0.016s
1 tree, 77 leaves, max depth = 14, train loss: 0.51590, val loss: 0.50923, in 0.016s
1 tree, 126 leaves, max depth = 19, train loss: 0.49800, val loss: 0.49216, in 0.016s
1 tree, 77 leaves, max depth = 20, train loss: 0.48316, val loss: 0.47640, in 0.000s
1 tree, 77 leaves, max depth = 13, train loss: 0.47050, val loss: 0.46276, in 0.016s
1 tree, 77 leaves, max depth = 12, train loss: 0.45947, val loss: 0.45086, in 0.016s
1 tree, 126 leaves, max depth = 20, train loss: 0.44605, val loss: 0.43823, in 0.016s
1 tree, 77 leaves, max depth = 22, train loss: 0.43775, val loss: 0.42930, in 0.000s
1 tree, 77 leaves, max depth = 19, train loss: 0.43073, val loss: 0.42172, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.42425, val loss: 0.41501, in 0.000s
1 tree, 168 leaves, max depth = 13, train loss: 0.41656, val loss: 0.40836, in 0.016s
1 tree, 126 leaves, max depth = 19, train loss: 0.40664, val loss: 0.39923, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.40134, val loss: 0.39370, in 0.016s
1 tree, 76 leaves, max depth = 12, train loss: 0.39587, val loss: 0.38800, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.39144, val loss: 0.38338, in 0.016s
1 tree, 77 leaves, max depth = 12, train loss: 0.38684, val loss: 0.37835, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.38314, val loss: 0.37445, in 0.000s
1 tree, 153 leaves, max depth = 23, train loss: 0.37833, val loss: 0.37084, in 0.016s
1 tree, 126 leaves, max depth = 19, train loss: 0.37046, val loss: 0.36376, in 0.016s
1 tree, 77 leaves, max depth = 12, train loss: 0.36682, val loss: 0.35978, in 0.016s
1 tree, 163 leaves, max depth = 16, train loss: 0.36212, val loss: 0.35617, in 0.016s
1 tree, 126 leaves, max depth = 20, train loss: 0.35600, val loss: 0.35075, in 0.016s
1 tree, 126 leaves, max depth = 20, train loss: 0.35080, val loss: 0.34618, in 0.016s
1 tree, 97 leaves, max depth = 15, train loss: 0.34775, val loss: 0.34290, in 0.000s
1 tree, 126 leaves, max depth = 20, train loss: 0.34327, val loss: 0.33899, in 0.031s
1 tree, 126 leaves, max depth = 20, train loss: 0.33945, val loss: 0.33571, in 0.016s
1 tree, 76 leaves, max depth = 14, train loss: 0.33677, val loss: 0.33296, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.33336, val loss: 0.32938, in 0.016s
1 tree, 97 leaves, max depth = 18, train loss: 0.33103, val loss: 0.32673, in 0.000s
1 tree, 126 leaves, max depth = 20, train loss: 0.32760, val loss: 0.32381, in 0.016s
1 tree, 96 leaves, max depth = 16, train loss: 0.32559, val loss: 0.32149, in 0.016s
1 tree, 75 leaves, max depth = 13, train loss: 0.32372, val loss: 0.31953, in 0.016s
1 tree, 163 leaves, max depth = 17, train loss: 0.32131, val loss: 0.31803, in 0.016s
1 tree, 126 leaves, max depth = 20, train loss: 0.31853, val loss: 0.31571, in 0.016s
1 tree, 97 leaves, max depth = 15, train loss: 0.31682, val loss: 0.31383, in 0.000s
1 tree, 162 leaves, max depth = 16, train loss: 0.31493, val loss: 0.31275, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.31181, val loss: 0.30948, in 0.016s
1 tree, 96 leaves, max depth = 15, train loss: 0.31011, val loss: 0.30765, in 0.000s
1 tree, 152 leaves, max depth = 24, train loss: 0.30812, val loss: 0.30663, in 0.016s
1 tree, 126 leaves, max depth = 21, train loss: 0.30594, val loss: 0.30486, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.30317, val loss: 0.30194, in 0.016s
1 tree, 97 leaves, max depth = 15, train loss: 0.30164, val loss: 0.30029, in 0.000s
Fit 46 trees in 0.798 s, (4239 total leaves)
Time spent computing histograms: 0.224s
Time spent finding best splits:  0.082s
Time spent applying splits:      0.059s
Time spent predicting:           0.016s
Trial 9, Fold 2: Log loss = 0.30944503444618643, Average precision = 0.952180089458451, ROC-AUC = 0.9511007936105684, Elapsed Time = 0.8179369999998016 seconds
Trial 9, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 9, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
Binning 0.040 GB of training data: 0.158 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 75 leaves, max depth = 15, train loss: 0.64923, val loss: 0.64769, in 0.016s
1 tree, 76 leaves, max depth = 13, train loss: 0.61303, val loss: 0.61001, in 0.000s
1 tree, 76 leaves, max depth = 14, train loss: 0.58324, val loss: 0.57897, in 0.016s
1 tree, 76 leaves, max depth = 14, train loss: 0.55878, val loss: 0.55332, in 0.016s
1 tree, 121 leaves, max depth = 14, train loss: 0.53606, val loss: 0.53243, in 0.016s
1 tree, 76 leaves, max depth = 15, train loss: 0.51700, val loss: 0.51227, in 0.000s
1 tree, 121 leaves, max depth = 14, train loss: 0.49913, val loss: 0.49593, in 0.016s
1 tree, 76 leaves, max depth = 19, train loss: 0.48443, val loss: 0.48029, in 0.016s
1 tree, 76 leaves, max depth = 15, train loss: 0.47188, val loss: 0.46681, in 0.000s
1 tree, 76 leaves, max depth = 13, train loss: 0.46090, val loss: 0.45537, in 0.016s
1 tree, 121 leaves, max depth = 15, train loss: 0.44753, val loss: 0.44321, in 0.016s
1 tree, 76 leaves, max depth = 19, train loss: 0.43929, val loss: 0.43432, in 0.016s
1 tree, 75 leaves, max depth = 13, train loss: 0.43195, val loss: 0.42628, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.42523, val loss: 0.42008, in 0.016s
1 tree, 168 leaves, max depth = 14, train loss: 0.41756, val loss: 0.41361, in 0.016s
1 tree, 121 leaves, max depth = 14, train loss: 0.40779, val loss: 0.40487, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.40230, val loss: 0.39978, in 0.000s
1 tree, 75 leaves, max depth = 15, train loss: 0.39701, val loss: 0.39394, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.39243, val loss: 0.38970, in 0.000s
1 tree, 76 leaves, max depth = 13, train loss: 0.38784, val loss: 0.38491, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.38401, val loss: 0.38136, in 0.000s
1 tree, 153 leaves, max depth = 25, train loss: 0.37916, val loss: 0.37772, in 0.016s
1 tree, 121 leaves, max depth = 14, train loss: 0.37143, val loss: 0.37093, in 0.016s
1 tree, 76 leaves, max depth = 13, train loss: 0.36780, val loss: 0.36713, in 0.016s
1 tree, 153 leaves, max depth = 25, train loss: 0.36418, val loss: 0.36460, in 0.016s
1 tree, 121 leaves, max depth = 15, train loss: 0.35802, val loss: 0.35925, in 0.000s
1 tree, 121 leaves, max depth = 14, train loss: 0.35279, val loss: 0.35475, in 0.016s
1 tree, 46 leaves, max depth = 11, train loss: 0.35018, val loss: 0.35204, in 0.016s
1 tree, 121 leaves, max depth = 14, train loss: 0.34577, val loss: 0.34829, in 0.016s
1 tree, 121 leaves, max depth = 14, train loss: 0.34203, val loss: 0.34514, in 0.016s
1 tree, 75 leaves, max depth = 14, train loss: 0.33936, val loss: 0.34210, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.33587, val loss: 0.33883, in 0.000s
1 tree, 96 leaves, max depth = 16, train loss: 0.33348, val loss: 0.33595, in 0.016s
1 tree, 121 leaves, max depth = 14, train loss: 0.33012, val loss: 0.33316, in 0.016s
1 tree, 75 leaves, max depth = 14, train loss: 0.32806, val loss: 0.33085, in 0.000s
1 tree, 96 leaves, max depth = 16, train loss: 0.32598, val loss: 0.32826, in 0.016s
1 tree, 163 leaves, max depth = 18, train loss: 0.32344, val loss: 0.32641, in 0.031s
1 tree, 121 leaves, max depth = 15, train loss: 0.32075, val loss: 0.32421, in 0.016s
1 tree, 92 leaves, max depth = 22, train loss: 0.31898, val loss: 0.32186, in 0.000s
1 tree, 162 leaves, max depth = 18, train loss: 0.31699, val loss: 0.32049, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.31381, val loss: 0.31751, in 0.016s
1 tree, 96 leaves, max depth = 20, train loss: 0.31209, val loss: 0.31529, in 0.016s
1 tree, 153 leaves, max depth = 24, train loss: 0.31029, val loss: 0.31449, in 0.016s
1 tree, 121 leaves, max depth = 19, train loss: 0.30820, val loss: 0.31283, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.30538, val loss: 0.31019, in 0.000s
1 tree, 96 leaves, max depth = 20, train loss: 0.30384, val loss: 0.30819, in 0.016s
Fit 46 trees in 0.830 s, (4096 total leaves)
Time spent computing histograms: 0.227s
Time spent finding best splits:  0.087s
Time spent applying splits:      0.062s
Time spent predicting:           0.000s
Trial 9, Fold 3: Log loss = 0.3068492381650382, Average precision = 0.9564745796419276, ROC-AUC = 0.9535465835107049, Elapsed Time = 0.8307597000002716 seconds
Trial 9, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 9, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
Binning 0.040 GB of training data: 0.158 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 75 leaves, max depth = 14, train loss: 0.64907, val loss: 0.64680, in 0.000s
1 tree, 76 leaves, max depth = 14, train loss: 0.61275, val loss: 0.60823, in 0.016s
1 tree, 76 leaves, max depth = 13, train loss: 0.58283, val loss: 0.57639, in 0.016s
1 tree, 76 leaves, max depth = 13, train loss: 0.55820, val loss: 0.55014, in 0.016s
1 tree, 124 leaves, max depth = 16, train loss: 0.53569, val loss: 0.52826, in 0.016s
1 tree, 76 leaves, max depth = 13, train loss: 0.51655, val loss: 0.50746, in 0.000s
1 tree, 124 leaves, max depth = 16, train loss: 0.49887, val loss: 0.49034, in 0.016s
1 tree, 76 leaves, max depth = 13, train loss: 0.48407, val loss: 0.47406, in 0.031s
1 tree, 76 leaves, max depth = 13, train loss: 0.47145, val loss: 0.46009, in 0.016s
1 tree, 76 leaves, max depth = 14, train loss: 0.46054, val loss: 0.44777, in 0.000s
1 tree, 124 leaves, max depth = 16, train loss: 0.44735, val loss: 0.43517, in 0.031s
1 tree, 76 leaves, max depth = 13, train loss: 0.43906, val loss: 0.42575, in 0.000s
1 tree, 75 leaves, max depth = 15, train loss: 0.43157, val loss: 0.41737, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.42510, val loss: 0.41044, in 0.016s
1 tree, 168 leaves, max depth = 14, train loss: 0.41760, val loss: 0.40375, in 0.016s
1 tree, 124 leaves, max depth = 14, train loss: 0.40800, val loss: 0.39474, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.40270, val loss: 0.38905, in 0.000s
1 tree, 75 leaves, max depth = 12, train loss: 0.39727, val loss: 0.38279, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.39286, val loss: 0.37804, in 0.000s
1 tree, 76 leaves, max depth = 13, train loss: 0.38832, val loss: 0.37273, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.38463, val loss: 0.36875, in 0.016s
1 tree, 152 leaves, max depth = 27, train loss: 0.37991, val loss: 0.36440, in 0.016s
1 tree, 124 leaves, max depth = 15, train loss: 0.37232, val loss: 0.35750, in 0.016s
1 tree, 76 leaves, max depth = 11, train loss: 0.36873, val loss: 0.35327, in 0.016s
1 tree, 163 leaves, max depth = 18, train loss: 0.36407, val loss: 0.34928, in 0.016s
1 tree, 124 leaves, max depth = 14, train loss: 0.35821, val loss: 0.34402, in 0.016s
1 tree, 124 leaves, max depth = 14, train loss: 0.35324, val loss: 0.33957, in 0.016s
1 tree, 46 leaves, max depth = 13, train loss: 0.35063, val loss: 0.33688, in 0.016s
1 tree, 124 leaves, max depth = 15, train loss: 0.34645, val loss: 0.33317, in 0.016s
1 tree, 124 leaves, max depth = 15, train loss: 0.34291, val loss: 0.33005, in 0.016s
1 tree, 96 leaves, max depth = 14, train loss: 0.34010, val loss: 0.32708, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.33672, val loss: 0.32338, in 0.000s
1 tree, 97 leaves, max depth = 14, train loss: 0.33440, val loss: 0.32095, in 0.016s
1 tree, 124 leaves, max depth = 13, train loss: 0.33119, val loss: 0.31816, in 0.016s
1 tree, 75 leaves, max depth = 12, train loss: 0.32896, val loss: 0.31561, in 0.016s
1 tree, 97 leaves, max depth = 15, train loss: 0.32694, val loss: 0.31360, in 0.016s
1 tree, 163 leaves, max depth = 22, train loss: 0.32451, val loss: 0.31184, in 0.016s
1 tree, 124 leaves, max depth = 15, train loss: 0.32193, val loss: 0.30963, in 0.016s
1 tree, 97 leaves, max depth = 19, train loss: 0.32023, val loss: 0.30776, in 0.000s
1 tree, 163 leaves, max depth = 21, train loss: 0.31830, val loss: 0.30644, in 0.031s
1 tree, 5 leaves, max depth = 3, train loss: 0.31521, val loss: 0.30306, in 0.000s
1 tree, 97 leaves, max depth = 16, train loss: 0.31352, val loss: 0.30142, in 0.016s
1 tree, 152 leaves, max depth = 30, train loss: 0.31152, val loss: 0.29988, in 0.016s
1 tree, 124 leaves, max depth = 14, train loss: 0.30952, val loss: 0.29822, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.30679, val loss: 0.29522, in 0.016s
1 tree, 75 leaves, max depth = 13, train loss: 0.30525, val loss: 0.29346, in 0.016s
Fit 46 trees in 0.923 s, (4149 total leaves)
Time spent computing histograms: 0.242s
Time spent finding best splits:  0.098s
Time spent applying splits:      0.070s
Time spent predicting:           0.000s
Trial 9, Fold 4: Log loss = 0.3106716960208532, Average precision = 0.9542641250120021, ROC-AUC = 0.9510418082409973, Elapsed Time = 0.9211533999987296 seconds
Trial 9, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 9, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
Binning 0.040 GB of training data: 0.157 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 76 leaves, max depth = 12, train loss: 0.64857, val loss: 0.64569, in 0.016s
1 tree, 76 leaves, max depth = 14, train loss: 0.61206, val loss: 0.60642, in 0.016s
1 tree, 76 leaves, max depth = 16, train loss: 0.58208, val loss: 0.57397, in 0.016s
1 tree, 76 leaves, max depth = 14, train loss: 0.55722, val loss: 0.54677, in 0.016s
1 tree, 121 leaves, max depth = 17, train loss: 0.53504, val loss: 0.52573, in 0.000s
1 tree, 76 leaves, max depth = 15, train loss: 0.51560, val loss: 0.50445, in 0.016s
1 tree, 121 leaves, max depth = 18, train loss: 0.49819, val loss: 0.48810, in 0.031s
1 tree, 76 leaves, max depth = 14, train loss: 0.48315, val loss: 0.47132, in 0.000s
1 tree, 76 leaves, max depth = 15, train loss: 0.47030, val loss: 0.45701, in 0.016s
1 tree, 76 leaves, max depth = 15, train loss: 0.45915, val loss: 0.44442, in 0.016s
1 tree, 121 leaves, max depth = 19, train loss: 0.44613, val loss: 0.43252, in 0.016s
1 tree, 76 leaves, max depth = 16, train loss: 0.43768, val loss: 0.42285, in 0.016s
1 tree, 76 leaves, max depth = 14, train loss: 0.43054, val loss: 0.41463, in 0.016s
[14/46] 1 tree, 5 leaves, max depth = 3, train loss: 0.42404, val loss: 0.40839, in 0.000s
1 tree, 168 leaves, max depth = 13, train loss: 0.41646, val loss: 0.40243, in 0.016s
1 tree, 121 leaves, max depth = 20, train loss: 0.40693, val loss: 0.39395, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.40160, val loss: 0.38887, in 0.016s
1 tree, 76 leaves, max depth = 14, train loss: 0.39611, val loss: 0.38223, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.39169, val loss: 0.37802, in 0.016s
1 tree, 76 leaves, max depth = 14, train loss: 0.38699, val loss: 0.37250, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.38329, val loss: 0.36900, in 0.016s
1 tree, 152 leaves, max depth = 30, train loss: 0.37850, val loss: 0.36462, in 0.016s
1 tree, 121 leaves, max depth = 20, train loss: 0.37098, val loss: 0.35818, in 0.016s
1 tree, 76 leaves, max depth = 14, train loss: 0.36725, val loss: 0.35376, in 0.016s
1 tree, 164 leaves, max depth = 18, train loss: 0.36264, val loss: 0.34991, in 0.016s
1 tree, 121 leaves, max depth = 20, train loss: 0.35684, val loss: 0.34505, in 0.016s
1 tree, 121 leaves, max depth = 19, train loss: 0.35193, val loss: 0.34098, in 0.016s
1 tree, 97 leaves, max depth = 13, train loss: 0.34875, val loss: 0.33742, in 0.016s
1 tree, 121 leaves, max depth = 21, train loss: 0.34455, val loss: 0.33397, in 0.016s
1 tree, 121 leaves, max depth = 21, train loss: 0.34099, val loss: 0.33109, in 0.016s
1 tree, 76 leaves, max depth = 13, train loss: 0.33828, val loss: 0.32763, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.33488, val loss: 0.32438, in 0.016s
1 tree, 97 leaves, max depth = 13, train loss: 0.33245, val loss: 0.32181, in 0.016s
1 tree, 121 leaves, max depth = 21, train loss: 0.32925, val loss: 0.31928, in 0.016s
1 tree, 97 leaves, max depth = 14, train loss: 0.32715, val loss: 0.31705, in 0.000s
1 tree, 76 leaves, max depth = 17, train loss: 0.32524, val loss: 0.31468, in 0.016s
1 tree, 164 leaves, max depth = 17, train loss: 0.32281, val loss: 0.31290, in 0.063s
1 tree, 121 leaves, max depth = 20, train loss: 0.32024, val loss: 0.31091, in 0.031s
1 tree, 94 leaves, max depth = 18, train loss: 0.31844, val loss: 0.30896, in 0.031s
1 tree, 97 leaves, max depth = 15, train loss: 0.31676, val loss: 0.30707, in 0.031s
1 tree, 5 leaves, max depth = 3, train loss: 0.31371, val loss: 0.30412, in 0.000s
1 tree, 46 leaves, max depth = 11, train loss: 0.31235, val loss: 0.30254, in 0.016s
1 tree, 152 leaves, max depth = 28, train loss: 0.31024, val loss: 0.30088, in 0.031s
1 tree, 121 leaves, max depth = 19, train loss: 0.30806, val loss: 0.29925, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.30536, val loss: 0.29662, in 0.016s
1 tree, 121 leaves, max depth = 20, train loss: 0.30346, val loss: 0.29523, in 0.016s
Fit 46 trees in 1.017 s, (4076 total leaves)
Time spent computing histograms: 0.284s
Time spent finding best splits:  0.123s
Time spent applying splits:      0.090s
Time spent predicting:           0.000s
Trial 9, Fold 5: Log loss = 0.31477065490313444, Average precision = 0.9525868854943338, ROC-AUC = 0.9487047544729948, Elapsed Time = 1.028342600000542 seconds
Optimization Progress:  10%|#         | 10/100 [02:02<18:03, 12.04s/it]
Trial 10, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 10, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
0.190 s 0.040 GB of training data: 
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 77 leaves, max depth = 19, train loss: 0.68188, val loss: 0.68157, in 0.000s
1 tree, 80 leaves, max depth = 17, train loss: 0.67108, val loss: 0.67043, in 0.016s
1 tree, 80 leaves, max depth = 17, train loss: 0.66079, val loss: 0.65980, in 0.016s
1 tree, 76 leaves, max depth = 17, train loss: 0.65117, val loss: 0.64997, in 0.000s
1 tree, 80 leaves, max depth = 15, train loss: 0.64190, val loss: 0.64041, in 0.016s
1 tree, 80 leaves, max depth = 15, train loss: 0.63304, val loss: 0.63127, in 0.016s
1 tree, 73 leaves, max depth = 19, train loss: 0.62468, val loss: 0.62268, in 0.000s
1 tree, 78 leaves, max depth = 18, train loss: 0.61668, val loss: 0.61449, in 0.016s
1 tree, 80 leaves, max depth = 15, train loss: 0.60894, val loss: 0.60648, in 0.016s
1 tree, 79 leaves, max depth = 16, train loss: 0.60142, val loss: 0.59868, in 0.000s
1 tree, 79 leaves, max depth = 16, train loss: 0.59439, val loss: 0.59138, in 0.016s
1 tree, 80 leaves, max depth = 13, train loss: 0.58759, val loss: 0.58439, in 0.016s
1 tree, 80 leaves, max depth = 16, train loss: 0.58109, val loss: 0.57766, in 0.016s
1 tree, 80 leaves, max depth = 16, train loss: 0.57477, val loss: 0.57107, in 0.016s
1 tree, 80 leaves, max depth = 16, train loss: 0.56881, val loss: 0.56489, in 0.016s
1 tree, 80 leaves, max depth = 16, train loss: 0.56310, val loss: 0.55896, in 0.000s
1 tree, 80 leaves, max depth = 14, train loss: 0.55761, val loss: 0.55332, in 0.016s
1 tree, 80 leaves, max depth = 14, train loss: 0.55235, val loss: 0.54791, in 0.016s
1 tree, 78 leaves, max depth = 16, train loss: 0.54737, val loss: 0.54280, in 0.000s
1 tree, 80 leaves, max depth = 16, train loss: 0.54244, val loss: 0.53763, in 0.016s
1 tree, 80 leaves, max depth = 16, train loss: 0.53772, val loss: 0.53267, in 0.016s
1 tree, 80 leaves, max depth = 16, train loss: 0.53319, val loss: 0.52790, in 0.016s
1 tree, 123 leaves, max depth = 17, train loss: 0.52786, val loss: 0.52299, in 0.000s
1 tree, 80 leaves, max depth = 16, train loss: 0.52360, val loss: 0.51850, in 0.000s
1 tree, 80 leaves, max depth = 15, train loss: 0.51959, val loss: 0.51436, in 0.016s
1 tree, 123 leaves, max depth = 17, train loss: 0.51461, val loss: 0.50978, in 0.016s
1 tree, 123 leaves, max depth = 16, train loss: 0.50983, val loss: 0.50539, in 0.016s
1 tree, 79 leaves, max depth = 16, train loss: 0.50614, val loss: 0.50151, in 0.000s
1 tree, 122 leaves, max depth = 19, train loss: 0.50161, val loss: 0.49735, in 0.016s
1 tree, 123 leaves, max depth = 19, train loss: 0.49725, val loss: 0.49336, in 0.016s
1 tree, 78 leaves, max depth = 14, train loss: 0.49386, val loss: 0.48984, in 0.016s
1 tree, 79 leaves, max depth = 16, train loss: 0.49058, val loss: 0.48637, in 0.016s
1 tree, 80 leaves, max depth = 17, train loss: 0.48734, val loss: 0.48293, in 0.000s
1 tree, 123 leaves, max depth = 19, train loss: 0.48328, val loss: 0.47923, in 0.016s
1 tree, 80 leaves, max depth = 17, train loss: 0.48023, val loss: 0.47597, in 0.000s
1 tree, 79 leaves, max depth = 14, train loss: 0.47736, val loss: 0.47301, in 0.016s
1 tree, 80 leaves, max depth = 17, train loss: 0.47454, val loss: 0.46999, in 0.016s
1 tree, 123 leaves, max depth = 14, train loss: 0.47074, val loss: 0.46654, in 0.016s
1 tree, 80 leaves, max depth = 14, train loss: 0.46815, val loss: 0.46385, in 0.016s
1 tree, 79 leaves, max depth = 16, train loss: 0.46565, val loss: 0.46120, in 0.016s
1 tree, 80 leaves, max depth = 14, train loss: 0.46325, val loss: 0.45872, in 0.000s
1 tree, 123 leaves, max depth = 14, train loss: 0.45968, val loss: 0.45549, in 0.016s
1 tree, 79 leaves, max depth = 14, train loss: 0.45738, val loss: 0.45306, in 0.016s
1 tree, 122 leaves, max depth = 13, train loss: 0.45397, val loss: 0.45000, in 0.016s
1 tree, 79 leaves, max depth = 14, train loss: 0.45180, val loss: 0.44770, in 0.016s
1 tree, 123 leaves, max depth = 14, train loss: 0.44855, val loss: 0.44478, in 0.000s
1 tree, 123 leaves, max depth = 14, train loss: 0.44542, val loss: 0.44198, in 0.016s
1 tree, 80 leaves, max depth = 17, train loss: 0.44336, val loss: 0.43974, in 0.016s
1 tree, 123 leaves, max depth = 14, train loss: 0.44037, val loss: 0.43707, in 0.016s
1 tree, 123 leaves, max depth = 14, train loss: 0.43749, val loss: 0.43450, in 0.016s
1 tree, 79 leaves, max depth = 14, train loss: 0.43554, val loss: 0.43245, in 0.016s
1 tree, 123 leaves, max depth = 14, train loss: 0.43279, val loss: 0.43001, in 0.016s
1 tree, 122 leaves, max depth = 14, train loss: 0.43015, val loss: 0.42766, in 0.016s
1 tree, 79 leaves, max depth = 14, train loss: 0.42834, val loss: 0.42569, in 0.000s
1 tree, 123 leaves, max depth = 14, train loss: 0.42581, val loss: 0.42345, in 0.016s
Fit 55 trees in 0.987 s, (5055 total leaves)
Time spent computing histograms: 0.277s
Time spent finding best splits:  0.073s
Time spent applying splits:      0.086s
Time spent predicting:           0.016s
Trial 10, Fold 1: Log loss = 0.42790914008392067, Average precision = 0.9175382538763569, ROC-AUC = 0.9268080588654212, Elapsed Time = 0.9964141999989806 seconds
Trial 10, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 10, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
Binning 0.040 GB of training data: 0.221 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 76 leaves, max depth = 19, train loss: 0.68194, val loss: 0.68148, in 0.000s
1 tree, 80 leaves, max depth = 17, train loss: 0.67119, val loss: 0.67021, in 0.016s
1 tree, 80 leaves, max depth = 17, train loss: 0.66094, val loss: 0.65946, in 0.016s
1 tree, 78 leaves, max depth = 16, train loss: 0.65128, val loss: 0.64939, in 0.016s
1 tree, 79 leaves, max depth = 17, train loss: 0.64206, val loss: 0.63972, in 0.000s
1 tree, 79 leaves, max depth = 17, train loss: 0.63327, val loss: 0.63048, in 0.016s
1 tree, 76 leaves, max depth = 19, train loss: 0.62486, val loss: 0.62170, in 0.016s
1 tree, 77 leaves, max depth = 18, train loss: 0.61683, val loss: 0.61326, in 0.016s
1 tree, 80 leaves, max depth = 14, train loss: 0.60909, val loss: 0.60513, in 0.016s
1 tree, 79 leaves, max depth = 14, train loss: 0.60161, val loss: 0.59725, in 0.000s
1 tree, 79 leaves, max depth = 16, train loss: 0.59458, val loss: 0.58988, in 0.016s
1 tree, 77 leaves, max depth = 19, train loss: 0.58786, val loss: 0.58279, in 0.016s
1 tree, 80 leaves, max depth = 14, train loss: 0.58136, val loss: 0.57594, in 0.000s
1 tree, 79 leaves, max depth = 14, train loss: 0.57507, val loss: 0.56929, in 0.016s
1 tree, 79 leaves, max depth = 17, train loss: 0.56915, val loss: 0.56308, in 0.016s
1 tree, 80 leaves, max depth = 14, train loss: 0.56344, val loss: 0.55704, in 0.016s
1 tree, 79 leaves, max depth = 17, train loss: 0.55800, val loss: 0.55129, in 0.000s
1 tree, 79 leaves, max depth = 17, train loss: 0.55279, val loss: 0.54578, in 0.016s
1 tree, 77 leaves, max depth = 19, train loss: 0.54781, val loss: 0.54048, in 0.016s
1 tree, 79 leaves, max depth = 14, train loss: 0.54291, val loss: 0.53528, in 0.000s
1 tree, 79 leaves, max depth = 14, train loss: 0.53820, val loss: 0.53028, in 0.016s
1 tree, 79 leaves, max depth = 14, train loss: 0.53369, val loss: 0.52548, in 0.016s
1 tree, 123 leaves, max depth = 15, train loss: 0.52845, val loss: 0.52047, in 0.016s
1 tree, 79 leaves, max depth = 14, train loss: 0.52421, val loss: 0.51595, in 0.000s
1 tree, 79 leaves, max depth = 17, train loss: 0.52024, val loss: 0.51172, in 0.016s
1 tree, 123 leaves, max depth = 17, train loss: 0.51534, val loss: 0.50705, in 0.016s
1 tree, 123 leaves, max depth = 17, train loss: 0.51064, val loss: 0.50257, in 0.016s
1 tree, 79 leaves, max depth = 19, train loss: 0.50697, val loss: 0.49866, in 0.016s
1 tree, 123 leaves, max depth = 17, train loss: 0.50251, val loss: 0.49441, in 0.000s
1 tree, 123 leaves, max depth = 17, train loss: 0.49824, val loss: 0.49035, in 0.016s
1 tree, 76 leaves, max depth = 18, train loss: 0.49482, val loss: 0.48674, in 0.016s
1 tree, 77 leaves, max depth = 19, train loss: 0.49157, val loss: 0.48325, in 0.016s
1 tree, 80 leaves, max depth = 14, train loss: 0.48834, val loss: 0.47979, in 0.016s
1 tree, 123 leaves, max depth = 15, train loss: 0.48436, val loss: 0.47601, in 0.016s
1 tree, 80 leaves, max depth = 14, train loss: 0.48131, val loss: 0.47275, in 0.000s
1 tree, 80 leaves, max depth = 12, train loss: 0.47843, val loss: 0.46966, in 0.016s
1 tree, 79 leaves, max depth = 14, train loss: 0.47562, val loss: 0.46664, in 0.016s
1 tree, 123 leaves, max depth = 14, train loss: 0.47190, val loss: 0.46312, in 0.016s
1 tree, 79 leaves, max depth = 16, train loss: 0.46932, val loss: 0.46036, in 0.000s
1 tree, 79 leaves, max depth = 16, train loss: 0.46684, val loss: 0.45771, in 0.016s
1 tree, 79 leaves, max depth = 25, train loss: 0.46445, val loss: 0.45518, in 0.016s
1 tree, 123 leaves, max depth = 18, train loss: 0.46095, val loss: 0.45188, in 0.016s
1 tree, 80 leaves, max depth = 12, train loss: 0.45867, val loss: 0.44942, in 0.000s
1 tree, 123 leaves, max depth = 17, train loss: 0.45532, val loss: 0.44628, in 0.016s
1 tree, 79 leaves, max depth = 16, train loss: 0.45320, val loss: 0.44400, in 0.016s
[46/55] 1 tree, 123 leaves, max depth = 17, train loss: 0.45000, val loss: 0.44100, in 0.016s
1 tree, 123 leaves, max depth = 17, train loss: 0.44692, val loss: 0.43812, in 0.016s
1 tree, 80 leaves, max depth = 14, train loss: 0.44487, val loss: 0.43590, in 0.016s
1 tree, 123 leaves, max depth = 17, train loss: 0.44193, val loss: 0.43316, in 0.016s
1 tree, 123 leaves, max depth = 17, train loss: 0.43911, val loss: 0.43052, in 0.016s
1 tree, 79 leaves, max depth = 16, train loss: 0.43726, val loss: 0.42853, in 0.000s
1 tree, 123 leaves, max depth = 15, train loss: 0.43455, val loss: 0.42601, in 0.016s
1 tree, 123 leaves, max depth = 15, train loss: 0.43195, val loss: 0.42360, in 0.016s
1 tree, 80 leaves, max depth = 14, train loss: 0.43015, val loss: 0.42165, in 0.016s
1 tree, 123 leaves, max depth = 15, train loss: 0.42766, val loss: 0.41934, in 0.016s
Fit 55 trees in 1.049 s, (5042 total leaves)
Time spent computing histograms: 0.283s
Time spent finding best splits:  0.071s
Time spent applying splits:      0.085s
Time spent predicting:           0.000s
Trial 10, Fold 2: Log loss = 0.4291473245433425, Average precision = 0.9123761684258548, ROC-AUC = 0.9273861049408776, Elapsed Time = 1.0500720000000001 seconds
Trial 10, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 10, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
Binning 0.040 GB of training data: 0.174 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 72 leaves, max depth = 13, train loss: 0.68197, val loss: 0.68161, in 0.016s
1 tree, 79 leaves, max depth = 15, train loss: 0.67128, val loss: 0.67061, in 0.016s
1 tree, 79 leaves, max depth = 15, train loss: 0.66109, val loss: 0.66011, in 0.016s
1 tree, 77 leaves, max depth = 16, train loss: 0.65154, val loss: 0.65022, in 0.000s
1 tree, 79 leaves, max depth = 14, train loss: 0.64238, val loss: 0.64081, in 0.016s
1 tree, 79 leaves, max depth = 12, train loss: 0.63361, val loss: 0.63168, in 0.016s
1 tree, 77 leaves, max depth = 16, train loss: 0.62527, val loss: 0.62304, in 0.016s
1 tree, 77 leaves, max depth = 17, train loss: 0.61732, val loss: 0.61480, in 0.016s
1 tree, 79 leaves, max depth = 12, train loss: 0.60965, val loss: 0.60680, in 0.000s
1 tree, 79 leaves, max depth = 15, train loss: 0.60221, val loss: 0.59913, in 0.016s
1 tree, 79 leaves, max depth = 14, train loss: 0.59521, val loss: 0.59193, in 0.016s
1 tree, 79 leaves, max depth = 13, train loss: 0.58847, val loss: 0.58492, in 0.016s
1 tree, 79 leaves, max depth = 12, train loss: 0.58204, val loss: 0.57819, in 0.016s
1 tree, 79 leaves, max depth = 15, train loss: 0.57578, val loss: 0.57173, in 0.000s
1 tree, 79 leaves, max depth = 12, train loss: 0.56988, val loss: 0.56554, in 0.016s
1 tree, 79 leaves, max depth = 12, train loss: 0.56423, val loss: 0.55962, in 0.016s
1 tree, 79 leaves, max depth = 13, train loss: 0.55880, val loss: 0.55394, in 0.016s
1 tree, 78 leaves, max depth = 14, train loss: 0.55360, val loss: 0.54860, in 0.016s
1 tree, 77 leaves, max depth = 18, train loss: 0.54865, val loss: 0.54344, in 0.016s
1 tree, 79 leaves, max depth = 15, train loss: 0.54378, val loss: 0.53839, in 0.000s
1 tree, 79 leaves, max depth = 15, train loss: 0.53911, val loss: 0.53355, in 0.016s
1 tree, 79 leaves, max depth = 15, train loss: 0.53463, val loss: 0.52890, in 0.016s
1 tree, 120 leaves, max depth = 15, train loss: 0.52929, val loss: 0.52398, in 0.016s
1 tree, 78 leaves, max depth = 15, train loss: 0.52508, val loss: 0.51960, in 0.016s
1 tree, 79 leaves, max depth = 13, train loss: 0.52111, val loss: 0.51542, in 0.016s
1 tree, 120 leaves, max depth = 15, train loss: 0.51612, val loss: 0.51082, in 0.016s
1 tree, 120 leaves, max depth = 15, train loss: 0.51133, val loss: 0.50642, in 0.016s
1 tree, 78 leaves, max depth = 17, train loss: 0.50767, val loss: 0.50263, in 0.016s
1 tree, 120 leaves, max depth = 15, train loss: 0.50312, val loss: 0.49845, in 0.016s
1 tree, 120 leaves, max depth = 15, train loss: 0.49876, val loss: 0.49445, in 0.016s
1 tree, 73 leaves, max depth = 18, train loss: 0.49539, val loss: 0.49090, in 0.000s
1 tree, 79 leaves, max depth = 17, train loss: 0.49213, val loss: 0.48752, in 0.000s
1 tree, 79 leaves, max depth = 15, train loss: 0.48894, val loss: 0.48417, in 0.016s
1 tree, 120 leaves, max depth = 14, train loss: 0.48487, val loss: 0.48045, in 0.016s
1 tree, 79 leaves, max depth = 15, train loss: 0.48187, val loss: 0.47729, in 0.016s
1 tree, 78 leaves, max depth = 13, train loss: 0.47902, val loss: 0.47427, in 0.016s
1 tree, 79 leaves, max depth = 15, train loss: 0.47624, val loss: 0.47135, in 0.000s
1 tree, 120 leaves, max depth = 15, train loss: 0.47244, val loss: 0.46788, in 0.016s
1 tree, 79 leaves, max depth = 17, train loss: 0.46986, val loss: 0.46520, in 0.000s
1 tree, 79 leaves, max depth = 17, train loss: 0.46739, val loss: 0.46262, in 0.000s
1 tree, 79 leaves, max depth = 13, train loss: 0.46501, val loss: 0.46008, in 0.000s
1 tree, 120 leaves, max depth = 14, train loss: 0.46144, val loss: 0.45681, in 0.016s
1 tree, 78 leaves, max depth = 13, train loss: 0.45918, val loss: 0.45440, in 0.016s
1 tree, 120 leaves, max depth = 14, train loss: 0.45577, val loss: 0.45129, in 0.016s
1 tree, 78 leaves, max depth = 13, train loss: 0.45364, val loss: 0.44901, in 0.016s
1 tree, 120 leaves, max depth = 14, train loss: 0.45038, val loss: 0.44605, in 0.016s
1 tree, 120 leaves, max depth = 14, train loss: 0.44724, val loss: 0.44320, in 0.016s
1 tree, 78 leaves, max depth = 15, train loss: 0.44522, val loss: 0.44105, in 0.016s
1 tree, 120 leaves, max depth = 14, train loss: 0.44222, val loss: 0.43833, in 0.016s
1 tree, 120 leaves, max depth = 14, train loss: 0.43934, val loss: 0.43573, in 0.016s
1 tree, 78 leaves, max depth = 12, train loss: 0.43741, val loss: 0.43367, in 0.016s
1 tree, 120 leaves, max depth = 14, train loss: 0.43466, val loss: 0.43118, in 0.016s
1 tree, 120 leaves, max depth = 14, train loss: 0.43201, val loss: 0.42880, in 0.016s
1 tree, 79 leaves, max depth = 13, train loss: 0.43024, val loss: 0.42690, in 0.016s
1 tree, 120 leaves, max depth = 14, train loss: 0.42770, val loss: 0.42462, in 0.000s
Fit 55 trees in 1.065 s, (4972 total leaves)
Time spent computing histograms: 0.309s
Time spent finding best splits:  0.079s
Time spent applying splits:      0.095s
Time spent predicting:           0.000s
Trial 10, Fold 3: Log loss = 0.4249057059562685, Average precision = 0.9207502708543311, ROC-AUC = 0.9313444142404484, Elapsed Time = 1.0818947999996453 seconds
Trial 10, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 10, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
Binning 0.040 GB of training data: 0.173 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 75 leaves, max depth = 15, train loss: 0.68197, val loss: 0.68140, in 0.016s
1 tree, 79 leaves, max depth = 17, train loss: 0.67128, val loss: 0.67007, in 0.000s
1 tree, 79 leaves, max depth = 17, train loss: 0.66109, val loss: 0.65926, in 0.016s
1 tree, 79 leaves, max depth = 14, train loss: 0.65149, val loss: 0.64915, in 0.016s
1 tree, 79 leaves, max depth = 19, train loss: 0.64228, val loss: 0.63940, in 0.016s
1 tree, 79 leaves, max depth = 19, train loss: 0.63349, val loss: 0.63008, in 0.000s
1 tree, 78 leaves, max depth = 14, train loss: 0.62513, val loss: 0.62123, in 0.016s
1 tree, 75 leaves, max depth = 16, train loss: 0.61714, val loss: 0.61276, in 0.016s
1 tree, 79 leaves, max depth = 19, train loss: 0.60945, val loss: 0.60458, in 0.000s
1 tree, 78 leaves, max depth = 16, train loss: 0.60202, val loss: 0.59662, in 0.016s
1 tree, 80 leaves, max depth = 18, train loss: 0.59500, val loss: 0.58908, in 0.016s
1 tree, 79 leaves, max depth = 13, train loss: 0.58824, val loss: 0.58189, in 0.000s
1 tree, 79 leaves, max depth = 19, train loss: 0.58179, val loss: 0.57499, in 0.000s
1 tree, 78 leaves, max depth = 16, train loss: 0.57554, val loss: 0.56825, in 0.016s
1 tree, 78 leaves, max depth = 16, train loss: 0.56966, val loss: 0.56195, in 0.016s
1 tree, 78 leaves, max depth = 16, train loss: 0.56402, val loss: 0.55591, in 0.016s
1 tree, 79 leaves, max depth = 13, train loss: 0.55856, val loss: 0.55007, in 0.000s
1 tree, 79 leaves, max depth = 13, train loss: 0.55333, val loss: 0.54446, in 0.016s
1 tree, 74 leaves, max depth = 15, train loss: 0.54835, val loss: 0.53911, in 0.016s
1 tree, 78 leaves, max depth = 16, train loss: 0.54349, val loss: 0.53381, in 0.000s
1 tree, 78 leaves, max depth = 16, train loss: 0.53882, val loss: 0.52872, in 0.016s
1 tree, 79 leaves, max depth = 16, train loss: 0.53434, val loss: 0.52382, in 0.016s
1 tree, 124 leaves, max depth = 17, train loss: 0.52907, val loss: 0.51871, in 0.016s
1 tree, 79 leaves, max depth = 16, train loss: 0.52486, val loss: 0.51410, in 0.016s
1 tree, 79 leaves, max depth = 13, train loss: 0.52087, val loss: 0.50978, in 0.000s
1 tree, 124 leaves, max depth = 16, train loss: 0.51595, val loss: 0.50500, in 0.031s
1 tree, 124 leaves, max depth = 16, train loss: 0.51122, val loss: 0.50043, in 0.016s
1 tree, 80 leaves, max depth = 16, train loss: 0.50754, val loss: 0.49637, in 0.000s
1 tree, 124 leaves, max depth = 13, train loss: 0.50306, val loss: 0.49203, in 0.031s
1 tree, 124 leaves, max depth = 13, train loss: 0.49877, val loss: 0.48787, in 0.016s
1 tree, 75 leaves, max depth = 14, train loss: 0.49537, val loss: 0.48416, in 0.000s
1 tree, 78 leaves, max depth = 13, train loss: 0.49199, val loss: 0.48048, in 0.016s
1 tree, 79 leaves, max depth = 13, train loss: 0.48879, val loss: 0.47694, in 0.016s
1 tree, 124 leaves, max depth = 14, train loss: 0.48480, val loss: 0.47308, in 0.016s
1 tree, 79 leaves, max depth = 13, train loss: 0.48179, val loss: 0.46973, in 0.016s
1 tree, 79 leaves, max depth = 14, train loss: 0.47894, val loss: 0.46660, in 0.016s
1 tree, 79 leaves, max depth = 13, train loss: 0.47616, val loss: 0.46349, in 0.000s
1 tree, 124 leaves, max depth = 14, train loss: 0.47242, val loss: 0.45989, in 0.016s
1 tree, 78 leaves, max depth = 13, train loss: 0.46973, val loss: 0.45693, in 0.016s
1 tree, 80 leaves, max depth = 13, train loss: 0.46723, val loss: 0.45413, in 0.016s
1 tree, 79 leaves, max depth = 14, train loss: 0.46484, val loss: 0.45149, in 0.016s
1 tree, 124 leaves, max depth = 16, train loss: 0.46134, val loss: 0.44812, in 0.016s
1 tree, 78 leaves, max depth = 15, train loss: 0.45907, val loss: 0.44556, in 0.016s
1 tree, 124 leaves, max depth = 13, train loss: 0.45573, val loss: 0.44236, in 0.016s
1 tree, 78 leaves, max depth = 15, train loss: 0.45358, val loss: 0.43994, in 0.016s
1 tree, 124 leaves, max depth = 13, train loss: 0.45039, val loss: 0.43688, in 0.016s
1 tree, 124 leaves, max depth = 13, train loss: 0.44733, val loss: 0.43394, in 0.016s
1 tree, 79 leaves, max depth = 13, train loss: 0.44530, val loss: 0.43163, in 0.000s
1 tree, 124 leaves, max depth = 13, train loss: 0.44237, val loss: 0.42882, in 0.016s
1 tree, 124 leaves, max depth = 13, train loss: 0.43955, val loss: 0.42612, in 0.016s
1 tree, 78 leaves, max depth = 13, train loss: 0.43761, val loss: 0.42396, in 0.016s
1 tree, 124 leaves, max depth = 13, train loss: 0.43492, val loss: 0.42139, in 0.016s
1 tree, 124 leaves, max depth = 13, train loss: 0.43233, val loss: 0.41891, in 0.016s
1 tree, 79 leaves, max depth = 12, train loss: 0.43056, val loss: 0.41688, in 0.016s
1 tree, 124 leaves, max depth = 13, train loss: 0.42808, val loss: 0.41451, in 0.016s
Fit 55 trees in 1.017 s, (5039 total leaves)
Time spent computing histograms: 0.294s
Time spent finding best splits:  0.075s
Time spent applying splits:      0.091s
Time spent predicting:           0.000s
Trial 10, Fold 4: Log loss = 0.42822515332766903, Average precision = 0.9172057006370695, ROC-AUC = 0.9287372940764108, Elapsed Time = 1.0293785000012576 seconds
Trial 10, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 10, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
Binning 0.040 GB of training data: 0.158 s
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 78 leaves, max depth = 13, train loss: 0.68180, val loss: 0.68110, in 0.016s
1 tree, 78 leaves, max depth = 16, train loss: 0.67100, val loss: 0.66961, in 0.000s
1 tree, 79 leaves, max depth = 14, train loss: 0.66078, val loss: 0.65869, in 0.016s
1 tree, 79 leaves, max depth = 14, train loss: 0.65106, val loss: 0.64833, in 0.016s
1 tree, 78 leaves, max depth = 15, train loss: 0.64177, val loss: 0.63840, in 0.016s
1 tree, 78 leaves, max depth = 15, train loss: 0.63291, val loss: 0.62891, in 0.000s
1 tree, 77 leaves, max depth = 14, train loss: 0.62445, val loss: 0.61984, in 0.016s
1 tree, 77 leaves, max depth = 13, train loss: 0.61637, val loss: 0.61118, in 0.000s
1 tree, 79 leaves, max depth = 13, train loss: 0.60861, val loss: 0.60283, in 0.016s
1 tree, 79 leaves, max depth = 17, train loss: 0.60110, val loss: 0.59474, in 0.016s
1 tree, 78 leaves, max depth = 15, train loss: 0.59401, val loss: 0.58709, in 0.000s
1 tree, 79 leaves, max depth = 14, train loss: 0.58719, val loss: 0.57971, in 0.016s
1 tree, 79 leaves, max depth = 13, train loss: 0.58068, val loss: 0.57266, in 0.016s
1 tree, 79 leaves, max depth = 17, train loss: 0.57436, val loss: 0.56582, in 0.016s
1 tree, 78 leaves, max depth = 13, train loss: 0.56840, val loss: 0.55936, in 0.000s
1 tree, 78 leaves, max depth = 13, train loss: 0.56269, val loss: 0.55316, in 0.016s
1 tree, 79 leaves, max depth = 13, train loss: 0.55719, val loss: 0.54715, in 0.016s
1 tree, 79 leaves, max depth = 13, train loss: 0.55191, val loss: 0.54138, in 0.016s
1 tree, 77 leaves, max depth = 14, train loss: 0.54688, val loss: 0.53590, in 0.000s
1 tree, 79 leaves, max depth = 16, train loss: 0.54195, val loss: 0.53052, in 0.016s
1 tree, 79 leaves, max depth = 16, train loss: 0.53723, val loss: 0.52535, in 0.016s
1 tree, 79 leaves, max depth = 16, train loss: 0.53270, val loss: 0.52039, in 0.016s
1 tree, 122 leaves, max depth = 17, train loss: 0.52751, val loss: 0.51548, in 0.016s
1 tree, 78 leaves, max depth = 16, train loss: 0.52324, val loss: 0.51080, in 0.000s
1 tree, 79 leaves, max depth = 14, train loss: 0.51921, val loss: 0.50634, in 0.016s
1 tree, 122 leaves, max depth = 18, train loss: 0.51436, val loss: 0.50177, in 0.016s
1 tree, 122 leaves, max depth = 18, train loss: 0.50970, val loss: 0.49740, in 0.031s
1 tree, 78 leaves, max depth = 15, train loss: 0.50598, val loss: 0.49328, in 0.000s
1 tree, 122 leaves, max depth = 19, train loss: 0.50156, val loss: 0.48914, in 0.016s
1 tree, 122 leaves, max depth = 19, train loss: 0.49732, val loss: 0.48517, in 0.016s
1 tree, 78 leaves, max depth = 14, train loss: 0.49388, val loss: 0.48135, in 0.016s
1 tree, 77 leaves, max depth = 15, train loss: 0.49056, val loss: 0.47767, in 0.016s
1 tree, 79 leaves, max depth = 15, train loss: 0.48732, val loss: 0.47408, in 0.000s
1 tree, 122 leaves, max depth = 17, train loss: 0.48337, val loss: 0.47040, in 0.016s
1 tree, 79 leaves, max depth = 15, train loss: 0.48031, val loss: 0.46699, in 0.016s
1 tree, 77 leaves, max depth = 15, train loss: 0.47743, val loss: 0.46379, in 0.016s
1 tree, 79 leaves, max depth = 15, train loss: 0.47461, val loss: 0.46063, in 0.000s
1 tree, 122 leaves, max depth = 17, train loss: 0.47091, val loss: 0.45721, in 0.031s
1 tree, 78 leaves, max depth = 15, train loss: 0.46828, val loss: 0.45427, in 0.016s
1 tree, 78 leaves, max depth = 15, train loss: 0.46577, val loss: 0.45143, in 0.000s
1 tree, 78 leaves, max depth = 14, train loss: 0.46335, val loss: 0.44870, in 0.016s
1 tree, 122 leaves, max depth = 18, train loss: 0.45987, val loss: 0.44551, in 0.016s
1 tree, 78 leaves, max depth = 15, train loss: 0.45758, val loss: 0.44292, in 0.016s
1 tree, 122 leaves, max depth = 17, train loss: 0.45426, val loss: 0.43988, in 0.016s
1 tree, 78 leaves, max depth = 15, train loss: 0.45210, val loss: 0.43743, in 0.016s
1 tree, 122 leaves, max depth = 17, train loss: 0.44892, val loss: 0.43453, in 0.016s
1 tree, 122 leaves, max depth = 17, train loss: 0.44586, val loss: 0.43174, in 0.016s
1 tree, 79 leaves, max depth = 15, train loss: 0.44380, val loss: 0.42940, in 0.016s
1 tree, 122 leaves, max depth = 17, train loss: 0.44088, val loss: 0.42675, in 0.016s
1 tree, 122 leaves, max depth = 17, train loss: 0.43807, val loss: 0.42421, in 0.016s
1 tree, 78 leaves, max depth = 13, train loss: 0.43618, val loss: 0.42206, in 0.016s
1 tree, 122 leaves, max depth = 17, train loss: 0.43349, val loss: 0.41963, in 0.016s
1 tree, 122 leaves, max depth = 17, train loss: 0.43090, val loss: 0.41729, in 0.016s
1 tree, 79 leaves, max depth = 16, train loss: 0.42909, val loss: 0.41523, in 0.016s
1 tree, 122 leaves, max depth = 17, train loss: 0.42661, val loss: 0.41300, in 0.016s
Fit 55 trees in 1.018 s, (5007 total leaves)
Time spent computing histograms: 0.299s
Time spent finding best splits:  0.075s
Time spent applying splits:      0.090s
Time spent predicting:           0.016s
Trial 10, Fold 5: Log loss = 0.4337304957267745, Average precision = 0.912977732762567, ROC-AUC = 0.9249993497203797, Elapsed Time = 1.0223001000013028 seconds
Optimization Progress:  11%|#1        | 11/100 [02:15<18:14, 12.30s/it]
Trial 11, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 11, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
0.142 s 0.040 GB of training data: 
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 43 leaves, max depth = 11, train loss: 0.68296, val loss: 0.68266, in 0.016s
1 tree, 6 leaves, max depth = 4, train loss: 0.67751, val loss: 0.67678, in 0.000s
1 tree, 43 leaves, max depth = 12, train loss: 0.66797, val loss: 0.66700, in 0.000s
1 tree, 7 leaves, max depth = 5, train loss: 0.65892, val loss: 0.65761, in 0.000s
1 tree, 7 leaves, max depth = 5, train loss: 0.65025, val loss: 0.64861, in 0.000s
1 tree, 42 leaves, max depth = 13, train loss: 0.64185, val loss: 0.63997, in 0.016s
1 tree, 7 leaves, max depth = 5, train loss: 0.63387, val loss: 0.63168, in 0.000s
1 tree, 42 leaves, max depth = 13, train loss: 0.62624, val loss: 0.62380, in 0.016s
1 tree, 24 leaves, max depth = 8, train loss: 0.62171, val loss: 0.61895, in 0.000s
1 tree, 42 leaves, max depth = 14, train loss: 0.61435, val loss: 0.61134, in 0.016s
1 tree, 42 leaves, max depth = 14, train loss: 0.60746, val loss: 0.60426, in 0.000s
1 tree, 24 leaves, max depth = 8, train loss: 0.60343, val loss: 0.59992, in 0.016s
1 tree, 42 leaves, max depth = 13, train loss: 0.59686, val loss: 0.59314, in 0.000s
1 tree, 42 leaves, max depth = 11, train loss: 0.59066, val loss: 0.58673, in 0.016s
1 tree, 44 leaves, max depth = 12, train loss: 0.58468, val loss: 0.58055, in 0.000s
1 tree, 43 leaves, max depth = 11, train loss: 0.57893, val loss: 0.57461, in 0.016s
1 tree, 42 leaves, max depth = 12, train loss: 0.57362, val loss: 0.56912, in 0.016s
1 tree, 41 leaves, max depth = 13, train loss: 0.56826, val loss: 0.56351, in 0.000s
1 tree, 42 leaves, max depth = 11, train loss: 0.56314, val loss: 0.55820, in 0.016s
1 tree, 42 leaves, max depth = 14, train loss: 0.55802, val loss: 0.55290, in 0.000s
1 tree, 42 leaves, max depth = 14, train loss: 0.55310, val loss: 0.54779, in 0.016s
1 tree, 42 leaves, max depth = 14, train loss: 0.54836, val loss: 0.54288, in 0.000s
1 tree, 40 leaves, max depth = 10, train loss: 0.54417, val loss: 0.53856, in 0.016s
1 tree, 42 leaves, max depth = 14, train loss: 0.53976, val loss: 0.53398, in 0.000s
1 tree, 41 leaves, max depth = 13, train loss: 0.53564, val loss: 0.52965, in 0.016s
1 tree, 42 leaves, max depth = 12, train loss: 0.53161, val loss: 0.52549, in 0.016s
1 tree, 41 leaves, max depth = 13, train loss: 0.52779, val loss: 0.52146, in 0.000s
1 tree, 43 leaves, max depth = 14, train loss: 0.52413, val loss: 0.51767, in 0.016s
1 tree, 43 leaves, max depth = 11, train loss: 0.52061, val loss: 0.51400, in 0.000s
1 tree, 72 leaves, max depth = 11, train loss: 0.51605, val loss: 0.50979, in 0.016s
1 tree, 43 leaves, max depth = 14, train loss: 0.51271, val loss: 0.50633, in 0.016s
1 tree, 7 leaves, max depth = 5, train loss: 0.50949, val loss: 0.50290, in 0.000s
1 tree, 42 leaves, max depth = 13, train loss: 0.50631, val loss: 0.49959, in 0.016s
1 tree, 7 leaves, max depth = 5, train loss: 0.50331, val loss: 0.49640, in 0.000s
1 tree, 42 leaves, max depth = 14, train loss: 0.50029, val loss: 0.49323, in 0.016s
1 tree, 44 leaves, max depth = 13, train loss: 0.49752, val loss: 0.49034, in 0.016s
1 tree, 43 leaves, max depth = 14, train loss: 0.49484, val loss: 0.48756, in 0.000s
1 tree, 72 leaves, max depth = 11, train loss: 0.49072, val loss: 0.48378, in 0.016s
1 tree, 42 leaves, max depth = 14, train loss: 0.48811, val loss: 0.48107, in 0.000s
1 tree, 43 leaves, max depth = 14, train loss: 0.48568, val loss: 0.47853, in 0.016s
1 tree, 42 leaves, max depth = 14, train loss: 0.48325, val loss: 0.47600, in 0.000s
1 tree, 41 leaves, max depth = 10, train loss: 0.48095, val loss: 0.47358, in 0.016s
1 tree, 45 leaves, max depth = 13, train loss: 0.47877, val loss: 0.47128, in 0.016s
1 tree, 7 leaves, max depth = 5, train loss: 0.47664, val loss: 0.46898, in 0.000s
1 tree, 89 leaves, max depth = 14, train loss: 0.47393, val loss: 0.46651, in 0.016s
1 tree, 88 leaves, max depth = 10, train loss: 0.47132, val loss: 0.46414, in 0.016s
1 tree, 72 leaves, max depth = 11, train loss: 0.46768, val loss: 0.46083, in 0.000s
1 tree, 49 leaves, max depth = 13, train loss: 0.46576, val loss: 0.45898, in 0.016s
1 tree, 87 leaves, max depth = 10, train loss: 0.46338, val loss: 0.45684, in 0.016s
1 tree, 42 leaves, max depth = 15, train loss: 0.46146, val loss: 0.45483, in 0.016s
1 tree, 42 leaves, max depth = 10, train loss: 0.45964, val loss: 0.45290, in 0.000s
1 tree, 71 leaves, max depth = 11, train loss: 0.45630, val loss: 0.44988, in 0.016s
Fit 52 trees in 0.735 s, (2175 total leaves)
Time spent computing histograms: 0.228s
Time spent finding best splits:  0.036s
Time spent applying splits:      0.039s
Time spent predicting:           0.000s
Trial 11, Fold 1: Log loss = 0.45635692276042383, Average precision = 0.9041750786722952, ROC-AUC = 0.9112115870387099, Elapsed Time = 0.7370410000003176 seconds
Trial 11, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 11, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
Binning 0.040 GB of training data: 0.141 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 40 leaves, max depth = 11, train loss: 0.68307, val loss: 0.68258, in 0.016s
1 tree, 6 leaves, max depth = 4, train loss: 0.67755, val loss: 0.67652, in 0.000s
1 tree, 39 leaves, max depth = 12, train loss: 0.66803, val loss: 0.66655, in 0.016s
1 tree, 7 leaves, max depth = 5, train loss: 0.65895, val loss: 0.65703, in 0.000s
1 tree, 7 leaves, max depth = 5, train loss: 0.65025, val loss: 0.64790, in 0.016s
1 tree, 40 leaves, max depth = 13, train loss: 0.64190, val loss: 0.63917, in 0.000s
1 tree, 7 leaves, max depth = 5, train loss: 0.63391, val loss: 0.63077, in 0.016s
1 tree, 42 leaves, max depth = 12, train loss: 0.62624, val loss: 0.62275, in 0.000s
1 tree, 25 leaves, max depth = 9, train loss: 0.62168, val loss: 0.61800, in 0.016s
1 tree, 42 leaves, max depth = 11, train loss: 0.61436, val loss: 0.61029, in 0.000s
1 tree, 45 leaves, max depth = 19, train loss: 0.60745, val loss: 0.60302, in 0.016s
1 tree, 25 leaves, max depth = 9, train loss: 0.60339, val loss: 0.59878, in 0.000s
1 tree, 45 leaves, max depth = 14, train loss: 0.59688, val loss: 0.59195, in 0.016s
1 tree, 41 leaves, max depth = 11, train loss: 0.59069, val loss: 0.58541, in 0.000s
1 tree, 42 leaves, max depth = 12, train loss: 0.58469, val loss: 0.57912, in 0.016s
1 tree, 43 leaves, max depth = 12, train loss: 0.57892, val loss: 0.57303, in 0.016s
1 tree, 42 leaves, max depth = 10, train loss: 0.57365, val loss: 0.56751, in 0.000s
1 tree, 39 leaves, max depth = 13, train loss: 0.56830, val loss: 0.56187, in 0.016s
1 tree, 41 leaves, max depth = 11, train loss: 0.56318, val loss: 0.55645, in 0.000s
1 tree, 43 leaves, max depth = 10, train loss: 0.55811, val loss: 0.55106, in 0.016s
1 tree, 43 leaves, max depth = 10, train loss: 0.55324, val loss: 0.54588, in 0.016s
1 tree, 43 leaves, max depth = 10, train loss: 0.54855, val loss: 0.54089, in 0.000s
1 tree, 38 leaves, max depth = 16, train loss: 0.54433, val loss: 0.53648, in 0.016s
1 tree, 43 leaves, max depth = 10, train loss: 0.53997, val loss: 0.53183, in 0.016s
1 tree, 39 leaves, max depth = 12, train loss: 0.53585, val loss: 0.52748, in 0.016s
[26/52] 1 tree, 41 leaves, max depth = 11, train loss: 0.53185, val loss: 0.52323, in 0.000s
1 tree, 39 leaves, max depth = 12, train loss: 0.52804, val loss: 0.51920, in 0.016s
1 tree, 46 leaves, max depth = 19, train loss: 0.52436, val loss: 0.51528, in 0.000s
1 tree, 44 leaves, max depth = 14, train loss: 0.52083, val loss: 0.51151, in 0.016s
1 tree, 73 leaves, max depth = 15, train loss: 0.51626, val loss: 0.50714, in 0.016s
1 tree, 46 leaves, max depth = 19, train loss: 0.51291, val loss: 0.50357, in 0.000s
1 tree, 7 leaves, max depth = 5, train loss: 0.50967, val loss: 0.50011, in 0.016s
1 tree, 42 leaves, max depth = 11, train loss: 0.50652, val loss: 0.49674, in 0.000s
1 tree, 7 leaves, max depth = 5, train loss: 0.50351, val loss: 0.49351, in 0.016s
1 tree, 42 leaves, max depth = 10, train loss: 0.50054, val loss: 0.49031, in 0.000s
1 tree, 41 leaves, max depth = 16, train loss: 0.49775, val loss: 0.48735, in 0.016s
1 tree, 46 leaves, max depth = 19, train loss: 0.49507, val loss: 0.48448, in 0.016s
1 tree, 73 leaves, max depth = 14, train loss: 0.49093, val loss: 0.48054, in 0.016s
1 tree, 41 leaves, max depth = 11, train loss: 0.48835, val loss: 0.47779, in 0.000s
1 tree, 46 leaves, max depth = 19, train loss: 0.48591, val loss: 0.47516, in 0.016s
1 tree, 42 leaves, max depth = 11, train loss: 0.48351, val loss: 0.47260, in 0.016s
1 tree, 41 leaves, max depth = 11, train loss: 0.48121, val loss: 0.47012, in 0.000s
1 tree, 45 leaves, max depth = 17, train loss: 0.47903, val loss: 0.46776, in 0.016s
1 tree, 7 leaves, max depth = 5, train loss: 0.47690, val loss: 0.46545, in 0.016s
1 tree, 92 leaves, max depth = 11, train loss: 0.47423, val loss: 0.46300, in 0.016s
1 tree, 92 leaves, max depth = 11, train loss: 0.47166, val loss: 0.46066, in 0.016s
1 tree, 73 leaves, max depth = 13, train loss: 0.46801, val loss: 0.45720, in 0.000s
1 tree, 52 leaves, max depth = 12, train loss: 0.46599, val loss: 0.45540, in 0.000s
1 tree, 92 leaves, max depth = 11, train loss: 0.46366, val loss: 0.45327, in 0.016s
1 tree, 47 leaves, max depth = 15, train loss: 0.46176, val loss: 0.45122, in 0.016s
1 tree, 45 leaves, max depth = 13, train loss: 0.45994, val loss: 0.44925, in 0.016s
1 tree, 73 leaves, max depth = 13, train loss: 0.45659, val loss: 0.44610, in 0.000s
Fit 52 trees in 0.766 s, (2202 total leaves)
Time spent computing histograms: 0.251s
Time spent finding best splits:  0.040s
Time spent applying splits:      0.045s
Time spent predicting:           0.000s
Trial 11, Fold 2: Log loss = 0.45779518972191213, Average precision = 0.9013062927885805, ROC-AUC = 0.9160451583199821, Elapsed Time = 0.7835273000000598 seconds
Trial 11, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 11, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
Binning 0.040 GB of training data: 0.174 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 40 leaves, max depth = 11, train loss: 0.68304, val loss: 0.68274, in 0.016s
1 tree, 6 leaves, max depth = 4, train loss: 0.67750, val loss: 0.67707, in 0.000s
1 tree, 42 leaves, max depth = 10, train loss: 0.66806, val loss: 0.66726, in 0.016s
1 tree, 7 leaves, max depth = 4, train loss: 0.65903, val loss: 0.65791, in 0.000s
1 tree, 7 leaves, max depth = 4, train loss: 0.65039, val loss: 0.64895, in 0.016s
1 tree, 41 leaves, max depth = 12, train loss: 0.64211, val loss: 0.64041, in 0.016s
1 tree, 7 leaves, max depth = 4, train loss: 0.63416, val loss: 0.63216, in 0.000s
1 tree, 44 leaves, max depth = 16, train loss: 0.62657, val loss: 0.62429, in 0.016s
1 tree, 24 leaves, max depth = 8, train loss: 0.62201, val loss: 0.61967, in 0.000s
1 tree, 45 leaves, max depth = 11, train loss: 0.61473, val loss: 0.61214, in 0.016s
1 tree, 44 leaves, max depth = 11, train loss: 0.60788, val loss: 0.60504, in 0.016s
1 tree, 23 leaves, max depth = 7, train loss: 0.60381, val loss: 0.60092, in 0.000s
1 tree, 40 leaves, max depth = 9, train loss: 0.59732, val loss: 0.59412, in 0.016s
1 tree, 39 leaves, max depth = 10, train loss: 0.59117, val loss: 0.58775, in 0.000s
1 tree, 43 leaves, max depth = 10, train loss: 0.58523, val loss: 0.58155, in 0.016s
1 tree, 44 leaves, max depth = 14, train loss: 0.57952, val loss: 0.57561, in 0.016s
1 tree, 41 leaves, max depth = 11, train loss: 0.57426, val loss: 0.57013, in 0.000s
1 tree, 42 leaves, max depth = 12, train loss: 0.56894, val loss: 0.56462, in 0.016s
1 tree, 39 leaves, max depth = 10, train loss: 0.56386, val loss: 0.55934, in 0.000s
1 tree, 45 leaves, max depth = 11, train loss: 0.55882, val loss: 0.55411, in 0.016s
1 tree, 45 leaves, max depth = 11, train loss: 0.55397, val loss: 0.54907, in 0.016s
1 tree, 45 leaves, max depth = 10, train loss: 0.54931, val loss: 0.54422, in 0.000s
1 tree, 39 leaves, max depth = 10, train loss: 0.54515, val loss: 0.53987, in 0.016s
1 tree, 45 leaves, max depth = 10, train loss: 0.54081, val loss: 0.53535, in 0.016s
1 tree, 42 leaves, max depth = 12, train loss: 0.53672, val loss: 0.53110, in 0.000s
1 tree, 42 leaves, max depth = 11, train loss: 0.53275, val loss: 0.52693, in 0.016s
1 tree, 41 leaves, max depth = 12, train loss: 0.52896, val loss: 0.52298, in 0.016s
1 tree, 44 leaves, max depth = 15, train loss: 0.52533, val loss: 0.51920, in 0.000s
1 tree, 44 leaves, max depth = 15, train loss: 0.52183, val loss: 0.51553, in 0.016s
1 tree, 70 leaves, max depth = 13, train loss: 0.51720, val loss: 0.51122, in 0.016s
1 tree, 43 leaves, max depth = 15, train loss: 0.51390, val loss: 0.50776, in 0.000s
1 tree, 7 leaves, max depth = 5, train loss: 0.51068, val loss: 0.50438, in 0.016s
1 tree, 42 leaves, max depth = 9, train loss: 0.50756, val loss: 0.50107, in 0.016s
1 tree, 7 leaves, max depth = 5, train loss: 0.50457, val loss: 0.49792, in 0.000s
1 tree, 45 leaves, max depth = 10, train loss: 0.50161, val loss: 0.49482, in 0.016s
1 tree, 43 leaves, max depth = 14, train loss: 0.49886, val loss: 0.49192, in 0.000s
1 tree, 44 leaves, max depth = 15, train loss: 0.49621, val loss: 0.48915, in 0.016s
1 tree, 70 leaves, max depth = 12, train loss: 0.49202, val loss: 0.48526, in 0.016s
1 tree, 40 leaves, max depth = 11, train loss: 0.48947, val loss: 0.48255, in 0.000s
1 tree, 44 leaves, max depth = 14, train loss: 0.48705, val loss: 0.48000, in 0.016s
1 tree, 40 leaves, max depth = 11, train loss: 0.48467, val loss: 0.47747, in 0.016s
1 tree, 42 leaves, max depth = 11, train loss: 0.48240, val loss: 0.47507, in 0.000s
1 tree, 42 leaves, max depth = 14, train loss: 0.48024, val loss: 0.47280, in 0.016s
1 tree, 7 leaves, max depth = 5, train loss: 0.47812, val loss: 0.47055, in 0.000s
1 tree, 96 leaves, max depth = 10, train loss: 0.47541, val loss: 0.46812, in 0.016s
1 tree, 96 leaves, max depth = 10, train loss: 0.47280, val loss: 0.46579, in 0.016s
1 tree, 70 leaves, max depth = 11, train loss: 0.46911, val loss: 0.46238, in 0.016s
1 tree, 51 leaves, max depth = 12, train loss: 0.46713, val loss: 0.46059, in 0.016s
1 tree, 96 leaves, max depth = 12, train loss: 0.46475, val loss: 0.45850, in 0.016s
1 tree, 43 leaves, max depth = 17, train loss: 0.46288, val loss: 0.45652, in 0.016s
1 tree, 43 leaves, max depth = 13, train loss: 0.46108, val loss: 0.45462, in 0.000s
1 tree, 71 leaves, max depth = 11, train loss: 0.45769, val loss: 0.45150, in 0.016s
Fit 52 trees in 0.831 s, (2202 total leaves)
Time spent computing histograms: 0.260s
Time spent finding best splits:  0.041s
Time spent applying splits:      0.046s
Time spent predicting:           0.000s
Trial 11, Fold 3: Log loss = 0.4524066912549197, Average precision = 0.9099322068591883, ROC-AUC = 0.9202060469657424, Elapsed Time = 0.8413478000002215 seconds
Trial 11, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 11, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
Binning 0.040 GB of training data: 0.174 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 40 leaves, max depth = 12, train loss: 0.68307, val loss: 0.68251, in 0.016s
1 tree, 6 leaves, max depth = 4, train loss: 0.67749, val loss: 0.67657, in 0.000s
1 tree, 41 leaves, max depth = 12, train loss: 0.66801, val loss: 0.66657, in 0.016s
1 tree, 6 leaves, max depth = 4, train loss: 0.65898, val loss: 0.65704, in 0.000s
1 tree, 6 leaves, max depth = 4, train loss: 0.65034, val loss: 0.64791, in 0.016s
1 tree, 39 leaves, max depth = 11, train loss: 0.64199, val loss: 0.63907, in 0.000s
1 tree, 7 leaves, max depth = 5, train loss: 0.63404, val loss: 0.63064, in 0.016s
1 tree, 43 leaves, max depth = 11, train loss: 0.62641, val loss: 0.62254, in 0.000s
1 tree, 27 leaves, max depth = 9, train loss: 0.62181, val loss: 0.61779, in 0.016s
1 tree, 40 leaves, max depth = 12, train loss: 0.61454, val loss: 0.61001, in 0.000s
1 tree, 41 leaves, max depth = 11, train loss: 0.60766, val loss: 0.60268, in 0.016s
1 tree, 27 leaves, max depth = 9, train loss: 0.60356, val loss: 0.59843, in 0.000s
1 tree, 40 leaves, max depth = 11, train loss: 0.59704, val loss: 0.59148, in 0.016s
1 tree, 38 leaves, max depth = 11, train loss: 0.59088, val loss: 0.58488, in 0.016s
1 tree, 41 leaves, max depth = 12, train loss: 0.58490, val loss: 0.57850, in 0.000s
1 tree, 39 leaves, max depth = 11, train loss: 0.57917, val loss: 0.57234, in 0.016s
1 tree, 37 leaves, max depth = 10, train loss: 0.57396, val loss: 0.56663, in 0.016s
1 tree, 40 leaves, max depth = 11, train loss: 0.56865, val loss: 0.56091, in 0.000s
1 tree, 39 leaves, max depth = 11, train loss: 0.56356, val loss: 0.55542, in 0.016s
1 tree, 39 leaves, max depth = 12, train loss: 0.55852, val loss: 0.54996, in 0.016s
1 tree, 39 leaves, max depth = 12, train loss: 0.55367, val loss: 0.54469, in 0.000s
1 tree, 39 leaves, max depth = 12, train loss: 0.54900, val loss: 0.53962, in 0.016s
1 tree, 41 leaves, max depth = 10, train loss: 0.54479, val loss: 0.53504, in 0.016s
1 tree, 39 leaves, max depth = 12, train loss: 0.54045, val loss: 0.53031, in 0.000s
1 tree, 40 leaves, max depth = 15, train loss: 0.53636, val loss: 0.52582, in 0.016s
1 tree, 41 leaves, max depth = 11, train loss: 0.53238, val loss: 0.52150, in 0.016s
1 tree, 40 leaves, max depth = 11, train loss: 0.52859, val loss: 0.51735, in 0.000s
1 tree, 42 leaves, max depth = 11, train loss: 0.52493, val loss: 0.51336, in 0.016s
1 tree, 40 leaves, max depth = 11, train loss: 0.52142, val loss: 0.50950, in 0.016s
1 tree, 72 leaves, max depth = 15, train loss: 0.51677, val loss: 0.50500, in 0.000s
1 tree, 41 leaves, max depth = 11, train loss: 0.51344, val loss: 0.50134, in 0.016s
1 tree, 7 leaves, max depth = 5, train loss: 0.51023, val loss: 0.49782, in 0.000s
1 tree, 42 leaves, max depth = 11, train loss: 0.50709, val loss: 0.49436, in 0.016s
1 tree, 7 leaves, max depth = 5, train loss: 0.50411, val loss: 0.49108, in 0.000s
1 tree, 40 leaves, max depth = 12, train loss: 0.50114, val loss: 0.48779, in 0.016s
1 tree, 40 leaves, max depth = 11, train loss: 0.49837, val loss: 0.48474, in 0.016s
1 tree, 42 leaves, max depth = 11, train loss: 0.49570, val loss: 0.48176, in 0.000s
1 tree, 72 leaves, max depth = 15, train loss: 0.49149, val loss: 0.47770, in 0.016s
1 tree, 41 leaves, max depth = 11, train loss: 0.48892, val loss: 0.47486, in 0.016s
1 tree, 42 leaves, max depth = 11, train loss: 0.48649, val loss: 0.47214, in 0.016s
1 tree, 41 leaves, max depth = 11, train loss: 0.48410, val loss: 0.46950, in 0.000s
1 tree, 39 leaves, max depth = 11, train loss: 0.48183, val loss: 0.46695, in 0.016s
1 tree, 42 leaves, max depth = 12, train loss: 0.47965, val loss: 0.46451, in 0.016s
1 tree, 7 leaves, max depth = 5, train loss: 0.47754, val loss: 0.46214, in 0.000s
1 tree, 93 leaves, max depth = 11, train loss: 0.47488, val loss: 0.45964, in 0.016s
1 tree, 93 leaves, max depth = 11, train loss: 0.47234, val loss: 0.45723, in 0.016s
1 tree, 72 leaves, max depth = 15, train loss: 0.46862, val loss: 0.45367, in 0.016s
1 tree, 51 leaves, max depth = 10, train loss: 0.46660, val loss: 0.45167, in 0.016s
1 tree, 94 leaves, max depth = 11, train loss: 0.46428, val loss: 0.44948, in 0.016s
1 tree, 41 leaves, max depth = 11, train loss: 0.46240, val loss: 0.44734, in 0.000s
1 tree, 43 leaves, max depth = 12, train loss: 0.46058, val loss: 0.44528, in 0.016s
1 tree, 71 leaves, max depth = 15, train loss: 0.45717, val loss: 0.44201, in 0.031s
Fit 52 trees in 0.846 s, (2130 total leaves)
Time spent computing histograms: 0.263s
Time spent finding best splits:  0.047s
Time spent applying splits:      0.051s
Time spent predicting:           0.000s
Trial 11, Fold 4: Log loss = 0.45743982263479455, Average precision = 0.9038198909903736, ROC-AUC = 0.9152758106397132, Elapsed Time = 0.8614232999989326 seconds
Trial 11, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 11, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
Binning 0.040 GB of training data: 0.158 s
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 39 leaves, max depth = 13, train loss: 0.68291, val loss: 0.68219, in 0.016s
1 tree, 6 leaves, max depth = 4, train loss: 0.67746, val loss: 0.67617, in 0.000s
1 tree, 42 leaves, max depth = 11, train loss: 0.66789, val loss: 0.66593, in 0.016s
1 tree, 7 leaves, max depth = 5, train loss: 0.65874, val loss: 0.65617, in 0.000s
1 tree, 7 leaves, max depth = 5, train loss: 0.64997, val loss: 0.64681, in 0.016s
1 tree, 41 leaves, max depth = 11, train loss: 0.64158, val loss: 0.63786, in 0.000s
1 tree, 7 leaves, max depth = 5, train loss: 0.63352, val loss: 0.62923, in 0.016s
1 tree, 45 leaves, max depth = 12, train loss: 0.62581, val loss: 0.62098, in 0.000s
1 tree, 25 leaves, max depth = 9, train loss: 0.62129, val loss: 0.61613, in 0.016s
1 tree, 43 leaves, max depth = 14, train loss: 0.61394, val loss: 0.60821, in 0.016s
1 tree, 43 leaves, max depth = 12, train loss: 0.60697, val loss: 0.60075, in 0.000s
1 tree, 26 leaves, max depth = 9, train loss: 0.60294, val loss: 0.59641, in 0.016s
1 tree, 44 leaves, max depth = 12, train loss: 0.59636, val loss: 0.58931, in 0.000s
1 tree, 39 leaves, max depth = 11, train loss: 0.59012, val loss: 0.58251, in 0.016s
1 tree, 44 leaves, max depth = 12, train loss: 0.58407, val loss: 0.57600, in 0.016s
1 tree, 41 leaves, max depth = 11, train loss: 0.57825, val loss: 0.56971, in 0.000s
1 tree, 41 leaves, max depth = 10, train loss: 0.57295, val loss: 0.56397, in 0.016s
1 tree, 40 leaves, max depth = 11, train loss: 0.56756, val loss: 0.55816, in 0.016s
1 tree, 39 leaves, max depth = 11, train loss: 0.56241, val loss: 0.55250, in 0.000s
1 tree, 43 leaves, max depth = 14, train loss: 0.55731, val loss: 0.54694, in 0.016s
1 tree, 43 leaves, max depth = 14, train loss: 0.55241, val loss: 0.54159, in 0.016s
1 tree, 43 leaves, max depth = 14, train loss: 0.54769, val loss: 0.53643, in 0.000s
1 tree, 38 leaves, max depth = 9, train loss: 0.54345, val loss: 0.53178, in 0.016s
1 tree, 43 leaves, max depth = 14, train loss: 0.53906, val loss: 0.52696, in 0.000s
1 tree, 40 leaves, max depth = 11, train loss: 0.53492, val loss: 0.52244, in 0.016s
1 tree, 42 leaves, max depth = 12, train loss: 0.53089, val loss: 0.51800, in 0.016s
1 tree, 40 leaves, max depth = 11, train loss: 0.52706, val loss: 0.51380, in 0.000s
1 tree, 44 leaves, max depth = 12, train loss: 0.52335, val loss: 0.50973, in 0.016s
1 tree, 41 leaves, max depth = 12, train loss: 0.51978, val loss: 0.50580, in 0.016s
1 tree, 72 leaves, max depth = 14, train loss: 0.51520, val loss: 0.50151, in 0.016s
1 tree, 44 leaves, max depth = 12, train loss: 0.51182, val loss: 0.49779, in 0.000s
1 tree, 7 leaves, max depth = 5, train loss: 0.50856, val loss: 0.49416, in 0.016s
1 tree, 42 leaves, max depth = 12, train loss: 0.50540, val loss: 0.49065, in 0.000s
1 tree, 7 leaves, max depth = 5, train loss: 0.50237, val loss: 0.48726, in 0.016s
1 tree, 43 leaves, max depth = 14, train loss: 0.49937, val loss: 0.48391, in 0.000s
1 tree, 44 leaves, max depth = 12, train loss: 0.49657, val loss: 0.48079, in 0.016s
1 tree, 44 leaves, max depth = 12, train loss: 0.49386, val loss: 0.47777, in 0.016s
1 tree, 73 leaves, max depth = 13, train loss: 0.48971, val loss: 0.47392, in 0.016s
1 tree, 42 leaves, max depth = 12, train loss: 0.48712, val loss: 0.47101, in 0.000s
1 tree, 44 leaves, max depth = 12, train loss: 0.48465, val loss: 0.46824, in 0.016s
1 tree, 42 leaves, max depth = 12, train loss: 0.48225, val loss: 0.46553, in 0.016s
[42/52] 1 tree, 39 leaves, max depth = 11, train loss: 0.47995, val loss: 0.46291, in 0.000s
1 tree, 44 leaves, max depth = 13, train loss: 0.47774, val loss: 0.46042, in 0.016s
[44/52] 1 tree, 7 leaves, max depth = 5, train loss: 0.47559, val loss: 0.45797, in 0.000s
1 tree, 93 leaves, max depth = 10, train loss: 0.47296, val loss: 0.45572, in 0.031s
1 tree, 93 leaves, max depth = 10, train loss: 0.47044, val loss: 0.45356, in 0.016s
1 tree, 73 leaves, max depth = 13, train loss: 0.46678, val loss: 0.45019, in 0.000s
1 tree, 53 leaves, max depth = 10, train loss: 0.46480, val loss: 0.44834, in 0.016s
1 tree, 41 leaves, max depth = 12, train loss: 0.46286, val loss: 0.44614, in 0.016s
1 tree, 44 leaves, max depth = 12, train loss: 0.46098, val loss: 0.44400, in 0.016s
1 tree, 45 leaves, max depth = 12, train loss: 0.45919, val loss: 0.44194, in 0.000s
1 tree, 72 leaves, max depth = 13, train loss: 0.45577, val loss: 0.43881, in 0.016s
Fit 52 trees in 0.845 s, (2144 total leaves)
Time spent computing histograms: 0.254s
Time spent finding best splits:  0.043s
Time spent applying splits:      0.045s
Time spent predicting:           0.000s
Trial 11, Fold 5: Log loss = 0.46344583690742613, Average precision = 0.8988737071441618, ROC-AUC = 0.9086291734017056, Elapsed Time = 0.8489270999998553 seconds
Optimization Progress:  12%|#2        | 12/100 [02:26<17:21, 11.83s/it]
Trial 12, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 12, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
0.142 s 0.040 GB of training data: 
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 13 leaves, max depth = 7, train loss: 0.67249, val loss: 0.67184, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.66180, val loss: 0.66028, in 0.016s
1 tree, 17 leaves, max depth = 8, train loss: 0.64349, val loss: 0.64145, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.62687, val loss: 0.62418, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.61165, val loss: 0.60833, in 0.000s
1 tree, 14 leaves, max depth = 9, train loss: 0.59754, val loss: 0.59373, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.58471, val loss: 0.58032, in 0.000s
1 tree, 16 leaves, max depth = 8, train loss: 0.57300, val loss: 0.56817, in 0.016s
1 tree, 34 leaves, max depth = 7, train loss: 0.56509, val loss: 0.56073, in 0.000s
1 tree, 26 leaves, max depth = 10, train loss: 0.56039, val loss: 0.55528, in 0.016s
1 tree, 18 leaves, max depth = 10, train loss: 0.55022, val loss: 0.54472, in 0.000s
1 tree, 30 leaves, max depth = 9, train loss: 0.54603, val loss: 0.53972, in 0.016s
1 tree, 17 leaves, max depth = 10, train loss: 0.53694, val loss: 0.53028, in 0.000s
1 tree, 17 leaves, max depth = 8, train loss: 0.52853, val loss: 0.52149, in 0.016s
1 tree, 16 leaves, max depth = 9, train loss: 0.52079, val loss: 0.51342, in 0.000s
1 tree, 18 leaves, max depth = 9, train loss: 0.51364, val loss: 0.50592, in 0.016s
Fit 16 trees in 0.407 s, (256 total leaves)
Time spent computing histograms: 0.073s
Time spent finding best splits:  0.007s
Time spent applying splits:      0.006s
Time spent predicting:           0.000s
Trial 12, Fold 1: Log loss = 0.5121890687203764, Average precision = 0.8792070370375461, ROC-AUC = 0.8829421836987328, Elapsed Time = 0.41822590000083437 seconds
Trial 12, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 12, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
0.158 s 0.040 GB of training data: 
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 20 leaves, max depth = 8, train loss: 0.67249, val loss: 0.67142, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.66164, val loss: 0.65946, in 0.000s
1 tree, 15 leaves, max depth = 7, train loss: 0.64336, val loss: 0.64029, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.62670, val loss: 0.62279, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.61144, val loss: 0.60674, in 0.000s
1 tree, 16 leaves, max depth = 7, train loss: 0.59742, val loss: 0.59188, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.58454, val loss: 0.57830, in 0.000s
1 tree, 16 leaves, max depth = 8, train loss: 0.57280, val loss: 0.56587, in 0.016s
1 tree, 42 leaves, max depth = 8, train loss: 0.56496, val loss: 0.55839, in 0.000s
1 tree, 30 leaves, max depth = 10, train loss: 0.56033, val loss: 0.55325, in 0.016s
1 tree, 21 leaves, max depth = 13, train loss: 0.55010, val loss: 0.54242, in 0.000s
1 tree, 29 leaves, max depth = 9, train loss: 0.54594, val loss: 0.53788, in 0.016s
1 tree, 21 leaves, max depth = 13, train loss: 0.53681, val loss: 0.52818, in 0.000s
1 tree, 21 leaves, max depth = 8, train loss: 0.52843, val loss: 0.51920, in 0.016s
1 tree, 17 leaves, max depth = 8, train loss: 0.52066, val loss: 0.51091, in 0.000s
1 tree, 19 leaves, max depth = 9, train loss: 0.51347, val loss: 0.50322, in 0.016s
Fit 16 trees in 0.423 s, (287 total leaves)
Time spent computing histograms: 0.068s
Time spent finding best splits:  0.007s
Time spent applying splits:      0.006s
Time spent predicting:           0.000s
Trial 12, Fold 2: Log loss = 0.5132259896944191, Average precision = 0.8776855526440213, ROC-AUC = 0.8887734256850123, Elapsed Time = 0.4338880999985122 seconds
Trial 12, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 12, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
Binning 0.040 GB of training data: 0.143 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 15 leaves, max depth = 8, train loss: 0.67258, val loss: 0.67186, in 0.000s
1 tree, 6 leaves, max depth = 4, train loss: 0.66170, val loss: 0.66070, in 0.000s
1 tree, 15 leaves, max depth = 8, train loss: 0.64355, val loss: 0.64184, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.62700, val loss: 0.62466, in 0.000s
1 tree, 6 leaves, max depth = 4, train loss: 0.61184, val loss: 0.60891, in 0.016s
1 tree, 18 leaves, max depth = 9, train loss: 0.59796, val loss: 0.59448, in 0.000s
1 tree, 6 leaves, max depth = 4, train loss: 0.58518, val loss: 0.58117, in 0.000s
1 tree, 21 leaves, max depth = 10, train loss: 0.57352, val loss: 0.56894, in 0.016s
1 tree, 39 leaves, max depth = 7, train loss: 0.56559, val loss: 0.56164, in 0.000s
1 tree, 29 leaves, max depth = 11, train loss: 0.56103, val loss: 0.55613, in 0.016s
1 tree, 22 leaves, max depth = 11, train loss: 0.55092, val loss: 0.54549, in 0.000s
1 tree, 31 leaves, max depth = 12, train loss: 0.54682, val loss: 0.54073, in 0.016s
1 tree, 23 leaves, max depth = 12, train loss: 0.53780, val loss: 0.53121, in 0.000s
1 tree, 17 leaves, max depth = 7, train loss: 0.52948, val loss: 0.52248, in 0.000s
1 tree, 20 leaves, max depth = 11, train loss: 0.52178, val loss: 0.51434, in 0.016s
1 tree, 24 leaves, max depth = 11, train loss: 0.51468, val loss: 0.50681, in 0.000s
Fit 16 trees in 0.393 s, (297 total leaves)
Time spent computing histograms: 0.060s
Time spent finding best splits:  0.006s
Time spent applying splits:      0.006s
Time spent predicting:           0.000s
Trial 12, Fold 3: Log loss = 0.509757626563093, Average precision = 0.8850018372669946, ROC-AUC = 0.8930221495747899, Elapsed Time = 0.39575520000107645 seconds
Trial 12, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 12, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
0.126 s 0.040 GB of training data: 
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 11 leaves, max depth = 6, train loss: 0.67255, val loss: 0.67132, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.66159, val loss: 0.65961, in 0.000s
1 tree, 15 leaves, max depth = 8, train loss: 0.64345, val loss: 0.64035, in 0.016s
1 tree, 6 leaves, max depth = 4, train loss: 0.62691, val loss: 0.62281, in 0.000s
1 tree, 6 leaves, max depth = 4, train loss: 0.61175, val loss: 0.60669, in 0.000s
1 tree, 19 leaves, max depth = 11, train loss: 0.59786, val loss: 0.59179, in 0.016s
1 tree, 6 leaves, max depth = 4, train loss: 0.58509, val loss: 0.57813, in 0.000s
1 tree, 22 leaves, max depth = 9, train loss: 0.57336, val loss: 0.56553, in 0.016s
1 tree, 38 leaves, max depth = 8, train loss: 0.56555, val loss: 0.55778, in 0.000s
1 tree, 27 leaves, max depth = 11, train loss: 0.56087, val loss: 0.55270, in 0.016s
1 tree, 17 leaves, max depth = 9, train loss: 0.55072, val loss: 0.54175, in 0.000s
1 tree, 30 leaves, max depth = 10, train loss: 0.54660, val loss: 0.53713, in 0.000s
1 tree, 17 leaves, max depth = 9, train loss: 0.53752, val loss: 0.52731, in 0.000s
1 tree, 19 leaves, max depth = 9, train loss: 0.52917, val loss: 0.51816, in 0.000s
1 tree, 22 leaves, max depth = 9, train loss: 0.52143, val loss: 0.50968, in 0.016s
1 tree, 23 leaves, max depth = 9, train loss: 0.51428, val loss: 0.50184, in 0.000s
Fit 16 trees in 0.360 s, (283 total leaves)
Time spent computing histograms: 0.062s
Time spent finding best splits:  0.007s
Time spent applying splits:      0.006s
Time spent predicting:           0.000s
Trial 12, Fold 4: Log loss = 0.5130938883208326, Average precision = 0.8756620847959189, ROC-AUC = 0.8863719953456319, Elapsed Time = 0.37896379999983765 seconds
Trial 12, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 12, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
0.142 s 0.040 GB of training data: 
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 14 leaves, max depth = 7, train loss: 0.67229, val loss: 0.67077, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.66158, val loss: 0.65890, in 0.000s
1 tree, 11 leaves, max depth = 6, train loss: 0.64322, val loss: 0.63926, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.62642, val loss: 0.62127, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.61104, val loss: 0.60474, in 0.000s
1 tree, 15 leaves, max depth = 8, train loss: 0.59699, val loss: 0.58945, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.58402, val loss: 0.57542, in 0.000s
1 tree, 23 leaves, max depth = 9, train loss: 0.57218, val loss: 0.56244, in 0.000s
1 tree, 45 leaves, max depth = 7, train loss: 0.56445, val loss: 0.55537, in 0.016s
1 tree, 26 leaves, max depth = 10, train loss: 0.55972, val loss: 0.55005, in 0.000s
1 tree, 17 leaves, max depth = 9, train loss: 0.54943, val loss: 0.53871, in 0.016s
1 tree, 27 leaves, max depth = 9, train loss: 0.54517, val loss: 0.53409, in 0.000s
1 tree, 18 leaves, max depth = 10, train loss: 0.53597, val loss: 0.52391, in 0.016s
1 tree, 15 leaves, max depth = 7, train loss: 0.52752, val loss: 0.51448, in 0.000s
1 tree, 19 leaves, max depth = 11, train loss: 0.51969, val loss: 0.50570, in 0.000s
1 tree, 19 leaves, max depth = 12, train loss: 0.51245, val loss: 0.49755, in 0.016s
Fit 16 trees in 0.376 s, (269 total leaves)
Time spent computing histograms: 0.058s
Time spent finding best splits:  0.006s
Time spent applying splits:      0.006s
Time spent predicting:           0.000s
Trial 12, Fold 5: Log loss = 0.5175040686108638, Average precision = 0.8760296722679743, ROC-AUC = 0.8810527841257455, Elapsed Time = 0.38353559999995923 seconds
Optimization Progress:  13%|#3        | 13/100 [02:35<15:56, 11.00s/it]
Trial 13, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 13, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
0.157 s 0.040 GB of training data: 
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 6 leaves, max depth = 4, train loss: 0.67771, val loss: 0.67720, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.66296, val loss: 0.66204, in 0.000s
1 tree, 5 leaves, max depth = 4, train loss: 0.64917, val loss: 0.64781, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.63630, val loss: 0.63442, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.62422, val loss: 0.62195, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.61289, val loss: 0.61025, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.60240, val loss: 0.59937, in 0.000s
1 tree, 4 leaves, max depth = 3, train loss: 0.59256, val loss: 0.58915, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.58317, val loss: 0.57942, in 0.016s
1 tree, 7 leaves, max depth = 4, train loss: 0.57418, val loss: 0.57006, in 0.000s
1 tree, 4 leaves, max depth = 3, train loss: 0.56596, val loss: 0.56146, in 0.000s
1 tree, 4 leaves, max depth = 3, train loss: 0.55827, val loss: 0.55344, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.55091, val loss: 0.54577, in 0.000s
1 tree, 6 leaves, max depth = 4, train loss: 0.54406, val loss: 0.53859, in 0.016s
1 tree, 6 leaves, max depth = 4, train loss: 0.53752, val loss: 0.53176, in 0.000s
1 tree, 21 leaves, max depth = 10, train loss: 0.53026, val loss: 0.52504, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.52424, val loss: 0.51875, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.51857, val loss: 0.51282, in 0.000s
1 tree, 4 leaves, max depth = 3, train loss: 0.51318, val loss: 0.50707, in 0.016s
1 tree, 6 leaves, max depth = 4, train loss: 0.50799, val loss: 0.50157, in 0.000s
1 tree, 6 leaves, max depth = 4, train loss: 0.50309, val loss: 0.49638, in 0.000s
1 tree, 6 leaves, max depth = 4, train loss: 0.49848, val loss: 0.49146, in 0.016s
1 tree, 8 leaves, max depth = 4, train loss: 0.49412, val loss: 0.48682, in 0.000s
1 tree, 8 leaves, max depth = 5, train loss: 0.49001, val loss: 0.48242, in 0.016s
1 tree, 7 leaves, max depth = 5, train loss: 0.48628, val loss: 0.47842, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.48272, val loss: 0.47465, in 0.016s
1 tree, 7 leaves, max depth = 5, train loss: 0.47940, val loss: 0.47107, in 0.000s
1 tree, 9 leaves, max depth = 7, train loss: 0.47628, val loss: 0.46775, in 0.000s
1 tree, 22 leaves, max depth = 7, train loss: 0.47213, val loss: 0.46399, in 0.016s
1 tree, 19 leaves, max depth = 9, train loss: 0.46651, val loss: 0.45890, in 0.000s
1 tree, 20 leaves, max depth = 6, train loss: 0.46282, val loss: 0.45556, in 0.016s
1 tree, 7 leaves, max depth = 4, train loss: 0.46007, val loss: 0.45257, in 0.000s
1 tree, 9 leaves, max depth = 6, train loss: 0.45732, val loss: 0.44960, in 0.016s
1 tree, 9 leaves, max depth = 7, train loss: 0.45489, val loss: 0.44698, in 0.000s
1 tree, 10 leaves, max depth = 6, train loss: 0.45244, val loss: 0.44430, in 0.000s
1 tree, 29 leaves, max depth = 12, train loss: 0.44999, val loss: 0.44186, in 0.016s
1 tree, 22 leaves, max depth = 10, train loss: 0.44738, val loss: 0.43949, in 0.000s
1 tree, 22 leaves, max depth = 9, train loss: 0.44264, val loss: 0.43519, in 0.016s
1 tree, 12 leaves, max depth = 6, train loss: 0.44039, val loss: 0.43280, in 0.000s
1 tree, 22 leaves, max depth = 7, train loss: 0.43741, val loss: 0.43016, in 0.016s
1 tree, 3 leaves, max depth = 2, train loss: 0.43518, val loss: 0.42771, in 0.000s
1 tree, 10 leaves, max depth = 6, train loss: 0.43317, val loss: 0.42551, in 0.016s
1 tree, 20 leaves, max depth = 7, train loss: 0.43111, val loss: 0.42347, in 0.000s
1 tree, 21 leaves, max depth = 8, train loss: 0.42695, val loss: 0.41972, in 0.016s
1 tree, 24 leaves, max depth = 7, train loss: 0.42437, val loss: 0.41747, in 0.000s
1 tree, 26 leaves, max depth = 8, train loss: 0.42194, val loss: 0.41537, in 0.016s
1 tree, 20 leaves, max depth = 8, train loss: 0.41828, val loss: 0.41216, in 0.000s
1 tree, 10 leaves, max depth = 6, train loss: 0.41651, val loss: 0.41028, in 0.016s
1 tree, 26 leaves, max depth = 7, train loss: 0.41437, val loss: 0.40844, in 0.000s
1 tree, 26 leaves, max depth = 11, train loss: 0.41100, val loss: 0.40548, in 0.016s
1 tree, 29 leaves, max depth = 10, train loss: 0.40942, val loss: 0.40392, in 0.000s
Fit 51 trees in 0.610 s, (590 total leaves)
Time spent computing histograms: 0.197s
Time spent finding best splits:  0.016s
Time spent applying splits:      0.013s
Time spent predicting:           0.000s
Trial 13, Fold 1: Log loss = 0.4108587589253095, Average precision = 0.9460757513044896, ROC-AUC = 0.9415504778394344, Elapsed Time = 0.6094315999998798 seconds
Trial 13, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 13, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
Binning 0.040 GB of training data: 0.159 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 4 leaves, max depth = 3, train loss: 0.67769, val loss: 0.67689, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.66307, val loss: 0.66155, in 0.000s
1 tree, 4 leaves, max depth = 3, train loss: 0.64932, val loss: 0.64706, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.63642, val loss: 0.63352, in 0.000s
1 tree, 4 leaves, max depth = 3, train loss: 0.62445, val loss: 0.62088, in 0.000s
1 tree, 4 leaves, max depth = 3, train loss: 0.61321, val loss: 0.60901, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.60268, val loss: 0.59791, in 0.000s
1 tree, 5 leaves, max depth = 4, train loss: 0.59281, val loss: 0.58744, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.58347, val loss: 0.57750, in 0.000s
1 tree, 6 leaves, max depth = 4, train loss: 0.57454, val loss: 0.56801, in 0.000s
1 tree, 4 leaves, max depth = 3, train loss: 0.56632, val loss: 0.55927, in 0.016s
1 tree, 5 leaves, max depth = 4, train loss: 0.55860, val loss: 0.55103, in 0.000s
1 tree, 4 leaves, max depth = 3, train loss: 0.55133, val loss: 0.54329, in 0.016s
1 tree, 8 leaves, max depth = 5, train loss: 0.54436, val loss: 0.53586, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.53785, val loss: 0.52886, in 0.000s
1 tree, 17 leaves, max depth = 10, train loss: 0.53061, val loss: 0.52180, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.52464, val loss: 0.51534, in 0.000s
1 tree, 7 leaves, max depth = 4, train loss: 0.51904, val loss: 0.50933, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.51363, val loss: 0.50355, in 0.000s
1 tree, 6 leaves, max depth = 4, train loss: 0.50848, val loss: 0.49799, in 0.016s
1 tree, 6 leaves, max depth = 4, train loss: 0.50362, val loss: 0.49274, in 0.000s
1 tree, 6 leaves, max depth = 4, train loss: 0.49903, val loss: 0.48779, in 0.016s
1 tree, 7 leaves, max depth = 4, train loss: 0.49470, val loss: 0.48310, in 0.000s
1 tree, 7 leaves, max depth = 4, train loss: 0.49062, val loss: 0.47866, in 0.000s
1 tree, 7 leaves, max depth = 4, train loss: 0.48690, val loss: 0.47461, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.48335, val loss: 0.47072, in 0.000s
1 tree, 8 leaves, max depth = 4, train loss: 0.48003, val loss: 0.46710, in 0.016s
1 tree, 8 leaves, max depth = 6, train loss: 0.47689, val loss: 0.46368, in 0.000s
1 tree, 21 leaves, max depth = 7, train loss: 0.47283, val loss: 0.45983, in 0.016s
1 tree, 15 leaves, max depth = 6, train loss: 0.46721, val loss: 0.45443, in 0.000s
1 tree, 17 leaves, max depth = 7, train loss: 0.46360, val loss: 0.45104, in 0.016s
1 tree, 8 leaves, max depth = 4, train loss: 0.46086, val loss: 0.44803, in 0.000s
1 tree, 12 leaves, max depth = 6, train loss: 0.45813, val loss: 0.44505, in 0.016s
1 tree, 9 leaves, max depth = 6, train loss: 0.45569, val loss: 0.44236, in 0.000s
1 tree, 12 leaves, max depth = 6, train loss: 0.45325, val loss: 0.43968, in 0.016s
1 tree, 27 leaves, max depth = 13, train loss: 0.45075, val loss: 0.43753, in 0.000s
1 tree, 8 leaves, max depth = 6, train loss: 0.44859, val loss: 0.43515, in 0.016s
1 tree, 19 leaves, max depth = 9, train loss: 0.44376, val loss: 0.43052, in 0.000s
1 tree, 8 leaves, max depth = 6, train loss: 0.44179, val loss: 0.42833, in 0.000s
1 tree, 23 leaves, max depth = 8, train loss: 0.43879, val loss: 0.42556, in 0.016s
1 tree, 3 leaves, max depth = 2, train loss: 0.43662, val loss: 0.42331, in 0.000s
1 tree, 12 leaves, max depth = 6, train loss: 0.43470, val loss: 0.42118, in 0.016s
1 tree, 27 leaves, max depth = 13, train loss: 0.43253, val loss: 0.41937, in 0.000s
1 tree, 17 leaves, max depth = 8, train loss: 0.42826, val loss: 0.41531, in 0.016s
1 tree, 32 leaves, max depth = 10, train loss: 0.42558, val loss: 0.41290, in 0.016s
1 tree, 25 leaves, max depth = 8, train loss: 0.42307, val loss: 0.41062, in 0.000s
1 tree, 16 leaves, max depth = 8, train loss: 0.41932, val loss: 0.40704, in 0.016s
1 tree, 11 leaves, max depth = 6, train loss: 0.41765, val loss: 0.40519, in 0.000s
1 tree, 29 leaves, max depth = 7, train loss: 0.41543, val loss: 0.40321, in 0.016s
1 tree, 14 leaves, max depth = 6, train loss: 0.41201, val loss: 0.39996, in 0.000s
1 tree, 30 leaves, max depth = 11, train loss: 0.41034, val loss: 0.39862, in 0.016s
Fit 51 trees in 0.643 s, (558 total leaves)
Time spent computing histograms: 0.225s
Time spent finding best splits:  0.017s
Time spent applying splits:      0.015s
Time spent predicting:           0.000s
Trial 13, Fold 2: Log loss = 0.411491134302576, Average precision = 0.9436506570711057, ROC-AUC = 0.9425052679243726, Elapsed Time = 0.6547442999999475 seconds
Trial 13, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 13, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
Binning 0.040 GB of training data: 0.158 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 4 leaves, max depth = 3, train loss: 0.67776, val loss: 0.67719, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.66317, val loss: 0.66199, in 0.000s
1 tree, 4 leaves, max depth = 3, train loss: 0.64951, val loss: 0.64775, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.63668, val loss: 0.63446, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.62472, val loss: 0.62196, in 0.000s
1 tree, 4 leaves, max depth = 3, train loss: 0.61357, val loss: 0.61043, in 0.000s
1 tree, 4 leaves, max depth = 3, train loss: 0.60315, val loss: 0.59955, in 0.000s
1 tree, 4 leaves, max depth = 3, train loss: 0.59337, val loss: 0.58930, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.58408, val loss: 0.57956, in 0.000s
1 tree, 7 leaves, max depth = 4, train loss: 0.57519, val loss: 0.57031, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.56703, val loss: 0.56183, in 0.000s
1 tree, 4 leaves, max depth = 3, train loss: 0.55940, val loss: 0.55379, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.55212, val loss: 0.54612, in 0.016s
1 tree, 6 leaves, max depth = 4, train loss: 0.54533, val loss: 0.53897, in 0.000s
1 tree, 7 leaves, max depth = 4, train loss: 0.53886, val loss: 0.53216, in 0.016s
1 tree, 22 leaves, max depth = 10, train loss: 0.53147, val loss: 0.52526, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.52554, val loss: 0.51897, in 0.016s
1 tree, 6 leaves, max depth = 3, train loss: 0.51995, val loss: 0.51305, in 0.000s
1 tree, 4 leaves, max depth = 3, train loss: 0.51458, val loss: 0.50742, in 0.016s
1 tree, 6 leaves, max depth = 3, train loss: 0.50948, val loss: 0.50206, in 0.000s
1 tree, 6 leaves, max depth = 3, train loss: 0.50466, val loss: 0.49700, in 0.016s
1 tree, 6 leaves, max depth = 3, train loss: 0.50012, val loss: 0.49222, in 0.000s
1 tree, 6 leaves, max depth = 3, train loss: 0.49583, val loss: 0.48771, in 0.000s
1 tree, 8 leaves, max depth = 5, train loss: 0.49178, val loss: 0.48344, in 0.016s
1 tree, 8 leaves, max depth = 6, train loss: 0.48810, val loss: 0.47956, in 0.000s
1 tree, 6 leaves, max depth = 3, train loss: 0.48460, val loss: 0.47580, in 0.000s
1 tree, 8 leaves, max depth = 6, train loss: 0.48131, val loss: 0.47233, in 0.016s
1 tree, 6 leaves, max depth = 5, train loss: 0.47823, val loss: 0.46898, in 0.000s
1 tree, 22 leaves, max depth = 6, train loss: 0.47408, val loss: 0.46521, in 0.016s
1 tree, 20 leaves, max depth = 9, train loss: 0.46837, val loss: 0.45993, in 0.000s
1 tree, 23 leaves, max depth = 6, train loss: 0.46467, val loss: 0.45659, in 0.016s
1 tree, 8 leaves, max depth = 4, train loss: 0.46195, val loss: 0.45371, in 0.000s
1 tree, 7 leaves, max depth = 4, train loss: 0.45927, val loss: 0.45086, in 0.016s
1 tree, 6 leaves, max depth = 5, train loss: 0.45688, val loss: 0.44822, in 0.000s
1 tree, 8 leaves, max depth = 5, train loss: 0.45448, val loss: 0.44566, in 0.016s
1 tree, 30 leaves, max depth = 14, train loss: 0.45195, val loss: 0.44342, in 0.000s
1 tree, 20 leaves, max depth = 9, train loss: 0.44935, val loss: 0.44102, in 0.016s
1 tree, 23 leaves, max depth = 8, train loss: 0.44453, val loss: 0.43661, in 0.000s
1 tree, 13 leaves, max depth = 6, train loss: 0.44235, val loss: 0.43420, in 0.016s
1 tree, 23 leaves, max depth = 7, train loss: 0.43937, val loss: 0.43152, in 0.000s
1 tree, 3 leaves, max depth = 2, train loss: 0.43711, val loss: 0.42946, in 0.016s
1 tree, 8 leaves, max depth = 5, train loss: 0.43516, val loss: 0.42736, in 0.000s
1 tree, 29 leaves, max depth = 11, train loss: 0.43301, val loss: 0.42549, in 0.016s
1 tree, 20 leaves, max depth = 9, train loss: 0.42876, val loss: 0.42162, in 0.000s
1 tree, 36 leaves, max depth = 9, train loss: 0.42614, val loss: 0.41920, in 0.016s
1 tree, 21 leaves, max depth = 6, train loss: 0.42365, val loss: 0.41699, in 0.000s
1 tree, 25 leaves, max depth = 10, train loss: 0.41985, val loss: 0.41356, in 0.016s
1 tree, 11 leaves, max depth = 8, train loss: 0.41815, val loss: 0.41167, in 0.000s
1 tree, 23 leaves, max depth = 6, train loss: 0.41595, val loss: 0.40975, in 0.000s
1 tree, 26 leaves, max depth = 10, train loss: 0.41248, val loss: 0.40663, in 0.000s
1 tree, 38 leaves, max depth = 16, train loss: 0.41082, val loss: 0.40525, in 0.016s
Fit 51 trees in 0.658 s, (612 total leaves)
Time spent computing histograms: 0.224s
Time spent finding best splits:  0.017s
Time spent applying splits:      0.016s
Time spent predicting:           0.000s
Trial 13, Fold 3: Log loss = 0.4052344243260207, Average precision = 0.951163209490254, ROC-AUC = 0.9481140778043167, Elapsed Time = 0.6698638999987452 seconds
Trial 13, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 13, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
Binning 0.040 GB of training data: 0.142 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 4 leaves, max depth = 3, train loss: 0.67774, val loss: 0.67683, in 0.000s
1 tree, 6 leaves, max depth = 4, train loss: 0.66312, val loss: 0.66140, in 0.000s
1 tree, 6 leaves, max depth = 4, train loss: 0.64942, val loss: 0.64689, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.63659, val loss: 0.63332, in 0.000s
1 tree, 6 leaves, max depth = 4, train loss: 0.62462, val loss: 0.62060, in 0.016s
1 tree, 6 leaves, max depth = 4, train loss: 0.61340, val loss: 0.60865, in 0.000s
1 tree, 4 leaves, max depth = 3, train loss: 0.60294, val loss: 0.59749, in 0.000s
1 tree, 5 leaves, max depth = 4, train loss: 0.59312, val loss: 0.58701, in 0.016s
1 tree, 7 leaves, max depth = 4, train loss: 0.58382, val loss: 0.57704, in 0.000s
1 tree, 6 leaves, max depth = 3, train loss: 0.57493, val loss: 0.56742, in 0.000s
1 tree, 6 leaves, max depth = 4, train loss: 0.56676, val loss: 0.55856, in 0.000s
1 tree, 5 leaves, max depth = 4, train loss: 0.55908, val loss: 0.55030, in 0.000s
1 tree, 7 leaves, max depth = 4, train loss: 0.55180, val loss: 0.54240, in 0.016s
1 tree, 6 leaves, max depth = 4, train loss: 0.54499, val loss: 0.53494, in 0.000s
1 tree, 7 leaves, max depth = 4, train loss: 0.53851, val loss: 0.52789, in 0.016s
1 tree, 22 leaves, max depth = 10, train loss: 0.53121, val loss: 0.52067, in 0.000s
1 tree, 6 leaves, max depth = 3, train loss: 0.52525, val loss: 0.51415, in 0.016s
1 tree, 6 leaves, max depth = 3, train loss: 0.51965, val loss: 0.50798, in 0.000s
1 tree, 4 leaves, max depth = 3, train loss: 0.51427, val loss: 0.50211, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.50916, val loss: 0.49645, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.50435, val loss: 0.49110, in 0.016s
1 tree, 8 leaves, max depth = 6, train loss: 0.49980, val loss: 0.48603, in 0.000s
1 tree, 8 leaves, max depth = 6, train loss: 0.49551, val loss: 0.48124, in 0.016s
1 tree, 8 leaves, max depth = 6, train loss: 0.49146, val loss: 0.47669, in 0.000s
1 tree, 9 leaves, max depth = 5, train loss: 0.48775, val loss: 0.47252, in 0.000s
1 tree, 7 leaves, max depth = 4, train loss: 0.48424, val loss: 0.46853, in 0.016s
1 tree, 9 leaves, max depth = 5, train loss: 0.48093, val loss: 0.46479, in 0.000s
1 tree, 8 leaves, max depth = 5, train loss: 0.47781, val loss: 0.46128, in 0.016s
1 tree, 16 leaves, max depth = 6, train loss: 0.47377, val loss: 0.45737, in 0.000s
1 tree, 17 leaves, max depth = 8, train loss: 0.46812, val loss: 0.45187, in 0.016s
1 tree, 16 leaves, max depth = 6, train loss: 0.46453, val loss: 0.44840, in 0.000s
1 tree, 12 leaves, max depth = 6, train loss: 0.46162, val loss: 0.44512, in 0.016s
1 tree, 9 leaves, max depth = 6, train loss: 0.45893, val loss: 0.44204, in 0.000s
1 tree, 8 leaves, max depth = 5, train loss: 0.45650, val loss: 0.43926, in 0.016s
1 tree, 10 leaves, max depth = 6, train loss: 0.45409, val loss: 0.43647, in 0.000s
1 tree, 20 leaves, max depth = 7, train loss: 0.45162, val loss: 0.43391, in 0.016s
[37/51] 1 tree, 8 leaves, max depth = 4, train loss: 0.44946, val loss: 0.43141, in 0.000s
1 tree, 15 leaves, max depth = 7, train loss: 0.44463, val loss: 0.42671, in 0.016s
1 tree, 13 leaves, max depth = 6, train loss: 0.44248, val loss: 0.42425, in 0.000s
1 tree, 23 leaves, max depth = 7, train loss: 0.43954, val loss: 0.42146, in 0.016s
1 tree, 3 leaves, max depth = 2, train loss: 0.43737, val loss: 0.41917, in 0.000s
1 tree, 9 leaves, max depth = 6, train loss: 0.43549, val loss: 0.41696, in 0.016s
1 tree, 25 leaves, max depth = 9, train loss: 0.43336, val loss: 0.41476, in 0.000s
1 tree, 17 leaves, max depth = 8, train loss: 0.42910, val loss: 0.41066, in 0.016s
1 tree, 29 leaves, max depth = 9, train loss: 0.42647, val loss: 0.40812, in 0.000s
1 tree, 23 leaves, max depth = 7, train loss: 0.42401, val loss: 0.40581, in 0.016s
1 tree, 17 leaves, max depth = 8, train loss: 0.42025, val loss: 0.40219, in 0.000s
1 tree, 13 leaves, max depth = 8, train loss: 0.41859, val loss: 0.40025, in 0.016s
1 tree, 22 leaves, max depth = 6, train loss: 0.41642, val loss: 0.39824, in 0.000s
1 tree, 16 leaves, max depth = 8, train loss: 0.41299, val loss: 0.39493, in 0.016s
1 tree, 25 leaves, max depth = 9, train loss: 0.41135, val loss: 0.39325, in 0.000s
Fit 51 trees in 0.658 s, (556 total leaves)
Time spent computing histograms: 0.222s
Time spent finding best splits:  0.017s
Time spent applying splits:      0.014s
Time spent predicting:           0.016s
Trial 13, Fold 4: Log loss = 0.4107153777528465, Average precision = 0.945980256226045, ROC-AUC = 0.9420119739097883, Elapsed Time = 0.6631233000007342 seconds
Trial 13, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 13, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
Binning 0.040 GB of training data: 0.142 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 4 leaves, max depth = 3, train loss: 0.67755, val loss: 0.67641, in 0.000s
1 tree, 4 leaves, max depth = 3, train loss: 0.66285, val loss: 0.66070, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.64902, val loss: 0.64585, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.63603, val loss: 0.63194, in 0.000s
1 tree, 4 leaves, max depth = 3, train loss: 0.62399, val loss: 0.61899, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.61269, val loss: 0.60682, in 0.000s
1 tree, 4 leaves, max depth = 3, train loss: 0.60211, val loss: 0.59540, in 0.000s
1 tree, 4 leaves, max depth = 3, train loss: 0.59218, val loss: 0.58463, in 0.016s
1 tree, 6 leaves, max depth = 4, train loss: 0.58277, val loss: 0.57439, in 0.000s
1 tree, 6 leaves, max depth = 3, train loss: 0.57379, val loss: 0.56461, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.56552, val loss: 0.55559, in 0.000s
1 tree, 4 leaves, max depth = 3, train loss: 0.55776, val loss: 0.54710, in 0.000s
1 tree, 6 leaves, max depth = 4, train loss: 0.55039, val loss: 0.53899, in 0.016s
1 tree, 6 leaves, max depth = 4, train loss: 0.54351, val loss: 0.53135, in 0.000s
1 tree, 6 leaves, max depth = 4, train loss: 0.53696, val loss: 0.52411, in 0.016s
1 tree, 22 leaves, max depth = 11, train loss: 0.52974, val loss: 0.51731, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.52371, val loss: 0.51062, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.51803, val loss: 0.50429, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.51261, val loss: 0.49827, in 0.000s
1 tree, 9 leaves, max depth = 5, train loss: 0.50743, val loss: 0.49250, in 0.016s
1 tree, 9 leaves, max depth = 5, train loss: 0.50256, val loss: 0.48704, in 0.000s
1 tree, 9 leaves, max depth = 5, train loss: 0.49795, val loss: 0.48188, in 0.016s
1 tree, 9 leaves, max depth = 5, train loss: 0.49361, val loss: 0.47698, in 0.000s
1 tree, 9 leaves, max depth = 5, train loss: 0.48951, val loss: 0.47235, in 0.016s
1 tree, 9 leaves, max depth = 5, train loss: 0.48577, val loss: 0.46809, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.48220, val loss: 0.46400, in 0.000s
1 tree, 9 leaves, max depth = 5, train loss: 0.47886, val loss: 0.46018, in 0.016s
1 tree, 6 leaves, max depth = 5, train loss: 0.47572, val loss: 0.45657, in 0.000s
1 tree, 22 leaves, max depth = 7, train loss: 0.47170, val loss: 0.45301, in 0.016s
1 tree, 16 leaves, max depth = 8, train loss: 0.46612, val loss: 0.44789, in 0.000s
1 tree, 22 leaves, max depth = 7, train loss: 0.46254, val loss: 0.44475, in 0.016s
1 tree, 9 leaves, max depth = 5, train loss: 0.45978, val loss: 0.44155, in 0.000s
1 tree, 9 leaves, max depth = 6, train loss: 0.45705, val loss: 0.43838, in 0.016s
1 tree, 6 leaves, max depth = 5, train loss: 0.45460, val loss: 0.43551, in 0.000s
1 tree, 9 leaves, max depth = 5, train loss: 0.45216, val loss: 0.43265, in 0.016s
1 tree, 20 leaves, max depth = 6, train loss: 0.44969, val loss: 0.43019, in 0.000s
1 tree, 7 leaves, max depth = 4, train loss: 0.44750, val loss: 0.42760, in 0.016s
1 tree, 19 leaves, max depth = 9, train loss: 0.44270, val loss: 0.42327, in 0.000s
1 tree, 7 leaves, max depth = 4, train loss: 0.44070, val loss: 0.42089, in 0.016s
1 tree, 22 leaves, max depth = 8, train loss: 0.43773, val loss: 0.41836, in 0.000s
1 tree, 3 leaves, max depth = 2, train loss: 0.43556, val loss: 0.41626, in 0.016s
1 tree, 10 leaves, max depth = 5, train loss: 0.43365, val loss: 0.41398, in 0.000s
1 tree, 21 leaves, max depth = 8, train loss: 0.43152, val loss: 0.41187, in 0.016s
1 tree, 17 leaves, max depth = 8, train loss: 0.42728, val loss: 0.40810, in 0.000s
1 tree, 30 leaves, max depth = 11, train loss: 0.42466, val loss: 0.40560, in 0.016s
1 tree, 24 leaves, max depth = 7, train loss: 0.42218, val loss: 0.40354, in 0.000s
1 tree, 19 leaves, max depth = 10, train loss: 0.41843, val loss: 0.40023, in 0.016s
1 tree, 15 leaves, max depth = 9, train loss: 0.41676, val loss: 0.39827, in 0.000s
1 tree, 27 leaves, max depth = 7, train loss: 0.41457, val loss: 0.39648, in 0.016s
1 tree, 16 leaves, max depth = 6, train loss: 0.41115, val loss: 0.39349, in 0.000s
1 tree, 36 leaves, max depth = 14, train loss: 0.40950, val loss: 0.39189, in 0.016s
Fit 51 trees in 0.642 s, (569 total leaves)
Time spent computing histograms: 0.214s
Time spent finding best splits:  0.017s
Time spent applying splits:      0.015s
Time spent predicting:           0.000s
Trial 13, Fold 5: Log loss = 0.4152681992329296, Average precision = 0.9466220671297173, ROC-AUC = 0.9429014361889898, Elapsed Time = 0.6470339000006788 seconds
Optimization Progress:  14%|#4        | 14/100 [02:45<15:11, 10.60s/it]
Trial 14, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 14, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
0.142 s 0.040 GB of training data: 
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 37 leaves, max depth = 11, train loss: 0.68557, val loss: 0.68534, in 0.000s
1 tree, 37 leaves, max depth = 14, train loss: 0.67812, val loss: 0.67769, in 0.016s
1 tree, 38 leaves, max depth = 10, train loss: 0.67091, val loss: 0.67028, in 0.000s
1 tree, 6 leaves, max depth = 4, train loss: 0.66395, val loss: 0.66305, in 0.016s
1 tree, 38 leaves, max depth = 14, train loss: 0.65719, val loss: 0.65609, in 0.000s
1 tree, 38 leaves, max depth = 14, train loss: 0.65064, val loss: 0.64936, in 0.016s
1 tree, 37 leaves, max depth = 13, train loss: 0.64434, val loss: 0.64289, in 0.000s
1 tree, 37 leaves, max depth = 12, train loss: 0.63825, val loss: 0.63663, in 0.016s
1 tree, 38 leaves, max depth = 14, train loss: 0.63229, val loss: 0.63049, in 0.000s
1 tree, 37 leaves, max depth = 13, train loss: 0.62642, val loss: 0.62444, in 0.016s
1 tree, 36 leaves, max depth = 13, train loss: 0.62084, val loss: 0.61867, in 0.000s
1 tree, 36 leaves, max depth = 11, train loss: 0.61545, val loss: 0.61309, in 0.016s
1 tree, 36 leaves, max depth = 13, train loss: 0.61017, val loss: 0.60764, in 0.000s
1 tree, 37 leaves, max depth = 11, train loss: 0.60512, val loss: 0.60240, in 0.016s
1 tree, 36 leaves, max depth = 14, train loss: 0.60020, val loss: 0.59733, in 0.000s
1 tree, 34 leaves, max depth = 11, train loss: 0.59543, val loss: 0.59238, in 0.016s
1 tree, 36 leaves, max depth = 10, train loss: 0.59074, val loss: 0.58754, in 0.000s
1 tree, 37 leaves, max depth = 10, train loss: 0.58619, val loss: 0.58284, in 0.016s
1 tree, 6 leaves, max depth = 4, train loss: 0.58177, val loss: 0.57820, in 0.000s
1 tree, 37 leaves, max depth = 13, train loss: 0.57739, val loss: 0.57367, in 0.016s
1 tree, 37 leaves, max depth = 13, train loss: 0.57314, val loss: 0.56927, in 0.000s
1 tree, 37 leaves, max depth = 13, train loss: 0.56902, val loss: 0.56499, in 0.016s
1 tree, 36 leaves, max depth = 13, train loss: 0.56511, val loss: 0.56092, in 0.000s
1 tree, 37 leaves, max depth = 13, train loss: 0.56122, val loss: 0.55688, in 0.016s
1 tree, 36 leaves, max depth = 13, train loss: 0.55754, val loss: 0.55303, in 0.000s
1 tree, 37 leaves, max depth = 12, train loss: 0.55393, val loss: 0.54930, in 0.016s
1 tree, 36 leaves, max depth = 13, train loss: 0.55046, val loss: 0.54567, in 0.016s
1 tree, 37 leaves, max depth = 13, train loss: 0.54708, val loss: 0.54218, in 0.000s
1 tree, 34 leaves, max depth = 14, train loss: 0.54381, val loss: 0.53878, in 0.016s
1 tree, 57 leaves, max depth = 10, train loss: 0.54006, val loss: 0.53530, in 0.000s
1 tree, 37 leaves, max depth = 13, train loss: 0.53692, val loss: 0.53205, in 0.016s
1 tree, 6 leaves, max depth = 4, train loss: 0.53383, val loss: 0.52877, in 0.000s
1 tree, 38 leaves, max depth = 13, train loss: 0.53075, val loss: 0.52554, in 0.016s
1 tree, 36 leaves, max depth = 18, train loss: 0.52787, val loss: 0.52255, in 0.000s
1 tree, 38 leaves, max depth = 13, train loss: 0.52496, val loss: 0.51949, in 0.016s
1 tree, 36 leaves, max depth = 16, train loss: 0.52220, val loss: 0.51661, in 0.000s
1 tree, 35 leaves, max depth = 16, train loss: 0.51954, val loss: 0.51386, in 0.016s
1 tree, 57 leaves, max depth = 10, train loss: 0.51612, val loss: 0.51070, in 0.000s
1 tree, 37 leaves, max depth = 11, train loss: 0.51354, val loss: 0.50801, in 0.016s
1 tree, 35 leaves, max depth = 12, train loss: 0.51106, val loss: 0.50540, in 0.000s
1 tree, 37 leaves, max depth = 11, train loss: 0.50863, val loss: 0.50286, in 0.016s
1 tree, 38 leaves, max depth = 13, train loss: 0.50619, val loss: 0.50028, in 0.016s
1 tree, 37 leaves, max depth = 11, train loss: 0.50389, val loss: 0.49788, in 0.000s
1 tree, 57 leaves, max depth = 10, train loss: 0.50068, val loss: 0.49493, in 0.016s
1 tree, 6 leaves, max depth = 4, train loss: 0.49845, val loss: 0.49254, in 0.000s
1 tree, 38 leaves, max depth = 11, train loss: 0.49630, val loss: 0.49029, in 0.016s
1 tree, 57 leaves, max depth = 10, train loss: 0.49321, val loss: 0.48746, in 0.000s
1 tree, 36 leaves, max depth = 9, train loss: 0.49123, val loss: 0.48538, in 0.016s
1 tree, 37 leaves, max depth = 14, train loss: 0.48922, val loss: 0.48326, in 0.000s
1 tree, 57 leaves, max depth = 10, train loss: 0.48627, val loss: 0.48056, in 0.016s
1 tree, 37 leaves, max depth = 15, train loss: 0.48436, val loss: 0.47856, in 0.000s
1 tree, 57 leaves, max depth = 10, train loss: 0.48150, val loss: 0.47594, in 0.016s
1 tree, 36 leaves, max depth = 17, train loss: 0.47967, val loss: 0.47403, in 0.016s
1 tree, 36 leaves, max depth = 16, train loss: 0.47789, val loss: 0.47216, in 0.000s
1 tree, 36 leaves, max depth = 11, train loss: 0.47613, val loss: 0.47031, in 0.016s
1 tree, 57 leaves, max depth = 10, train loss: 0.47340, val loss: 0.46781, in 0.016s
1 tree, 6 leaves, max depth = 4, train loss: 0.47169, val loss: 0.46597, in 0.000s
1 tree, 38 leaves, max depth = 16, train loss: 0.47005, val loss: 0.46423, in 0.016s
1 tree, 57 leaves, max depth = 10, train loss: 0.46741, val loss: 0.46184, in 0.000s
1 tree, 36 leaves, max depth = 15, train loss: 0.46586, val loss: 0.46020, in 0.016s
1 tree, 38 leaves, max depth = 16, train loss: 0.46433, val loss: 0.45858, in 0.000s
1 tree, 57 leaves, max depth = 11, train loss: 0.46179, val loss: 0.45628, in 0.016s
1 tree, 57 leaves, max depth = 10, train loss: 0.46003, val loss: 0.45469, in 0.016s
1 tree, 37 leaves, max depth = 11, train loss: 0.45857, val loss: 0.45314, in 0.016s
1 tree, 37 leaves, max depth = 15, train loss: 0.45718, val loss: 0.45167, in 0.000s
1 tree, 57 leaves, max depth = 10, train loss: 0.45549, val loss: 0.45015, in 0.016s
1 tree, 37 leaves, max depth = 13, train loss: 0.45412, val loss: 0.44870, in 0.000s
1 tree, 37 leaves, max depth = 11, train loss: 0.45279, val loss: 0.44729, in 0.016s
1 tree, 57 leaves, max depth = 11, train loss: 0.45043, val loss: 0.44517, in 0.016s
1 tree, 39 leaves, max depth = 9, train loss: 0.44910, val loss: 0.44372, in 0.000s
1 tree, 39 leaves, max depth = 9, train loss: 0.44780, val loss: 0.44231, in 0.016s
1 tree, 37 leaves, max depth = 11, train loss: 0.44659, val loss: 0.44103, in 0.000s
1 tree, 3 leaves, max depth = 2, train loss: 0.44542, val loss: 0.43975, in 0.016s
1 tree, 38 leaves, max depth = 11, train loss: 0.44424, val loss: 0.43848, in 0.000s
1 tree, 3 leaves, max depth = 2, train loss: 0.44311, val loss: 0.43724, in 0.016s
Fit 75 trees in 0.908 s, (2782 total leaves)
Time spent computing histograms: 0.325s
Time spent finding best splits:  0.042s
Time spent applying splits:      0.050s
Time spent predicting:           0.000s
Trial 14, Fold 1: Log loss = 0.4434066575603427, Average precision = 0.9299184649791674, ROC-AUC = 0.9270560000573551, Elapsed Time = 0.9077753000001394 seconds
Trial 14, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 14, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
Binning 0.040 GB of training data: 0.158 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 36 leaves, max depth = 10, train loss: 0.68557, val loss: 0.68520, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.67813, val loss: 0.67738, in 0.000s
1 tree, 34 leaves, max depth = 11, train loss: 0.67091, val loss: 0.66982, in 0.016s
1 tree, 6 leaves, max depth = 4, train loss: 0.66392, val loss: 0.66250, in 0.000s
1 tree, 36 leaves, max depth = 14, train loss: 0.65720, val loss: 0.65546, in 0.016s
1 tree, 36 leaves, max depth = 13, train loss: 0.65067, val loss: 0.64863, in 0.000s
1 tree, 39 leaves, max depth = 18, train loss: 0.64436, val loss: 0.64202, in 0.016s
1 tree, 36 leaves, max depth = 14, train loss: 0.63824, val loss: 0.63561, in 0.000s
1 tree, 35 leaves, max depth = 11, train loss: 0.63229, val loss: 0.62933, in 0.016s
1 tree, 36 leaves, max depth = 10, train loss: 0.62645, val loss: 0.62317, in 0.016s
1 tree, 36 leaves, max depth = 13, train loss: 0.62086, val loss: 0.61732, in 0.000s
1 tree, 35 leaves, max depth = 13, train loss: 0.61548, val loss: 0.61165, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.61021, val loss: 0.60608, in 0.000s
1 tree, 37 leaves, max depth = 10, train loss: 0.60517, val loss: 0.60075, in 0.016s
1 tree, 36 leaves, max depth = 13, train loss: 0.60023, val loss: 0.59557, in 0.000s
1 tree, 37 leaves, max depth = 16, train loss: 0.59544, val loss: 0.59053, in 0.016s
1 tree, 33 leaves, max depth = 10, train loss: 0.59075, val loss: 0.58558, in 0.000s
1 tree, 33 leaves, max depth = 10, train loss: 0.58620, val loss: 0.58078, in 0.016s
1 tree, 6 leaves, max depth = 4, train loss: 0.58178, val loss: 0.57611, in 0.000s
1 tree, 37 leaves, max depth = 10, train loss: 0.57746, val loss: 0.57150, in 0.016s
1 tree, 36 leaves, max depth = 10, train loss: 0.57324, val loss: 0.56701, in 0.000s
1 tree, 37 leaves, max depth = 10, train loss: 0.56914, val loss: 0.56265, in 0.016s
1 tree, 36 leaves, max depth = 13, train loss: 0.56523, val loss: 0.55854, in 0.016s
1 tree, 36 leaves, max depth = 10, train loss: 0.56138, val loss: 0.55443, in 0.000s
1 tree, 36 leaves, max depth = 13, train loss: 0.55770, val loss: 0.55054, in 0.016s
1 tree, 34 leaves, max depth = 11, train loss: 0.55409, val loss: 0.54672, in 0.000s
1 tree, 36 leaves, max depth = 13, train loss: 0.55062, val loss: 0.54305, in 0.016s
1 tree, 38 leaves, max depth = 15, train loss: 0.54724, val loss: 0.53947, in 0.016s
1 tree, 37 leaves, max depth = 17, train loss: 0.54396, val loss: 0.53599, in 0.000s
1 tree, 57 leaves, max depth = 12, train loss: 0.54027, val loss: 0.53241, in 0.016s
1 tree, 38 leaves, max depth = 14, train loss: 0.53712, val loss: 0.52908, in 0.000s
1 tree, 6 leaves, max depth = 4, train loss: 0.53403, val loss: 0.52579, in 0.016s
1 tree, 37 leaves, max depth = 10, train loss: 0.53100, val loss: 0.52252, in 0.000s
1 tree, 39 leaves, max depth = 14, train loss: 0.52811, val loss: 0.51945, in 0.016s
1 tree, 37 leaves, max depth = 10, train loss: 0.52524, val loss: 0.51635, in 0.016s
1 tree, 37 leaves, max depth = 10, train loss: 0.52249, val loss: 0.51340, in 0.000s
1 tree, 38 leaves, max depth = 15, train loss: 0.51984, val loss: 0.51058, in 0.016s
1 tree, 57 leaves, max depth = 12, train loss: 0.51645, val loss: 0.50731, in 0.000s
1 tree, 35 leaves, max depth = 11, train loss: 0.51388, val loss: 0.50457, in 0.016s
1 tree, 36 leaves, max depth = 13, train loss: 0.51140, val loss: 0.50193, in 0.016s
1 tree, 36 leaves, max depth = 11, train loss: 0.50897, val loss: 0.49932, in 0.000s
1 tree, 36 leaves, max depth = 10, train loss: 0.50657, val loss: 0.49672, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.50427, val loss: 0.49426, in 0.000s
1 tree, 57 leaves, max depth = 11, train loss: 0.50110, val loss: 0.49121, in 0.016s
1 tree, 6 leaves, max depth = 4, train loss: 0.49887, val loss: 0.48882, in 0.000s
1 tree, 36 leaves, max depth = 11, train loss: 0.49673, val loss: 0.48653, in 0.016s
1 tree, 57 leaves, max depth = 12, train loss: 0.49369, val loss: 0.48360, in 0.016s
1 tree, 38 leaves, max depth = 14, train loss: 0.49170, val loss: 0.48150, in 0.000s
1 tree, 36 leaves, max depth = 13, train loss: 0.48971, val loss: 0.47937, in 0.016s
1 tree, 57 leaves, max depth = 10, train loss: 0.48679, val loss: 0.47657, in 0.000s
1 tree, 36 leaves, max depth = 14, train loss: 0.48488, val loss: 0.47452, in 0.016s
1 tree, 57 leaves, max depth = 13, train loss: 0.48206, val loss: 0.47182, in 0.016s
1 tree, 37 leaves, max depth = 17, train loss: 0.48023, val loss: 0.46985, in 0.000s
1 tree, 37 leaves, max depth = 17, train loss: 0.47844, val loss: 0.46794, in 0.016s
1 tree, 37 leaves, max depth = 11, train loss: 0.47669, val loss: 0.46605, in 0.000s
1 tree, 57 leaves, max depth = 13, train loss: 0.47399, val loss: 0.46347, in 0.016s
1 tree, 6 leaves, max depth = 4, train loss: 0.47229, val loss: 0.46162, in 0.000s
1 tree, 38 leaves, max depth = 17, train loss: 0.47067, val loss: 0.45988, in 0.016s
1 tree, 57 leaves, max depth = 11, train loss: 0.46807, val loss: 0.45739, in 0.016s
1 tree, 36 leaves, max depth = 16, train loss: 0.46652, val loss: 0.45573, in 0.000s
1 tree, 36 leaves, max depth = 13, train loss: 0.46501, val loss: 0.45410, in 0.016s
1 tree, 57 leaves, max depth = 11, train loss: 0.46250, val loss: 0.45171, in 0.016s
1 tree, 37 leaves, max depth = 11, train loss: 0.46103, val loss: 0.45012, in 0.000s
1 tree, 38 leaves, max depth = 10, train loss: 0.45960, val loss: 0.44856, in 0.016s
1 tree, 37 leaves, max depth = 14, train loss: 0.45823, val loss: 0.44707, in 0.000s
1 tree, 57 leaves, max depth = 9, train loss: 0.45652, val loss: 0.44551, in 0.016s
1 tree, 37 leaves, max depth = 11, train loss: 0.45519, val loss: 0.44406, in 0.016s
1 tree, 37 leaves, max depth = 14, train loss: 0.45390, val loss: 0.44265, in 0.000s
1 tree, 57 leaves, max depth = 12, train loss: 0.45155, val loss: 0.44042, in 0.016s
1 tree, 36 leaves, max depth = 10, train loss: 0.45024, val loss: 0.43900, in 0.000s
1 tree, 37 leaves, max depth = 9, train loss: 0.44898, val loss: 0.43760, in 0.000s
1 tree, 38 leaves, max depth = 10, train loss: 0.44779, val loss: 0.43630, in 0.016s
1 tree, 3 leaves, max depth = 2, train loss: 0.44664, val loss: 0.43512, in 0.000s
1 tree, 38 leaves, max depth = 11, train loss: 0.44550, val loss: 0.43388, in 0.016s
1 tree, 3 leaves, max depth = 2, train loss: 0.44440, val loss: 0.43273, in 0.000s
Fit 75 trees in 0.955 s, (2737 total leaves)
Time spent computing histograms: 0.343s
Time spent finding best splits:  0.045s
Time spent applying splits:      0.054s
Time spent predicting:           0.016s
Trial 14, Fold 2: Log loss = 0.4453315201550713, Average precision = 0.9283547951673355, ROC-AUC = 0.9304742482490582, Elapsed Time = 0.9605992000015249 seconds
Trial 14, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 14, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
Binning 0.040 GB of training data: 0.158 s
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 34 leaves, max depth = 12, train loss: 0.68561, val loss: 0.68537, in 0.000s
1 tree, 34 leaves, max depth = 9, train loss: 0.67820, val loss: 0.67771, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.67104, val loss: 0.67028, in 0.016s
1 tree, 6 leaves, max depth = 4, train loss: 0.66409, val loss: 0.66309, in 0.000s
1 tree, 36 leaves, max depth = 10, train loss: 0.65738, val loss: 0.65614, in 0.016s
1 tree, 36 leaves, max depth = 10, train loss: 0.65087, val loss: 0.64940, in 0.016s
1 tree, 36 leaves, max depth = 11, train loss: 0.64461, val loss: 0.64287, in 0.016s
1 tree, 36 leaves, max depth = 11, train loss: 0.63854, val loss: 0.63656, in 0.016s
1 tree, 36 leaves, max depth = 11, train loss: 0.63262, val loss: 0.63042, in 0.016s
1 tree, 36 leaves, max depth = 9, train loss: 0.62680, val loss: 0.62441, in 0.016s
1 tree, 36 leaves, max depth = 12, train loss: 0.62125, val loss: 0.61866, in 0.016s
1 tree, 37 leaves, max depth = 9, train loss: 0.61589, val loss: 0.61307, in 0.000s
1 tree, 36 leaves, max depth = 11, train loss: 0.61065, val loss: 0.60763, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.60563, val loss: 0.60243, in 0.000s
1 tree, 38 leaves, max depth = 13, train loss: 0.60074, val loss: 0.59732, in 0.016s
1 tree, 38 leaves, max depth = 13, train loss: 0.59599, val loss: 0.59238, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.59134, val loss: 0.58753, in 0.000s
1 tree, 35 leaves, max depth = 9, train loss: 0.58683, val loss: 0.58282, in 0.016s
1 tree, 6 leaves, max depth = 4, train loss: 0.58243, val loss: 0.57824, in 0.000s
1 tree, 36 leaves, max depth = 9, train loss: 0.57810, val loss: 0.57376, in 0.016s
1 tree, 36 leaves, max depth = 9, train loss: 0.57390, val loss: 0.56941, in 0.016s
1 tree, 36 leaves, max depth = 9, train loss: 0.56982, val loss: 0.56518, in 0.000s
1 tree, 36 leaves, max depth = 12, train loss: 0.56593, val loss: 0.56115, in 0.016s
1 tree, 36 leaves, max depth = 9, train loss: 0.56208, val loss: 0.55715, in 0.000s
1 tree, 37 leaves, max depth = 12, train loss: 0.55841, val loss: 0.55335, in 0.016s
1 tree, 34 leaves, max depth = 9, train loss: 0.55484, val loss: 0.54961, in 0.000s
1 tree, 37 leaves, max depth = 12, train loss: 0.55139, val loss: 0.54601, in 0.016s
1 tree, 36 leaves, max depth = 10, train loss: 0.54804, val loss: 0.54249, in 0.000s
1 tree, 39 leaves, max depth = 14, train loss: 0.54479, val loss: 0.53910, in 0.016s
1 tree, 57 leaves, max depth = 12, train loss: 0.54107, val loss: 0.53566, in 0.016s
1 tree, 36 leaves, max depth = 11, train loss: 0.53795, val loss: 0.53238, in 0.000s
1 tree, 6 leaves, max depth = 4, train loss: 0.53487, val loss: 0.52916, in 0.016s
1 tree, 36 leaves, max depth = 9, train loss: 0.53184, val loss: 0.52600, in 0.000s
1 tree, 36 leaves, max depth = 11, train loss: 0.52898, val loss: 0.52299, in 0.016s
1 tree, 37 leaves, max depth = 9, train loss: 0.52611, val loss: 0.52000, in 0.016s
1 tree, 37 leaves, max depth = 11, train loss: 0.52338, val loss: 0.51713, in 0.000s
1 tree, 36 leaves, max depth = 10, train loss: 0.52075, val loss: 0.51436, in 0.016s
1 tree, 57 leaves, max depth = 12, train loss: 0.51735, val loss: 0.51122, in 0.000s
1 tree, 35 leaves, max depth = 9, train loss: 0.51480, val loss: 0.50853, in 0.016s
1 tree, 36 leaves, max depth = 12, train loss: 0.51233, val loss: 0.50595, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.50993, val loss: 0.50341, in 0.000s
1 tree, 37 leaves, max depth = 9, train loss: 0.50753, val loss: 0.50091, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.50525, val loss: 0.49850, in 0.000s
1 tree, 57 leaves, max depth = 12, train loss: 0.50206, val loss: 0.49557, in 0.016s
1 tree, 6 leaves, max depth = 4, train loss: 0.49985, val loss: 0.49324, in 0.000s
1 tree, 35 leaves, max depth = 9, train loss: 0.49773, val loss: 0.49099, in 0.016s
1 tree, 57 leaves, max depth = 11, train loss: 0.49466, val loss: 0.48818, in 0.000s
1 tree, 35 leaves, max depth = 9, train loss: 0.49271, val loss: 0.48611, in 0.016s
1 tree, 37 leaves, max depth = 11, train loss: 0.49072, val loss: 0.48400, in 0.016s
1 tree, 57 leaves, max depth = 11, train loss: 0.48778, val loss: 0.48131, in 0.000s
1 tree, 35 leaves, max depth = 14, train loss: 0.48589, val loss: 0.47932, in 0.016s
1 tree, 57 leaves, max depth = 11, train loss: 0.48306, val loss: 0.47672, in 0.000s
1 tree, 37 leaves, max depth = 10, train loss: 0.48124, val loss: 0.47480, in 0.016s
1 tree, 37 leaves, max depth = 10, train loss: 0.47948, val loss: 0.47293, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.47775, val loss: 0.47108, in 0.000s
1 tree, 57 leaves, max depth = 12, train loss: 0.47503, val loss: 0.46859, in 0.016s
1 tree, 6 leaves, max depth = 4, train loss: 0.47333, val loss: 0.46679, in 0.000s
1 tree, 37 leaves, max depth = 10, train loss: 0.47171, val loss: 0.46507, in 0.016s
1 tree, 57 leaves, max depth = 12, train loss: 0.46909, val loss: 0.46267, in 0.000s
1 tree, 36 leaves, max depth = 14, train loss: 0.46755, val loss: 0.46104, in 0.016s
1 tree, 37 leaves, max depth = 10, train loss: 0.46604, val loss: 0.45942, in 0.000s
1 tree, 57 leaves, max depth = 11, train loss: 0.46352, val loss: 0.45712, in 0.016s
1 tree, 57 leaves, max depth = 8, train loss: 0.46176, val loss: 0.45556, in 0.016s
1 tree, 37 leaves, max depth = 10, train loss: 0.46033, val loss: 0.45402, in 0.000s
1 tree, 36 leaves, max depth = 14, train loss: 0.45894, val loss: 0.45254, in 0.016s
1 tree, 57 leaves, max depth = 8, train loss: 0.45726, val loss: 0.45104, in 0.016s
1 tree, 36 leaves, max depth = 10, train loss: 0.45592, val loss: 0.44959, in 0.000s
1 tree, 37 leaves, max depth = 10, train loss: 0.45461, val loss: 0.44818, in 0.000s
1 tree, 57 leaves, max depth = 11, train loss: 0.45227, val loss: 0.44606, in 0.016s
1 tree, 37 leaves, max depth = 9, train loss: 0.45095, val loss: 0.44468, in 0.016s
1 tree, 37 leaves, max depth = 9, train loss: 0.44968, val loss: 0.44333, in 0.000s
1 tree, 37 leaves, max depth = 10, train loss: 0.44849, val loss: 0.44204, in 0.016s
1 tree, 3 leaves, max depth = 2, train loss: 0.44731, val loss: 0.44096, in 0.000s
1 tree, 35 leaves, max depth = 11, train loss: 0.44614, val loss: 0.43972, in 0.016s
1 tree, 3 leaves, max depth = 2, train loss: 0.44500, val loss: 0.43867, in 0.000s
Fit 75 trees in 1.017 s, (2741 total leaves)
Time spent computing histograms: 0.352s
Time spent finding best splits:  0.049s
Time spent applying splits:      0.059s
Time spent predicting:           0.000s
Trial 14, Fold 3: Log loss = 0.4398167373771425, Average precision = 0.9355649438911422, ROC-AUC = 0.9350192560506977, Elapsed Time = 1.0301173999996536 seconds
Trial 14, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 14, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
Binning 0.040 GB of training data: 0.158 s
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 35 leaves, max depth = 14, train loss: 0.68559, val loss: 0.68516, in 0.016s
1 tree, 39 leaves, max depth = 10, train loss: 0.67818, val loss: 0.67735, in 0.000s
1 tree, 36 leaves, max depth = 11, train loss: 0.67100, val loss: 0.66979, in 0.016s
1 tree, 6 leaves, max depth = 4, train loss: 0.66406, val loss: 0.66247, in 0.000s
1 tree, 39 leaves, max depth = 10, train loss: 0.65733, val loss: 0.65535, in 0.016s
1 tree, 39 leaves, max depth = 10, train loss: 0.65081, val loss: 0.64846, in 0.000s
1 tree, 36 leaves, max depth = 11, train loss: 0.64452, val loss: 0.64181, in 0.016s
1 tree, 34 leaves, max depth = 11, train loss: 0.63843, val loss: 0.63536, in 0.000s
1 tree, 38 leaves, max depth = 12, train loss: 0.63250, val loss: 0.62906, in 0.016s
1 tree, 37 leaves, max depth = 13, train loss: 0.62670, val loss: 0.62287, in 0.000s
1 tree, 35 leaves, max depth = 14, train loss: 0.62114, val loss: 0.61693, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.61575, val loss: 0.61123, in 0.000s
1 tree, 39 leaves, max depth = 12, train loss: 0.61050, val loss: 0.60563, in 0.016s
1 tree, 35 leaves, max depth = 14, train loss: 0.60547, val loss: 0.60025, in 0.016s
1 tree, 34 leaves, max depth = 11, train loss: 0.60054, val loss: 0.59500, in 0.000s
1 tree, 37 leaves, max depth = 11, train loss: 0.59577, val loss: 0.58990, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.59111, val loss: 0.58493, in 0.000s
1 tree, 35 leaves, max depth = 11, train loss: 0.58659, val loss: 0.58011, in 0.016s
1 tree, 6 leaves, max depth = 4, train loss: 0.58219, val loss: 0.57540, in 0.000s
1 tree, 38 leaves, max depth = 12, train loss: 0.57787, val loss: 0.57074, in 0.016s
1 tree, 38 leaves, max depth = 12, train loss: 0.57368, val loss: 0.56621, in 0.000s
1 tree, 38 leaves, max depth = 12, train loss: 0.56961, val loss: 0.56181, in 0.016s
1 tree, 36 leaves, max depth = 13, train loss: 0.56572, val loss: 0.55760, in 0.000s
1 tree, 38 leaves, max depth = 12, train loss: 0.56187, val loss: 0.55343, in 0.016s
1 tree, 36 leaves, max depth = 13, train loss: 0.55820, val loss: 0.54945, in 0.016s
1 tree, 36 leaves, max depth = 11, train loss: 0.55461, val loss: 0.54559, in 0.000s
1 tree, 35 leaves, max depth = 12, train loss: 0.55115, val loss: 0.54182, in 0.016s
1 tree, 36 leaves, max depth = 11, train loss: 0.54779, val loss: 0.53819, in 0.000s
1 tree, 33 leaves, max depth = 11, train loss: 0.54452, val loss: 0.53465, in 0.016s
[30/75] 1 tree, 57 leaves, max depth = 12, train loss: 0.54078, val loss: 0.53100, in 0.000s
1 tree, 35 leaves, max depth = 11, train loss: 0.53765, val loss: 0.52761, in 0.016s
1 tree, 6 leaves, max depth = 4, train loss: 0.53457, val loss: 0.52427, in 0.000s
1 tree, 39 leaves, max depth = 12, train loss: 0.53154, val loss: 0.52096, in 0.016s
1 tree, 36 leaves, max depth = 11, train loss: 0.52867, val loss: 0.51784, in 0.016s
1 tree, 39 leaves, max depth = 12, train loss: 0.52581, val loss: 0.51470, in 0.000s
1 tree, 39 leaves, max depth = 12, train loss: 0.52307, val loss: 0.51171, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.52043, val loss: 0.50882, in 0.000s
1 tree, 57 leaves, max depth = 13, train loss: 0.51701, val loss: 0.50550, in 0.016s
1 tree, 37 leaves, max depth = 11, train loss: 0.51445, val loss: 0.50271, in 0.000s
1 tree, 35 leaves, max depth = 12, train loss: 0.51199, val loss: 0.49998, in 0.016s
1 tree, 37 leaves, max depth = 11, train loss: 0.50956, val loss: 0.49734, in 0.016s
1 tree, 38 leaves, max depth = 12, train loss: 0.50717, val loss: 0.49470, in 0.000s
1 tree, 37 leaves, max depth = 11, train loss: 0.50489, val loss: 0.49220, in 0.016s
1 tree, 57 leaves, max depth = 12, train loss: 0.50169, val loss: 0.48907, in 0.000s
1 tree, 6 leaves, max depth = 4, train loss: 0.49947, val loss: 0.48664, in 0.016s
1 tree, 36 leaves, max depth = 11, train loss: 0.49734, val loss: 0.48429, in 0.000s
1 tree, 57 leaves, max depth = 12, train loss: 0.49427, val loss: 0.48130, in 0.016s
1 tree, 32 leaves, max depth = 9, train loss: 0.49227, val loss: 0.47908, in 0.016s
1 tree, 39 leaves, max depth = 11, train loss: 0.49028, val loss: 0.47686, in 0.000s
1 tree, 57 leaves, max depth = 11, train loss: 0.48734, val loss: 0.47399, in 0.016s
1 tree, 37 leaves, max depth = 11, train loss: 0.48544, val loss: 0.47188, in 0.000s
1 tree, 57 leaves, max depth = 11, train loss: 0.48260, val loss: 0.46911, in 0.016s
1 tree, 37 leaves, max depth = 11, train loss: 0.48078, val loss: 0.46708, in 0.016s
1 tree, 36 leaves, max depth = 11, train loss: 0.47901, val loss: 0.46512, in 0.000s
1 tree, 36 leaves, max depth = 11, train loss: 0.47727, val loss: 0.46318, in 0.016s
1 tree, 57 leaves, max depth = 12, train loss: 0.47454, val loss: 0.46054, in 0.000s
1 tree, 6 leaves, max depth = 4, train loss: 0.47284, val loss: 0.45865, in 0.016s
1 tree, 39 leaves, max depth = 12, train loss: 0.47122, val loss: 0.45682, in 0.000s
1 tree, 57 leaves, max depth = 11, train loss: 0.46860, val loss: 0.45428, in 0.016s
1 tree, 37 leaves, max depth = 10, train loss: 0.46697, val loss: 0.45247, in 0.016s
1 tree, 39 leaves, max depth = 13, train loss: 0.46546, val loss: 0.45075, in 0.000s
1 tree, 57 leaves, max depth = 11, train loss: 0.46293, val loss: 0.44830, in 0.016s
1 tree, 36 leaves, max depth = 11, train loss: 0.46147, val loss: 0.44667, in 0.016s
1 tree, 39 leaves, max depth = 13, train loss: 0.46006, val loss: 0.44505, in 0.000s
1 tree, 36 leaves, max depth = 13, train loss: 0.45867, val loss: 0.44347, in 0.016s
1 tree, 57 leaves, max depth = 9, train loss: 0.45698, val loss: 0.44187, in 0.000s
1 tree, 36 leaves, max depth = 11, train loss: 0.45565, val loss: 0.44037, in 0.016s
1 tree, 38 leaves, max depth = 13, train loss: 0.45436, val loss: 0.43890, in 0.000s
1 tree, 57 leaves, max depth = 11, train loss: 0.45200, val loss: 0.43663, in 0.016s
1 tree, 38 leaves, max depth = 11, train loss: 0.45072, val loss: 0.43515, in 0.016s
1 tree, 38 leaves, max depth = 11, train loss: 0.44947, val loss: 0.43371, in 0.000s
1 tree, 39 leaves, max depth = 13, train loss: 0.44830, val loss: 0.43236, in 0.016s
1 tree, 3 leaves, max depth = 2, train loss: 0.44716, val loss: 0.43116, in 0.000s
1 tree, 37 leaves, max depth = 11, train loss: 0.44603, val loss: 0.42987, in 0.016s
1 tree, 3 leaves, max depth = 2, train loss: 0.44493, val loss: 0.42871, in 0.000s
Fit 75 trees in 0.971 s, (2757 total leaves)
Time spent computing histograms: 0.343s
Time spent finding best splits:  0.044s
Time spent applying splits:      0.053s
Time spent predicting:           0.000s
Trial 14, Fold 4: Log loss = 0.44463370939777047, Average precision = 0.9301654073756966, ROC-AUC = 0.9295039563319194, Elapsed Time = 0.9744702000007237 seconds
Trial 14, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 14, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
Binning 0.040 GB of training data: 0.158 s
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 36 leaves, max depth = 12, train loss: 0.68550, val loss: 0.68497, in 0.000s
1 tree, 35 leaves, max depth = 11, train loss: 0.67801, val loss: 0.67699, in 0.016s
1 tree, 35 leaves, max depth = 12, train loss: 0.67075, val loss: 0.66924, in 0.000s
1 tree, 6 leaves, max depth = 4, train loss: 0.66371, val loss: 0.66174, in 0.016s
1 tree, 36 leaves, max depth = 12, train loss: 0.65694, val loss: 0.65447, in 0.000s
1 tree, 36 leaves, max depth = 12, train loss: 0.65037, val loss: 0.64742, in 0.016s
1 tree, 37 leaves, max depth = 12, train loss: 0.64401, val loss: 0.64062, in 0.000s
1 tree, 37 leaves, max depth = 12, train loss: 0.63785, val loss: 0.63399, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.63186, val loss: 0.62757, in 0.000s
1 tree, 38 leaves, max depth = 11, train loss: 0.62600, val loss: 0.62126, in 0.016s
1 tree, 37 leaves, max depth = 11, train loss: 0.62038, val loss: 0.61520, in 0.000s
1 tree, 37 leaves, max depth = 9, train loss: 0.61496, val loss: 0.60933, in 0.016s
1 tree, 36 leaves, max depth = 11, train loss: 0.60967, val loss: 0.60362, in 0.000s
1 tree, 35 leaves, max depth = 11, train loss: 0.60459, val loss: 0.59809, in 0.016s
1 tree, 37 leaves, max depth = 12, train loss: 0.59962, val loss: 0.59269, in 0.000s
1 tree, 34 leaves, max depth = 10, train loss: 0.59480, val loss: 0.58745, in 0.000s
1 tree, 35 leaves, max depth = 12, train loss: 0.59008, val loss: 0.58235, in 0.016s
1 tree, 38 leaves, max depth = 13, train loss: 0.58553, val loss: 0.57740, in 0.000s
1 tree, 6 leaves, max depth = 4, train loss: 0.58108, val loss: 0.57258, in 0.000s
1 tree, 37 leaves, max depth = 13, train loss: 0.57673, val loss: 0.56783, in 0.016s
1 tree, 38 leaves, max depth = 13, train loss: 0.57250, val loss: 0.56322, in 0.016s
1 tree, 38 leaves, max depth = 13, train loss: 0.56839, val loss: 0.55873, in 0.000s
1 tree, 38 leaves, max depth = 11, train loss: 0.56446, val loss: 0.55442, in 0.016s
1 tree, 38 leaves, max depth = 13, train loss: 0.56059, val loss: 0.55018, in 0.000s
1 tree, 38 leaves, max depth = 11, train loss: 0.55688, val loss: 0.54610, in 0.016s
1 tree, 35 leaves, max depth = 12, train loss: 0.55326, val loss: 0.54215, in 0.000s
1 tree, 38 leaves, max depth = 11, train loss: 0.54976, val loss: 0.53829, in 0.016s
1 tree, 38 leaves, max depth = 12, train loss: 0.54636, val loss: 0.53457, in 0.016s
1 tree, 37 leaves, max depth = 12, train loss: 0.54305, val loss: 0.53092, in 0.000s
1 tree, 57 leaves, max depth = 14, train loss: 0.53940, val loss: 0.52745, in 0.016s
1 tree, 39 leaves, max depth = 12, train loss: 0.53624, val loss: 0.52397, in 0.000s
1 tree, 6 leaves, max depth = 4, train loss: 0.53312, val loss: 0.52054, in 0.016s
1 tree, 38 leaves, max depth = 13, train loss: 0.53007, val loss: 0.51716, in 0.000s
1 tree, 38 leaves, max depth = 12, train loss: 0.52716, val loss: 0.51396, in 0.016s
1 tree, 38 leaves, max depth = 13, train loss: 0.52427, val loss: 0.51075, in 0.000s
1 tree, 37 leaves, max depth = 12, train loss: 0.52152, val loss: 0.50769, in 0.016s
1 tree, 39 leaves, max depth = 12, train loss: 0.51885, val loss: 0.50473, in 0.016s
1 tree, 57 leaves, max depth = 14, train loss: 0.51551, val loss: 0.50157, in 0.000s
1 tree, 35 leaves, max depth = 10, train loss: 0.51293, val loss: 0.49871, in 0.016s
1 tree, 37 leaves, max depth = 12, train loss: 0.51043, val loss: 0.49591, in 0.000s
1 tree, 39 leaves, max depth = 12, train loss: 0.50801, val loss: 0.49321, in 0.016s
1 tree, 38 leaves, max depth = 13, train loss: 0.50559, val loss: 0.49051, in 0.000s
1 tree, 36 leaves, max depth = 15, train loss: 0.50329, val loss: 0.48795, in 0.016s
1 tree, 57 leaves, max depth = 14, train loss: 0.50017, val loss: 0.48501, in 0.016s
1 tree, 6 leaves, max depth = 4, train loss: 0.49792, val loss: 0.48251, in 0.000s
1 tree, 35 leaves, max depth = 11, train loss: 0.49577, val loss: 0.48009, in 0.016s
1 tree, 57 leaves, max depth = 12, train loss: 0.49277, val loss: 0.47728, in 0.000s
1 tree, 34 leaves, max depth = 10, train loss: 0.49076, val loss: 0.47505, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.48874, val loss: 0.47278, in 0.031s
1 tree, 57 leaves, max depth = 13, train loss: 0.48586, val loss: 0.47009, in 0.032s
1 tree, 36 leaves, max depth = 12, train loss: 0.48394, val loss: 0.46793, in 0.047s
1 tree, 57 leaves, max depth = 13, train loss: 0.48115, val loss: 0.46533, in 0.016s
1 tree, 39 leaves, max depth = 12, train loss: 0.47930, val loss: 0.46325, in 0.000s
1 tree, 39 leaves, max depth = 12, train loss: 0.47751, val loss: 0.46122, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.47574, val loss: 0.45923, in 0.000s
1 tree, 57 leaves, max depth = 12, train loss: 0.47307, val loss: 0.45674, in 0.016s
1 tree, 6 leaves, max depth = 4, train loss: 0.47135, val loss: 0.45479, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.46971, val loss: 0.45293, in 0.000s
1 tree, 57 leaves, max depth = 12, train loss: 0.46714, val loss: 0.45055, in 0.016s
1 tree, 36 leaves, max depth = 12, train loss: 0.46557, val loss: 0.44874, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.46403, val loss: 0.44699, in 0.016s
1 tree, 57 leaves, max depth = 13, train loss: 0.46156, val loss: 0.44471, in 0.016s
1 tree, 35 leaves, max depth = 12, train loss: 0.46009, val loss: 0.44304, in 0.016s
1 tree, 36 leaves, max depth = 11, train loss: 0.45865, val loss: 0.44139, in 0.000s
1 tree, 36 leaves, max depth = 12, train loss: 0.45727, val loss: 0.43978, in 0.016s
1 tree, 57 leaves, max depth = 9, train loss: 0.45559, val loss: 0.43837, in 0.016s
1 tree, 35 leaves, max depth = 14, train loss: 0.45423, val loss: 0.43682, in 0.000s
1 tree, 35 leaves, max depth = 12, train loss: 0.45293, val loss: 0.43530, in 0.016s
1 tree, 57 leaves, max depth = 12, train loss: 0.45061, val loss: 0.43318, in 0.016s
1 tree, 38 leaves, max depth = 9, train loss: 0.44930, val loss: 0.43167, in 0.016s
1 tree, 38 leaves, max depth = 9, train loss: 0.44803, val loss: 0.43019, in 0.016s
1 tree, 35 leaves, max depth = 13, train loss: 0.44683, val loss: 0.42881, in 0.016s
1 tree, 3 leaves, max depth = 2, train loss: 0.44569, val loss: 0.42770, in 0.000s
1 tree, 35 leaves, max depth = 13, train loss: 0.44455, val loss: 0.42638, in 0.016s
1 tree, 3 leaves, max depth = 2, train loss: 0.44344, val loss: 0.42531, in 0.000s
Fit 75 trees in 1.096 s, (2748 total leaves)
Time spent computing histograms: 0.393s
Time spent finding best splits:  0.071s
Time spent applying splits:      0.082s
Time spent predicting:           0.000s
Trial 14, Fold 5: Log loss = 0.4511288488395596, Average precision = 0.925886440789031, ROC-AUC = 0.924868922207978, Elapsed Time = 1.1130646000001434 seconds
Optimization Progress:  15%|#5        | 15/100 [02:56<15:20, 10.83s/it]
Trial 15, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 15, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
0.158 s 0.040 GB of training data: 
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 49 leaves, max depth = 10, train loss: 0.66220, val loss: 0.66243, in 0.016s
1 tree, 48 leaves, max depth = 11, train loss: 0.63453, val loss: 0.63502, in 0.015s
1 tree, 48 leaves, max depth = 11, train loss: 0.60959, val loss: 0.61019, in 0.016s
1 tree, 51 leaves, max depth = 13, train loss: 0.58790, val loss: 0.58839, in 0.016s
1 tree, 51 leaves, max depth = 11, train loss: 0.56691, val loss: 0.56733, in 0.016s
1 tree, 50 leaves, max depth = 12, train loss: 0.54896, val loss: 0.54928, in 0.016s
1 tree, 49 leaves, max depth = 10, train loss: 0.53166, val loss: 0.53207, in 0.016s
1 tree, 51 leaves, max depth = 11, train loss: 0.51556, val loss: 0.51590, in 0.016s
1 tree, 48 leaves, max depth = 10, train loss: 0.50083, val loss: 0.50105, in 0.016s
1 tree, 49 leaves, max depth = 10, train loss: 0.48729, val loss: 0.48745, in 0.016s
1 tree, 50 leaves, max depth = 11, train loss: 0.47482, val loss: 0.47483, in 0.031s
1 tree, 51 leaves, max depth = 10, train loss: 0.46334, val loss: 0.46327, in 0.016s
1 tree, 50 leaves, max depth = 11, train loss: 0.45272, val loss: 0.45257, in 0.016s
1 tree, 51 leaves, max depth = 10, train loss: 0.44290, val loss: 0.44274, in 0.016s
1 tree, 49 leaves, max depth = 11, train loss: 0.42909, val loss: 0.42941, in 0.016s
1 tree, 50 leaves, max depth = 10, train loss: 0.42059, val loss: 0.42090, in 0.016s
1 tree, 52 leaves, max depth = 13, train loss: 0.41303, val loss: 0.41326, in 0.016s
1 tree, 51 leaves, max depth = 11, train loss: 0.40140, val loss: 0.40211, in 0.016s
1 tree, 49 leaves, max depth = 10, train loss: 0.39446, val loss: 0.39517, in 0.016s
1 tree, 49 leaves, max depth = 10, train loss: 0.38798, val loss: 0.38875, in 0.016s
1 tree, 49 leaves, max depth = 11, train loss: 0.37808, val loss: 0.37933, in 0.031s
1 tree, 50 leaves, max depth = 11, train loss: 0.36904, val loss: 0.37072, in 0.016s
1 tree, 56 leaves, max depth = 10, train loss: 0.36375, val loss: 0.36547, in 0.016s
1 tree, 26 leaves, max depth = 9, train loss: 0.35613, val loss: 0.35795, in 0.016s
1 tree, 27 leaves, max depth = 10, train loss: 0.34919, val loss: 0.35110, in 0.016s
1 tree, 53 leaves, max depth = 12, train loss: 0.34368, val loss: 0.34559, in 0.016s
1 tree, 27 leaves, max depth = 9, train loss: 0.33755, val loss: 0.33954, in 0.016s
1 tree, 52 leaves, max depth = 14, train loss: 0.33259, val loss: 0.33460, in 0.016s
1 tree, 43 leaves, max depth = 13, train loss: 0.32679, val loss: 0.32945, in 0.016s
1 tree, 44 leaves, max depth = 13, train loss: 0.32149, val loss: 0.32476, in 0.031s
1 tree, 26 leaves, max depth = 15, train loss: 0.31666, val loss: 0.32003, in 0.016s
1 tree, 62 leaves, max depth = 12, train loss: 0.31236, val loss: 0.31581, in 0.016s
1 tree, 44 leaves, max depth = 10, train loss: 0.30788, val loss: 0.31189, in 0.016s
1 tree, 26 leaves, max depth = 9, train loss: 0.30379, val loss: 0.30784, in 0.016s
1 tree, 52 leaves, max depth = 11, train loss: 0.30006, val loss: 0.30426, in 0.016s
1 tree, 44 leaves, max depth = 12, train loss: 0.29624, val loss: 0.30096, in 0.016s
1 tree, 25 leaves, max depth = 9, train loss: 0.29278, val loss: 0.29754, in 0.016s
1 tree, 43 leaves, max depth = 11, train loss: 0.28941, val loss: 0.29468, in 0.016s
1 tree, 25 leaves, max depth = 9, train loss: 0.28638, val loss: 0.29168, in 0.016s
1 tree, 60 leaves, max depth = 12, train loss: 0.28314, val loss: 0.28844, in 0.016s
1 tree, 42 leaves, max depth = 12, train loss: 0.28028, val loss: 0.28603, in 0.016s
1 tree, 25 leaves, max depth = 9, train loss: 0.27770, val loss: 0.28343, in 0.016s
1 tree, 52 leaves, max depth = 12, train loss: 0.27489, val loss: 0.28071, in 0.016s
1 tree, 43 leaves, max depth = 12, train loss: 0.27243, val loss: 0.27870, in 0.016s
1 tree, 26 leaves, max depth = 9, train loss: 0.27023, val loss: 0.27649, in 0.016s
Fit 45 trees in 1.095 s, (2018 total leaves)
Time spent computing histograms: 0.297s
Time spent finding best splits:  0.064s
Time spent applying splits:      0.043s
Time spent predicting:           0.000s
Trial 15, Fold 1: Log loss = 0.2758642335299996, Average precision = 0.963677897916902, ROC-AUC = 0.9577225914454912, Elapsed Time = 1.1047766000010597 seconds
Trial 15, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 15, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
Binning 0.040 GB of training data: 0.173 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 49 leaves, max depth = 10, train loss: 0.66197, val loss: 0.66118, in 0.016s
1 tree, 49 leaves, max depth = 10, train loss: 0.63397, val loss: 0.63262, in 0.016s
1 tree, 47 leaves, max depth = 10, train loss: 0.60884, val loss: 0.60702, in 0.016s
1 tree, 50 leaves, max depth = 15, train loss: 0.58717, val loss: 0.58512, in 0.031s
1 tree, 50 leaves, max depth = 10, train loss: 0.56631, val loss: 0.56377, in 0.016s
1 tree, 51 leaves, max depth = 15, train loss: 0.54836, val loss: 0.54555, in 0.016s
1 tree, 51 leaves, max depth = 10, train loss: 0.53069, val loss: 0.52757, in 0.031s
1 tree, 50 leaves, max depth = 10, train loss: 0.51452, val loss: 0.51102, in 0.016s
1 tree, 52 leaves, max depth = 10, train loss: 0.49971, val loss: 0.49597, in 0.016s
1 tree, 50 leaves, max depth = 10, train loss: 0.48612, val loss: 0.48203, in 0.016s
1 tree, 49 leaves, max depth = 10, train loss: 0.47362, val loss: 0.46922, in 0.026s
1 tree, 49 leaves, max depth = 10, train loss: 0.46217, val loss: 0.45764, in 0.022s
1 tree, 48 leaves, max depth = 11, train loss: 0.45155, val loss: 0.44675, in 0.016s
1 tree, 52 leaves, max depth = 11, train loss: 0.44169, val loss: 0.43673, in 0.016s
1 tree, 50 leaves, max depth = 12, train loss: 0.42806, val loss: 0.42331, in 0.016s
1 tree, 48 leaves, max depth = 11, train loss: 0.41963, val loss: 0.41470, in 0.031s
1 tree, 54 leaves, max depth = 12, train loss: 0.41218, val loss: 0.40725, in 0.016s
1 tree, 50 leaves, max depth = 12, train loss: 0.40070, val loss: 0.39601, in 0.016s
1 tree, 50 leaves, max depth = 11, train loss: 0.39372, val loss: 0.38892, in 0.031s
1 tree, 52 leaves, max depth = 10, train loss: 0.38727, val loss: 0.38246, in 0.016s
1 tree, 50 leaves, max depth = 13, train loss: 0.37751, val loss: 0.37294, in 0.016s
1 tree, 50 leaves, max depth = 11, train loss: 0.36854, val loss: 0.36413, in 0.016s
1 tree, 59 leaves, max depth = 9, train loss: 0.36320, val loss: 0.35876, in 0.016s
1 tree, 27 leaves, max depth = 11, train loss: 0.35564, val loss: 0.35134, in 0.016s
1 tree, 27 leaves, max depth = 11, train loss: 0.34879, val loss: 0.34467, in 0.016s
1 tree, 51 leaves, max depth = 13, train loss: 0.34330, val loss: 0.33931, in 0.016s
1 tree, 44 leaves, max depth = 11, train loss: 0.33707, val loss: 0.33352, in 0.016s
1 tree, 27 leaves, max depth = 10, train loss: 0.33134, val loss: 0.32790, in 0.016s
1 tree, 48 leaves, max depth = 11, train loss: 0.32654, val loss: 0.32323, in 0.016s
1 tree, 44 leaves, max depth = 11, train loss: 0.32131, val loss: 0.31844, in 0.016s
1 tree, 25 leaves, max depth = 14, train loss: 0.31654, val loss: 0.31376, in 0.016s
1 tree, 60 leaves, max depth = 12, train loss: 0.31223, val loss: 0.30962, in 0.031s
1 tree, 43 leaves, max depth = 13, train loss: 0.30782, val loss: 0.30560, in 0.016s
1 tree, 28 leaves, max depth = 12, train loss: 0.30377, val loss: 0.30165, in 0.016s
1 tree, 43 leaves, max depth = 13, train loss: 0.29991, val loss: 0.29813, in 0.016s
1 tree, 28 leaves, max depth = 11, train loss: 0.29639, val loss: 0.29472, in 0.016s
1 tree, 44 leaves, max depth = 13, train loss: 0.29297, val loss: 0.29158, in 0.016s
1 tree, 27 leaves, max depth = 11, train loss: 0.28989, val loss: 0.28860, in 0.016s
1 tree, 58 leaves, max depth = 12, train loss: 0.28646, val loss: 0.28534, in 0.016s
1 tree, 49 leaves, max depth = 11, train loss: 0.28332, val loss: 0.28228, in 0.016s
1 tree, 43 leaves, max depth = 12, train loss: 0.28050, val loss: 0.27975, in 0.016s
1 tree, 26 leaves, max depth = 16, train loss: 0.27799, val loss: 0.27728, in 0.016s
1 tree, 61 leaves, max depth = 15, train loss: 0.27512, val loss: 0.27458, in 0.016s
1 tree, 43 leaves, max depth = 12, train loss: 0.27271, val loss: 0.27241, in 0.031s
1 tree, 28 leaves, max depth = 10, train loss: 0.27055, val loss: 0.27030, in 0.016s
Fit 45 trees in 1.189 s, (2034 total leaves)
Time spent computing histograms: 0.317s
Time spent finding best splits:  0.067s
Time spent applying splits:      0.047s
Time spent predicting:           0.000s
Trial 15, Fold 2: Log loss = 0.2732321948794839, Average precision = 0.9636508261454557, ROC-AUC = 0.9604291852266854, Elapsed Time = 1.1898820999995223 seconds
Trial 15, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 15, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
Binning 0.040 GB of training data: 0.158 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 49 leaves, max depth = 11, train loss: 0.66244, val loss: 0.66232, in 0.016s
1 tree, 50 leaves, max depth = 10, train loss: 0.63447, val loss: 0.63435, in 0.016s
1 tree, 49 leaves, max depth = 10, train loss: 0.60927, val loss: 0.60911, in 0.016s
1 tree, 53 leaves, max depth = 11, train loss: 0.58756, val loss: 0.58750, in 0.031s
1 tree, 50 leaves, max depth = 12, train loss: 0.56673, val loss: 0.56670, in 0.016s
1 tree, 52 leaves, max depth = 12, train loss: 0.54872, val loss: 0.54872, in 0.016s
1 tree, 52 leaves, max depth = 11, train loss: 0.53142, val loss: 0.53127, in 0.031s
1 tree, 51 leaves, max depth = 12, train loss: 0.51548, val loss: 0.51535, in 0.016s
1 tree, 52 leaves, max depth = 11, train loss: 0.50085, val loss: 0.50067, in 0.016s
1 tree, 52 leaves, max depth = 13, train loss: 0.48813, val loss: 0.48804, in 0.016s
1 tree, 52 leaves, max depth = 11, train loss: 0.47569, val loss: 0.47557, in 0.031s
1 tree, 53 leaves, max depth = 12, train loss: 0.46421, val loss: 0.46414, in 0.016s
1 tree, 52 leaves, max depth = 12, train loss: 0.45352, val loss: 0.45332, in 0.016s
1 tree, 53 leaves, max depth = 11, train loss: 0.44369, val loss: 0.44344, in 0.016s
1 tree, 51 leaves, max depth = 12, train loss: 0.42989, val loss: 0.43066, in 0.016s
1 tree, 51 leaves, max depth = 11, train loss: 0.42134, val loss: 0.42206, in 0.016s
1 tree, 51 leaves, max depth = 12, train loss: 0.41391, val loss: 0.41472, in 0.031s
1 tree, 50 leaves, max depth = 11, train loss: 0.40229, val loss: 0.40410, in 0.016s
1 tree, 53 leaves, max depth = 12, train loss: 0.39528, val loss: 0.39704, in 0.016s
1 tree, 50 leaves, max depth = 11, train loss: 0.38505, val loss: 0.38776, in 0.031s
1 tree, 52 leaves, max depth = 11, train loss: 0.37884, val loss: 0.38151, in 0.016s
1 tree, 51 leaves, max depth = 12, train loss: 0.36979, val loss: 0.37332, in 0.016s
1 tree, 53 leaves, max depth = 12, train loss: 0.36441, val loss: 0.36789, in 0.016s
1 tree, 27 leaves, max depth = 10, train loss: 0.35673, val loss: 0.36089, in 0.016s
1 tree, 27 leaves, max depth = 10, train loss: 0.34974, val loss: 0.35454, in 0.016s
1 tree, 41 leaves, max depth = 11, train loss: 0.34325, val loss: 0.34905, in 0.016s
1 tree, 27 leaves, max depth = 10, train loss: 0.33724, val loss: 0.34358, in 0.016s
1 tree, 52 leaves, max depth = 14, train loss: 0.33215, val loss: 0.33813, in 0.016s
1 tree, 51 leaves, max depth = 13, train loss: 0.32741, val loss: 0.33303, in 0.016s
1 tree, 41 leaves, max depth = 11, train loss: 0.32212, val loss: 0.32858, in 0.000s
1 tree, 27 leaves, max depth = 10, train loss: 0.31726, val loss: 0.32414, in 0.016s
1 tree, 51 leaves, max depth = 12, train loss: 0.31312, val loss: 0.31975, in 0.031s
1 tree, 42 leaves, max depth = 11, train loss: 0.30865, val loss: 0.31605, in 0.016s
1 tree, 27 leaves, max depth = 10, train loss: 0.30456, val loss: 0.31233, in 0.016s
1 tree, 43 leaves, max depth = 10, train loss: 0.30066, val loss: 0.30912, in 0.016s
1 tree, 27 leaves, max depth = 10, train loss: 0.29713, val loss: 0.30587, in 0.016s
1 tree, 49 leaves, max depth = 12, train loss: 0.29369, val loss: 0.30226, in 0.016s
1 tree, 26 leaves, max depth = 10, train loss: 0.29050, val loss: 0.29944, in 0.016s
1 tree, 58 leaves, max depth = 11, train loss: 0.28723, val loss: 0.29587, in 0.016s
1 tree, 41 leaves, max depth = 13, train loss: 0.28417, val loss: 0.29338, in 0.031s
1 tree, 43 leaves, max depth = 10, train loss: 0.28134, val loss: 0.29112, in 0.016s
1 tree, 26 leaves, max depth = 12, train loss: 0.27878, val loss: 0.28881, in 0.000s
1 tree, 42 leaves, max depth = 10, train loss: 0.27624, val loss: 0.28681, in 0.016s
1 tree, 27 leaves, max depth = 10, train loss: 0.27398, val loss: 0.28477, in 0.016s
1 tree, 51 leaves, max depth = 14, train loss: 0.27135, val loss: 0.28186, in 0.016s
Fit 45 trees in 1.143 s, (2028 total leaves)
Time spent computing histograms: 0.312s
Time spent finding best splits:  0.064s
Time spent applying splits:      0.045s
Time spent predicting:           0.000s
Trial 15, Fold 3: Log loss = 0.27160769915984334, Average precision = 0.9628420835833131, ROC-AUC = 0.9593151972917529, Elapsed Time = 1.1477941000011924 seconds
Trial 15, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 15, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
Binning 0.040 GB of training data: 0.166 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 51 leaves, max depth = 10, train loss: 0.66237, val loss: 0.66117, in 0.016s
1 tree, 48 leaves, max depth = 9, train loss: 0.63481, val loss: 0.63244, in 0.016s
1 tree, 50 leaves, max depth = 9, train loss: 0.60993, val loss: 0.60653, in 0.031s
1 tree, 49 leaves, max depth = 13, train loss: 0.58852, val loss: 0.58411, in 0.016s
1 tree, 49 leaves, max depth = 12, train loss: 0.56757, val loss: 0.56228, in 0.016s
1 tree, 50 leaves, max depth = 11, train loss: 0.54987, val loss: 0.54374, in 0.016s
1 tree, 51 leaves, max depth = 11, train loss: 0.53227, val loss: 0.52533, in 0.016s
1 tree, 48 leaves, max depth = 12, train loss: 0.51624, val loss: 0.50850, in 0.031s
1 tree, 52 leaves, max depth = 11, train loss: 0.50151, val loss: 0.49305, in 0.016s
1 tree, 49 leaves, max depth = 10, train loss: 0.48804, val loss: 0.47885, in 0.016s
1 tree, 48 leaves, max depth = 10, train loss: 0.47561, val loss: 0.46574, in 0.016s
1 tree, 48 leaves, max depth = 10, train loss: 0.46415, val loss: 0.45366, in 0.016s
1 tree, 48 leaves, max depth = 10, train loss: 0.45363, val loss: 0.44252, in 0.016s
1 tree, 48 leaves, max depth = 10, train loss: 0.44388, val loss: 0.43219, in 0.031s
1 tree, 47 leaves, max depth = 11, train loss: 0.43489, val loss: 0.42258, in 0.016s
1 tree, 47 leaves, max depth = 12, train loss: 0.42176, val loss: 0.40926, in 0.016s
1 tree, 54 leaves, max depth = 14, train loss: 0.41434, val loss: 0.40140, in 0.016s
1 tree, 48 leaves, max depth = 12, train loss: 0.40284, val loss: 0.38972, in 0.031s
1 tree, 51 leaves, max depth = 11, train loss: 0.39587, val loss: 0.38238, in 0.016s
1 tree, 48 leaves, max depth = 9, train loss: 0.38939, val loss: 0.37551, in 0.016s
1 tree, 48 leaves, max depth = 12, train loss: 0.37960, val loss: 0.36560, in 0.016s
1 tree, 48 leaves, max depth = 12, train loss: 0.37065, val loss: 0.35655, in 0.016s
1 tree, 49 leaves, max depth = 10, train loss: 0.36536, val loss: 0.35091, in 0.032s
1 tree, 26 leaves, max depth = 11, train loss: 0.35778, val loss: 0.34306, in 0.000s
1 tree, 26 leaves, max depth = 11, train loss: 0.35087, val loss: 0.33590, in 0.016s
1 tree, 51 leaves, max depth = 11, train loss: 0.34544, val loss: 0.33042, in 0.031s
1 tree, 44 leaves, max depth = 9, train loss: 0.33917, val loss: 0.32456, in 0.031s
1 tree, 26 leaves, max depth = 10, train loss: 0.33342, val loss: 0.31856, in 0.016s
1 tree, 57 leaves, max depth = 11, train loss: 0.32856, val loss: 0.31359, in 0.031s
1 tree, 44 leaves, max depth = 11, train loss: 0.32328, val loss: 0.30871, in 0.031s
1 tree, 26 leaves, max depth = 12, train loss: 0.31847, val loss: 0.30369, in 0.016s
1 tree, 49 leaves, max depth = 12, train loss: 0.31431, val loss: 0.29953, in 0.016s
1 tree, 58 leaves, max depth = 12, train loss: 0.31021, val loss: 0.29529, in 0.031s
1 tree, 46 leaves, max depth = 11, train loss: 0.30588, val loss: 0.29131, in 0.016s
1 tree, 25 leaves, max depth = 10, train loss: 0.30193, val loss: 0.28718, in 0.016s
1 tree, 46 leaves, max depth = 11, train loss: 0.29813, val loss: 0.28361, in 0.016s
1 tree, 25 leaves, max depth = 10, train loss: 0.29468, val loss: 0.27991, in 0.016s
1 tree, 46 leaves, max depth = 11, train loss: 0.29135, val loss: 0.27689, in 0.031s
1 tree, 25 leaves, max depth = 10, train loss: 0.28834, val loss: 0.27371, in 0.016s
1 tree, 57 leaves, max depth = 11, train loss: 0.28506, val loss: 0.27032, in 0.016s
1 tree, 45 leaves, max depth = 10, train loss: 0.28221, val loss: 0.26775, in 0.016s
1 tree, 25 leaves, max depth = 10, train loss: 0.27968, val loss: 0.26501, in 0.016s
1 tree, 50 leaves, max depth = 12, train loss: 0.27689, val loss: 0.26223, in 0.031s
1 tree, 47 leaves, max depth = 11, train loss: 0.27444, val loss: 0.26005, in 0.016s
1 tree, 25 leaves, max depth = 10, train loss: 0.27226, val loss: 0.25774, in 0.016s
Fit 45 trees in 1.236 s, (1998 total leaves)
Time spent computing histograms: 0.333s
Time spent finding best splits:  0.074s
Time spent applying splits:      0.050s
Time spent predicting:           0.000s
Trial 15, Fold 4: Log loss = 0.2721552688535955, Average precision = 0.9637009760932421, ROC-AUC = 0.9597320132048613, Elapsed Time = 1.2341109000008146 seconds
Trial 15, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 15, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
Binning 0.040 GB of training data: 0.157 s
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 50 leaves, max depth = 12, train loss: 0.66188, val loss: 0.66069, in 0.016s
1 tree, 49 leaves, max depth = 12, train loss: 0.63385, val loss: 0.63157, in 0.016s
1 tree, 49 leaves, max depth = 12, train loss: 0.60861, val loss: 0.60532, in 0.016s
1 tree, 50 leaves, max depth = 12, train loss: 0.58686, val loss: 0.58256, in 0.031s
1 tree, 50 leaves, max depth = 12, train loss: 0.56588, val loss: 0.56065, in 0.016s
1 tree, 50 leaves, max depth = 12, train loss: 0.54790, val loss: 0.54184, in 0.016s
1 tree, 50 leaves, max depth = 11, train loss: 0.53037, val loss: 0.52360, in 0.016s
1 tree, 52 leaves, max depth = 12, train loss: 0.51431, val loss: 0.50682, in 0.031s
1 tree, 50 leaves, max depth = 11, train loss: 0.49961, val loss: 0.49151, in 0.016s
1 tree, 52 leaves, max depth = 12, train loss: 0.48604, val loss: 0.47746, in 0.016s
1 tree, 50 leaves, max depth = 10, train loss: 0.47363, val loss: 0.46450, in 0.016s
1 tree, 50 leaves, max depth = 11, train loss: 0.46218, val loss: 0.45253, in 0.016s
1 tree, 51 leaves, max depth = 11, train loss: 0.45166, val loss: 0.44155, in 0.016s
1 tree, 51 leaves, max depth = 12, train loss: 0.44191, val loss: 0.43140, in 0.016s
1 tree, 51 leaves, max depth = 15, train loss: 0.42819, val loss: 0.41770, in 0.016s
1 tree, 49 leaves, max depth = 10, train loss: 0.41969, val loss: 0.40884, in 0.031s
1 tree, 51 leaves, max depth = 11, train loss: 0.41221, val loss: 0.40106, in 0.016s
1 tree, 50 leaves, max depth = 12, train loss: 0.40065, val loss: 0.38961, in 0.016s
1 tree, 53 leaves, max depth = 11, train loss: 0.39365, val loss: 0.38248, in 0.016s
1 tree, 51 leaves, max depth = 11, train loss: 0.38718, val loss: 0.37591, in 0.031s
1 tree, 49 leaves, max depth = 15, train loss: 0.37734, val loss: 0.36618, in 0.016s
1 tree, 48 leaves, max depth = 12, train loss: 0.36833, val loss: 0.35731, in 0.016s
1 tree, 61 leaves, max depth = 10, train loss: 0.36316, val loss: 0.35204, in 0.031s
1 tree, 25 leaves, max depth = 10, train loss: 0.35550, val loss: 0.34428, in 0.000s
1 tree, 27 leaves, max depth = 11, train loss: 0.34857, val loss: 0.33709, in 0.016s
1 tree, 49 leaves, max depth = 10, train loss: 0.34312, val loss: 0.33164, in 0.016s
1 tree, 26 leaves, max depth = 10, train loss: 0.33694, val loss: 0.32538, in 0.016s
1 tree, 42 leaves, max depth = 12, train loss: 0.33113, val loss: 0.32024, in 0.016s
1 tree, 49 leaves, max depth = 12, train loss: 0.32637, val loss: 0.31550, in 0.016s
1 tree, 25 leaves, max depth = 10, train loss: 0.32124, val loss: 0.31018, in 0.016s
1 tree, 57 leaves, max depth = 12, train loss: 0.31684, val loss: 0.30572, in 0.031s
1 tree, 43 leaves, max depth = 12, train loss: 0.31210, val loss: 0.30158, in 0.016s
1 tree, 26 leaves, max depth = 14, train loss: 0.30776, val loss: 0.29715, in 0.016s
1 tree, 50 leaves, max depth = 12, train loss: 0.30395, val loss: 0.29341, in 0.016s
1 tree, 43 leaves, max depth = 12, train loss: 0.29995, val loss: 0.28997, in 0.016s
1 tree, 27 leaves, max depth = 10, train loss: 0.29629, val loss: 0.28623, in 0.016s
1 tree, 43 leaves, max depth = 12, train loss: 0.29279, val loss: 0.28312, in 0.016s
1 tree, 27 leaves, max depth = 11, train loss: 0.28960, val loss: 0.27986, in 0.031s
1 tree, 58 leaves, max depth = 11, train loss: 0.28627, val loss: 0.27655, in 0.016s
1 tree, 42 leaves, max depth = 14, train loss: 0.28329, val loss: 0.27394, in 0.016s
1 tree, 27 leaves, max depth = 11, train loss: 0.28057, val loss: 0.27106, in 0.016s
1 tree, 49 leaves, max depth = 11, train loss: 0.27773, val loss: 0.26832, in 0.016s
1 tree, 43 leaves, max depth = 12, train loss: 0.27518, val loss: 0.26612, in 0.016s
1 tree, 24 leaves, max depth = 9, train loss: 0.27287, val loss: 0.26377, in 0.016s
1 tree, 60 leaves, max depth = 11, train loss: 0.27023, val loss: 0.26117, in 0.016s
Fit 45 trees in 1.173 s, (2029 total leaves)
Time spent computing histograms: 0.307s
Time spent finding best splits:  0.063s
Time spent applying splits:      0.045s
Time spent predicting:           0.000s
Trial 15, Fold 5: Log loss = 0.27970886264491346, Average precision = 0.959729690231182, ROC-AUC = 0.9547875815171952, Elapsed Time = 1.1758101999985229 seconds
Optimization Progress:  16%|#6        | 16/100 [03:10<16:26, 11.75s/it]
Trial 16, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 16, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
0.142 s 0.040 GB of training data: 
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 37 leaves, max depth = 12, train loss: 0.68427, val loss: 0.68410, in 0.000s
1 tree, 39 leaves, max depth = 12, train loss: 0.67611, val loss: 0.67582, in 0.016s
1 tree, 32 leaves, max depth = 11, train loss: 0.66815, val loss: 0.66773, in 0.016s
1 tree, 36 leaves, max depth = 10, train loss: 0.66014, val loss: 0.65970, in 0.016s
1 tree, 38 leaves, max depth = 11, train loss: 0.65280, val loss: 0.65227, in 0.016s
1 tree, 40 leaves, max depth = 11, train loss: 0.64479, val loss: 0.64426, in 0.016s
1 tree, 22 leaves, max depth = 9, train loss: 0.63793, val loss: 0.63733, in 0.016s
1 tree, 36 leaves, max depth = 10, train loss: 0.63064, val loss: 0.63002, in 0.016s
1 tree, 33 leaves, max depth = 9, train loss: 0.62411, val loss: 0.62344, in 0.016s
1 tree, 38 leaves, max depth = 11, train loss: 0.61737, val loss: 0.61652, in 0.000s
1 tree, 34 leaves, max depth = 12, train loss: 0.61094, val loss: 0.61000, in 0.016s
1 tree, 33 leaves, max depth = 11, train loss: 0.60445, val loss: 0.60347, in 0.016s
1 tree, 18 leaves, max depth = 9, train loss: 0.59835, val loss: 0.59723, in 0.000s
1 tree, 36 leaves, max depth = 12, train loss: 0.59276, val loss: 0.59162, in 0.016s
1 tree, 36 leaves, max depth = 12, train loss: 0.58708, val loss: 0.58584, in 0.016s
1 tree, 23 leaves, max depth = 8, train loss: 0.58167, val loss: 0.58031, in 0.016s
1 tree, 41 leaves, max depth = 11, train loss: 0.57597, val loss: 0.57453, in 0.016s
1 tree, 34 leaves, max depth = 10, train loss: 0.57082, val loss: 0.56935, in 0.016s
1 tree, 38 leaves, max depth = 12, train loss: 0.56533, val loss: 0.56382, in 0.031s
1 tree, 41 leaves, max depth = 11, train loss: 0.55986, val loss: 0.55835, in 0.016s
1 tree, 33 leaves, max depth = 9, train loss: 0.55472, val loss: 0.55317, in 0.016s
1 tree, 33 leaves, max depth = 10, train loss: 0.54950, val loss: 0.54795, in 0.016s
1 tree, 32 leaves, max depth = 13, train loss: 0.54478, val loss: 0.54320, in 0.016s
1 tree, 38 leaves, max depth = 10, train loss: 0.54038, val loss: 0.53870, in 0.016s
1 tree, 35 leaves, max depth = 12, train loss: 0.53565, val loss: 0.53392, in 0.016s
1 tree, 32 leaves, max depth = 11, train loss: 0.53171, val loss: 0.52989, in 0.016s
1 tree, 33 leaves, max depth = 11, train loss: 0.52715, val loss: 0.52526, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.52292, val loss: 0.52092, in 0.016s
1 tree, 35 leaves, max depth = 12, train loss: 0.51857, val loss: 0.51648, in 0.016s
1 tree, 40 leaves, max depth = 11, train loss: 0.51442, val loss: 0.51231, in 0.016s
1 tree, 37 leaves, max depth = 10, train loss: 0.51021, val loss: 0.50804, in 0.031s
1 tree, 33 leaves, max depth = 10, train loss: 0.50655, val loss: 0.50431, in 0.016s
1 tree, 37 leaves, max depth = 10, train loss: 0.50243, val loss: 0.50015, in 0.016s
1 tree, 35 leaves, max depth = 12, train loss: 0.49858, val loss: 0.49630, in 0.016s
1 tree, 39 leaves, max depth = 9, train loss: 0.49472, val loss: 0.49246, in 0.016s
1 tree, 28 leaves, max depth = 9, train loss: 0.49125, val loss: 0.48899, in 0.016s
1 tree, 31 leaves, max depth = 13, train loss: 0.48788, val loss: 0.48549, in 0.016s
1 tree, 14 leaves, max depth = 9, train loss: 0.48334, val loss: 0.48101, in 0.016s
1 tree, 39 leaves, max depth = 10, train loss: 0.48006, val loss: 0.47769, in 0.016s
1 tree, 7 leaves, max depth = 4, train loss: 0.47557, val loss: 0.47331, in 0.016s
1 tree, 41 leaves, max depth = 10, train loss: 0.47264, val loss: 0.47034, in 0.016s
1 tree, 40 leaves, max depth = 10, train loss: 0.46937, val loss: 0.46706, in 0.016s
1 tree, 38 leaves, max depth = 15, train loss: 0.46641, val loss: 0.46403, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.46330, val loss: 0.46083, in 0.031s
1 tree, 8 leaves, max depth = 5, train loss: 0.46063, val loss: 0.45800, in 0.016s
1 tree, 45 leaves, max depth = 12, train loss: 0.45789, val loss: 0.45523, in 0.016s
1 tree, 36 leaves, max depth = 13, train loss: 0.45502, val loss: 0.45233, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.45250, val loss: 0.44973, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.45008, val loss: 0.44713, in 0.016s
1 tree, 36 leaves, max depth = 12, train loss: 0.44732, val loss: 0.44428, in 0.016s
1 tree, 39 leaves, max depth = 9, train loss: 0.44465, val loss: 0.44161, in 0.016s
1 tree, 22 leaves, max depth = 8, train loss: 0.44226, val loss: 0.43910, in 0.016s
1 tree, 15 leaves, max depth = 9, train loss: 0.43848, val loss: 0.43544, in 0.016s
1 tree, 33 leaves, max depth = 11, train loss: 0.43591, val loss: 0.43278, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.43237, val loss: 0.42937, in 0.016s
1 tree, 39 leaves, max depth = 11, train loss: 0.42989, val loss: 0.42688, in 0.016s
1 tree, 37 leaves, max depth = 10, train loss: 0.42780, val loss: 0.42479, in 0.016s
1 tree, 33 leaves, max depth = 10, train loss: 0.42575, val loss: 0.42272, in 0.016s
1 tree, 34 leaves, max depth = 14, train loss: 0.42357, val loss: 0.42047, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.42173, val loss: 0.41859, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.41953, val loss: 0.41639, in 0.031s
1 tree, 38 leaves, max depth = 13, train loss: 0.41718, val loss: 0.41403, in 0.016s
1 tree, 33 leaves, max depth = 12, train loss: 0.41505, val loss: 0.41194, in 0.016s
1 tree, 21 leaves, max depth = 8, train loss: 0.41193, val loss: 0.40893, in 0.016s
1 tree, 55 leaves, max depth = 12, train loss: 0.40956, val loss: 0.40656, in 0.016s
1 tree, 37 leaves, max depth = 14, train loss: 0.40755, val loss: 0.40454, in 0.016s
1 tree, 41 leaves, max depth = 11, train loss: 0.40584, val loss: 0.40279, in 0.016s
1 tree, 36 leaves, max depth = 11, train loss: 0.40391, val loss: 0.40084, in 0.016s
1 tree, 38 leaves, max depth = 10, train loss: 0.40186, val loss: 0.39886, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.39963, val loss: 0.39667, in 0.016s
1 tree, 36 leaves, max depth = 11, train loss: 0.39750, val loss: 0.39455, in 0.016s
1 tree, 11 leaves, max depth = 7, train loss: 0.39565, val loss: 0.39262, in 0.016s
1 tree, 28 leaves, max depth = 8, train loss: 0.39398, val loss: 0.39086, in 0.016s
1 tree, 8 leaves, max depth = 4, train loss: 0.39232, val loss: 0.38908, in 0.016s
Fit 74 trees in 1.517 s, (2394 total leaves)
Time spent computing histograms: 0.512s
Time spent finding best splits:  0.062s
Time spent applying splits:      0.054s
Time spent predicting:           0.000s
Trial 16, Fold 1: Log loss = 0.3938402636701185, Average precision = 0.9528827845173964, ROC-AUC = 0.9465832359492026, Elapsed Time = 1.5249160999992455 seconds
Trial 16, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 16, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
Binning 0.040 GB of training data: 0.174 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 34 leaves, max depth = 9, train loss: 0.68432, val loss: 0.68414, in 0.016s
1 tree, 31 leaves, max depth = 8, train loss: 0.67584, val loss: 0.67557, in 0.016s
1 tree, 38 leaves, max depth = 16, train loss: 0.66782, val loss: 0.66742, in 0.016s
1 tree, 34 leaves, max depth = 11, train loss: 0.66017, val loss: 0.65948, in 0.016s
1 tree, 36 leaves, max depth = 12, train loss: 0.65277, val loss: 0.65180, in 0.031s
1 tree, 32 leaves, max depth = 11, train loss: 0.64499, val loss: 0.64385, in 0.000s
1 tree, 28 leaves, max depth = 8, train loss: 0.63818, val loss: 0.63678, in 0.031s
1 tree, 42 leaves, max depth = 11, train loss: 0.63110, val loss: 0.62954, in 0.016s
1 tree, 35 leaves, max depth = 12, train loss: 0.62431, val loss: 0.62272, in 0.016s
1 tree, 42 leaves, max depth = 12, train loss: 0.61733, val loss: 0.61556, in 0.016s
1 tree, 34 leaves, max depth = 12, train loss: 0.61098, val loss: 0.60907, in 0.016s
1 tree, 34 leaves, max depth = 13, train loss: 0.60466, val loss: 0.60265, in 0.016s
1 tree, 30 leaves, max depth = 11, train loss: 0.59875, val loss: 0.59650, in 0.016s
1 tree, 37 leaves, max depth = 11, train loss: 0.59236, val loss: 0.58996, in 0.016s
1 tree, 34 leaves, max depth = 11, train loss: 0.58671, val loss: 0.58416, in 0.016s
1 tree, 39 leaves, max depth = 12, train loss: 0.58109, val loss: 0.57838, in 0.016s
1 tree, 33 leaves, max depth = 12, train loss: 0.57564, val loss: 0.57282, in 0.016s
1 tree, 35 leaves, max depth = 12, train loss: 0.56987, val loss: 0.56703, in 0.016s
1 tree, 35 leaves, max depth = 13, train loss: 0.56460, val loss: 0.56162, in 0.031s
1 tree, 38 leaves, max depth = 11, train loss: 0.55918, val loss: 0.55608, in 0.016s
1 tree, 33 leaves, max depth = 12, train loss: 0.55427, val loss: 0.55105, in 0.016s
1 tree, 35 leaves, max depth = 16, train loss: 0.54940, val loss: 0.54610, in 0.016s
1 tree, 33 leaves, max depth = 11, train loss: 0.54474, val loss: 0.54129, in 0.016s
1 tree, 38 leaves, max depth = 9, train loss: 0.54002, val loss: 0.53651, in 0.016s
1 tree, 34 leaves, max depth = 11, train loss: 0.53567, val loss: 0.53205, in 0.016s
1 tree, 33 leaves, max depth = 12, train loss: 0.53104, val loss: 0.52730, in 0.016s
1 tree, 35 leaves, max depth = 12, train loss: 0.52689, val loss: 0.52308, in 0.016s
1 tree, 34 leaves, max depth = 10, train loss: 0.52270, val loss: 0.51882, in 0.016s
1 tree, 34 leaves, max depth = 12, train loss: 0.51853, val loss: 0.51460, in 0.016s
1 tree, 29 leaves, max depth = 8, train loss: 0.51482, val loss: 0.51076, in 0.016s
1 tree, 34 leaves, max depth = 11, train loss: 0.51100, val loss: 0.50684, in 0.016s
1 tree, 22 leaves, max depth = 11, train loss: 0.50715, val loss: 0.50292, in 0.016s
1 tree, 34 leaves, max depth = 10, train loss: 0.50312, val loss: 0.49884, in 0.016s
1 tree, 35 leaves, max depth = 13, train loss: 0.49957, val loss: 0.49515, in 0.016s
1 tree, 37 leaves, max depth = 12, train loss: 0.49614, val loss: 0.49160, in 0.016s
1 tree, 37 leaves, max depth = 12, train loss: 0.49264, val loss: 0.48812, in 0.016s
1 tree, 32 leaves, max depth = 11, train loss: 0.48930, val loss: 0.48475, in 0.016s
1 tree, 34 leaves, max depth = 12, train loss: 0.48582, val loss: 0.48123, in 0.031s
1 tree, 36 leaves, max depth = 9, train loss: 0.48249, val loss: 0.47789, in 0.016s
1 tree, 36 leaves, max depth = 12, train loss: 0.47936, val loss: 0.47471, in 0.016s
1 tree, 30 leaves, max depth = 9, train loss: 0.47651, val loss: 0.47175, in 0.016s
1 tree, 40 leaves, max depth = 13, train loss: 0.47316, val loss: 0.46838, in 0.016s
1 tree, 35 leaves, max depth = 12, train loss: 0.47023, val loss: 0.46542, in 0.031s
1 tree, 34 leaves, max depth = 11, train loss: 0.46703, val loss: 0.46216, in 0.016s
1 tree, 27 leaves, max depth = 12, train loss: 0.46429, val loss: 0.45934, in 0.016s
1 tree, 36 leaves, max depth = 13, train loss: 0.46157, val loss: 0.45661, in 0.016s
1 tree, 11 leaves, max depth = 7, train loss: 0.45754, val loss: 0.45257, in 0.016s
1 tree, 13 leaves, max depth = 7, train loss: 0.45489, val loss: 0.44988, in 0.016s
1 tree, 41 leaves, max depth = 9, train loss: 0.45204, val loss: 0.44700, in 0.016s
1 tree, 36 leaves, max depth = 14, train loss: 0.44930, val loss: 0.44419, in 0.031s
1 tree, 35 leaves, max depth = 9, train loss: 0.44660, val loss: 0.44143, in 0.016s
1 tree, 20 leaves, max depth = 9, train loss: 0.44422, val loss: 0.43895, in 0.016s
1 tree, 12 leaves, max depth = 6, train loss: 0.44045, val loss: 0.43522, in 0.016s
1 tree, 36 leaves, max depth = 11, train loss: 0.43783, val loss: 0.43258, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.43430, val loss: 0.42904, in 0.016s
1 tree, 42 leaves, max depth = 11, train loss: 0.43178, val loss: 0.42651, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.42955, val loss: 0.42422, in 0.031s
1 tree, 35 leaves, max depth = 10, train loss: 0.42743, val loss: 0.42205, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.42530, val loss: 0.41996, in 0.016s
1 tree, 39 leaves, max depth = 12, train loss: 0.42347, val loss: 0.41808, in 0.031s
1 tree, 36 leaves, max depth = 11, train loss: 0.42104, val loss: 0.41563, in 0.016s
1 tree, 32 leaves, max depth = 10, train loss: 0.41847, val loss: 0.41307, in 0.031s
1 tree, 37 leaves, max depth = 12, train loss: 0.41598, val loss: 0.41061, in 0.031s
1 tree, 35 leaves, max depth = 10, train loss: 0.41433, val loss: 0.40884, in 0.016s
1 tree, 44 leaves, max depth = 11, train loss: 0.41189, val loss: 0.40643, in 0.016s
1 tree, 39 leaves, max depth = 11, train loss: 0.40998, val loss: 0.40454, in 0.031s
1 tree, 33 leaves, max depth = 12, train loss: 0.40837, val loss: 0.40289, in 0.016s
1 tree, 36 leaves, max depth = 10, train loss: 0.40608, val loss: 0.40062, in 0.016s
1 tree, 42 leaves, max depth = 11, train loss: 0.40401, val loss: 0.39851, in 0.016s
1 tree, 43 leaves, max depth = 11, train loss: 0.40178, val loss: 0.39631, in 0.031s
1 tree, 5 leaves, max depth = 3, train loss: 0.39906, val loss: 0.39371, in 0.016s
1 tree, 7 leaves, max depth = 4, train loss: 0.39722, val loss: 0.39184, in 0.016s
1 tree, 23 leaves, max depth = 9, train loss: 0.39560, val loss: 0.39021, in 0.016s
1 tree, 11 leaves, max depth = 7, train loss: 0.39299, val loss: 0.38773, in 0.016s
Fit 74 trees in 1.706 s, (2392 total leaves)
Time spent computing histograms: 0.574s
Time spent finding best splits:  0.070s
Time spent applying splits:      0.062s
Time spent predicting:           0.000s
Trial 16, Fold 2: Log loss = 0.3954167607410999, Average precision = 0.9511083064377481, ROC-AUC = 0.9473437460640419, Elapsed Time = 1.711078099999213 seconds
Trial 16, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 16, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
Binning 0.040 GB of training data: 0.173 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 33 leaves, max depth = 13, train loss: 0.68432, val loss: 0.68415, in 0.016s
1 tree, 41 leaves, max depth = 10, train loss: 0.67628, val loss: 0.67603, in 0.016s
1 tree, 33 leaves, max depth = 13, train loss: 0.66836, val loss: 0.66792, in 0.031s
1 tree, 35 leaves, max depth = 11, train loss: 0.66030, val loss: 0.65979, in 0.016s
1 tree, 39 leaves, max depth = 10, train loss: 0.65290, val loss: 0.65227, in 0.016s
1 tree, 39 leaves, max depth = 10, train loss: 0.64492, val loss: 0.64428, in 0.016s
1 tree, 37 leaves, max depth = 14, train loss: 0.63789, val loss: 0.63716, in 0.016s
1 tree, 35 leaves, max depth = 13, train loss: 0.63093, val loss: 0.63018, in 0.016s
1 tree, 34 leaves, max depth = 11, train loss: 0.62433, val loss: 0.62348, in 0.016s
1 tree, 33 leaves, max depth = 11, train loss: 0.61767, val loss: 0.61671, in 0.016s
1 tree, 35 leaves, max depth = 12, train loss: 0.61120, val loss: 0.61010, in 0.031s
1 tree, 35 leaves, max depth = 10, train loss: 0.60466, val loss: 0.60351, in 0.016s
1 tree, 33 leaves, max depth = 12, train loss: 0.59862, val loss: 0.59745, in 0.016s
1 tree, 26 leaves, max depth = 9, train loss: 0.59273, val loss: 0.59144, in 0.016s
1 tree, 37 leaves, max depth = 9, train loss: 0.58705, val loss: 0.58572, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.58160, val loss: 0.58016, in 0.016s
1 tree, 33 leaves, max depth = 9, train loss: 0.57574, val loss: 0.57422, in 0.031s
1 tree, 30 leaves, max depth = 11, train loss: 0.57043, val loss: 0.56882, in 0.016s
1 tree, 28 leaves, max depth = 8, train loss: 0.56522, val loss: 0.56357, in 0.016s
1 tree, 34 leaves, max depth = 11, train loss: 0.56029, val loss: 0.55855, in 0.016s
1 tree, 34 leaves, max depth = 14, train loss: 0.55492, val loss: 0.55315, in 0.031s
1 tree, 43 leaves, max depth = 11, train loss: 0.55041, val loss: 0.54858, in 0.016s
1 tree, 37 leaves, max depth = 11, train loss: 0.54545, val loss: 0.54357, in 0.031s
1 tree, 26 leaves, max depth = 8, train loss: 0.54085, val loss: 0.53897, in 0.016s
1 tree, 34 leaves, max depth = 11, train loss: 0.53646, val loss: 0.53456, in 0.016s
1 tree, 33 leaves, max depth = 10, train loss: 0.53253, val loss: 0.53054, in 0.016s
1 tree, 33 leaves, max depth = 10, train loss: 0.52793, val loss: 0.52592, in 0.031s
1 tree, 34 leaves, max depth = 11, train loss: 0.52374, val loss: 0.52160, in 0.016s
1 tree, 34 leaves, max depth = 12, train loss: 0.51941, val loss: 0.51727, in 0.016s
1 tree, 35 leaves, max depth = 12, train loss: 0.51518, val loss: 0.51302, in 0.016s
1 tree, 32 leaves, max depth = 11, train loss: 0.51136, val loss: 0.50924, in 0.016s
1 tree, 27 leaves, max depth = 9, train loss: 0.50748, val loss: 0.50530, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.50364, val loss: 0.50148, in 0.031s
1 tree, 34 leaves, max depth = 11, train loss: 0.50002, val loss: 0.49790, in 0.016s
1 tree, 36 leaves, max depth = 13, train loss: 0.49611, val loss: 0.49400, in 0.016s
1 tree, 48 leaves, max depth = 10, train loss: 0.49242, val loss: 0.49027, in 0.016s
1 tree, 32 leaves, max depth = 13, train loss: 0.48908, val loss: 0.48692, in 0.031s
1 tree, 19 leaves, max depth = 9, train loss: 0.48455, val loss: 0.48265, in 0.016s
1 tree, 39 leaves, max depth = 11, train loss: 0.48125, val loss: 0.47931, in 0.016s
1 tree, 7 leaves, max depth = 4, train loss: 0.47685, val loss: 0.47515, in 0.016s
1 tree, 6 leaves, max depth = 3, train loss: 0.47404, val loss: 0.47235, in 0.016s
1 tree, 46 leaves, max depth = 11, train loss: 0.47072, val loss: 0.46902, in 0.016s
1 tree, 34 leaves, max depth = 10, train loss: 0.46783, val loss: 0.46610, in 0.016s
1 tree, 37 leaves, max depth = 10, train loss: 0.46473, val loss: 0.46299, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.46201, val loss: 0.46028, in 0.016s
1 tree, 39 leaves, max depth = 14, train loss: 0.45926, val loss: 0.45753, in 0.031s
1 tree, 10 leaves, max depth = 5, train loss: 0.45538, val loss: 0.45391, in 0.016s
1 tree, 13 leaves, max depth = 6, train loss: 0.45288, val loss: 0.45130, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.45026, val loss: 0.44870, in 0.016s
1 tree, 34 leaves, max depth = 13, train loss: 0.44774, val loss: 0.44621, in 0.016s
1 tree, 8 leaves, max depth = 4, train loss: 0.44533, val loss: 0.44383, in 0.016s
1 tree, 18 leaves, max depth = 7, train loss: 0.44278, val loss: 0.44125, in 0.031s
1 tree, 4 leaves, max depth = 2, train loss: 0.44061, val loss: 0.43910, in 0.016s
1 tree, 40 leaves, max depth = 11, train loss: 0.43827, val loss: 0.43681, in 0.016s
1 tree, 31 leaves, max depth = 12, train loss: 0.43604, val loss: 0.43457, in 0.016s
1 tree, 33 leaves, max depth = 12, train loss: 0.43353, val loss: 0.43207, in 0.031s
1 tree, 35 leaves, max depth = 12, train loss: 0.43116, val loss: 0.42978, in 0.016s
1 tree, 34 leaves, max depth = 10, train loss: 0.42879, val loss: 0.42740, in 0.016s
1 tree, 46 leaves, max depth = 12, train loss: 0.42612, val loss: 0.42469, in 0.031s
1 tree, 33 leaves, max depth = 12, train loss: 0.42426, val loss: 0.42284, in 0.016s
1 tree, 36 leaves, max depth = 10, train loss: 0.42167, val loss: 0.42020, in 0.016s
1 tree, 36 leaves, max depth = 11, train loss: 0.41935, val loss: 0.41791, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.41717, val loss: 0.41570, in 0.031s
1 tree, 40 leaves, max depth = 12, train loss: 0.41470, val loss: 0.41315, in 0.016s
1 tree, 37 leaves, max depth = 10, train loss: 0.41263, val loss: 0.41112, in 0.016s
1 tree, 36 leaves, max depth = 11, train loss: 0.41081, val loss: 0.40927, in 0.031s
1 tree, 35 leaves, max depth = 14, train loss: 0.40876, val loss: 0.40725, in 0.016s
1 tree, 37 leaves, max depth = 10, train loss: 0.40682, val loss: 0.40532, in 0.016s
1 tree, 40 leaves, max depth = 11, train loss: 0.40474, val loss: 0.40324, in 0.016s
1 tree, 38 leaves, max depth = 11, train loss: 0.40253, val loss: 0.40096, in 0.031s
1 tree, 35 leaves, max depth = 11, train loss: 0.40068, val loss: 0.39916, in 0.016s
1 tree, 13 leaves, max depth = 6, train loss: 0.39868, val loss: 0.39730, in 0.016s
1 tree, 11 leaves, max depth = 7, train loss: 0.39575, val loss: 0.39462, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.39411, val loss: 0.39301, in 0.016s
Fit 74 trees in 1.752 s, (2308 total leaves)
Time spent computing histograms: 0.604s
Time spent finding best splits:  0.070s
Time spent applying splits:      0.061s
Time spent predicting:           0.000s
Trial 16, Fold 3: Log loss = 0.39023381825865555, Average precision = 0.9561549356669634, ROC-AUC = 0.9512085745105027, Elapsed Time = 1.7605428999995638 seconds
Trial 16, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 16, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
Binning 0.040 GB of training data: 0.157 s
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 32 leaves, max depth = 11, train loss: 0.68448, val loss: 0.68410, in 0.016s
1 tree, 34 leaves, max depth = 11, train loss: 0.67644, val loss: 0.67568, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.66844, val loss: 0.66726, in 0.016s
1 tree, 36 leaves, max depth = 12, train loss: 0.66060, val loss: 0.65906, in 0.016s
1 tree, 38 leaves, max depth = 17, train loss: 0.65309, val loss: 0.65120, in 0.016s
1 tree, 34 leaves, max depth = 11, train loss: 0.64586, val loss: 0.64373, in 0.031s
1 tree, 34 leaves, max depth = 10, train loss: 0.63842, val loss: 0.63592, in 0.016s
1 tree, 34 leaves, max depth = 12, train loss: 0.63161, val loss: 0.62882, in 0.016s
1 tree, 39 leaves, max depth = 10, train loss: 0.62463, val loss: 0.62156, in 0.016s
1 tree, 32 leaves, max depth = 12, train loss: 0.61781, val loss: 0.61437, in 0.016s
1 tree, 32 leaves, max depth = 12, train loss: 0.61140, val loss: 0.60764, in 0.016s
1 tree, 33 leaves, max depth = 12, train loss: 0.60514, val loss: 0.60107, in 0.031s
1 tree, 32 leaves, max depth = 15, train loss: 0.59923, val loss: 0.59500, in 0.016s
1 tree, 32 leaves, max depth = 11, train loss: 0.59306, val loss: 0.58853, in 0.016s
1 tree, 39 leaves, max depth = 11, train loss: 0.58740, val loss: 0.58263, in 0.016s
1 tree, 33 leaves, max depth = 10, train loss: 0.58194, val loss: 0.57695, in 0.016s
1 tree, 35 leaves, max depth = 16, train loss: 0.57594, val loss: 0.57071, in 0.031s
1 tree, 34 leaves, max depth = 11, train loss: 0.57038, val loss: 0.56485, in 0.016s
1 tree, 36 leaves, max depth = 12, train loss: 0.56533, val loss: 0.55956, in 0.016s
1 tree, 36 leaves, max depth = 12, train loss: 0.55999, val loss: 0.55390, in 0.016s
1 tree, 29 leaves, max depth = 9, train loss: 0.55519, val loss: 0.54875, in 0.016s
1 tree, 37 leaves, max depth = 11, train loss: 0.55030, val loss: 0.54363, in 0.016s
1 tree, 36 leaves, max depth = 13, train loss: 0.54530, val loss: 0.53835, in 0.031s
1 tree, 35 leaves, max depth = 13, train loss: 0.54065, val loss: 0.53345, in 0.016s
1 tree, 28 leaves, max depth = 11, train loss: 0.53616, val loss: 0.52865, in 0.016s
1 tree, 25 leaves, max depth = 8, train loss: 0.53199, val loss: 0.52431, in 0.016s
1 tree, 30 leaves, max depth = 11, train loss: 0.52779, val loss: 0.51979, in 0.016s
1 tree, 37 leaves, max depth = 11, train loss: 0.52372, val loss: 0.51546, in 0.016s
1 tree, 37 leaves, max depth = 13, train loss: 0.51958, val loss: 0.51108, in 0.031s
1 tree, 31 leaves, max depth = 9, train loss: 0.51595, val loss: 0.50717, in 0.016s
1 tree, 33 leaves, max depth = 11, train loss: 0.51220, val loss: 0.50325, in 0.016s
1 tree, 25 leaves, max depth = 8, train loss: 0.50836, val loss: 0.49918, in 0.016s
1 tree, 34 leaves, max depth = 12, train loss: 0.50437, val loss: 0.49499, in 0.016s
1 tree, 34 leaves, max depth = 9, train loss: 0.50082, val loss: 0.49131, in 0.016s
1 tree, 36 leaves, max depth = 13, train loss: 0.49743, val loss: 0.48775, in 0.016s
1 tree, 32 leaves, max depth = 13, train loss: 0.49433, val loss: 0.48450, in 0.016s
1 tree, 39 leaves, max depth = 11, train loss: 0.49088, val loss: 0.48085, in 0.031s
1 tree, 35 leaves, max depth = 13, train loss: 0.48760, val loss: 0.47738, in 0.016s
1 tree, 8 leaves, max depth = 4, train loss: 0.48309, val loss: 0.47272, in 0.016s
1 tree, 34 leaves, max depth = 11, train loss: 0.48005, val loss: 0.46954, in 0.031s
1 tree, 5 leaves, max depth = 3, train loss: 0.47590, val loss: 0.46526, in 0.016s
1 tree, 35 leaves, max depth = 14, train loss: 0.47310, val loss: 0.46239, in 0.016s
1 tree, 36 leaves, max depth = 13, train loss: 0.46987, val loss: 0.45896, in 0.016s
1 tree, 41 leaves, max depth = 12, train loss: 0.46659, val loss: 0.45552, in 0.031s
1 tree, 34 leaves, max depth = 11, train loss: 0.46387, val loss: 0.45269, in 0.016s
1 tree, 33 leaves, max depth = 12, train loss: 0.46120, val loss: 0.44990, in 0.016s
1 tree, 35 leaves, max depth = 12, train loss: 0.45833, val loss: 0.44684, in 0.016s
1 tree, 31 leaves, max depth = 12, train loss: 0.45548, val loss: 0.44376, in 0.031s
1 tree, 35 leaves, max depth = 11, train loss: 0.45294, val loss: 0.44102, in 0.016s
1 tree, 37 leaves, max depth = 14, train loss: 0.45015, val loss: 0.43806, in 0.016s
1 tree, 37 leaves, max depth = 15, train loss: 0.44769, val loss: 0.43542, in 0.016s
1 tree, 37 leaves, max depth = 12, train loss: 0.44502, val loss: 0.43257, in 0.031s
1 tree, 30 leaves, max depth = 14, train loss: 0.44293, val loss: 0.43040, in 0.016s
1 tree, 36 leaves, max depth = 11, train loss: 0.44039, val loss: 0.42769, in 0.016s
1 tree, 11 leaves, max depth = 8, train loss: 0.43693, val loss: 0.42415, in 0.016s
1 tree, 41 leaves, max depth = 11, train loss: 0.43439, val loss: 0.42143, in 0.016s
1 tree, 41 leaves, max depth = 11, train loss: 0.43216, val loss: 0.41904, in 0.031s
1 tree, 33 leaves, max depth = 11, train loss: 0.42983, val loss: 0.41656, in 0.016s
1 tree, 31 leaves, max depth = 12, train loss: 0.42790, val loss: 0.41456, in 0.016s
1 tree, 48 leaves, max depth = 11, train loss: 0.42588, val loss: 0.41243, in 0.016s
1 tree, 35 leaves, max depth = 12, train loss: 0.42370, val loss: 0.41011, in 0.016s
1 tree, 37 leaves, max depth = 8, train loss: 0.42174, val loss: 0.40800, in 0.016s
1 tree, 36 leaves, max depth = 9, train loss: 0.41976, val loss: 0.40596, in 0.016s
1 tree, 39 leaves, max depth = 12, train loss: 0.41752, val loss: 0.40364, in 0.016s
1 tree, 36 leaves, max depth = 12, train loss: 0.41569, val loss: 0.40166, in 0.016s
1 tree, 4 leaves, max depth = 2, train loss: 0.41395, val loss: 0.39977, in 0.031s
1 tree, 31 leaves, max depth = 13, train loss: 0.41219, val loss: 0.39786, in 0.016s
1 tree, 37 leaves, max depth = 12, train loss: 0.41006, val loss: 0.39568, in 0.016s
1 tree, 42 leaves, max depth = 12, train loss: 0.40784, val loss: 0.39347, in 0.031s
1 tree, 40 leaves, max depth = 13, train loss: 0.40561, val loss: 0.39121, in 0.016s
1 tree, 11 leaves, max depth = 6, train loss: 0.40360, val loss: 0.38915, in 0.016s
1 tree, 38 leaves, max depth = 9, train loss: 0.40171, val loss: 0.38717, in 0.031s
1 tree, 35 leaves, max depth = 12, train loss: 0.40018, val loss: 0.38555, in 0.016s
1 tree, 34 leaves, max depth = 11, train loss: 0.39847, val loss: 0.38367, in 0.016s
Fit 74 trees in 1.751 s, (2447 total leaves)
Time spent computing histograms: 0.598s
Time spent finding best splits:  0.072s
Time spent applying splits:      0.064s
Time spent predicting:           0.000s
Trial 16, Fold 4: Log loss = 0.3980620539886425, Average precision = 0.951851052319919, ROC-AUC = 0.9459866359793129, Elapsed Time = 1.7685146000003442 seconds
Trial 16, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 16, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
Binning 0.040 GB of training data: 0.158 s
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 34 leaves, max depth = 11, train loss: 0.68426, val loss: 0.68370, in 0.016s
1 tree, 41 leaves, max depth = 11, train loss: 0.67617, val loss: 0.67516, in 0.016s
1 tree, 33 leaves, max depth = 11, train loss: 0.66823, val loss: 0.66676, in 0.016s
1 tree, 30 leaves, max depth = 10, train loss: 0.66016, val loss: 0.65826, in 0.016s
1 tree, 41 leaves, max depth = 11, train loss: 0.65272, val loss: 0.65057, in 0.016s
1 tree, 36 leaves, max depth = 12, train loss: 0.64472, val loss: 0.64225, in 0.016s
1 tree, 32 leaves, max depth = 11, train loss: 0.63796, val loss: 0.63515, in 0.031s
1 tree, 38 leaves, max depth = 12, train loss: 0.63079, val loss: 0.62764, in 0.016s
1 tree, 40 leaves, max depth = 11, train loss: 0.62391, val loss: 0.62044, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.61718, val loss: 0.61326, in 0.016s
1 tree, 40 leaves, max depth = 12, train loss: 0.61083, val loss: 0.60659, in 0.016s
1 tree, 34 leaves, max depth = 13, train loss: 0.60419, val loss: 0.59965, in 0.016s
1 tree, 35 leaves, max depth = 14, train loss: 0.59815, val loss: 0.59330, in 0.016s
1 tree, 25 leaves, max depth = 9, train loss: 0.59213, val loss: 0.58705, in 0.016s
1 tree, 33 leaves, max depth = 12, train loss: 0.58642, val loss: 0.58118, in 0.016s
1 tree, 39 leaves, max depth = 10, train loss: 0.58092, val loss: 0.57545, in 0.031s
1 tree, 41 leaves, max depth = 11, train loss: 0.57514, val loss: 0.56935, in 0.016s
1 tree, 15 leaves, max depth = 6, train loss: 0.56979, val loss: 0.56366, in 0.016s
1 tree, 21 leaves, max depth = 9, train loss: 0.56468, val loss: 0.55828, in 0.016s
1 tree, 36 leaves, max depth = 11, train loss: 0.55916, val loss: 0.55257, in 0.016s
1 tree, 36 leaves, max depth = 12, train loss: 0.55392, val loss: 0.54711, in 0.016s
1 tree, 38 leaves, max depth = 11, train loss: 0.54862, val loss: 0.54158, in 0.031s
1 tree, 28 leaves, max depth = 9, train loss: 0.54397, val loss: 0.53668, in 0.031s
1 tree, 27 leaves, max depth = 7, train loss: 0.53959, val loss: 0.53213, in 0.031s
1 tree, 34 leaves, max depth = 13, train loss: 0.53524, val loss: 0.52760, in 0.031s
1 tree, 35 leaves, max depth = 12, train loss: 0.53128, val loss: 0.52340, in 0.032s
1 tree, 34 leaves, max depth = 12, train loss: 0.52661, val loss: 0.51851, in 0.031s
1 tree, 35 leaves, max depth = 11, train loss: 0.52240, val loss: 0.51400, in 0.016s
1 tree, 34 leaves, max depth = 12, train loss: 0.51810, val loss: 0.50953, in 0.031s
1 tree, 34 leaves, max depth = 11, train loss: 0.51382, val loss: 0.50500, in 0.016s
1 tree, 38 leaves, max depth = 12, train loss: 0.50992, val loss: 0.50086, in 0.016s
1 tree, 32 leaves, max depth = 9, train loss: 0.50612, val loss: 0.49682, in 0.016s
1 tree, 38 leaves, max depth = 12, train loss: 0.50201, val loss: 0.49254, in 0.016s
1 tree, 22 leaves, max depth = 7, train loss: 0.49802, val loss: 0.48838, in 0.031s
1 tree, 41 leaves, max depth = 13, train loss: 0.49408, val loss: 0.48431, in 0.016s
1 tree, 39 leaves, max depth = 14, train loss: 0.49034, val loss: 0.48044, in 0.031s
1 tree, 38 leaves, max depth = 11, train loss: 0.48725, val loss: 0.47718, in 0.016s
1 tree, 33 leaves, max depth = 10, train loss: 0.48394, val loss: 0.47367, in 0.016s
1 tree, 38 leaves, max depth = 12, train loss: 0.48064, val loss: 0.47009, in 0.016s
1 tree, 7 leaves, max depth = 4, train loss: 0.47614, val loss: 0.46555, in 0.031s
1 tree, 42 leaves, max depth = 11, train loss: 0.47315, val loss: 0.46246, in 0.016s
1 tree, 45 leaves, max depth = 12, train loss: 0.46988, val loss: 0.45909, in 0.016s
1 tree, 36 leaves, max depth = 13, train loss: 0.46698, val loss: 0.45599, in 0.031s
1 tree, 35 leaves, max depth = 12, train loss: 0.46389, val loss: 0.45271, in 0.016s
1 tree, 8 leaves, max depth = 5, train loss: 0.46123, val loss: 0.44994, in 0.016s
1 tree, 40 leaves, max depth = 12, train loss: 0.45850, val loss: 0.44717, in 0.031s
1 tree, 9 leaves, max depth = 5, train loss: 0.45461, val loss: 0.44322, in 0.016s
1 tree, 16 leaves, max depth = 7, train loss: 0.45204, val loss: 0.44062, in 0.016s
1 tree, 42 leaves, max depth = 10, train loss: 0.44935, val loss: 0.43771, in 0.016s
1 tree, 36 leaves, max depth = 11, train loss: 0.44656, val loss: 0.43475, in 0.031s
1 tree, 35 leaves, max depth = 11, train loss: 0.44416, val loss: 0.43224, in 0.016s
1 tree, 38 leaves, max depth = 11, train loss: 0.44160, val loss: 0.42949, in 0.016s
1 tree, 9 leaves, max depth = 4, train loss: 0.43792, val loss: 0.42574, in 0.016s
1 tree, 36 leaves, max depth = 12, train loss: 0.43554, val loss: 0.42323, in 0.031s
1 tree, 39 leaves, max depth = 11, train loss: 0.43333, val loss: 0.42091, in 0.016s
1 tree, 34 leaves, max depth = 12, train loss: 0.43080, val loss: 0.41827, in 0.031s
1 tree, 32 leaves, max depth = 11, train loss: 0.42855, val loss: 0.41591, in 0.016s
1 tree, 35 leaves, max depth = 12, train loss: 0.42641, val loss: 0.41369, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.42427, val loss: 0.41141, in 0.016s
1 tree, 27 leaves, max depth = 8, train loss: 0.42244, val loss: 0.40947, in 0.016s
1 tree, 36 leaves, max depth = 11, train loss: 0.41985, val loss: 0.40685, in 0.031s
1 tree, 33 leaves, max depth = 11, train loss: 0.41770, val loss: 0.40461, in 0.016s
1 tree, 39 leaves, max depth = 10, train loss: 0.41524, val loss: 0.40211, in 0.016s
1 tree, 33 leaves, max depth = 9, train loss: 0.41362, val loss: 0.40034, in 0.016s
1 tree, 36 leaves, max depth = 12, train loss: 0.41120, val loss: 0.39789, in 0.016s
1 tree, 34 leaves, max depth = 12, train loss: 0.40942, val loss: 0.39594, in 0.031s
1 tree, 37 leaves, max depth = 12, train loss: 0.40741, val loss: 0.39380, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.40550, val loss: 0.39180, in 0.016s
1 tree, 44 leaves, max depth = 14, train loss: 0.40345, val loss: 0.38970, in 0.016s
1 tree, 42 leaves, max depth = 11, train loss: 0.40175, val loss: 0.38799, in 0.031s
1 tree, 31 leaves, max depth = 9, train loss: 0.39960, val loss: 0.38582, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.39673, val loss: 0.38290, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.39411, val loss: 0.38014, in 0.016s
1 tree, 9 leaves, max depth = 4, train loss: 0.39244, val loss: 0.37839, in 0.016s
Fit 74 trees in 1.846 s, (2384 total leaves)
Time spent computing histograms: 0.627s
Time spent finding best splits:  0.081s
Time spent applying splits:      0.078s
Time spent predicting:           0.000s
Trial 16, Fold 5: Log loss = 0.3984597419290703, Average precision = 0.9524122235739496, ROC-AUC = 0.9470420452223027, Elapsed Time = 1.8619557000001805 seconds
Optimization Progress:  17%|#7        | 17/100 [03:25<17:42, 12.80s/it]
Trial 17, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 17, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
0.143 s 0.040 GB of training data: 
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 32 leaves, max depth = 9, train loss: 0.66507, val loss: 0.66472, in 0.016s
1 tree, 19 leaves, max depth = 8, train loss: 0.63841, val loss: 0.63797, in 0.016s
1 tree, 11 leaves, max depth = 5, train loss: 0.61633, val loss: 0.61549, in 0.016s
1 tree, 20 leaves, max depth = 8, train loss: 0.59559, val loss: 0.59439, in 0.016s
1 tree, 20 leaves, max depth = 8, train loss: 0.57693, val loss: 0.57546, in 0.016s
1 tree, 43 leaves, max depth = 11, train loss: 0.55961, val loss: 0.55818, in 0.016s
1 tree, 32 leaves, max depth = 8, train loss: 0.54219, val loss: 0.54094, in 0.016s
1 tree, 22 leaves, max depth = 10, train loss: 0.52658, val loss: 0.52496, in 0.000s
1 tree, 22 leaves, max depth = 8, train loss: 0.51319, val loss: 0.51126, in 0.016s
1 tree, 32 leaves, max depth = 10, train loss: 0.50033, val loss: 0.49815, in 0.016s
1 tree, 37 leaves, max depth = 10, train loss: 0.48853, val loss: 0.48650, in 0.016s
1 tree, 22 leaves, max depth = 8, train loss: 0.47672, val loss: 0.47460, in 0.016s
1 tree, 24 leaves, max depth = 9, train loss: 0.46670, val loss: 0.46432, in 0.016s
1 tree, 9 leaves, max depth = 5, train loss: 0.45308, val loss: 0.45113, in 0.016s
1 tree, 21 leaves, max depth = 7, train loss: 0.44400, val loss: 0.44195, in 0.016s
1 tree, 8 leaves, max depth = 4, train loss: 0.43655, val loss: 0.43440, in 0.000s
1 tree, 31 leaves, max depth = 11, train loss: 0.42880, val loss: 0.42653, in 0.016s
1 tree, 21 leaves, max depth = 7, train loss: 0.42121, val loss: 0.41879, in 0.016s
1 tree, 20 leaves, max depth = 7, train loss: 0.41505, val loss: 0.41242, in 0.016s
1 tree, 10 leaves, max depth = 5, train loss: 0.40483, val loss: 0.40253, in 0.016s
1 tree, 12 leaves, max depth = 5, train loss: 0.39548, val loss: 0.39352, in 0.016s
Fit 21 trees in 0.596 s, (468 total leaves)
Time spent computing histograms: 0.123s
Time spent finding best splits:  0.013s
Time spent applying splits:      0.009s
Time spent predicting:           0.000s
Trial 17, Fold 1: Log loss = 0.39756739316111345, Average precision = 0.9541704790921671, ROC-AUC = 0.9475914305549582, Elapsed Time = 0.5942180999991251 seconds
Trial 17, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 17, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
Binning 0.040 GB of training data: 0.157 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 25 leaves, max depth = 9, train loss: 0.66539, val loss: 0.66473, in 0.016s
1 tree, 40 leaves, max depth = 10, train loss: 0.64026, val loss: 0.63928, in 0.016s
1 tree, 34 leaves, max depth = 11, train loss: 0.61738, val loss: 0.61586, in 0.016s
1 tree, 21 leaves, max depth = 8, train loss: 0.59498, val loss: 0.59293, in 0.016s
1 tree, 40 leaves, max depth = 12, train loss: 0.57621, val loss: 0.57385, in 0.016s
1 tree, 14 leaves, max depth = 7, train loss: 0.55887, val loss: 0.55629, in 0.016s
1 tree, 19 leaves, max depth = 9, train loss: 0.54307, val loss: 0.54021, in 0.000s
1 tree, 20 leaves, max depth = 9, train loss: 0.52964, val loss: 0.52629, in 0.016s
1 tree, 38 leaves, max depth = 12, train loss: 0.51611, val loss: 0.51239, in 0.016s
1 tree, 24 leaves, max depth = 10, train loss: 0.50330, val loss: 0.49944, in 0.016s
1 tree, 38 leaves, max depth = 10, train loss: 0.49164, val loss: 0.48767, in 0.016s
1 tree, 18 leaves, max depth = 8, train loss: 0.47942, val loss: 0.47515, in 0.016s
1 tree, 18 leaves, max depth = 9, train loss: 0.46952, val loss: 0.46529, in 0.000s
1 tree, 21 leaves, max depth = 7, train loss: 0.46090, val loss: 0.45620, in 0.000s
1 tree, 26 leaves, max depth = 9, train loss: 0.45187, val loss: 0.44693, in 0.016s
1 tree, 23 leaves, max depth = 8, train loss: 0.44443, val loss: 0.43914, in 0.016s
1 tree, 13 leaves, max depth = 7, train loss: 0.43653, val loss: 0.43132, in 0.016s
1 tree, 22 leaves, max depth = 9, train loss: 0.42810, val loss: 0.42291, in 0.016s
1 tree, 33 leaves, max depth = 10, train loss: 0.42129, val loss: 0.41596, in 0.016s
1 tree, 19 leaves, max depth = 7, train loss: 0.41343, val loss: 0.40812, in 0.016s
1 tree, 20 leaves, max depth = 7, train loss: 0.40291, val loss: 0.39803, in 0.016s
Fit 21 trees in 0.610 s, (526 total leaves)
Time spent computing histograms: 0.122s
Time spent finding best splits:  0.014s
Time spent applying splits:      0.010s
Time spent predicting:           0.000s
Trial 17, Fold 2: Log loss = 0.40518344337243584, Average precision = 0.9527342274576989, ROC-AUC = 0.949551524594826, Elapsed Time = 0.6115257000001293 seconds
Trial 17, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 17, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
0.157 s 0.040 GB of training data: 
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 27 leaves, max depth = 9, train loss: 0.66559, val loss: 0.66505, in 0.016s
1 tree, 43 leaves, max depth = 13, train loss: 0.64046, val loss: 0.63985, in 0.016s
1 tree, 42 leaves, max depth = 12, train loss: 0.61773, val loss: 0.61666, in 0.016s
1 tree, 21 leaves, max depth = 8, train loss: 0.59592, val loss: 0.59467, in 0.016s
1 tree, 30 leaves, max depth = 11, train loss: 0.57611, val loss: 0.57468, in 0.031s
1 tree, 12 leaves, max depth = 6, train loss: 0.55841, val loss: 0.55707, in 0.016s
1 tree, 27 leaves, max depth = 10, train loss: 0.54289, val loss: 0.54170, in 0.000s
1 tree, 15 leaves, max depth = 8, train loss: 0.52858, val loss: 0.52736, in 0.016s
1 tree, 26 leaves, max depth = 9, train loss: 0.51444, val loss: 0.51341, in 0.016s
1 tree, 38 leaves, max depth = 9, train loss: 0.50159, val loss: 0.50054, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.49013, val loss: 0.48903, in 0.016s
1 tree, 20 leaves, max depth = 9, train loss: 0.47809, val loss: 0.47697, in 0.016s
1 tree, 24 leaves, max depth = 8, train loss: 0.46787, val loss: 0.46705, in 0.016s
1 tree, 9 leaves, max depth = 5, train loss: 0.45428, val loss: 0.45429, in 0.000s
1 tree, 27 leaves, max depth = 9, train loss: 0.44552, val loss: 0.44544, in 0.016s
1 tree, 12 leaves, max depth = 7, train loss: 0.43799, val loss: 0.43780, in 0.016s
1 tree, 34 leaves, max depth = 10, train loss: 0.43013, val loss: 0.42978, in 0.016s
1 tree, 8 leaves, max depth = 5, train loss: 0.42302, val loss: 0.42272, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.41618, val loss: 0.41609, in 0.016s
1 tree, 7 leaves, max depth = 4, train loss: 0.40647, val loss: 0.40718, in 0.016s
1 tree, 7 leaves, max depth = 4, train loss: 0.39716, val loss: 0.39861, in 0.016s
Fit 21 trees in 0.673 s, (481 total leaves)
Time spent computing histograms: 0.130s
Time spent finding best splits:  0.013s
Time spent applying splits:      0.010s
Time spent predicting:           0.000s
Trial 17, Fold 3: Log loss = 0.39428931547288537, Average precision = 0.9567107082320997, ROC-AUC = 0.9522550466623647, Elapsed Time = 0.6815104000015708 seconds
Trial 17, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 17, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
Binning 0.040 GB of training data: 0.158 s
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 25 leaves, max depth = 8, train loss: 0.66547, val loss: 0.66418, in 0.016s
1 tree, 26 leaves, max depth = 9, train loss: 0.64126, val loss: 0.63883, in 0.016s
1 tree, 20 leaves, max depth = 6, train loss: 0.61845, val loss: 0.61484, in 0.016s
1 tree, 44 leaves, max depth = 11, train loss: 0.59738, val loss: 0.59317, in 0.016s
1 tree, 17 leaves, max depth = 8, train loss: 0.58069, val loss: 0.57572, in 0.016s
1 tree, 36 leaves, max depth = 9, train loss: 0.56350, val loss: 0.55780, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.54596, val loss: 0.53959, in 0.016s
1 tree, 25 leaves, max depth = 9, train loss: 0.52977, val loss: 0.52266, in 0.016s
1 tree, 23 leaves, max depth = 7, train loss: 0.51579, val loss: 0.50794, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.50278, val loss: 0.49436, in 0.016s
1 tree, 46 leaves, max depth = 11, train loss: 0.49103, val loss: 0.48235, in 0.016s
1 tree, 42 leaves, max depth = 10, train loss: 0.48049, val loss: 0.47128, in 0.016s
1 tree, 25 leaves, max depth = 9, train loss: 0.46961, val loss: 0.45968, in 0.016s
1 tree, 9 leaves, max depth = 5, train loss: 0.45616, val loss: 0.44584, in 0.016s
1 tree, 29 leaves, max depth = 9, train loss: 0.44701, val loss: 0.43613, in 0.016s
1 tree, 42 leaves, max depth = 9, train loss: 0.43925, val loss: 0.42805, in 0.016s
1 tree, 44 leaves, max depth = 9, train loss: 0.43107, val loss: 0.41942, in 0.016s
1 tree, 21 leaves, max depth = 8, train loss: 0.42377, val loss: 0.41158, in 0.016s
1 tree, 15 leaves, max depth = 7, train loss: 0.41728, val loss: 0.40452, in 0.016s
1 tree, 11 leaves, max depth = 7, train loss: 0.41171, val loss: 0.39847, in 0.016s
1 tree, 26 leaves, max depth = 8, train loss: 0.40425, val loss: 0.39089, in 0.000s
Fit 21 trees in 0.674 s, (588 total leaves)
Time spent computing histograms: 0.143s
Time spent finding best splits:  0.016s
Time spent applying splits:      0.012s
Time spent predicting:           0.000s
Trial 17, Fold 4: Log loss = 0.4040348495124786, Average precision = 0.954412483575146, ROC-AUC = 0.9494533442539301, Elapsed Time = 0.6907933000002231 seconds
Trial 17, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 17, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
Binning 0.040 GB of training data: 0.158 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 28 leaves, max depth = 11, train loss: 0.66527, val loss: 0.66365, in 0.016s
1 tree, 24 leaves, max depth = 8, train loss: 0.63975, val loss: 0.63669, in 0.016s
1 tree, 15 leaves, max depth = 7, train loss: 0.61722, val loss: 0.61283, in 0.016s
1 tree, 20 leaves, max depth = 8, train loss: 0.59465, val loss: 0.58947, in 0.016s
1 tree, 41 leaves, max depth = 10, train loss: 0.57630, val loss: 0.57058, in 0.016s
1 tree, 21 leaves, max depth = 9, train loss: 0.55935, val loss: 0.55280, in 0.016s
1 tree, 30 leaves, max depth = 10, train loss: 0.54307, val loss: 0.53549, in 0.016s
1 tree, 46 leaves, max depth = 9, train loss: 0.52860, val loss: 0.52063, in 0.016s
1 tree, 19 leaves, max depth = 7, train loss: 0.51506, val loss: 0.50630, in 0.016s
1 tree, 21 leaves, max depth = 8, train loss: 0.50106, val loss: 0.49176, in 0.016s
1 tree, 22 leaves, max depth = 7, train loss: 0.48909, val loss: 0.47910, in 0.016s
1 tree, 13 leaves, max depth = 8, train loss: 0.47812, val loss: 0.46755, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.46845, val loss: 0.45713, in 0.016s
1 tree, 32 leaves, max depth = 10, train loss: 0.45849, val loss: 0.44673, in 0.016s
1 tree, 42 leaves, max depth = 12, train loss: 0.44993, val loss: 0.43794, in 0.016s
1 tree, 17 leaves, max depth = 8, train loss: 0.44236, val loss: 0.42992, in 0.000s
1 tree, 42 leaves, max depth = 11, train loss: 0.43443, val loss: 0.42171, in 0.016s
1 tree, 30 leaves, max depth = 10, train loss: 0.42614, val loss: 0.41325, in 0.016s
1 tree, 20 leaves, max depth = 8, train loss: 0.41917, val loss: 0.40599, in 0.031s
1 tree, 21 leaves, max depth = 8, train loss: 0.41301, val loss: 0.39956, in 0.016s
1 tree, 21 leaves, max depth = 9, train loss: 0.40720, val loss: 0.39350, in 0.016s
Fit 21 trees in 0.690 s, (556 total leaves)
Time spent computing histograms: 0.138s
Time spent finding best splits:  0.015s
Time spent applying splits:      0.012s
Time spent predicting:           0.000s
Trial 17, Fold 5: Log loss = 0.41314811982724015, Average precision = 0.9197794119472843, ROC-AUC = 0.9302998346431822, Elapsed Time = 0.6928602000007231 seconds
Optimization Progress:  18%|#8        | 18/100 [03:35<16:17, 11.92s/it]
Trial 18, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 18, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
0.158 s 0.040 GB of training data: 
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 52 leaves, max depth = 16, train loss: 0.66318, val loss: 0.66227, in 0.015s
1 tree, 52 leaves, max depth = 16, train loss: 0.63692, val loss: 0.63517, in 0.000s
1 tree, 51 leaves, max depth = 16, train loss: 0.61382, val loss: 0.61127, in 0.016s
1 tree, 54 leaves, max depth = 17, train loss: 0.59360, val loss: 0.59055, in 0.016s
1 tree, 52 leaves, max depth = 17, train loss: 0.57550, val loss: 0.57175, in 0.000s
Fit 5 trees in 0.314 s, (261 total leaves)
Time spent computing histograms: 0.024s
Time spent finding best splits:  0.005s
Time spent applying splits:      0.004s
Time spent predicting:           0.000s
Trial 18, Fold 1: Log loss = 0.5758696069682718, Average precision = 0.8140119097748746, ROC-AUC = 0.8614314929524958, Elapsed Time = 0.3241126000011718 seconds
Trial 18, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 18, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
0.157 s 0.040 GB of training data: 
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 52 leaves, max depth = 11, train loss: 0.66356, val loss: 0.66219, in 0.016s
1 tree, 53 leaves, max depth = 11, train loss: 0.63764, val loss: 0.63497, in 0.016s
1 tree, 52 leaves, max depth = 14, train loss: 0.61482, val loss: 0.61105, in 0.000s
1 tree, 53 leaves, max depth = 11, train loss: 0.59466, val loss: 0.58981, in 0.016s
1 tree, 52 leaves, max depth = 14, train loss: 0.57680, val loss: 0.57101, in 0.016s
Fit 5 trees in 0.313 s, (262 total leaves)
Time spent computing histograms: 0.023s
Time spent finding best splits:  0.005s
Time spent applying splits:      0.004s
Time spent predicting:           0.000s
Trial 18, Fold 2: Log loss = 0.5768084023705091, Average precision = 0.8172011369396084, ROC-AUC = 0.8660990439586019, Elapsed Time = 0.3173349999997299 seconds
Trial 18, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 18, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
Binning 0.040 GB of training data: 0.126 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 51 leaves, max depth = 10, train loss: 0.66371, val loss: 0.66260, in 0.000s
1 tree, 51 leaves, max depth = 10, train loss: 0.63793, val loss: 0.63579, in 0.016s
1 tree, 51 leaves, max depth = 10, train loss: 0.61525, val loss: 0.61217, in 0.016s
1 tree, 52 leaves, max depth = 15, train loss: 0.59520, val loss: 0.59141, in 0.000s
1 tree, 54 leaves, max depth = 13, train loss: 0.57718, val loss: 0.57284, in 0.016s
Fit 5 trees in 0.314 s, (259 total leaves)
Time spent computing histograms: 0.021s
Time spent finding best splits:  0.005s
Time spent applying splits:      0.004s
Time spent predicting:           0.000s
Trial 18, Fold 3: Log loss = 0.5743287091850737, Average precision = 0.8266571807986699, ROC-AUC = 0.8716865477314095, Elapsed Time = 0.31165280000095663 seconds
Trial 18, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 18, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
0.143 s 0.040 GB of training data: 
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 53 leaves, max depth = 12, train loss: 0.66348, val loss: 0.66164, in 0.000s
1 tree, 53 leaves, max depth = 12, train loss: 0.63749, val loss: 0.63393, in 0.016s
1 tree, 53 leaves, max depth = 12, train loss: 0.61463, val loss: 0.60946, in 0.016s
1 tree, 53 leaves, max depth = 14, train loss: 0.59452, val loss: 0.58806, in 0.016s
1 tree, 52 leaves, max depth = 12, train loss: 0.57663, val loss: 0.56873, in 0.000s
Fit 5 trees in 0.315 s, (264 total leaves)
Time spent computing histograms: 0.023s
Time spent finding best splits:  0.005s
Time spent applying splits:      0.005s
Time spent predicting:           0.000s
Trial 18, Fold 4: Log loss = 0.5755665976093852, Average precision = 0.8188990668810218, ROC-AUC = 0.8676090003123578, Elapsed Time = 0.3268798999997671 seconds
Trial 18, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 18, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
Binning 0.040 GB of training data: 0.157 s
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 54 leaves, max depth = 15, train loss: 0.66343, val loss: 0.66149, in 0.000s
1 tree, 48 leaves, max depth = 14, train loss: 0.63709, val loss: 0.63332, in 0.016s
1 tree, 48 leaves, max depth = 14, train loss: 0.61391, val loss: 0.60844, in 0.016s
1 tree, 54 leaves, max depth = 17, train loss: 0.59365, val loss: 0.58660, in 0.000s
1 tree, 48 leaves, max depth = 15, train loss: 0.57548, val loss: 0.56694, in 0.016s
Fit 5 trees in 0.344 s, (252 total leaves)
Time spent computing histograms: 0.024s
Time spent finding best splits:  0.005s
Time spent applying splits:      0.005s
Time spent predicting:           0.000s
Trial 18, Fold 5: Log loss = 0.5793771384317353, Average precision = 0.8077174872619299, ROC-AUC = 0.858010664585772, Elapsed Time = 0.34722619999956805 seconds
Optimization Progress:  19%|#9        | 19/100 [03:43<14:33, 10.79s/it]
Trial 19, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 19, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
0.126 s 0.040 GB of training data: 
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 42 leaves, max depth = 9, train loss: 0.68003, val loss: 0.68007, in 0.016s
1 tree, 45 leaves, max depth = 10, train loss: 0.66750, val loss: 0.66754, in 0.016s
1 tree, 44 leaves, max depth = 11, train loss: 0.65551, val loss: 0.65557, in 0.016s
1 tree, 44 leaves, max depth = 9, train loss: 0.64402, val loss: 0.64410, in 0.016s
1 tree, 48 leaves, max depth = 13, train loss: 0.63301, val loss: 0.63311, in 0.016s
1 tree, 49 leaves, max depth = 13, train loss: 0.62247, val loss: 0.62257, in 0.016s
1 tree, 55 leaves, max depth = 11, train loss: 0.61234, val loss: 0.61250, in 0.016s
1 tree, 43 leaves, max depth = 11, train loss: 0.60261, val loss: 0.60279, in 0.016s
1 tree, 53 leaves, max depth = 13, train loss: 0.59328, val loss: 0.59340, in 0.016s
1 tree, 49 leaves, max depth = 11, train loss: 0.58429, val loss: 0.58446, in 0.016s
1 tree, 53 leaves, max depth = 13, train loss: 0.57564, val loss: 0.57588, in 0.016s
1 tree, 47 leaves, max depth = 9, train loss: 0.56743, val loss: 0.56764, in 0.016s
1 tree, 48 leaves, max depth = 13, train loss: 0.55974, val loss: 0.55988, in 0.016s
1 tree, 52 leaves, max depth = 13, train loss: 0.55200, val loss: 0.55216, in 0.016s
1 tree, 50 leaves, max depth = 11, train loss: 0.54454, val loss: 0.54470, in 0.031s
1 tree, 46 leaves, max depth = 11, train loss: 0.53745, val loss: 0.53753, in 0.000s
1 tree, 49 leaves, max depth = 13, train loss: 0.53080, val loss: 0.53082, in 0.031s
1 tree, 48 leaves, max depth = 13, train loss: 0.52438, val loss: 0.52435, in 0.016s
1 tree, 53 leaves, max depth = 13, train loss: 0.51789, val loss: 0.51787, in 0.016s
1 tree, 56 leaves, max depth = 13, train loss: 0.51162, val loss: 0.51162, in 0.016s
1 tree, 52 leaves, max depth = 11, train loss: 0.50558, val loss: 0.50559, in 0.016s
1 tree, 49 leaves, max depth = 12, train loss: 0.49989, val loss: 0.49982, in 0.016s
1 tree, 53 leaves, max depth = 12, train loss: 0.49424, val loss: 0.49420, in 0.016s
1 tree, 53 leaves, max depth = 14, train loss: 0.48901, val loss: 0.48894, in 0.016s
1 tree, 56 leaves, max depth = 12, train loss: 0.48372, val loss: 0.48366, in 0.016s
1 tree, 56 leaves, max depth = 14, train loss: 0.47860, val loss: 0.47858, in 0.016s
1 tree, 58 leaves, max depth = 14, train loss: 0.47365, val loss: 0.47367, in 0.016s
Fit 27 trees in 0.736 s, (1351 total leaves)
Time spent computing histograms: 0.188s
Time spent finding best splits:  0.033s
Time spent applying splits:      0.023s
Time spent predicting:           0.000s
Trial 19, Fold 1: Log loss = 0.4759809969926131, Average precision = 0.9192054238246357, ROC-AUC = 0.9301531738862362, Elapsed Time = 0.743226999999024 seconds
Trial 19, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 19, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
Binning 0.040 GB of training data: 0.127 s
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 38 leaves, max depth = 9, train loss: 0.68007, val loss: 0.67983, in 0.016s
1 tree, 36 leaves, max depth = 8, train loss: 0.66738, val loss: 0.66688, in 0.016s
1 tree, 36 leaves, max depth = 8, train loss: 0.65524, val loss: 0.65450, in 0.016s
1 tree, 36 leaves, max depth = 8, train loss: 0.64361, val loss: 0.64263, in 0.016s
1 tree, 37 leaves, max depth = 8, train loss: 0.63247, val loss: 0.63127, in 0.016s
1 tree, 40 leaves, max depth = 9, train loss: 0.62183, val loss: 0.62049, in 0.016s
1 tree, 40 leaves, max depth = 8, train loss: 0.61157, val loss: 0.61003, in 0.016s
1 tree, 42 leaves, max depth = 8, train loss: 0.60173, val loss: 0.60000, in 0.016s
1 tree, 40 leaves, max depth = 8, train loss: 0.59227, val loss: 0.59034, in 0.016s
1 tree, 47 leaves, max depth = 9, train loss: 0.58318, val loss: 0.58107, in 0.016s
1 tree, 42 leaves, max depth = 8, train loss: 0.57445, val loss: 0.57215, in 0.016s
1 tree, 42 leaves, max depth = 8, train loss: 0.56605, val loss: 0.56358, in 0.016s
1 tree, 48 leaves, max depth = 9, train loss: 0.55795, val loss: 0.55534, in 0.016s
1 tree, 47 leaves, max depth = 9, train loss: 0.55023, val loss: 0.54752, in 0.031s
1 tree, 43 leaves, max depth = 9, train loss: 0.54272, val loss: 0.53985, in 0.016s
1 tree, 43 leaves, max depth = 9, train loss: 0.53548, val loss: 0.53245, in 0.016s
1 tree, 44 leaves, max depth = 10, train loss: 0.52885, val loss: 0.52577, in 0.016s
1 tree, 44 leaves, max depth = 11, train loss: 0.52210, val loss: 0.51887, in 0.016s
1 tree, 44 leaves, max depth = 11, train loss: 0.51559, val loss: 0.51221, in 0.016s
1 tree, 49 leaves, max depth = 9, train loss: 0.50926, val loss: 0.50588, in 0.031s
1 tree, 43 leaves, max depth = 8, train loss: 0.50320, val loss: 0.49971, in 0.016s
1 tree, 53 leaves, max depth = 11, train loss: 0.49734, val loss: 0.49376, in 0.016s
1 tree, 43 leaves, max depth = 9, train loss: 0.49168, val loss: 0.48796, in 0.016s
1 tree, 45 leaves, max depth = 11, train loss: 0.48621, val loss: 0.48239, in 0.000s
1 tree, 55 leaves, max depth = 9, train loss: 0.48103, val loss: 0.47720, in 0.016s
1 tree, 45 leaves, max depth = 11, train loss: 0.47591, val loss: 0.47200, in 0.016s
1 tree, 52 leaves, max depth = 10, train loss: 0.47091, val loss: 0.46702, in 0.016s
Fit 27 trees in 0.750 s, (1174 total leaves)
Time spent computing histograms: 0.182s
Time spent finding best splits:  0.032s
Time spent applying splits:      0.022s
Time spent predicting:           0.000s
Trial 19, Fold 2: Log loss = 0.47331780547530794, Average precision = 0.915780116516474, ROC-AUC = 0.9320602402288841, Elapsed Time = 0.765905100000964 seconds
Trial 19, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 19, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
Binning 0.040 GB of training data: 0.158 s
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 34 leaves, max depth = 8, train loss: 0.67995, val loss: 0.67999, in 0.016s
1 tree, 36 leaves, max depth = 8, train loss: 0.66734, val loss: 0.66740, in 0.016s
1 tree, 37 leaves, max depth = 8, train loss: 0.65528, val loss: 0.65533, in 0.016s
1 tree, 33 leaves, max depth = 8, train loss: 0.64434, val loss: 0.64440, in 0.016s
1 tree, 36 leaves, max depth = 8, train loss: 0.63324, val loss: 0.63329, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.62318, val loss: 0.62325, in 0.000s
1 tree, 38 leaves, max depth = 8, train loss: 0.61294, val loss: 0.61299, in 0.016s
1 tree, 37 leaves, max depth = 8, train loss: 0.60311, val loss: 0.60314, in 0.031s
1 tree, 37 leaves, max depth = 9, train loss: 0.59366, val loss: 0.59371, in 0.016s
1 tree, 47 leaves, max depth = 12, train loss: 0.58507, val loss: 0.58519, in 0.016s
1 tree, 49 leaves, max depth = 12, train loss: 0.57630, val loss: 0.57648, in 0.016s
1 tree, 48 leaves, max depth = 12, train loss: 0.56786, val loss: 0.56810, in 0.016s
1 tree, 50 leaves, max depth = 12, train loss: 0.55974, val loss: 0.56005, in 0.016s
1 tree, 55 leaves, max depth = 12, train loss: 0.55191, val loss: 0.55228, in 0.016s
1 tree, 50 leaves, max depth = 12, train loss: 0.54436, val loss: 0.54481, in 0.016s
1 tree, 53 leaves, max depth = 12, train loss: 0.53711, val loss: 0.53760, in 0.016s
1 tree, 55 leaves, max depth = 12, train loss: 0.53009, val loss: 0.53064, in 0.016s
1 tree, 52 leaves, max depth = 12, train loss: 0.52333, val loss: 0.52395, in 0.016s
1 tree, 57 leaves, max depth = 12, train loss: 0.51682, val loss: 0.51748, in 0.016s
1 tree, 47 leaves, max depth = 12, train loss: 0.51084, val loss: 0.51157, in 0.031s
1 tree, 56 leaves, max depth = 13, train loss: 0.50474, val loss: 0.50550, in 0.016s
1 tree, 48 leaves, max depth = 9, train loss: 0.49891, val loss: 0.49972, in 0.016s
1 tree, 58 leaves, max depth = 13, train loss: 0.49322, val loss: 0.49406, in 0.016s
1 tree, 58 leaves, max depth = 13, train loss: 0.48772, val loss: 0.48861, in 0.016s
1 tree, 48 leaves, max depth = 9, train loss: 0.48246, val loss: 0.48339, in 0.016s
1 tree, 62 leaves, max depth = 14, train loss: 0.47732, val loss: 0.47833, in 0.016s
1 tree, 64 leaves, max depth = 14, train loss: 0.47231, val loss: 0.47339, in 0.016s
Fit 27 trees in 0.799 s, (1280 total leaves)
Time spent computing histograms: 0.187s
Time spent finding best splits:  0.033s
Time spent applying splits:      0.023s
Time spent predicting:           0.000s
Trial 19, Fold 3: Log loss = 0.47063638550940856, Average precision = 0.9229946765596092, ROC-AUC = 0.9352773376929819, Elapsed Time = 0.803773600000568 seconds
Trial 19, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 19, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
Binning 0.040 GB of training data: 0.142 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 38 leaves, max depth = 8, train loss: 0.68012, val loss: 0.67960, in 0.016s
1 tree, 37 leaves, max depth = 8, train loss: 0.66767, val loss: 0.66664, in 0.016s
1 tree, 41 leaves, max depth = 9, train loss: 0.65575, val loss: 0.65424, in 0.016s
1 tree, 38 leaves, max depth = 8, train loss: 0.64433, val loss: 0.64235, in 0.031s
1 tree, 46 leaves, max depth = 10, train loss: 0.63339, val loss: 0.63096, in 0.000s
1 tree, 44 leaves, max depth = 8, train loss: 0.62290, val loss: 0.62002, in 0.031s
1 tree, 43 leaves, max depth = 9, train loss: 0.61284, val loss: 0.60952, in 0.016s
1 tree, 45 leaves, max depth = 10, train loss: 0.60318, val loss: 0.59943, in 0.016s
1 tree, 44 leaves, max depth = 8, train loss: 0.59390, val loss: 0.58973, in 0.016s
1 tree, 48 leaves, max depth = 11, train loss: 0.58498, val loss: 0.58043, in 0.016s
1 tree, 45 leaves, max depth = 8, train loss: 0.57640, val loss: 0.57147, in 0.016s
1 tree, 44 leaves, max depth = 8, train loss: 0.56816, val loss: 0.56285, in 0.016s
1 tree, 48 leaves, max depth = 11, train loss: 0.56022, val loss: 0.55455, in 0.016s
1 tree, 49 leaves, max depth = 12, train loss: 0.55268, val loss: 0.54658, in 0.016s
1 tree, 47 leaves, max depth = 10, train loss: 0.54531, val loss: 0.53886, in 0.016s
1 tree, 45 leaves, max depth = 9, train loss: 0.53821, val loss: 0.53141, in 0.016s
1 tree, 46 leaves, max depth = 10, train loss: 0.53165, val loss: 0.52453, in 0.016s
1 tree, 51 leaves, max depth = 10, train loss: 0.52503, val loss: 0.51760, in 0.016s
1 tree, 52 leaves, max depth = 12, train loss: 0.51874, val loss: 0.51094, in 0.031s
1 tree, 53 leaves, max depth = 10, train loss: 0.51242, val loss: 0.50437, in 0.016s
1 tree, 46 leaves, max depth = 10, train loss: 0.50647, val loss: 0.49810, in 0.016s
1 tree, 53 leaves, max depth = 10, train loss: 0.50071, val loss: 0.49207, in 0.016s
1 tree, 54 leaves, max depth = 10, train loss: 0.49515, val loss: 0.48623, in 0.016s
1 tree, 61 leaves, max depth = 12, train loss: 0.48977, val loss: 0.48059, in 0.016s
1 tree, 60 leaves, max depth = 12, train loss: 0.48465, val loss: 0.47513, in 0.016s
1 tree, 50 leaves, max depth = 10, train loss: 0.47962, val loss: 0.46983, in 0.016s
1 tree, 68 leaves, max depth = 14, train loss: 0.47474, val loss: 0.46470, in 0.016s
Fit 27 trees in 0.814 s, (1296 total leaves)
Time spent computing histograms: 0.194s
Time spent finding best splits:  0.036s
Time spent applying splits:      0.025s
Time spent predicting:           0.000s
Trial 19, Fold 4: Log loss = 0.47506838673715585, Average precision = 0.9201397556504622, ROC-AUC = 0.9309760313168379, Elapsed Time = 0.8261987999994744 seconds
Trial 19, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 19, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
Binning 0.040 GB of training data: 0.158 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 41 leaves, max depth = 9, train loss: 0.67988, val loss: 0.67937, in 0.000s
1 tree, 41 leaves, max depth = 9, train loss: 0.66720, val loss: 0.66622, in 0.016s
1 tree, 43 leaves, max depth = 9, train loss: 0.65514, val loss: 0.65370, in 0.016s
1 tree, 41 leaves, max depth = 9, train loss: 0.64419, val loss: 0.64229, in 0.031s
1 tree, 46 leaves, max depth = 9, train loss: 0.63301, val loss: 0.63067, in 0.016s
1 tree, 46 leaves, max depth = 9, train loss: 0.62230, val loss: 0.61952, in 0.016s
1 tree, 43 leaves, max depth = 10, train loss: 0.61263, val loss: 0.60946, in 0.016s
1 tree, 48 leaves, max depth = 10, train loss: 0.60273, val loss: 0.59917, in 0.016s
1 tree, 46 leaves, max depth = 10, train loss: 0.59322, val loss: 0.58929, in 0.016s
1 tree, 49 leaves, max depth = 10, train loss: 0.58408, val loss: 0.57978, in 0.016s
1 tree, 47 leaves, max depth = 10, train loss: 0.57529, val loss: 0.57064, in 0.016s
1 tree, 47 leaves, max depth = 10, train loss: 0.56684, val loss: 0.56184, in 0.016s
1 tree, 48 leaves, max depth = 10, train loss: 0.55870, val loss: 0.55339, in 0.016s
1 tree, 48 leaves, max depth = 10, train loss: 0.55086, val loss: 0.54521, in 0.016s
1 tree, 50 leaves, max depth = 10, train loss: 0.54331, val loss: 0.53738, in 0.016s
1 tree, 45 leaves, max depth = 9, train loss: 0.53613, val loss: 0.52987, in 0.016s
1 tree, 49 leaves, max depth = 10, train loss: 0.52911, val loss: 0.52258, in 0.016s
1 tree, 50 leaves, max depth = 10, train loss: 0.52271, val loss: 0.51592, in 0.031s
1 tree, 53 leaves, max depth = 12, train loss: 0.51616, val loss: 0.50911, in 0.016s
1 tree, 49 leaves, max depth = 10, train loss: 0.50984, val loss: 0.50253, in 0.016s
1 tree, 55 leaves, max depth = 9, train loss: 0.50383, val loss: 0.49623, in 0.016s
1 tree, 56 leaves, max depth = 12, train loss: 0.49792, val loss: 0.49014, in 0.016s
1 tree, 56 leaves, max depth = 11, train loss: 0.49223, val loss: 0.48420, in 0.016s
1 tree, 59 leaves, max depth = 12, train loss: 0.48672, val loss: 0.47850, in 0.016s
1 tree, 58 leaves, max depth = 12, train loss: 0.48140, val loss: 0.47298, in 0.016s
1 tree, 57 leaves, max depth = 11, train loss: 0.47625, val loss: 0.46764, in 0.016s
1 tree, 54 leaves, max depth = 12, train loss: 0.47153, val loss: 0.46276, in 0.016s
Fit 27 trees in 0.799 s, (1325 total leaves)
Time spent computing histograms: 0.182s
Time spent finding best splits:  0.034s
Time spent applying splits:      0.024s
Time spent predicting:           0.000s
Trial 19, Fold 5: Log loss = 0.47660104352388494, Average precision = 0.9162011773787254, ROC-AUC = 0.9303857458707244, Elapsed Time = 0.8032679999996617 seconds
Optimization Progress:  20%|##        | 20/100 [03:54<14:10, 10.63s/it]
Trial 20, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 20, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
0.142 s 0.040 GB of training data: 
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 101 leaves, max depth = 15, train loss: 0.68483, val loss: 0.68463, in 0.000s
1 tree, 104 leaves, max depth = 17, train loss: 0.67663, val loss: 0.67620, in 0.016s
1 tree, 104 leaves, max depth = 17, train loss: 0.66871, val loss: 0.66805, in 0.016s
1 tree, 106 leaves, max depth = 14, train loss: 0.66116, val loss: 0.66033, in 0.016s
1 tree, 105 leaves, max depth = 19, train loss: 0.65386, val loss: 0.65283, in 0.000s
1 tree, 105 leaves, max depth = 19, train loss: 0.64681, val loss: 0.64558, in 0.000s
1 tree, 104 leaves, max depth = 21, train loss: 0.64007, val loss: 0.63868, in 0.016s
1 tree, 104 leaves, max depth = 19, train loss: 0.63356, val loss: 0.63201, in 0.016s
1 tree, 105 leaves, max depth = 19, train loss: 0.62719, val loss: 0.62545, in 0.016s
1 tree, 105 leaves, max depth = 18, train loss: 0.62095, val loss: 0.61901, in 0.016s
1 tree, 105 leaves, max depth = 18, train loss: 0.61505, val loss: 0.61289, in 0.016s
1 tree, 105 leaves, max depth = 19, train loss: 0.60928, val loss: 0.60695, in 0.016s
1 tree, 105 leaves, max depth = 19, train loss: 0.60371, val loss: 0.60120, in 0.016s
1 tree, 104 leaves, max depth = 18, train loss: 0.59824, val loss: 0.59554, in 0.016s
1 tree, 105 leaves, max depth = 19, train loss: 0.59302, val loss: 0.59015, in 0.016s
1 tree, 105 leaves, max depth = 19, train loss: 0.58797, val loss: 0.58494, in 0.016s
1 tree, 105 leaves, max depth = 18, train loss: 0.58300, val loss: 0.57979, in 0.016s
1 tree, 106 leaves, max depth = 16, train loss: 0.57827, val loss: 0.57493, in 0.031s
1 tree, 103 leaves, max depth = 20, train loss: 0.57372, val loss: 0.57026, in 0.016s
1 tree, 105 leaves, max depth = 18, train loss: 0.56920, val loss: 0.56557, in 0.016s
1 tree, 105 leaves, max depth = 18, train loss: 0.56483, val loss: 0.56102, in 0.016s
1 tree, 105 leaves, max depth = 18, train loss: 0.56059, val loss: 0.55661, in 0.016s
1 tree, 105 leaves, max depth = 18, train loss: 0.55648, val loss: 0.55233, in 0.031s
1 tree, 105 leaves, max depth = 18, train loss: 0.55250, val loss: 0.54818, in 0.016s
1 tree, 105 leaves, max depth = 20, train loss: 0.54870, val loss: 0.54425, in 0.016s
1 tree, 159 leaves, max depth = 15, train loss: 0.54448, val loss: 0.54029, in 0.016s
1 tree, 159 leaves, max depth = 15, train loss: 0.54039, val loss: 0.53647, in 0.031s
1 tree, 106 leaves, max depth = 17, train loss: 0.53684, val loss: 0.53276, in 0.016s
1 tree, 159 leaves, max depth = 15, train loss: 0.53292, val loss: 0.52909, in 0.016s
1 tree, 159 leaves, max depth = 15, train loss: 0.52913, val loss: 0.52555, in 0.031s
1 tree, 104 leaves, max depth = 16, train loss: 0.52578, val loss: 0.52210, in 0.000s
1 tree, 106 leaves, max depth = 16, train loss: 0.52253, val loss: 0.51869, in 0.016s
1 tree, 105 leaves, max depth = 20, train loss: 0.51931, val loss: 0.51530, in 0.016s
1 tree, 159 leaves, max depth = 15, train loss: 0.51573, val loss: 0.51197, in 0.031s
1 tree, 159 leaves, max depth = 15, train loss: 0.51227, val loss: 0.50875, in 0.016s
1 tree, 105 leaves, max depth = 19, train loss: 0.50928, val loss: 0.50563, in 0.016s
1 tree, 105 leaves, max depth = 20, train loss: 0.50632, val loss: 0.50251, in 0.016s
1 tree, 159 leaves, max depth = 15, train loss: 0.50302, val loss: 0.49945, in 0.016s
1 tree, 105 leaves, max depth = 19, train loss: 0.50025, val loss: 0.49654, in 0.016s
1 tree, 106 leaves, max depth = 17, train loss: 0.49757, val loss: 0.49371, in 0.000s
1 tree, 106 leaves, max depth = 21, train loss: 0.49495, val loss: 0.49101, in 0.016s
1 tree, 159 leaves, max depth = 16, train loss: 0.49183, val loss: 0.48812, in 0.031s
1 tree, 105 leaves, max depth = 19, train loss: 0.48933, val loss: 0.48550, in 0.016s
1 tree, 159 leaves, max depth = 16, train loss: 0.48632, val loss: 0.48271, in 0.016s
1 tree, 159 leaves, max depth = 16, train loss: 0.48340, val loss: 0.48001, in 0.016s
1 tree, 159 leaves, max depth = 16, train loss: 0.48057, val loss: 0.47739, in 0.031s
1 tree, 159 leaves, max depth = 16, train loss: 0.47782, val loss: 0.47485, in 0.016s
1 tree, 105 leaves, max depth = 17, train loss: 0.47546, val loss: 0.47234, in 0.016s
1 tree, 159 leaves, max depth = 16, train loss: 0.47281, val loss: 0.46990, in 0.016s
1 tree, 159 leaves, max depth = 16, train loss: 0.47024, val loss: 0.46752, in 0.031s
1 tree, 105 leaves, max depth = 17, train loss: 0.46805, val loss: 0.46519, in 0.000s
1 tree, 159 leaves, max depth = 16, train loss: 0.46557, val loss: 0.46291, in 0.031s
1 tree, 159 leaves, max depth = 16, train loss: 0.46316, val loss: 0.46069, in 0.016s
1 tree, 105 leaves, max depth = 17, train loss: 0.46102, val loss: 0.45841, in 0.016s
1 tree, 159 leaves, max depth = 16, train loss: 0.45870, val loss: 0.45628, in 0.016s
1 tree, 159 leaves, max depth = 16, train loss: 0.45644, val loss: 0.45421, in 0.031s
1 tree, 159 leaves, max depth = 16, train loss: 0.45424, val loss: 0.45220, in 0.016s
1 tree, 106 leaves, max depth = 14, train loss: 0.45228, val loss: 0.45010, in 0.016s
1 tree, 159 leaves, max depth = 16, train loss: 0.45016, val loss: 0.44816, in 0.016s
1 tree, 104 leaves, max depth = 18, train loss: 0.44821, val loss: 0.44612, in 0.016s
1 tree, 106 leaves, max depth = 14, train loss: 0.44638, val loss: 0.44415, in 0.016s
1 tree, 159 leaves, max depth = 16, train loss: 0.44434, val loss: 0.44229, in 0.016s
1 tree, 159 leaves, max depth = 16, train loss: 0.44236, val loss: 0.44049, in 0.031s
1 tree, 104 leaves, max depth = 20, train loss: 0.44062, val loss: 0.43867, in 0.016s
1 tree, 159 leaves, max depth = 16, train loss: 0.43871, val loss: 0.43693, in 0.016s
1 tree, 104 leaves, max depth = 14, train loss: 0.43697, val loss: 0.43508, in 0.016s
1 tree, 106 leaves, max depth = 14, train loss: 0.43533, val loss: 0.43331, in 0.016s
1 tree, 104 leaves, max depth = 17, train loss: 0.43369, val loss: 0.43158, in 0.016s
1 tree, 159 leaves, max depth = 16, train loss: 0.43186, val loss: 0.42992, in 0.031s
1 tree, 159 leaves, max depth = 16, train loss: 0.43008, val loss: 0.42832, in 0.016s
1 tree, 159 leaves, max depth = 16, train loss: 0.42835, val loss: 0.42675, in 0.016s
1 tree, 159 leaves, max depth = 16, train loss: 0.42667, val loss: 0.42524, in 0.016s
1 tree, 106 leaves, max depth = 14, train loss: 0.42517, val loss: 0.42361, in 0.016s
1 tree, 104 leaves, max depth = 21, train loss: 0.42372, val loss: 0.42209, in 0.016s
1 tree, 104 leaves, max depth = 16, train loss: 0.42230, val loss: 0.42057, in 0.016s
1 tree, 106 leaves, max depth = 14, train loss: 0.42093, val loss: 0.41907, in 0.016s
1 tree, 105 leaves, max depth = 17, train loss: 0.41955, val loss: 0.41757, in 0.016s
1 tree, 104 leaves, max depth = 17, train loss: 0.41821, val loss: 0.41615, in 0.016s
1 tree, 104 leaves, max depth = 14, train loss: 0.41690, val loss: 0.41475, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.41565, val loss: 0.41338, in 0.016s
1 tree, 105 leaves, max depth = 17, train loss: 0.41439, val loss: 0.41200, in 0.016s
1 tree, 159 leaves, max depth = 16, train loss: 0.41280, val loss: 0.41058, in 0.016s
1 tree, 105 leaves, max depth = 17, train loss: 0.41159, val loss: 0.40925, in 0.016s
1 tree, 159 leaves, max depth = 16, train loss: 0.41005, val loss: 0.40788, in 0.016s
1 tree, 159 leaves, max depth = 16, train loss: 0.40856, val loss: 0.40655, in 0.031s
1 tree, 5 leaves, max depth = 3, train loss: 0.40737, val loss: 0.40524, in 0.000s
1 tree, 159 leaves, max depth = 16, train loss: 0.40592, val loss: 0.40395, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.40476, val loss: 0.40268, in 0.016s
1 tree, 105 leaves, max depth = 18, train loss: 0.40362, val loss: 0.40143, in 0.016s
1 tree, 159 leaves, max depth = 16, train loss: 0.40222, val loss: 0.40019, in 0.016s
1 tree, 105 leaves, max depth = 18, train loss: 0.40112, val loss: 0.39898, in 0.016s
Fit 91 trees in 1.815 s, (10974 total leaves)
Time spent computing histograms: 0.564s
Time spent finding best splits:  0.175s
Time spent applying splits:      0.207s
Time spent predicting:           0.000s
Trial 20, Fold 1: Log loss = 0.4053307122495676, Average precision = 0.9457528949247934, ROC-AUC = 0.9421825785167024, Elapsed Time = 1.8230208000004495 seconds
Trial 20, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 20, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
Binning 0.040 GB of training data: 0.158 s
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 105 leaves, max depth = 18, train loss: 0.68479, val loss: 0.68447, in 0.016s
1 tree, 106 leaves, max depth = 15, train loss: 0.67669, val loss: 0.67601, in 0.016s
1 tree, 106 leaves, max depth = 15, train loss: 0.66888, val loss: 0.66784, in 0.016s
1 tree, 106 leaves, max depth = 15, train loss: 0.66134, val loss: 0.65995, in 0.016s
1 tree, 105 leaves, max depth = 17, train loss: 0.65408, val loss: 0.65235, in 0.000s
1 tree, 105 leaves, max depth = 17, train loss: 0.64707, val loss: 0.64501, in 0.016s
1 tree, 106 leaves, max depth = 21, train loss: 0.64030, val loss: 0.63796, in 0.016s
1 tree, 104 leaves, max depth = 21, train loss: 0.63376, val loss: 0.63114, in 0.016s
1 tree, 8 leaves, max depth = 6, train loss: 0.62743, val loss: 0.62449, in 0.016s
1 tree, 106 leaves, max depth = 14, train loss: 0.62123, val loss: 0.61798, in 0.016s
1 tree, 104 leaves, max depth = 16, train loss: 0.61531, val loss: 0.61177, in 0.016s
1 tree, 106 leaves, max depth = 15, train loss: 0.60956, val loss: 0.60573, in 0.016s
1 tree, 105 leaves, max depth = 16, train loss: 0.60402, val loss: 0.59991, in 0.016s
1 tree, 106 leaves, max depth = 14, train loss: 0.59858, val loss: 0.59419, in 0.016s
1 tree, 105 leaves, max depth = 19, train loss: 0.59340, val loss: 0.58877, in 0.000s
1 tree, 106 leaves, max depth = 17, train loss: 0.58837, val loss: 0.58353, in 0.016s
1 tree, 106 leaves, max depth = 14, train loss: 0.58342, val loss: 0.57832, in 0.016s
1 tree, 106 leaves, max depth = 15, train loss: 0.57869, val loss: 0.57334, in 0.016s
1 tree, 104 leaves, max depth = 23, train loss: 0.57413, val loss: 0.56857, in 0.016s
1 tree, 106 leaves, max depth = 14, train loss: 0.56963, val loss: 0.56383, in 0.016s
1 tree, 106 leaves, max depth = 14, train loss: 0.56528, val loss: 0.55923, in 0.016s
1 tree, 106 leaves, max depth = 14, train loss: 0.56106, val loss: 0.55478, in 0.016s
1 tree, 106 leaves, max depth = 14, train loss: 0.55697, val loss: 0.55046, in 0.016s
1 tree, 106 leaves, max depth = 14, train loss: 0.55300, val loss: 0.54628, in 0.016s
1 tree, 106 leaves, max depth = 15, train loss: 0.54921, val loss: 0.54227, in 0.016s
1 tree, 159 leaves, max depth = 17, train loss: 0.54499, val loss: 0.53820, in 0.016s
1 tree, 159 leaves, max depth = 17, train loss: 0.54089, val loss: 0.53427, in 0.031s
1 tree, 105 leaves, max depth = 17, train loss: 0.53734, val loss: 0.53051, in 0.016s
1 tree, 159 leaves, max depth = 17, train loss: 0.53342, val loss: 0.52674, in 0.016s
1 tree, 159 leaves, max depth = 17, train loss: 0.52961, val loss: 0.52309, in 0.016s
1 tree, 106 leaves, max depth = 18, train loss: 0.52626, val loss: 0.51957, in 0.016s
1 tree, 104 leaves, max depth = 17, train loss: 0.52301, val loss: 0.51613, in 0.016s
1 tree, 106 leaves, max depth = 14, train loss: 0.51980, val loss: 0.51272, in 0.031s
1 tree, 159 leaves, max depth = 16, train loss: 0.51622, val loss: 0.50930, in 0.016s
1 tree, 159 leaves, max depth = 16, train loss: 0.51275, val loss: 0.50598, in 0.016s
1 tree, 106 leaves, max depth = 15, train loss: 0.50977, val loss: 0.50281, in 0.016s
1 tree, 106 leaves, max depth = 14, train loss: 0.50683, val loss: 0.49968, in 0.016s
1 tree, 159 leaves, max depth = 16, train loss: 0.50352, val loss: 0.49652, in 0.031s
1 tree, 106 leaves, max depth = 15, train loss: 0.50075, val loss: 0.49358, in 0.016s
1 tree, 105 leaves, max depth = 16, train loss: 0.49807, val loss: 0.49073, in 0.000s
1 tree, 106 leaves, max depth = 15, train loss: 0.49547, val loss: 0.48795, in 0.016s
1 tree, 159 leaves, max depth = 17, train loss: 0.49234, val loss: 0.48496, in 0.032s
1 tree, 106 leaves, max depth = 15, train loss: 0.48984, val loss: 0.48230, in 0.016s
1 tree, 159 leaves, max depth = 17, train loss: 0.48683, val loss: 0.47943, in 0.016s
1 tree, 159 leaves, max depth = 17, train loss: 0.48391, val loss: 0.47665, in 0.016s
1 tree, 159 leaves, max depth = 17, train loss: 0.48107, val loss: 0.47394, in 0.016s
1 tree, 159 leaves, max depth = 17, train loss: 0.47832, val loss: 0.47132, in 0.016s
1 tree, 106 leaves, max depth = 15, train loss: 0.47597, val loss: 0.46882, in 0.016s
1 tree, 159 leaves, max depth = 16, train loss: 0.47331, val loss: 0.46629, in 0.016s
1 tree, 159 leaves, max depth = 16, train loss: 0.47074, val loss: 0.46384, in 0.016s
1 tree, 105 leaves, max depth = 14, train loss: 0.46855, val loss: 0.46151, in 0.016s
1 tree, 159 leaves, max depth = 16, train loss: 0.46606, val loss: 0.45915, in 0.016s
1 tree, 159 leaves, max depth = 16, train loss: 0.46365, val loss: 0.45686, in 0.031s
1 tree, 106 leaves, max depth = 14, train loss: 0.46153, val loss: 0.45459, in 0.016s
1 tree, 159 leaves, max depth = 16, train loss: 0.45920, val loss: 0.45238, in 0.016s
1 tree, 159 leaves, max depth = 16, train loss: 0.45694, val loss: 0.45024, in 0.031s
1 tree, 159 leaves, max depth = 16, train loss: 0.45474, val loss: 0.44816, in 0.016s
1 tree, 105 leaves, max depth = 14, train loss: 0.45278, val loss: 0.44607, in 0.016s
1 tree, 159 leaves, max depth = 16, train loss: 0.45066, val loss: 0.44406, in 0.031s
1 tree, 106 leaves, max depth = 13, train loss: 0.44877, val loss: 0.44204, in 0.016s
1 tree, 105 leaves, max depth = 14, train loss: 0.44694, val loss: 0.44008, in 0.000s
1 tree, 159 leaves, max depth = 16, train loss: 0.44490, val loss: 0.43816, in 0.016s
1 tree, 159 leaves, max depth = 16, train loss: 0.44292, val loss: 0.43629, in 0.016s
1 tree, 105 leaves, max depth = 20, train loss: 0.44117, val loss: 0.43444, in 0.016s
1 tree, 159 leaves, max depth = 15, train loss: 0.43925, val loss: 0.43264, in 0.016s
1 tree, 105 leaves, max depth = 14, train loss: 0.43757, val loss: 0.43084, in 0.016s
1 tree, 105 leaves, max depth = 14, train loss: 0.43594, val loss: 0.42909, in 0.016s
1 tree, 104 leaves, max depth = 21, train loss: 0.43436, val loss: 0.42742, in 0.016s
1 tree, 159 leaves, max depth = 16, train loss: 0.43252, val loss: 0.42569, in 0.016s
1 tree, 159 leaves, max depth = 16, train loss: 0.43073, val loss: 0.42402, in 0.016s
1 tree, 159 leaves, max depth = 16, train loss: 0.42899, val loss: 0.42239, in 0.031s
1 tree, 159 leaves, max depth = 16, train loss: 0.42730, val loss: 0.42081, in 0.016s
1 tree, 105 leaves, max depth = 15, train loss: 0.42581, val loss: 0.41921, in 0.016s
1 tree, 105 leaves, max depth = 19, train loss: 0.42436, val loss: 0.41768, in 0.016s
1 tree, 105 leaves, max depth = 15, train loss: 0.42295, val loss: 0.41617, in 0.016s
1 tree, 105 leaves, max depth = 15, train loss: 0.42159, val loss: 0.41471, in 0.016s
1 tree, 106 leaves, max depth = 17, train loss: 0.42023, val loss: 0.41324, in 0.016s
1 tree, 106 leaves, max depth = 13, train loss: 0.41894, val loss: 0.41185, in 0.016s
1 tree, 106 leaves, max depth = 13, train loss: 0.41770, val loss: 0.41051, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.41648, val loss: 0.40924, in 0.000s
1 tree, 106 leaves, max depth = 18, train loss: 0.41524, val loss: 0.40790, in 0.000s
1 tree, 159 leaves, max depth = 15, train loss: 0.41364, val loss: 0.40642, in 0.031s
1 tree, 106 leaves, max depth = 18, train loss: 0.41245, val loss: 0.40512, in 0.016s
1 tree, 159 leaves, max depth = 15, train loss: 0.41089, val loss: 0.40368, in 0.016s
1 tree, 159 leaves, max depth = 15, train loss: 0.40939, val loss: 0.40229, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.40822, val loss: 0.40108, in 0.016s
1 tree, 159 leaves, max depth = 15, train loss: 0.40676, val loss: 0.39972, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.40563, val loss: 0.39855, in 0.016s
1 tree, 105 leaves, max depth = 13, train loss: 0.40452, val loss: 0.39736, in 0.016s
1 tree, 159 leaves, max depth = 15, train loss: 0.40310, val loss: 0.39604, in 0.016s
1 tree, 105 leaves, max depth = 18, train loss: 0.40198, val loss: 0.39488, in 0.016s
Fit 91 trees in 1.830 s, (10912 total leaves)
Time spent computing histograms: 0.555s
Time spent finding best splits:  0.167s
Time spent applying splits:      0.202s
Time spent predicting:           0.000s
Trial 20, Fold 2: Log loss = 0.4061719563790699, Average precision = 0.9421895019520601, ROC-AUC = 0.9427703713055864, Elapsed Time = 1.8393180000002758 seconds
Trial 20, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 20, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
Binning 0.040 GB of training data: 0.157 s
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 99 leaves, max depth = 15, train loss: 0.68486, val loss: 0.68458, in 0.016s
1 tree, 105 leaves, max depth = 14, train loss: 0.67682, val loss: 0.67626, in 0.000s
1 tree, 105 leaves, max depth = 14, train loss: 0.66905, val loss: 0.66824, in 0.016s
1 tree, 105 leaves, max depth = 14, train loss: 0.66155, val loss: 0.66049, in 0.016s
1 tree, 105 leaves, max depth = 16, train loss: 0.65433, val loss: 0.65307, in 0.016s
1 tree, 105 leaves, max depth = 16, train loss: 0.64736, val loss: 0.64591, in 0.016s
1 tree, 101 leaves, max depth = 15, train loss: 0.64065, val loss: 0.63895, in 0.016s
1 tree, 103 leaves, max depth = 19, train loss: 0.63417, val loss: 0.63224, in 0.016s
1 tree, 105 leaves, max depth = 15, train loss: 0.62786, val loss: 0.62569, in 0.016s
1 tree, 105 leaves, max depth = 14, train loss: 0.62168, val loss: 0.61932, in 0.016s
1 tree, 102 leaves, max depth = 20, train loss: 0.61582, val loss: 0.61326, in 0.016s
1 tree, 104 leaves, max depth = 14, train loss: 0.61011, val loss: 0.60733, in 0.016s
1 tree, 105 leaves, max depth = 16, train loss: 0.60460, val loss: 0.60167, in 0.016s
1 tree, 105 leaves, max depth = 14, train loss: 0.59917, val loss: 0.59608, in 0.016s
1 tree, 102 leaves, max depth = 20, train loss: 0.59404, val loss: 0.59076, in 0.016s
1 tree, 105 leaves, max depth = 14, train loss: 0.58903, val loss: 0.58553, in 0.016s
1 tree, 105 leaves, max depth = 14, train loss: 0.58411, val loss: 0.58045, in 0.016s
1 tree, 104 leaves, max depth = 14, train loss: 0.57941, val loss: 0.57557, in 0.016s
1 tree, 103 leaves, max depth = 20, train loss: 0.57489, val loss: 0.57087, in 0.016s
1 tree, 104 leaves, max depth = 14, train loss: 0.57041, val loss: 0.56624, in 0.016s
1 tree, 104 leaves, max depth = 14, train loss: 0.56607, val loss: 0.56176, in 0.016s
1 tree, 104 leaves, max depth = 14, train loss: 0.56187, val loss: 0.55742, in 0.016s
1 tree, 159 leaves, max depth = 18, train loss: 0.55739, val loss: 0.55329, in 0.031s
1 tree, 105 leaves, max depth = 14, train loss: 0.55338, val loss: 0.54914, in 0.016s
1 tree, 105 leaves, max depth = 15, train loss: 0.54955, val loss: 0.54515, in 0.016s
1 tree, 159 leaves, max depth = 18, train loss: 0.54531, val loss: 0.54125, in 0.016s
1 tree, 159 leaves, max depth = 18, train loss: 0.54121, val loss: 0.53748, in 0.031s
1 tree, 105 leaves, max depth = 16, train loss: 0.53761, val loss: 0.53376, in 0.016s
1 tree, 159 leaves, max depth = 17, train loss: 0.53368, val loss: 0.53015, in 0.016s
1 tree, 159 leaves, max depth = 17, train loss: 0.52987, val loss: 0.52665, in 0.031s
1 tree, 101 leaves, max depth = 15, train loss: 0.52649, val loss: 0.52312, in 0.016s
1 tree, 105 leaves, max depth = 16, train loss: 0.52321, val loss: 0.51972, in 0.016s
1 tree, 105 leaves, max depth = 14, train loss: 0.51996, val loss: 0.51634, in 0.016s
1 tree, 159 leaves, max depth = 18, train loss: 0.51637, val loss: 0.51305, in 0.016s
1 tree, 159 leaves, max depth = 18, train loss: 0.51289, val loss: 0.50987, in 0.031s
1 tree, 104 leaves, max depth = 15, train loss: 0.50989, val loss: 0.50671, in 0.016s
1 tree, 105 leaves, max depth = 14, train loss: 0.50692, val loss: 0.50361, in 0.016s
1 tree, 159 leaves, max depth = 18, train loss: 0.50360, val loss: 0.50058, in 0.016s
1 tree, 105 leaves, max depth = 15, train loss: 0.50081, val loss: 0.49764, in 0.016s
1 tree, 105 leaves, max depth = 16, train loss: 0.49810, val loss: 0.49482, in 0.031s
1 tree, 104 leaves, max depth = 15, train loss: 0.49548, val loss: 0.49204, in 0.016s
1 tree, 159 leaves, max depth = 17, train loss: 0.49234, val loss: 0.48918, in 0.016s
1 tree, 105 leaves, max depth = 15, train loss: 0.48983, val loss: 0.48652, in 0.016s
1 tree, 159 leaves, max depth = 18, train loss: 0.48681, val loss: 0.48378, in 0.016s
[45/91] 1 tree, 159 leaves, max depth = 18, train loss: 0.48389, val loss: 0.48111, in 0.031s
1 tree, 159 leaves, max depth = 18, train loss: 0.48104, val loss: 0.47853, in 0.031s
1 tree, 159 leaves, max depth = 18, train loss: 0.47828, val loss: 0.47603, in 0.016s
1 tree, 105 leaves, max depth = 14, train loss: 0.47591, val loss: 0.47353, in 0.016s
1 tree, 159 leaves, max depth = 18, train loss: 0.47325, val loss: 0.47112, in 0.031s
1 tree, 159 leaves, max depth = 18, train loss: 0.47067, val loss: 0.46878, in 0.016s
1 tree, 104 leaves, max depth = 15, train loss: 0.46846, val loss: 0.46647, in 0.016s
1 tree, 159 leaves, max depth = 18, train loss: 0.46596, val loss: 0.46421, in 0.016s
1 tree, 159 leaves, max depth = 18, train loss: 0.46354, val loss: 0.46203, in 0.031s
1 tree, 105 leaves, max depth = 14, train loss: 0.46141, val loss: 0.45977, in 0.016s
1 tree, 159 leaves, max depth = 15, train loss: 0.45907, val loss: 0.45766, in 0.031s
1 tree, 159 leaves, max depth = 15, train loss: 0.45680, val loss: 0.45562, in 0.016s
1 tree, 159 leaves, max depth = 18, train loss: 0.45459, val loss: 0.45364, in 0.016s
1 tree, 104 leaves, max depth = 16, train loss: 0.45261, val loss: 0.45156, in 0.031s
1 tree, 159 leaves, max depth = 17, train loss: 0.45048, val loss: 0.44965, in 0.016s
1 tree, 105 leaves, max depth = 15, train loss: 0.44859, val loss: 0.44761, in 0.016s
1 tree, 105 leaves, max depth = 18, train loss: 0.44674, val loss: 0.44567, in 0.016s
1 tree, 159 leaves, max depth = 16, train loss: 0.44469, val loss: 0.44383, in 0.031s
1 tree, 159 leaves, max depth = 16, train loss: 0.44270, val loss: 0.44205, in 0.031s
1 tree, 105 leaves, max depth = 15, train loss: 0.44095, val loss: 0.44016, in 0.016s
1 tree, 159 leaves, max depth = 16, train loss: 0.43903, val loss: 0.43845, in 0.016s
1 tree, 105 leaves, max depth = 15, train loss: 0.43735, val loss: 0.43663, in 0.016s
1 tree, 105 leaves, max depth = 15, train loss: 0.43571, val loss: 0.43486, in 0.031s
1 tree, 104 leaves, max depth = 14, train loss: 0.43407, val loss: 0.43310, in 0.016s
1 tree, 159 leaves, max depth = 16, train loss: 0.43222, val loss: 0.43146, in 0.016s
1 tree, 159 leaves, max depth = 16, train loss: 0.43043, val loss: 0.42986, in 0.016s
1 tree, 159 leaves, max depth = 16, train loss: 0.42869, val loss: 0.42832, in 0.031s
1 tree, 159 leaves, max depth = 16, train loss: 0.42700, val loss: 0.42682, in 0.016s
1 tree, 105 leaves, max depth = 15, train loss: 0.42551, val loss: 0.42520, in 0.016s
1 tree, 104 leaves, max depth = 16, train loss: 0.42406, val loss: 0.42364, in 0.016s
1 tree, 105 leaves, max depth = 15, train loss: 0.42266, val loss: 0.42211, in 0.031s
1 tree, 104 leaves, max depth = 13, train loss: 0.42128, val loss: 0.42062, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.42000, val loss: 0.41943, in 0.016s
1 tree, 104 leaves, max depth = 13, train loss: 0.41863, val loss: 0.41795, in 0.016s
1 tree, 105 leaves, max depth = 15, train loss: 0.41735, val loss: 0.41656, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.41612, val loss: 0.41541, in 0.016s
1 tree, 104 leaves, max depth = 13, train loss: 0.41483, val loss: 0.41403, in 0.016s
1 tree, 159 leaves, max depth = 16, train loss: 0.41323, val loss: 0.41262, in 0.016s
1 tree, 105 leaves, max depth = 14, train loss: 0.41200, val loss: 0.41129, in 0.016s
1 tree, 159 leaves, max depth = 16, train loss: 0.41045, val loss: 0.40993, in 0.031s
1 tree, 159 leaves, max depth = 16, train loss: 0.40895, val loss: 0.40861, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.40777, val loss: 0.40751, in 0.000s
1 tree, 159 leaves, max depth = 16, train loss: 0.40631, val loss: 0.40623, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.40517, val loss: 0.40517, in 0.016s
1 tree, 104 leaves, max depth = 13, train loss: 0.40404, val loss: 0.40394, in 0.016s
1 tree, 159 leaves, max depth = 16, train loss: 0.40262, val loss: 0.40271, in 0.016s
1 tree, 103 leaves, max depth = 13, train loss: 0.40150, val loss: 0.40149, in 0.016s
Fit 91 trees in 1.955 s, (10896 total leaves)
Time spent computing histograms: 0.608s
Time spent finding best splits:  0.184s
Time spent applying splits:      0.224s
Time spent predicting:           0.063s
Trial 20, Fold 3: Log loss = 0.40054153446053187, Average precision = 0.948156783525392, ROC-AUC = 0.9469457128651367, Elapsed Time = 1.969958299998325 seconds
Trial 20, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 20, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
Binning 0.040 GB of training data: 0.158 s
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 96 leaves, max depth = 16, train loss: 0.68486, val loss: 0.68446, in 0.016s
1 tree, 102 leaves, max depth = 20, train loss: 0.67674, val loss: 0.67586, in 0.016s
1 tree, 102 leaves, max depth = 20, train loss: 0.66890, val loss: 0.66754, in 0.016s
1 tree, 103 leaves, max depth = 19, train loss: 0.66138, val loss: 0.65964, in 0.016s
1 tree, 105 leaves, max depth = 13, train loss: 0.65413, val loss: 0.65198, in 0.016s
1 tree, 103 leaves, max depth = 19, train loss: 0.64716, val loss: 0.64458, in 0.016s
1 tree, 103 leaves, max depth = 15, train loss: 0.64044, val loss: 0.63746, in 0.016s
1 tree, 103 leaves, max depth = 15, train loss: 0.63394, val loss: 0.63057, in 0.000s
1 tree, 105 leaves, max depth = 13, train loss: 0.62762, val loss: 0.62386, in 0.031s
1 tree, 105 leaves, max depth = 20, train loss: 0.62144, val loss: 0.61727, in 0.000s
1 tree, 103 leaves, max depth = 19, train loss: 0.61556, val loss: 0.61098, in 0.016s
1 tree, 104 leaves, max depth = 19, train loss: 0.60981, val loss: 0.60492, in 0.016s
1 tree, 105 leaves, max depth = 13, train loss: 0.60428, val loss: 0.59902, in 0.000s
1 tree, 105 leaves, max depth = 20, train loss: 0.59887, val loss: 0.59321, in 0.031s
1 tree, 104 leaves, max depth = 13, train loss: 0.59368, val loss: 0.58768, in 0.016s
1 tree, 104 leaves, max depth = 13, train loss: 0.58867, val loss: 0.58231, in 0.000s
1 tree, 104 leaves, max depth = 19, train loss: 0.58376, val loss: 0.57702, in 0.000s
1 tree, 104 leaves, max depth = 18, train loss: 0.57903, val loss: 0.57201, in 0.016s
1 tree, 104 leaves, max depth = 14, train loss: 0.57450, val loss: 0.56716, in 0.016s
1 tree, 105 leaves, max depth = 20, train loss: 0.57003, val loss: 0.56233, in 0.016s
1 tree, 105 leaves, max depth = 20, train loss: 0.56571, val loss: 0.55765, in 0.047s
1 tree, 105 leaves, max depth = 20, train loss: 0.56152, val loss: 0.55310, in 0.016s
1 tree, 105 leaves, max depth = 20, train loss: 0.55746, val loss: 0.54870, in 0.063s
1 tree, 105 leaves, max depth = 20, train loss: 0.55352, val loss: 0.54443, in 0.016s
1 tree, 104 leaves, max depth = 21, train loss: 0.54974, val loss: 0.54038, in 0.031s
1 tree, 159 leaves, max depth = 16, train loss: 0.54552, val loss: 0.53628, in 0.031s
1 tree, 159 leaves, max depth = 18, train loss: 0.54143, val loss: 0.53232, in 0.031s
1 tree, 103 leaves, max depth = 21, train loss: 0.53789, val loss: 0.52848, in 0.031s
1 tree, 159 leaves, max depth = 16, train loss: 0.53397, val loss: 0.52468, in 0.031s
1 tree, 159 leaves, max depth = 16, train loss: 0.53018, val loss: 0.52100, in 0.031s
1 tree, 103 leaves, max depth = 15, train loss: 0.52685, val loss: 0.51742, in 0.016s
1 tree, 103 leaves, max depth = 20, train loss: 0.52361, val loss: 0.51390, in 0.016s
1 tree, 104 leaves, max depth = 20, train loss: 0.52042, val loss: 0.51041, in 0.016s
1 tree, 159 leaves, max depth = 16, train loss: 0.51685, val loss: 0.50696, in 0.031s
1 tree, 159 leaves, max depth = 16, train loss: 0.51339, val loss: 0.50361, in 0.016s
1 tree, 104 leaves, max depth = 21, train loss: 0.51041, val loss: 0.50040, in 0.016s
1 tree, 105 leaves, max depth = 18, train loss: 0.50750, val loss: 0.49720, in 0.016s
1 tree, 159 leaves, max depth = 18, train loss: 0.50420, val loss: 0.49401, in 0.031s
1 tree, 104 leaves, max depth = 21, train loss: 0.50144, val loss: 0.49103, in 0.016s
1 tree, 103 leaves, max depth = 22, train loss: 0.49877, val loss: 0.48810, in 0.031s
1 tree, 104 leaves, max depth = 21, train loss: 0.49616, val loss: 0.48529, in 0.016s
1 tree, 159 leaves, max depth = 18, train loss: 0.49304, val loss: 0.48228, in 0.016s
1 tree, 104 leaves, max depth = 21, train loss: 0.49055, val loss: 0.47959, in 0.016s
1 tree, 159 leaves, max depth = 18, train loss: 0.48755, val loss: 0.47670, in 0.031s
1 tree, 159 leaves, max depth = 18, train loss: 0.48464, val loss: 0.47389, in 0.016s
1 tree, 159 leaves, max depth = 18, train loss: 0.48181, val loss: 0.47117, in 0.031s
1 tree, 159 leaves, max depth = 18, train loss: 0.47907, val loss: 0.46853, in 0.016s
1 tree, 105 leaves, max depth = 15, train loss: 0.47674, val loss: 0.46594, in 0.016s
1 tree, 159 leaves, max depth = 19, train loss: 0.47410, val loss: 0.46340, in 0.031s
1 tree, 159 leaves, max depth = 19, train loss: 0.47153, val loss: 0.46093, in 0.016s
1 tree, 103 leaves, max depth = 20, train loss: 0.46935, val loss: 0.45852, in 0.016s
1 tree, 159 leaves, max depth = 19, train loss: 0.46688, val loss: 0.45614, in 0.031s
1 tree, 159 leaves, max depth = 19, train loss: 0.46447, val loss: 0.45383, in 0.016s
1 tree, 105 leaves, max depth = 15, train loss: 0.46237, val loss: 0.45149, in 0.016s
1 tree, 159 leaves, max depth = 18, train loss: 0.46006, val loss: 0.44927, in 0.031s
1 tree, 159 leaves, max depth = 18, train loss: 0.45780, val loss: 0.44710, in 0.031s
1 tree, 159 leaves, max depth = 21, train loss: 0.45561, val loss: 0.44501, in 0.016s
1 tree, 104 leaves, max depth = 14, train loss: 0.45360, val loss: 0.44281, in 0.016s
1 tree, 159 leaves, max depth = 18, train loss: 0.45149, val loss: 0.44079, in 0.031s
1 tree, 104 leaves, max depth = 14, train loss: 0.44961, val loss: 0.43871, in 0.016s
1 tree, 103 leaves, max depth = 19, train loss: 0.44778, val loss: 0.43668, in 0.016s
1 tree, 159 leaves, max depth = 16, train loss: 0.44576, val loss: 0.43474, in 0.031s
1 tree, 159 leaves, max depth = 16, train loss: 0.44379, val loss: 0.43286, in 0.031s
1 tree, 104 leaves, max depth = 14, train loss: 0.44199, val loss: 0.43089, in 0.016s
1 tree, 159 leaves, max depth = 18, train loss: 0.44009, val loss: 0.42908, in 0.031s
1 tree, 103 leaves, max depth = 17, train loss: 0.43841, val loss: 0.42720, in 0.016s
1 tree, 104 leaves, max depth = 15, train loss: 0.43672, val loss: 0.42536, in 0.031s
1 tree, 104 leaves, max depth = 16, train loss: 0.43515, val loss: 0.42360, in 0.031s
1 tree, 159 leaves, max depth = 18, train loss: 0.43333, val loss: 0.42186, in 0.016s
1 tree, 159 leaves, max depth = 18, train loss: 0.43156, val loss: 0.42018, in 0.031s
1 tree, 159 leaves, max depth = 18, train loss: 0.42985, val loss: 0.41855, in 0.016s
1 tree, 159 leaves, max depth = 18, train loss: 0.42818, val loss: 0.41696, in 0.031s
1 tree, 103 leaves, max depth = 17, train loss: 0.42668, val loss: 0.41528, in 0.016s
1 tree, 104 leaves, max depth = 15, train loss: 0.42523, val loss: 0.41367, in 0.016s
1 tree, 103 leaves, max depth = 16, train loss: 0.42382, val loss: 0.41209, in 0.016s
1 tree, 104 leaves, max depth = 15, train loss: 0.42246, val loss: 0.41057, in 0.016s
1 tree, 105 leaves, max depth = 17, train loss: 0.42111, val loss: 0.40902, in 0.016s
1 tree, 104 leaves, max depth = 14, train loss: 0.41977, val loss: 0.40755, in 0.016s
1 tree, 103 leaves, max depth = 16, train loss: 0.41852, val loss: 0.40613, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.41730, val loss: 0.40482, in 0.000s
1 tree, 105 leaves, max depth = 17, train loss: 0.41607, val loss: 0.40341, in 0.016s
1 tree, 159 leaves, max depth = 17, train loss: 0.41449, val loss: 0.40192, in 0.031s
1 tree, 105 leaves, max depth = 17, train loss: 0.41331, val loss: 0.40056, in 0.016s
1 tree, 159 leaves, max depth = 17, train loss: 0.41178, val loss: 0.39911, in 0.016s
1 tree, 159 leaves, max depth = 17, train loss: 0.41029, val loss: 0.39771, in 0.031s
1 tree, 5 leaves, max depth = 3, train loss: 0.40913, val loss: 0.39646, in 0.016s
1 tree, 159 leaves, max depth = 17, train loss: 0.40769, val loss: 0.39510, in 0.031s
1 tree, 5 leaves, max depth = 3, train loss: 0.40656, val loss: 0.39389, in 0.000s
1 tree, 105 leaves, max depth = 17, train loss: 0.40545, val loss: 0.39261, in 0.031s
1 tree, 159 leaves, max depth = 20, train loss: 0.40405, val loss: 0.39130, in 0.031s
1 tree, 105 leaves, max depth = 16, train loss: 0.40298, val loss: 0.39005, in 0.016s
Fit 91 trees in 2.221 s, (10918 total leaves)
Time spent computing histograms: 0.679s
Time spent finding best splits:  0.226s
Time spent applying splits:      0.279s
Time spent predicting:           0.000s
Trial 20, Fold 4: Log loss = 0.40511396654818094, Average precision = 0.9467197122369408, ROC-AUC = 0.9438916339406426, Elapsed Time = 2.220715299999938 seconds
Trial 20, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 20, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
Binning 0.040 GB of training data: 0.157 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 102 leaves, max depth = 13, train loss: 0.68473, val loss: 0.68423, in 0.016s
1 tree, 103 leaves, max depth = 16, train loss: 0.67653, val loss: 0.67551, in 0.016s
1 tree, 103 leaves, max depth = 16, train loss: 0.66861, val loss: 0.66709, in 0.016s
1 tree, 101 leaves, max depth = 14, train loss: 0.66105, val loss: 0.65905, in 0.016s
1 tree, 105 leaves, max depth = 17, train loss: 0.65374, val loss: 0.65127, in 0.016s
1 tree, 105 leaves, max depth = 17, train loss: 0.64667, val loss: 0.64375, in 0.016s
1 tree, 99 leaves, max depth = 15, train loss: 0.63986, val loss: 0.63650, in 0.016s
1 tree, 103 leaves, max depth = 18, train loss: 0.63328, val loss: 0.62949, in 0.016s
1 tree, 105 leaves, max depth = 15, train loss: 0.62689, val loss: 0.62263, in 0.016s
1 tree, 105 leaves, max depth = 16, train loss: 0.62065, val loss: 0.61595, in 0.016s
1 tree, 105 leaves, max depth = 17, train loss: 0.61469, val loss: 0.60952, in 0.016s
1 tree, 104 leaves, max depth = 15, train loss: 0.60891, val loss: 0.60330, in 0.016s
1 tree, 105 leaves, max depth = 17, train loss: 0.60333, val loss: 0.59727, in 0.016s
1 tree, 105 leaves, max depth = 16, train loss: 0.59785, val loss: 0.59138, in 0.016s
1 tree, 100 leaves, max depth = 20, train loss: 0.59263, val loss: 0.58579, in 0.016s
1 tree, 104 leaves, max depth = 15, train loss: 0.58756, val loss: 0.58030, in 0.016s
1 tree, 105 leaves, max depth = 16, train loss: 0.58258, val loss: 0.57494, in 0.016s
1 tree, 105 leaves, max depth = 17, train loss: 0.57783, val loss: 0.56982, in 0.016s
1 tree, 103 leaves, max depth = 18, train loss: 0.57324, val loss: 0.56487, in 0.016s
1 tree, 105 leaves, max depth = 16, train loss: 0.56871, val loss: 0.55997, in 0.016s
1 tree, 105 leaves, max depth = 16, train loss: 0.56433, val loss: 0.55523, in 0.016s
1 tree, 105 leaves, max depth = 16, train loss: 0.56008, val loss: 0.55062, in 0.016s
1 tree, 105 leaves, max depth = 16, train loss: 0.55597, val loss: 0.54615, in 0.016s
1 tree, 105 leaves, max depth = 16, train loss: 0.55198, val loss: 0.54182, in 0.016s
1 tree, 105 leaves, max depth = 17, train loss: 0.54816, val loss: 0.53768, in 0.016s
1 tree, 159 leaves, max depth = 17, train loss: 0.54406, val loss: 0.53379, in 0.016s
1 tree, 158 leaves, max depth = 17, train loss: 0.54008, val loss: 0.53004, in 0.031s
1 tree, 105 leaves, max depth = 17, train loss: 0.53649, val loss: 0.52613, in 0.016s
1 tree, 159 leaves, max depth = 17, train loss: 0.53267, val loss: 0.52254, in 0.016s
1 tree, 159 leaves, max depth = 17, train loss: 0.52898, val loss: 0.51906, in 0.031s
1 tree, 102 leaves, max depth = 15, train loss: 0.52559, val loss: 0.51536, in 0.016s
1 tree, 105 leaves, max depth = 17, train loss: 0.52231, val loss: 0.51174, in 0.016s
1 tree, 105 leaves, max depth = 16, train loss: 0.51907, val loss: 0.50820, in 0.016s
1 tree, 159 leaves, max depth = 16, train loss: 0.51560, val loss: 0.50494, in 0.016s
1 tree, 159 leaves, max depth = 16, train loss: 0.51223, val loss: 0.50179, in 0.031s
1 tree, 103 leaves, max depth = 18, train loss: 0.50922, val loss: 0.49850, in 0.016s
1 tree, 105 leaves, max depth = 14, train loss: 0.50625, val loss: 0.49523, in 0.016s
1 tree, 159 leaves, max depth = 18, train loss: 0.50304, val loss: 0.49224, in 0.016s
1 tree, 105 leaves, max depth = 17, train loss: 0.50024, val loss: 0.48916, in 0.016s
1 tree, 105 leaves, max depth = 17, train loss: 0.49752, val loss: 0.48617, in 0.016s
1 tree, 102 leaves, max depth = 15, train loss: 0.49489, val loss: 0.48326, in 0.016s
1 tree, 159 leaves, max depth = 18, train loss: 0.49185, val loss: 0.48044, in 0.016s
1 tree, 105 leaves, max depth = 17, train loss: 0.48933, val loss: 0.47765, in 0.016s
1 tree, 158 leaves, max depth = 17, train loss: 0.48640, val loss: 0.47495, in 0.031s
1 tree, 159 leaves, max depth = 17, train loss: 0.48357, val loss: 0.47232, in 0.016s
1 tree, 159 leaves, max depth = 17, train loss: 0.48082, val loss: 0.46978, in 0.031s
1 tree, 159 leaves, max depth = 18, train loss: 0.47815, val loss: 0.46732, in 0.031s
1 tree, 105 leaves, max depth = 14, train loss: 0.47576, val loss: 0.46468, in 0.016s
1 tree, 159 leaves, max depth = 17, train loss: 0.47319, val loss: 0.46231, in 0.016s
1 tree, 159 leaves, max depth = 17, train loss: 0.47069, val loss: 0.46001, in 0.016s
1 tree, 105 leaves, max depth = 16, train loss: 0.46847, val loss: 0.45754, in 0.016s
1 tree, 159 leaves, max depth = 17, train loss: 0.46606, val loss: 0.45534, in 0.031s
1 tree, 159 leaves, max depth = 17, train loss: 0.46372, val loss: 0.45320, in 0.016s
1 tree, 105 leaves, max depth = 13, train loss: 0.46157, val loss: 0.45080, in 0.016s
1 tree, 159 leaves, max depth = 16, train loss: 0.45931, val loss: 0.44874, in 0.031s
1 tree, 159 leaves, max depth = 16, train loss: 0.45712, val loss: 0.44674, in 0.016s
1 tree, 159 leaves, max depth = 16, train loss: 0.45500, val loss: 0.44481, in 0.016s
1 tree, 105 leaves, max depth = 16, train loss: 0.45300, val loss: 0.44258, in 0.016s
1 tree, 159 leaves, max depth = 16, train loss: 0.45095, val loss: 0.44071, in 0.031s
1 tree, 103 leaves, max depth = 16, train loss: 0.44903, val loss: 0.43857, in 0.016s
1 tree, 105 leaves, max depth = 16, train loss: 0.44716, val loss: 0.43648, in 0.016s
1 tree, 159 leaves, max depth = 15, train loss: 0.44519, val loss: 0.43470, in 0.016s
1 tree, 159 leaves, max depth = 15, train loss: 0.44327, val loss: 0.43297, in 0.016s
1 tree, 100 leaves, max depth = 15, train loss: 0.44149, val loss: 0.43099, in 0.016s
1 tree, 159 leaves, max depth = 15, train loss: 0.43964, val loss: 0.42932, in 0.031s
1 tree, 105 leaves, max depth = 16, train loss: 0.43792, val loss: 0.42739, in 0.016s
1 tree, 105 leaves, max depth = 16, train loss: 0.43625, val loss: 0.42551, in 0.016s
1 tree, 103 leaves, max depth = 17, train loss: 0.43464, val loss: 0.42369, in 0.000s
1 tree, 159 leaves, max depth = 16, train loss: 0.43287, val loss: 0.42211, in 0.016s
1 tree, 159 leaves, max depth = 16, train loss: 0.43114, val loss: 0.42057, in 0.016s
1 tree, 159 leaves, max depth = 15, train loss: 0.42947, val loss: 0.41907, in 0.016s
1 tree, 159 leaves, max depth = 15, train loss: 0.42784, val loss: 0.41763, in 0.031s
1 tree, 105 leaves, max depth = 16, train loss: 0.42631, val loss: 0.41589, in 0.016s
1 tree, 100 leaves, max depth = 15, train loss: 0.42483, val loss: 0.41423, in 0.016s
1 tree, 105 leaves, max depth = 16, train loss: 0.42339, val loss: 0.41259, in 0.016s
1 tree, 105 leaves, max depth = 16, train loss: 0.42199, val loss: 0.41100, in 0.016s
1 tree, 105 leaves, max depth = 19, train loss: 0.42059, val loss: 0.40942, in 0.016s
1 tree, 102 leaves, max depth = 15, train loss: 0.41928, val loss: 0.40792, in 0.031s
1 tree, 105 leaves, max depth = 16, train loss: 0.41800, val loss: 0.40645, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.41678, val loss: 0.40527, in 0.000s
1 tree, 105 leaves, max depth = 19, train loss: 0.41550, val loss: 0.40382, in 0.016s
1 tree, 159 leaves, max depth = 17, train loss: 0.41397, val loss: 0.40247, in 0.031s
1 tree, 105 leaves, max depth = 19, train loss: 0.41274, val loss: 0.40107, in 0.016s
1 tree, 159 leaves, max depth = 19, train loss: 0.41124, val loss: 0.39977, in 0.031s
1 tree, 159 leaves, max depth = 17, train loss: 0.40980, val loss: 0.39850, in 0.063s
1 tree, 5 leaves, max depth = 3, train loss: 0.40863, val loss: 0.39738, in 0.016s
1 tree, 159 leaves, max depth = 17, train loss: 0.40723, val loss: 0.39616, in 0.031s
1 tree, 5 leaves, max depth = 3, train loss: 0.40610, val loss: 0.39508, in 0.016s
1 tree, 105 leaves, max depth = 19, train loss: 0.40494, val loss: 0.39375, in 0.031s
1 tree, 159 leaves, max depth = 19, train loss: 0.40358, val loss: 0.39258, in 0.016s
1 tree, 105 leaves, max depth = 19, train loss: 0.40246, val loss: 0.39130, in 0.031s
Fit 91 trees in 2.017 s, (10928 total leaves)
Time spent computing histograms: 0.633s
Time spent finding best splits:  0.190s
Time spent applying splits:      0.236s
Time spent predicting:           0.031s
Trial 20, Fold 5: Log loss = 0.41079164018892395, Average precision = 0.9446465371422839, ROC-AUC = 0.941807182802891, Elapsed Time = 2.021551100000579 seconds
Optimization Progress:  21%|##1       | 21/100 [04:10<16:19, 12.40s/it]
Trial 21, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 21, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
0.142 s 0.040 GB of training data: 
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 17 leaves, max depth = 8, train loss: 0.67808, val loss: 0.67764, in 0.000s
1 tree, 17 leaves, max depth = 8, train loss: 0.66413, val loss: 0.66327, in 0.000s
1 tree, 17 leaves, max depth = 8, train loss: 0.65103, val loss: 0.64976, in 0.016s
1 tree, 17 leaves, max depth = 8, train loss: 0.63883, val loss: 0.63723, in 0.000s
1 tree, 17 leaves, max depth = 7, train loss: 0.62725, val loss: 0.62526, in 0.016s
1 tree, 17 leaves, max depth = 8, train loss: 0.61634, val loss: 0.61397, in 0.000s
1 tree, 17 leaves, max depth = 8, train loss: 0.60617, val loss: 0.60351, in 0.016s
1 tree, 17 leaves, max depth = 8, train loss: 0.59648, val loss: 0.59347, in 0.000s
1 tree, 17 leaves, max depth = 7, train loss: 0.58734, val loss: 0.58398, in 0.016s
1 tree, 17 leaves, max depth = 8, train loss: 0.57871, val loss: 0.57502, in 0.000s
1 tree, 17 leaves, max depth = 7, train loss: 0.57057, val loss: 0.56655, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.56287, val loss: 0.55853, in 0.000s
1 tree, 17 leaves, max depth = 9, train loss: 0.55573, val loss: 0.55108, in 0.000s
1 tree, 17 leaves, max depth = 7, train loss: 0.54884, val loss: 0.54389, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.54233, val loss: 0.53709, in 0.000s
1 tree, 17 leaves, max depth = 8, train loss: 0.53543, val loss: 0.53057, in 0.016s
1 tree, 17 leaves, max depth = 8, train loss: 0.52942, val loss: 0.52428, in 0.000s
1 tree, 17 leaves, max depth = 8, train loss: 0.52301, val loss: 0.51826, in 0.016s
1 tree, 17 leaves, max depth = 8, train loss: 0.51758, val loss: 0.51255, in 0.000s
1 tree, 17 leaves, max depth = 8, train loss: 0.51162, val loss: 0.50695, in 0.000s
1 tree, 17 leaves, max depth = 8, train loss: 0.50598, val loss: 0.50168, in 0.016s
1 tree, 17 leaves, max depth = 8, train loss: 0.50064, val loss: 0.49668, in 0.000s
1 tree, 17 leaves, max depth = 8, train loss: 0.49559, val loss: 0.49197, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.49077, val loss: 0.48687, in 0.000s
1 tree, 17 leaves, max depth = 8, train loss: 0.48604, val loss: 0.48246, in 0.016s
1 tree, 17 leaves, max depth = 9, train loss: 0.48164, val loss: 0.47778, in 0.000s
1 tree, 17 leaves, max depth = 6, train loss: 0.47723, val loss: 0.47370, in 0.016s
1 tree, 17 leaves, max depth = 9, train loss: 0.47313, val loss: 0.46935, in 0.000s
1 tree, 17 leaves, max depth = 6, train loss: 0.46899, val loss: 0.46552, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.46509, val loss: 0.46137, in 0.000s
1 tree, 17 leaves, max depth = 7, train loss: 0.46143, val loss: 0.45750, in 0.000s
1 tree, 17 leaves, max depth = 6, train loss: 0.45760, val loss: 0.45397, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.45415, val loss: 0.45027, in 0.000s
1 tree, 17 leaves, max depth = 8, train loss: 0.45054, val loss: 0.44694, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.44712, val loss: 0.44379, in 0.000s
1 tree, 17 leaves, max depth = 7, train loss: 0.44388, val loss: 0.44082, in 0.016s
1 tree, 17 leaves, max depth = 10, train loss: 0.44081, val loss: 0.43759, in 0.000s
1 tree, 17 leaves, max depth = 6, train loss: 0.43776, val loss: 0.43480, in 0.000s
1 tree, 17 leaves, max depth = 9, train loss: 0.43479, val loss: 0.43167, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.43192, val loss: 0.42909, in 0.000s
1 tree, 17 leaves, max depth = 9, train loss: 0.42922, val loss: 0.42617, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.42651, val loss: 0.42372, in 0.000s
1 tree, 17 leaves, max depth = 9, train loss: 0.42396, val loss: 0.42098, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.42141, val loss: 0.41871, in 0.000s
1 tree, 17 leaves, max depth = 8, train loss: 0.41899, val loss: 0.41654, in 0.000s
1 tree, 17 leaves, max depth = 10, train loss: 0.41662, val loss: 0.41397, in 0.016s
1 tree, 17 leaves, max depth = 8, train loss: 0.41433, val loss: 0.41191, in 0.000s
1 tree, 17 leaves, max depth = 11, train loss: 0.41213, val loss: 0.40956, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.40994, val loss: 0.40714, in 0.000s
1 tree, 17 leaves, max depth = 8, train loss: 0.40778, val loss: 0.40519, in 0.000s
1 tree, 17 leaves, max depth = 9, train loss: 0.40565, val loss: 0.40294, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.40361, val loss: 0.40115, in 0.000s
1 tree, 17 leaves, max depth = 9, train loss: 0.40165, val loss: 0.39903, in 0.016s
1 tree, 17 leaves, max depth = 10, train loss: 0.39979, val loss: 0.39700, in 0.000s
1 tree, 17 leaves, max depth = 7, train loss: 0.39785, val loss: 0.39529, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.39584, val loss: 0.39306, in 0.000s
1 tree, 17 leaves, max depth = 7, train loss: 0.39400, val loss: 0.39145, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.39210, val loss: 0.38933, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.39030, val loss: 0.38733, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.38859, val loss: 0.38542, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.38697, val loss: 0.38361, in 0.000s
1 tree, 17 leaves, max depth = 7, train loss: 0.38522, val loss: 0.38211, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.38356, val loss: 0.38070, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.38202, val loss: 0.37897, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.38057, val loss: 0.37733, in 0.000s
1 tree, 17 leaves, max depth = 8, train loss: 0.37887, val loss: 0.37556, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.37749, val loss: 0.37400, in 0.000s
1 tree, 17 leaves, max depth = 8, train loss: 0.37593, val loss: 0.37237, in 0.016s
1 tree, 17 leaves, max depth = 9, train loss: 0.37437, val loss: 0.37115, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.37306, val loss: 0.36967, in 0.000s
Fit 70 trees in 0.736 s, (1070 total leaves)
Time spent computing histograms: 0.281s
Time spent finding best splits:  0.029s
Time spent applying splits:      0.023s
Time spent predicting:           0.000s
Trial 21, Fold 1: Log loss = 0.374868091326567, Average precision = 0.9452746861639605, ROC-AUC = 0.9428602346539338, Elapsed Time = 0.7437253999996756 seconds
Trial 21, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 21, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
Binning 0.040 GB of training data: 0.143 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 17 leaves, max depth = 8, train loss: 0.67826, val loss: 0.67752, in 0.016s
1 tree, 17 leaves, max depth = 8, train loss: 0.66446, val loss: 0.66307, in 0.016s
1 tree, 17 leaves, max depth = 8, train loss: 0.65141, val loss: 0.64934, in 0.000s
1 tree, 17 leaves, max depth = 8, train loss: 0.63924, val loss: 0.63656, in 0.016s
1 tree, 17 leaves, max depth = 8, train loss: 0.62769, val loss: 0.62439, in 0.000s
1 tree, 17 leaves, max depth = 8, train loss: 0.61683, val loss: 0.61291, in 0.000s
1 tree, 17 leaves, max depth = 8, train loss: 0.60668, val loss: 0.60222, in 0.016s
1 tree, 17 leaves, max depth = 8, train loss: 0.59703, val loss: 0.59201, in 0.000s
1 tree, 17 leaves, max depth = 7, train loss: 0.58793, val loss: 0.58236, in 0.016s
1 tree, 17 leaves, max depth = 8, train loss: 0.57934, val loss: 0.57325, in 0.000s
1 tree, 17 leaves, max depth = 7, train loss: 0.57123, val loss: 0.56465, in 0.016s
1 tree, 17 leaves, max depth = 8, train loss: 0.56364, val loss: 0.55660, in 0.000s
1 tree, 17 leaves, max depth = 12, train loss: 0.55648, val loss: 0.54903, in 0.016s
1 tree, 17 leaves, max depth = 8, train loss: 0.54969, val loss: 0.54182, in 0.000s
1 tree, 17 leaves, max depth = 8, train loss: 0.54321, val loss: 0.53489, in 0.000s
1 tree, 17 leaves, max depth = 8, train loss: 0.53621, val loss: 0.52810, in 0.016s
1 tree, 17 leaves, max depth = 12, train loss: 0.53031, val loss: 0.52184, in 0.016s
1 tree, 17 leaves, max depth = 8, train loss: 0.52381, val loss: 0.51553, in 0.000s
1 tree, 17 leaves, max depth = 12, train loss: 0.51836, val loss: 0.50974, in 0.000s
1 tree, 17 leaves, max depth = 8, train loss: 0.51231, val loss: 0.50388, in 0.016s
1 tree, 17 leaves, max depth = 8, train loss: 0.50658, val loss: 0.49834, in 0.000s
1 tree, 17 leaves, max depth = 8, train loss: 0.50117, val loss: 0.49311, in 0.016s
1 tree, 17 leaves, max depth = 8, train loss: 0.49604, val loss: 0.48816, in 0.000s
1 tree, 17 leaves, max depth = 8, train loss: 0.49132, val loss: 0.48310, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.48656, val loss: 0.47856, in 0.000s
1 tree, 17 leaves, max depth = 11, train loss: 0.48216, val loss: 0.47386, in 0.000s
1 tree, 17 leaves, max depth = 8, train loss: 0.47767, val loss: 0.46954, in 0.016s
1 tree, 17 leaves, max depth = 10, train loss: 0.47358, val loss: 0.46518, in 0.000s
1 tree, 17 leaves, max depth = 7, train loss: 0.46941, val loss: 0.46122, in 0.016s
1 tree, 17 leaves, max depth = 8, train loss: 0.46543, val loss: 0.45739, in 0.000s
1 tree, 17 leaves, max depth = 11, train loss: 0.46168, val loss: 0.45335, in 0.016s
1 tree, 17 leaves, max depth = 8, train loss: 0.45793, val loss: 0.44978, in 0.000s
1 tree, 17 leaves, max depth = 10, train loss: 0.45443, val loss: 0.44604, in 0.016s
1 tree, 17 leaves, max depth = 8, train loss: 0.45091, val loss: 0.44268, in 0.000s
1 tree, 17 leaves, max depth = 8, train loss: 0.44764, val loss: 0.43917, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.44433, val loss: 0.43603, in 0.000s
1 tree, 17 leaves, max depth = 6, train loss: 0.44121, val loss: 0.43310, in 0.016s
1 tree, 17 leaves, max depth = 9, train loss: 0.43819, val loss: 0.42986, in 0.000s
1 tree, 17 leaves, max depth = 9, train loss: 0.43533, val loss: 0.42679, in 0.000s
1 tree, 17 leaves, max depth = 8, train loss: 0.43239, val loss: 0.42397, in 0.000s
1 tree, 17 leaves, max depth = 9, train loss: 0.42971, val loss: 0.42109, in 0.000s
1 tree, 17 leaves, max depth = 8, train loss: 0.42692, val loss: 0.41846, in 0.016s
1 tree, 17 leaves, max depth = 11, train loss: 0.42441, val loss: 0.41572, in 0.000s
1 tree, 17 leaves, max depth = 6, train loss: 0.42178, val loss: 0.41321, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.41931, val loss: 0.41089, in 0.000s
1 tree, 17 leaves, max depth = 8, train loss: 0.41693, val loss: 0.40865, in 0.016s
1 tree, 17 leaves, max depth = 8, train loss: 0.41464, val loss: 0.40612, in 0.000s
1 tree, 17 leaves, max depth = 8, train loss: 0.41239, val loss: 0.40400, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.41026, val loss: 0.40179, in 0.000s
1 tree, 17 leaves, max depth = 10, train loss: 0.40812, val loss: 0.39943, in 0.016s
1 tree, 17 leaves, max depth = 8, train loss: 0.40599, val loss: 0.39745, in 0.000s
1 tree, 17 leaves, max depth = 8, train loss: 0.40399, val loss: 0.39523, in 0.000s
1 tree, 17 leaves, max depth = 8, train loss: 0.40197, val loss: 0.39336, in 0.016s
1 tree, 17 leaves, max depth = 11, train loss: 0.40005, val loss: 0.39126, in 0.000s
1 tree, 17 leaves, max depth = 8, train loss: 0.39814, val loss: 0.38948, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.39619, val loss: 0.38746, in 0.000s
1 tree, 17 leaves, max depth = 8, train loss: 0.39437, val loss: 0.38578, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.39252, val loss: 0.38386, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.39077, val loss: 0.38204, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.38911, val loss: 0.38030, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.38754, val loss: 0.37866, in 0.000s
1 tree, 17 leaves, max depth = 8, train loss: 0.38581, val loss: 0.37708, in 0.016s
1 tree, 17 leaves, max depth = 8, train loss: 0.38406, val loss: 0.37518, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.38257, val loss: 0.37362, in 0.016s
1 tree, 17 leaves, max depth = 8, train loss: 0.38092, val loss: 0.37212, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.37951, val loss: 0.37064, in 0.016s
1 tree, 17 leaves, max depth = 9, train loss: 0.37783, val loss: 0.36885, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.37649, val loss: 0.36744, in 0.000s
1 tree, 17 leaves, max depth = 8, train loss: 0.37492, val loss: 0.36602, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.37337, val loss: 0.36434, in 0.000s
Fit 70 trees in 0.768 s, (1082 total leaves)
Time spent computing histograms: 0.291s
Time spent finding best splits:  0.032s
Time spent applying splits:      0.025s
Time spent predicting:           0.000s
Trial 21, Fold 2: Log loss = 0.3752575024880809, Average precision = 0.9417755109207956, ROC-AUC = 0.9443603087278346, Elapsed Time = 0.7794021999998222 seconds
Trial 21, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 21, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
Binning 0.040 GB of training data: 0.141 s
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 17 leaves, max depth = 8, train loss: 0.67827, val loss: 0.67781, in 0.016s
1 tree, 17 leaves, max depth = 9, train loss: 0.66471, val loss: 0.66373, in 0.000s
1 tree, 17 leaves, max depth = 8, train loss: 0.65184, val loss: 0.65036, in 0.016s
1 tree, 17 leaves, max depth = 8, train loss: 0.63975, val loss: 0.63779, in 0.000s
1 tree, 17 leaves, max depth = 8, train loss: 0.62838, val loss: 0.62596, in 0.016s
1 tree, 17 leaves, max depth = 8, train loss: 0.61755, val loss: 0.61477, in 0.000s
1 tree, 17 leaves, max depth = 8, train loss: 0.60747, val loss: 0.60425, in 0.016s
1 tree, 17 leaves, max depth = 9, train loss: 0.59800, val loss: 0.59445, in 0.000s
1 tree, 17 leaves, max depth = 8, train loss: 0.58903, val loss: 0.58508, in 0.000s
1 tree, 17 leaves, max depth = 8, train loss: 0.58045, val loss: 0.57619, in 0.016s
1 tree, 17 leaves, max depth = 9, train loss: 0.57255, val loss: 0.56790, in 0.016s
1 tree, 17 leaves, max depth = 8, train loss: 0.56489, val loss: 0.55994, in 0.000s
1 tree, 17 leaves, max depth = 8, train loss: 0.55775, val loss: 0.55245, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.55090, val loss: 0.54533, in 0.000s
1 tree, 17 leaves, max depth = 9, train loss: 0.54442, val loss: 0.53860, in 0.000s
1 tree, 17 leaves, max depth = 8, train loss: 0.53733, val loss: 0.53192, in 0.000s
1 tree, 17 leaves, max depth = 12, train loss: 0.53149, val loss: 0.52576, in 0.000s
1 tree, 17 leaves, max depth = 7, train loss: 0.52490, val loss: 0.51956, in 0.016s
1 tree, 17 leaves, max depth = 10, train loss: 0.51948, val loss: 0.51389, in 0.000s
1 tree, 17 leaves, max depth = 7, train loss: 0.51335, val loss: 0.50813, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.50755, val loss: 0.50269, in 0.000s
1 tree, 17 leaves, max depth = 7, train loss: 0.50205, val loss: 0.49756, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.49685, val loss: 0.49272, in 0.000s
1 tree, 17 leaves, max depth = 9, train loss: 0.49219, val loss: 0.48775, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.48733, val loss: 0.48321, in 0.000s
1 tree, 17 leaves, max depth = 7, train loss: 0.48272, val loss: 0.47894, in 0.016s
1 tree, 17 leaves, max depth = 9, train loss: 0.47843, val loss: 0.47440, in 0.000s
1 tree, 17 leaves, max depth = 9, train loss: 0.47441, val loss: 0.47010, in 0.016s
[29/70] 1 tree, 17 leaves, max depth = 7, train loss: 0.47015, val loss: 0.46615, in 0.000s
1 tree, 17 leaves, max depth = 7, train loss: 0.46610, val loss: 0.46241, in 0.016s
[31/70] 1 tree, 17 leaves, max depth = 9, train loss: 0.46243, val loss: 0.45848, in 0.000s
1 tree, 17 leaves, max depth = 7, train loss: 0.45863, val loss: 0.45498, in 0.016s
1 tree, 17 leaves, max depth = 9, train loss: 0.45516, val loss: 0.45132, in 0.000s
1 tree, 17 leaves, max depth = 7, train loss: 0.45158, val loss: 0.44804, in 0.000s
1 tree, 17 leaves, max depth = 7, train loss: 0.44818, val loss: 0.44494, in 0.016s
1 tree, 17 leaves, max depth = 10, train loss: 0.44499, val loss: 0.44156, in 0.000s
1 tree, 17 leaves, max depth = 7, train loss: 0.44179, val loss: 0.43864, in 0.016s
1 tree, 17 leaves, max depth = 12, train loss: 0.43880, val loss: 0.43545, in 0.000s
1 tree, 17 leaves, max depth = 10, train loss: 0.43596, val loss: 0.43242, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.43296, val loss: 0.42970, in 0.000s
1 tree, 17 leaves, max depth = 7, train loss: 0.43026, val loss: 0.42675, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.42744, val loss: 0.42419, in 0.000s
1 tree, 17 leaves, max depth = 12, train loss: 0.42494, val loss: 0.42152, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.42227, val loss: 0.41911, in 0.000s
1 tree, 17 leaves, max depth = 7, train loss: 0.41973, val loss: 0.41682, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.41732, val loss: 0.41465, in 0.000s
1 tree, 17 leaves, max depth = 10, train loss: 0.41507, val loss: 0.41219, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.41279, val loss: 0.41015, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.41057, val loss: 0.40809, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.40841, val loss: 0.40617, in 0.000s
1 tree, 17 leaves, max depth = 7, train loss: 0.40627, val loss: 0.40382, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.40422, val loss: 0.40201, in 0.000s
1 tree, 17 leaves, max depth = 12, train loss: 0.40227, val loss: 0.39987, in 0.016s
1 tree, 17 leaves, max depth = 8, train loss: 0.40040, val loss: 0.39779, in 0.000s
1 tree, 17 leaves, max depth = 7, train loss: 0.39846, val loss: 0.39609, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.39643, val loss: 0.39420, in 0.000s
1 tree, 17 leaves, max depth = 6, train loss: 0.39459, val loss: 0.39260, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.39267, val loss: 0.39081, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.39085, val loss: 0.38912, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.38912, val loss: 0.38751, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.38749, val loss: 0.38599, in 0.000s
1 tree, 17 leaves, max depth = 6, train loss: 0.38573, val loss: 0.38448, in 0.016s
1 tree, 17 leaves, max depth = 8, train loss: 0.38406, val loss: 0.38304, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.38251, val loss: 0.38159, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.38103, val loss: 0.38022, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.37963, val loss: 0.37892, in 0.016s
1 tree, 17 leaves, max depth = 8, train loss: 0.37792, val loss: 0.37706, in 0.000s
1 tree, 17 leaves, max depth = 7, train loss: 0.37635, val loss: 0.37532, in 0.016s
1 tree, 17 leaves, max depth = 8, train loss: 0.37475, val loss: 0.37396, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.37342, val loss: 0.37272, in 0.016s
Fit 70 trees in 0.829 s, (1070 total leaves)
Time spent computing histograms: 0.313s
Time spent finding best splits:  0.032s
Time spent applying splits:      0.026s
Time spent predicting:           0.000s
Trial 21, Fold 3: Log loss = 0.3701317586291271, Average precision = 0.9496235379991602, ROC-AUC = 0.9494132893837101, Elapsed Time = 0.8211824000009074 seconds
Trial 21, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 21, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
Binning 0.040 GB of training data: 0.157 s
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 17 leaves, max depth = 7, train loss: 0.67830, val loss: 0.67744, in 0.000s
1 tree, 17 leaves, max depth = 7, train loss: 0.66447, val loss: 0.66278, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.65149, val loss: 0.64898, in 0.000s
1 tree, 17 leaves, max depth = 10, train loss: 0.63937, val loss: 0.63615, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.62790, val loss: 0.62391, in 0.000s
1 tree, 17 leaves, max depth = 7, train loss: 0.61709, val loss: 0.61236, in 0.016s
1 tree, 17 leaves, max depth = 10, train loss: 0.60699, val loss: 0.60160, in 0.000s
1 tree, 17 leaves, max depth = 7, train loss: 0.59739, val loss: 0.59130, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.58834, val loss: 0.58157, in 0.000s
1 tree, 17 leaves, max depth = 7, train loss: 0.57980, val loss: 0.57236, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.57174, val loss: 0.56365, in 0.000s
1 tree, 17 leaves, max depth = 7, train loss: 0.56412, val loss: 0.55541, in 0.016s
1 tree, 17 leaves, max depth = 10, train loss: 0.55699, val loss: 0.54765, in 0.000s
1 tree, 17 leaves, max depth = 7, train loss: 0.55018, val loss: 0.54024, in 0.016s
1 tree, 17 leaves, max depth = 8, train loss: 0.54374, val loss: 0.53323, in 0.000s
1 tree, 17 leaves, max depth = 7, train loss: 0.53663, val loss: 0.52625, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.53070, val loss: 0.51976, in 0.000s
1 tree, 17 leaves, max depth = 7, train loss: 0.52409, val loss: 0.51329, in 0.016s
1 tree, 17 leaves, max depth = 10, train loss: 0.51867, val loss: 0.50732, in 0.000s
1 tree, 17 leaves, max depth = 7, train loss: 0.51253, val loss: 0.50132, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.50671, val loss: 0.49561, in 0.000s
1 tree, 17 leaves, max depth = 7, train loss: 0.50120, val loss: 0.49021, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.49599, val loss: 0.48511, in 0.000s
1 tree, 17 leaves, max depth = 6, train loss: 0.49128, val loss: 0.47999, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.48641, val loss: 0.47523, in 0.000s
1 tree, 17 leaves, max depth = 8, train loss: 0.48179, val loss: 0.47070, in 0.016s
1 tree, 17 leaves, max depth = 9, train loss: 0.47751, val loss: 0.46601, in 0.000s
1 tree, 17 leaves, max depth = 9, train loss: 0.47346, val loss: 0.46149, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.46919, val loss: 0.45732, in 0.000s
1 tree, 17 leaves, max depth = 7, train loss: 0.46513, val loss: 0.45336, in 0.016s
1 tree, 17 leaves, max depth = 8, train loss: 0.46140, val loss: 0.44921, in 0.000s
1 tree, 17 leaves, max depth = 7, train loss: 0.45760, val loss: 0.44550, in 0.016s
1 tree, 17 leaves, max depth = 9, train loss: 0.45414, val loss: 0.44164, in 0.000s
1 tree, 17 leaves, max depth = 7, train loss: 0.45056, val loss: 0.43818, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.44716, val loss: 0.43487, in 0.000s
1 tree, 17 leaves, max depth = 9, train loss: 0.44398, val loss: 0.43130, in 0.016s
1 tree, 17 leaves, max depth = 8, train loss: 0.44078, val loss: 0.42820, in 0.000s
1 tree, 17 leaves, max depth = 8, train loss: 0.43781, val loss: 0.42487, in 0.016s
1 tree, 17 leaves, max depth = 10, train loss: 0.43489, val loss: 0.42161, in 0.000s
1 tree, 17 leaves, max depth = 7, train loss: 0.43190, val loss: 0.41872, in 0.000s
1 tree, 17 leaves, max depth = 8, train loss: 0.42926, val loss: 0.41574, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.42644, val loss: 0.41302, in 0.000s
1 tree, 17 leaves, max depth = 8, train loss: 0.42396, val loss: 0.41018, in 0.016s
1 tree, 17 leaves, max depth = 8, train loss: 0.42130, val loss: 0.40760, in 0.000s
1 tree, 17 leaves, max depth = 7, train loss: 0.41877, val loss: 0.40517, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.41636, val loss: 0.40285, in 0.000s
1 tree, 17 leaves, max depth = 9, train loss: 0.41410, val loss: 0.40029, in 0.016s
1 tree, 17 leaves, max depth = 8, train loss: 0.41183, val loss: 0.39807, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.40969, val loss: 0.39577, in 0.000s
1 tree, 17 leaves, max depth = 8, train loss: 0.40753, val loss: 0.39368, in 0.016s
1 tree, 17 leaves, max depth = 10, train loss: 0.40542, val loss: 0.39127, in 0.000s
1 tree, 17 leaves, max depth = 7, train loss: 0.40337, val loss: 0.38930, in 0.016s
1 tree, 17 leaves, max depth = 8, train loss: 0.40140, val loss: 0.38702, in 0.000s
1 tree, 17 leaves, max depth = 10, train loss: 0.39952, val loss: 0.38484, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.39758, val loss: 0.38298, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.39561, val loss: 0.38086, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.39377, val loss: 0.37909, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.39191, val loss: 0.37708, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.39015, val loss: 0.37518, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.38848, val loss: 0.37337, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.38690, val loss: 0.37166, in 0.016s
1 tree, 17 leaves, max depth = 8, train loss: 0.38515, val loss: 0.36999, in 0.000s
1 tree, 17 leaves, max depth = 8, train loss: 0.38347, val loss: 0.36839, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.38197, val loss: 0.36676, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.38054, val loss: 0.36522, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.37883, val loss: 0.36328, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.37748, val loss: 0.36182, in 0.000s
1 tree, 17 leaves, max depth = 9, train loss: 0.37591, val loss: 0.36000, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.37430, val loss: 0.35849, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.37302, val loss: 0.35710, in 0.016s
Fit 70 trees in 0.830 s, (1070 total leaves)
Time spent computing histograms: 0.304s
Time spent finding best splits:  0.032s
Time spent applying splits:      0.026s
Time spent predicting:           0.000s
Trial 21, Fold 4: Log loss = 0.3720529426817586, Average precision = 0.948352786118597, ROC-AUC = 0.9466053903159581, Elapsed Time = 0.8287339999988035 seconds
Trial 21, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 21, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
Binning 0.040 GB of training data: 0.157 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 17 leaves, max depth = 8, train loss: 0.67829, val loss: 0.67724, in 0.016s
1 tree, 17 leaves, max depth = 11, train loss: 0.66452, val loss: 0.66246, in 0.000s
1 tree, 17 leaves, max depth = 8, train loss: 0.65138, val loss: 0.64845, in 0.016s
1 tree, 17 leaves, max depth = 8, train loss: 0.63921, val loss: 0.63531, in 0.000s
1 tree, 17 leaves, max depth = 8, train loss: 0.62759, val loss: 0.62286, in 0.016s
1 tree, 17 leaves, max depth = 8, train loss: 0.61665, val loss: 0.61113, in 0.000s
1 tree, 17 leaves, max depth = 9, train loss: 0.60649, val loss: 0.60012, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.59677, val loss: 0.58961, in 0.000s
1 tree, 17 leaves, max depth = 6, train loss: 0.58760, val loss: 0.57970, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.57894, val loss: 0.57032, in 0.000s
1 tree, 17 leaves, max depth = 8, train loss: 0.57077, val loss: 0.56146, in 0.016s
1 tree, 17 leaves, max depth = 9, train loss: 0.56316, val loss: 0.55310, in 0.000s
1 tree, 17 leaves, max depth = 9, train loss: 0.55597, val loss: 0.54518, in 0.016s
1 tree, 17 leaves, max depth = 9, train loss: 0.54905, val loss: 0.53759, in 0.000s
1 tree, 17 leaves, max depth = 7, train loss: 0.54251, val loss: 0.53042, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.53552, val loss: 0.52372, in 0.000s
1 tree, 17 leaves, max depth = 9, train loss: 0.52949, val loss: 0.51706, in 0.000s
1 tree, 17 leaves, max depth = 6, train loss: 0.52301, val loss: 0.51087, in 0.016s
1 tree, 17 leaves, max depth = 8, train loss: 0.51753, val loss: 0.50481, in 0.000s
1 tree, 17 leaves, max depth = 6, train loss: 0.51150, val loss: 0.49908, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.50578, val loss: 0.49365, in 0.000s
1 tree, 17 leaves, max depth = 6, train loss: 0.50038, val loss: 0.48851, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.49526, val loss: 0.48366, in 0.000s
1 tree, 17 leaves, max depth = 10, train loss: 0.49050, val loss: 0.47837, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.48573, val loss: 0.47386, in 0.000s
1 tree, 17 leaves, max depth = 9, train loss: 0.48129, val loss: 0.46892, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.47682, val loss: 0.46472, in 0.000s
1 tree, 17 leaves, max depth = 9, train loss: 0.47269, val loss: 0.46010, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.46850, val loss: 0.45616, in 0.000s
1 tree, 17 leaves, max depth = 9, train loss: 0.46465, val loss: 0.45185, in 0.016s
1 tree, 17 leaves, max depth = 11, train loss: 0.46101, val loss: 0.44778, in 0.000s
1 tree, 17 leaves, max depth = 6, train loss: 0.45712, val loss: 0.44415, in 0.016s
1 tree, 17 leaves, max depth = 9, train loss: 0.45372, val loss: 0.44031, in 0.000s
1 tree, 17 leaves, max depth = 6, train loss: 0.45006, val loss: 0.43690, in 0.000s
1 tree, 17 leaves, max depth = 6, train loss: 0.44658, val loss: 0.43367, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.44328, val loss: 0.43061, in 0.000s
1 tree, 17 leaves, max depth = 10, train loss: 0.44022, val loss: 0.42714, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.43711, val loss: 0.42427, in 0.000s
1 tree, 17 leaves, max depth = 8, train loss: 0.43423, val loss: 0.42098, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.43130, val loss: 0.41828, in 0.000s
1 tree, 17 leaves, max depth = 10, train loss: 0.42859, val loss: 0.41520, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.42582, val loss: 0.41266, in 0.000s
1 tree, 17 leaves, max depth = 8, train loss: 0.42329, val loss: 0.40976, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.42067, val loss: 0.40738, in 0.000s
1 tree, 17 leaves, max depth = 6, train loss: 0.41817, val loss: 0.40513, in 0.000s
1 tree, 17 leaves, max depth = 8, train loss: 0.41582, val loss: 0.40242, in 0.000s
1 tree, 17 leaves, max depth = 6, train loss: 0.41346, val loss: 0.40030, in 0.000s
1 tree, 17 leaves, max depth = 6, train loss: 0.41121, val loss: 0.39828, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.40906, val loss: 0.39620, in 0.016s
1 tree, 17 leaves, max depth = 11, train loss: 0.40690, val loss: 0.39376, in 0.000s
1 tree, 17 leaves, max depth = 8, train loss: 0.40477, val loss: 0.39182, in 0.016s
1 tree, 17 leaves, max depth = 11, train loss: 0.40275, val loss: 0.38947, in 0.047s
1 tree, 17 leaves, max depth = 8, train loss: 0.40073, val loss: 0.38765, in 0.016s
1 tree, 17 leaves, max depth = 8, train loss: 0.39880, val loss: 0.38540, in 0.016s
1 tree, 17 leaves, max depth = 8, train loss: 0.39688, val loss: 0.38367, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.39490, val loss: 0.38177, in 0.016s
1 tree, 17 leaves, max depth = 10, train loss: 0.39307, val loss: 0.38014, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.39120, val loss: 0.37834, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.38942, val loss: 0.37664, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.38774, val loss: 0.37503, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.38615, val loss: 0.37350, in 0.016s
1 tree, 17 leaves, max depth = 10, train loss: 0.38440, val loss: 0.37195, in 0.000s
1 tree, 17 leaves, max depth = 10, train loss: 0.38266, val loss: 0.36990, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.38116, val loss: 0.36846, in 0.000s
1 tree, 17 leaves, max depth = 10, train loss: 0.37949, val loss: 0.36698, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.37806, val loss: 0.36561, in 0.000s
1 tree, 17 leaves, max depth = 9, train loss: 0.37639, val loss: 0.36368, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.37504, val loss: 0.36238, in 0.000s
1 tree, 17 leaves, max depth = 10, train loss: 0.37345, val loss: 0.36099, in 0.016s
1 tree, 17 leaves, max depth = 9, train loss: 0.37188, val loss: 0.35917, in 0.000s
Fit 70 trees in 0.892 s, (1082 total leaves)
Time spent computing histograms: 0.331s
Time spent finding best splits:  0.038s
Time spent applying splits:      0.050s
Time spent predicting:           0.000s
Trial 21, Fold 5: Log loss = 0.37708270831005164, Average precision = 0.9454919453985955, ROC-AUC = 0.9430249521579993, Elapsed Time = 0.8994215000002441 seconds
Optimization Progress:  22%|##2       | 22/100 [04:21<15:25, 11.86s/it]
Trial 22, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 22, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
0.142 s 0.040 GB of training data: 
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 48 leaves, max depth = 11, train loss: 0.66298, val loss: 0.66325, in 0.016s
1 tree, 48 leaves, max depth = 11, train loss: 0.63598, val loss: 0.63648, in 0.016s
1 tree, 48 leaves, max depth = 12, train loss: 0.61156, val loss: 0.61214, in 0.016s
1 tree, 48 leaves, max depth = 12, train loss: 0.59031, val loss: 0.59090, in 0.016s
1 tree, 48 leaves, max depth = 12, train loss: 0.57004, val loss: 0.57076, in 0.016s
1 tree, 48 leaves, max depth = 12, train loss: 0.55147, val loss: 0.55217, in 0.031s
1 tree, 48 leaves, max depth = 11, train loss: 0.53410, val loss: 0.53461, in 0.016s
1 tree, 48 leaves, max depth = 10, train loss: 0.51818, val loss: 0.51851, in 0.016s
1 tree, 48 leaves, max depth = 11, train loss: 0.50361, val loss: 0.50378, in 0.016s
1 tree, 48 leaves, max depth = 10, train loss: 0.49016, val loss: 0.49020, in 0.016s
1 tree, 48 leaves, max depth = 11, train loss: 0.47775, val loss: 0.47766, in 0.016s
1 tree, 48 leaves, max depth = 10, train loss: 0.46631, val loss: 0.46614, in 0.016s
1 tree, 48 leaves, max depth = 11, train loss: 0.45572, val loss: 0.45536, in 0.016s
1 tree, 48 leaves, max depth = 10, train loss: 0.44593, val loss: 0.44558, in 0.016s
1 tree, 48 leaves, max depth = 11, train loss: 0.43681, val loss: 0.43644, in 0.016s
1 tree, 48 leaves, max depth = 11, train loss: 0.42369, val loss: 0.42385, in 0.016s
1 tree, 48 leaves, max depth = 10, train loss: 0.41572, val loss: 0.41585, in 0.016s
1 tree, 48 leaves, max depth = 12, train loss: 0.40408, val loss: 0.40468, in 0.016s
1 tree, 48 leaves, max depth = 11, train loss: 0.39707, val loss: 0.39776, in 0.016s
1 tree, 48 leaves, max depth = 12, train loss: 0.38678, val loss: 0.38794, in 0.016s
1 tree, 48 leaves, max depth = 11, train loss: 0.38060, val loss: 0.38185, in 0.016s
1 tree, 48 leaves, max depth = 12, train loss: 0.37148, val loss: 0.37323, in 0.031s
1 tree, 48 leaves, max depth = 12, train loss: 0.36600, val loss: 0.36783, in 0.016s
1 tree, 48 leaves, max depth = 12, train loss: 0.36089, val loss: 0.36290, in 0.016s
1 tree, 48 leaves, max depth = 13, train loss: 0.35341, val loss: 0.35553, in 0.016s
1 tree, 48 leaves, max depth = 13, train loss: 0.34659, val loss: 0.34879, in 0.016s
1 tree, 48 leaves, max depth = 13, train loss: 0.34034, val loss: 0.34263, in 0.016s
1 tree, 48 leaves, max depth = 11, train loss: 0.33428, val loss: 0.33721, in 0.016s
1 tree, 48 leaves, max depth = 13, train loss: 0.32888, val loss: 0.33187, in 0.016s
1 tree, 48 leaves, max depth = 15, train loss: 0.32410, val loss: 0.32731, in 0.016s
1 tree, 48 leaves, max depth = 11, train loss: 0.31893, val loss: 0.32273, in 0.016s
1 tree, 48 leaves, max depth = 14, train loss: 0.31438, val loss: 0.31823, in 0.016s
1 tree, 48 leaves, max depth = 15, train loss: 0.31018, val loss: 0.31424, in 0.016s
1 tree, 48 leaves, max depth = 11, train loss: 0.30577, val loss: 0.31037, in 0.016s
1 tree, 48 leaves, max depth = 14, train loss: 0.30192, val loss: 0.30656, in 0.016s
1 tree, 48 leaves, max depth = 16, train loss: 0.29789, val loss: 0.30251, in 0.016s
1 tree, 48 leaves, max depth = 11, train loss: 0.29413, val loss: 0.29926, in 0.016s
1 tree, 48 leaves, max depth = 14, train loss: 0.29084, val loss: 0.29601, in 0.016s
1 tree, 48 leaves, max depth = 11, train loss: 0.28726, val loss: 0.29245, in 0.016s
1 tree, 48 leaves, max depth = 11, train loss: 0.28403, val loss: 0.28970, in 0.016s
1 tree, 48 leaves, max depth = 14, train loss: 0.28123, val loss: 0.28694, in 0.016s
1 tree, 48 leaves, max depth = 16, train loss: 0.27825, val loss: 0.28410, in 0.016s
1 tree, 48 leaves, max depth = 10, train loss: 0.27546, val loss: 0.28176, in 0.016s
1 tree, 48 leaves, max depth = 13, train loss: 0.27252, val loss: 0.27874, in 0.016s
1 tree, 48 leaves, max depth = 12, train loss: 0.27001, val loss: 0.27664, in 0.016s
1 tree, 47 leaves, max depth = 14, train loss: 0.26779, val loss: 0.27445, in 0.016s
1 tree, 47 leaves, max depth = 14, train loss: 0.26573, val loss: 0.27247, in 0.016s
1 tree, 48 leaves, max depth = 10, train loss: 0.26311, val loss: 0.26984, in 0.016s
1 tree, 48 leaves, max depth = 11, train loss: 0.26101, val loss: 0.26806, in 0.016s
Fit 49 trees in 1.127 s, (2350 total leaves)
Time spent computing histograms: 0.317s
Time spent finding best splits:  0.069s
Time spent applying splits:      0.046s
Time spent predicting:           0.000s
Trial 22, Fold 1: Log loss = 0.2685903231207794, Average precision = 0.9631391826305978, ROC-AUC = 0.9585063694001137, Elapsed Time = 1.1325500999992073 seconds
Trial 22, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 22, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
Binning 0.040 GB of training data: 0.142 s
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 48 leaves, max depth = 10, train loss: 0.66256, val loss: 0.66211, in 0.016s
1 tree, 48 leaves, max depth = 11, train loss: 0.63534, val loss: 0.63440, in 0.016s
1 tree, 48 leaves, max depth = 10, train loss: 0.61048, val loss: 0.60922, in 0.016s
1 tree, 48 leaves, max depth = 10, train loss: 0.58929, val loss: 0.58780, in 0.016s
1 tree, 48 leaves, max depth = 10, train loss: 0.56878, val loss: 0.56697, in 0.031s
1 tree, 48 leaves, max depth = 10, train loss: 0.54985, val loss: 0.54777, in 0.016s
1 tree, 48 leaves, max depth = 9, train loss: 0.53252, val loss: 0.53025, in 0.016s
1 tree, 48 leaves, max depth = 10, train loss: 0.51664, val loss: 0.51419, in 0.031s
1 tree, 48 leaves, max depth = 13, train loss: 0.50203, val loss: 0.49938, in 0.016s
1 tree, 48 leaves, max depth = 12, train loss: 0.48862, val loss: 0.48574, in 0.016s
1 tree, 48 leaves, max depth = 12, train loss: 0.47624, val loss: 0.47313, in 0.031s
1 tree, 48 leaves, max depth = 12, train loss: 0.46481, val loss: 0.46151, in 0.016s
1 tree, 48 leaves, max depth = 13, train loss: 0.45427, val loss: 0.45082, in 0.016s
1 tree, 48 leaves, max depth = 13, train loss: 0.44450, val loss: 0.44090, in 0.016s
1 tree, 48 leaves, max depth = 12, train loss: 0.43091, val loss: 0.42745, in 0.031s
1 tree, 48 leaves, max depth = 11, train loss: 0.42238, val loss: 0.41882, in 0.016s
1 tree, 48 leaves, max depth = 11, train loss: 0.41448, val loss: 0.41078, in 0.016s
1 tree, 48 leaves, max depth = 14, train loss: 0.40304, val loss: 0.39959, in 0.016s
1 tree, 48 leaves, max depth = 13, train loss: 0.39610, val loss: 0.39252, in 0.016s
1 tree, 48 leaves, max depth = 13, train loss: 0.38602, val loss: 0.38265, in 0.031s
1 tree, 48 leaves, max depth = 12, train loss: 0.37669, val loss: 0.37346, in 0.016s
1 tree, 48 leaves, max depth = 13, train loss: 0.37085, val loss: 0.36759, in 0.016s
1 tree, 48 leaves, max depth = 10, train loss: 0.36546, val loss: 0.36231, in 0.016s
1 tree, 48 leaves, max depth = 16, train loss: 0.35748, val loss: 0.35458, in 0.016s
1 tree, 48 leaves, max depth = 12, train loss: 0.35262, val loss: 0.34966, in 0.016s
1 tree, 48 leaves, max depth = 15, train loss: 0.34589, val loss: 0.34315, in 0.016s
1 tree, 48 leaves, max depth = 15, train loss: 0.33971, val loss: 0.33715, in 0.016s
1 tree, 48 leaves, max depth = 12, train loss: 0.33374, val loss: 0.33164, in 0.016s
1 tree, 48 leaves, max depth = 15, train loss: 0.32840, val loss: 0.32647, in 0.016s
1 tree, 48 leaves, max depth = 11, train loss: 0.32355, val loss: 0.32177, in 0.016s
1 tree, 48 leaves, max depth = 14, train loss: 0.31849, val loss: 0.31711, in 0.016s
1 tree, 48 leaves, max depth = 15, train loss: 0.31398, val loss: 0.31275, in 0.016s
1 tree, 48 leaves, max depth = 11, train loss: 0.30971, val loss: 0.30860, in 0.016s
1 tree, 48 leaves, max depth = 11, train loss: 0.30537, val loss: 0.30460, in 0.016s
1 tree, 48 leaves, max depth = 18, train loss: 0.30156, val loss: 0.30092, in 0.016s
1 tree, 48 leaves, max depth = 13, train loss: 0.29784, val loss: 0.29724, in 0.016s
1 tree, 48 leaves, max depth = 12, train loss: 0.29413, val loss: 0.29384, in 0.016s
1 tree, 48 leaves, max depth = 19, train loss: 0.29090, val loss: 0.29073, in 0.016s
1 tree, 48 leaves, max depth = 12, train loss: 0.28749, val loss: 0.28747, in 0.031s
1 tree, 48 leaves, max depth = 12, train loss: 0.28431, val loss: 0.28459, in 0.016s
1 tree, 48 leaves, max depth = 22, train loss: 0.28154, val loss: 0.28190, in 0.016s
1 tree, 48 leaves, max depth = 11, train loss: 0.27849, val loss: 0.27898, in 0.016s
1 tree, 48 leaves, max depth = 12, train loss: 0.27574, val loss: 0.27649, in 0.016s
1 tree, 48 leaves, max depth = 15, train loss: 0.27338, val loss: 0.27422, in 0.016s
1 tree, 48 leaves, max depth = 12, train loss: 0.27072, val loss: 0.27165, in 0.016s
1 tree, 48 leaves, max depth = 12, train loss: 0.26834, val loss: 0.26950, in 0.016s
1 tree, 48 leaves, max depth = 16, train loss: 0.26632, val loss: 0.26759, in 0.031s
1 tree, 48 leaves, max depth = 13, train loss: 0.26421, val loss: 0.26571, in 0.016s
1 tree, 48 leaves, max depth = 16, train loss: 0.26242, val loss: 0.26399, in 0.016s
Fit 49 trees in 1.221 s, (2352 total leaves)
Time spent computing histograms: 0.351s
Time spent finding best splits:  0.074s
Time spent applying splits:      0.050s
Time spent predicting:           0.000s
Trial 22, Fold 2: Log loss = 0.26610610846642424, Average precision = 0.9634199782399377, ROC-AUC = 0.960638900666203, Elapsed Time = 1.2277723000006517 seconds
Trial 22, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 22, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
Binning 0.040 GB of training data: 0.174 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 48 leaves, max depth = 12, train loss: 0.66322, val loss: 0.66334, in 0.016s
1 tree, 48 leaves, max depth = 9, train loss: 0.63585, val loss: 0.63615, in 0.016s
1 tree, 48 leaves, max depth = 8, train loss: 0.61153, val loss: 0.61171, in 0.031s
1 tree, 48 leaves, max depth = 8, train loss: 0.58904, val loss: 0.58938, in 0.016s
1 tree, 48 leaves, max depth = 9, train loss: 0.56858, val loss: 0.56905, in 0.016s
1 tree, 48 leaves, max depth = 10, train loss: 0.54991, val loss: 0.55048, in 0.016s
1 tree, 48 leaves, max depth = 10, train loss: 0.53351, val loss: 0.53414, in 0.031s
1 tree, 48 leaves, max depth = 9, train loss: 0.51787, val loss: 0.51852, in 0.016s
1 tree, 48 leaves, max depth = 12, train loss: 0.50330, val loss: 0.50410, in 0.016s
1 tree, 48 leaves, max depth = 12, train loss: 0.48989, val loss: 0.49086, in 0.016s
1 tree, 48 leaves, max depth = 12, train loss: 0.47757, val loss: 0.47859, in 0.031s
1 tree, 48 leaves, max depth = 13, train loss: 0.46616, val loss: 0.46729, in 0.016s
1 tree, 48 leaves, max depth = 13, train loss: 0.45561, val loss: 0.45676, in 0.016s
1 tree, 48 leaves, max depth = 13, train loss: 0.44584, val loss: 0.44707, in 0.016s
1 tree, 48 leaves, max depth = 12, train loss: 0.43209, val loss: 0.43434, in 0.031s
1 tree, 48 leaves, max depth = 12, train loss: 0.42358, val loss: 0.42593, in 0.016s
1 tree, 48 leaves, max depth = 13, train loss: 0.41564, val loss: 0.41810, in 0.016s
1 tree, 48 leaves, max depth = 12, train loss: 0.40399, val loss: 0.40739, in 0.016s
1 tree, 48 leaves, max depth = 12, train loss: 0.39333, val loss: 0.39761, in 0.016s
1 tree, 48 leaves, max depth = 12, train loss: 0.38673, val loss: 0.39117, in 0.031s
1 tree, 48 leaves, max depth = 13, train loss: 0.37729, val loss: 0.38253, in 0.016s
1 tree, 48 leaves, max depth = 10, train loss: 0.37147, val loss: 0.37676, in 0.016s
1 tree, 48 leaves, max depth = 11, train loss: 0.36309, val loss: 0.36917, in 0.016s
1 tree, 48 leaves, max depth = 14, train loss: 0.35789, val loss: 0.36400, in 0.031s
1 tree, 48 leaves, max depth = 11, train loss: 0.35048, val loss: 0.35747, in 0.016s
1 tree, 47 leaves, max depth = 14, train loss: 0.34394, val loss: 0.35150, in 0.028s
1 tree, 47 leaves, max depth = 14, train loss: 0.33795, val loss: 0.34605, in 0.004s
1 tree, 48 leaves, max depth = 15, train loss: 0.33289, val loss: 0.34080, in 0.031s
1 tree, 48 leaves, max depth = 12, train loss: 0.32712, val loss: 0.33591, in 0.016s
1 tree, 48 leaves, max depth = 13, train loss: 0.32209, val loss: 0.33135, in 0.016s
1 tree, 48 leaves, max depth = 16, train loss: 0.31766, val loss: 0.32673, in 0.016s
1 tree, 48 leaves, max depth = 12, train loss: 0.31274, val loss: 0.32259, in 0.016s
1 tree, 48 leaves, max depth = 15, train loss: 0.30848, val loss: 0.31878, in 0.016s
1 tree, 48 leaves, max depth = 14, train loss: 0.30459, val loss: 0.31476, in 0.016s
1 tree, 48 leaves, max depth = 13, train loss: 0.30038, val loss: 0.31122, in 0.016s
1 tree, 48 leaves, max depth = 12, train loss: 0.29649, val loss: 0.30794, in 0.031s
1 tree, 47 leaves, max depth = 12, train loss: 0.29306, val loss: 0.30488, in 0.016s
1 tree, 48 leaves, max depth = 12, train loss: 0.28972, val loss: 0.30139, in 0.016s
1 tree, 48 leaves, max depth = 13, train loss: 0.28636, val loss: 0.29851, in 0.016s
1 tree, 48 leaves, max depth = 12, train loss: 0.28346, val loss: 0.29593, in 0.016s
1 tree, 48 leaves, max depth = 9, train loss: 0.28024, val loss: 0.29230, in 0.016s
1 tree, 48 leaves, max depth = 13, train loss: 0.27735, val loss: 0.28991, in 0.016s
1 tree, 48 leaves, max depth = 13, train loss: 0.27487, val loss: 0.28771, in 0.031s
1 tree, 48 leaves, max depth = 13, train loss: 0.27230, val loss: 0.28560, in 0.016s
1 tree, 48 leaves, max depth = 12, train loss: 0.27009, val loss: 0.28369, in 0.016s
1 tree, 48 leaves, max depth = 10, train loss: 0.26733, val loss: 0.28056, in 0.016s
1 tree, 47 leaves, max depth = 12, train loss: 0.26537, val loss: 0.27883, in 0.016s
1 tree, 48 leaves, max depth = 12, train loss: 0.26301, val loss: 0.27628, in 0.016s
1 tree, 48 leaves, max depth = 14, train loss: 0.26091, val loss: 0.27460, in 0.016s
Fit 49 trees in 1.268 s, (2348 total leaves)
Time spent computing histograms: 0.353s
Time spent finding best splits:  0.077s
Time spent applying splits:      0.051s
Time spent predicting:           0.016s
Trial 22, Fold 3: Log loss = 0.2634322686868408, Average precision = 0.9640817460306497, ROC-AUC = 0.9609534514017972, Elapsed Time = 1.279824600000211 seconds
Trial 22, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 22, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
Binning 0.040 GB of training data: 0.174 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 48 leaves, max depth = 11, train loss: 0.66321, val loss: 0.66211, in 0.016s
1 tree, 48 leaves, max depth = 10, train loss: 0.63633, val loss: 0.63405, in 0.031s
1 tree, 48 leaves, max depth = 11, train loss: 0.61205, val loss: 0.60880, in 0.016s
1 tree, 48 leaves, max depth = 12, train loss: 0.59116, val loss: 0.58710, in 0.016s
1 tree, 48 leaves, max depth = 11, train loss: 0.57097, val loss: 0.56607, in 0.031s
1 tree, 48 leaves, max depth = 10, train loss: 0.55206, val loss: 0.54636, in 0.016s
1 tree, 48 leaves, max depth = 10, train loss: 0.53478, val loss: 0.52829, in 0.016s
1 tree, 48 leaves, max depth = 10, train loss: 0.51894, val loss: 0.51173, in 0.016s
1 tree, 48 leaves, max depth = 10, train loss: 0.50438, val loss: 0.49650, in 0.016s
1 tree, 48 leaves, max depth = 13, train loss: 0.49180, val loss: 0.48327, in 0.016s
1 tree, 48 leaves, max depth = 9, train loss: 0.47950, val loss: 0.47023, in 0.016s
1 tree, 48 leaves, max depth = 10, train loss: 0.46798, val loss: 0.45819, in 0.031s
1 tree, 48 leaves, max depth = 10, train loss: 0.45734, val loss: 0.44703, in 0.016s
1 tree, 48 leaves, max depth = 10, train loss: 0.44749, val loss: 0.43668, in 0.016s
1 tree, 48 leaves, max depth = 11, train loss: 0.43837, val loss: 0.42708, in 0.016s
1 tree, 48 leaves, max depth = 14, train loss: 0.42536, val loss: 0.41391, in 0.016s
1 tree, 48 leaves, max depth = 11, train loss: 0.41739, val loss: 0.40548, in 0.031s
1 tree, 48 leaves, max depth = 14, train loss: 0.40595, val loss: 0.39390, in 0.016s
1 tree, 48 leaves, max depth = 11, train loss: 0.39896, val loss: 0.38648, in 0.016s
1 tree, 48 leaves, max depth = 12, train loss: 0.38885, val loss: 0.37627, in 0.016s
1 tree, 48 leaves, max depth = 11, train loss: 0.38269, val loss: 0.36967, in 0.031s
1 tree, 48 leaves, max depth = 12, train loss: 0.37373, val loss: 0.36062, in 0.016s
1 tree, 48 leaves, max depth = 10, train loss: 0.36838, val loss: 0.35498, in 0.016s
1 tree, 48 leaves, max depth = 13, train loss: 0.36326, val loss: 0.34953, in 0.016s
1 tree, 48 leaves, max depth = 13, train loss: 0.35586, val loss: 0.34188, in 0.031s
1 tree, 48 leaves, max depth = 12, train loss: 0.34910, val loss: 0.33492, in 0.016s
1 tree, 48 leaves, max depth = 12, train loss: 0.34291, val loss: 0.32850, in 0.016s
1 tree, 48 leaves, max depth = 14, train loss: 0.33782, val loss: 0.32340, in 0.016s
1 tree, 48 leaves, max depth = 11, train loss: 0.33192, val loss: 0.31791, in 0.031s
1 tree, 48 leaves, max depth = 13, train loss: 0.32673, val loss: 0.31251, in 0.016s
1 tree, 48 leaves, max depth = 15, train loss: 0.32227, val loss: 0.30803, in 0.016s
1 tree, 48 leaves, max depth = 15, train loss: 0.31785, val loss: 0.30341, in 0.031s
1 tree, 48 leaves, max depth = 11, train loss: 0.31297, val loss: 0.29893, in 0.016s
1 tree, 48 leaves, max depth = 12, train loss: 0.30872, val loss: 0.29452, in 0.031s
1 tree, 48 leaves, max depth = 11, train loss: 0.30444, val loss: 0.29062, in 0.016s
1 tree, 48 leaves, max depth = 11, train loss: 0.30049, val loss: 0.28703, in 0.031s
1 tree, 48 leaves, max depth = 14, train loss: 0.29697, val loss: 0.28339, in 0.016s
1 tree, 48 leaves, max depth = 12, train loss: 0.29348, val loss: 0.28023, in 0.031s
1 tree, 48 leaves, max depth = 13, train loss: 0.29038, val loss: 0.27699, in 0.016s
1 tree, 48 leaves, max depth = 13, train loss: 0.28691, val loss: 0.27332, in 0.032s
1 tree, 48 leaves, max depth = 13, train loss: 0.28392, val loss: 0.27063, in 0.031s
1 tree, 48 leaves, max depth = 12, train loss: 0.28127, val loss: 0.26784, in 0.016s
1 tree, 48 leaves, max depth = 13, train loss: 0.27818, val loss: 0.26452, in 0.031s
1 tree, 48 leaves, max depth = 12, train loss: 0.27560, val loss: 0.26221, in 0.016s
1 tree, 48 leaves, max depth = 12, train loss: 0.27334, val loss: 0.25982, in 0.016s
1 tree, 48 leaves, max depth = 13, train loss: 0.27073, val loss: 0.25724, in 0.031s
1 tree, 48 leaves, max depth = 12, train loss: 0.26871, val loss: 0.25510, in 0.016s
1 tree, 48 leaves, max depth = 12, train loss: 0.26613, val loss: 0.25235, in 0.031s
1 tree, 48 leaves, max depth = 11, train loss: 0.26404, val loss: 0.25051, in 0.031s
Fit 49 trees in 1.377 s, (2352 total leaves)
Time spent computing histograms: 0.393s
Time spent finding best splits:  0.091s
Time spent applying splits:      0.061s
Time spent predicting:           0.000s
Trial 22, Fold 4: Log loss = 0.2667903100689803, Average precision = 0.9642265018935711, ROC-AUC = 0.9601925023426827, Elapsed Time = 1.3921172999998817 seconds
Trial 22, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 22, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
Binning 0.040 GB of training data: 0.158 s
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 48 leaves, max depth = 14, train loss: 0.66293, val loss: 0.66200, in 0.016s
1 tree, 48 leaves, max depth = 11, train loss: 0.63540, val loss: 0.63352, in 0.016s
1 tree, 48 leaves, max depth = 12, train loss: 0.61051, val loss: 0.60776, in 0.016s
1 tree, 48 leaves, max depth = 12, train loss: 0.58918, val loss: 0.58564, in 0.031s
1 tree, 48 leaves, max depth = 12, train loss: 0.56846, val loss: 0.56419, in 0.016s
1 tree, 48 leaves, max depth = 12, train loss: 0.54957, val loss: 0.54461, in 0.016s
1 tree, 48 leaves, max depth = 13, train loss: 0.53228, val loss: 0.52670, in 0.016s
1 tree, 48 leaves, max depth = 13, train loss: 0.51643, val loss: 0.51028, in 0.016s
1 tree, 48 leaves, max depth = 13, train loss: 0.50187, val loss: 0.49512, in 0.031s
1 tree, 48 leaves, max depth = 12, train loss: 0.48916, val loss: 0.48194, in 0.016s
1 tree, 48 leaves, max depth = 12, train loss: 0.47670, val loss: 0.46903, in 0.016s
1 tree, 48 leaves, max depth = 12, train loss: 0.46519, val loss: 0.45709, in 0.016s
1 tree, 48 leaves, max depth = 13, train loss: 0.45456, val loss: 0.44609, in 0.016s
1 tree, 48 leaves, max depth = 12, train loss: 0.44472, val loss: 0.43584, in 0.031s
1 tree, 48 leaves, max depth = 12, train loss: 0.43559, val loss: 0.42640, in 0.016s
1 tree, 48 leaves, max depth = 13, train loss: 0.42252, val loss: 0.41342, in 0.016s
1 tree, 48 leaves, max depth = 11, train loss: 0.41452, val loss: 0.40521, in 0.016s
1 tree, 48 leaves, max depth = 13, train loss: 0.40301, val loss: 0.39379, in 0.016s
1 tree, 48 leaves, max depth = 13, train loss: 0.39598, val loss: 0.38658, in 0.016s
1 tree, 48 leaves, max depth = 15, train loss: 0.38580, val loss: 0.37649, in 0.031s
1 tree, 48 leaves, max depth = 12, train loss: 0.37959, val loss: 0.37013, in 0.031s
1 tree, 48 leaves, max depth = 15, train loss: 0.37057, val loss: 0.36122, in 0.016s
1 tree, 48 leaves, max depth = 12, train loss: 0.36507, val loss: 0.35558, in 0.016s
1 tree, 48 leaves, max depth = 13, train loss: 0.35994, val loss: 0.35036, in 0.016s
1 tree, 48 leaves, max depth = 12, train loss: 0.35252, val loss: 0.34274, in 0.016s
1 tree, 48 leaves, max depth = 8, train loss: 0.34798, val loss: 0.33825, in 0.016s
1 tree, 46 leaves, max depth = 12, train loss: 0.34136, val loss: 0.33158, in 0.016s
1 tree, 46 leaves, max depth = 15, train loss: 0.33531, val loss: 0.32546, in 0.016s
1 tree, 48 leaves, max depth = 14, train loss: 0.32939, val loss: 0.32014, in 0.016s
1 tree, 47 leaves, max depth = 12, train loss: 0.32416, val loss: 0.31481, in 0.016s
1 tree, 48 leaves, max depth = 15, train loss: 0.31955, val loss: 0.31025, in 0.016s
1 tree, 48 leaves, max depth = 16, train loss: 0.31452, val loss: 0.30578, in 0.016s
1 tree, 47 leaves, max depth = 14, train loss: 0.31012, val loss: 0.30134, in 0.016s
1 tree, 48 leaves, max depth = 12, train loss: 0.30590, val loss: 0.29708, in 0.016s
1 tree, 48 leaves, max depth = 14, train loss: 0.30161, val loss: 0.29318, in 0.031s
1 tree, 46 leaves, max depth = 13, train loss: 0.29788, val loss: 0.28928, in 0.016s
1 tree, 46 leaves, max depth = 13, train loss: 0.29444, val loss: 0.28567, in 0.016s
1 tree, 48 leaves, max depth = 12, train loss: 0.29077, val loss: 0.28200, in 0.016s
1 tree, 48 leaves, max depth = 14, train loss: 0.28725, val loss: 0.27887, in 0.031s
1 tree, 48 leaves, max depth = 14, train loss: 0.28398, val loss: 0.27606, in 0.016s
1 tree, 47 leaves, max depth = 13, train loss: 0.28120, val loss: 0.27314, in 0.016s
1 tree, 48 leaves, max depth = 15, train loss: 0.27809, val loss: 0.27016, in 0.016s
1 tree, 48 leaves, max depth = 14, train loss: 0.27528, val loss: 0.26767, in 0.016s
1 tree, 48 leaves, max depth = 14, train loss: 0.27267, val loss: 0.26547, in 0.031s
1 tree, 47 leaves, max depth = 11, train loss: 0.27036, val loss: 0.26309, in 0.016s
1 tree, 48 leaves, max depth = 11, train loss: 0.26754, val loss: 0.26026, in 0.031s
1 tree, 48 leaves, max depth = 15, train loss: 0.26548, val loss: 0.25812, in 0.016s
1 tree, 48 leaves, max depth = 12, train loss: 0.26289, val loss: 0.25549, in 0.016s
1 tree, 48 leaves, max depth = 15, train loss: 0.26078, val loss: 0.25368, in 0.031s
Fit 49 trees in 1.298 s, (2340 total leaves)
Time spent computing histograms: 0.360s
Time spent finding best splits:  0.078s
Time spent applying splits:      0.053s
Time spent predicting:           0.000s
Trial 22, Fold 5: Log loss = 0.27211823887263314, Average precision = 0.9607680900911164, ROC-AUC = 0.9565815357746688, Elapsed Time = 1.3137683000004472 seconds
Optimization Progress:  23%|##3       | 23/100 [04:34<15:40, 12.21s/it]
Trial 23, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 23, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
0.157 s 0.040 GB of training data: 
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 51 leaves, max depth = 15, train loss: 0.67715, val loss: 0.67667, in 0.016s
1 tree, 50 leaves, max depth = 15, train loss: 0.66222, val loss: 0.66127, in 0.000s
1 tree, 51 leaves, max depth = 15, train loss: 0.64827, val loss: 0.64687, in 0.016s
1 tree, 50 leaves, max depth = 15, train loss: 0.63522, val loss: 0.63339, in 0.016s
1 tree, 51 leaves, max depth = 15, train loss: 0.62300, val loss: 0.62075, in 0.000s
1 tree, 50 leaves, max depth = 15, train loss: 0.61154, val loss: 0.60888, in 0.016s
1 tree, 51 leaves, max depth = 15, train loss: 0.60079, val loss: 0.59774, in 0.016s
1 tree, 51 leaves, max depth = 15, train loss: 0.59070, val loss: 0.58727, in 0.000s
1 tree, 50 leaves, max depth = 15, train loss: 0.58122, val loss: 0.57742, in 0.016s
1 tree, 51 leaves, max depth = 15, train loss: 0.57231, val loss: 0.56815, in 0.016s
1 tree, 50 leaves, max depth = 15, train loss: 0.56392, val loss: 0.55942, in 0.000s
1 tree, 50 leaves, max depth = 14, train loss: 0.55603, val loss: 0.55121, in 0.016s
1 tree, 81 leaves, max depth = 12, train loss: 0.54798, val loss: 0.54375, in 0.016s
1 tree, 50 leaves, max depth = 14, train loss: 0.54076, val loss: 0.53621, in 0.000s
1 tree, 51 leaves, max depth = 14, train loss: 0.53396, val loss: 0.52910, in 0.016s
1 tree, 83 leaves, max depth = 13, train loss: 0.52670, val loss: 0.52240, in 0.016s
1 tree, 82 leaves, max depth = 13, train loss: 0.51986, val loss: 0.51611, in 0.000s
1 tree, 83 leaves, max depth = 13, train loss: 0.51341, val loss: 0.51019, in 0.016s
1 tree, 50 leaves, max depth = 14, train loss: 0.50748, val loss: 0.50395, in 0.016s
1 tree, 83 leaves, max depth = 13, train loss: 0.50151, val loss: 0.49849, in 0.016s
1 tree, 50 leaves, max depth = 14, train loss: 0.49605, val loss: 0.49273, in 0.000s
1 tree, 82 leaves, max depth = 13, train loss: 0.49051, val loss: 0.48767, in 0.016s
1 tree, 51 leaves, max depth = 15, train loss: 0.48552, val loss: 0.48250, in 0.016s
1 tree, 82 leaves, max depth = 11, train loss: 0.48037, val loss: 0.47782, in 0.016s
1 tree, 52 leaves, max depth = 14, train loss: 0.47576, val loss: 0.47295, in 0.000s
1 tree, 82 leaves, max depth = 11, train loss: 0.47096, val loss: 0.46861, in 0.016s
1 tree, 82 leaves, max depth = 11, train loss: 0.46643, val loss: 0.46451, in 0.016s
1 tree, 53 leaves, max depth = 14, train loss: 0.46223, val loss: 0.46001, in 0.016s
1 tree, 81 leaves, max depth = 12, train loss: 0.45799, val loss: 0.45620, in 0.000s
1 tree, 53 leaves, max depth = 13, train loss: 0.45409, val loss: 0.45202, in 0.016s
1 tree, 81 leaves, max depth = 12, train loss: 0.45013, val loss: 0.44846, in 0.016s
1 tree, 49 leaves, max depth = 12, train loss: 0.44648, val loss: 0.44460, in 0.000s
1 tree, 82 leaves, max depth = 12, train loss: 0.44277, val loss: 0.44128, in 0.016s
1 tree, 52 leaves, max depth = 13, train loss: 0.43940, val loss: 0.43764, in 0.016s
1 tree, 83 leaves, max depth = 12, train loss: 0.43592, val loss: 0.43455, in 0.016s
1 tree, 85 leaves, max depth = 12, train loss: 0.43263, val loss: 0.43162, in 0.000s
1 tree, 52 leaves, max depth = 12, train loss: 0.42953, val loss: 0.42827, in 0.016s
1 tree, 83 leaves, max depth = 11, train loss: 0.42644, val loss: 0.42553, in 0.016s
1 tree, 49 leaves, max depth = 12, train loss: 0.42353, val loss: 0.42243, in 0.000s
1 tree, 83 leaves, max depth = 11, train loss: 0.42063, val loss: 0.41986, in 0.016s
1 tree, 53 leaves, max depth = 12, train loss: 0.41793, val loss: 0.41692, in 0.016s
1 tree, 82 leaves, max depth = 11, train loss: 0.41519, val loss: 0.41451, in 0.016s
1 tree, 52 leaves, max depth = 12, train loss: 0.41267, val loss: 0.41175, in 0.000s
1 tree, 84 leaves, max depth = 12, train loss: 0.41009, val loss: 0.40949, in 0.016s
1 tree, 3 leaves, max depth = 2, train loss: 0.40779, val loss: 0.40697, in 0.016s
1 tree, 83 leaves, max depth = 12, train loss: 0.40535, val loss: 0.40486, in 0.000s
1 tree, 3 leaves, max depth = 2, train loss: 0.40319, val loss: 0.40249, in 0.016s
1 tree, 51 leaves, max depth = 12, train loss: 0.40089, val loss: 0.40003, in 0.000s
1 tree, 84 leaves, max depth = 12, train loss: 0.39860, val loss: 0.39807, in 0.016s
1 tree, 3 leaves, max depth = 2, train loss: 0.39658, val loss: 0.39585, in 0.000s
1 tree, 3 leaves, max depth = 2, train loss: 0.39467, val loss: 0.39376, in 0.016s
1 tree, 82 leaves, max depth = 11, train loss: 0.39250, val loss: 0.39193, in 0.000s
1 tree, 53 leaves, max depth = 13, train loss: 0.39040, val loss: 0.38969, in 0.000s
1 tree, 3 leaves, max depth = 2, train loss: 0.38861, val loss: 0.38772, in 0.016s
1 tree, 80 leaves, max depth = 11, train loss: 0.38656, val loss: 0.38600, in 0.000s
1 tree, 3 leaves, max depth = 2, train loss: 0.38486, val loss: 0.38413, in 0.016s
1 tree, 85 leaves, max depth = 12, train loss: 0.38292, val loss: 0.38252, in 0.016s
1 tree, 53 leaves, max depth = 13, train loss: 0.38098, val loss: 0.38047, in 0.000s
1 tree, 3 leaves, max depth = 2, train loss: 0.37938, val loss: 0.37871, in 0.016s
1 tree, 82 leaves, max depth = 12, train loss: 0.37754, val loss: 0.37720, in 0.000s
1 tree, 2 leaves, max depth = 1, train loss: 0.37609, val loss: 0.37551, in 0.016s
1 tree, 50 leaves, max depth = 12, train loss: 0.37426, val loss: 0.37358, in 0.000s
1 tree, 83 leaves, max depth = 13, train loss: 0.37251, val loss: 0.37215, in 0.016s
1 tree, 2 leaves, max depth = 1, train loss: 0.37114, val loss: 0.37056, in 0.016s
1 tree, 52 leaves, max depth = 12, train loss: 0.36944, val loss: 0.36878, in 0.000s
1 tree, 82 leaves, max depth = 12, train loss: 0.36777, val loss: 0.36745, in 0.016s
1 tree, 2 leaves, max depth = 1, train loss: 0.36648, val loss: 0.36594, in 0.000s
1 tree, 51 leaves, max depth = 12, train loss: 0.36488, val loss: 0.36427, in 0.016s
1 tree, 3 leaves, max depth = 2, train loss: 0.36366, val loss: 0.36282, in 0.016s
1 tree, 50 leaves, max depth = 12, train loss: 0.36216, val loss: 0.36126, in 0.000s
1 tree, 83 leaves, max depth = 12, train loss: 0.36055, val loss: 0.36000, in 0.016s
1 tree, 3 leaves, max depth = 2, train loss: 0.35939, val loss: 0.35861, in 0.000s
1 tree, 48 leaves, max depth = 12, train loss: 0.35795, val loss: 0.35711, in 0.016s
1 tree, 83 leaves, max depth = 13, train loss: 0.35642, val loss: 0.35591, in 0.016s
1 tree, 2 leaves, max depth = 1, train loss: 0.35531, val loss: 0.35460, in 0.000s
1 tree, 52 leaves, max depth = 12, train loss: 0.35397, val loss: 0.35322, in 0.016s
Fit 76 trees in 1.032 s, (4129 total leaves)
Time spent computing histograms: 0.350s
Time spent finding best splits:  0.070s
Time spent applying splits:      0.070s
Time spent predicting:           0.000s
Trial 23, Fold 1: Log loss = 0.35838270196769895, Average precision = 0.946139576660052, ROC-AUC = 0.9448678861788617, Elapsed Time = 1.0405602000009821 seconds
Trial 23, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 23, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
Binning 0.040 GB of training data: 0.142 s
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 48 leaves, max depth = 16, train loss: 0.67725, val loss: 0.67651, in 0.000s
1 tree, 47 leaves, max depth = 14, train loss: 0.66252, val loss: 0.66111, in 0.016s
1 tree, 49 leaves, max depth = 14, train loss: 0.64864, val loss: 0.64657, in 0.016s
1 tree, 48 leaves, max depth = 14, train loss: 0.63577, val loss: 0.63309, in 0.000s
1 tree, 50 leaves, max depth = 13, train loss: 0.62361, val loss: 0.62031, in 0.016s
1 tree, 51 leaves, max depth = 15, train loss: 0.61229, val loss: 0.60843, in 0.016s
1 tree, 49 leaves, max depth = 14, train loss: 0.60169, val loss: 0.59729, in 0.000s
1 tree, 48 leaves, max depth = 16, train loss: 0.59175, val loss: 0.58682, in 0.016s
1 tree, 50 leaves, max depth = 13, train loss: 0.58228, val loss: 0.57683, in 0.016s
1 tree, 48 leaves, max depth = 16, train loss: 0.57350, val loss: 0.56755, in 0.000s
1 tree, 50 leaves, max depth = 13, train loss: 0.56512, val loss: 0.55868, in 0.016s
1 tree, 51 leaves, max depth = 14, train loss: 0.55732, val loss: 0.55045, in 0.016s
1 tree, 83 leaves, max depth = 13, train loss: 0.54932, val loss: 0.54275, in 0.016s
1 tree, 51 leaves, max depth = 18, train loss: 0.54218, val loss: 0.53519, in 0.000s
1 tree, 51 leaves, max depth = 13, train loss: 0.53534, val loss: 0.52793, in 0.016s
1 tree, 82 leaves, max depth = 14, train loss: 0.52812, val loss: 0.52099, in 0.016s
1 tree, 82 leaves, max depth = 14, train loss: 0.52133, val loss: 0.51446, in 0.016s
1 tree, 81 leaves, max depth = 14, train loss: 0.51492, val loss: 0.50832, in 0.016s
1 tree, 53 leaves, max depth = 14, train loss: 0.50904, val loss: 0.50209, in 0.000s
1 tree, 82 leaves, max depth = 14, train loss: 0.50311, val loss: 0.49641, in 0.016s
1 tree, 53 leaves, max depth = 14, train loss: 0.49768, val loss: 0.49066, in 0.016s
1 tree, 82 leaves, max depth = 15, train loss: 0.49217, val loss: 0.48540, in 0.016s
1 tree, 53 leaves, max depth = 14, train loss: 0.48716, val loss: 0.48009, in 0.000s
1 tree, 82 leaves, max depth = 15, train loss: 0.48204, val loss: 0.47520, in 0.016s
1 tree, 83 leaves, max depth = 15, train loss: 0.47721, val loss: 0.47059, in 0.016s
1 tree, 53 leaves, max depth = 12, train loss: 0.47266, val loss: 0.46577, in 0.016s
1 tree, 80 leaves, max depth = 15, train loss: 0.46815, val loss: 0.46149, in 0.016s
1 tree, 53 leaves, max depth = 12, train loss: 0.46394, val loss: 0.45701, in 0.016s
1 tree, 81 leaves, max depth = 15, train loss: 0.45973, val loss: 0.45303, in 0.016s
1 tree, 53 leaves, max depth = 15, train loss: 0.45583, val loss: 0.44887, in 0.000s
1 tree, 79 leaves, max depth = 15, train loss: 0.45189, val loss: 0.44516, in 0.016s
1 tree, 53 leaves, max depth = 15, train loss: 0.44826, val loss: 0.44128, in 0.016s
1 tree, 80 leaves, max depth = 13, train loss: 0.44457, val loss: 0.43780, in 0.016s
1 tree, 81 leaves, max depth = 13, train loss: 0.44109, val loss: 0.43450, in 0.016s
1 tree, 53 leaves, max depth = 17, train loss: 0.43776, val loss: 0.43095, in 0.000s
1 tree, 81 leaves, max depth = 14, train loss: 0.43449, val loss: 0.42788, in 0.016s
1 tree, 53 leaves, max depth = 17, train loss: 0.43139, val loss: 0.42456, in 0.016s
1 tree, 80 leaves, max depth = 14, train loss: 0.42832, val loss: 0.42169, in 0.016s
1 tree, 53 leaves, max depth = 13, train loss: 0.42543, val loss: 0.41860, in 0.016s
1 tree, 78 leaves, max depth = 12, train loss: 0.42256, val loss: 0.41594, in 0.000s
1 tree, 52 leaves, max depth = 11, train loss: 0.41986, val loss: 0.41305, in 0.016s
1 tree, 79 leaves, max depth = 12, train loss: 0.41716, val loss: 0.41055, in 0.016s
1 tree, 79 leaves, max depth = 12, train loss: 0.41459, val loss: 0.40816, in 0.016s
1 tree, 52 leaves, max depth = 10, train loss: 0.41209, val loss: 0.40548, in 0.016s
1 tree, 3 leaves, max depth = 2, train loss: 0.40985, val loss: 0.40316, in 0.000s
1 tree, 82 leaves, max depth = 12, train loss: 0.40744, val loss: 0.40093, in 0.016s
1 tree, 53 leaves, max depth = 11, train loss: 0.40512, val loss: 0.39844, in 0.016s
1 tree, 3 leaves, max depth = 2, train loss: 0.40304, val loss: 0.39627, in 0.000s
1 tree, 80 leaves, max depth = 13, train loss: 0.40075, val loss: 0.39416, in 0.016s
1 tree, 3 leaves, max depth = 2, train loss: 0.39878, val loss: 0.39211, in 0.016s
1 tree, 82 leaves, max depth = 12, train loss: 0.39662, val loss: 0.39012, in 0.000s
1 tree, 53 leaves, max depth = 11, train loss: 0.39451, val loss: 0.38785, in 0.016s
1 tree, 3 leaves, max depth = 2, train loss: 0.39267, val loss: 0.38594, in 0.016s
1 tree, 81 leaves, max depth = 13, train loss: 0.39064, val loss: 0.38408, in 0.016s
1 tree, 3 leaves, max depth = 2, train loss: 0.38890, val loss: 0.38227, in 0.000s
1 tree, 84 leaves, max depth = 14, train loss: 0.38697, val loss: 0.38051, in 0.016s
1 tree, 51 leaves, max depth = 11, train loss: 0.38503, val loss: 0.37843, in 0.016s
1 tree, 3 leaves, max depth = 2, train loss: 0.38339, val loss: 0.37673, in 0.000s
1 tree, 84 leaves, max depth = 14, train loss: 0.38157, val loss: 0.37506, in 0.016s
1 tree, 2 leaves, max depth = 1, train loss: 0.38009, val loss: 0.37351, in 0.016s
1 tree, 52 leaves, max depth = 12, train loss: 0.37824, val loss: 0.37152, in 0.000s
1 tree, 82 leaves, max depth = 12, train loss: 0.37650, val loss: 0.36996, in 0.016s
1 tree, 2 leaves, max depth = 1, train loss: 0.37510, val loss: 0.36850, in 0.000s
1 tree, 52 leaves, max depth = 11, train loss: 0.37337, val loss: 0.36664, in 0.016s
1 tree, 2 leaves, max depth = 1, train loss: 0.37205, val loss: 0.36526, in 0.000s
1 tree, 79 leaves, max depth = 13, train loss: 0.37040, val loss: 0.36379, in 0.016s
1 tree, 52 leaves, max depth = 11, train loss: 0.36877, val loss: 0.36204, in 0.016s
1 tree, 2 leaves, max depth = 1, train loss: 0.36752, val loss: 0.36073, in 0.000s
1 tree, 80 leaves, max depth = 12, train loss: 0.36594, val loss: 0.35933, in 0.016s
1 tree, 52 leaves, max depth = 11, train loss: 0.36442, val loss: 0.35770, in 0.016s
1 tree, 2 leaves, max depth = 1, train loss: 0.36322, val loss: 0.35645, in 0.000s
1 tree, 52 leaves, max depth = 11, train loss: 0.36179, val loss: 0.35491, in 0.016s
1 tree, 80 leaves, max depth = 14, train loss: 0.36028, val loss: 0.35358, in 0.016s
1 tree, 2 leaves, max depth = 1, train loss: 0.35914, val loss: 0.35239, in 0.000s
1 tree, 52 leaves, max depth = 15, train loss: 0.35775, val loss: 0.35090, in 0.016s
1 tree, 80 leaves, max depth = 14, train loss: 0.35632, val loss: 0.34963, in 0.016s
Fit 76 trees in 1.142 s, (4173 total leaves)
Time spent computing histograms: 0.378s
Time spent finding best splits:  0.081s
Time spent applying splits:      0.085s
Time spent predicting:           0.000s
Trial 23, Fold 2: Log loss = 0.3599834861148231, Average precision = 0.9429529922221647, ROC-AUC = 0.9446858836015578, Elapsed Time = 1.1502019999988988 seconds
Trial 23, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 23, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
Binning 0.040 GB of training data: 0.157 s
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 51 leaves, max depth = 13, train loss: 0.67733, val loss: 0.67686, in 0.016s
1 tree, 51 leaves, max depth = 13, train loss: 0.66257, val loss: 0.66165, in 0.000s
1 tree, 51 leaves, max depth = 13, train loss: 0.64877, val loss: 0.64742, in 0.016s
1 tree, 51 leaves, max depth = 11, train loss: 0.63587, val loss: 0.63409, in 0.000s
1 tree, 51 leaves, max depth = 13, train loss: 0.62378, val loss: 0.62160, in 0.016s
1 tree, 51 leaves, max depth = 11, train loss: 0.61246, val loss: 0.60989, in 0.016s
1 tree, 49 leaves, max depth = 13, train loss: 0.60193, val loss: 0.59899, in 0.016s
1 tree, 51 leaves, max depth = 13, train loss: 0.59194, val loss: 0.58865, in 0.000s
1 tree, 47 leaves, max depth = 14, train loss: 0.58266, val loss: 0.57897, in 0.016s
1 tree, 51 leaves, max depth = 13, train loss: 0.57383, val loss: 0.56981, in 0.016s
1 tree, 49 leaves, max depth = 13, train loss: 0.56561, val loss: 0.56130, in 0.000s
1 tree, 51 leaves, max depth = 13, train loss: 0.55779, val loss: 0.55317, in 0.016s
1 tree, 80 leaves, max depth = 12, train loss: 0.54966, val loss: 0.54560, in 0.016s
1 tree, 82 leaves, max depth = 12, train loss: 0.54202, val loss: 0.53850, in 0.016s
1 tree, 49 leaves, max depth = 13, train loss: 0.53512, val loss: 0.53131, in 0.000s
1 tree, 81 leaves, max depth = 12, train loss: 0.52807, val loss: 0.52478, in 0.016s
1 tree, 49 leaves, max depth = 13, train loss: 0.52174, val loss: 0.51817, in 0.016s
1 tree, 81 leaves, max depth = 12, train loss: 0.51522, val loss: 0.51214, in 0.000s
1 tree, 49 leaves, max depth = 13, train loss: 0.50941, val loss: 0.50605, in 0.016s
1 tree, 81 leaves, max depth = 12, train loss: 0.50337, val loss: 0.50047, in 0.016s
1 tree, 50 leaves, max depth = 13, train loss: 0.49801, val loss: 0.49483, in 0.016s
1 tree, 81 leaves, max depth = 12, train loss: 0.49240, val loss: 0.48967, in 0.016s
1 tree, 80 leaves, max depth = 12, train loss: 0.48710, val loss: 0.48481, in 0.016s
1 tree, 49 leaves, max depth = 13, train loss: 0.48226, val loss: 0.47971, in 0.000s
1 tree, 80 leaves, max depth = 14, train loss: 0.47733, val loss: 0.47520, in 0.016s
1 tree, 48 leaves, max depth = 13, train loss: 0.47285, val loss: 0.47047, in 0.016s
1 tree, 81 leaves, max depth = 13, train loss: 0.46825, val loss: 0.46627, in 0.016s
1 tree, 50 leaves, max depth = 13, train loss: 0.46411, val loss: 0.46188, in 0.016s
1 tree, 81 leaves, max depth = 13, train loss: 0.45981, val loss: 0.45797, in 0.016s
1 tree, 81 leaves, max depth = 13, train loss: 0.45574, val loss: 0.45428, in 0.016s
1 tree, 48 leaves, max depth = 16, train loss: 0.45198, val loss: 0.45026, in 0.000s
1 tree, 80 leaves, max depth = 12, train loss: 0.44817, val loss: 0.44682, in 0.016s
1 tree, 49 leaves, max depth = 13, train loss: 0.44466, val loss: 0.44308, in 0.016s
1 tree, 81 leaves, max depth = 12, train loss: 0.44109, val loss: 0.43985, in 0.016s
1 tree, 51 leaves, max depth = 13, train loss: 0.43783, val loss: 0.43637, in 0.016s
1 tree, 82 leaves, max depth = 12, train loss: 0.43447, val loss: 0.43336, in 0.000s
1 tree, 50 leaves, max depth = 13, train loss: 0.43144, val loss: 0.43011, in 0.016s
1 tree, 82 leaves, max depth = 12, train loss: 0.42828, val loss: 0.42728, in 0.016s
1 tree, 80 leaves, max depth = 12, train loss: 0.42530, val loss: 0.42462, in 0.016s
1 tree, 49 leaves, max depth = 13, train loss: 0.42250, val loss: 0.42160, in 0.016s
1 tree, 80 leaves, max depth = 13, train loss: 0.41969, val loss: 0.41911, in 0.000s
1 tree, 52 leaves, max depth = 13, train loss: 0.41707, val loss: 0.41628, in 0.016s
1 tree, 78 leaves, max depth = 13, train loss: 0.41442, val loss: 0.41393, in 0.016s
1 tree, 3 leaves, max depth = 2, train loss: 0.41208, val loss: 0.41178, in 0.000s
1 tree, 81 leaves, max depth = 13, train loss: 0.40958, val loss: 0.40959, in 0.016s
1 tree, 50 leaves, max depth = 11, train loss: 0.40717, val loss: 0.40692, in 0.016s
1 tree, 3 leaves, max depth = 2, train loss: 0.40499, val loss: 0.40491, in 0.000s
1 tree, 81 leaves, max depth = 12, train loss: 0.40262, val loss: 0.40285, in 0.016s
1 tree, 3 leaves, max depth = 2, train loss: 0.40057, val loss: 0.40096, in 0.016s
1 tree, 50 leaves, max depth = 11, train loss: 0.39836, val loss: 0.39851, in 0.000s
1 tree, 81 leaves, max depth = 12, train loss: 0.39614, val loss: 0.39657, in 0.016s
1 tree, 3 leaves, max depth = 2, train loss: 0.39422, val loss: 0.39480, in 0.016s
1 tree, 3 leaves, max depth = 2, train loss: 0.39240, val loss: 0.39313, in 0.000s
1 tree, 80 leaves, max depth = 12, train loss: 0.39029, val loss: 0.39131, in 0.016s
1 tree, 49 leaves, max depth = 11, train loss: 0.38826, val loss: 0.38906, in 0.016s
1 tree, 3 leaves, max depth = 2, train loss: 0.38656, val loss: 0.38749, in 0.000s
1 tree, 82 leaves, max depth = 13, train loss: 0.38456, val loss: 0.38578, in 0.016s
1 tree, 3 leaves, max depth = 2, train loss: 0.38295, val loss: 0.38430, in 0.016s
1 tree, 82 leaves, max depth = 12, train loss: 0.38104, val loss: 0.38267, in 0.016s
1 tree, 47 leaves, max depth = 12, train loss: 0.37917, val loss: 0.38058, in 0.000s
1 tree, 3 leaves, max depth = 2, train loss: 0.37765, val loss: 0.37918, in 0.016s
1 tree, 80 leaves, max depth = 13, train loss: 0.37585, val loss: 0.37766, in 0.000s
1 tree, 2 leaves, max depth = 1, train loss: 0.37446, val loss: 0.37638, in 0.000s
1 tree, 49 leaves, max depth = 15, train loss: 0.37268, val loss: 0.37442, in 0.016s
1 tree, 82 leaves, max depth = 13, train loss: 0.37096, val loss: 0.37299, in 0.000s
1 tree, 3 leaves, max depth = 2, train loss: 0.36965, val loss: 0.37177, in 0.016s
1 tree, 49 leaves, max depth = 15, train loss: 0.36797, val loss: 0.36993, in 0.016s
1 tree, 50 leaves, max depth = 15, train loss: 0.36639, val loss: 0.36819, in 0.000s
1 tree, 82 leaves, max depth = 13, train loss: 0.36474, val loss: 0.36681, in 0.016s
1 tree, 2 leaves, max depth = 1, train loss: 0.36350, val loss: 0.36567, in 0.016s
1 tree, 49 leaves, max depth = 13, train loss: 0.36202, val loss: 0.36403, in 0.000s
1 tree, 3 leaves, max depth = 2, train loss: 0.36084, val loss: 0.36294, in 0.016s
1 tree, 83 leaves, max depth = 14, train loss: 0.35926, val loss: 0.36164, in 0.016s
1 tree, 48 leaves, max depth = 10, train loss: 0.35788, val loss: 0.36009, in 0.000s
1 tree, 3 leaves, max depth = 2, train loss: 0.35676, val loss: 0.35905, in 0.000s
1 tree, 82 leaves, max depth = 14, train loss: 0.35525, val loss: 0.35782, in 0.016s
Fit 76 trees in 1.173 s, (4073 total leaves)
Time spent computing histograms: 0.378s
Time spent finding best splits:  0.081s
Time spent applying splits:      0.084s
Time spent predicting:           0.000s
Trial 23, Fold 3: Log loss = 0.3552397610452097, Average precision = 0.9484253015879371, ROC-AUC = 0.9490830502210323, Elapsed Time = 1.1743360999989818 seconds
Trial 23, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 23, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
Binning 0.040 GB of training data: 0.157 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 48 leaves, max depth = 15, train loss: 0.67731, val loss: 0.67637, in 0.000s
1 tree, 48 leaves, max depth = 15, train loss: 0.66252, val loss: 0.66068, in 0.016s
1 tree, 49 leaves, max depth = 15, train loss: 0.64870, val loss: 0.64600, in 0.016s
1 tree, 47 leaves, max depth = 15, train loss: 0.63578, val loss: 0.63222, in 0.000s
1 tree, 48 leaves, max depth = 13, train loss: 0.62368, val loss: 0.61930, in 0.016s
1 tree, 49 leaves, max depth = 13, train loss: 0.61233, val loss: 0.60716, in 0.016s
1 tree, 48 leaves, max depth = 15, train loss: 0.60169, val loss: 0.59574, in 0.016s
1 tree, 48 leaves, max depth = 13, train loss: 0.59170, val loss: 0.58500, in 0.016s
1 tree, 50 leaves, max depth = 15, train loss: 0.58232, val loss: 0.57489, in 0.016s
1 tree, 48 leaves, max depth = 13, train loss: 0.57350, val loss: 0.56536, in 0.016s
1 tree, 49 leaves, max depth = 13, train loss: 0.56521, val loss: 0.55637, in 0.016s
1 tree, 48 leaves, max depth = 15, train loss: 0.55740, val loss: 0.54790, in 0.016s
1 tree, 79 leaves, max depth = 13, train loss: 0.54941, val loss: 0.54010, in 0.031s
1 tree, 49 leaves, max depth = 15, train loss: 0.54227, val loss: 0.53231, in 0.016s
1 tree, 49 leaves, max depth = 15, train loss: 0.53554, val loss: 0.52497, in 0.016s
1 tree, 78 leaves, max depth = 13, train loss: 0.52833, val loss: 0.51795, in 0.016s
1 tree, 77 leaves, max depth = 13, train loss: 0.52153, val loss: 0.51133, in 0.016s
1 tree, 78 leaves, max depth = 13, train loss: 0.51514, val loss: 0.50510, in 0.016s
1 tree, 47 leaves, max depth = 12, train loss: 0.50927, val loss: 0.49874, in 0.016s
1 tree, 79 leaves, max depth = 13, train loss: 0.50334, val loss: 0.49296, in 0.016s
1 tree, 49 leaves, max depth = 12, train loss: 0.49794, val loss: 0.48709, in 0.016s
1 tree, 80 leaves, max depth = 13, train loss: 0.49244, val loss: 0.48174, in 0.016s
1 tree, 47 leaves, max depth = 12, train loss: 0.48745, val loss: 0.47631, in 0.016s
1 tree, 79 leaves, max depth = 13, train loss: 0.48234, val loss: 0.47134, in 0.016s
1 tree, 49 leaves, max depth = 12, train loss: 0.47774, val loss: 0.46631, in 0.016s
1 tree, 80 leaves, max depth = 15, train loss: 0.47298, val loss: 0.46170, in 0.016s
1 tree, 80 leaves, max depth = 15, train loss: 0.46848, val loss: 0.45736, in 0.016s
1 tree, 50 leaves, max depth = 15, train loss: 0.46429, val loss: 0.45272, in 0.000s
1 tree, 79 leaves, max depth = 14, train loss: 0.46009, val loss: 0.44867, in 0.016s
1 tree, 50 leaves, max depth = 15, train loss: 0.45619, val loss: 0.44434, in 0.016s
1 tree, 78 leaves, max depth = 13, train loss: 0.45228, val loss: 0.44056, in 0.016s
1 tree, 50 leaves, max depth = 15, train loss: 0.44866, val loss: 0.43655, in 0.016s
1 tree, 77 leaves, max depth = 14, train loss: 0.44499, val loss: 0.43299, in 0.016s
1 tree, 50 leaves, max depth = 14, train loss: 0.44162, val loss: 0.42924, in 0.000s
1 tree, 78 leaves, max depth = 13, train loss: 0.43818, val loss: 0.42593, in 0.031s
1 tree, 79 leaves, max depth = 13, train loss: 0.43491, val loss: 0.42278, in 0.016s
1 tree, 50 leaves, max depth = 14, train loss: 0.43183, val loss: 0.41932, in 0.000s
1 tree, 79 leaves, max depth = 13, train loss: 0.42876, val loss: 0.41638, in 0.031s
1 tree, 50 leaves, max depth = 12, train loss: 0.42588, val loss: 0.41314, in 0.016s
1 tree, 79 leaves, max depth = 14, train loss: 0.42301, val loss: 0.41038, in 0.016s
1 tree, 51 leaves, max depth = 15, train loss: 0.42032, val loss: 0.40734, in 0.000s
1 tree, 80 leaves, max depth = 14, train loss: 0.41761, val loss: 0.40475, in 0.016s
1 tree, 50 leaves, max depth = 12, train loss: 0.41510, val loss: 0.40190, in 0.016s
1 tree, 78 leaves, max depth = 14, train loss: 0.41255, val loss: 0.39948, in 0.016s
1 tree, 3 leaves, max depth = 2, train loss: 0.41031, val loss: 0.39711, in 0.000s
1 tree, 78 leaves, max depth = 12, train loss: 0.40790, val loss: 0.39481, in 0.000s
1 tree, 3 leaves, max depth = 2, train loss: 0.40580, val loss: 0.39257, in 0.016s
1 tree, 49 leaves, max depth = 12, train loss: 0.40353, val loss: 0.38999, in 0.016s
1 tree, 80 leaves, max depth = 12, train loss: 0.40126, val loss: 0.38783, in 0.016s
1 tree, 3 leaves, max depth = 2, train loss: 0.39929, val loss: 0.38574, in 0.000s
1 tree, 80 leaves, max depth = 12, train loss: 0.39714, val loss: 0.38372, in 0.016s
1 tree, 3 leaves, max depth = 2, train loss: 0.39528, val loss: 0.38174, in 0.000s
1 tree, 49 leaves, max depth = 12, train loss: 0.39320, val loss: 0.37938, in 0.016s
1 tree, 3 leaves, max depth = 2, train loss: 0.39146, val loss: 0.37753, in 0.000s
1 tree, 79 leaves, max depth = 12, train loss: 0.38943, val loss: 0.37562, in 0.016s
1 tree, 2 leaves, max depth = 1, train loss: 0.38785, val loss: 0.37393, in 0.000s
1 tree, 80 leaves, max depth = 12, train loss: 0.38592, val loss: 0.37213, in 0.016s
1 tree, 49 leaves, max depth = 11, train loss: 0.38401, val loss: 0.36996, in 0.016s
1 tree, 2 leaves, max depth = 1, train loss: 0.38251, val loss: 0.36837, in 0.000s
1 tree, 80 leaves, max depth = 12, train loss: 0.38068, val loss: 0.36666, in 0.031s
1 tree, 2 leaves, max depth = 1, train loss: 0.37926, val loss: 0.36515, in 0.000s
1 tree, 53 leaves, max depth = 13, train loss: 0.37745, val loss: 0.36308, in 0.016s
1 tree, 81 leaves, max depth = 12, train loss: 0.37571, val loss: 0.36148, in 0.016s
1 tree, 2 leaves, max depth = 1, train loss: 0.37437, val loss: 0.36005, in 0.000s
1 tree, 51 leaves, max depth = 12, train loss: 0.37270, val loss: 0.35815, in 0.016s
1 tree, 80 leaves, max depth = 13, train loss: 0.37104, val loss: 0.35662, in 0.016s
1 tree, 2 leaves, max depth = 1, train loss: 0.36977, val loss: 0.35526, in 0.016s
1 tree, 54 leaves, max depth = 14, train loss: 0.36817, val loss: 0.35344, in 0.016s
1 tree, 80 leaves, max depth = 12, train loss: 0.36659, val loss: 0.35200, in 0.016s
1 tree, 2 leaves, max depth = 1, train loss: 0.36538, val loss: 0.35071, in 0.000s
1 tree, 51 leaves, max depth = 13, train loss: 0.36390, val loss: 0.34902, in 0.016s
1 tree, 2 leaves, max depth = 1, train loss: 0.36276, val loss: 0.34780, in 0.016s
1 tree, 51 leaves, max depth = 12, train loss: 0.36136, val loss: 0.34620, in 0.000s
1 tree, 80 leaves, max depth = 12, train loss: 0.35984, val loss: 0.34482, in 0.016s
1 tree, 2 leaves, max depth = 1, train loss: 0.35875, val loss: 0.34366, in 0.016s
1 tree, 51 leaves, max depth = 13, train loss: 0.35744, val loss: 0.34216, in 0.000s
Fit 76 trees in 1.329 s, (3975 total leaves)
Time spent computing histograms: 0.444s
Time spent finding best splits:  0.101s
Time spent applying splits:      0.101s
Time spent predicting:           0.000s
Trial 23, Fold 4: Log loss = 0.35819156661274026, Average precision = 0.9483507958808596, ROC-AUC = 0.946384683386148, Elapsed Time = 1.3260851000013645 seconds
Trial 23, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 23, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
Binning 0.040 GB of training data: 0.158 s
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 47 leaves, max depth = 12, train loss: 0.67714, val loss: 0.67608, in 0.000s
1 tree, 47 leaves, max depth = 12, train loss: 0.66220, val loss: 0.66013, in 0.016s
1 tree, 48 leaves, max depth = 13, train loss: 0.64837, val loss: 0.64533, in 0.016s
1 tree, 48 leaves, max depth = 12, train loss: 0.63529, val loss: 0.63130, in 0.000s
1 tree, 47 leaves, max depth = 12, train loss: 0.62304, val loss: 0.61814, in 0.016s
1 tree, 48 leaves, max depth = 12, train loss: 0.61156, val loss: 0.60577, in 0.016s
1 tree, 48 leaves, max depth = 12, train loss: 0.60078, val loss: 0.59414, in 0.016s
1 tree, 49 leaves, max depth = 13, train loss: 0.59075, val loss: 0.58330, in 0.000s
1 tree, 47 leaves, max depth = 12, train loss: 0.58124, val loss: 0.57299, in 0.016s
1 tree, 48 leaves, max depth = 12, train loss: 0.57230, val loss: 0.56327, in 0.016s
1 tree, 48 leaves, max depth = 13, train loss: 0.56395, val loss: 0.55418, in 0.000s
1 tree, 48 leaves, max depth = 12, train loss: 0.55602, val loss: 0.54554, in 0.016s
1 tree, 84 leaves, max depth = 14, train loss: 0.54801, val loss: 0.53787, in 0.016s
1 tree, 48 leaves, max depth = 13, train loss: 0.54082, val loss: 0.53000, in 0.000s
1 tree, 48 leaves, max depth = 12, train loss: 0.53397, val loss: 0.52248, in 0.016s
1 tree, 84 leaves, max depth = 15, train loss: 0.52676, val loss: 0.51564, in 0.016s
1 tree, 84 leaves, max depth = 16, train loss: 0.51996, val loss: 0.50921, in 0.016s
1 tree, 47 leaves, max depth = 13, train loss: 0.51390, val loss: 0.50254, in 0.016s
1 tree, 84 leaves, max depth = 16, train loss: 0.50760, val loss: 0.49656, in 0.000s
1 tree, 84 leaves, max depth = 16, train loss: 0.50165, val loss: 0.49093, in 0.016s
1 tree, 47 leaves, max depth = 13, train loss: 0.49621, val loss: 0.48492, in 0.016s
1 tree, 83 leaves, max depth = 13, train loss: 0.49069, val loss: 0.47971, in 0.016s
1 tree, 48 leaves, max depth = 11, train loss: 0.48559, val loss: 0.47405, in 0.016s
1 tree, 83 leaves, max depth = 17, train loss: 0.48046, val loss: 0.46923, in 0.016s
1 tree, 49 leaves, max depth = 13, train loss: 0.47579, val loss: 0.46402, in 0.000s
1 tree, 84 leaves, max depth = 17, train loss: 0.47100, val loss: 0.45954, in 0.016s
1 tree, 49 leaves, max depth = 13, train loss: 0.46668, val loss: 0.45471, in 0.016s
1 tree, 84 leaves, max depth = 16, train loss: 0.46221, val loss: 0.45054, in 0.016s
1 tree, 85 leaves, max depth = 16, train loss: 0.45799, val loss: 0.44660, in 0.016s
1 tree, 49 leaves, max depth = 13, train loss: 0.45405, val loss: 0.44218, in 0.016s
1 tree, 84 leaves, max depth = 15, train loss: 0.45010, val loss: 0.43852, in 0.016s
1 tree, 49 leaves, max depth = 13, train loss: 0.44644, val loss: 0.43440, in 0.016s
1 tree, 85 leaves, max depth = 13, train loss: 0.44273, val loss: 0.43100, in 0.000s
1 tree, 48 leaves, max depth = 13, train loss: 0.43935, val loss: 0.42719, in 0.016s
1 tree, 84 leaves, max depth = 12, train loss: 0.43588, val loss: 0.42400, in 0.016s
1 tree, 85 leaves, max depth = 11, train loss: 0.43258, val loss: 0.42100, in 0.016s
1 tree, 50 leaves, max depth = 13, train loss: 0.42946, val loss: 0.41744, in 0.016s
1 tree, 82 leaves, max depth = 14, train loss: 0.42636, val loss: 0.41464, in 0.016s
1 tree, 50 leaves, max depth = 13, train loss: 0.42345, val loss: 0.41131, in 0.016s
1 tree, 81 leaves, max depth = 14, train loss: 0.42053, val loss: 0.40866, in 0.016s
1 tree, 50 leaves, max depth = 13, train loss: 0.41781, val loss: 0.40554, in 0.000s
1 tree, 85 leaves, max depth = 13, train loss: 0.41507, val loss: 0.40309, in 0.016s
1 tree, 49 leaves, max depth = 13, train loss: 0.41253, val loss: 0.40015, in 0.016s
1 tree, 83 leaves, max depth = 14, train loss: 0.40994, val loss: 0.39785, in 0.016s
1 tree, 3 leaves, max depth = 2, train loss: 0.40770, val loss: 0.39567, in 0.016s
1 tree, 82 leaves, max depth = 14, train loss: 0.40525, val loss: 0.39349, in 0.016s
1 tree, 50 leaves, max depth = 13, train loss: 0.40292, val loss: 0.39080, in 0.000s
1 tree, 3 leaves, max depth = 2, train loss: 0.40083, val loss: 0.38877, in 0.016s
1 tree, 82 leaves, max depth = 14, train loss: 0.39852, val loss: 0.38674, in 0.016s
1 tree, 3 leaves, max depth = 2, train loss: 0.39655, val loss: 0.38483, in 0.000s
1 tree, 82 leaves, max depth = 13, train loss: 0.39436, val loss: 0.38290, in 0.031s
1 tree, 51 leaves, max depth = 12, train loss: 0.39224, val loss: 0.38044, in 0.016s
1 tree, 3 leaves, max depth = 2, train loss: 0.39039, val loss: 0.37866, in 0.000s
1 tree, 82 leaves, max depth = 13, train loss: 0.38832, val loss: 0.37684, in 0.016s
1 tree, 2 leaves, max depth = 1, train loss: 0.38665, val loss: 0.37518, in 0.000s
1 tree, 80 leaves, max depth = 14, train loss: 0.38468, val loss: 0.37348, in 0.016s
1 tree, 51 leaves, max depth = 11, train loss: 0.38272, val loss: 0.37119, in 0.016s
1 tree, 2 leaves, max depth = 1, train loss: 0.38116, val loss: 0.36963, in 0.016s
1 tree, 82 leaves, max depth = 14, train loss: 0.37929, val loss: 0.36804, in 0.016s
1 tree, 2 leaves, max depth = 1, train loss: 0.37780, val loss: 0.36656, in 0.000s
1 tree, 50 leaves, max depth = 11, train loss: 0.37598, val loss: 0.36443, in 0.016s
1 tree, 82 leaves, max depth = 14, train loss: 0.37420, val loss: 0.36292, in 0.016s
1 tree, 2 leaves, max depth = 1, train loss: 0.37280, val loss: 0.36152, in 0.000s
1 tree, 50 leaves, max depth = 11, train loss: 0.37106, val loss: 0.35952, in 0.016s
1 tree, 2 leaves, max depth = 1, train loss: 0.36973, val loss: 0.35820, in 0.016s
1 tree, 79 leaves, max depth = 15, train loss: 0.36803, val loss: 0.35676, in 0.016s
1 tree, 51 leaves, max depth = 12, train loss: 0.36640, val loss: 0.35487, in 0.000s
1 tree, 2 leaves, max depth = 1, train loss: 0.36514, val loss: 0.35362, in 0.016s
1 tree, 80 leaves, max depth = 15, train loss: 0.36352, val loss: 0.35224, in 0.016s
1 tree, 50 leaves, max depth = 12, train loss: 0.36199, val loss: 0.35047, in 0.016s
1 tree, 2 leaves, max depth = 1, train loss: 0.36080, val loss: 0.34929, in 0.000s
1 tree, 51 leaves, max depth = 10, train loss: 0.35937, val loss: 0.34760, in 0.016s
1 tree, 81 leaves, max depth = 15, train loss: 0.35780, val loss: 0.34630, in 0.016s
1 tree, 2 leaves, max depth = 1, train loss: 0.35667, val loss: 0.34517, in 0.000s
1 tree, 50 leaves, max depth = 10, train loss: 0.35533, val loss: 0.34359, in 0.016s
1 tree, 81 leaves, max depth = 15, train loss: 0.35383, val loss: 0.34235, in 0.016s
Fit 76 trees in 1.236 s, (4137 total leaves)
Time spent computing histograms: 0.417s
Time spent finding best splits:  0.088s
Time spent applying splits:      0.089s
Time spent predicting:           0.016s
Trial 23, Fold 5: Log loss = 0.3607328935159004, Average precision = 0.946009279757922, ROC-AUC = 0.9442421269717407, Elapsed Time = 1.2442246000009618 seconds
Optimization Progress:  24%|##4       | 24/100 [04:47<15:47, 12.46s/it]
Trial 24, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 24, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
0.142 s 0.040 GB of training data: 
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 20 leaves, max depth = 8, train loss: 0.68729, val loss: 0.68711, in 0.000s
1 tree, 20 leaves, max depth = 8, train loss: 0.68158, val loss: 0.68122, in 0.016s
1 tree, 20 leaves, max depth = 8, train loss: 0.67600, val loss: 0.67547, in 0.000s
1 tree, 20 leaves, max depth = 8, train loss: 0.67057, val loss: 0.66986, in 0.016s
1 tree, 20 leaves, max depth = 8, train loss: 0.66526, val loss: 0.66438, in 0.000s
1 tree, 20 leaves, max depth = 8, train loss: 0.66009, val loss: 0.65904, in 0.016s
1 tree, 20 leaves, max depth = 8, train loss: 0.65503, val loss: 0.65382, in 0.000s
1 tree, 20 leaves, max depth = 8, train loss: 0.65010, val loss: 0.64872, in 0.016s
1 tree, 20 leaves, max depth = 8, train loss: 0.64529, val loss: 0.64374, in 0.000s
1 tree, 20 leaves, max depth = 8, train loss: 0.64059, val loss: 0.63888, in 0.000s
Fit 10 trees in 0.314 s, (200 total leaves)
Time spent computing histograms: 0.038s
Time spent finding best splits:  0.004s
Time spent applying splits:      0.004s
Time spent predicting:           0.000s
Trial 24, Fold 1: Log loss = 0.640623233035379, Average precision = 0.8109263008444648, ROC-AUC = 0.8600171079422435, Elapsed Time = 0.32782589999987977 seconds
Trial 24, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 24, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
Binning 0.040 GB of training data: 0.142 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 20 leaves, max depth = 8, train loss: 0.68733, val loss: 0.68703, in 0.000s
1 tree, 19 leaves, max depth = 9, train loss: 0.68170, val loss: 0.68113, in 0.016s
1 tree, 20 leaves, max depth = 8, train loss: 0.67616, val loss: 0.67530, in 0.000s
1 tree, 20 leaves, max depth = 9, train loss: 0.67081, val loss: 0.66969, in 0.000s
1 tree, 20 leaves, max depth = 8, train loss: 0.66553, val loss: 0.66413, in 0.016s
1 tree, 20 leaves, max depth = 9, train loss: 0.66043, val loss: 0.65878, in 0.000s
1 tree, 20 leaves, max depth = 8, train loss: 0.65541, val loss: 0.65348, in 0.016s
1 tree, 20 leaves, max depth = 9, train loss: 0.65055, val loss: 0.64837, in 0.000s
1 tree, 20 leaves, max depth = 8, train loss: 0.64575, val loss: 0.64331, in 0.016s
1 tree, 20 leaves, max depth = 9, train loss: 0.64112, val loss: 0.63844, in 0.000s
Fit 10 trees in 0.329 s, (199 total leaves)
Time spent computing histograms: 0.042s
Time spent finding best splits:  0.005s
Time spent applying splits:      0.004s
Time spent predicting:           0.000s
Trial 24, Fold 2: Log loss = 0.6409561541008428, Average precision = 0.8198208757306849, ROC-AUC = 0.8671791846955682, Elapsed Time = 0.33721589999913704 seconds
Trial 24, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 24, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
Binning 0.040 GB of training data: 0.142 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 20 leaves, max depth = 7, train loss: 0.68734, val loss: 0.68716, in 0.000s
1 tree, 20 leaves, max depth = 7, train loss: 0.68167, val loss: 0.68131, in 0.016s
1 tree, 20 leaves, max depth = 7, train loss: 0.67614, val loss: 0.67561, in 0.000s
1 tree, 20 leaves, max depth = 7, train loss: 0.67075, val loss: 0.67004, in 0.016s
1 tree, 20 leaves, max depth = 7, train loss: 0.66549, val loss: 0.66461, in 0.000s
1 tree, 20 leaves, max depth = 7, train loss: 0.66035, val loss: 0.65931, in 0.016s
1 tree, 20 leaves, max depth = 7, train loss: 0.65534, val loss: 0.65413, in 0.000s
1 tree, 20 leaves, max depth = 7, train loss: 0.65045, val loss: 0.64907, in 0.000s
1 tree, 20 leaves, max depth = 7, train loss: 0.64567, val loss: 0.64413, in 0.016s
1 tree, 20 leaves, max depth = 7, train loss: 0.64101, val loss: 0.63931, in 0.000s
Fit 10 trees in 0.330 s, (200 total leaves)
Time spent computing histograms: 0.038s
Time spent finding best splits:  0.004s
Time spent applying splits:      0.004s
Time spent predicting:           0.000s
Trial 24, Fold 3: Log loss = 0.6396197595777118, Average precision = 0.8252803499994151, ROC-AUC = 0.8716421546792383, Elapsed Time = 0.33429789999900095 seconds
Trial 24, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 24, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
Binning 0.040 GB of training data: 0.142 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 19 leaves, max depth = 9, train loss: 0.68737, val loss: 0.68704, in 0.016s
1 tree, 19 leaves, max depth = 9, train loss: 0.68173, val loss: 0.68108, in 0.000s
1 tree, 19 leaves, max depth = 9, train loss: 0.67624, val loss: 0.67526, in 0.016s
1 tree, 19 leaves, max depth = 9, train loss: 0.67087, val loss: 0.66957, in 0.000s
1 tree, 19 leaves, max depth = 9, train loss: 0.66564, val loss: 0.66402, in 0.016s
1 tree, 19 leaves, max depth = 9, train loss: 0.66053, val loss: 0.65860, in 0.000s
1 tree, 19 leaves, max depth = 9, train loss: 0.65555, val loss: 0.65331, in 0.016s
1 tree, 20 leaves, max depth = 7, train loss: 0.65065, val loss: 0.64809, in 0.000s
1 tree, 19 leaves, max depth = 9, train loss: 0.64590, val loss: 0.64303, in 0.000s
1 tree, 20 leaves, max depth = 7, train loss: 0.64124, val loss: 0.63804, in 0.016s
Fit 10 trees in 0.329 s, (192 total leaves)
Time spent computing histograms: 0.040s
Time spent finding best splits:  0.004s
Time spent applying splits:      0.004s
Time spent predicting:           0.000s
Trial 24, Fold 4: Log loss = 0.640927237290986, Average precision = 0.8185837745206201, ROC-AUC = 0.8648848121442758, Elapsed Time = 0.3400923999997758 seconds
Trial 24, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 24, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
0.142 s 0.040 GB of training data: 
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 20 leaves, max depth = 8, train loss: 0.68734, val loss: 0.68696, in 0.016s
1 tree, 20 leaves, max depth = 8, train loss: 0.68167, val loss: 0.68091, in 0.000s
1 tree, 20 leaves, max depth = 8, train loss: 0.67614, val loss: 0.67502, in 0.016s
1 tree, 20 leaves, max depth = 8, train loss: 0.67075, val loss: 0.66926, in 0.000s
1 tree, 20 leaves, max depth = 9, train loss: 0.66547, val loss: 0.66359, in 0.000s
1 tree, 20 leaves, max depth = 8, train loss: 0.66033, val loss: 0.65810, in 0.016s
1 tree, 20 leaves, max depth = 9, train loss: 0.65530, val loss: 0.65269, in 0.000s
1 tree, 20 leaves, max depth = 8, train loss: 0.65041, val loss: 0.64745, in 0.016s
1 tree, 20 leaves, max depth = 9, train loss: 0.64561, val loss: 0.64228, in 0.000s
1 tree, 20 leaves, max depth = 8, train loss: 0.64095, val loss: 0.63728, in 0.016s
Fit 10 trees in 0.345 s, (200 total leaves)
Time spent computing histograms: 0.041s
Time spent finding best splits:  0.005s
Time spent applying splits:      0.004s
Time spent predicting:           0.000s
Trial 24, Fold 5: Log loss = 0.6424195026971156, Average precision = 0.8047005213484206, ROC-AUC = 0.8551868160451852, Elapsed Time = 0.35267949999979464 seconds
Optimization Progress:  25%|##5       | 25/100 [04:55<13:56, 11.15s/it]
Trial 25, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 25, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
0.205 s 0.040 GB of training data: 
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 6 leaves, max depth = 4, train loss: 0.66934, val loss: 0.66854, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.65718, val loss: 0.65539, in 0.000s
1 tree, 7 leaves, max depth = 4, train loss: 0.63648, val loss: 0.63409, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.61800, val loss: 0.61486, in 0.000s
1 tree, 7 leaves, max depth = 4, train loss: 0.60112, val loss: 0.59737, in 0.016s
1 tree, 8 leaves, max depth = 5, train loss: 0.58600, val loss: 0.58163, in 0.000s
1 tree, 4 leaves, max depth = 3, train loss: 0.57225, val loss: 0.56724, in 0.016s
1 tree, 8 leaves, max depth = 5, train loss: 0.55985, val loss: 0.55430, in 0.000s
1 tree, 28 leaves, max depth = 7, train loss: 0.55106, val loss: 0.54609, in 0.000s
1 tree, 8 leaves, max depth = 4, train loss: 0.53971, val loss: 0.53425, in 0.000s
1 tree, 10 leaves, max depth = 7, train loss: 0.52971, val loss: 0.52376, in 0.000s
1 tree, 12 leaves, max depth = 6, train loss: 0.52050, val loss: 0.51412, in 0.016s
1 tree, 7 leaves, max depth = 4, train loss: 0.51201, val loss: 0.50520, in 0.000s
1 tree, 9 leaves, max depth = 6, train loss: 0.50444, val loss: 0.49722, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.49754, val loss: 0.48992, in 0.000s
1 tree, 28 leaves, max depth = 10, train loss: 0.48797, val loss: 0.48101, in 0.016s
1 tree, 8 leaves, max depth = 4, train loss: 0.48173, val loss: 0.47441, in 0.000s
1 tree, 14 leaves, max depth = 7, train loss: 0.47615, val loss: 0.46844, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.47098, val loss: 0.46285, in 0.000s
1 tree, 8 leaves, max depth = 4, train loss: 0.46601, val loss: 0.45755, in 0.016s
1 tree, 8 leaves, max depth = 4, train loss: 0.46147, val loss: 0.45268, in 0.000s
1 tree, 28 leaves, max depth = 8, train loss: 0.45596, val loss: 0.44773, in 0.016s
1 tree, 16 leaves, max depth = 6, train loss: 0.45228, val loss: 0.44378, in 0.000s
1 tree, 10 leaves, max depth = 5, train loss: 0.44856, val loss: 0.43975, in 0.016s
1 tree, 28 leaves, max depth = 9, train loss: 0.44366, val loss: 0.43536, in 0.000s
1 tree, 16 leaves, max depth = 6, train loss: 0.44050, val loss: 0.43199, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.43722, val loss: 0.42867, in 0.000s
1 tree, 24 leaves, max depth = 9, train loss: 0.43451, val loss: 0.42578, in 0.016s
1 tree, 28 leaves, max depth = 8, train loss: 0.43022, val loss: 0.42200, in 0.016s
Fit 29 trees in 0.580 s, (364 total leaves)
Time spent computing histograms: 0.133s
Time spent finding best splits:  0.010s
Time spent applying splits:      0.010s
Time spent predicting:           0.000s
Trial 25, Fold 1: Log loss = 0.43042724292040524, Average precision = 0.904210098361593, ROC-AUC = 0.9104416997653222, Elapsed Time = 0.5778432999995857 seconds
Trial 25, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 25, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
Binning 0.040 GB of training data: 0.173 s
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 6 leaves, max depth = 4, train loss: 0.66933, val loss: 0.66808, in 0.000s
1 tree, 4 leaves, max depth = 3, train loss: 0.65699, val loss: 0.65449, in 0.016s
1 tree, 7 leaves, max depth = 5, train loss: 0.63639, val loss: 0.63281, in 0.000s
1 tree, 4 leaves, max depth = 3, train loss: 0.61785, val loss: 0.61333, in 0.000s
1 tree, 8 leaves, max depth = 5, train loss: 0.60106, val loss: 0.59560, in 0.016s
1 tree, 8 leaves, max depth = 5, train loss: 0.58594, val loss: 0.57960, in 0.000s
1 tree, 4 leaves, max depth = 3, train loss: 0.57215, val loss: 0.56503, in 0.016s
1 tree, 5 leaves, max depth = 4, train loss: 0.55970, val loss: 0.55184, in 0.000s
1 tree, 28 leaves, max depth = 7, train loss: 0.55103, val loss: 0.54364, in 0.016s
1 tree, 7 leaves, max depth = 4, train loss: 0.53980, val loss: 0.53169, in 0.000s
1 tree, 8 leaves, max depth = 5, train loss: 0.52982, val loss: 0.52101, in 0.016s
1 tree, 13 leaves, max depth = 7, train loss: 0.52064, val loss: 0.51121, in 0.000s
1 tree, 9 leaves, max depth = 6, train loss: 0.51233, val loss: 0.50229, in 0.016s
1 tree, 13 leaves, max depth = 7, train loss: 0.50478, val loss: 0.49415, in 0.000s
1 tree, 7 leaves, max depth = 5, train loss: 0.49785, val loss: 0.48671, in 0.000s
1 tree, 28 leaves, max depth = 9, train loss: 0.48808, val loss: 0.47735, in 0.000s
1 tree, 10 leaves, max depth = 6, train loss: 0.48191, val loss: 0.47069, in 0.016s
1 tree, 12 leaves, max depth = 5, train loss: 0.47635, val loss: 0.46469, in 0.000s
1 tree, 4 leaves, max depth = 3, train loss: 0.47117, val loss: 0.45907, in 0.000s
1 tree, 11 leaves, max depth = 5, train loss: 0.46630, val loss: 0.45375, in 0.016s
1 tree, 11 leaves, max depth = 5, train loss: 0.46184, val loss: 0.44887, in 0.000s
1 tree, 28 leaves, max depth = 7, train loss: 0.45641, val loss: 0.44392, in 0.017s
1 tree, 18 leaves, max depth = 7, train loss: 0.45268, val loss: 0.43995, in 0.000s
1 tree, 13 leaves, max depth = 6, train loss: 0.44903, val loss: 0.43593, in 0.015s
1 tree, 28 leaves, max depth = 8, train loss: 0.44412, val loss: 0.43148, in 0.016s
1 tree, 13 leaves, max depth = 7, train loss: 0.44100, val loss: 0.42807, in 0.000s
1 tree, 15 leaves, max depth = 8, train loss: 0.43820, val loss: 0.42501, in 0.016s
1 tree, 21 leaves, max depth = 13, train loss: 0.43563, val loss: 0.42215, in 0.000s
1 tree, 28 leaves, max depth = 7, train loss: 0.43121, val loss: 0.41822, in 0.016s
Fit 29 trees in 0.549 s, (371 total leaves)
Time spent computing histograms: 0.131s
Time spent finding best splits:  0.010s
Time spent applying splits:      0.010s
Time spent predicting:           0.000s
Trial 25, Fold 2: Log loss = 0.43183056693913974, Average precision = 0.8994185525865448, ROC-AUC = 0.9132483707599013, Elapsed Time = 0.5474708000001556 seconds
Trial 25, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 25, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
Binning 0.040 GB of training data: 0.158 s
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 7 leaves, max depth = 5, train loss: 0.66943, val loss: 0.66856, in 0.000s
1 tree, 4 leaves, max depth = 3, train loss: 0.65706, val loss: 0.65587, in 0.016s
1 tree, 6 leaves, max depth = 4, train loss: 0.63667, val loss: 0.63463, in 0.000s
1 tree, 4 leaves, max depth = 3, train loss: 0.61824, val loss: 0.61550, in 0.000s
1 tree, 6 leaves, max depth = 4, train loss: 0.60161, val loss: 0.59821, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.58658, val loss: 0.58263, in 0.000s
1 tree, 4 leaves, max depth = 3, train loss: 0.57286, val loss: 0.56833, in 0.016s
1 tree, 7 leaves, max depth = 5, train loss: 0.56057, val loss: 0.55544, in 0.000s
1 tree, 28 leaves, max depth = 8, train loss: 0.55180, val loss: 0.54733, in 0.016s
1 tree, 8 leaves, max depth = 5, train loss: 0.54066, val loss: 0.53569, in 0.000s
1 tree, 8 leaves, max depth = 5, train loss: 0.53074, val loss: 0.52535, in 0.000s
1 tree, 9 leaves, max depth = 6, train loss: 0.52161, val loss: 0.51567, in 0.016s
1 tree, 8 leaves, max depth = 5, train loss: 0.51327, val loss: 0.50689, in 0.000s
1 tree, 12 leaves, max depth = 6, train loss: 0.50575, val loss: 0.49892, in 0.016s
1 tree, 18 leaves, max depth = 12, train loss: 0.49890, val loss: 0.49167, in 0.000s
1 tree, 28 leaves, max depth = 10, train loss: 0.48918, val loss: 0.48259, in 0.016s
1 tree, 7 leaves, max depth = 4, train loss: 0.48306, val loss: 0.47604, in 0.000s
1 tree, 16 leaves, max depth = 10, train loss: 0.47751, val loss: 0.47022, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.47235, val loss: 0.46476, in 0.000s
1 tree, 11 leaves, max depth = 5, train loss: 0.46752, val loss: 0.45967, in 0.000s
1 tree, 14 leaves, max depth = 7, train loss: 0.46311, val loss: 0.45504, in 0.016s
1 tree, 28 leaves, max depth = 7, train loss: 0.45761, val loss: 0.45007, in 0.000s
1 tree, 20 leaves, max depth = 8, train loss: 0.45397, val loss: 0.44612, in 0.016s
1 tree, 14 leaves, max depth = 7, train loss: 0.45035, val loss: 0.44230, in 0.000s
1 tree, 28 leaves, max depth = 9, train loss: 0.44540, val loss: 0.43770, in 0.016s
1 tree, 13 leaves, max depth = 7, train loss: 0.44231, val loss: 0.43433, in 0.000s
1 tree, 17 leaves, max depth = 5, train loss: 0.43907, val loss: 0.43145, in 0.016s
1 tree, 28 leaves, max depth = 11, train loss: 0.43635, val loss: 0.42854, in 0.000s
1 tree, 28 leaves, max depth = 7, train loss: 0.43207, val loss: 0.42474, in 0.000s
Fit 29 trees in 0.517 s, (390 total leaves)
Time spent computing histograms: 0.123s
Time spent finding best splits:  0.010s
Time spent applying splits:      0.010s
Time spent predicting:           0.000s
Trial 25, Fold 3: Log loss = 0.4254316361822161, Average precision = 0.910916950452471, ROC-AUC = 0.9208799936916721, Elapsed Time = 0.5182334000000992 seconds
Trial 25, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 25, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
Binning 0.040 GB of training data: 0.174 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 6 leaves, max depth = 4, train loss: 0.66940, val loss: 0.66796, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.65694, val loss: 0.65464, in 0.000s
1 tree, 8 leaves, max depth = 4, train loss: 0.63642, val loss: 0.63287, in 0.000s
1 tree, 4 leaves, max depth = 3, train loss: 0.61802, val loss: 0.61333, in 0.016s
1 tree, 9 leaves, max depth = 5, train loss: 0.60130, val loss: 0.59547, in 0.000s
1 tree, 9 leaves, max depth = 5, train loss: 0.58617, val loss: 0.57925, in 0.000s
1 tree, 4 leaves, max depth = 3, train loss: 0.57247, val loss: 0.56456, in 0.000s
1 tree, 9 leaves, max depth = 6, train loss: 0.56011, val loss: 0.55124, in 0.000s
1 tree, 28 leaves, max depth = 7, train loss: 0.55149, val loss: 0.54270, in 0.016s
1 tree, 8 leaves, max depth = 4, train loss: 0.54031, val loss: 0.53052, in 0.000s
1 tree, 8 leaves, max depth = 5, train loss: 0.53042, val loss: 0.51977, in 0.016s
1 tree, 14 leaves, max depth = 9, train loss: 0.52123, val loss: 0.50979, in 0.000s
1 tree, 15 leaves, max depth = 7, train loss: 0.51286, val loss: 0.50062, in 0.000s
1 tree, 12 leaves, max depth = 8, train loss: 0.50534, val loss: 0.49229, in 0.016s
1 tree, 19 leaves, max depth = 9, train loss: 0.49842, val loss: 0.48466, in 0.000s
1 tree, 28 leaves, max depth = 10, train loss: 0.48904, val loss: 0.47543, in 0.016s
1 tree, 12 leaves, max depth = 6, train loss: 0.48289, val loss: 0.46855, in 0.000s
1 tree, 20 leaves, max depth = 9, train loss: 0.47738, val loss: 0.46238, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.47221, val loss: 0.45659, in 0.000s
1 tree, 13 leaves, max depth = 6, train loss: 0.46733, val loss: 0.45104, in 0.016s
1 tree, 14 leaves, max depth = 6, train loss: 0.46287, val loss: 0.44594, in 0.000s
1 tree, 28 leaves, max depth = 7, train loss: 0.45752, val loss: 0.44083, in 0.016s
1 tree, 17 leaves, max depth = 8, train loss: 0.45382, val loss: 0.43656, in 0.000s
1 tree, 14 leaves, max depth = 6, train loss: 0.45017, val loss: 0.43232, in 0.016s
1 tree, 28 leaves, max depth = 7, train loss: 0.44530, val loss: 0.42758, in 0.000s
1 tree, 20 leaves, max depth = 9, train loss: 0.44219, val loss: 0.42396, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.43900, val loss: 0.42105, in 0.000s
1 tree, 22 leaves, max depth = 10, train loss: 0.43628, val loss: 0.41782, in 0.016s
1 tree, 28 leaves, max depth = 7, train loss: 0.43211, val loss: 0.41386, in 0.016s
Fit 29 trees in 0.518 s, (422 total leaves)
Time spent computing histograms: 0.117s
Time spent finding best splits:  0.011s
Time spent applying splits:      0.011s
Time spent predicting:           0.000s
Trial 25, Fold 4: Log loss = 0.4316979696405457, Average precision = 0.9031565302826969, ROC-AUC = 0.9134650749539213, Elapsed Time = 0.5205359000010503 seconds
Trial 25, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 25, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
Binning 0.040 GB of training data: 0.174 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 6 leaves, max depth = 5, train loss: 0.66911, val loss: 0.66737, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.65694, val loss: 0.65387, in 0.000s
1 tree, 8 leaves, max depth = 5, train loss: 0.63619, val loss: 0.63160, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.61751, val loss: 0.61156, in 0.000s
1 tree, 4 leaves, max depth = 3, train loss: 0.60063, val loss: 0.59337, in 0.016s
1 tree, 9 leaves, max depth = 6, train loss: 0.58542, val loss: 0.57687, in 0.000s
1 tree, 4 leaves, max depth = 3, train loss: 0.57154, val loss: 0.56182, in 0.000s
1 tree, 6 leaves, max depth = 4, train loss: 0.55903, val loss: 0.54818, in 0.016s
1 tree, 28 leaves, max depth = 9, train loss: 0.55049, val loss: 0.54039, in 0.000s
1 tree, 9 leaves, max depth = 5, train loss: 0.53917, val loss: 0.52793, in 0.016s
1 tree, 13 leaves, max depth = 7, train loss: 0.52911, val loss: 0.51685, in 0.000s
1 tree, 16 leaves, max depth = 7, train loss: 0.51986, val loss: 0.50663, in 0.016s
1 tree, 11 leaves, max depth = 6, train loss: 0.51136, val loss: 0.49722, in 0.000s
1 tree, 6 leaves, max depth = 4, train loss: 0.50376, val loss: 0.48862, in 0.016s
1 tree, 18 leaves, max depth = 11, train loss: 0.49675, val loss: 0.48080, in 0.000s
1 tree, 28 leaves, max depth = 12, train loss: 0.48716, val loss: 0.47176, in 0.016s
1 tree, 11 leaves, max depth = 5, train loss: 0.48092, val loss: 0.46469, in 0.016s
1 tree, 15 leaves, max depth = 7, train loss: 0.47531, val loss: 0.45830, in 0.000s
1 tree, 4 leaves, max depth = 3, train loss: 0.47010, val loss: 0.45236, in 0.000s
1 tree, 11 leaves, max depth = 5, train loss: 0.46518, val loss: 0.44670, in 0.016s
1 tree, 11 leaves, max depth = 5, train loss: 0.46069, val loss: 0.44149, in 0.000s
1 tree, 28 leaves, max depth = 8, train loss: 0.45537, val loss: 0.43694, in 0.016s
1 tree, 16 leaves, max depth = 6, train loss: 0.45163, val loss: 0.43264, in 0.000s
1 tree, 11 leaves, max depth = 5, train loss: 0.44794, val loss: 0.42831, in 0.016s
1 tree, 28 leaves, max depth = 10, train loss: 0.44315, val loss: 0.42368, in 0.000s
1 tree, 17 leaves, max depth = 8, train loss: 0.43999, val loss: 0.41995, in 0.016s
1 tree, 22 leaves, max depth = 9, train loss: 0.43715, val loss: 0.41660, in 0.000s
1 tree, 28 leaves, max depth = 11, train loss: 0.43454, val loss: 0.41348, in 0.016s
1 tree, 28 leaves, max depth = 8, train loss: 0.43022, val loss: 0.40992, in 0.016s
Fit 29 trees in 0.549 s, (404 total leaves)
Time spent computing histograms: 0.129s
Time spent finding best splits:  0.010s
Time spent applying splits:      0.010s
Time spent predicting:           0.016s
Trial 25, Fold 5: Log loss = 0.4376364533477417, Average precision = 0.9000715888354822, ROC-AUC = 0.9088322092785612, Elapsed Time = 0.5473820000006526 seconds
Optimization Progress:  26%|##6       | 26/100 [05:05<13:24, 10.88s/it]
Trial 26, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 26, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
0.143 s 0.040 GB of training data: 
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 190 leaves, max depth = 15, train loss: 0.68518, val loss: 0.68533, in 0.016s
1 tree, 190 leaves, max depth = 15, train loss: 0.67727, val loss: 0.67748, in 0.031s
1 tree, 190 leaves, max depth = 14, train loss: 0.66946, val loss: 0.66980, in 0.031s
1 tree, 190 leaves, max depth = 14, train loss: 0.66197, val loss: 0.66236, in 0.016s
1 tree, 190 leaves, max depth = 14, train loss: 0.65457, val loss: 0.65492, in 0.031s
1 tree, 190 leaves, max depth = 16, train loss: 0.64780, val loss: 0.64813, in 0.016s
1 tree, 190 leaves, max depth = 14, train loss: 0.64114, val loss: 0.64149, in 0.016s
1 tree, 190 leaves, max depth = 16, train loss: 0.63454, val loss: 0.63490, in 0.038s
1 tree, 190 leaves, max depth = 16, train loss: 0.62810, val loss: 0.62848, in 0.009s
1 tree, 190 leaves, max depth = 14, train loss: 0.62143, val loss: 0.62186, in 0.031s
1 tree, 190 leaves, max depth = 14, train loss: 0.61512, val loss: 0.61559, in 0.031s
1 tree, 190 leaves, max depth = 14, train loss: 0.60919, val loss: 0.60987, in 0.016s
1 tree, 190 leaves, max depth = 14, train loss: 0.60318, val loss: 0.60388, in 0.031s
1 tree, 190 leaves, max depth = 14, train loss: 0.59720, val loss: 0.59803, in 0.016s
1 tree, 190 leaves, max depth = 14, train loss: 0.59128, val loss: 0.59214, in 0.031s
1 tree, 190 leaves, max depth = 15, train loss: 0.58601, val loss: 0.58684, in 0.031s
1 tree, 190 leaves, max depth = 14, train loss: 0.58044, val loss: 0.58138, in 0.016s
1 tree, 190 leaves, max depth = 14, train loss: 0.57510, val loss: 0.57608, in 0.031s
1 tree, 190 leaves, max depth = 14, train loss: 0.57009, val loss: 0.57109, in 0.016s
1 tree, 190 leaves, max depth = 15, train loss: 0.56530, val loss: 0.56626, in 0.031s
Fit 20 trees in 0.783 s, (3800 total leaves)
Time spent computing histograms: 0.153s
Time spent finding best splits:  0.066s
Time spent applying splits:      0.051s
Time spent predicting:           0.016s
Trial 26, Fold 1: Log loss = 0.569108795906292, Average precision = 0.9202807195831728, ROC-AUC = 0.9315492620314211, Elapsed Time = 0.7984632000006968 seconds
Trial 26, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 26, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
Binning 0.040 GB of training data: 0.127 s
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 190 leaves, max depth = 15, train loss: 0.68526, val loss: 0.68526, in 0.016s
1 tree, 190 leaves, max depth = 15, train loss: 0.67718, val loss: 0.67722, in 0.031s
1 tree, 190 leaves, max depth = 18, train loss: 0.66990, val loss: 0.66999, in 0.016s
1 tree, 190 leaves, max depth = 15, train loss: 0.66234, val loss: 0.66253, in 0.031s
1 tree, 190 leaves, max depth = 15, train loss: 0.65538, val loss: 0.65564, in 0.031s
1 tree, 190 leaves, max depth = 17, train loss: 0.64807, val loss: 0.64842, in 0.031s
1 tree, 190 leaves, max depth = 14, train loss: 0.64096, val loss: 0.64137, in 0.016s
1 tree, 190 leaves, max depth = 14, train loss: 0.63448, val loss: 0.63492, in 0.031s
1 tree, 190 leaves, max depth = 15, train loss: 0.62772, val loss: 0.62822, in 0.016s
1 tree, 190 leaves, max depth = 16, train loss: 0.62110, val loss: 0.62174, in 0.031s
1 tree, 190 leaves, max depth = 19, train loss: 0.61505, val loss: 0.61569, in 0.031s
1 tree, 190 leaves, max depth = 15, train loss: 0.60889, val loss: 0.60957, in 0.031s
1 tree, 190 leaves, max depth = 15, train loss: 0.60289, val loss: 0.60361, in 0.016s
1 tree, 190 leaves, max depth = 15, train loss: 0.59698, val loss: 0.59780, in 0.031s
1 tree, 190 leaves, max depth = 16, train loss: 0.59112, val loss: 0.59202, in 0.031s
1 tree, 190 leaves, max depth = 16, train loss: 0.58541, val loss: 0.58637, in 0.031s
1 tree, 190 leaves, max depth = 16, train loss: 0.58010, val loss: 0.58107, in 0.031s
1 tree, 190 leaves, max depth = 16, train loss: 0.57464, val loss: 0.57569, in 0.016s
1 tree, 190 leaves, max depth = 19, train loss: 0.56974, val loss: 0.57081, in 0.031s
1 tree, 190 leaves, max depth = 14, train loss: 0.56483, val loss: 0.56595, in 0.031s
Fit 20 trees in 0.846 s, (3800 total leaves)
Time spent computing histograms: 0.177s
Time spent finding best splits:  0.074s
Time spent applying splits:      0.059s
Time spent predicting:           0.000s
Trial 26, Fold 2: Log loss = 0.5681891539256246, Average precision = 0.9177697630994953, ROC-AUC = 0.9322516700602529, Elapsed Time = 0.8451671000002534 seconds
Trial 26, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 26, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
Binning 0.040 GB of training data: 0.157 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 190 leaves, max depth = 13, train loss: 0.68511, val loss: 0.68519, in 0.031s
1 tree, 190 leaves, max depth = 15, train loss: 0.67719, val loss: 0.67737, in 0.031s
1 tree, 190 leaves, max depth = 15, train loss: 0.66938, val loss: 0.66965, in 0.016s
1 tree, 190 leaves, max depth = 15, train loss: 0.66185, val loss: 0.66224, in 0.031s
1 tree, 190 leaves, max depth = 15, train loss: 0.65452, val loss: 0.65501, in 0.016s
1 tree, 190 leaves, max depth = 15, train loss: 0.64778, val loss: 0.64843, in 0.031s
1 tree, 190 leaves, max depth = 13, train loss: 0.64098, val loss: 0.64179, in 0.031s
1 tree, 190 leaves, max depth = 13, train loss: 0.63436, val loss: 0.63532, in 0.031s
1 tree, 190 leaves, max depth = 15, train loss: 0.62801, val loss: 0.62913, in 0.016s
1 tree, 190 leaves, max depth = 14, train loss: 0.62142, val loss: 0.62264, in 0.031s
1 tree, 190 leaves, max depth = 14, train loss: 0.61500, val loss: 0.61634, in 0.031s
1 tree, 190 leaves, max depth = 14, train loss: 0.60882, val loss: 0.61026, in 0.031s
1 tree, 190 leaves, max depth = 14, train loss: 0.60271, val loss: 0.60427, in 0.031s
1 tree, 190 leaves, max depth = 14, train loss: 0.59673, val loss: 0.59839, in 0.016s
1 tree, 190 leaves, max depth = 15, train loss: 0.59090, val loss: 0.59265, in 0.031s
1 tree, 190 leaves, max depth = 15, train loss: 0.58554, val loss: 0.58744, in 0.031s
1 tree, 190 leaves, max depth = 14, train loss: 0.57998, val loss: 0.58200, in 0.016s
1 tree, 190 leaves, max depth = 15, train loss: 0.57462, val loss: 0.57673, in 0.031s
1 tree, 190 leaves, max depth = 15, train loss: 0.56961, val loss: 0.57186, in 0.031s
1 tree, 190 leaves, max depth = 14, train loss: 0.56472, val loss: 0.56711, in 0.016s
Fit 20 trees in 0.860 s, (3800 total leaves)
Time spent computing histograms: 0.176s
Time spent finding best splits:  0.073s
Time spent applying splits:      0.059s
Time spent predicting:           0.000s
Trial 26, Fold 3: Log loss = 0.5659776112241304, Average precision = 0.921698723740934, ROC-AUC = 0.9365434425894001, Elapsed Time = 0.8646678000004613 seconds
Trial 26, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 26, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
Binning 0.040 GB of training data: 0.142 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 190 leaves, max depth = 14, train loss: 0.68512, val loss: 0.68504, in 0.031s
1 tree, 190 leaves, max depth = 15, train loss: 0.67723, val loss: 0.67691, in 0.031s
1 tree, 190 leaves, max depth = 15, train loss: 0.66944, val loss: 0.66890, in 0.031s
1 tree, 190 leaves, max depth = 15, train loss: 0.66192, val loss: 0.66114, in 0.031s
1 tree, 190 leaves, max depth = 14, train loss: 0.65462, val loss: 0.65365, in 0.016s
1 tree, 190 leaves, max depth = 17, train loss: 0.64785, val loss: 0.64666, in 0.031s
1 tree, 190 leaves, max depth = 16, train loss: 0.64082, val loss: 0.63943, in 0.016s
1 tree, 190 leaves, max depth = 15, train loss: 0.63409, val loss: 0.63252, in 0.031s
1 tree, 190 leaves, max depth = 19, train loss: 0.62780, val loss: 0.62608, in 0.016s
1 tree, 190 leaves, max depth = 14, train loss: 0.62127, val loss: 0.61938, in 0.031s
1 tree, 190 leaves, max depth = 15, train loss: 0.61495, val loss: 0.61284, in 0.031s
1 tree, 190 leaves, max depth = 16, train loss: 0.60908, val loss: 0.60697, in 0.016s
1 tree, 190 leaves, max depth = 15, train loss: 0.60305, val loss: 0.60073, in 0.016s
1 tree, 190 leaves, max depth = 15, train loss: 0.59707, val loss: 0.59462, in 0.031s
1 tree, 190 leaves, max depth = 16, train loss: 0.59164, val loss: 0.58903, in 0.031s
1 tree, 190 leaves, max depth = 15, train loss: 0.58632, val loss: 0.58350, in 0.016s
1 tree, 190 leaves, max depth = 15, train loss: 0.58073, val loss: 0.57779, in 0.031s
1 tree, 190 leaves, max depth = 15, train loss: 0.57537, val loss: 0.57224, in 0.031s
1 tree, 190 leaves, max depth = 15, train loss: 0.57003, val loss: 0.56679, in 0.031s
1 tree, 190 leaves, max depth = 16, train loss: 0.56485, val loss: 0.56147, in 0.031s
Fit 20 trees in 0.861 s, (3800 total leaves)
Time spent computing histograms: 0.170s
Time spent finding best splits:  0.073s
Time spent applying splits:      0.059s
Time spent predicting:           0.000s
Trial 26, Fold 4: Log loss = 0.5667484174551578, Average precision = 0.9241353906034142, ROC-AUC = 0.9350950246905633, Elapsed Time = 0.8688241999989259 seconds
Trial 26, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 26, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
Binning 0.040 GB of training data: 0.142 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 190 leaves, max depth = 14, train loss: 0.68510, val loss: 0.68483, in 0.016s
1 tree, 190 leaves, max depth = 15, train loss: 0.67701, val loss: 0.67665, in 0.031s
1 tree, 190 leaves, max depth = 17, train loss: 0.66914, val loss: 0.66863, in 0.031s
1 tree, 190 leaves, max depth = 16, train loss: 0.66156, val loss: 0.66084, in 0.016s
1 tree, 190 leaves, max depth = 16, train loss: 0.65417, val loss: 0.65324, in 0.031s
1 tree, 190 leaves, max depth = 17, train loss: 0.64732, val loss: 0.64616, in 0.031s
1 tree, 190 leaves, max depth = 16, train loss: 0.64065, val loss: 0.63930, in 0.031s
1 tree, 190 leaves, max depth = 15, train loss: 0.63371, val loss: 0.63230, in 0.016s
1 tree, 190 leaves, max depth = 16, train loss: 0.62702, val loss: 0.62541, in 0.031s
1 tree, 190 leaves, max depth = 17, train loss: 0.62053, val loss: 0.61880, in 0.016s
1 tree, 190 leaves, max depth = 16, train loss: 0.61406, val loss: 0.61214, in 0.031s
1 tree, 190 leaves, max depth = 14, train loss: 0.60809, val loss: 0.60622, in 0.031s
1 tree, 190 leaves, max depth = 15, train loss: 0.60205, val loss: 0.60012, in 0.031s
1 tree, 190 leaves, max depth = 16, train loss: 0.59603, val loss: 0.59393, in 0.016s
1 tree, 190 leaves, max depth = 17, train loss: 0.59056, val loss: 0.58826, in 0.031s
1 tree, 190 leaves, max depth = 14, train loss: 0.58512, val loss: 0.58288, in 0.016s
1 tree, 190 leaves, max depth = 15, train loss: 0.57962, val loss: 0.57732, in 0.016s
1 tree, 190 leaves, max depth = 16, train loss: 0.57422, val loss: 0.57177, in 0.031s
1 tree, 190 leaves, max depth = 17, train loss: 0.56910, val loss: 0.56652, in 0.031s
1 tree, 190 leaves, max depth = 18, train loss: 0.56424, val loss: 0.56147, in 0.016s
Fit 20 trees in 0.845 s, (3800 total leaves)
Time spent computing histograms: 0.172s
Time spent finding best splits:  0.074s
Time spent applying splits:      0.059s
Time spent predicting:           0.000s
Trial 26, Fold 5: Log loss = 0.5685353232718314, Average precision = 0.9194077007754793, ROC-AUC = 0.9328047117403341, Elapsed Time = 0.863473299999896 seconds
Optimization Progress:  27%|##7       | 27/100 [05:16<13:20, 10.96s/it]
Trial 27, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 27, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
0.142 s 0.040 GB of training data: 
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 39 leaves, max depth = 9, train loss: 0.66511, val loss: 0.66498, in 0.016s
1 tree, 52 leaves, max depth = 10, train loss: 0.63941, val loss: 0.63945, in 0.016s
1 tree, 55 leaves, max depth = 9, train loss: 0.61638, val loss: 0.61627, in 0.016s
1 tree, 59 leaves, max depth = 10, train loss: 0.59531, val loss: 0.59502, in 0.016s
1 tree, 51 leaves, max depth = 10, train loss: 0.57582, val loss: 0.57567, in 0.016s
1 tree, 53 leaves, max depth = 10, train loss: 0.55789, val loss: 0.55776, in 0.016s
1 tree, 54 leaves, max depth = 12, train loss: 0.54232, val loss: 0.54228, in 0.016s
1 tree, 56 leaves, max depth = 10, train loss: 0.52719, val loss: 0.52691, in 0.016s
1 tree, 56 leaves, max depth = 10, train loss: 0.51281, val loss: 0.51246, in 0.016s
1 tree, 56 leaves, max depth = 12, train loss: 0.49997, val loss: 0.49944, in 0.016s
1 tree, 57 leaves, max depth = 11, train loss: 0.48764, val loss: 0.48698, in 0.016s
1 tree, 54 leaves, max depth = 11, train loss: 0.47663, val loss: 0.47582, in 0.016s
1 tree, 55 leaves, max depth = 10, train loss: 0.46598, val loss: 0.46512, in 0.016s
1 tree, 56 leaves, max depth = 10, train loss: 0.45610, val loss: 0.45520, in 0.016s
1 tree, 53 leaves, max depth = 10, train loss: 0.44700, val loss: 0.44608, in 0.032s
1 tree, 43 leaves, max depth = 9, train loss: 0.43879, val loss: 0.43762, in 0.016s
1 tree, 56 leaves, max depth = 12, train loss: 0.43107, val loss: 0.42985, in 0.016s
1 tree, 46 leaves, max depth = 10, train loss: 0.41904, val loss: 0.41825, in 0.016s
1 tree, 61 leaves, max depth = 10, train loss: 0.41213, val loss: 0.41119, in 0.016s
1 tree, 60 leaves, max depth = 10, train loss: 0.40570, val loss: 0.40462, in 0.000s
1 tree, 48 leaves, max depth = 10, train loss: 0.39540, val loss: 0.39482, in 0.016s
1 tree, 46 leaves, max depth = 10, train loss: 0.38581, val loss: 0.38569, in 0.016s
1 tree, 54 leaves, max depth = 11, train loss: 0.37722, val loss: 0.37743, in 0.016s
1 tree, 54 leaves, max depth = 10, train loss: 0.37196, val loss: 0.37233, in 0.016s
1 tree, 57 leaves, max depth = 10, train loss: 0.36713, val loss: 0.36730, in 0.016s
1 tree, 23 leaves, max depth = 10, train loss: 0.35995, val loss: 0.36020, in 0.016s
1 tree, 22 leaves, max depth = 9, train loss: 0.35337, val loss: 0.35368, in 0.016s
1 tree, 51 leaves, max depth = 13, train loss: 0.34821, val loss: 0.34851, in 0.016s
1 tree, 38 leaves, max depth = 10, train loss: 0.34237, val loss: 0.34335, in 0.016s
1 tree, 54 leaves, max depth = 10, train loss: 0.33768, val loss: 0.33866, in 0.016s
1 tree, 24 leaves, max depth = 10, train loss: 0.33225, val loss: 0.33329, in 0.000s
1 tree, 55 leaves, max depth = 10, train loss: 0.32873, val loss: 0.33000, in 0.031s
1 tree, 38 leaves, max depth = 10, train loss: 0.32390, val loss: 0.32579, in 0.016s
1 tree, 55 leaves, max depth = 11, train loss: 0.31994, val loss: 0.32182, in 0.000s
1 tree, 23 leaves, max depth = 9, train loss: 0.31547, val loss: 0.31741, in 0.016s
1 tree, 38 leaves, max depth = 10, train loss: 0.31134, val loss: 0.31386, in 0.016s
1 tree, 24 leaves, max depth = 11, train loss: 0.30744, val loss: 0.31001, in 0.016s
1 tree, 38 leaves, max depth = 10, train loss: 0.30380, val loss: 0.30691, in 0.016s
1 tree, 25 leaves, max depth = 9, train loss: 0.30036, val loss: 0.30352, in 0.016s
1 tree, 39 leaves, max depth = 10, train loss: 0.29713, val loss: 0.30080, in 0.016s
1 tree, 60 leaves, max depth = 11, train loss: 0.29484, val loss: 0.29845, in 0.016s
1 tree, 24 leaves, max depth = 9, train loss: 0.29186, val loss: 0.29550, in 0.016s
1 tree, 38 leaves, max depth = 11, train loss: 0.28905, val loss: 0.29316, in 0.016s
1 tree, 23 leaves, max depth = 8, train loss: 0.28645, val loss: 0.29055, in 0.000s
1 tree, 63 leaves, max depth = 10, train loss: 0.28350, val loss: 0.28762, in 0.016s
1 tree, 38 leaves, max depth = 11, train loss: 0.28107, val loss: 0.28564, in 0.016s
1 tree, 23 leaves, max depth = 8, train loss: 0.27881, val loss: 0.28335, in 0.016s
1 tree, 53 leaves, max depth = 12, train loss: 0.27621, val loss: 0.28080, in 0.016s
Fit 48 trees in 1.018 s, (2200 total leaves)
Time spent computing histograms: 0.277s
Time spent finding best splits:  0.044s
Time spent applying splits:      0.041s
Time spent predicting:           0.016s
Trial 27, Fold 1: Log loss = 0.2806973281608503, Average precision = 0.9612056199159554, ROC-AUC = 0.9552867932779856, Elapsed Time = 1.0278072000000975 seconds
Trial 27, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 27, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
Binning 0.040 GB of training data: 0.143 s
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 39 leaves, max depth = 10, train loss: 0.66495, val loss: 0.66419, in 0.016s
1 tree, 50 leaves, max depth = 10, train loss: 0.63895, val loss: 0.63762, in 0.016s
1 tree, 50 leaves, max depth = 10, train loss: 0.61502, val loss: 0.61328, in 0.016s
1 tree, 53 leaves, max depth = 10, train loss: 0.59406, val loss: 0.59219, in 0.016s
1 tree, 60 leaves, max depth = 12, train loss: 0.57545, val loss: 0.57337, in 0.016s
1 tree, 53 leaves, max depth = 11, train loss: 0.55696, val loss: 0.55455, in 0.016s
1 tree, 53 leaves, max depth = 11, train loss: 0.54118, val loss: 0.53836, in 0.016s
1 tree, 60 leaves, max depth = 10, train loss: 0.52571, val loss: 0.52277, in 0.016s
1 tree, 54 leaves, max depth = 11, train loss: 0.51120, val loss: 0.50800, in 0.016s
1 tree, 59 leaves, max depth = 12, train loss: 0.49886, val loss: 0.49557, in 0.031s
1 tree, 53 leaves, max depth = 11, train loss: 0.48638, val loss: 0.48286, in 0.016s
1 tree, 55 leaves, max depth = 12, train loss: 0.47557, val loss: 0.47196, in 0.016s
1 tree, 53 leaves, max depth = 11, train loss: 0.46478, val loss: 0.46098, in 0.016s
1 tree, 53 leaves, max depth = 11, train loss: 0.45477, val loss: 0.45074, in 0.016s
1 tree, 59 leaves, max depth = 11, train loss: 0.44568, val loss: 0.44156, in 0.016s
1 tree, 42 leaves, max depth = 11, train loss: 0.43731, val loss: 0.43298, in 0.016s
1 tree, 55 leaves, max depth = 14, train loss: 0.42975, val loss: 0.42539, in 0.016s
1 tree, 47 leaves, max depth = 11, train loss: 0.41788, val loss: 0.41377, in 0.016s
1 tree, 58 leaves, max depth = 11, train loss: 0.41092, val loss: 0.40674, in 0.016s
1 tree, 59 leaves, max depth = 11, train loss: 0.40447, val loss: 0.40022, in 0.016s
1 tree, 47 leaves, max depth = 11, train loss: 0.39428, val loss: 0.39026, in 0.016s
1 tree, 46 leaves, max depth = 11, train loss: 0.38482, val loss: 0.38102, in 0.016s
1 tree, 57 leaves, max depth = 11, train loss: 0.37934, val loss: 0.37558, in 0.031s
1 tree, 47 leaves, max depth = 12, train loss: 0.37097, val loss: 0.36741, in 0.016s
1 tree, 42 leaves, max depth = 11, train loss: 0.36624, val loss: 0.36260, in 0.016s
1 tree, 23 leaves, max depth = 11, train loss: 0.35916, val loss: 0.35565, in 0.016s
1 tree, 51 leaves, max depth = 10, train loss: 0.35384, val loss: 0.35042, in 0.016s
1 tree, 39 leaves, max depth = 12, train loss: 0.34750, val loss: 0.34458, in 0.016s
1 tree, 51 leaves, max depth = 11, train loss: 0.34263, val loss: 0.33979, in 0.016s
1 tree, 23 leaves, max depth = 9, train loss: 0.33685, val loss: 0.33417, in 0.000s
1 tree, 40 leaves, max depth = 12, train loss: 0.33148, val loss: 0.32923, in 0.016s
1 tree, 23 leaves, max depth = 11, train loss: 0.32643, val loss: 0.32427, in 0.016s
1 tree, 40 leaves, max depth = 12, train loss: 0.32173, val loss: 0.31996, in 0.016s
1 tree, 59 leaves, max depth = 11, train loss: 0.31753, val loss: 0.31593, in 0.016s
1 tree, 23 leaves, max depth = 10, train loss: 0.31325, val loss: 0.31169, in 0.016s
1 tree, 52 leaves, max depth = 11, train loss: 0.30952, val loss: 0.30805, in 0.016s
1 tree, 23 leaves, max depth = 12, train loss: 0.30569, val loss: 0.30431, in 0.016s
1 tree, 59 leaves, max depth = 12, train loss: 0.30215, val loss: 0.30095, in 0.016s
1 tree, 52 leaves, max depth = 13, train loss: 0.29900, val loss: 0.29791, in 0.016s
1 tree, 60 leaves, max depth = 11, train loss: 0.29591, val loss: 0.29498, in 0.016s
1 tree, 41 leaves, max depth = 12, train loss: 0.29248, val loss: 0.29189, in 0.016s
1 tree, 23 leaves, max depth = 12, train loss: 0.28934, val loss: 0.28885, in 0.016s
1 tree, 51 leaves, max depth = 12, train loss: 0.28667, val loss: 0.28631, in 0.016s
1 tree, 59 leaves, max depth = 12, train loss: 0.28403, val loss: 0.28385, in 0.016s
1 tree, 23 leaves, max depth = 12, train loss: 0.28125, val loss: 0.28118, in 0.016s
1 tree, 40 leaves, max depth = 12, train loss: 0.27851, val loss: 0.27870, in 0.016s
1 tree, 42 leaves, max depth = 12, train loss: 0.27597, val loss: 0.27644, in 0.016s
1 tree, 23 leaves, max depth = 12, train loss: 0.27364, val loss: 0.27416, in 0.016s
Fit 48 trees in 1.096 s, (2224 total leaves)
Time spent computing histograms: 0.310s
Time spent finding best splits:  0.046s
Time spent applying splits:      0.044s
Time spent predicting:           0.000s
Trial 27, Fold 2: Log loss = 0.2768179417669449, Average precision = 0.9623801790052878, ROC-AUC = 0.9598748505663682, Elapsed Time = 1.1042353000011644 seconds
Trial 27, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 27, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
Binning 0.040 GB of training data: 0.158 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 40 leaves, max depth = 10, train loss: 0.66514, val loss: 0.66509, in 0.016s
1 tree, 50 leaves, max depth = 10, train loss: 0.63945, val loss: 0.63947, in 0.016s
1 tree, 50 leaves, max depth = 11, train loss: 0.61602, val loss: 0.61600, in 0.016s
1 tree, 52 leaves, max depth = 10, train loss: 0.59528, val loss: 0.59509, in 0.016s
1 tree, 60 leaves, max depth = 10, train loss: 0.57657, val loss: 0.57649, in 0.016s
1 tree, 51 leaves, max depth = 11, train loss: 0.55824, val loss: 0.55830, in 0.016s
1 tree, 54 leaves, max depth = 11, train loss: 0.54254, val loss: 0.54254, in 0.016s
1 tree, 60 leaves, max depth = 10, train loss: 0.52716, val loss: 0.52721, in 0.016s
1 tree, 61 leaves, max depth = 12, train loss: 0.51374, val loss: 0.51390, in 0.016s
1 tree, 55 leaves, max depth = 11, train loss: 0.50110, val loss: 0.50136, in 0.016s
1 tree, 53 leaves, max depth = 11, train loss: 0.48866, val loss: 0.48897, in 0.016s
1 tree, 51 leaves, max depth = 11, train loss: 0.47715, val loss: 0.47752, in 0.031s
1 tree, 57 leaves, max depth = 11, train loss: 0.46669, val loss: 0.46713, in 0.016s
1 tree, 59 leaves, max depth = 11, train loss: 0.45699, val loss: 0.45748, in 0.016s
1 tree, 59 leaves, max depth = 11, train loss: 0.44802, val loss: 0.44859, in 0.016s
1 tree, 42 leaves, max depth = 12, train loss: 0.43970, val loss: 0.44031, in 0.016s
1 tree, 52 leaves, max depth = 11, train loss: 0.43207, val loss: 0.43275, in 0.016s
1 tree, 44 leaves, max depth = 10, train loss: 0.42001, val loss: 0.42170, in 0.031s
1 tree, 62 leaves, max depth = 12, train loss: 0.41307, val loss: 0.41475, in 0.016s
1 tree, 64 leaves, max depth = 12, train loss: 0.40660, val loss: 0.40830, in 0.016s
1 tree, 48 leaves, max depth = 10, train loss: 0.39627, val loss: 0.39888, in 0.016s
1 tree, 47 leaves, max depth = 11, train loss: 0.38672, val loss: 0.39025, in 0.016s
1 tree, 34 leaves, max depth = 10, train loss: 0.37824, val loss: 0.38257, in 0.016s
1 tree, 50 leaves, max depth = 12, train loss: 0.37214, val loss: 0.37624, in 0.016s
1 tree, 51 leaves, max depth = 12, train loss: 0.36655, val loss: 0.37041, in 0.016s
1 tree, 24 leaves, max depth = 9, train loss: 0.35934, val loss: 0.36380, in 0.016s
1 tree, 22 leaves, max depth = 9, train loss: 0.35271, val loss: 0.35778, in 0.016s
1 tree, 50 leaves, max depth = 12, train loss: 0.34769, val loss: 0.35258, in 0.016s
1 tree, 41 leaves, max depth = 12, train loss: 0.34171, val loss: 0.34754, in 0.016s
1 tree, 52 leaves, max depth = 10, train loss: 0.33727, val loss: 0.34298, in 0.016s
1 tree, 41 leaves, max depth = 12, train loss: 0.33182, val loss: 0.33846, in 0.016s
1 tree, 52 leaves, max depth = 12, train loss: 0.32768, val loss: 0.33416, in 0.016s
1 tree, 50 leaves, max depth = 12, train loss: 0.32383, val loss: 0.33018, in 0.016s
1 tree, 23 leaves, max depth = 9, train loss: 0.31898, val loss: 0.32580, in 0.016s
1 tree, 41 leaves, max depth = 12, train loss: 0.31447, val loss: 0.32207, in 0.016s
1 tree, 23 leaves, max depth = 9, train loss: 0.31016, val loss: 0.31819, in 0.016s
1 tree, 51 leaves, max depth = 12, train loss: 0.30682, val loss: 0.31469, in 0.016s
1 tree, 41 leaves, max depth = 11, train loss: 0.30298, val loss: 0.31151, in 0.016s
1 tree, 41 leaves, max depth = 10, train loss: 0.30046, val loss: 0.30906, in 0.016s
1 tree, 44 leaves, max depth = 11, train loss: 0.29644, val loss: 0.30545, in 0.016s
1 tree, 59 leaves, max depth = 13, train loss: 0.29354, val loss: 0.30230, in 0.016s
1 tree, 46 leaves, max depth = 11, train loss: 0.28993, val loss: 0.29909, in 0.016s
1 tree, 44 leaves, max depth = 10, train loss: 0.28801, val loss: 0.29723, in 0.016s
1 tree, 41 leaves, max depth = 11, train loss: 0.28497, val loss: 0.29486, in 0.016s
1 tree, 24 leaves, max depth = 10, train loss: 0.28217, val loss: 0.29239, in 0.000s
1 tree, 62 leaves, max depth = 12, train loss: 0.27970, val loss: 0.28969, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.27701, val loss: 0.28729, in 0.031s
1 tree, 23 leaves, max depth = 9, train loss: 0.27468, val loss: 0.28520, in 0.016s
Fit 48 trees in 1.142 s, (2236 total leaves)
Time spent computing histograms: 0.324s
Time spent finding best splits:  0.047s
Time spent applying splits:      0.045s
Time spent predicting:           0.000s
Trial 27, Fold 3: Log loss = 0.27394828088236733, Average precision = 0.9627584780797169, ROC-AUC = 0.9596843368069268, Elapsed Time = 1.136641499999314 seconds
Trial 27, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 27, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
Binning 0.040 GB of training data: 0.174 s
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 41 leaves, max depth = 9, train loss: 0.66513, val loss: 0.66404, in 0.016s
1 tree, 54 leaves, max depth = 10, train loss: 0.63931, val loss: 0.63718, in 0.016s
1 tree, 57 leaves, max depth = 9, train loss: 0.61626, val loss: 0.61303, in 0.016s
1 tree, 52 leaves, max depth = 10, train loss: 0.59551, val loss: 0.59139, in 0.016s
1 tree, 61 leaves, max depth = 12, train loss: 0.57706, val loss: 0.57190, in 0.016s
1 tree, 55 leaves, max depth = 10, train loss: 0.55892, val loss: 0.55295, in 0.031s
1 tree, 54 leaves, max depth = 11, train loss: 0.54313, val loss: 0.53670, in 0.016s
1 tree, 61 leaves, max depth = 10, train loss: 0.52778, val loss: 0.52044, in 0.016s
1 tree, 54 leaves, max depth = 12, train loss: 0.51330, val loss: 0.50525, in 0.016s
1 tree, 62 leaves, max depth = 12, train loss: 0.50104, val loss: 0.49215, in 0.016s
1 tree, 54 leaves, max depth = 12, train loss: 0.48854, val loss: 0.47900, in 0.016s
1 tree, 54 leaves, max depth = 12, train loss: 0.47777, val loss: 0.46763, in 0.016s
1 tree, 55 leaves, max depth = 12, train loss: 0.46696, val loss: 0.45624, in 0.016s
1 tree, 55 leaves, max depth = 12, train loss: 0.45693, val loss: 0.44565, in 0.031s
1 tree, 57 leaves, max depth = 10, train loss: 0.44765, val loss: 0.43582, in 0.016s
1 tree, 42 leaves, max depth = 10, train loss: 0.43938, val loss: 0.42702, in 0.016s
1 tree, 55 leaves, max depth = 12, train loss: 0.43185, val loss: 0.41905, in 0.016s
1 tree, 44 leaves, max depth = 11, train loss: 0.42000, val loss: 0.40704, in 0.016s
1 tree, 60 leaves, max depth = 10, train loss: 0.41309, val loss: 0.39959, in 0.016s
1 tree, 62 leaves, max depth = 10, train loss: 0.40662, val loss: 0.39261, in 0.016s
1 tree, 45 leaves, max depth = 10, train loss: 0.39644, val loss: 0.38234, in 0.016s
1 tree, 49 leaves, max depth = 11, train loss: 0.38709, val loss: 0.37294, in 0.016s
1 tree, 62 leaves, max depth = 10, train loss: 0.38164, val loss: 0.36704, in 0.016s
1 tree, 43 leaves, max depth = 11, train loss: 0.37324, val loss: 0.35855, in 0.016s
1 tree, 53 leaves, max depth = 12, train loss: 0.36749, val loss: 0.35263, in 0.016s
1 tree, 20 leaves, max depth = 9, train loss: 0.36039, val loss: 0.34528, in 0.016s
1 tree, 42 leaves, max depth = 11, train loss: 0.35376, val loss: 0.33901, in 0.016s
1 tree, 54 leaves, max depth = 11, train loss: 0.34866, val loss: 0.33389, in 0.016s
1 tree, 21 leaves, max depth = 10, train loss: 0.34269, val loss: 0.32769, in 0.016s
1 tree, 51 leaves, max depth = 11, train loss: 0.33802, val loss: 0.32293, in 0.016s
1 tree, 42 leaves, max depth = 11, train loss: 0.33254, val loss: 0.31768, in 0.016s
1 tree, 20 leaves, max depth = 9, train loss: 0.32748, val loss: 0.31242, in 0.016s
1 tree, 61 leaves, max depth = 11, train loss: 0.32327, val loss: 0.30810, in 0.016s
1 tree, 22 leaves, max depth = 11, train loss: 0.31875, val loss: 0.30341, in 0.016s
1 tree, 21 leaves, max depth = 10, train loss: 0.31457, val loss: 0.29905, in 0.000s
1 tree, 54 leaves, max depth = 12, train loss: 0.31091, val loss: 0.29533, in 0.016s
1 tree, 42 leaves, max depth = 11, train loss: 0.30683, val loss: 0.29150, in 0.031s
1 tree, 42 leaves, max depth = 11, train loss: 0.30299, val loss: 0.28789, in 0.000s
1 tree, 42 leaves, max depth = 11, train loss: 0.29944, val loss: 0.28461, in 0.016s
1 tree, 20 leaves, max depth = 9, train loss: 0.29619, val loss: 0.28120, in 0.016s
1 tree, 60 leaves, max depth = 11, train loss: 0.29300, val loss: 0.27791, in 0.016s
1 tree, 54 leaves, max depth = 12, train loss: 0.29010, val loss: 0.27507, in 0.016s
1 tree, 53 leaves, max depth = 11, train loss: 0.28737, val loss: 0.27237, in 0.016s
1 tree, 42 leaves, max depth = 11, train loss: 0.28451, val loss: 0.26970, in 0.016s
1 tree, 22 leaves, max depth = 10, train loss: 0.28185, val loss: 0.26686, in 0.016s
1 tree, 21 leaves, max depth = 9, train loss: 0.27940, val loss: 0.26422, in 0.016s
1 tree, 54 leaves, max depth = 10, train loss: 0.27746, val loss: 0.26223, in 0.016s
1 tree, 23 leaves, max depth = 10, train loss: 0.27527, val loss: 0.25988, in 0.000s
Fit 48 trees in 1.143 s, (2224 total leaves)
Time spent computing histograms: 0.302s
Time spent finding best splits:  0.046s
Time spent applying splits:      0.044s
Time spent predicting:           0.000s
Trial 27, Fold 4: Log loss = 0.274923146793898, Average precision = 0.9643716512099001, ROC-AUC = 0.9600594224721084, Elapsed Time = 1.1375859999989189 seconds
Trial 27, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 27, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
Binning 0.040 GB of training data: 0.157 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 38 leaves, max depth = 10, train loss: 0.66504, val loss: 0.66373, in 0.016s
1 tree, 48 leaves, max depth = 10, train loss: 0.63879, val loss: 0.63634, in 0.031s
1 tree, 50 leaves, max depth = 11, train loss: 0.61501, val loss: 0.61152, in 0.016s
1 tree, 39 leaves, max depth = 10, train loss: 0.59374, val loss: 0.58917, in 0.016s
1 tree, 51 leaves, max depth = 12, train loss: 0.57486, val loss: 0.56936, in 0.016s
1 tree, 51 leaves, max depth = 10, train loss: 0.55654, val loss: 0.55020, in 0.016s
1 tree, 61 leaves, max depth = 10, train loss: 0.54081, val loss: 0.53366, in 0.016s
1 tree, 51 leaves, max depth = 12, train loss: 0.52516, val loss: 0.51733, in 0.016s
1 tree, 61 leaves, max depth = 11, train loss: 0.51192, val loss: 0.50337, in 0.016s
1 tree, 52 leaves, max depth = 12, train loss: 0.49945, val loss: 0.49027, in 0.016s
1 tree, 53 leaves, max depth = 10, train loss: 0.48694, val loss: 0.47720, in 0.016s
1 tree, 52 leaves, max depth = 12, train loss: 0.47628, val loss: 0.46601, in 0.016s
1 tree, 60 leaves, max depth = 10, train loss: 0.46569, val loss: 0.45490, in 0.016s
1 tree, 61 leaves, max depth = 11, train loss: 0.45599, val loss: 0.44476, in 0.016s
1 tree, 61 leaves, max depth = 10, train loss: 0.44689, val loss: 0.43521, in 0.016s
1 tree, 44 leaves, max depth = 9, train loss: 0.43855, val loss: 0.42632, in 0.016s
1 tree, 52 leaves, max depth = 11, train loss: 0.43105, val loss: 0.41849, in 0.016s
1 tree, 44 leaves, max depth = 10, train loss: 0.41910, val loss: 0.40661, in 0.016s
1 tree, 60 leaves, max depth = 10, train loss: 0.41211, val loss: 0.39926, in 0.016s
1 tree, 61 leaves, max depth = 11, train loss: 0.40570, val loss: 0.39254, in 0.016s
1 tree, 46 leaves, max depth = 12, train loss: 0.39540, val loss: 0.38234, in 0.031s
1 tree, 45 leaves, max depth = 11, train loss: 0.38592, val loss: 0.37299, in 0.016s
1 tree, 59 leaves, max depth = 9, train loss: 0.38045, val loss: 0.36724, in 0.016s
1 tree, 56 leaves, max depth = 12, train loss: 0.37506, val loss: 0.36171, in 0.016s
1 tree, 52 leaves, max depth = 10, train loss: 0.36999, val loss: 0.35647, in 0.016s
1 tree, 24 leaves, max depth = 12, train loss: 0.36247, val loss: 0.34881, in 0.016s
1 tree, 44 leaves, max depth = 12, train loss: 0.35543, val loss: 0.34243, in 0.016s
1 tree, 22 leaves, max depth = 9, train loss: 0.34889, val loss: 0.33576, in 0.016s
1 tree, 52 leaves, max depth = 12, train loss: 0.34388, val loss: 0.33075, in 0.016s
1 tree, 52 leaves, max depth = 12, train loss: 0.33918, val loss: 0.32606, in 0.016s
1 tree, 44 leaves, max depth = 13, train loss: 0.33317, val loss: 0.32061, in 0.016s
1 tree, 53 leaves, max depth = 15, train loss: 0.32956, val loss: 0.31710, in 0.016s
1 tree, 51 leaves, max depth = 12, train loss: 0.32551, val loss: 0.31304, in 0.016s
1 tree, 54 leaves, max depth = 13, train loss: 0.32003, val loss: 0.30792, in 0.031s
1 tree, 21 leaves, max depth = 8, train loss: 0.31529, val loss: 0.30300, in 0.016s
1 tree, 46 leaves, max depth = 11, train loss: 0.31051, val loss: 0.29842, in 0.016s
1 tree, 63 leaves, max depth = 12, train loss: 0.30690, val loss: 0.29472, in 0.016s
1 tree, 21 leaves, max depth = 8, train loss: 0.30294, val loss: 0.29072, in 0.000s
1 tree, 41 leaves, max depth = 13, train loss: 0.29901, val loss: 0.28733, in 0.016s
1 tree, 56 leaves, max depth = 10, train loss: 0.29648, val loss: 0.28483, in 0.016s
1 tree, 63 leaves, max depth = 12, train loss: 0.29339, val loss: 0.28166, in 0.016s
1 tree, 21 leaves, max depth = 9, train loss: 0.29009, val loss: 0.27823, in 0.016s
1 tree, 54 leaves, max depth = 12, train loss: 0.28729, val loss: 0.27552, in 0.016s
1 tree, 42 leaves, max depth = 12, train loss: 0.28406, val loss: 0.27276, in 0.016s
1 tree, 42 leaves, max depth = 12, train loss: 0.28108, val loss: 0.27013, in 0.000s
1 tree, 22 leaves, max depth = 10, train loss: 0.27839, val loss: 0.26740, in 0.016s
1 tree, 21 leaves, max depth = 9, train loss: 0.27588, val loss: 0.26477, in 0.016s
1 tree, 41 leaves, max depth = 13, train loss: 0.27335, val loss: 0.26259, in 0.016s
Fit 48 trees in 1.110 s, (2258 total leaves)
Time spent computing histograms: 0.307s
Time spent finding best splits:  0.046s
Time spent applying splits:      0.043s
Time spent predicting:           0.000s
Trial 27, Fold 5: Log loss = 0.28149502590144165, Average precision = 0.9592432659407226, ROC-AUC = 0.9550485108596697, Elapsed Time = 1.121284700000615 seconds
Optimization Progress:  28%|##8       | 28/100 [05:28<13:31, 11.27s/it]
Trial 28, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 28, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
0.142 s 0.040 GB of training data: 
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 35 leaves, max depth = 8, train loss: 0.67338, val loss: 0.67336, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.65449, val loss: 0.65448, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.63710, val loss: 0.63698, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.62067, val loss: 0.62051, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.60563, val loss: 0.60531, in 0.016s
1 tree, 35 leaves, max depth = 8, train loss: 0.59171, val loss: 0.59136, in 0.016s
1 tree, 35 leaves, max depth = 8, train loss: 0.57880, val loss: 0.57822, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.56571, val loss: 0.56515, in 0.016s
1 tree, 35 leaves, max depth = 8, train loss: 0.55338, val loss: 0.55287, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.54237, val loss: 0.54183, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.53133, val loss: 0.53078, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.52150, val loss: 0.52093, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.51158, val loss: 0.51102, in 0.016s
1 tree, 35 leaves, max depth = 7, train loss: 0.50201, val loss: 0.50144, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.49341, val loss: 0.49268, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.48497, val loss: 0.48428, in 0.016s
1 tree, 35 leaves, max depth = 7, train loss: 0.47679, val loss: 0.47611, in 0.016s
1 tree, 35 leaves, max depth = 7, train loss: 0.46903, val loss: 0.46837, in 0.016s
1 tree, 35 leaves, max depth = 7, train loss: 0.46179, val loss: 0.46101, in 0.016s
1 tree, 35 leaves, max depth = 8, train loss: 0.45478, val loss: 0.45405, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.44543, val loss: 0.44496, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.43901, val loss: 0.43859, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.43291, val loss: 0.43252, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.42443, val loss: 0.42442, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.41881, val loss: 0.41886, in 0.016s
1 tree, 35 leaves, max depth = 8, train loss: 0.41360, val loss: 0.41356, in 0.016s
1 tree, 35 leaves, max depth = 8, train loss: 0.40851, val loss: 0.40845, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.40112, val loss: 0.40146, in 0.016s
1 tree, 35 leaves, max depth = 7, train loss: 0.39644, val loss: 0.39682, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.38976, val loss: 0.39040, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.38552, val loss: 0.38629, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.37917, val loss: 0.38033, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.37516, val loss: 0.37642, in 0.016s
1 tree, 35 leaves, max depth = 8, train loss: 0.37159, val loss: 0.37289, in 0.016s
1 tree, 35 leaves, max depth = 8, train loss: 0.36586, val loss: 0.36754, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.36239, val loss: 0.36422, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.35736, val loss: 0.35930, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.35262, val loss: 0.35466, in 0.000s
1 tree, 35 leaves, max depth = 10, train loss: 0.34816, val loss: 0.35029, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.34451, val loss: 0.34675, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.34039, val loss: 0.34272, in 0.016s
1 tree, 35 leaves, max depth = 8, train loss: 0.33698, val loss: 0.33941, in 0.016s
1 tree, 35 leaves, max depth = 12, train loss: 0.33276, val loss: 0.33552, in 0.016s
1 tree, 35 leaves, max depth = 12, train loss: 0.32913, val loss: 0.33198, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.32600, val loss: 0.32898, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.32230, val loss: 0.32568, in 0.000s
1 tree, 35 leaves, max depth = 11, train loss: 0.31880, val loss: 0.32257, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.31548, val loss: 0.31964, in 0.016s
1 tree, 35 leaves, max depth = 12, train loss: 0.31234, val loss: 0.31688, in 0.016s
1 tree, 35 leaves, max depth = 14, train loss: 0.30943, val loss: 0.31405, in 0.016s
1 tree, 35 leaves, max depth = 14, train loss: 0.30667, val loss: 0.31138, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.30385, val loss: 0.30888, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.30118, val loss: 0.30652, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.29895, val loss: 0.30443, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.29691, val loss: 0.30242, in 0.016s
1 tree, 35 leaves, max depth = 14, train loss: 0.29457, val loss: 0.30015, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.29205, val loss: 0.29757, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.28975, val loss: 0.29537, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.28747, val loss: 0.29340, in 0.016s
1 tree, 35 leaves, max depth = 12, train loss: 0.28530, val loss: 0.29149, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.28306, val loss: 0.28918, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.28110, val loss: 0.28727, in 0.016s
1 tree, 35 leaves, max depth = 15, train loss: 0.27912, val loss: 0.28553, in 0.000s
1 tree, 35 leaves, max depth = 11, train loss: 0.27732, val loss: 0.28381, in 0.000s
Fit 64 trees in 1.283 s, (2240 total leaves)
Time spent computing histograms: 0.388s
Time spent finding best splits:  0.073s
Time spent applying splits:      0.044s
Time spent predicting:           0.000s
Trial 28, Fold 1: Log loss = 0.28464131570100243, Average precision = 0.9620374566234541, ROC-AUC = 0.9565089491834071, Elapsed Time = 1.2902052999997977 seconds
Trial 28, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 28, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
Binning 0.040 GB of training data: 0.143 s
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 35 leaves, max depth = 9, train loss: 0.67318, val loss: 0.67290, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.65421, val loss: 0.65369, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.63651, val loss: 0.63571, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.62079, val loss: 0.61980, in 0.016s
1 tree, 35 leaves, max depth = 8, train loss: 0.60512, val loss: 0.60407, in 0.016s
1 tree, 35 leaves, max depth = 8, train loss: 0.59126, val loss: 0.59005, in 0.016s
1 tree, 35 leaves, max depth = 8, train loss: 0.57844, val loss: 0.57718, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.56506, val loss: 0.56373, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.55252, val loss: 0.55104, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.54155, val loss: 0.53993, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.53026, val loss: 0.52859, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.52048, val loss: 0.51868, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.51040, val loss: 0.50853, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.50079, val loss: 0.49885, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.49169, val loss: 0.48968, in 0.016s
1 tree, 35 leaves, max depth = 8, train loss: 0.48383, val loss: 0.48172, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.47560, val loss: 0.47341, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.46781, val loss: 0.46563, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.46039, val loss: 0.45815, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.45335, val loss: 0.45106, in 0.016s
1 tree, 35 leaves, max depth = 8, train loss: 0.44401, val loss: 0.44186, in 0.031s
1 tree, 35 leaves, max depth = 9, train loss: 0.43757, val loss: 0.43541, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.43146, val loss: 0.42930, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.42575, val loss: 0.42364, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.42032, val loss: 0.41827, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.41237, val loss: 0.41050, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.40727, val loss: 0.40542, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.39996, val loss: 0.39828, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.39527, val loss: 0.39362, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.39080, val loss: 0.38920, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.38418, val loss: 0.38280, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.37805, val loss: 0.37692, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.37439, val loss: 0.37323, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.37067, val loss: 0.36954, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.36700, val loss: 0.36584, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.36146, val loss: 0.36046, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.35623, val loss: 0.35538, in 0.016s
1 tree, 35 leaves, max depth = 13, train loss: 0.35154, val loss: 0.35082, in 0.031s
1 tree, 35 leaves, max depth = 14, train loss: 0.34711, val loss: 0.34654, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.34347, val loss: 0.34304, in 0.016s
1 tree, 35 leaves, max depth = 13, train loss: 0.33939, val loss: 0.33910, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.33589, val loss: 0.33578, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.33188, val loss: 0.33200, in 0.016s
1 tree, 35 leaves, max depth = 8, train loss: 0.32869, val loss: 0.32893, in 0.016s
1 tree, 35 leaves, max depth = 13, train loss: 0.32482, val loss: 0.32525, in 0.016s
1 tree, 35 leaves, max depth = 13, train loss: 0.32126, val loss: 0.32193, in 0.016s
1 tree, 35 leaves, max depth = 13, train loss: 0.31788, val loss: 0.31879, in 0.016s
1 tree, 35 leaves, max depth = 13, train loss: 0.31473, val loss: 0.31573, in 0.016s
1 tree, 35 leaves, max depth = 13, train loss: 0.31163, val loss: 0.31286, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.30876, val loss: 0.31008, in 0.016s
1 tree, 35 leaves, max depth = 13, train loss: 0.30591, val loss: 0.30742, in 0.000s
1 tree, 35 leaves, max depth = 10, train loss: 0.30328, val loss: 0.30489, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.30056, val loss: 0.30236, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.29848, val loss: 0.30039, in 0.016s
1 tree, 35 leaves, max depth = 13, train loss: 0.29593, val loss: 0.29804, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.29344, val loss: 0.29574, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.29118, val loss: 0.29358, in 0.016s
1 tree, 35 leaves, max depth = 13, train loss: 0.28888, val loss: 0.29147, in 0.016s
1 tree, 35 leaves, max depth = 12, train loss: 0.28680, val loss: 0.28948, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.28452, val loss: 0.28740, in 0.016s
1 tree, 35 leaves, max depth = 13, train loss: 0.28245, val loss: 0.28552, in 0.016s
1 tree, 35 leaves, max depth = 12, train loss: 0.28048, val loss: 0.28369, in 0.016s
1 tree, 35 leaves, max depth = 13, train loss: 0.27865, val loss: 0.28196, in 0.000s
1 tree, 35 leaves, max depth = 12, train loss: 0.27683, val loss: 0.28030, in 0.016s
Fit 64 trees in 1.346 s, (2240 total leaves)
Time spent computing histograms: 0.406s
Time spent finding best splits:  0.076s
Time spent applying splits:      0.047s
Time spent predicting:           0.000s
Trial 28, Fold 2: Log loss = 0.2808506028525122, Average precision = 0.9628806869606636, ROC-AUC = 0.959936611915054, Elapsed Time = 1.3568864999997459 seconds
Trial 28, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 28, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
Binning 0.040 GB of training data: 0.173 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 35 leaves, max depth = 9, train loss: 0.67335, val loss: 0.67322, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.65453, val loss: 0.65434, in 0.031s
1 tree, 35 leaves, max depth = 9, train loss: 0.63715, val loss: 0.63693, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.62067, val loss: 0.62039, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.60562, val loss: 0.60540, in 0.016s
1 tree, 35 leaves, max depth = 8, train loss: 0.59174, val loss: 0.59162, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.57887, val loss: 0.57886, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.56579, val loss: 0.56578, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.55348, val loss: 0.55346, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.54228, val loss: 0.54237, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.53115, val loss: 0.53122, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.52114, val loss: 0.52132, in 0.016s
1 tree, 35 leaves, max depth = 8, train loss: 0.51106, val loss: 0.51132, in 0.031s
1 tree, 35 leaves, max depth = 8, train loss: 0.50152, val loss: 0.50178, in 0.016s
1 tree, 35 leaves, max depth = 8, train loss: 0.49273, val loss: 0.49296, in 0.016s
1 tree, 35 leaves, max depth = 8, train loss: 0.48417, val loss: 0.48443, in 0.016s
1 tree, 35 leaves, max depth = 8, train loss: 0.47606, val loss: 0.47637, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.46836, val loss: 0.46871, in 0.016s
1 tree, 35 leaves, max depth = 8, train loss: 0.46114, val loss: 0.46155, in 0.016s
1 tree, 35 leaves, max depth = 8, train loss: 0.45418, val loss: 0.45461, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.44479, val loss: 0.44593, in 0.016s
1 tree, 35 leaves, max depth = 8, train loss: 0.43843, val loss: 0.43961, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.43237, val loss: 0.43361, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.42387, val loss: 0.42581, in 0.016s
1 tree, 35 leaves, max depth = 8, train loss: 0.41833, val loss: 0.42038, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.41314, val loss: 0.41534, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.40556, val loss: 0.40838, in 0.016s
1 tree, 35 leaves, max depth = 8, train loss: 0.40102, val loss: 0.40397, in 0.016s
1 tree, 35 leaves, max depth = 12, train loss: 0.39398, val loss: 0.39757, in 0.031s
1 tree, 35 leaves, max depth = 8, train loss: 0.38959, val loss: 0.39324, in 0.000s
1 tree, 35 leaves, max depth = 12, train loss: 0.38312, val loss: 0.38740, in 0.031s
1 tree, 35 leaves, max depth = 8, train loss: 0.37916, val loss: 0.38347, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.37314, val loss: 0.37806, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.36928, val loss: 0.37430, in 0.016s
1 tree, 35 leaves, max depth = 8, train loss: 0.36558, val loss: 0.37072, in 0.016s
1 tree, 35 leaves, max depth = 14, train loss: 0.36040, val loss: 0.36602, in 0.016s
1 tree, 35 leaves, max depth = 14, train loss: 0.35554, val loss: 0.36158, in 0.016s
1 tree, 35 leaves, max depth = 14, train loss: 0.35094, val loss: 0.35742, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.34637, val loss: 0.35360, in 0.016s
1 tree, 35 leaves, max depth = 8, train loss: 0.34272, val loss: 0.34984, in 0.016s
1 tree, 35 leaves, max depth = 14, train loss: 0.33865, val loss: 0.34613, in 0.016s
1 tree, 35 leaves, max depth = 7, train loss: 0.33525, val loss: 0.34265, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.33123, val loss: 0.33931, in 0.016s
1 tree, 35 leaves, max depth = 8, train loss: 0.32805, val loss: 0.33601, in 0.016s
1 tree, 35 leaves, max depth = 8, train loss: 0.32502, val loss: 0.33286, in 0.000s
1 tree, 35 leaves, max depth = 8, train loss: 0.32212, val loss: 0.32984, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.31851, val loss: 0.32691, in 0.016s
1 tree, 35 leaves, max depth = 14, train loss: 0.31520, val loss: 0.32396, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.31189, val loss: 0.32129, in 0.016s
1 tree, 35 leaves, max depth = 13, train loss: 0.30888, val loss: 0.31861, in 0.031s
1 tree, 35 leaves, max depth = 10, train loss: 0.30586, val loss: 0.31613, in 0.016s
1 tree, 35 leaves, max depth = 13, train loss: 0.30311, val loss: 0.31366, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.30030, val loss: 0.31132, in 0.016s
1 tree, 35 leaves, max depth = 18, train loss: 0.29782, val loss: 0.30907, in 0.016s
1 tree, 35 leaves, max depth = 14, train loss: 0.29523, val loss: 0.30696, in 0.016s
1 tree, 35 leaves, max depth = 13, train loss: 0.29294, val loss: 0.30489, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.29063, val loss: 0.30252, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.28842, val loss: 0.30019, in 0.016s
1 tree, 35 leaves, max depth = 14, train loss: 0.28612, val loss: 0.29834, in 0.016s
1 tree, 35 leaves, max depth = 14, train loss: 0.28394, val loss: 0.29656, in 0.016s
1 tree, 35 leaves, max depth = 15, train loss: 0.28198, val loss: 0.29480, in 0.016s
1 tree, 35 leaves, max depth = 14, train loss: 0.27996, val loss: 0.29315, in 0.016s
1 tree, 35 leaves, max depth = 15, train loss: 0.27816, val loss: 0.29153, in 0.016s
1 tree, 35 leaves, max depth = 8, train loss: 0.27609, val loss: 0.28921, in 0.016s
Fit 64 trees in 1.408 s, (2240 total leaves)
Time spent computing histograms: 0.420s
Time spent finding best splits:  0.080s
Time spent applying splits:      0.046s
Time spent predicting:           0.000s
Trial 28, Fold 3: Log loss = 0.2772330542065684, Average precision = 0.9641706060782602, ROC-AUC = 0.96079649562751, Elapsed Time = 1.4219327000009798 seconds
Trial 28, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 28, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
Binning 0.040 GB of training data: 0.174 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 35 leaves, max depth = 9, train loss: 0.67344, val loss: 0.67260, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.65470, val loss: 0.65308, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.63721, val loss: 0.63482, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.62083, val loss: 0.61772, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.60547, val loss: 0.60166, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.59105, val loss: 0.58656, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.57827, val loss: 0.57307, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.56518, val loss: 0.55936, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.55289, val loss: 0.54650, in 0.016s
1 tree, 35 leaves, max depth = 8, train loss: 0.54198, val loss: 0.53512, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.53099, val loss: 0.52358, in 0.016s
1 tree, 35 leaves, max depth = 8, train loss: 0.52104, val loss: 0.51329, in 0.016s
1 tree, 35 leaves, max depth = 8, train loss: 0.51112, val loss: 0.50285, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.50178, val loss: 0.49304, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.49299, val loss: 0.48372, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.48455, val loss: 0.47481, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.47658, val loss: 0.46648, in 0.016s
1 tree, 35 leaves, max depth = 8, train loss: 0.46896, val loss: 0.45847, in 0.016s
1 tree, 35 leaves, max depth = 8, train loss: 0.46185, val loss: 0.45095, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.45498, val loss: 0.44370, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.44575, val loss: 0.43430, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.43946, val loss: 0.42765, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.43348, val loss: 0.42132, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.42512, val loss: 0.41287, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.41965, val loss: 0.40707, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.41457, val loss: 0.40162, in 0.016s
1 tree, 35 leaves, max depth = 8, train loss: 0.40962, val loss: 0.39639, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.40227, val loss: 0.38904, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.39770, val loss: 0.38417, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.39105, val loss: 0.37742, in 0.016s
1 tree, 35 leaves, max depth = 12, train loss: 0.38459, val loss: 0.37089, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.38056, val loss: 0.36662, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.37459, val loss: 0.36057, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.37104, val loss: 0.35676, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.36743, val loss: 0.35294, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.36205, val loss: 0.34758, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.35725, val loss: 0.34257, in 0.016s
1 tree, 35 leaves, max depth = 12, train loss: 0.35270, val loss: 0.33784, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.34900, val loss: 0.33410, in 0.016s
1 tree, 35 leaves, max depth = 12, train loss: 0.34479, val loss: 0.32973, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.34133, val loss: 0.32624, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.33731, val loss: 0.32252, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.33357, val loss: 0.31862, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.33040, val loss: 0.31543, in 0.016s
1 tree, 35 leaves, max depth = 8, train loss: 0.32737, val loss: 0.31237, in 0.016s
1 tree, 35 leaves, max depth = 8, train loss: 0.32447, val loss: 0.30944, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.32101, val loss: 0.30625, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.31779, val loss: 0.30292, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.31463, val loss: 0.30003, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.31170, val loss: 0.29695, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.30878, val loss: 0.29425, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.30610, val loss: 0.29146, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.30342, val loss: 0.28900, in 0.016s
1 tree, 35 leaves, max depth = 13, train loss: 0.30096, val loss: 0.28642, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.29844, val loss: 0.28373, in 0.016s
1 tree, 35 leaves, max depth = 16, train loss: 0.29603, val loss: 0.28147, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.29368, val loss: 0.27894, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.29143, val loss: 0.27652, in 0.016s
1 tree, 35 leaves, max depth = 12, train loss: 0.28923, val loss: 0.27448, in 0.016s
1 tree, 35 leaves, max depth = 8, train loss: 0.28718, val loss: 0.27241, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.28516, val loss: 0.27032, in 0.016s
1 tree, 35 leaves, max depth = 12, train loss: 0.28317, val loss: 0.26852, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.28131, val loss: 0.26657, in 0.016s
1 tree, 35 leaves, max depth = 12, train loss: 0.27948, val loss: 0.26491, in 0.016s
Fit 64 trees in 1.361 s, (2240 total leaves)
Time spent computing histograms: 0.400s
Time spent finding best splits:  0.075s
Time spent applying splits:      0.045s
Time spent predicting:           0.000s
Trial 28, Fold 4: Log loss = 0.28037042522834454, Average precision = 0.9633028006312216, ROC-AUC = 0.959027792680339, Elapsed Time = 1.3770490999995673 seconds
Trial 28, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 28, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
Binning 0.040 GB of training data: 0.174 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 35 leaves, max depth = 9, train loss: 0.67321, val loss: 0.67246, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.65418, val loss: 0.65279, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.63649, val loss: 0.63452, in 0.031s
1 tree, 35 leaves, max depth = 9, train loss: 0.61979, val loss: 0.61728, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.60436, val loss: 0.60127, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.59016, val loss: 0.58647, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.57704, val loss: 0.57269, in 0.016s
1 tree, 35 leaves, max depth = 8, train loss: 0.56379, val loss: 0.55892, in 0.016s
1 tree, 35 leaves, max depth = 8, train loss: 0.55130, val loss: 0.54598, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.54029, val loss: 0.53455, in 0.016s
1 tree, 35 leaves, max depth = 8, train loss: 0.52912, val loss: 0.52297, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.51929, val loss: 0.51277, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.50935, val loss: 0.50248, in 0.016s
1 tree, 35 leaves, max depth = 8, train loss: 0.49985, val loss: 0.49262, in 0.031s
1 tree, 35 leaves, max depth = 8, train loss: 0.49115, val loss: 0.48341, in 0.000s
1 tree, 35 leaves, max depth = 9, train loss: 0.48261, val loss: 0.47454, in 0.016s
1 tree, 35 leaves, max depth = 8, train loss: 0.47451, val loss: 0.46617, in 0.016s
1 tree, 35 leaves, max depth = 8, train loss: 0.46682, val loss: 0.45823, in 0.016s
1 tree, 35 leaves, max depth = 8, train loss: 0.45966, val loss: 0.45079, in 0.031s
1 tree, 35 leaves, max depth = 8, train loss: 0.45272, val loss: 0.44362, in 0.000s
1 tree, 35 leaves, max depth = 8, train loss: 0.44343, val loss: 0.43433, in 0.016s
1 tree, 35 leaves, max depth = 8, train loss: 0.43708, val loss: 0.42779, in 0.016s
1 tree, 35 leaves, max depth = 8, train loss: 0.43104, val loss: 0.42157, in 0.031s
1 tree, 35 leaves, max depth = 11, train loss: 0.42260, val loss: 0.41323, in 0.016s
1 tree, 35 leaves, max depth = 8, train loss: 0.41706, val loss: 0.40753, in 0.016s
1 tree, 35 leaves, max depth = 8, train loss: 0.41194, val loss: 0.40224, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.40694, val loss: 0.39717, in 0.016s
1 tree, 35 leaves, max depth = 14, train loss: 0.39956, val loss: 0.38992, in 0.016s
1 tree, 35 leaves, max depth = 8, train loss: 0.39492, val loss: 0.38517, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.38826, val loss: 0.37860, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.38404, val loss: 0.37429, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.37795, val loss: 0.36815, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.37403, val loss: 0.36422, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.36833, val loss: 0.35861, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.36466, val loss: 0.35495, in 0.016s
1 tree, 35 leaves, max depth = 13, train loss: 0.35925, val loss: 0.34973, in 0.016s
1 tree, 35 leaves, max depth = 12, train loss: 0.35406, val loss: 0.34471, in 0.031s
1 tree, 35 leaves, max depth = 11, train loss: 0.34920, val loss: 0.33996, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.34601, val loss: 0.33673, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.34178, val loss: 0.33236, in 0.016s
1 tree, 35 leaves, max depth = 8, train loss: 0.33825, val loss: 0.32891, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.33415, val loss: 0.32520, in 0.016s
1 tree, 35 leaves, max depth = 13, train loss: 0.33034, val loss: 0.32137, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.32711, val loss: 0.31823, in 0.016s
1 tree, 35 leaves, max depth = 8, train loss: 0.32398, val loss: 0.31516, in 0.016s
1 tree, 35 leaves, max depth = 12, train loss: 0.32027, val loss: 0.31158, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.31679, val loss: 0.30846, in 0.016s
1 tree, 35 leaves, max depth = 13, train loss: 0.31363, val loss: 0.30530, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.31045, val loss: 0.30245, in 0.016s
1 tree, 35 leaves, max depth = 13, train loss: 0.30756, val loss: 0.29956, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.30477, val loss: 0.29668, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.30190, val loss: 0.29414, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.29920, val loss: 0.29170, in 0.016s
1 tree, 35 leaves, max depth = 16, train loss: 0.29671, val loss: 0.28919, in 0.016s
1 tree, 35 leaves, max depth = 8, train loss: 0.29422, val loss: 0.28685, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.29176, val loss: 0.28466, in 0.000s
1 tree, 35 leaves, max depth = 11, train loss: 0.28952, val loss: 0.28240, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.28716, val loss: 0.27998, in 0.016s
1 tree, 35 leaves, max depth = 8, train loss: 0.28520, val loss: 0.27822, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.28301, val loss: 0.27628, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.28104, val loss: 0.27423, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.27902, val loss: 0.27249, in 0.016s
1 tree, 35 leaves, max depth = 8, train loss: 0.27697, val loss: 0.27057, in 0.016s
1 tree, 35 leaves, max depth = 12, train loss: 0.27509, val loss: 0.26888, in 0.016s
Fit 64 trees in 1.424 s, (2240 total leaves)
Time spent computing histograms: 0.441s
Time spent finding best splits:  0.080s
Time spent applying splits:      0.047s
Time spent predicting:           0.000s
Trial 28, Fold 5: Log loss = 0.2856709964198882, Average precision = 0.9600743059726313, ROC-AUC = 0.9556647529866413, Elapsed Time = 1.4378190000006725 seconds
Optimization Progress:  29%|##9       | 29/100 [05:42<14:05, 11.91s/it]
Trial 29, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 29, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
0.158 s 0.040 GB of training data: 
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 13 leaves, max depth = 5, train loss: 0.68812, val loss: 0.68797, in 0.015s
1 tree, 13 leaves, max depth = 5, train loss: 0.68339, val loss: 0.68309, in 0.000s
1 tree, 13 leaves, max depth = 5, train loss: 0.67875, val loss: 0.67831, in 0.000s
1 tree, 16 leaves, max depth = 9, train loss: 0.67425, val loss: 0.67371, in 0.016s
1 tree, 13 leaves, max depth = 5, train loss: 0.66979, val loss: 0.66911, in 0.000s
1 tree, 13 leaves, max depth = 5, train loss: 0.66543, val loss: 0.66461, in 0.016s
1 tree, 14 leaves, max depth = 5, train loss: 0.66114, val loss: 0.66019, in 0.000s
1 tree, 14 leaves, max depth = 5, train loss: 0.65695, val loss: 0.65586, in 0.016s
1 tree, 14 leaves, max depth = 5, train loss: 0.65283, val loss: 0.65162, in 0.000s
1 tree, 14 leaves, max depth = 5, train loss: 0.64880, val loss: 0.64746, in 0.016s
1 tree, 14 leaves, max depth = 5, train loss: 0.64484, val loss: 0.64337, in 0.000s
1 tree, 14 leaves, max depth = 5, train loss: 0.64096, val loss: 0.63937, in 0.000s
1 tree, 15 leaves, max depth = 9, train loss: 0.63721, val loss: 0.63550, in 0.016s
1 tree, 14 leaves, max depth = 5, train loss: 0.63348, val loss: 0.63164, in 0.000s
1 tree, 14 leaves, max depth = 5, train loss: 0.62982, val loss: 0.62786, in 0.016s
1 tree, 19 leaves, max depth = 7, train loss: 0.62627, val loss: 0.62421, in 0.000s
1 tree, 14 leaves, max depth = 5, train loss: 0.62274, val loss: 0.62056, in 0.000s
1 tree, 14 leaves, max depth = 5, train loss: 0.61929, val loss: 0.61699, in 0.016s
1 tree, 20 leaves, max depth = 10, train loss: 0.61596, val loss: 0.61353, in 0.000s
1 tree, 14 leaves, max depth = 5, train loss: 0.61263, val loss: 0.61008, in 0.000s
1 tree, 14 leaves, max depth = 5, train loss: 0.60936, val loss: 0.60670, in 0.016s
1 tree, 14 leaves, max depth = 5, train loss: 0.60616, val loss: 0.60338, in 0.000s
1 tree, 14 leaves, max depth = 5, train loss: 0.60301, val loss: 0.60012, in 0.016s
1 tree, 15 leaves, max depth = 5, train loss: 0.59992, val loss: 0.59691, in 0.000s
1 tree, 17 leaves, max depth = 9, train loss: 0.59692, val loss: 0.59383, in 0.000s
1 tree, 16 leaves, max depth = 6, train loss: 0.59395, val loss: 0.59074, in 0.016s
1 tree, 16 leaves, max depth = 9, train loss: 0.59106, val loss: 0.58777, in 0.000s
1 tree, 23 leaves, max depth = 10, train loss: 0.58825, val loss: 0.58484, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.58543, val loss: 0.58192, in 0.000s
1 tree, 17 leaves, max depth = 6, train loss: 0.58266, val loss: 0.57904, in 0.000s
1 tree, 17 leaves, max depth = 6, train loss: 0.57994, val loss: 0.57622, in 0.016s
1 tree, 15 leaves, max depth = 5, train loss: 0.57728, val loss: 0.57345, in 0.000s
1 tree, 17 leaves, max depth = 6, train loss: 0.57465, val loss: 0.57073, in 0.016s
1 tree, 24 leaves, max depth = 10, train loss: 0.57214, val loss: 0.56809, in 0.000s
1 tree, 18 leaves, max depth = 7, train loss: 0.56961, val loss: 0.56546, in 0.016s
1 tree, 15 leaves, max depth = 5, train loss: 0.56713, val loss: 0.56288, in 0.000s
1 tree, 20 leaves, max depth = 8, train loss: 0.56469, val loss: 0.56034, in 0.016s
1 tree, 20 leaves, max depth = 8, train loss: 0.56229, val loss: 0.55785, in 0.000s
1 tree, 22 leaves, max depth = 10, train loss: 0.55997, val loss: 0.55545, in 0.016s
1 tree, 22 leaves, max depth = 10, train loss: 0.55769, val loss: 0.55310, in 0.000s
Fit 40 trees in 0.517 s, (642 total leaves)
Time spent computing histograms: 0.152s
Time spent finding best splits:  0.016s
Time spent applying splits:      0.012s
Time spent predicting:           0.000s
Trial 29, Fold 1: Log loss = 0.5583189356982935, Average precision = 0.8176059446619469, ROC-AUC = 0.8618765922006664, Elapsed Time = 0.5281728000009025 seconds
Trial 29, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 29, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
Binning 0.040 GB of training data: 0.142 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 16 leaves, max depth = 8, train loss: 0.68832, val loss: 0.68809, in 0.016s
1 tree, 16 leaves, max depth = 8, train loss: 0.68365, val loss: 0.68321, in 0.000s
1 tree, 17 leaves, max depth = 8, train loss: 0.67907, val loss: 0.67842, in 0.000s
1 tree, 17 leaves, max depth = 8, train loss: 0.67459, val loss: 0.67373, in 0.016s
1 tree, 21 leaves, max depth = 12, train loss: 0.67021, val loss: 0.66913, in 0.000s
1 tree, 21 leaves, max depth = 12, train loss: 0.66592, val loss: 0.66463, in 0.000s
1 tree, 18 leaves, max depth = 8, train loss: 0.66169, val loss: 0.66021, in 0.016s
1 tree, 21 leaves, max depth = 12, train loss: 0.65757, val loss: 0.65587, in 0.000s
1 tree, 20 leaves, max depth = 8, train loss: 0.65351, val loss: 0.65162, in 0.016s
1 tree, 21 leaves, max depth = 12, train loss: 0.64954, val loss: 0.64745, in 0.000s
1 tree, 20 leaves, max depth = 8, train loss: 0.64564, val loss: 0.64336, in 0.000s
1 tree, 21 leaves, max depth = 12, train loss: 0.64182, val loss: 0.63935, in 0.016s
1 tree, 21 leaves, max depth = 12, train loss: 0.63808, val loss: 0.63541, in 0.000s
1 tree, 20 leaves, max depth = 8, train loss: 0.63439, val loss: 0.63155, in 0.016s
1 tree, 13 leaves, max depth = 7, train loss: 0.63075, val loss: 0.62769, in 0.000s
1 tree, 20 leaves, max depth = 8, train loss: 0.62721, val loss: 0.62397, in 0.016s
1 tree, 21 leaves, max depth = 12, train loss: 0.62374, val loss: 0.62032, in 0.000s
1 tree, 21 leaves, max depth = 12, train loss: 0.62034, val loss: 0.61674, in 0.016s
1 tree, 21 leaves, max depth = 12, train loss: 0.61700, val loss: 0.61322, in 0.000s
1 tree, 21 leaves, max depth = 12, train loss: 0.61372, val loss: 0.60977, in 0.000s
1 tree, 21 leaves, max depth = 8, train loss: 0.61050, val loss: 0.60638, in 0.016s
1 tree, 21 leaves, max depth = 8, train loss: 0.60733, val loss: 0.60305, in 0.000s
1 tree, 21 leaves, max depth = 8, train loss: 0.60423, val loss: 0.59978, in 0.016s
1 tree, 14 leaves, max depth = 7, train loss: 0.60115, val loss: 0.59651, in 0.000s
1 tree, 21 leaves, max depth = 12, train loss: 0.59816, val loss: 0.59336, in 0.016s
1 tree, 21 leaves, max depth = 12, train loss: 0.59524, val loss: 0.59027, in 0.000s
1 tree, 21 leaves, max depth = 8, train loss: 0.59235, val loss: 0.58723, in 0.016s
1 tree, 21 leaves, max depth = 12, train loss: 0.58953, val loss: 0.58425, in 0.000s
1 tree, 14 leaves, max depth = 7, train loss: 0.58671, val loss: 0.58126, in 0.000s
1 tree, 15 leaves, max depth = 7, train loss: 0.58394, val loss: 0.57832, in 0.000s
1 tree, 15 leaves, max depth = 7, train loss: 0.58122, val loss: 0.57544, in 0.000s
1 tree, 27 leaves, max depth = 12, train loss: 0.57859, val loss: 0.57266, in 0.016s
1 tree, 15 leaves, max depth = 7, train loss: 0.57597, val loss: 0.56988, in 0.000s
1 tree, 27 leaves, max depth = 12, train loss: 0.57344, val loss: 0.56720, in 0.016s
1 tree, 16 leaves, max depth = 7, train loss: 0.57091, val loss: 0.56452, in 0.016s
1 tree, 16 leaves, max depth = 7, train loss: 0.56843, val loss: 0.56188, in 0.000s
1 tree, 16 leaves, max depth = 7, train loss: 0.56599, val loss: 0.55928, in 0.000s
1 tree, 17 leaves, max depth = 7, train loss: 0.56359, val loss: 0.55673, in 0.016s
1 tree, 27 leaves, max depth = 12, train loss: 0.56128, val loss: 0.55428, in 0.000s
1 tree, 49 leaves, max depth = 10, train loss: 0.55885, val loss: 0.55194, in 0.016s
Fit 40 trees in 0.548 s, (802 total leaves)
Time spent computing histograms: 0.157s
Time spent finding best splits:  0.020s
Time spent applying splits:      0.015s
Time spent predicting:           0.000s
Trial 29, Fold 2: Log loss = 0.558383755264408, Average precision = 0.8717239998038205, ROC-AUC = 0.8998013166244859, Elapsed Time = 0.5486139999993611 seconds
Trial 29, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 29, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
Binning 0.040 GB of training data: 0.174 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 18 leaves, max depth = 8, train loss: 0.68828, val loss: 0.68813, in 0.016s
1 tree, 18 leaves, max depth = 8, train loss: 0.68366, val loss: 0.68335, in 0.000s
1 tree, 18 leaves, max depth = 8, train loss: 0.67913, val loss: 0.67867, in 0.016s
1 tree, 18 leaves, max depth = 8, train loss: 0.67469, val loss: 0.67409, in 0.000s
1 tree, 14 leaves, max depth = 6, train loss: 0.67027, val loss: 0.66953, in 0.016s
1 tree, 16 leaves, max depth = 7, train loss: 0.66595, val loss: 0.66506, in 0.000s
1 tree, 16 leaves, max depth = 7, train loss: 0.66170, val loss: 0.66068, in 0.000s
1 tree, 16 leaves, max depth = 7, train loss: 0.65754, val loss: 0.65638, in 0.016s
1 tree, 16 leaves, max depth = 7, train loss: 0.65347, val loss: 0.65217, in 0.000s
1 tree, 23 leaves, max depth = 11, train loss: 0.64953, val loss: 0.64809, in 0.016s
1 tree, 16 leaves, max depth = 7, train loss: 0.64561, val loss: 0.64404, in 0.000s
1 tree, 15 leaves, max depth = 7, train loss: 0.64176, val loss: 0.64006, in 0.016s
1 tree, 23 leaves, max depth = 11, train loss: 0.63804, val loss: 0.63621, in 0.000s
1 tree, 15 leaves, max depth = 7, train loss: 0.63434, val loss: 0.63239, in 0.016s
1 tree, 16 leaves, max depth = 7, train loss: 0.63071, val loss: 0.62863, in 0.000s
1 tree, 28 leaves, max depth = 10, train loss: 0.62721, val loss: 0.62501, in 0.016s
1 tree, 14 leaves, max depth = 7, train loss: 0.62372, val loss: 0.62139, in 0.000s
1 tree, 16 leaves, max depth = 7, train loss: 0.62029, val loss: 0.61784, in 0.000s
1 tree, 28 leaves, max depth = 12, train loss: 0.61698, val loss: 0.61441, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.61368, val loss: 0.61099, in 0.000s
1 tree, 16 leaves, max depth = 7, train loss: 0.61044, val loss: 0.60763, in 0.016s
1 tree, 16 leaves, max depth = 7, train loss: 0.60726, val loss: 0.60434, in 0.000s
1 tree, 17 leaves, max depth = 7, train loss: 0.60414, val loss: 0.60110, in 0.016s
1 tree, 16 leaves, max depth = 7, train loss: 0.60107, val loss: 0.59793, in 0.000s
1 tree, 28 leaves, max depth = 12, train loss: 0.59811, val loss: 0.59485, in 0.016s
1 tree, 16 leaves, max depth = 7, train loss: 0.59516, val loss: 0.59179, in 0.016s
1 tree, 28 leaves, max depth = 12, train loss: 0.59231, val loss: 0.58883, in 0.000s
1 tree, 28 leaves, max depth = 12, train loss: 0.58951, val loss: 0.58592, in 0.016s
1 tree, 18 leaves, max depth = 7, train loss: 0.58671, val loss: 0.58302, in 0.000s
1 tree, 16 leaves, max depth = 7, train loss: 0.58397, val loss: 0.58017, in 0.016s
1 tree, 16 leaves, max depth = 7, train loss: 0.58128, val loss: 0.57737, in 0.000s
1 tree, 18 leaves, max depth = 7, train loss: 0.57863, val loss: 0.57463, in 0.016s
1 tree, 16 leaves, max depth = 7, train loss: 0.57603, val loss: 0.57193, in 0.000s
1 tree, 28 leaves, max depth = 13, train loss: 0.57352, val loss: 0.56931, in 0.016s
1 tree, 16 leaves, max depth = 7, train loss: 0.57102, val loss: 0.56671, in 0.000s
1 tree, 18 leaves, max depth = 7, train loss: 0.56856, val loss: 0.56415, in 0.016s
1 tree, 16 leaves, max depth = 7, train loss: 0.56614, val loss: 0.56164, in 0.000s
1 tree, 16 leaves, max depth = 7, train loss: 0.56377, val loss: 0.55917, in 0.016s
1 tree, 28 leaves, max depth = 13, train loss: 0.56147, val loss: 0.55678, in 0.000s
1 tree, 52 leaves, max depth = 12, train loss: 0.55905, val loss: 0.55452, in 0.016s
Fit 40 trees in 0.612 s, (784 total leaves)
Time spent computing histograms: 0.175s
Time spent finding best splits:  0.022s
Time spent applying splits:      0.017s
Time spent predicting:           0.000s
Trial 29, Fold 3: Log loss = 0.5556490839538677, Average precision = 0.8730575981840494, ROC-AUC = 0.9010974714921364, Elapsed Time = 0.614298399999825 seconds
Trial 29, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 29, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
Binning 0.040 GB of training data: 0.142 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 10 leaves, max depth = 5, train loss: 0.68826, val loss: 0.68798, in 0.000s
1 tree, 10 leaves, max depth = 5, train loss: 0.68357, val loss: 0.68301, in 0.016s
1 tree, 10 leaves, max depth = 5, train loss: 0.67898, val loss: 0.67814, in 0.000s
1 tree, 17 leaves, max depth = 8, train loss: 0.67451, val loss: 0.67343, in 0.016s
1 tree, 10 leaves, max depth = 5, train loss: 0.67010, val loss: 0.66875, in 0.000s
1 tree, 10 leaves, max depth = 5, train loss: 0.66577, val loss: 0.66416, in 0.000s
1 tree, 10 leaves, max depth = 5, train loss: 0.66154, val loss: 0.65965, in 0.016s
1 tree, 10 leaves, max depth = 5, train loss: 0.65738, val loss: 0.65524, in 0.000s
1 tree, 10 leaves, max depth = 5, train loss: 0.65331, val loss: 0.65091, in 0.016s
1 tree, 14 leaves, max depth = 7, train loss: 0.64932, val loss: 0.64666, in 0.000s
1 tree, 14 leaves, max depth = 7, train loss: 0.64540, val loss: 0.64249, in 0.000s
1 tree, 14 leaves, max depth = 7, train loss: 0.64157, val loss: 0.63840, in 0.000s
1 tree, 25 leaves, max depth = 14, train loss: 0.63785, val loss: 0.63444, in 0.000s
1 tree, 14 leaves, max depth = 6, train loss: 0.63415, val loss: 0.63050, in 0.016s
1 tree, 14 leaves, max depth = 7, train loss: 0.63053, val loss: 0.62663, in 0.000s
1 tree, 17 leaves, max depth = 9, train loss: 0.62700, val loss: 0.62288, in 0.016s
1 tree, 13 leaves, max depth = 6, train loss: 0.62351, val loss: 0.61915, in 0.000s
1 tree, 16 leaves, max depth = 8, train loss: 0.62009, val loss: 0.61550, in 0.016s
1 tree, 25 leaves, max depth = 14, train loss: 0.61677, val loss: 0.61195, in 0.000s
1 tree, 13 leaves, max depth = 6, train loss: 0.61348, val loss: 0.60842, in 0.016s
1 tree, 13 leaves, max depth = 6, train loss: 0.61025, val loss: 0.60496, in 0.000s
1 tree, 13 leaves, max depth = 6, train loss: 0.60707, val loss: 0.60155, in 0.016s
1 tree, 16 leaves, max depth = 8, train loss: 0.60396, val loss: 0.59821, in 0.000s
1 tree, 13 leaves, max depth = 6, train loss: 0.60090, val loss: 0.59493, in 0.016s
1 tree, 19 leaves, max depth = 10, train loss: 0.59792, val loss: 0.59175, in 0.000s
1 tree, 14 leaves, max depth = 6, train loss: 0.59498, val loss: 0.58858, in 0.016s
1 tree, 19 leaves, max depth = 10, train loss: 0.59211, val loss: 0.58551, in 0.000s
1 tree, 25 leaves, max depth = 14, train loss: 0.58930, val loss: 0.58249, in 0.016s
1 tree, 14 leaves, max depth = 6, train loss: 0.58651, val loss: 0.57949, in 0.000s
1 tree, 17 leaves, max depth = 8, train loss: 0.58378, val loss: 0.57654, in 0.016s
1 tree, 14 leaves, max depth = 6, train loss: 0.58109, val loss: 0.57364, in 0.000s
1 tree, 14 leaves, max depth = 6, train loss: 0.57845, val loss: 0.57078, in 0.000s
1 tree, 20 leaves, max depth = 8, train loss: 0.57586, val loss: 0.56798, in 0.016s
1 tree, 27 leaves, max depth = 15, train loss: 0.57335, val loss: 0.56527, in 0.000s
1 tree, 14 leaves, max depth = 6, train loss: 0.57085, val loss: 0.56256, in 0.016s
1 tree, 14 leaves, max depth = 6, train loss: 0.56840, val loss: 0.55990, in 0.000s
1 tree, 14 leaves, max depth = 6, train loss: 0.56599, val loss: 0.55729, in 0.016s
1 tree, 14 leaves, max depth = 6, train loss: 0.56362, val loss: 0.55472, in 0.000s
1 tree, 19 leaves, max depth = 10, train loss: 0.56131, val loss: 0.55223, in 0.016s
1 tree, 19 leaves, max depth = 10, train loss: 0.55904, val loss: 0.54978, in 0.000s
Fit 40 trees in 0.579 s, (608 total leaves)
Time spent computing histograms: 0.166s
Time spent finding best splits:  0.019s
Time spent applying splits:      0.014s
Time spent predicting:           0.000s
Trial 29, Fold 4: Log loss = 0.557560007433454, Average precision = 0.8233037899458493, ROC-AUC = 0.8688659568278767, Elapsed Time = 0.5799826999991637 seconds
Trial 29, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 29, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
Binning 0.040 GB of training data: 0.143 s
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 14 leaves, max depth = 6, train loss: 0.68817, val loss: 0.68786, in 0.000s
1 tree, 14 leaves, max depth = 6, train loss: 0.68343, val loss: 0.68281, in 0.016s
1 tree, 14 leaves, max depth = 6, train loss: 0.67879, val loss: 0.67787, in 0.000s
1 tree, 17 leaves, max depth = 8, train loss: 0.67429, val loss: 0.67308, in 0.000s
1 tree, 15 leaves, max depth = 7, train loss: 0.66983, val loss: 0.66832, in 0.016s
1 tree, 15 leaves, max depth = 7, train loss: 0.66546, val loss: 0.66366, in 0.000s
1 tree, 15 leaves, max depth = 7, train loss: 0.66118, val loss: 0.65909, in 0.016s
1 tree, 15 leaves, max depth = 7, train loss: 0.65698, val loss: 0.65460, in 0.000s
1 tree, 15 leaves, max depth = 7, train loss: 0.65287, val loss: 0.65020, in 0.000s
1 tree, 15 leaves, max depth = 7, train loss: 0.64883, val loss: 0.64589, in 0.016s
1 tree, 15 leaves, max depth = 7, train loss: 0.64487, val loss: 0.64165, in 0.000s
1 tree, 15 leaves, max depth = 7, train loss: 0.64099, val loss: 0.63749, in 0.016s
1 tree, 18 leaves, max depth = 9, train loss: 0.63723, val loss: 0.63347, in 0.000s
1 tree, 15 leaves, max depth = 7, train loss: 0.63349, val loss: 0.62947, in 0.016s
1 tree, 15 leaves, max depth = 7, train loss: 0.62983, val loss: 0.62554, in 0.000s
1 tree, 23 leaves, max depth = 13, train loss: 0.62627, val loss: 0.62173, in 0.016s
1 tree, 15 leaves, max depth = 7, train loss: 0.62275, val loss: 0.61794, in 0.000s
1 tree, 15 leaves, max depth = 7, train loss: 0.61929, val loss: 0.61422, in 0.016s
1 tree, 24 leaves, max depth = 9, train loss: 0.61593, val loss: 0.61062, in 0.000s
1 tree, 15 leaves, max depth = 7, train loss: 0.61260, val loss: 0.60703, in 0.016s
1 tree, 15 leaves, max depth = 7, train loss: 0.60933, val loss: 0.60351, in 0.000s
1 tree, 15 leaves, max depth = 7, train loss: 0.60612, val loss: 0.60006, in 0.016s
1 tree, 15 leaves, max depth = 7, train loss: 0.60297, val loss: 0.59666, in 0.000s
1 tree, 15 leaves, max depth = 7, train loss: 0.59988, val loss: 0.59333, in 0.016s
1 tree, 25 leaves, max depth = 9, train loss: 0.59688, val loss: 0.59010, in 0.000s
1 tree, 15 leaves, max depth = 7, train loss: 0.59390, val loss: 0.58688, in 0.016s
1 tree, 25 leaves, max depth = 9, train loss: 0.59101, val loss: 0.58376, in 0.000s
1 tree, 25 leaves, max depth = 9, train loss: 0.58817, val loss: 0.58070, in 0.016s
1 tree, 15 leaves, max depth = 7, train loss: 0.58535, val loss: 0.57764, in 0.000s
1 tree, 15 leaves, max depth = 7, train loss: 0.58258, val loss: 0.57464, in 0.000s
1 tree, 15 leaves, max depth = 7, train loss: 0.57986, val loss: 0.57170, in 0.016s
1 tree, 15 leaves, max depth = 7, train loss: 0.57719, val loss: 0.56880, in 0.000s
1 tree, 15 leaves, max depth = 7, train loss: 0.57457, val loss: 0.56595, in 0.016s
1 tree, 27 leaves, max depth = 12, train loss: 0.57203, val loss: 0.56320, in 0.000s
1 tree, 16 leaves, max depth = 7, train loss: 0.56950, val loss: 0.56045, in 0.016s
1 tree, 16 leaves, max depth = 7, train loss: 0.56702, val loss: 0.55775, in 0.000s
1 tree, 16 leaves, max depth = 7, train loss: 0.56458, val loss: 0.55509, in 0.000s
1 tree, 16 leaves, max depth = 7, train loss: 0.56218, val loss: 0.55248, in 0.016s
1 tree, 26 leaves, max depth = 12, train loss: 0.55986, val loss: 0.54996, in 0.000s
1 tree, 27 leaves, max depth = 12, train loss: 0.55757, val loss: 0.54748, in 0.016s
Fit 40 trees in 0.565 s, (688 total leaves)
Time spent computing histograms: 0.165s
Time spent finding best splits:  0.021s
Time spent applying splits:      0.015s
Time spent predicting:           0.000s
Trial 29, Fold 5: Log loss = 0.5614857329703491, Average precision = 0.816707956377658, ROC-AUC = 0.8589935157832154, Elapsed Time = 0.5744513999998162 seconds
Optimization Progress:  30%|###       | 30/100 [05:51<12:56, 11.09s/it]
Trial 30, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 30, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
0.155 s 0.040 GB of training data: 
0.003 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 79 leaves, max depth = 17, train loss: 0.68638, val loss: 0.68620, in 0.016s
1 tree, 84 leaves, max depth = 19, train loss: 0.67984, val loss: 0.67947, in 0.000s
1 tree, 84 leaves, max depth = 19, train loss: 0.67349, val loss: 0.67292, in 0.016s
1 tree, 83 leaves, max depth = 15, train loss: 0.66737, val loss: 0.66667, in 0.016s
1 tree, 84 leaves, max depth = 17, train loss: 0.66144, val loss: 0.66057, in 0.016s
1 tree, 84 leaves, max depth = 17, train loss: 0.65567, val loss: 0.65464, in 0.000s
1 tree, 80 leaves, max depth = 18, train loss: 0.65010, val loss: 0.64890, in 0.016s
1 tree, 83 leaves, max depth = 17, train loss: 0.64467, val loss: 0.64335, in 0.016s
1 tree, 84 leaves, max depth = 17, train loss: 0.63935, val loss: 0.63787, in 0.000s
1 tree, 84 leaves, max depth = 18, train loss: 0.63410, val loss: 0.63244, in 0.016s
1 tree, 85 leaves, max depth = 14, train loss: 0.62907, val loss: 0.62724, in 0.016s
1 tree, 84 leaves, max depth = 16, train loss: 0.62415, val loss: 0.62220, in 0.000s
1 tree, 84 leaves, max depth = 17, train loss: 0.61937, val loss: 0.61727, in 0.016s
1 tree, 84 leaves, max depth = 18, train loss: 0.61464, val loss: 0.61238, in 0.016s
1 tree, 84 leaves, max depth = 20, train loss: 0.61010, val loss: 0.60770, in 0.016s
1 tree, 84 leaves, max depth = 20, train loss: 0.60569, val loss: 0.60314, in 0.000s
1 tree, 84 leaves, max depth = 16, train loss: 0.60137, val loss: 0.59871, in 0.016s
1 tree, 84 leaves, max depth = 15, train loss: 0.59716, val loss: 0.59439, in 0.000s
1 tree, 83 leaves, max depth = 17, train loss: 0.59310, val loss: 0.59023, in 0.016s
1 tree, 84 leaves, max depth = 18, train loss: 0.58904, val loss: 0.58602, in 0.016s
1 tree, 84 leaves, max depth = 18, train loss: 0.58509, val loss: 0.58191, in 0.000s
1 tree, 84 leaves, max depth = 18, train loss: 0.58123, val loss: 0.57791, in 0.016s
1 tree, 84 leaves, max depth = 18, train loss: 0.57747, val loss: 0.57401, in 0.016s
1 tree, 84 leaves, max depth = 18, train loss: 0.57381, val loss: 0.57020, in 0.016s
1 tree, 84 leaves, max depth = 15, train loss: 0.57029, val loss: 0.56659, in 0.000s
1 tree, 84 leaves, max depth = 15, train loss: 0.56686, val loss: 0.56306, in 0.016s
1 tree, 85 leaves, max depth = 14, train loss: 0.56352, val loss: 0.55958, in 0.016s
1 tree, 85 leaves, max depth = 14, train loss: 0.56027, val loss: 0.55619, in 0.000s
1 tree, 80 leaves, max depth = 16, train loss: 0.55711, val loss: 0.55292, in 0.016s
1 tree, 127 leaves, max depth = 16, train loss: 0.55380, val loss: 0.54983, in 0.016s
1 tree, 80 leaves, max depth = 15, train loss: 0.55075, val loss: 0.54668, in 0.000s
1 tree, 85 leaves, max depth = 14, train loss: 0.54777, val loss: 0.54356, in 0.016s
1 tree, 84 leaves, max depth = 18, train loss: 0.54479, val loss: 0.54045, in 0.016s
1 tree, 128 leaves, max depth = 16, train loss: 0.54164, val loss: 0.53752, in 0.016s
1 tree, 84 leaves, max depth = 18, train loss: 0.53876, val loss: 0.53452, in 0.016s
1 tree, 84 leaves, max depth = 15, train loss: 0.53601, val loss: 0.53167, in 0.000s
1 tree, 84 leaves, max depth = 18, train loss: 0.53327, val loss: 0.52881, in 0.016s
1 tree, 128 leaves, max depth = 16, train loss: 0.53027, val loss: 0.52603, in 0.016s
1 tree, 84 leaves, max depth = 14, train loss: 0.52768, val loss: 0.52335, in 0.016s
1 tree, 85 leaves, max depth = 14, train loss: 0.52515, val loss: 0.52070, in 0.000s
1 tree, 84 leaves, max depth = 14, train loss: 0.52268, val loss: 0.51815, in 0.016s
1 tree, 128 leaves, max depth = 16, train loss: 0.51982, val loss: 0.51550, in 0.016s
1 tree, 84 leaves, max depth = 16, train loss: 0.51743, val loss: 0.51304, in 0.016s
1 tree, 128 leaves, max depth = 16, train loss: 0.51466, val loss: 0.51048, in 0.016s
1 tree, 85 leaves, max depth = 14, train loss: 0.51236, val loss: 0.50806, in 0.016s
1 tree, 127 leaves, max depth = 16, train loss: 0.50968, val loss: 0.50558, in 0.016s
1 tree, 127 leaves, max depth = 16, train loss: 0.50706, val loss: 0.50316, in 0.016s
1 tree, 84 leaves, max depth = 18, train loss: 0.50481, val loss: 0.50079, in 0.016s
1 tree, 127 leaves, max depth = 15, train loss: 0.50227, val loss: 0.49845, in 0.016s
1 tree, 127 leaves, max depth = 15, train loss: 0.49979, val loss: 0.49617, in 0.016s
1 tree, 85 leaves, max depth = 13, train loss: 0.49769, val loss: 0.49395, in 0.016s
1 tree, 128 leaves, max depth = 15, train loss: 0.49528, val loss: 0.49174, in 0.016s
[53/100] 1 tree, 128 leaves, max depth = 15, train loss: 0.49294, val loss: 0.48958, in 0.016s
1 tree, 84 leaves, max depth = 13, train loss: 0.49087, val loss: 0.48740, in 0.000s
1 tree, 128 leaves, max depth = 15, train loss: 0.48860, val loss: 0.48531, in 0.016s
1 tree, 127 leaves, max depth = 15, train loss: 0.48637, val loss: 0.48328, in 0.016s
1 tree, 127 leaves, max depth = 15, train loss: 0.48420, val loss: 0.48129, in 0.016s
1 tree, 84 leaves, max depth = 13, train loss: 0.48229, val loss: 0.47926, in 0.016s
1 tree, 128 leaves, max depth = 15, train loss: 0.48018, val loss: 0.47733, in 0.016s
1 tree, 83 leaves, max depth = 19, train loss: 0.47833, val loss: 0.47542, in 0.000s
1 tree, 85 leaves, max depth = 13, train loss: 0.47652, val loss: 0.47349, in 0.016s
1 tree, 128 leaves, max depth = 16, train loss: 0.47448, val loss: 0.47164, in 0.016s
1 tree, 128 leaves, max depth = 16, train loss: 0.47249, val loss: 0.46982, in 0.016s
1 tree, 84 leaves, max depth = 14, train loss: 0.47070, val loss: 0.46796, in 0.016s
1 tree, 128 leaves, max depth = 16, train loss: 0.46877, val loss: 0.46620, in 0.016s
1 tree, 84 leaves, max depth = 14, train loss: 0.46709, val loss: 0.46445, in 0.000s
1 tree, 84 leaves, max depth = 14, train loss: 0.46544, val loss: 0.46273, in 0.016s
1 tree, 83 leaves, max depth = 19, train loss: 0.46384, val loss: 0.46107, in 0.016s
1 tree, 128 leaves, max depth = 16, train loss: 0.46198, val loss: 0.45938, in 0.016s
1 tree, 128 leaves, max depth = 16, train loss: 0.46017, val loss: 0.45774, in 0.016s
1 tree, 128 leaves, max depth = 16, train loss: 0.45839, val loss: 0.45613, in 0.016s
1 tree, 84 leaves, max depth = 16, train loss: 0.45686, val loss: 0.45453, in 0.016s
1 tree, 84 leaves, max depth = 13, train loss: 0.45532, val loss: 0.45292, in 0.000s
1 tree, 80 leaves, max depth = 18, train loss: 0.45386, val loss: 0.45138, in 0.016s
1 tree, 84 leaves, max depth = 14, train loss: 0.45243, val loss: 0.44987, in 0.016s
1 tree, 85 leaves, max depth = 14, train loss: 0.45102, val loss: 0.44836, in 0.016s
1 tree, 84 leaves, max depth = 13, train loss: 0.44961, val loss: 0.44685, in 0.000s
1 tree, 84 leaves, max depth = 14, train loss: 0.44822, val loss: 0.44541, in 0.016s
1 tree, 84 leaves, max depth = 17, train loss: 0.44692, val loss: 0.44404, in 0.016s
1 tree, 84 leaves, max depth = 13, train loss: 0.44561, val loss: 0.44263, in 0.000s
1 tree, 84 leaves, max depth = 13, train loss: 0.44432, val loss: 0.44125, in 0.016s
1 tree, 128 leaves, max depth = 15, train loss: 0.44266, val loss: 0.43975, in 0.016s
1 tree, 84 leaves, max depth = 13, train loss: 0.44141, val loss: 0.43841, in 0.016s
1 tree, 127 leaves, max depth = 15, train loss: 0.43979, val loss: 0.43696, in 0.016s
1 tree, 127 leaves, max depth = 15, train loss: 0.43821, val loss: 0.43554, in 0.016s
1 tree, 84 leaves, max depth = 13, train loss: 0.43701, val loss: 0.43424, in 0.016s
1 tree, 127 leaves, max depth = 15, train loss: 0.43547, val loss: 0.43286, in 0.016s
1 tree, 84 leaves, max depth = 13, train loss: 0.43431, val loss: 0.43161, in 0.000s
1 tree, 84 leaves, max depth = 13, train loss: 0.43318, val loss: 0.43038, in 0.016s
1 tree, 127 leaves, max depth = 15, train loss: 0.43168, val loss: 0.42904, in 0.016s
1 tree, 84 leaves, max depth = 17, train loss: 0.43058, val loss: 0.42785, in 0.016s
1 tree, 127 leaves, max depth = 15, train loss: 0.42912, val loss: 0.42655, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.42810, val loss: 0.42543, in 0.000s
1 tree, 84 leaves, max depth = 14, train loss: 0.42706, val loss: 0.42432, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.42607, val loss: 0.42323, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.42511, val loss: 0.42216, in 0.000s
1 tree, 84 leaves, max depth = 17, train loss: 0.42408, val loss: 0.42105, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.42315, val loss: 0.42002, in 0.000s
1 tree, 128 leaves, max depth = 15, train loss: 0.42173, val loss: 0.41877, in 0.016s
1 tree, 84 leaves, max depth = 17, train loss: 0.42074, val loss: 0.41770, in 0.016s
Fit 100 trees in 1.485 s, (9287 total leaves)
Time spent computing histograms: 0.510s
Time spent finding best splits:  0.125s
Time spent applying splits:      0.152s
Time spent predicting:           0.000s
Trial 30, Fold 1: Log loss = 0.4232796007217628, Average precision = 0.9459918060041705, ROC-AUC = 0.9419506190762966, Elapsed Time = 1.4879868999996688 seconds
Trial 30, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 30, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
Binning 0.040 GB of training data: 0.158 s
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 77 leaves, max depth = 21, train loss: 0.68647, val loss: 0.68619, in 0.000s
1 tree, 81 leaves, max depth = 18, train loss: 0.68003, val loss: 0.67948, in 0.016s
1 tree, 84 leaves, max depth = 17, train loss: 0.67370, val loss: 0.67283, in 0.016s
1 tree, 82 leaves, max depth = 14, train loss: 0.66763, val loss: 0.66652, in 0.016s
1 tree, 82 leaves, max depth = 18, train loss: 0.66171, val loss: 0.66034, in 0.000s
1 tree, 82 leaves, max depth = 18, train loss: 0.65596, val loss: 0.65432, in 0.016s
1 tree, 78 leaves, max depth = 21, train loss: 0.65037, val loss: 0.64849, in 0.016s
1 tree, 83 leaves, max depth = 20, train loss: 0.64493, val loss: 0.64280, in 0.000s
1 tree, 85 leaves, max depth = 11, train loss: 0.63960, val loss: 0.63719, in 0.016s
1 tree, 84 leaves, max depth = 17, train loss: 0.63437, val loss: 0.63169, in 0.016s
1 tree, 81 leaves, max depth = 18, train loss: 0.62934, val loss: 0.62642, in 0.016s
1 tree, 84 leaves, max depth = 19, train loss: 0.62443, val loss: 0.62127, in 0.000s
1 tree, 85 leaves, max depth = 11, train loss: 0.61964, val loss: 0.61623, in 0.016s
1 tree, 83 leaves, max depth = 17, train loss: 0.61493, val loss: 0.61126, in 0.016s
1 tree, 82 leaves, max depth = 16, train loss: 0.61042, val loss: 0.60655, in 0.000s
1 tree, 85 leaves, max depth = 11, train loss: 0.60600, val loss: 0.60188, in 0.016s
1 tree, 82 leaves, max depth = 17, train loss: 0.60170, val loss: 0.59737, in 0.016s
1 tree, 82 leaves, max depth = 17, train loss: 0.59752, val loss: 0.59298, in 0.000s
1 tree, 83 leaves, max depth = 23, train loss: 0.59345, val loss: 0.58871, in 0.016s
1 tree, 85 leaves, max depth = 15, train loss: 0.58941, val loss: 0.58443, in 0.016s
1 tree, 85 leaves, max depth = 15, train loss: 0.58548, val loss: 0.58027, in 0.000s
1 tree, 85 leaves, max depth = 15, train loss: 0.58164, val loss: 0.57621, in 0.016s
1 tree, 85 leaves, max depth = 15, train loss: 0.57790, val loss: 0.57225, in 0.016s
1 tree, 85 leaves, max depth = 15, train loss: 0.57425, val loss: 0.56839, in 0.016s
1 tree, 82 leaves, max depth = 16, train loss: 0.57075, val loss: 0.56470, in 0.000s
1 tree, 82 leaves, max depth = 16, train loss: 0.56734, val loss: 0.56111, in 0.016s
1 tree, 82 leaves, max depth = 18, train loss: 0.56401, val loss: 0.55761, in 0.016s
1 tree, 82 leaves, max depth = 18, train loss: 0.56077, val loss: 0.55419, in 0.000s
1 tree, 128 leaves, max depth = 16, train loss: 0.55742, val loss: 0.55097, in 0.016s
1 tree, 85 leaves, max depth = 19, train loss: 0.55428, val loss: 0.54764, in 0.016s
1 tree, 78 leaves, max depth = 18, train loss: 0.55122, val loss: 0.54443, in 0.016s
1 tree, 82 leaves, max depth = 17, train loss: 0.54824, val loss: 0.54129, in 0.016s
1 tree, 85 leaves, max depth = 15, train loss: 0.54528, val loss: 0.53814, in 0.000s
1 tree, 128 leaves, max depth = 17, train loss: 0.54213, val loss: 0.53511, in 0.016s
1 tree, 85 leaves, max depth = 15, train loss: 0.53927, val loss: 0.53206, in 0.016s
1 tree, 85 leaves, max depth = 19, train loss: 0.53652, val loss: 0.52915, in 0.016s
1 tree, 85 leaves, max depth = 15, train loss: 0.53379, val loss: 0.52625, in 0.000s
1 tree, 127 leaves, max depth = 16, train loss: 0.53079, val loss: 0.52336, in 0.016s
1 tree, 84 leaves, max depth = 18, train loss: 0.52821, val loss: 0.52062, in 0.016s
1 tree, 82 leaves, max depth = 16, train loss: 0.52569, val loss: 0.51796, in 0.000s
1 tree, 85 leaves, max depth = 18, train loss: 0.52322, val loss: 0.51534, in 0.016s
1 tree, 128 leaves, max depth = 17, train loss: 0.52036, val loss: 0.51259, in 0.016s
1 tree, 82 leaves, max depth = 17, train loss: 0.51799, val loss: 0.51009, in 0.016s
1 tree, 127 leaves, max depth = 17, train loss: 0.51522, val loss: 0.50743, in 0.016s
1 tree, 82 leaves, max depth = 17, train loss: 0.51293, val loss: 0.50500, in 0.000s
1 tree, 127 leaves, max depth = 17, train loss: 0.51025, val loss: 0.50243, in 0.031s
1 tree, 127 leaves, max depth = 17, train loss: 0.50762, val loss: 0.49992, in 0.000s
1 tree, 85 leaves, max depth = 16, train loss: 0.50539, val loss: 0.49752, in 0.016s
1 tree, 127 leaves, max depth = 17, train loss: 0.50285, val loss: 0.49509, in 0.016s
1 tree, 127 leaves, max depth = 17, train loss: 0.50037, val loss: 0.49272, in 0.016s
1 tree, 82 leaves, max depth = 17, train loss: 0.49828, val loss: 0.49050, in 0.016s
1 tree, 127 leaves, max depth = 17, train loss: 0.49587, val loss: 0.48820, in 0.016s
1 tree, 127 leaves, max depth = 17, train loss: 0.49352, val loss: 0.48596, in 0.016s
1 tree, 85 leaves, max depth = 16, train loss: 0.49147, val loss: 0.48376, in 0.000s
1 tree, 127 leaves, max depth = 17, train loss: 0.48920, val loss: 0.48159, in 0.016s
1 tree, 127 leaves, max depth = 17, train loss: 0.48697, val loss: 0.47947, in 0.016s
1 tree, 127 leaves, max depth = 17, train loss: 0.48480, val loss: 0.47740, in 0.016s
1 tree, 81 leaves, max depth = 17, train loss: 0.48290, val loss: 0.47538, in 0.016s
1 tree, 127 leaves, max depth = 17, train loss: 0.48079, val loss: 0.47337, in 0.000s
1 tree, 83 leaves, max depth = 26, train loss: 0.47895, val loss: 0.47141, in 0.016s
1 tree, 82 leaves, max depth = 17, train loss: 0.47715, val loss: 0.46949, in 0.016s
1 tree, 127 leaves, max depth = 15, train loss: 0.47511, val loss: 0.46756, in 0.016s
1 tree, 127 leaves, max depth = 15, train loss: 0.47312, val loss: 0.46567, in 0.016s
1 tree, 85 leaves, max depth = 12, train loss: 0.47138, val loss: 0.46379, in 0.016s
1 tree, 127 leaves, max depth = 18, train loss: 0.46945, val loss: 0.46196, in 0.016s
1 tree, 82 leaves, max depth = 14, train loss: 0.46778, val loss: 0.46017, in 0.000s
1 tree, 82 leaves, max depth = 14, train loss: 0.46614, val loss: 0.45843, in 0.016s
1 tree, 83 leaves, max depth = 26, train loss: 0.46455, val loss: 0.45673, in 0.016s
1 tree, 128 leaves, max depth = 16, train loss: 0.46269, val loss: 0.45496, in 0.016s
1 tree, 128 leaves, max depth = 16, train loss: 0.46087, val loss: 0.45324, in 0.016s
1 tree, 128 leaves, max depth = 16, train loss: 0.45909, val loss: 0.45156, in 0.016s
1 tree, 85 leaves, max depth = 12, train loss: 0.45756, val loss: 0.44990, in 0.016s
1 tree, 82 leaves, max depth = 14, train loss: 0.45607, val loss: 0.44831, in 0.000s
1 tree, 79 leaves, max depth = 19, train loss: 0.45462, val loss: 0.44677, in 0.016s
1 tree, 82 leaves, max depth = 14, train loss: 0.45320, val loss: 0.44525, in 0.016s
1 tree, 82 leaves, max depth = 14, train loss: 0.45181, val loss: 0.44377, in 0.016s
1 tree, 85 leaves, max depth = 16, train loss: 0.45042, val loss: 0.44226, in 0.000s
1 tree, 78 leaves, max depth = 19, train loss: 0.44909, val loss: 0.44086, in 0.016s
1 tree, 82 leaves, max depth = 13, train loss: 0.44780, val loss: 0.43948, in 0.016s
1 tree, 85 leaves, max depth = 16, train loss: 0.44650, val loss: 0.43806, in 0.016s
1 tree, 85 leaves, max depth = 16, train loss: 0.44523, val loss: 0.43668, in 0.016s
1 tree, 128 leaves, max depth = 16, train loss: 0.44356, val loss: 0.43511, in 0.016s
1 tree, 84 leaves, max depth = 16, train loss: 0.44233, val loss: 0.43377, in 0.004s
1 tree, 128 leaves, max depth = 16, train loss: 0.44070, val loss: 0.43223, in 0.013s
1 tree, 128 leaves, max depth = 16, train loss: 0.43911, val loss: 0.43074, in 0.016s
1 tree, 85 leaves, max depth = 16, train loss: 0.43793, val loss: 0.42945, in 0.016s
1 tree, 128 leaves, max depth = 16, train loss: 0.43638, val loss: 0.42799, in 0.016s
1 tree, 85 leaves, max depth = 16, train loss: 0.43523, val loss: 0.42674, in 0.016s
1 tree, 85 leaves, max depth = 16, train loss: 0.43411, val loss: 0.42552, in 0.016s
1 tree, 128 leaves, max depth = 16, train loss: 0.43261, val loss: 0.42411, in 0.016s
1 tree, 85 leaves, max depth = 16, train loss: 0.43152, val loss: 0.42292, in 0.016s
1 tree, 128 leaves, max depth = 18, train loss: 0.43005, val loss: 0.42154, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.42906, val loss: 0.42052, in 0.000s
1 tree, 83 leaves, max depth = 22, train loss: 0.42804, val loss: 0.41942, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.42708, val loss: 0.41842, in 0.000s
1 tree, 83 leaves, max depth = 22, train loss: 0.42609, val loss: 0.41735, in 0.016s
1 tree, 85 leaves, max depth = 17, train loss: 0.42509, val loss: 0.41626, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.42416, val loss: 0.41529, in 0.000s
1 tree, 127 leaves, max depth = 17, train loss: 0.42274, val loss: 0.41397, in 0.016s
1 tree, 84 leaves, max depth = 14, train loss: 0.42178, val loss: 0.41294, in 0.016s
Fit 100 trees in 1.534 s, (9315 total leaves)
Time spent computing histograms: 0.519s
Time spent finding best splits:  0.128s
Time spent applying splits:      0.154s
Time spent predicting:           0.016s
Trial 30, Fold 2: Log loss = 0.42380552050116543, Average precision = 0.9431906991089813, ROC-AUC = 0.9425675603903075, Elapsed Time = 1.5441061000001355 seconds
Trial 30, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 30, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
Binning 0.040 GB of training data: 0.159 s
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 81 leaves, max depth = 14, train loss: 0.68646, val loss: 0.68625, in 0.016s
1 tree, 84 leaves, max depth = 13, train loss: 0.68007, val loss: 0.67968, in 0.000s
1 tree, 81 leaves, max depth = 14, train loss: 0.67377, val loss: 0.67320, in 0.000s
1 tree, 83 leaves, max depth = 13, train loss: 0.66771, val loss: 0.66692, in 0.016s
1 tree, 84 leaves, max depth = 13, train loss: 0.66183, val loss: 0.66087, in 0.016s
1 tree, 84 leaves, max depth = 13, train loss: 0.65612, val loss: 0.65498, in 0.016s
1 tree, 81 leaves, max depth = 16, train loss: 0.65059, val loss: 0.64926, in 0.016s
1 tree, 80 leaves, max depth = 18, train loss: 0.64520, val loss: 0.64369, in 0.000s
1 tree, 84 leaves, max depth = 14, train loss: 0.63991, val loss: 0.63818, in 0.016s
1 tree, 83 leaves, max depth = 14, train loss: 0.63471, val loss: 0.63282, in 0.016s
1 tree, 84 leaves, max depth = 13, train loss: 0.62971, val loss: 0.62767, in 0.016s
1 tree, 84 leaves, max depth = 13, train loss: 0.62483, val loss: 0.62260, in 0.000s
1 tree, 84 leaves, max depth = 14, train loss: 0.62008, val loss: 0.61765, in 0.016s
1 tree, 84 leaves, max depth = 14, train loss: 0.61540, val loss: 0.61282, in 0.016s
1 tree, 83 leaves, max depth = 15, train loss: 0.61094, val loss: 0.60819, in 0.016s
1 tree, 83 leaves, max depth = 18, train loss: 0.60657, val loss: 0.60367, in 0.016s
1 tree, 84 leaves, max depth = 13, train loss: 0.60231, val loss: 0.59927, in 0.016s
1 tree, 84 leaves, max depth = 13, train loss: 0.59815, val loss: 0.59497, in 0.000s
1 tree, 80 leaves, max depth = 19, train loss: 0.59411, val loss: 0.59077, in 0.016s
1 tree, 83 leaves, max depth = 14, train loss: 0.59009, val loss: 0.58662, in 0.016s
1 tree, 83 leaves, max depth = 14, train loss: 0.58617, val loss: 0.58258, in 0.016s
1 tree, 83 leaves, max depth = 14, train loss: 0.58235, val loss: 0.57863, in 0.016s
1 tree, 83 leaves, max depth = 14, train loss: 0.57863, val loss: 0.57479, in 0.000s
1 tree, 83 leaves, max depth = 14, train loss: 0.57500, val loss: 0.57103, in 0.016s
1 tree, 84 leaves, max depth = 13, train loss: 0.57152, val loss: 0.56743, in 0.016s
1 tree, 84 leaves, max depth = 13, train loss: 0.56813, val loss: 0.56392, in 0.016s
1 tree, 84 leaves, max depth = 13, train loss: 0.56482, val loss: 0.56050, in 0.000s
1 tree, 84 leaves, max depth = 13, train loss: 0.56160, val loss: 0.55716, in 0.016s
1 tree, 125 leaves, max depth = 14, train loss: 0.55818, val loss: 0.55399, in 0.016s
1 tree, 125 leaves, max depth = 14, train loss: 0.55485, val loss: 0.55090, in 0.016s
1 tree, 81 leaves, max depth = 15, train loss: 0.55179, val loss: 0.54770, in 0.016s
1 tree, 84 leaves, max depth = 13, train loss: 0.54879, val loss: 0.54459, in 0.016s
1 tree, 84 leaves, max depth = 14, train loss: 0.54580, val loss: 0.54149, in 0.000s
1 tree, 125 leaves, max depth = 15, train loss: 0.54264, val loss: 0.53856, in 0.016s
1 tree, 84 leaves, max depth = 14, train loss: 0.53976, val loss: 0.53557, in 0.000s
1 tree, 84 leaves, max depth = 14, train loss: 0.53699, val loss: 0.53265, in 0.016s
1 tree, 84 leaves, max depth = 14, train loss: 0.53425, val loss: 0.52980, in 0.016s
1 tree, 125 leaves, max depth = 15, train loss: 0.53124, val loss: 0.52702, in 0.016s
1 tree, 84 leaves, max depth = 14, train loss: 0.52864, val loss: 0.52432, in 0.016s
1 tree, 84 leaves, max depth = 14, train loss: 0.52611, val loss: 0.52168, in 0.016s
1 tree, 84 leaves, max depth = 14, train loss: 0.52363, val loss: 0.51907, in 0.000s
1 tree, 124 leaves, max depth = 15, train loss: 0.52076, val loss: 0.51641, in 0.016s
1 tree, 84 leaves, max depth = 14, train loss: 0.51837, val loss: 0.51393, in 0.000s
1 tree, 125 leaves, max depth = 15, train loss: 0.51559, val loss: 0.51136, in 0.016s
1 tree, 84 leaves, max depth = 14, train loss: 0.51329, val loss: 0.50896, in 0.000s
1 tree, 125 leaves, max depth = 15, train loss: 0.51059, val loss: 0.50647, in 0.016s
1 tree, 125 leaves, max depth = 15, train loss: 0.50796, val loss: 0.50404, in 0.016s
1 tree, 84 leaves, max depth = 14, train loss: 0.50571, val loss: 0.50169, in 0.016s
1 tree, 125 leaves, max depth = 15, train loss: 0.50316, val loss: 0.49934, in 0.016s
1 tree, 125 leaves, max depth = 15, train loss: 0.50067, val loss: 0.49705, in 0.016s
1 tree, 84 leaves, max depth = 14, train loss: 0.49857, val loss: 0.49485, in 0.016s
1 tree, 125 leaves, max depth = 15, train loss: 0.49615, val loss: 0.49263, in 0.016s
1 tree, 125 leaves, max depth = 15, train loss: 0.49380, val loss: 0.49046, in 0.016s
1 tree, 84 leaves, max depth = 14, train loss: 0.49173, val loss: 0.48830, in 0.016s
1 tree, 125 leaves, max depth = 15, train loss: 0.48945, val loss: 0.48620, in 0.016s
1 tree, 125 leaves, max depth = 15, train loss: 0.48722, val loss: 0.48416, in 0.016s
1 tree, 125 leaves, max depth = 15, train loss: 0.48504, val loss: 0.48216, in 0.016s
1 tree, 84 leaves, max depth = 14, train loss: 0.48312, val loss: 0.48015, in 0.016s
1 tree, 125 leaves, max depth = 15, train loss: 0.48100, val loss: 0.47821, in 0.016s
1 tree, 80 leaves, max depth = 23, train loss: 0.47915, val loss: 0.47624, in 0.016s
1 tree, 84 leaves, max depth = 14, train loss: 0.47734, val loss: 0.47434, in 0.000s
1 tree, 124 leaves, max depth = 15, train loss: 0.47530, val loss: 0.47247, in 0.016s
1 tree, 125 leaves, max depth = 15, train loss: 0.47330, val loss: 0.47065, in 0.016s
1 tree, 84 leaves, max depth = 14, train loss: 0.47155, val loss: 0.46877, in 0.016s
1 tree, 125 leaves, max depth = 15, train loss: 0.46961, val loss: 0.46700, in 0.031s
1 tree, 84 leaves, max depth = 14, train loss: 0.46793, val loss: 0.46522, in 0.016s
1 tree, 84 leaves, max depth = 14, train loss: 0.46628, val loss: 0.46349, in 0.016s
1 tree, 80 leaves, max depth = 23, train loss: 0.46468, val loss: 0.46178, in 0.000s
1 tree, 125 leaves, max depth = 14, train loss: 0.46281, val loss: 0.46008, in 0.031s
1 tree, 125 leaves, max depth = 14, train loss: 0.46099, val loss: 0.45842, in 0.016s
1 tree, 125 leaves, max depth = 15, train loss: 0.45920, val loss: 0.45680, in 0.016s
1 tree, 84 leaves, max depth = 16, train loss: 0.45766, val loss: 0.45513, in 0.016s
1 tree, 83 leaves, max depth = 14, train loss: 0.45617, val loss: 0.45355, in 0.016s
1 tree, 83 leaves, max depth = 18, train loss: 0.45471, val loss: 0.45200, in 0.016s
1 tree, 84 leaves, max depth = 14, train loss: 0.45328, val loss: 0.45049, in 0.016s
1 tree, 84 leaves, max depth = 14, train loss: 0.45189, val loss: 0.44901, in 0.000s
1 tree, 84 leaves, max depth = 14, train loss: 0.45048, val loss: 0.44753, in 0.016s
1 tree, 83 leaves, max depth = 13, train loss: 0.44910, val loss: 0.44604, in 0.016s
1 tree, 83 leaves, max depth = 14, train loss: 0.44780, val loss: 0.44466, in 0.016s
1 tree, 84 leaves, max depth = 14, train loss: 0.44649, val loss: 0.44327, in 0.016s
1 tree, 84 leaves, max depth = 14, train loss: 0.44521, val loss: 0.44192, in 0.000s
1 tree, 125 leaves, max depth = 15, train loss: 0.44353, val loss: 0.44040, in 0.016s
1 tree, 84 leaves, max depth = 14, train loss: 0.44229, val loss: 0.43908, in 0.016s
1 tree, 124 leaves, max depth = 15, train loss: 0.44066, val loss: 0.43761, in 0.016s
1 tree, 125 leaves, max depth = 15, train loss: 0.43907, val loss: 0.43617, in 0.016s
1 tree, 84 leaves, max depth = 14, train loss: 0.43787, val loss: 0.43490, in 0.016s
1 tree, 125 leaves, max depth = 15, train loss: 0.43632, val loss: 0.43350, in 0.016s
1 tree, 84 leaves, max depth = 14, train loss: 0.43517, val loss: 0.43227, in 0.016s
1 tree, 84 leaves, max depth = 14, train loss: 0.43404, val loss: 0.43107, in 0.016s
1 tree, 125 leaves, max depth = 15, train loss: 0.43253, val loss: 0.42971, in 0.016s
1 tree, 84 leaves, max depth = 14, train loss: 0.43143, val loss: 0.42854, in 0.016s
1 tree, 125 leaves, max depth = 15, train loss: 0.42996, val loss: 0.42722, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.42894, val loss: 0.42627, in 0.016s
1 tree, 83 leaves, max depth = 16, train loss: 0.42789, val loss: 0.42515, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.42690, val loss: 0.42423, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.42592, val loss: 0.42333, in 0.000s
1 tree, 83 leaves, max depth = 16, train loss: 0.42491, val loss: 0.42225, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.42397, val loss: 0.42138, in 0.016s
1 tree, 124 leaves, max depth = 15, train loss: 0.42254, val loss: 0.42010, in 0.016s
1 tree, 83 leaves, max depth = 16, train loss: 0.42157, val loss: 0.41906, in 0.000s
Fit 100 trees in 1.674 s, (9225 total leaves)
Time spent computing histograms: 0.570s
Time spent finding best splits:  0.144s
Time spent applying splits:      0.172s
Time spent predicting:           0.031s
Trial 30, Fold 3: Log loss = 0.4193375609257467, Average precision = 0.9465947262409895, ROC-AUC = 0.945489996966224, Elapsed Time = 1.6884934999998222 seconds
Trial 30, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 30, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
Binning 0.040 GB of training data: 0.173 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 79 leaves, max depth = 13, train loss: 0.68647, val loss: 0.68614, in 0.016s
1 tree, 81 leaves, max depth = 19, train loss: 0.68000, val loss: 0.67930, in 0.016s
1 tree, 81 leaves, max depth = 19, train loss: 0.67370, val loss: 0.67264, in 0.000s
1 tree, 81 leaves, max depth = 16, train loss: 0.66762, val loss: 0.66623, in 0.016s
1 tree, 84 leaves, max depth = 14, train loss: 0.66171, val loss: 0.66000, in 0.016s
1 tree, 84 leaves, max depth = 14, train loss: 0.65597, val loss: 0.65394, in 0.000s
1 tree, 82 leaves, max depth = 13, train loss: 0.65042, val loss: 0.64808, in 0.016s
1 tree, 80 leaves, max depth = 14, train loss: 0.64503, val loss: 0.64239, in 0.016s
1 tree, 84 leaves, max depth = 14, train loss: 0.63973, val loss: 0.63678, in 0.000s
1 tree, 84 leaves, max depth = 20, train loss: 0.63453, val loss: 0.63125, in 0.016s
1 tree, 82 leaves, max depth = 19, train loss: 0.62953, val loss: 0.62593, in 0.016s
1 tree, 81 leaves, max depth = 14, train loss: 0.62464, val loss: 0.62074, in 0.016s
1 tree, 84 leaves, max depth = 14, train loss: 0.61988, val loss: 0.61569, in 0.016s
1 tree, 84 leaves, max depth = 20, train loss: 0.61519, val loss: 0.61069, in 0.000s
1 tree, 84 leaves, max depth = 14, train loss: 0.61068, val loss: 0.60590, in 0.016s
1 tree, 82 leaves, max depth = 14, train loss: 0.60632, val loss: 0.60125, in 0.016s
1 tree, 81 leaves, max depth = 14, train loss: 0.60202, val loss: 0.59668, in 0.016s
1 tree, 81 leaves, max depth = 14, train loss: 0.59784, val loss: 0.59223, in 0.000s
1 tree, 80 leaves, max depth = 13, train loss: 0.59380, val loss: 0.58793, in 0.016s
1 tree, 83 leaves, max depth = 20, train loss: 0.58979, val loss: 0.58363, in 0.016s
1 tree, 83 leaves, max depth = 20, train loss: 0.58587, val loss: 0.57943, in 0.000s
1 tree, 83 leaves, max depth = 20, train loss: 0.58206, val loss: 0.57533, in 0.016s
1 tree, 83 leaves, max depth = 20, train loss: 0.57834, val loss: 0.57133, in 0.016s
1 tree, 83 leaves, max depth = 20, train loss: 0.57472, val loss: 0.56742, in 0.000s
1 tree, 81 leaves, max depth = 14, train loss: 0.57122, val loss: 0.56368, in 0.016s
1 tree, 81 leaves, max depth = 14, train loss: 0.56781, val loss: 0.56002, in 0.016s
1 tree, 83 leaves, max depth = 19, train loss: 0.56449, val loss: 0.55644, in 0.016s
1 tree, 83 leaves, max depth = 19, train loss: 0.56126, val loss: 0.55294, in 0.000s
1 tree, 128 leaves, max depth = 13, train loss: 0.55781, val loss: 0.54959, in 0.031s
1 tree, 81 leaves, max depth = 14, train loss: 0.55468, val loss: 0.54622, in 0.016s
1 tree, 80 leaves, max depth = 13, train loss: 0.55164, val loss: 0.54296, in 0.000s
1 tree, 83 leaves, max depth = 17, train loss: 0.54868, val loss: 0.53974, in 0.016s
1 tree, 84 leaves, max depth = 17, train loss: 0.54573, val loss: 0.53654, in 0.016s
1 tree, 128 leaves, max depth = 14, train loss: 0.54249, val loss: 0.53340, in 0.016s
1 tree, 84 leaves, max depth = 20, train loss: 0.53966, val loss: 0.53031, in 0.016s
1 tree, 81 leaves, max depth = 14, train loss: 0.53692, val loss: 0.52736, in 0.016s
1 tree, 84 leaves, max depth = 20, train loss: 0.53422, val loss: 0.52441, in 0.000s
1 tree, 128 leaves, max depth = 14, train loss: 0.53114, val loss: 0.52142, in 0.016s
1 tree, 81 leaves, max depth = 15, train loss: 0.52856, val loss: 0.51863, in 0.016s
1 tree, 83 leaves, max depth = 17, train loss: 0.52605, val loss: 0.51589, in 0.016s
1 tree, 81 leaves, max depth = 14, train loss: 0.52360, val loss: 0.51323, in 0.000s
[42/100] 1 tree, 128 leaves, max depth = 16, train loss: 0.52066, val loss: 0.51038, in 0.016s
1 tree, 81 leaves, max depth = 15, train loss: 0.51829, val loss: 0.50781, in 0.016s
1 tree, 128 leaves, max depth = 16, train loss: 0.51544, val loss: 0.50505, in 0.016s
1 tree, 83 leaves, max depth = 17, train loss: 0.51316, val loss: 0.50254, in 0.016s
1 tree, 128 leaves, max depth = 16, train loss: 0.51040, val loss: 0.49987, in 0.016s
1 tree, 128 leaves, max depth = 16, train loss: 0.50770, val loss: 0.49726, in 0.016s
1 tree, 84 leaves, max depth = 20, train loss: 0.50549, val loss: 0.49483, in 0.000s
1 tree, 128 leaves, max depth = 16, train loss: 0.50288, val loss: 0.49230, in 0.031s
1 tree, 128 leaves, max depth = 16, train loss: 0.50032, val loss: 0.48983, in 0.016s
1 tree, 83 leaves, max depth = 17, train loss: 0.49824, val loss: 0.48754, in 0.000s
1 tree, 128 leaves, max depth = 16, train loss: 0.49576, val loss: 0.48514, in 0.016s
1 tree, 128 leaves, max depth = 16, train loss: 0.49334, val loss: 0.48281, in 0.016s
1 tree, 84 leaves, max depth = 20, train loss: 0.49132, val loss: 0.48058, in 0.000s
1 tree, 128 leaves, max depth = 14, train loss: 0.48898, val loss: 0.47831, in 0.016s
1 tree, 128 leaves, max depth = 14, train loss: 0.48668, val loss: 0.47610, in 0.016s
1 tree, 128 leaves, max depth = 14, train loss: 0.48445, val loss: 0.47393, in 0.016s
1 tree, 82 leaves, max depth = 18, train loss: 0.48255, val loss: 0.47184, in 0.016s
1 tree, 128 leaves, max depth = 15, train loss: 0.48038, val loss: 0.46974, in 0.016s
1 tree, 80 leaves, max depth = 13, train loss: 0.47857, val loss: 0.46776, in 0.016s
1 tree, 83 leaves, max depth = 18, train loss: 0.47678, val loss: 0.46578, in 0.000s
1 tree, 128 leaves, max depth = 15, train loss: 0.47467, val loss: 0.46375, in 0.016s
1 tree, 128 leaves, max depth = 15, train loss: 0.47262, val loss: 0.46177, in 0.031s
1 tree, 83 leaves, max depth = 12, train loss: 0.47085, val loss: 0.45984, in 0.000s
1 tree, 128 leaves, max depth = 17, train loss: 0.46886, val loss: 0.45791, in 0.016s
1 tree, 81 leaves, max depth = 17, train loss: 0.46720, val loss: 0.45609, in 0.016s
1 tree, 81 leaves, max depth = 17, train loss: 0.46558, val loss: 0.45430, in 0.016s
1 tree, 83 leaves, max depth = 12, train loss: 0.46394, val loss: 0.45250, in 0.016s
1 tree, 128 leaves, max depth = 16, train loss: 0.46202, val loss: 0.45066, in 0.016s
1 tree, 128 leaves, max depth = 16, train loss: 0.46015, val loss: 0.44886, in 0.016s
1 tree, 128 leaves, max depth = 16, train loss: 0.45832, val loss: 0.44709, in 0.016s
1 tree, 80 leaves, max depth = 16, train loss: 0.45682, val loss: 0.44543, in 0.016s
1 tree, 83 leaves, max depth = 13, train loss: 0.45529, val loss: 0.44374, in 0.016s
1 tree, 81 leaves, max depth = 16, train loss: 0.45386, val loss: 0.44214, in 0.016s
1 tree, 80 leaves, max depth = 16, train loss: 0.45246, val loss: 0.44058, in 0.031s
1 tree, 83 leaves, max depth = 15, train loss: 0.45108, val loss: 0.43904, in 0.031s
1 tree, 84 leaves, max depth = 17, train loss: 0.44971, val loss: 0.43750, in 0.016s
1 tree, 83 leaves, max depth = 12, train loss: 0.44834, val loss: 0.43599, in 0.031s
1 tree, 81 leaves, max depth = 16, train loss: 0.44707, val loss: 0.43457, in 0.016s
1 tree, 84 leaves, max depth = 17, train loss: 0.44579, val loss: 0.43312, in 0.016s
1 tree, 84 leaves, max depth = 17, train loss: 0.44454, val loss: 0.43171, in 0.016s
1 tree, 128 leaves, max depth = 17, train loss: 0.44283, val loss: 0.43007, in 0.031s
1 tree, 84 leaves, max depth = 17, train loss: 0.44162, val loss: 0.42870, in 0.016s
1 tree, 128 leaves, max depth = 17, train loss: 0.43995, val loss: 0.42710, in 0.016s
1 tree, 128 leaves, max depth = 17, train loss: 0.43832, val loss: 0.42554, in 0.016s
1 tree, 84 leaves, max depth = 17, train loss: 0.43716, val loss: 0.42422, in 0.016s
1 tree, 128 leaves, max depth = 17, train loss: 0.43556, val loss: 0.42270, in 0.031s
1 tree, 84 leaves, max depth = 17, train loss: 0.43444, val loss: 0.42142, in 0.016s
1 tree, 84 leaves, max depth = 17, train loss: 0.43334, val loss: 0.42017, in 0.016s
1 tree, 128 leaves, max depth = 16, train loss: 0.43179, val loss: 0.41869, in 0.016s
1 tree, 84 leaves, max depth = 17, train loss: 0.43073, val loss: 0.41748, in 0.016s
1 tree, 128 leaves, max depth = 17, train loss: 0.42922, val loss: 0.41604, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.42823, val loss: 0.41498, in 0.000s
1 tree, 83 leaves, max depth = 15, train loss: 0.42721, val loss: 0.41382, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.42625, val loss: 0.41279, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.42531, val loss: 0.41178, in 0.000s
1 tree, 84 leaves, max depth = 17, train loss: 0.42432, val loss: 0.41065, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.42340, val loss: 0.40967, in 0.016s
1 tree, 128 leaves, max depth = 16, train loss: 0.42194, val loss: 0.40828, in 0.016s
1 tree, 84 leaves, max depth = 17, train loss: 0.42098, val loss: 0.40718, in 0.000s
Fit 100 trees in 1.720 s, (9210 total leaves)
Time spent computing histograms: 0.582s
Time spent finding best splits:  0.158s
Time spent applying splits:      0.190s
Time spent predicting:           0.031s
Trial 30, Fold 4: Log loss = 0.4216082511273579, Average precision = 0.9466523855487519, ROC-AUC = 0.9441516091189367, Elapsed Time = 1.738201800000752 seconds
Trial 30, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 30, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
Binning 0.040 GB of training data: 0.173 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 83 leaves, max depth = 14, train loss: 0.68635, val loss: 0.68594, in 0.016s
1 tree, 80 leaves, max depth = 16, train loss: 0.67980, val loss: 0.67897, in 0.016s
1 tree, 80 leaves, max depth = 16, train loss: 0.67344, val loss: 0.67218, in 0.016s
1 tree, 81 leaves, max depth = 18, train loss: 0.66733, val loss: 0.66569, in 0.000s
1 tree, 83 leaves, max depth = 15, train loss: 0.66137, val loss: 0.65935, in 0.016s
1 tree, 83 leaves, max depth = 15, train loss: 0.65558, val loss: 0.65317, in 0.016s
1 tree, 83 leaves, max depth = 15, train loss: 0.64995, val loss: 0.64719, in 0.000s
1 tree, 83 leaves, max depth = 13, train loss: 0.64448, val loss: 0.64137, in 0.016s
1 tree, 7 leaves, max depth = 5, train loss: 0.63913, val loss: 0.63565, in 0.016s
1 tree, 84 leaves, max depth = 16, train loss: 0.63387, val loss: 0.63001, in 0.000s
1 tree, 83 leaves, max depth = 15, train loss: 0.62881, val loss: 0.62459, in 0.016s
1 tree, 84 leaves, max depth = 14, train loss: 0.62387, val loss: 0.61928, in 0.016s
1 tree, 83 leaves, max depth = 16, train loss: 0.61908, val loss: 0.61417, in 0.000s
1 tree, 84 leaves, max depth = 16, train loss: 0.61434, val loss: 0.60908, in 0.000s
1 tree, 83 leaves, max depth = 16, train loss: 0.60979, val loss: 0.60422, in 0.016s
1 tree, 83 leaves, max depth = 16, train loss: 0.60536, val loss: 0.59949, in 0.000s
1 tree, 84 leaves, max depth = 14, train loss: 0.60103, val loss: 0.59480, in 0.016s
1 tree, 84 leaves, max depth = 14, train loss: 0.59681, val loss: 0.59024, in 0.016s
1 tree, 82 leaves, max depth = 15, train loss: 0.59271, val loss: 0.58584, in 0.016s
1 tree, 84 leaves, max depth = 15, train loss: 0.58865, val loss: 0.58145, in 0.000s
1 tree, 84 leaves, max depth = 15, train loss: 0.58468, val loss: 0.57716, in 0.016s
1 tree, 84 leaves, max depth = 15, train loss: 0.58082, val loss: 0.57298, in 0.016s
1 tree, 84 leaves, max depth = 15, train loss: 0.57706, val loss: 0.56890, in 0.016s
1 tree, 84 leaves, max depth = 15, train loss: 0.57339, val loss: 0.56492, in 0.016s
1 tree, 83 leaves, max depth = 15, train loss: 0.56986, val loss: 0.56110, in 0.000s
1 tree, 84 leaves, max depth = 14, train loss: 0.56641, val loss: 0.55735, in 0.000s
1 tree, 83 leaves, max depth = 15, train loss: 0.56305, val loss: 0.55371, in 0.016s
1 tree, 83 leaves, max depth = 15, train loss: 0.55977, val loss: 0.55015, in 0.016s
1 tree, 83 leaves, max depth = 16, train loss: 0.55658, val loss: 0.54671, in 0.000s
1 tree, 128 leaves, max depth = 20, train loss: 0.55323, val loss: 0.54354, in 0.016s
1 tree, 83 leaves, max depth = 14, train loss: 0.55016, val loss: 0.54019, in 0.000s
1 tree, 83 leaves, max depth = 17, train loss: 0.54715, val loss: 0.53692, in 0.016s
1 tree, 84 leaves, max depth = 15, train loss: 0.54417, val loss: 0.53365, in 0.016s
1 tree, 128 leaves, max depth = 20, train loss: 0.54098, val loss: 0.53066, in 0.016s
1 tree, 84 leaves, max depth = 15, train loss: 0.53810, val loss: 0.52750, in 0.016s
1 tree, 84 leaves, max depth = 14, train loss: 0.53533, val loss: 0.52447, in 0.016s
1 tree, 84 leaves, max depth = 15, train loss: 0.53259, val loss: 0.52146, in 0.000s
1 tree, 128 leaves, max depth = 18, train loss: 0.52956, val loss: 0.51862, in 0.016s
1 tree, 84 leaves, max depth = 14, train loss: 0.52695, val loss: 0.51575, in 0.000s
1 tree, 82 leaves, max depth = 17, train loss: 0.52441, val loss: 0.51297, in 0.016s
1 tree, 84 leaves, max depth = 14, train loss: 0.52193, val loss: 0.51023, in 0.016s
1 tree, 128 leaves, max depth = 18, train loss: 0.51904, val loss: 0.50753, in 0.016s
1 tree, 84 leaves, max depth = 14, train loss: 0.51664, val loss: 0.50489, in 0.016s
1 tree, 128 leaves, max depth = 19, train loss: 0.51384, val loss: 0.50227, in 0.016s
1 tree, 83 leaves, max depth = 17, train loss: 0.51153, val loss: 0.49973, in 0.016s
1 tree, 128 leaves, max depth = 19, train loss: 0.50881, val loss: 0.49719, in 0.000s
1 tree, 128 leaves, max depth = 19, train loss: 0.50616, val loss: 0.49473, in 0.031s
1 tree, 84 leaves, max depth = 15, train loss: 0.50391, val loss: 0.49224, in 0.000s
1 tree, 128 leaves, max depth = 20, train loss: 0.50134, val loss: 0.48985, in 0.016s
1 tree, 128 leaves, max depth = 20, train loss: 0.49883, val loss: 0.48752, in 0.016s
1 tree, 83 leaves, max depth = 17, train loss: 0.49672, val loss: 0.48519, in 0.016s
1 tree, 128 leaves, max depth = 21, train loss: 0.49428, val loss: 0.48293, in 0.016s
1 tree, 128 leaves, max depth = 21, train loss: 0.49190, val loss: 0.48073, in 0.016s
1 tree, 84 leaves, max depth = 15, train loss: 0.48984, val loss: 0.47844, in 0.016s
1 tree, 128 leaves, max depth = 19, train loss: 0.48753, val loss: 0.47631, in 0.016s
1 tree, 128 leaves, max depth = 19, train loss: 0.48528, val loss: 0.47422, in 0.016s
1 tree, 128 leaves, max depth = 19, train loss: 0.48308, val loss: 0.47219, in 0.016s
1 tree, 82 leaves, max depth = 17, train loss: 0.48116, val loss: 0.47006, in 0.016s
1 tree, 128 leaves, max depth = 18, train loss: 0.47902, val loss: 0.46809, in 0.016s
1 tree, 83 leaves, max depth = 14, train loss: 0.47717, val loss: 0.46605, in 0.016s
1 tree, 83 leaves, max depth = 17, train loss: 0.47535, val loss: 0.46403, in 0.016s
1 tree, 128 leaves, max depth = 19, train loss: 0.47328, val loss: 0.46213, in 0.000s
1 tree, 128 leaves, max depth = 19, train loss: 0.47127, val loss: 0.46028, in 0.016s
1 tree, 84 leaves, max depth = 14, train loss: 0.46952, val loss: 0.45833, in 0.016s
1 tree, 128 leaves, max depth = 19, train loss: 0.46756, val loss: 0.45654, in 0.016s
1 tree, 83 leaves, max depth = 17, train loss: 0.46587, val loss: 0.45465, in 0.016s
1 tree, 83 leaves, max depth = 17, train loss: 0.46422, val loss: 0.45281, in 0.000s
1 tree, 81 leaves, max depth = 14, train loss: 0.46262, val loss: 0.45101, in 0.016s
1 tree, 128 leaves, max depth = 16, train loss: 0.46073, val loss: 0.44929, in 0.031s
1 tree, 128 leaves, max depth = 16, train loss: 0.45889, val loss: 0.44761, in 0.016s
1 tree, 128 leaves, max depth = 16, train loss: 0.45708, val loss: 0.44596, in 0.016s
1 tree, 84 leaves, max depth = 14, train loss: 0.45555, val loss: 0.44424, in 0.016s
1 tree, 83 leaves, max depth = 14, train loss: 0.45405, val loss: 0.44255, in 0.016s
1 tree, 83 leaves, max depth = 15, train loss: 0.45259, val loss: 0.44093, in 0.000s
1 tree, 81 leaves, max depth = 14, train loss: 0.45117, val loss: 0.43933, in 0.016s
1 tree, 83 leaves, max depth = 14, train loss: 0.44977, val loss: 0.43775, in 0.016s
1 tree, 84 leaves, max depth = 15, train loss: 0.44837, val loss: 0.43617, in 0.016s
1 tree, 81 leaves, max depth = 14, train loss: 0.44705, val loss: 0.43467, in 0.016s
1 tree, 83 leaves, max depth = 14, train loss: 0.44575, val loss: 0.43320, in 0.016s
1 tree, 84 leaves, max depth = 15, train loss: 0.44444, val loss: 0.43171, in 0.000s
1 tree, 84 leaves, max depth = 15, train loss: 0.44316, val loss: 0.43026, in 0.016s
1 tree, 128 leaves, max depth = 18, train loss: 0.44147, val loss: 0.42873, in 0.016s
1 tree, 84 leaves, max depth = 15, train loss: 0.44023, val loss: 0.42732, in 0.016s
1 tree, 128 leaves, max depth = 18, train loss: 0.43858, val loss: 0.42583, in 0.016s
1 tree, 128 leaves, max depth = 18, train loss: 0.43696, val loss: 0.42438, in 0.016s
1 tree, 84 leaves, max depth = 15, train loss: 0.43577, val loss: 0.42303, in 0.016s
1 tree, 128 leaves, max depth = 18, train loss: 0.43419, val loss: 0.42161, in 0.031s
1 tree, 84 leaves, max depth = 15, train loss: 0.43304, val loss: 0.42030, in 0.016s
1 tree, 84 leaves, max depth = 15, train loss: 0.43192, val loss: 0.41901, in 0.000s
1 tree, 128 leaves, max depth = 18, train loss: 0.43038, val loss: 0.41764, in 0.016s
1 tree, 84 leaves, max depth = 15, train loss: 0.42929, val loss: 0.41639, in 0.016s
1 tree, 128 leaves, max depth = 18, train loss: 0.42780, val loss: 0.41505, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.42680, val loss: 0.41409, in 0.016s
1 tree, 83 leaves, max depth = 15, train loss: 0.42578, val loss: 0.41293, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.42481, val loss: 0.41200, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.42386, val loss: 0.41108, in 0.000s
1 tree, 84 leaves, max depth = 15, train loss: 0.42284, val loss: 0.40991, in 0.016s
1 tree, 84 leaves, max depth = 15, train loss: 0.42185, val loss: 0.40877, in 0.016s
1 tree, 128 leaves, max depth = 17, train loss: 0.42040, val loss: 0.40749, in 0.016s
1 tree, 84 leaves, max depth = 15, train loss: 0.41944, val loss: 0.40637, in 0.016s
Fit 100 trees in 1.626 s, (9266 total leaves)
Time spent computing histograms: 0.551s
Time spent finding best splits:  0.141s
Time spent applying splits:      0.168s
Time spent predicting:           0.000s
Trial 30, Fold 5: Log loss = 0.427324505663281, Average precision = 0.9425507706044726, ROC-AUC = 0.9406792635118816, Elapsed Time = 1.635113199999978 seconds
Optimization Progress:  31%|###1      | 31/100 [06:06<14:02, 12.21s/it]
Trial 31, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 31, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
0.142 s 0.040 GB of training data: 
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 194 leaves, max depth = 18, train loss: 0.68167, val loss: 0.68189, in 0.016s
1 tree, 127 leaves, max depth = 15, train loss: 0.67091, val loss: 0.67120, in 0.031s
1 tree, 63 leaves, max depth = 10, train loss: 0.66041, val loss: 0.66056, in 0.016s
1 tree, 183 leaves, max depth = 15, train loss: 0.64992, val loss: 0.65031, in 0.031s
1 tree, 64 leaves, max depth = 13, train loss: 0.64028, val loss: 0.64060, in 0.016s
1 tree, 184 leaves, max depth = 16, train loss: 0.63057, val loss: 0.63109, in 0.016s
1 tree, 59 leaves, max depth = 11, train loss: 0.62149, val loss: 0.62184, in 0.016s
1 tree, 113 leaves, max depth = 15, train loss: 0.61202, val loss: 0.61229, in 0.016s
1 tree, 116 leaves, max depth = 16, train loss: 0.60307, val loss: 0.60325, in 0.031s
1 tree, 207 leaves, max depth = 15, train loss: 0.59509, val loss: 0.59550, in 0.016s
1 tree, 213 leaves, max depth = 16, train loss: 0.58714, val loss: 0.58771, in 0.031s
1 tree, 116 leaves, max depth = 16, train loss: 0.57905, val loss: 0.57950, in 0.031s
1 tree, 119 leaves, max depth = 15, train loss: 0.57112, val loss: 0.57159, in 0.016s
1 tree, 115 leaves, max depth = 14, train loss: 0.56406, val loss: 0.56447, in 0.031s
1 tree, 122 leaves, max depth = 20, train loss: 0.55662, val loss: 0.55708, in 0.016s
1 tree, 110 leaves, max depth = 16, train loss: 0.55005, val loss: 0.55044, in 0.016s
1 tree, 203 leaves, max depth = 17, train loss: 0.54366, val loss: 0.54406, in 0.031s
1 tree, 213 leaves, max depth = 13, train loss: 0.53740, val loss: 0.53798, in 0.016s
1 tree, 147 leaves, max depth = 17, train loss: 0.53085, val loss: 0.53154, in 0.031s
1 tree, 129 leaves, max depth = 15, train loss: 0.52507, val loss: 0.52568, in 0.016s
1 tree, 91 leaves, max depth = 14, train loss: 0.51936, val loss: 0.51989, in 0.016s
1 tree, 126 leaves, max depth = 17, train loss: 0.51393, val loss: 0.51441, in 0.031s
1 tree, 213 leaves, max depth = 16, train loss: 0.50856, val loss: 0.50915, in 0.016s
1 tree, 213 leaves, max depth = 14, train loss: 0.50345, val loss: 0.50418, in 0.031s
1 tree, 186 leaves, max depth = 17, train loss: 0.49822, val loss: 0.49891, in 0.031s
1 tree, 129 leaves, max depth = 14, train loss: 0.49343, val loss: 0.49405, in 0.016s
1 tree, 213 leaves, max depth = 18, train loss: 0.48866, val loss: 0.48948, in 0.031s
1 tree, 213 leaves, max depth = 16, train loss: 0.48393, val loss: 0.48494, in 0.031s
1 tree, 131 leaves, max depth = 15, train loss: 0.47906, val loss: 0.48000, in 0.031s
1 tree, 124 leaves, max depth = 17, train loss: 0.47431, val loss: 0.47516, in 0.016s
1 tree, 213 leaves, max depth = 16, train loss: 0.47004, val loss: 0.47111, in 0.031s
1 tree, 207 leaves, max depth = 17, train loss: 0.46604, val loss: 0.46727, in 0.016s
1 tree, 189 leaves, max depth = 18, train loss: 0.46204, val loss: 0.46321, in 0.031s
1 tree, 192 leaves, max depth = 15, train loss: 0.45788, val loss: 0.45918, in 0.031s
1 tree, 132 leaves, max depth = 17, train loss: 0.45365, val loss: 0.45499, in 0.016s
1 tree, 134 leaves, max depth = 15, train loss: 0.44950, val loss: 0.45086, in 0.031s
1 tree, 139 leaves, max depth = 14, train loss: 0.44561, val loss: 0.44686, in 0.016s
1 tree, 149 leaves, max depth = 17, train loss: 0.44168, val loss: 0.44284, in 0.031s
1 tree, 213 leaves, max depth = 17, train loss: 0.43817, val loss: 0.43953, in 0.031s
1 tree, 138 leaves, max depth = 15, train loss: 0.43452, val loss: 0.43588, in 0.016s
1 tree, 16 leaves, max depth = 8, train loss: 0.42957, val loss: 0.43108, in 0.016s
1 tree, 144 leaves, max depth = 14, train loss: 0.42597, val loss: 0.42756, in 0.031s
1 tree, 139 leaves, max depth = 18, train loss: 0.42270, val loss: 0.42427, in 0.016s
1 tree, 14 leaves, max depth = 5, train loss: 0.41806, val loss: 0.41979, in 0.016s
1 tree, 213 leaves, max depth = 16, train loss: 0.41507, val loss: 0.41698, in 0.016s
1 tree, 150 leaves, max depth = 14, train loss: 0.41205, val loss: 0.41388, in 0.031s
1 tree, 177 leaves, max depth = 19, train loss: 0.40905, val loss: 0.41080, in 0.016s
1 tree, 136 leaves, max depth = 15, train loss: 0.40599, val loss: 0.40782, in 0.031s
1 tree, 29 leaves, max depth = 10, train loss: 0.40165, val loss: 0.40362, in 0.016s
1 tree, 19 leaves, max depth = 7, train loss: 0.39753, val loss: 0.39963, in 0.016s
1 tree, 26 leaves, max depth = 8, train loss: 0.39337, val loss: 0.39562, in 0.016s
1 tree, 17 leaves, max depth = 5, train loss: 0.38953, val loss: 0.39189, in 0.016s
1 tree, 10 leaves, max depth = 4, train loss: 0.38583, val loss: 0.38828, in 0.016s
1 tree, 154 leaves, max depth = 13, train loss: 0.38284, val loss: 0.38542, in 0.016s
1 tree, 146 leaves, max depth = 15, train loss: 0.37996, val loss: 0.38256, in 0.031s
1 tree, 156 leaves, max depth = 14, train loss: 0.37713, val loss: 0.37985, in 0.031s
1 tree, 149 leaves, max depth = 24, train loss: 0.37506, val loss: 0.37779, in 0.016s
1 tree, 141 leaves, max depth = 14, train loss: 0.37237, val loss: 0.37519, in 0.031s
1 tree, 158 leaves, max depth = 15, train loss: 0.36973, val loss: 0.37252, in 0.016s
1 tree, 15 leaves, max depth = 6, train loss: 0.36751, val loss: 0.37024, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.36438, val loss: 0.36720, in 0.016s
1 tree, 135 leaves, max depth = 17, train loss: 0.36192, val loss: 0.36478, in 0.016s
1 tree, 212 leaves, max depth = 26, train loss: 0.36005, val loss: 0.36300, in 0.031s
1 tree, 123 leaves, max depth = 15, train loss: 0.35669, val loss: 0.36001, in 0.016s
1 tree, 27 leaves, max depth = 9, train loss: 0.35362, val loss: 0.35705, in 0.016s
1 tree, 24 leaves, max depth = 7, train loss: 0.35050, val loss: 0.35410, in 0.016s
1 tree, 20 leaves, max depth = 10, train loss: 0.34780, val loss: 0.35166, in 0.016s
1 tree, 153 leaves, max depth = 17, train loss: 0.34584, val loss: 0.34981, in 0.016s
1 tree, 36 leaves, max depth = 10, train loss: 0.34292, val loss: 0.34710, in 0.016s
1 tree, 165 leaves, max depth = 16, train loss: 0.34093, val loss: 0.34519, in 0.016s
Fit 70 trees in 1.845 s, (9063 total leaves)
Time spent computing histograms: 0.544s
Time spent finding best splits:  0.175s
Time spent applying splits:      0.138s
Time spent predicting:           0.016s
Trial 31, Fold 1: Log loss = 0.35161783356055243, Average precision = 0.9569503128917286, ROC-AUC = 0.9514637193329605, Elapsed Time = 1.8531702999989648 seconds
Trial 31, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 31, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
Binning 0.040 GB of training data: 0.157 s
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 156 leaves, max depth = 14, train loss: 0.68167, val loss: 0.68164, in 0.016s
1 tree, 121 leaves, max depth = 14, train loss: 0.67017, val loss: 0.67009, in 0.031s
1 tree, 105 leaves, max depth = 17, train loss: 0.65906, val loss: 0.65895, in 0.031s
1 tree, 83 leaves, max depth = 16, train loss: 0.64946, val loss: 0.64902, in 0.016s
1 tree, 135 leaves, max depth = 16, train loss: 0.63915, val loss: 0.63863, in 0.016s
1 tree, 126 leaves, max depth = 15, train loss: 0.62900, val loss: 0.62839, in 0.031s
1 tree, 109 leaves, max depth = 15, train loss: 0.61920, val loss: 0.61853, in 0.016s
1 tree, 110 leaves, max depth = 14, train loss: 0.61057, val loss: 0.60996, in 0.031s
1 tree, 113 leaves, max depth = 14, train loss: 0.60152, val loss: 0.60081, in 0.016s
1 tree, 111 leaves, max depth = 17, train loss: 0.59280, val loss: 0.59206, in 0.031s
1 tree, 213 leaves, max depth = 14, train loss: 0.58480, val loss: 0.58415, in 0.016s
1 tree, 119 leaves, max depth = 15, train loss: 0.57657, val loss: 0.57586, in 0.031s
1 tree, 116 leaves, max depth = 19, train loss: 0.56919, val loss: 0.56844, in 0.016s
1 tree, 103 leaves, max depth = 13, train loss: 0.56225, val loss: 0.56141, in 0.016s
1 tree, 136 leaves, max depth = 15, train loss: 0.55494, val loss: 0.55407, in 0.016s
1 tree, 126 leaves, max depth = 15, train loss: 0.54787, val loss: 0.54709, in 0.016s
1 tree, 84 leaves, max depth = 15, train loss: 0.54137, val loss: 0.54066, in 0.016s
1 tree, 85 leaves, max depth = 11, train loss: 0.53532, val loss: 0.53460, in 0.016s
1 tree, 147 leaves, max depth = 16, train loss: 0.52887, val loss: 0.52824, in 0.016s
1 tree, 213 leaves, max depth = 17, train loss: 0.52287, val loss: 0.52229, in 0.031s
1 tree, 131 leaves, max depth = 17, train loss: 0.51669, val loss: 0.51607, in 0.016s
1 tree, 179 leaves, max depth = 18, train loss: 0.51117, val loss: 0.51065, in 0.031s
1 tree, 165 leaves, max depth = 23, train loss: 0.50549, val loss: 0.50504, in 0.016s
1 tree, 164 leaves, max depth = 17, train loss: 0.49986, val loss: 0.49940, in 0.031s
1 tree, 205 leaves, max depth = 16, train loss: 0.49458, val loss: 0.49429, in 0.031s
1 tree, 144 leaves, max depth = 24, train loss: 0.48966, val loss: 0.48944, in 0.016s
1 tree, 120 leaves, max depth = 13, train loss: 0.48492, val loss: 0.48473, in 0.031s
1 tree, 126 leaves, max depth = 14, train loss: 0.48033, val loss: 0.48016, in 0.031s
1 tree, 140 leaves, max depth = 20, train loss: 0.47556, val loss: 0.47533, in 0.016s
1 tree, 139 leaves, max depth = 15, train loss: 0.47076, val loss: 0.47054, in 0.031s
1 tree, 170 leaves, max depth = 18, train loss: 0.46625, val loss: 0.46606, in 0.016s
1 tree, 152 leaves, max depth = 16, train loss: 0.46232, val loss: 0.46215, in 0.031s
1 tree, 140 leaves, max depth = 16, train loss: 0.45790, val loss: 0.45778, in 0.016s
1 tree, 172 leaves, max depth = 18, train loss: 0.45396, val loss: 0.45387, in 0.031s
1 tree, 170 leaves, max depth = 20, train loss: 0.44991, val loss: 0.44986, in 0.031s
1 tree, 174 leaves, max depth = 20, train loss: 0.44585, val loss: 0.44585, in 0.016s
1 tree, 32 leaves, max depth = 8, train loss: 0.44218, val loss: 0.44208, in 0.031s
1 tree, 157 leaves, max depth = 16, train loss: 0.43852, val loss: 0.43837, in 0.016s
1 tree, 181 leaves, max depth = 15, train loss: 0.43483, val loss: 0.43477, in 0.031s
1 tree, 133 leaves, max depth = 17, train loss: 0.43133, val loss: 0.43129, in 0.016s
1 tree, 138 leaves, max depth = 16, train loss: 0.42795, val loss: 0.42796, in 0.031s
1 tree, 178 leaves, max depth = 17, train loss: 0.42456, val loss: 0.42472, in 0.031s
1 tree, 10 leaves, max depth = 4, train loss: 0.41969, val loss: 0.41994, in 0.016s
1 tree, 173 leaves, max depth = 16, train loss: 0.41655, val loss: 0.41681, in 0.016s
1 tree, 168 leaves, max depth = 22, train loss: 0.41377, val loss: 0.41413, in 0.031s
1 tree, 213 leaves, max depth = 15, train loss: 0.41083, val loss: 0.41126, in 0.031s
1 tree, 24 leaves, max depth = 8, train loss: 0.40623, val loss: 0.40674, in 0.016s
1 tree, 121 leaves, max depth = 14, train loss: 0.40344, val loss: 0.40404, in 0.031s
1 tree, 207 leaves, max depth = 21, train loss: 0.40118, val loss: 0.40183, in 0.031s
1 tree, 33 leaves, max depth = 10, train loss: 0.39714, val loss: 0.39786, in 0.016s
1 tree, 116 leaves, max depth = 16, train loss: 0.39449, val loss: 0.39529, in 0.016s
1 tree, 163 leaves, max depth = 17, train loss: 0.39174, val loss: 0.39261, in 0.031s
1 tree, 213 leaves, max depth = 17, train loss: 0.38935, val loss: 0.39037, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.38534, val loss: 0.38647, in 0.016s
1 tree, 12 leaves, max depth = 5, train loss: 0.38164, val loss: 0.38279, in 0.031s
1 tree, 185 leaves, max depth = 22, train loss: 0.37942, val loss: 0.38069, in 0.016s
1 tree, 197 leaves, max depth = 20, train loss: 0.37727, val loss: 0.37867, in 0.031s
1 tree, 14 leaves, max depth = 5, train loss: 0.37383, val loss: 0.37531, in 0.016s
1 tree, 146 leaves, max depth = 18, train loss: 0.37145, val loss: 0.37300, in 0.031s
1 tree, 168 leaves, max depth = 18, train loss: 0.36886, val loss: 0.37053, in 0.031s
1 tree, 11 leaves, max depth = 5, train loss: 0.36558, val loss: 0.36731, in 0.016s
1 tree, 46 leaves, max depth = 10, train loss: 0.36229, val loss: 0.36417, in 0.016s
1 tree, 171 leaves, max depth = 17, train loss: 0.35981, val loss: 0.36182, in 0.031s
1 tree, 22 leaves, max depth = 9, train loss: 0.35683, val loss: 0.35902, in 0.016s
1 tree, 13 leaves, max depth = 6, train loss: 0.35391, val loss: 0.35617, in 0.016s
1 tree, 16 leaves, max depth = 6, train loss: 0.35120, val loss: 0.35358, in 0.016s
1 tree, 166 leaves, max depth = 18, train loss: 0.34892, val loss: 0.35144, in 0.031s
1 tree, 14 leaves, max depth = 6, train loss: 0.34637, val loss: 0.34889, in 0.000s
1 tree, 171 leaves, max depth = 15, train loss: 0.34409, val loss: 0.34679, in 0.031s
1 tree, 176 leaves, max depth = 16, train loss: 0.34186, val loss: 0.34475, in 0.031s
Fit 70 trees in 2.017 s, (8807 total leaves)
Time spent computing histograms: 0.589s
Time spent finding best splits:  0.188s
Time spent applying splits:      0.147s
Time spent predicting:           0.031s
Trial 31, Fold 2: Log loss = 0.3499749363102381, Average precision = 0.9564690837470304, ROC-AUC = 0.9537399152215471, Elapsed Time = 2.0252207000012277 seconds
Trial 31, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 31, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
Binning 0.040 GB of training data: 0.158 s
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 161 leaves, max depth = 15, train loss: 0.68177, val loss: 0.68167, in 0.016s
1 tree, 97 leaves, max depth = 13, train loss: 0.67085, val loss: 0.67080, in 0.031s
1 tree, 82 leaves, max depth = 13, train loss: 0.66039, val loss: 0.66035, in 0.016s
1 tree, 94 leaves, max depth = 13, train loss: 0.65019, val loss: 0.65022, in 0.016s
1 tree, 200 leaves, max depth = 15, train loss: 0.64067, val loss: 0.64094, in 0.031s
1 tree, 136 leaves, max depth = 18, train loss: 0.63065, val loss: 0.63098, in 0.031s
1 tree, 213 leaves, max depth = 18, train loss: 0.62170, val loss: 0.62227, in 0.016s
1 tree, 160 leaves, max depth = 17, train loss: 0.61269, val loss: 0.61324, in 0.031s
1 tree, 89 leaves, max depth = 13, train loss: 0.60408, val loss: 0.60463, in 0.016s
1 tree, 185 leaves, max depth = 18, train loss: 0.59600, val loss: 0.59648, in 0.016s
1 tree, 213 leaves, max depth = 16, train loss: 0.58819, val loss: 0.58875, in 0.031s
1 tree, 120 leaves, max depth = 18, train loss: 0.58004, val loss: 0.58067, in 0.031s
1 tree, 116 leaves, max depth = 14, train loss: 0.57219, val loss: 0.57286, in 0.016s
1 tree, 171 leaves, max depth = 17, train loss: 0.56493, val loss: 0.56560, in 0.031s
1 tree, 213 leaves, max depth = 17, train loss: 0.55823, val loss: 0.55903, in 0.031s
1 tree, 131 leaves, max depth = 14, train loss: 0.55096, val loss: 0.55189, in 0.031s
1 tree, 134 leaves, max depth = 20, train loss: 0.54443, val loss: 0.54548, in 0.031s
1 tree, 88 leaves, max depth = 11, train loss: 0.53797, val loss: 0.53920, in 0.016s
1 tree, 89 leaves, max depth = 11, train loss: 0.53190, val loss: 0.53319, in 0.016s
1 tree, 116 leaves, max depth = 16, train loss: 0.52556, val loss: 0.52692, in 0.031s
1 tree, 116 leaves, max depth = 14, train loss: 0.51945, val loss: 0.52091, in 0.031s
1 tree, 183 leaves, max depth = 16, train loss: 0.51390, val loss: 0.51539, in 0.016s
1 tree, 106 leaves, max depth = 15, train loss: 0.50851, val loss: 0.51013, in 0.031s
1 tree, 208 leaves, max depth = 17, train loss: 0.50320, val loss: 0.50482, in 0.031s
1 tree, 213 leaves, max depth = 17, train loss: 0.49807, val loss: 0.49980, in 0.016s
1 tree, 151 leaves, max depth = 15, train loss: 0.49266, val loss: 0.49453, in 0.031s
1 tree, 109 leaves, max depth = 15, train loss: 0.48755, val loss: 0.48950, in 0.016s
1 tree, 7 leaves, max depth = 4, train loss: 0.48325, val loss: 0.48518, in 0.016s
1 tree, 129 leaves, max depth = 15, train loss: 0.47842, val loss: 0.48042, in 0.031s
1 tree, 115 leaves, max depth = 15, train loss: 0.47366, val loss: 0.47572, in 0.016s
1 tree, 213 leaves, max depth = 17, train loss: 0.46953, val loss: 0.47170, in 0.031s
1 tree, 86 leaves, max depth = 13, train loss: 0.46542, val loss: 0.46762, in 0.031s
1 tree, 14 leaves, max depth = 7, train loss: 0.45968, val loss: 0.46218, in 0.016s
1 tree, 137 leaves, max depth = 17, train loss: 0.45558, val loss: 0.45813, in 0.016s
1 tree, 9 leaves, max depth = 4, train loss: 0.45029, val loss: 0.45313, in 0.016s
1 tree, 163 leaves, max depth = 20, train loss: 0.44659, val loss: 0.44956, in 0.031s
1 tree, 42 leaves, max depth = 10, train loss: 0.44139, val loss: 0.44469, in 0.016s
1 tree, 158 leaves, max depth = 20, train loss: 0.43745, val loss: 0.44083, in 0.016s
1 tree, 139 leaves, max depth = 17, train loss: 0.43400, val loss: 0.43749, in 0.031s
1 tree, 141 leaves, max depth = 16, train loss: 0.43042, val loss: 0.43411, in 0.016s
1 tree, 134 leaves, max depth = 18, train loss: 0.42690, val loss: 0.43064, in 0.031s
1 tree, 18 leaves, max depth = 7, train loss: 0.42242, val loss: 0.42651, in 0.016s
1 tree, 146 leaves, max depth = 17, train loss: 0.41913, val loss: 0.42332, in 0.031s
1 tree, 136 leaves, max depth = 16, train loss: 0.41585, val loss: 0.42009, in 0.016s
1 tree, 155 leaves, max depth = 18, train loss: 0.41270, val loss: 0.41711, in 0.031s
1 tree, 169 leaves, max depth = 19, train loss: 0.40979, val loss: 0.41430, in 0.031s
1 tree, 26 leaves, max depth = 7, train loss: 0.40553, val loss: 0.41039, in 0.016s
1 tree, 46 leaves, max depth = 10, train loss: 0.40282, val loss: 0.40772, in 0.016s
1 tree, 195 leaves, max depth = 24, train loss: 0.40022, val loss: 0.40525, in 0.031s
1 tree, 12 leaves, max depth = 6, train loss: 0.39627, val loss: 0.40160, in 0.016s
1 tree, 162 leaves, max depth = 16, train loss: 0.39358, val loss: 0.39904, in 0.016s
1 tree, 155 leaves, max depth = 18, train loss: 0.39078, val loss: 0.39639, in 0.031s
1 tree, 176 leaves, max depth = 18, train loss: 0.38812, val loss: 0.39390, in 0.031s
1 tree, 116 leaves, max depth = 12, train loss: 0.38523, val loss: 0.39097, in 0.016s
1 tree, 177 leaves, max depth = 18, train loss: 0.38296, val loss: 0.38891, in 0.031s
1 tree, 132 leaves, max depth = 12, train loss: 0.38017, val loss: 0.38610, in 0.031s
1 tree, 18 leaves, max depth = 8, train loss: 0.37670, val loss: 0.38291, in 0.016s
1 tree, 157 leaves, max depth = 20, train loss: 0.37439, val loss: 0.38079, in 0.031s
1 tree, 97 leaves, max depth = 14, train loss: 0.37088, val loss: 0.37779, in 0.016s
1 tree, 169 leaves, max depth = 14, train loss: 0.36827, val loss: 0.37520, in 0.031s
1 tree, 94 leaves, max depth = 15, train loss: 0.36492, val loss: 0.37235, in 0.031s
1 tree, 113 leaves, max depth = 13, train loss: 0.36270, val loss: 0.37017, in 0.016s
1 tree, 141 leaves, max depth = 18, train loss: 0.36055, val loss: 0.36812, in 0.031s
1 tree, 132 leaves, max depth = 16, train loss: 0.35822, val loss: 0.36574, in 0.031s
1 tree, 16 leaves, max depth = 5, train loss: 0.35520, val loss: 0.36296, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.35235, val loss: 0.36048, in 0.000s
1 tree, 134 leaves, max depth = 13, train loss: 0.35010, val loss: 0.35817, in 0.016s
1 tree, 7 leaves, max depth = 3, train loss: 0.34749, val loss: 0.35569, in 0.000s
1 tree, 39 leaves, max depth = 9, train loss: 0.34454, val loss: 0.35304, in 0.016s
1 tree, 12 leaves, max depth = 7, train loss: 0.34191, val loss: 0.35063, in 0.016s
Fit 70 trees in 2.002 s, (8348 total leaves)
Time spent computing histograms: 0.591s
Time spent finding best splits:  0.183s
Time spent applying splits:      0.143s
Time spent predicting:           0.000s
Trial 31, Fold 3: Log loss = 0.346050974179654, Average precision = 0.9593904984587104, ROC-AUC = 0.9552786649699992, Elapsed Time = 2.0108632999999827 seconds
Trial 31, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 31, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
Binning 0.040 GB of training data: 0.158 s
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 169 leaves, max depth = 15, train loss: 0.68174, val loss: 0.68155, in 0.016s
1 tree, 137 leaves, max depth = 16, train loss: 0.67022, val loss: 0.66958, in 0.031s
1 tree, 207 leaves, max depth = 17, train loss: 0.65982, val loss: 0.65903, in 0.031s
1 tree, 113 leaves, max depth = 13, train loss: 0.64919, val loss: 0.64805, in 0.016s
1 tree, 180 leaves, max depth = 15, train loss: 0.63929, val loss: 0.63789, in 0.032s
1 tree, 93 leaves, max depth = 20, train loss: 0.63000, val loss: 0.62821, in 0.016s
1 tree, 127 leaves, max depth = 20, train loss: 0.62052, val loss: 0.61838, in 0.031s
1 tree, 107 leaves, max depth = 18, train loss: 0.61204, val loss: 0.60955, in 0.016s
1 tree, 84 leaves, max depth = 13, train loss: 0.60386, val loss: 0.60093, in 0.016s
1 tree, 148 leaves, max depth = 17, train loss: 0.59577, val loss: 0.59254, in 0.031s
1 tree, 110 leaves, max depth = 16, train loss: 0.58718, val loss: 0.58365, in 0.016s
1 tree, 135 leaves, max depth = 20, train loss: 0.57895, val loss: 0.57517, in 0.031s
1 tree, 130 leaves, max depth = 14, train loss: 0.57093, val loss: 0.56691, in 0.016s
1 tree, 185 leaves, max depth = 17, train loss: 0.56379, val loss: 0.55948, in 0.031s
1 tree, 133 leaves, max depth = 19, train loss: 0.55711, val loss: 0.55255, in 0.016s
1 tree, 132 leaves, max depth = 15, train loss: 0.55014, val loss: 0.54533, in 0.031s
1 tree, 181 leaves, max depth = 22, train loss: 0.54370, val loss: 0.53868, in 0.016s
1 tree, 127 leaves, max depth = 14, train loss: 0.53749, val loss: 0.53225, in 0.031s
1 tree, 135 leaves, max depth = 19, train loss: 0.53143, val loss: 0.52598, in 0.031s
1 tree, 213 leaves, max depth = 18, train loss: 0.52564, val loss: 0.52013, in 0.031s
1 tree, 126 leaves, max depth = 17, train loss: 0.51993, val loss: 0.51418, in 0.016s
1 tree, 106 leaves, max depth = 17, train loss: 0.51396, val loss: 0.50797, in 0.031s
1 tree, 183 leaves, max depth = 15, train loss: 0.50855, val loss: 0.50231, in 0.016s
1 tree, 213 leaves, max depth = 18, train loss: 0.50321, val loss: 0.49709, in 0.031s
1 tree, 123 leaves, max depth = 19, train loss: 0.49842, val loss: 0.49208, in 0.031s
1 tree, 124 leaves, max depth = 16, train loss: 0.49303, val loss: 0.48645, in 0.016s
1 tree, 130 leaves, max depth = 13, train loss: 0.48798, val loss: 0.48118, in 0.031s
1 tree, 17 leaves, max depth = 6, train loss: 0.48171, val loss: 0.47475, in 0.016s
1 tree, 122 leaves, max depth = 19, train loss: 0.47736, val loss: 0.47018, in 0.031s
1 tree, 144 leaves, max depth = 18, train loss: 0.47252, val loss: 0.46525, in 0.016s
1 tree, 128 leaves, max depth = 22, train loss: 0.46833, val loss: 0.46088, in 0.031s
1 tree, 19 leaves, max depth = 10, train loss: 0.46291, val loss: 0.45528, in 0.016s
1 tree, 136 leaves, max depth = 13, train loss: 0.45842, val loss: 0.45070, in 0.031s
1 tree, 178 leaves, max depth = 17, train loss: 0.45435, val loss: 0.44652, in 0.031s
1 tree, 168 leaves, max depth = 15, train loss: 0.45007, val loss: 0.44218, in 0.031s
1 tree, 172 leaves, max depth = 15, train loss: 0.44592, val loss: 0.43795, in 0.016s
1 tree, 32 leaves, max depth = 10, train loss: 0.44232, val loss: 0.43411, in 0.031s
1 tree, 139 leaves, max depth = 20, train loss: 0.43858, val loss: 0.43020, in 0.016s
1 tree, 140 leaves, max depth = 17, train loss: 0.43499, val loss: 0.42641, in 0.031s
1 tree, 138 leaves, max depth = 20, train loss: 0.43146, val loss: 0.42272, in 0.016s
1 tree, 30 leaves, max depth = 8, train loss: 0.42669, val loss: 0.41779, in 0.016s
1 tree, 165 leaves, max depth = 15, train loss: 0.42320, val loss: 0.41421, in 0.031s
1 tree, 14 leaves, max depth = 6, train loss: 0.41871, val loss: 0.40958, in 0.016s
1 tree, 213 leaves, max depth = 18, train loss: 0.41556, val loss: 0.40652, in 0.031s
1 tree, 154 leaves, max depth = 15, train loss: 0.41238, val loss: 0.40323, in 0.031s
1 tree, 167 leaves, max depth = 17, train loss: 0.40931, val loss: 0.40011, in 0.016s
1 tree, 187 leaves, max depth = 18, train loss: 0.40632, val loss: 0.39700, in 0.031s
1 tree, 171 leaves, max depth = 17, train loss: 0.40345, val loss: 0.39399, in 0.031s
1 tree, 148 leaves, max depth = 15, train loss: 0.40062, val loss: 0.39103, in 0.031s
1 tree, 27 leaves, max depth = 8, train loss: 0.39660, val loss: 0.38688, in 0.016s
1 tree, 19 leaves, max depth = 8, train loss: 0.39275, val loss: 0.38293, in 0.016s
1 tree, 167 leaves, max depth = 15, train loss: 0.39012, val loss: 0.38023, in 0.016s
1 tree, 119 leaves, max depth = 14, train loss: 0.38721, val loss: 0.37743, in 0.016s
1 tree, 155 leaves, max depth = 17, train loss: 0.38456, val loss: 0.37475, in 0.031s
1 tree, 207 leaves, max depth = 17, train loss: 0.38195, val loss: 0.37209, in 0.031s
1 tree, 200 leaves, max depth = 19, train loss: 0.37940, val loss: 0.36954, in 0.031s
1 tree, 150 leaves, max depth = 15, train loss: 0.37709, val loss: 0.36715, in 0.016s
1 tree, 156 leaves, max depth = 15, train loss: 0.37439, val loss: 0.36460, in 0.047s
1 tree, 20 leaves, max depth = 7, train loss: 0.37126, val loss: 0.36125, in 0.000s
1 tree, 142 leaves, max depth = 18, train loss: 0.36870, val loss: 0.35876, in 0.031s
1 tree, 21 leaves, max depth = 10, train loss: 0.36553, val loss: 0.35574, in 0.016s
1 tree, 142 leaves, max depth = 13, train loss: 0.36306, val loss: 0.35335, in 0.016s
1 tree, 129 leaves, max depth = 13, train loss: 0.36075, val loss: 0.35108, in 0.031s
1 tree, 21 leaves, max depth = 8, train loss: 0.35775, val loss: 0.34794, in 0.016s
1 tree, 211 leaves, max depth = 17, train loss: 0.35582, val loss: 0.34606, in 0.031s
1 tree, 171 leaves, max depth = 16, train loss: 0.35351, val loss: 0.34382, in 0.031s
1 tree, 19 leaves, max depth = 10, train loss: 0.35065, val loss: 0.34083, in 0.016s
1 tree, 153 leaves, max depth = 14, train loss: 0.34850, val loss: 0.33873, in 0.016s
1 tree, 13 leaves, max depth = 5, train loss: 0.34575, val loss: 0.33587, in 0.016s
1 tree, 152 leaves, max depth = 15, train loss: 0.34365, val loss: 0.33382, in 0.031s
Fit 70 trees in 2.033 s, (9027 total leaves)
Time spent computing histograms: 0.594s
Time spent finding best splits:  0.194s
Time spent applying splits:      0.153s
Time spent predicting:           0.016s
Trial 31, Fold 4: Log loss = 0.3492675491284612, Average precision = 0.9588931444376244, ROC-AUC = 0.9539327560677268, Elapsed Time = 2.041462600000159 seconds
Trial 31, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 31, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
Binning 0.040 GB of training data: 0.158 s
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 176 leaves, max depth = 16, train loss: 0.68165, val loss: 0.68129, in 0.016s
1 tree, 190 leaves, max depth = 19, train loss: 0.67054, val loss: 0.66977, in 0.031s
1 tree, 213 leaves, max depth = 14, train loss: 0.65976, val loss: 0.65893, in 0.031s
1 tree, 119 leaves, max depth = 14, train loss: 0.64884, val loss: 0.64774, in 0.016s
1 tree, 122 leaves, max depth = 16, train loss: 0.63859, val loss: 0.63713, in 0.031s
1 tree, 57 leaves, max depth = 12, train loss: 0.62920, val loss: 0.62738, in 0.016s
1 tree, 67 leaves, max depth = 14, train loss: 0.62012, val loss: 0.61796, in 0.016s
1 tree, 121 leaves, max depth = 14, train loss: 0.61083, val loss: 0.60839, in 0.031s
1 tree, 106 leaves, max depth = 15, train loss: 0.60194, val loss: 0.59918, in 0.016s
1 tree, 213 leaves, max depth = 15, train loss: 0.59378, val loss: 0.59103, in 0.031s
1 tree, 62 leaves, max depth = 16, train loss: 0.58628, val loss: 0.58335, in 0.016s
1 tree, 184 leaves, max depth = 16, train loss: 0.57871, val loss: 0.57548, in 0.031s
1 tree, 119 leaves, max depth = 14, train loss: 0.57071, val loss: 0.56729, in 0.016s
1 tree, 182 leaves, max depth = 15, train loss: 0.56330, val loss: 0.55971, in 0.031s
1 tree, 126 leaves, max depth = 15, train loss: 0.55579, val loss: 0.55204, in 0.031s
1 tree, 115 leaves, max depth = 13, train loss: 0.54856, val loss: 0.54462, in 0.016s
1 tree, 135 leaves, max depth = 16, train loss: 0.54151, val loss: 0.53752, in 0.016s
1 tree, 138 leaves, max depth = 14, train loss: 0.53472, val loss: 0.53058, in 0.031s
1 tree, 104 leaves, max depth = 13, train loss: 0.52860, val loss: 0.52431, in 0.016s
1 tree, 136 leaves, max depth = 14, train loss: 0.52227, val loss: 0.51788, in 0.016s
1 tree, 103 leaves, max depth = 15, train loss: 0.51659, val loss: 0.51205, in 0.031s
1 tree, 138 leaves, max depth = 18, train loss: 0.51082, val loss: 0.50619, in 0.031s
1 tree, 213 leaves, max depth = 18, train loss: 0.50537, val loss: 0.50078, in 0.016s
1 tree, 112 leaves, max depth = 16, train loss: 0.49974, val loss: 0.49502, in 0.031s
1 tree, 194 leaves, max depth = 17, train loss: 0.49442, val loss: 0.48963, in 0.016s
1 tree, 106 leaves, max depth = 21, train loss: 0.48950, val loss: 0.48461, in 0.016s
1 tree, 24 leaves, max depth = 12, train loss: 0.48323, val loss: 0.47817, in 0.016s
1 tree, 166 leaves, max depth = 19, train loss: 0.47858, val loss: 0.47341, in 0.016s
1 tree, 142 leaves, max depth = 17, train loss: 0.47362, val loss: 0.46843, in 0.031s
1 tree, 127 leaves, max depth = 16, train loss: 0.46888, val loss: 0.46360, in 0.031s
1 tree, 129 leaves, max depth = 16, train loss: 0.46465, val loss: 0.45927, in 0.016s
1 tree, 162 leaves, max depth = 14, train loss: 0.46013, val loss: 0.45471, in 0.031s
1 tree, 18 leaves, max depth = 8, train loss: 0.45465, val loss: 0.44918, in 0.016s
1 tree, 11 leaves, max depth = 6, train loss: 0.44922, val loss: 0.44369, in 0.016s
1 tree, 146 leaves, max depth = 16, train loss: 0.44520, val loss: 0.43961, in 0.031s
1 tree, 136 leaves, max depth = 15, train loss: 0.44127, val loss: 0.43565, in 0.016s
1 tree, 213 leaves, max depth = 16, train loss: 0.43770, val loss: 0.43210, in 0.031s
1 tree, 118 leaves, max depth = 13, train loss: 0.43387, val loss: 0.42825, in 0.031s
1 tree, 139 leaves, max depth = 15, train loss: 0.43023, val loss: 0.42451, in 0.016s
1 tree, 212 leaves, max depth = 19, train loss: 0.42679, val loss: 0.42103, in 0.031s
1 tree, 154 leaves, max depth = 15, train loss: 0.42332, val loss: 0.41760, in 0.031s
1 tree, 213 leaves, max depth = 16, train loss: 0.42023, val loss: 0.41459, in 0.031s
1 tree, 139 leaves, max depth = 14, train loss: 0.41682, val loss: 0.41118, in 0.016s
1 tree, 171 leaves, max depth = 18, train loss: 0.41369, val loss: 0.40794, in 0.031s
1 tree, 134 leaves, max depth = 16, train loss: 0.41065, val loss: 0.40492, in 0.016s
1 tree, 118 leaves, max depth = 15, train loss: 0.40759, val loss: 0.40188, in 0.031s
1 tree, 165 leaves, max depth = 18, train loss: 0.40464, val loss: 0.39894, in 0.031s
1 tree, 163 leaves, max depth = 14, train loss: 0.40162, val loss: 0.39604, in 0.031s
1 tree, 204 leaves, max depth = 16, train loss: 0.39872, val loss: 0.39324, in 0.031s
1 tree, 17 leaves, max depth = 6, train loss: 0.39462, val loss: 0.38914, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.39068, val loss: 0.38528, in 0.016s
1 tree, 213 leaves, max depth = 19, train loss: 0.38826, val loss: 0.38304, in 0.031s
1 tree, 196 leaves, max depth = 15, train loss: 0.38574, val loss: 0.38052, in 0.031s
1 tree, 11 leaves, max depth = 5, train loss: 0.38341, val loss: 0.37812, in 0.016s
1 tree, 189 leaves, max depth = 20, train loss: 0.38081, val loss: 0.37563, in 0.031s
1 tree, 213 leaves, max depth = 18, train loss: 0.37858, val loss: 0.37359, in 0.031s
1 tree, 213 leaves, max depth = 21, train loss: 0.37639, val loss: 0.37148, in 0.031s
1 tree, 173 leaves, max depth = 16, train loss: 0.37401, val loss: 0.36919, in 0.016s
1 tree, 20 leaves, max depth = 7, train loss: 0.37053, val loss: 0.36567, in 0.016s
1 tree, 213 leaves, max depth = 20, train loss: 0.36839, val loss: 0.36367, in 0.031s
1 tree, 19 leaves, max depth = 7, train loss: 0.36511, val loss: 0.36030, in 0.016s
1 tree, 168 leaves, max depth = 19, train loss: 0.36262, val loss: 0.35791, in 0.031s
1 tree, 11 leaves, max depth = 5, train loss: 0.35948, val loss: 0.35474, in 0.016s
1 tree, 25 leaves, max depth = 9, train loss: 0.35658, val loss: 0.35204, in 0.016s
1 tree, 137 leaves, max depth = 13, train loss: 0.35419, val loss: 0.34974, in 0.016s
1 tree, 9 leaves, max depth = 5, train loss: 0.35128, val loss: 0.34681, in 0.016s
1 tree, 158 leaves, max depth = 15, train loss: 0.34899, val loss: 0.34466, in 0.031s
1 tree, 148 leaves, max depth = 16, train loss: 0.34719, val loss: 0.34290, in 0.016s
1 tree, 171 leaves, max depth = 17, train loss: 0.34529, val loss: 0.34113, in 0.031s
1 tree, 109 leaves, max depth = 12, train loss: 0.34322, val loss: 0.33928, in 0.016s
Fit 70 trees in 2.033 s, (9185 total leaves)
Time spent computing histograms: 0.593s
Time spent finding best splits:  0.195s
Time spent applying splits:      0.156s
Time spent predicting:           0.000s
Trial 31, Fold 5: Log loss = 0.3565390565096313, Average precision = 0.9549716549634287, ROC-AUC = 0.9506515801794772, Elapsed Time = 2.0520335999990493 seconds
Optimization Progress:  32%|###2      | 32/100 [06:23<15:39, 13.81s/it]
Trial 32, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 32, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
0.142 s 0.040 GB of training data: 
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 27 leaves, max depth = 9, train loss: 0.67187, val loss: 0.67116, in 0.000s
1 tree, 29 leaves, max depth = 11, train loss: 0.65255, val loss: 0.65121, in 0.016s
1 tree, 26 leaves, max depth = 9, train loss: 0.63460, val loss: 0.63274, in 0.000s
1 tree, 8 leaves, max depth = 5, train loss: 0.61833, val loss: 0.61577, in 0.000s
1 tree, 26 leaves, max depth = 9, train loss: 0.60334, val loss: 0.60025, in 0.016s
1 tree, 26 leaves, max depth = 9, train loss: 0.58961, val loss: 0.58601, in 0.000s
1 tree, 30 leaves, max depth = 12, train loss: 0.57720, val loss: 0.57310, in 0.016s
1 tree, 29 leaves, max depth = 10, train loss: 0.56586, val loss: 0.56125, in 0.000s
1 tree, 27 leaves, max depth = 10, train loss: 0.55519, val loss: 0.55013, in 0.016s
1 tree, 27 leaves, max depth = 10, train loss: 0.54514, val loss: 0.53959, in 0.000s
1 tree, 28 leaves, max depth = 10, train loss: 0.53623, val loss: 0.53019, in 0.016s
1 tree, 29 leaves, max depth = 9, train loss: 0.52809, val loss: 0.52163, in 0.000s
1 tree, 28 leaves, max depth = 10, train loss: 0.52038, val loss: 0.51355, in 0.016s
1 tree, 27 leaves, max depth = 9, train loss: 0.51336, val loss: 0.50612, in 0.000s
1 tree, 28 leaves, max depth = 10, train loss: 0.50678, val loss: 0.49920, in 0.016s
1 tree, 57 leaves, max depth = 11, train loss: 0.49755, val loss: 0.49064, in 0.000s
1 tree, 28 leaves, max depth = 9, train loss: 0.49169, val loss: 0.48450, in 0.016s
1 tree, 28 leaves, max depth = 9, train loss: 0.48629, val loss: 0.47883, in 0.000s
1 tree, 9 leaves, max depth = 6, train loss: 0.48124, val loss: 0.47337, in 0.016s
1 tree, 27 leaves, max depth = 8, train loss: 0.47644, val loss: 0.46821, in 0.000s
1 tree, 63 leaves, max depth = 13, train loss: 0.47118, val loss: 0.46335, in 0.016s
1 tree, 58 leaves, max depth = 10, train loss: 0.46580, val loss: 0.45842, in 0.000s
1 tree, 26 leaves, max depth = 8, train loss: 0.46162, val loss: 0.45390, in 0.016s
1 tree, 26 leaves, max depth = 8, train loss: 0.45776, val loss: 0.44972, in 0.000s
1 tree, 63 leaves, max depth = 14, train loss: 0.45326, val loss: 0.44560, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.44992, val loss: 0.44191, in 0.000s
1 tree, 29 leaves, max depth = 9, train loss: 0.44672, val loss: 0.43842, in 0.016s
1 tree, 31 leaves, max depth = 13, train loss: 0.44379, val loss: 0.43530, in 0.000s
1 tree, 61 leaves, max depth = 9, train loss: 0.43932, val loss: 0.43129, in 0.016s
1 tree, 53 leaves, max depth = 12, train loss: 0.43299, val loss: 0.42560, in 0.016s
1 tree, 61 leaves, max depth = 9, train loss: 0.42917, val loss: 0.42221, in 0.000s
1 tree, 58 leaves, max depth = 17, train loss: 0.42631, val loss: 0.41937, in 0.016s
1 tree, 58 leaves, max depth = 17, train loss: 0.42367, val loss: 0.41675, in 0.000s
1 tree, 31 leaves, max depth = 16, train loss: 0.42125, val loss: 0.41417, in 0.016s
1 tree, 28 leaves, max depth = 8, train loss: 0.41881, val loss: 0.41149, in 0.000s
1 tree, 60 leaves, max depth = 15, train loss: 0.41638, val loss: 0.40911, in 0.016s
1 tree, 54 leaves, max depth = 9, train loss: 0.41384, val loss: 0.40689, in 0.016s
1 tree, 55 leaves, max depth = 11, train loss: 0.40898, val loss: 0.40257, in 0.000s
1 tree, 28 leaves, max depth = 8, train loss: 0.40685, val loss: 0.40031, in 0.016s
1 tree, 60 leaves, max depth = 10, train loss: 0.40402, val loss: 0.39787, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.40128, val loss: 0.39485, in 0.016s
1 tree, 27 leaves, max depth = 8, train loss: 0.39936, val loss: 0.39273, in 0.000s
1 tree, 59 leaves, max depth = 13, train loss: 0.39737, val loss: 0.39082, in 0.016s
1 tree, 55 leaves, max depth = 11, train loss: 0.39322, val loss: 0.38718, in 0.000s
1 tree, 60 leaves, max depth = 10, train loss: 0.39088, val loss: 0.38522, in 0.016s
1 tree, 61 leaves, max depth = 10, train loss: 0.38874, val loss: 0.38344, in 0.016s
1 tree, 55 leaves, max depth = 13, train loss: 0.38518, val loss: 0.38035, in 0.000s
1 tree, 29 leaves, max depth = 8, train loss: 0.38353, val loss: 0.37858, in 0.016s
1 tree, 62 leaves, max depth = 10, train loss: 0.38172, val loss: 0.37710, in 0.000s
1 tree, 54 leaves, max depth = 12, train loss: 0.37855, val loss: 0.37436, in 0.016s
1 tree, 59 leaves, max depth = 15, train loss: 0.37715, val loss: 0.37302, in 0.016s
1 tree, 55 leaves, max depth = 12, train loss: 0.37430, val loss: 0.37058, in 0.000s
1 tree, 55 leaves, max depth = 12, train loss: 0.37167, val loss: 0.36833, in 0.016s
1 tree, 29 leaves, max depth = 10, train loss: 0.37022, val loss: 0.36676, in 0.000s
1 tree, 29 leaves, max depth = 10, train loss: 0.36888, val loss: 0.36531, in 0.016s
1 tree, 54 leaves, max depth = 12, train loss: 0.36644, val loss: 0.36324, in 0.000s
1 tree, 9 leaves, max depth = 5, train loss: 0.36513, val loss: 0.36177, in 0.016s
1 tree, 27 leaves, max depth = 9, train loss: 0.36400, val loss: 0.36057, in 0.000s
1 tree, 54 leaves, max depth = 12, train loss: 0.36172, val loss: 0.35865, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.35928, val loss: 0.35595, in 0.000s
1 tree, 64 leaves, max depth = 12, train loss: 0.35791, val loss: 0.35487, in 0.016s
1 tree, 54 leaves, max depth = 12, train loss: 0.35588, val loss: 0.35319, in 0.000s
1 tree, 37 leaves, max depth = 10, train loss: 0.35480, val loss: 0.35191, in 0.016s
1 tree, 16 leaves, max depth = 6, train loss: 0.35386, val loss: 0.35090, in 0.000s
1 tree, 38 leaves, max depth = 10, train loss: 0.35292, val loss: 0.34974, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.35165, val loss: 0.34863, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.34939, val loss: 0.34612, in 0.016s
1 tree, 63 leaves, max depth = 13, train loss: 0.34820, val loss: 0.34523, in 0.000s
1 tree, 53 leaves, max depth = 12, train loss: 0.34643, val loss: 0.34380, in 0.016s
1 tree, 54 leaves, max depth = 12, train loss: 0.34480, val loss: 0.34249, in 0.016s
1 tree, 36 leaves, max depth = 10, train loss: 0.34385, val loss: 0.34134, in 0.000s
1 tree, 37 leaves, max depth = 15, train loss: 0.34299, val loss: 0.34031, in 0.016s
Fit 72 trees in 0.845 s, (2787 total leaves)
Time spent computing histograms: 0.299s
Time spent finding best splits:  0.056s
Time spent applying splits:      0.047s
Time spent predicting:           0.000s
Trial 32, Fold 1: Log loss = 0.3466410806136022, Average precision = 0.9522885844063388, ROC-AUC = 0.947052009100338, Elapsed Time = 0.8517611000006582 seconds
Trial 32, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 32, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
Binning 0.040 GB of training data: 0.157 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 28 leaves, max depth = 9, train loss: 0.67195, val loss: 0.67090, in 0.016s
1 tree, 29 leaves, max depth = 11, train loss: 0.65251, val loss: 0.65046, in 0.000s
1 tree, 31 leaves, max depth = 9, train loss: 0.63462, val loss: 0.63170, in 0.000s
1 tree, 9 leaves, max depth = 7, train loss: 0.61829, val loss: 0.61452, in 0.016s
1 tree, 29 leaves, max depth = 14, train loss: 0.60349, val loss: 0.59889, in 0.000s
1 tree, 29 leaves, max depth = 11, train loss: 0.58987, val loss: 0.58451, in 0.016s
1 tree, 28 leaves, max depth = 10, train loss: 0.57741, val loss: 0.57134, in 0.000s
1 tree, 29 leaves, max depth = 14, train loss: 0.56599, val loss: 0.55921, in 0.000s
1 tree, 28 leaves, max depth = 10, train loss: 0.55534, val loss: 0.54785, in 0.016s
1 tree, 30 leaves, max depth = 8, train loss: 0.54537, val loss: 0.53725, in 0.000s
1 tree, 29 leaves, max depth = 10, train loss: 0.53644, val loss: 0.52773, in 0.016s
1 tree, 31 leaves, max depth = 14, train loss: 0.52825, val loss: 0.51896, in 0.000s
1 tree, 28 leaves, max depth = 12, train loss: 0.52068, val loss: 0.51085, in 0.016s
1 tree, 30 leaves, max depth = 9, train loss: 0.51367, val loss: 0.50333, in 0.000s
1 tree, 30 leaves, max depth = 13, train loss: 0.50725, val loss: 0.49636, in 0.016s
1 tree, 54 leaves, max depth = 12, train loss: 0.49811, val loss: 0.48760, in 0.000s
1 tree, 30 leaves, max depth = 9, train loss: 0.49227, val loss: 0.48133, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.48688, val loss: 0.47553, in 0.000s
1 tree, 9 leaves, max depth = 6, train loss: 0.48184, val loss: 0.47009, in 0.000s
1 tree, 31 leaves, max depth = 8, train loss: 0.47708, val loss: 0.46493, in 0.016s
1 tree, 63 leaves, max depth = 12, train loss: 0.47181, val loss: 0.46008, in 0.016s
1 tree, 63 leaves, max depth = 9, train loss: 0.46650, val loss: 0.45517, in 0.000s
1 tree, 31 leaves, max depth = 9, train loss: 0.46235, val loss: 0.45066, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.45851, val loss: 0.44649, in 0.016s
1 tree, 61 leaves, max depth = 11, train loss: 0.45401, val loss: 0.44238, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.45074, val loss: 0.43901, in 0.016s
1 tree, 32 leaves, max depth = 10, train loss: 0.44754, val loss: 0.43551, in 0.000s
1 tree, 29 leaves, max depth = 12, train loss: 0.44461, val loss: 0.43228, in 0.016s
1 tree, 63 leaves, max depth = 9, train loss: 0.44021, val loss: 0.42827, in 0.016s
1 tree, 53 leaves, max depth = 11, train loss: 0.43396, val loss: 0.42236, in 0.000s
1 tree, 63 leaves, max depth = 9, train loss: 0.43021, val loss: 0.41898, in 0.016s
1 tree, 58 leaves, max depth = 12, train loss: 0.42724, val loss: 0.41645, in 0.016s
1 tree, 58 leaves, max depth = 12, train loss: 0.42451, val loss: 0.41414, in 0.000s
1 tree, 58 leaves, max depth = 12, train loss: 0.42199, val loss: 0.41203, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.41942, val loss: 0.40920, in 0.000s
1 tree, 59 leaves, max depth = 12, train loss: 0.41711, val loss: 0.40728, in 0.016s
1 tree, 31 leaves, max depth = 15, train loss: 0.41491, val loss: 0.40482, in 0.016s
1 tree, 56 leaves, max depth = 13, train loss: 0.41009, val loss: 0.40030, in 0.000s
1 tree, 30 leaves, max depth = 14, train loss: 0.40812, val loss: 0.39809, in 0.016s
1 tree, 64 leaves, max depth = 9, train loss: 0.40527, val loss: 0.39559, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.40261, val loss: 0.39282, in 0.016s
1 tree, 29 leaves, max depth = 9, train loss: 0.40070, val loss: 0.39071, in 0.000s
1 tree, 59 leaves, max depth = 10, train loss: 0.39874, val loss: 0.38914, in 0.016s
1 tree, 56 leaves, max depth = 12, train loss: 0.39461, val loss: 0.38528, in 0.016s
1 tree, 62 leaves, max depth = 11, train loss: 0.39214, val loss: 0.38315, in 0.000s
1 tree, 62 leaves, max depth = 9, train loss: 0.38989, val loss: 0.38123, in 0.016s
1 tree, 56 leaves, max depth = 12, train loss: 0.38640, val loss: 0.37797, in 0.016s
1 tree, 30 leaves, max depth = 9, train loss: 0.38474, val loss: 0.37614, in 0.000s
1 tree, 62 leaves, max depth = 9, train loss: 0.38283, val loss: 0.37455, in 0.016s
1 tree, 56 leaves, max depth = 11, train loss: 0.37973, val loss: 0.37167, in 0.016s
1 tree, 60 leaves, max depth = 12, train loss: 0.37834, val loss: 0.37062, in 0.000s
1 tree, 56 leaves, max depth = 11, train loss: 0.37555, val loss: 0.36804, in 0.016s
1 tree, 56 leaves, max depth = 11, train loss: 0.37298, val loss: 0.36567, in 0.016s
1 tree, 31 leaves, max depth = 14, train loss: 0.37162, val loss: 0.36413, in 0.000s
1 tree, 30 leaves, max depth = 9, train loss: 0.37026, val loss: 0.36261, in 0.016s
1 tree, 57 leaves, max depth = 11, train loss: 0.36787, val loss: 0.36041, in 0.016s
1 tree, 9 leaves, max depth = 5, train loss: 0.36656, val loss: 0.35893, in 0.000s
1 tree, 31 leaves, max depth = 15, train loss: 0.36549, val loss: 0.35772, in 0.016s
1 tree, 57 leaves, max depth = 12, train loss: 0.36325, val loss: 0.35566, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.36089, val loss: 0.35320, in 0.016s
1 tree, 63 leaves, max depth = 11, train loss: 0.35951, val loss: 0.35211, in 0.000s
1 tree, 56 leaves, max depth = 11, train loss: 0.35752, val loss: 0.35030, in 0.016s
1 tree, 38 leaves, max depth = 10, train loss: 0.35647, val loss: 0.34905, in 0.000s
1 tree, 16 leaves, max depth = 7, train loss: 0.35554, val loss: 0.34810, in 0.016s
1 tree, 37 leaves, max depth = 13, train loss: 0.35462, val loss: 0.34703, in 0.000s
1 tree, 36 leaves, max depth = 10, train loss: 0.35324, val loss: 0.34594, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.35105, val loss: 0.34365, in 0.000s
1 tree, 64 leaves, max depth = 11, train loss: 0.34986, val loss: 0.34274, in 0.016s
1 tree, 55 leaves, max depth = 11, train loss: 0.34813, val loss: 0.34118, in 0.016s
1 tree, 55 leaves, max depth = 11, train loss: 0.34654, val loss: 0.33974, in 0.000s
1 tree, 37 leaves, max depth = 13, train loss: 0.34561, val loss: 0.33865, in 0.016s
1 tree, 36 leaves, max depth = 15, train loss: 0.34475, val loss: 0.33761, in 0.000s
Fit 72 trees in 0.892 s, (2879 total leaves)
Time spent computing histograms: 0.315s
Time spent finding best splits:  0.063s
Time spent applying splits:      0.053s
Time spent predicting:           0.000s
Trial 32, Fold 2: Log loss = 0.34737620853550505, Average precision = 0.949150803587019, ROC-AUC = 0.94730262241418, Elapsed Time = 0.9006196000009368 seconds
Trial 32, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 32, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
Binning 0.040 GB of training data: 0.158 s
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 28 leaves, max depth = 10, train loss: 0.67201, val loss: 0.67129, in 0.000s
1 tree, 27 leaves, max depth = 9, train loss: 0.65277, val loss: 0.65134, in 0.016s
1 tree, 23 leaves, max depth = 8, train loss: 0.63501, val loss: 0.63285, in 0.000s
1 tree, 8 leaves, max depth = 6, train loss: 0.61876, val loss: 0.61601, in 0.016s
1 tree, 30 leaves, max depth = 9, train loss: 0.60411, val loss: 0.60073, in 0.000s
1 tree, 28 leaves, max depth = 10, train loss: 0.59060, val loss: 0.58675, in 0.016s
1 tree, 30 leaves, max depth = 9, train loss: 0.57827, val loss: 0.57386, in 0.000s
1 tree, 29 leaves, max depth = 9, train loss: 0.56697, val loss: 0.56202, in 0.016s
1 tree, 29 leaves, max depth = 8, train loss: 0.55639, val loss: 0.55091, in 0.000s
1 tree, 27 leaves, max depth = 9, train loss: 0.54644, val loss: 0.54056, in 0.000s
1 tree, 29 leaves, max depth = 10, train loss: 0.53757, val loss: 0.53134, in 0.016s
1 tree, 30 leaves, max depth = 11, train loss: 0.52948, val loss: 0.52279, in 0.000s
1 tree, 29 leaves, max depth = 8, train loss: 0.52185, val loss: 0.51472, in 0.016s
1 tree, 29 leaves, max depth = 11, train loss: 0.51490, val loss: 0.50738, in 0.000s
1 tree, 29 leaves, max depth = 8, train loss: 0.50840, val loss: 0.50047, in 0.016s
1 tree, 54 leaves, max depth = 12, train loss: 0.49925, val loss: 0.49204, in 0.000s
1 tree, 29 leaves, max depth = 9, train loss: 0.49347, val loss: 0.48588, in 0.016s
1 tree, 30 leaves, max depth = 9, train loss: 0.48814, val loss: 0.48018, in 0.000s
1 tree, 9 leaves, max depth = 7, train loss: 0.48310, val loss: 0.47489, in 0.016s
1 tree, 27 leaves, max depth = 9, train loss: 0.47837, val loss: 0.46992, in 0.000s
1 tree, 61 leaves, max depth = 12, train loss: 0.47308, val loss: 0.46496, in 0.016s
1 tree, 61 leaves, max depth = 9, train loss: 0.46770, val loss: 0.46007, in 0.016s
1 tree, 27 leaves, max depth = 9, train loss: 0.46358, val loss: 0.45572, in 0.000s
1 tree, 28 leaves, max depth = 9, train loss: 0.45979, val loss: 0.45170, in 0.016s
1 tree, 60 leaves, max depth = 12, train loss: 0.45526, val loss: 0.44748, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.45188, val loss: 0.44438, in 0.016s
1 tree, 28 leaves, max depth = 10, train loss: 0.44870, val loss: 0.44103, in 0.000s
1 tree, 30 leaves, max depth = 12, train loss: 0.44581, val loss: 0.43789, in 0.016s
1 tree, 64 leaves, max depth = 9, train loss: 0.44135, val loss: 0.43389, in 0.000s
1 tree, 54 leaves, max depth = 13, train loss: 0.43511, val loss: 0.42824, in 0.016s
1 tree, 62 leaves, max depth = 9, train loss: 0.43131, val loss: 0.42487, in 0.016s
1 tree, 59 leaves, max depth = 16, train loss: 0.42832, val loss: 0.42231, in 0.000s
1 tree, 60 leaves, max depth = 16, train loss: 0.42558, val loss: 0.41996, in 0.016s
1 tree, 60 leaves, max depth = 16, train loss: 0.42305, val loss: 0.41781, in 0.000s
1 tree, 25 leaves, max depth = 8, train loss: 0.42052, val loss: 0.41509, in 0.016s
1 tree, 60 leaves, max depth = 15, train loss: 0.41819, val loss: 0.41314, in 0.016s
1 tree, 30 leaves, max depth = 8, train loss: 0.41587, val loss: 0.41054, in 0.000s
1 tree, 52 leaves, max depth = 13, train loss: 0.41107, val loss: 0.40626, in 0.016s
1 tree, 29 leaves, max depth = 8, train loss: 0.40899, val loss: 0.40391, in 0.016s
1 tree, 62 leaves, max depth = 9, train loss: 0.40614, val loss: 0.40146, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.40338, val loss: 0.39892, in 0.016s
1 tree, 25 leaves, max depth = 9, train loss: 0.40150, val loss: 0.39688, in 0.000s
1 tree, 60 leaves, max depth = 14, train loss: 0.39954, val loss: 0.39530, in 0.016s
1 tree, 53 leaves, max depth = 11, train loss: 0.39542, val loss: 0.39167, in 0.000s
1 tree, 60 leaves, max depth = 10, train loss: 0.39298, val loss: 0.38946, in 0.016s
1 tree, 64 leaves, max depth = 9, train loss: 0.39072, val loss: 0.38758, in 0.016s
1 tree, 52 leaves, max depth = 11, train loss: 0.38723, val loss: 0.38455, in 0.000s
1 tree, 29 leaves, max depth = 9, train loss: 0.38563, val loss: 0.38276, in 0.016s
1 tree, 64 leaves, max depth = 9, train loss: 0.38372, val loss: 0.38120, in 0.016s
1 tree, 52 leaves, max depth = 11, train loss: 0.38061, val loss: 0.37852, in 0.000s
1 tree, 60 leaves, max depth = 14, train loss: 0.37920, val loss: 0.37746, in 0.016s
1 tree, 54 leaves, max depth = 11, train loss: 0.37640, val loss: 0.37507, in 0.016s
1 tree, 53 leaves, max depth = 11, train loss: 0.37381, val loss: 0.37287, in 0.000s
1 tree, 29 leaves, max depth = 9, train loss: 0.37240, val loss: 0.37127, in 0.016s
1 tree, 29 leaves, max depth = 9, train loss: 0.37110, val loss: 0.36980, in 0.000s
1 tree, 53 leaves, max depth = 11, train loss: 0.36868, val loss: 0.36776, in 0.016s
1 tree, 8 leaves, max depth = 6, train loss: 0.36739, val loss: 0.36633, in 0.000s
1 tree, 31 leaves, max depth = 12, train loss: 0.36632, val loss: 0.36513, in 0.016s
1 tree, 54 leaves, max depth = 13, train loss: 0.36405, val loss: 0.36324, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.36161, val loss: 0.36096, in 0.000s
1 tree, 60 leaves, max depth = 11, train loss: 0.36025, val loss: 0.35980, in 0.016s
1 tree, 53 leaves, max depth = 11, train loss: 0.35823, val loss: 0.35815, in 0.000s
1 tree, 8 leaves, max depth = 5, train loss: 0.35714, val loss: 0.35693, in 0.016s
1 tree, 18 leaves, max depth = 7, train loss: 0.35626, val loss: 0.35602, in 0.000s
1 tree, 36 leaves, max depth = 10, train loss: 0.35535, val loss: 0.35475, in 0.016s
1 tree, 37 leaves, max depth = 11, train loss: 0.35401, val loss: 0.35372, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.35175, val loss: 0.35162, in 0.016s
1 tree, 61 leaves, max depth = 11, train loss: 0.35060, val loss: 0.35065, in 0.000s
1 tree, 53 leaves, max depth = 11, train loss: 0.34884, val loss: 0.34924, in 0.016s
1 tree, 53 leaves, max depth = 11, train loss: 0.34722, val loss: 0.34795, in 0.016s
1 tree, 37 leaves, max depth = 10, train loss: 0.34630, val loss: 0.34666, in 0.000s
1 tree, 37 leaves, max depth = 15, train loss: 0.34545, val loss: 0.34546, in 0.016s
Fit 72 trees in 0.924 s, (2769 total leaves)
Time spent computing histograms: 0.318s
Time spent finding best splits:  0.063s
Time spent applying splits:      0.052s
Time spent predicting:           0.016s
Trial 32, Fold 3: Log loss = 0.34113213388950736, Average precision = 0.9536000701204155, ROC-AUC = 0.9504115311178742, Elapsed Time = 0.9310433000009652 seconds
Trial 32, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 32, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
Binning 0.040 GB of training data: 0.158 s
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 27 leaves, max depth = 9, train loss: 0.67201, val loss: 0.67076, in 0.000s
1 tree, 31 leaves, max depth = 11, train loss: 0.65271, val loss: 0.65035, in 0.016s
1 tree, 29 leaves, max depth = 11, train loss: 0.63491, val loss: 0.63146, in 0.000s
1 tree, 7 leaves, max depth = 5, train loss: 0.61867, val loss: 0.61423, in 0.000s
1 tree, 26 leaves, max depth = 11, train loss: 0.60396, val loss: 0.59852, in 0.016s
1 tree, 30 leaves, max depth = 14, train loss: 0.59042, val loss: 0.58401, in 0.016s
1 tree, 29 leaves, max depth = 11, train loss: 0.57803, val loss: 0.57073, in 0.000s
1 tree, 26 leaves, max depth = 11, train loss: 0.56668, val loss: 0.55848, in 0.016s
1 tree, 29 leaves, max depth = 9, train loss: 0.55607, val loss: 0.54701, in 0.000s
1 tree, 26 leaves, max depth = 12, train loss: 0.54615, val loss: 0.53620, in 0.016s
1 tree, 30 leaves, max depth = 14, train loss: 0.53726, val loss: 0.52650, in 0.000s
1 tree, 27 leaves, max depth = 11, train loss: 0.52912, val loss: 0.51758, in 0.016s
1 tree, 29 leaves, max depth = 9, train loss: 0.52146, val loss: 0.50918, in 0.000s
1 tree, 29 leaves, max depth = 10, train loss: 0.51449, val loss: 0.50143, in 0.016s
1 tree, 29 leaves, max depth = 9, train loss: 0.50795, val loss: 0.49420, in 0.000s
1 tree, 51 leaves, max depth = 12, train loss: 0.49881, val loss: 0.48525, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.49300, val loss: 0.47880, in 0.000s
1 tree, 31 leaves, max depth = 11, train loss: 0.48765, val loss: 0.47282, in 0.016s
1 tree, 8 leaves, max depth = 5, train loss: 0.48262, val loss: 0.46722, in 0.000s
1 tree, 25 leaves, max depth = 11, train loss: 0.47791, val loss: 0.46188, in 0.016s
1 tree, 64 leaves, max depth = 11, train loss: 0.47270, val loss: 0.45673, in 0.000s
1 tree, 60 leaves, max depth = 8, train loss: 0.46742, val loss: 0.45163, in 0.016s
1 tree, 26 leaves, max depth = 9, train loss: 0.46333, val loss: 0.44695, in 0.016s
1 tree, 26 leaves, max depth = 9, train loss: 0.45955, val loss: 0.44261, in 0.000s
1 tree, 64 leaves, max depth = 11, train loss: 0.45509, val loss: 0.43822, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.45184, val loss: 0.43476, in 0.000s
1 tree, 31 leaves, max depth = 11, train loss: 0.44865, val loss: 0.43108, in 0.016s
1 tree, 30 leaves, max depth = 15, train loss: 0.44572, val loss: 0.42770, in 0.000s
1 tree, 63 leaves, max depth = 10, train loss: 0.44134, val loss: 0.42355, in 0.016s
1 tree, 51 leaves, max depth = 10, train loss: 0.43509, val loss: 0.41754, in 0.016s
1 tree, 63 leaves, max depth = 10, train loss: 0.43136, val loss: 0.41401, in 0.000s
1 tree, 58 leaves, max depth = 17, train loss: 0.42841, val loss: 0.41103, in 0.016s
1 tree, 58 leaves, max depth = 17, train loss: 0.42569, val loss: 0.40828, in 0.016s
1 tree, 58 leaves, max depth = 17, train loss: 0.42319, val loss: 0.40574, in 0.000s
1 tree, 27 leaves, max depth = 8, train loss: 0.42068, val loss: 0.40279, in 0.016s
1 tree, 58 leaves, max depth = 16, train loss: 0.41838, val loss: 0.40045, in 0.016s
1 tree, 29 leaves, max depth = 8, train loss: 0.41600, val loss: 0.39770, in 0.000s
1 tree, 50 leaves, max depth = 10, train loss: 0.41120, val loss: 0.39313, in 0.016s
1 tree, 30 leaves, max depth = 8, train loss: 0.40907, val loss: 0.39064, in 0.016s
1 tree, 61 leaves, max depth = 10, train loss: 0.40628, val loss: 0.38806, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.40363, val loss: 0.38521, in 0.016s
1 tree, 28 leaves, max depth = 8, train loss: 0.40177, val loss: 0.38298, in 0.000s
1 tree, 58 leaves, max depth = 15, train loss: 0.39983, val loss: 0.38104, in 0.016s
1 tree, 50 leaves, max depth = 10, train loss: 0.39574, val loss: 0.37716, in 0.016s
1 tree, 64 leaves, max depth = 13, train loss: 0.39332, val loss: 0.37487, in 0.000s
1 tree, 61 leaves, max depth = 10, train loss: 0.39111, val loss: 0.37286, in 0.016s
1 tree, 51 leaves, max depth = 10, train loss: 0.38763, val loss: 0.36958, in 0.016s
1 tree, 29 leaves, max depth = 9, train loss: 0.38602, val loss: 0.36766, in 0.000s
1 tree, 61 leaves, max depth = 9, train loss: 0.38414, val loss: 0.36597, in 0.016s
1 tree, 53 leaves, max depth = 11, train loss: 0.38105, val loss: 0.36307, in 0.016s
1 tree, 30 leaves, max depth = 9, train loss: 0.37961, val loss: 0.36136, in 0.000s
1 tree, 53 leaves, max depth = 11, train loss: 0.37675, val loss: 0.35866, in 0.016s
1 tree, 53 leaves, max depth = 11, train loss: 0.37410, val loss: 0.35618, in 0.016s
1 tree, 30 leaves, max depth = 10, train loss: 0.37278, val loss: 0.35459, in 0.000s
1 tree, 30 leaves, max depth = 10, train loss: 0.37156, val loss: 0.35312, in 0.016s
1 tree, 53 leaves, max depth = 11, train loss: 0.36910, val loss: 0.35082, in 0.000s
1 tree, 8 leaves, max depth = 6, train loss: 0.36789, val loss: 0.34940, in 0.000s
1 tree, 28 leaves, max depth = 8, train loss: 0.36673, val loss: 0.34846, in 0.000s
1 tree, 53 leaves, max depth = 11, train loss: 0.36450, val loss: 0.34639, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.36215, val loss: 0.34383, in 0.016s
1 tree, 65 leaves, max depth = 15, train loss: 0.36077, val loss: 0.34259, in 0.000s
1 tree, 51 leaves, max depth = 11, train loss: 0.35880, val loss: 0.34075, in 0.016s
1 tree, 8 leaves, max depth = 5, train loss: 0.35771, val loss: 0.33948, in 0.016s
1 tree, 19 leaves, max depth = 7, train loss: 0.35681, val loss: 0.33855, in 0.000s
1 tree, 37 leaves, max depth = 14, train loss: 0.35589, val loss: 0.33752, in 0.016s
1 tree, 34 leaves, max depth = 9, train loss: 0.35448, val loss: 0.33617, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.35230, val loss: 0.33380, in 0.016s
1 tree, 65 leaves, max depth = 15, train loss: 0.35113, val loss: 0.33276, in 0.016s
1 tree, 51 leaves, max depth = 11, train loss: 0.34942, val loss: 0.33119, in 0.000s
1 tree, 51 leaves, max depth = 11, train loss: 0.34784, val loss: 0.32974, in 0.016s
1 tree, 36 leaves, max depth = 14, train loss: 0.34690, val loss: 0.32871, in 0.016s
1 tree, 38 leaves, max depth = 17, train loss: 0.34603, val loss: 0.32777, in 0.000s
Fit 72 trees in 0.955 s, (2725 total leaves)
Time spent computing histograms: 0.337s
Time spent finding best splits:  0.065s
Time spent applying splits:      0.055s
Time spent predicting:           0.000s
Trial 32, Fold 4: Log loss = 0.3466575263630329, Average precision = 0.9518809856724947, ROC-AUC = 0.9474566246056781, Elapsed Time = 0.9676071000012598 seconds
Trial 32, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 32, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
Binning 0.040 GB of training data: 0.174 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 27 leaves, max depth = 10, train loss: 0.67170, val loss: 0.67020, in 0.016s
1 tree, 30 leaves, max depth = 10, train loss: 0.65213, val loss: 0.64928, in 0.016s
1 tree, 27 leaves, max depth = 10, train loss: 0.63415, val loss: 0.63004, in 0.000s
1 tree, 8 leaves, max depth = 5, train loss: 0.61770, val loss: 0.61238, in 0.016s
1 tree, 27 leaves, max depth = 9, train loss: 0.60282, val loss: 0.59636, in 0.000s
1 tree, 28 leaves, max depth = 9, train loss: 0.58913, val loss: 0.58154, in 0.016s
1 tree, 30 leaves, max depth = 9, train loss: 0.57659, val loss: 0.56794, in 0.016s
1 tree, 27 leaves, max depth = 9, train loss: 0.56511, val loss: 0.55546, in 0.000s
1 tree, 26 leaves, max depth = 9, train loss: 0.55441, val loss: 0.54380, in 0.016s
1 tree, 26 leaves, max depth = 9, train loss: 0.54442, val loss: 0.53282, in 0.000s
1 tree, 29 leaves, max depth = 10, train loss: 0.53543, val loss: 0.52290, in 0.016s
1 tree, 27 leaves, max depth = 9, train loss: 0.52719, val loss: 0.51381, in 0.016s
1 tree, 29 leaves, max depth = 9, train loss: 0.51959, val loss: 0.50540, in 0.000s
1 tree, 29 leaves, max depth = 10, train loss: 0.51254, val loss: 0.49748, in 0.016s
1 tree, 28 leaves, max depth = 9, train loss: 0.50595, val loss: 0.49013, in 0.016s
1 tree, 55 leaves, max depth = 10, train loss: 0.49695, val loss: 0.48163, in 0.016s
1 tree, 28 leaves, max depth = 10, train loss: 0.49108, val loss: 0.47504, in 0.000s
1 tree, 28 leaves, max depth = 10, train loss: 0.48566, val loss: 0.46892, in 0.016s
1 tree, 9 leaves, max depth = 6, train loss: 0.48057, val loss: 0.46315, in 0.000s
1 tree, 30 leaves, max depth = 9, train loss: 0.47581, val loss: 0.45770, in 0.016s
1 tree, 30 leaves, max depth = 10, train loss: 0.47142, val loss: 0.45264, in 0.016s
1 tree, 63 leaves, max depth = 8, train loss: 0.46604, val loss: 0.44799, in 0.016s
1 tree, 29 leaves, max depth = 9, train loss: 0.46211, val loss: 0.44343, in 0.000s
1 tree, 29 leaves, max depth = 9, train loss: 0.45847, val loss: 0.43919, in 0.016s
1 tree, 64 leaves, max depth = 15, train loss: 0.45379, val loss: 0.43466, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.45053, val loss: 0.43152, in 0.000s
1 tree, 30 leaves, max depth = 10, train loss: 0.44747, val loss: 0.42791, in 0.016s
1 tree, 52 leaves, max depth = 10, train loss: 0.44405, val loss: 0.42437, in 0.016s
1 tree, 63 leaves, max depth = 8, train loss: 0.43972, val loss: 0.42076, in 0.016s
1 tree, 57 leaves, max depth = 12, train loss: 0.43350, val loss: 0.41510, in 0.016s
1 tree, 63 leaves, max depth = 8, train loss: 0.42981, val loss: 0.41209, in 0.000s
1 tree, 60 leaves, max depth = 21, train loss: 0.42690, val loss: 0.40919, in 0.016s
1 tree, 60 leaves, max depth = 21, train loss: 0.42422, val loss: 0.40651, in 0.016s
1 tree, 31 leaves, max depth = 15, train loss: 0.42175, val loss: 0.40359, in 0.016s
1 tree, 29 leaves, max depth = 10, train loss: 0.41935, val loss: 0.40072, in 0.000s
1 tree, 59 leaves, max depth = 19, train loss: 0.41688, val loss: 0.39827, in 0.016s
1 tree, 31 leaves, max depth = 15, train loss: 0.41479, val loss: 0.39576, in 0.016s
1 tree, 55 leaves, max depth = 11, train loss: 0.40986, val loss: 0.39136, in 0.016s
1 tree, 30 leaves, max depth = 10, train loss: 0.40785, val loss: 0.38893, in 0.000s
1 tree, 64 leaves, max depth = 11, train loss: 0.40498, val loss: 0.38629, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.40232, val loss: 0.38374, in 0.016s
1 tree, 28 leaves, max depth = 8, train loss: 0.40036, val loss: 0.38221, in 0.000s
1 tree, 61 leaves, max depth = 18, train loss: 0.39835, val loss: 0.38024, in 0.016s
1 tree, 54 leaves, max depth = 10, train loss: 0.39425, val loss: 0.37663, in 0.016s
1 tree, 64 leaves, max depth = 12, train loss: 0.39186, val loss: 0.37446, in 0.016s
1 tree, 64 leaves, max depth = 9, train loss: 0.38963, val loss: 0.37280, in 0.016s
1 tree, 55 leaves, max depth = 10, train loss: 0.38615, val loss: 0.36977, in 0.016s
1 tree, 26 leaves, max depth = 9, train loss: 0.38452, val loss: 0.36781, in 0.000s
1 tree, 63 leaves, max depth = 8, train loss: 0.38264, val loss: 0.36645, in 0.016s
1 tree, 55 leaves, max depth = 10, train loss: 0.37955, val loss: 0.36378, in 0.016s
1 tree, 61 leaves, max depth = 20, train loss: 0.37812, val loss: 0.36237, in 0.016s
1 tree, 55 leaves, max depth = 10, train loss: 0.37535, val loss: 0.35999, in 0.016s
1 tree, 55 leaves, max depth = 10, train loss: 0.37279, val loss: 0.35780, in 0.016s
1 tree, 28 leaves, max depth = 14, train loss: 0.37141, val loss: 0.35609, in 0.000s
1 tree, 30 leaves, max depth = 9, train loss: 0.37008, val loss: 0.35444, in 0.016s
1 tree, 55 leaves, max depth = 10, train loss: 0.36768, val loss: 0.35241, in 0.016s
1 tree, 8 leaves, max depth = 5, train loss: 0.36638, val loss: 0.35081, in 0.016s
1 tree, 30 leaves, max depth = 11, train loss: 0.36525, val loss: 0.34937, in 0.000s
1 tree, 54 leaves, max depth = 10, train loss: 0.36301, val loss: 0.34749, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.36063, val loss: 0.34523, in 0.016s
1 tree, 64 leaves, max depth = 12, train loss: 0.35929, val loss: 0.34409, in 0.016s
1 tree, 55 leaves, max depth = 10, train loss: 0.35730, val loss: 0.34245, in 0.016s
1 tree, 37 leaves, max depth = 10, train loss: 0.35617, val loss: 0.34121, in 0.000s
1 tree, 37 leaves, max depth = 10, train loss: 0.35514, val loss: 0.34006, in 0.016s
1 tree, 30 leaves, max depth = 9, train loss: 0.35422, val loss: 0.33889, in 0.016s
1 tree, 36 leaves, max depth = 12, train loss: 0.35284, val loss: 0.33775, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.35065, val loss: 0.33565, in 0.000s
1 tree, 65 leaves, max depth = 11, train loss: 0.34947, val loss: 0.33469, in 0.016s
1 tree, 54 leaves, max depth = 10, train loss: 0.34772, val loss: 0.33327, in 0.016s
1 tree, 54 leaves, max depth = 10, train loss: 0.34611, val loss: 0.33197, in 0.016s
1 tree, 39 leaves, max depth = 11, train loss: 0.34516, val loss: 0.33092, in 0.016s
1 tree, 18 leaves, max depth = 7, train loss: 0.34438, val loss: 0.33003, in 0.000s
Fit 72 trees in 1.127 s, (2802 total leaves)
Time spent computing histograms: 0.405s
Time spent finding best splits:  0.086s
Time spent applying splits:      0.070s
Time spent predicting:           0.000s
Trial 32, Fold 5: Log loss = 0.35100856288112636, Average precision = 0.950646473005743, ROC-AUC = 0.9457785704996006, Elapsed Time = 1.1419028999989678 seconds
Optimization Progress:  33%|###3      | 33/100 [06:35<14:38, 13.11s/it]
Trial 33, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 33, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
0.143 s 0.040 GB of training data: 
0.015 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 27 leaves, max depth = 12, train loss: 0.67757, val loss: 0.67715, in 0.000s
1 tree, 28 leaves, max depth = 12, train loss: 0.66302, val loss: 0.66218, in 0.016s
1 tree, 29 leaves, max depth = 12, train loss: 0.64939, val loss: 0.64816, in 0.000s
1 tree, 29 leaves, max depth = 12, train loss: 0.63662, val loss: 0.63501, in 0.016s
1 tree, 29 leaves, max depth = 12, train loss: 0.62465, val loss: 0.62267, in 0.000s
1 tree, 29 leaves, max depth = 12, train loss: 0.61341, val loss: 0.61107, in 0.016s
1 tree, 29 leaves, max depth = 12, train loss: 0.60286, val loss: 0.60017, in 0.000s
1 tree, 29 leaves, max depth = 12, train loss: 0.59293, val loss: 0.58991, in 0.016s
1 tree, 30 leaves, max depth = 12, train loss: 0.58360, val loss: 0.58024, in 0.000s
1 tree, 31 leaves, max depth = 12, train loss: 0.57481, val loss: 0.57113, in 0.016s
1 tree, 28 leaves, max depth = 11, train loss: 0.56654, val loss: 0.56255, in 0.000s
1 tree, 28 leaves, max depth = 11, train loss: 0.55874, val loss: 0.55446, in 0.016s
1 tree, 31 leaves, max depth = 14, train loss: 0.55145, val loss: 0.54680, in 0.000s
Fit 13 trees in 0.361 s, (377 total leaves)
Time spent computing histograms: 0.052s
Time spent finding best splits:  0.007s
Time spent applying splits:      0.006s
Time spent predicting:           0.000s
Trial 33, Fold 1: Log loss = 0.5523366704044703, Average precision = 0.8140478710455946, ROC-AUC = 0.8619023571739245, Elapsed Time = 0.36222819999966305 seconds
Trial 33, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 33, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
Binning 0.040 GB of training data: 0.143 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 29 leaves, max depth = 9, train loss: 0.67775, val loss: 0.67702, in 0.016s
1 tree, 29 leaves, max depth = 9, train loss: 0.66336, val loss: 0.66191, in 0.000s
1 tree, 29 leaves, max depth = 9, train loss: 0.64990, val loss: 0.64776, in 0.000s
1 tree, 29 leaves, max depth = 9, train loss: 0.63728, val loss: 0.63449, in 0.016s
1 tree, 29 leaves, max depth = 9, train loss: 0.62545, val loss: 0.62202, in 0.000s
1 tree, 30 leaves, max depth = 11, train loss: 0.61434, val loss: 0.61036, in 0.016s
1 tree, 29 leaves, max depth = 9, train loss: 0.60390, val loss: 0.59934, in 0.000s
1 tree, 30 leaves, max depth = 11, train loss: 0.59408, val loss: 0.58902, in 0.016s
1 tree, 29 leaves, max depth = 9, train loss: 0.58485, val loss: 0.57923, in 0.000s
1 tree, 31 leaves, max depth = 11, train loss: 0.57615, val loss: 0.57007, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.56796, val loss: 0.56144, in 0.000s
1 tree, 28 leaves, max depth = 9, train loss: 0.56010, val loss: 0.55310, in 0.016s
1 tree, 54 leaves, max depth = 11, train loss: 0.55226, val loss: 0.54561, in 0.000s
Fit 13 trees in 0.377 s, (407 total leaves)
Time spent computing histograms: 0.049s
Time spent finding best splits:  0.007s
Time spent applying splits:      0.007s
Time spent predicting:           0.000s
Trial 33, Fold 2: Log loss = 0.5521814942210168, Average precision = 0.8878644760545134, ROC-AUC = 0.9063182770192243, Elapsed Time = 0.3772525999993377 seconds
Trial 33, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 33, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
0.158 s 0.040 GB of training data: 
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 28 leaves, max depth = 11, train loss: 0.67789, val loss: 0.67739, in 0.016s
1 tree, 28 leaves, max depth = 11, train loss: 0.66362, val loss: 0.66265, in 0.000s
1 tree, 21 leaves, max depth = 8, train loss: 0.65013, val loss: 0.64872, in 0.016s
1 tree, 28 leaves, max depth = 11, train loss: 0.63762, val loss: 0.63576, in 0.000s
1 tree, 21 leaves, max depth = 8, train loss: 0.62575, val loss: 0.62348, in 0.016s
1 tree, 28 leaves, max depth = 11, train loss: 0.61473, val loss: 0.61205, in 0.000s
1 tree, 20 leaves, max depth = 8, train loss: 0.60425, val loss: 0.60119, in 0.016s
1 tree, 28 leaves, max depth = 11, train loss: 0.59452, val loss: 0.59107, in 0.000s
1 tree, 21 leaves, max depth = 7, train loss: 0.58525, val loss: 0.58145, in 0.016s
1 tree, 29 leaves, max depth = 11, train loss: 0.57663, val loss: 0.57247, in 0.000s
1 tree, 22 leaves, max depth = 7, train loss: 0.56840, val loss: 0.56391, in 0.000s
1 tree, 29 leaves, max depth = 11, train loss: 0.56075, val loss: 0.55593, in 0.000s
1 tree, 54 leaves, max depth = 12, train loss: 0.55293, val loss: 0.54866, in 0.016s
Fit 13 trees in 0.377 s, (357 total leaves)
Time spent computing histograms: 0.053s
Time spent finding best splits:  0.007s
Time spent applying splits:      0.007s
Time spent predicting:           0.000s
Trial 33, Fold 3: Log loss = 0.5495966806223601, Average precision = 0.8902275823415812, ROC-AUC = 0.9079231666120907, Elapsed Time = 0.3846448000003875 seconds
Trial 33, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 33, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
0.157 s 0.040 GB of training data: 
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 27 leaves, max depth = 12, train loss: 0.67773, val loss: 0.67684, in 0.000s
1 tree, 27 leaves, max depth = 12, train loss: 0.66332, val loss: 0.66157, in 0.016s
1 tree, 27 leaves, max depth = 12, train loss: 0.64984, val loss: 0.64725, in 0.000s
1 tree, 27 leaves, max depth = 12, train loss: 0.63721, val loss: 0.63381, in 0.016s
1 tree, 27 leaves, max depth = 12, train loss: 0.62536, val loss: 0.62117, in 0.000s
1 tree, 27 leaves, max depth = 12, train loss: 0.61424, val loss: 0.60929, in 0.000s
1 tree, 29 leaves, max depth = 12, train loss: 0.60379, val loss: 0.59810, in 0.016s
1 tree, 32 leaves, max depth = 13, train loss: 0.59403, val loss: 0.58762, in 0.000s
1 tree, 29 leaves, max depth = 12, train loss: 0.58478, val loss: 0.57768, in 0.016s
1 tree, 32 leaves, max depth = 13, train loss: 0.57613, val loss: 0.56836, in 0.000s
1 tree, 30 leaves, max depth = 11, train loss: 0.56792, val loss: 0.55950, in 0.016s
1 tree, 33 leaves, max depth = 13, train loss: 0.56024, val loss: 0.55118, in 0.000s
1 tree, 31 leaves, max depth = 11, train loss: 0.55295, val loss: 0.54326, in 0.016s
Fit 13 trees in 0.376 s, (378 total leaves)
Time spent computing histograms: 0.051s
Time spent finding best splits:  0.007s
Time spent applying splits:      0.007s
Time spent predicting:           0.000s
Trial 33, Fold 4: Log loss = 0.5517180277152086, Average precision = 0.8204335615774272, ROC-AUC = 0.8677278482370867, Elapsed Time = 0.38067810000029567 seconds
Trial 33, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 33, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
Binning 0.040 GB of training data: 0.142 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 27 leaves, max depth = 10, train loss: 0.67760, val loss: 0.67656, in 0.016s
1 tree, 27 leaves, max depth = 10, train loss: 0.66306, val loss: 0.66102, in 0.000s
1 tree, 27 leaves, max depth = 11, train loss: 0.64952, val loss: 0.64655, in 0.016s
1 tree, 27 leaves, max depth = 10, train loss: 0.63675, val loss: 0.63285, in 0.000s
1 tree, 27 leaves, max depth = 12, train loss: 0.62485, val loss: 0.62008, in 0.016s
1 tree, 27 leaves, max depth = 10, train loss: 0.61360, val loss: 0.60796, in 0.000s
1 tree, 27 leaves, max depth = 12, train loss: 0.60309, val loss: 0.59664, in 0.016s
1 tree, 27 leaves, max depth = 10, train loss: 0.59314, val loss: 0.58587, in 0.000s
1 tree, 27 leaves, max depth = 12, train loss: 0.58385, val loss: 0.57582, in 0.000s
1 tree, 26 leaves, max depth = 12, train loss: 0.57510, val loss: 0.56633, in 0.016s
1 tree, 28 leaves, max depth = 10, train loss: 0.56678, val loss: 0.55726, in 0.000s
1 tree, 30 leaves, max depth = 12, train loss: 0.55901, val loss: 0.54880, in 0.016s
1 tree, 51 leaves, max depth = 11, train loss: 0.55133, val loss: 0.54145, in 0.016s
Fit 13 trees in 0.377 s, (378 total leaves)
Time spent computing histograms: 0.050s
Time spent finding best splits:  0.007s
Time spent applying splits:      0.007s
Time spent predicting:           0.000s
Trial 33, Fold 5: Log loss = 0.5551145966827552, Average precision = 0.8855353230294833, ROC-AUC = 0.9000196941827843, Elapsed Time = 0.3723337999999785 seconds
Optimization Progress:  34%|###4      | 34/100 [06:44<12:59, 11.81s/it]
Trial 34, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 34, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
0.141 s 0.040 GB of training data: 
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 64 leaves, max depth = 16, train loss: 0.65572, val loss: 0.65450, in 0.000s
1 tree, 64 leaves, max depth = 16, train loss: 0.62421, val loss: 0.62186, in 0.016s
1 tree, 68 leaves, max depth = 19, train loss: 0.59754, val loss: 0.59454, in 0.016s
1 tree, 68 leaves, max depth = 19, train loss: 0.57469, val loss: 0.57110, in 0.000s
1 tree, 68 leaves, max depth = 15, train loss: 0.55484, val loss: 0.55033, in 0.016s
1 tree, 107 leaves, max depth = 18, train loss: 0.53642, val loss: 0.53322, in 0.000s
1 tree, 67 leaves, max depth = 18, train loss: 0.52055, val loss: 0.51683, in 0.016s
1 tree, 106 leaves, max depth = 18, train loss: 0.50540, val loss: 0.50285, in 0.016s
1 tree, 67 leaves, max depth = 13, train loss: 0.49249, val loss: 0.48932, in 0.000s
1 tree, 107 leaves, max depth = 18, train loss: 0.47987, val loss: 0.47776, in 0.016s
1 tree, 67 leaves, max depth = 12, train loss: 0.46924, val loss: 0.46663, in 0.016s
1 tree, 107 leaves, max depth = 15, train loss: 0.45859, val loss: 0.45695, in 0.000s
1 tree, 107 leaves, max depth = 15, train loss: 0.44930, val loss: 0.44856, in 0.016s
1 tree, 68 leaves, max depth = 14, train loss: 0.44082, val loss: 0.43960, in 0.016s
1 tree, 68 leaves, max depth = 13, train loss: 0.43316, val loss: 0.43122, in 0.000s
1 tree, 107 leaves, max depth = 13, train loss: 0.42538, val loss: 0.42429, in 0.016s
1 tree, 67 leaves, max depth = 14, train loss: 0.41903, val loss: 0.41746, in 0.016s
1 tree, 107 leaves, max depth = 13, train loss: 0.41233, val loss: 0.41156, in 0.000s
1 tree, 4 leaves, max depth = 3, train loss: 0.40685, val loss: 0.40550, in 0.016s
1 tree, 107 leaves, max depth = 15, train loss: 0.40102, val loss: 0.40048, in 0.000s
1 tree, 4 leaves, max depth = 3, train loss: 0.39630, val loss: 0.39522, in 0.016s
1 tree, 107 leaves, max depth = 13, train loss: 0.39119, val loss: 0.39094, in 0.000s
1 tree, 67 leaves, max depth = 14, train loss: 0.38623, val loss: 0.38562, in 0.016s
1 tree, 67 leaves, max depth = 14, train loss: 0.38193, val loss: 0.38099, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.37791, val loss: 0.37649, in 0.000s
1 tree, 107 leaves, max depth = 14, train loss: 0.37343, val loss: 0.37285, in 0.016s
1 tree, 107 leaves, max depth = 14, train loss: 0.36951, val loss: 0.36970, in 0.000s
1 tree, 4 leaves, max depth = 3, train loss: 0.36598, val loss: 0.36571, in 0.016s
1 tree, 68 leaves, max depth = 12, train loss: 0.36221, val loss: 0.36159, in 0.000s
1 tree, 106 leaves, max depth = 14, train loss: 0.35873, val loss: 0.35888, in 0.016s
1 tree, 67 leaves, max depth = 13, train loss: 0.35557, val loss: 0.35549, in 0.000s
1 tree, 4 leaves, max depth = 3, train loss: 0.35248, val loss: 0.35198, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.34978, val loss: 0.34889, in 0.000s
1 tree, 67 leaves, max depth = 13, train loss: 0.34705, val loss: 0.34604, in 0.016s
1 tree, 107 leaves, max depth = 14, train loss: 0.34384, val loss: 0.34366, in 0.000s
1 tree, 4 leaves, max depth = 3, train loss: 0.34142, val loss: 0.34082, in 0.016s
1 tree, 86 leaves, max depth = 17, train loss: 0.33886, val loss: 0.33801, in 0.016s
1 tree, 107 leaves, max depth = 14, train loss: 0.33596, val loss: 0.33592, in 0.000s
1 tree, 86 leaves, max depth = 15, train loss: 0.33371, val loss: 0.33340, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.33152, val loss: 0.33086, in 0.000s
1 tree, 4 leaves, max depth = 3, train loss: 0.32959, val loss: 0.32860, in 0.016s
1 tree, 107 leaves, max depth = 14, train loss: 0.32690, val loss: 0.32672, in 0.000s
1 tree, 85 leaves, max depth = 14, train loss: 0.32465, val loss: 0.32419, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.32288, val loss: 0.32207, in 0.000s
1 tree, 67 leaves, max depth = 14, train loss: 0.32097, val loss: 0.32013, in 0.016s
1 tree, 107 leaves, max depth = 14, train loss: 0.31851, val loss: 0.31845, in 0.016s
1 tree, 85 leaves, max depth = 15, train loss: 0.31671, val loss: 0.31651, in 0.016s
[48/85] 1 tree, 107 leaves, max depth = 14, train loss: 0.31454, val loss: 0.31507, in 0.000s
1 tree, 4 leaves, max depth = 3, train loss: 0.31287, val loss: 0.31308, in 0.016s
1 tree, 67 leaves, max depth = 13, train loss: 0.31129, val loss: 0.31145, in 0.000s
1 tree, 107 leaves, max depth = 15, train loss: 0.30933, val loss: 0.31021, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.30780, val loss: 0.30834, in 0.000s
1 tree, 86 leaves, max depth = 15, train loss: 0.30623, val loss: 0.30665, in 0.016s
1 tree, 107 leaves, max depth = 14, train loss: 0.30443, val loss: 0.30553, in 0.016s
1 tree, 85 leaves, max depth = 19, train loss: 0.30294, val loss: 0.30382, in 0.000s
1 tree, 4 leaves, max depth = 3, train loss: 0.30152, val loss: 0.30207, in 0.000s
1 tree, 107 leaves, max depth = 14, train loss: 0.29989, val loss: 0.30112, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.29861, val loss: 0.29952, in 0.016s
1 tree, 67 leaves, max depth = 15, train loss: 0.29723, val loss: 0.29826, in 0.000s
1 tree, 4 leaves, max depth = 3, train loss: 0.29609, val loss: 0.29684, in 0.016s
1 tree, 85 leaves, max depth = 19, train loss: 0.29481, val loss: 0.29542, in 0.000s
1 tree, 107 leaves, max depth = 15, train loss: 0.29323, val loss: 0.29450, in 0.016s
1 tree, 40 leaves, max depth = 11, train loss: 0.29213, val loss: 0.29350, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.29104, val loss: 0.29212, in 0.000s
1 tree, 107 leaves, max depth = 15, train loss: 0.28966, val loss: 0.29138, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.28868, val loss: 0.29012, in 0.000s
1 tree, 85 leaves, max depth = 19, train loss: 0.28753, val loss: 0.28883, in 0.016s
1 tree, 132 leaves, max depth = 24, train loss: 0.28595, val loss: 0.28798, in 0.016s
1 tree, 64 leaves, max depth = 15, train loss: 0.28497, val loss: 0.28696, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.28404, val loss: 0.28575, in 0.000s
1 tree, 140 leaves, max depth = 18, train loss: 0.28243, val loss: 0.28485, in 0.016s
1 tree, 140 leaves, max depth = 20, train loss: 0.28082, val loss: 0.28362, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.27996, val loss: 0.28251, in 0.000s
1 tree, 83 leaves, max depth = 17, train loss: 0.27896, val loss: 0.28156, in 0.016s
1 tree, 140 leaves, max depth = 18, train loss: 0.27753, val loss: 0.28080, in 0.016s
1 tree, 131 leaves, max depth = 24, train loss: 0.27623, val loss: 0.28017, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.27541, val loss: 0.27910, in 0.000s
1 tree, 40 leaves, max depth = 10, train loss: 0.27456, val loss: 0.27836, in 0.000s
1 tree, 4 leaves, max depth = 3, train loss: 0.27383, val loss: 0.27738, in 0.016s
1 tree, 85 leaves, max depth = 17, train loss: 0.27293, val loss: 0.27635, in 0.016s
1 tree, 84 leaves, max depth = 13, train loss: 0.27138, val loss: 0.27547, in 0.000s
1 tree, 140 leaves, max depth = 17, train loss: 0.27018, val loss: 0.27490, in 0.016s
1 tree, 140 leaves, max depth = 17, train loss: 0.26887, val loss: 0.27393, in 0.016s
1 tree, 85 leaves, max depth = 14, train loss: 0.26804, val loss: 0.27291, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.26732, val loss: 0.27195, in 0.000s
Fit 85 trees in 1.064 s, (5840 total leaves)
Time spent computing histograms: 0.358s
Time spent finding best splits:  0.077s
Time spent applying splits:      0.088s
Time spent predicting:           0.000s
Trial 34, Fold 1: Log loss = 0.2807773485256574, Average precision = 0.9522178452405305, ROC-AUC = 0.9524693001008493, Elapsed Time = 1.0698346000008314 seconds
Trial 34, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 34, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
Binning 0.040 GB of training data: 0.142 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 65 leaves, max depth = 15, train loss: 0.65638, val loss: 0.65475, in 0.016s
1 tree, 68 leaves, max depth = 11, train loss: 0.62534, val loss: 0.62225, in 0.000s
1 tree, 67 leaves, max depth = 14, train loss: 0.59839, val loss: 0.59387, in 0.016s
1 tree, 67 leaves, max depth = 14, train loss: 0.57553, val loss: 0.56976, in 0.000s
1 tree, 67 leaves, max depth = 14, train loss: 0.55565, val loss: 0.54871, in 0.016s
1 tree, 107 leaves, max depth = 15, train loss: 0.53740, val loss: 0.53119, in 0.016s
1 tree, 67 leaves, max depth = 20, train loss: 0.52158, val loss: 0.51443, in 0.016s
1 tree, 107 leaves, max depth = 15, train loss: 0.50660, val loss: 0.50012, in 0.000s
1 tree, 68 leaves, max depth = 12, train loss: 0.49338, val loss: 0.48599, in 0.016s
1 tree, 107 leaves, max depth = 16, train loss: 0.48096, val loss: 0.47419, in 0.016s
1 tree, 68 leaves, max depth = 14, train loss: 0.47029, val loss: 0.46280, in 0.000s
1 tree, 107 leaves, max depth = 15, train loss: 0.45981, val loss: 0.45289, in 0.016s
1 tree, 68 leaves, max depth = 17, train loss: 0.45088, val loss: 0.44338, in 0.000s
1 tree, 68 leaves, max depth = 15, train loss: 0.44292, val loss: 0.43475, in 0.016s
1 tree, 68 leaves, max depth = 18, train loss: 0.43616, val loss: 0.42749, in 0.016s
1 tree, 107 leaves, max depth = 14, train loss: 0.42762, val loss: 0.41952, in 0.000s
1 tree, 4 leaves, max depth = 3, train loss: 0.42217, val loss: 0.41385, in 0.016s
1 tree, 106 leaves, max depth = 14, train loss: 0.41477, val loss: 0.40700, in 0.000s
1 tree, 68 leaves, max depth = 14, train loss: 0.40943, val loss: 0.40129, in 0.016s
1 tree, 107 leaves, max depth = 14, train loss: 0.40301, val loss: 0.39541, in 0.016s
1 tree, 107 leaves, max depth = 14, train loss: 0.39738, val loss: 0.39024, in 0.016s
1 tree, 106 leaves, max depth = 14, train loss: 0.39249, val loss: 0.38583, in 0.000s
1 tree, 4 leaves, max depth = 3, train loss: 0.38798, val loss: 0.38112, in 0.016s
1 tree, 67 leaves, max depth = 12, train loss: 0.38358, val loss: 0.37632, in 0.000s
1 tree, 4 leaves, max depth = 3, train loss: 0.37970, val loss: 0.37226, in 0.016s
1 tree, 107 leaves, max depth = 15, train loss: 0.37539, val loss: 0.36839, in 0.000s
1 tree, 107 leaves, max depth = 15, train loss: 0.37166, val loss: 0.36510, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.36824, val loss: 0.36151, in 0.016s
1 tree, 67 leaves, max depth = 11, train loss: 0.36446, val loss: 0.35744, in 0.000s
1 tree, 107 leaves, max depth = 17, train loss: 0.36117, val loss: 0.35458, in 0.016s
1 tree, 67 leaves, max depth = 14, train loss: 0.35795, val loss: 0.35107, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.35495, val loss: 0.34791, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.35233, val loss: 0.34513, in 0.000s
1 tree, 67 leaves, max depth = 15, train loss: 0.34955, val loss: 0.34213, in 0.000s
1 tree, 107 leaves, max depth = 18, train loss: 0.34648, val loss: 0.33949, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.34413, val loss: 0.33700, in 0.016s
1 tree, 86 leaves, max depth = 16, train loss: 0.34166, val loss: 0.33427, in 0.000s
1 tree, 107 leaves, max depth = 18, train loss: 0.33894, val loss: 0.33197, in 0.016s
1 tree, 67 leaves, max depth = 14, train loss: 0.33661, val loss: 0.32957, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.33448, val loss: 0.32730, in 0.000s
1 tree, 83 leaves, max depth = 15, train loss: 0.33225, val loss: 0.32487, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.33036, val loss: 0.32284, in 0.000s
1 tree, 83 leaves, max depth = 15, train loss: 0.32843, val loss: 0.32074, in 0.016s
1 tree, 107 leaves, max depth = 17, train loss: 0.32585, val loss: 0.31858, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.32412, val loss: 0.31673, in 0.000s
1 tree, 107 leaves, max depth = 18, train loss: 0.32184, val loss: 0.31484, in 0.016s
1 tree, 67 leaves, max depth = 13, train loss: 0.32009, val loss: 0.31302, in 0.016s
1 tree, 106 leaves, max depth = 17, train loss: 0.31808, val loss: 0.31138, in 0.000s
1 tree, 4 leaves, max depth = 3, train loss: 0.31646, val loss: 0.30964, in 0.000s
1 tree, 83 leaves, max depth = 16, train loss: 0.31474, val loss: 0.30775, in 0.016s
1 tree, 106 leaves, max depth = 16, train loss: 0.31286, val loss: 0.30621, in 0.000s
1 tree, 4 leaves, max depth = 3, train loss: 0.31137, val loss: 0.30461, in 0.016s
1 tree, 86 leaves, max depth = 16, train loss: 0.30985, val loss: 0.30298, in 0.016s
1 tree, 107 leaves, max depth = 16, train loss: 0.30814, val loss: 0.30158, in 0.000s
1 tree, 67 leaves, max depth = 14, train loss: 0.30675, val loss: 0.30017, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.30537, val loss: 0.29869, in 0.000s
1 tree, 83 leaves, max depth = 16, train loss: 0.30406, val loss: 0.29725, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.30283, val loss: 0.29591, in 0.000s
1 tree, 107 leaves, max depth = 17, train loss: 0.30122, val loss: 0.29464, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.30010, val loss: 0.29341, in 0.000s
1 tree, 84 leaves, max depth = 14, train loss: 0.29813, val loss: 0.29203, in 0.016s
1 tree, 83 leaves, max depth = 16, train loss: 0.29687, val loss: 0.29064, in 0.016s
1 tree, 41 leaves, max depth = 10, train loss: 0.29578, val loss: 0.28960, in 0.000s
1 tree, 4 leaves, max depth = 3, train loss: 0.29473, val loss: 0.28845, in 0.016s
1 tree, 106 leaves, max depth = 17, train loss: 0.29333, val loss: 0.28733, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.29238, val loss: 0.28628, in 0.000s
1 tree, 83 leaves, max depth = 16, train loss: 0.29129, val loss: 0.28509, in 0.016s
1 tree, 131 leaves, max depth = 21, train loss: 0.28970, val loss: 0.28436, in 0.016s
1 tree, 65 leaves, max depth = 18, train loss: 0.28867, val loss: 0.28342, in 0.000s
1 tree, 4 leaves, max depth = 3, train loss: 0.28777, val loss: 0.28242, in 0.016s
1 tree, 106 leaves, max depth = 17, train loss: 0.28655, val loss: 0.28151, in 0.016s
1 tree, 140 leaves, max depth = 18, train loss: 0.28494, val loss: 0.28081, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.28409, val loss: 0.27987, in 0.000s
1 tree, 83 leaves, max depth = 17, train loss: 0.28304, val loss: 0.27882, in 0.000s
1 tree, 84 leaves, max depth = 14, train loss: 0.28152, val loss: 0.27780, in 0.016s
1 tree, 86 leaves, max depth = 21, train loss: 0.28056, val loss: 0.27673, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.27976, val loss: 0.27584, in 0.000s
1 tree, 140 leaves, max depth = 17, train loss: 0.27823, val loss: 0.27522, in 0.016s
1 tree, 131 leaves, max depth = 21, train loss: 0.27700, val loss: 0.27475, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.27626, val loss: 0.27392, in 0.016s
1 tree, 41 leaves, max depth = 10, train loss: 0.27544, val loss: 0.27315, in 0.000s
1 tree, 140 leaves, max depth = 18, train loss: 0.27406, val loss: 0.27262, in 0.016s
1 tree, 86 leaves, max depth = 21, train loss: 0.27322, val loss: 0.27167, in 0.016s
1 tree, 140 leaves, max depth = 18, train loss: 0.27199, val loss: 0.27124, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.27128, val loss: 0.27043, in 0.000s
Fit 85 trees in 1.126 s, (5776 total leaves)
Time spent computing histograms: 0.381s
Time spent finding best splits:  0.081s
Time spent applying splits:      0.093s
Time spent predicting:           0.000s
Trial 34, Fold 2: Log loss = 0.2800543717383729, Average precision = 0.9508008089697524, ROC-AUC = 0.9531672190791882, Elapsed Time = 1.1384725000007165 seconds
Trial 34, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 34, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
Binning 0.040 GB of training data: 0.158 s
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 65 leaves, max depth = 13, train loss: 0.65601, val loss: 0.65491, in 0.000s
1 tree, 65 leaves, max depth = 13, train loss: 0.62471, val loss: 0.62261, in 0.016s
1 tree, 65 leaves, max depth = 13, train loss: 0.59805, val loss: 0.59502, in 0.016s
1 tree, 67 leaves, max depth = 16, train loss: 0.57547, val loss: 0.57137, in 0.000s
1 tree, 66 leaves, max depth = 13, train loss: 0.55579, val loss: 0.55095, in 0.016s
1 tree, 106 leaves, max depth = 13, train loss: 0.53741, val loss: 0.53400, in 0.016s
1 tree, 66 leaves, max depth = 17, train loss: 0.52169, val loss: 0.51748, in 0.000s
1 tree, 106 leaves, max depth = 13, train loss: 0.50658, val loss: 0.50363, in 0.016s
1 tree, 66 leaves, max depth = 17, train loss: 0.49376, val loss: 0.49006, in 0.016s
1 tree, 106 leaves, max depth = 13, train loss: 0.48118, val loss: 0.47860, in 0.016s
1 tree, 66 leaves, max depth = 13, train loss: 0.47049, val loss: 0.46743, in 0.000s
1 tree, 106 leaves, max depth = 14, train loss: 0.45989, val loss: 0.45783, in 0.016s
1 tree, 67 leaves, max depth = 13, train loss: 0.45098, val loss: 0.44844, in 0.016s
1 tree, 67 leaves, max depth = 13, train loss: 0.44313, val loss: 0.44007, in 0.000s
1 tree, 67 leaves, max depth = 13, train loss: 0.43639, val loss: 0.43281, in 0.016s
1 tree, 106 leaves, max depth = 12, train loss: 0.42773, val loss: 0.42502, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.42206, val loss: 0.41978, in 0.000s
1 tree, 106 leaves, max depth = 12, train loss: 0.41456, val loss: 0.41311, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.40969, val loss: 0.40860, in 0.016s
1 tree, 106 leaves, max depth = 12, train loss: 0.40315, val loss: 0.40285, in 0.000s
1 tree, 106 leaves, max depth = 12, train loss: 0.39742, val loss: 0.39788, in 0.016s
1 tree, 106 leaves, max depth = 12, train loss: 0.39241, val loss: 0.39358, in 0.016s
1 tree, 67 leaves, max depth = 13, train loss: 0.38754, val loss: 0.38822, in 0.016s
1 tree, 67 leaves, max depth = 14, train loss: 0.38330, val loss: 0.38357, in 0.000s
1 tree, 4 leaves, max depth = 3, train loss: 0.37922, val loss: 0.37978, in 0.016s
1 tree, 106 leaves, max depth = 15, train loss: 0.37483, val loss: 0.37605, in 0.016s
1 tree, 106 leaves, max depth = 15, train loss: 0.37099, val loss: 0.37285, in 0.000s
1 tree, 4 leaves, max depth = 3, train loss: 0.36740, val loss: 0.36951, in 0.016s
1 tree, 67 leaves, max depth = 14, train loss: 0.36374, val loss: 0.36541, in 0.000s
1 tree, 106 leaves, max depth = 17, train loss: 0.36033, val loss: 0.36262, in 0.016s
1 tree, 66 leaves, max depth = 13, train loss: 0.35724, val loss: 0.35919, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.35410, val loss: 0.35626, in 0.000s
1 tree, 4 leaves, max depth = 3, train loss: 0.35135, val loss: 0.35370, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.34895, val loss: 0.35144, in 0.000s
1 tree, 67 leaves, max depth = 13, train loss: 0.34628, val loss: 0.34854, in 0.016s
1 tree, 105 leaves, max depth = 17, train loss: 0.34308, val loss: 0.34598, in 0.016s
1 tree, 85 leaves, max depth = 15, train loss: 0.34066, val loss: 0.34293, in 0.000s
1 tree, 106 leaves, max depth = 17, train loss: 0.33783, val loss: 0.34070, in 0.016s
1 tree, 67 leaves, max depth = 12, train loss: 0.33553, val loss: 0.33832, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.33330, val loss: 0.33623, in 0.000s
1 tree, 4 leaves, max depth = 3, train loss: 0.33133, val loss: 0.33438, in 0.016s
1 tree, 83 leaves, max depth = 14, train loss: 0.32915, val loss: 0.33179, in 0.000s
1 tree, 83 leaves, max depth = 14, train loss: 0.32729, val loss: 0.32954, in 0.016s
1 tree, 103 leaves, max depth = 14, train loss: 0.32460, val loss: 0.32744, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.32278, val loss: 0.32572, in 0.000s
1 tree, 105 leaves, max depth = 14, train loss: 0.32039, val loss: 0.32389, in 0.016s
[47/85] 1 tree, 67 leaves, max depth = 13, train loss: 0.31874, val loss: 0.32211, in 0.016s
1 tree, 106 leaves, max depth = 15, train loss: 0.31662, val loss: 0.32052, in 0.000s
1 tree, 4 leaves, max depth = 3, train loss: 0.31492, val loss: 0.31893, in 0.016s
1 tree, 83 leaves, max depth = 15, train loss: 0.31326, val loss: 0.31689, in 0.000s
1 tree, 106 leaves, max depth = 15, train loss: 0.31133, val loss: 0.31547, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.30977, val loss: 0.31399, in 0.016s
1 tree, 67 leaves, max depth = 13, train loss: 0.30838, val loss: 0.31250, in 0.000s
1 tree, 106 leaves, max depth = 15, train loss: 0.30662, val loss: 0.31123, in 0.016s
1 tree, 83 leaves, max depth = 15, train loss: 0.30519, val loss: 0.30943, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.30374, val loss: 0.30805, in 0.000s
1 tree, 106 leaves, max depth = 13, train loss: 0.30213, val loss: 0.30692, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.30083, val loss: 0.30569, in 0.016s
1 tree, 83 leaves, max depth = 15, train loss: 0.29952, val loss: 0.30404, in 0.000s
1 tree, 4 leaves, max depth = 3, train loss: 0.29835, val loss: 0.30292, in 0.016s
1 tree, 129 leaves, max depth = 19, train loss: 0.29654, val loss: 0.30210, in 0.016s
1 tree, 67 leaves, max depth = 14, train loss: 0.29534, val loss: 0.30085, in 0.016s
1 tree, 105 leaves, max depth = 15, train loss: 0.29392, val loss: 0.29988, in 0.000s
1 tree, 4 leaves, max depth = 3, train loss: 0.29281, val loss: 0.29883, in 0.016s
1 tree, 83 leaves, max depth = 17, train loss: 0.29169, val loss: 0.29740, in 0.000s
1 tree, 4 leaves, max depth = 3, train loss: 0.29069, val loss: 0.29644, in 0.016s
1 tree, 106 leaves, max depth = 15, train loss: 0.28934, val loss: 0.29554, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.28843, val loss: 0.29467, in 0.000s
1 tree, 64 leaves, max depth = 16, train loss: 0.28739, val loss: 0.29361, in 0.016s
1 tree, 106 leaves, max depth = 15, train loss: 0.28617, val loss: 0.29282, in 0.000s
1 tree, 83 leaves, max depth = 17, train loss: 0.28518, val loss: 0.29153, in 0.016s
1 tree, 130 leaves, max depth = 20, train loss: 0.28374, val loss: 0.29104, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.28285, val loss: 0.29020, in 0.000s
1 tree, 40 leaves, max depth = 10, train loss: 0.28192, val loss: 0.28939, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.28112, val loss: 0.28862, in 0.000s
1 tree, 83 leaves, max depth = 13, train loss: 0.27949, val loss: 0.28740, in 0.016s
1 tree, 85 leaves, max depth = 15, train loss: 0.27847, val loss: 0.28630, in 0.000s
1 tree, 140 leaves, max depth = 15, train loss: 0.27708, val loss: 0.28525, in 0.031s
1 tree, 4 leaves, max depth = 3, train loss: 0.27632, val loss: 0.28451, in 0.000s
1 tree, 140 leaves, max depth = 17, train loss: 0.27491, val loss: 0.28359, in 0.016s
1 tree, 85 leaves, max depth = 15, train loss: 0.27401, val loss: 0.28259, in 0.016s
1 tree, 140 leaves, max depth = 15, train loss: 0.27277, val loss: 0.28166, in 0.016s
1 tree, 131 leaves, max depth = 18, train loss: 0.27160, val loss: 0.28135, in 0.016s
1 tree, 83 leaves, max depth = 21, train loss: 0.27085, val loss: 0.28032, in 0.000s
1 tree, 4 leaves, max depth = 3, train loss: 0.27010, val loss: 0.27961, in 0.016s
Fit 85 trees in 1.189 s, (5765 total leaves)
Time spent computing histograms: 0.402s
Time spent finding best splits:  0.084s
Time spent applying splits:      0.096s
Time spent predicting:           0.000s
Trial 34, Fold 3: Log loss = 0.2744742454659397, Average precision = 0.9557721692213328, ROC-AUC = 0.9553627860417409, Elapsed Time = 1.1863267999997333 seconds
Trial 34, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 34, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
Binning 0.040 GB of training data: 0.158 s
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 67 leaves, max depth = 13, train loss: 0.65633, val loss: 0.65431, in 0.000s
1 tree, 67 leaves, max depth = 13, train loss: 0.62527, val loss: 0.62136, in 0.016s
1 tree, 67 leaves, max depth = 15, train loss: 0.59856, val loss: 0.59275, in 0.016s
1 tree, 67 leaves, max depth = 13, train loss: 0.57590, val loss: 0.56845, in 0.000s
1 tree, 67 leaves, max depth = 15, train loss: 0.55621, val loss: 0.54711, in 0.000s
1 tree, 101 leaves, max depth = 14, train loss: 0.53808, val loss: 0.52926, in 0.016s
1 tree, 67 leaves, max depth = 15, train loss: 0.52227, val loss: 0.51210, in 0.016s
1 tree, 104 leaves, max depth = 15, train loss: 0.50739, val loss: 0.49748, in 0.000s
1 tree, 67 leaves, max depth = 14, train loss: 0.49449, val loss: 0.48334, in 0.016s
1 tree, 104 leaves, max depth = 15, train loss: 0.48211, val loss: 0.47120, in 0.016s
1 tree, 67 leaves, max depth = 14, train loss: 0.47129, val loss: 0.45916, in 0.000s
1 tree, 104 leaves, max depth = 15, train loss: 0.46088, val loss: 0.44898, in 0.016s
1 tree, 67 leaves, max depth = 13, train loss: 0.45197, val loss: 0.43904, in 0.016s
1 tree, 67 leaves, max depth = 14, train loss: 0.44410, val loss: 0.43012, in 0.000s
1 tree, 67 leaves, max depth = 13, train loss: 0.43733, val loss: 0.42244, in 0.016s
1 tree, 104 leaves, max depth = 14, train loss: 0.42888, val loss: 0.41423, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.42340, val loss: 0.40837, in 0.000s
1 tree, 104 leaves, max depth = 14, train loss: 0.41607, val loss: 0.40125, in 0.016s
1 tree, 68 leaves, max depth = 16, train loss: 0.41067, val loss: 0.39502, in 0.016s
1 tree, 104 leaves, max depth = 13, train loss: 0.40432, val loss: 0.38885, in 0.000s
1 tree, 104 leaves, max depth = 13, train loss: 0.39876, val loss: 0.38346, in 0.016s
1 tree, 104 leaves, max depth = 13, train loss: 0.39390, val loss: 0.37877, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.38935, val loss: 0.37387, in 0.000s
1 tree, 67 leaves, max depth = 13, train loss: 0.38507, val loss: 0.36895, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.38117, val loss: 0.36474, in 0.000s
1 tree, 104 leaves, max depth = 14, train loss: 0.37690, val loss: 0.36070, in 0.016s
1 tree, 104 leaves, max depth = 14, train loss: 0.37317, val loss: 0.35713, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.36973, val loss: 0.35340, in 0.016s
1 tree, 67 leaves, max depth = 12, train loss: 0.36603, val loss: 0.34919, in 0.000s
1 tree, 104 leaves, max depth = 15, train loss: 0.36274, val loss: 0.34606, in 0.016s
1 tree, 67 leaves, max depth = 14, train loss: 0.35961, val loss: 0.34245, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.35659, val loss: 0.33918, in 0.000s
1 tree, 4 leaves, max depth = 3, train loss: 0.35395, val loss: 0.33632, in 0.016s
1 tree, 67 leaves, max depth = 14, train loss: 0.35126, val loss: 0.33324, in 0.000s
1 tree, 104 leaves, max depth = 14, train loss: 0.34821, val loss: 0.33045, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.34585, val loss: 0.32788, in 0.000s
1 tree, 86 leaves, max depth = 17, train loss: 0.34338, val loss: 0.32537, in 0.016s
1 tree, 104 leaves, max depth = 14, train loss: 0.34062, val loss: 0.32287, in 0.016s
1 tree, 67 leaves, max depth = 13, train loss: 0.33836, val loss: 0.32036, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.33622, val loss: 0.31803, in 0.000s
1 tree, 85 leaves, max depth = 15, train loss: 0.33404, val loss: 0.31583, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.33214, val loss: 0.31374, in 0.000s
1 tree, 85 leaves, max depth = 15, train loss: 0.33025, val loss: 0.31185, in 0.016s
1 tree, 104 leaves, max depth = 14, train loss: 0.32763, val loss: 0.30951, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.32590, val loss: 0.30762, in 0.000s
1 tree, 104 leaves, max depth = 14, train loss: 0.32355, val loss: 0.30553, in 0.016s
1 tree, 41 leaves, max depth = 11, train loss: 0.32190, val loss: 0.30399, in 0.000s
1 tree, 104 leaves, max depth = 14, train loss: 0.31988, val loss: 0.30222, in 0.000s
1 tree, 4 leaves, max depth = 3, train loss: 0.31825, val loss: 0.30042, in 0.016s
1 tree, 85 leaves, max depth = 18, train loss: 0.31654, val loss: 0.29874, in 0.016s
1 tree, 64 leaves, max depth = 12, train loss: 0.31507, val loss: 0.29701, in 0.000s
1 tree, 4 leaves, max depth = 3, train loss: 0.31360, val loss: 0.29541, in 0.016s
1 tree, 104 leaves, max depth = 16, train loss: 0.31167, val loss: 0.29378, in 0.000s
1 tree, 86 leaves, max depth = 15, train loss: 0.31021, val loss: 0.29233, in 0.016s
1 tree, 104 leaves, max depth = 15, train loss: 0.30851, val loss: 0.29084, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.30712, val loss: 0.28930, in 0.000s
1 tree, 85 leaves, max depth = 18, train loss: 0.30580, val loss: 0.28806, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.30456, val loss: 0.28669, in 0.016s
1 tree, 104 leaves, max depth = 15, train loss: 0.30295, val loss: 0.28536, in 0.000s
1 tree, 4 leaves, max depth = 3, train loss: 0.30182, val loss: 0.28411, in 0.016s
1 tree, 67 leaves, max depth = 13, train loss: 0.30057, val loss: 0.28289, in 0.000s
1 tree, 83 leaves, max depth = 16, train loss: 0.29856, val loss: 0.28111, in 0.016s
1 tree, 85 leaves, max depth = 18, train loss: 0.29740, val loss: 0.28004, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.29635, val loss: 0.27889, in 0.000s
1 tree, 103 leaves, max depth = 15, train loss: 0.29495, val loss: 0.27771, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.29400, val loss: 0.27664, in 0.000s
1 tree, 41 leaves, max depth = 11, train loss: 0.29294, val loss: 0.27581, in 0.000s
1 tree, 140 leaves, max depth = 19, train loss: 0.29124, val loss: 0.27474, in 0.016s
1 tree, 85 leaves, max depth = 18, train loss: 0.29019, val loss: 0.27380, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.28928, val loss: 0.27279, in 0.000s
1 tree, 83 leaves, max depth = 13, train loss: 0.28759, val loss: 0.27131, in 0.016s
1 tree, 140 leaves, max depth = 18, train loss: 0.28592, val loss: 0.27038, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.28509, val loss: 0.26946, in 0.000s
1 tree, 86 leaves, max depth = 17, train loss: 0.28409, val loss: 0.26859, in 0.016s
1 tree, 129 leaves, max depth = 23, train loss: 0.28256, val loss: 0.26746, in 0.016s
1 tree, 64 leaves, max depth = 12, train loss: 0.28172, val loss: 0.26646, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.28093, val loss: 0.26559, in 0.000s
1 tree, 140 leaves, max depth = 20, train loss: 0.27946, val loss: 0.26469, in 0.016s
1 tree, 85 leaves, max depth = 17, train loss: 0.27862, val loss: 0.26397, in 0.016s
1 tree, 140 leaves, max depth = 18, train loss: 0.27731, val loss: 0.26317, in 0.016s
1 tree, 85 leaves, max depth = 17, train loss: 0.27655, val loss: 0.26251, in 0.016s
1 tree, 129 leaves, max depth = 22, train loss: 0.27523, val loss: 0.26151, in 0.016s
1 tree, 140 leaves, max depth = 18, train loss: 0.27410, val loss: 0.26084, in 0.016s
1 tree, 140 leaves, max depth = 20, train loss: 0.27280, val loss: 0.26014, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.27198, val loss: 0.25924, in 0.000s
Fit 85 trees in 1.190 s, (5883 total leaves)
Time spent computing histograms: 0.410s
Time spent finding best splits:  0.086s
Time spent applying splits:      0.100s
Time spent predicting:           0.016s
Trial 34, Fold 4: Log loss = 0.2763670443208256, Average precision = 0.9559006771424874, ROC-AUC = 0.9546079165981148, Elapsed Time = 1.2050694999998086 seconds
Trial 34, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 34, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
Binning 0.040 GB of training data: 0.158 s
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 64 leaves, max depth = 14, train loss: 0.65604, val loss: 0.65344, in 0.000s
1 tree, 67 leaves, max depth = 14, train loss: 0.62471, val loss: 0.61997, in 0.016s
1 tree, 67 leaves, max depth = 14, train loss: 0.59802, val loss: 0.59130, in 0.016s
1 tree, 66 leaves, max depth = 15, train loss: 0.57510, val loss: 0.56637, in 0.000s
1 tree, 67 leaves, max depth = 14, train loss: 0.55541, val loss: 0.54499, in 0.016s
1 tree, 105 leaves, max depth = 19, train loss: 0.53731, val loss: 0.52774, in 0.016s
1 tree, 68 leaves, max depth = 13, train loss: 0.52132, val loss: 0.51023, in 0.016s
1 tree, 105 leaves, max depth = 19, train loss: 0.50645, val loss: 0.49620, in 0.016s
1 tree, 67 leaves, max depth = 14, train loss: 0.49308, val loss: 0.48144, in 0.000s
1 tree, 105 leaves, max depth = 22, train loss: 0.48073, val loss: 0.46989, in 0.016s
1 tree, 68 leaves, max depth = 13, train loss: 0.46990, val loss: 0.45785, in 0.016s
1 tree, 105 leaves, max depth = 20, train loss: 0.45949, val loss: 0.44824, in 0.000s
1 tree, 68 leaves, max depth = 13, train loss: 0.45047, val loss: 0.43812, in 0.016s
1 tree, 67 leaves, max depth = 16, train loss: 0.44241, val loss: 0.42900, in 0.016s
1 tree, 67 leaves, max depth = 16, train loss: 0.43540, val loss: 0.42099, in 0.000s
1 tree, 105 leaves, max depth = 18, train loss: 0.42689, val loss: 0.41333, in 0.016s
1 tree, 67 leaves, max depth = 13, train loss: 0.42117, val loss: 0.40674, in 0.016s
1 tree, 105 leaves, max depth = 18, train loss: 0.41382, val loss: 0.40021, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.40852, val loss: 0.39513, in 0.000s
1 tree, 105 leaves, max depth = 18, train loss: 0.40214, val loss: 0.38953, in 0.016s
1 tree, 105 leaves, max depth = 18, train loss: 0.39658, val loss: 0.38469, in 0.016s
1 tree, 105 leaves, max depth = 18, train loss: 0.39173, val loss: 0.38051, in 0.031s
1 tree, 5 leaves, max depth = 3, train loss: 0.38719, val loss: 0.37617, in 0.016s
1 tree, 67 leaves, max depth = 12, train loss: 0.38276, val loss: 0.37099, in 0.031s
1 tree, 5 leaves, max depth = 3, train loss: 0.37885, val loss: 0.36726, in 0.000s
1 tree, 105 leaves, max depth = 18, train loss: 0.37460, val loss: 0.36370, in 0.031s
1 tree, 105 leaves, max depth = 19, train loss: 0.37089, val loss: 0.36062, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.36745, val loss: 0.35734, in 0.000s
1 tree, 67 leaves, max depth = 12, train loss: 0.36360, val loss: 0.35295, in 0.016s
1 tree, 67 leaves, max depth = 14, train loss: 0.36035, val loss: 0.34910, in 0.016s
1 tree, 67 leaves, max depth = 16, train loss: 0.35744, val loss: 0.34567, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.35447, val loss: 0.34284, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.35188, val loss: 0.34037, in 0.016s
1 tree, 105 leaves, max depth = 19, train loss: 0.34842, val loss: 0.33763, in 0.000s
1 tree, 105 leaves, max depth = 19, train loss: 0.34543, val loss: 0.33528, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.34307, val loss: 0.33303, in 0.016s
1 tree, 86 leaves, max depth = 13, train loss: 0.34050, val loss: 0.33033, in 0.016s
1 tree, 67 leaves, max depth = 14, train loss: 0.33823, val loss: 0.32759, in 0.000s
1 tree, 83 leaves, max depth = 15, train loss: 0.33619, val loss: 0.32532, in 0.016s
1 tree, 104 leaves, max depth = 18, train loss: 0.33341, val loss: 0.32319, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.33126, val loss: 0.32113, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.32937, val loss: 0.31933, in 0.000s
1 tree, 84 leaves, max depth = 14, train loss: 0.32726, val loss: 0.31710, in 0.016s
1 tree, 105 leaves, max depth = 18, train loss: 0.32470, val loss: 0.31517, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.32298, val loss: 0.31353, in 0.000s
1 tree, 104 leaves, max depth = 18, train loss: 0.32071, val loss: 0.31185, in 0.016s
1 tree, 67 leaves, max depth = 15, train loss: 0.31894, val loss: 0.30969, in 0.016s
1 tree, 86 leaves, max depth = 18, train loss: 0.31725, val loss: 0.30792, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.31566, val loss: 0.30640, in 0.000s
1 tree, 105 leaves, max depth = 17, train loss: 0.31357, val loss: 0.30488, in 0.016s
1 tree, 84 leaves, max depth = 15, train loss: 0.31193, val loss: 0.30315, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.31047, val loss: 0.30175, in 0.000s
1 tree, 105 leaves, max depth = 17, train loss: 0.30857, val loss: 0.30041, in 0.016s
1 tree, 67 leaves, max depth = 16, train loss: 0.30720, val loss: 0.29869, in 0.000s
1 tree, 105 leaves, max depth = 17, train loss: 0.30553, val loss: 0.29754, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.30415, val loss: 0.29622, in 0.000s
1 tree, 84 leaves, max depth = 15, train loss: 0.30272, val loss: 0.29471, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.30150, val loss: 0.29353, in 0.000s
1 tree, 105 leaves, max depth = 17, train loss: 0.29992, val loss: 0.29247, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.29881, val loss: 0.29140, in 0.016s
1 tree, 84 leaves, max depth = 16, train loss: 0.29749, val loss: 0.29003, in 0.000s
1 tree, 105 leaves, max depth = 18, train loss: 0.29607, val loss: 0.28907, in 0.016s
1 tree, 63 leaves, max depth = 13, train loss: 0.29491, val loss: 0.28767, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.29386, val loss: 0.28666, in 0.016s
1 tree, 105 leaves, max depth = 18, train loss: 0.29257, val loss: 0.28581, in 0.000s
1 tree, 84 leaves, max depth = 16, train loss: 0.29143, val loss: 0.28463, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.29046, val loss: 0.28369, in 0.016s
1 tree, 83 leaves, max depth = 14, train loss: 0.28862, val loss: 0.28234, in 0.000s
1 tree, 84 leaves, max depth = 16, train loss: 0.28758, val loss: 0.28125, in 0.000s
1 tree, 140 leaves, max depth = 21, train loss: 0.28593, val loss: 0.28015, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.28501, val loss: 0.27926, in 0.016s
1 tree, 128 leaves, max depth = 24, train loss: 0.28349, val loss: 0.27819, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.28266, val loss: 0.27738, in 0.000s
1 tree, 84 leaves, max depth = 19, train loss: 0.28166, val loss: 0.27645, in 0.016s
1 tree, 140 leaves, max depth = 18, train loss: 0.28017, val loss: 0.27547, in 0.016s
1 tree, 66 leaves, max depth = 13, train loss: 0.27930, val loss: 0.27437, in 0.016s
[77/85] 1 tree, 5 leaves, max depth = 3, train loss: 0.27852, val loss: 0.27361, in 0.000s
1 tree, 140 leaves, max depth = 18, train loss: 0.27692, val loss: 0.27319, in 0.016s
1 tree, 140 leaves, max depth = 21, train loss: 0.27561, val loss: 0.27234, in 0.016s
1 tree, 83 leaves, max depth = 15, train loss: 0.27474, val loss: 0.27145, in 0.016s
1 tree, 82 leaves, max depth = 12, train loss: 0.27336, val loss: 0.27049, in 0.000s
1 tree, 129 leaves, max depth = 25, train loss: 0.27214, val loss: 0.26968, in 0.016s
1 tree, 39 leaves, max depth = 11, train loss: 0.27138, val loss: 0.26886, in 0.016s
1 tree, 140 leaves, max depth = 18, train loss: 0.27032, val loss: 0.26821, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.26952, val loss: 0.26741, in 0.016s
Fit 85 trees in 1.330 s, (5901 total leaves)
Time spent computing histograms: 0.461s
Time spent finding best splits:  0.103s
Time spent applying splits:      0.131s
Time spent predicting:           0.000s
Trial 34, Fold 5: Log loss = 0.2834021484954921, Average precision = 0.9528156004014571, ROC-AUC = 0.9512490199357152, Elapsed Time = 1.3338655999996263 seconds
Optimization Progress:  35%|###5      | 35/100 [06:56<12:58, 11.97s/it]
Trial 35, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 35, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
0.158 s 0.040 GB of training data: 
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 15 leaves, max depth = 6, train loss: 0.68744, val loss: 0.68725, in 0.000s
1 tree, 15 leaves, max depth = 6, train loss: 0.68204, val loss: 0.68167, in 0.016s
1 tree, 15 leaves, max depth = 6, train loss: 0.67677, val loss: 0.67623, in 0.000s
1 tree, 16 leaves, max depth = 7, train loss: 0.67163, val loss: 0.67090, in 0.016s
1 tree, 15 leaves, max depth = 6, train loss: 0.66660, val loss: 0.66570, in 0.000s
1 tree, 15 leaves, max depth = 6, train loss: 0.66168, val loss: 0.66061, in 0.016s
1 tree, 15 leaves, max depth = 6, train loss: 0.65688, val loss: 0.65564, in 0.000s
1 tree, 15 leaves, max depth = 6, train loss: 0.65218, val loss: 0.65077, in 0.016s
1 tree, 15 leaves, max depth = 6, train loss: 0.64759, val loss: 0.64601, in 0.000s
1 tree, 18 leaves, max depth = 8, train loss: 0.64310, val loss: 0.64136, in 0.016s
1 tree, 15 leaves, max depth = 6, train loss: 0.63871, val loss: 0.63681, in 0.016s
1 tree, 15 leaves, max depth = 6, train loss: 0.63442, val loss: 0.63235, in 0.000s
1 tree, 15 leaves, max depth = 6, train loss: 0.63022, val loss: 0.62799, in 0.016s
1 tree, 15 leaves, max depth = 6, train loss: 0.62611, val loss: 0.62373, in 0.000s
1 tree, 15 leaves, max depth = 6, train loss: 0.62209, val loss: 0.61955, in 0.016s
1 tree, 18 leaves, max depth = 8, train loss: 0.61816, val loss: 0.61547, in 0.000s
1 tree, 15 leaves, max depth = 6, train loss: 0.61431, val loss: 0.61147, in 0.016s
1 tree, 15 leaves, max depth = 6, train loss: 0.61055, val loss: 0.60755, in 0.000s
1 tree, 15 leaves, max depth = 6, train loss: 0.60686, val loss: 0.60372, in 0.016s
1 tree, 15 leaves, max depth = 6, train loss: 0.60326, val loss: 0.59996, in 0.000s
1 tree, 15 leaves, max depth = 6, train loss: 0.59973, val loss: 0.59629, in 0.016s
1 tree, 26 leaves, max depth = 10, train loss: 0.59632, val loss: 0.59275, in 0.000s
1 tree, 16 leaves, max depth = 6, train loss: 0.59294, val loss: 0.58922, in 0.016s
1 tree, 16 leaves, max depth = 6, train loss: 0.58963, val loss: 0.58577, in 0.000s
1 tree, 28 leaves, max depth = 11, train loss: 0.58643, val loss: 0.58244, in 0.000s
1 tree, 16 leaves, max depth = 6, train loss: 0.58326, val loss: 0.57913, in 0.016s
1 tree, 16 leaves, max depth = 6, train loss: 0.58015, val loss: 0.57588, in 0.000s
1 tree, 16 leaves, max depth = 6, train loss: 0.57710, val loss: 0.57270, in 0.016s
1 tree, 16 leaves, max depth = 6, train loss: 0.57412, val loss: 0.56959, in 0.000s
1 tree, 28 leaves, max depth = 11, train loss: 0.57124, val loss: 0.56659, in 0.016s
1 tree, 16 leaves, max depth = 6, train loss: 0.56838, val loss: 0.56359, in 0.000s
1 tree, 16 leaves, max depth = 6, train loss: 0.56557, val loss: 0.56066, in 0.016s
1 tree, 28 leaves, max depth = 11, train loss: 0.56287, val loss: 0.55784, in 0.000s
1 tree, 16 leaves, max depth = 6, train loss: 0.56017, val loss: 0.55502, in 0.016s
1 tree, 49 leaves, max depth = 11, train loss: 0.55745, val loss: 0.55248, in 0.016s
1 tree, 19 leaves, max depth = 8, train loss: 0.55483, val loss: 0.54974, in 0.000s
1 tree, 49 leaves, max depth = 11, train loss: 0.55218, val loss: 0.54728, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.54969, val loss: 0.54466, in 0.000s
1 tree, 50 leaves, max depth = 11, train loss: 0.54711, val loss: 0.54227, in 0.016s
[40/65] 1 tree, 31 leaves, max depth = 11, train loss: 0.54468, val loss: 0.53973, in 0.016s
1 tree, 50 leaves, max depth = 11, train loss: 0.54218, val loss: 0.53740, in 0.000s
1 tree, 31 leaves, max depth = 11, train loss: 0.53982, val loss: 0.53493, in 0.016s
1 tree, 50 leaves, max depth = 11, train loss: 0.53738, val loss: 0.53267, in 0.000s
1 tree, 20 leaves, max depth = 7, train loss: 0.53506, val loss: 0.53022, in 0.016s
1 tree, 50 leaves, max depth = 11, train loss: 0.53268, val loss: 0.52803, in 0.000s
1 tree, 50 leaves, max depth = 11, train loss: 0.53036, val loss: 0.52587, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.52815, val loss: 0.52356, in 0.016s
1 tree, 51 leaves, max depth = 11, train loss: 0.52589, val loss: 0.52147, in 0.000s
1 tree, 31 leaves, max depth = 11, train loss: 0.52375, val loss: 0.51921, in 0.016s
1 tree, 51 leaves, max depth = 11, train loss: 0.52154, val loss: 0.51718, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.51946, val loss: 0.51498, in 0.016s
1 tree, 51 leaves, max depth = 11, train loss: 0.51731, val loss: 0.51300, in 0.062s
1 tree, 22 leaves, max depth = 10, train loss: 0.51525, val loss: 0.51082, in 0.016s
1 tree, 51 leaves, max depth = 11, train loss: 0.51316, val loss: 0.50890, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.51119, val loss: 0.50682, in 0.016s
1 tree, 51 leaves, max depth = 11, train loss: 0.50915, val loss: 0.50494, in 0.000s
1 tree, 51 leaves, max depth = 11, train loss: 0.50716, val loss: 0.50311, in 0.016s
1 tree, 31 leaves, max depth = 12, train loss: 0.50525, val loss: 0.50110, in 0.016s
1 tree, 51 leaves, max depth = 11, train loss: 0.50331, val loss: 0.49931, in 0.016s
1 tree, 31 leaves, max depth = 12, train loss: 0.50146, val loss: 0.49736, in 0.000s
1 tree, 49 leaves, max depth = 11, train loss: 0.49956, val loss: 0.49561, in 0.016s
1 tree, 31 leaves, max depth = 12, train loss: 0.49776, val loss: 0.49371, in 0.016s
1 tree, 50 leaves, max depth = 11, train loss: 0.49591, val loss: 0.49201, in 0.016s
1 tree, 31 leaves, max depth = 12, train loss: 0.49415, val loss: 0.49016, in 0.000s
1 tree, 49 leaves, max depth = 10, train loss: 0.49235, val loss: 0.48850, in 0.016s
Fit 65 trees in 1.002 s, (1831 total leaves)
Time spent computing histograms: 0.335s
Time spent finding best splits:  0.067s
Time spent applying splits:      0.065s
Time spent predicting:           0.000s
Trial 35, Fold 1: Log loss = 0.4928565760604082, Average precision = 0.9120125022297894, ROC-AUC = 0.9211213185213862, Elapsed Time = 1.0007270999994944 seconds
Trial 35, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 35, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
Binning 0.040 GB of training data: 0.158 s
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 30 leaves, max depth = 11, train loss: 0.68764, val loss: 0.68738, in 0.000s
1 tree, 30 leaves, max depth = 11, train loss: 0.68233, val loss: 0.68181, in 0.016s
1 tree, 30 leaves, max depth = 11, train loss: 0.67715, val loss: 0.67638, in 0.016s
1 tree, 30 leaves, max depth = 11, train loss: 0.67208, val loss: 0.67106, in 0.000s
1 tree, 30 leaves, max depth = 11, train loss: 0.66713, val loss: 0.66586, in 0.016s
1 tree, 30 leaves, max depth = 11, train loss: 0.66229, val loss: 0.66078, in 0.000s
1 tree, 30 leaves, max depth = 11, train loss: 0.65755, val loss: 0.65582, in 0.016s
1 tree, 30 leaves, max depth = 11, train loss: 0.65293, val loss: 0.65096, in 0.000s
1 tree, 30 leaves, max depth = 11, train loss: 0.64841, val loss: 0.64621, in 0.016s
1 tree, 30 leaves, max depth = 11, train loss: 0.64399, val loss: 0.64156, in 0.000s
1 tree, 30 leaves, max depth = 11, train loss: 0.63967, val loss: 0.63702, in 0.016s
1 tree, 30 leaves, max depth = 11, train loss: 0.63544, val loss: 0.63257, in 0.016s
1 tree, 16 leaves, max depth = 8, train loss: 0.63124, val loss: 0.62814, in 0.000s
1 tree, 18 leaves, max depth = 8, train loss: 0.62713, val loss: 0.62379, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.62317, val loss: 0.61963, in 0.016s
1 tree, 18 leaves, max depth = 8, train loss: 0.61923, val loss: 0.61547, in 0.000s
1 tree, 18 leaves, max depth = 8, train loss: 0.61538, val loss: 0.61139, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.61167, val loss: 0.60749, in 0.016s
1 tree, 18 leaves, max depth = 8, train loss: 0.60798, val loss: 0.60358, in 0.000s
1 tree, 22 leaves, max depth = 10, train loss: 0.60444, val loss: 0.59984, in 0.016s
1 tree, 18 leaves, max depth = 8, train loss: 0.60090, val loss: 0.59610, in 0.016s
1 tree, 24 leaves, max depth = 11, train loss: 0.59749, val loss: 0.59251, in 0.000s
1 tree, 19 leaves, max depth = 8, train loss: 0.59410, val loss: 0.58891, in 0.016s
1 tree, 19 leaves, max depth = 8, train loss: 0.59078, val loss: 0.58539, in 0.016s
1 tree, 24 leaves, max depth = 11, train loss: 0.58759, val loss: 0.58201, in 0.000s
1 tree, 32 leaves, max depth = 11, train loss: 0.58446, val loss: 0.57871, in 0.016s
1 tree, 19 leaves, max depth = 8, train loss: 0.58134, val loss: 0.57539, in 0.016s
1 tree, 25 leaves, max depth = 11, train loss: 0.57834, val loss: 0.57222, in 0.000s
1 tree, 19 leaves, max depth = 8, train loss: 0.57535, val loss: 0.56904, in 0.016s
1 tree, 21 leaves, max depth = 8, train loss: 0.57242, val loss: 0.56592, in 0.016s
1 tree, 25 leaves, max depth = 11, train loss: 0.56960, val loss: 0.56293, in 0.000s
1 tree, 21 leaves, max depth = 8, train loss: 0.56679, val loss: 0.55994, in 0.016s
1 tree, 32 leaves, max depth = 11, train loss: 0.56408, val loss: 0.55708, in 0.016s
1 tree, 29 leaves, max depth = 13, train loss: 0.56143, val loss: 0.55427, in 0.016s
1 tree, 47 leaves, max depth = 11, train loss: 0.55873, val loss: 0.55166, in 0.000s
1 tree, 32 leaves, max depth = 11, train loss: 0.55616, val loss: 0.54893, in 0.016s
1 tree, 47 leaves, max depth = 11, train loss: 0.55353, val loss: 0.54639, in 0.016s
1 tree, 32 leaves, max depth = 11, train loss: 0.55103, val loss: 0.54375, in 0.000s
1 tree, 46 leaves, max depth = 12, train loss: 0.54848, val loss: 0.54128, in 0.016s
1 tree, 32 leaves, max depth = 11, train loss: 0.54605, val loss: 0.53871, in 0.016s
1 tree, 47 leaves, max depth = 12, train loss: 0.54357, val loss: 0.53631, in 0.000s
1 tree, 21 leaves, max depth = 8, train loss: 0.54116, val loss: 0.53373, in 0.016s
1 tree, 47 leaves, max depth = 12, train loss: 0.53874, val loss: 0.53140, in 0.016s
1 tree, 32 leaves, max depth = 11, train loss: 0.53645, val loss: 0.52897, in 0.000s
1 tree, 47 leaves, max depth = 12, train loss: 0.53410, val loss: 0.52670, in 0.016s
1 tree, 47 leaves, max depth = 12, train loss: 0.53180, val loss: 0.52448, in 0.016s
1 tree, 32 leaves, max depth = 11, train loss: 0.52959, val loss: 0.52214, in 0.016s
1 tree, 47 leaves, max depth = 12, train loss: 0.52735, val loss: 0.51997, in 0.000s
1 tree, 32 leaves, max depth = 11, train loss: 0.52521, val loss: 0.51769, in 0.016s
1 tree, 47 leaves, max depth = 12, train loss: 0.52302, val loss: 0.51559, in 0.016s
1 tree, 32 leaves, max depth = 11, train loss: 0.52094, val loss: 0.51337, in 0.000s
1 tree, 47 leaves, max depth = 12, train loss: 0.51881, val loss: 0.51132, in 0.016s
1 tree, 29 leaves, max depth = 17, train loss: 0.51678, val loss: 0.50915, in 0.016s
1 tree, 47 leaves, max depth = 12, train loss: 0.51471, val loss: 0.50715, in 0.016s
1 tree, 31 leaves, max depth = 12, train loss: 0.51274, val loss: 0.50505, in 0.016s
1 tree, 47 leaves, max depth = 12, train loss: 0.51072, val loss: 0.50311, in 0.000s
1 tree, 47 leaves, max depth = 12, train loss: 0.50874, val loss: 0.50121, in 0.016s
1 tree, 31 leaves, max depth = 12, train loss: 0.50684, val loss: 0.49918, in 0.016s
1 tree, 47 leaves, max depth = 12, train loss: 0.50491, val loss: 0.49733, in 0.016s
1 tree, 31 leaves, max depth = 12, train loss: 0.50306, val loss: 0.49535, in 0.000s
1 tree, 47 leaves, max depth = 12, train loss: 0.50118, val loss: 0.49355, in 0.016s
1 tree, 31 leaves, max depth = 12, train loss: 0.49937, val loss: 0.49162, in 0.016s
1 tree, 47 leaves, max depth = 12, train loss: 0.49754, val loss: 0.48986, in 0.016s
1 tree, 31 leaves, max depth = 12, train loss: 0.49578, val loss: 0.48799, in 0.016s
1 tree, 47 leaves, max depth = 12, train loss: 0.49400, val loss: 0.48627, in 0.016s
Fit 65 trees in 1.017 s, (2086 total leaves)
Time spent computing histograms: 0.354s
Time spent finding best splits:  0.055s
Time spent applying splits:      0.055s
Time spent predicting:           0.000s
Trial 35, Fold 2: Log loss = 0.49437818976459447, Average precision = 0.9123666380898829, ROC-AUC = 0.927085492577864, Elapsed Time = 1.021703800000978 seconds
Trial 35, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 35, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
Binning 0.040 GB of training data: 0.173 s
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 13 leaves, max depth = 8, train loss: 0.68755, val loss: 0.68738, in 0.000s
1 tree, 13 leaves, max depth = 8, train loss: 0.68223, val loss: 0.68189, in 0.016s
1 tree, 13 leaves, max depth = 8, train loss: 0.67703, val loss: 0.67652, in 0.016s
1 tree, 13 leaves, max depth = 8, train loss: 0.67195, val loss: 0.67127, in 0.000s
1 tree, 13 leaves, max depth = 8, train loss: 0.66699, val loss: 0.66615, in 0.016s
1 tree, 13 leaves, max depth = 8, train loss: 0.66214, val loss: 0.66113, in 0.000s
1 tree, 13 leaves, max depth = 7, train loss: 0.65740, val loss: 0.65623, in 0.016s
1 tree, 13 leaves, max depth = 8, train loss: 0.65276, val loss: 0.65143, in 0.000s
1 tree, 13 leaves, max depth = 7, train loss: 0.64823, val loss: 0.64674, in 0.016s
1 tree, 21 leaves, max depth = 11, train loss: 0.64383, val loss: 0.64220, in 0.000s
1 tree, 13 leaves, max depth = 8, train loss: 0.63950, val loss: 0.63771, in 0.016s
1 tree, 21 leaves, max depth = 11, train loss: 0.63529, val loss: 0.63336, in 0.016s
1 tree, 14 leaves, max depth = 9, train loss: 0.63114, val loss: 0.62906, in 0.000s
1 tree, 21 leaves, max depth = 11, train loss: 0.62711, val loss: 0.62490, in 0.016s
1 tree, 14 leaves, max depth = 9, train loss: 0.62314, val loss: 0.62078, in 0.000s
1 tree, 20 leaves, max depth = 11, train loss: 0.61928, val loss: 0.61679, in 0.016s
1 tree, 14 leaves, max depth = 9, train loss: 0.61548, val loss: 0.61285, in 0.000s
1 tree, 20 leaves, max depth = 11, train loss: 0.61179, val loss: 0.60903, in 0.016s
1 tree, 20 leaves, max depth = 11, train loss: 0.60817, val loss: 0.60529, in 0.000s
1 tree, 14 leaves, max depth = 9, train loss: 0.60460, val loss: 0.60159, in 0.016s
1 tree, 20 leaves, max depth = 11, train loss: 0.60114, val loss: 0.59800, in 0.000s
1 tree, 14 leaves, max depth = 9, train loss: 0.59772, val loss: 0.59445, in 0.016s
1 tree, 20 leaves, max depth = 11, train loss: 0.59440, val loss: 0.59101, in 0.000s
1 tree, 14 leaves, max depth = 9, train loss: 0.59112, val loss: 0.58760, in 0.016s
1 tree, 20 leaves, max depth = 11, train loss: 0.58794, val loss: 0.58430, in 0.000s
[26/65] 1 tree, 15 leaves, max depth = 10, train loss: 0.58480, val loss: 0.58103, in 0.000s
1 tree, 20 leaves, max depth = 11, train loss: 0.58174, val loss: 0.57787, in 0.016s
1 tree, 20 leaves, max depth = 11, train loss: 0.57875, val loss: 0.57477, in 0.000s
1 tree, 15 leaves, max depth = 10, train loss: 0.57580, val loss: 0.57169, in 0.016s
1 tree, 20 leaves, max depth = 11, train loss: 0.57293, val loss: 0.56872, in 0.000s
1 tree, 15 leaves, max depth = 10, train loss: 0.57010, val loss: 0.56576, in 0.016s
1 tree, 20 leaves, max depth = 11, train loss: 0.56734, val loss: 0.56291, in 0.016s
1 tree, 15 leaves, max depth = 10, train loss: 0.56462, val loss: 0.56007, in 0.000s
1 tree, 20 leaves, max depth = 11, train loss: 0.56198, val loss: 0.55733, in 0.016s
1 tree, 15 leaves, max depth = 10, train loss: 0.55937, val loss: 0.55460, in 0.000s
1 tree, 48 leaves, max depth = 13, train loss: 0.55670, val loss: 0.55213, in 0.016s
1 tree, 20 leaves, max depth = 11, train loss: 0.55419, val loss: 0.54952, in 0.016s
1 tree, 49 leaves, max depth = 13, train loss: 0.55159, val loss: 0.54712, in 0.016s
1 tree, 20 leaves, max depth = 11, train loss: 0.54916, val loss: 0.54458, in 0.000s
1 tree, 49 leaves, max depth = 13, train loss: 0.54663, val loss: 0.54225, in 0.016s
1 tree, 20 leaves, max depth = 11, train loss: 0.54426, val loss: 0.53978, in 0.016s
1 tree, 48 leaves, max depth = 13, train loss: 0.54181, val loss: 0.53752, in 0.000s
1 tree, 22 leaves, max depth = 11, train loss: 0.53951, val loss: 0.53512, in 0.016s
1 tree, 48 leaves, max depth = 13, train loss: 0.53712, val loss: 0.53292, in 0.016s
1 tree, 22 leaves, max depth = 11, train loss: 0.53489, val loss: 0.53058, in 0.000s
1 tree, 49 leaves, max depth = 13, train loss: 0.53256, val loss: 0.52845, in 0.016s
1 tree, 22 leaves, max depth = 11, train loss: 0.53039, val loss: 0.52617, in 0.016s
1 tree, 49 leaves, max depth = 13, train loss: 0.52813, val loss: 0.52409, in 0.000s
1 tree, 49 leaves, max depth = 13, train loss: 0.52591, val loss: 0.52206, in 0.016s
1 tree, 26 leaves, max depth = 10, train loss: 0.52382, val loss: 0.51985, in 0.016s
1 tree, 49 leaves, max depth = 13, train loss: 0.52167, val loss: 0.51786, in 0.016s
1 tree, 22 leaves, max depth = 11, train loss: 0.51963, val loss: 0.51573, in 0.000s
1 tree, 49 leaves, max depth = 13, train loss: 0.51753, val loss: 0.51380, in 0.016s
1 tree, 22 leaves, max depth = 11, train loss: 0.51555, val loss: 0.51173, in 0.016s
1 tree, 49 leaves, max depth = 13, train loss: 0.51350, val loss: 0.50985, in 0.016s
1 tree, 26 leaves, max depth = 10, train loss: 0.51158, val loss: 0.50781, in 0.000s
1 tree, 49 leaves, max depth = 13, train loss: 0.50958, val loss: 0.50599, in 0.016s
1 tree, 25 leaves, max depth = 11, train loss: 0.50771, val loss: 0.50402, in 0.016s
1 tree, 49 leaves, max depth = 13, train loss: 0.50576, val loss: 0.50224, in 0.000s
1 tree, 49 leaves, max depth = 13, train loss: 0.50386, val loss: 0.50050, in 0.016s
1 tree, 25 leaves, max depth = 11, train loss: 0.50205, val loss: 0.49860, in 0.016s
1 tree, 49 leaves, max depth = 13, train loss: 0.50019, val loss: 0.49690, in 0.016s
1 tree, 25 leaves, max depth = 11, train loss: 0.49843, val loss: 0.49505, in 0.000s
1 tree, 48 leaves, max depth = 13, train loss: 0.49662, val loss: 0.49340, in 0.016s
1 tree, 25 leaves, max depth = 11, train loss: 0.49490, val loss: 0.49160, in 0.016s
Fit 65 trees in 0.970 s, (1674 total leaves)
Time spent computing histograms: 0.343s
Time spent finding best splits:  0.047s
Time spent applying splits:      0.045s
Time spent predicting:           0.000s
Trial 35, Fold 3: Log loss = 0.4913007819756683, Average precision = 0.916790820488213, ROC-AUC = 0.9291073053808592, Elapsed Time = 0.9722562999995716 seconds
Trial 35, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 35, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
Binning 0.040 GB of training data: 0.173 s
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 14 leaves, max depth = 6, train loss: 0.68757, val loss: 0.68725, in 0.000s
1 tree, 14 leaves, max depth = 6, train loss: 0.68222, val loss: 0.68160, in 0.016s
1 tree, 14 leaves, max depth = 6, train loss: 0.67700, val loss: 0.67607, in 0.000s
1 tree, 14 leaves, max depth = 6, train loss: 0.67190, val loss: 0.67066, in 0.016s
1 tree, 14 leaves, max depth = 6, train loss: 0.66691, val loss: 0.66537, in 0.000s
1 tree, 16 leaves, max depth = 8, train loss: 0.66204, val loss: 0.66020, in 0.016s
1 tree, 16 leaves, max depth = 8, train loss: 0.65728, val loss: 0.65515, in 0.000s
1 tree, 16 leaves, max depth = 8, train loss: 0.65263, val loss: 0.65021, in 0.000s
1 tree, 16 leaves, max depth = 8, train loss: 0.64807, val loss: 0.64537, in 0.016s
1 tree, 16 leaves, max depth = 8, train loss: 0.64362, val loss: 0.64064, in 0.000s
1 tree, 16 leaves, max depth = 8, train loss: 0.63927, val loss: 0.63600, in 0.016s
1 tree, 16 leaves, max depth = 8, train loss: 0.63502, val loss: 0.63147, in 0.000s
1 tree, 17 leaves, max depth = 8, train loss: 0.63086, val loss: 0.62704, in 0.016s
1 tree, 17 leaves, max depth = 8, train loss: 0.62679, val loss: 0.62270, in 0.000s
1 tree, 17 leaves, max depth = 8, train loss: 0.62280, val loss: 0.61845, in 0.016s
1 tree, 17 leaves, max depth = 8, train loss: 0.61891, val loss: 0.61429, in 0.000s
1 tree, 17 leaves, max depth = 8, train loss: 0.61510, val loss: 0.61021, in 0.016s
1 tree, 17 leaves, max depth = 8, train loss: 0.61137, val loss: 0.60622, in 0.000s
1 tree, 22 leaves, max depth = 12, train loss: 0.60772, val loss: 0.60231, in 0.016s
1 tree, 23 leaves, max depth = 12, train loss: 0.60414, val loss: 0.59847, in 0.000s
1 tree, 25 leaves, max depth = 11, train loss: 0.60068, val loss: 0.59478, in 0.016s
1 tree, 23 leaves, max depth = 12, train loss: 0.59725, val loss: 0.59110, in 0.000s
1 tree, 23 leaves, max depth = 12, train loss: 0.59390, val loss: 0.58749, in 0.016s
1 tree, 23 leaves, max depth = 12, train loss: 0.59062, val loss: 0.58396, in 0.000s
1 tree, 25 leaves, max depth = 11, train loss: 0.58743, val loss: 0.58056, in 0.016s
1 tree, 23 leaves, max depth = 12, train loss: 0.58429, val loss: 0.57716, in 0.000s
1 tree, 23 leaves, max depth = 12, train loss: 0.58120, val loss: 0.57384, in 0.016s
[28/65] 1 tree, 24 leaves, max depth = 12, train loss: 0.57818, val loss: 0.57058, in 0.000s
1 tree, 24 leaves, max depth = 12, train loss: 0.57523, val loss: 0.56739, in 0.016s
1 tree, 27 leaves, max depth = 11, train loss: 0.57236, val loss: 0.56431, in 0.000s
1 tree, 24 leaves, max depth = 12, train loss: 0.56952, val loss: 0.56124, in 0.016s
1 tree, 24 leaves, max depth = 12, train loss: 0.56674, val loss: 0.55823, in 0.000s
1 tree, 24 leaves, max depth = 12, train loss: 0.56402, val loss: 0.55528, in 0.016s
1 tree, 27 leaves, max depth = 11, train loss: 0.56138, val loss: 0.55244, in 0.016s
1 tree, 44 leaves, max depth = 11, train loss: 0.55867, val loss: 0.54976, in 0.000s
1 tree, 27 leaves, max depth = 11, train loss: 0.55610, val loss: 0.54700, in 0.016s
1 tree, 43 leaves, max depth = 12, train loss: 0.55347, val loss: 0.54440, in 0.016s
1 tree, 24 leaves, max depth = 12, train loss: 0.55095, val loss: 0.54167, in 0.000s
1 tree, 43 leaves, max depth = 12, train loss: 0.54839, val loss: 0.53914, in 0.016s
1 tree, 25 leaves, max depth = 12, train loss: 0.54597, val loss: 0.53654, in 0.000s
1 tree, 42 leaves, max depth = 12, train loss: 0.54348, val loss: 0.53408, in 0.016s
1 tree, 42 leaves, max depth = 12, train loss: 0.54104, val loss: 0.53168, in 0.016s
1 tree, 25 leaves, max depth = 12, train loss: 0.53872, val loss: 0.52916, in 0.000s
1 tree, 43 leaves, max depth = 12, train loss: 0.53634, val loss: 0.52683, in 0.016s
1 tree, 27 leaves, max depth = 11, train loss: 0.53408, val loss: 0.52438, in 0.000s
1 tree, 43 leaves, max depth = 12, train loss: 0.53177, val loss: 0.52211, in 0.016s
1 tree, 24 leaves, max depth = 12, train loss: 0.52958, val loss: 0.51973, in 0.000s
1 tree, 43 leaves, max depth = 12, train loss: 0.52733, val loss: 0.51751, in 0.016s
1 tree, 24 leaves, max depth = 12, train loss: 0.52520, val loss: 0.51520, in 0.016s
1 tree, 43 leaves, max depth = 12, train loss: 0.52301, val loss: 0.51305, in 0.000s
1 tree, 24 leaves, max depth = 12, train loss: 0.52093, val loss: 0.51080, in 0.016s
1 tree, 44 leaves, max depth = 12, train loss: 0.51880, val loss: 0.50870, in 0.000s
1 tree, 44 leaves, max depth = 12, train loss: 0.51672, val loss: 0.50665, in 0.016s
1 tree, 24 leaves, max depth = 12, train loss: 0.51472, val loss: 0.50447, in 0.000s
1 tree, 44 leaves, max depth = 12, train loss: 0.51268, val loss: 0.50248, in 0.016s
[56/65] 1 tree, 24 leaves, max depth = 12, train loss: 0.51074, val loss: 0.50036, in 0.016s
[57/65] 1 tree, 45 leaves, max depth = 12, train loss: 0.50876, val loss: 0.49841, in 0.000s
1 tree, 28 leaves, max depth = 12, train loss: 0.50687, val loss: 0.49635, in 0.016s
1 tree, 46 leaves, max depth = 12, train loss: 0.50494, val loss: 0.49445, in 0.016s
1 tree, 22 leaves, max depth = 11, train loss: 0.50307, val loss: 0.49240, in 0.000s
1 tree, 44 leaves, max depth = 13, train loss: 0.50119, val loss: 0.49055, in 0.016s
1 tree, 28 leaves, max depth = 12, train loss: 0.49940, val loss: 0.48859, in 0.000s
1 tree, 44 leaves, max depth = 13, train loss: 0.49756, val loss: 0.48679, in 0.000s
1 tree, 44 leaves, max depth = 13, train loss: 0.49577, val loss: 0.48503, in 0.016s
1 tree, 28 leaves, max depth = 12, train loss: 0.49403, val loss: 0.48313, in 0.016s
Fit 65 trees in 0.892 s, (1763 total leaves)
Time spent computing histograms: 0.306s
Time spent finding best splits:  0.040s
Time spent applying splits:      0.040s
Time spent predicting:           0.000s
Trial 35, Fold 4: Log loss = 0.4927859316438387, Average precision = 0.916245593193003, ROC-AUC = 0.9274143192152431, Elapsed Time = 0.8943703999993886 seconds
Trial 35, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 35, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
Binning 0.040 GB of training data: 0.158 s
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 28 leaves, max depth = 10, train loss: 0.68753, val loss: 0.68718, in 0.016s
1 tree, 26 leaves, max depth = 11, train loss: 0.68220, val loss: 0.68150, in 0.016s
1 tree, 27 leaves, max depth = 11, train loss: 0.67698, val loss: 0.67595, in 0.000s
1 tree, 30 leaves, max depth = 10, train loss: 0.67188, val loss: 0.67051, in 0.016s
1 tree, 30 leaves, max depth = 10, train loss: 0.66689, val loss: 0.66519, in 0.000s
1 tree, 27 leaves, max depth = 11, train loss: 0.66203, val loss: 0.66000, in 0.016s
1 tree, 30 leaves, max depth = 10, train loss: 0.65726, val loss: 0.65491, in 0.016s
1 tree, 28 leaves, max depth = 11, train loss: 0.65262, val loss: 0.64995, in 0.000s
1 tree, 30 leaves, max depth = 10, train loss: 0.64806, val loss: 0.64508, in 0.016s
1 tree, 28 leaves, max depth = 11, train loss: 0.64362, val loss: 0.64032, in 0.000s
1 tree, 30 leaves, max depth = 10, train loss: 0.63926, val loss: 0.63566, in 0.016s
1 tree, 28 leaves, max depth = 11, train loss: 0.63501, val loss: 0.63110, in 0.016s
1 tree, 30 leaves, max depth = 10, train loss: 0.63085, val loss: 0.62663, in 0.000s
1 tree, 28 leaves, max depth = 11, train loss: 0.62678, val loss: 0.62227, in 0.016s
1 tree, 17 leaves, max depth = 8, train loss: 0.62274, val loss: 0.61791, in 0.000s
1 tree, 17 leaves, max depth = 8, train loss: 0.61878, val loss: 0.61364, in 0.016s
1 tree, 30 leaves, max depth = 10, train loss: 0.61496, val loss: 0.60954, in 0.000s
1 tree, 17 leaves, max depth = 8, train loss: 0.61117, val loss: 0.60544, in 0.016s
1 tree, 17 leaves, max depth = 8, train loss: 0.60746, val loss: 0.60144, in 0.000s
1 tree, 17 leaves, max depth = 8, train loss: 0.60382, val loss: 0.59751, in 0.000s
1 tree, 17 leaves, max depth = 8, train loss: 0.60027, val loss: 0.59366, in 0.000s
1 tree, 17 leaves, max depth = 8, train loss: 0.59679, val loss: 0.58989, in 0.000s
1 tree, 30 leaves, max depth = 10, train loss: 0.59344, val loss: 0.58628, in 0.016s
1 tree, 17 leaves, max depth = 8, train loss: 0.59011, val loss: 0.58266, in 0.000s
1 tree, 31 leaves, max depth = 10, train loss: 0.58689, val loss: 0.57918, in 0.016s
1 tree, 17 leaves, max depth = 8, train loss: 0.58369, val loss: 0.57570, in 0.000s
1 tree, 17 leaves, max depth = 8, train loss: 0.58056, val loss: 0.57229, in 0.016s
1 tree, 17 leaves, max depth = 8, train loss: 0.57749, val loss: 0.56896, in 0.000s
1 tree, 17 leaves, max depth = 8, train loss: 0.57448, val loss: 0.56568, in 0.016s
1 tree, 17 leaves, max depth = 8, train loss: 0.57154, val loss: 0.56248, in 0.000s
1 tree, 17 leaves, max depth = 8, train loss: 0.56866, val loss: 0.55933, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.56588, val loss: 0.55631, in 0.016s
1 tree, 17 leaves, max depth = 8, train loss: 0.56311, val loss: 0.55328, in 0.000s
1 tree, 17 leaves, max depth = 8, train loss: 0.56040, val loss: 0.55032, in 0.016s
1 tree, 17 leaves, max depth = 8, train loss: 0.55775, val loss: 0.54741, in 0.000s
1 tree, 40 leaves, max depth = 13, train loss: 0.55511, val loss: 0.54489, in 0.016s
1 tree, 32 leaves, max depth = 10, train loss: 0.55257, val loss: 0.54212, in 0.000s
1 tree, 40 leaves, max depth = 13, train loss: 0.55001, val loss: 0.53966, in 0.016s
1 tree, 32 leaves, max depth = 10, train loss: 0.54755, val loss: 0.53697, in 0.000s
1 tree, 42 leaves, max depth = 13, train loss: 0.54506, val loss: 0.53459, in 0.016s
1 tree, 18 leaves, max depth = 8, train loss: 0.54262, val loss: 0.53192, in 0.000s
1 tree, 42 leaves, max depth = 13, train loss: 0.54020, val loss: 0.52961, in 0.016s
1 tree, 32 leaves, max depth = 10, train loss: 0.53787, val loss: 0.52706, in 0.000s
1 tree, 42 leaves, max depth = 13, train loss: 0.53551, val loss: 0.52481, in 0.016s
1 tree, 32 leaves, max depth = 10, train loss: 0.53325, val loss: 0.52233, in 0.000s
1 tree, 41 leaves, max depth = 13, train loss: 0.53096, val loss: 0.52015, in 0.016s
1 tree, 27 leaves, max depth = 11, train loss: 0.52872, val loss: 0.51768, in 0.016s
1 tree, 41 leaves, max depth = 13, train loss: 0.52649, val loss: 0.51556, in 0.000s
1 tree, 42 leaves, max depth = 13, train loss: 0.52430, val loss: 0.51348, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.52219, val loss: 0.51116, in 0.016s
1 tree, 41 leaves, max depth = 13, train loss: 0.52006, val loss: 0.50914, in 0.000s
1 tree, 32 leaves, max depth = 10, train loss: 0.51800, val loss: 0.50687, in 0.016s
1 tree, 41 leaves, max depth = 13, train loss: 0.51592, val loss: 0.50491, in 0.000s
1 tree, 31 leaves, max depth = 10, train loss: 0.51391, val loss: 0.50270, in 0.016s
1 tree, 41 leaves, max depth = 13, train loss: 0.51190, val loss: 0.50079, in 0.000s
1 tree, 31 leaves, max depth = 10, train loss: 0.50995, val loss: 0.49864, in 0.016s
1 tree, 41 leaves, max depth = 13, train loss: 0.50798, val loss: 0.49678, in 0.000s
1 tree, 31 leaves, max depth = 10, train loss: 0.50608, val loss: 0.49469, in 0.016s
1 tree, 42 leaves, max depth = 12, train loss: 0.50416, val loss: 0.49288, in 0.000s
1 tree, 25 leaves, max depth = 10, train loss: 0.50228, val loss: 0.49079, in 0.016s
1 tree, 42 leaves, max depth = 12, train loss: 0.50041, val loss: 0.48903, in 0.016s
1 tree, 43 leaves, max depth = 12, train loss: 0.49858, val loss: 0.48731, in 0.000s
1 tree, 31 leaves, max depth = 10, train loss: 0.49679, val loss: 0.48533, in 0.016s
1 tree, 44 leaves, max depth = 12, train loss: 0.49501, val loss: 0.48365, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.49326, val loss: 0.48173, in 0.000s
Fit 65 trees in 0.861 s, (1892 total leaves)
Time spent computing histograms: 0.304s
Time spent finding best splits:  0.041s
Time spent applying splits:      0.041s
Time spent predicting:           0.000s
Trial 35, Fold 5: Log loss = 0.49773541235725716, Average precision = 0.9113811010579826, ROC-AUC = 0.9217625922003606, Elapsed Time = 0.8786968999993405 seconds
Optimization Progress:  36%|###6      | 36/100 [07:08<12:41, 11.90s/it]
Trial 36, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 36, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
0.142 s 0.040 GB of training data: 
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 15 leaves, max depth = 6, train loss: 0.67203, val loss: 0.67173, in 0.016s
1 tree, 15 leaves, max depth = 6, train loss: 0.64883, val loss: 0.64854, in 0.000s
1 tree, 11 leaves, max depth = 6, train loss: 0.63083, val loss: 0.63017, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.61309, val loss: 0.61227, in 0.016s
1 tree, 13 leaves, max depth = 5, train loss: 0.59605, val loss: 0.59496, in 0.016s
1 tree, 20 leaves, max depth = 6, train loss: 0.58042, val loss: 0.57903, in 0.016s
1 tree, 16 leaves, max depth = 6, train loss: 0.56613, val loss: 0.56452, in 0.016s
1 tree, 21 leaves, max depth = 6, train loss: 0.55287, val loss: 0.55115, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.54003, val loss: 0.53814, in 0.016s
1 tree, 16 leaves, max depth = 7, train loss: 0.52689, val loss: 0.52513, in 0.016s
1 tree, 18 leaves, max depth = 8, train loss: 0.51701, val loss: 0.51499, in 0.016s
1 tree, 20 leaves, max depth = 7, train loss: 0.50699, val loss: 0.50494, in 0.016s
1 tree, 15 leaves, max depth = 5, train loss: 0.49662, val loss: 0.49455, in 0.016s
1 tree, 10 leaves, max depth = 5, train loss: 0.48687, val loss: 0.48464, in 0.016s
1 tree, 6 leaves, max depth = 3, train loss: 0.47487, val loss: 0.47280, in 0.016s
1 tree, 10 leaves, max depth = 5, train loss: 0.46607, val loss: 0.46385, in 0.016s
1 tree, 21 leaves, max depth = 7, train loss: 0.45818, val loss: 0.45578, in 0.000s
1 tree, 13 leaves, max depth = 5, train loss: 0.45051, val loss: 0.44806, in 0.031s
1 tree, 6 leaves, max depth = 4, train loss: 0.44352, val loss: 0.44080, in 0.000s
1 tree, 15 leaves, max depth = 6, train loss: 0.43701, val loss: 0.43415, in 0.016s
1 tree, 22 leaves, max depth = 7, train loss: 0.43012, val loss: 0.42724, in 0.016s
Fit 21 trees in 0.595 s, (317 total leaves)
Time spent computing histograms: 0.128s
Time spent finding best splits:  0.012s
Time spent applying splits:      0.008s
Time spent predicting:           0.000s
Trial 36, Fold 1: Log loss = 0.4301857673216879, Average precision = 0.9524029040859325, ROC-AUC = 0.9449978312613816, Elapsed Time = 0.6013696999998501 seconds
Trial 36, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 36, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
Binning 0.040 GB of training data: 0.142 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 17 leaves, max depth = 6, train loss: 0.67213, val loss: 0.67116, in 0.016s
1 tree, 15 leaves, max depth = 6, train loss: 0.64904, val loss: 0.64762, in 0.025s
1 tree, 9 leaves, max depth = 5, train loss: 0.63110, val loss: 0.62876, in 0.007s
1 tree, 16 leaves, max depth = 6, train loss: 0.61269, val loss: 0.60995, in 0.031s
1 tree, 14 leaves, max depth = 5, train loss: 0.59576, val loss: 0.59279, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.58018, val loss: 0.57680, in 0.016s
1 tree, 16 leaves, max depth = 6, train loss: 0.56589, val loss: 0.56193, in 0.016s
1 tree, 19 leaves, max depth = 7, train loss: 0.55240, val loss: 0.54800, in 0.016s
1 tree, 16 leaves, max depth = 6, train loss: 0.53940, val loss: 0.53478, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.52623, val loss: 0.52130, in 0.000s
1 tree, 17 leaves, max depth = 8, train loss: 0.51637, val loss: 0.51100, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.50619, val loss: 0.50043, in 0.016s
1 tree, 10 leaves, max depth = 6, train loss: 0.49527, val loss: 0.48930, in 0.000s
1 tree, 13 leaves, max depth = 5, train loss: 0.48534, val loss: 0.47909, in 0.031s
1 tree, 6 leaves, max depth = 3, train loss: 0.47334, val loss: 0.46706, in 0.000s
1 tree, 10 leaves, max depth = 5, train loss: 0.46457, val loss: 0.45801, in 0.031s
1 tree, 13 leaves, max depth = 7, train loss: 0.45382, val loss: 0.44729, in 0.016s
1 tree, 18 leaves, max depth = 7, train loss: 0.44675, val loss: 0.44008, in 0.016s
1 tree, 6 leaves, max depth = 4, train loss: 0.43996, val loss: 0.43310, in 0.016s
1 tree, 10 leaves, max depth = 5, train loss: 0.43343, val loss: 0.42674, in 0.016s
1 tree, 11 leaves, max depth = 5, train loss: 0.42688, val loss: 0.41994, in 0.000s
Fit 21 trees in 0.643 s, (287 total leaves)
Time spent computing histograms: 0.149s
Time spent finding best splits:  0.012s
Time spent applying splits:      0.008s
Time spent predicting:           0.000s
Trial 36, Fold 2: Log loss = 0.42915409712140934, Average precision = 0.9483329192762742, ROC-AUC = 0.9447445341206413, Elapsed Time = 0.6612169999989419 seconds
Trial 36, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 36, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
Binning 0.040 GB of training data: 0.143 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 11 leaves, max depth = 5, train loss: 0.67219, val loss: 0.67183, in 0.000s
1 tree, 13 leaves, max depth = 6, train loss: 0.64908, val loss: 0.64859, in 0.000s
1 tree, 15 leaves, max depth = 8, train loss: 0.63112, val loss: 0.63017, in 0.031s
1 tree, 16 leaves, max depth = 6, train loss: 0.61350, val loss: 0.61224, in 0.016s
1 tree, 15 leaves, max depth = 7, train loss: 0.59642, val loss: 0.59520, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.58089, val loss: 0.57936, in 0.016s
1 tree, 20 leaves, max depth = 6, train loss: 0.56666, val loss: 0.56496, in 0.016s
1 tree, 19 leaves, max depth = 6, train loss: 0.55325, val loss: 0.55150, in 0.000s
1 tree, 13 leaves, max depth = 6, train loss: 0.54038, val loss: 0.53819, in 0.016s
1 tree, 13 leaves, max depth = 6, train loss: 0.52731, val loss: 0.52502, in 0.016s
1 tree, 12 leaves, max depth = 6, train loss: 0.51712, val loss: 0.51475, in 0.016s
1 tree, 14 leaves, max depth = 7, train loss: 0.50690, val loss: 0.50426, in 0.016s
1 tree, 13 leaves, max depth = 5, train loss: 0.49637, val loss: 0.49373, in 0.016s
1 tree, 13 leaves, max depth = 6, train loss: 0.48654, val loss: 0.48385, in 0.016s
1 tree, 13 leaves, max depth = 6, train loss: 0.47732, val loss: 0.47469, in 0.016s
1 tree, 12 leaves, max depth = 6, train loss: 0.46857, val loss: 0.46593, in 0.016s
1 tree, 9 leaves, max depth = 5, train loss: 0.45721, val loss: 0.45532, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.45023, val loss: 0.44818, in 0.016s
1 tree, 6 leaves, max depth = 4, train loss: 0.44326, val loss: 0.44133, in 0.016s
1 tree, 16 leaves, max depth = 7, train loss: 0.43679, val loss: 0.43498, in 0.016s
1 tree, 20 leaves, max depth = 6, train loss: 0.43075, val loss: 0.42901, in 0.016s
Fit 21 trees in 0.643 s, (297 total leaves)
Time spent computing histograms: 0.140s
Time spent finding best splits:  0.012s
Time spent applying splits:      0.008s
Time spent predicting:           0.000s
Trial 36, Fold 3: Log loss = 0.4271048823779159, Average precision = 0.9534852152384319, ROC-AUC = 0.9482975189249838, Elapsed Time = 0.6425328000004811 seconds
Trial 36, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 36, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
Binning 0.040 GB of training data: 0.159 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 15 leaves, max depth = 6, train loss: 0.67212, val loss: 0.67149, in 0.016s
1 tree, 12 leaves, max depth = 6, train loss: 0.64917, val loss: 0.64765, in 0.016s
1 tree, 13 leaves, max depth = 7, train loss: 0.63125, val loss: 0.62907, in 0.016s
1 tree, 16 leaves, max depth = 8, train loss: 0.61354, val loss: 0.61074, in 0.016s
1 tree, 16 leaves, max depth = 5, train loss: 0.59675, val loss: 0.59298, in 0.019s
1 tree, 18 leaves, max depth = 6, train loss: 0.58124, val loss: 0.57680, in 0.013s
1 tree, 18 leaves, max depth = 7, train loss: 0.56701, val loss: 0.56201, in 0.016s
1 tree, 19 leaves, max depth = 7, train loss: 0.55372, val loss: 0.54812, in 0.016s
1 tree, 15 leaves, max depth = 7, train loss: 0.54078, val loss: 0.53446, in 0.016s
1 tree, 15 leaves, max depth = 6, train loss: 0.52768, val loss: 0.52065, in 0.016s
1 tree, 27 leaves, max depth = 8, train loss: 0.51741, val loss: 0.50975, in 0.016s
1 tree, 23 leaves, max depth = 8, train loss: 0.50742, val loss: 0.49935, in 0.016s
1 tree, 11 leaves, max depth = 5, train loss: 0.49837, val loss: 0.48986, in 0.016s
1 tree, 14 leaves, max depth = 6, train loss: 0.48822, val loss: 0.47908, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.48048, val loss: 0.47076, in 0.016s
1 tree, 16 leaves, max depth = 6, train loss: 0.47240, val loss: 0.46203, in 0.016s
1 tree, 24 leaves, max depth = 7, train loss: 0.46540, val loss: 0.45467, in 0.016s
1 tree, 10 leaves, max depth = 6, train loss: 0.45757, val loss: 0.44616, in 0.000s
1 tree, 6 leaves, max depth = 4, train loss: 0.45065, val loss: 0.43878, in 0.016s
1 tree, 13 leaves, max depth = 7, train loss: 0.44425, val loss: 0.43186, in 0.016s
1 tree, 10 leaves, max depth = 5, train loss: 0.43762, val loss: 0.42464, in 0.016s
Fit 21 trees in 0.643 s, (316 total leaves)
Time spent computing histograms: 0.143s
Time spent finding best splits:  0.012s
Time spent applying splits:      0.009s
Time spent predicting:           0.000s
Trial 36, Fold 4: Log loss = 0.4373566537181545, Average precision = 0.9497832966419613, ROC-AUC = 0.9433571535869868, Elapsed Time = 0.6570773000003101 seconds
Trial 36, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 36, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
Binning 0.040 GB of training data: 0.142 s
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 16 leaves, max depth = 6, train loss: 0.67196, val loss: 0.67085, in 0.016s
1 tree, 11 leaves, max depth = 5, train loss: 0.64876, val loss: 0.64661, in 0.016s
1 tree, 11 leaves, max depth = 8, train loss: 0.63074, val loss: 0.62734, in 0.016s
1 tree, 16 leaves, max depth = 6, train loss: 0.61288, val loss: 0.60873, in 0.016s
1 tree, 16 leaves, max depth = 6, train loss: 0.59587, val loss: 0.59070, in 0.016s
1 tree, 14 leaves, max depth = 6, train loss: 0.58037, val loss: 0.57407, in 0.016s
1 tree, 21 leaves, max depth = 9, train loss: 0.56713, val loss: 0.55997, in 0.016s
1 tree, 14 leaves, max depth = 6, train loss: 0.55273, val loss: 0.54472, in 0.016s
1 tree, 16 leaves, max depth = 7, train loss: 0.53949, val loss: 0.53058, in 0.000s
1 tree, 16 leaves, max depth = 7, train loss: 0.52626, val loss: 0.51686, in 0.016s
1 tree, 26 leaves, max depth = 9, train loss: 0.51593, val loss: 0.50572, in 0.016s
1 tree, 23 leaves, max depth = 7, train loss: 0.50589, val loss: 0.49534, in 0.016s
1 tree, 23 leaves, max depth = 8, train loss: 0.49665, val loss: 0.48572, in 0.016s
1 tree, 10 leaves, max depth = 5, train loss: 0.48687, val loss: 0.47538, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.47846, val loss: 0.46647, in 0.016s
1 tree, 18 leaves, max depth = 6, train loss: 0.47090, val loss: 0.45854, in 0.016s
1 tree, 11 leaves, max depth = 6, train loss: 0.46266, val loss: 0.44977, in 0.016s
1 tree, 6 leaves, max depth = 3, train loss: 0.45560, val loss: 0.44230, in 0.016s
1 tree, 16 leaves, max depth = 7, train loss: 0.44905, val loss: 0.43545, in 0.016s
1 tree, 16 leaves, max depth = 6, train loss: 0.44193, val loss: 0.42788, in 0.000s
1 tree, 16 leaves, max depth = 9, train loss: 0.43561, val loss: 0.42133, in 0.031s
Fit 21 trees in 0.642 s, (333 total leaves)
Time spent computing histograms: 0.137s
Time spent finding best splits:  0.013s
Time spent applying splits:      0.009s
Time spent predicting:           0.000s
Trial 36, Fold 5: Log loss = 0.44078513005362435, Average precision = 0.9495169038635765, ROC-AUC = 0.9432683425301451, Elapsed Time = 0.6378953999992518 seconds
Optimization Progress:  37%|###7      | 37/100 [07:18<11:52, 11.31s/it]
Trial 37, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 37, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
0.142 s 0.040 GB of training data: 
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 66 leaves, max depth = 10, train loss: 0.67495, val loss: 0.67482, in 0.016s
1 tree, 66 leaves, max depth = 13, train loss: 0.65724, val loss: 0.65695, in 0.016s
1 tree, 66 leaves, max depth = 14, train loss: 0.64077, val loss: 0.64054, in 0.031s
1 tree, 66 leaves, max depth = 12, train loss: 0.62631, val loss: 0.62619, in 0.016s
1 tree, 66 leaves, max depth = 12, train loss: 0.61130, val loss: 0.61109, in 0.016s
1 tree, 66 leaves, max depth = 10, train loss: 0.59835, val loss: 0.59787, in 0.031s
1 tree, 66 leaves, max depth = 16, train loss: 0.58679, val loss: 0.58628, in 0.016s
1 tree, 66 leaves, max depth = 13, train loss: 0.57493, val loss: 0.57441, in 0.016s
1 tree, 66 leaves, max depth = 14, train loss: 0.56390, val loss: 0.56324, in 0.031s
1 tree, 66 leaves, max depth = 13, train loss: 0.55273, val loss: 0.55193, in 0.016s
1 tree, 66 leaves, max depth = 13, train loss: 0.54219, val loss: 0.54137, in 0.016s
Fit 11 trees in 0.532 s, (726 total leaves)
Time spent computing histograms: 0.092s
Time spent finding best splits:  0.020s
Time spent applying splits:      0.014s
Time spent predicting:           0.000s
Trial 37, Fold 1: Log loss = 0.5447501958689264, Average precision = 0.923759226037229, ROC-AUC = 0.932276730450285, Elapsed Time = 0.5382616000006237 seconds
Trial 37, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 37, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
Binning 0.040 GB of training data: 0.158 s
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 66 leaves, max depth = 12, train loss: 0.67491, val loss: 0.67488, in 0.016s
1 tree, 66 leaves, max depth = 14, train loss: 0.65725, val loss: 0.65710, in 0.016s
1 tree, 66 leaves, max depth = 14, train loss: 0.64050, val loss: 0.64020, in 0.031s
1 tree, 66 leaves, max depth = 17, train loss: 0.62601, val loss: 0.62566, in 0.016s
1 tree, 66 leaves, max depth = 14, train loss: 0.61097, val loss: 0.61042, in 0.016s
1 tree, 66 leaves, max depth = 13, train loss: 0.59763, val loss: 0.59692, in 0.016s
1 tree, 66 leaves, max depth = 15, train loss: 0.58429, val loss: 0.58353, in 0.016s
1 tree, 66 leaves, max depth = 14, train loss: 0.57186, val loss: 0.57115, in 0.031s
1 tree, 66 leaves, max depth = 14, train loss: 0.56021, val loss: 0.55946, in 0.031s
1 tree, 66 leaves, max depth = 11, train loss: 0.54943, val loss: 0.54862, in 0.016s
1 tree, 66 leaves, max depth = 12, train loss: 0.53884, val loss: 0.53792, in 0.016s
Fit 11 trees in 0.580 s, (726 total leaves)
Time spent computing histograms: 0.093s
Time spent finding best splits:  0.022s
Time spent applying splits:      0.015s
Time spent predicting:           0.000s
Trial 37, Fold 2: Log loss = 0.5415810970872361, Average precision = 0.9166716562949201, ROC-AUC = 0.9333092003771843, Elapsed Time = 0.6004226000004564 seconds
Trial 37, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 37, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
Binning 0.040 GB of training data: 0.174 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 66 leaves, max depth = 11, train loss: 0.67505, val loss: 0.67510, in 0.031s
1 tree, 66 leaves, max depth = 13, train loss: 0.65762, val loss: 0.65776, in 0.016s
1 tree, 66 leaves, max depth = 14, train loss: 0.64113, val loss: 0.64134, in 0.016s
1 tree, 66 leaves, max depth = 14, train loss: 0.62692, val loss: 0.62707, in 0.031s
1 tree, 66 leaves, max depth = 13, train loss: 0.61189, val loss: 0.61223, in 0.016s
1 tree, 66 leaves, max depth = 11, train loss: 0.59878, val loss: 0.59919, in 0.016s
1 tree, 66 leaves, max depth = 14, train loss: 0.58667, val loss: 0.58714, in 0.031s
1 tree, 66 leaves, max depth = 12, train loss: 0.57446, val loss: 0.57494, in 0.016s
1 tree, 66 leaves, max depth = 13, train loss: 0.56293, val loss: 0.56332, in 0.016s
1 tree, 66 leaves, max depth = 13, train loss: 0.55215, val loss: 0.55263, in 0.016s
1 tree, 66 leaves, max depth = 12, train loss: 0.54137, val loss: 0.54185, in 0.031s
Fit 11 trees in 0.596 s, (726 total leaves)
Time spent computing histograms: 0.092s
Time spent finding best splits:  0.020s
Time spent applying splits:      0.013s
Time spent predicting:           0.000s
Trial 37, Fold 3: Log loss = 0.5414732280783043, Average precision = 0.9260159673414275, ROC-AUC = 0.9363839285714284, Elapsed Time = 0.597768299998279 seconds
Trial 37, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 37, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
Binning 0.040 GB of training data: 0.190 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 66 leaves, max depth = 12, train loss: 0.67506, val loss: 0.67440, in 0.016s
1 tree, 66 leaves, max depth = 15, train loss: 0.65755, val loss: 0.65637, in 0.016s
1 tree, 66 leaves, max depth = 17, train loss: 0.64099, val loss: 0.63931, in 0.016s
1 tree, 66 leaves, max depth = 12, train loss: 0.62663, val loss: 0.62472, in 0.031s
1 tree, 66 leaves, max depth = 18, train loss: 0.61191, val loss: 0.60943, in 0.016s
1 tree, 66 leaves, max depth = 13, train loss: 0.59869, val loss: 0.59588, in 0.016s
1 tree, 66 leaves, max depth = 13, train loss: 0.58542, val loss: 0.58223, in 0.031s
1 tree, 66 leaves, max depth = 14, train loss: 0.57315, val loss: 0.56937, in 0.016s
1 tree, 66 leaves, max depth = 14, train loss: 0.56174, val loss: 0.55759, in 0.016s
1 tree, 66 leaves, max depth = 13, train loss: 0.55118, val loss: 0.54669, in 0.016s
1 tree, 66 leaves, max depth = 15, train loss: 0.54061, val loss: 0.53566, in 0.031s
Fit 11 trees in 0.612 s, (726 total leaves)
Time spent computing histograms: 0.087s
Time spent finding best splits:  0.020s
Time spent applying splits:      0.013s
Time spent predicting:           0.000s
Trial 37, Fold 4: Log loss = 0.5420898621380901, Average precision = 0.9209019511537924, ROC-AUC = 0.9337531980896108, Elapsed Time = 0.6224643000005017 seconds
Trial 37, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 37, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
Binning 0.040 GB of training data: 0.159 s
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 66 leaves, max depth = 14, train loss: 0.67501, val loss: 0.67422, in 0.016s
1 tree, 66 leaves, max depth = 13, train loss: 0.65740, val loss: 0.65604, in 0.016s
1 tree, 66 leaves, max depth = 13, train loss: 0.64071, val loss: 0.63888, in 0.031s
1 tree, 66 leaves, max depth = 16, train loss: 0.62738, val loss: 0.62485, in 0.016s
1 tree, 66 leaves, max depth = 12, train loss: 0.61236, val loss: 0.60946, in 0.031s
1 tree, 66 leaves, max depth = 11, train loss: 0.59937, val loss: 0.59588, in 0.016s
1 tree, 66 leaves, max depth = 13, train loss: 0.58713, val loss: 0.58337, in 0.016s
1 tree, 66 leaves, max depth = 13, train loss: 0.57437, val loss: 0.57023, in 0.031s
1 tree, 66 leaves, max depth = 15, train loss: 0.56272, val loss: 0.55809, in 0.016s
1 tree, 66 leaves, max depth = 11, train loss: 0.55205, val loss: 0.54700, in 0.016s
1 tree, 66 leaves, max depth = 13, train loss: 0.54132, val loss: 0.53598, in 0.031s
Fit 11 trees in 0.596 s, (726 total leaves)
Time spent computing histograms: 0.092s
Time spent finding best splits:  0.020s
Time spent applying splits:      0.014s
Time spent predicting:           0.016s
Trial 37, Fold 5: Log loss = 0.5459636127053025, Average precision = 0.9183070200349506, ROC-AUC = 0.9307324378053993, Elapsed Time = 0.6025019000007887 seconds
Optimization Progress:  38%|###8      | 38/100 [07:27<11:05, 10.74s/it]
Trial 38, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 38, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
0.157 s 0.040 GB of training data: 
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 25 leaves, max depth = 10, train loss: 0.68529, val loss: 0.68505, in 0.016s
1 tree, 25 leaves, max depth = 14, train loss: 0.67774, val loss: 0.67729, in 0.000s
1 tree, 25 leaves, max depth = 9, train loss: 0.67044, val loss: 0.66980, in 0.016s
1 tree, 6 leaves, max depth = 4, train loss: 0.66340, val loss: 0.66250, in 0.000s
1 tree, 25 leaves, max depth = 15, train loss: 0.65656, val loss: 0.65546, in 0.016s
1 tree, 25 leaves, max depth = 13, train loss: 0.64994, val loss: 0.64864, in 0.000s
1 tree, 25 leaves, max depth = 14, train loss: 0.64359, val loss: 0.64211, in 0.016s
1 tree, 25 leaves, max depth = 10, train loss: 0.63744, val loss: 0.63578, in 0.000s
1 tree, 25 leaves, max depth = 12, train loss: 0.63142, val loss: 0.62957, in 0.016s
1 tree, 25 leaves, max depth = 13, train loss: 0.62550, val loss: 0.62347, in 0.000s
1 tree, 25 leaves, max depth = 11, train loss: 0.61988, val loss: 0.61765, in 0.016s
1 tree, 25 leaves, max depth = 9, train loss: 0.61446, val loss: 0.61206, in 0.000s
1 tree, 25 leaves, max depth = 14, train loss: 0.60914, val loss: 0.60657, in 0.016s
1 tree, 25 leaves, max depth = 10, train loss: 0.60405, val loss: 0.60129, in 0.000s
1 tree, 25 leaves, max depth = 14, train loss: 0.59905, val loss: 0.59611, in 0.016s
1 tree, 25 leaves, max depth = 11, train loss: 0.59425, val loss: 0.59114, in 0.000s
1 tree, 25 leaves, max depth = 9, train loss: 0.58953, val loss: 0.58628, in 0.016s
1 tree, 25 leaves, max depth = 9, train loss: 0.58496, val loss: 0.58156, in 0.000s
1 tree, 6 leaves, max depth = 4, train loss: 0.58052, val loss: 0.57691, in 0.016s
1 tree, 25 leaves, max depth = 11, train loss: 0.57612, val loss: 0.57233, in 0.000s
1 tree, 25 leaves, max depth = 11, train loss: 0.57186, val loss: 0.56788, in 0.000s
1 tree, 25 leaves, max depth = 11, train loss: 0.56772, val loss: 0.56356, in 0.000s
1 tree, 25 leaves, max depth = 11, train loss: 0.56370, val loss: 0.55937, in 0.016s
1 tree, 25 leaves, max depth = 11, train loss: 0.55980, val loss: 0.55530, in 0.000s
1 tree, 25 leaves, max depth = 9, train loss: 0.55607, val loss: 0.55145, in 0.016s
1 tree, 25 leaves, max depth = 8, train loss: 0.55217, val loss: 0.54778, in 0.000s
1 tree, 25 leaves, max depth = 11, train loss: 0.54864, val loss: 0.54409, in 0.016s
1 tree, 25 leaves, max depth = 14, train loss: 0.54521, val loss: 0.54055, in 0.000s
1 tree, 25 leaves, max depth = 13, train loss: 0.54189, val loss: 0.53710, in 0.016s
1 tree, 25 leaves, max depth = 8, train loss: 0.53821, val loss: 0.53366, in 0.000s
1 tree, 25 leaves, max depth = 14, train loss: 0.53502, val loss: 0.53037, in 0.016s
1 tree, 25 leaves, max depth = 10, train loss: 0.53191, val loss: 0.52710, in 0.000s
1 tree, 25 leaves, max depth = 9, train loss: 0.52880, val loss: 0.52383, in 0.016s
1 tree, 25 leaves, max depth = 9, train loss: 0.52532, val loss: 0.52058, in 0.000s
1 tree, 25 leaves, max depth = 12, train loss: 0.52233, val loss: 0.51743, in 0.016s
1 tree, 25 leaves, max depth = 9, train loss: 0.51949, val loss: 0.51448, in 0.016s
1 tree, 25 leaves, max depth = 9, train loss: 0.51672, val loss: 0.51160, in 0.000s
1 tree, 25 leaves, max depth = 10, train loss: 0.51343, val loss: 0.50854, in 0.016s
1 tree, 25 leaves, max depth = 10, train loss: 0.51077, val loss: 0.50578, in 0.000s
1 tree, 25 leaves, max depth = 10, train loss: 0.50822, val loss: 0.50309, in 0.016s
1 tree, 25 leaves, max depth = 9, train loss: 0.50571, val loss: 0.50048, in 0.000s
1 tree, 25 leaves, max depth = 12, train loss: 0.50322, val loss: 0.49784, in 0.016s
1 tree, 25 leaves, max depth = 9, train loss: 0.50085, val loss: 0.49536, in 0.016s
1 tree, 25 leaves, max depth = 8, train loss: 0.49777, val loss: 0.49250, in 0.000s
1 tree, 25 leaves, max depth = 11, train loss: 0.49553, val loss: 0.49015, in 0.016s
1 tree, 25 leaves, max depth = 9, train loss: 0.49332, val loss: 0.48785, in 0.000s
1 tree, 25 leaves, max depth = 9, train loss: 0.49036, val loss: 0.48511, in 0.016s
1 tree, 25 leaves, max depth = 12, train loss: 0.48819, val loss: 0.48280, in 0.000s
1 tree, 25 leaves, max depth = 12, train loss: 0.48608, val loss: 0.48055, in 0.016s
1 tree, 25 leaves, max depth = 9, train loss: 0.48324, val loss: 0.47793, in 0.000s
1 tree, 25 leaves, max depth = 10, train loss: 0.48128, val loss: 0.47585, in 0.016s
Fit 51 trees in 0.689 s, (1237 total leaves)
Time spent computing histograms: 0.222s
Time spent finding best splits:  0.027s
Time spent applying splits:      0.027s
Time spent predicting:           0.000s
Trial 38, Fold 1: Log loss = 0.4815927550429081, Average precision = 0.9019773409073615, ROC-AUC = 0.9093855599049819, Elapsed Time = 0.7017431000003853 seconds
Trial 38, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 38, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
Binning 0.040 GB of training data: 0.143 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 25 leaves, max depth = 8, train loss: 0.68540, val loss: 0.68502, in 0.016s
1 tree, 25 leaves, max depth = 12, train loss: 0.67789, val loss: 0.67716, in 0.000s
1 tree, 25 leaves, max depth = 11, train loss: 0.67058, val loss: 0.66951, in 0.016s
1 tree, 6 leaves, max depth = 4, train loss: 0.66351, val loss: 0.66210, in 0.000s
1 tree, 25 leaves, max depth = 12, train loss: 0.65671, val loss: 0.65496, in 0.016s
1 tree, 25 leaves, max depth = 11, train loss: 0.65011, val loss: 0.64806, in 0.000s
1 tree, 25 leaves, max depth = 13, train loss: 0.64373, val loss: 0.64136, in 0.016s
1 tree, 25 leaves, max depth = 14, train loss: 0.63755, val loss: 0.63486, in 0.000s
1 tree, 25 leaves, max depth = 9, train loss: 0.63154, val loss: 0.62851, in 0.016s
1 tree, 25 leaves, max depth = 10, train loss: 0.62565, val loss: 0.62229, in 0.000s
1 tree, 25 leaves, max depth = 11, train loss: 0.62002, val loss: 0.61639, in 0.016s
1 tree, 25 leaves, max depth = 15, train loss: 0.61457, val loss: 0.61064, in 0.000s
1 tree, 25 leaves, max depth = 13, train loss: 0.60929, val loss: 0.60507, in 0.016s
1 tree, 25 leaves, max depth = 9, train loss: 0.60421, val loss: 0.59971, in 0.016s
1 tree, 25 leaves, max depth = 12, train loss: 0.59924, val loss: 0.59448, in 0.000s
1 tree, 25 leaves, max depth = 14, train loss: 0.59442, val loss: 0.58938, in 0.016s
1 tree, 25 leaves, max depth = 8, train loss: 0.58970, val loss: 0.58440, in 0.000s
1 tree, 25 leaves, max depth = 8, train loss: 0.58512, val loss: 0.57956, in 0.016s
1 tree, 6 leaves, max depth = 4, train loss: 0.58068, val loss: 0.57487, in 0.000s
1 tree, 25 leaves, max depth = 9, train loss: 0.57633, val loss: 0.57023, in 0.016s
1 tree, 25 leaves, max depth = 8, train loss: 0.57214, val loss: 0.56580, in 0.000s
1 tree, 25 leaves, max depth = 9, train loss: 0.56804, val loss: 0.56143, in 0.016s
1 tree, 25 leaves, max depth = 9, train loss: 0.56406, val loss: 0.55718, in 0.000s
1 tree, 25 leaves, max depth = 9, train loss: 0.56020, val loss: 0.55305, in 0.016s
1 tree, 25 leaves, max depth = 8, train loss: 0.55648, val loss: 0.54911, in 0.000s
1 tree, 25 leaves, max depth = 9, train loss: 0.55257, val loss: 0.54540, in 0.016s
1 tree, 25 leaves, max depth = 11, train loss: 0.54903, val loss: 0.54166, in 0.000s
1 tree, 25 leaves, max depth = 14, train loss: 0.54559, val loss: 0.53801, in 0.000s
1 tree, 25 leaves, max depth = 12, train loss: 0.54226, val loss: 0.53447, in 0.016s
1 tree, 25 leaves, max depth = 9, train loss: 0.53858, val loss: 0.53098, in 0.000s
1 tree, 25 leaves, max depth = 14, train loss: 0.53538, val loss: 0.52758, in 0.016s
1 tree, 25 leaves, max depth = 11, train loss: 0.53226, val loss: 0.52429, in 0.000s
1 tree, 25 leaves, max depth = 9, train loss: 0.52918, val loss: 0.52098, in 0.016s
1 tree, 25 leaves, max depth = 8, train loss: 0.52571, val loss: 0.51769, in 0.000s
1 tree, 25 leaves, max depth = 8, train loss: 0.52279, val loss: 0.51457, in 0.016s
1 tree, 25 leaves, max depth = 8, train loss: 0.51995, val loss: 0.51154, in 0.000s
1 tree, 25 leaves, max depth = 8, train loss: 0.51719, val loss: 0.50860, in 0.016s
1 tree, 25 leaves, max depth = 7, train loss: 0.51391, val loss: 0.50549, in 0.016s
1 tree, 25 leaves, max depth = 8, train loss: 0.51126, val loss: 0.50266, in 0.000s
1 tree, 25 leaves, max depth = 11, train loss: 0.50870, val loss: 0.49996, in 0.016s
1 tree, 25 leaves, max depth = 8, train loss: 0.50621, val loss: 0.49729, in 0.000s
1 tree, 25 leaves, max depth = 9, train loss: 0.50374, val loss: 0.49462, in 0.016s
1 tree, 25 leaves, max depth = 8, train loss: 0.50138, val loss: 0.49209, in 0.016s
1 tree, 25 leaves, max depth = 7, train loss: 0.49831, val loss: 0.48918, in 0.000s
1 tree, 25 leaves, max depth = 14, train loss: 0.49606, val loss: 0.48678, in 0.016s
1 tree, 25 leaves, max depth = 8, train loss: 0.49386, val loss: 0.48442, in 0.000s
1 tree, 25 leaves, max depth = 9, train loss: 0.49091, val loss: 0.48164, in 0.016s
1 tree, 25 leaves, max depth = 9, train loss: 0.48876, val loss: 0.47931, in 0.016s
1 tree, 25 leaves, max depth = 9, train loss: 0.48667, val loss: 0.47704, in 0.000s
1 tree, 25 leaves, max depth = 7, train loss: 0.48384, val loss: 0.47438, in 0.016s
1 tree, 25 leaves, max depth = 11, train loss: 0.48187, val loss: 0.47229, in 0.000s
Fit 51 trees in 0.737 s, (1237 total leaves)
Time spent computing histograms: 0.245s
Time spent finding best splits:  0.028s
Time spent applying splits:      0.029s
Time spent predicting:           0.000s
Trial 38, Fold 2: Log loss = 0.4818755510950665, Average precision = 0.899719366512129, ROC-AUC = 0.9126473736555525, Elapsed Time = 0.7471654999990278 seconds
Trial 38, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 38, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
Binning 0.040 GB of training data: 0.174 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 25 leaves, max depth = 10, train loss: 0.68537, val loss: 0.68512, in 0.000s
1 tree, 25 leaves, max depth = 13, train loss: 0.67792, val loss: 0.67741, in 0.016s
1 tree, 25 leaves, max depth = 8, train loss: 0.67066, val loss: 0.66988, in 0.000s
1 tree, 6 leaves, max depth = 4, train loss: 0.66363, val loss: 0.66261, in 0.016s
1 tree, 25 leaves, max depth = 11, train loss: 0.65689, val loss: 0.65563, in 0.000s
1 tree, 25 leaves, max depth = 9, train loss: 0.65032, val loss: 0.64884, in 0.016s
1 tree, 25 leaves, max depth = 10, train loss: 0.64399, val loss: 0.64228, in 0.000s
1 tree, 25 leaves, max depth = 11, train loss: 0.63787, val loss: 0.63593, in 0.016s
1 tree, 25 leaves, max depth = 8, train loss: 0.63189, val loss: 0.62970, in 0.000s
1 tree, 25 leaves, max depth = 7, train loss: 0.62602, val loss: 0.62363, in 0.016s
1 tree, 25 leaves, max depth = 9, train loss: 0.62041, val loss: 0.61783, in 0.016s
1 tree, 25 leaves, max depth = 11, train loss: 0.61501, val loss: 0.61223, in 0.000s
1 tree, 25 leaves, max depth = 8, train loss: 0.60974, val loss: 0.60672, in 0.016s
1 tree, 25 leaves, max depth = 9, train loss: 0.60468, val loss: 0.60147, in 0.000s
1 tree, 25 leaves, max depth = 8, train loss: 0.59972, val loss: 0.59628, in 0.016s
1 tree, 25 leaves, max depth = 14, train loss: 0.59495, val loss: 0.59130, in 0.016s
1 tree, 25 leaves, max depth = 8, train loss: 0.59026, val loss: 0.58641, in 0.000s
1 tree, 25 leaves, max depth = 8, train loss: 0.58572, val loss: 0.58167, in 0.016s
1 tree, 6 leaves, max depth = 4, train loss: 0.58130, val loss: 0.57707, in 0.000s
1 tree, 25 leaves, max depth = 8, train loss: 0.57695, val loss: 0.57256, in 0.016s
1 tree, 25 leaves, max depth = 7, train loss: 0.57273, val loss: 0.56819, in 0.016s
1 tree, 25 leaves, max depth = 7, train loss: 0.56864, val loss: 0.56394, in 0.000s
1 tree, 25 leaves, max depth = 7, train loss: 0.56466, val loss: 0.55982, in 0.016s
1 tree, 25 leaves, max depth = 7, train loss: 0.56081, val loss: 0.55581, in 0.000s
1 tree, 25 leaves, max depth = 8, train loss: 0.55712, val loss: 0.55194, in 0.016s
1 tree, 25 leaves, max depth = 8, train loss: 0.55318, val loss: 0.54824, in 0.016s
1 tree, 25 leaves, max depth = 11, train loss: 0.54966, val loss: 0.54457, in 0.000s
1 tree, 25 leaves, max depth = 11, train loss: 0.54625, val loss: 0.54101, in 0.016s
1 tree, 25 leaves, max depth = 12, train loss: 0.54296, val loss: 0.53756, in 0.000s
1 tree, 25 leaves, max depth = 8, train loss: 0.53925, val loss: 0.53408, in 0.016s
1 tree, 25 leaves, max depth = 11, train loss: 0.53608, val loss: 0.53076, in 0.016s
1 tree, 25 leaves, max depth = 9, train loss: 0.53299, val loss: 0.52753, in 0.000s
1 tree, 25 leaves, max depth = 8, train loss: 0.52991, val loss: 0.52432, in 0.016s
1 tree, 25 leaves, max depth = 7, train loss: 0.52642, val loss: 0.52104, in 0.016s
1 tree, 25 leaves, max depth = 7, train loss: 0.52347, val loss: 0.51796, in 0.000s
1 tree, 25 leaves, max depth = 8, train loss: 0.52066, val loss: 0.51499, in 0.016s
1 tree, 25 leaves, max depth = 8, train loss: 0.51793, val loss: 0.51211, in 0.016s
[38/51] 1 tree, 25 leaves, max depth = 7, train loss: 0.51462, val loss: 0.50901, in 0.000s
1 tree, 25 leaves, max depth = 8, train loss: 0.51200, val loss: 0.50624, in 0.016s
1 tree, 25 leaves, max depth = 11, train loss: 0.50946, val loss: 0.50358, in 0.000s
1 tree, 25 leaves, max depth = 8, train loss: 0.50699, val loss: 0.50096, in 0.016s
1 tree, 25 leaves, max depth = 7, train loss: 0.50453, val loss: 0.49839, in 0.016s
1 tree, 25 leaves, max depth = 8, train loss: 0.50220, val loss: 0.49591, in 0.000s
1 tree, 25 leaves, max depth = 8, train loss: 0.49909, val loss: 0.49300, in 0.016s
1 tree, 25 leaves, max depth = 12, train loss: 0.49688, val loss: 0.49066, in 0.016s
1 tree, 25 leaves, max depth = 8, train loss: 0.49470, val loss: 0.48834, in 0.000s
1 tree, 25 leaves, max depth = 8, train loss: 0.49173, val loss: 0.48556, in 0.016s
1 tree, 25 leaves, max depth = 8, train loss: 0.48959, val loss: 0.48331, in 0.016s
1 tree, 25 leaves, max depth = 8, train loss: 0.48750, val loss: 0.48113, in 0.000s
1 tree, 25 leaves, max depth = 9, train loss: 0.48465, val loss: 0.47846, in 0.016s
1 tree, 25 leaves, max depth = 11, train loss: 0.48270, val loss: 0.47641, in 0.000s
Fit 51 trees in 0.799 s, (1237 total leaves)
Time spent computing histograms: 0.268s
Time spent finding best splits:  0.030s
Time spent applying splits:      0.032s
Time spent predicting:           0.000s
Trial 38, Fold 3: Log loss = 0.4776605345559353, Average precision = 0.908762631514317, ROC-AUC = 0.9183635095732487, Elapsed Time = 0.7995752000006178 seconds
Trial 38, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 38, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
Binning 0.040 GB of training data: 0.159 s
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 25 leaves, max depth = 11, train loss: 0.68539, val loss: 0.68493, in 0.000s
1 tree, 25 leaves, max depth = 10, train loss: 0.67788, val loss: 0.67701, in 0.016s
1 tree, 25 leaves, max depth = 9, train loss: 0.67061, val loss: 0.66935, in 0.016s
1 tree, 6 leaves, max depth = 4, train loss: 0.66358, val loss: 0.66194, in 0.000s
1 tree, 25 leaves, max depth = 10, train loss: 0.65678, val loss: 0.65474, in 0.016s
1 tree, 25 leaves, max depth = 10, train loss: 0.65019, val loss: 0.64777, in 0.000s
1 tree, 25 leaves, max depth = 10, train loss: 0.64385, val loss: 0.64106, in 0.016s
1 tree, 25 leaves, max depth = 9, train loss: 0.63770, val loss: 0.63455, in 0.016s
1 tree, 25 leaves, max depth = 10, train loss: 0.63172, val loss: 0.62819, in 0.000s
1 tree, 25 leaves, max depth = 10, train loss: 0.62585, val loss: 0.62191, in 0.016s
1 tree, 25 leaves, max depth = 13, train loss: 0.62024, val loss: 0.61592, in 0.000s
1 tree, 25 leaves, max depth = 11, train loss: 0.61483, val loss: 0.61016, in 0.016s
1 tree, 25 leaves, max depth = 10, train loss: 0.60954, val loss: 0.60453, in 0.000s
1 tree, 25 leaves, max depth = 12, train loss: 0.60446, val loss: 0.59907, in 0.016s
1 tree, 25 leaves, max depth = 11, train loss: 0.59949, val loss: 0.59376, in 0.000s
1 tree, 25 leaves, max depth = 10, train loss: 0.59470, val loss: 0.58864, in 0.016s
1 tree, 25 leaves, max depth = 9, train loss: 0.59000, val loss: 0.58363, in 0.016s
1 tree, 25 leaves, max depth = 9, train loss: 0.58544, val loss: 0.57876, in 0.000s
1 tree, 6 leaves, max depth = 4, train loss: 0.58101, val loss: 0.57403, in 0.016s
1 tree, 25 leaves, max depth = 11, train loss: 0.57667, val loss: 0.56933, in 0.000s
1 tree, 25 leaves, max depth = 11, train loss: 0.57245, val loss: 0.56477, in 0.016s
1 tree, 25 leaves, max depth = 10, train loss: 0.56836, val loss: 0.56034, in 0.016s
1 tree, 25 leaves, max depth = 11, train loss: 0.56440, val loss: 0.55603, in 0.000s
1 tree, 25 leaves, max depth = 10, train loss: 0.56054, val loss: 0.55184, in 0.016s
1 tree, 25 leaves, max depth = 9, train loss: 0.55684, val loss: 0.54787, in 0.000s
1 tree, 25 leaves, max depth = 9, train loss: 0.55300, val loss: 0.54408, in 0.016s
1 tree, 25 leaves, max depth = 12, train loss: 0.54947, val loss: 0.54024, in 0.000s
1 tree, 25 leaves, max depth = 10, train loss: 0.54605, val loss: 0.53655, in 0.016s
1 tree, 25 leaves, max depth = 10, train loss: 0.54274, val loss: 0.53296, in 0.016s
1 tree, 25 leaves, max depth = 9, train loss: 0.53911, val loss: 0.52938, in 0.000s
1 tree, 25 leaves, max depth = 10, train loss: 0.53592, val loss: 0.52594, in 0.016s
1 tree, 25 leaves, max depth = 12, train loss: 0.53282, val loss: 0.52254, in 0.000s
1 tree, 25 leaves, max depth = 10, train loss: 0.52975, val loss: 0.51917, in 0.016s
1 tree, 25 leaves, max depth = 9, train loss: 0.52632, val loss: 0.51579, in 0.016s
1 tree, 25 leaves, max depth = 10, train loss: 0.52338, val loss: 0.51256, in 0.000s
1 tree, 25 leaves, max depth = 10, train loss: 0.52055, val loss: 0.50949, in 0.016s
1 tree, 25 leaves, max depth = 10, train loss: 0.51781, val loss: 0.50650, in 0.016s
1 tree, 25 leaves, max depth = 9, train loss: 0.51456, val loss: 0.50330, in 0.000s
1 tree, 25 leaves, max depth = 10, train loss: 0.51193, val loss: 0.50043, in 0.016s
1 tree, 25 leaves, max depth = 12, train loss: 0.50938, val loss: 0.49762, in 0.000s
1 tree, 25 leaves, max depth = 10, train loss: 0.50690, val loss: 0.49490, in 0.016s
1 tree, 25 leaves, max depth = 10, train loss: 0.50444, val loss: 0.49217, in 0.016s
1 tree, 25 leaves, max depth = 10, train loss: 0.50210, val loss: 0.48960, in 0.000s
1 tree, 25 leaves, max depth = 8, train loss: 0.49906, val loss: 0.48662, in 0.016s
1 tree, 25 leaves, max depth = 12, train loss: 0.49682, val loss: 0.48415, in 0.016s
1 tree, 25 leaves, max depth = 10, train loss: 0.49463, val loss: 0.48174, in 0.000s
1 tree, 25 leaves, max depth = 9, train loss: 0.49172, val loss: 0.47890, in 0.016s
1 tree, 25 leaves, max depth = 10, train loss: 0.48958, val loss: 0.47651, in 0.016s
1 tree, 25 leaves, max depth = 11, train loss: 0.48750, val loss: 0.47418, in 0.000s
1 tree, 25 leaves, max depth = 8, train loss: 0.48470, val loss: 0.47145, in 0.016s
1 tree, 25 leaves, max depth = 12, train loss: 0.48275, val loss: 0.46925, in 0.016s
Fit 51 trees in 0.799 s, (1237 total leaves)
Time spent computing histograms: 0.257s
Time spent finding best splits:  0.031s
Time spent applying splits:      0.032s
Time spent predicting:           0.047s
Trial 38, Fold 4: Log loss = 0.4817802276329943, Average precision = 0.9027976986375478, ROC-AUC = 0.9135190222250835, Elapsed Time = 0.7933245000003808 seconds
Trial 38, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 38, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
Binning 0.040 GB of training data: 0.172 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 25 leaves, max depth = 10, train loss: 0.68526, val loss: 0.68471, in 0.016s
1 tree, 25 leaves, max depth = 11, train loss: 0.67770, val loss: 0.67668, in 0.000s
1 tree, 25 leaves, max depth = 9, train loss: 0.67035, val loss: 0.66885, in 0.016s
1 tree, 6 leaves, max depth = 4, train loss: 0.66324, val loss: 0.66126, in 0.000s
1 tree, 25 leaves, max depth = 9, train loss: 0.65640, val loss: 0.65396, in 0.016s
1 tree, 25 leaves, max depth = 12, train loss: 0.64976, val loss: 0.64687, in 0.000s
1 tree, 25 leaves, max depth = 11, train loss: 0.64334, val loss: 0.64001, in 0.016s
1 tree, 25 leaves, max depth = 9, train loss: 0.63714, val loss: 0.63336, in 0.016s
1 tree, 25 leaves, max depth = 10, train loss: 0.63108, val loss: 0.62688, in 0.000s
1 tree, 25 leaves, max depth = 9, train loss: 0.62516, val loss: 0.62052, in 0.016s
1 tree, 25 leaves, max depth = 9, train loss: 0.61950, val loss: 0.61439, in 0.000s
1 tree, 25 leaves, max depth = 9, train loss: 0.61403, val loss: 0.60851, in 0.016s
1 tree, 25 leaves, max depth = 11, train loss: 0.60868, val loss: 0.60277, in 0.000s
1 tree, 25 leaves, max depth = 9, train loss: 0.60356, val loss: 0.59720, in 0.016s
1 tree, 25 leaves, max depth = 10, train loss: 0.59852, val loss: 0.59178, in 0.016s
1 tree, 25 leaves, max depth = 10, train loss: 0.59367, val loss: 0.58654, in 0.000s
1 tree, 25 leaves, max depth = 9, train loss: 0.58893, val loss: 0.58143, in 0.016s
1 tree, 25 leaves, max depth = 9, train loss: 0.58436, val loss: 0.57643, in 0.000s
1 tree, 6 leaves, max depth = 4, train loss: 0.57989, val loss: 0.57159, in 0.016s
1 tree, 25 leaves, max depth = 9, train loss: 0.57551, val loss: 0.56683, in 0.000s
1 tree, 25 leaves, max depth = 9, train loss: 0.57126, val loss: 0.56221, in 0.016s
1 tree, 25 leaves, max depth = 9, train loss: 0.56713, val loss: 0.55772, in 0.016s
1 tree, 25 leaves, max depth = 9, train loss: 0.56313, val loss: 0.55335, in 0.000s
1 tree, 25 leaves, max depth = 9, train loss: 0.55924, val loss: 0.54911, in 0.016s
1 tree, 25 leaves, max depth = 9, train loss: 0.55552, val loss: 0.54505, in 0.000s
1 tree, 25 leaves, max depth = 9, train loss: 0.55167, val loss: 0.54135, in 0.016s
1 tree, 25 leaves, max depth = 9, train loss: 0.54810, val loss: 0.53745, in 0.016s
1 tree, 25 leaves, max depth = 11, train loss: 0.54465, val loss: 0.53367, in 0.000s
1 tree, 25 leaves, max depth = 13, train loss: 0.54129, val loss: 0.52999, in 0.016s
1 tree, 25 leaves, max depth = 10, train loss: 0.53765, val loss: 0.52651, in 0.000s
1 tree, 25 leaves, max depth = 11, train loss: 0.53444, val loss: 0.52298, in 0.016s
1 tree, 25 leaves, max depth = 9, train loss: 0.53130, val loss: 0.51951, in 0.016s
1 tree, 25 leaves, max depth = 9, train loss: 0.52820, val loss: 0.51610, in 0.016s
1 tree, 25 leaves, max depth = 10, train loss: 0.52476, val loss: 0.51283, in 0.000s
1 tree, 25 leaves, max depth = 9, train loss: 0.52178, val loss: 0.50956, in 0.016s
1 tree, 25 leaves, max depth = 11, train loss: 0.51895, val loss: 0.50644, in 0.016s
1 tree, 25 leaves, max depth = 11, train loss: 0.51620, val loss: 0.50340, in 0.000s
1 tree, 25 leaves, max depth = 10, train loss: 0.51295, val loss: 0.50031, in 0.016s
1 tree, 25 leaves, max depth = 10, train loss: 0.51029, val loss: 0.49738, in 0.016s
1 tree, 25 leaves, max depth = 9, train loss: 0.50772, val loss: 0.49449, in 0.016s
1 tree, 25 leaves, max depth = 9, train loss: 0.50520, val loss: 0.49171, in 0.000s
1 tree, 25 leaves, max depth = 9, train loss: 0.50271, val loss: 0.48895, in 0.016s
1 tree, 25 leaves, max depth = 9, train loss: 0.50035, val loss: 0.48632, in 0.016s
1 tree, 25 leaves, max depth = 8, train loss: 0.49730, val loss: 0.48344, in 0.000s
1 tree, 25 leaves, max depth = 11, train loss: 0.49504, val loss: 0.48093, in 0.016s
1 tree, 25 leaves, max depth = 9, train loss: 0.49282, val loss: 0.47846, in 0.016s
1 tree, 25 leaves, max depth = 10, train loss: 0.48990, val loss: 0.47571, in 0.000s
1 tree, 25 leaves, max depth = 9, train loss: 0.48773, val loss: 0.47329, in 0.016s
1 tree, 25 leaves, max depth = 9, train loss: 0.48561, val loss: 0.47094, in 0.016s
1 tree, 25 leaves, max depth = 10, train loss: 0.48281, val loss: 0.46831, in 0.016s
1 tree, 25 leaves, max depth = 9, train loss: 0.48083, val loss: 0.46608, in 0.000s
Fit 51 trees in 0.813 s, (1237 total leaves)
Time spent computing histograms: 0.269s
Time spent finding best splits:  0.033s
Time spent applying splits:      0.033s
Time spent predicting:           0.000s
Trial 38, Fold 5: Log loss = 0.4873302464752492, Average precision = 0.8985605879198201, ROC-AUC = 0.9079279861769133, Elapsed Time = 0.8260637000003044 seconds
Optimization Progress:  39%|###9      | 39/100 [07:38<11:00, 10.82s/it]
Trial 39, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 39, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
0.157 s 0.040 GB of training data: 
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 64 leaves, max depth = 16, train loss: 0.68187, val loss: 0.68155, in 0.016s
1 tree, 64 leaves, max depth = 16, train loss: 0.67129, val loss: 0.67067, in 0.016s
1 tree, 64 leaves, max depth = 16, train loss: 0.66120, val loss: 0.66028, in 0.000s
1 tree, 64 leaves, max depth = 16, train loss: 0.65157, val loss: 0.65035, in 0.016s
1 tree, 64 leaves, max depth = 16, train loss: 0.64237, val loss: 0.64087, in 0.016s
1 tree, 64 leaves, max depth = 16, train loss: 0.63357, val loss: 0.63179, in 0.000s
1 tree, 64 leaves, max depth = 16, train loss: 0.62517, val loss: 0.62311, in 0.016s
1 tree, 64 leaves, max depth = 16, train loss: 0.61712, val loss: 0.61480, in 0.000s
1 tree, 64 leaves, max depth = 16, train loss: 0.60943, val loss: 0.60685, in 0.016s
1 tree, 64 leaves, max depth = 16, train loss: 0.60206, val loss: 0.59922, in 0.016s
1 tree, 64 leaves, max depth = 16, train loss: 0.59501, val loss: 0.59192, in 0.000s
1 tree, 64 leaves, max depth = 16, train loss: 0.58825, val loss: 0.58492, in 0.016s
1 tree, 66 leaves, max depth = 17, train loss: 0.58188, val loss: 0.57829, in 0.016s
1 tree, 66 leaves, max depth = 16, train loss: 0.57567, val loss: 0.57184, in 0.000s
1 tree, 66 leaves, max depth = 16, train loss: 0.56971, val loss: 0.56566, in 0.016s
1 tree, 67 leaves, max depth = 15, train loss: 0.56405, val loss: 0.55985, in 0.016s
1 tree, 66 leaves, max depth = 17, train loss: 0.55857, val loss: 0.55415, in 0.000s
1 tree, 105 leaves, max depth = 14, train loss: 0.55301, val loss: 0.54893, in 0.016s
1 tree, 66 leaves, max depth = 15, train loss: 0.54793, val loss: 0.54362, in 0.016s
1 tree, 105 leaves, max depth = 14, train loss: 0.54268, val loss: 0.53870, in 0.016s
1 tree, 66 leaves, max depth = 17, train loss: 0.53782, val loss: 0.53363, in 0.000s
1 tree, 105 leaves, max depth = 14, train loss: 0.53285, val loss: 0.52898, in 0.016s
1 tree, 66 leaves, max depth = 17, train loss: 0.52827, val loss: 0.52419, in 0.000s
1 tree, 66 leaves, max depth = 17, train loss: 0.52387, val loss: 0.51958, in 0.031s
1 tree, 106 leaves, max depth = 16, train loss: 0.51922, val loss: 0.51525, in 0.016s
1 tree, 106 leaves, max depth = 16, train loss: 0.51477, val loss: 0.51110, in 0.016s
1 tree, 106 leaves, max depth = 16, train loss: 0.51049, val loss: 0.50711, in 0.000s
1 tree, 67 leaves, max depth = 15, train loss: 0.50653, val loss: 0.50295, in 0.016s
1 tree, 106 leaves, max depth = 16, train loss: 0.50247, val loss: 0.49918, in 0.016s
1 tree, 66 leaves, max depth = 14, train loss: 0.49867, val loss: 0.49518, in 0.016s
1 tree, 66 leaves, max depth = 14, train loss: 0.49502, val loss: 0.49133, in 0.000s
1 tree, 106 leaves, max depth = 17, train loss: 0.49120, val loss: 0.48780, in 0.016s
1 tree, 106 leaves, max depth = 17, train loss: 0.48754, val loss: 0.48440, in 0.016s
1 tree, 66 leaves, max depth = 16, train loss: 0.48418, val loss: 0.48086, in 0.016s
1 tree, 106 leaves, max depth = 17, train loss: 0.48069, val loss: 0.47763, in 0.000s
1 tree, 106 leaves, max depth = 17, train loss: 0.47733, val loss: 0.47453, in 0.016s
1 tree, 67 leaves, max depth = 15, train loss: 0.47415, val loss: 0.47117, in 0.016s
1 tree, 106 leaves, max depth = 16, train loss: 0.47095, val loss: 0.46822, in 0.016s
1 tree, 67 leaves, max depth = 17, train loss: 0.46798, val loss: 0.46506, in 0.000s
1 tree, 106 leaves, max depth = 16, train loss: 0.46493, val loss: 0.46226, in 0.016s
1 tree, 68 leaves, max depth = 15, train loss: 0.46206, val loss: 0.45921, in 0.016s
1 tree, 105 leaves, max depth = 17, train loss: 0.45915, val loss: 0.45655, in 0.016s
1 tree, 67 leaves, max depth = 17, train loss: 0.45646, val loss: 0.45368, in 0.000s
1 tree, 106 leaves, max depth = 17, train loss: 0.45369, val loss: 0.45114, in 0.016s
1 tree, 106 leaves, max depth = 17, train loss: 0.45101, val loss: 0.44869, in 0.016s
1 tree, 67 leaves, max depth = 17, train loss: 0.44848, val loss: 0.44599, in 0.016s
1 tree, 106 leaves, max depth = 17, train loss: 0.44593, val loss: 0.44366, in 0.000s
1 tree, 67 leaves, max depth = 14, train loss: 0.44350, val loss: 0.44108, in 0.016s
1 tree, 106 leaves, max depth = 17, train loss: 0.44106, val loss: 0.43886, in 0.016s
1 tree, 65 leaves, max depth = 14, train loss: 0.43879, val loss: 0.43647, in 0.000s
1 tree, 106 leaves, max depth = 17, train loss: 0.43645, val loss: 0.43435, in 0.016s
1 tree, 67 leaves, max depth = 14, train loss: 0.43425, val loss: 0.43201, in 0.016s
1 tree, 106 leaves, max depth = 16, train loss: 0.43202, val loss: 0.42999, in 0.016s
1 tree, 106 leaves, max depth = 16, train loss: 0.42986, val loss: 0.42805, in 0.016s
[55/82] 1 tree, 67 leaves, max depth = 13, train loss: 0.42780, val loss: 0.42582, in 0.000s
1 tree, 106 leaves, max depth = 16, train loss: 0.42574, val loss: 0.42397, in 0.016s
1 tree, 67 leaves, max depth = 13, train loss: 0.42377, val loss: 0.42183, in 0.016s
1 tree, 106 leaves, max depth = 16, train loss: 0.42179, val loss: 0.42006, in 0.000s
1 tree, 67 leaves, max depth = 12, train loss: 0.41985, val loss: 0.41800, in 0.016s
1 tree, 106 leaves, max depth = 16, train loss: 0.41796, val loss: 0.41631, in 0.016s
1 tree, 67 leaves, max depth = 13, train loss: 0.41617, val loss: 0.41436, in 0.000s
1 tree, 106 leaves, max depth = 16, train loss: 0.41435, val loss: 0.41274, in 0.016s
1 tree, 106 leaves, max depth = 16, train loss: 0.41259, val loss: 0.41118, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.41095, val loss: 0.40936, in 0.000s
1 tree, 4 leaves, max depth = 3, train loss: 0.40937, val loss: 0.40761, in 0.000s
1 tree, 67 leaves, max depth = 12, train loss: 0.40765, val loss: 0.40578, in 0.016s
1 tree, 106 leaves, max depth = 16, train loss: 0.40597, val loss: 0.40431, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.40447, val loss: 0.40264, in 0.000s
1 tree, 106 leaves, max depth = 16, train loss: 0.40286, val loss: 0.40124, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.40142, val loss: 0.39963, in 0.000s
1 tree, 67 leaves, max depth = 14, train loss: 0.39984, val loss: 0.39795, in 0.016s
1 tree, 105 leaves, max depth = 14, train loss: 0.39829, val loss: 0.39661, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.39692, val loss: 0.39507, in 0.000s
1 tree, 67 leaves, max depth = 14, train loss: 0.39541, val loss: 0.39348, in 0.016s
1 tree, 104 leaves, max depth = 14, train loss: 0.39393, val loss: 0.39220, in 0.016s
1 tree, 67 leaves, max depth = 13, train loss: 0.39249, val loss: 0.39064, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.39118, val loss: 0.38917, in 0.000s
1 tree, 4 leaves, max depth = 3, train loss: 0.38992, val loss: 0.38776, in 0.000s
1 tree, 4 leaves, max depth = 3, train loss: 0.38871, val loss: 0.38640, in 0.016s
1 tree, 67 leaves, max depth = 14, train loss: 0.38734, val loss: 0.38496, in 0.016s
1 tree, 67 leaves, max depth = 14, train loss: 0.38599, val loss: 0.38356, in 0.000s
1 tree, 105 leaves, max depth = 13, train loss: 0.38456, val loss: 0.38235, in 0.016s
Fit 82 trees in 1.173 s, (6143 total leaves)
Time spent computing histograms: 0.399s
Time spent finding best splits:  0.091s
Time spent applying splits:      0.104s
Time spent predicting:           0.000s
Trial 39, Fold 1: Log loss = 0.3873480272135086, Average precision = 0.9476433109414608, ROC-AUC = 0.9442173392743628, Elapsed Time = 1.1750902000003407 seconds
Trial 39, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 39, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
Binning 0.040 GB of training data: 0.174 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 66 leaves, max depth = 16, train loss: 0.68205, val loss: 0.68153, in 0.016s
1 tree, 66 leaves, max depth = 16, train loss: 0.67152, val loss: 0.67051, in 0.016s
1 tree, 66 leaves, max depth = 16, train loss: 0.66149, val loss: 0.65999, in 0.000s
1 tree, 66 leaves, max depth = 16, train loss: 0.65190, val loss: 0.64994, in 0.016s
1 tree, 66 leaves, max depth = 16, train loss: 0.64275, val loss: 0.64033, in 0.000s
1 tree, 66 leaves, max depth = 16, train loss: 0.63400, val loss: 0.63114, in 0.000s
1 tree, 66 leaves, max depth = 16, train loss: 0.62564, val loss: 0.62235, in 0.016s
1 tree, 68 leaves, max depth = 15, train loss: 0.61771, val loss: 0.61406, in 0.000s
1 tree, 67 leaves, max depth = 16, train loss: 0.61005, val loss: 0.60599, in 0.016s
1 tree, 68 leaves, max depth = 15, train loss: 0.60279, val loss: 0.59838, in 0.000s
1 tree, 67 leaves, max depth = 16, train loss: 0.59577, val loss: 0.59097, in 0.000s
1 tree, 68 leaves, max depth = 15, train loss: 0.58911, val loss: 0.58398, in 0.016s
1 tree, 68 leaves, max depth = 15, train loss: 0.58273, val loss: 0.57729, in 0.016s
1 tree, 67 leaves, max depth = 16, train loss: 0.57654, val loss: 0.57074, in 0.000s
1 tree, 67 leaves, max depth = 16, train loss: 0.57060, val loss: 0.56446, in 0.016s
1 tree, 65 leaves, max depth = 16, train loss: 0.56497, val loss: 0.55852, in 0.000s
1 tree, 67 leaves, max depth = 16, train loss: 0.55951, val loss: 0.55273, in 0.016s
1 tree, 106 leaves, max depth = 16, train loss: 0.55394, val loss: 0.54741, in 0.016s
1 tree, 67 leaves, max depth = 15, train loss: 0.54887, val loss: 0.54206, in 0.000s
1 tree, 106 leaves, max depth = 16, train loss: 0.54361, val loss: 0.53704, in 0.016s
1 tree, 66 leaves, max depth = 16, train loss: 0.53882, val loss: 0.53196, in 0.016s
1 tree, 106 leaves, max depth = 17, train loss: 0.53385, val loss: 0.52722, in 0.016s
1 tree, 68 leaves, max depth = 14, train loss: 0.52934, val loss: 0.52246, in 0.016s
1 tree, 68 leaves, max depth = 11, train loss: 0.52494, val loss: 0.51778, in 0.016s
1 tree, 106 leaves, max depth = 15, train loss: 0.52029, val loss: 0.51335, in 0.000s
1 tree, 106 leaves, max depth = 15, train loss: 0.51583, val loss: 0.50911, in 0.016s
1 tree, 106 leaves, max depth = 15, train loss: 0.51155, val loss: 0.50504, in 0.016s
1 tree, 68 leaves, max depth = 13, train loss: 0.50760, val loss: 0.50085, in 0.016s
1 tree, 105 leaves, max depth = 15, train loss: 0.50353, val loss: 0.49699, in 0.016s
1 tree, 68 leaves, max depth = 14, train loss: 0.49979, val loss: 0.49302, in 0.000s
1 tree, 64 leaves, max depth = 16, train loss: 0.49618, val loss: 0.48921, in 0.016s
1 tree, 105 leaves, max depth = 18, train loss: 0.49236, val loss: 0.48559, in 0.016s
1 tree, 105 leaves, max depth = 18, train loss: 0.48869, val loss: 0.48211, in 0.016s
1 tree, 68 leaves, max depth = 13, train loss: 0.48534, val loss: 0.47856, in 0.000s
1 tree, 105 leaves, max depth = 18, train loss: 0.48184, val loss: 0.47525, in 0.016s
1 tree, 105 leaves, max depth = 18, train loss: 0.47848, val loss: 0.47208, in 0.016s
1 tree, 66 leaves, max depth = 17, train loss: 0.47535, val loss: 0.46873, in 0.016s
1 tree, 106 leaves, max depth = 19, train loss: 0.47215, val loss: 0.46571, in 0.016s
1 tree, 68 leaves, max depth = 13, train loss: 0.46917, val loss: 0.46255, in 0.000s
1 tree, 105 leaves, max depth = 18, train loss: 0.46612, val loss: 0.45968, in 0.016s
1 tree, 68 leaves, max depth = 13, train loss: 0.46330, val loss: 0.45667, in 0.016s
1 tree, 106 leaves, max depth = 18, train loss: 0.46038, val loss: 0.45393, in 0.016s
1 tree, 68 leaves, max depth = 13, train loss: 0.45770, val loss: 0.45108, in 0.000s
1 tree, 106 leaves, max depth = 17, train loss: 0.45492, val loss: 0.44846, in 0.016s
1 tree, 106 leaves, max depth = 17, train loss: 0.45224, val loss: 0.44596, in 0.016s
1 tree, 68 leaves, max depth = 13, train loss: 0.44972, val loss: 0.44327, in 0.016s
1 tree, 106 leaves, max depth = 18, train loss: 0.44716, val loss: 0.44087, in 0.016s
1 tree, 66 leaves, max depth = 17, train loss: 0.44477, val loss: 0.43831, in 0.016s
1 tree, 106 leaves, max depth = 16, train loss: 0.44232, val loss: 0.43602, in 0.016s
1 tree, 66 leaves, max depth = 15, train loss: 0.44005, val loss: 0.43357, in 0.016s
1 tree, 106 leaves, max depth = 20, train loss: 0.43770, val loss: 0.43139, in 0.016s
1 tree, 106 leaves, max depth = 20, train loss: 0.43544, val loss: 0.42929, in 0.016s
1 tree, 66 leaves, max depth = 15, train loss: 0.43330, val loss: 0.42698, in 0.000s
1 tree, 106 leaves, max depth = 20, train loss: 0.43114, val loss: 0.42498, in 0.016s
1 tree, 68 leaves, max depth = 12, train loss: 0.42909, val loss: 0.42278, in 0.016s
1 tree, 106 leaves, max depth = 20, train loss: 0.42701, val loss: 0.42086, in 0.016s
1 tree, 68 leaves, max depth = 12, train loss: 0.42506, val loss: 0.41876, in 0.016s
1 tree, 106 leaves, max depth = 20, train loss: 0.42307, val loss: 0.41693, in 0.016s
1 tree, 66 leaves, max depth = 15, train loss: 0.42121, val loss: 0.41492, in 0.000s
1 tree, 105 leaves, max depth = 20, train loss: 0.41931, val loss: 0.41317, in 0.031s
1 tree, 68 leaves, max depth = 12, train loss: 0.41747, val loss: 0.41117, in 0.000s
1 tree, 105 leaves, max depth = 20, train loss: 0.41564, val loss: 0.40949, in 0.016s
1 tree, 105 leaves, max depth = 20, train loss: 0.41388, val loss: 0.40787, in 0.031s
1 tree, 3 leaves, max depth = 2, train loss: 0.41230, val loss: 0.40624, in 0.000s
1 tree, 68 leaves, max depth = 12, train loss: 0.41062, val loss: 0.40444, in 0.016s
1 tree, 3 leaves, max depth = 2, train loss: 0.40912, val loss: 0.40288, in 0.000s
1 tree, 106 leaves, max depth = 19, train loss: 0.40743, val loss: 0.40134, in 0.031s
1 tree, 105 leaves, max depth = 19, train loss: 0.40581, val loss: 0.39986, in 0.016s
1 tree, 3 leaves, max depth = 2, train loss: 0.40436, val loss: 0.39837, in 0.000s
1 tree, 66 leaves, max depth = 15, train loss: 0.40281, val loss: 0.39668, in 0.016s
1 tree, 106 leaves, max depth = 19, train loss: 0.40125, val loss: 0.39526, in 0.016s
1 tree, 3 leaves, max depth = 2, train loss: 0.39987, val loss: 0.39384, in 0.000s
1 tree, 68 leaves, max depth = 12, train loss: 0.39838, val loss: 0.39224, in 0.016s
1 tree, 106 leaves, max depth = 19, train loss: 0.39688, val loss: 0.39088, in 0.016s
1 tree, 3 leaves, max depth = 2, train loss: 0.39557, val loss: 0.38952, in 0.016s
1 tree, 68 leaves, max depth = 12, train loss: 0.39415, val loss: 0.38799, in 0.000s
1 tree, 3 leaves, max depth = 2, train loss: 0.39289, val loss: 0.38669, in 0.016s
1 tree, 3 leaves, max depth = 2, train loss: 0.39169, val loss: 0.38543, in 0.000s
1 tree, 3 leaves, max depth = 2, train loss: 0.39053, val loss: 0.38422, in 0.016s
1 tree, 67 leaves, max depth = 15, train loss: 0.38915, val loss: 0.38272, in 0.000s
1 tree, 67 leaves, max depth = 11, train loss: 0.38780, val loss: 0.38129, in 0.016s
1 tree, 106 leaves, max depth = 19, train loss: 0.38636, val loss: 0.38000, in 0.016s
Fit 82 trees in 1.284 s, (6181 total leaves)
Time spent computing histograms: 0.431s
Time spent finding best splits:  0.100s
Time spent applying splits:      0.115s
Time spent predicting:           0.000s
Trial 39, Fold 2: Log loss = 0.38897657752043585, Average precision = 0.9443297070994459, ROC-AUC = 0.9444997649426803, Elapsed Time = 1.2912431999993714 seconds
Trial 39, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 39, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
Binning 0.040 GB of training data: 0.173 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 65 leaves, max depth = 12, train loss: 0.68203, val loss: 0.68171, in 0.016s
1 tree, 64 leaves, max depth = 12, train loss: 0.67165, val loss: 0.67095, in 0.016s
1 tree, 66 leaves, max depth = 12, train loss: 0.66167, val loss: 0.66065, in 0.000s
1 tree, 66 leaves, max depth = 12, train loss: 0.65213, val loss: 0.65082, in 0.016s
1 tree, 67 leaves, max depth = 12, train loss: 0.64310, val loss: 0.64144, in 0.016s
1 tree, 67 leaves, max depth = 12, train loss: 0.63449, val loss: 0.63256, in 0.000s
1 tree, 67 leaves, max depth = 12, train loss: 0.62623, val loss: 0.62397, in 0.016s
1 tree, 66 leaves, max depth = 12, train loss: 0.61826, val loss: 0.61573, in 0.016s
1 tree, 66 leaves, max depth = 12, train loss: 0.61063, val loss: 0.60785, in 0.000s
1 tree, 67 leaves, max depth = 12, train loss: 0.60342, val loss: 0.60038, in 0.016s
1 tree, 67 leaves, max depth = 13, train loss: 0.59649, val loss: 0.59316, in 0.016s
1 tree, 67 leaves, max depth = 12, train loss: 0.58978, val loss: 0.58622, in 0.000s
1 tree, 67 leaves, max depth = 13, train loss: 0.58342, val loss: 0.57958, in 0.016s
1 tree, 67 leaves, max depth = 12, train loss: 0.57734, val loss: 0.57328, in 0.016s
1 tree, 67 leaves, max depth = 12, train loss: 0.57143, val loss: 0.56715, in 0.000s
1 tree, 67 leaves, max depth = 13, train loss: 0.56583, val loss: 0.56128, in 0.016s
1 tree, 67 leaves, max depth = 12, train loss: 0.56039, val loss: 0.55564, in 0.016s
1 tree, 104 leaves, max depth = 14, train loss: 0.55483, val loss: 0.55049, in 0.016s
1 tree, 67 leaves, max depth = 13, train loss: 0.54978, val loss: 0.54524, in 0.000s
1 tree, 104 leaves, max depth = 14, train loss: 0.54453, val loss: 0.54038, in 0.016s
1 tree, 67 leaves, max depth = 12, train loss: 0.53971, val loss: 0.53536, in 0.016s
1 tree, 104 leaves, max depth = 14, train loss: 0.53475, val loss: 0.53077, in 0.016s
1 tree, 68 leaves, max depth = 13, train loss: 0.53026, val loss: 0.52609, in 0.000s
1 tree, 68 leaves, max depth = 13, train loss: 0.52595, val loss: 0.52160, in 0.016s
1 tree, 104 leaves, max depth = 15, train loss: 0.52130, val loss: 0.51731, in 0.016s
1 tree, 104 leaves, max depth = 15, train loss: 0.51685, val loss: 0.51321, in 0.016s
1 tree, 104 leaves, max depth = 15, train loss: 0.51257, val loss: 0.50928, in 0.016s
1 tree, 68 leaves, max depth = 13, train loss: 0.50864, val loss: 0.50516, in 0.000s
1 tree, 104 leaves, max depth = 15, train loss: 0.50458, val loss: 0.50143, in 0.000s
1 tree, 68 leaves, max depth = 13, train loss: 0.50086, val loss: 0.49752, in 0.016s
1 tree, 64 leaves, max depth = 16, train loss: 0.49730, val loss: 0.49375, in 0.016s
1 tree, 104 leaves, max depth = 15, train loss: 0.49348, val loss: 0.49025, in 0.016s
1 tree, 104 leaves, max depth = 15, train loss: 0.48981, val loss: 0.48690, in 0.016s
1 tree, 104 leaves, max depth = 15, train loss: 0.48629, val loss: 0.48368, in 0.016s
1 tree, 68 leaves, max depth = 14, train loss: 0.48300, val loss: 0.48021, in 0.016s
1 tree, 104 leaves, max depth = 15, train loss: 0.47965, val loss: 0.47717, in 0.000s
1 tree, 67 leaves, max depth = 16, train loss: 0.47656, val loss: 0.47386, in 0.016s
1 tree, 104 leaves, max depth = 15, train loss: 0.47336, val loss: 0.47096, in 0.016s
1 tree, 68 leaves, max depth = 14, train loss: 0.47040, val loss: 0.46783, in 0.016s
1 tree, 104 leaves, max depth = 15, train loss: 0.46736, val loss: 0.46507, in 0.016s
1 tree, 68 leaves, max depth = 14, train loss: 0.46455, val loss: 0.46209, in 0.016s
1 tree, 104 leaves, max depth = 15, train loss: 0.46164, val loss: 0.45946, in 0.016s
1 tree, 68 leaves, max depth = 14, train loss: 0.45897, val loss: 0.45663, in 0.000s
1 tree, 104 leaves, max depth = 15, train loss: 0.45620, val loss: 0.45413, in 0.031s
1 tree, 104 leaves, max depth = 15, train loss: 0.45352, val loss: 0.45172, in 0.000s
1 tree, 68 leaves, max depth = 14, train loss: 0.45102, val loss: 0.44905, in 0.016s
1 tree, 104 leaves, max depth = 15, train loss: 0.44846, val loss: 0.44676, in 0.016s
1 tree, 67 leaves, max depth = 17, train loss: 0.44611, val loss: 0.44421, in 0.016s
1 tree, 103 leaves, max depth = 15, train loss: 0.44367, val loss: 0.44203, in 0.016s
1 tree, 103 leaves, max depth = 15, train loss: 0.44132, val loss: 0.43993, in 0.016s
1 tree, 66 leaves, max depth = 15, train loss: 0.43907, val loss: 0.43752, in 0.016s
1 tree, 103 leaves, max depth = 15, train loss: 0.43682, val loss: 0.43552, in 0.000s
1 tree, 67 leaves, max depth = 18, train loss: 0.43470, val loss: 0.43322, in 0.016s
1 tree, 104 leaves, max depth = 14, train loss: 0.43255, val loss: 0.43130, in 0.016s
1 tree, 68 leaves, max depth = 15, train loss: 0.43050, val loss: 0.42910, in 0.016s
1 tree, 104 leaves, max depth = 14, train loss: 0.42844, val loss: 0.42728, in 0.016s
1 tree, 67 leaves, max depth = 13, train loss: 0.42649, val loss: 0.42517, in 0.000s
1 tree, 104 leaves, max depth = 14, train loss: 0.42452, val loss: 0.42343, in 0.031s
1 tree, 67 leaves, max depth = 16, train loss: 0.42269, val loss: 0.42142, in 0.000s
1 tree, 104 leaves, max depth = 14, train loss: 0.42080, val loss: 0.41975, in 0.016s
1 tree, 67 leaves, max depth = 13, train loss: 0.41898, val loss: 0.41777, in 0.016s
1 tree, 104 leaves, max depth = 14, train loss: 0.41716, val loss: 0.41619, in 0.016s
1 tree, 104 leaves, max depth = 14, train loss: 0.41542, val loss: 0.41467, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.41377, val loss: 0.41313, in 0.000s
1 tree, 4 leaves, max depth = 3, train loss: 0.41218, val loss: 0.41166, in 0.016s
1 tree, 66 leaves, max depth = 14, train loss: 0.41048, val loss: 0.40979, in 0.016s
1 tree, 104 leaves, max depth = 13, train loss: 0.40882, val loss: 0.40833, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.40731, val loss: 0.40693, in 0.000s
1 tree, 4 leaves, max depth = 3, train loss: 0.40585, val loss: 0.40558, in 0.000s
1 tree, 104 leaves, max depth = 13, train loss: 0.40425, val loss: 0.40419, in 0.016s
1 tree, 67 leaves, max depth = 12, train loss: 0.40270, val loss: 0.40249, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.40131, val loss: 0.40120, in 0.016s
1 tree, 104 leaves, max depth = 13, train loss: 0.39978, val loss: 0.39988, in 0.016s
1 tree, 66 leaves, max depth = 12, train loss: 0.39831, val loss: 0.39826, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.39698, val loss: 0.39703, in 0.000s
1 tree, 66 leaves, max depth = 12, train loss: 0.39557, val loss: 0.39549, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.39430, val loss: 0.39431, in 0.000s
1 tree, 4 leaves, max depth = 3, train loss: 0.39308, val loss: 0.39318, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.39191, val loss: 0.39209, in 0.000s
1 tree, 66 leaves, max depth = 11, train loss: 0.39057, val loss: 0.39062, in 0.016s
1 tree, 67 leaves, max depth = 15, train loss: 0.38924, val loss: 0.38916, in 0.016s
1 tree, 102 leaves, max depth = 15, train loss: 0.38777, val loss: 0.38791, in 0.016s
Fit 82 trees in 1.298 s, (6025 total leaves)
Time spent computing histograms: 0.445s
Time spent finding best splits:  0.100s
Time spent applying splits:      0.117s
Time spent predicting:           0.000s
Trial 39, Fold 3: Log loss = 0.38644798890290544, Average precision = 0.9486453359713937, ROC-AUC = 0.9473633085253923, Elapsed Time = 1.3085611999995308 seconds
Trial 39, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 39, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
Binning 0.040 GB of training data: 0.158 s
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 65 leaves, max depth = 16, train loss: 0.68206, val loss: 0.68143, in 0.000s
1 tree, 65 leaves, max depth = 16, train loss: 0.67159, val loss: 0.67034, in 0.016s
1 tree, 65 leaves, max depth = 16, train loss: 0.66160, val loss: 0.65975, in 0.016s
1 tree, 65 leaves, max depth = 16, train loss: 0.65206, val loss: 0.64962, in 0.000s
1 tree, 65 leaves, max depth = 16, train loss: 0.64295, val loss: 0.63994, in 0.016s
1 tree, 65 leaves, max depth = 16, train loss: 0.63425, val loss: 0.63067, in 0.016s
1 tree, 65 leaves, max depth = 16, train loss: 0.62593, val loss: 0.62179, in 0.000s
1 tree, 65 leaves, max depth = 16, train loss: 0.61797, val loss: 0.61329, in 0.016s
1 tree, 65 leaves, max depth = 16, train loss: 0.61035, val loss: 0.60514, in 0.016s
1 tree, 65 leaves, max depth = 16, train loss: 0.60306, val loss: 0.59733, in 0.016s
1 tree, 65 leaves, max depth = 16, train loss: 0.59608, val loss: 0.58984, in 0.000s
1 tree, 65 leaves, max depth = 16, train loss: 0.58940, val loss: 0.58266, in 0.016s
1 tree, 68 leaves, max depth = 15, train loss: 0.58303, val loss: 0.57579, in 0.016s
1 tree, 65 leaves, max depth = 16, train loss: 0.57688, val loss: 0.56917, in 0.016s
1 tree, 65 leaves, max depth = 16, train loss: 0.57100, val loss: 0.56281, in 0.000s
1 tree, 68 leaves, max depth = 18, train loss: 0.56536, val loss: 0.55678, in 0.016s
1 tree, 66 leaves, max depth = 16, train loss: 0.55994, val loss: 0.55091, in 0.000s
1 tree, 107 leaves, max depth = 16, train loss: 0.55436, val loss: 0.54549, in 0.031s
1 tree, 68 leaves, max depth = 15, train loss: 0.54929, val loss: 0.53998, in 0.000s
1 tree, 107 leaves, max depth = 16, train loss: 0.54402, val loss: 0.53486, in 0.016s
1 tree, 66 leaves, max depth = 15, train loss: 0.53922, val loss: 0.52964, in 0.016s
1 tree, 107 leaves, max depth = 14, train loss: 0.53424, val loss: 0.52481, in 0.016s
1 tree, 68 leaves, max depth = 15, train loss: 0.52973, val loss: 0.51989, in 0.016s
1 tree, 67 leaves, max depth = 14, train loss: 0.52539, val loss: 0.51513, in 0.000s
1 tree, 107 leaves, max depth = 14, train loss: 0.52073, val loss: 0.51063, in 0.016s
1 tree, 107 leaves, max depth = 14, train loss: 0.51626, val loss: 0.50631, in 0.016s
1 tree, 107 leaves, max depth = 14, train loss: 0.51197, val loss: 0.50216, in 0.016s
1 tree, 68 leaves, max depth = 15, train loss: 0.50803, val loss: 0.49782, in 0.016s
1 tree, 107 leaves, max depth = 14, train loss: 0.50395, val loss: 0.49389, in 0.016s
1 tree, 68 leaves, max depth = 15, train loss: 0.50022, val loss: 0.48978, in 0.016s
1 tree, 64 leaves, max depth = 13, train loss: 0.49662, val loss: 0.48588, in 0.000s
1 tree, 107 leaves, max depth = 16, train loss: 0.49279, val loss: 0.48219, in 0.016s
1 tree, 107 leaves, max depth = 16, train loss: 0.48912, val loss: 0.47865, in 0.016s
1 tree, 107 leaves, max depth = 16, train loss: 0.48558, val loss: 0.47525, in 0.016s
1 tree, 68 leaves, max depth = 14, train loss: 0.48229, val loss: 0.47160, in 0.016s
1 tree, 106 leaves, max depth = 17, train loss: 0.47893, val loss: 0.46836, in 0.016s
1 tree, 63 leaves, max depth = 13, train loss: 0.47579, val loss: 0.46494, in 0.016s
1 tree, 107 leaves, max depth = 17, train loss: 0.47259, val loss: 0.46186, in 0.016s
1 tree, 68 leaves, max depth = 15, train loss: 0.46963, val loss: 0.45857, in 0.000s
1 tree, 106 leaves, max depth = 16, train loss: 0.46657, val loss: 0.45564, in 0.000s
1 tree, 68 leaves, max depth = 15, train loss: 0.46377, val loss: 0.45250, in 0.016s
1 tree, 107 leaves, max depth = 17, train loss: 0.46085, val loss: 0.44971, in 0.016s
1 tree, 68 leaves, max depth = 19, train loss: 0.45817, val loss: 0.44675, in 0.016s
1 tree, 107 leaves, max depth = 16, train loss: 0.45539, val loss: 0.44409, in 0.016s
1 tree, 107 leaves, max depth = 16, train loss: 0.45271, val loss: 0.44153, in 0.016s
1 tree, 67 leaves, max depth = 14, train loss: 0.45019, val loss: 0.43872, in 0.000s
1 tree, 107 leaves, max depth = 16, train loss: 0.44764, val loss: 0.43627, in 0.016s
1 tree, 67 leaves, max depth = 14, train loss: 0.44525, val loss: 0.43360, in 0.016s
1 tree, 106 leaves, max depth = 16, train loss: 0.44281, val loss: 0.43126, in 0.016s
1 tree, 67 leaves, max depth = 15, train loss: 0.44054, val loss: 0.42874, in 0.016s
1 tree, 107 leaves, max depth = 14, train loss: 0.43820, val loss: 0.42652, in 0.000s
1 tree, 107 leaves, max depth = 14, train loss: 0.43596, val loss: 0.42437, in 0.016s
1 tree, 67 leaves, max depth = 15, train loss: 0.43382, val loss: 0.42199, in 0.016s
1 tree, 107 leaves, max depth = 15, train loss: 0.43167, val loss: 0.41994, in 0.016s
1 tree, 68 leaves, max depth = 17, train loss: 0.42962, val loss: 0.41762, in 0.016s
1 tree, 107 leaves, max depth = 17, train loss: 0.42756, val loss: 0.41566, in 0.016s
1 tree, 67 leaves, max depth = 14, train loss: 0.42561, val loss: 0.41345, in 0.000s
1 tree, 107 leaves, max depth = 16, train loss: 0.42363, val loss: 0.41157, in 0.016s
1 tree, 68 leaves, max depth = 17, train loss: 0.42176, val loss: 0.40949, in 0.016s
1 tree, 106 leaves, max depth = 15, train loss: 0.41987, val loss: 0.40769, in 0.016s
1 tree, 67 leaves, max depth = 11, train loss: 0.41805, val loss: 0.40563, in 0.016s
1 tree, 106 leaves, max depth = 16, train loss: 0.41624, val loss: 0.40391, in 0.016s
1 tree, 106 leaves, max depth = 16, train loss: 0.41449, val loss: 0.40226, in 0.016s
1 tree, 3 leaves, max depth = 2, train loss: 0.41292, val loss: 0.40059, in 0.000s
1 tree, 68 leaves, max depth = 18, train loss: 0.41124, val loss: 0.39867, in 0.016s
1 tree, 3 leaves, max depth = 2, train loss: 0.40973, val loss: 0.39707, in 0.016s
1 tree, 107 leaves, max depth = 15, train loss: 0.40807, val loss: 0.39550, in 0.000s
1 tree, 3 leaves, max depth = 2, train loss: 0.40663, val loss: 0.39396, in 0.016s
1 tree, 106 leaves, max depth = 15, train loss: 0.40502, val loss: 0.39246, in 0.016s
1 tree, 67 leaves, max depth = 13, train loss: 0.40346, val loss: 0.39067, in 0.016s
1 tree, 3 leaves, max depth = 2, train loss: 0.40208, val loss: 0.38921, in 0.000s
1 tree, 107 leaves, max depth = 15, train loss: 0.40055, val loss: 0.38777, in 0.016s
1 tree, 3 leaves, max depth = 2, train loss: 0.39923, val loss: 0.38636, in 0.000s
1 tree, 107 leaves, max depth = 15, train loss: 0.39774, val loss: 0.38498, in 0.016s
1 tree, 67 leaves, max depth = 13, train loss: 0.39627, val loss: 0.38330, in 0.016s
1 tree, 67 leaves, max depth = 13, train loss: 0.39486, val loss: 0.38169, in 0.016s
1 tree, 3 leaves, max depth = 2, train loss: 0.39360, val loss: 0.38035, in 0.000s
1 tree, 3 leaves, max depth = 2, train loss: 0.39240, val loss: 0.37906, in 0.016s
1 tree, 3 leaves, max depth = 2, train loss: 0.39128, val loss: 0.37787, in 0.000s
1 tree, 67 leaves, max depth = 13, train loss: 0.38994, val loss: 0.37634, in 0.016s
1 tree, 67 leaves, max depth = 15, train loss: 0.38860, val loss: 0.37484, in 0.016s
1 tree, 106 leaves, max depth = 14, train loss: 0.38719, val loss: 0.37352, in 0.016s
Fit 82 trees in 1.299 s, (6190 total leaves)
Time spent computing histograms: 0.448s
Time spent finding best splits:  0.103s
Time spent applying splits:      0.117s
Time spent predicting:           0.031s
Trial 39, Fold 4: Log loss = 0.3883433454778819, Average precision = 0.9477614116286428, ROC-AUC = 0.9447609003292871, Elapsed Time = 1.3085276000001613 seconds
Trial 39, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 39, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
Binning 0.040 GB of training data: 0.158 s
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 64 leaves, max depth = 13, train loss: 0.68203, val loss: 0.68127, in 0.000s
1 tree, 64 leaves, max depth = 13, train loss: 0.67156, val loss: 0.67007, in 0.016s
1 tree, 64 leaves, max depth = 13, train loss: 0.66158, val loss: 0.65936, in 0.016s
1 tree, 68 leaves, max depth = 15, train loss: 0.65192, val loss: 0.64903, in 0.000s
1 tree, 64 leaves, max depth = 13, train loss: 0.64281, val loss: 0.63924, in 0.016s
1 tree, 68 leaves, max depth = 15, train loss: 0.63398, val loss: 0.62977, in 0.000s
1 tree, 68 leaves, max depth = 15, train loss: 0.62554, val loss: 0.62071, in 0.016s
1 tree, 68 leaves, max depth = 15, train loss: 0.61746, val loss: 0.61203, in 0.016s
1 tree, 68 leaves, max depth = 15, train loss: 0.60974, val loss: 0.60371, in 0.016s
1 tree, 68 leaves, max depth = 15, train loss: 0.60234, val loss: 0.59574, in 0.000s
1 tree, 65 leaves, max depth = 13, train loss: 0.59535, val loss: 0.58814, in 0.016s
1 tree, 67 leaves, max depth = 15, train loss: 0.58856, val loss: 0.58080, in 0.016s
1 tree, 65 leaves, max depth = 13, train loss: 0.58214, val loss: 0.57381, in 0.000s
1 tree, 68 leaves, max depth = 15, train loss: 0.57590, val loss: 0.56704, in 0.016s
1 tree, 68 leaves, max depth = 15, train loss: 0.56991, val loss: 0.56054, in 0.016s
1 tree, 65 leaves, max depth = 19, train loss: 0.56425, val loss: 0.55443, in 0.000s
1 tree, 68 leaves, max depth = 15, train loss: 0.55875, val loss: 0.54842, in 0.016s
1 tree, 68 leaves, max depth = 15, train loss: 0.55346, val loss: 0.54265, in 0.016s
1 tree, 67 leaves, max depth = 12, train loss: 0.54844, val loss: 0.53719, in 0.016s
1 tree, 105 leaves, max depth = 13, train loss: 0.54317, val loss: 0.53217, in 0.016s
1 tree, 105 leaves, max depth = 13, train loss: 0.53812, val loss: 0.52737, in 0.000s
1 tree, 65 leaves, max depth = 13, train loss: 0.53349, val loss: 0.52226, in 0.016s
1 tree, 104 leaves, max depth = 13, train loss: 0.52871, val loss: 0.51773, in 0.016s
1 tree, 68 leaves, max depth = 14, train loss: 0.52428, val loss: 0.51286, in 0.000s
1 tree, 105 leaves, max depth = 13, train loss: 0.51975, val loss: 0.50858, in 0.016s
1 tree, 104 leaves, max depth = 13, train loss: 0.51541, val loss: 0.50448, in 0.016s
1 tree, 65 leaves, max depth = 12, train loss: 0.51136, val loss: 0.49998, in 0.016s
1 tree, 105 leaves, max depth = 13, train loss: 0.50724, val loss: 0.49610, in 0.016s
1 tree, 65 leaves, max depth = 13, train loss: 0.50341, val loss: 0.49185, in 0.000s
1 tree, 105 leaves, max depth = 14, train loss: 0.49950, val loss: 0.48818, in 0.016s
1 tree, 65 leaves, max depth = 14, train loss: 0.49587, val loss: 0.48419, in 0.016s
1 tree, 105 leaves, max depth = 14, train loss: 0.49215, val loss: 0.48071, in 0.016s
1 tree, 67 leaves, max depth = 13, train loss: 0.48869, val loss: 0.47690, in 0.000s
1 tree, 105 leaves, max depth = 14, train loss: 0.48516, val loss: 0.47360, in 0.000s
1 tree, 105 leaves, max depth = 14, train loss: 0.48177, val loss: 0.47043, in 0.016s
1 tree, 67 leaves, max depth = 13, train loss: 0.47853, val loss: 0.46686, in 0.016s
[37/82] 1 tree, 105 leaves, max depth = 14, train loss: 0.47530, val loss: 0.46385, in 0.016s
1 tree, 66 leaves, max depth = 13, train loss: 0.47223, val loss: 0.46045, in 0.000s
1 tree, 105 leaves, max depth = 14, train loss: 0.46915, val loss: 0.45760, in 0.032s
1 tree, 67 leaves, max depth = 13, train loss: 0.46623, val loss: 0.45436, in 0.000s
1 tree, 67 leaves, max depth = 13, train loss: 0.46343, val loss: 0.45125, in 0.016s
1 tree, 105 leaves, max depth = 14, train loss: 0.46052, val loss: 0.44856, in 0.016s
1 tree, 67 leaves, max depth = 13, train loss: 0.45786, val loss: 0.44559, in 0.016s
1 tree, 105 leaves, max depth = 15, train loss: 0.45508, val loss: 0.44303, in 0.016s
1 tree, 105 leaves, max depth = 15, train loss: 0.45240, val loss: 0.44057, in 0.016s
1 tree, 105 leaves, max depth = 15, train loss: 0.44982, val loss: 0.43821, in 0.000s
1 tree, 65 leaves, max depth = 17, train loss: 0.44737, val loss: 0.43548, in 0.000s
1 tree, 105 leaves, max depth = 14, train loss: 0.44491, val loss: 0.43323, in 0.016s
1 tree, 67 leaves, max depth = 12, train loss: 0.44255, val loss: 0.43059, in 0.016s
1 tree, 105 leaves, max depth = 13, train loss: 0.44020, val loss: 0.42845, in 0.016s
1 tree, 67 leaves, max depth = 12, train loss: 0.43795, val loss: 0.42593, in 0.016s
1 tree, 105 leaves, max depth = 17, train loss: 0.43570, val loss: 0.42388, in 0.016s
1 tree, 66 leaves, max depth = 19, train loss: 0.43358, val loss: 0.42151, in 0.000s
1 tree, 105 leaves, max depth = 16, train loss: 0.43142, val loss: 0.41956, in 0.016s
1 tree, 105 leaves, max depth = 16, train loss: 0.42935, val loss: 0.41769, in 0.016s
1 tree, 64 leaves, max depth = 17, train loss: 0.42735, val loss: 0.41545, in 0.016s
1 tree, 105 leaves, max depth = 16, train loss: 0.42536, val loss: 0.41366, in 0.016s
1 tree, 67 leaves, max depth = 12, train loss: 0.42343, val loss: 0.41147, in 0.000s
1 tree, 104 leaves, max depth = 15, train loss: 0.42152, val loss: 0.40976, in 0.016s
1 tree, 64 leaves, max depth = 19, train loss: 0.41970, val loss: 0.40771, in 0.016s
1 tree, 67 leaves, max depth = 11, train loss: 0.41793, val loss: 0.40569, in 0.016s
1 tree, 105 leaves, max depth = 15, train loss: 0.41611, val loss: 0.40407, in 0.016s
1 tree, 105 leaves, max depth = 15, train loss: 0.41435, val loss: 0.40251, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.41275, val loss: 0.40096, in 0.000s
1 tree, 105 leaves, max depth = 15, train loss: 0.41106, val loss: 0.39947, in 0.016s
1 tree, 67 leaves, max depth = 11, train loss: 0.40940, val loss: 0.39758, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.40787, val loss: 0.39611, in 0.016s
1 tree, 105 leaves, max depth = 15, train loss: 0.40626, val loss: 0.39469, in 0.000s
1 tree, 4 leaves, max depth = 3, train loss: 0.40480, val loss: 0.39327, in 0.016s
1 tree, 105 leaves, max depth = 14, train loss: 0.40325, val loss: 0.39191, in 0.016s
1 tree, 67 leaves, max depth = 11, train loss: 0.40169, val loss: 0.39013, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.40029, val loss: 0.38879, in 0.000s
1 tree, 105 leaves, max depth = 14, train loss: 0.39880, val loss: 0.38749, in 0.016s
1 tree, 67 leaves, max depth = 11, train loss: 0.39732, val loss: 0.38579, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.39599, val loss: 0.38451, in 0.000s
1 tree, 67 leaves, max depth = 13, train loss: 0.39457, val loss: 0.38286, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.39330, val loss: 0.38163, in 0.000s
1 tree, 4 leaves, max depth = 3, train loss: 0.39207, val loss: 0.38046, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.39089, val loss: 0.37932, in 0.000s
1 tree, 66 leaves, max depth = 12, train loss: 0.38955, val loss: 0.37776, in 0.016s
1 tree, 67 leaves, max depth = 13, train loss: 0.38823, val loss: 0.37619, in 0.016s
1 tree, 105 leaves, max depth = 16, train loss: 0.38680, val loss: 0.37496, in 0.016s
Fit 82 trees in 1.283 s, (6101 total leaves)
Time spent computing histograms: 0.427s
Time spent finding best splits:  0.098s
Time spent applying splits:      0.112s
Time spent predicting:           0.016s
Trial 39, Fold 5: Log loss = 0.3938249430487527, Average precision = 0.9446614020697768, ROC-AUC = 0.941758430410791, Elapsed Time = 1.2814244000001054 seconds
Optimization Progress:  40%|####      | 40/100 [07:52<11:48, 11.80s/it]
Trial 40, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 40, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
0.126 s 0.040 GB of training data: 
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 33 leaves, max depth = 11, train loss: 0.67951, val loss: 0.67910, in 0.000s
1 tree, 33 leaves, max depth = 11, train loss: 0.66665, val loss: 0.66584, in 0.016s
1 tree, 33 leaves, max depth = 11, train loss: 0.65451, val loss: 0.65332, in 0.000s
1 tree, 29 leaves, max depth = 10, train loss: 0.64315, val loss: 0.64166, in 0.016s
1 tree, 33 leaves, max depth = 11, train loss: 0.63231, val loss: 0.63046, in 0.000s
1 tree, 34 leaves, max depth = 11, train loss: 0.62205, val loss: 0.61985, in 0.016s
1 tree, 32 leaves, max depth = 12, train loss: 0.61254, val loss: 0.61001, in 0.000s
1 tree, 34 leaves, max depth = 11, train loss: 0.60333, val loss: 0.60047, in 0.016s
1 tree, 34 leaves, max depth = 11, train loss: 0.59460, val loss: 0.59142, in 0.000s
1 tree, 34 leaves, max depth = 11, train loss: 0.58632, val loss: 0.58282, in 0.016s
1 tree, 34 leaves, max depth = 11, train loss: 0.57846, val loss: 0.57467, in 0.000s
1 tree, 30 leaves, max depth = 10, train loss: 0.57110, val loss: 0.56707, in 0.016s
1 tree, 32 leaves, max depth = 12, train loss: 0.56415, val loss: 0.55980, in 0.000s
1 tree, 34 leaves, max depth = 11, train loss: 0.55742, val loss: 0.55279, in 0.000s
1 tree, 34 leaves, max depth = 11, train loss: 0.55103, val loss: 0.54612, in 0.016s
1 tree, 54 leaves, max depth = 12, train loss: 0.54434, val loss: 0.53986, in 0.000s
1 tree, 34 leaves, max depth = 11, train loss: 0.53841, val loss: 0.53366, in 0.016s
1 tree, 54 leaves, max depth = 10, train loss: 0.53217, val loss: 0.52784, in 0.000s
1 tree, 33 leaves, max depth = 12, train loss: 0.52676, val loss: 0.52213, in 0.016s
1 tree, 54 leaves, max depth = 11, train loss: 0.52093, val loss: 0.51671, in 0.000s
1 tree, 54 leaves, max depth = 11, train loss: 0.51539, val loss: 0.51156, in 0.016s
1 tree, 54 leaves, max depth = 10, train loss: 0.51012, val loss: 0.50668, in 0.016s
1 tree, 31 leaves, max depth = 13, train loss: 0.50519, val loss: 0.50149, in 0.000s
1 tree, 32 leaves, max depth = 13, train loss: 0.50051, val loss: 0.49656, in 0.016s
1 tree, 52 leaves, max depth = 11, train loss: 0.49564, val loss: 0.49206, in 0.000s
1 tree, 52 leaves, max depth = 11, train loss: 0.49101, val loss: 0.48779, in 0.016s
1 tree, 33 leaves, max depth = 12, train loss: 0.48679, val loss: 0.48330, in 0.000s
1 tree, 33 leaves, max depth = 12, train loss: 0.48277, val loss: 0.47903, in 0.016s
1 tree, 52 leaves, max depth = 10, train loss: 0.47847, val loss: 0.47508, in 0.016s
1 tree, 51 leaves, max depth = 10, train loss: 0.47437, val loss: 0.47133, in 0.000s
1 tree, 32 leaves, max depth = 12, train loss: 0.47069, val loss: 0.46748, in 0.016s
1 tree, 53 leaves, max depth = 11, train loss: 0.46682, val loss: 0.46396, in 0.000s
1 tree, 30 leaves, max depth = 9, train loss: 0.46329, val loss: 0.46020, in 0.016s
1 tree, 52 leaves, max depth = 10, train loss: 0.45965, val loss: 0.45689, in 0.000s
1 tree, 53 leaves, max depth = 10, train loss: 0.45617, val loss: 0.45374, in 0.000s
Fit 35 trees in 0.533 s, (1386 total leaves)
Time spent computing histograms: 0.144s
Time spent finding best splits:  0.024s
Time spent applying splits:      0.025s
Time spent predicting:           0.000s
Trial 40, Fold 1: Log loss = 0.4573582800681732, Average precision = 0.9182890017667679, ROC-AUC = 0.9282691942329476, Elapsed Time = 0.5440377000013541 seconds
Trial 40, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 40, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
Binning 0.040 GB of training data: 0.157 s
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 31 leaves, max depth = 11, train loss: 0.67970, val loss: 0.67910, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.66701, val loss: 0.66584, in 0.000s
1 tree, 31 leaves, max depth = 11, train loss: 0.65504, val loss: 0.65332, in 0.000s
1 tree, 30 leaves, max depth = 11, train loss: 0.64374, val loss: 0.64147, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.63306, val loss: 0.63028, in 0.000s
1 tree, 31 leaves, max depth = 10, train loss: 0.62297, val loss: 0.61968, in 0.000s
1 tree, 32 leaves, max depth = 12, train loss: 0.61342, val loss: 0.60964, in 0.000s
1 tree, 30 leaves, max depth = 12, train loss: 0.60425, val loss: 0.59993, in 0.016s
1 tree, 30 leaves, max depth = 12, train loss: 0.59557, val loss: 0.59072, in 0.000s
1 tree, 31 leaves, max depth = 11, train loss: 0.58744, val loss: 0.58215, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.57972, val loss: 0.57402, in 0.000s
1 tree, 29 leaves, max depth = 10, train loss: 0.57237, val loss: 0.56624, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.56541, val loss: 0.55890, in 0.000s
1 tree, 29 leaves, max depth = 10, train loss: 0.55878, val loss: 0.55187, in 0.016s
1 tree, 29 leaves, max depth = 10, train loss: 0.55238, val loss: 0.54506, in 0.000s
1 tree, 52 leaves, max depth = 10, train loss: 0.54578, val loss: 0.53877, in 0.016s
1 tree, 29 leaves, max depth = 10, train loss: 0.53993, val loss: 0.53256, in 0.000s
1 tree, 52 leaves, max depth = 11, train loss: 0.53378, val loss: 0.52670, in 0.016s
1 tree, 32 leaves, max depth = 12, train loss: 0.52836, val loss: 0.52095, in 0.000s
1 tree, 52 leaves, max depth = 11, train loss: 0.52261, val loss: 0.51549, in 0.016s
1 tree, 52 leaves, max depth = 11, train loss: 0.51714, val loss: 0.51031, in 0.000s
1 tree, 53 leaves, max depth = 11, train loss: 0.51195, val loss: 0.50539, in 0.016s
1 tree, 32 leaves, max depth = 12, train loss: 0.50710, val loss: 0.50023, in 0.016s
1 tree, 31 leaves, max depth = 15, train loss: 0.50249, val loss: 0.49530, in 0.000s
1 tree, 52 leaves, max depth = 11, train loss: 0.49769, val loss: 0.49076, in 0.016s
1 tree, 53 leaves, max depth = 11, train loss: 0.49312, val loss: 0.48645, in 0.000s
1 tree, 31 leaves, max depth = 11, train loss: 0.48889, val loss: 0.48193, in 0.016s
1 tree, 32 leaves, max depth = 11, train loss: 0.48486, val loss: 0.47763, in 0.000s
1 tree, 54 leaves, max depth = 12, train loss: 0.48062, val loss: 0.47365, in 0.016s
1 tree, 54 leaves, max depth = 12, train loss: 0.47657, val loss: 0.46986, in 0.016s
1 tree, 32 leaves, max depth = 16, train loss: 0.47286, val loss: 0.46588, in 0.000s
1 tree, 53 leaves, max depth = 11, train loss: 0.46906, val loss: 0.46232, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.46558, val loss: 0.45859, in 0.000s
1 tree, 54 leaves, max depth = 10, train loss: 0.46199, val loss: 0.45524, in 0.016s
1 tree, 53 leaves, max depth = 11, train loss: 0.45856, val loss: 0.45205, in 0.016s
Fit 35 trees in 0.610 s, (1341 total leaves)
Time spent computing histograms: 0.147s
Time spent finding best splits:  0.025s
Time spent applying splits:      0.025s
Time spent predicting:           0.000s
Trial 40, Fold 2: Log loss = 0.4593806726958846, Average precision = 0.9127169139881373, ROC-AUC = 0.9285808911297653, Elapsed Time = 0.6046727999982977 seconds
Trial 40, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 40, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
Binning 0.040 GB of training data: 0.158 s
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 30 leaves, max depth = 11, train loss: 0.67980, val loss: 0.67938, in 0.016s
1 tree, 30 leaves, max depth = 11, train loss: 0.66721, val loss: 0.66640, in 0.000s
1 tree, 30 leaves, max depth = 9, train loss: 0.65522, val loss: 0.65400, in 0.016s
1 tree, 30 leaves, max depth = 11, train loss: 0.64399, val loss: 0.64240, in 0.000s
1 tree, 30 leaves, max depth = 9, train loss: 0.63328, val loss: 0.63130, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.62323, val loss: 0.62091, in 0.000s
1 tree, 30 leaves, max depth = 10, train loss: 0.61375, val loss: 0.61104, in 0.016s
1 tree, 30 leaves, max depth = 8, train loss: 0.60464, val loss: 0.60159, in 0.000s
1 tree, 30 leaves, max depth = 8, train loss: 0.59600, val loss: 0.59262, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.58790, val loss: 0.58421, in 0.000s
1 tree, 31 leaves, max depth = 11, train loss: 0.58022, val loss: 0.57624, in 0.016s
1 tree, 31 leaves, max depth = 14, train loss: 0.57297, val loss: 0.56867, in 0.000s
1 tree, 31 leaves, max depth = 11, train loss: 0.56605, val loss: 0.56147, in 0.016s
1 tree, 30 leaves, max depth = 8, train loss: 0.55933, val loss: 0.55450, in 0.016s
1 tree, 29 leaves, max depth = 8, train loss: 0.55296, val loss: 0.54787, in 0.000s
1 tree, 54 leaves, max depth = 12, train loss: 0.54630, val loss: 0.54169, in 0.016s
1 tree, 30 leaves, max depth = 9, train loss: 0.54042, val loss: 0.53555, in 0.000s
1 tree, 54 leaves, max depth = 12, train loss: 0.53421, val loss: 0.52981, in 0.016s
1 tree, 32 leaves, max depth = 11, train loss: 0.52883, val loss: 0.52417, in 0.000s
1 tree, 54 leaves, max depth = 12, train loss: 0.52302, val loss: 0.51880, in 0.016s
1 tree, 53 leaves, max depth = 12, train loss: 0.51751, val loss: 0.51372, in 0.016s
1 tree, 53 leaves, max depth = 12, train loss: 0.51227, val loss: 0.50888, in 0.000s
1 tree, 32 leaves, max depth = 11, train loss: 0.50745, val loss: 0.50382, in 0.016s
1 tree, 30 leaves, max depth = 8, train loss: 0.50277, val loss: 0.49889, in 0.000s
1 tree, 55 leaves, max depth = 13, train loss: 0.49793, val loss: 0.49445, in 0.016s
1 tree, 55 leaves, max depth = 13, train loss: 0.49332, val loss: 0.49022, in 0.016s
1 tree, 32 leaves, max depth = 11, train loss: 0.48912, val loss: 0.48579, in 0.000s
1 tree, 32 leaves, max depth = 11, train loss: 0.48512, val loss: 0.48156, in 0.016s
1 tree, 53 leaves, max depth = 12, train loss: 0.48083, val loss: 0.47764, in 0.000s
1 tree, 53 leaves, max depth = 12, train loss: 0.47676, val loss: 0.47391, in 0.016s
1 tree, 30 leaves, max depth = 12, train loss: 0.47309, val loss: 0.47000, in 0.000s
1 tree, 52 leaves, max depth = 12, train loss: 0.46925, val loss: 0.46650, in 0.016s
1 tree, 32 leaves, max depth = 11, train loss: 0.46580, val loss: 0.46284, in 0.016s
1 tree, 52 leaves, max depth = 12, train loss: 0.46217, val loss: 0.45954, in 0.000s
1 tree, 51 leaves, max depth = 12, train loss: 0.45872, val loss: 0.45641, in 0.016s
Fit 35 trees in 0.627 s, (1343 total leaves)
Time spent computing histograms: 0.159s
Time spent finding best splits:  0.026s
Time spent applying splits:      0.026s
Time spent predicting:           0.000s
Trial 40, Fold 3: Log loss = 0.4559647888321185, Average precision = 0.9192760922522056, ROC-AUC = 0.9317236362454374, Elapsed Time = 0.6254176999991614 seconds
Trial 40, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 40, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
Binning 0.040 GB of training data: 0.142 s
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 29 leaves, max depth = 14, train loss: 0.67964, val loss: 0.67882, in 0.016s
1 tree, 29 leaves, max depth = 14, train loss: 0.66689, val loss: 0.66529, in 0.000s
1 tree, 29 leaves, max depth = 14, train loss: 0.65487, val loss: 0.65250, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.64359, val loss: 0.64053, in 0.000s
1 tree, 29 leaves, max depth = 14, train loss: 0.63285, val loss: 0.62906, in 0.016s
1 tree, 30 leaves, max depth = 15, train loss: 0.62268, val loss: 0.61820, in 0.000s
1 tree, 31 leaves, max depth = 11, train loss: 0.61319, val loss: 0.60811, in 0.016s
1 tree, 29 leaves, max depth = 13, train loss: 0.60407, val loss: 0.59832, in 0.000s
1 tree, 30 leaves, max depth = 14, train loss: 0.59542, val loss: 0.58902, in 0.000s
1 tree, 30 leaves, max depth = 14, train loss: 0.58723, val loss: 0.58020, in 0.016s
1 tree, 30 leaves, max depth = 14, train loss: 0.57946, val loss: 0.57181, in 0.000s
1 tree, 31 leaves, max depth = 11, train loss: 0.57214, val loss: 0.56393, in 0.016s
1 tree, 33 leaves, max depth = 13, train loss: 0.56523, val loss: 0.55645, in 0.000s
1 tree, 29 leaves, max depth = 13, train loss: 0.55858, val loss: 0.54922, in 0.016s
1 tree, 29 leaves, max depth = 13, train loss: 0.55225, val loss: 0.54233, in 0.000s
1 tree, 57 leaves, max depth = 11, train loss: 0.54555, val loss: 0.53577, in 0.016s
1 tree, 29 leaves, max depth = 13, train loss: 0.53969, val loss: 0.52937, in 0.000s
1 tree, 57 leaves, max depth = 11, train loss: 0.53343, val loss: 0.52325, in 0.016s
1 tree, 33 leaves, max depth = 13, train loss: 0.52806, val loss: 0.51738, in 0.016s
1 tree, 58 leaves, max depth = 11, train loss: 0.52220, val loss: 0.51168, in 0.000s
1 tree, 58 leaves, max depth = 11, train loss: 0.51664, val loss: 0.50626, in 0.016s
1 tree, 58 leaves, max depth = 11, train loss: 0.51135, val loss: 0.50111, in 0.016s
1 tree, 32 leaves, max depth = 14, train loss: 0.50650, val loss: 0.49577, in 0.000s
1 tree, 32 leaves, max depth = 14, train loss: 0.50188, val loss: 0.49067, in 0.016s
1 tree, 58 leaves, max depth = 11, train loss: 0.49699, val loss: 0.48592, in 0.000s
1 tree, 58 leaves, max depth = 11, train loss: 0.49234, val loss: 0.48140, in 0.016s
1 tree, 58 leaves, max depth = 12, train loss: 0.48791, val loss: 0.47710, in 0.016s
1 tree, 33 leaves, max depth = 12, train loss: 0.48379, val loss: 0.47255, in 0.000s
1 tree, 58 leaves, max depth = 13, train loss: 0.47962, val loss: 0.46850, in 0.016s
1 tree, 31 leaves, max depth = 13, train loss: 0.47573, val loss: 0.46418, in 0.000s
1 tree, 29 leaves, max depth = 10, train loss: 0.47206, val loss: 0.46013, in 0.016s
1 tree, 57 leaves, max depth = 12, train loss: 0.46818, val loss: 0.45637, in 0.016s
1 tree, 32 leaves, max depth = 13, train loss: 0.46471, val loss: 0.45249, in 0.000s
1 tree, 57 leaves, max depth = 12, train loss: 0.46104, val loss: 0.44895, in 0.016s
1 tree, 58 leaves, max depth = 12, train loss: 0.45755, val loss: 0.44557, in 0.000s
Fit 35 trees in 0.596 s, (1392 total leaves)
Time spent computing histograms: 0.151s
Time spent finding best splits:  0.026s
Time spent applying splits:      0.026s
Time spent predicting:           0.000s
Trial 40, Fold 4: Log loss = 0.45723244672666946, Average precision = 0.917938386877304, ROC-AUC = 0.9294905440269343, Elapsed Time = 0.6093828999983089 seconds
Trial 40, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 40, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
Binning 0.040 GB of training data: 0.157 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 32 leaves, max depth = 12, train loss: 0.67949, val loss: 0.67861, in 0.016s
1 tree, 32 leaves, max depth = 12, train loss: 0.66662, val loss: 0.66489, in 0.000s
1 tree, 32 leaves, max depth = 12, train loss: 0.65446, val loss: 0.65191, in 0.016s
1 tree, 32 leaves, max depth = 10, train loss: 0.64311, val loss: 0.63976, in 0.000s
1 tree, 31 leaves, max depth = 12, train loss: 0.63225, val loss: 0.62813, in 0.016s
1 tree, 31 leaves, max depth = 12, train loss: 0.62197, val loss: 0.61710, in 0.000s
1 tree, 31 leaves, max depth = 11, train loss: 0.61235, val loss: 0.60677, in 0.016s
1 tree, 32 leaves, max depth = 12, train loss: 0.60313, val loss: 0.59684, in 0.000s
1 tree, 32 leaves, max depth = 12, train loss: 0.59439, val loss: 0.58740, in 0.016s
1 tree, 32 leaves, max depth = 12, train loss: 0.58610, val loss: 0.57845, in 0.000s
1 tree, 32 leaves, max depth = 12, train loss: 0.57824, val loss: 0.56993, in 0.016s
1 tree, 33 leaves, max depth = 12, train loss: 0.57086, val loss: 0.56192, in 0.000s
1 tree, 31 leaves, max depth = 10, train loss: 0.56386, val loss: 0.55431, in 0.016s
1 tree, 32 leaves, max depth = 12, train loss: 0.55713, val loss: 0.54696, in 0.000s
1 tree, 32 leaves, max depth = 12, train loss: 0.55073, val loss: 0.53997, in 0.016s
1 tree, 53 leaves, max depth = 12, train loss: 0.54421, val loss: 0.53375, in 0.016s
1 tree, 32 leaves, max depth = 12, train loss: 0.53827, val loss: 0.52724, in 0.000s
1 tree, 53 leaves, max depth = 12, train loss: 0.53219, val loss: 0.52146, in 0.016s
1 tree, 32 leaves, max depth = 10, train loss: 0.52674, val loss: 0.51547, in 0.000s
1 tree, 54 leaves, max depth = 12, train loss: 0.52105, val loss: 0.51005, in 0.016s
1 tree, 54 leaves, max depth = 12, train loss: 0.51566, val loss: 0.50492, in 0.000s
1 tree, 33 leaves, max depth = 12, train loss: 0.51061, val loss: 0.49936, in 0.016s
1 tree, 54 leaves, max depth = 12, train loss: 0.50556, val loss: 0.49459, in 0.016s
1 tree, 33 leaves, max depth = 12, train loss: 0.50090, val loss: 0.48945, in 0.000s
1 tree, 53 leaves, max depth = 12, train loss: 0.49617, val loss: 0.48497, in 0.016s
1 tree, 32 leaves, max depth = 10, train loss: 0.49182, val loss: 0.48016, in 0.000s
1 tree, 54 leaves, max depth = 13, train loss: 0.48737, val loss: 0.47596, in 0.016s
1 tree, 32 leaves, max depth = 10, train loss: 0.48331, val loss: 0.47144, in 0.016s
1 tree, 54 leaves, max depth = 13, train loss: 0.47912, val loss: 0.46751, in 0.000s
1 tree, 54 leaves, max depth = 13, train loss: 0.47513, val loss: 0.46377, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.47139, val loss: 0.45960, in 0.000s
1 tree, 54 leaves, max depth = 13, train loss: 0.46764, val loss: 0.45609, in 0.016s
1 tree, 32 leaves, max depth = 9, train loss: 0.46408, val loss: 0.45211, in 0.016s
1 tree, 54 leaves, max depth = 12, train loss: 0.46053, val loss: 0.44880, in 0.000s
1 tree, 30 leaves, max depth = 11, train loss: 0.45725, val loss: 0.44509, in 0.016s
Fit 35 trees in 0.626 s, (1355 total leaves)
Time spent computing histograms: 0.164s
Time spent finding best splits:  0.026s
Time spent applying splits:      0.026s
Time spent predicting:           0.000s
Trial 40, Fold 5: Log loss = 0.4623714122151564, Average precision = 0.9148543808130059, ROC-AUC = 0.925672444865578, Elapsed Time = 0.6308587999992596 seconds
Optimization Progress:  41%|####1     | 41/100 [08:02<10:59, 11.18s/it]
Trial 41, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 41, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
0.142 s 0.040 GB of training data: 
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 26 leaves, max depth = 11, train loss: 0.65757, val loss: 0.65670, in 0.016s
1 tree, 29 leaves, max depth = 11, train loss: 0.62692, val loss: 0.62497, in 0.000s
1 tree, 28 leaves, max depth = 11, train loss: 0.60070, val loss: 0.59791, in 0.016s
1 tree, 29 leaves, max depth = 11, train loss: 0.57855, val loss: 0.57500, in 0.000s
1 tree, 30 leaves, max depth = 12, train loss: 0.55919, val loss: 0.55488, in 0.016s
1 tree, 56 leaves, max depth = 10, train loss: 0.54133, val loss: 0.53809, in 0.000s
1 tree, 31 leaves, max depth = 14, train loss: 0.52568, val loss: 0.52173, in 0.016s
1 tree, 55 leaves, max depth = 11, train loss: 0.51091, val loss: 0.50792, in 0.016s
1 tree, 30 leaves, max depth = 12, train loss: 0.49790, val loss: 0.49422, in 0.000s
1 tree, 56 leaves, max depth = 10, train loss: 0.48555, val loss: 0.48276, in 0.016s
1 tree, 29 leaves, max depth = 10, train loss: 0.47475, val loss: 0.47127, in 0.000s
1 tree, 53 leaves, max depth = 10, train loss: 0.46427, val loss: 0.46161, in 0.016s
1 tree, 29 leaves, max depth = 9, train loss: 0.45521, val loss: 0.45183, in 0.016s
1 tree, 30 leaves, max depth = 9, train loss: 0.44704, val loss: 0.44305, in 0.016s
1 tree, 29 leaves, max depth = 10, train loss: 0.44003, val loss: 0.43551, in 0.000s
1 tree, 56 leaves, max depth = 11, train loss: 0.43145, val loss: 0.42780, in 0.016s
1 tree, 30 leaves, max depth = 9, train loss: 0.42540, val loss: 0.42119, in 0.016s
1 tree, 28 leaves, max depth = 10, train loss: 0.42023, val loss: 0.41556, in 0.000s
1 tree, 3 leaves, max depth = 2, train loss: 0.41506, val loss: 0.40991, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.41052, val loss: 0.40494, in 0.000s
1 tree, 3 leaves, max depth = 2, train loss: 0.40610, val loss: 0.40008, in 0.016s
1 tree, 64 leaves, max depth = 9, train loss: 0.40086, val loss: 0.39564, in 0.016s
1 tree, 54 leaves, max depth = 10, train loss: 0.39414, val loss: 0.38976, in 0.016s
1 tree, 28 leaves, max depth = 11, train loss: 0.39059, val loss: 0.38594, in 0.000s
1 tree, 3 leaves, max depth = 2, train loss: 0.38680, val loss: 0.38176, in 0.016s
1 tree, 51 leaves, max depth = 10, train loss: 0.38090, val loss: 0.37675, in 0.016s
1 tree, 51 leaves, max depth = 11, train loss: 0.37572, val loss: 0.37238, in 0.000s
1 tree, 3 leaves, max depth = 2, train loss: 0.37236, val loss: 0.36866, in 0.016s
1 tree, 50 leaves, max depth = 10, train loss: 0.36778, val loss: 0.36480, in 0.016s
1 tree, 51 leaves, max depth = 12, train loss: 0.36374, val loss: 0.36150, in 0.000s
1 tree, 29 leaves, max depth = 9, train loss: 0.36089, val loss: 0.35842, in 0.016s
1 tree, 2 leaves, max depth = 1, train loss: 0.35802, val loss: 0.35506, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.35543, val loss: 0.35228, in 0.000s
1 tree, 51 leaves, max depth = 10, train loss: 0.35178, val loss: 0.34930, in 0.016s
1 tree, 51 leaves, max depth = 11, train loss: 0.34858, val loss: 0.34669, in 0.016s
1 tree, 3 leaves, max depth = 2, train loss: 0.34598, val loss: 0.34360, in 0.000s
1 tree, 37 leaves, max depth = 11, train loss: 0.34373, val loss: 0.34107, in 0.016s
1 tree, 51 leaves, max depth = 12, train loss: 0.34083, val loss: 0.33887, in 0.016s
Fit 38 trees in 0.642 s, (1301 total leaves)
Time spent computing histograms: 0.187s
Time spent finding best splits:  0.030s
Time spent applying splits:      0.031s
Time spent predicting:           0.000s
Trial 41, Fold 1: Log loss = 0.34574342825086174, Average precision = 0.9481375195266801, ROC-AUC = 0.945188492063492, Elapsed Time = 0.6465646999986348 seconds
Trial 41, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 41, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
Binning 0.040 GB of training data: 0.173 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 31 leaves, max depth = 10, train loss: 0.65762, val loss: 0.65594, in 0.000s
1 tree, 31 leaves, max depth = 10, train loss: 0.62741, val loss: 0.62422, in 0.016s
1 tree, 30 leaves, max depth = 11, train loss: 0.60154, val loss: 0.59697, in 0.016s
1 tree, 30 leaves, max depth = 11, train loss: 0.57922, val loss: 0.57339, in 0.000s
1 tree, 30 leaves, max depth = 11, train loss: 0.55994, val loss: 0.55297, in 0.016s
1 tree, 53 leaves, max depth = 11, train loss: 0.54195, val loss: 0.53566, in 0.016s
1 tree, 29 leaves, max depth = 13, train loss: 0.52626, val loss: 0.51900, in 0.000s
1 tree, 52 leaves, max depth = 11, train loss: 0.51138, val loss: 0.50481, in 0.016s
1 tree, 6 leaves, max depth = 4, train loss: 0.49827, val loss: 0.49077, in 0.016s
1 tree, 53 leaves, max depth = 13, train loss: 0.48580, val loss: 0.47889, in 0.000s
1 tree, 31 leaves, max depth = 11, train loss: 0.47504, val loss: 0.46737, in 0.016s
1 tree, 52 leaves, max depth = 11, train loss: 0.46447, val loss: 0.45732, in 0.016s
1 tree, 28 leaves, max depth = 11, train loss: 0.45547, val loss: 0.44765, in 0.016s
1 tree, 30 leaves, max depth = 9, train loss: 0.44738, val loss: 0.43881, in 0.000s
1 tree, 28 leaves, max depth = 16, train loss: 0.44054, val loss: 0.43136, in 0.016s
1 tree, 52 leaves, max depth = 10, train loss: 0.43185, val loss: 0.42328, in 0.016s
1 tree, 29 leaves, max depth = 9, train loss: 0.42587, val loss: 0.41668, in 0.016s
1 tree, 30 leaves, max depth = 11, train loss: 0.42080, val loss: 0.41118, in 0.000s
1 tree, 3 leaves, max depth = 2, train loss: 0.41579, val loss: 0.40600, in 0.000s
1 tree, 30 leaves, max depth = 9, train loss: 0.41134, val loss: 0.40103, in 0.016s
1 tree, 3 leaves, max depth = 2, train loss: 0.40705, val loss: 0.39658, in 0.000s
1 tree, 65 leaves, max depth = 10, train loss: 0.40185, val loss: 0.39205, in 0.016s
1 tree, 53 leaves, max depth = 13, train loss: 0.39502, val loss: 0.38582, in 0.016s
1 tree, 30 leaves, max depth = 12, train loss: 0.39142, val loss: 0.38187, in 0.016s
1 tree, 3 leaves, max depth = 2, train loss: 0.38775, val loss: 0.37804, in 0.000s
1 tree, 51 leaves, max depth = 12, train loss: 0.38173, val loss: 0.37262, in 0.016s
1 tree, 54 leaves, max depth = 12, train loss: 0.37643, val loss: 0.36776, in 0.016s
1 tree, 2 leaves, max depth = 1, train loss: 0.37330, val loss: 0.36452, in 0.000s
1 tree, 52 leaves, max depth = 12, train loss: 0.36860, val loss: 0.36029, in 0.016s
1 tree, 54 leaves, max depth = 12, train loss: 0.36446, val loss: 0.35655, in 0.016s
1 tree, 29 leaves, max depth = 9, train loss: 0.36158, val loss: 0.35345, in 0.016s
1 tree, 2 leaves, max depth = 1, train loss: 0.35878, val loss: 0.35055, in 0.000s
1 tree, 29 leaves, max depth = 9, train loss: 0.35625, val loss: 0.34772, in 0.016s
1 tree, 51 leaves, max depth = 12, train loss: 0.35250, val loss: 0.34441, in 0.016s
1 tree, 52 leaves, max depth = 14, train loss: 0.34921, val loss: 0.34147, in 0.016s
1 tree, 2 leaves, max depth = 1, train loss: 0.34668, val loss: 0.33883, in 0.000s
1 tree, 37 leaves, max depth = 11, train loss: 0.34449, val loss: 0.33647, in 0.000s
1 tree, 51 leaves, max depth = 13, train loss: 0.34151, val loss: 0.33388, in 0.016s
Fit 38 trees in 0.751 s, (1278 total leaves)
Time spent computing histograms: 0.214s
Time spent finding best splits:  0.034s
Time spent applying splits:      0.035s
Time spent predicting:           0.000s
Trial 41, Fold 2: Log loss = 0.34458307528002785, Average precision = 0.9450730202217352, ROC-AUC = 0.9462417536459682, Elapsed Time = 0.7594802000003256 seconds
Trial 41, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 41, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
Binning 0.040 GB of training data: 0.205 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 29 leaves, max depth = 9, train loss: 0.65779, val loss: 0.65641, in 0.016s
1 tree, 29 leaves, max depth = 8, train loss: 0.62758, val loss: 0.62519, in 0.016s
1 tree, 30 leaves, max depth = 9, train loss: 0.60187, val loss: 0.59830, in 0.000s
1 tree, 30 leaves, max depth = 11, train loss: 0.57979, val loss: 0.57524, in 0.016s
1 tree, 32 leaves, max depth = 11, train loss: 0.56060, val loss: 0.55532, in 0.016s
1 tree, 50 leaves, max depth = 13, train loss: 0.54267, val loss: 0.53863, in 0.016s
1 tree, 29 leaves, max depth = 10, train loss: 0.52711, val loss: 0.52225, in 0.016s
1 tree, 50 leaves, max depth = 13, train loss: 0.51228, val loss: 0.50855, in 0.016s
1 tree, 6 leaves, max depth = 4, train loss: 0.49921, val loss: 0.49478, in 0.016s
1 tree, 50 leaves, max depth = 13, train loss: 0.48680, val loss: 0.48334, in 0.016s
1 tree, 32 leaves, max depth = 12, train loss: 0.47606, val loss: 0.47207, in 0.016s
1 tree, 51 leaves, max depth = 12, train loss: 0.46554, val loss: 0.46249, in 0.000s
1 tree, 32 leaves, max depth = 12, train loss: 0.45657, val loss: 0.45301, in 0.016s
1 tree, 28 leaves, max depth = 8, train loss: 0.44853, val loss: 0.44443, in 0.016s
1 tree, 30 leaves, max depth = 10, train loss: 0.44164, val loss: 0.43702, in 0.016s
1 tree, 50 leaves, max depth = 10, train loss: 0.43303, val loss: 0.42922, in 0.016s
1 tree, 28 leaves, max depth = 8, train loss: 0.42708, val loss: 0.42277, in 0.016s
1 tree, 30 leaves, max depth = 9, train loss: 0.42181, val loss: 0.41699, in 0.000s
1 tree, 3 leaves, max depth = 2, train loss: 0.41660, val loss: 0.41220, in 0.016s
1 tree, 28 leaves, max depth = 9, train loss: 0.41215, val loss: 0.40735, in 0.016s
1 tree, 3 leaves, max depth = 2, train loss: 0.40768, val loss: 0.40326, in 0.000s
1 tree, 63 leaves, max depth = 10, train loss: 0.40253, val loss: 0.39897, in 0.016s
1 tree, 50 leaves, max depth = 11, train loss: 0.39584, val loss: 0.39298, in 0.016s
1 tree, 30 leaves, max depth = 9, train loss: 0.39221, val loss: 0.38896, in 0.016s
1 tree, 3 leaves, max depth = 2, train loss: 0.38839, val loss: 0.38545, in 0.016s
1 tree, 50 leaves, max depth = 11, train loss: 0.38250, val loss: 0.38021, in 0.000s
1 tree, 51 leaves, max depth = 11, train loss: 0.37733, val loss: 0.37565, in 0.016s
1 tree, 3 leaves, max depth = 2, train loss: 0.37394, val loss: 0.37253, in 0.016s
1 tree, 50 leaves, max depth = 11, train loss: 0.36936, val loss: 0.36860, in 0.016s
1 tree, 49 leaves, max depth = 11, train loss: 0.36535, val loss: 0.36521, in 0.016s
1 tree, 30 leaves, max depth = 9, train loss: 0.36250, val loss: 0.36198, in 0.016s
1 tree, 2 leaves, max depth = 1, train loss: 0.35960, val loss: 0.35930, in 0.000s
1 tree, 29 leaves, max depth = 8, train loss: 0.35706, val loss: 0.35647, in 0.016s
1 tree, 50 leaves, max depth = 11, train loss: 0.35343, val loss: 0.35348, in 0.016s
1 tree, 51 leaves, max depth = 11, train loss: 0.35025, val loss: 0.35094, in 0.016s
1 tree, 2 leaves, max depth = 1, train loss: 0.34763, val loss: 0.34851, in 0.016s
1 tree, 28 leaves, max depth = 9, train loss: 0.34547, val loss: 0.34605, in 0.000s
1 tree, 50 leaves, max depth = 11, train loss: 0.34260, val loss: 0.34369, in 0.016s
Fit 38 trees in 0.846 s, (1241 total leaves)
Time spent computing histograms: 0.240s
Time spent finding best splits:  0.039s
Time spent applying splits:      0.039s
Time spent predicting:           0.000s
Trial 41, Fold 3: Log loss = 0.3399037601455915, Average precision = 0.9499262049311503, ROC-AUC = 0.9491582926823396, Elapsed Time = 0.8536236999989342 seconds
Trial 41, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 41, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
Binning 0.040 GB of training data: 0.204 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 30 leaves, max depth = 10, train loss: 0.65777, val loss: 0.65584, in 0.000s
1 tree, 31 leaves, max depth = 12, train loss: 0.62740, val loss: 0.62347, in 0.016s
1 tree, 32 leaves, max depth = 13, train loss: 0.60138, val loss: 0.59562, in 0.016s
1 tree, 30 leaves, max depth = 11, train loss: 0.57921, val loss: 0.57190, in 0.016s
1 tree, 30 leaves, max depth = 14, train loss: 0.56002, val loss: 0.55118, in 0.000s
1 tree, 53 leaves, max depth = 10, train loss: 0.54240, val loss: 0.53375, in 0.016s
1 tree, 30 leaves, max depth = 10, train loss: 0.52671, val loss: 0.51681, in 0.016s
1 tree, 52 leaves, max depth = 10, train loss: 0.51216, val loss: 0.50245, in 0.016s
1 tree, 6 leaves, max depth = 4, train loss: 0.49907, val loss: 0.48825, in 0.016s
1 tree, 52 leaves, max depth = 11, train loss: 0.48690, val loss: 0.47624, in 0.016s
1 tree, 29 leaves, max depth = 10, train loss: 0.47617, val loss: 0.46448, in 0.016s
1 tree, 52 leaves, max depth = 12, train loss: 0.46589, val loss: 0.45437, in 0.016s
1 tree, 28 leaves, max depth = 10, train loss: 0.45687, val loss: 0.44432, in 0.000s
1 tree, 31 leaves, max depth = 8, train loss: 0.44882, val loss: 0.43525, in 0.016s
1 tree, 30 leaves, max depth = 11, train loss: 0.44186, val loss: 0.42734, in 0.016s
1 tree, 51 leaves, max depth = 12, train loss: 0.43345, val loss: 0.41917, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.42749, val loss: 0.41234, in 0.000s
1 tree, 29 leaves, max depth = 10, train loss: 0.42236, val loss: 0.40639, in 0.016s
1 tree, 3 leaves, max depth = 2, train loss: 0.41737, val loss: 0.40110, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.41292, val loss: 0.39592, in 0.016s
1 tree, 3 leaves, max depth = 2, train loss: 0.40864, val loss: 0.39139, in 0.000s
1 tree, 65 leaves, max depth = 10, train loss: 0.40358, val loss: 0.38684, in 0.016s
1 tree, 53 leaves, max depth = 11, train loss: 0.39700, val loss: 0.38054, in 0.016s
1 tree, 30 leaves, max depth = 10, train loss: 0.39348, val loss: 0.37638, in 0.016s
1 tree, 3 leaves, max depth = 2, train loss: 0.38982, val loss: 0.37250, in 0.016s
1 tree, 51 leaves, max depth = 10, train loss: 0.38403, val loss: 0.36701, in 0.016s
1 tree, 50 leaves, max depth = 10, train loss: 0.37896, val loss: 0.36219, in 0.016s
1 tree, 2 leaves, max depth = 1, train loss: 0.37584, val loss: 0.35886, in 0.000s
1 tree, 51 leaves, max depth = 10, train loss: 0.37134, val loss: 0.35460, in 0.016s
1 tree, 50 leaves, max depth = 10, train loss: 0.36739, val loss: 0.35086, in 0.016s
1 tree, 32 leaves, max depth = 9, train loss: 0.36456, val loss: 0.34755, in 0.016s
1 tree, 2 leaves, max depth = 1, train loss: 0.36176, val loss: 0.34456, in 0.000s
1 tree, 27 leaves, max depth = 9, train loss: 0.35920, val loss: 0.34160, in 0.016s
1 tree, 51 leaves, max depth = 11, train loss: 0.35564, val loss: 0.33831, in 0.016s
1 tree, 51 leaves, max depth = 11, train loss: 0.35254, val loss: 0.33536, in 0.016s
1 tree, 2 leaves, max depth = 1, train loss: 0.35000, val loss: 0.33264, in 0.016s
1 tree, 31 leaves, max depth = 12, train loss: 0.34785, val loss: 0.33013, in 0.016s
1 tree, 52 leaves, max depth = 11, train loss: 0.34503, val loss: 0.32754, in 0.016s
Fit 38 trees in 0.860 s, (1267 total leaves)
Time spent computing histograms: 0.238s
Time spent finding best splits:  0.039s
Time spent applying splits:      0.039s
Time spent predicting:           0.031s
Trial 41, Fold 4: Log loss = 0.3451491793687913, Average precision = 0.948128226802262, ROC-AUC = 0.9461292534889876, Elapsed Time = 0.857237800000803 seconds
Trial 41, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 41, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
Binning 0.040 GB of training data: 0.189 s
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 31 leaves, max depth = 10, train loss: 0.65751, val loss: 0.65527, in 0.016s
1 tree, 29 leaves, max depth = 10, train loss: 0.62691, val loss: 0.62237, in 0.000s
1 tree, 30 leaves, max depth = 11, train loss: 0.60094, val loss: 0.59422, in 0.016s
1 tree, 32 leaves, max depth = 11, train loss: 0.57858, val loss: 0.57007, in 0.016s
1 tree, 29 leaves, max depth = 10, train loss: 0.55918, val loss: 0.54879, in 0.000s
1 tree, 51 leaves, max depth = 11, train loss: 0.54135, val loss: 0.53196, in 0.016s
1 tree, 29 leaves, max depth = 11, train loss: 0.52559, val loss: 0.51452, in 0.016s
1 tree, 52 leaves, max depth = 13, train loss: 0.51084, val loss: 0.50075, in 0.016s
1 tree, 30 leaves, max depth = 9, train loss: 0.49775, val loss: 0.48623, in 0.000s
1 tree, 52 leaves, max depth = 13, train loss: 0.48541, val loss: 0.47479, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.47451, val loss: 0.46267, in 0.016s
1 tree, 51 leaves, max depth = 13, train loss: 0.46406, val loss: 0.45304, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.45494, val loss: 0.44281, in 0.016s
1 tree, 29 leaves, max depth = 9, train loss: 0.44676, val loss: 0.43357, in 0.000s
1 tree, 30 leaves, max depth = 11, train loss: 0.43984, val loss: 0.42565, in 0.016s
1 tree, 52 leaves, max depth = 12, train loss: 0.43126, val loss: 0.41793, in 0.016s
1 tree, 28 leaves, max depth = 9, train loss: 0.42521, val loss: 0.41096, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.42006, val loss: 0.40497, in 0.016s
1 tree, 3 leaves, max depth = 2, train loss: 0.41504, val loss: 0.40012, in 0.000s
1 tree, 29 leaves, max depth = 10, train loss: 0.41052, val loss: 0.39483, in 0.016s
1 tree, 3 leaves, max depth = 2, train loss: 0.40622, val loss: 0.39071, in 0.000s
1 tree, 63 leaves, max depth = 10, train loss: 0.40111, val loss: 0.38667, in 0.031s
1 tree, 52 leaves, max depth = 12, train loss: 0.39436, val loss: 0.38083, in 0.000s
1 tree, 28 leaves, max depth = 10, train loss: 0.39077, val loss: 0.37653, in 0.016s
1 tree, 3 leaves, max depth = 2, train loss: 0.38709, val loss: 0.37302, in 0.016s
1 tree, 52 leaves, max depth = 12, train loss: 0.38113, val loss: 0.36795, in 0.016s
1 tree, 51 leaves, max depth = 12, train loss: 0.37590, val loss: 0.36358, in 0.016s
1 tree, 2 leaves, max depth = 1, train loss: 0.37279, val loss: 0.36050, in 0.000s
1 tree, 51 leaves, max depth = 12, train loss: 0.36814, val loss: 0.35663, in 0.016s
1 tree, 51 leaves, max depth = 14, train loss: 0.36406, val loss: 0.35330, in 0.016s
1 tree, 29 leaves, max depth = 10, train loss: 0.36119, val loss: 0.34982, in 0.016s
1 tree, 2 leaves, max depth = 1, train loss: 0.35840, val loss: 0.34706, in 0.000s
1 tree, 31 leaves, max depth = 9, train loss: 0.35583, val loss: 0.34398, in 0.000s
1 tree, 53 leaves, max depth = 13, train loss: 0.35213, val loss: 0.34100, in 0.016s
1 tree, 52 leaves, max depth = 15, train loss: 0.34889, val loss: 0.33843, in 0.016s
1 tree, 2 leaves, max depth = 1, train loss: 0.34637, val loss: 0.33594, in 0.016s
1 tree, 39 leaves, max depth = 11, train loss: 0.34408, val loss: 0.33343, in 0.016s
1 tree, 54 leaves, max depth = 15, train loss: 0.34114, val loss: 0.33111, in 0.016s
Fit 38 trees in 0.862 s, (1298 total leaves)
Time spent computing histograms: 0.233s
Time spent finding best splits:  0.036s
Time spent applying splits:      0.037s
Time spent predicting:           0.000s
Trial 41, Fold 5: Log loss = 0.34752194627777483, Average precision = 0.947238785766397, ROC-AUC = 0.9448383033275738, Elapsed Time = 0.8585003999996843 seconds
Optimization Progress:  42%|####2     | 42/100 [08:13<10:41, 11.06s/it]
Trial 42, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 42, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
0.142 s 0.040 GB of training data: 
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 13 leaves, max depth = 5, train loss: 0.68314, val loss: 0.68317, in 0.016s
1 tree, 13 leaves, max depth = 5, train loss: 0.67366, val loss: 0.67373, in 0.016s
1 tree, 13 leaves, max depth = 5, train loss: 0.66449, val loss: 0.66459, in 0.016s
1 tree, 15 leaves, max depth = 6, train loss: 0.65563, val loss: 0.65577, in 0.016s
1 tree, 15 leaves, max depth = 6, train loss: 0.64705, val loss: 0.64724, in 0.016s
1 tree, 15 leaves, max depth = 6, train loss: 0.63875, val loss: 0.63897, in 0.016s
1 tree, 16 leaves, max depth = 5, train loss: 0.63053, val loss: 0.63071, in 0.000s
1 tree, 14 leaves, max depth = 5, train loss: 0.62275, val loss: 0.62293, in 0.016s
1 tree, 14 leaves, max depth = 5, train loss: 0.61521, val loss: 0.61540, in 0.016s
1 tree, 16 leaves, max depth = 5, train loss: 0.60771, val loss: 0.60786, in 0.016s
1 tree, 16 leaves, max depth = 5, train loss: 0.60045, val loss: 0.60056, in 0.016s
1 tree, 17 leaves, max depth = 5, train loss: 0.59340, val loss: 0.59347, in 0.016s
1 tree, 17 leaves, max depth = 5, train loss: 0.58656, val loss: 0.58660, in 0.016s
1 tree, 17 leaves, max depth = 5, train loss: 0.57992, val loss: 0.57992, in 0.016s
1 tree, 17 leaves, max depth = 5, train loss: 0.57348, val loss: 0.57344, in 0.000s
1 tree, 17 leaves, max depth = 5, train loss: 0.56723, val loss: 0.56715, in 0.016s
1 tree, 17 leaves, max depth = 5, train loss: 0.56115, val loss: 0.56104, in 0.016s
Fit 17 trees in 0.532 s, (262 total leaves)
Time spent computing histograms: 0.101s
Time spent finding best splits:  0.008s
Time spent applying splits:      0.006s
Time spent predicting:           0.000s
Trial 42, Fold 1: Log loss = 0.5622161983440589, Average precision = 0.9122596955287137, ROC-AUC = 0.9279834644135683, Elapsed Time = 0.5323338999987755 seconds
Trial 42, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 42, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
Binning 0.040 GB of training data: 0.158 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 14 leaves, max depth = 5, train loss: 0.68307, val loss: 0.68285, in 0.016s
1 tree, 14 leaves, max depth = 5, train loss: 0.67339, val loss: 0.67297, in 0.016s
1 tree, 14 leaves, max depth = 5, train loss: 0.66404, val loss: 0.66341, in 0.016s
1 tree, 14 leaves, max depth = 5, train loss: 0.65500, val loss: 0.65418, in 0.016s
1 tree, 14 leaves, max depth = 5, train loss: 0.64624, val loss: 0.64523, in 0.016s
1 tree, 14 leaves, max depth = 5, train loss: 0.63778, val loss: 0.63658, in 0.016s
1 tree, 14 leaves, max depth = 5, train loss: 0.62957, val loss: 0.62819, in 0.016s
1 tree, 14 leaves, max depth = 5, train loss: 0.62163, val loss: 0.62007, in 0.016s
1 tree, 14 leaves, max depth = 5, train loss: 0.61393, val loss: 0.61221, in 0.016s
1 tree, 14 leaves, max depth = 5, train loss: 0.60647, val loss: 0.60458, in 0.000s
1 tree, 14 leaves, max depth = 5, train loss: 0.59924, val loss: 0.59718, in 0.016s
1 tree, 14 leaves, max depth = 5, train loss: 0.59222, val loss: 0.59000, in 0.016s
1 tree, 15 leaves, max depth = 5, train loss: 0.58541, val loss: 0.58304, in 0.016s
1 tree, 15 leaves, max depth = 5, train loss: 0.57880, val loss: 0.57626, in 0.016s
1 tree, 15 leaves, max depth = 5, train loss: 0.57239, val loss: 0.56967, in 0.016s
1 tree, 15 leaves, max depth = 5, train loss: 0.56615, val loss: 0.56330, in 0.016s
1 tree, 16 leaves, max depth = 5, train loss: 0.56010, val loss: 0.55709, in 0.016s
Fit 17 trees in 0.580 s, (244 total leaves)
Time spent computing histograms: 0.120s
Time spent finding best splits:  0.009s
Time spent applying splits:      0.006s
Time spent predicting:           0.016s
Trial 42, Fold 2: Log loss = 0.5607065966168168, Average precision = 0.9051654203427068, ROC-AUC = 0.9282734501164058, Elapsed Time = 0.5888044000002992 seconds
Trial 42, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 42, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
Binning 0.040 GB of training data: 0.142 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 13 leaves, max depth = 6, train loss: 0.68317, val loss: 0.68310, in 0.000s
1 tree, 13 leaves, max depth = 6, train loss: 0.67368, val loss: 0.67354, in 0.016s
1 tree, 14 leaves, max depth = 6, train loss: 0.66450, val loss: 0.66429, in 0.016s
1 tree, 14 leaves, max depth = 6, train loss: 0.65562, val loss: 0.65534, in 0.000s
1 tree, 16 leaves, max depth = 5, train loss: 0.64689, val loss: 0.64656, in 0.016s
1 tree, 16 leaves, max depth = 5, train loss: 0.63844, val loss: 0.63806, in 0.000s
1 tree, 16 leaves, max depth = 5, train loss: 0.63025, val loss: 0.62983, in 0.016s
1 tree, 16 leaves, max depth = 5, train loss: 0.62232, val loss: 0.62186, in 0.000s
1 tree, 16 leaves, max depth = 5, train loss: 0.61464, val loss: 0.61413, in 0.016s
1 tree, 16 leaves, max depth = 5, train loss: 0.60720, val loss: 0.60664, in 0.016s
1 tree, 16 leaves, max depth = 5, train loss: 0.59998, val loss: 0.59937, in 0.016s
1 tree, 16 leaves, max depth = 5, train loss: 0.59297, val loss: 0.59233, in 0.016s
1 tree, 16 leaves, max depth = 5, train loss: 0.58618, val loss: 0.58549, in 0.016s
1 tree, 20 leaves, max depth = 6, train loss: 0.57959, val loss: 0.57888, in 0.016s
1 tree, 20 leaves, max depth = 6, train loss: 0.57319, val loss: 0.57246, in 0.016s
1 tree, 21 leaves, max depth = 6, train loss: 0.56698, val loss: 0.56623, in 0.000s
1 tree, 20 leaves, max depth = 6, train loss: 0.56094, val loss: 0.56016, in 0.031s
Fit 17 trees in 0.563 s, (279 total leaves)
Time spent computing histograms: 0.109s
Time spent finding best splits:  0.009s
Time spent applying splits:      0.007s
Time spent predicting:           0.000s
Trial 42, Fold 3: Log loss = 0.5586542076405022, Average precision = 0.9144956673682674, ROC-AUC = 0.9326203758992979, Elapsed Time = 0.5713990999993257 seconds
Trial 42, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 42, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
Binning 0.040 GB of training data: 0.141 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 13 leaves, max depth = 5, train loss: 0.68327, val loss: 0.68287, in 0.000s
1 tree, 13 leaves, max depth = 5, train loss: 0.67384, val loss: 0.67303, in 0.016s
1 tree, 13 leaves, max depth = 5, train loss: 0.66471, val loss: 0.66352, in 0.016s
1 tree, 13 leaves, max depth = 5, train loss: 0.65589, val loss: 0.65432, in 0.016s
1 tree, 16 leaves, max depth = 6, train loss: 0.64718, val loss: 0.64521, in 0.016s
1 tree, 15 leaves, max depth = 5, train loss: 0.63873, val loss: 0.63639, in 0.016s
1 tree, 15 leaves, max depth = 5, train loss: 0.63056, val loss: 0.62785, in 0.016s
1 tree, 16 leaves, max depth = 6, train loss: 0.62265, val loss: 0.61957, in 0.000s
1 tree, 16 leaves, max depth = 5, train loss: 0.61497, val loss: 0.61153, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.60754, val loss: 0.60375, in 0.016s
1 tree, 15 leaves, max depth = 5, train loss: 0.60034, val loss: 0.59623, in 0.016s
1 tree, 16 leaves, max depth = 5, train loss: 0.59333, val loss: 0.58889, in 0.016s
1 tree, 16 leaves, max depth = 5, train loss: 0.58655, val loss: 0.58181, in 0.016s
1 tree, 16 leaves, max depth = 5, train loss: 0.57996, val loss: 0.57490, in 0.016s
1 tree, 16 leaves, max depth = 5, train loss: 0.57357, val loss: 0.56822, in 0.016s
1 tree, 17 leaves, max depth = 5, train loss: 0.56737, val loss: 0.56168, in 0.000s
1 tree, 16 leaves, max depth = 5, train loss: 0.56133, val loss: 0.55534, in 0.031s
Fit 17 trees in 0.578 s, (259 total leaves)
Time spent computing histograms: 0.110s
Time spent finding best splits:  0.009s
Time spent applying splits:      0.007s
Time spent predicting:           0.000s
Trial 42, Fold 4: Log loss = 0.5609712921412567, Average precision = 0.9131328401100529, ROC-AUC = 0.9301575826615258, Elapsed Time = 0.5790532999999414 seconds
Trial 42, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 42, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
Binning 0.040 GB of training data: 0.142 s
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 15 leaves, max depth = 5, train loss: 0.68300, val loss: 0.68258, in 0.016s
1 tree, 15 leaves, max depth = 5, train loss: 0.67333, val loss: 0.67250, in 0.016s
1 tree, 15 leaves, max depth = 5, train loss: 0.66399, val loss: 0.66276, in 0.016s
1 tree, 15 leaves, max depth = 5, train loss: 0.65495, val loss: 0.65333, in 0.016s
1 tree, 15 leaves, max depth = 5, train loss: 0.64621, val loss: 0.64421, in 0.016s
1 tree, 14 leaves, max depth = 5, train loss: 0.63775, val loss: 0.63539, in 0.000s
1 tree, 15 leaves, max depth = 5, train loss: 0.62956, val loss: 0.62684, in 0.000s
1 tree, 16 leaves, max depth = 5, train loss: 0.62162, val loss: 0.61855, in 0.016s
1 tree, 15 leaves, max depth = 5, train loss: 0.61394, val loss: 0.61053, in 0.016s
1 tree, 16 leaves, max depth = 5, train loss: 0.60648, val loss: 0.60275, in 0.016s
1 tree, 17 leaves, max depth = 5, train loss: 0.59926, val loss: 0.59521, in 0.016s
1 tree, 16 leaves, max depth = 5, train loss: 0.59225, val loss: 0.58789, in 0.016s
1 tree, 18 leaves, max depth = 5, train loss: 0.58545, val loss: 0.58078, in 0.016s
1 tree, 18 leaves, max depth = 5, train loss: 0.57885, val loss: 0.57389, in 0.016s
1 tree, 17 leaves, max depth = 5, train loss: 0.57245, val loss: 0.56719, in 0.016s
1 tree, 17 leaves, max depth = 5, train loss: 0.56622, val loss: 0.56070, in 0.016s
1 tree, 18 leaves, max depth = 5, train loss: 0.56018, val loss: 0.55437, in 0.016s
Fit 17 trees in 0.595 s, (272 total leaves)
Time spent computing histograms: 0.122s
Time spent finding best splits:  0.010s
Time spent applying splits:      0.007s
Time spent predicting:           0.000s
Trial 42, Fold 5: Log loss = 0.5621848627885069, Average precision = 0.9096137397135172, ROC-AUC = 0.9274200248964197, Elapsed Time = 0.605163199999879 seconds
Optimization Progress:  43%|####3     | 43/100 [08:22<10:07, 10.65s/it]
Trial 43, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 43, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
0.127 s 0.040 GB of training data: 
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 29 leaves, max depth = 10, train loss: 0.68834, val loss: 0.68819, in 0.016s
1 tree, 29 leaves, max depth = 12, train loss: 0.68379, val loss: 0.68350, in 0.000s
1 tree, 27 leaves, max depth = 10, train loss: 0.67926, val loss: 0.67884, in 0.016s
1 tree, 7 leaves, max depth = 5, train loss: 0.67485, val loss: 0.67427, in 0.000s
1 tree, 29 leaves, max depth = 11, train loss: 0.67055, val loss: 0.66984, in 0.000s
1 tree, 29 leaves, max depth = 12, train loss: 0.66632, val loss: 0.66546, in 0.016s
1 tree, 29 leaves, max depth = 10, train loss: 0.66220, val loss: 0.66121, in 0.000s
1 tree, 30 leaves, max depth = 12, train loss: 0.65815, val loss: 0.65703, in 0.016s
1 tree, 29 leaves, max depth = 11, train loss: 0.65413, val loss: 0.65288, in 0.000s
1 tree, 29 leaves, max depth = 11, train loss: 0.65013, val loss: 0.64874, in 0.016s
1 tree, 29 leaves, max depth = 12, train loss: 0.64630, val loss: 0.64476, in 0.000s
1 tree, 30 leaves, max depth = 12, train loss: 0.64255, val loss: 0.64089, in 0.016s
1 tree, 29 leaves, max depth = 11, train loss: 0.63888, val loss: 0.63709, in 0.000s
1 tree, 29 leaves, max depth = 10, train loss: 0.63529, val loss: 0.63337, in 0.016s
1 tree, 29 leaves, max depth = 12, train loss: 0.63175, val loss: 0.62972, in 0.000s
1 tree, 29 leaves, max depth = 11, train loss: 0.62828, val loss: 0.62613, in 0.016s
1 tree, 29 leaves, max depth = 10, train loss: 0.62482, val loss: 0.62256, in 0.000s
1 tree, 29 leaves, max depth = 12, train loss: 0.62146, val loss: 0.61905, in 0.016s
1 tree, 7 leaves, max depth = 5, train loss: 0.61814, val loss: 0.61559, in 0.000s
1 tree, 29 leaves, max depth = 11, train loss: 0.61482, val loss: 0.61215, in 0.016s
1 tree, 29 leaves, max depth = 11, train loss: 0.61156, val loss: 0.60876, in 0.000s
1 tree, 29 leaves, max depth = 11, train loss: 0.60837, val loss: 0.60543, in 0.000s
1 tree, 29 leaves, max depth = 12, train loss: 0.60530, val loss: 0.60224, in 0.016s
1 tree, 29 leaves, max depth = 11, train loss: 0.60222, val loss: 0.59903, in 0.016s
1 tree, 29 leaves, max depth = 12, train loss: 0.59927, val loss: 0.59594, in 0.000s
1 tree, 29 leaves, max depth = 10, train loss: 0.59633, val loss: 0.59291, in 0.016s
1 tree, 29 leaves, max depth = 12, train loss: 0.59349, val loss: 0.58993, in 0.000s
1 tree, 29 leaves, max depth = 10, train loss: 0.59071, val loss: 0.58705, in 0.016s
1 tree, 29 leaves, max depth = 12, train loss: 0.58798, val loss: 0.58422, in 0.000s
1 tree, 29 leaves, max depth = 12, train loss: 0.58530, val loss: 0.58144, in 0.016s
1 tree, 29 leaves, max depth = 10, train loss: 0.58267, val loss: 0.57870, in 0.000s
1 tree, 7 leaves, max depth = 5, train loss: 0.58005, val loss: 0.57596, in 0.016s
1 tree, 29 leaves, max depth = 9, train loss: 0.57742, val loss: 0.57321, in 0.000s
1 tree, 29 leaves, max depth = 10, train loss: 0.57492, val loss: 0.57062, in 0.016s
1 tree, 29 leaves, max depth = 9, train loss: 0.57239, val loss: 0.56797, in 0.000s
1 tree, 29 leaves, max depth = 11, train loss: 0.56993, val loss: 0.56541, in 0.016s
1 tree, 29 leaves, max depth = 10, train loss: 0.56751, val loss: 0.56291, in 0.000s
1 tree, 30 leaves, max depth = 12, train loss: 0.56519, val loss: 0.56049, in 0.016s
1 tree, 29 leaves, max depth = 10, train loss: 0.56286, val loss: 0.55807, in 0.000s
1 tree, 29 leaves, max depth = 12, train loss: 0.56060, val loss: 0.55570, in 0.016s
1 tree, 29 leaves, max depth = 10, train loss: 0.55835, val loss: 0.55336, in 0.000s
1 tree, 29 leaves, max depth = 9, train loss: 0.55610, val loss: 0.55100, in 0.016s
1 tree, 29 leaves, max depth = 10, train loss: 0.55393, val loss: 0.54874, in 0.016s
1 tree, 48 leaves, max depth = 11, train loss: 0.55170, val loss: 0.54668, in 0.000s
1 tree, 7 leaves, max depth = 5, train loss: 0.54959, val loss: 0.54445, in 0.016s
1 tree, 29 leaves, max depth = 10, train loss: 0.54751, val loss: 0.54229, in 0.000s
1 tree, 48 leaves, max depth = 11, train loss: 0.54534, val loss: 0.54029, in 0.016s
1 tree, 30 leaves, max depth = 9, train loss: 0.54330, val loss: 0.53815, in 0.000s
1 tree, 29 leaves, max depth = 10, train loss: 0.54130, val loss: 0.53607, in 0.016s
1 tree, 48 leaves, max depth = 11, train loss: 0.53920, val loss: 0.53413, in 0.016s
1 tree, 28 leaves, max depth = 12, train loss: 0.53728, val loss: 0.53210, in 0.000s
1 tree, 48 leaves, max depth = 11, train loss: 0.53523, val loss: 0.53021, in 0.016s
1 tree, 29 leaves, max depth = 12, train loss: 0.53337, val loss: 0.52827, in 0.000s
1 tree, 29 leaves, max depth = 12, train loss: 0.53154, val loss: 0.52636, in 0.016s
1 tree, 28 leaves, max depth = 10, train loss: 0.52970, val loss: 0.52444, in 0.000s
1 tree, 48 leaves, max depth = 11, train loss: 0.52772, val loss: 0.52261, in 0.016s
1 tree, 7 leaves, max depth = 5, train loss: 0.52593, val loss: 0.52072, in 0.000s
1 tree, 29 leaves, max depth = 10, train loss: 0.52417, val loss: 0.51887, in 0.016s
1 tree, 48 leaves, max depth = 11, train loss: 0.52224, val loss: 0.51710, in 0.016s
1 tree, 30 leaves, max depth = 11, train loss: 0.52055, val loss: 0.51534, in 0.000s
1 tree, 29 leaves, max depth = 9, train loss: 0.51886, val loss: 0.51357, in 0.016s
1 tree, 49 leaves, max depth = 12, train loss: 0.51698, val loss: 0.51185, in 0.000s
1 tree, 29 leaves, max depth = 11, train loss: 0.51533, val loss: 0.51012, in 0.016s
1 tree, 29 leaves, max depth = 9, train loss: 0.51371, val loss: 0.50842, in 0.000s
1 tree, 29 leaves, max depth = 10, train loss: 0.51215, val loss: 0.50678, in 0.016s
1 tree, 29 leaves, max depth = 11, train loss: 0.51058, val loss: 0.50514, in 0.000s
1 tree, 29 leaves, max depth = 11, train loss: 0.50904, val loss: 0.50352, in 0.016s
1 tree, 29 leaves, max depth = 9, train loss: 0.50752, val loss: 0.50193, in 0.000s
1 tree, 50 leaves, max depth = 10, train loss: 0.50573, val loss: 0.50029, in 0.016s
1 tree, 50 leaves, max depth = 10, train loss: 0.50396, val loss: 0.49868, in 0.016s
1 tree, 29 leaves, max depth = 11, train loss: 0.50246, val loss: 0.49709, in 0.000s
1 tree, 29 leaves, max depth = 9, train loss: 0.50101, val loss: 0.49557, in 0.016s
1 tree, 29 leaves, max depth = 12, train loss: 0.49962, val loss: 0.49408, in 0.000s
1 tree, 29 leaves, max depth = 11, train loss: 0.49822, val loss: 0.49261, in 0.016s
1 tree, 29 leaves, max depth = 11, train loss: 0.49685, val loss: 0.49117, in 0.000s
1 tree, 7 leaves, max depth = 5, train loss: 0.49549, val loss: 0.48972, in 0.016s
1 tree, 30 leaves, max depth = 12, train loss: 0.49418, val loss: 0.48832, in 0.000s
1 tree, 29 leaves, max depth = 12, train loss: 0.49291, val loss: 0.48697, in 0.016s
1 tree, 29 leaves, max depth = 10, train loss: 0.49162, val loss: 0.48562, in 0.000s
Fit 79 trees in 0.877 s, (2337 total leaves)
Time spent computing histograms: 0.344s
Time spent finding best splits:  0.053s
Time spent applying splits:      0.045s
Time spent predicting:           0.000s
Trial 43, Fold 1: Log loss = 0.49087564799547156, Average precision = 0.9016494024047659, ROC-AUC = 0.9077731953465921, Elapsed Time = 0.8890620000001945 seconds
Trial 43, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 43, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
Binning 0.040 GB of training data: 0.157 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 27 leaves, max depth = 8, train loss: 0.68846, val loss: 0.68822, in 0.016s
1 tree, 28 leaves, max depth = 10, train loss: 0.68389, val loss: 0.68343, in 0.016s
1 tree, 28 leaves, max depth = 10, train loss: 0.67937, val loss: 0.67870, in 0.000s
1 tree, 7 leaves, max depth = 5, train loss: 0.67494, val loss: 0.67406, in 0.000s
1 tree, 28 leaves, max depth = 13, train loss: 0.67064, val loss: 0.66954, in 0.016s
1 tree, 30 leaves, max depth = 11, train loss: 0.66640, val loss: 0.66509, in 0.016s
1 tree, 28 leaves, max depth = 12, train loss: 0.66225, val loss: 0.66074, in 0.000s
1 tree, 28 leaves, max depth = 10, train loss: 0.65819, val loss: 0.65648, in 0.016s
1 tree, 28 leaves, max depth = 9, train loss: 0.65418, val loss: 0.65226, in 0.000s
1 tree, 30 leaves, max depth = 10, train loss: 0.65026, val loss: 0.64814, in 0.016s
1 tree, 30 leaves, max depth = 10, train loss: 0.64642, val loss: 0.64410, in 0.000s
1 tree, 29 leaves, max depth = 13, train loss: 0.64266, val loss: 0.64015, in 0.016s
1 tree, 28 leaves, max depth = 12, train loss: 0.63897, val loss: 0.63626, in 0.000s
1 tree, 26 leaves, max depth = 8, train loss: 0.63538, val loss: 0.63247, in 0.016s
1 tree, 28 leaves, max depth = 10, train loss: 0.63183, val loss: 0.62873, in 0.000s
1 tree, 29 leaves, max depth = 11, train loss: 0.62834, val loss: 0.62506, in 0.016s
1 tree, 28 leaves, max depth = 9, train loss: 0.62489, val loss: 0.62142, in 0.000s
1 tree, 30 leaves, max depth = 10, train loss: 0.62152, val loss: 0.61788, in 0.016s
1 tree, 7 leaves, max depth = 5, train loss: 0.61819, val loss: 0.61438, in 0.000s
1 tree, 29 leaves, max depth = 9, train loss: 0.61489, val loss: 0.61089, in 0.016s
1 tree, 29 leaves, max depth = 9, train loss: 0.61166, val loss: 0.60746, in 0.000s
1 tree, 29 leaves, max depth = 9, train loss: 0.60848, val loss: 0.60410, in 0.016s
1 tree, 30 leaves, max depth = 10, train loss: 0.60541, val loss: 0.60086, in 0.000s
1 tree, 28 leaves, max depth = 11, train loss: 0.60241, val loss: 0.59769, in 0.016s
1 tree, 30 leaves, max depth = 10, train loss: 0.59945, val loss: 0.59457, in 0.000s
1 tree, 28 leaves, max depth = 9, train loss: 0.59653, val loss: 0.59148, in 0.016s
[27/79] 1 tree, 30 leaves, max depth = 10, train loss: 0.59368, val loss: 0.58847, in 0.000s
1 tree, 28 leaves, max depth = 12, train loss: 0.59088, val loss: 0.58551, in 0.016s
1 tree, 28 leaves, max depth = 11, train loss: 0.58814, val loss: 0.58261, in 0.000s
1 tree, 28 leaves, max depth = 11, train loss: 0.58545, val loss: 0.57977, in 0.016s
1 tree, 28 leaves, max depth = 12, train loss: 0.58281, val loss: 0.57696, in 0.000s
1 tree, 7 leaves, max depth = 5, train loss: 0.58017, val loss: 0.57418, in 0.016s
1 tree, 29 leaves, max depth = 9, train loss: 0.57756, val loss: 0.57140, in 0.000s
1 tree, 29 leaves, max depth = 12, train loss: 0.57506, val loss: 0.56874, in 0.016s
1 tree, 29 leaves, max depth = 9, train loss: 0.57253, val loss: 0.56606, in 0.000s
1 tree, 28 leaves, max depth = 11, train loss: 0.57012, val loss: 0.56350, in 0.016s
1 tree, 28 leaves, max depth = 9, train loss: 0.56772, val loss: 0.56095, in 0.000s
1 tree, 28 leaves, max depth = 11, train loss: 0.56539, val loss: 0.55847, in 0.016s
1 tree, 28 leaves, max depth = 9, train loss: 0.56307, val loss: 0.55601, in 0.000s
1 tree, 30 leaves, max depth = 10, train loss: 0.56081, val loss: 0.55362, in 0.016s
1 tree, 28 leaves, max depth = 9, train loss: 0.55857, val loss: 0.55124, in 0.000s
1 tree, 29 leaves, max depth = 9, train loss: 0.55634, val loss: 0.54886, in 0.016s
1 tree, 30 leaves, max depth = 10, train loss: 0.55420, val loss: 0.54658, in 0.000s
1 tree, 50 leaves, max depth = 11, train loss: 0.55192, val loss: 0.54437, in 0.016s
1 tree, 7 leaves, max depth = 5, train loss: 0.54980, val loss: 0.54212, in 0.000s
1 tree, 28 leaves, max depth = 9, train loss: 0.54773, val loss: 0.53992, in 0.016s
1 tree, 50 leaves, max depth = 11, train loss: 0.54552, val loss: 0.53778, in 0.016s
1 tree, 30 leaves, max depth = 9, train loss: 0.54348, val loss: 0.53562, in 0.000s
1 tree, 30 leaves, max depth = 9, train loss: 0.54151, val loss: 0.53352, in 0.016s
1 tree, 49 leaves, max depth = 11, train loss: 0.53936, val loss: 0.53145, in 0.000s
1 tree, 30 leaves, max depth = 9, train loss: 0.53744, val loss: 0.52940, in 0.016s
1 tree, 49 leaves, max depth = 11, train loss: 0.53534, val loss: 0.52737, in 0.016s
1 tree, 30 leaves, max depth = 12, train loss: 0.53347, val loss: 0.52538, in 0.000s
1 tree, 30 leaves, max depth = 12, train loss: 0.53164, val loss: 0.52342, in 0.016s
1 tree, 30 leaves, max depth = 9, train loss: 0.52983, val loss: 0.52149, in 0.000s
1 tree, 50 leaves, max depth = 11, train loss: 0.52780, val loss: 0.51953, in 0.016s
1 tree, 7 leaves, max depth = 5, train loss: 0.52601, val loss: 0.51762, in 0.000s
1 tree, 30 leaves, max depth = 12, train loss: 0.52428, val loss: 0.51577, in 0.016s
1 tree, 51 leaves, max depth = 11, train loss: 0.52230, val loss: 0.51386, in 0.000s
1 tree, 30 leaves, max depth = 14, train loss: 0.52062, val loss: 0.51206, in 0.016s
1 tree, 30 leaves, max depth = 13, train loss: 0.51896, val loss: 0.51028, in 0.000s
1 tree, 51 leaves, max depth = 11, train loss: 0.51704, val loss: 0.50843, in 0.016s
1 tree, 28 leaves, max depth = 9, train loss: 0.51540, val loss: 0.50667, in 0.016s
1 tree, 29 leaves, max depth = 9, train loss: 0.51379, val loss: 0.50494, in 0.000s
1 tree, 30 leaves, max depth = 14, train loss: 0.51223, val loss: 0.50326, in 0.016s
1 tree, 28 leaves, max depth = 9, train loss: 0.51068, val loss: 0.50159, in 0.000s
1 tree, 28 leaves, max depth = 9, train loss: 0.50915, val loss: 0.49995, in 0.016s
1 tree, 30 leaves, max depth = 14, train loss: 0.50766, val loss: 0.49835, in 0.000s
1 tree, 51 leaves, max depth = 12, train loss: 0.50583, val loss: 0.49659, in 0.016s
1 tree, 51 leaves, max depth = 12, train loss: 0.50402, val loss: 0.49485, in 0.016s
1 tree, 29 leaves, max depth = 9, train loss: 0.50253, val loss: 0.49324, in 0.000s
1 tree, 29 leaves, max depth = 9, train loss: 0.50110, val loss: 0.49170, in 0.016s
1 tree, 30 leaves, max depth = 9, train loss: 0.49970, val loss: 0.49020, in 0.000s
1 tree, 29 leaves, max depth = 12, train loss: 0.49834, val loss: 0.48872, in 0.016s
1 tree, 30 leaves, max depth = 9, train loss: 0.49699, val loss: 0.48727, in 0.000s
1 tree, 7 leaves, max depth = 5, train loss: 0.49564, val loss: 0.48582, in 0.016s
1 tree, 30 leaves, max depth = 9, train loss: 0.49433, val loss: 0.48442, in 0.000s
1 tree, 29 leaves, max depth = 12, train loss: 0.49306, val loss: 0.48304, in 0.016s
1 tree, 30 leaves, max depth = 9, train loss: 0.49180, val loss: 0.48168, in 0.016s
Fit 79 trees in 0.970 s, (2345 total leaves)
Time spent computing histograms: 0.363s
Time spent finding best splits:  0.054s
Time spent applying splits:      0.046s
Time spent predicting:           0.000s
Trial 43, Fold 2: Log loss = 0.49183921891558047, Average precision = 0.8987425486197136, ROC-AUC = 0.9109339894098256, Elapsed Time = 0.9627939000001788 seconds
Trial 43, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 43, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
Binning 0.040 GB of training data: 0.173 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 30 leaves, max depth = 9, train loss: 0.68841, val loss: 0.68825, in 0.016s
1 tree, 30 leaves, max depth = 11, train loss: 0.68388, val loss: 0.68356, in 0.000s
1 tree, 30 leaves, max depth = 9, train loss: 0.67939, val loss: 0.67891, in 0.016s
1 tree, 7 leaves, max depth = 4, train loss: 0.67499, val loss: 0.67436, in 0.000s
1 tree, 26 leaves, max depth = 10, train loss: 0.67073, val loss: 0.66993, in 0.016s
1 tree, 27 leaves, max depth = 10, train loss: 0.66652, val loss: 0.66560, in 0.000s
1 tree, 29 leaves, max depth = 9, train loss: 0.66242, val loss: 0.66134, in 0.016s
1 tree, 26 leaves, max depth = 10, train loss: 0.65840, val loss: 0.65716, in 0.000s
1 tree, 27 leaves, max depth = 9, train loss: 0.65441, val loss: 0.65302, in 0.016s
1 tree, 26 leaves, max depth = 9, train loss: 0.65045, val loss: 0.64893, in 0.000s
1 tree, 27 leaves, max depth = 10, train loss: 0.64664, val loss: 0.64500, in 0.016s
1 tree, 26 leaves, max depth = 10, train loss: 0.64292, val loss: 0.64114, in 0.000s
1 tree, 29 leaves, max depth = 10, train loss: 0.63927, val loss: 0.63734, in 0.016s
1 tree, 30 leaves, max depth = 10, train loss: 0.63570, val loss: 0.63364, in 0.000s
1 tree, 30 leaves, max depth = 11, train loss: 0.63219, val loss: 0.62998, in 0.016s
1 tree, 29 leaves, max depth = 10, train loss: 0.62874, val loss: 0.62640, in 0.000s
1 tree, 30 leaves, max depth = 10, train loss: 0.62531, val loss: 0.62283, in 0.016s
1 tree, 27 leaves, max depth = 10, train loss: 0.62197, val loss: 0.61938, in 0.000s
1 tree, 7 leaves, max depth = 4, train loss: 0.61866, val loss: 0.61595, in 0.000s
1 tree, 26 leaves, max depth = 9, train loss: 0.61538, val loss: 0.61255, in 0.016s
1 tree, 26 leaves, max depth = 9, train loss: 0.61215, val loss: 0.60921, in 0.000s
1 tree, 26 leaves, max depth = 9, train loss: 0.60898, val loss: 0.60594, in 0.016s
1 tree, 27 leaves, max depth = 10, train loss: 0.60594, val loss: 0.60279, in 0.016s
1 tree, 26 leaves, max depth = 9, train loss: 0.60289, val loss: 0.59963, in 0.000s
1 tree, 28 leaves, max depth = 10, train loss: 0.59995, val loss: 0.59660, in 0.000s
1 tree, 30 leaves, max depth = 10, train loss: 0.59705, val loss: 0.59357, in 0.016s
1 tree, 28 leaves, max depth = 10, train loss: 0.59422, val loss: 0.59065, in 0.016s
1 tree, 30 leaves, max depth = 11, train loss: 0.59146, val loss: 0.58775, in 0.000s
1 tree, 29 leaves, max depth = 9, train loss: 0.58875, val loss: 0.58492, in 0.016s
1 tree, 29 leaves, max depth = 11, train loss: 0.58609, val loss: 0.58214, in 0.000s
1 tree, 30 leaves, max depth = 11, train loss: 0.58347, val loss: 0.57940, in 0.016s
1 tree, 7 leaves, max depth = 4, train loss: 0.58086, val loss: 0.57668, in 0.000s
1 tree, 26 leaves, max depth = 9, train loss: 0.57826, val loss: 0.57398, in 0.016s
1 tree, 30 leaves, max depth = 11, train loss: 0.57578, val loss: 0.57138, in 0.000s
1 tree, 26 leaves, max depth = 9, train loss: 0.57327, val loss: 0.56878, in 0.016s
1 tree, 29 leaves, max depth = 11, train loss: 0.57088, val loss: 0.56628, in 0.000s
1 tree, 30 leaves, max depth = 10, train loss: 0.56849, val loss: 0.56378, in 0.016s
1 tree, 29 leaves, max depth = 9, train loss: 0.56619, val loss: 0.56136, in 0.000s
1 tree, 30 leaves, max depth = 10, train loss: 0.56388, val loss: 0.55895, in 0.016s
1 tree, 30 leaves, max depth = 11, train loss: 0.56164, val loss: 0.55662, in 0.016s
1 tree, 30 leaves, max depth = 10, train loss: 0.55941, val loss: 0.55429, in 0.000s
1 tree, 27 leaves, max depth = 9, train loss: 0.55719, val loss: 0.55198, in 0.016s
1 tree, 30 leaves, max depth = 10, train loss: 0.55504, val loss: 0.54973, in 0.000s
1 tree, 50 leaves, max depth = 12, train loss: 0.55278, val loss: 0.54764, in 0.016s
1 tree, 7 leaves, max depth = 5, train loss: 0.55068, val loss: 0.54544, in 0.000s
1 tree, 30 leaves, max depth = 10, train loss: 0.54862, val loss: 0.54328, in 0.016s
1 tree, 49 leaves, max depth = 12, train loss: 0.54642, val loss: 0.54125, in 0.000s
1 tree, 29 leaves, max depth = 9, train loss: 0.54441, val loss: 0.53912, in 0.016s
1 tree, 29 leaves, max depth = 10, train loss: 0.54245, val loss: 0.53710, in 0.000s
1 tree, 50 leaves, max depth = 12, train loss: 0.54032, val loss: 0.53513, in 0.016s
1 tree, 29 leaves, max depth = 10, train loss: 0.53842, val loss: 0.53315, in 0.016s
1 tree, 50 leaves, max depth = 12, train loss: 0.53634, val loss: 0.53123, in 0.000s
1 tree, 29 leaves, max depth = 11, train loss: 0.53449, val loss: 0.52928, in 0.016s
1 tree, 29 leaves, max depth = 11, train loss: 0.53268, val loss: 0.52736, in 0.000s
1 tree, 30 leaves, max depth = 10, train loss: 0.53087, val loss: 0.52545, in 0.016s
1 tree, 49 leaves, max depth = 12, train loss: 0.52885, val loss: 0.52359, in 0.016s
1 tree, 7 leaves, max depth = 5, train loss: 0.52707, val loss: 0.52172, in 0.000s
1 tree, 27 leaves, max depth = 9, train loss: 0.52534, val loss: 0.51989, in 0.016s
1 tree, 49 leaves, max depth = 13, train loss: 0.52338, val loss: 0.51808, in 0.000s
1 tree, 29 leaves, max depth = 10, train loss: 0.52172, val loss: 0.51633, in 0.016s
1 tree, 29 leaves, max depth = 9, train loss: 0.52005, val loss: 0.51456, in 0.000s
1 tree, 49 leaves, max depth = 13, train loss: 0.51815, val loss: 0.51281, in 0.016s
1 tree, 30 leaves, max depth = 10, train loss: 0.51652, val loss: 0.51109, in 0.016s
1 tree, 29 leaves, max depth = 9, train loss: 0.51492, val loss: 0.50939, in 0.000s
1 tree, 29 leaves, max depth = 12, train loss: 0.51338, val loss: 0.50776, in 0.016s
1 tree, 30 leaves, max depth = 10, train loss: 0.51184, val loss: 0.50612, in 0.000s
1 tree, 30 leaves, max depth = 10, train loss: 0.51032, val loss: 0.50451, in 0.016s
1 tree, 27 leaves, max depth = 9, train loss: 0.50882, val loss: 0.50293, in 0.000s
1 tree, 48 leaves, max depth = 11, train loss: 0.50700, val loss: 0.50126, in 0.016s
1 tree, 48 leaves, max depth = 11, train loss: 0.50522, val loss: 0.49961, in 0.000s
1 tree, 29 leaves, max depth = 9, train loss: 0.50373, val loss: 0.49806, in 0.016s
1 tree, 29 leaves, max depth = 9, train loss: 0.50231, val loss: 0.49655, in 0.000s
1 tree, 29 leaves, max depth = 10, train loss: 0.50093, val loss: 0.49511, in 0.016s
1 tree, 30 leaves, max depth = 10, train loss: 0.49955, val loss: 0.49364, in 0.000s
1 tree, 30 leaves, max depth = 10, train loss: 0.49820, val loss: 0.49221, in 0.016s
1 tree, 7 leaves, max depth = 5, train loss: 0.49685, val loss: 0.49079, in 0.000s
1 tree, 29 leaves, max depth = 10, train loss: 0.49556, val loss: 0.48944, in 0.016s
1 tree, 29 leaves, max depth = 11, train loss: 0.49430, val loss: 0.48809, in 0.000s
1 tree, 30 leaves, max depth = 10, train loss: 0.49303, val loss: 0.48674, in 0.016s
Fit 79 trees in 0.955 s, (2315 total leaves)
Time spent computing histograms: 0.352s
Time spent finding best splits:  0.055s
Time spent applying splits:      0.046s
Time spent predicting:           0.000s
Trial 43, Fold 3: Log loss = 0.48784348922773413, Average precision = 0.9067597255312809, ROC-AUC = 0.9150396738449983, Elapsed Time = 0.9549069000004238 seconds
Trial 43, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 43, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
Binning 0.040 GB of training data: 0.158 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 30 leaves, max depth = 11, train loss: 0.68844, val loss: 0.68818, in 0.000s
1 tree, 29 leaves, max depth = 10, train loss: 0.68389, val loss: 0.68339, in 0.016s
1 tree, 29 leaves, max depth = 11, train loss: 0.67939, val loss: 0.67864, in 0.000s
1 tree, 6 leaves, max depth = 4, train loss: 0.67499, val loss: 0.67401, in 0.016s
1 tree, 29 leaves, max depth = 10, train loss: 0.67070, val loss: 0.66948, in 0.000s
1 tree, 30 leaves, max depth = 11, train loss: 0.66649, val loss: 0.66500, in 0.016s
1 tree, 28 leaves, max depth = 10, train loss: 0.66237, val loss: 0.66064, in 0.000s
1 tree, 29 leaves, max depth = 10, train loss: 0.65833, val loss: 0.65637, in 0.016s
1 tree, 30 leaves, max depth = 11, train loss: 0.65434, val loss: 0.65213, in 0.000s
1 tree, 30 leaves, max depth = 11, train loss: 0.65044, val loss: 0.64798, in 0.016s
1 tree, 29 leaves, max depth = 10, train loss: 0.64663, val loss: 0.64394, in 0.016s
1 tree, 29 leaves, max depth = 10, train loss: 0.64289, val loss: 0.63997, in 0.000s
1 tree, 29 leaves, max depth = 10, train loss: 0.63922, val loss: 0.63607, in 0.016s
1 tree, 30 leaves, max depth = 11, train loss: 0.63565, val loss: 0.63226, in 0.000s
1 tree, 29 leaves, max depth = 9, train loss: 0.63212, val loss: 0.62852, in 0.016s
1 tree, 30 leaves, max depth = 10, train loss: 0.62865, val loss: 0.62484, in 0.000s
1 tree, 29 leaves, max depth = 10, train loss: 0.62521, val loss: 0.62118, in 0.016s
1 tree, 30 leaves, max depth = 11, train loss: 0.62187, val loss: 0.61759, in 0.000s
1 tree, 6 leaves, max depth = 4, train loss: 0.61856, val loss: 0.61408, in 0.016s
1 tree, 30 leaves, max depth = 11, train loss: 0.61528, val loss: 0.61056, in 0.000s
1 tree, 30 leaves, max depth = 11, train loss: 0.61206, val loss: 0.60710, in 0.016s
1 tree, 30 leaves, max depth = 11, train loss: 0.60890, val loss: 0.60371, in 0.016s
1 tree, 30 leaves, max depth = 11, train loss: 0.60585, val loss: 0.60043, in 0.000s
1 tree, 30 leaves, max depth = 11, train loss: 0.60280, val loss: 0.59715, in 0.000s
1 tree, 29 leaves, max depth = 11, train loss: 0.59987, val loss: 0.59401, in 0.016s
1 tree, 29 leaves, max depth = 10, train loss: 0.59696, val loss: 0.59089, in 0.016s
1 tree, 30 leaves, max depth = 10, train loss: 0.59413, val loss: 0.58787, in 0.000s
1 tree, 29 leaves, max depth = 11, train loss: 0.59136, val loss: 0.58489, in 0.016s
1 tree, 29 leaves, max depth = 10, train loss: 0.58863, val loss: 0.58198, in 0.016s
1 tree, 29 leaves, max depth = 10, train loss: 0.58595, val loss: 0.57912, in 0.000s
1 tree, 29 leaves, max depth = 11, train loss: 0.58333, val loss: 0.57629, in 0.016s
1 tree, 6 leaves, max depth = 4, train loss: 0.58071, val loss: 0.57349, in 0.000s
1 tree, 30 leaves, max depth = 11, train loss: 0.57812, val loss: 0.57068, in 0.016s
1 tree, 29 leaves, max depth = 11, train loss: 0.57563, val loss: 0.56800, in 0.000s
1 tree, 30 leaves, max depth = 11, train loss: 0.57312, val loss: 0.56528, in 0.016s
1 tree, 29 leaves, max depth = 11, train loss: 0.57072, val loss: 0.56270, in 0.000s
1 tree, 29 leaves, max depth = 10, train loss: 0.56832, val loss: 0.56011, in 0.016s
1 tree, 29 leaves, max depth = 11, train loss: 0.56600, val loss: 0.55762, in 0.016s
1 tree, 29 leaves, max depth = 10, train loss: 0.56369, val loss: 0.55512, in 0.000s
1 tree, 29 leaves, max depth = 12, train loss: 0.56145, val loss: 0.55270, in 0.016s
1 tree, 29 leaves, max depth = 10, train loss: 0.55921, val loss: 0.55028, in 0.016s
1 tree, 30 leaves, max depth = 11, train loss: 0.55699, val loss: 0.54786, in 0.000s
1 tree, 30 leaves, max depth = 10, train loss: 0.55484, val loss: 0.54553, in 0.016s
1 tree, 52 leaves, max depth = 11, train loss: 0.55254, val loss: 0.54325, in 0.016s
1 tree, 6 leaves, max depth = 4, train loss: 0.55044, val loss: 0.54098, in 0.000s
1 tree, 30 leaves, max depth = 10, train loss: 0.54837, val loss: 0.53874, in 0.016s
1 tree, 53 leaves, max depth = 11, train loss: 0.54614, val loss: 0.53652, in 0.000s
1 tree, 30 leaves, max depth = 10, train loss: 0.54411, val loss: 0.53432, in 0.016s
1 tree, 30 leaves, max depth = 11, train loss: 0.54213, val loss: 0.53217, in 0.016s
1 tree, 53 leaves, max depth = 11, train loss: 0.53997, val loss: 0.53002, in 0.000s
1 tree, 29 leaves, max depth = 10, train loss: 0.53807, val loss: 0.52795, in 0.016s
1 tree, 53 leaves, max depth = 11, train loss: 0.53596, val loss: 0.52586, in 0.016s
1 tree, 29 leaves, max depth = 12, train loss: 0.53410, val loss: 0.52384, in 0.000s
1 tree, 29 leaves, max depth = 12, train loss: 0.53228, val loss: 0.52185, in 0.016s
1 tree, 30 leaves, max depth = 11, train loss: 0.53046, val loss: 0.51986, in 0.016s
1 tree, 53 leaves, max depth = 11, train loss: 0.52842, val loss: 0.51784, in 0.000s
1 tree, 7 leaves, max depth = 5, train loss: 0.52664, val loss: 0.51591, in 0.016s
1 tree, 30 leaves, max depth = 10, train loss: 0.52490, val loss: 0.51400, in 0.016s
1 tree, 53 leaves, max depth = 11, train loss: 0.52291, val loss: 0.51203, in 0.000s
1 tree, 30 leaves, max depth = 10, train loss: 0.52124, val loss: 0.51020, in 0.016s
1 tree, 30 leaves, max depth = 10, train loss: 0.51957, val loss: 0.50837, in 0.000s
1 tree, 53 leaves, max depth = 12, train loss: 0.51763, val loss: 0.50646, in 0.016s
1 tree, 30 leaves, max depth = 11, train loss: 0.51600, val loss: 0.50467, in 0.016s
1 tree, 30 leaves, max depth = 10, train loss: 0.51440, val loss: 0.50291, in 0.000s
1 tree, 30 leaves, max depth = 10, train loss: 0.51285, val loss: 0.50120, in 0.016s
1 tree, 30 leaves, max depth = 11, train loss: 0.51130, val loss: 0.49950, in 0.000s
1 tree, 30 leaves, max depth = 11, train loss: 0.50977, val loss: 0.49782, in 0.016s
1 tree, 29 leaves, max depth = 10, train loss: 0.50828, val loss: 0.49617, in 0.016s
1 tree, 52 leaves, max depth = 10, train loss: 0.50643, val loss: 0.49435, in 0.016s
1 tree, 52 leaves, max depth = 10, train loss: 0.50462, val loss: 0.49256, in 0.000s
1 tree, 29 leaves, max depth = 10, train loss: 0.50314, val loss: 0.49091, in 0.016s
1 tree, 29 leaves, max depth = 10, train loss: 0.50172, val loss: 0.48934, in 0.016s
1 tree, 30 leaves, max depth = 10, train loss: 0.50034, val loss: 0.48783, in 0.000s
1 tree, 30 leaves, max depth = 11, train loss: 0.49896, val loss: 0.48630, in 0.016s
1 tree, 30 leaves, max depth = 11, train loss: 0.49760, val loss: 0.48480, in 0.016s
1 tree, 7 leaves, max depth = 5, train loss: 0.49626, val loss: 0.48332, in 0.000s
1 tree, 30 leaves, max depth = 10, train loss: 0.49497, val loss: 0.48189, in 0.000s
1 tree, 29 leaves, max depth = 12, train loss: 0.49370, val loss: 0.48048, in 0.000s
1 tree, 30 leaves, max depth = 11, train loss: 0.49243, val loss: 0.47907, in 0.016s
Fit 79 trees in 1.033 s, (2401 total leaves)
Time spent computing histograms: 0.394s
Time spent finding best splits:  0.063s
Time spent applying splits:      0.054s
Time spent predicting:           0.016s
Trial 43, Fold 4: Log loss = 0.4912654405101837, Average precision = 0.8998129762974338, ROC-AUC = 0.9107013204861334, Elapsed Time = 1.0364403000003222 seconds
Trial 43, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 43, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
Binning 0.040 GB of training data: 0.159 s
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 29 leaves, max depth = 12, train loss: 0.68834, val loss: 0.68802, in 0.000s
1 tree, 29 leaves, max depth = 9, train loss: 0.68374, val loss: 0.68312, in 0.016s
1 tree, 28 leaves, max depth = 11, train loss: 0.67920, val loss: 0.67829, in 0.000s
1 tree, 6 leaves, max depth = 4, train loss: 0.67474, val loss: 0.67354, in 0.016s
1 tree, 29 leaves, max depth = 9, train loss: 0.67041, val loss: 0.66892, in 0.000s
1 tree, 27 leaves, max depth = 10, train loss: 0.66615, val loss: 0.66438, in 0.016s
1 tree, 28 leaves, max depth = 13, train loss: 0.66198, val loss: 0.65993, in 0.000s
1 tree, 29 leaves, max depth = 9, train loss: 0.65790, val loss: 0.65556, in 0.016s
1 tree, 28 leaves, max depth = 9, train loss: 0.65386, val loss: 0.65126, in 0.000s
1 tree, 27 leaves, max depth = 10, train loss: 0.64992, val loss: 0.64705, in 0.016s
1 tree, 27 leaves, max depth = 10, train loss: 0.64606, val loss: 0.64291, in 0.000s
1 tree, 29 leaves, max depth = 9, train loss: 0.64228, val loss: 0.63886, in 0.016s
1 tree, 30 leaves, max depth = 10, train loss: 0.63857, val loss: 0.63488, in 0.016s
1 tree, 29 leaves, max depth = 12, train loss: 0.63495, val loss: 0.63097, in 0.000s
1 tree, 29 leaves, max depth = 9, train loss: 0.63138, val loss: 0.62715, in 0.016s
1 tree, 30 leaves, max depth = 10, train loss: 0.62787, val loss: 0.62338, in 0.000s
1 tree, 28 leaves, max depth = 11, train loss: 0.62440, val loss: 0.61966, in 0.016s
1 tree, 29 leaves, max depth = 10, train loss: 0.62101, val loss: 0.61602, in 0.000s
1 tree, 6 leaves, max depth = 4, train loss: 0.61766, val loss: 0.61242, in 0.000s
1 tree, 29 leaves, max depth = 10, train loss: 0.61441, val loss: 0.60892, in 0.016s
1 tree, 30 leaves, max depth = 13, train loss: 0.61116, val loss: 0.60542, in 0.016s
1 tree, 28 leaves, max depth = 10, train loss: 0.60803, val loss: 0.60204, in 0.000s
1 tree, 29 leaves, max depth = 11, train loss: 0.60494, val loss: 0.59872, in 0.016s
1 tree, 28 leaves, max depth = 10, train loss: 0.60193, val loss: 0.59546, in 0.000s
1 tree, 29 leaves, max depth = 11, train loss: 0.59896, val loss: 0.59225, in 0.016s
1 tree, 28 leaves, max depth = 11, train loss: 0.59601, val loss: 0.58907, in 0.000s
1 tree, 29 leaves, max depth = 11, train loss: 0.59315, val loss: 0.58597, in 0.016s
1 tree, 29 leaves, max depth = 13, train loss: 0.59033, val loss: 0.58293, in 0.016s
1 tree, 29 leaves, max depth = 10, train loss: 0.58758, val loss: 0.57995, in 0.000s
1 tree, 29 leaves, max depth = 10, train loss: 0.58488, val loss: 0.57702, in 0.016s
1 tree, 29 leaves, max depth = 13, train loss: 0.58221, val loss: 0.57414, in 0.000s
1 tree, 6 leaves, max depth = 4, train loss: 0.57957, val loss: 0.57127, in 0.016s
1 tree, 30 leaves, max depth = 13, train loss: 0.57694, val loss: 0.56842, in 0.000s
1 tree, 29 leaves, max depth = 13, train loss: 0.57442, val loss: 0.56568, in 0.016s
1 tree, 30 leaves, max depth = 13, train loss: 0.57189, val loss: 0.56293, in 0.000s
1 tree, 29 leaves, max depth = 10, train loss: 0.56946, val loss: 0.56029, in 0.016s
1 tree, 29 leaves, max depth = 13, train loss: 0.56707, val loss: 0.55768, in 0.016s
1 tree, 29 leaves, max depth = 10, train loss: 0.56473, val loss: 0.55513, in 0.000s
1 tree, 29 leaves, max depth = 13, train loss: 0.56242, val loss: 0.55262, in 0.016s
1 tree, 29 leaves, max depth = 11, train loss: 0.56015, val loss: 0.55013, in 0.000s
1 tree, 29 leaves, max depth = 13, train loss: 0.55792, val loss: 0.54770, in 0.016s
1 tree, 29 leaves, max depth = 13, train loss: 0.55568, val loss: 0.54525, in 0.016s
1 tree, 29 leaves, max depth = 11, train loss: 0.55352, val loss: 0.54289, in 0.016s
1 tree, 52 leaves, max depth = 12, train loss: 0.55125, val loss: 0.54069, in 0.016s
1 tree, 6 leaves, max depth = 4, train loss: 0.54912, val loss: 0.53837, in 0.016s
1 tree, 29 leaves, max depth = 11, train loss: 0.54703, val loss: 0.53608, in 0.016s
1 tree, 51 leaves, max depth = 12, train loss: 0.54482, val loss: 0.53396, in 0.031s
1 tree, 29 leaves, max depth = 8, train loss: 0.54278, val loss: 0.53171, in 0.016s
1 tree, 29 leaves, max depth = 11, train loss: 0.54080, val loss: 0.52954, in 0.016s
1 tree, 50 leaves, max depth = 12, train loss: 0.53866, val loss: 0.52748, in 0.016s
1 tree, 29 leaves, max depth = 11, train loss: 0.53673, val loss: 0.52535, in 0.016s
1 tree, 50 leaves, max depth = 12, train loss: 0.53464, val loss: 0.52334, in 0.000s
1 tree, 29 leaves, max depth = 13, train loss: 0.53276, val loss: 0.52128, in 0.016s
1 tree, 29 leaves, max depth = 13, train loss: 0.53092, val loss: 0.51925, in 0.016s
1 tree, 29 leaves, max depth = 10, train loss: 0.52907, val loss: 0.51722, in 0.016s
1 tree, 50 leaves, max depth = 11, train loss: 0.52705, val loss: 0.51528, in 0.016s
1 tree, 6 leaves, max depth = 4, train loss: 0.52525, val loss: 0.51330, in 0.000s
1 tree, 29 leaves, max depth = 13, train loss: 0.52351, val loss: 0.51138, in 0.016s
1 tree, 52 leaves, max depth = 12, train loss: 0.52154, val loss: 0.50950, in 0.016s
1 tree, 28 leaves, max depth = 10, train loss: 0.51985, val loss: 0.50762, in 0.016s
1 tree, 28 leaves, max depth = 10, train loss: 0.51819, val loss: 0.50578, in 0.000s
1 tree, 52 leaves, max depth = 12, train loss: 0.51628, val loss: 0.50395, in 0.016s
1 tree, 29 leaves, max depth = 10, train loss: 0.51462, val loss: 0.50213, in 0.016s
1 tree, 29 leaves, max depth = 10, train loss: 0.51300, val loss: 0.50034, in 0.016s
1 tree, 28 leaves, max depth = 10, train loss: 0.51144, val loss: 0.49860, in 0.000s
1 tree, 29 leaves, max depth = 10, train loss: 0.50987, val loss: 0.49686, in 0.000s
1 tree, 29 leaves, max depth = 10, train loss: 0.50832, val loss: 0.49515, in 0.016s
1 tree, 28 leaves, max depth = 10, train loss: 0.50683, val loss: 0.49349, in 0.016s
1 tree, 52 leaves, max depth = 11, train loss: 0.50500, val loss: 0.49175, in 0.000s
1 tree, 52 leaves, max depth = 11, train loss: 0.50320, val loss: 0.49004, in 0.016s
1 tree, 29 leaves, max depth = 10, train loss: 0.50171, val loss: 0.48837, in 0.016s
1 tree, 29 leaves, max depth = 10, train loss: 0.50026, val loss: 0.48678, in 0.016s
1 tree, 30 leaves, max depth = 11, train loss: 0.49886, val loss: 0.48521, in 0.000s
1 tree, 29 leaves, max depth = 10, train loss: 0.49746, val loss: 0.48365, in 0.016s
1 tree, 29 leaves, max depth = 10, train loss: 0.49608, val loss: 0.48212, in 0.000s
1 tree, 7 leaves, max depth = 5, train loss: 0.49472, val loss: 0.48060, in 0.016s
1 tree, 30 leaves, max depth = 11, train loss: 0.49341, val loss: 0.47913, in 0.000s
1 tree, 29 leaves, max depth = 13, train loss: 0.49213, val loss: 0.47769, in 0.016s
1 tree, 29 leaves, max depth = 10, train loss: 0.49084, val loss: 0.47625, in 0.000s
Fit 79 trees in 1.112 s, (2344 total leaves)
Time spent computing histograms: 0.421s
Time spent finding best splits:  0.074s
Time spent applying splits:      0.072s
Time spent predicting:           0.031s
Trial 43, Fold 5: Log loss = 0.49696672240271467, Average precision = 0.8970724235639036, ROC-AUC = 0.9054165691247237, Elapsed Time = 1.1303874000004726 seconds
Optimization Progress:  44%|####4     | 44/100 [08:34<10:11, 10.92s/it]
Trial 44, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 44, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
0.158 s 0.040 GB of training data: 
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 26 leaves, max depth = 7, train loss: 0.67205, val loss: 0.67196, in 0.016s
1 tree, 34 leaves, max depth = 10, train loss: 0.65221, val loss: 0.65221, in 0.016s
1 tree, 31 leaves, max depth = 7, train loss: 0.63397, val loss: 0.63387, in 0.016s
1 tree, 38 leaves, max depth = 10, train loss: 0.61672, val loss: 0.61668, in 0.016s
1 tree, 38 leaves, max depth = 7, train loss: 0.60176, val loss: 0.60165, in 0.016s
1 tree, 23 leaves, max depth = 7, train loss: 0.58762, val loss: 0.58727, in 0.016s
1 tree, 33 leaves, max depth = 7, train loss: 0.57428, val loss: 0.57371, in 0.016s
1 tree, 37 leaves, max depth = 10, train loss: 0.56062, val loss: 0.56003, in 0.016s
1 tree, 38 leaves, max depth = 10, train loss: 0.54851, val loss: 0.54784, in 0.016s
1 tree, 39 leaves, max depth = 12, train loss: 0.53724, val loss: 0.53658, in 0.016s
1 tree, 36 leaves, max depth = 12, train loss: 0.52592, val loss: 0.52539, in 0.016s
1 tree, 39 leaves, max depth = 8, train loss: 0.51609, val loss: 0.51539, in 0.031s
1 tree, 38 leaves, max depth = 12, train loss: 0.50597, val loss: 0.50541, in 0.016s
1 tree, 39 leaves, max depth = 8, train loss: 0.49656, val loss: 0.49587, in 0.016s
1 tree, 39 leaves, max depth = 10, train loss: 0.48761, val loss: 0.48667, in 0.000s
1 tree, 43 leaves, max depth = 11, train loss: 0.47885, val loss: 0.47785, in 0.031s
1 tree, 40 leaves, max depth = 13, train loss: 0.47078, val loss: 0.46990, in 0.016s
1 tree, 43 leaves, max depth = 11, train loss: 0.46288, val loss: 0.46196, in 0.016s
1 tree, 44 leaves, max depth = 8, train loss: 0.45558, val loss: 0.45451, in 0.016s
1 tree, 44 leaves, max depth = 11, train loss: 0.44853, val loss: 0.44744, in 0.016s
1 tree, 44 leaves, max depth = 11, train loss: 0.44180, val loss: 0.44070, in 0.016s
1 tree, 44 leaves, max depth = 11, train loss: 0.43546, val loss: 0.43437, in 0.016s
1 tree, 44 leaves, max depth = 11, train loss: 0.42619, val loss: 0.42549, in 0.016s
1 tree, 45 leaves, max depth = 11, train loss: 0.41750, val loss: 0.41718, in 0.016s
1 tree, 45 leaves, max depth = 11, train loss: 0.41195, val loss: 0.41167, in 0.016s
1 tree, 42 leaves, max depth = 7, train loss: 0.40683, val loss: 0.40642, in 0.016s
1 tree, 37 leaves, max depth = 11, train loss: 0.39910, val loss: 0.39913, in 0.016s
1 tree, 43 leaves, max depth = 11, train loss: 0.39426, val loss: 0.39432, in 0.016s
1 tree, 50 leaves, max depth = 11, train loss: 0.38712, val loss: 0.38755, in 0.016s
1 tree, 51 leaves, max depth = 12, train loss: 0.38039, val loss: 0.38116, in 0.016s
1 tree, 45 leaves, max depth = 13, train loss: 0.37601, val loss: 0.37684, in 0.016s
1 tree, 44 leaves, max depth = 10, train loss: 0.37193, val loss: 0.37282, in 0.016s
1 tree, 50 leaves, max depth = 13, train loss: 0.36588, val loss: 0.36714, in 0.016s
1 tree, 45 leaves, max depth = 13, train loss: 0.36238, val loss: 0.36371, in 0.016s
1 tree, 46 leaves, max depth = 13, train loss: 0.35869, val loss: 0.36011, in 0.016s
1 tree, 30 leaves, max depth = 11, train loss: 0.35355, val loss: 0.35501, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.34871, val loss: 0.35022, in 0.016s
1 tree, 62 leaves, max depth = 13, train loss: 0.34395, val loss: 0.34594, in 0.016s
1 tree, 46 leaves, max depth = 16, train loss: 0.34025, val loss: 0.34238, in 0.016s
1 tree, 54 leaves, max depth = 12, train loss: 0.33565, val loss: 0.33817, in 0.016s
1 tree, 47 leaves, max depth = 13, train loss: 0.33281, val loss: 0.33546, in 0.016s
1 tree, 63 leaves, max depth = 14, train loss: 0.32869, val loss: 0.33177, in 0.016s
1 tree, 33 leaves, max depth = 11, train loss: 0.32490, val loss: 0.32804, in 0.016s
1 tree, 47 leaves, max depth = 16, train loss: 0.32175, val loss: 0.32498, in 0.016s
1 tree, 64 leaves, max depth = 14, train loss: 0.31806, val loss: 0.32171, in 0.016s
1 tree, 34 leaves, max depth = 11, train loss: 0.31471, val loss: 0.31840, in 0.016s
1 tree, 52 leaves, max depth = 10, train loss: 0.31230, val loss: 0.31602, in 0.016s
1 tree, 35 leaves, max depth = 13, train loss: 0.30921, val loss: 0.31302, in 0.016s
1 tree, 33 leaves, max depth = 11, train loss: 0.30629, val loss: 0.31013, in 0.016s
1 tree, 50 leaves, max depth = 16, train loss: 0.30335, val loss: 0.30718, in 0.016s
1 tree, 66 leaves, max depth = 12, train loss: 0.30033, val loss: 0.30454, in 0.016s
1 tree, 65 leaves, max depth = 12, train loss: 0.29747, val loss: 0.30205, in 0.032s
1 tree, 65 leaves, max depth = 12, train loss: 0.29477, val loss: 0.29969, in 0.016s
1 tree, 35 leaves, max depth = 14, train loss: 0.29237, val loss: 0.29735, in 0.016s
1 tree, 54 leaves, max depth = 16, train loss: 0.28976, val loss: 0.29474, in 0.016s
1 tree, 52 leaves, max depth = 14, train loss: 0.28784, val loss: 0.29298, in 0.016s
1 tree, 68 leaves, max depth = 12, train loss: 0.28549, val loss: 0.29097, in 0.031s
1 tree, 51 leaves, max depth = 14, train loss: 0.28373, val loss: 0.28931, in 0.016s
1 tree, 67 leaves, max depth = 13, train loss: 0.28152, val loss: 0.28741, in 0.016s
1 tree, 32 leaves, max depth = 11, train loss: 0.27952, val loss: 0.28544, in 0.016s
1 tree, 67 leaves, max depth = 12, train loss: 0.27749, val loss: 0.28371, in 0.016s
1 tree, 31 leaves, max depth = 14, train loss: 0.27567, val loss: 0.28195, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.27395, val loss: 0.28025, in 0.016s
1 tree, 46 leaves, max depth = 18, train loss: 0.27197, val loss: 0.27835, in 0.016s
1 tree, 64 leaves, max depth = 12, train loss: 0.27020, val loss: 0.27688, in 0.016s
1 tree, 55 leaves, max depth = 15, train loss: 0.26791, val loss: 0.27455, in 0.031s
1 tree, 66 leaves, max depth = 13, train loss: 0.26626, val loss: 0.27317, in 0.016s
1 tree, 42 leaves, max depth = 15, train loss: 0.26473, val loss: 0.27161, in 0.016s
1 tree, 66 leaves, max depth = 12, train loss: 0.26320, val loss: 0.27035, in 0.016s
1 tree, 39 leaves, max depth = 14, train loss: 0.26183, val loss: 0.26896, in 0.016s
1 tree, 30 leaves, max depth = 7, train loss: 0.25995, val loss: 0.26706, in 0.016s
1 tree, 69 leaves, max depth = 13, train loss: 0.25856, val loss: 0.26586, in 0.016s
1 tree, 42 leaves, max depth = 16, train loss: 0.25728, val loss: 0.26455, in 0.031s
1 tree, 35 leaves, max depth = 11, train loss: 0.25611, val loss: 0.26339, in 0.016s
1 tree, 47 leaves, max depth = 16, train loss: 0.25455, val loss: 0.26195, in 0.016s
1 tree, 32 leaves, max depth = 8, train loss: 0.25289, val loss: 0.26027, in 0.016s
1 tree, 67 leaves, max depth = 16, train loss: 0.25170, val loss: 0.25930, in 0.016s
1 tree, 43 leaves, max depth = 16, train loss: 0.25062, val loss: 0.25819, in 0.016s
1 tree, 48 leaves, max depth = 18, train loss: 0.24923, val loss: 0.25693, in 0.016s
1 tree, 65 leaves, max depth = 16, train loss: 0.24815, val loss: 0.25607, in 0.016s
1 tree, 43 leaves, max depth = 15, train loss: 0.24716, val loss: 0.25503, in 0.016s
1 tree, 55 leaves, max depth = 18, train loss: 0.24568, val loss: 0.25350, in 0.016s
1 tree, 66 leaves, max depth = 17, train loss: 0.24470, val loss: 0.25273, in 0.016s
1 tree, 38 leaves, max depth = 11, train loss: 0.24380, val loss: 0.25179, in 0.031s
1 tree, 56 leaves, max depth = 16, train loss: 0.24242, val loss: 0.25037, in 0.016s
Fit 85 trees in 1.721 s, (3900 total leaves)
Time spent computing histograms: 0.533s
Time spent finding best splits:  0.108s
Time spent applying splits:      0.079s
Time spent predicting:           0.000s
Trial 44, Fold 1: Log loss = 0.2527531781390743, Average precision = 0.9657645417180376, ROC-AUC = 0.9605238041467716, Elapsed Time = 1.7215439999999944 seconds
Trial 44, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 44, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
Binning 0.040 GB of training data: 0.158 s
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 24 leaves, max depth = 6, train loss: 0.67210, val loss: 0.67157, in 0.016s
1 tree, 36 leaves, max depth = 11, train loss: 0.65188, val loss: 0.65105, in 0.016s
1 tree, 29 leaves, max depth = 8, train loss: 0.63335, val loss: 0.63239, in 0.016s
1 tree, 37 leaves, max depth = 12, train loss: 0.61578, val loss: 0.61457, in 0.016s
1 tree, 34 leaves, max depth = 12, train loss: 0.59954, val loss: 0.59802, in 0.016s
1 tree, 40 leaves, max depth = 12, train loss: 0.58422, val loss: 0.58243, in 0.016s
1 tree, 39 leaves, max depth = 10, train loss: 0.56999, val loss: 0.56795, in 0.016s
1 tree, 43 leaves, max depth = 10, train loss: 0.55749, val loss: 0.55515, in 0.016s
1 tree, 37 leaves, max depth = 12, train loss: 0.54478, val loss: 0.54216, in 0.016s
1 tree, 37 leaves, max depth = 10, train loss: 0.53282, val loss: 0.52998, in 0.016s
1 tree, 37 leaves, max depth = 10, train loss: 0.52216, val loss: 0.51922, in 0.016s
1 tree, 40 leaves, max depth = 10, train loss: 0.51146, val loss: 0.50840, in 0.031s
1 tree, 39 leaves, max depth = 10, train loss: 0.50147, val loss: 0.49823, in 0.016s
1 tree, 40 leaves, max depth = 10, train loss: 0.49198, val loss: 0.48855, in 0.016s
1 tree, 39 leaves, max depth = 12, train loss: 0.48301, val loss: 0.47943, in 0.016s
1 tree, 33 leaves, max depth = 8, train loss: 0.47469, val loss: 0.47101, in 0.016s
1 tree, 38 leaves, max depth = 12, train loss: 0.46697, val loss: 0.46310, in 0.016s
1 tree, 37 leaves, max depth = 11, train loss: 0.45933, val loss: 0.45534, in 0.016s
1 tree, 38 leaves, max depth = 11, train loss: 0.45210, val loss: 0.44797, in 0.016s
1 tree, 36 leaves, max depth = 11, train loss: 0.44526, val loss: 0.44099, in 0.031s
1 tree, 36 leaves, max depth = 7, train loss: 0.43889, val loss: 0.43456, in 0.016s
1 tree, 32 leaves, max depth = 11, train loss: 0.42954, val loss: 0.42537, in 0.016s
1 tree, 37 leaves, max depth = 8, train loss: 0.42371, val loss: 0.41956, in 0.016s
1 tree, 35 leaves, max depth = 12, train loss: 0.41516, val loss: 0.41116, in 0.016s
1 tree, 37 leaves, max depth = 8, train loss: 0.40996, val loss: 0.40596, in 0.031s
1 tree, 35 leaves, max depth = 12, train loss: 0.40213, val loss: 0.39828, in 0.016s
1 tree, 37 leaves, max depth = 8, train loss: 0.39738, val loss: 0.39355, in 0.016s
1 tree, 38 leaves, max depth = 12, train loss: 0.39021, val loss: 0.38655, in 0.016s
1 tree, 38 leaves, max depth = 12, train loss: 0.38345, val loss: 0.37994, in 0.016s
1 tree, 39 leaves, max depth = 11, train loss: 0.37932, val loss: 0.37587, in 0.016s
1 tree, 46 leaves, max depth = 8, train loss: 0.37524, val loss: 0.37184, in 0.031s
1 tree, 47 leaves, max depth = 9, train loss: 0.37138, val loss: 0.36805, in 0.016s
1 tree, 36 leaves, max depth = 8, train loss: 0.36559, val loss: 0.36245, in 0.016s
1 tree, 45 leaves, max depth = 12, train loss: 0.36189, val loss: 0.35874, in 0.016s
1 tree, 19 leaves, max depth = 10, train loss: 0.35670, val loss: 0.35366, in 0.016s
1 tree, 19 leaves, max depth = 11, train loss: 0.35184, val loss: 0.34888, in 0.016s
1 tree, 38 leaves, max depth = 14, train loss: 0.34797, val loss: 0.34518, in 0.016s
1 tree, 64 leaves, max depth = 15, train loss: 0.34332, val loss: 0.34093, in 0.016s
1 tree, 21 leaves, max depth = 11, train loss: 0.33904, val loss: 0.33676, in 0.016s
1 tree, 37 leaves, max depth = 14, train loss: 0.33553, val loss: 0.33339, in 0.000s
1 tree, 21 leaves, max depth = 11, train loss: 0.33162, val loss: 0.32957, in 0.016s
1 tree, 18 leaves, max depth = 9, train loss: 0.32791, val loss: 0.32598, in 0.016s
1 tree, 39 leaves, max depth = 17, train loss: 0.32469, val loss: 0.32291, in 0.016s
1 tree, 25 leaves, max depth = 14, train loss: 0.32127, val loss: 0.31956, in 0.016s
1 tree, 40 leaves, max depth = 17, train loss: 0.31825, val loss: 0.31666, in 0.016s
1 tree, 47 leaves, max depth = 17, train loss: 0.31524, val loss: 0.31374, in 0.016s
1 tree, 48 leaves, max depth = 18, train loss: 0.31238, val loss: 0.31098, in 0.016s
1 tree, 26 leaves, max depth = 14, train loss: 0.30932, val loss: 0.30804, in 0.016s
1 tree, 26 leaves, max depth = 14, train loss: 0.30645, val loss: 0.30528, in 0.016s
1 tree, 53 leaves, max depth = 16, train loss: 0.30380, val loss: 0.30273, in 0.016s
1 tree, 61 leaves, max depth = 16, train loss: 0.30064, val loss: 0.29986, in 0.016s
1 tree, 62 leaves, max depth = 16, train loss: 0.29765, val loss: 0.29717, in 0.031s
1 tree, 51 leaves, max depth = 18, train loss: 0.29524, val loss: 0.29485, in 0.016s
1 tree, 65 leaves, max depth = 15, train loss: 0.29246, val loss: 0.29236, in 0.016s
1 tree, 65 leaves, max depth = 16, train loss: 0.28984, val loss: 0.29002, in 0.031s
1 tree, 63 leaves, max depth = 15, train loss: 0.28737, val loss: 0.28781, in 0.016s
1 tree, 28 leaves, max depth = 11, train loss: 0.28521, val loss: 0.28573, in 0.016s
1 tree, 43 leaves, max depth = 16, train loss: 0.28313, val loss: 0.28374, in 0.016s
1 tree, 64 leaves, max depth = 16, train loss: 0.28090, val loss: 0.28173, in 0.016s
1 tree, 66 leaves, max depth = 15, train loss: 0.27879, val loss: 0.27985, in 0.031s
1 tree, 65 leaves, max depth = 15, train loss: 0.27680, val loss: 0.27809, in 0.016s
1 tree, 41 leaves, max depth = 17, train loss: 0.27492, val loss: 0.27632, in 0.031s
1 tree, 26 leaves, max depth = 11, train loss: 0.27316, val loss: 0.27462, in 0.016s
1 tree, 66 leaves, max depth = 16, train loss: 0.27135, val loss: 0.27300, in 0.016s
1 tree, 28 leaves, max depth = 11, train loss: 0.26974, val loss: 0.27144, in 0.016s
1 tree, 66 leaves, max depth = 16, train loss: 0.26808, val loss: 0.26998, in 0.031s
1 tree, 33 leaves, max depth = 16, train loss: 0.26660, val loss: 0.26856, in 0.016s
1 tree, 28 leaves, max depth = 8, train loss: 0.26522, val loss: 0.26731, in 0.016s
1 tree, 63 leaves, max depth = 16, train loss: 0.26371, val loss: 0.26599, in 0.031s
1 tree, 63 leaves, max depth = 16, train loss: 0.26228, val loss: 0.26475, in 0.016s
1 tree, 31 leaves, max depth = 16, train loss: 0.26099, val loss: 0.26351, in 0.016s
1 tree, 53 leaves, max depth = 17, train loss: 0.25934, val loss: 0.26197, in 0.031s
1 tree, 65 leaves, max depth = 16, train loss: 0.25804, val loss: 0.26082, in 0.016s
1 tree, 59 leaves, max depth = 17, train loss: 0.25650, val loss: 0.25942, in 0.031s
1 tree, 34 leaves, max depth = 12, train loss: 0.25535, val loss: 0.25834, in 0.016s
1 tree, 44 leaves, max depth = 17, train loss: 0.25398, val loss: 0.25702, in 0.016s
1 tree, 35 leaves, max depth = 13, train loss: 0.25288, val loss: 0.25598, in 0.016s
1 tree, 67 leaves, max depth = 14, train loss: 0.25172, val loss: 0.25498, in 0.031s
1 tree, 32 leaves, max depth = 11, train loss: 0.25073, val loss: 0.25408, in 0.016s
1 tree, 83 leaves, max depth = 15, train loss: 0.24972, val loss: 0.25337, in 0.016s
1 tree, 41 leaves, max depth = 15, train loss: 0.24873, val loss: 0.25240, in 0.016s
1 tree, 52 leaves, max depth = 17, train loss: 0.24740, val loss: 0.25118, in 0.031s
1 tree, 72 leaves, max depth = 17, train loss: 0.24637, val loss: 0.25044, in 0.016s
1 tree, 52 leaves, max depth = 17, train loss: 0.24512, val loss: 0.24931, in 0.031s
1 tree, 65 leaves, max depth = 17, train loss: 0.24415, val loss: 0.24851, in 0.016s
Fit 85 trees in 1.908 s, (3644 total leaves)
Time spent computing histograms: 0.606s
Time spent finding best splits:  0.121s
Time spent applying splits:      0.087s
Time spent predicting:           0.000s
Trial 44, Fold 2: Log loss = 0.24867228386667295, Average precision = 0.9662767909458208, ROC-AUC = 0.9632831058279951, Elapsed Time = 1.924355299999661 seconds
Trial 44, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 44, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
Binning 0.040 GB of training data: 0.173 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 25 leaves, max depth = 7, train loss: 0.67210, val loss: 0.67202, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.65208, val loss: 0.65216, in 0.031s
1 tree, 27 leaves, max depth = 7, train loss: 0.63387, val loss: 0.63392, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.61667, val loss: 0.61675, in 0.016s
1 tree, 36 leaves, max depth = 11, train loss: 0.60062, val loss: 0.60066, in 0.016s
1 tree, 32 leaves, max depth = 9, train loss: 0.58539, val loss: 0.58551, in 0.016s
1 tree, 36 leaves, max depth = 9, train loss: 0.57116, val loss: 0.57136, in 0.016s
1 tree, 44 leaves, max depth = 12, train loss: 0.55877, val loss: 0.55893, in 0.031s
1 tree, 37 leaves, max depth = 9, train loss: 0.54613, val loss: 0.54641, in 0.016s
1 tree, 33 leaves, max depth = 10, train loss: 0.53427, val loss: 0.53463, in 0.016s
1 tree, 29 leaves, max depth = 8, train loss: 0.52346, val loss: 0.52395, in 0.016s
1 tree, 34 leaves, max depth = 11, train loss: 0.51289, val loss: 0.51341, in 0.016s
1 tree, 36 leaves, max depth = 11, train loss: 0.50292, val loss: 0.50347, in 0.016s
1 tree, 36 leaves, max depth = 10, train loss: 0.49343, val loss: 0.49413, in 0.016s
1 tree, 38 leaves, max depth = 10, train loss: 0.48474, val loss: 0.48547, in 0.016s
1 tree, 36 leaves, max depth = 11, train loss: 0.47628, val loss: 0.47707, in 0.016s
1 tree, 44 leaves, max depth = 12, train loss: 0.46856, val loss: 0.46940, in 0.016s
1 tree, 39 leaves, max depth = 12, train loss: 0.46098, val loss: 0.46191, in 0.031s
1 tree, 37 leaves, max depth = 12, train loss: 0.45380, val loss: 0.45479, in 0.016s
1 tree, 40 leaves, max depth = 12, train loss: 0.44700, val loss: 0.44803, in 0.016s
1 tree, 39 leaves, max depth = 8, train loss: 0.44060, val loss: 0.44180, in 0.016s
1 tree, 41 leaves, max depth = 12, train loss: 0.43110, val loss: 0.43302, in 0.016s
1 tree, 40 leaves, max depth = 12, train loss: 0.42523, val loss: 0.42720, in 0.031s
1 tree, 44 leaves, max depth = 15, train loss: 0.41654, val loss: 0.41921, in 0.016s
1 tree, 36 leaves, max depth = 9, train loss: 0.40849, val loss: 0.41188, in 0.016s
1 tree, 42 leaves, max depth = 12, train loss: 0.40330, val loss: 0.40682, in 0.016s
1 tree, 48 leaves, max depth = 12, train loss: 0.39588, val loss: 0.40002, in 0.016s
1 tree, 34 leaves, max depth = 7, train loss: 0.39137, val loss: 0.39556, in 0.031s
1 tree, 45 leaves, max depth = 11, train loss: 0.38449, val loss: 0.38932, in 0.016s
1 tree, 48 leaves, max depth = 15, train loss: 0.37800, val loss: 0.38344, in 0.016s
1 tree, 45 leaves, max depth = 11, train loss: 0.37190, val loss: 0.37793, in 0.016s
1 tree, 45 leaves, max depth = 12, train loss: 0.36788, val loss: 0.37396, in 0.016s
1 tree, 50 leaves, max depth = 12, train loss: 0.36437, val loss: 0.37051, in 0.031s
1 tree, 40 leaves, max depth = 8, train loss: 0.36076, val loss: 0.36706, in 0.016s
1 tree, 30 leaves, max depth = 10, train loss: 0.35566, val loss: 0.36246, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.35087, val loss: 0.35814, in 0.016s
1 tree, 40 leaves, max depth = 14, train loss: 0.34698, val loss: 0.35415, in 0.016s
1 tree, 34 leaves, max depth = 10, train loss: 0.34262, val loss: 0.35022, in 0.031s
1 tree, 39 leaves, max depth = 14, train loss: 0.33900, val loss: 0.34650, in 0.016s
1 tree, 61 leaves, max depth = 12, train loss: 0.33473, val loss: 0.34288, in 0.016s
1 tree, 63 leaves, max depth = 12, train loss: 0.33071, val loss: 0.33949, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.32695, val loss: 0.33613, in 0.016s
1 tree, 40 leaves, max depth = 16, train loss: 0.32372, val loss: 0.33276, in 0.031s
1 tree, 62 leaves, max depth = 12, train loss: 0.32012, val loss: 0.32978, in 0.016s
1 tree, 49 leaves, max depth = 14, train loss: 0.31707, val loss: 0.32647, in 0.031s
1 tree, 32 leaves, max depth = 11, train loss: 0.31381, val loss: 0.32353, in 0.016s
1 tree, 50 leaves, max depth = 9, train loss: 0.31138, val loss: 0.32139, in 0.016s
1 tree, 32 leaves, max depth = 11, train loss: 0.30837, val loss: 0.31870, in 0.016s
1 tree, 42 leaves, max depth = 16, train loss: 0.30570, val loss: 0.31590, in 0.016s
1 tree, 60 leaves, max depth = 14, train loss: 0.30270, val loss: 0.31345, in 0.016s
1 tree, 33 leaves, max depth = 9, train loss: 0.30003, val loss: 0.31108, in 0.031s
1 tree, 63 leaves, max depth = 15, train loss: 0.29729, val loss: 0.30887, in 0.016s
1 tree, 47 leaves, max depth = 14, train loss: 0.29531, val loss: 0.30703, in 0.016s
1 tree, 61 leaves, max depth = 12, train loss: 0.29277, val loss: 0.30496, in 0.016s
1 tree, 51 leaves, max depth = 8, train loss: 0.29086, val loss: 0.30331, in 0.016s
1 tree, 66 leaves, max depth = 14, train loss: 0.28849, val loss: 0.30137, in 0.031s
1 tree, 35 leaves, max depth = 10, train loss: 0.28633, val loss: 0.29947, in 0.016s
1 tree, 66 leaves, max depth = 14, train loss: 0.28416, val loss: 0.29771, in 0.031s
1 tree, 33 leaves, max depth = 12, train loss: 0.28218, val loss: 0.29592, in 0.016s
1 tree, 61 leaves, max depth = 12, train loss: 0.28019, val loss: 0.29427, in 0.016s
1 tree, 39 leaves, max depth = 13, train loss: 0.27838, val loss: 0.29265, in 0.016s
1 tree, 46 leaves, max depth = 14, train loss: 0.27640, val loss: 0.29055, in 0.031s
1 tree, 61 leaves, max depth = 16, train loss: 0.27460, val loss: 0.28918, in 0.016s
1 tree, 36 leaves, max depth = 10, train loss: 0.27295, val loss: 0.28778, in 0.031s
1 tree, 62 leaves, max depth = 13, train loss: 0.27131, val loss: 0.28639, in 0.016s
1 tree, 31 leaves, max depth = 13, train loss: 0.26983, val loss: 0.28505, in 0.016s
1 tree, 44 leaves, max depth = 16, train loss: 0.26803, val loss: 0.28310, in 0.031s
1 tree, 65 leaves, max depth = 15, train loss: 0.26653, val loss: 0.28193, in 0.016s
1 tree, 36 leaves, max depth = 10, train loss: 0.26519, val loss: 0.28072, in 0.016s
1 tree, 42 leaves, max depth = 15, train loss: 0.26352, val loss: 0.27892, in 0.016s
1 tree, 36 leaves, max depth = 11, train loss: 0.26227, val loss: 0.27785, in 0.016s
1 tree, 45 leaves, max depth = 14, train loss: 0.26071, val loss: 0.27619, in 0.031s
1 tree, 69 leaves, max depth = 11, train loss: 0.25918, val loss: 0.27474, in 0.016s
1 tree, 63 leaves, max depth = 16, train loss: 0.25790, val loss: 0.27370, in 0.031s
1 tree, 44 leaves, max depth = 13, train loss: 0.25673, val loss: 0.27256, in 0.016s
1 tree, 55 leaves, max depth = 14, train loss: 0.25519, val loss: 0.27082, in 0.016s
1 tree, 62 leaves, max depth = 19, train loss: 0.25403, val loss: 0.26996, in 0.031s
1 tree, 43 leaves, max depth = 13, train loss: 0.25297, val loss: 0.26893, in 0.016s
1 tree, 56 leaves, max depth = 12, train loss: 0.25189, val loss: 0.26814, in 0.031s
1 tree, 64 leaves, max depth = 15, train loss: 0.25087, val loss: 0.26728, in 0.016s
1 tree, 39 leaves, max depth = 12, train loss: 0.24996, val loss: 0.26652, in 0.016s
1 tree, 49 leaves, max depth = 16, train loss: 0.24869, val loss: 0.26515, in 0.016s
1 tree, 83 leaves, max depth = 13, train loss: 0.24778, val loss: 0.26429, in 0.031s
1 tree, 46 leaves, max depth = 16, train loss: 0.24657, val loss: 0.26297, in 0.016s
1 tree, 83 leaves, max depth = 13, train loss: 0.24572, val loss: 0.26217, in 0.016s
Fit 85 trees in 2.029 s, (3817 total leaves)
Time spent computing histograms: 0.649s
Time spent finding best splits:  0.132s
Time spent applying splits:      0.095s
Time spent predicting:           0.016s
Trial 44, Fold 3: Log loss = 0.24909530956709083, Average precision = 0.9658513285518209, ROC-AUC = 0.9626928313798384, Elapsed Time = 2.0412577999995847 seconds
Trial 44, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 44, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
Binning 0.040 GB of training data: 0.173 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 23 leaves, max depth = 7, train loss: 0.67219, val loss: 0.67130, in 0.016s
1 tree, 23 leaves, max depth = 7, train loss: 0.65285, val loss: 0.65112, in 0.016s
1 tree, 30 leaves, max depth = 9, train loss: 0.63549, val loss: 0.63305, in 0.016s
1 tree, 26 leaves, max depth = 7, train loss: 0.61862, val loss: 0.61541, in 0.031s
1 tree, 35 leaves, max depth = 9, train loss: 0.60253, val loss: 0.59870, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.58742, val loss: 0.58303, in 0.016s
1 tree, 22 leaves, max depth = 8, train loss: 0.57448, val loss: 0.56937, in 0.016s
1 tree, 36 leaves, max depth = 9, train loss: 0.56112, val loss: 0.55550, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.54938, val loss: 0.54323, in 0.016s
1 tree, 26 leaves, max depth = 8, train loss: 0.53785, val loss: 0.53110, in 0.016s
1 tree, 39 leaves, max depth = 10, train loss: 0.52673, val loss: 0.51947, in 0.016s
1 tree, 37 leaves, max depth = 8, train loss: 0.51690, val loss: 0.50902, in 0.016s
1 tree, 39 leaves, max depth = 9, train loss: 0.50670, val loss: 0.49838, in 0.016s
1 tree, 42 leaves, max depth = 10, train loss: 0.49701, val loss: 0.48830, in 0.016s
1 tree, 43 leaves, max depth = 10, train loss: 0.48860, val loss: 0.47946, in 0.016s
1 tree, 42 leaves, max depth = 10, train loss: 0.47991, val loss: 0.47039, in 0.016s
1 tree, 42 leaves, max depth = 10, train loss: 0.46908, val loss: 0.45941, in 0.031s
1 tree, 45 leaves, max depth = 10, train loss: 0.46192, val loss: 0.45187, in 0.016s
1 tree, 42 leaves, max depth = 10, train loss: 0.45441, val loss: 0.44407, in 0.016s
1 tree, 42 leaves, max depth = 10, train loss: 0.44731, val loss: 0.43668, in 0.016s
1 tree, 44 leaves, max depth = 11, train loss: 0.44058, val loss: 0.42967, in 0.031s
1 tree, 38 leaves, max depth = 11, train loss: 0.43424, val loss: 0.42301, in 0.016s
1 tree, 41 leaves, max depth = 11, train loss: 0.42820, val loss: 0.41657, in 0.016s
1 tree, 46 leaves, max depth = 12, train loss: 0.42244, val loss: 0.41054, in 0.016s
1 tree, 41 leaves, max depth = 13, train loss: 0.41437, val loss: 0.40236, in 0.031s
1 tree, 58 leaves, max depth = 13, train loss: 0.40657, val loss: 0.39448, in 0.016s
1 tree, 44 leaves, max depth = 8, train loss: 0.40164, val loss: 0.38922, in 0.016s
1 tree, 42 leaves, max depth = 11, train loss: 0.39684, val loss: 0.38411, in 0.031s
1 tree, 51 leaves, max depth = 12, train loss: 0.38980, val loss: 0.37699, in 0.016s
1 tree, 56 leaves, max depth = 14, train loss: 0.38323, val loss: 0.37037, in 0.031s
1 tree, 47 leaves, max depth = 12, train loss: 0.37896, val loss: 0.36585, in 0.016s
1 tree, 43 leaves, max depth = 12, train loss: 0.37494, val loss: 0.36160, in 0.031s
1 tree, 50 leaves, max depth = 11, train loss: 0.37138, val loss: 0.35795, in 0.016s
1 tree, 49 leaves, max depth = 10, train loss: 0.36565, val loss: 0.35214, in 0.016s
1 tree, 23 leaves, max depth = 9, train loss: 0.36044, val loss: 0.34672, in 0.016s
1 tree, 25 leaves, max depth = 10, train loss: 0.35553, val loss: 0.34160, in 0.016s
1 tree, 40 leaves, max depth = 14, train loss: 0.35159, val loss: 0.33759, in 0.016s
1 tree, 56 leaves, max depth = 13, train loss: 0.34693, val loss: 0.33320, in 0.031s
1 tree, 31 leaves, max depth = 14, train loss: 0.34261, val loss: 0.32870, in 0.016s
1 tree, 42 leaves, max depth = 14, train loss: 0.33898, val loss: 0.32503, in 0.016s
1 tree, 35 leaves, max depth = 13, train loss: 0.33505, val loss: 0.32086, in 0.016s
1 tree, 54 leaves, max depth = 13, train loss: 0.33100, val loss: 0.31713, in 0.016s
1 tree, 51 leaves, max depth = 14, train loss: 0.32754, val loss: 0.31356, in 0.016s
1 tree, 40 leaves, max depth = 14, train loss: 0.32438, val loss: 0.31036, in 0.031s
1 tree, 49 leaves, max depth = 13, train loss: 0.32194, val loss: 0.30777, in 0.016s
1 tree, 56 leaves, max depth = 13, train loss: 0.31835, val loss: 0.30447, in 0.016s
1 tree, 24 leaves, max depth = 10, train loss: 0.31509, val loss: 0.30107, in 0.016s
1 tree, 55 leaves, max depth = 13, train loss: 0.31182, val loss: 0.29809, in 0.031s
1 tree, 20 leaves, max depth = 8, train loss: 0.30887, val loss: 0.29501, in 0.016s
1 tree, 42 leaves, max depth = 15, train loss: 0.30618, val loss: 0.29227, in 0.016s
1 tree, 58 leaves, max depth = 13, train loss: 0.30325, val loss: 0.28962, in 0.031s
1 tree, 35 leaves, max depth = 11, train loss: 0.30063, val loss: 0.28687, in 0.016s
1 tree, 33 leaves, max depth = 12, train loss: 0.29815, val loss: 0.28428, in 0.016s
1 tree, 51 leaves, max depth = 15, train loss: 0.29555, val loss: 0.28158, in 0.016s
1 tree, 57 leaves, max depth = 12, train loss: 0.29301, val loss: 0.27931, in 0.031s
1 tree, 59 leaves, max depth = 12, train loss: 0.29061, val loss: 0.27716, in 0.016s
1 tree, 69 leaves, max depth = 9, train loss: 0.28877, val loss: 0.27526, in 0.016s
1 tree, 61 leaves, max depth = 13, train loss: 0.28654, val loss: 0.27323, in 0.031s
1 tree, 33 leaves, max depth = 11, train loss: 0.28451, val loss: 0.27106, in 0.016s
1 tree, 60 leaves, max depth = 12, train loss: 0.28245, val loss: 0.26922, in 0.031s
1 tree, 35 leaves, max depth = 15, train loss: 0.28060, val loss: 0.26726, in 0.016s
1 tree, 56 leaves, max depth = 12, train loss: 0.27870, val loss: 0.26559, in 0.016s
1 tree, 39 leaves, max depth = 15, train loss: 0.27669, val loss: 0.26355, in 0.016s
1 tree, 33 leaves, max depth = 10, train loss: 0.27503, val loss: 0.26175, in 0.016s
1 tree, 48 leaves, max depth = 14, train loss: 0.27298, val loss: 0.25961, in 0.031s
1 tree, 41 leaves, max depth = 13, train loss: 0.27118, val loss: 0.25775, in 0.016s
1 tree, 52 leaves, max depth = 13, train loss: 0.26931, val loss: 0.25578, in 0.016s
1 tree, 70 leaves, max depth = 15, train loss: 0.26769, val loss: 0.25434, in 0.031s
1 tree, 71 leaves, max depth = 15, train loss: 0.26617, val loss: 0.25301, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.26478, val loss: 0.25151, in 0.016s
1 tree, 69 leaves, max depth = 15, train loss: 0.26337, val loss: 0.25026, in 0.016s
1 tree, 36 leaves, max depth = 11, train loss: 0.26209, val loss: 0.24891, in 0.016s
1 tree, 52 leaves, max depth = 15, train loss: 0.26042, val loss: 0.24717, in 0.016s
1 tree, 42 leaves, max depth = 11, train loss: 0.25915, val loss: 0.24592, in 0.031s
1 tree, 64 leaves, max depth = 15, train loss: 0.25791, val loss: 0.24481, in 0.016s
1 tree, 42 leaves, max depth = 11, train loss: 0.25673, val loss: 0.24366, in 0.031s
1 tree, 56 leaves, max depth = 14, train loss: 0.25523, val loss: 0.24212, in 0.016s
1 tree, 71 leaves, max depth = 15, train loss: 0.25408, val loss: 0.24112, in 0.016s
1 tree, 45 leaves, max depth = 13, train loss: 0.25305, val loss: 0.24014, in 0.031s
1 tree, 38 leaves, max depth = 11, train loss: 0.25207, val loss: 0.23907, in 0.016s
1 tree, 68 leaves, max depth = 14, train loss: 0.25105, val loss: 0.23819, in 0.016s
1 tree, 91 leaves, max depth = 13, train loss: 0.25006, val loss: 0.23747, in 0.031s
1 tree, 52 leaves, max depth = 13, train loss: 0.24869, val loss: 0.23606, in 0.016s
1 tree, 69 leaves, max depth = 13, train loss: 0.24773, val loss: 0.23520, in 0.016s
1 tree, 92 leaves, max depth = 13, train loss: 0.24680, val loss: 0.23454, in 0.031s
Fit 85 trees in 2.049 s, (3880 total leaves)
Time spent computing histograms: 0.643s
Time spent finding best splits:  0.138s
Time spent applying splits:      0.099s
Time spent predicting:           0.000s
Trial 44, Fold 4: Log loss = 0.24959408851622875, Average precision = 0.9669935233028454, ROC-AUC = 0.9628425502693191, Elapsed Time = 2.0559398000004876 seconds
Trial 44, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 44, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
Binning 0.040 GB of training data: 0.173 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 24 leaves, max depth = 7, train loss: 0.67203, val loss: 0.67121, in 0.016s
1 tree, 34 leaves, max depth = 11, train loss: 0.65215, val loss: 0.65058, in 0.016s
1 tree, 34 leaves, max depth = 12, train loss: 0.63366, val loss: 0.63152, in 0.000s
1 tree, 30 leaves, max depth = 7, train loss: 0.61643, val loss: 0.61361, in 0.031s
1 tree, 35 leaves, max depth = 10, train loss: 0.59999, val loss: 0.59658, in 0.016s
1 tree, 32 leaves, max depth = 10, train loss: 0.58547, val loss: 0.58147, in 0.016s
1 tree, 34 leaves, max depth = 10, train loss: 0.57106, val loss: 0.56656, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.55842, val loss: 0.55332, in 0.016s
1 tree, 36 leaves, max depth = 10, train loss: 0.54642, val loss: 0.54085, in 0.016s
1 tree, 31 leaves, max depth = 7, train loss: 0.53459, val loss: 0.52842, in 0.031s
1 tree, 39 leaves, max depth = 12, train loss: 0.52314, val loss: 0.51659, in 0.016s
1 tree, 32 leaves, max depth = 9, train loss: 0.51321, val loss: 0.50617, in 0.016s
1 tree, 37 leaves, max depth = 12, train loss: 0.50364, val loss: 0.49622, in 0.016s
1 tree, 44 leaves, max depth = 12, train loss: 0.49477, val loss: 0.48718, in 0.016s
1 tree, 38 leaves, max depth = 13, train loss: 0.48636, val loss: 0.47846, in 0.031s
1 tree, 34 leaves, max depth = 7, train loss: 0.47785, val loss: 0.46962, in 0.016s
1 tree, 37 leaves, max depth = 9, train loss: 0.46982, val loss: 0.46128, in 0.016s
1 tree, 36 leaves, max depth = 9, train loss: 0.46216, val loss: 0.45333, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.45491, val loss: 0.44580, in 0.016s
1 tree, 39 leaves, max depth = 12, train loss: 0.44781, val loss: 0.43852, in 0.031s
1 tree, 39 leaves, max depth = 12, train loss: 0.44113, val loss: 0.43165, in 0.016s
1 tree, 37 leaves, max depth = 12, train loss: 0.43474, val loss: 0.42512, in 0.016s
1 tree, 36 leaves, max depth = 8, train loss: 0.42896, val loss: 0.41897, in 0.016s
1 tree, 40 leaves, max depth = 9, train loss: 0.42021, val loss: 0.41023, in 0.031s
1 tree, 38 leaves, max depth = 12, train loss: 0.41177, val loss: 0.40187, in 0.016s
1 tree, 48 leaves, max depth = 9, train loss: 0.40709, val loss: 0.39711, in 0.016s
1 tree, 38 leaves, max depth = 12, train loss: 0.40202, val loss: 0.39197, in 0.016s
1 tree, 40 leaves, max depth = 12, train loss: 0.39447, val loss: 0.38453, in 0.031s
1 tree, 38 leaves, max depth = 12, train loss: 0.38977, val loss: 0.37975, in 0.016s
1 tree, 40 leaves, max depth = 12, train loss: 0.38572, val loss: 0.37561, in 0.016s
1 tree, 48 leaves, max depth = 12, train loss: 0.37917, val loss: 0.36918, in 0.016s
1 tree, 40 leaves, max depth = 12, train loss: 0.37501, val loss: 0.36499, in 0.031s
1 tree, 26 leaves, max depth = 11, train loss: 0.36909, val loss: 0.35903, in 0.016s
1 tree, 19 leaves, max depth = 7, train loss: 0.36353, val loss: 0.35338, in 0.016s
1 tree, 42 leaves, max depth = 15, train loss: 0.35927, val loss: 0.34917, in 0.016s
1 tree, 43 leaves, max depth = 15, train loss: 0.35521, val loss: 0.34516, in 0.016s
1 tree, 63 leaves, max depth = 15, train loss: 0.34999, val loss: 0.34050, in 0.031s
1 tree, 22 leaves, max depth = 7, train loss: 0.34520, val loss: 0.33565, in 0.016s
1 tree, 62 leaves, max depth = 15, train loss: 0.34052, val loss: 0.33149, in 0.016s
1 tree, 27 leaves, max depth = 11, train loss: 0.33621, val loss: 0.32712, in 0.016s
1 tree, 43 leaves, max depth = 15, train loss: 0.33270, val loss: 0.32365, in 0.016s
1 tree, 27 leaves, max depth = 11, train loss: 0.32873, val loss: 0.31963, in 0.016s
1 tree, 43 leaves, max depth = 15, train loss: 0.32540, val loss: 0.31639, in 0.016s
1 tree, 41 leaves, max depth = 15, train loss: 0.32226, val loss: 0.31335, in 0.016s
1 tree, 62 leaves, max depth = 15, train loss: 0.31836, val loss: 0.30997, in 0.016s
1 tree, 63 leaves, max depth = 15, train loss: 0.31469, val loss: 0.30672, in 0.031s
1 tree, 27 leaves, max depth = 12, train loss: 0.31137, val loss: 0.30337, in 0.016s
1 tree, 64 leaves, max depth = 15, train loss: 0.30801, val loss: 0.30049, in 0.016s
1 tree, 50 leaves, max depth = 12, train loss: 0.30568, val loss: 0.29821, in 0.031s
1 tree, 63 leaves, max depth = 15, train loss: 0.30256, val loss: 0.29548, in 0.016s
1 tree, 35 leaves, max depth = 12, train loss: 0.29974, val loss: 0.29256, in 0.016s
1 tree, 65 leaves, max depth = 14, train loss: 0.29689, val loss: 0.29009, in 0.016s
1 tree, 47 leaves, max depth = 14, train loss: 0.29420, val loss: 0.28738, in 0.031s
1 tree, 35 leaves, max depth = 12, train loss: 0.29169, val loss: 0.28476, in 0.016s
1 tree, 34 leaves, max depth = 15, train loss: 0.28932, val loss: 0.28230, in 0.016s
1 tree, 44 leaves, max depth = 11, train loss: 0.28749, val loss: 0.28057, in 0.031s
1 tree, 63 leaves, max depth = 12, train loss: 0.28505, val loss: 0.27849, in 0.016s
1 tree, 61 leaves, max depth = 12, train loss: 0.28274, val loss: 0.27658, in 0.016s
1 tree, 28 leaves, max depth = 10, train loss: 0.28068, val loss: 0.27443, in 0.016s
1 tree, 53 leaves, max depth = 13, train loss: 0.27903, val loss: 0.27288, in 0.031s
1 tree, 52 leaves, max depth = 14, train loss: 0.27681, val loss: 0.27064, in 0.016s
1 tree, 65 leaves, max depth = 15, train loss: 0.27476, val loss: 0.26892, in 0.016s
1 tree, 29 leaves, max depth = 10, train loss: 0.27295, val loss: 0.26704, in 0.016s
1 tree, 67 leaves, max depth = 15, train loss: 0.27107, val loss: 0.26554, in 0.016s
1 tree, 53 leaves, max depth = 16, train loss: 0.26907, val loss: 0.26357, in 0.031s
1 tree, 37 leaves, max depth = 12, train loss: 0.26748, val loss: 0.26190, in 0.016s
1 tree, 67 leaves, max depth = 15, train loss: 0.26578, val loss: 0.26050, in 0.016s
1 tree, 29 leaves, max depth = 11, train loss: 0.26429, val loss: 0.25895, in 0.016s
1 tree, 66 leaves, max depth = 15, train loss: 0.26272, val loss: 0.25767, in 0.031s
1 tree, 44 leaves, max depth = 13, train loss: 0.26130, val loss: 0.25624, in 0.016s
1 tree, 45 leaves, max depth = 14, train loss: 0.25961, val loss: 0.25467, in 0.016s
1 tree, 68 leaves, max depth = 16, train loss: 0.25816, val loss: 0.25351, in 0.031s
1 tree, 46 leaves, max depth = 13, train loss: 0.25684, val loss: 0.25220, in 0.016s
1 tree, 45 leaves, max depth = 13, train loss: 0.25529, val loss: 0.25075, in 0.016s
1 tree, 40 leaves, max depth = 11, train loss: 0.25344, val loss: 0.24885, in 0.016s
1 tree, 63 leaves, max depth = 18, train loss: 0.25214, val loss: 0.24778, in 0.016s
1 tree, 30 leaves, max depth = 15, train loss: 0.25100, val loss: 0.24657, in 0.031s
1 tree, 55 leaves, max depth = 14, train loss: 0.24945, val loss: 0.24504, in 0.016s
1 tree, 68 leaves, max depth = 16, train loss: 0.24828, val loss: 0.24413, in 0.031s
1 tree, 33 leaves, max depth = 17, train loss: 0.24725, val loss: 0.24305, in 0.016s
1 tree, 42 leaves, max depth = 13, train loss: 0.24624, val loss: 0.24204, in 0.016s
1 tree, 67 leaves, max depth = 16, train loss: 0.24519, val loss: 0.24119, in 0.016s
1 tree, 84 leaves, max depth = 15, train loss: 0.24418, val loss: 0.24057, in 0.031s
1 tree, 57 leaves, max depth = 14, train loss: 0.24280, val loss: 0.23921, in 0.016s
1 tree, 49 leaves, max depth = 14, train loss: 0.24161, val loss: 0.23814, in 0.031s
Fit 85 trees in 2.018 s, (3727 total leaves)
Time spent computing histograms: 0.640s
Time spent finding best splits:  0.129s
Time spent applying splits:      0.093s
Time spent predicting:           0.031s
Trial 44, Fold 5: Log loss = 0.25348226199119334, Average precision = 0.964379013424272, ROC-AUC = 0.9602610779778162, Elapsed Time = 2.019451999998637 seconds
Optimization Progress:  45%|####5     | 45/100 [08:50<11:33, 12.62s/it]
Trial 45, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 45, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
0.158 s 0.040 GB of training data: 
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 47 leaves, max depth = 11, train loss: 0.65211, val loss: 0.65237, in 0.016s
1 tree, 49 leaves, max depth = 12, train loss: 0.61671, val loss: 0.61729, in 0.016s
1 tree, 47 leaves, max depth = 11, train loss: 0.58596, val loss: 0.58668, in 0.016s
1 tree, 48 leaves, max depth = 12, train loss: 0.55998, val loss: 0.56050, in 0.016s
1 tree, 49 leaves, max depth = 10, train loss: 0.53547, val loss: 0.53585, in 0.000s
1 tree, 52 leaves, max depth = 12, train loss: 0.51513, val loss: 0.51539, in 0.000s
1 tree, 51 leaves, max depth = 10, train loss: 0.49554, val loss: 0.49563, in 0.016s
1 tree, 51 leaves, max depth = 10, train loss: 0.47806, val loss: 0.47795, in 0.016s
1 tree, 50 leaves, max depth = 10, train loss: 0.46248, val loss: 0.46226, in 0.016s
1 tree, 50 leaves, max depth = 10, train loss: 0.44852, val loss: 0.44814, in 0.016s
1 tree, 52 leaves, max depth = 10, train loss: 0.43593, val loss: 0.43551, in 0.016s
1 tree, 49 leaves, max depth = 10, train loss: 0.41817, val loss: 0.41837, in 0.016s
1 tree, 57 leaves, max depth = 9, train loss: 0.40785, val loss: 0.40788, in 0.016s
1 tree, 50 leaves, max depth = 10, train loss: 0.39293, val loss: 0.39359, in 0.016s
1 tree, 50 leaves, max depth = 11, train loss: 0.38415, val loss: 0.38484, in 0.016s
1 tree, 50 leaves, max depth = 11, train loss: 0.37156, val loss: 0.37281, in 0.016s
1 tree, 51 leaves, max depth = 11, train loss: 0.36457, val loss: 0.36579, in 0.016s
1 tree, 26 leaves, max depth = 9, train loss: 0.35444, val loss: 0.35578, in 0.016s
1 tree, 51 leaves, max depth = 12, train loss: 0.34694, val loss: 0.34834, in 0.016s
1 tree, 27 leaves, max depth = 9, train loss: 0.33832, val loss: 0.33980, in 0.000s
1 tree, 52 leaves, max depth = 12, train loss: 0.33177, val loss: 0.33345, in 0.016s
1 tree, 43 leaves, max depth = 12, train loss: 0.32408, val loss: 0.32670, in 0.016s
1 tree, 60 leaves, max depth = 12, train loss: 0.31820, val loss: 0.32078, in 0.016s
1 tree, 26 leaves, max depth = 9, train loss: 0.31169, val loss: 0.31436, in 0.016s
1 tree, 44 leaves, max depth = 12, train loss: 0.30558, val loss: 0.30909, in 0.016s
1 tree, 27 leaves, max depth = 11, train loss: 0.30023, val loss: 0.30389, in 0.000s
1 tree, 43 leaves, max depth = 12, train loss: 0.29511, val loss: 0.29956, in 0.016s
1 tree, 27 leaves, max depth = 11, train loss: 0.29067, val loss: 0.29520, in 0.016s
1 tree, 53 leaves, max depth = 12, train loss: 0.28628, val loss: 0.29099, in 0.016s
1 tree, 51 leaves, max depth = 12, train loss: 0.28233, val loss: 0.28732, in 0.016s
Fit 30 trees in 0.721 s, (1383 total leaves)
Time spent computing histograms: 0.161s
Time spent finding best splits:  0.028s
Time spent applying splits:      0.023s
Time spent predicting:           0.000s
Trial 45, Fold 1: Log loss = 0.287727311305024, Average precision = 0.9614434610463919, ROC-AUC = 0.9557613168724279, Elapsed Time = 0.7307570999983 seconds
Trial 45, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 45, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
Binning 0.040 GB of training data: 0.143 s
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 50 leaves, max depth = 10, train loss: 0.65176, val loss: 0.65098, in 0.016s
1 tree, 51 leaves, max depth = 10, train loss: 0.61607, val loss: 0.61459, in 0.016s
1 tree, 51 leaves, max depth = 9, train loss: 0.58506, val loss: 0.58286, in 0.016s
1 tree, 55 leaves, max depth = 14, train loss: 0.55912, val loss: 0.55660, in 0.016s
1 tree, 49 leaves, max depth = 10, train loss: 0.53470, val loss: 0.53154, in 0.016s
1 tree, 54 leaves, max depth = 12, train loss: 0.51431, val loss: 0.51097, in 0.016s
1 tree, 50 leaves, max depth = 10, train loss: 0.49489, val loss: 0.49101, in 0.016s
1 tree, 49 leaves, max depth = 9, train loss: 0.47748, val loss: 0.47333, in 0.016s
1 tree, 48 leaves, max depth = 11, train loss: 0.46223, val loss: 0.45771, in 0.016s
1 tree, 50 leaves, max depth = 11, train loss: 0.44269, val loss: 0.43837, in 0.031s
1 tree, 58 leaves, max depth = 9, train loss: 0.43023, val loss: 0.42573, in 0.016s
1 tree, 48 leaves, max depth = 13, train loss: 0.41857, val loss: 0.41395, in 0.016s
1 tree, 50 leaves, max depth = 11, train loss: 0.40804, val loss: 0.40331, in 0.016s
1 tree, 50 leaves, max depth = 11, train loss: 0.39323, val loss: 0.38871, in 0.016s
1 tree, 51 leaves, max depth = 11, train loss: 0.38023, val loss: 0.37595, in 0.016s
1 tree, 49 leaves, max depth = 10, train loss: 0.37220, val loss: 0.36777, in 0.016s
1 tree, 54 leaves, max depth = 12, train loss: 0.36535, val loss: 0.36099, in 0.016s
1 tree, 28 leaves, max depth = 10, train loss: 0.35530, val loss: 0.35114, in 0.016s
1 tree, 27 leaves, max depth = 10, train loss: 0.34647, val loss: 0.34254, in 0.016s
1 tree, 51 leaves, max depth = 13, train loss: 0.33927, val loss: 0.33561, in 0.016s
1 tree, 43 leaves, max depth = 12, train loss: 0.33150, val loss: 0.32845, in 0.016s
1 tree, 27 leaves, max depth = 10, train loss: 0.32454, val loss: 0.32161, in 0.016s
1 tree, 50 leaves, max depth = 13, train loss: 0.31854, val loss: 0.31586, in 0.016s
1 tree, 45 leaves, max depth = 11, train loss: 0.31238, val loss: 0.31023, in 0.016s
1 tree, 27 leaves, max depth = 11, train loss: 0.30687, val loss: 0.30480, in 0.016s
1 tree, 60 leaves, max depth = 12, train loss: 0.30172, val loss: 0.29996, in 0.016s
1 tree, 44 leaves, max depth = 11, train loss: 0.29679, val loss: 0.29547, in 0.016s
1 tree, 26 leaves, max depth = 9, train loss: 0.29240, val loss: 0.29119, in 0.016s
1 tree, 53 leaves, max depth = 13, train loss: 0.28808, val loss: 0.28710, in 0.016s
1 tree, 42 leaves, max depth = 11, train loss: 0.28412, val loss: 0.28351, in 0.000s
Fit 30 trees in 0.799 s, (1390 total leaves)
Time spent computing histograms: 0.195s
Time spent finding best splits:  0.032s
Time spent applying splits:      0.027s
Time spent predicting:           0.000s
Trial 45, Fold 2: Log loss = 0.28666063128091607, Average precision = 0.9612289400368368, ROC-AUC = 0.9578255725975062, Elapsed Time = 0.8044456999996328 seconds
Trial 45, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 45, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
Binning 0.040 GB of training data: 0.159 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 50 leaves, max depth = 10, train loss: 0.65237, val loss: 0.65226, in 0.016s
1 tree, 50 leaves, max depth = 10, train loss: 0.61727, val loss: 0.61706, in 0.016s
1 tree, 48 leaves, max depth = 9, train loss: 0.58671, val loss: 0.58640, in 0.016s
1 tree, 52 leaves, max depth = 11, train loss: 0.56054, val loss: 0.56038, in 0.016s
1 tree, 49 leaves, max depth = 11, train loss: 0.53621, val loss: 0.53613, in 0.031s
1 tree, 50 leaves, max depth = 11, train loss: 0.51577, val loss: 0.51584, in 0.016s
1 tree, 49 leaves, max depth = 10, train loss: 0.49645, val loss: 0.49658, in 0.016s
1 tree, 48 leaves, max depth = 10, train loss: 0.47921, val loss: 0.47937, in 0.016s
1 tree, 49 leaves, max depth = 11, train loss: 0.46380, val loss: 0.46392, in 0.016s
1 tree, 49 leaves, max depth = 10, train loss: 0.44995, val loss: 0.45018, in 0.016s
1 tree, 49 leaves, max depth = 11, train loss: 0.43753, val loss: 0.43779, in 0.031s
1 tree, 48 leaves, max depth = 11, train loss: 0.41978, val loss: 0.42138, in 0.000s
1 tree, 60 leaves, max depth = 11, train loss: 0.40953, val loss: 0.41135, in 0.031s
1 tree, 48 leaves, max depth = 11, train loss: 0.39462, val loss: 0.39768, in 0.016s
1 tree, 52 leaves, max depth = 11, train loss: 0.38592, val loss: 0.38897, in 0.016s
1 tree, 48 leaves, max depth = 11, train loss: 0.37333, val loss: 0.37751, in 0.016s
1 tree, 50 leaves, max depth = 12, train loss: 0.36638, val loss: 0.37069, in 0.016s
1 tree, 26 leaves, max depth = 10, train loss: 0.35618, val loss: 0.36137, in 0.016s
1 tree, 26 leaves, max depth = 10, train loss: 0.34717, val loss: 0.35318, in 0.000s
1 tree, 50 leaves, max depth = 11, train loss: 0.34005, val loss: 0.34571, in 0.016s
1 tree, 42 leaves, max depth = 12, train loss: 0.33202, val loss: 0.33894, in 0.016s
1 tree, 26 leaves, max depth = 10, train loss: 0.32498, val loss: 0.33248, in 0.016s
1 tree, 49 leaves, max depth = 10, train loss: 0.31903, val loss: 0.32624, in 0.016s
1 tree, 42 leaves, max depth = 12, train loss: 0.31263, val loss: 0.32101, in 0.016s
1 tree, 42 leaves, max depth = 12, train loss: 0.30694, val loss: 0.31633, in 0.016s
1 tree, 26 leaves, max depth = 11, train loss: 0.30180, val loss: 0.31157, in 0.000s
1 tree, 49 leaves, max depth = 12, train loss: 0.29699, val loss: 0.30647, in 0.016s
1 tree, 42 leaves, max depth = 14, train loss: 0.29239, val loss: 0.30282, in 0.016s
1 tree, 26 leaves, max depth = 11, train loss: 0.28825, val loss: 0.29915, in 0.016s
1 tree, 50 leaves, max depth = 13, train loss: 0.28417, val loss: 0.29480, in 0.016s
Fit 30 trees in 0.815 s, (1345 total leaves)
Time spent computing histograms: 0.189s
Time spent finding best splits:  0.032s
Time spent applying splits:      0.027s
Time spent predicting:           0.000s
Trial 45, Fold 3: Log loss = 0.2838568494431637, Average precision = 0.9616779725832294, ROC-AUC = 0.957759032706995, Elapsed Time = 0.8189179000000877 seconds
Trial 45, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 45, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
Binning 0.040 GB of training data: 0.158 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 48 leaves, max depth = 10, train loss: 0.65248, val loss: 0.65076, in 0.016s
1 tree, 47 leaves, max depth = 9, train loss: 0.61743, val loss: 0.61438, in 0.031s
1 tree, 47 leaves, max depth = 10, train loss: 0.58690, val loss: 0.58236, in 0.016s
1 tree, 52 leaves, max depth = 11, train loss: 0.56104, val loss: 0.55526, in 0.016s
1 tree, 52 leaves, max depth = 11, train loss: 0.53656, val loss: 0.52966, in 0.016s
1 tree, 53 leaves, max depth = 11, train loss: 0.51633, val loss: 0.50842, in 0.016s
1 tree, 50 leaves, max depth = 11, train loss: 0.49689, val loss: 0.48814, in 0.016s
1 tree, 53 leaves, max depth = 12, train loss: 0.47959, val loss: 0.46988, in 0.016s
1 tree, 50 leaves, max depth = 10, train loss: 0.46411, val loss: 0.45344, in 0.016s
1 tree, 50 leaves, max depth = 11, train loss: 0.45025, val loss: 0.43888, in 0.016s
1 tree, 49 leaves, max depth = 10, train loss: 0.43772, val loss: 0.42558, in 0.016s
1 tree, 49 leaves, max depth = 13, train loss: 0.42022, val loss: 0.40783, in 0.031s
1 tree, 60 leaves, max depth = 11, train loss: 0.40997, val loss: 0.39678, in 0.016s
1 tree, 49 leaves, max depth = 13, train loss: 0.39528, val loss: 0.38190, in 0.016s
1 tree, 48 leaves, max depth = 10, train loss: 0.38658, val loss: 0.37264, in 0.016s
1 tree, 47 leaves, max depth = 13, train loss: 0.37417, val loss: 0.36009, in 0.016s
1 tree, 50 leaves, max depth = 12, train loss: 0.36726, val loss: 0.35272, in 0.016s
1 tree, 25 leaves, max depth = 12, train loss: 0.35719, val loss: 0.34231, in 0.000s
1 tree, 26 leaves, max depth = 12, train loss: 0.34831, val loss: 0.33310, in 0.016s
1 tree, 53 leaves, max depth = 10, train loss: 0.34113, val loss: 0.32580, in 0.016s
1 tree, 50 leaves, max depth = 12, train loss: 0.33463, val loss: 0.31921, in 0.016s
1 tree, 43 leaves, max depth = 11, train loss: 0.32713, val loss: 0.31218, in 0.016s
1 tree, 44 leaves, max depth = 11, train loss: 0.32046, val loss: 0.30596, in 0.016s
1 tree, 26 leaves, max depth = 10, train loss: 0.31425, val loss: 0.29951, in 0.016s
1 tree, 27 leaves, max depth = 10, train loss: 0.30873, val loss: 0.29376, in 0.016s
1 tree, 51 leaves, max depth = 11, train loss: 0.30360, val loss: 0.28860, in 0.016s
1 tree, 59 leaves, max depth = 12, train loss: 0.29877, val loss: 0.28358, in 0.016s
1 tree, 42 leaves, max depth = 10, train loss: 0.29401, val loss: 0.27922, in 0.016s
1 tree, 44 leaves, max depth = 10, train loss: 0.28977, val loss: 0.27537, in 0.016s
1 tree, 27 leaves, max depth = 10, train loss: 0.28579, val loss: 0.27122, in 0.016s
Fit 30 trees in 0.830 s, (1371 total leaves)
Time spent computing histograms: 0.194s
Time spent finding best splits:  0.033s
Time spent applying splits:      0.027s
Time spent predicting:           0.000s
Trial 45, Fold 4: Log loss = 0.2852568290057377, Average precision = 0.9622366021799464, ROC-AUC = 0.9579074926500568, Elapsed Time = 0.8325492999993003 seconds
Trial 45, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 45, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
Binning 0.040 GB of training data: 0.158 s
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 48 leaves, max depth = 11, train loss: 0.65224, val loss: 0.65067, in 0.016s
1 tree, 47 leaves, max depth = 11, train loss: 0.61645, val loss: 0.61336, in 0.016s
1 tree, 48 leaves, max depth = 10, train loss: 0.58518, val loss: 0.58074, in 0.016s
1 tree, 51 leaves, max depth = 11, train loss: 0.55912, val loss: 0.55340, in 0.016s
1 tree, 48 leaves, max depth = 10, train loss: 0.53470, val loss: 0.52790, in 0.016s
1 tree, 50 leaves, max depth = 11, train loss: 0.51432, val loss: 0.50646, in 0.016s
1 tree, 50 leaves, max depth = 10, train loss: 0.49490, val loss: 0.48611, in 0.016s
1 tree, 51 leaves, max depth = 11, train loss: 0.47758, val loss: 0.46801, in 0.016s
1 tree, 47 leaves, max depth = 11, train loss: 0.46203, val loss: 0.45182, in 0.016s
1 tree, 48 leaves, max depth = 11, train loss: 0.44234, val loss: 0.43204, in 0.016s
1 tree, 57 leaves, max depth = 9, train loss: 0.42975, val loss: 0.41885, in 0.016s
1 tree, 48 leaves, max depth = 10, train loss: 0.41818, val loss: 0.40677, in 0.016s
1 tree, 49 leaves, max depth = 10, train loss: 0.40776, val loss: 0.39574, in 0.031s
1 tree, 51 leaves, max depth = 11, train loss: 0.39296, val loss: 0.38104, in 0.016s
1 tree, 53 leaves, max depth = 11, train loss: 0.37986, val loss: 0.36802, in 0.016s
1 tree, 48 leaves, max depth = 11, train loss: 0.37172, val loss: 0.35953, in 0.016s
1 tree, 53 leaves, max depth = 11, train loss: 0.36482, val loss: 0.35243, in 0.016s
1 tree, 28 leaves, max depth = 10, train loss: 0.35463, val loss: 0.34207, in 0.016s
1 tree, 28 leaves, max depth = 9, train loss: 0.34563, val loss: 0.33289, in 0.016s
1 tree, 49 leaves, max depth = 11, train loss: 0.33841, val loss: 0.32569, in 0.016s
1 tree, 41 leaves, max depth = 13, train loss: 0.33066, val loss: 0.31870, in 0.016s
1 tree, 28 leaves, max depth = 10, train loss: 0.32361, val loss: 0.31147, in 0.016s
1 tree, 50 leaves, max depth = 11, train loss: 0.31758, val loss: 0.30555, in 0.016s
1 tree, 41 leaves, max depth = 13, train loss: 0.31142, val loss: 0.30024, in 0.016s
1 tree, 27 leaves, max depth = 9, train loss: 0.30584, val loss: 0.29453, in 0.000s
1 tree, 50 leaves, max depth = 11, train loss: 0.30076, val loss: 0.28957, in 0.016s
1 tree, 41 leaves, max depth = 13, train loss: 0.29582, val loss: 0.28537, in 0.016s
1 tree, 25 leaves, max depth = 10, train loss: 0.29139, val loss: 0.28069, in 0.016s
1 tree, 52 leaves, max depth = 12, train loss: 0.28699, val loss: 0.27636, in 0.016s
1 tree, 41 leaves, max depth = 13, train loss: 0.28302, val loss: 0.27291, in 0.016s
Fit 30 trees in 0.830 s, (1348 total leaves)
Time spent computing histograms: 0.189s
Time spent finding best splits:  0.032s
Time spent applying splits:      0.027s
Time spent predicting:           0.000s
Trial 45, Fold 5: Log loss = 0.2918554722452596, Average precision = 0.9588748949249177, ROC-AUC = 0.9535981271946937, Elapsed Time = 0.8407050000005256 seconds
Optimization Progress:  46%|####6     | 46/100 [09:01<10:47, 11.99s/it]
Trial 46, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 46, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
0.190 s 0.040 GB of training data: 
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 41 leaves, max depth = 11, train loss: 0.68146, val loss: 0.68153, in 0.016s
1 tree, 41 leaves, max depth = 11, train loss: 0.67023, val loss: 0.67037, in 0.016s
1 tree, 41 leaves, max depth = 11, train loss: 0.65945, val loss: 0.65964, in 0.016s
1 tree, 41 leaves, max depth = 11, train loss: 0.64907, val loss: 0.64933, in 0.031s
1 tree, 41 leaves, max depth = 11, train loss: 0.63909, val loss: 0.63941, in 0.016s
1 tree, 41 leaves, max depth = 11, train loss: 0.62948, val loss: 0.62986, in 0.031s
1 tree, 41 leaves, max depth = 11, train loss: 0.62022, val loss: 0.62065, in 0.031s
1 tree, 41 leaves, max depth = 11, train loss: 0.61129, val loss: 0.61177, in 0.016s
1 tree, 41 leaves, max depth = 11, train loss: 0.60268, val loss: 0.60319, in 0.016s
1 tree, 41 leaves, max depth = 11, train loss: 0.59438, val loss: 0.59493, in 0.016s
1 tree, 41 leaves, max depth = 11, train loss: 0.58636, val loss: 0.58695, in 0.016s
1 tree, 41 leaves, max depth = 11, train loss: 0.57862, val loss: 0.57925, in 0.031s
1 tree, 41 leaves, max depth = 11, train loss: 0.57114, val loss: 0.57180, in 0.016s
1 tree, 41 leaves, max depth = 11, train loss: 0.56392, val loss: 0.56458, in 0.031s
1 tree, 41 leaves, max depth = 11, train loss: 0.55694, val loss: 0.55763, in 0.000s
1 tree, 41 leaves, max depth = 11, train loss: 0.55020, val loss: 0.55090, in 0.031s
1 tree, 41 leaves, max depth = 10, train loss: 0.54353, val loss: 0.54416, in 0.000s
1 tree, 41 leaves, max depth = 10, train loss: 0.53704, val loss: 0.53761, in 0.031s
1 tree, 41 leaves, max depth = 10, train loss: 0.53075, val loss: 0.53127, in 0.016s
1 tree, 41 leaves, max depth = 10, train loss: 0.52472, val loss: 0.52515, in 0.016s
1 tree, 41 leaves, max depth = 10, train loss: 0.51883, val loss: 0.51921, in 0.016s
1 tree, 41 leaves, max depth = 10, train loss: 0.51316, val loss: 0.51347, in 0.016s
1 tree, 41 leaves, max depth = 9, train loss: 0.50764, val loss: 0.50789, in 0.016s
1 tree, 41 leaves, max depth = 10, train loss: 0.50232, val loss: 0.50250, in 0.016s
1 tree, 41 leaves, max depth = 9, train loss: 0.49713, val loss: 0.49726, in 0.016s
1 tree, 41 leaves, max depth = 10, train loss: 0.49211, val loss: 0.49218, in 0.016s
1 tree, 41 leaves, max depth = 9, train loss: 0.48724, val loss: 0.48726, in 0.016s
1 tree, 41 leaves, max depth = 10, train loss: 0.48252, val loss: 0.48249, in 0.016s
1 tree, 41 leaves, max depth = 9, train loss: 0.47794, val loss: 0.47787, in 0.016s
1 tree, 41 leaves, max depth = 9, train loss: 0.47350, val loss: 0.47339, in 0.016s
1 tree, 41 leaves, max depth = 11, train loss: 0.46919, val loss: 0.46902, in 0.016s
1 tree, 41 leaves, max depth = 9, train loss: 0.46501, val loss: 0.46480, in 0.016s
1 tree, 41 leaves, max depth = 11, train loss: 0.46095, val loss: 0.46069, in 0.031s
1 tree, 41 leaves, max depth = 11, train loss: 0.45700, val loss: 0.45670, in 0.016s
1 tree, 41 leaves, max depth = 10, train loss: 0.45317, val loss: 0.45282, in 0.016s
1 tree, 41 leaves, max depth = 10, train loss: 0.44945, val loss: 0.44905, in 0.016s
1 tree, 41 leaves, max depth = 9, train loss: 0.44584, val loss: 0.44541, in 0.016s
Fit 37 trees in 1.035 s, (1517 total leaves)
Time spent computing histograms: 0.268s
Time spent finding best splits:  0.052s
Time spent applying splits:      0.035s
Time spent predicting:           0.000s
Trial 46, Fold 1: Log loss = 0.44786320029330523, Average precision = 0.9199227180329279, ROC-AUC = 0.9306717593190043, Elapsed Time = 1.0404746000003797 seconds
Trial 46, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 46, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
Binning 0.040 GB of training data: 0.174 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 41 leaves, max depth = 11, train loss: 0.68142, val loss: 0.68126, in 0.016s
1 tree, 41 leaves, max depth = 11, train loss: 0.67015, val loss: 0.66985, in 0.016s
1 tree, 41 leaves, max depth = 11, train loss: 0.65932, val loss: 0.65887, in 0.016s
1 tree, 41 leaves, max depth = 11, train loss: 0.64890, val loss: 0.64833, in 0.031s
1 tree, 41 leaves, max depth = 11, train loss: 0.63889, val loss: 0.63818, in 0.016s
1 tree, 41 leaves, max depth = 11, train loss: 0.62924, val loss: 0.62841, in 0.016s
1 tree, 41 leaves, max depth = 11, train loss: 0.61995, val loss: 0.61899, in 0.016s
1 tree, 41 leaves, max depth = 11, train loss: 0.61099, val loss: 0.60991, in 0.016s
1 tree, 41 leaves, max depth = 11, train loss: 0.60235, val loss: 0.60117, in 0.016s
1 tree, 41 leaves, max depth = 10, train loss: 0.59390, val loss: 0.59255, in 0.016s
1 tree, 41 leaves, max depth = 10, train loss: 0.58572, val loss: 0.58422, in 0.016s
1 tree, 41 leaves, max depth = 10, train loss: 0.57781, val loss: 0.57618, in 0.016s
1 tree, 41 leaves, max depth = 10, train loss: 0.57016, val loss: 0.56842, in 0.016s
1 tree, 41 leaves, max depth = 10, train loss: 0.56278, val loss: 0.56092, in 0.016s
1 tree, 41 leaves, max depth = 10, train loss: 0.55568, val loss: 0.55367, in 0.016s
1 tree, 41 leaves, max depth = 10, train loss: 0.54878, val loss: 0.54665, in 0.016s
1 tree, 41 leaves, max depth = 10, train loss: 0.54210, val loss: 0.53988, in 0.016s
1 tree, 41 leaves, max depth = 10, train loss: 0.53568, val loss: 0.53332, in 0.016s
1 tree, 41 leaves, max depth = 10, train loss: 0.52943, val loss: 0.52698, in 0.016s
1 tree, 41 leaves, max depth = 10, train loss: 0.52338, val loss: 0.52083, in 0.016s
1 tree, 41 leaves, max depth = 10, train loss: 0.51755, val loss: 0.51492, in 0.031s
1 tree, 41 leaves, max depth = 10, train loss: 0.51190, val loss: 0.50920, in 0.016s
1 tree, 41 leaves, max depth = 10, train loss: 0.50640, val loss: 0.50361, in 0.016s
1 tree, 41 leaves, max depth = 10, train loss: 0.50109, val loss: 0.49820, in 0.016s
1 tree, 41 leaves, max depth = 10, train loss: 0.49592, val loss: 0.49296, in 0.016s
1 tree, 41 leaves, max depth = 10, train loss: 0.49094, val loss: 0.48791, in 0.016s
1 tree, 41 leaves, max depth = 10, train loss: 0.48608, val loss: 0.48297, in 0.016s
1 tree, 41 leaves, max depth = 9, train loss: 0.48137, val loss: 0.47819, in 0.016s
1 tree, 41 leaves, max depth = 10, train loss: 0.47683, val loss: 0.47358, in 0.016s
1 tree, 41 leaves, max depth = 9, train loss: 0.47239, val loss: 0.46906, in 0.031s
1 tree, 41 leaves, max depth = 9, train loss: 0.46808, val loss: 0.46468, in 0.016s
1 tree, 41 leaves, max depth = 10, train loss: 0.46393, val loss: 0.46047, in 0.016s
1 tree, 41 leaves, max depth = 9, train loss: 0.45987, val loss: 0.45635, in 0.016s
1 tree, 41 leaves, max depth = 9, train loss: 0.45595, val loss: 0.45238, in 0.016s
1 tree, 41 leaves, max depth = 9, train loss: 0.45213, val loss: 0.44849, in 0.016s
1 tree, 41 leaves, max depth = 8, train loss: 0.44841, val loss: 0.44471, in 0.016s
1 tree, 41 leaves, max depth = 10, train loss: 0.44483, val loss: 0.44107, in 0.016s
Fit 37 trees in 1.002 s, (1517 total leaves)
Time spent computing histograms: 0.273s
Time spent finding best splits:  0.045s
Time spent applying splits:      0.030s
Time spent predicting:           0.000s
Trial 46, Fold 2: Log loss = 0.4479834310936089, Average precision = 0.9135194459769991, ROC-AUC = 0.9308501275136641, Elapsed Time = 1.01119479999943 seconds
Trial 46, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 46, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
Binning 0.040 GB of training data: 0.157 s
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 41 leaves, max depth = 9, train loss: 0.68137, val loss: 0.68143, in 0.016s
1 tree, 41 leaves, max depth = 9, train loss: 0.67007, val loss: 0.67017, in 0.016s
1 tree, 41 leaves, max depth = 9, train loss: 0.65919, val loss: 0.65935, in 0.016s
1 tree, 41 leaves, max depth = 9, train loss: 0.64875, val loss: 0.64892, in 0.016s
1 tree, 41 leaves, max depth = 9, train loss: 0.63868, val loss: 0.63890, in 0.016s
1 tree, 41 leaves, max depth = 9, train loss: 0.62898, val loss: 0.62924, in 0.016s
1 tree, 41 leaves, max depth = 9, train loss: 0.61966, val loss: 0.61993, in 0.016s
1 tree, 41 leaves, max depth = 8, train loss: 0.61065, val loss: 0.61097, in 0.016s
1 tree, 41 leaves, max depth = 8, train loss: 0.60196, val loss: 0.60227, in 0.016s
1 tree, 41 leaves, max depth = 8, train loss: 0.59357, val loss: 0.59393, in 0.016s
1 tree, 41 leaves, max depth = 8, train loss: 0.58546, val loss: 0.58589, in 0.031s
1 tree, 41 leaves, max depth = 8, train loss: 0.57765, val loss: 0.57806, in 0.016s
1 tree, 41 leaves, max depth = 9, train loss: 0.57008, val loss: 0.57057, in 0.016s
1 tree, 41 leaves, max depth = 8, train loss: 0.56277, val loss: 0.56332, in 0.063s
1 tree, 41 leaves, max depth = 8, train loss: 0.55571, val loss: 0.55631, in 0.047s
1 tree, 41 leaves, max depth = 8, train loss: 0.54888, val loss: 0.54954, in 0.016s
1 tree, 41 leaves, max depth = 14, train loss: 0.54228, val loss: 0.54292, in 0.031s
1 tree, 41 leaves, max depth = 9, train loss: 0.53588, val loss: 0.53658, in 0.016s
1 tree, 41 leaves, max depth = 10, train loss: 0.52970, val loss: 0.53044, in 0.016s
1 tree, 41 leaves, max depth = 9, train loss: 0.52376, val loss: 0.52449, in 0.016s
1 tree, 41 leaves, max depth = 14, train loss: 0.51796, val loss: 0.51867, in 0.031s
1 tree, 41 leaves, max depth = 9, train loss: 0.51239, val loss: 0.51309, in 0.016s
1 tree, 41 leaves, max depth = 9, train loss: 0.50700, val loss: 0.50770, in 0.016s
1 tree, 41 leaves, max depth = 13, train loss: 0.50172, val loss: 0.50240, in 0.016s
1 tree, 41 leaves, max depth = 9, train loss: 0.49666, val loss: 0.49736, in 0.016s
1 tree, 41 leaves, max depth = 13, train loss: 0.49169, val loss: 0.49237, in 0.031s
1 tree, 41 leaves, max depth = 9, train loss: 0.48693, val loss: 0.48761, in 0.016s
1 tree, 41 leaves, max depth = 10, train loss: 0.48226, val loss: 0.48293, in 0.016s
1 tree, 41 leaves, max depth = 9, train loss: 0.47778, val loss: 0.47844, in 0.016s
1 tree, 41 leaves, max depth = 9, train loss: 0.47343, val loss: 0.47412, in 0.016s
1 tree, 41 leaves, max depth = 13, train loss: 0.46916, val loss: 0.46985, in 0.016s
1 tree, 41 leaves, max depth = 9, train loss: 0.46506, val loss: 0.46574, in 0.016s
1 tree, 41 leaves, max depth = 14, train loss: 0.46104, val loss: 0.46171, in 0.016s
1 tree, 41 leaves, max depth = 9, train loss: 0.45717, val loss: 0.45788, in 0.016s
1 tree, 41 leaves, max depth = 9, train loss: 0.45335, val loss: 0.45412, in 0.016s
1 tree, 41 leaves, max depth = 10, train loss: 0.44785, val loss: 0.44901, in 0.016s
1 tree, 41 leaves, max depth = 10, train loss: 0.44429, val loss: 0.44547, in 0.016s
Fit 37 trees in 1.079 s, (1517 total leaves)
Time spent computing histograms: 0.312s
Time spent finding best splits:  0.063s
Time spent applying splits:      0.050s
Time spent predicting:           0.016s
Trial 46, Fold 3: Log loss = 0.441979098882662, Average precision = 0.9501071518342736, ROC-AUC = 0.9485726806059848, Elapsed Time = 1.092893199998798 seconds
Trial 46, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 46, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
Binning 0.040 GB of training data: 0.173 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 41 leaves, max depth = 11, train loss: 0.68149, val loss: 0.68109, in 0.016s
1 tree, 41 leaves, max depth = 11, train loss: 0.67029, val loss: 0.66950, in 0.016s
1 tree, 41 leaves, max depth = 11, train loss: 0.65952, val loss: 0.65836, in 0.031s
1 tree, 41 leaves, max depth = 11, train loss: 0.64917, val loss: 0.64763, in 0.016s
1 tree, 41 leaves, max depth = 11, train loss: 0.63921, val loss: 0.63731, in 0.016s
1 tree, 41 leaves, max depth = 11, train loss: 0.62962, val loss: 0.62735, in 0.016s
1 tree, 41 leaves, max depth = 11, train loss: 0.62039, val loss: 0.61776, in 0.016s
1 tree, 41 leaves, max depth = 11, train loss: 0.61149, val loss: 0.60852, in 0.016s
1 tree, 41 leaves, max depth = 11, train loss: 0.60291, val loss: 0.59959, in 0.031s
1 tree, 41 leaves, max depth = 11, train loss: 0.59463, val loss: 0.59099, in 0.000s
1 tree, 41 leaves, max depth = 10, train loss: 0.58648, val loss: 0.58249, in 0.031s
1 tree, 41 leaves, max depth = 10, train loss: 0.57861, val loss: 0.57428, in 0.016s
1 tree, 41 leaves, max depth = 10, train loss: 0.57102, val loss: 0.56636, in 0.016s
1 tree, 41 leaves, max depth = 10, train loss: 0.56368, val loss: 0.55869, in 0.016s
1 tree, 41 leaves, max depth = 10, train loss: 0.55658, val loss: 0.55127, in 0.016s
1 tree, 41 leaves, max depth = 10, train loss: 0.54973, val loss: 0.54410, in 0.016s
1 tree, 41 leaves, max depth = 10, train loss: 0.54309, val loss: 0.53718, in 0.016s
1 tree, 41 leaves, max depth = 10, train loss: 0.53668, val loss: 0.53046, in 0.016s
1 tree, 41 leaves, max depth = 10, train loss: 0.53047, val loss: 0.52397, in 0.016s
1 tree, 41 leaves, max depth = 10, train loss: 0.52446, val loss: 0.51768, in 0.016s
1 tree, 41 leaves, max depth = 10, train loss: 0.51864, val loss: 0.51159, in 0.016s
1 tree, 41 leaves, max depth = 10, train loss: 0.51300, val loss: 0.50569, in 0.016s
1 tree, 41 leaves, max depth = 10, train loss: 0.50753, val loss: 0.49999, in 0.016s
1 tree, 41 leaves, max depth = 10, train loss: 0.50223, val loss: 0.49446, in 0.016s
1 tree, 41 leaves, max depth = 10, train loss: 0.49710, val loss: 0.48908, in 0.016s
1 tree, 41 leaves, max depth = 11, train loss: 0.49212, val loss: 0.48386, in 0.031s
1 tree, 41 leaves, max depth = 11, train loss: 0.48729, val loss: 0.47881, in 0.016s
1 tree, 41 leaves, max depth = 11, train loss: 0.48261, val loss: 0.47390, in 0.016s
1 tree, 41 leaves, max depth = 11, train loss: 0.47807, val loss: 0.46914, in 0.016s
1 tree, 41 leaves, max depth = 11, train loss: 0.47367, val loss: 0.46451, in 0.016s
1 tree, 41 leaves, max depth = 11, train loss: 0.46940, val loss: 0.46003, in 0.016s
1 tree, 41 leaves, max depth = 11, train loss: 0.46525, val loss: 0.45566, in 0.016s
1 tree, 41 leaves, max depth = 11, train loss: 0.46123, val loss: 0.45140, in 0.016s
1 tree, 41 leaves, max depth = 9, train loss: 0.45732, val loss: 0.44727, in 0.016s
1 tree, 41 leaves, max depth = 10, train loss: 0.45353, val loss: 0.44325, in 0.016s
1 tree, 41 leaves, max depth = 11, train loss: 0.44984, val loss: 0.43933, in 0.016s
1 tree, 41 leaves, max depth = 9, train loss: 0.44627, val loss: 0.43554, in 0.016s
Fit 37 trees in 1.064 s, (1517 total leaves)
Time spent computing histograms: 0.276s
Time spent finding best splits:  0.044s
Time spent applying splits:      0.032s
Time spent predicting:           0.000s
Trial 46, Fold 4: Log loss = 0.44626257917322193, Average precision = 0.9205991301635942, ROC-AUC = 0.9328650054483764, Elapsed Time = 1.0834144000000379 seconds
Trial 46, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 46, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
Binning 0.040 GB of training data: 0.157 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 41 leaves, max depth = 12, train loss: 0.68147, val loss: 0.68108, in 0.016s
1 tree, 41 leaves, max depth = 11, train loss: 0.67026, val loss: 0.66948, in 0.016s
1 tree, 41 leaves, max depth = 12, train loss: 0.65948, val loss: 0.65835, in 0.016s
1 tree, 41 leaves, max depth = 12, train loss: 0.64896, val loss: 0.64744, in 0.016s
1 tree, 41 leaves, max depth = 12, train loss: 0.63883, val loss: 0.63695, in 0.016s
1 tree, 41 leaves, max depth = 12, train loss: 0.62908, val loss: 0.62684, in 0.016s
1 tree, 41 leaves, max depth = 12, train loss: 0.61969, val loss: 0.61710, in 0.016s
1 tree, 41 leaves, max depth = 11, train loss: 0.61064, val loss: 0.60771, in 0.016s
1 tree, 41 leaves, max depth = 11, train loss: 0.60191, val loss: 0.59865, in 0.016s
1 tree, 41 leaves, max depth = 13, train loss: 0.59349, val loss: 0.58991, in 0.016s
1 tree, 41 leaves, max depth = 10, train loss: 0.58536, val loss: 0.58147, in 0.016s
1 tree, 41 leaves, max depth = 13, train loss: 0.57752, val loss: 0.57333, in 0.016s
1 tree, 41 leaves, max depth = 10, train loss: 0.56994, val loss: 0.56546, in 0.016s
1 tree, 41 leaves, max depth = 13, train loss: 0.56259, val loss: 0.55781, in 0.016s
1 tree, 41 leaves, max depth = 13, train loss: 0.55552, val loss: 0.55045, in 0.016s
1 tree, 41 leaves, max depth = 13, train loss: 0.54865, val loss: 0.54330, in 0.016s
1 tree, 41 leaves, max depth = 13, train loss: 0.54201, val loss: 0.53638, in 0.016s
1 tree, 41 leaves, max depth = 12, train loss: 0.53561, val loss: 0.52973, in 0.016s
1 tree, 41 leaves, max depth = 13, train loss: 0.52939, val loss: 0.52325, in 0.016s
1 tree, 41 leaves, max depth = 12, train loss: 0.52340, val loss: 0.51701, in 0.016s
1 tree, 41 leaves, max depth = 11, train loss: 0.51757, val loss: 0.51092, in 0.016s
1 tree, 41 leaves, max depth = 12, train loss: 0.51191, val loss: 0.50504, in 0.016s
1 tree, 41 leaves, max depth = 11, train loss: 0.50645, val loss: 0.49938, in 0.016s
1 tree, 41 leaves, max depth = 11, train loss: 0.50115, val loss: 0.49384, in 0.016s
1 tree, 41 leaves, max depth = 11, train loss: 0.49602, val loss: 0.48851, in 0.016s
1 tree, 41 leaves, max depth = 11, train loss: 0.49102, val loss: 0.48329, in 0.016s
1 tree, 41 leaves, max depth = 11, train loss: 0.48621, val loss: 0.47827, in 0.016s
1 tree, 41 leaves, max depth = 12, train loss: 0.48153, val loss: 0.47341, in 0.031s
[29/37] 1 tree, 41 leaves, max depth = 11, train loss: 0.47697, val loss: 0.46865, in 0.000s
1 tree, 41 leaves, max depth = 11, train loss: 0.47254, val loss: 0.46402, in 0.016s
1 tree, 41 leaves, max depth = 11, train loss: 0.46828, val loss: 0.45957, in 0.031s
1 tree, 41 leaves, max depth = 11, train loss: 0.46411, val loss: 0.45521, in 0.016s
1 tree, 41 leaves, max depth = 14, train loss: 0.46008, val loss: 0.45101, in 0.031s
1 tree, 41 leaves, max depth = 11, train loss: 0.45615, val loss: 0.44690, in 0.016s
1 tree, 41 leaves, max depth = 11, train loss: 0.45235, val loss: 0.44297, in 0.016s
1 tree, 41 leaves, max depth = 11, train loss: 0.44864, val loss: 0.43908, in 0.016s
1 tree, 41 leaves, max depth = 11, train loss: 0.44506, val loss: 0.43536, in 0.031s
Fit 37 trees in 0.985 s, (1517 total leaves)
Time spent computing histograms: 0.252s
Time spent finding best splits:  0.045s
Time spent applying splits:      0.035s
Time spent predicting:           0.000s
Trial 46, Fold 5: Log loss = 0.45017790444983935, Average precision = 0.9146362790173244, ROC-AUC = 0.9284197462051541, Elapsed Time = 0.9993957999995473 seconds
Optimization Progress:  47%|####6     | 47/100 [09:14<10:56, 12.39s/it]
Trial 47, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 47, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
0.127 s 0.040 GB of training data: 
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 65 leaves, max depth = 17, train loss: 0.67073, val loss: 0.67007, in 0.000s
1 tree, 65 leaves, max depth = 17, train loss: 0.65039, val loss: 0.64911, in 0.016s
1 tree, 65 leaves, max depth = 17, train loss: 0.63191, val loss: 0.63002, in 0.016s
1 tree, 65 leaves, max depth = 17, train loss: 0.61507, val loss: 0.61262, in 0.000s
1 tree, 65 leaves, max depth = 17, train loss: 0.59970, val loss: 0.59670, in 0.016s
1 tree, 65 leaves, max depth = 17, train loss: 0.58565, val loss: 0.58213, in 0.000s
1 tree, 65 leaves, max depth = 17, train loss: 0.57279, val loss: 0.56878, in 0.016s
1 tree, 65 leaves, max depth = 17, train loss: 0.56100, val loss: 0.55651, in 0.000s
1 tree, 65 leaves, max depth = 17, train loss: 0.55018, val loss: 0.54524, in 0.016s
1 tree, 75 leaves, max depth = 11, train loss: 0.53934, val loss: 0.53514, in 0.016s
1 tree, 67 leaves, max depth = 18, train loss: 0.52979, val loss: 0.52515, in 0.000s
1 tree, 75 leaves, max depth = 12, train loss: 0.52013, val loss: 0.51619, in 0.016s
1 tree, 67 leaves, max depth = 17, train loss: 0.51166, val loss: 0.50729, in 0.000s
1 tree, 75 leaves, max depth = 13, train loss: 0.50302, val loss: 0.49931, in 0.000s
1 tree, 75 leaves, max depth = 13, train loss: 0.49507, val loss: 0.49199, in 0.016s
1 tree, 68 leaves, max depth = 15, train loss: 0.48782, val loss: 0.48436, in 0.000s
1 tree, 75 leaves, max depth = 12, train loss: 0.48066, val loss: 0.47780, in 0.016s
1 tree, 68 leaves, max depth = 15, train loss: 0.47418, val loss: 0.47094, in 0.016s
1 tree, 75 leaves, max depth = 11, train loss: 0.46771, val loss: 0.46504, in 0.000s
1 tree, 68 leaves, max depth = 14, train loss: 0.46189, val loss: 0.45887, in 0.016s
1 tree, 75 leaves, max depth = 11, train loss: 0.45602, val loss: 0.45354, in 0.000s
1 tree, 67 leaves, max depth = 15, train loss: 0.45075, val loss: 0.44796, in 0.000s
1 tree, 75 leaves, max depth = 11, train loss: 0.44542, val loss: 0.44315, in 0.016s
1 tree, 75 leaves, max depth = 11, train loss: 0.44050, val loss: 0.43872, in 0.016s
1 tree, 68 leaves, max depth = 14, train loss: 0.43586, val loss: 0.43375, in 0.000s
1 tree, 75 leaves, max depth = 12, train loss: 0.43137, val loss: 0.42973, in 0.016s
1 tree, 68 leaves, max depth = 14, train loss: 0.42717, val loss: 0.42521, in 0.000s
1 tree, 75 leaves, max depth = 11, train loss: 0.42306, val loss: 0.42157, in 0.016s
1 tree, 67 leaves, max depth = 16, train loss: 0.41924, val loss: 0.41747, in 0.016s
1 tree, 75 leaves, max depth = 11, train loss: 0.41548, val loss: 0.41416, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.41218, val loss: 0.41052, in 0.016s
1 tree, 67 leaves, max depth = 16, train loss: 0.40876, val loss: 0.40686, in 0.000s
1 tree, 75 leaves, max depth = 12, train loss: 0.40533, val loss: 0.40388, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.40234, val loss: 0.40057, in 0.000s
1 tree, 75 leaves, max depth = 12, train loss: 0.39918, val loss: 0.39786, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.39643, val loss: 0.39481, in 0.000s
1 tree, 67 leaves, max depth = 16, train loss: 0.39342, val loss: 0.39161, in 0.016s
1 tree, 75 leaves, max depth = 12, train loss: 0.39052, val loss: 0.38916, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.38801, val loss: 0.38635, in 0.016s
1 tree, 75 leaves, max depth = 11, train loss: 0.38532, val loss: 0.38412, in 0.000s
1 tree, 68 leaves, max depth = 13, train loss: 0.38258, val loss: 0.38119, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.38028, val loss: 0.37861, in 0.000s
1 tree, 75 leaves, max depth = 11, train loss: 0.37779, val loss: 0.37657, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.37566, val loss: 0.37417, in 0.000s
1 tree, 67 leaves, max depth = 12, train loss: 0.37320, val loss: 0.37158, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.37124, val loss: 0.36936, in 0.000s
1 tree, 67 leaves, max depth = 12, train loss: 0.36898, val loss: 0.36700, in 0.016s
1 tree, 75 leaves, max depth = 12, train loss: 0.36665, val loss: 0.36514, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.36483, val loss: 0.36307, in 0.016s
1 tree, 67 leaves, max depth = 12, train loss: 0.36276, val loss: 0.36092, in 0.000s
1 tree, 75 leaves, max depth = 12, train loss: 0.36059, val loss: 0.35921, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.35890, val loss: 0.35728, in 0.000s
1 tree, 67 leaves, max depth = 12, train loss: 0.35700, val loss: 0.35531, in 0.016s
1 tree, 75 leaves, max depth = 12, train loss: 0.35497, val loss: 0.35374, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.35339, val loss: 0.35192, in 0.016s
1 tree, 67 leaves, max depth = 12, train loss: 0.35165, val loss: 0.35012, in 0.000s
1 tree, 75 leaves, max depth = 12, train loss: 0.34974, val loss: 0.34868, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.34827, val loss: 0.34697, in 0.000s
1 tree, 67 leaves, max depth = 13, train loss: 0.34666, val loss: 0.34531, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.34528, val loss: 0.34371, in 0.000s
1 tree, 75 leaves, max depth = 12, train loss: 0.34370, val loss: 0.34190, in 0.000s
1 tree, 75 leaves, max depth = 12, train loss: 0.34189, val loss: 0.34056, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.34059, val loss: 0.33905, in 0.000s
1 tree, 67 leaves, max depth = 14, train loss: 0.33918, val loss: 0.33760, in 0.016s
1 tree, 75 leaves, max depth = 12, train loss: 0.33748, val loss: 0.33635, in 0.000s
1 tree, 75 leaves, max depth = 12, train loss: 0.33605, val loss: 0.33471, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.33483, val loss: 0.33327, in 0.000s
1 tree, 75 leaves, max depth = 12, train loss: 0.33351, val loss: 0.33176, in 0.016s
1 tree, 75 leaves, max depth = 13, train loss: 0.33190, val loss: 0.33060, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.33075, val loss: 0.32923, in 0.000s
1 tree, 67 leaves, max depth = 15, train loss: 0.32955, val loss: 0.32802, in 0.016s
Fit 71 trees in 0.861 s, (4031 total leaves)
Time spent computing histograms: 0.294s
Time spent finding best splits:  0.060s
Time spent applying splits:      0.059s
Time spent predicting:           0.000s
Trial 47, Fold 1: Log loss = 0.3353796802663027, Average precision = 0.9461633419759852, ROC-AUC = 0.9449084380063377, Elapsed Time = 0.8692676999999094 seconds
Trial 47, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 47, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
Binning 0.040 GB of training data: 0.142 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 68 leaves, max depth = 15, train loss: 0.67100, val loss: 0.66996, in 0.016s
1 tree, 68 leaves, max depth = 15, train loss: 0.65092, val loss: 0.64891, in 0.000s
1 tree, 68 leaves, max depth = 15, train loss: 0.63267, val loss: 0.62973, in 0.016s
1 tree, 68 leaves, max depth = 15, train loss: 0.61604, val loss: 0.61223, in 0.000s
1 tree, 68 leaves, max depth = 15, train loss: 0.60087, val loss: 0.59624, in 0.016s
1 tree, 68 leaves, max depth = 15, train loss: 0.58701, val loss: 0.58159, in 0.016s
1 tree, 68 leaves, max depth = 15, train loss: 0.57432, val loss: 0.56816, in 0.000s
1 tree, 68 leaves, max depth = 15, train loss: 0.56270, val loss: 0.55583, in 0.016s
1 tree, 75 leaves, max depth = 13, train loss: 0.55133, val loss: 0.54490, in 0.016s
1 tree, 68 leaves, max depth = 15, train loss: 0.54103, val loss: 0.53402, in 0.000s
1 tree, 75 leaves, max depth = 16, train loss: 0.53092, val loss: 0.52431, in 0.016s
1 tree, 68 leaves, max depth = 15, train loss: 0.52179, val loss: 0.51466, in 0.000s
1 tree, 75 leaves, max depth = 16, train loss: 0.51276, val loss: 0.50600, in 0.016s
1 tree, 68 leaves, max depth = 15, train loss: 0.50464, val loss: 0.49741, in 0.016s
1 tree, 75 leaves, max depth = 14, train loss: 0.49653, val loss: 0.48968, in 0.000s
1 tree, 68 leaves, max depth = 15, train loss: 0.48929, val loss: 0.48201, in 0.016s
1 tree, 75 leaves, max depth = 16, train loss: 0.48198, val loss: 0.47507, in 0.016s
1 tree, 75 leaves, max depth = 16, train loss: 0.47524, val loss: 0.46869, in 0.000s
1 tree, 68 leaves, max depth = 15, train loss: 0.46894, val loss: 0.46199, in 0.016s
1 tree, 75 leaves, max depth = 17, train loss: 0.46284, val loss: 0.45624, in 0.016s
1 tree, 68 leaves, max depth = 16, train loss: 0.45718, val loss: 0.45020, in 0.000s
1 tree, 75 leaves, max depth = 15, train loss: 0.45164, val loss: 0.44500, in 0.016s
1 tree, 68 leaves, max depth = 17, train loss: 0.44654, val loss: 0.43955, in 0.016s
1 tree, 75 leaves, max depth = 12, train loss: 0.44149, val loss: 0.43483, in 0.016s
1 tree, 68 leaves, max depth = 15, train loss: 0.43688, val loss: 0.42991, in 0.000s
1 tree, 75 leaves, max depth = 15, train loss: 0.43226, val loss: 0.42561, in 0.016s
1 tree, 75 leaves, max depth = 15, train loss: 0.42800, val loss: 0.42166, in 0.016s
1 tree, 68 leaves, max depth = 14, train loss: 0.42390, val loss: 0.41727, in 0.000s
1 tree, 75 leaves, max depth = 13, train loss: 0.41999, val loss: 0.41367, in 0.016s
1 tree, 68 leaves, max depth = 14, train loss: 0.41627, val loss: 0.40967, in 0.016s
1 tree, 75 leaves, max depth = 13, train loss: 0.41267, val loss: 0.40637, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.40946, val loss: 0.40305, in 0.016s
1 tree, 68 leaves, max depth = 14, train loss: 0.40614, val loss: 0.39948, in 0.000s
1 tree, 75 leaves, max depth = 12, train loss: 0.40284, val loss: 0.39648, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.39994, val loss: 0.39346, in 0.000s
1 tree, 75 leaves, max depth = 11, train loss: 0.39689, val loss: 0.39071, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.39422, val loss: 0.38793, in 0.016s
1 tree, 68 leaves, max depth = 14, train loss: 0.39129, val loss: 0.38479, in 0.000s
1 tree, 75 leaves, max depth = 11, train loss: 0.38848, val loss: 0.38226, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.38604, val loss: 0.37971, in 0.000s
1 tree, 75 leaves, max depth = 13, train loss: 0.38343, val loss: 0.37739, in 0.016s
1 tree, 67 leaves, max depth = 12, train loss: 0.38073, val loss: 0.37448, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.37849, val loss: 0.37214, in 0.000s
1 tree, 75 leaves, max depth = 13, train loss: 0.37607, val loss: 0.37000, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.37400, val loss: 0.36783, in 0.000s
1 tree, 67 leaves, max depth = 11, train loss: 0.37155, val loss: 0.36519, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.36964, val loss: 0.36319, in 0.016s
1 tree, 67 leaves, max depth = 11, train loss: 0.36740, val loss: 0.36077, in 0.000s
1 tree, 75 leaves, max depth = 14, train loss: 0.36513, val loss: 0.35879, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.36336, val loss: 0.35692, in 0.016s
1 tree, 67 leaves, max depth = 11, train loss: 0.36130, val loss: 0.35471, in 0.000s
1 tree, 75 leaves, max depth = 14, train loss: 0.35918, val loss: 0.35288, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.35753, val loss: 0.35114, in 0.016s
1 tree, 68 leaves, max depth = 16, train loss: 0.35558, val loss: 0.34897, in 0.000s
1 tree, 75 leaves, max depth = 13, train loss: 0.35359, val loss: 0.34726, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.35205, val loss: 0.34564, in 0.000s
1 tree, 67 leaves, max depth = 11, train loss: 0.35031, val loss: 0.34376, in 0.016s
1 tree, 75 leaves, max depth = 15, train loss: 0.34844, val loss: 0.34217, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.34700, val loss: 0.34065, in 0.000s
1 tree, 75 leaves, max depth = 15, train loss: 0.34528, val loss: 0.33878, in 0.016s
1 tree, 67 leaves, max depth = 11, train loss: 0.34375, val loss: 0.33714, in 0.000s
1 tree, 75 leaves, max depth = 15, train loss: 0.34199, val loss: 0.33564, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.34063, val loss: 0.33420, in 0.000s
1 tree, 75 leaves, max depth = 15, train loss: 0.33911, val loss: 0.33254, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.33784, val loss: 0.33120, in 0.016s
1 tree, 75 leaves, max depth = 15, train loss: 0.33616, val loss: 0.32979, in 0.000s
1 tree, 75 leaves, max depth = 14, train loss: 0.33474, val loss: 0.32824, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.33354, val loss: 0.32697, in 0.000s
1 tree, 67 leaves, max depth = 12, train loss: 0.33225, val loss: 0.32560, in 0.016s
1 tree, 75 leaves, max depth = 15, train loss: 0.33066, val loss: 0.32427, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.32953, val loss: 0.32307, in 0.000s
Fit 71 trees in 0.955 s, (4072 total leaves)
Time spent computing histograms: 0.323s
Time spent finding best splits:  0.067s
Time spent applying splits:      0.067s
Time spent predicting:           0.000s
Trial 47, Fold 2: Log loss = 0.33349052582425665, Average precision = 0.9443356104667668, ROC-AUC = 0.9468951037363036, Elapsed Time = 0.9595272000005934 seconds
Trial 47, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 47, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
Binning 0.040 GB of training data: 0.174 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 66 leaves, max depth = 14, train loss: 0.67121, val loss: 0.67047, in 0.016s
1 tree, 67 leaves, max depth = 12, train loss: 0.65104, val loss: 0.64967, in 0.016s
1 tree, 67 leaves, max depth = 15, train loss: 0.63295, val loss: 0.63092, in 0.000s
1 tree, 67 leaves, max depth = 12, train loss: 0.61621, val loss: 0.61363, in 0.016s
1 tree, 67 leaves, max depth = 12, train loss: 0.60094, val loss: 0.59784, in 0.000s
1 tree, 67 leaves, max depth = 12, train loss: 0.58698, val loss: 0.58338, in 0.016s
1 tree, 67 leaves, max depth = 12, train loss: 0.57421, val loss: 0.57013, in 0.016s
1 tree, 67 leaves, max depth = 15, train loss: 0.56267, val loss: 0.55807, in 0.000s
1 tree, 75 leaves, max depth = 15, train loss: 0.55122, val loss: 0.54750, in 0.016s
1 tree, 67 leaves, max depth = 12, train loss: 0.54088, val loss: 0.53672, in 0.016s
1 tree, 75 leaves, max depth = 13, train loss: 0.53071, val loss: 0.52735, in 0.000s
1 tree, 67 leaves, max depth = 15, train loss: 0.52170, val loss: 0.51786, in 0.016s
1 tree, 75 leaves, max depth = 13, train loss: 0.51261, val loss: 0.50951, in 0.016s
1 tree, 67 leaves, max depth = 15, train loss: 0.50461, val loss: 0.50105, in 0.000s
1 tree, 75 leaves, max depth = 15, train loss: 0.49645, val loss: 0.49357, in 0.016s
1 tree, 67 leaves, max depth = 15, train loss: 0.48932, val loss: 0.48600, in 0.000s
1 tree, 75 leaves, max depth = 14, train loss: 0.48196, val loss: 0.47930, in 0.016s
1 tree, 75 leaves, max depth = 14, train loss: 0.47518, val loss: 0.47313, in 0.016s
1 tree, 67 leaves, max depth = 14, train loss: 0.46898, val loss: 0.46651, in 0.016s
1 tree, 75 leaves, max depth = 14, train loss: 0.46284, val loss: 0.46095, in 0.000s
1 tree, 67 leaves, max depth = 14, train loss: 0.45728, val loss: 0.45497, in 0.016s
1 tree, 75 leaves, max depth = 13, train loss: 0.45169, val loss: 0.44994, in 0.016s
1 tree, 67 leaves, max depth = 14, train loss: 0.44669, val loss: 0.44454, in 0.000s
1 tree, 75 leaves, max depth = 13, train loss: 0.44159, val loss: 0.43997, in 0.016s
1 tree, 67 leaves, max depth = 14, train loss: 0.43708, val loss: 0.43510, in 0.016s
1 tree, 75 leaves, max depth = 12, train loss: 0.43242, val loss: 0.43094, in 0.016s
1 tree, 75 leaves, max depth = 12, train loss: 0.42812, val loss: 0.42712, in 0.000s
1 tree, 67 leaves, max depth = 14, train loss: 0.42411, val loss: 0.42276, in 0.000s
1 tree, 75 leaves, max depth = 13, train loss: 0.42016, val loss: 0.41927, in 0.016s
1 tree, 67 leaves, max depth = 13, train loss: 0.41652, val loss: 0.41530, in 0.016s
1 tree, 75 leaves, max depth = 13, train loss: 0.41289, val loss: 0.41211, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.40959, val loss: 0.40904, in 0.016s
1 tree, 75 leaves, max depth = 13, train loss: 0.40625, val loss: 0.40612, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.40323, val loss: 0.40330, in 0.000s
1 tree, 66 leaves, max depth = 12, train loss: 0.40002, val loss: 0.39980, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.39726, val loss: 0.39723, in 0.016s
1 tree, 75 leaves, max depth = 13, train loss: 0.39420, val loss: 0.39459, in 0.000s
1 tree, 66 leaves, max depth = 12, train loss: 0.39131, val loss: 0.39142, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.38879, val loss: 0.38907, in 0.000s
1 tree, 75 leaves, max depth = 14, train loss: 0.38597, val loss: 0.38665, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.38364, val loss: 0.38448, in 0.016s
1 tree, 75 leaves, max depth = 15, train loss: 0.38102, val loss: 0.38226, in 0.000s
1 tree, 66 leaves, max depth = 12, train loss: 0.37843, val loss: 0.37942, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.37629, val loss: 0.37742, in 0.000s
1 tree, 75 leaves, max depth = 15, train loss: 0.37386, val loss: 0.37538, in 0.016s
1 tree, 67 leaves, max depth = 12, train loss: 0.37141, val loss: 0.37270, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.36943, val loss: 0.37085, in 0.000s
1 tree, 66 leaves, max depth = 11, train loss: 0.36720, val loss: 0.36840, in 0.016s
1 tree, 75 leaves, max depth = 15, train loss: 0.36493, val loss: 0.36650, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.36309, val loss: 0.36478, in 0.000s
1 tree, 67 leaves, max depth = 11, train loss: 0.36105, val loss: 0.36253, in 0.016s
1 tree, 75 leaves, max depth = 15, train loss: 0.35892, val loss: 0.36078, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.35722, val loss: 0.35918, in 0.016s
1 tree, 66 leaves, max depth = 11, train loss: 0.35538, val loss: 0.35717, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.35380, val loss: 0.35569, in 0.000s
1 tree, 75 leaves, max depth = 15, train loss: 0.35180, val loss: 0.35405, in 0.016s
1 tree, 66 leaves, max depth = 11, train loss: 0.35011, val loss: 0.35219, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.34863, val loss: 0.35081, in 0.000s
1 tree, 75 leaves, max depth = 13, train loss: 0.34675, val loss: 0.34928, in 0.016s
1 tree, 67 leaves, max depth = 14, train loss: 0.34514, val loss: 0.34751, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.34375, val loss: 0.34621, in 0.016s
1 tree, 75 leaves, max depth = 13, train loss: 0.34219, val loss: 0.34436, in 0.000s
1 tree, 75 leaves, max depth = 13, train loss: 0.34041, val loss: 0.34293, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.33911, val loss: 0.34170, in 0.016s
1 tree, 75 leaves, max depth = 13, train loss: 0.33766, val loss: 0.33997, in 0.000s
1 tree, 75 leaves, max depth = 13, train loss: 0.33598, val loss: 0.33863, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.33475, val loss: 0.33747, in 0.016s
1 tree, 66 leaves, max depth = 11, train loss: 0.33343, val loss: 0.33603, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.33228, val loss: 0.33495, in 0.000s
1 tree, 75 leaves, max depth = 13, train loss: 0.33098, val loss: 0.33340, in 0.016s
1 tree, 75 leaves, max depth = 11, train loss: 0.32937, val loss: 0.33213, in 0.016s
Fit 71 trees in 1.002 s, (4043 total leaves)
Time spent computing histograms: 0.324s
Time spent finding best splits:  0.070s
Time spent applying splits:      0.069s
Time spent predicting:           0.016s
Trial 47, Fold 3: Log loss = 0.3298393523478352, Average precision = 0.9500604398322146, ROC-AUC = 0.9502438156716203, Elapsed Time = 1.0118590999991284 seconds
Trial 47, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 47, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
Binning 0.040 GB of training data: 0.158 s
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 65 leaves, max depth = 13, train loss: 0.67094, val loss: 0.66962, in 0.016s
1 tree, 65 leaves, max depth = 13, train loss: 0.65081, val loss: 0.64823, in 0.000s
1 tree, 65 leaves, max depth = 13, train loss: 0.63251, val loss: 0.62873, in 0.016s
1 tree, 65 leaves, max depth = 13, train loss: 0.61584, val loss: 0.61092, in 0.000s
1 tree, 65 leaves, max depth = 13, train loss: 0.60063, val loss: 0.59461, in 0.016s
1 tree, 65 leaves, max depth = 13, train loss: 0.58672, val loss: 0.57966, in 0.016s
1 tree, 65 leaves, max depth = 13, train loss: 0.57400, val loss: 0.56592, in 0.016s
1 tree, 65 leaves, max depth = 13, train loss: 0.56234, val loss: 0.55330, in 0.000s
1 tree, 75 leaves, max depth = 13, train loss: 0.55088, val loss: 0.54203, in 0.016s
1 tree, 67 leaves, max depth = 12, train loss: 0.54060, val loss: 0.53085, in 0.016s
1 tree, 75 leaves, max depth = 15, train loss: 0.53042, val loss: 0.52085, in 0.000s
1 tree, 67 leaves, max depth = 15, train loss: 0.52137, val loss: 0.51098, in 0.016s
1 tree, 75 leaves, max depth = 12, train loss: 0.51226, val loss: 0.50206, in 0.016s
1 tree, 67 leaves, max depth = 13, train loss: 0.50418, val loss: 0.49319, in 0.000s
1 tree, 75 leaves, max depth = 12, train loss: 0.49601, val loss: 0.48520, in 0.016s
1 tree, 67 leaves, max depth = 16, train loss: 0.48885, val loss: 0.47732, in 0.016s
1 tree, 75 leaves, max depth = 14, train loss: 0.48148, val loss: 0.47012, in 0.016s
1 tree, 75 leaves, max depth = 14, train loss: 0.47468, val loss: 0.46348, in 0.000s
1 tree, 67 leaves, max depth = 15, train loss: 0.46846, val loss: 0.45660, in 0.016s
1 tree, 75 leaves, max depth = 14, train loss: 0.46230, val loss: 0.45059, in 0.016s
1 tree, 67 leaves, max depth = 15, train loss: 0.45672, val loss: 0.44438, in 0.000s
1 tree, 75 leaves, max depth = 14, train loss: 0.45111, val loss: 0.43893, in 0.016s
1 tree, 66 leaves, max depth = 15, train loss: 0.44605, val loss: 0.43331, in 0.016s
1 tree, 75 leaves, max depth = 12, train loss: 0.44095, val loss: 0.42835, in 0.000s
1 tree, 67 leaves, max depth = 15, train loss: 0.43642, val loss: 0.42325, in 0.016s
1 tree, 75 leaves, max depth = 12, train loss: 0.43175, val loss: 0.41874, in 0.016s
1 tree, 75 leaves, max depth = 12, train loss: 0.42743, val loss: 0.41456, in 0.016s
1 tree, 67 leaves, max depth = 14, train loss: 0.42340, val loss: 0.41000, in 0.000s
1 tree, 75 leaves, max depth = 14, train loss: 0.41943, val loss: 0.40617, in 0.016s
1 tree, 66 leaves, max depth = 13, train loss: 0.41577, val loss: 0.40203, in 0.016s
1 tree, 75 leaves, max depth = 14, train loss: 0.41213, val loss: 0.39852, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.40893, val loss: 0.39506, in 0.016s
1 tree, 75 leaves, max depth = 13, train loss: 0.40557, val loss: 0.39184, in 0.000s
1 tree, 67 leaves, max depth = 13, train loss: 0.40234, val loss: 0.38814, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.39945, val loss: 0.38500, in 0.000s
1 tree, 75 leaves, max depth = 13, train loss: 0.39635, val loss: 0.38205, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.39369, val loss: 0.37916, in 0.000s
1 tree, 66 leaves, max depth = 14, train loss: 0.39081, val loss: 0.37589, in 0.000s
1 tree, 75 leaves, max depth = 11, train loss: 0.38795, val loss: 0.37318, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.38551, val loss: 0.37053, in 0.000s
1 tree, 75 leaves, max depth = 14, train loss: 0.38285, val loss: 0.36803, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.38060, val loss: 0.36558, in 0.000s
1 tree, 66 leaves, max depth = 14, train loss: 0.37803, val loss: 0.36267, in 0.016s
1 tree, 75 leaves, max depth = 14, train loss: 0.37556, val loss: 0.36036, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.37348, val loss: 0.35810, in 0.000s
1 tree, 66 leaves, max depth = 13, train loss: 0.37114, val loss: 0.35544, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.36923, val loss: 0.35336, in 0.000s
1 tree, 75 leaves, max depth = 12, train loss: 0.36691, val loss: 0.35122, in 0.016s
1 tree, 66 leaves, max depth = 13, train loss: 0.36477, val loss: 0.34879, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.36299, val loss: 0.34685, in 0.000s
1 tree, 75 leaves, max depth = 15, train loss: 0.36082, val loss: 0.34486, in 0.016s
1 tree, 66 leaves, max depth = 13, train loss: 0.35886, val loss: 0.34264, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.35721, val loss: 0.34083, in 0.000s
1 tree, 67 leaves, max depth = 13, train loss: 0.35537, val loss: 0.33870, in 0.016s
1 tree, 75 leaves, max depth = 14, train loss: 0.35333, val loss: 0.33684, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.35178, val loss: 0.33515, in 0.016s
1 tree, 66 leaves, max depth = 13, train loss: 0.35012, val loss: 0.33326, in 0.000s
1 tree, 75 leaves, max depth = 14, train loss: 0.34820, val loss: 0.33152, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.34675, val loss: 0.32994, in 0.016s
1 tree, 75 leaves, max depth = 12, train loss: 0.34507, val loss: 0.32821, in 0.016s
1 tree, 75 leaves, max depth = 15, train loss: 0.34325, val loss: 0.32658, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.34189, val loss: 0.32509, in 0.016s
1 tree, 75 leaves, max depth = 13, train loss: 0.34032, val loss: 0.32349, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.33906, val loss: 0.32210, in 0.016s
1 tree, 66 leaves, max depth = 13, train loss: 0.33767, val loss: 0.32056, in 0.000s
1 tree, 75 leaves, max depth = 14, train loss: 0.33593, val loss: 0.31900, in 0.016s
1 tree, 75 leaves, max depth = 13, train loss: 0.33453, val loss: 0.31759, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.33333, val loss: 0.31627, in 0.000s
1 tree, 75 leaves, max depth = 12, train loss: 0.33168, val loss: 0.31480, in 0.016s
1 tree, 75 leaves, max depth = 14, train loss: 0.33037, val loss: 0.31349, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.32924, val loss: 0.31224, in 0.000s
Fit 71 trees in 1.002 s, (4034 total leaves)
Time spent computing histograms: 0.330s
Time spent finding best splits:  0.071s
Time spent applying splits:      0.070s
Time spent predicting:           0.000s
Trial 47, Fold 4: Log loss = 0.3291262557638443, Average precision = 0.9527007277982728, ROC-AUC = 0.9509306351352318, Elapsed Time = 1.0164748999995936 seconds
Trial 47, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 47, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
Binning 0.040 GB of training data: 0.157 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 67 leaves, max depth = 14, train loss: 0.67071, val loss: 0.66926, in 0.016s
1 tree, 67 leaves, max depth = 14, train loss: 0.65037, val loss: 0.64754, in 0.016s
1 tree, 67 leaves, max depth = 14, train loss: 0.63187, val loss: 0.62774, in 0.016s
1 tree, 67 leaves, max depth = 14, train loss: 0.61518, val loss: 0.60975, in 0.000s
1 tree, 67 leaves, max depth = 14, train loss: 0.59978, val loss: 0.59316, in 0.016s
1 tree, 67 leaves, max depth = 14, train loss: 0.58570, val loss: 0.57796, in 0.016s
1 tree, 67 leaves, max depth = 14, train loss: 0.57282, val loss: 0.56400, in 0.000s
1 tree, 67 leaves, max depth = 12, train loss: 0.56110, val loss: 0.55116, in 0.016s
1 tree, 67 leaves, max depth = 14, train loss: 0.55024, val loss: 0.53931, in 0.000s
1 tree, 75 leaves, max depth = 17, train loss: 0.53943, val loss: 0.52898, in 0.016s
1 tree, 67 leaves, max depth = 14, train loss: 0.52993, val loss: 0.51860, in 0.000s
1 tree, 75 leaves, max depth = 15, train loss: 0.52029, val loss: 0.50942, in 0.016s
1 tree, 75 leaves, max depth = 15, train loss: 0.51144, val loss: 0.50102, in 0.016s
1 tree, 67 leaves, max depth = 13, train loss: 0.50328, val loss: 0.49205, in 0.000s
1 tree, 75 leaves, max depth = 15, train loss: 0.49533, val loss: 0.48455, in 0.016s
1 tree, 67 leaves, max depth = 13, train loss: 0.48805, val loss: 0.47650, in 0.016s
1 tree, 75 leaves, max depth = 15, train loss: 0.48089, val loss: 0.46978, in 0.000s
1 tree, 67 leaves, max depth = 13, train loss: 0.47438, val loss: 0.46249, in 0.016s
1 tree, 75 leaves, max depth = 15, train loss: 0.46791, val loss: 0.45645, in 0.016s
1 tree, 67 leaves, max depth = 13, train loss: 0.46206, val loss: 0.44992, in 0.000s
1 tree, 75 leaves, max depth = 15, train loss: 0.45619, val loss: 0.44446, in 0.016s
1 tree, 75 leaves, max depth = 16, train loss: 0.45077, val loss: 0.43944, in 0.016s
1 tree, 67 leaves, max depth = 13, train loss: 0.44562, val loss: 0.43367, in 0.016s
1 tree, 75 leaves, max depth = 16, train loss: 0.44068, val loss: 0.42912, in 0.000s
1 tree, 67 leaves, max depth = 13, train loss: 0.43603, val loss: 0.42387, in 0.016s
1 tree, 75 leaves, max depth = 16, train loss: 0.43151, val loss: 0.41974, in 0.016s
[27/71] 1 tree, 67 leaves, max depth = 13, train loss: 0.42731, val loss: 0.41496, in 0.000s
1 tree, 75 leaves, max depth = 15, train loss: 0.42317, val loss: 0.41121, in 0.016s
1 tree, 67 leaves, max depth = 13, train loss: 0.41936, val loss: 0.40685, in 0.016s
1 tree, 75 leaves, max depth = 11, train loss: 0.41557, val loss: 0.40343, in 0.000s
1 tree, 67 leaves, max depth = 12, train loss: 0.41210, val loss: 0.39944, in 0.016s
[32/71] 1 tree, 75 leaves, max depth = 16, train loss: 0.40861, val loss: 0.39631, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.40543, val loss: 0.39325, in 0.000s
1 tree, 75 leaves, max depth = 16, train loss: 0.40221, val loss: 0.39039, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.39930, val loss: 0.38759, in 0.000s
1 tree, 67 leaves, max depth = 12, train loss: 0.39627, val loss: 0.38409, in 0.016s
1 tree, 75 leaves, max depth = 17, train loss: 0.39330, val loss: 0.38149, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.39065, val loss: 0.37894, in 0.000s
1 tree, 75 leaves, max depth = 17, train loss: 0.38791, val loss: 0.37655, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.38546, val loss: 0.37421, in 0.000s
1 tree, 67 leaves, max depth = 13, train loss: 0.38274, val loss: 0.37104, in 0.016s
1 tree, 75 leaves, max depth = 16, train loss: 0.38020, val loss: 0.36885, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.37796, val loss: 0.36670, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.37588, val loss: 0.36471, in 0.016s
1 tree, 67 leaves, max depth = 12, train loss: 0.37338, val loss: 0.36185, in 0.000s
1 tree, 75 leaves, max depth = 16, train loss: 0.37101, val loss: 0.35983, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.36909, val loss: 0.35800, in 0.000s
1 tree, 67 leaves, max depth = 12, train loss: 0.36684, val loss: 0.35535, in 0.016s
1 tree, 75 leaves, max depth = 16, train loss: 0.36462, val loss: 0.35349, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.36284, val loss: 0.35179, in 0.000s
1 tree, 67 leaves, max depth = 12, train loss: 0.36078, val loss: 0.34935, in 0.016s
1 tree, 75 leaves, max depth = 16, train loss: 0.35871, val loss: 0.34764, in 0.000s
1 tree, 67 leaves, max depth = 12, train loss: 0.35676, val loss: 0.34539, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.35511, val loss: 0.34381, in 0.000s
1 tree, 75 leaves, max depth = 16, train loss: 0.35317, val loss: 0.34222, in 0.016s
1 tree, 67 leaves, max depth = 12, train loss: 0.35142, val loss: 0.34012, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.34987, val loss: 0.33864, in 0.000s
1 tree, 75 leaves, max depth = 13, train loss: 0.34815, val loss: 0.33677, in 0.016s
1 tree, 75 leaves, max depth = 17, train loss: 0.34631, val loss: 0.33529, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.34487, val loss: 0.33390, in 0.000s
1 tree, 67 leaves, max depth = 13, train loss: 0.34333, val loss: 0.33206, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.34199, val loss: 0.33077, in 0.000s
1 tree, 75 leaves, max depth = 13, train loss: 0.34045, val loss: 0.32910, in 0.016s
1 tree, 75 leaves, max depth = 17, train loss: 0.33869, val loss: 0.32770, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.33742, val loss: 0.32649, in 0.000s
1 tree, 75 leaves, max depth = 15, train loss: 0.33599, val loss: 0.32493, in 0.016s
1 tree, 75 leaves, max depth = 17, train loss: 0.33433, val loss: 0.32361, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.33313, val loss: 0.32247, in 0.016s
1 tree, 67 leaves, max depth = 14, train loss: 0.33184, val loss: 0.32090, in 0.000s
1 tree, 75 leaves, max depth = 17, train loss: 0.33028, val loss: 0.31967, in 0.016s
1 tree, 75 leaves, max depth = 14, train loss: 0.32897, val loss: 0.31826, in 0.016s
Fit 71 trees in 1.001 s, (4121 total leaves)
Time spent computing histograms: 0.319s
Time spent finding best splits:  0.069s
Time spent applying splits:      0.070s
Time spent predicting:           0.000s
Trial 47, Fold 5: Log loss = 0.33656045996488004, Average precision = 0.9484047206945048, ROC-AUC = 0.9457466882187912, Elapsed Time = 1.0071942000013223 seconds
Optimization Progress:  48%|####8     | 48/100 [09:26<10:32, 12.16s/it]
Trial 48, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 48, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
0.143 s 0.040 GB of training data: 
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 21 leaves, max depth = 8, train loss: 0.68062, val loss: 0.68020, in 0.016s
1 tree, 21 leaves, max depth = 8, train loss: 0.66875, val loss: 0.66793, in 0.000s
1 tree, 21 leaves, max depth = 8, train loss: 0.65749, val loss: 0.65628, in 0.016s
1 tree, 21 leaves, max depth = 8, train loss: 0.64681, val loss: 0.64522, in 0.000s
1 tree, 21 leaves, max depth = 8, train loss: 0.63667, val loss: 0.63471, in 0.016s
1 tree, 21 leaves, max depth = 8, train loss: 0.62704, val loss: 0.62471, in 0.000s
1 tree, 21 leaves, max depth = 8, train loss: 0.61789, val loss: 0.61520, in 0.016s
1 tree, 21 leaves, max depth = 8, train loss: 0.60918, val loss: 0.60615, in 0.000s
1 tree, 21 leaves, max depth = 8, train loss: 0.60090, val loss: 0.59753, in 0.000s
1 tree, 21 leaves, max depth = 8, train loss: 0.59301, val loss: 0.58931, in 0.016s
1 tree, 21 leaves, max depth = 8, train loss: 0.58550, val loss: 0.58148, in 0.000s
1 tree, 21 leaves, max depth = 8, train loss: 0.57835, val loss: 0.57401, in 0.016s
1 tree, 21 leaves, max depth = 8, train loss: 0.57153, val loss: 0.56689, in 0.000s
1 tree, 21 leaves, max depth = 8, train loss: 0.56503, val loss: 0.56009, in 0.000s
1 tree, 21 leaves, max depth = 9, train loss: 0.55888, val loss: 0.55368, in 0.016s
1 tree, 21 leaves, max depth = 9, train loss: 0.55301, val loss: 0.54757, in 0.000s
1 tree, 38 leaves, max depth = 9, train loss: 0.54710, val loss: 0.54203, in 0.016s
1 tree, 21 leaves, max depth = 8, train loss: 0.54157, val loss: 0.53622, in 0.000s
1 tree, 21 leaves, max depth = 9, train loss: 0.53634, val loss: 0.53076, in 0.016s
1 tree, 39 leaves, max depth = 9, train loss: 0.53088, val loss: 0.52566, in 0.000s
1 tree, 39 leaves, max depth = 9, train loss: 0.52568, val loss: 0.52081, in 0.016s
1 tree, 39 leaves, max depth = 9, train loss: 0.52072, val loss: 0.51620, in 0.000s
1 tree, 21 leaves, max depth = 8, train loss: 0.51596, val loss: 0.51117, in 0.016s
1 tree, 39 leaves, max depth = 9, train loss: 0.51130, val loss: 0.50685, in 0.000s
1 tree, 21 leaves, max depth = 11, train loss: 0.50691, val loss: 0.50221, in 0.016s
1 tree, 39 leaves, max depth = 9, train loss: 0.50252, val loss: 0.49814, in 0.000s
1 tree, 21 leaves, max depth = 11, train loss: 0.49840, val loss: 0.49378, in 0.016s
1 tree, 39 leaves, max depth = 9, train loss: 0.49426, val loss: 0.48996, in 0.000s
1 tree, 21 leaves, max depth = 10, train loss: 0.49038, val loss: 0.48584, in 0.016s
1 tree, 39 leaves, max depth = 9, train loss: 0.48647, val loss: 0.48225, in 0.000s
1 tree, 21 leaves, max depth = 11, train loss: 0.48282, val loss: 0.47841, in 0.016s
1 tree, 39 leaves, max depth = 9, train loss: 0.47913, val loss: 0.47503, in 0.000s
1 tree, 39 leaves, max depth = 9, train loss: 0.47560, val loss: 0.47180, in 0.016s
1 tree, 21 leaves, max depth = 8, train loss: 0.47221, val loss: 0.46819, in 0.000s
1 tree, 39 leaves, max depth = 9, train loss: 0.46887, val loss: 0.46514, in 0.016s
1 tree, 21 leaves, max depth = 8, train loss: 0.46567, val loss: 0.46173, in 0.000s
1 tree, 39 leaves, max depth = 9, train loss: 0.46250, val loss: 0.45885, in 0.016s
1 tree, 20 leaves, max depth = 10, train loss: 0.45946, val loss: 0.45562, in 0.000s
1 tree, 39 leaves, max depth = 9, train loss: 0.45646, val loss: 0.45291, in 0.016s
1 tree, 21 leaves, max depth = 8, train loss: 0.45361, val loss: 0.44985, in 0.000s
1 tree, 21 leaves, max depth = 8, train loss: 0.45089, val loss: 0.44692, in 0.016s
1 tree, 39 leaves, max depth = 9, train loss: 0.44806, val loss: 0.44438, in 0.000s
1 tree, 39 leaves, max depth = 9, train loss: 0.44536, val loss: 0.44196, in 0.016s
1 tree, 20 leaves, max depth = 10, train loss: 0.44279, val loss: 0.43922, in 0.000s
1 tree, 39 leaves, max depth = 9, train loss: 0.44023, val loss: 0.43693, in 0.016s
1 tree, 39 leaves, max depth = 9, train loss: 0.43778, val loss: 0.43476, in 0.016s
1 tree, 21 leaves, max depth = 7, train loss: 0.43538, val loss: 0.43217, in 0.000s
1 tree, 39 leaves, max depth = 9, train loss: 0.43305, val loss: 0.43010, in 0.016s
1 tree, 20 leaves, max depth = 10, train loss: 0.43077, val loss: 0.42766, in 0.000s
1 tree, 39 leaves, max depth = 9, train loss: 0.42856, val loss: 0.42571, in 0.016s
1 tree, 21 leaves, max depth = 7, train loss: 0.42641, val loss: 0.42337, in 0.000s
1 tree, 39 leaves, max depth = 9, train loss: 0.42430, val loss: 0.42152, in 0.016s
1 tree, 20 leaves, max depth = 10, train loss: 0.42225, val loss: 0.41931, in 0.000s
1 tree, 39 leaves, max depth = 9, train loss: 0.42024, val loss: 0.41757, in 0.000s
1 tree, 21 leaves, max depth = 8, train loss: 0.41830, val loss: 0.41544, in 0.016s
[56/80] 1 tree, 39 leaves, max depth = 9, train loss: 0.41638, val loss: 0.41379, in 0.000s
1 tree, 21 leaves, max depth = 8, train loss: 0.41454, val loss: 0.41176, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.41273, val loss: 0.40976, in 0.000s
1 tree, 39 leaves, max depth = 9, train loss: 0.41091, val loss: 0.40820, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.40918, val loss: 0.40629, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.40753, val loss: 0.40445, in 0.000s
1 tree, 39 leaves, max depth = 9, train loss: 0.40579, val loss: 0.40299, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.40422, val loss: 0.40124, in 0.000s
1 tree, 39 leaves, max depth = 9, train loss: 0.40256, val loss: 0.39985, in 0.016s
1 tree, 20 leaves, max depth = 10, train loss: 0.40088, val loss: 0.39805, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.39938, val loss: 0.39638, in 0.016s
1 tree, 39 leaves, max depth = 9, train loss: 0.39780, val loss: 0.39507, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.39637, val loss: 0.39348, in 0.000s
1 tree, 20 leaves, max depth = 9, train loss: 0.39479, val loss: 0.39179, in 0.016s
1 tree, 39 leaves, max depth = 9, train loss: 0.39328, val loss: 0.39055, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.39192, val loss: 0.38903, in 0.016s
1 tree, 20 leaves, max depth = 8, train loss: 0.39039, val loss: 0.38742, in 0.000s
1 tree, 39 leaves, max depth = 9, train loss: 0.38895, val loss: 0.38626, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.38765, val loss: 0.38480, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.38640, val loss: 0.38341, in 0.000s
1 tree, 39 leaves, max depth = 9, train loss: 0.38502, val loss: 0.38231, in 0.016s
1 tree, 20 leaves, max depth = 9, train loss: 0.38358, val loss: 0.38078, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.38239, val loss: 0.37944, in 0.016s
1 tree, 39 leaves, max depth = 9, train loss: 0.38107, val loss: 0.37840, in 0.000s
1 tree, 20 leaves, max depth = 9, train loss: 0.37972, val loss: 0.37697, in 0.016s
Fit 80 trees in 0.846 s, (2050 total leaves)
Time spent computing histograms: 0.312s
Time spent finding best splits:  0.046s
Time spent applying splits:      0.039s
Time spent predicting:           0.016s
Trial 48, Fold 1: Log loss = 0.380684813906018, Average precision = 0.9458659296414029, ROC-AUC = 0.9424333575897487, Elapsed Time = 0.8453929999996035 seconds
Trial 48, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 48, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
Binning 0.040 GB of training data: 0.142 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 21 leaves, max depth = 8, train loss: 0.68065, val loss: 0.68003, in 0.016s
1 tree, 21 leaves, max depth = 8, train loss: 0.66881, val loss: 0.66760, in 0.000s
1 tree, 21 leaves, max depth = 9, train loss: 0.65775, val loss: 0.65597, in 0.016s
1 tree, 21 leaves, max depth = 8, train loss: 0.64709, val loss: 0.64476, in 0.000s
1 tree, 21 leaves, max depth = 8, train loss: 0.63697, val loss: 0.63411, in 0.000s
1 tree, 21 leaves, max depth = 8, train loss: 0.62736, val loss: 0.62397, in 0.016s
1 tree, 21 leaves, max depth = 8, train loss: 0.61823, val loss: 0.61434, in 0.000s
1 tree, 21 leaves, max depth = 9, train loss: 0.60967, val loss: 0.60530, in 0.000s
1 tree, 21 leaves, max depth = 8, train loss: 0.60140, val loss: 0.59656, in 0.016s
1 tree, 21 leaves, max depth = 8, train loss: 0.59353, val loss: 0.58822, in 0.000s
1 tree, 21 leaves, max depth = 8, train loss: 0.58603, val loss: 0.58028, in 0.016s
1 tree, 21 leaves, max depth = 9, train loss: 0.57900, val loss: 0.57283, in 0.000s
1 tree, 21 leaves, max depth = 8, train loss: 0.57219, val loss: 0.56560, in 0.016s
1 tree, 21 leaves, max depth = 8, train loss: 0.56570, val loss: 0.55870, in 0.000s
1 tree, 21 leaves, max depth = 8, train loss: 0.55951, val loss: 0.55212, in 0.000s
1 tree, 21 leaves, max depth = 9, train loss: 0.55371, val loss: 0.54594, in 0.016s
1 tree, 39 leaves, max depth = 10, train loss: 0.54783, val loss: 0.54020, in 0.000s
1 tree, 21 leaves, max depth = 8, train loss: 0.54241, val loss: 0.53442, in 0.016s
1 tree, 21 leaves, max depth = 8, train loss: 0.53724, val loss: 0.52889, in 0.000s
1 tree, 39 leaves, max depth = 11, train loss: 0.53179, val loss: 0.52360, in 0.016s
1 tree, 39 leaves, max depth = 11, train loss: 0.52660, val loss: 0.51856, in 0.000s
1 tree, 39 leaves, max depth = 11, train loss: 0.52166, val loss: 0.51376, in 0.016s
1 tree, 21 leaves, max depth = 8, train loss: 0.51699, val loss: 0.50876, in 0.000s
1 tree, 39 leaves, max depth = 11, train loss: 0.51234, val loss: 0.50425, in 0.016s
1 tree, 21 leaves, max depth = 8, train loss: 0.50796, val loss: 0.49956, in 0.000s
1 tree, 39 leaves, max depth = 11, train loss: 0.50358, val loss: 0.49531, in 0.016s
1 tree, 21 leaves, max depth = 8, train loss: 0.49947, val loss: 0.49089, in 0.000s
1 tree, 39 leaves, max depth = 11, train loss: 0.49533, val loss: 0.48689, in 0.016s
1 tree, 21 leaves, max depth = 8, train loss: 0.49146, val loss: 0.48273, in 0.000s
1 tree, 39 leaves, max depth = 11, train loss: 0.48756, val loss: 0.47896, in 0.016s
1 tree, 21 leaves, max depth = 10, train loss: 0.48391, val loss: 0.47504, in 0.016s
1 tree, 39 leaves, max depth = 11, train loss: 0.48022, val loss: 0.47148, in 0.000s
1 tree, 39 leaves, max depth = 11, train loss: 0.47670, val loss: 0.46808, in 0.016s
[34/80] 1 tree, 21 leaves, max depth = 8, train loss: 0.47332, val loss: 0.46443, in 0.000s
1 tree, 39 leaves, max depth = 10, train loss: 0.46998, val loss: 0.46123, in 0.016s
1 tree, 21 leaves, max depth = 8, train loss: 0.46680, val loss: 0.45778, in 0.000s
1 tree, 39 leaves, max depth = 10, train loss: 0.46364, val loss: 0.45475, in 0.016s
1 tree, 21 leaves, max depth = 8, train loss: 0.46063, val loss: 0.45149, in 0.000s
1 tree, 39 leaves, max depth = 9, train loss: 0.45764, val loss: 0.44862, in 0.016s
1 tree, 21 leaves, max depth = 8, train loss: 0.45480, val loss: 0.44554, in 0.000s
1 tree, 21 leaves, max depth = 8, train loss: 0.45208, val loss: 0.44259, in 0.016s
1 tree, 39 leaves, max depth = 9, train loss: 0.44926, val loss: 0.43989, in 0.016s
1 tree, 39 leaves, max depth = 9, train loss: 0.44656, val loss: 0.43731, in 0.000s
1 tree, 39 leaves, max depth = 9, train loss: 0.44398, val loss: 0.43484, in 0.016s
1 tree, 21 leaves, max depth = 8, train loss: 0.44147, val loss: 0.43211, in 0.000s
1 tree, 39 leaves, max depth = 11, train loss: 0.43902, val loss: 0.42977, in 0.016s
1 tree, 21 leaves, max depth = 8, train loss: 0.43664, val loss: 0.42718, in 0.000s
1 tree, 39 leaves, max depth = 10, train loss: 0.43431, val loss: 0.42496, in 0.016s
1 tree, 21 leaves, max depth = 8, train loss: 0.43205, val loss: 0.42250, in 0.000s
1 tree, 38 leaves, max depth = 10, train loss: 0.42983, val loss: 0.42039, in 0.016s
1 tree, 21 leaves, max depth = 8, train loss: 0.42770, val loss: 0.41805, in 0.000s
1 tree, 39 leaves, max depth = 10, train loss: 0.42558, val loss: 0.41605, in 0.016s
1 tree, 21 leaves, max depth = 11, train loss: 0.42356, val loss: 0.41383, in 0.000s
1 tree, 39 leaves, max depth = 10, train loss: 0.42154, val loss: 0.41192, in 0.016s
1 tree, 21 leaves, max depth = 8, train loss: 0.41962, val loss: 0.40982, in 0.016s
1 tree, 39 leaves, max depth = 10, train loss: 0.41770, val loss: 0.40800, in 0.000s
1 tree, 39 leaves, max depth = 10, train loss: 0.41586, val loss: 0.40627, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.41408, val loss: 0.40443, in 0.000s
1 tree, 21 leaves, max depth = 10, train loss: 0.41229, val loss: 0.40246, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.41061, val loss: 0.40072, in 0.000s
1 tree, 39 leaves, max depth = 9, train loss: 0.40887, val loss: 0.39908, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.40726, val loss: 0.39741, in 0.000s
1 tree, 39 leaves, max depth = 9, train loss: 0.40560, val loss: 0.39586, in 0.016s
1 tree, 21 leaves, max depth = 7, train loss: 0.40392, val loss: 0.39402, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.40241, val loss: 0.39243, in 0.016s
1 tree, 39 leaves, max depth = 9, train loss: 0.40082, val loss: 0.39095, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.39937, val loss: 0.38944, in 0.016s
1 tree, 21 leaves, max depth = 10, train loss: 0.39776, val loss: 0.38767, in 0.000s
1 tree, 39 leaves, max depth = 9, train loss: 0.39625, val loss: 0.38626, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.39487, val loss: 0.38482, in 0.000s
1 tree, 39 leaves, max depth = 10, train loss: 0.39342, val loss: 0.38348, in 0.016s
1 tree, 21 leaves, max depth = 9, train loss: 0.39189, val loss: 0.38182, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.39058, val loss: 0.38044, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.38932, val loss: 0.37913, in 0.000s
1 tree, 39 leaves, max depth = 10, train loss: 0.38793, val loss: 0.37785, in 0.016s
1 tree, 21 leaves, max depth = 10, train loss: 0.38651, val loss: 0.37627, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.38530, val loss: 0.37501, in 0.016s
1 tree, 39 leaves, max depth = 10, train loss: 0.38398, val loss: 0.37379, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.38283, val loss: 0.37259, in 0.016s
1 tree, 21 leaves, max depth = 9, train loss: 0.38147, val loss: 0.37110, in 0.000s
Fit 80 trees in 0.876 s, (2059 total leaves)
Time spent computing histograms: 0.341s
Time spent finding best splits:  0.050s
Time spent applying splits:      0.043s
Time spent predicting:           0.000s
Trial 48, Fold 2: Log loss = 0.3832620626880714, Average precision = 0.9423748606646112, ROC-AUC = 0.9432571023274772, Elapsed Time = 0.8830567999993946 seconds
Trial 48, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 48, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
Binning 0.040 GB of training data: 0.157 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 21 leaves, max depth = 7, train loss: 0.68072, val loss: 0.68033, in 0.016s
1 tree, 21 leaves, max depth = 7, train loss: 0.66895, val loss: 0.66819, in 0.000s
1 tree, 21 leaves, max depth = 7, train loss: 0.65779, val loss: 0.65666, in 0.016s
1 tree, 21 leaves, max depth = 7, train loss: 0.64720, val loss: 0.64572, in 0.000s
1 tree, 21 leaves, max depth = 7, train loss: 0.63715, val loss: 0.63533, in 0.016s
1 tree, 21 leaves, max depth = 7, train loss: 0.62760, val loss: 0.62544, in 0.000s
1 tree, 21 leaves, max depth = 7, train loss: 0.61852, val loss: 0.61605, in 0.016s
1 tree, 21 leaves, max depth = 7, train loss: 0.60989, val loss: 0.60710, in 0.000s
1 tree, 21 leaves, max depth = 7, train loss: 0.60168, val loss: 0.59858, in 0.000s
1 tree, 21 leaves, max depth = 7, train loss: 0.59386, val loss: 0.59047, in 0.000s
1 tree, 21 leaves, max depth = 7, train loss: 0.58642, val loss: 0.58274, in 0.000s
1 tree, 21 leaves, max depth = 7, train loss: 0.57932, val loss: 0.57537, in 0.016s
1 tree, 21 leaves, max depth = 7, train loss: 0.57257, val loss: 0.56834, in 0.000s
1 tree, 21 leaves, max depth = 7, train loss: 0.56613, val loss: 0.56163, in 0.016s
1 tree, 21 leaves, max depth = 7, train loss: 0.55999, val loss: 0.55523, in 0.016s
1 tree, 41 leaves, max depth = 10, train loss: 0.55385, val loss: 0.54946, in 0.000s
1 tree, 21 leaves, max depth = 8, train loss: 0.54818, val loss: 0.54354, in 0.016s
1 tree, 41 leaves, max depth = 10, train loss: 0.54243, val loss: 0.53814, in 0.000s
1 tree, 21 leaves, max depth = 8, train loss: 0.53713, val loss: 0.53259, in 0.016s
1 tree, 41 leaves, max depth = 10, train loss: 0.53173, val loss: 0.52753, in 0.000s
1 tree, 21 leaves, max depth = 8, train loss: 0.52677, val loss: 0.52232, in 0.016s
1 tree, 41 leaves, max depth = 10, train loss: 0.52170, val loss: 0.51757, in 0.000s
1 tree, 41 leaves, max depth = 10, train loss: 0.51685, val loss: 0.51304, in 0.016s
1 tree, 21 leaves, max depth = 8, train loss: 0.51229, val loss: 0.50824, in 0.016s
1 tree, 41 leaves, max depth = 10, train loss: 0.50773, val loss: 0.50398, in 0.000s
1 tree, 21 leaves, max depth = 8, train loss: 0.50345, val loss: 0.49947, in 0.016s
1 tree, 41 leaves, max depth = 10, train loss: 0.49915, val loss: 0.49546, in 0.000s
1 tree, 21 leaves, max depth = 8, train loss: 0.49514, val loss: 0.49121, in 0.016s
1 tree, 41 leaves, max depth = 10, train loss: 0.49108, val loss: 0.48744, in 0.000s
1 tree, 21 leaves, max depth = 8, train loss: 0.48730, val loss: 0.48343, in 0.016s
1 tree, 41 leaves, max depth = 10, train loss: 0.48346, val loss: 0.47987, in 0.000s
1 tree, 21 leaves, max depth = 8, train loss: 0.47991, val loss: 0.47610, in 0.016s
1 tree, 41 leaves, max depth = 10, train loss: 0.47628, val loss: 0.47274, in 0.016s
1 tree, 41 leaves, max depth = 10, train loss: 0.47281, val loss: 0.46953, in 0.000s
1 tree, 21 leaves, max depth = 8, train loss: 0.46951, val loss: 0.46601, in 0.016s
1 tree, 41 leaves, max depth = 10, train loss: 0.46623, val loss: 0.46298, in 0.000s
1 tree, 21 leaves, max depth = 8, train loss: 0.46312, val loss: 0.45966, in 0.016s
1 tree, 41 leaves, max depth = 10, train loss: 0.46000, val loss: 0.45679, in 0.000s
1 tree, 20 leaves, max depth = 7, train loss: 0.45707, val loss: 0.45365, in 0.016s
1 tree, 41 leaves, max depth = 10, train loss: 0.45411, val loss: 0.45093, in 0.000s
1 tree, 21 leaves, max depth = 8, train loss: 0.45134, val loss: 0.44796, in 0.016s
1 tree, 41 leaves, max depth = 10, train loss: 0.44854, val loss: 0.44539, in 0.016s
1 tree, 41 leaves, max depth = 10, train loss: 0.44585, val loss: 0.44293, in 0.000s
1 tree, 21 leaves, max depth = 8, train loss: 0.44326, val loss: 0.44015, in 0.000s
1 tree, 41 leaves, max depth = 10, train loss: 0.44071, val loss: 0.43782, in 0.016s
1 tree, 21 leaves, max depth = 8, train loss: 0.43826, val loss: 0.43517, in 0.016s
1 tree, 40 leaves, max depth = 10, train loss: 0.43584, val loss: 0.43296, in 0.000s
1 tree, 19 leaves, max depth = 9, train loss: 0.43351, val loss: 0.43041, in 0.016s
1 tree, 41 leaves, max depth = 10, train loss: 0.43120, val loss: 0.42831, in 0.000s
1 tree, 21 leaves, max depth = 10, train loss: 0.42903, val loss: 0.42591, in 0.016s
1 tree, 41 leaves, max depth = 10, train loss: 0.42683, val loss: 0.42392, in 0.000s
1 tree, 21 leaves, max depth = 8, train loss: 0.42474, val loss: 0.42166, in 0.016s
1 tree, 41 leaves, max depth = 10, train loss: 0.42265, val loss: 0.41977, in 0.016s
1 tree, 41 leaves, max depth = 10, train loss: 0.42065, val loss: 0.41797, in 0.000s
1 tree, 20 leaves, max depth = 10, train loss: 0.41868, val loss: 0.41579, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.41683, val loss: 0.41407, in 0.016s
[57/80] 1 tree, 41 leaves, max depth = 10, train loss: 0.41493, val loss: 0.41236, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.41316, val loss: 0.41072, in 0.016s
1 tree, 20 leaves, max depth = 10, train loss: 0.41133, val loss: 0.40868, in 0.000s
1 tree, 41 leaves, max depth = 10, train loss: 0.40952, val loss: 0.40707, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.40785, val loss: 0.40552, in 0.000s
1 tree, 40 leaves, max depth = 10, train loss: 0.40613, val loss: 0.40398, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.40453, val loss: 0.40250, in 0.000s
1 tree, 20 leaves, max depth = 10, train loss: 0.40283, val loss: 0.40060, in 0.000s
1 tree, 41 leaves, max depth = 10, train loss: 0.40118, val loss: 0.39915, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.39967, val loss: 0.39774, in 0.000s
1 tree, 20 leaves, max depth = 10, train loss: 0.39806, val loss: 0.39594, in 0.016s
1 tree, 41 leaves, max depth = 10, train loss: 0.39649, val loss: 0.39456, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.39505, val loss: 0.39322, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.39367, val loss: 0.39194, in 0.000s
1 tree, 40 leaves, max depth = 9, train loss: 0.39216, val loss: 0.39062, in 0.016s
1 tree, 20 leaves, max depth = 8, train loss: 0.39063, val loss: 0.38890, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.38932, val loss: 0.38768, in 0.016s
1 tree, 40 leaves, max depth = 9, train loss: 0.38788, val loss: 0.38643, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.38662, val loss: 0.38526, in 0.016s
1 tree, 40 leaves, max depth = 9, train loss: 0.38525, val loss: 0.38408, in 0.000s
1 tree, 20 leaves, max depth = 7, train loss: 0.38380, val loss: 0.38245, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.38259, val loss: 0.38133, in 0.000s
1 tree, 41 leaves, max depth = 10, train loss: 0.38128, val loss: 0.38020, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.38012, val loss: 0.37913, in 0.000s
Fit 80 trees in 0.938 s, (2110 total leaves)
Time spent computing histograms: 0.345s
Time spent finding best splits:  0.051s
Time spent applying splits:      0.044s
Time spent predicting:           0.000s
Trial 48, Fold 3: Log loss = 0.3766158102262939, Average precision = 0.9477626147986153, ROC-AUC = 0.9475018298966589, Elapsed Time = 0.9445581999989372 seconds
Trial 48, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 48, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
Binning 0.040 GB of training data: 0.159 s
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 20 leaves, max depth = 9, train loss: 0.68079, val loss: 0.68008, in 0.000s
1 tree, 20 leaves, max depth = 9, train loss: 0.66909, val loss: 0.66768, in 0.016s
1 tree, 20 leaves, max depth = 9, train loss: 0.65800, val loss: 0.65590, in 0.000s
1 tree, 20 leaves, max depth = 9, train loss: 0.64747, val loss: 0.64471, in 0.016s
1 tree, 20 leaves, max depth = 9, train loss: 0.63748, val loss: 0.63407, in 0.000s
1 tree, 20 leaves, max depth = 9, train loss: 0.62799, val loss: 0.62394, in 0.016s
1 tree, 20 leaves, max depth = 9, train loss: 0.61897, val loss: 0.61429, in 0.000s
1 tree, 20 leaves, max depth = 9, train loss: 0.61039, val loss: 0.60510, in 0.016s
1 tree, 20 leaves, max depth = 9, train loss: 0.60223, val loss: 0.59634, in 0.000s
1 tree, 20 leaves, max depth = 9, train loss: 0.59446, val loss: 0.58799, in 0.016s
1 tree, 20 leaves, max depth = 9, train loss: 0.58707, val loss: 0.58002, in 0.000s
1 tree, 20 leaves, max depth = 9, train loss: 0.58003, val loss: 0.57241, in 0.016s
1 tree, 20 leaves, max depth = 9, train loss: 0.57332, val loss: 0.56515, in 0.000s
1 tree, 20 leaves, max depth = 9, train loss: 0.56693, val loss: 0.55822, in 0.016s
1 tree, 20 leaves, max depth = 9, train loss: 0.56083, val loss: 0.55159, in 0.000s
1 tree, 20 leaves, max depth = 9, train loss: 0.55502, val loss: 0.54525, in 0.016s
1 tree, 38 leaves, max depth = 10, train loss: 0.54914, val loss: 0.53933, in 0.000s
1 tree, 21 leaves, max depth = 9, train loss: 0.54371, val loss: 0.53340, in 0.016s
1 tree, 21 leaves, max depth = 9, train loss: 0.53854, val loss: 0.52773, in 0.000s
1 tree, 38 leaves, max depth = 10, train loss: 0.53311, val loss: 0.52227, in 0.016s
1 tree, 38 leaves, max depth = 10, train loss: 0.52794, val loss: 0.51706, in 0.000s
1 tree, 38 leaves, max depth = 10, train loss: 0.52302, val loss: 0.51210, in 0.016s
1 tree, 21 leaves, max depth = 11, train loss: 0.51835, val loss: 0.50697, in 0.000s
1 tree, 38 leaves, max depth = 10, train loss: 0.51371, val loss: 0.50230, in 0.016s
1 tree, 21 leaves, max depth = 11, train loss: 0.50933, val loss: 0.49747, in 0.000s
1 tree, 38 leaves, max depth = 11, train loss: 0.50496, val loss: 0.49307, in 0.016s
1 tree, 21 leaves, max depth = 11, train loss: 0.50084, val loss: 0.48852, in 0.016s
1 tree, 38 leaves, max depth = 10, train loss: 0.49673, val loss: 0.48438, in 0.000s
1 tree, 21 leaves, max depth = 11, train loss: 0.49286, val loss: 0.48009, in 0.016s
1 tree, 38 leaves, max depth = 11, train loss: 0.48897, val loss: 0.47618, in 0.000s
1 tree, 21 leaves, max depth = 9, train loss: 0.48534, val loss: 0.47218, in 0.016s
1 tree, 38 leaves, max depth = 10, train loss: 0.48167, val loss: 0.46849, in 0.000s
1 tree, 38 leaves, max depth = 10, train loss: 0.47817, val loss: 0.46496, in 0.016s
1 tree, 21 leaves, max depth = 10, train loss: 0.47478, val loss: 0.46119, in 0.000s
1 tree, 38 leaves, max depth = 11, train loss: 0.47146, val loss: 0.45785, in 0.016s
1 tree, 21 leaves, max depth = 10, train loss: 0.46827, val loss: 0.45429, in 0.000s
1 tree, 38 leaves, max depth = 10, train loss: 0.46513, val loss: 0.45113, in 0.016s
1 tree, 20 leaves, max depth = 12, train loss: 0.46212, val loss: 0.44776, in 0.000s
1 tree, 38 leaves, max depth = 10, train loss: 0.45915, val loss: 0.44477, in 0.016s
1 tree, 21 leaves, max depth = 10, train loss: 0.45630, val loss: 0.44157, in 0.000s
1 tree, 20 leaves, max depth = 12, train loss: 0.45358, val loss: 0.43851, in 0.016s
1 tree, 38 leaves, max depth = 10, train loss: 0.45078, val loss: 0.43570, in 0.000s
1 tree, 38 leaves, max depth = 10, train loss: 0.44811, val loss: 0.43301, in 0.016s
1 tree, 38 leaves, max depth = 10, train loss: 0.44555, val loss: 0.43044, in 0.016s
1 tree, 21 leaves, max depth = 9, train loss: 0.44303, val loss: 0.42759, in 0.000s
1 tree, 38 leaves, max depth = 11, train loss: 0.44060, val loss: 0.42514, in 0.016s
1 tree, 21 leaves, max depth = 11, train loss: 0.43821, val loss: 0.42244, in 0.000s
1 tree, 38 leaves, max depth = 11, train loss: 0.43590, val loss: 0.42011, in 0.016s
1 tree, 20 leaves, max depth = 11, train loss: 0.43364, val loss: 0.41756, in 0.000s
1 tree, 38 leaves, max depth = 11, train loss: 0.43145, val loss: 0.41535, in 0.016s
1 tree, 21 leaves, max depth = 11, train loss: 0.42930, val loss: 0.41290, in 0.016s
1 tree, 38 leaves, max depth = 11, train loss: 0.42721, val loss: 0.41080, in 0.000s
1 tree, 21 leaves, max depth = 9, train loss: 0.42519, val loss: 0.40851, in 0.016s
1 tree, 38 leaves, max depth = 11, train loss: 0.42320, val loss: 0.40651, in 0.000s
1 tree, 20 leaves, max depth = 10, train loss: 0.42128, val loss: 0.40431, in 0.016s
1 tree, 38 leaves, max depth = 10, train loss: 0.41939, val loss: 0.40240, in 0.000s
1 tree, 38 leaves, max depth = 11, train loss: 0.41758, val loss: 0.40058, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.41580, val loss: 0.39866, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.41411, val loss: 0.39683, in 0.000s
1 tree, 20 leaves, max depth = 10, train loss: 0.41232, val loss: 0.39479, in 0.016s
1 tree, 38 leaves, max depth = 10, train loss: 0.41061, val loss: 0.39307, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.40900, val loss: 0.39134, in 0.016s
1 tree, 19 leaves, max depth = 10, train loss: 0.40732, val loss: 0.38941, in 0.000s
1 tree, 38 leaves, max depth = 10, train loss: 0.40569, val loss: 0.38777, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.40416, val loss: 0.38613, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.40270, val loss: 0.38455, in 0.016s
1 tree, 38 leaves, max depth = 10, train loss: 0.40115, val loss: 0.38300, in 0.000s
1 tree, 20 leaves, max depth = 9, train loss: 0.39957, val loss: 0.38119, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.39819, val loss: 0.37969, in 0.016s
1 tree, 38 leaves, max depth = 11, train loss: 0.39670, val loss: 0.37821, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.39537, val loss: 0.37678, in 0.016s
1 tree, 38 leaves, max depth = 11, train loss: 0.39395, val loss: 0.37536, in 0.000s
1 tree, 20 leaves, max depth = 9, train loss: 0.39247, val loss: 0.37367, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.39121, val loss: 0.37230, in 0.000s
1 tree, 38 leaves, max depth = 11, train loss: 0.38985, val loss: 0.37095, in 0.000s
1 tree, 20 leaves, max depth = 9, train loss: 0.38845, val loss: 0.36934, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.38724, val loss: 0.36803, in 0.000s
1 tree, 38 leaves, max depth = 11, train loss: 0.38593, val loss: 0.36674, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.38478, val loss: 0.36548, in 0.000s
1 tree, 21 leaves, max depth = 7, train loss: 0.38339, val loss: 0.36388, in 0.016s
Fit 80 trees in 0.925 s, (2004 total leaves)
Time spent computing histograms: 0.343s
Time spent finding best splits:  0.050s
Time spent applying splits:      0.043s
Time spent predicting:           0.000s
Trial 48, Fold 4: Log loss = 0.38315428635904075, Average precision = 0.945962665443955, ROC-AUC = 0.9426270026061598, Elapsed Time = 0.938886599999023 seconds
Trial 48, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 48, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
Binning 0.040 GB of training data: 0.159 s
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 21 leaves, max depth = 8, train loss: 0.68073, val loss: 0.67990, in 0.000s
1 tree, 21 leaves, max depth = 8, train loss: 0.66897, val loss: 0.66733, in 0.016s
1 tree, 21 leaves, max depth = 9, train loss: 0.65776, val loss: 0.65533, in 0.000s
1 tree, 21 leaves, max depth = 9, train loss: 0.64712, val loss: 0.64392, in 0.016s
1 tree, 21 leaves, max depth = 8, train loss: 0.63707, val loss: 0.63313, in 0.000s
1 tree, 21 leaves, max depth = 9, train loss: 0.62748, val loss: 0.62280, in 0.016s
1 tree, 21 leaves, max depth = 9, train loss: 0.61836, val loss: 0.61297, in 0.000s
1 tree, 21 leaves, max depth = 8, train loss: 0.60973, val loss: 0.60365, in 0.016s
1 tree, 21 leaves, max depth = 9, train loss: 0.60147, val loss: 0.59471, in 0.000s
1 tree, 21 leaves, max depth = 9, train loss: 0.59361, val loss: 0.58620, in 0.016s
1 tree, 21 leaves, max depth = 8, train loss: 0.58617, val loss: 0.57811, in 0.000s
1 tree, 21 leaves, max depth = 9, train loss: 0.57904, val loss: 0.57035, in 0.016s
1 tree, 21 leaves, max depth = 9, train loss: 0.57225, val loss: 0.56294, in 0.000s
1 tree, 21 leaves, max depth = 8, train loss: 0.56581, val loss: 0.55590, in 0.016s
1 tree, 21 leaves, max depth = 9, train loss: 0.55964, val loss: 0.54914, in 0.000s
1 tree, 21 leaves, max depth = 9, train loss: 0.55375, val loss: 0.54268, in 0.016s
1 tree, 39 leaves, max depth = 10, train loss: 0.54790, val loss: 0.53706, in 0.000s
1 tree, 21 leaves, max depth = 8, train loss: 0.54242, val loss: 0.53103, in 0.016s
1 tree, 21 leaves, max depth = 8, train loss: 0.53720, val loss: 0.52527, in 0.000s
1 tree, 39 leaves, max depth = 10, train loss: 0.53179, val loss: 0.52010, in 0.016s
1 tree, 39 leaves, max depth = 10, train loss: 0.52664, val loss: 0.51518, in 0.016s
1 tree, 21 leaves, max depth = 8, train loss: 0.52184, val loss: 0.50986, in 0.000s
1 tree, 39 leaves, max depth = 10, train loss: 0.51699, val loss: 0.50525, in 0.016s
1 tree, 21 leaves, max depth = 8, train loss: 0.51249, val loss: 0.50025, in 0.000s
1 tree, 39 leaves, max depth = 10, train loss: 0.50792, val loss: 0.49592, in 0.016s
1 tree, 39 leaves, max depth = 10, train loss: 0.50357, val loss: 0.49179, in 0.000s
1 tree, 21 leaves, max depth = 8, train loss: 0.49941, val loss: 0.48716, in 0.016s
1 tree, 39 leaves, max depth = 10, train loss: 0.49530, val loss: 0.48328, in 0.000s
1 tree, 21 leaves, max depth = 8, train loss: 0.49139, val loss: 0.47892, in 0.016s
1 tree, 39 leaves, max depth = 11, train loss: 0.48751, val loss: 0.47527, in 0.000s
1 tree, 21 leaves, max depth = 8, train loss: 0.48383, val loss: 0.47115, in 0.016s
1 tree, 39 leaves, max depth = 11, train loss: 0.48017, val loss: 0.46771, in 0.016s
1 tree, 21 leaves, max depth = 8, train loss: 0.47670, val loss: 0.46382, in 0.000s
1 tree, 39 leaves, max depth = 11, train loss: 0.47324, val loss: 0.46058, in 0.016s
1 tree, 21 leaves, max depth = 8, train loss: 0.46996, val loss: 0.45690, in 0.000s
1 tree, 39 leaves, max depth = 10, train loss: 0.46668, val loss: 0.45384, in 0.016s
1 tree, 39 leaves, max depth = 10, train loss: 0.46355, val loss: 0.45093, in 0.000s
1 tree, 21 leaves, max depth = 7, train loss: 0.46050, val loss: 0.44749, in 0.016s
1 tree, 39 leaves, max depth = 9, train loss: 0.45752, val loss: 0.44473, in 0.016s
1 tree, 21 leaves, max depth = 7, train loss: 0.45464, val loss: 0.44147, in 0.000s
1 tree, 21 leaves, max depth = 7, train loss: 0.45189, val loss: 0.43835, in 0.016s
1 tree, 39 leaves, max depth = 11, train loss: 0.44909, val loss: 0.43577, in 0.000s
1 tree, 39 leaves, max depth = 9, train loss: 0.44641, val loss: 0.43330, in 0.016s
1 tree, 21 leaves, max depth = 7, train loss: 0.44383, val loss: 0.43037, in 0.000s
1 tree, 39 leaves, max depth = 11, train loss: 0.44129, val loss: 0.42804, in 0.016s
1 tree, 21 leaves, max depth = 7, train loss: 0.43885, val loss: 0.42526, in 0.000s
1 tree, 39 leaves, max depth = 10, train loss: 0.43643, val loss: 0.42305, in 0.016s
1 tree, 39 leaves, max depth = 10, train loss: 0.43411, val loss: 0.42094, in 0.000s
1 tree, 21 leaves, max depth = 7, train loss: 0.43182, val loss: 0.41832, in 0.016s
1 tree, 39 leaves, max depth = 10, train loss: 0.42962, val loss: 0.41633, in 0.000s
1 tree, 21 leaves, max depth = 7, train loss: 0.42745, val loss: 0.41384, in 0.016s
1 tree, 39 leaves, max depth = 10, train loss: 0.42535, val loss: 0.41194, in 0.000s
1 tree, 21 leaves, max depth = 8, train loss: 0.42330, val loss: 0.40958, in 0.016s
1 tree, 39 leaves, max depth = 10, train loss: 0.42130, val loss: 0.40779, in 0.000s
1 tree, 21 leaves, max depth = 7, train loss: 0.41935, val loss: 0.40553, in 0.016s
1 tree, 39 leaves, max depth = 10, train loss: 0.41744, val loss: 0.40382, in 0.000s
1 tree, 21 leaves, max depth = 7, train loss: 0.41558, val loss: 0.40167, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.41381, val loss: 0.39997, in 0.000s
1 tree, 39 leaves, max depth = 10, train loss: 0.41199, val loss: 0.39836, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.41030, val loss: 0.39674, in 0.000s
1 tree, 39 leaves, max depth = 9, train loss: 0.40857, val loss: 0.39521, in 0.016s
1 tree, 21 leaves, max depth = 9, train loss: 0.40686, val loss: 0.39322, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.40526, val loss: 0.39169, in 0.016s
1 tree, 39 leaves, max depth = 9, train loss: 0.40361, val loss: 0.39024, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.40208, val loss: 0.38878, in 0.016s
1 tree, 39 leaves, max depth = 9, train loss: 0.40050, val loss: 0.38741, in 0.000s
1 tree, 20 leaves, max depth = 10, train loss: 0.39889, val loss: 0.38550, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.39744, val loss: 0.38411, in 0.000s
1 tree, 38 leaves, max depth = 10, train loss: 0.39593, val loss: 0.38281, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.39455, val loss: 0.38148, in 0.000s
1 tree, 20 leaves, max depth = 10, train loss: 0.39303, val loss: 0.37968, in 0.000s
1 tree, 39 leaves, max depth = 10, train loss: 0.39159, val loss: 0.37844, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.39027, val loss: 0.37718, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.38900, val loss: 0.37597, in 0.016s
1 tree, 39 leaves, max depth = 10, train loss: 0.38763, val loss: 0.37480, in 0.000s
1 tree, 20 leaves, max depth = 9, train loss: 0.38620, val loss: 0.37310, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.38499, val loss: 0.37195, in 0.000s
1 tree, 39 leaves, max depth = 10, train loss: 0.38367, val loss: 0.37083, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.38251, val loss: 0.36973, in 0.000s
1 tree, 20 leaves, max depth = 8, train loss: 0.38116, val loss: 0.36811, in 0.000s
Fit 80 trees in 0.925 s, (2055 total leaves)
Time spent computing histograms: 0.338s
Time spent finding best splits:  0.051s
Time spent applying splits:      0.044s
Time spent predicting:           0.016s
Trial 48, Fold 5: Log loss = 0.3854740829711175, Average precision = 0.9450464468313988, ROC-AUC = 0.9415032978466454, Elapsed Time = 0.9400613999987399 seconds
Optimization Progress:  49%|####9     | 49/100 [09:37<10:02, 11.81s/it]
Trial 49, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 49, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
0.173 s 0.040 GB of training data: 
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 6 leaves, max depth = 4, train loss: 0.64967, val loss: 0.64819, in 0.016s
1 tree, 7 leaves, max depth = 4, train loss: 0.61370, val loss: 0.61102, in 0.000s
1 tree, 8 leaves, max depth = 5, train loss: 0.58401, val loss: 0.58024, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.55969, val loss: 0.55494, in 0.000s
1 tree, 41 leaves, max depth = 10, train loss: 0.53764, val loss: 0.53467, in 0.016s
1 tree, 7 leaves, max depth = 4, train loss: 0.51850, val loss: 0.51461, in 0.000s
1 tree, 40 leaves, max depth = 13, train loss: 0.50111, val loss: 0.49877, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.48634, val loss: 0.48316, in 0.000s
1 tree, 7 leaves, max depth = 4, train loss: 0.47365, val loss: 0.46970, in 0.016s
1 tree, 28 leaves, max depth = 10, train loss: 0.46025, val loss: 0.45770, in 0.000s
1 tree, 40 leaves, max depth = 11, train loss: 0.44879, val loss: 0.44734, in 0.016s
Fit 11 trees in 0.376 s, (194 total leaves)
Time spent computing histograms: 0.050s
Time spent finding best splits:  0.007s
Time spent applying splits:      0.005s
Time spent predicting:           0.000s
Trial 49, Fold 1: Log loss = 0.44881063567524554, Average precision = 0.9169794849465421, ROC-AUC = 0.9279338014940995, Elapsed Time = 0.3875459000009869 seconds
Trial 49, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 49, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
0.189 s 0.040 GB of training data: 
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 6 leaves, max depth = 4, train loss: 0.64957, val loss: 0.64758, in 0.016s
1 tree, 6 leaves, max depth = 3, train loss: 0.61375, val loss: 0.60978, in 0.000s
1 tree, 8 leaves, max depth = 4, train loss: 0.58416, val loss: 0.57844, in 0.016s
1 tree, 6 leaves, max depth = 4, train loss: 0.55976, val loss: 0.55260, in 0.000s
1 tree, 47 leaves, max depth = 12, train loss: 0.53801, val loss: 0.53182, in 0.016s
1 tree, 8 leaves, max depth = 4, train loss: 0.51902, val loss: 0.51149, in 0.000s
1 tree, 45 leaves, max depth = 14, train loss: 0.50178, val loss: 0.49514, in 0.016s
1 tree, 7 leaves, max depth = 4, train loss: 0.48697, val loss: 0.47920, in 0.016s
1 tree, 8 leaves, max depth = 4, train loss: 0.47434, val loss: 0.46545, in 0.000s
1 tree, 46 leaves, max depth = 14, train loss: 0.46095, val loss: 0.45293, in 0.016s
1 tree, 46 leaves, max depth = 14, train loss: 0.44960, val loss: 0.44238, in 0.016s
Fit 11 trees in 0.423 s, (233 total leaves)
Time spent computing histograms: 0.055s
Time spent finding best splits:  0.008s
Time spent applying splits:      0.007s
Time spent predicting:           0.000s
Trial 49, Fold 2: Log loss = 0.4507257352537522, Average precision = 0.9085994456637089, ROC-AUC = 0.9257551956165527, Elapsed Time = 0.43212510000012117 seconds
Trial 49, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 49, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
0.173 s 0.040 GB of training data: 
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 6 leaves, max depth = 4, train loss: 0.64996, val loss: 0.64830, in 0.016s
1 tree, 7 leaves, max depth = 4, train loss: 0.61434, val loss: 0.61120, in 0.000s
1 tree, 8 leaves, max depth = 4, train loss: 0.58491, val loss: 0.58043, in 0.016s
1 tree, 8 leaves, max depth = 6, train loss: 0.56077, val loss: 0.55505, in 0.000s
1 tree, 44 leaves, max depth = 14, train loss: 0.53865, val loss: 0.53467, in 0.000s
1 tree, 8 leaves, max depth = 4, train loss: 0.51975, val loss: 0.51466, in 0.016s
1 tree, 46 leaves, max depth = 14, train loss: 0.50227, val loss: 0.49871, in 0.000s
1 tree, 4 leaves, max depth = 3, train loss: 0.48767, val loss: 0.48304, in 0.016s
1 tree, 9 leaves, max depth = 5, train loss: 0.47517, val loss: 0.46960, in 0.000s
1 tree, 48 leaves, max depth = 11, train loss: 0.46140, val loss: 0.45717, in 0.016s
1 tree, 56 leaves, max depth = 13, train loss: 0.44957, val loss: 0.44655, in 0.016s
Fit 11 trees in 0.423 s, (244 total leaves)
Time spent computing histograms: 0.055s
Time spent finding best splits:  0.007s
Time spent applying splits:      0.006s
Time spent predicting:           0.000s
Trial 49, Fold 3: Log loss = 0.44611069590815944, Average precision = 0.9203295690550128, ROC-AUC = 0.9321823895320279, Elapsed Time = 0.42323920000126236 seconds
Trial 49, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 49, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
0.173 s 0.040 GB of training data: 
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 6 leaves, max depth = 5, train loss: 0.64985, val loss: 0.64751, in 0.000s
1 tree, 8 leaves, max depth = 5, train loss: 0.61415, val loss: 0.60949, in 0.016s
1 tree, 9 leaves, max depth = 5, train loss: 0.58463, val loss: 0.57790, in 0.000s
1 tree, 8 leaves, max depth = 5, train loss: 0.56034, val loss: 0.55181, in 0.016s
1 tree, 38 leaves, max depth = 12, train loss: 0.53858, val loss: 0.53068, in 0.000s
1 tree, 14 leaves, max depth = 6, train loss: 0.51959, val loss: 0.51003, in 0.016s
1 tree, 47 leaves, max depth = 13, train loss: 0.50202, val loss: 0.49318, in 0.016s
1 tree, 7 leaves, max depth = 4, train loss: 0.48731, val loss: 0.47699, in 0.000s
1 tree, 13 leaves, max depth = 5, train loss: 0.47475, val loss: 0.46303, in 0.016s
1 tree, 37 leaves, max depth = 11, train loss: 0.46148, val loss: 0.45029, in 0.000s
1 tree, 41 leaves, max depth = 10, train loss: 0.45011, val loss: 0.43939, in 0.000s
Fit 11 trees in 0.407 s, (228 total leaves)
Time spent computing histograms: 0.054s
Time spent finding best splits:  0.007s
Time spent applying splits:      0.006s
Time spent predicting:           0.000s
Trial 49, Fold 4: Log loss = 0.45057004868577993, Average precision = 0.9144949064218189, ROC-AUC = 0.9272061304367524, Elapsed Time = 0.42175010000028124 seconds
Trial 49, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 49, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
0.174 s 0.040 GB of training data: 
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 5 leaves, max depth = 4, train loss: 0.64929, val loss: 0.64638, in 0.000s
1 tree, 6 leaves, max depth = 4, train loss: 0.61341, val loss: 0.60766, in 0.000s
1 tree, 8 leaves, max depth = 4, train loss: 0.58357, val loss: 0.57537, in 0.016s
1 tree, 5 leaves, max depth = 4, train loss: 0.55901, val loss: 0.54834, in 0.000s
1 tree, 46 leaves, max depth = 16, train loss: 0.53697, val loss: 0.52722, in 0.016s
1 tree, 8 leaves, max depth = 5, train loss: 0.51789, val loss: 0.50611, in 0.016s
1 tree, 47 leaves, max depth = 14, train loss: 0.50044, val loss: 0.48961, in 0.000s
1 tree, 5 leaves, max depth = 4, train loss: 0.48557, val loss: 0.47292, in 0.016s
1 tree, 9 leaves, max depth = 4, train loss: 0.47283, val loss: 0.45868, in 0.000s
1 tree, 40 leaves, max depth = 10, train loss: 0.45932, val loss: 0.44610, in 0.016s
1 tree, 49 leaves, max depth = 14, train loss: 0.44784, val loss: 0.43552, in 0.016s
Fit 11 trees in 0.424 s, (228 total leaves)
Time spent computing histograms: 0.054s
Time spent finding best splits:  0.007s
Time spent applying splits:      0.006s
Time spent predicting:           0.000s
Trial 49, Fold 5: Log loss = 0.45311330864184113, Average precision = 0.9136391070882067, ROC-AUC = 0.9254636122103934, Elapsed Time = 0.42471040000054927 seconds
Optimization Progress:  50%|#####     | 50/100 [09:46<09:11, 11.02s/it]
Trial 50, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 50, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
0.143 s 0.040 GB of training data: 
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 93 leaves, max depth = 19, train loss: 0.67691, val loss: 0.67669, in 0.016s
1 tree, 93 leaves, max depth = 12, train loss: 0.66030, val loss: 0.66018, in 0.016s
1 tree, 51 leaves, max depth = 14, train loss: 0.64396, val loss: 0.64362, in 0.016s
1 tree, 93 leaves, max depth = 19, train loss: 0.63039, val loss: 0.62996, in 0.016s
1 tree, 93 leaves, max depth = 12, train loss: 0.61710, val loss: 0.61688, in 0.016s
1 tree, 93 leaves, max depth = 17, train loss: 0.60445, val loss: 0.60429, in 0.016s
1 tree, 92 leaves, max depth = 14, train loss: 0.59204, val loss: 0.59153, in 0.016s
1 tree, 93 leaves, max depth = 17, train loss: 0.57944, val loss: 0.57868, in 0.016s
1 tree, 93 leaves, max depth = 16, train loss: 0.56946, val loss: 0.56846, in 0.016s
1 tree, 93 leaves, max depth = 14, train loss: 0.55847, val loss: 0.55736, in 0.016s
1 tree, 93 leaves, max depth = 12, train loss: 0.54744, val loss: 0.54619, in 0.016s
1 tree, 12 leaves, max depth = 6, train loss: 0.53563, val loss: 0.53445, in 0.000s
1 tree, 54 leaves, max depth = 10, train loss: 0.52659, val loss: 0.52521, in 0.016s
1 tree, 93 leaves, max depth = 18, train loss: 0.51887, val loss: 0.51738, in 0.031s
1 tree, 93 leaves, max depth = 17, train loss: 0.51029, val loss: 0.50873, in 0.016s
1 tree, 93 leaves, max depth = 17, train loss: 0.50283, val loss: 0.50141, in 0.016s
1 tree, 93 leaves, max depth = 20, train loss: 0.49582, val loss: 0.49441, in 0.031s
1 tree, 93 leaves, max depth = 16, train loss: 0.48930, val loss: 0.48782, in 0.016s
1 tree, 93 leaves, max depth = 17, train loss: 0.48209, val loss: 0.48047, in 0.016s
1 tree, 38 leaves, max depth = 10, train loss: 0.47611, val loss: 0.47438, in 0.016s
1 tree, 93 leaves, max depth = 13, train loss: 0.47000, val loss: 0.46813, in 0.016s
1 tree, 93 leaves, max depth = 15, train loss: 0.46400, val loss: 0.46182, in 0.016s
1 tree, 93 leaves, max depth = 12, train loss: 0.45805, val loss: 0.45567, in 0.016s
1 tree, 59 leaves, max depth = 12, train loss: 0.45242, val loss: 0.44982, in 0.016s
1 tree, 93 leaves, max depth = 11, train loss: 0.44640, val loss: 0.44398, in 0.031s
1 tree, 93 leaves, max depth = 14, train loss: 0.44121, val loss: 0.43887, in 0.016s
1 tree, 15 leaves, max depth = 10, train loss: 0.43418, val loss: 0.43195, in 0.016s
1 tree, 93 leaves, max depth = 12, train loss: 0.42909, val loss: 0.42692, in 0.016s
1 tree, 93 leaves, max depth = 14, train loss: 0.42403, val loss: 0.42180, in 0.016s
1 tree, 13 leaves, max depth = 5, train loss: 0.41933, val loss: 0.41696, in 0.016s
1 tree, 93 leaves, max depth = 19, train loss: 0.41568, val loss: 0.41328, in 0.031s
1 tree, 93 leaves, max depth = 18, train loss: 0.41229, val loss: 0.40987, in 0.016s
1 tree, 93 leaves, max depth = 14, train loss: 0.40863, val loss: 0.40613, in 0.016s
1 tree, 93 leaves, max depth = 22, train loss: 0.40466, val loss: 0.40203, in 0.016s
1 tree, 93 leaves, max depth = 15, train loss: 0.40183, val loss: 0.39911, in 0.031s
1 tree, 93 leaves, max depth = 13, train loss: 0.39699, val loss: 0.39437, in 0.016s
1 tree, 93 leaves, max depth = 13, train loss: 0.39320, val loss: 0.39046, in 0.016s
1 tree, 93 leaves, max depth = 15, train loss: 0.38875, val loss: 0.38606, in 0.016s
1 tree, 93 leaves, max depth = 17, train loss: 0.38455, val loss: 0.38189, in 0.016s
1 tree, 12 leaves, max depth = 6, train loss: 0.37911, val loss: 0.37662, in 0.000s
1 tree, 34 leaves, max depth = 13, train loss: 0.37666, val loss: 0.37410, in 0.016s
1 tree, 14 leaves, max depth = 8, train loss: 0.37159, val loss: 0.36924, in 0.016s
1 tree, 93 leaves, max depth = 13, train loss: 0.36869, val loss: 0.36636, in 0.016s
1 tree, 49 leaves, max depth = 10, train loss: 0.36650, val loss: 0.36412, in 0.016s
1 tree, 93 leaves, max depth = 15, train loss: 0.36391, val loss: 0.36149, in 0.016s
1 tree, 14 leaves, max depth = 5, train loss: 0.35946, val loss: 0.35716, in 0.016s
1 tree, 93 leaves, max depth = 19, train loss: 0.35696, val loss: 0.35467, in 0.016s
1 tree, 36 leaves, max depth = 10, train loss: 0.35512, val loss: 0.35292, in 0.016s
1 tree, 85 leaves, max depth = 16, train loss: 0.35318, val loss: 0.35100, in 0.016s
1 tree, 93 leaves, max depth = 11, train loss: 0.35115, val loss: 0.34906, in 0.016s
1 tree, 10 leaves, max depth = 5, train loss: 0.34746, val loss: 0.34561, in 0.016s
1 tree, 27 leaves, max depth = 8, train loss: 0.34361, val loss: 0.34186, in 0.016s
1 tree, 93 leaves, max depth = 13, train loss: 0.34156, val loss: 0.33997, in 0.016s
1 tree, 9 leaves, max depth = 4, train loss: 0.33846, val loss: 0.33677, in 0.016s
1 tree, 22 leaves, max depth = 11, train loss: 0.33484, val loss: 0.33356, in 0.000s
1 tree, 23 leaves, max depth = 8, train loss: 0.33145, val loss: 0.33027, in 0.000s
1 tree, 21 leaves, max depth = 9, train loss: 0.32824, val loss: 0.32715, in 0.016s
1 tree, 93 leaves, max depth = 15, train loss: 0.32645, val loss: 0.32547, in 0.031s
Fit 58 trees in 1.283 s, (4131 total leaves)
Time spent computing histograms: 0.386s
Time spent finding best splits:  0.075s
Time spent applying splits:      0.066s
Time spent predicting:           0.000s
Trial 50, Fold 1: Log loss = 0.332543159481717, Average precision = 0.960439148332299, ROC-AUC = 0.9515212984471115, Elapsed Time = 1.2974546999994345 seconds
Trial 50, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 50, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
Binning 0.040 GB of training data: 0.157 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 93 leaves, max depth = 14, train loss: 0.67712, val loss: 0.67655, in 0.016s
1 tree, 93 leaves, max depth = 12, train loss: 0.66063, val loss: 0.65961, in 0.031s
1 tree, 93 leaves, max depth = 17, train loss: 0.64447, val loss: 0.64340, in 0.016s
1 tree, 93 leaves, max depth = 17, train loss: 0.62999, val loss: 0.62871, in 0.016s
1 tree, 93 leaves, max depth = 13, train loss: 0.61492, val loss: 0.61359, in 0.016s
1 tree, 93 leaves, max depth = 19, train loss: 0.60232, val loss: 0.60093, in 0.016s
1 tree, 93 leaves, max depth = 15, train loss: 0.59010, val loss: 0.58830, in 0.031s
1 tree, 44 leaves, max depth = 13, train loss: 0.57760, val loss: 0.57544, in 0.016s
1 tree, 28 leaves, max depth = 10, train loss: 0.56719, val loss: 0.56463, in 0.016s
1 tree, 93 leaves, max depth = 16, train loss: 0.55622, val loss: 0.55374, in 0.016s
1 tree, 93 leaves, max depth = 12, train loss: 0.54582, val loss: 0.54325, in 0.016s
1 tree, 93 leaves, max depth = 15, train loss: 0.53720, val loss: 0.53424, in 0.031s
1 tree, 93 leaves, max depth = 16, train loss: 0.52793, val loss: 0.52493, in 0.016s
1 tree, 93 leaves, max depth = 18, train loss: 0.51891, val loss: 0.51599, in 0.016s
1 tree, 93 leaves, max depth = 18, train loss: 0.51000, val loss: 0.50705, in 0.016s
1 tree, 93 leaves, max depth = 13, train loss: 0.50179, val loss: 0.49877, in 0.031s
1 tree, 93 leaves, max depth = 11, train loss: 0.49437, val loss: 0.49129, in 0.016s
1 tree, 93 leaves, max depth = 16, train loss: 0.48643, val loss: 0.48321, in 0.016s
1 tree, 82 leaves, max depth = 13, train loss: 0.47894, val loss: 0.47571, in 0.016s
1 tree, 93 leaves, max depth = 14, train loss: 0.47201, val loss: 0.46873, in 0.031s
1 tree, 93 leaves, max depth = 17, train loss: 0.46525, val loss: 0.46193, in 0.016s
1 tree, 93 leaves, max depth = 13, train loss: 0.45915, val loss: 0.45589, in 0.016s
1 tree, 93 leaves, max depth = 19, train loss: 0.45419, val loss: 0.45077, in 0.031s
1 tree, 93 leaves, max depth = 15, train loss: 0.44872, val loss: 0.44524, in 0.016s
1 tree, 93 leaves, max depth = 14, train loss: 0.44314, val loss: 0.43967, in 0.016s
1 tree, 31 leaves, max depth = 12, train loss: 0.43878, val loss: 0.43512, in 0.016s
1 tree, 93 leaves, max depth = 17, train loss: 0.43392, val loss: 0.43036, in 0.016s
1 tree, 93 leaves, max depth = 16, train loss: 0.42930, val loss: 0.42583, in 0.016s
1 tree, 93 leaves, max depth = 12, train loss: 0.42494, val loss: 0.42148, in 0.016s
1 tree, 93 leaves, max depth = 17, train loss: 0.42125, val loss: 0.41768, in 0.031s
1 tree, 93 leaves, max depth = 16, train loss: 0.41641, val loss: 0.41302, in 0.016s
1 tree, 93 leaves, max depth = 18, train loss: 0.41129, val loss: 0.40802, in 0.016s
1 tree, 93 leaves, max depth = 15, train loss: 0.40671, val loss: 0.40348, in 0.016s
1 tree, 13 leaves, max depth = 5, train loss: 0.40006, val loss: 0.39694, in 0.016s
1 tree, 10 leaves, max depth = 5, train loss: 0.39391, val loss: 0.39091, in 0.016s
1 tree, 14 leaves, max depth = 5, train loss: 0.38824, val loss: 0.38532, in 0.016s
1 tree, 11 leaves, max depth = 5, train loss: 0.38280, val loss: 0.38002, in 0.000s
1 tree, 9 leaves, max depth = 4, train loss: 0.37745, val loss: 0.37473, in 0.016s
1 tree, 32 leaves, max depth = 10, train loss: 0.37253, val loss: 0.36987, in 0.016s
1 tree, 93 leaves, max depth = 15, train loss: 0.36838, val loss: 0.36592, in 0.016s
1 tree, 14 leaves, max depth = 7, train loss: 0.36414, val loss: 0.36161, in 0.016s
1 tree, 93 leaves, max depth = 13, train loss: 0.36101, val loss: 0.35856, in 0.031s
1 tree, 15 leaves, max depth = 6, train loss: 0.35658, val loss: 0.35428, in 0.000s
1 tree, 32 leaves, max depth = 9, train loss: 0.35267, val loss: 0.35069, in 0.016s
1 tree, 93 leaves, max depth = 17, train loss: 0.34950, val loss: 0.34769, in 0.031s
1 tree, 31 leaves, max depth = 9, train loss: 0.34791, val loss: 0.34631, in 0.000s
1 tree, 11 leaves, max depth = 5, train loss: 0.34401, val loss: 0.34257, in 0.016s
1 tree, 93 leaves, max depth = 14, train loss: 0.34155, val loss: 0.34014, in 0.031s
1 tree, 93 leaves, max depth = 15, train loss: 0.33926, val loss: 0.33803, in 0.016s
1 tree, 93 leaves, max depth = 15, train loss: 0.33713, val loss: 0.33602, in 0.016s
1 tree, 93 leaves, max depth = 20, train loss: 0.33436, val loss: 0.33322, in 0.016s
1 tree, 93 leaves, max depth = 11, train loss: 0.33118, val loss: 0.33035, in 0.031s
1 tree, 93 leaves, max depth = 13, train loss: 0.32926, val loss: 0.32849, in 0.016s
1 tree, 16 leaves, max depth = 6, train loss: 0.32614, val loss: 0.32572, in 0.016s
1 tree, 82 leaves, max depth = 12, train loss: 0.32423, val loss: 0.32381, in 0.016s
1 tree, 93 leaves, max depth = 20, train loss: 0.32235, val loss: 0.32212, in 0.016s
1 tree, 7 leaves, max depth = 4, train loss: 0.31964, val loss: 0.31937, in 0.016s
1 tree, 93 leaves, max depth = 12, train loss: 0.31793, val loss: 0.31775, in 0.016s
Fit 58 trees in 1.361 s, (4202 total leaves)
Time spent computing histograms: 0.402s
Time spent finding best splits:  0.078s
Time spent applying splits:      0.070s
Time spent predicting:           0.016s
Trial 50, Fold 2: Log loss = 0.322366115525635, Average precision = 0.9586962201836953, ROC-AUC = 0.9532182063351058, Elapsed Time = 1.3756073000004108 seconds
Trial 50, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 50, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
Binning 0.040 GB of training data: 0.174 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 93 leaves, max depth = 16, train loss: 0.67699, val loss: 0.67687, in 0.016s
1 tree, 93 leaves, max depth = 14, train loss: 0.66059, val loss: 0.66047, in 0.016s
1 tree, 93 leaves, max depth = 17, train loss: 0.64392, val loss: 0.64371, in 0.016s
1 tree, 93 leaves, max depth = 12, train loss: 0.63036, val loss: 0.63012, in 0.016s
1 tree, 93 leaves, max depth = 12, train loss: 0.61582, val loss: 0.61557, in 0.016s
1 tree, 93 leaves, max depth = 13, train loss: 0.60270, val loss: 0.60208, in 0.016s
1 tree, 93 leaves, max depth = 13, train loss: 0.58979, val loss: 0.58909, in 0.016s
1 tree, 93 leaves, max depth = 14, train loss: 0.57838, val loss: 0.57759, in 0.031s
1 tree, 93 leaves, max depth = 13, train loss: 0.56866, val loss: 0.56741, in 0.016s
1 tree, 93 leaves, max depth = 16, train loss: 0.55778, val loss: 0.55665, in 0.031s
1 tree, 93 leaves, max depth = 18, train loss: 0.54750, val loss: 0.54645, in 0.016s
1 tree, 93 leaves, max depth = 12, train loss: 0.53805, val loss: 0.53703, in 0.016s
1 tree, 93 leaves, max depth = 15, train loss: 0.52802, val loss: 0.52715, in 0.016s
1 tree, 67 leaves, max depth = 14, train loss: 0.51983, val loss: 0.51872, in 0.016s
1 tree, 45 leaves, max depth = 12, train loss: 0.51229, val loss: 0.51097, in 0.016s
1 tree, 93 leaves, max depth = 14, train loss: 0.50429, val loss: 0.50288, in 0.016s
1 tree, 93 leaves, max depth = 19, train loss: 0.49686, val loss: 0.49552, in 0.031s
1 tree, 93 leaves, max depth = 18, train loss: 0.48960, val loss: 0.48841, in 0.016s
1 tree, 93 leaves, max depth = 11, train loss: 0.48313, val loss: 0.48199, in 0.031s
1 tree, 93 leaves, max depth = 15, train loss: 0.47729, val loss: 0.47620, in 0.016s
1 tree, 93 leaves, max depth = 18, train loss: 0.47177, val loss: 0.47068, in 0.016s
1 tree, 93 leaves, max depth = 18, train loss: 0.46518, val loss: 0.46392, in 0.031s
1 tree, 93 leaves, max depth = 14, train loss: 0.45945, val loss: 0.45830, in 0.016s
1 tree, 93 leaves, max depth = 13, train loss: 0.45456, val loss: 0.45366, in 0.016s
1 tree, 9 leaves, max depth = 4, train loss: 0.44924, val loss: 0.44841, in 0.016s
1 tree, 93 leaves, max depth = 15, train loss: 0.44406, val loss: 0.44327, in 0.016s
1 tree, 14 leaves, max depth = 8, train loss: 0.43627, val loss: 0.43604, in 0.016s
1 tree, 93 leaves, max depth = 15, train loss: 0.43106, val loss: 0.43091, in 0.031s
1 tree, 93 leaves, max depth = 16, train loss: 0.42579, val loss: 0.42578, in 0.016s
1 tree, 93 leaves, max depth = 17, train loss: 0.42053, val loss: 0.42059, in 0.016s
1 tree, 93 leaves, max depth = 15, train loss: 0.41531, val loss: 0.41527, in 0.016s
1 tree, 93 leaves, max depth = 16, train loss: 0.41015, val loss: 0.41007, in 0.031s
1 tree, 93 leaves, max depth = 20, train loss: 0.40523, val loss: 0.40504, in 0.016s
1 tree, 93 leaves, max depth = 15, train loss: 0.40136, val loss: 0.40132, in 0.016s
1 tree, 93 leaves, max depth = 18, train loss: 0.39694, val loss: 0.39681, in 0.016s
1 tree, 50 leaves, max depth = 10, train loss: 0.39388, val loss: 0.39364, in 0.016s
1 tree, 26 leaves, max depth = 10, train loss: 0.39107, val loss: 0.39071, in 0.016s
1 tree, 93 leaves, max depth = 15, train loss: 0.38772, val loss: 0.38755, in 0.016s
1 tree, 93 leaves, max depth = 14, train loss: 0.38493, val loss: 0.38493, in 0.016s
1 tree, 11 leaves, max depth = 4, train loss: 0.37915, val loss: 0.37970, in 0.016s
1 tree, 28 leaves, max depth = 13, train loss: 0.37680, val loss: 0.37723, in 0.016s
1 tree, 93 leaves, max depth = 16, train loss: 0.37407, val loss: 0.37460, in 0.016s
1 tree, 11 leaves, max depth = 4, train loss: 0.36892, val loss: 0.36985, in 0.016s
1 tree, 93 leaves, max depth = 12, train loss: 0.36651, val loss: 0.36755, in 0.016s
1 tree, 93 leaves, max depth = 16, train loss: 0.36409, val loss: 0.36527, in 0.016s
1 tree, 93 leaves, max depth = 14, train loss: 0.36140, val loss: 0.36232, in 0.031s
1 tree, 93 leaves, max depth = 19, train loss: 0.35951, val loss: 0.36054, in 0.016s
1 tree, 93 leaves, max depth = 13, train loss: 0.35618, val loss: 0.35716, in 0.016s
1 tree, 93 leaves, max depth = 15, train loss: 0.35396, val loss: 0.35499, in 0.031s
1 tree, 93 leaves, max depth = 14, train loss: 0.35196, val loss: 0.35315, in 0.016s
1 tree, 93 leaves, max depth = 16, train loss: 0.34994, val loss: 0.35125, in 0.016s
1 tree, 93 leaves, max depth = 13, train loss: 0.34801, val loss: 0.34936, in 0.031s
1 tree, 93 leaves, max depth = 17, train loss: 0.34655, val loss: 0.34796, in 0.016s
1 tree, 16 leaves, max depth = 5, train loss: 0.34205, val loss: 0.34429, in 0.016s
1 tree, 93 leaves, max depth = 14, train loss: 0.34021, val loss: 0.34257, in 0.016s
1 tree, 93 leaves, max depth = 19, train loss: 0.33858, val loss: 0.34114, in 0.016s
1 tree, 93 leaves, max depth = 19, train loss: 0.33663, val loss: 0.33936, in 0.031s
1 tree, 93 leaves, max depth = 19, train loss: 0.33458, val loss: 0.33749, in 0.016s
Fit 58 trees in 1.456 s, (4741 total leaves)
Time spent computing histograms: 0.423s
Time spent finding best splits:  0.089s
Time spent applying splits:      0.080s
Time spent predicting:           0.000s
Trial 50, Fold 3: Log loss = 0.33476056901817486, Average precision = 0.9590247945597814, ROC-AUC = 0.9532110773757355, Elapsed Time = 1.46114309999939 seconds
Trial 50, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 50, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
Binning 0.040 GB of training data: 0.158 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 93 leaves, max depth = 16, train loss: 0.67706, val loss: 0.67662, in 0.031s
1 tree, 93 leaves, max depth = 15, train loss: 0.66065, val loss: 0.65963, in 0.016s
1 tree, 93 leaves, max depth = 16, train loss: 0.64404, val loss: 0.64234, in 0.031s
1 tree, 93 leaves, max depth = 23, train loss: 0.63095, val loss: 0.62891, in 0.016s
1 tree, 93 leaves, max depth = 12, train loss: 0.61819, val loss: 0.61587, in 0.016s
1 tree, 93 leaves, max depth = 15, train loss: 0.60411, val loss: 0.60139, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.59182, val loss: 0.58841, in 0.016s
1 tree, 93 leaves, max depth = 14, train loss: 0.57956, val loss: 0.57559, in 0.031s
1 tree, 86 leaves, max depth = 14, train loss: 0.56887, val loss: 0.56458, in 0.016s
1 tree, 93 leaves, max depth = 12, train loss: 0.55849, val loss: 0.55400, in 0.016s
1 tree, 93 leaves, max depth = 17, train loss: 0.54768, val loss: 0.54265, in 0.031s
1 tree, 93 leaves, max depth = 18, train loss: 0.53934, val loss: 0.53406, in 0.016s
1 tree, 93 leaves, max depth = 17, train loss: 0.52996, val loss: 0.52418, in 0.016s
1 tree, 93 leaves, max depth = 14, train loss: 0.52248, val loss: 0.51605, in 0.016s
1 tree, 93 leaves, max depth = 15, train loss: 0.51347, val loss: 0.50652, in 0.031s
1 tree, 93 leaves, max depth = 12, train loss: 0.50528, val loss: 0.49792, in 0.016s
1 tree, 35 leaves, max depth = 12, train loss: 0.49812, val loss: 0.49043, in 0.016s
1 tree, 93 leaves, max depth = 14, train loss: 0.49035, val loss: 0.48221, in 0.016s
1 tree, 93 leaves, max depth = 13, train loss: 0.48311, val loss: 0.47458, in 0.016s
1 tree, 93 leaves, max depth = 15, train loss: 0.47595, val loss: 0.46706, in 0.031s
1 tree, 93 leaves, max depth = 16, train loss: 0.46919, val loss: 0.45981, in 0.016s
1 tree, 93 leaves, max depth = 13, train loss: 0.46239, val loss: 0.45270, in 0.016s
1 tree, 31 leaves, max depth = 12, train loss: 0.45693, val loss: 0.44701, in 0.016s
1 tree, 93 leaves, max depth = 19, train loss: 0.45220, val loss: 0.44209, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.44424, val loss: 0.43376, in 0.016s
1 tree, 93 leaves, max depth = 17, train loss: 0.43880, val loss: 0.42793, in 0.016s
1 tree, 93 leaves, max depth = 21, train loss: 0.43397, val loss: 0.42281, in 0.016s
1 tree, 93 leaves, max depth = 15, train loss: 0.42901, val loss: 0.41748, in 0.016s
1 tree, 79 leaves, max depth = 13, train loss: 0.42493, val loss: 0.41326, in 0.016s
1 tree, 93 leaves, max depth = 15, train loss: 0.42092, val loss: 0.40902, in 0.016s
1 tree, 93 leaves, max depth = 12, train loss: 0.41696, val loss: 0.40488, in 0.016s
1 tree, 9 leaves, max depth = 4, train loss: 0.41041, val loss: 0.39815, in 0.016s
1 tree, 36 leaves, max depth = 12, train loss: 0.40405, val loss: 0.39168, in 0.016s
1 tree, 93 leaves, max depth = 14, train loss: 0.40064, val loss: 0.38802, in 0.016s
1 tree, 93 leaves, max depth = 13, train loss: 0.39686, val loss: 0.38430, in 0.016s
1 tree, 93 leaves, max depth = 16, train loss: 0.39228, val loss: 0.37961, in 0.016s
1 tree, 93 leaves, max depth = 15, train loss: 0.38937, val loss: 0.37661, in 0.031s
1 tree, 8 leaves, max depth = 4, train loss: 0.38389, val loss: 0.37111, in 0.016s
1 tree, 29 leaves, max depth = 9, train loss: 0.37882, val loss: 0.36581, in 0.016s
1 tree, 10 leaves, max depth = 4, train loss: 0.37444, val loss: 0.36104, in 0.000s
1 tree, 34 leaves, max depth = 11, train loss: 0.36958, val loss: 0.35611, in 0.016s
1 tree, 93 leaves, max depth = 16, train loss: 0.36581, val loss: 0.35236, in 0.031s
1 tree, 17 leaves, max depth = 6, train loss: 0.36170, val loss: 0.34789, in 0.000s
1 tree, 93 leaves, max depth = 15, train loss: 0.35880, val loss: 0.34478, in 0.031s
1 tree, 93 leaves, max depth = 17, train loss: 0.35558, val loss: 0.34145, in 0.016s
1 tree, 93 leaves, max depth = 18, train loss: 0.35367, val loss: 0.33964, in 0.016s
1 tree, 93 leaves, max depth = 19, train loss: 0.35128, val loss: 0.33703, in 0.016s
1 tree, 25 leaves, max depth = 11, train loss: 0.34947, val loss: 0.33508, in 0.016s
1 tree, 93 leaves, max depth = 16, train loss: 0.34721, val loss: 0.33300, in 0.016s
1 tree, 93 leaves, max depth = 14, train loss: 0.34421, val loss: 0.33030, in 0.016s
1 tree, 93 leaves, max depth = 14, train loss: 0.34245, val loss: 0.32862, in 0.016s
1 tree, 93 leaves, max depth = 17, train loss: 0.34063, val loss: 0.32694, in 0.031s
1 tree, 84 leaves, max depth = 13, train loss: 0.33614, val loss: 0.32258, in 0.016s
1 tree, 11 leaves, max depth = 4, train loss: 0.33288, val loss: 0.31933, in 0.016s
1 tree, 17 leaves, max depth = 8, train loss: 0.32976, val loss: 0.31607, in 0.000s
1 tree, 16 leaves, max depth = 6, train loss: 0.32660, val loss: 0.31279, in 0.016s
1 tree, 93 leaves, max depth = 13, train loss: 0.32374, val loss: 0.30998, in 0.016s
1 tree, 9 leaves, max depth = 4, train loss: 0.32194, val loss: 0.30798, in 0.016s
Fit 58 trees in 1.393 s, (4225 total leaves)
Time spent computing histograms: 0.404s
Time spent finding best splits:  0.080s
Time spent applying splits:      0.071s
Time spent predicting:           0.000s
Trial 50, Fold 4: Log loss = 0.3254950150277615, Average precision = 0.9597598144707746, ROC-AUC = 0.9523395232611097, Elapsed Time = 1.4036365000010846 seconds
Trial 50, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 50, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
Binning 0.040 GB of training data: 0.142 s
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 93 leaves, max depth = 18, train loss: 0.67693, val loss: 0.67613, in 0.016s
1 tree, 93 leaves, max depth = 16, train loss: 0.65889, val loss: 0.65740, in 0.016s
1 tree, 93 leaves, max depth = 15, train loss: 0.64240, val loss: 0.64030, in 0.016s
1 tree, 93 leaves, max depth = 22, train loss: 0.62800, val loss: 0.62499, in 0.032s
1 tree, 93 leaves, max depth = 16, train loss: 0.61531, val loss: 0.61168, in 0.016s
1 tree, 93 leaves, max depth = 15, train loss: 0.60245, val loss: 0.59855, in 0.016s
1 tree, 93 leaves, max depth = 15, train loss: 0.59016, val loss: 0.58574, in 0.016s
1 tree, 29 leaves, max depth = 8, train loss: 0.57771, val loss: 0.57269, in 0.016s
1 tree, 93 leaves, max depth = 11, train loss: 0.56684, val loss: 0.56124, in 0.031s
1 tree, 93 leaves, max depth = 17, train loss: 0.55727, val loss: 0.55110, in 0.016s
1 tree, 93 leaves, max depth = 14, train loss: 0.54609, val loss: 0.53956, in 0.016s
1 tree, 93 leaves, max depth = 17, train loss: 0.53762, val loss: 0.53042, in 0.019s
1 tree, 93 leaves, max depth = 13, train loss: 0.52816, val loss: 0.52042, in 0.029s
1 tree, 93 leaves, max depth = 13, train loss: 0.52061, val loss: 0.51222, in 0.016s
1 tree, 93 leaves, max depth = 15, train loss: 0.51164, val loss: 0.50288, in 0.016s
1 tree, 19 leaves, max depth = 9, train loss: 0.50482, val loss: 0.49561, in 0.016s
1 tree, 93 leaves, max depth = 16, train loss: 0.49646, val loss: 0.48679, in 0.016s
1 tree, 93 leaves, max depth = 13, train loss: 0.48951, val loss: 0.47963, in 0.016s
1 tree, 93 leaves, max depth = 14, train loss: 0.48216, val loss: 0.47189, in 0.031s
1 tree, 93 leaves, max depth = 15, train loss: 0.47498, val loss: 0.46430, in 0.016s
1 tree, 93 leaves, max depth = 19, train loss: 0.46818, val loss: 0.45719, in 0.016s
1 tree, 93 leaves, max depth = 13, train loss: 0.46127, val loss: 0.45012, in 0.016s
1 tree, 29 leaves, max depth = 10, train loss: 0.45575, val loss: 0.44449, in 0.016s
1 tree, 93 leaves, max depth = 14, train loss: 0.45080, val loss: 0.43934, in 0.031s
1 tree, 25 leaves, max depth = 8, train loss: 0.44602, val loss: 0.43428, in 0.016s
1 tree, 21 leaves, max depth = 9, train loss: 0.44189, val loss: 0.42987, in 0.000s
1 tree, 43 leaves, max depth = 11, train loss: 0.43739, val loss: 0.42532, in 0.000s
1 tree, 9 leaves, max depth = 5, train loss: 0.43228, val loss: 0.42012, in 0.016s
1 tree, 93 leaves, max depth = 12, train loss: 0.42779, val loss: 0.41536, in 0.031s
1 tree, 93 leaves, max depth = 16, train loss: 0.42416, val loss: 0.41150, in 0.016s
1 tree, 93 leaves, max depth = 13, train loss: 0.41970, val loss: 0.40683, in 0.016s
1 tree, 93 leaves, max depth = 18, train loss: 0.41632, val loss: 0.40326, in 0.016s
1 tree, 93 leaves, max depth = 19, train loss: 0.41188, val loss: 0.39883, in 0.031s
1 tree, 11 leaves, max depth = 4, train loss: 0.40569, val loss: 0.39258, in 0.016s
1 tree, 93 leaves, max depth = 12, train loss: 0.40210, val loss: 0.38896, in 0.016s
1 tree, 20 leaves, max depth = 6, train loss: 0.39591, val loss: 0.38266, in 0.016s
1 tree, 87 leaves, max depth = 12, train loss: 0.39288, val loss: 0.37956, in 0.016s
1 tree, 80 leaves, max depth = 13, train loss: 0.39003, val loss: 0.37664, in 0.016s
1 tree, 16 leaves, max depth = 6, train loss: 0.38470, val loss: 0.37127, in 0.016s
1 tree, 10 leaves, max depth = 5, train loss: 0.37984, val loss: 0.36636, in 0.016s
1 tree, 39 leaves, max depth = 10, train loss: 0.37727, val loss: 0.36380, in 0.016s
1 tree, 93 leaves, max depth = 15, train loss: 0.37367, val loss: 0.36030, in 0.016s
1 tree, 9 leaves, max depth = 4, train loss: 0.36912, val loss: 0.35571, in 0.016s
1 tree, 93 leaves, max depth = 13, train loss: 0.36596, val loss: 0.35251, in 0.016s
1 tree, 93 leaves, max depth = 18, train loss: 0.36289, val loss: 0.34947, in 0.016s
1 tree, 93 leaves, max depth = 21, train loss: 0.36098, val loss: 0.34743, in 0.016s
1 tree, 93 leaves, max depth = 13, train loss: 0.35840, val loss: 0.34480, in 0.031s
1 tree, 27 leaves, max depth = 10, train loss: 0.35658, val loss: 0.34275, in 0.016s
1 tree, 93 leaves, max depth = 11, train loss: 0.35402, val loss: 0.34021, in 0.016s
1 tree, 14 leaves, max depth = 6, train loss: 0.35000, val loss: 0.33615, in 0.016s
1 tree, 93 leaves, max depth = 17, train loss: 0.34782, val loss: 0.33397, in 0.016s
1 tree, 93 leaves, max depth = 12, train loss: 0.34502, val loss: 0.33118, in 0.016s
1 tree, 93 leaves, max depth = 16, train loss: 0.34276, val loss: 0.32882, in 0.016s
1 tree, 93 leaves, max depth = 16, train loss: 0.34009, val loss: 0.32605, in 0.031s
1 tree, 93 leaves, max depth = 20, train loss: 0.33788, val loss: 0.32392, in 0.016s
1 tree, 93 leaves, max depth = 13, train loss: 0.33598, val loss: 0.32193, in 0.016s
1 tree, 13 leaves, max depth = 7, train loss: 0.33236, val loss: 0.31829, in 0.016s
1 tree, 93 leaves, max depth = 16, train loss: 0.32964, val loss: 0.31577, in 0.016s
Fit 58 trees in 1.377 s, (4221 total leaves)
Time spent computing histograms: 0.400s
Time spent finding best splits:  0.082s
Time spent applying splits:      0.072s
Time spent predicting:           0.016s
Trial 50, Fold 5: Log loss = 0.34125740585444037, Average precision = 0.9561758490398942, ROC-AUC = 0.9489523066347101, Elapsed Time = 1.3802941999983886 seconds
Optimization Progress:  51%|#####1    | 51/100 [10:01<09:53, 12.12s/it]
Trial 51, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 51, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
0.125 s 0.040 GB of training data: 
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 69 leaves, max depth = 14, train loss: 0.64940, val loss: 0.64810, in 0.016s
1 tree, 73 leaves, max depth = 19, train loss: 0.61326, val loss: 0.61086, in 0.000s
1 tree, 73 leaves, max depth = 17, train loss: 0.58337, val loss: 0.58023, in 0.016s
1 tree, 68 leaves, max depth = 16, train loss: 0.55886, val loss: 0.55494, in 0.000s
1 tree, 115 leaves, max depth = 16, train loss: 0.53645, val loss: 0.53415, in 0.016s
1 tree, 74 leaves, max depth = 19, train loss: 0.51724, val loss: 0.51411, in 0.016s
1 tree, 115 leaves, max depth = 17, train loss: 0.49958, val loss: 0.49786, in 0.000s
1 tree, 69 leaves, max depth = 16, train loss: 0.48468, val loss: 0.48225, in 0.016s
1 tree, 74 leaves, max depth = 19, train loss: 0.47194, val loss: 0.46881, in 0.016s
1 tree, 115 leaves, max depth = 17, train loss: 0.45826, val loss: 0.45640, in 0.000s
1 tree, 114 leaves, max depth = 17, train loss: 0.44667, val loss: 0.44597, in 0.016s
Fit 11 trees in 0.360 s, (959 total leaves)
Time spent computing histograms: 0.047s
Time spent finding best splits:  0.011s
Time spent applying splits:      0.013s
Time spent predicting:           0.000s
Trial 51, Fold 1: Log loss = 0.44821471458833306, Average precision = 0.9172839742555474, ROC-AUC = 0.927829023936183, Elapsed Time = 0.36683610000000044 seconds
Trial 51, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 51, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
Binning 0.040 GB of training data: 0.142 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 72 leaves, max depth = 18, train loss: 0.64926, val loss: 0.64729, in 0.000s
1 tree, 71 leaves, max depth = 15, train loss: 0.61334, val loss: 0.60958, in 0.016s
1 tree, 73 leaves, max depth = 13, train loss: 0.58351, val loss: 0.57807, in 0.016s
1 tree, 71 leaves, max depth = 20, train loss: 0.55890, val loss: 0.55214, in 0.000s
1 tree, 112 leaves, max depth = 15, train loss: 0.53652, val loss: 0.53076, in 0.016s
1 tree, 73 leaves, max depth = 15, train loss: 0.51744, val loss: 0.51051, in 0.016s
1 tree, 112 leaves, max depth = 15, train loss: 0.49976, val loss: 0.49373, in 0.000s
1 tree, 72 leaves, max depth = 18, train loss: 0.48486, val loss: 0.47787, in 0.016s
1 tree, 75 leaves, max depth = 14, train loss: 0.47209, val loss: 0.46413, in 0.016s
1 tree, 111 leaves, max depth = 15, train loss: 0.45839, val loss: 0.45127, in 0.000s
1 tree, 112 leaves, max depth = 15, train loss: 0.44679, val loss: 0.44042, in 0.016s
Fit 11 trees in 0.377 s, (954 total leaves)
Time spent computing histograms: 0.051s
Time spent finding best splits:  0.012s
Time spent applying splits:      0.014s
Time spent predicting:           0.000s
Trial 51, Fold 2: Log loss = 0.4485400674319748, Average precision = 0.9115079649576727, ROC-AUC = 0.9271467228093008, Elapsed Time = 0.38675879999937024 seconds
Trial 51, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 51, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
Binning 0.040 GB of training data: 0.126 s
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 69 leaves, max depth = 16, train loss: 0.64962, val loss: 0.64818, in 0.000s
1 tree, 73 leaves, max depth = 11, train loss: 0.61380, val loss: 0.61092, in 0.016s
1 tree, 72 leaves, max depth = 12, train loss: 0.58423, val loss: 0.58013, in 0.000s
1 tree, 67 leaves, max depth = 13, train loss: 0.55987, val loss: 0.55478, in 0.016s
1 tree, 110 leaves, max depth = 13, train loss: 0.53762, val loss: 0.53429, in 0.016s
1 tree, 72 leaves, max depth = 14, train loss: 0.51860, val loss: 0.51459, in 0.000s
1 tree, 110 leaves, max depth = 14, train loss: 0.50103, val loss: 0.49851, in 0.016s
1 tree, 70 leaves, max depth = 15, train loss: 0.48624, val loss: 0.48290, in 0.016s
1 tree, 74 leaves, max depth = 13, train loss: 0.47360, val loss: 0.46939, in 0.000s
1 tree, 110 leaves, max depth = 14, train loss: 0.46000, val loss: 0.45707, in 0.016s
1 tree, 110 leaves, max depth = 13, train loss: 0.44849, val loss: 0.44672, in 0.016s
Fit 11 trees in 0.360 s, (937 total leaves)
Time spent computing histograms: 0.045s
Time spent finding best splits:  0.011s
Time spent applying splits:      0.014s
Time spent predicting:           0.000s
Trial 51, Fold 3: Log loss = 0.4468223273437511, Average precision = 0.9191827663788117, ROC-AUC = 0.9306622660862364, Elapsed Time = 0.36531429999922693 seconds
Trial 51, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 51, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
Binning 0.040 GB of training data: 0.143 s
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 65 leaves, max depth = 12, train loss: 0.64952, val loss: 0.64734, in 0.000s
1 tree, 75 leaves, max depth = 12, train loss: 0.61360, val loss: 0.60925, in 0.016s
1 tree, 72 leaves, max depth = 13, train loss: 0.58385, val loss: 0.57766, in 0.016s
1 tree, 66 leaves, max depth = 13, train loss: 0.55935, val loss: 0.55142, in 0.000s
1 tree, 111 leaves, max depth = 14, train loss: 0.53742, val loss: 0.52987, in 0.016s
1 tree, 75 leaves, max depth = 13, train loss: 0.51833, val loss: 0.50917, in 0.016s
1 tree, 111 leaves, max depth = 13, train loss: 0.50107, val loss: 0.49227, in 0.000s
1 tree, 69 leaves, max depth = 18, train loss: 0.48619, val loss: 0.47602, in 0.016s
1 tree, 74 leaves, max depth = 13, train loss: 0.47353, val loss: 0.46203, in 0.016s
1 tree, 111 leaves, max depth = 13, train loss: 0.46021, val loss: 0.44909, in 0.000s
1 tree, 110 leaves, max depth = 14, train loss: 0.44894, val loss: 0.43815, in 0.016s
Fit 11 trees in 0.378 s, (939 total leaves)
Time spent computing histograms: 0.049s
Time spent finding best splits:  0.011s
Time spent applying splits:      0.013s
Time spent predicting:           0.000s
Trial 51, Fold 4: Log loss = 0.4507458387276249, Average precision = 0.9130938405507721, ROC-AUC = 0.9259754024287599, Elapsed Time = 0.37635580000096525 seconds
Trial 51, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 51, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
Binning 0.040 GB of training data: 0.127 s
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 68 leaves, max depth = 13, train loss: 0.64895, val loss: 0.64615, in 0.000s
1 tree, 69 leaves, max depth = 12, train loss: 0.61287, val loss: 0.60750, in 0.000s
1 tree, 74 leaves, max depth = 15, train loss: 0.58282, val loss: 0.57515, in 0.016s
1 tree, 71 leaves, max depth = 14, train loss: 0.55807, val loss: 0.54831, in 0.000s
1 tree, 114 leaves, max depth = 15, train loss: 0.53602, val loss: 0.52714, in 0.016s
1 tree, 75 leaves, max depth = 14, train loss: 0.51672, val loss: 0.50600, in 0.016s
1 tree, 114 leaves, max depth = 16, train loss: 0.49935, val loss: 0.48944, in 0.016s
1 tree, 69 leaves, max depth = 15, train loss: 0.48426, val loss: 0.47274, in 0.000s
1 tree, 75 leaves, max depth = 14, train loss: 0.47146, val loss: 0.45848, in 0.016s
1 tree, 114 leaves, max depth = 15, train loss: 0.45802, val loss: 0.44588, in 0.016s
1 tree, 114 leaves, max depth = 15, train loss: 0.44668, val loss: 0.43528, in 0.000s
Fit 11 trees in 0.361 s, (957 total leaves)
Time spent computing histograms: 0.052s
Time spent finding best splits:  0.011s
Time spent applying splits:      0.013s
Time spent predicting:           0.000s
Trial 51, Fold 5: Log loss = 0.4527107971310902, Average precision = 0.9107154606342183, ROC-AUC = 0.924461958642216, Elapsed Time = 0.3719161000008171 seconds
Optimization Progress:  52%|#####2    | 52/100 [10:09<08:46, 10.97s/it]
Trial 52, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 52, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
0.143 s 0.040 GB of training data: 
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 66 leaves, max depth = 10, train loss: 0.65370, val loss: 0.65348, in 0.016s
1 tree, 103 leaves, max depth = 12, train loss: 0.61890, val loss: 0.61892, in 0.016s
1 tree, 100 leaves, max depth = 12, train loss: 0.58839, val loss: 0.58842, in 0.031s
1 tree, 106 leaves, max depth = 13, train loss: 0.56160, val loss: 0.56168, in 0.016s
1 tree, 105 leaves, max depth = 12, train loss: 0.53781, val loss: 0.53800, in 0.016s
1 tree, 103 leaves, max depth = 12, train loss: 0.51668, val loss: 0.51697, in 0.016s
1 tree, 107 leaves, max depth = 13, train loss: 0.49788, val loss: 0.49834, in 0.016s
1 tree, 108 leaves, max depth = 12, train loss: 0.48101, val loss: 0.48159, in 0.016s
1 tree, 115 leaves, max depth = 13, train loss: 0.46603, val loss: 0.46647, in 0.016s
1 tree, 104 leaves, max depth = 15, train loss: 0.45190, val loss: 0.45229, in 0.031s
1 tree, 103 leaves, max depth = 12, train loss: 0.43940, val loss: 0.43978, in 0.016s
1 tree, 103 leaves, max depth = 14, train loss: 0.42183, val loss: 0.42258, in 0.031s
1 tree, 120 leaves, max depth = 15, train loss: 0.41137, val loss: 0.41191, in 0.016s
1 tree, 94 leaves, max depth = 14, train loss: 0.39639, val loss: 0.39753, in 0.016s
1 tree, 111 leaves, max depth = 15, train loss: 0.38739, val loss: 0.38863, in 0.031s
1 tree, 94 leaves, max depth = 12, train loss: 0.37471, val loss: 0.37665, in 0.016s
1 tree, 108 leaves, max depth = 13, train loss: 0.36718, val loss: 0.36926, in 0.016s
1 tree, 96 leaves, max depth = 13, train loss: 0.35633, val loss: 0.35898, in 0.031s
1 tree, 111 leaves, max depth = 12, train loss: 0.35006, val loss: 0.35301, in 0.016s
1 tree, 47 leaves, max depth = 12, train loss: 0.34132, val loss: 0.34437, in 0.016s
1 tree, 43 leaves, max depth = 11, train loss: 0.33353, val loss: 0.33673, in 0.016s
1 tree, 120 leaves, max depth = 16, train loss: 0.32708, val loss: 0.33031, in 0.031s
1 tree, 71 leaves, max depth = 13, train loss: 0.32026, val loss: 0.32425, in 0.016s
1 tree, 46 leaves, max depth = 11, train loss: 0.31410, val loss: 0.31816, in 0.016s
1 tree, 48 leaves, max depth = 12, train loss: 0.30864, val loss: 0.31282, in 0.016s
1 tree, 121 leaves, max depth = 16, train loss: 0.30337, val loss: 0.30774, in 0.016s
1 tree, 73 leaves, max depth = 14, train loss: 0.29820, val loss: 0.30317, in 0.016s
1 tree, 131 leaves, max depth = 18, train loss: 0.29462, val loss: 0.29965, in 0.031s
1 tree, 72 leaves, max depth = 14, train loss: 0.29010, val loss: 0.29567, in 0.016s
1 tree, 72 leaves, max depth = 14, train loss: 0.28605, val loss: 0.29213, in 0.016s
1 tree, 124 leaves, max depth = 16, train loss: 0.28272, val loss: 0.28908, in 0.031s
1 tree, 47 leaves, max depth = 13, train loss: 0.27917, val loss: 0.28566, in 0.016s
1 tree, 47 leaves, max depth = 13, train loss: 0.27600, val loss: 0.28260, in 0.016s
1 tree, 120 leaves, max depth = 17, train loss: 0.27228, val loss: 0.27895, in 0.016s
1 tree, 73 leaves, max depth = 12, train loss: 0.26925, val loss: 0.27635, in 0.016s
1 tree, 53 leaves, max depth = 16, train loss: 0.26652, val loss: 0.27351, in 0.016s
1 tree, 131 leaves, max depth = 14, train loss: 0.26299, val loss: 0.27006, in 0.031s
1 tree, 73 leaves, max depth = 13, train loss: 0.26053, val loss: 0.26794, in 0.016s
1 tree, 45 leaves, max depth = 13, train loss: 0.25836, val loss: 0.26573, in 0.016s
1 tree, 72 leaves, max depth = 14, train loss: 0.25625, val loss: 0.26394, in 0.016s
1 tree, 51 leaves, max depth = 17, train loss: 0.25427, val loss: 0.26181, in 0.016s
1 tree, 118 leaves, max depth = 15, train loss: 0.25150, val loss: 0.25915, in 0.016s
1 tree, 131 leaves, max depth = 17, train loss: 0.24858, val loss: 0.25623, in 0.031s
1 tree, 121 leaves, max depth = 17, train loss: 0.24623, val loss: 0.25401, in 0.031s
1 tree, 83 leaves, max depth = 19, train loss: 0.24446, val loss: 0.25248, in 0.016s
1 tree, 51 leaves, max depth = 11, train loss: 0.24263, val loss: 0.25104, in 0.016s
1 tree, 123 leaves, max depth = 15, train loss: 0.24099, val loss: 0.24920, in 0.031s
1 tree, 131 leaves, max depth = 18, train loss: 0.23840, val loss: 0.24660, in 0.016s
Fit 48 trees in 1.237 s, (4395 total leaves)
Time spent computing histograms: 0.326s
Time spent finding best splits:  0.090s
Time spent applying splits:      0.079s
Time spent predicting:           0.000s
Trial 52, Fold 1: Log loss = 0.24882506581240213, Average precision = 0.9662038143774188, ROC-AUC = 0.9609069180969588, Elapsed Time = 1.2518823999998858 seconds
Trial 52, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 52, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
Binning 0.040 GB of training data: 0.173 s
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 68 leaves, max depth = 12, train loss: 0.65352, val loss: 0.65276, in 0.016s
1 tree, 97 leaves, max depth = 15, train loss: 0.61851, val loss: 0.61723, in 0.031s
1 tree, 96 leaves, max depth = 14, train loss: 0.58731, val loss: 0.58581, in 0.016s
1 tree, 96 leaves, max depth = 14, train loss: 0.55988, val loss: 0.55818, in 0.031s
1 tree, 98 leaves, max depth = 14, train loss: 0.53563, val loss: 0.53374, in 0.031s
1 tree, 103 leaves, max depth = 14, train loss: 0.51409, val loss: 0.51187, in 0.016s
1 tree, 102 leaves, max depth = 14, train loss: 0.49495, val loss: 0.49261, in 0.016s
1 tree, 102 leaves, max depth = 14, train loss: 0.47775, val loss: 0.47514, in 0.031s
1 tree, 110 leaves, max depth = 12, train loss: 0.46249, val loss: 0.45972, in 0.016s
1 tree, 112 leaves, max depth = 15, train loss: 0.44880, val loss: 0.44604, in 0.031s
1 tree, 103 leaves, max depth = 16, train loss: 0.43625, val loss: 0.43341, in 0.016s
1 tree, 66 leaves, max depth = 13, train loss: 0.41929, val loss: 0.41669, in 0.016s
1 tree, 122 leaves, max depth = 17, train loss: 0.40911, val loss: 0.40661, in 0.031s
1 tree, 103 leaves, max depth = 14, train loss: 0.39460, val loss: 0.39253, in 0.016s
1 tree, 123 leaves, max depth = 17, train loss: 0.38574, val loss: 0.38398, in 0.016s
1 tree, 125 leaves, max depth = 19, train loss: 0.37802, val loss: 0.37638, in 0.031s
1 tree, 92 leaves, max depth = 16, train loss: 0.36608, val loss: 0.36476, in 0.016s
1 tree, 126 leaves, max depth = 18, train loss: 0.35961, val loss: 0.35842, in 0.016s
1 tree, 46 leaves, max depth = 14, train loss: 0.34997, val loss: 0.34900, in 0.016s
1 tree, 131 leaves, max depth = 17, train loss: 0.34247, val loss: 0.34198, in 0.031s
1 tree, 121 leaves, max depth = 17, train loss: 0.33575, val loss: 0.33555, in 0.016s
1 tree, 44 leaves, max depth = 12, train loss: 0.32783, val loss: 0.32788, in 0.031s
1 tree, 65 leaves, max depth = 12, train loss: 0.32042, val loss: 0.32104, in 0.016s
1 tree, 67 leaves, max depth = 12, train loss: 0.31382, val loss: 0.31497, in 0.016s
1 tree, 44 leaves, max depth = 12, train loss: 0.30781, val loss: 0.30909, in 0.031s
1 tree, 120 leaves, max depth = 17, train loss: 0.30257, val loss: 0.30414, in 0.016s
1 tree, 69 leaves, max depth = 12, train loss: 0.29727, val loss: 0.29926, in 0.016s
1 tree, 44 leaves, max depth = 12, train loss: 0.29246, val loss: 0.29453, in 0.016s
1 tree, 68 leaves, max depth = 12, train loss: 0.28800, val loss: 0.29044, in 0.016s
1 tree, 43 leaves, max depth = 12, train loss: 0.28403, val loss: 0.28651, in 0.031s
1 tree, 131 leaves, max depth = 18, train loss: 0.27969, val loss: 0.28259, in 0.032s
1 tree, 131 leaves, max depth = 17, train loss: 0.27567, val loss: 0.27903, in 0.016s
1 tree, 71 leaves, max depth = 14, train loss: 0.27216, val loss: 0.27581, in 0.031s
1 tree, 45 leaves, max depth = 12, train loss: 0.26899, val loss: 0.27274, in 0.016s
1 tree, 120 leaves, max depth = 19, train loss: 0.26563, val loss: 0.26966, in 0.016s
1 tree, 71 leaves, max depth = 12, train loss: 0.26277, val loss: 0.26706, in 0.031s
1 tree, 45 leaves, max depth = 13, train loss: 0.26019, val loss: 0.26457, in 0.016s
1 tree, 45 leaves, max depth = 13, train loss: 0.25789, val loss: 0.26237, in 0.031s
1 tree, 131 leaves, max depth = 19, train loss: 0.25487, val loss: 0.25983, in 0.031s
1 tree, 45 leaves, max depth = 13, train loss: 0.25286, val loss: 0.25795, in 0.016s
1 tree, 119 leaves, max depth = 19, train loss: 0.25028, val loss: 0.25565, in 0.016s
1 tree, 71 leaves, max depth = 13, train loss: 0.24821, val loss: 0.25380, in 0.016s
1 tree, 52 leaves, max depth = 12, train loss: 0.24604, val loss: 0.25215, in 0.031s
1 tree, 131 leaves, max depth = 17, train loss: 0.24365, val loss: 0.25017, in 0.031s
1 tree, 71 leaves, max depth = 14, train loss: 0.24202, val loss: 0.24873, in 0.016s
1 tree, 124 leaves, max depth = 15, train loss: 0.24035, val loss: 0.24758, in 0.031s
1 tree, 68 leaves, max depth = 13, train loss: 0.23888, val loss: 0.24631, in 0.016s
1 tree, 52 leaves, max depth = 14, train loss: 0.23742, val loss: 0.24485, in 0.031s
Fit 48 trees in 1.454 s, (4229 total leaves)
Time spent computing histograms: 0.381s
Time spent finding best splits:  0.102s
Time spent applying splits:      0.088s
Time spent predicting:           0.031s
Trial 52, Fold 2: Log loss = 0.2419004480603451, Average precision = 0.9674886909543102, ROC-AUC = 0.9645675749580949, Elapsed Time = 1.456512400000065 seconds
Trial 52, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 52, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
Binning 0.040 GB of training data: 0.174 s
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 69 leaves, max depth = 12, train loss: 0.65379, val loss: 0.65364, in 0.016s
1 tree, 86 leaves, max depth = 12, train loss: 0.61857, val loss: 0.61869, in 0.031s
1 tree, 100 leaves, max depth = 14, train loss: 0.58817, val loss: 0.58829, in 0.031s
1 tree, 97 leaves, max depth = 12, train loss: 0.56106, val loss: 0.56143, in 0.016s
1 tree, 97 leaves, max depth = 14, train loss: 0.53743, val loss: 0.53784, in 0.031s
1 tree, 103 leaves, max depth = 14, train loss: 0.51604, val loss: 0.51667, in 0.016s
1 tree, 103 leaves, max depth = 14, train loss: 0.49697, val loss: 0.49789, in 0.031s
1 tree, 102 leaves, max depth = 12, train loss: 0.47989, val loss: 0.48103, in 0.016s
1 tree, 120 leaves, max depth = 13, train loss: 0.46470, val loss: 0.46613, in 0.031s
1 tree, 105 leaves, max depth = 14, train loss: 0.45084, val loss: 0.45248, in 0.031s
1 tree, 93 leaves, max depth = 14, train loss: 0.43230, val loss: 0.43529, in 0.016s
1 tree, 91 leaves, max depth = 14, train loss: 0.41575, val loss: 0.42007, in 0.031s
1 tree, 83 leaves, max depth = 14, train loss: 0.40546, val loss: 0.40995, in 0.031s
1 tree, 121 leaves, max depth = 14, train loss: 0.39589, val loss: 0.40075, in 0.016s
1 tree, 120 leaves, max depth = 16, train loss: 0.38687, val loss: 0.39214, in 0.031s
1 tree, 121 leaves, max depth = 15, train loss: 0.37904, val loss: 0.38461, in 0.031s
1 tree, 42 leaves, max depth = 12, train loss: 0.36747, val loss: 0.37405, in 0.016s
1 tree, 45 leaves, max depth = 11, train loss: 0.35730, val loss: 0.36475, in 0.016s
1 tree, 66 leaves, max depth = 13, train loss: 0.34794, val loss: 0.35642, in 0.031s
1 tree, 121 leaves, max depth = 15, train loss: 0.34064, val loss: 0.34895, in 0.016s
1 tree, 115 leaves, max depth = 14, train loss: 0.33398, val loss: 0.34215, in 0.031s
1 tree, 70 leaves, max depth = 12, train loss: 0.32622, val loss: 0.33586, in 0.031s
1 tree, 45 leaves, max depth = 12, train loss: 0.31933, val loss: 0.32969, in 0.016s
1 tree, 117 leaves, max depth = 16, train loss: 0.31375, val loss: 0.32390, in 0.031s
1 tree, 70 leaves, max depth = 13, train loss: 0.30754, val loss: 0.31897, in 0.016s
1 tree, 47 leaves, max depth = 13, train loss: 0.30205, val loss: 0.31401, in 0.031s
1 tree, 121 leaves, max depth = 13, train loss: 0.29798, val loss: 0.31049, in 0.016s
1 tree, 119 leaves, max depth = 18, train loss: 0.29352, val loss: 0.30590, in 0.031s
1 tree, 71 leaves, max depth = 14, train loss: 0.28871, val loss: 0.30207, in 0.016s
1 tree, 120 leaves, max depth = 18, train loss: 0.28478, val loss: 0.29809, in 0.031s
1 tree, 73 leaves, max depth = 13, train loss: 0.28057, val loss: 0.29494, in 0.031s
1 tree, 43 leaves, max depth = 12, train loss: 0.27678, val loss: 0.29154, in 0.016s
1 tree, 123 leaves, max depth = 15, train loss: 0.27339, val loss: 0.28809, in 0.031s
1 tree, 131 leaves, max depth = 16, train loss: 0.27004, val loss: 0.28451, in 0.031s
1 tree, 71 leaves, max depth = 13, train loss: 0.26672, val loss: 0.28211, in 0.031s
1 tree, 94 leaves, max depth = 17, train loss: 0.26325, val loss: 0.27918, in 0.016s
1 tree, 71 leaves, max depth = 12, train loss: 0.26046, val loss: 0.27703, in 0.031s
1 tree, 44 leaves, max depth = 14, train loss: 0.25796, val loss: 0.27482, in 0.016s
1 tree, 72 leaves, max depth = 13, train loss: 0.25561, val loss: 0.27314, in 0.016s
1 tree, 41 leaves, max depth = 13, train loss: 0.25346, val loss: 0.27114, in 0.016s
1 tree, 131 leaves, max depth = 17, train loss: 0.25081, val loss: 0.26820, in 0.016s
1 tree, 40 leaves, max depth = 13, train loss: 0.24891, val loss: 0.26662, in 0.031s
1 tree, 73 leaves, max depth = 13, train loss: 0.24705, val loss: 0.26531, in 0.016s
1 tree, 41 leaves, max depth = 12, train loss: 0.24545, val loss: 0.26382, in 0.016s
1 tree, 81 leaves, max depth = 15, train loss: 0.24373, val loss: 0.26283, in 0.031s
1 tree, 123 leaves, max depth = 15, train loss: 0.24210, val loss: 0.26127, in 0.031s
1 tree, 58 leaves, max depth = 14, train loss: 0.24061, val loss: 0.25983, in 0.016s
1 tree, 131 leaves, max depth = 18, train loss: 0.23803, val loss: 0.25698, in 0.031s
Fit 48 trees in 1.549 s, (4221 total leaves)
Time spent computing histograms: 0.394s
Time spent finding best splits:  0.114s
Time spent applying splits:      0.098s
Time spent predicting:           0.000s
Trial 52, Fold 3: Log loss = 0.24269607928834366, Average precision = 0.9655662806077301, ROC-AUC = 0.9624913320684575, Elapsed Time = 1.555675900001006 seconds
Trial 52, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 52, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
Binning 0.040 GB of training data: 0.174 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 71 leaves, max depth = 12, train loss: 0.65354, val loss: 0.65202, in 0.016s
1 tree, 103 leaves, max depth = 16, train loss: 0.61862, val loss: 0.61578, in 0.031s
1 tree, 101 leaves, max depth = 16, train loss: 0.58794, val loss: 0.58395, in 0.016s
1 tree, 99 leaves, max depth = 13, train loss: 0.56067, val loss: 0.55556, in 0.031s
1 tree, 103 leaves, max depth = 14, train loss: 0.53660, val loss: 0.53051, in 0.016s
1 tree, 101 leaves, max depth = 13, train loss: 0.51512, val loss: 0.50806, in 0.031s
1 tree, 102 leaves, max depth = 14, train loss: 0.49603, val loss: 0.48810, in 0.016s
1 tree, 108 leaves, max depth = 14, train loss: 0.47889, val loss: 0.47012, in 0.031s
1 tree, 114 leaves, max depth = 13, train loss: 0.46372, val loss: 0.45401, in 0.031s
1 tree, 95 leaves, max depth = 14, train loss: 0.44419, val loss: 0.43419, in 0.016s
1 tree, 110 leaves, max depth = 14, train loss: 0.43143, val loss: 0.42076, in 0.016s
1 tree, 93 leaves, max depth = 14, train loss: 0.41517, val loss: 0.40437, in 0.031s
1 tree, 87 leaves, max depth = 13, train loss: 0.40487, val loss: 0.39337, in 0.016s
1 tree, 122 leaves, max depth = 13, train loss: 0.39539, val loss: 0.38323, in 0.031s
1 tree, 123 leaves, max depth = 15, train loss: 0.38657, val loss: 0.37421, in 0.031s
1 tree, 131 leaves, max depth = 15, train loss: 0.37909, val loss: 0.36632, in 0.031s
1 tree, 91 leaves, max depth = 17, train loss: 0.36712, val loss: 0.35433, in 0.016s
1 tree, 126 leaves, max depth = 18, train loss: 0.36058, val loss: 0.34755, in 0.031s
1 tree, 96 leaves, max depth = 18, train loss: 0.35030, val loss: 0.33728, in 0.031s
1 tree, 46 leaves, max depth = 14, train loss: 0.34168, val loss: 0.32832, in 0.016s
1 tree, 121 leaves, max depth = 14, train loss: 0.33481, val loss: 0.32139, in 0.016s
1 tree, 71 leaves, max depth = 12, train loss: 0.32704, val loss: 0.31409, in 0.031s
1 tree, 131 leaves, max depth = 15, train loss: 0.32071, val loss: 0.30766, in 0.016s
1 tree, 96 leaves, max depth = 18, train loss: 0.31363, val loss: 0.30059, in 0.031s
1 tree, 113 leaves, max depth = 18, train loss: 0.30927, val loss: 0.29607, in 0.016s
1 tree, 71 leaves, max depth = 13, train loss: 0.30334, val loss: 0.29055, in 0.016s
1 tree, 120 leaves, max depth = 15, train loss: 0.29863, val loss: 0.28578, in 0.016s
1 tree, 131 leaves, max depth = 15, train loss: 0.29402, val loss: 0.28107, in 0.016s
1 tree, 44 leaves, max depth = 11, train loss: 0.28916, val loss: 0.27598, in 0.016s
1 tree, 71 leaves, max depth = 14, train loss: 0.28458, val loss: 0.27171, in 0.016s
1 tree, 45 leaves, max depth = 13, train loss: 0.28050, val loss: 0.26750, in 0.016s
1 tree, 69 leaves, max depth = 14, train loss: 0.27661, val loss: 0.26397, in 0.016s
1 tree, 131 leaves, max depth = 14, train loss: 0.27286, val loss: 0.26014, in 0.031s
1 tree, 72 leaves, max depth = 14, train loss: 0.26949, val loss: 0.25709, in 0.016s
1 tree, 122 leaves, max depth = 16, train loss: 0.26685, val loss: 0.25468, in 0.016s
1 tree, 46 leaves, max depth = 12, train loss: 0.26390, val loss: 0.25162, in 0.016s
1 tree, 131 leaves, max depth = 14, train loss: 0.26072, val loss: 0.24837, in 0.031s
1 tree, 72 leaves, max depth = 16, train loss: 0.25797, val loss: 0.24582, in 0.016s
1 tree, 131 leaves, max depth = 20, train loss: 0.25428, val loss: 0.24258, in 0.031s
1 tree, 55 leaves, max depth = 12, train loss: 0.25180, val loss: 0.24015, in 0.016s
1 tree, 74 leaves, max depth = 12, train loss: 0.24951, val loss: 0.23802, in 0.016s
1 tree, 131 leaves, max depth = 21, train loss: 0.24631, val loss: 0.23524, in 0.016s
1 tree, 56 leaves, max depth = 16, train loss: 0.24432, val loss: 0.23338, in 0.016s
1 tree, 74 leaves, max depth = 13, train loss: 0.24241, val loss: 0.23162, in 0.016s
1 tree, 47 leaves, max depth = 11, train loss: 0.24071, val loss: 0.22979, in 0.016s
1 tree, 126 leaves, max depth = 15, train loss: 0.23912, val loss: 0.22855, in 0.031s
1 tree, 75 leaves, max depth = 13, train loss: 0.23749, val loss: 0.22706, in 0.031s
1 tree, 51 leaves, max depth = 12, train loss: 0.23581, val loss: 0.22576, in 0.016s
Fit 48 trees in 1.425 s, (4499 total leaves)
Time spent computing histograms: 0.369s
Time spent finding best splits:  0.101s
Time spent applying splits:      0.088s
Time spent predicting:           0.000s
Trial 52, Fold 4: Log loss = 0.24011756494161798, Average precision = 0.9684737999022944, ROC-AUC = 0.9645331713126891, Elapsed Time = 1.4310083000000304 seconds
Trial 52, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 52, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
Binning 0.040 GB of training data: 0.158 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 64 leaves, max depth = 12, train loss: 0.65348, val loss: 0.65181, in 0.016s
1 tree, 90 leaves, max depth = 14, train loss: 0.61798, val loss: 0.61507, in 0.016s
1 tree, 92 leaves, max depth = 14, train loss: 0.58694, val loss: 0.58286, in 0.016s
1 tree, 110 leaves, max depth = 13, train loss: 0.56174, val loss: 0.55693, in 0.016s
1 tree, 101 leaves, max depth = 13, train loss: 0.53773, val loss: 0.53183, in 0.031s
1 tree, 103 leaves, max depth = 14, train loss: 0.51594, val loss: 0.50921, in 0.016s
1 tree, 118 leaves, max depth = 15, train loss: 0.49745, val loss: 0.48993, in 0.016s
1 tree, 103 leaves, max depth = 14, train loss: 0.47993, val loss: 0.47181, in 0.016s
1 tree, 106 leaves, max depth = 12, train loss: 0.46458, val loss: 0.45581, in 0.031s
1 tree, 99 leaves, max depth = 12, train loss: 0.44531, val loss: 0.43637, in 0.016s
1 tree, 107 leaves, max depth = 13, train loss: 0.43220, val loss: 0.42288, in 0.016s
1 tree, 97 leaves, max depth = 15, train loss: 0.41571, val loss: 0.40652, in 0.016s
1 tree, 114 leaves, max depth = 14, train loss: 0.40467, val loss: 0.39516, in 0.031s
1 tree, 111 leaves, max depth = 13, train loss: 0.39470, val loss: 0.38490, in 0.016s
1 tree, 116 leaves, max depth = 13, train loss: 0.38600, val loss: 0.37581, in 0.016s
1 tree, 98 leaves, max depth = 15, train loss: 0.37336, val loss: 0.36330, in 0.016s
1 tree, 108 leaves, max depth = 13, train loss: 0.36559, val loss: 0.35539, in 0.031s
1 tree, 98 leaves, max depth = 15, train loss: 0.35482, val loss: 0.34476, in 0.016s
1 tree, 110 leaves, max depth = 13, train loss: 0.34822, val loss: 0.33808, in 0.016s
1 tree, 117 leaves, max depth = 16, train loss: 0.34101, val loss: 0.33094, in 0.031s
1 tree, 98 leaves, max depth = 14, train loss: 0.33208, val loss: 0.32221, in 0.016s
1 tree, 119 leaves, max depth = 16, train loss: 0.32582, val loss: 0.31605, in 0.016s
1 tree, 47 leaves, max depth = 11, train loss: 0.31869, val loss: 0.30875, in 0.016s
1 tree, 131 leaves, max depth = 17, train loss: 0.31291, val loss: 0.30299, in 0.016s
1 tree, 75 leaves, max depth = 12, train loss: 0.30637, val loss: 0.29700, in 0.016s
1 tree, 46 leaves, max depth = 14, train loss: 0.30066, val loss: 0.29120, in 0.016s
1 tree, 45 leaves, max depth = 13, train loss: 0.29554, val loss: 0.28598, in 0.016s
1 tree, 46 leaves, max depth = 14, train loss: 0.29099, val loss: 0.28134, in 0.016s
1 tree, 131 leaves, max depth = 16, train loss: 0.28627, val loss: 0.27669, in 0.031s
1 tree, 73 leaves, max depth = 12, train loss: 0.28163, val loss: 0.27267, in 0.016s
1 tree, 73 leaves, max depth = 12, train loss: 0.27746, val loss: 0.26897, in 0.016s
1 tree, 46 leaves, max depth = 13, train loss: 0.27405, val loss: 0.26547, in 0.016s
1 tree, 120 leaves, max depth = 17, train loss: 0.27040, val loss: 0.26210, in 0.031s
1 tree, 75 leaves, max depth = 14, train loss: 0.26697, val loss: 0.25905, in 0.016s
1 tree, 131 leaves, max depth = 16, train loss: 0.26348, val loss: 0.25561, in 0.016s
1 tree, 131 leaves, max depth = 23, train loss: 0.25932, val loss: 0.25186, in 0.031s
1 tree, 123 leaves, max depth = 18, train loss: 0.25652, val loss: 0.24925, in 0.016s
1 tree, 73 leaves, max depth = 12, train loss: 0.25376, val loss: 0.24683, in 0.031s
1 tree, 46 leaves, max depth = 12, train loss: 0.25129, val loss: 0.24437, in 0.016s
1 tree, 54 leaves, max depth = 13, train loss: 0.24884, val loss: 0.24223, in 0.016s
1 tree, 75 leaves, max depth = 13, train loss: 0.24663, val loss: 0.24028, in 0.016s
1 tree, 56 leaves, max depth = 15, train loss: 0.24458, val loss: 0.23826, in 0.016s
1 tree, 57 leaves, max depth = 15, train loss: 0.24274, val loss: 0.23640, in 0.016s
1 tree, 73 leaves, max depth = 12, train loss: 0.24097, val loss: 0.23499, in 0.016s
1 tree, 131 leaves, max depth = 18, train loss: 0.23903, val loss: 0.23380, in 0.016s
1 tree, 54 leaves, max depth = 11, train loss: 0.23751, val loss: 0.23233, in 0.016s
1 tree, 121 leaves, max depth = 18, train loss: 0.23545, val loss: 0.23050, in 0.031s
1 tree, 127 leaves, max depth = 17, train loss: 0.23394, val loss: 0.22978, in 0.016s
Fit 48 trees in 1.267 s, (4439 total leaves)
Time spent computing histograms: 0.329s
Time spent finding best splits:  0.089s
Time spent applying splits:      0.079s
Time spent predicting:           0.000s
Trial 52, Fold 5: Log loss = 0.24758770280829395, Average precision = 0.9640340686720158, ROC-AUC = 0.9600119651450124, Elapsed Time = 1.2826208999995288 seconds
Optimization Progress:  53%|#####3    | 53/100 [10:22<09:09, 11.70s/it]
Trial 53, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 53, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
0.157 s 0.040 GB of training data: 
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 37 leaves, max depth = 8, train loss: 0.68173, val loss: 0.68168, in 0.016s
1 tree, 44 leaves, max depth = 11, train loss: 0.67031, val loss: 0.67032, in 0.016s
1 tree, 45 leaves, max depth = 11, train loss: 0.65933, val loss: 0.65942, in 0.016s
1 tree, 51 leaves, max depth = 8, train loss: 0.64935, val loss: 0.64935, in 0.016s
1 tree, 59 leaves, max depth = 10, train loss: 0.63979, val loss: 0.63967, in 0.016s
1 tree, 45 leaves, max depth = 11, train loss: 0.62996, val loss: 0.62992, in 0.016s
1 tree, 42 leaves, max depth = 10, train loss: 0.62072, val loss: 0.62061, in 0.016s
1 tree, 39 leaves, max depth = 10, train loss: 0.61171, val loss: 0.61150, in 0.016s
1 tree, 44 leaves, max depth = 11, train loss: 0.60292, val loss: 0.60277, in 0.016s
1 tree, 61 leaves, max depth = 12, train loss: 0.59489, val loss: 0.59460, in 0.016s
1 tree, 42 leaves, max depth = 11, train loss: 0.58658, val loss: 0.58624, in 0.016s
1 tree, 46 leaves, max depth = 11, train loss: 0.57902, val loss: 0.57864, in 0.016s
1 tree, 46 leaves, max depth = 11, train loss: 0.57123, val loss: 0.57083, in 0.016s
1 tree, 45 leaves, max depth = 11, train loss: 0.56383, val loss: 0.56348, in 0.016s
1 tree, 50 leaves, max depth = 8, train loss: 0.55665, val loss: 0.55618, in 0.016s
1 tree, 31 leaves, max depth = 8, train loss: 0.54991, val loss: 0.54937, in 0.016s
1 tree, 52 leaves, max depth = 8, train loss: 0.54320, val loss: 0.54254, in 0.016s
1 tree, 46 leaves, max depth = 10, train loss: 0.53673, val loss: 0.53614, in 0.016s
1 tree, 34 leaves, max depth = 8, train loss: 0.53054, val loss: 0.52986, in 0.016s
1 tree, 65 leaves, max depth = 12, train loss: 0.52474, val loss: 0.52403, in 0.016s
1 tree, 49 leaves, max depth = 11, train loss: 0.51874, val loss: 0.51801, in 0.000s
1 tree, 53 leaves, max depth = 13, train loss: 0.51346, val loss: 0.51262, in 0.016s
1 tree, 46 leaves, max depth = 11, train loss: 0.50792, val loss: 0.50713, in 0.016s
1 tree, 65 leaves, max depth = 13, train loss: 0.50282, val loss: 0.50196, in 0.016s
1 tree, 57 leaves, max depth = 11, train loss: 0.49771, val loss: 0.49684, in 0.016s
1 tree, 50 leaves, max depth = 11, train loss: 0.49266, val loss: 0.49186, in 0.016s
1 tree, 54 leaves, max depth = 11, train loss: 0.48790, val loss: 0.48711, in 0.016s
1 tree, 62 leaves, max depth = 12, train loss: 0.48333, val loss: 0.48252, in 0.016s
1 tree, 51 leaves, max depth = 11, train loss: 0.47859, val loss: 0.47778, in 0.016s
1 tree, 46 leaves, max depth = 10, train loss: 0.47445, val loss: 0.47355, in 0.016s
1 tree, 60 leaves, max depth = 12, train loss: 0.47028, val loss: 0.46937, in 0.016s
1 tree, 58 leaves, max depth = 13, train loss: 0.46434, val loss: 0.46362, in 0.016s
1 tree, 61 leaves, max depth = 10, train loss: 0.46017, val loss: 0.45939, in 0.016s
1 tree, 58 leaves, max depth = 12, train loss: 0.45454, val loss: 0.45394, in 0.016s
1 tree, 56 leaves, max depth = 12, train loss: 0.45077, val loss: 0.45018, in 0.016s
1 tree, 59 leaves, max depth = 11, train loss: 0.44691, val loss: 0.44620, in 0.016s
1 tree, 45 leaves, max depth = 11, train loss: 0.44184, val loss: 0.44125, in 0.016s
1 tree, 47 leaves, max depth = 10, train loss: 0.43826, val loss: 0.43756, in 0.016s
1 tree, 67 leaves, max depth = 12, train loss: 0.43488, val loss: 0.43420, in 0.016s
1 tree, 54 leaves, max depth = 12, train loss: 0.43133, val loss: 0.43061, in 0.016s
1 tree, 50 leaves, max depth = 12, train loss: 0.42788, val loss: 0.42718, in 0.016s
1 tree, 62 leaves, max depth = 11, train loss: 0.42462, val loss: 0.42386, in 0.016s
1 tree, 60 leaves, max depth = 11, train loss: 0.42146, val loss: 0.42064, in 0.016s
1 tree, 65 leaves, max depth = 10, train loss: 0.41858, val loss: 0.41773, in 0.031s
1 tree, 52 leaves, max depth = 10, train loss: 0.41548, val loss: 0.41465, in 0.016s
1 tree, 69 leaves, max depth = 12, train loss: 0.41282, val loss: 0.41198, in 0.016s
1 tree, 53 leaves, max depth = 10, train loss: 0.41014, val loss: 0.40938, in 0.016s
1 tree, 70 leaves, max depth = 11, train loss: 0.40758, val loss: 0.40679, in 0.016s
1 tree, 51 leaves, max depth = 10, train loss: 0.40477, val loss: 0.40401, in 0.016s
1 tree, 38 leaves, max depth = 9, train loss: 0.40222, val loss: 0.40140, in 0.016s
1 tree, 51 leaves, max depth = 10, train loss: 0.39956, val loss: 0.39877, in 0.016s
1 tree, 60 leaves, max depth = 13, train loss: 0.39539, val loss: 0.39485, in 0.016s
1 tree, 58 leaves, max depth = 13, train loss: 0.39136, val loss: 0.39106, in 0.016s
1 tree, 55 leaves, max depth = 10, train loss: 0.38890, val loss: 0.38863, in 0.016s
1 tree, 55 leaves, max depth = 10, train loss: 0.38650, val loss: 0.38627, in 0.016s
1 tree, 58 leaves, max depth = 13, train loss: 0.38271, val loss: 0.38271, in 0.016s
1 tree, 36 leaves, max depth = 11, train loss: 0.37924, val loss: 0.37930, in 0.016s
1 tree, 32 leaves, max depth = 11, train loss: 0.37587, val loss: 0.37599, in 0.016s
1 tree, 32 leaves, max depth = 11, train loss: 0.37263, val loss: 0.37279, in 0.016s
Fit 59 trees in 1.236 s, (3029 total leaves)
Time spent computing histograms: 0.378s
Time spent finding best splits:  0.060s
Time spent applying splits:      0.048s
Time spent predicting:           0.016s
Trial 53, Fold 1: Log loss = 0.37719282831701495, Average precision = 0.9540985323839766, ROC-AUC = 0.9484556400347954, Elapsed Time = 1.235247900000104 seconds
Trial 53, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 53, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
Binning 0.040 GB of training data: 0.126 s
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 39 leaves, max depth = 7, train loss: 0.68182, val loss: 0.68164, in 0.016s
1 tree, 40 leaves, max depth = 11, train loss: 0.67031, val loss: 0.66989, in 0.016s
1 tree, 49 leaves, max depth = 8, train loss: 0.65940, val loss: 0.65880, in 0.016s
1 tree, 49 leaves, max depth = 11, train loss: 0.64920, val loss: 0.64844, in 0.016s
1 tree, 55 leaves, max depth = 12, train loss: 0.63965, val loss: 0.63878, in 0.016s
1 tree, 43 leaves, max depth = 11, train loss: 0.62965, val loss: 0.62862, in 0.016s
1 tree, 40 leaves, max depth = 10, train loss: 0.62006, val loss: 0.61886, in 0.016s
1 tree, 38 leaves, max depth = 8, train loss: 0.61100, val loss: 0.60957, in 0.016s
1 tree, 41 leaves, max depth = 10, train loss: 0.60209, val loss: 0.60051, in 0.016s
1 tree, 60 leaves, max depth = 12, train loss: 0.59415, val loss: 0.59250, in 0.000s
1 tree, 47 leaves, max depth = 11, train loss: 0.58584, val loss: 0.58404, in 0.016s
1 tree, 50 leaves, max depth = 10, train loss: 0.57828, val loss: 0.57635, in 0.016s
1 tree, 44 leaves, max depth = 11, train loss: 0.57048, val loss: 0.56839, in 0.031s
1 tree, 47 leaves, max depth = 11, train loss: 0.56298, val loss: 0.56076, in 0.000s
1 tree, 46 leaves, max depth = 8, train loss: 0.55579, val loss: 0.55349, in 0.031s
1 tree, 38 leaves, max depth = 10, train loss: 0.54890, val loss: 0.54649, in 0.016s
1 tree, 63 leaves, max depth = 12, train loss: 0.54263, val loss: 0.54019, in 0.016s
1 tree, 52 leaves, max depth = 11, train loss: 0.53670, val loss: 0.53406, in 0.016s
1 tree, 50 leaves, max depth = 9, train loss: 0.53038, val loss: 0.52768, in 0.016s
1 tree, 51 leaves, max depth = 9, train loss: 0.52426, val loss: 0.52151, in 0.016s
1 tree, 53 leaves, max depth = 11, train loss: 0.51876, val loss: 0.51585, in 0.016s
1 tree, 44 leaves, max depth = 11, train loss: 0.51290, val loss: 0.50991, in 0.016s
1 tree, 50 leaves, max depth = 9, train loss: 0.50734, val loss: 0.50430, in 0.016s
1 tree, 34 leaves, max depth = 8, train loss: 0.50202, val loss: 0.49886, in 0.016s
1 tree, 43 leaves, max depth = 11, train loss: 0.49669, val loss: 0.49346, in 0.016s
1 tree, 53 leaves, max depth = 9, train loss: 0.49167, val loss: 0.48839, in 0.016s
1 tree, 40 leaves, max depth = 11, train loss: 0.48685, val loss: 0.48345, in 0.016s
1 tree, 42 leaves, max depth = 8, train loss: 0.48216, val loss: 0.47860, in 0.016s
1 tree, 46 leaves, max depth = 12, train loss: 0.47743, val loss: 0.47381, in 0.016s
1 tree, 46 leaves, max depth = 12, train loss: 0.47285, val loss: 0.46918, in 0.016s
1 tree, 64 leaves, max depth = 12, train loss: 0.46869, val loss: 0.46498, in 0.016s
1 tree, 49 leaves, max depth = 12, train loss: 0.46441, val loss: 0.46058, in 0.016s
1 tree, 57 leaves, max depth = 9, train loss: 0.46030, val loss: 0.45649, in 0.016s
1 tree, 51 leaves, max depth = 12, train loss: 0.45463, val loss: 0.45089, in 0.016s
1 tree, 58 leaves, max depth = 12, train loss: 0.44925, val loss: 0.44563, in 0.016s
1 tree, 45 leaves, max depth = 12, train loss: 0.44536, val loss: 0.44170, in 0.016s
1 tree, 51 leaves, max depth = 13, train loss: 0.44015, val loss: 0.43656, in 0.016s
1 tree, 48 leaves, max depth = 10, train loss: 0.43661, val loss: 0.43294, in 0.016s
1 tree, 65 leaves, max depth = 12, train loss: 0.43327, val loss: 0.42958, in 0.016s
1 tree, 46 leaves, max depth = 12, train loss: 0.42977, val loss: 0.42604, in 0.016s
1 tree, 75 leaves, max depth = 12, train loss: 0.42671, val loss: 0.42301, in 0.016s
1 tree, 62 leaves, max depth = 10, train loss: 0.42349, val loss: 0.41979, in 0.031s
1 tree, 61 leaves, max depth = 10, train loss: 0.42036, val loss: 0.41668, in 0.016s
1 tree, 74 leaves, max depth = 15, train loss: 0.41748, val loss: 0.41387, in 0.016s
1 tree, 63 leaves, max depth = 11, train loss: 0.41455, val loss: 0.41092, in 0.016s
1 tree, 51 leaves, max depth = 13, train loss: 0.41008, val loss: 0.40654, in 0.016s
1 tree, 64 leaves, max depth = 9, train loss: 0.40757, val loss: 0.40402, in 0.016s
1 tree, 51 leaves, max depth = 12, train loss: 0.40472, val loss: 0.40116, in 0.016s
1 tree, 53 leaves, max depth = 15, train loss: 0.40066, val loss: 0.39721, in 0.016s
1 tree, 63 leaves, max depth = 12, train loss: 0.39655, val loss: 0.39318, in 0.016s
1 tree, 48 leaves, max depth = 10, train loss: 0.39403, val loss: 0.39065, in 0.016s
1 tree, 49 leaves, max depth = 11, train loss: 0.39160, val loss: 0.38818, in 0.016s
1 tree, 68 leaves, max depth = 13, train loss: 0.38928, val loss: 0.38586, in 0.016s
1 tree, 52 leaves, max depth = 12, train loss: 0.38560, val loss: 0.38227, in 0.016s
1 tree, 54 leaves, max depth = 14, train loss: 0.38194, val loss: 0.37870, in 0.016s
1 tree, 42 leaves, max depth = 12, train loss: 0.37974, val loss: 0.37645, in 0.016s
1 tree, 65 leaves, max depth = 11, train loss: 0.37756, val loss: 0.37425, in 0.016s
1 tree, 53 leaves, max depth = 12, train loss: 0.37531, val loss: 0.37195, in 0.016s
1 tree, 29 leaves, max depth = 12, train loss: 0.37209, val loss: 0.36879, in 0.016s
Fit 59 trees in 1.251 s, (3004 total leaves)
Time spent computing histograms: 0.376s
Time spent finding best splits:  0.066s
Time spent applying splits:      0.054s
Time spent predicting:           0.000s
Trial 53, Fold 2: Log loss = 0.37552071858201236, Average precision = 0.9503976093764905, ROC-AUC = 0.9489623638177436, Elapsed Time = 1.2549022000002878 seconds
Trial 53, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 53, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
Binning 0.040 GB of training data: 0.173 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 39 leaves, max depth = 8, train loss: 0.68181, val loss: 0.68159, in 0.016s
1 tree, 44 leaves, max depth = 12, train loss: 0.67045, val loss: 0.67018, in 0.016s
1 tree, 44 leaves, max depth = 12, train loss: 0.65954, val loss: 0.65922, in 0.016s
1 tree, 50 leaves, max depth = 8, train loss: 0.64958, val loss: 0.64910, in 0.016s
1 tree, 49 leaves, max depth = 8, train loss: 0.64008, val loss: 0.63964, in 0.016s
1 tree, 50 leaves, max depth = 11, train loss: 0.63069, val loss: 0.63028, in 0.016s
1 tree, 42 leaves, max depth = 11, train loss: 0.62150, val loss: 0.62117, in 0.016s
1 tree, 44 leaves, max depth = 13, train loss: 0.61235, val loss: 0.61200, in 0.016s
1 tree, 45 leaves, max depth = 13, train loss: 0.60388, val loss: 0.60357, in 0.016s
1 tree, 50 leaves, max depth = 9, train loss: 0.59583, val loss: 0.59557, in 0.016s
1 tree, 42 leaves, max depth = 8, train loss: 0.58759, val loss: 0.58736, in 0.016s
1 tree, 51 leaves, max depth = 9, train loss: 0.58010, val loss: 0.57993, in 0.016s
1 tree, 48 leaves, max depth = 9, train loss: 0.57251, val loss: 0.57232, in 0.016s
1 tree, 56 leaves, max depth = 10, train loss: 0.56552, val loss: 0.56540, in 0.031s
1 tree, 41 leaves, max depth = 12, train loss: 0.55819, val loss: 0.55811, in 0.016s
1 tree, 45 leaves, max depth = 12, train loss: 0.55146, val loss: 0.55147, in 0.016s
1 tree, 65 leaves, max depth = 11, train loss: 0.54520, val loss: 0.54526, in 0.016s
1 tree, 58 leaves, max depth = 10, train loss: 0.53903, val loss: 0.53917, in 0.016s
1 tree, 60 leaves, max depth = 12, train loss: 0.53314, val loss: 0.53335, in 0.016s
1 tree, 41 leaves, max depth = 12, train loss: 0.52685, val loss: 0.52711, in 0.016s
1 tree, 57 leaves, max depth = 12, train loss: 0.52125, val loss: 0.52155, in 0.016s
1 tree, 43 leaves, max depth = 12, train loss: 0.51531, val loss: 0.51568, in 0.016s
1 tree, 58 leaves, max depth = 12, train loss: 0.51006, val loss: 0.51048, in 0.016s
1 tree, 48 leaves, max depth = 12, train loss: 0.50499, val loss: 0.50538, in 0.016s
1 tree, 40 leaves, max depth = 8, train loss: 0.50023, val loss: 0.50058, in 0.016s
1 tree, 47 leaves, max depth = 8, train loss: 0.49552, val loss: 0.49593, in 0.016s
1 tree, 54 leaves, max depth = 12, train loss: 0.48900, val loss: 0.48983, in 0.031s
1 tree, 35 leaves, max depth = 7, train loss: 0.48415, val loss: 0.48500, in 0.016s
1 tree, 48 leaves, max depth = 12, train loss: 0.47947, val loss: 0.48029, in 0.000s
1 tree, 56 leaves, max depth = 12, train loss: 0.47340, val loss: 0.47464, in 0.016s
1 tree, 43 leaves, max depth = 13, train loss: 0.46880, val loss: 0.47008, in 0.016s
1 tree, 59 leaves, max depth = 14, train loss: 0.46463, val loss: 0.46601, in 0.016s
1 tree, 45 leaves, max depth = 13, train loss: 0.46028, val loss: 0.46172, in 0.016s
1 tree, 68 leaves, max depth = 11, train loss: 0.45482, val loss: 0.45669, in 0.016s
1 tree, 57 leaves, max depth = 13, train loss: 0.44950, val loss: 0.45171, in 0.016s
1 tree, 43 leaves, max depth = 12, train loss: 0.44546, val loss: 0.44772, in 0.016s
1 tree, 47 leaves, max depth = 13, train loss: 0.44154, val loss: 0.44385, in 0.016s
1 tree, 61 leaves, max depth = 10, train loss: 0.43787, val loss: 0.44023, in 0.016s
1 tree, 52 leaves, max depth = 13, train loss: 0.43450, val loss: 0.43693, in 0.016s
1 tree, 48 leaves, max depth = 15, train loss: 0.42983, val loss: 0.43258, in 0.016s
1 tree, 54 leaves, max depth = 12, train loss: 0.42512, val loss: 0.42825, in 0.016s
1 tree, 55 leaves, max depth = 13, train loss: 0.42055, val loss: 0.42403, in 0.016s
1 tree, 47 leaves, max depth = 12, train loss: 0.41716, val loss: 0.42069, in 0.016s
1 tree, 46 leaves, max depth = 13, train loss: 0.41388, val loss: 0.41744, in 0.016s
1 tree, 52 leaves, max depth = 15, train loss: 0.40976, val loss: 0.41366, in 0.031s
1 tree, 45 leaves, max depth = 13, train loss: 0.40662, val loss: 0.41057, in 0.016s
1 tree, 46 leaves, max depth = 13, train loss: 0.40358, val loss: 0.40756, in 0.016s
1 tree, 47 leaves, max depth = 13, train loss: 0.40062, val loss: 0.40464, in 0.000s
1 tree, 48 leaves, max depth = 13, train loss: 0.39776, val loss: 0.40180, in 0.031s
1 tree, 47 leaves, max depth = 12, train loss: 0.39496, val loss: 0.39906, in 0.016s
1 tree, 49 leaves, max depth = 10, train loss: 0.39236, val loss: 0.39652, in 0.016s
1 tree, 50 leaves, max depth = 12, train loss: 0.38971, val loss: 0.39392, in 0.016s
1 tree, 55 leaves, max depth = 15, train loss: 0.38600, val loss: 0.39054, in 0.016s
1 tree, 49 leaves, max depth = 12, train loss: 0.38347, val loss: 0.38806, in 0.016s
1 tree, 52 leaves, max depth = 13, train loss: 0.38130, val loss: 0.38591, in 0.016s
1 tree, 63 leaves, max depth = 14, train loss: 0.37906, val loss: 0.38376, in 0.031s
1 tree, 49 leaves, max depth = 12, train loss: 0.37671, val loss: 0.38146, in 0.000s
1 tree, 55 leaves, max depth = 15, train loss: 0.37329, val loss: 0.37838, in 0.031s
1 tree, 61 leaves, max depth = 9, train loss: 0.37113, val loss: 0.37626, in 0.016s
Fit 59 trees in 1.345 s, (2943 total leaves)
Time spent computing histograms: 0.411s
Time spent finding best splits:  0.067s
Time spent applying splits:      0.055s
Time spent predicting:           0.016s
Trial 53, Fold 3: Log loss = 0.37057968492038945, Average precision = 0.9563776470250097, ROC-AUC = 0.9524284052932168, Elapsed Time = 1.3416933999997127 seconds
Trial 53, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 53, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
Binning 0.040 GB of training data: 0.157 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 41 leaves, max depth = 8, train loss: 0.68182, val loss: 0.68135, in 0.016s
1 tree, 39 leaves, max depth = 11, train loss: 0.67044, val loss: 0.66956, in 0.016s
1 tree, 43 leaves, max depth = 11, train loss: 0.65950, val loss: 0.65821, in 0.031s
1 tree, 47 leaves, max depth = 12, train loss: 0.64982, val loss: 0.64805, in 0.016s
1 tree, 46 leaves, max depth = 9, train loss: 0.63982, val loss: 0.63759, in 0.016s
1 tree, 53 leaves, max depth = 13, train loss: 0.63054, val loss: 0.62793, in 0.016s
1 tree, 46 leaves, max depth = 9, train loss: 0.62125, val loss: 0.61820, in 0.016s
1 tree, 41 leaves, max depth = 10, train loss: 0.61205, val loss: 0.60863, in 0.016s
1 tree, 54 leaves, max depth = 13, train loss: 0.60374, val loss: 0.59997, in 0.016s
1 tree, 51 leaves, max depth = 12, train loss: 0.59579, val loss: 0.59166, in 0.016s
1 tree, 43 leaves, max depth = 11, train loss: 0.58759, val loss: 0.58314, in 0.016s
1 tree, 57 leaves, max depth = 14, train loss: 0.58012, val loss: 0.57535, in 0.016s
1 tree, 53 leaves, max depth = 10, train loss: 0.57241, val loss: 0.56725, in 0.016s
1 tree, 58 leaves, max depth = 9, train loss: 0.56549, val loss: 0.55999, in 0.016s
1 tree, 43 leaves, max depth = 10, train loss: 0.55816, val loss: 0.55233, in 0.016s
1 tree, 54 leaves, max depth = 13, train loss: 0.55159, val loss: 0.54548, in 0.016s
1 tree, 60 leaves, max depth = 16, train loss: 0.54547, val loss: 0.53905, in 0.031s
1 tree, 50 leaves, max depth = 10, train loss: 0.53956, val loss: 0.53297, in 0.016s
1 tree, 55 leaves, max depth = 10, train loss: 0.53319, val loss: 0.52626, in 0.016s
1 tree, 55 leaves, max depth = 10, train loss: 0.52704, val loss: 0.51977, in 0.016s
1 tree, 52 leaves, max depth = 11, train loss: 0.52126, val loss: 0.51375, in 0.016s
1 tree, 47 leaves, max depth = 11, train loss: 0.51537, val loss: 0.50758, in 0.016s
1 tree, 55 leaves, max depth = 10, train loss: 0.50977, val loss: 0.50167, in 0.016s
1 tree, 43 leaves, max depth = 9, train loss: 0.50441, val loss: 0.49602, in 0.016s
1 tree, 49 leaves, max depth = 11, train loss: 0.49906, val loss: 0.49041, in 0.016s
1 tree, 44 leaves, max depth = 9, train loss: 0.49403, val loss: 0.48511, in 0.016s
1 tree, 61 leaves, max depth = 10, train loss: 0.48911, val loss: 0.47989, in 0.016s
1 tree, 56 leaves, max depth = 12, train loss: 0.48281, val loss: 0.47346, in 0.031s
1 tree, 66 leaves, max depth = 15, train loss: 0.47846, val loss: 0.46889, in 0.016s
1 tree, 65 leaves, max depth = 10, train loss: 0.47426, val loss: 0.46455, in 0.016s
1 tree, 54 leaves, max depth = 11, train loss: 0.47009, val loss: 0.46027, in 0.016s
1 tree, 67 leaves, max depth = 13, train loss: 0.46614, val loss: 0.45611, in 0.016s
1 tree, 60 leaves, max depth = 13, train loss: 0.46049, val loss: 0.45035, in 0.031s
1 tree, 54 leaves, max depth = 13, train loss: 0.45503, val loss: 0.44483, in 0.016s
1 tree, 56 leaves, max depth = 11, train loss: 0.45129, val loss: 0.44098, in 0.016s
1 tree, 51 leaves, max depth = 11, train loss: 0.44731, val loss: 0.43681, in 0.016s
1 tree, 52 leaves, max depth = 10, train loss: 0.44387, val loss: 0.43309, in 0.016s
1 tree, 65 leaves, max depth = 17, train loss: 0.44049, val loss: 0.42953, in 0.016s
1 tree, 50 leaves, max depth = 11, train loss: 0.43682, val loss: 0.42568, in 0.031s
1 tree, 52 leaves, max depth = 11, train loss: 0.43326, val loss: 0.42193, in 0.000s
1 tree, 49 leaves, max depth = 11, train loss: 0.42981, val loss: 0.41829, in 0.031s
1 tree, 60 leaves, max depth = 12, train loss: 0.42510, val loss: 0.41352, in 0.016s
1 tree, 65 leaves, max depth = 10, train loss: 0.42190, val loss: 0.41012, in 0.016s
1 tree, 56 leaves, max depth = 13, train loss: 0.41740, val loss: 0.40557, in 0.016s
1 tree, 66 leaves, max depth = 10, train loss: 0.41436, val loss: 0.40233, in 0.016s
1 tree, 74 leaves, max depth = 13, train loss: 0.41018, val loss: 0.39809, in 0.031s
1 tree, 72 leaves, max depth = 12, train loss: 0.40613, val loss: 0.39398, in 0.016s
1 tree, 53 leaves, max depth = 11, train loss: 0.40320, val loss: 0.39089, in 0.016s
1 tree, 53 leaves, max depth = 11, train loss: 0.40037, val loss: 0.38790, in 0.016s
1 tree, 50 leaves, max depth = 11, train loss: 0.39760, val loss: 0.38498, in 0.016s
1 tree, 45 leaves, max depth = 9, train loss: 0.39504, val loss: 0.38224, in 0.016s
1 tree, 51 leaves, max depth = 11, train loss: 0.39242, val loss: 0.37948, in 0.016s
1 tree, 49 leaves, max depth = 11, train loss: 0.38988, val loss: 0.37677, in 0.016s
1 tree, 56 leaves, max depth = 13, train loss: 0.38631, val loss: 0.37315, in 0.016s
1 tree, 56 leaves, max depth = 11, train loss: 0.38409, val loss: 0.37091, in 0.016s
1 tree, 56 leaves, max depth = 11, train loss: 0.38172, val loss: 0.36839, in 0.031s
1 tree, 69 leaves, max depth = 15, train loss: 0.37965, val loss: 0.36620, in 0.016s
1 tree, 60 leaves, max depth = 11, train loss: 0.37738, val loss: 0.36381, in 0.016s
1 tree, 65 leaves, max depth = 14, train loss: 0.37398, val loss: 0.36039, in 0.016s
Fit 59 trees in 1.376 s, (3193 total leaves)
Time spent computing histograms: 0.434s
Time spent finding best splits:  0.074s
Time spent applying splits:      0.061s
Time spent predicting:           0.000s
Trial 53, Fold 4: Log loss = 0.3745417032972952, Average precision = 0.9548845195369, ROC-AUC = 0.9501723183139361, Elapsed Time = 1.3895826000007219 seconds
Trial 53, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 53, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
Binning 0.040 GB of training data: 0.158 s
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 41 leaves, max depth = 9, train loss: 0.68162, val loss: 0.68100, in 0.016s
1 tree, 44 leaves, max depth = 13, train loss: 0.67017, val loss: 0.66914, in 0.016s
1 tree, 43 leaves, max depth = 12, train loss: 0.65905, val loss: 0.65762, in 0.016s
1 tree, 51 leaves, max depth = 12, train loss: 0.64873, val loss: 0.64679, in 0.016s
1 tree, 54 leaves, max depth = 10, train loss: 0.63915, val loss: 0.63675, in 0.016s
1 tree, 42 leaves, max depth = 12, train loss: 0.62918, val loss: 0.62645, in 0.016s
1 tree, 38 leaves, max depth = 9, train loss: 0.61979, val loss: 0.61659, in 0.016s
1 tree, 41 leaves, max depth = 12, train loss: 0.61058, val loss: 0.60702, in 0.016s
1 tree, 47 leaves, max depth = 12, train loss: 0.60218, val loss: 0.59827, in 0.016s
1 tree, 48 leaves, max depth = 12, train loss: 0.59403, val loss: 0.58976, in 0.016s
1 tree, 41 leaves, max depth = 12, train loss: 0.58569, val loss: 0.58114, in 0.016s
1 tree, 55 leaves, max depth = 12, train loss: 0.57815, val loss: 0.57328, in 0.016s
1 tree, 49 leaves, max depth = 10, train loss: 0.57047, val loss: 0.56529, in 0.016s
1 tree, 58 leaves, max depth = 10, train loss: 0.56349, val loss: 0.55799, in 0.016s
1 tree, 42 leaves, max depth = 13, train loss: 0.55618, val loss: 0.55042, in 0.016s
1 tree, 49 leaves, max depth = 12, train loss: 0.54953, val loss: 0.54350, in 0.016s
1 tree, 62 leaves, max depth = 10, train loss: 0.54329, val loss: 0.53697, in 0.016s
1 tree, 54 leaves, max depth = 13, train loss: 0.53732, val loss: 0.53088, in 0.016s
1 tree, 55 leaves, max depth = 12, train loss: 0.53136, val loss: 0.52467, in 0.016s
1 tree, 43 leaves, max depth = 12, train loss: 0.52510, val loss: 0.51820, in 0.016s
1 tree, 60 leaves, max depth = 12, train loss: 0.51951, val loss: 0.51238, in 0.000s
1 tree, 41 leaves, max depth = 12, train loss: 0.51363, val loss: 0.50630, in 0.016s
1 tree, 60 leaves, max depth = 12, train loss: 0.50839, val loss: 0.50085, in 0.016s
1 tree, 51 leaves, max depth = 12, train loss: 0.50324, val loss: 0.49562, in 0.016s
1 tree, 41 leaves, max depth = 8, train loss: 0.49850, val loss: 0.49073, in 0.016s
1 tree, 44 leaves, max depth = 8, train loss: 0.49391, val loss: 0.48599, in 0.016s
1 tree, 59 leaves, max depth = 13, train loss: 0.48748, val loss: 0.47952, in 0.016s
1 tree, 36 leaves, max depth = 7, train loss: 0.48268, val loss: 0.47448, in 0.016s
1 tree, 46 leaves, max depth = 12, train loss: 0.47802, val loss: 0.46958, in 0.016s
1 tree, 48 leaves, max depth = 12, train loss: 0.47371, val loss: 0.46519, in 0.016s
1 tree, 45 leaves, max depth = 12, train loss: 0.46915, val loss: 0.46049, in 0.016s
1 tree, 56 leaves, max depth = 12, train loss: 0.46505, val loss: 0.45626, in 0.016s
1 tree, 49 leaves, max depth = 13, train loss: 0.46074, val loss: 0.45182, in 0.016s
1 tree, 51 leaves, max depth = 13, train loss: 0.45517, val loss: 0.44622, in 0.016s
1 tree, 48 leaves, max depth = 13, train loss: 0.45108, val loss: 0.44202, in 0.016s
1 tree, 54 leaves, max depth = 13, train loss: 0.44579, val loss: 0.43674, in 0.016s
1 tree, 57 leaves, max depth = 13, train loss: 0.44069, val loss: 0.43165, in 0.016s
1 tree, 47 leaves, max depth = 10, train loss: 0.43712, val loss: 0.42787, in 0.031s
1 tree, 47 leaves, max depth = 12, train loss: 0.43344, val loss: 0.42407, in 0.016s
1 tree, 56 leaves, max depth = 14, train loss: 0.42865, val loss: 0.41929, in 0.016s
1 tree, 52 leaves, max depth = 13, train loss: 0.42401, val loss: 0.41465, in 0.016s
1 tree, 54 leaves, max depth = 13, train loss: 0.41948, val loss: 0.41013, in 0.016s
1 tree, 47 leaves, max depth = 12, train loss: 0.41611, val loss: 0.40668, in 0.016s
1 tree, 50 leaves, max depth = 13, train loss: 0.41283, val loss: 0.40334, in 0.016s
1 tree, 53 leaves, max depth = 15, train loss: 0.40878, val loss: 0.39917, in 0.016s
1 tree, 50 leaves, max depth = 13, train loss: 0.40566, val loss: 0.39599, in 0.031s
1 tree, 49 leaves, max depth = 13, train loss: 0.40263, val loss: 0.39289, in 0.016s
1 tree, 49 leaves, max depth = 12, train loss: 0.39968, val loss: 0.38989, in 0.016s
1 tree, 51 leaves, max depth = 13, train loss: 0.39684, val loss: 0.38696, in 0.016s
1 tree, 50 leaves, max depth = 12, train loss: 0.39406, val loss: 0.38411, in 0.016s
1 tree, 48 leaves, max depth = 11, train loss: 0.39149, val loss: 0.38139, in 0.016s
1 tree, 48 leaves, max depth = 13, train loss: 0.38886, val loss: 0.37871, in 0.016s
1 tree, 50 leaves, max depth = 12, train loss: 0.38630, val loss: 0.37609, in 0.016s
1 tree, 49 leaves, max depth = 12, train loss: 0.38282, val loss: 0.37257, in 0.016s
1 tree, 56 leaves, max depth = 13, train loss: 0.38060, val loss: 0.37036, in 0.016s
1 tree, 48 leaves, max depth = 11, train loss: 0.37836, val loss: 0.36797, in 0.016s
1 tree, 77 leaves, max depth = 11, train loss: 0.37626, val loss: 0.36584, in 0.016s
1 tree, 53 leaves, max depth = 10, train loss: 0.37400, val loss: 0.36352, in 0.016s
1 tree, 72 leaves, max depth = 13, train loss: 0.37200, val loss: 0.36151, in 0.016s
Fit 59 trees in 1.330 s, (2959 total leaves)
Time spent computing histograms: 0.412s
Time spent finding best splits:  0.067s
Time spent applying splits:      0.054s
Time spent predicting:           0.000s
Trial 53, Fold 5: Log loss = 0.3786991673644685, Average precision = 0.9511013074293633, ROC-AUC = 0.9479524366906341, Elapsed Time = 1.3397103999996034 seconds
Optimization Progress:  54%|#####4    | 54/100 [10:36<09:18, 12.14s/it]
Trial 54, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 54, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
0.126 s 0.040 GB of training data: 
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 74 leaves, max depth = 19, train loss: 0.64843, val loss: 0.64717, in 0.016s
1 tree, 76 leaves, max depth = 19, train loss: 0.61204, val loss: 0.60963, in 0.000s
1 tree, 79 leaves, max depth = 19, train loss: 0.58201, val loss: 0.57855, in 0.016s
1 tree, 76 leaves, max depth = 17, train loss: 0.55730, val loss: 0.55297, in 0.016s
1 tree, 132 leaves, max depth = 16, train loss: 0.53483, val loss: 0.53240, in 0.016s
1 tree, 79 leaves, max depth = 19, train loss: 0.51546, val loss: 0.51214, in 0.016s
1 tree, 131 leaves, max depth = 17, train loss: 0.49773, val loss: 0.49607, in 0.016s
1 tree, 79 leaves, max depth = 15, train loss: 0.48253, val loss: 0.48004, in 0.016s
1 tree, 132 leaves, max depth = 15, train loss: 0.46827, val loss: 0.46726, in 0.016s
1 tree, 81 leaves, max depth = 19, train loss: 0.45626, val loss: 0.45455, in 0.000s
1 tree, 128 leaves, max depth = 15, train loss: 0.44458, val loss: 0.44423, in 0.031s
1 tree, 81 leaves, max depth = 14, train loss: 0.43486, val loss: 0.43399, in 0.000s
1 tree, 123 leaves, max depth = 14, train loss: 0.42523, val loss: 0.42559, in 0.016s
1 tree, 124 leaves, max depth = 14, train loss: 0.41702, val loss: 0.41851, in 0.016s
1 tree, 81 leaves, max depth = 14, train loss: 0.40946, val loss: 0.41047, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.40288, val loss: 0.40318, in 0.016s
1 tree, 82 leaves, max depth = 16, train loss: 0.39661, val loss: 0.39655, in 0.000s
1 tree, 130 leaves, max depth = 16, train loss: 0.38983, val loss: 0.39093, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.38442, val loss: 0.38487, in 0.016s
1 tree, 127 leaves, max depth = 16, train loss: 0.37863, val loss: 0.38022, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.37403, val loss: 0.37505, in 0.000s
1 tree, 85 leaves, max depth = 15, train loss: 0.36899, val loss: 0.36967, in 0.016s
1 tree, 129 leaves, max depth = 15, train loss: 0.36400, val loss: 0.36579, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.36010, val loss: 0.36136, in 0.016s
1 tree, 80 leaves, max depth = 14, train loss: 0.35581, val loss: 0.35697, in 0.000s
1 tree, 126 leaves, max depth = 14, train loss: 0.35149, val loss: 0.35374, in 0.031s
1 tree, 86 leaves, max depth = 16, train loss: 0.34799, val loss: 0.35007, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.34463, val loss: 0.34620, in 0.000s
1 tree, 126 leaves, max depth = 15, train loss: 0.34084, val loss: 0.34347, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.33791, val loss: 0.34007, in 0.000s
1 tree, 109 leaves, max depth = 13, train loss: 0.33453, val loss: 0.33617, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.33201, val loss: 0.33320, in 0.016s
1 tree, 108 leaves, max depth = 13, train loss: 0.32916, val loss: 0.32993, in 0.016s
1 tree, 122 leaves, max depth = 15, train loss: 0.32567, val loss: 0.32751, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.32341, val loss: 0.32483, in 0.000s
1 tree, 85 leaves, max depth = 15, train loss: 0.32095, val loss: 0.32236, in 0.016s
1 tree, 121 leaves, max depth = 15, train loss: 0.31786, val loss: 0.32030, in 0.031s
1 tree, 107 leaves, max depth = 13, train loss: 0.31554, val loss: 0.31757, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.31349, val loss: 0.31510, in 0.000s
1 tree, 123 leaves, max depth = 16, train loss: 0.31075, val loss: 0.31334, in 0.016s
1 tree, 4 leaves, max depth = 2, train loss: 0.30894, val loss: 0.31113, in 0.016s
1 tree, 105 leaves, max depth = 13, train loss: 0.30686, val loss: 0.30872, in 0.000s
1 tree, 82 leaves, max depth = 14, train loss: 0.30509, val loss: 0.30695, in 0.016s
1 tree, 123 leaves, max depth = 16, train loss: 0.30260, val loss: 0.30540, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.30094, val loss: 0.30334, in 0.016s
1 tree, 120 leaves, max depth = 16, train loss: 0.29877, val loss: 0.30208, in 0.016s
1 tree, 105 leaves, max depth = 15, train loss: 0.29704, val loss: 0.30002, in 0.016s
1 tree, 83 leaves, max depth = 15, train loss: 0.29561, val loss: 0.29856, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.29408, val loss: 0.29665, in 0.000s
1 tree, 123 leaves, max depth = 16, train loss: 0.29209, val loss: 0.29551, in 0.016s
1 tree, 105 leaves, max depth = 13, train loss: 0.29070, val loss: 0.29383, in 0.031s
1 tree, 5 leaves, max depth = 3, train loss: 0.28932, val loss: 0.29207, in 0.000s
1 tree, 125 leaves, max depth = 16, train loss: 0.28755, val loss: 0.29109, in 0.016s
1 tree, 111 leaves, max depth = 15, train loss: 0.28620, val loss: 0.28980, in 0.016s
1 tree, 176 leaves, max depth = 20, train loss: 0.28417, val loss: 0.28865, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.28287, val loss: 0.28699, in 0.000s
1 tree, 106 leaves, max depth = 17, train loss: 0.28173, val loss: 0.28561, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.28061, val loss: 0.28414, in 0.000s
1 tree, 126 leaves, max depth = 17, train loss: 0.27908, val loss: 0.28334, in 0.031s
1 tree, 5 leaves, max depth = 3, train loss: 0.27808, val loss: 0.28201, in 0.000s
1 tree, 110 leaves, max depth = 14, train loss: 0.27608, val loss: 0.28086, in 0.016s
1 tree, 107 leaves, max depth = 16, train loss: 0.27498, val loss: 0.27952, in 0.016s
1 tree, 182 leaves, max depth = 20, train loss: 0.27304, val loss: 0.27829, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.27211, val loss: 0.27704, in 0.016s
1 tree, 84 leaves, max depth = 20, train loss: 0.27104, val loss: 0.27603, in 0.016s
1 tree, 163 leaves, max depth = 27, train loss: 0.26927, val loss: 0.27515, in 0.016s
1 tree, 175 leaves, max depth = 21, train loss: 0.26764, val loss: 0.27434, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.26676, val loss: 0.27315, in 0.016s
1 tree, 49 leaves, max depth = 13, train loss: 0.26580, val loss: 0.27233, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.26503, val loss: 0.27127, in 0.016s
1 tree, 165 leaves, max depth = 27, train loss: 0.26351, val loss: 0.27061, in 0.016s
1 tree, 108 leaves, max depth = 15, train loss: 0.26252, val loss: 0.26971, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.26183, val loss: 0.26873, in 0.016s
1 tree, 177 leaves, max depth = 21, train loss: 0.26035, val loss: 0.26808, in 0.016s
Fit 74 trees in 1.236 s, (6131 total leaves)
Time spent computing histograms: 0.410s
Time spent finding best splits:  0.103s
Time spent applying splits:      0.112s
Time spent predicting:           0.016s
Trial 54, Fold 1: Log loss = 0.2760671375760067, Average precision = 0.9524626419368991, ROC-AUC = 0.9524715405333066, Elapsed Time = 1.2402317999985826 seconds
Trial 54, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 54, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
Binning 0.040 GB of training data: 0.174 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 78 leaves, max depth = 19, train loss: 0.64875, val loss: 0.64656, in 0.016s
1 tree, 77 leaves, max depth = 16, train loss: 0.61286, val loss: 0.60902, in 0.016s
1 tree, 80 leaves, max depth = 20, train loss: 0.58280, val loss: 0.57733, in 0.016s
1 tree, 78 leaves, max depth = 14, train loss: 0.55806, val loss: 0.55117, in 0.016s
1 tree, 132 leaves, max depth = 17, train loss: 0.53579, val loss: 0.52983, in 0.016s
1 tree, 80 leaves, max depth = 14, train loss: 0.51670, val loss: 0.50966, in 0.016s
1 tree, 130 leaves, max depth = 15, train loss: 0.49910, val loss: 0.49290, in 0.016s
1 tree, 78 leaves, max depth = 15, train loss: 0.48410, val loss: 0.47699, in 0.016s
1 tree, 129 leaves, max depth = 15, train loss: 0.46992, val loss: 0.46358, in 0.016s
1 tree, 76 leaves, max depth = 14, train loss: 0.45802, val loss: 0.45083, in 0.016s
1 tree, 127 leaves, max depth = 16, train loss: 0.44642, val loss: 0.43995, in 0.016s
1 tree, 77 leaves, max depth = 17, train loss: 0.43683, val loss: 0.42969, in 0.016s
1 tree, 127 leaves, max depth = 15, train loss: 0.42720, val loss: 0.42075, in 0.016s
1 tree, 129 leaves, max depth = 15, train loss: 0.41900, val loss: 0.41320, in 0.016s
1 tree, 75 leaves, max depth = 19, train loss: 0.41140, val loss: 0.40505, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.40501, val loss: 0.39841, in 0.016s
1 tree, 81 leaves, max depth = 12, train loss: 0.39883, val loss: 0.39176, in 0.000s
1 tree, 124 leaves, max depth = 15, train loss: 0.39204, val loss: 0.38562, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.38680, val loss: 0.38015, in 0.016s
1 tree, 126 leaves, max depth = 16, train loss: 0.38099, val loss: 0.37499, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.37655, val loss: 0.37031, in 0.000s
1 tree, 84 leaves, max depth = 12, train loss: 0.37131, val loss: 0.36470, in 0.016s
1 tree, 126 leaves, max depth = 17, train loss: 0.36634, val loss: 0.36036, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.36255, val loss: 0.35636, in 0.016s
1 tree, 82 leaves, max depth = 12, train loss: 0.35820, val loss: 0.35171, in 0.016s
1 tree, 127 leaves, max depth = 19, train loss: 0.35390, val loss: 0.34801, in 0.016s
1 tree, 85 leaves, max depth = 13, train loss: 0.35031, val loss: 0.34426, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.34704, val loss: 0.34080, in 0.000s
1 tree, 127 leaves, max depth = 18, train loss: 0.34326, val loss: 0.33762, in 0.031s
1 tree, 5 leaves, max depth = 3, train loss: 0.34041, val loss: 0.33459, in 0.000s
1 tree, 99 leaves, max depth = 15, train loss: 0.33699, val loss: 0.33095, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.33453, val loss: 0.32833, in 0.016s
1 tree, 87 leaves, max depth = 16, train loss: 0.33170, val loss: 0.32533, in 0.016s
1 tree, 126 leaves, max depth = 20, train loss: 0.32823, val loss: 0.32244, in 0.016s
1 tree, 101 leaves, max depth = 16, train loss: 0.32554, val loss: 0.31960, in 0.016s
1 tree, 4 leaves, max depth = 2, train loss: 0.32333, val loss: 0.31722, in 0.016s
1 tree, 126 leaves, max depth = 19, train loss: 0.32026, val loss: 0.31471, in 0.016s
1 tree, 102 leaves, max depth = 16, train loss: 0.31792, val loss: 0.31223, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.31593, val loss: 0.31009, in 0.000s
1 tree, 130 leaves, max depth = 19, train loss: 0.31319, val loss: 0.30785, in 0.016s
1 tree, 85 leaves, max depth = 14, train loss: 0.31113, val loss: 0.30587, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.30934, val loss: 0.30394, in 0.016s
1 tree, 102 leaves, max depth = 16, train loss: 0.30743, val loss: 0.30195, in 0.016s
1 tree, 127 leaves, max depth = 20, train loss: 0.30495, val loss: 0.29995, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.30334, val loss: 0.29820, in 0.000s
1 tree, 131 leaves, max depth = 20, train loss: 0.30117, val loss: 0.29647, in 0.031s
1 tree, 102 leaves, max depth = 17, train loss: 0.29942, val loss: 0.29461, in 0.016s
1 tree, 84 leaves, max depth = 13, train loss: 0.29788, val loss: 0.29322, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.29639, val loss: 0.29160, in 0.016s
1 tree, 130 leaves, max depth = 17, train loss: 0.29440, val loss: 0.29003, in 0.016s
1 tree, 103 leaves, max depth = 16, train loss: 0.29299, val loss: 0.28856, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.29164, val loss: 0.28708, in 0.000s
1 tree, 130 leaves, max depth = 19, train loss: 0.28985, val loss: 0.28569, in 0.016s
1 tree, 105 leaves, max depth = 15, train loss: 0.28764, val loss: 0.28416, in 0.016s
1 tree, 101 leaves, max depth = 15, train loss: 0.28632, val loss: 0.28274, in 0.031s
1 tree, 5 leaves, max depth = 3, train loss: 0.28506, val loss: 0.28135, in 0.000s
1 tree, 158 leaves, max depth = 21, train loss: 0.28320, val loss: 0.28065, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.28208, val loss: 0.27940, in 0.016s
1 tree, 85 leaves, max depth = 20, train loss: 0.28077, val loss: 0.27834, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.27979, val loss: 0.27724, in 0.016s
1 tree, 108 leaves, max depth = 15, train loss: 0.27786, val loss: 0.27594, in 0.016s
1 tree, 101 leaves, max depth = 15, train loss: 0.27672, val loss: 0.27473, in 0.016s
1 tree, 182 leaves, max depth = 19, train loss: 0.27482, val loss: 0.27404, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.27390, val loss: 0.27301, in 0.016s
1 tree, 130 leaves, max depth = 17, train loss: 0.27256, val loss: 0.27206, in 0.016s
1 tree, 47 leaves, max depth = 9, train loss: 0.27151, val loss: 0.27110, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.27066, val loss: 0.27015, in 0.000s
1 tree, 187 leaves, max depth = 18, train loss: 0.26879, val loss: 0.26947, in 0.016s
[69/74] 1 tree, 113 leaves, max depth = 15, train loss: 0.26762, val loss: 0.26843, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.26685, val loss: 0.26755, in 0.016s
1 tree, 157 leaves, max depth = 21, train loss: 0.26538, val loss: 0.26712, in 0.016s
1 tree, 108 leaves, max depth = 23, train loss: 0.26427, val loss: 0.26617, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.26357, val loss: 0.26536, in 0.016s
1 tree, 186 leaves, max depth = 18, train loss: 0.26191, val loss: 0.26482, in 0.016s
Fit 74 trees in 1.330 s, (6067 total leaves)
Time spent computing histograms: 0.421s
Time spent finding best splits:  0.109s
Time spent applying splits:      0.120s
Time spent predicting:           0.016s
Trial 54, Fold 2: Log loss = 0.27454362553156353, Average precision = 0.9524217169185072, ROC-AUC = 0.9541073724840596, Elapsed Time = 1.341459000001123 seconds
Trial 54, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 54, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
Binning 0.040 GB of training data: 0.205 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 80 leaves, max depth = 15, train loss: 0.64887, val loss: 0.64767, in 0.016s
1 tree, 80 leaves, max depth = 13, train loss: 0.61312, val loss: 0.61053, in 0.016s
1 tree, 82 leaves, max depth = 14, train loss: 0.58330, val loss: 0.57976, in 0.016s
1 tree, 72 leaves, max depth = 14, train loss: 0.55880, val loss: 0.55430, in 0.016s
1 tree, 130 leaves, max depth = 16, train loss: 0.53612, val loss: 0.53336, in 0.016s
1 tree, 80 leaves, max depth = 17, train loss: 0.51725, val loss: 0.51360, in 0.031s
1 tree, 132 leaves, max depth = 16, train loss: 0.49934, val loss: 0.49717, in 0.016s
1 tree, 81 leaves, max depth = 13, train loss: 0.48417, val loss: 0.48127, in 0.016s
1 tree, 134 leaves, max depth = 16, train loss: 0.46978, val loss: 0.46814, in 0.016s
1 tree, 78 leaves, max depth = 17, train loss: 0.45806, val loss: 0.45565, in 0.016s
1 tree, 134 leaves, max depth = 17, train loss: 0.44629, val loss: 0.44500, in 0.031s
1 tree, 77 leaves, max depth = 18, train loss: 0.43691, val loss: 0.43492, in 0.000s
1 tree, 133 leaves, max depth = 16, train loss: 0.42716, val loss: 0.42616, in 0.031s
1 tree, 134 leaves, max depth = 16, train loss: 0.41885, val loss: 0.41876, in 0.016s
1 tree, 87 leaves, max depth = 12, train loss: 0.41138, val loss: 0.41063, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.40475, val loss: 0.40446, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.39917, val loss: 0.39927, in 0.016s
1 tree, 131 leaves, max depth = 15, train loss: 0.39226, val loss: 0.39323, in 0.016s
1 tree, 87 leaves, max depth = 13, train loss: 0.38643, val loss: 0.38685, in 0.016s
1 tree, 130 leaves, max depth = 15, train loss: 0.38056, val loss: 0.38177, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.37590, val loss: 0.37742, in 0.016s
1 tree, 129 leaves, max depth = 16, train loss: 0.37085, val loss: 0.37313, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.36686, val loss: 0.36940, in 0.016s
1 tree, 80 leaves, max depth = 16, train loss: 0.36193, val loss: 0.36406, in 0.016s
1 tree, 80 leaves, max depth = 16, train loss: 0.35777, val loss: 0.35965, in 0.016s
1 tree, 125 leaves, max depth = 15, train loss: 0.35334, val loss: 0.35594, in 0.016s
1 tree, 127 leaves, max depth = 16, train loss: 0.34957, val loss: 0.35284, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.34612, val loss: 0.34959, in 0.016s
1 tree, 81 leaves, max depth = 15, train loss: 0.34255, val loss: 0.34559, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.33960, val loss: 0.34281, in 0.000s
1 tree, 103 leaves, max depth = 16, train loss: 0.33630, val loss: 0.33892, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.33376, val loss: 0.33652, in 0.016s
1 tree, 122 leaves, max depth = 16, train loss: 0.33022, val loss: 0.33368, in 0.016s
1 tree, 105 leaves, max depth = 15, train loss: 0.32737, val loss: 0.33029, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.32510, val loss: 0.32814, in 0.016s
1 tree, 86 leaves, max depth = 16, train loss: 0.32258, val loss: 0.32558, in 0.000s
1 tree, 124 leaves, max depth = 15, train loss: 0.31939, val loss: 0.32309, in 0.016s
1 tree, 105 leaves, max depth = 17, train loss: 0.31714, val loss: 0.32038, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.31507, val loss: 0.31842, in 0.000s
1 tree, 127 leaves, max depth = 15, train loss: 0.31223, val loss: 0.31622, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.31040, val loss: 0.31448, in 0.016s
1 tree, 79 leaves, max depth = 13, train loss: 0.30839, val loss: 0.31243, in 0.016s
1 tree, 104 leaves, max depth = 17, train loss: 0.30655, val loss: 0.31023, in 0.016s
1 tree, 126 leaves, max depth = 19, train loss: 0.30400, val loss: 0.30831, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.30231, val loss: 0.30669, in 0.016s
1 tree, 129 leaves, max depth = 19, train loss: 0.30001, val loss: 0.30497, in 0.016s
1 tree, 102 leaves, max depth = 17, train loss: 0.29833, val loss: 0.30290, in 0.016s
1 tree, 127 leaves, max depth = 17, train loss: 0.29635, val loss: 0.30143, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.29476, val loss: 0.29991, in 0.016s
1 tree, 87 leaves, max depth = 13, train loss: 0.29324, val loss: 0.29836, in 0.000s
1 tree, 105 leaves, max depth = 17, train loss: 0.29188, val loss: 0.29667, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.29047, val loss: 0.29532, in 0.016s
1 tree, 125 leaves, max depth = 16, train loss: 0.28858, val loss: 0.29402, in 0.016s
1 tree, 161 leaves, max depth = 24, train loss: 0.28644, val loss: 0.29312, in 0.031s
1 tree, 52 leaves, max depth = 11, train loss: 0.28526, val loss: 0.29197, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.28394, val loss: 0.29070, in 0.000s
1 tree, 105 leaves, max depth = 17, train loss: 0.28276, val loss: 0.28925, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.28162, val loss: 0.28814, in 0.016s
[59/74] 1 tree, 127 leaves, max depth = 17, train loss: 0.28001, val loss: 0.28700, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.27898, val loss: 0.28601, in 0.016s
1 tree, 105 leaves, max depth = 17, train loss: 0.27692, val loss: 0.28467, in 0.016s
1 tree, 107 leaves, max depth = 16, train loss: 0.27579, val loss: 0.28326, in 0.016s
1 tree, 175 leaves, max depth = 19, train loss: 0.27393, val loss: 0.28213, in 0.031s
1 tree, 5 leaves, max depth = 3, train loss: 0.27298, val loss: 0.28120, in 0.000s
1 tree, 81 leaves, max depth = 15, train loss: 0.27188, val loss: 0.28019, in 0.000s
1 tree, 180 leaves, max depth = 19, train loss: 0.27007, val loss: 0.27918, in 0.031s
1 tree, 5 leaves, max depth = 3, train loss: 0.26920, val loss: 0.27833, in 0.016s
1 tree, 161 leaves, max depth = 24, train loss: 0.26747, val loss: 0.27780, in 0.016s
1 tree, 113 leaves, max depth = 17, train loss: 0.26639, val loss: 0.27660, in 0.031s
1 tree, 182 leaves, max depth = 19, train loss: 0.26486, val loss: 0.27576, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.26403, val loss: 0.27495, in 0.016s
1 tree, 104 leaves, max depth = 17, train loss: 0.26317, val loss: 0.27384, in 0.016s
1 tree, 162 leaves, max depth = 24, train loss: 0.26170, val loss: 0.27349, in 0.031s
1 tree, 183 leaves, max depth = 19, train loss: 0.26039, val loss: 0.27279, in 0.016s
Fit 74 trees in 1.549 s, (6333 total leaves)
Time spent computing histograms: 0.471s
Time spent finding best splits:  0.125s
Time spent applying splits:      0.139s
Time spent predicting:           0.016s
Trial 54, Fold 3: Log loss = 0.26791224175750544, Average precision = 0.9581761600803326, ROC-AUC = 0.9569774140189347, Elapsed Time = 1.5581058999996458 seconds
Trial 54, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 54, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
Binning 0.040 GB of training data: 0.158 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 76 leaves, max depth = 17, train loss: 0.64889, val loss: 0.64626, in 0.016s
1 tree, 79 leaves, max depth = 17, train loss: 0.61281, val loss: 0.60783, in 0.016s
1 tree, 79 leaves, max depth = 17, train loss: 0.58304, val loss: 0.57592, in 0.016s
1 tree, 77 leaves, max depth = 14, train loss: 0.55838, val loss: 0.54963, in 0.016s
1 tree, 125 leaves, max depth = 16, train loss: 0.53601, val loss: 0.52804, in 0.016s
1 tree, 81 leaves, max depth = 18, train loss: 0.51684, val loss: 0.50717, in 0.016s
1 tree, 128 leaves, max depth = 16, train loss: 0.49919, val loss: 0.49024, in 0.016s
1 tree, 83 leaves, max depth = 16, train loss: 0.48424, val loss: 0.47395, in 0.016s
1 tree, 129 leaves, max depth = 18, train loss: 0.47006, val loss: 0.46042, in 0.016s
1 tree, 83 leaves, max depth = 15, train loss: 0.45807, val loss: 0.44708, in 0.016s
1 tree, 126 leaves, max depth = 16, train loss: 0.44649, val loss: 0.43609, in 0.016s
1 tree, 84 leaves, max depth = 18, train loss: 0.43687, val loss: 0.42531, in 0.016s
1 tree, 126 leaves, max depth = 16, train loss: 0.42731, val loss: 0.41629, in 0.016s
1 tree, 128 leaves, max depth = 17, train loss: 0.41917, val loss: 0.40864, in 0.031s
1 tree, 84 leaves, max depth = 15, train loss: 0.41166, val loss: 0.40008, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.40529, val loss: 0.39321, in 0.016s
1 tree, 84 leaves, max depth = 16, train loss: 0.39910, val loss: 0.38612, in 0.016s
1 tree, 127 leaves, max depth = 15, train loss: 0.39237, val loss: 0.37990, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.38713, val loss: 0.37423, in 0.000s
1 tree, 127 leaves, max depth = 16, train loss: 0.38138, val loss: 0.36899, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.37693, val loss: 0.36417, in 0.016s
1 tree, 86 leaves, max depth = 15, train loss: 0.37201, val loss: 0.35853, in 0.016s
1 tree, 126 leaves, max depth = 16, train loss: 0.36705, val loss: 0.35407, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.36328, val loss: 0.34997, in 0.016s
1 tree, 82 leaves, max depth = 13, train loss: 0.35903, val loss: 0.34528, in 0.000s
1 tree, 124 leaves, max depth = 15, train loss: 0.35471, val loss: 0.34149, in 0.016s
1 tree, 84 leaves, max depth = 16, train loss: 0.35127, val loss: 0.33750, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.34801, val loss: 0.33396, in 0.016s
1 tree, 126 leaves, max depth = 17, train loss: 0.34423, val loss: 0.33067, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.34139, val loss: 0.32757, in 0.016s
1 tree, 106 leaves, max depth = 16, train loss: 0.33808, val loss: 0.32419, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.33564, val loss: 0.32152, in 0.000s
1 tree, 83 leaves, max depth = 16, train loss: 0.33300, val loss: 0.31855, in 0.016s
1 tree, 122 leaves, max depth = 16, train loss: 0.32952, val loss: 0.31558, in 0.031s
1 tree, 106 leaves, max depth = 17, train loss: 0.32690, val loss: 0.31293, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.32469, val loss: 0.31051, in 0.000s
1 tree, 122 leaves, max depth = 18, train loss: 0.32158, val loss: 0.30790, in 0.031s
1 tree, 104 leaves, max depth = 16, train loss: 0.31927, val loss: 0.30560, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.31729, val loss: 0.30343, in 0.000s
1 tree, 119 leaves, max depth = 18, train loss: 0.31451, val loss: 0.30115, in 0.016s
1 tree, 104 leaves, max depth = 16, train loss: 0.31249, val loss: 0.29916, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.31071, val loss: 0.29720, in 0.016s
1 tree, 87 leaves, max depth = 15, train loss: 0.30897, val loss: 0.29531, in 0.016s
1 tree, 126 leaves, max depth = 19, train loss: 0.30645, val loss: 0.29323, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.30485, val loss: 0.29146, in 0.016s
1 tree, 123 leaves, max depth = 19, train loss: 0.30259, val loss: 0.28973, in 0.016s
1 tree, 105 leaves, max depth = 16, train loss: 0.30086, val loss: 0.28808, in 0.016s
1 tree, 48 leaves, max depth = 11, train loss: 0.29942, val loss: 0.28677, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.29794, val loss: 0.28513, in 0.000s
1 tree, 126 leaves, max depth = 15, train loss: 0.29594, val loss: 0.28356, in 0.016s
1 tree, 105 leaves, max depth = 16, train loss: 0.29452, val loss: 0.28223, in 0.031s
1 tree, 5 leaves, max depth = 3, train loss: 0.29318, val loss: 0.28074, in 0.000s
1 tree, 108 leaves, max depth = 17, train loss: 0.29069, val loss: 0.27878, in 0.016s
1 tree, 176 leaves, max depth = 26, train loss: 0.28861, val loss: 0.27773, in 0.031s
1 tree, 106 leaves, max depth = 17, train loss: 0.28728, val loss: 0.27648, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.28603, val loss: 0.27508, in 0.000s
1 tree, 174 leaves, max depth = 22, train loss: 0.28391, val loss: 0.27410, in 0.031s
1 tree, 5 leaves, max depth = 3, train loss: 0.28281, val loss: 0.27288, in 0.000s
1 tree, 128 leaves, max depth = 14, train loss: 0.28121, val loss: 0.27174, in 0.031s
1 tree, 111 leaves, max depth = 16, train loss: 0.27988, val loss: 0.27062, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.27888, val loss: 0.26950, in 0.000s
1 tree, 177 leaves, max depth = 26, train loss: 0.27705, val loss: 0.26864, in 0.031s
1 tree, 82 leaves, max depth = 15, train loss: 0.27593, val loss: 0.26727, in 0.016s
1 tree, 158 leaves, max depth = 28, train loss: 0.27411, val loss: 0.26609, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.27316, val loss: 0.26503, in 0.016s
1 tree, 113 leaves, max depth = 16, train loss: 0.27198, val loss: 0.26419, in 0.016s
1 tree, 175 leaves, max depth = 23, train loss: 0.27041, val loss: 0.26348, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.26954, val loss: 0.26252, in 0.016s
1 tree, 107 leaves, max depth = 19, train loss: 0.26861, val loss: 0.26171, in 0.016s
1 tree, 176 leaves, max depth = 23, train loss: 0.26720, val loss: 0.26108, in 0.016s
1 tree, 161 leaves, max depth = 26, train loss: 0.26567, val loss: 0.26010, in 0.031s
1 tree, 175 leaves, max depth = 21, train loss: 0.26405, val loss: 0.25945, in 0.031s
1 tree, 5 leaves, max depth = 3, train loss: 0.26322, val loss: 0.25852, in 0.000s
1 tree, 52 leaves, max depth = 10, train loss: 0.26233, val loss: 0.25781, in 0.016s
Fit 74 trees in 1.424 s, (6314 total leaves)
Time spent computing histograms: 0.459s
Time spent finding best splits:  0.118s
Time spent applying splits:      0.130s
Time spent predicting:           0.016s
Trial 54, Fold 4: Log loss = 0.27029483261290455, Average precision = 0.9573415530068022, ROC-AUC = 0.9565255036469547, Elapsed Time = 1.4256296999992628 seconds
Trial 54, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 54, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
Binning 0.040 GB of training data: 0.172 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 79 leaves, max depth = 16, train loss: 0.64882, val loss: 0.64587, in 0.016s
1 tree, 79 leaves, max depth = 16, train loss: 0.61270, val loss: 0.60711, in 0.016s
1 tree, 82 leaves, max depth = 21, train loss: 0.58241, val loss: 0.57442, in 0.016s
1 tree, 81 leaves, max depth = 15, train loss: 0.55762, val loss: 0.54747, in 0.000s
1 tree, 133 leaves, max depth = 16, train loss: 0.53517, val loss: 0.52612, in 0.016s
1 tree, 81 leaves, max depth = 21, train loss: 0.51564, val loss: 0.50472, in 0.031s
1 tree, 132 leaves, max depth = 16, train loss: 0.49794, val loss: 0.48806, in 0.016s
1 tree, 81 leaves, max depth = 15, train loss: 0.48261, val loss: 0.47111, in 0.016s
1 tree, 132 leaves, max depth = 20, train loss: 0.46838, val loss: 0.45786, in 0.016s
1 tree, 83 leaves, max depth = 16, train loss: 0.45638, val loss: 0.44452, in 0.016s
1 tree, 134 leaves, max depth = 17, train loss: 0.44474, val loss: 0.43381, in 0.016s
1 tree, 83 leaves, max depth = 16, train loss: 0.43510, val loss: 0.42297, in 0.016s
1 tree, 131 leaves, max depth = 19, train loss: 0.42544, val loss: 0.41420, in 0.016s
1 tree, 132 leaves, max depth = 19, train loss: 0.41722, val loss: 0.40677, in 0.016s
1 tree, 79 leaves, max depth = 15, train loss: 0.40957, val loss: 0.39811, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.40314, val loss: 0.39191, in 0.000s
1 tree, 82 leaves, max depth = 18, train loss: 0.39675, val loss: 0.38456, in 0.016s
1 tree, 131 leaves, max depth = 19, train loss: 0.38994, val loss: 0.37860, in 0.031s
1 tree, 5 leaves, max depth = 3, train loss: 0.38468, val loss: 0.37355, in 0.000s
1 tree, 129 leaves, max depth = 20, train loss: 0.37886, val loss: 0.36851, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.37438, val loss: 0.36423, in 0.016s
1 tree, 130 leaves, max depth = 20, train loss: 0.36937, val loss: 0.35994, in 0.016s
1 tree, 84 leaves, max depth = 15, train loss: 0.36440, val loss: 0.35410, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.36059, val loss: 0.35046, in 0.000s
1 tree, 83 leaves, max depth = 14, train loss: 0.35629, val loss: 0.34534, in 0.016s
1 tree, 131 leaves, max depth = 18, train loss: 0.35193, val loss: 0.34173, in 0.031s
1 tree, 81 leaves, max depth = 13, train loss: 0.34841, val loss: 0.33773, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.34514, val loss: 0.33459, in 0.016s
1 tree, 130 leaves, max depth = 19, train loss: 0.34130, val loss: 0.33147, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.33845, val loss: 0.32874, in 0.016s
1 tree, 103 leaves, max depth = 15, train loss: 0.33493, val loss: 0.32510, in 0.016s
1 tree, 130 leaves, max depth = 22, train loss: 0.33154, val loss: 0.32237, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.32901, val loss: 0.31996, in 0.016s
1 tree, 102 leaves, max depth = 15, train loss: 0.32597, val loss: 0.31680, in 0.016s
1 tree, 87 leaves, max depth = 15, train loss: 0.32351, val loss: 0.31381, in 0.016s
1 tree, 130 leaves, max depth = 23, train loss: 0.32044, val loss: 0.31141, in 0.016s
1 tree, 110 leaves, max depth = 15, train loss: 0.31806, val loss: 0.30900, in 0.016s
1 tree, 129 leaves, max depth = 24, train loss: 0.31541, val loss: 0.30694, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.31306, val loss: 0.30467, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.31105, val loss: 0.30274, in 0.000s
1 tree, 104 leaves, max depth = 16, train loss: 0.30887, val loss: 0.30053, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.30713, val loss: 0.29887, in 0.016s
1 tree, 88 leaves, max depth = 13, train loss: 0.30530, val loss: 0.29665, in 0.016s
1 tree, 124 leaves, max depth = 22, train loss: 0.30271, val loss: 0.29467, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.30113, val loss: 0.29316, in 0.000s
1 tree, 126 leaves, max depth = 22, train loss: 0.29887, val loss: 0.29145, in 0.016s
1 tree, 103 leaves, max depth = 15, train loss: 0.29701, val loss: 0.28960, in 0.031s
1 tree, 104 leaves, max depth = 14, train loss: 0.29547, val loss: 0.28806, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.29400, val loss: 0.28665, in 0.000s
1 tree, 125 leaves, max depth = 22, train loss: 0.29193, val loss: 0.28509, in 0.016s
1 tree, 83 leaves, max depth = 13, train loss: 0.29044, val loss: 0.28340, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.28910, val loss: 0.28211, in 0.016s
1 tree, 126 leaves, max depth = 23, train loss: 0.28725, val loss: 0.28076, in 0.016s
1 tree, 110 leaves, max depth = 18, train loss: 0.28585, val loss: 0.27942, in 0.016s
1 tree, 179 leaves, max depth = 21, train loss: 0.28379, val loss: 0.27799, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.28253, val loss: 0.27676, in 0.016s
1 tree, 184 leaves, max depth = 21, train loss: 0.28038, val loss: 0.27615, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.27927, val loss: 0.27508, in 0.016s
1 tree, 105 leaves, max depth = 16, train loss: 0.27803, val loss: 0.27389, in 0.016s
1 tree, 177 leaves, max depth = 22, train loss: 0.27617, val loss: 0.27259, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.27516, val loss: 0.27162, in 0.016s
1 tree, 129 leaves, max depth = 22, train loss: 0.27371, val loss: 0.27055, in 0.016s
1 tree, 102 leaves, max depth = 16, train loss: 0.27255, val loss: 0.26944, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.27163, val loss: 0.26856, in 0.016s
1 tree, 158 leaves, max depth = 21, train loss: 0.26980, val loss: 0.26755, in 0.016s
1 tree, 83 leaves, max depth = 15, train loss: 0.26868, val loss: 0.26629, in 0.016s
1 tree, 178 leaves, max depth = 22, train loss: 0.26716, val loss: 0.26528, in 0.031s
1 tree, 5 leaves, max depth = 3, train loss: 0.26628, val loss: 0.26443, in 0.000s
1 tree, 101 leaves, max depth = 17, train loss: 0.26529, val loss: 0.26352, in 0.016s
1 tree, 186 leaves, max depth = 21, train loss: 0.26360, val loss: 0.26318, in 0.031s
1 tree, 101 leaves, max depth = 16, train loss: 0.26181, val loss: 0.26204, in 0.016s
1 tree, 50 leaves, max depth = 13, train loss: 0.26095, val loss: 0.26113, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.26012, val loss: 0.26033, in 0.000s
1 tree, 160 leaves, max depth = 26, train loss: 0.25868, val loss: 0.25963, in 0.016s
Fit 74 trees in 1.391 s, (6375 total leaves)
Time spent computing histograms: 0.444s
Time spent finding best splits:  0.117s
Time spent applying splits:      0.131s
Time spent predicting:           0.016s
Trial 54, Fold 5: Log loss = 0.27748913248953944, Average precision = 0.9528118582715788, ROC-AUC = 0.9521970904631849, Elapsed Time = 1.414216099999976 seconds
Optimization Progress:  55%|#####5    | 55/100 [10:49<09:26, 12.59s/it]
Trial 55, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 55, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
Binning 0.040 GB of training data: 0.205 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 48 leaves, max depth = 11, train loss: 0.67308, val loss: 0.67327, in 0.016s
1 tree, 48 leaves, max depth = 11, train loss: 0.65437, val loss: 0.65472, in 0.016s
1 tree, 48 leaves, max depth = 12, train loss: 0.63687, val loss: 0.63735, in 0.016s
1 tree, 48 leaves, max depth = 11, train loss: 0.62118, val loss: 0.62166, in 0.016s
1 tree, 48 leaves, max depth = 12, train loss: 0.60572, val loss: 0.60620, in 0.016s
1 tree, 48 leaves, max depth = 11, train loss: 0.59126, val loss: 0.59186, in 0.031s
1 tree, 48 leaves, max depth = 12, train loss: 0.57761, val loss: 0.57821, in 0.016s
1 tree, 48 leaves, max depth = 11, train loss: 0.56481, val loss: 0.56552, in 0.016s
1 tree, 48 leaves, max depth = 11, train loss: 0.55271, val loss: 0.55342, in 0.031s
1 tree, 48 leaves, max depth = 11, train loss: 0.54103, val loss: 0.54164, in 0.016s
1 tree, 48 leaves, max depth = 10, train loss: 0.53002, val loss: 0.53053, in 0.016s
1 tree, 48 leaves, max depth = 11, train loss: 0.51963, val loss: 0.52003, in 0.031s
1 tree, 48 leaves, max depth = 10, train loss: 0.50978, val loss: 0.51010, in 0.016s
1 tree, 48 leaves, max depth = 11, train loss: 0.50045, val loss: 0.50067, in 0.016s
1 tree, 48 leaves, max depth = 10, train loss: 0.49163, val loss: 0.49181, in 0.016s
Fit 15 trees in 0.705 s, (720 total leaves)
Time spent computing histograms: 0.120s
Time spent finding best splits:  0.025s
Time spent applying splits:      0.016s
Time spent predicting:           0.000s
Trial 55, Fold 1: Log loss = 0.4936647614237157, Average precision = 0.9200493818973322, ROC-AUC = 0.9309645838411647, Elapsed Time = 0.7165882999997848 seconds
Trial 55, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 55, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
Binning 0.040 GB of training data: 0.189 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 48 leaves, max depth = 10, train loss: 0.67272, val loss: 0.67242, in 0.016s
1 tree, 48 leaves, max depth = 10, train loss: 0.65384, val loss: 0.65320, in 0.016s
1 tree, 48 leaves, max depth = 10, train loss: 0.63603, val loss: 0.63512, in 0.016s
1 tree, 48 leaves, max depth = 13, train loss: 0.62028, val loss: 0.61923, in 0.031s
1 tree, 48 leaves, max depth = 11, train loss: 0.60470, val loss: 0.60342, in 0.016s
1 tree, 48 leaves, max depth = 10, train loss: 0.58994, val loss: 0.58846, in 0.016s
1 tree, 48 leaves, max depth = 10, train loss: 0.57619, val loss: 0.57448, in 0.016s
1 tree, 48 leaves, max depth = 10, train loss: 0.56313, val loss: 0.56116, in 0.016s
1 tree, 48 leaves, max depth = 10, train loss: 0.55083, val loss: 0.54860, in 0.031s
1 tree, 48 leaves, max depth = 10, train loss: 0.53933, val loss: 0.53690, in 0.016s
1 tree, 48 leaves, max depth = 10, train loss: 0.52833, val loss: 0.52578, in 0.016s
1 tree, 48 leaves, max depth = 12, train loss: 0.51795, val loss: 0.51523, in 0.016s
1 tree, 48 leaves, max depth = 13, train loss: 0.50814, val loss: 0.50526, in 0.031s
1 tree, 48 leaves, max depth = 10, train loss: 0.49888, val loss: 0.49584, in 0.016s
1 tree, 48 leaves, max depth = 10, train loss: 0.49008, val loss: 0.48688, in 0.016s
Fit 15 trees in 0.689 s, (720 total leaves)
Time spent computing histograms: 0.119s
Time spent finding best splits:  0.026s
Time spent applying splits:      0.016s
Time spent predicting:           0.016s
Trial 55, Fold 2: Log loss = 0.4922320066760393, Average precision = 0.9170846482239512, ROC-AUC = 0.9323047059112742, Elapsed Time = 0.700506299999688 seconds
Trial 55, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 55, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
Binning 0.040 GB of training data: 0.142 s
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 48 leaves, max depth = 12, train loss: 0.67321, val loss: 0.67331, in 0.016s
1 tree, 48 leaves, max depth = 12, train loss: 0.65463, val loss: 0.65480, in 0.016s
1 tree, 48 leaves, max depth = 9, train loss: 0.63688, val loss: 0.63717, in 0.031s
1 tree, 48 leaves, max depth = 9, train loss: 0.62113, val loss: 0.62153, in 0.016s
1 tree, 48 leaves, max depth = 8, train loss: 0.60574, val loss: 0.60607, in 0.016s
1 tree, 48 leaves, max depth = 9, train loss: 0.59103, val loss: 0.59146, in 0.016s
1 tree, 48 leaves, max depth = 9, train loss: 0.57792, val loss: 0.57843, in 0.016s
1 tree, 48 leaves, max depth = 10, train loss: 0.56484, val loss: 0.56542, in 0.016s
1 tree, 48 leaves, max depth = 9, train loss: 0.55250, val loss: 0.55314, in 0.031s
1 tree, 48 leaves, max depth = 10, train loss: 0.54087, val loss: 0.54160, in 0.016s
1 tree, 48 leaves, max depth = 8, train loss: 0.52990, val loss: 0.53071, in 0.016s
1 tree, 48 leaves, max depth = 10, train loss: 0.51951, val loss: 0.52041, in 0.016s
1 tree, 48 leaves, max depth = 10, train loss: 0.50970, val loss: 0.51066, in 0.031s
1 tree, 48 leaves, max depth = 12, train loss: 0.50040, val loss: 0.50143, in 0.016s
1 tree, 48 leaves, max depth = 10, train loss: 0.49162, val loss: 0.49271, in 0.016s
Fit 15 trees in 0.626 s, (720 total leaves)
Time spent computing histograms: 0.114s
Time spent finding best splits:  0.023s
Time spent applying splits:      0.015s
Time spent predicting:           0.016s
Trial 55, Fold 3: Log loss = 0.49051110730915715, Average precision = 0.9220649795799083, ROC-AUC = 0.9346053472710462, Elapsed Time = 0.6382448000003933 seconds
Trial 55, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 55, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
Binning 0.040 GB of training data: 0.158 s
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 48 leaves, max depth = 11, train loss: 0.67318, val loss: 0.67245, in 0.016s
1 tree, 48 leaves, max depth = 11, train loss: 0.65456, val loss: 0.65314, in 0.016s
1 tree, 48 leaves, max depth = 11, train loss: 0.63718, val loss: 0.63508, in 0.016s
1 tree, 48 leaves, max depth = 12, train loss: 0.62175, val loss: 0.61907, in 0.031s
1 tree, 48 leaves, max depth = 11, train loss: 0.60642, val loss: 0.60308, in 0.016s
1 tree, 48 leaves, max depth = 11, train loss: 0.59198, val loss: 0.58805, in 0.016s
1 tree, 48 leaves, max depth = 10, train loss: 0.57833, val loss: 0.57381, in 0.016s
1 tree, 48 leaves, max depth = 10, train loss: 0.56549, val loss: 0.56039, in 0.032s
1 tree, 48 leaves, max depth = 10, train loss: 0.55316, val loss: 0.54755, in 0.016s
1 tree, 48 leaves, max depth = 12, train loss: 0.54228, val loss: 0.53615, in 0.016s
1 tree, 48 leaves, max depth = 8, train loss: 0.53138, val loss: 0.52466, in 0.020s
1 tree, 48 leaves, max depth = 10, train loss: 0.52096, val loss: 0.51377, in 0.011s
1 tree, 48 leaves, max depth = 10, train loss: 0.51110, val loss: 0.50345, in 0.031s
1 tree, 48 leaves, max depth = 10, train loss: 0.50178, val loss: 0.49369, in 0.016s
1 tree, 48 leaves, max depth = 11, train loss: 0.49293, val loss: 0.48448, in 0.016s
Fit 15 trees in 0.643 s, (720 total leaves)
Time spent computing histograms: 0.117s
Time spent finding best splits:  0.023s
Time spent applying splits:      0.015s
Time spent predicting:           0.000s
Trial 55, Fold 4: Log loss = 0.4929778956507077, Average precision = 0.9227369323898403, ROC-AUC = 0.9334807047599973, Elapsed Time = 0.6573267000003398 seconds
Trial 55, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 55, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
Binning 0.040 GB of training data: 0.158 s
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 48 leaves, max depth = 13, train loss: 0.67295, val loss: 0.67229, in 0.016s
1 tree, 48 leaves, max depth = 14, train loss: 0.65417, val loss: 0.65295, in 0.016s
1 tree, 48 leaves, max depth = 12, train loss: 0.63632, val loss: 0.63449, in 0.016s
1 tree, 48 leaves, max depth = 12, train loss: 0.62056, val loss: 0.61813, in 0.016s
1 tree, 48 leaves, max depth = 12, train loss: 0.60481, val loss: 0.60184, in 0.031s
1 tree, 48 leaves, max depth = 12, train loss: 0.59003, val loss: 0.58654, in 0.016s
1 tree, 48 leaves, max depth = 12, train loss: 0.57614, val loss: 0.57215, in 0.016s
1 tree, 48 leaves, max depth = 12, train loss: 0.56307, val loss: 0.55862, in 0.031s
1 tree, 48 leaves, max depth = 12, train loss: 0.55075, val loss: 0.54583, in 0.016s
1 tree, 48 leaves, max depth = 12, train loss: 0.53977, val loss: 0.53446, in 0.016s
1 tree, 48 leaves, max depth = 8, train loss: 0.52889, val loss: 0.52312, in 0.016s
1 tree, 48 leaves, max depth = 12, train loss: 0.51845, val loss: 0.51231, in 0.031s
1 tree, 48 leaves, max depth = 12, train loss: 0.50858, val loss: 0.50208, in 0.016s
1 tree, 48 leaves, max depth = 13, train loss: 0.49924, val loss: 0.49236, in 0.016s
1 tree, 48 leaves, max depth = 12, train loss: 0.49040, val loss: 0.48320, in 0.031s
Fit 15 trees in 0.658 s, (720 total leaves)
Time spent computing histograms: 0.122s
Time spent finding best splits:  0.023s
Time spent applying splits:      0.015s
Time spent predicting:           0.000s
Trial 55, Fold 5: Log loss = 0.49450128925862646, Average precision = 0.9170951877765856, ROC-AUC = 0.930760752838006, Elapsed Time = 0.6636056999996072 seconds
Optimization Progress:  56%|#####6    | 56/100 [11:00<08:50, 12.06s/it]
Trial 56, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 56, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
0.158 s 0.040 GB of training data: 
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 21 leaves, max depth = 8, train loss: 0.68062, val loss: 0.68020, in 0.000s
1 tree, 21 leaves, max depth = 8, train loss: 0.66875, val loss: 0.66793, in 0.016s
1 tree, 21 leaves, max depth = 8, train loss: 0.65749, val loss: 0.65628, in 0.000s
1 tree, 21 leaves, max depth = 8, train loss: 0.64681, val loss: 0.64522, in 0.016s
1 tree, 21 leaves, max depth = 8, train loss: 0.63667, val loss: 0.63471, in 0.000s
1 tree, 21 leaves, max depth = 8, train loss: 0.62704, val loss: 0.62471, in 0.016s
1 tree, 21 leaves, max depth = 8, train loss: 0.61789, val loss: 0.61520, in 0.016s
1 tree, 21 leaves, max depth = 8, train loss: 0.60918, val loss: 0.60615, in 0.000s
1 tree, 21 leaves, max depth = 8, train loss: 0.60090, val loss: 0.59753, in 0.016s
1 tree, 21 leaves, max depth = 8, train loss: 0.59301, val loss: 0.58931, in 0.000s
1 tree, 21 leaves, max depth = 8, train loss: 0.58550, val loss: 0.58148, in 0.016s
1 tree, 21 leaves, max depth = 8, train loss: 0.57835, val loss: 0.57401, in 0.000s
1 tree, 21 leaves, max depth = 8, train loss: 0.57153, val loss: 0.56689, in 0.016s
1 tree, 21 leaves, max depth = 8, train loss: 0.56503, val loss: 0.56009, in 0.000s
1 tree, 21 leaves, max depth = 9, train loss: 0.55888, val loss: 0.55368, in 0.016s
1 tree, 21 leaves, max depth = 9, train loss: 0.55301, val loss: 0.54757, in 0.000s
1 tree, 38 leaves, max depth = 9, train loss: 0.54710, val loss: 0.54203, in 0.016s
1 tree, 21 leaves, max depth = 8, train loss: 0.54157, val loss: 0.53622, in 0.016s
1 tree, 21 leaves, max depth = 9, train loss: 0.53634, val loss: 0.53076, in 0.000s
1 tree, 39 leaves, max depth = 9, train loss: 0.53088, val loss: 0.52566, in 0.016s
1 tree, 39 leaves, max depth = 9, train loss: 0.52568, val loss: 0.52081, in 0.000s
1 tree, 39 leaves, max depth = 9, train loss: 0.52072, val loss: 0.51620, in 0.016s
1 tree, 21 leaves, max depth = 8, train loss: 0.51596, val loss: 0.51117, in 0.000s
1 tree, 39 leaves, max depth = 9, train loss: 0.51130, val loss: 0.50685, in 0.016s
1 tree, 21 leaves, max depth = 11, train loss: 0.50691, val loss: 0.50221, in 0.016s
1 tree, 39 leaves, max depth = 9, train loss: 0.50252, val loss: 0.49814, in 0.000s
1 tree, 21 leaves, max depth = 11, train loss: 0.49840, val loss: 0.49378, in 0.019s
1 tree, 39 leaves, max depth = 9, train loss: 0.49426, val loss: 0.48996, in 0.000s
1 tree, 21 leaves, max depth = 10, train loss: 0.49038, val loss: 0.48584, in 0.013s
1 tree, 39 leaves, max depth = 9, train loss: 0.48647, val loss: 0.48225, in 0.016s
1 tree, 21 leaves, max depth = 11, train loss: 0.48282, val loss: 0.47841, in 0.000s
1 tree, 39 leaves, max depth = 9, train loss: 0.47913, val loss: 0.47503, in 0.016s
1 tree, 39 leaves, max depth = 9, train loss: 0.47560, val loss: 0.47180, in 0.000s
1 tree, 21 leaves, max depth = 8, train loss: 0.47221, val loss: 0.46819, in 0.016s
1 tree, 39 leaves, max depth = 9, train loss: 0.46887, val loss: 0.46514, in 0.016s
1 tree, 21 leaves, max depth = 8, train loss: 0.46567, val loss: 0.46173, in 0.000s
1 tree, 39 leaves, max depth = 9, train loss: 0.46250, val loss: 0.45885, in 0.016s
1 tree, 20 leaves, max depth = 10, train loss: 0.45946, val loss: 0.45562, in 0.000s
1 tree, 39 leaves, max depth = 9, train loss: 0.45646, val loss: 0.45291, in 0.016s
1 tree, 21 leaves, max depth = 8, train loss: 0.45361, val loss: 0.44985, in 0.016s
1 tree, 21 leaves, max depth = 8, train loss: 0.45089, val loss: 0.44692, in 0.000s
1 tree, 39 leaves, max depth = 9, train loss: 0.44806, val loss: 0.44438, in 0.016s
1 tree, 39 leaves, max depth = 9, train loss: 0.44536, val loss: 0.44196, in 0.016s
1 tree, 20 leaves, max depth = 10, train loss: 0.44279, val loss: 0.43922, in 0.000s
1 tree, 39 leaves, max depth = 9, train loss: 0.44023, val loss: 0.43693, in 0.016s
1 tree, 39 leaves, max depth = 9, train loss: 0.43778, val loss: 0.43476, in 0.000s
1 tree, 21 leaves, max depth = 7, train loss: 0.43538, val loss: 0.43217, in 0.000s
1 tree, 39 leaves, max depth = 9, train loss: 0.43305, val loss: 0.43010, in 0.016s
1 tree, 20 leaves, max depth = 10, train loss: 0.43077, val loss: 0.42766, in 0.000s
1 tree, 39 leaves, max depth = 9, train loss: 0.42856, val loss: 0.42571, in 0.016s
1 tree, 21 leaves, max depth = 7, train loss: 0.42641, val loss: 0.42337, in 0.016s
1 tree, 39 leaves, max depth = 9, train loss: 0.42430, val loss: 0.42152, in 0.000s
1 tree, 20 leaves, max depth = 10, train loss: 0.42225, val loss: 0.41931, in 0.016s
1 tree, 39 leaves, max depth = 9, train loss: 0.42024, val loss: 0.41757, in 0.000s
1 tree, 21 leaves, max depth = 8, train loss: 0.41830, val loss: 0.41544, in 0.016s
1 tree, 39 leaves, max depth = 9, train loss: 0.41638, val loss: 0.41379, in 0.016s
1 tree, 21 leaves, max depth = 8, train loss: 0.41454, val loss: 0.41176, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.41273, val loss: 0.40976, in 0.016s
1 tree, 39 leaves, max depth = 9, train loss: 0.41091, val loss: 0.40820, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.40918, val loss: 0.40629, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.40753, val loss: 0.40445, in 0.000s
1 tree, 39 leaves, max depth = 9, train loss: 0.40579, val loss: 0.40299, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.40422, val loss: 0.40124, in 0.000s
1 tree, 39 leaves, max depth = 9, train loss: 0.40256, val loss: 0.39985, in 0.016s
1 tree, 20 leaves, max depth = 10, train loss: 0.40088, val loss: 0.39805, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.39938, val loss: 0.39638, in 0.016s
1 tree, 39 leaves, max depth = 9, train loss: 0.39780, val loss: 0.39507, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.39637, val loss: 0.39348, in 0.016s
1 tree, 20 leaves, max depth = 9, train loss: 0.39479, val loss: 0.39179, in 0.000s
1 tree, 39 leaves, max depth = 9, train loss: 0.39328, val loss: 0.39055, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.39192, val loss: 0.38903, in 0.000s
1 tree, 20 leaves, max depth = 8, train loss: 0.39039, val loss: 0.38742, in 0.016s
1 tree, 39 leaves, max depth = 9, train loss: 0.38895, val loss: 0.38626, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.38765, val loss: 0.38480, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.38640, val loss: 0.38341, in 0.000s
1 tree, 39 leaves, max depth = 9, train loss: 0.38502, val loss: 0.38231, in 0.016s
1 tree, 20 leaves, max depth = 9, train loss: 0.38358, val loss: 0.38078, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.38239, val loss: 0.37944, in 0.016s
1 tree, 39 leaves, max depth = 9, train loss: 0.38107, val loss: 0.37840, in 0.000s
1 tree, 20 leaves, max depth = 9, train loss: 0.37972, val loss: 0.37697, in 0.016s
Fit 80 trees in 0.994 s, (2050 total leaves)
Time spent computing histograms: 0.374s
Time spent finding best splits:  0.059s
Time spent applying splits:      0.049s
Time spent predicting:           0.000s
Trial 56, Fold 1: Log loss = 0.380684813906018, Average precision = 0.9458659296414029, ROC-AUC = 0.9424333575897487, Elapsed Time = 1.0017719000006764 seconds
Trial 56, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 56, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
Binning 0.040 GB of training data: 0.173 s
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 21 leaves, max depth = 8, train loss: 0.68065, val loss: 0.68003, in 0.000s
1 tree, 21 leaves, max depth = 8, train loss: 0.66881, val loss: 0.66760, in 0.016s
1 tree, 21 leaves, max depth = 9, train loss: 0.65775, val loss: 0.65597, in 0.000s
1 tree, 21 leaves, max depth = 8, train loss: 0.64709, val loss: 0.64476, in 0.016s
1 tree, 21 leaves, max depth = 8, train loss: 0.63697, val loss: 0.63411, in 0.000s
1 tree, 21 leaves, max depth = 8, train loss: 0.62736, val loss: 0.62397, in 0.016s
1 tree, 21 leaves, max depth = 8, train loss: 0.61823, val loss: 0.61434, in 0.000s
1 tree, 21 leaves, max depth = 9, train loss: 0.60967, val loss: 0.60530, in 0.016s
1 tree, 21 leaves, max depth = 8, train loss: 0.60140, val loss: 0.59656, in 0.000s
1 tree, 21 leaves, max depth = 8, train loss: 0.59353, val loss: 0.58822, in 0.016s
1 tree, 21 leaves, max depth = 8, train loss: 0.58603, val loss: 0.58028, in 0.016s
1 tree, 21 leaves, max depth = 9, train loss: 0.57900, val loss: 0.57283, in 0.000s
1 tree, 21 leaves, max depth = 8, train loss: 0.57219, val loss: 0.56560, in 0.016s
1 tree, 21 leaves, max depth = 8, train loss: 0.56570, val loss: 0.55870, in 0.000s
1 tree, 21 leaves, max depth = 8, train loss: 0.55951, val loss: 0.55212, in 0.000s
1 tree, 21 leaves, max depth = 9, train loss: 0.55371, val loss: 0.54594, in 0.016s
1 tree, 39 leaves, max depth = 10, train loss: 0.54783, val loss: 0.54020, in 0.016s
1 tree, 21 leaves, max depth = 8, train loss: 0.54241, val loss: 0.53442, in 0.000s
1 tree, 21 leaves, max depth = 8, train loss: 0.53724, val loss: 0.52889, in 0.016s
1 tree, 39 leaves, max depth = 11, train loss: 0.53179, val loss: 0.52360, in 0.000s
1 tree, 39 leaves, max depth = 11, train loss: 0.52660, val loss: 0.51856, in 0.016s
1 tree, 39 leaves, max depth = 11, train loss: 0.52166, val loss: 0.51376, in 0.016s
1 tree, 21 leaves, max depth = 8, train loss: 0.51699, val loss: 0.50876, in 0.000s
1 tree, 39 leaves, max depth = 11, train loss: 0.51234, val loss: 0.50425, in 0.016s
1 tree, 21 leaves, max depth = 8, train loss: 0.50796, val loss: 0.49956, in 0.000s
1 tree, 39 leaves, max depth = 11, train loss: 0.50358, val loss: 0.49531, in 0.016s
1 tree, 21 leaves, max depth = 8, train loss: 0.49947, val loss: 0.49089, in 0.000s
1 tree, 39 leaves, max depth = 11, train loss: 0.49533, val loss: 0.48689, in 0.016s
1 tree, 21 leaves, max depth = 8, train loss: 0.49146, val loss: 0.48273, in 0.016s
1 tree, 39 leaves, max depth = 11, train loss: 0.48756, val loss: 0.47896, in 0.000s
1 tree, 21 leaves, max depth = 10, train loss: 0.48391, val loss: 0.47504, in 0.016s
1 tree, 39 leaves, max depth = 11, train loss: 0.48022, val loss: 0.47148, in 0.016s
1 tree, 39 leaves, max depth = 11, train loss: 0.47670, val loss: 0.46808, in 0.000s
1 tree, 21 leaves, max depth = 8, train loss: 0.47332, val loss: 0.46443, in 0.016s
1 tree, 39 leaves, max depth = 10, train loss: 0.46998, val loss: 0.46123, in 0.016s
1 tree, 21 leaves, max depth = 8, train loss: 0.46680, val loss: 0.45778, in 0.000s
1 tree, 39 leaves, max depth = 10, train loss: 0.46364, val loss: 0.45475, in 0.016s
1 tree, 21 leaves, max depth = 8, train loss: 0.46063, val loss: 0.45149, in 0.016s
1 tree, 39 leaves, max depth = 9, train loss: 0.45764, val loss: 0.44862, in 0.000s
1 tree, 21 leaves, max depth = 8, train loss: 0.45480, val loss: 0.44554, in 0.016s
1 tree, 21 leaves, max depth = 8, train loss: 0.45208, val loss: 0.44259, in 0.000s
1 tree, 39 leaves, max depth = 9, train loss: 0.44926, val loss: 0.43989, in 0.016s
1 tree, 39 leaves, max depth = 9, train loss: 0.44656, val loss: 0.43731, in 0.016s
1 tree, 39 leaves, max depth = 9, train loss: 0.44398, val loss: 0.43484, in 0.000s
1 tree, 21 leaves, max depth = 8, train loss: 0.44147, val loss: 0.43211, in 0.016s
1 tree, 39 leaves, max depth = 11, train loss: 0.43902, val loss: 0.42977, in 0.016s
1 tree, 21 leaves, max depth = 8, train loss: 0.43664, val loss: 0.42718, in 0.000s
1 tree, 39 leaves, max depth = 10, train loss: 0.43431, val loss: 0.42496, in 0.016s
1 tree, 21 leaves, max depth = 8, train loss: 0.43205, val loss: 0.42250, in 0.016s
1 tree, 38 leaves, max depth = 10, train loss: 0.42983, val loss: 0.42039, in 0.000s
1 tree, 21 leaves, max depth = 8, train loss: 0.42770, val loss: 0.41805, in 0.016s
1 tree, 39 leaves, max depth = 10, train loss: 0.42558, val loss: 0.41605, in 0.016s
1 tree, 21 leaves, max depth = 11, train loss: 0.42356, val loss: 0.41383, in 0.000s
1 tree, 39 leaves, max depth = 10, train loss: 0.42154, val loss: 0.41192, in 0.016s
1 tree, 21 leaves, max depth = 8, train loss: 0.41962, val loss: 0.40982, in 0.016s
1 tree, 39 leaves, max depth = 10, train loss: 0.41770, val loss: 0.40800, in 0.016s
1 tree, 39 leaves, max depth = 10, train loss: 0.41586, val loss: 0.40627, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.41408, val loss: 0.40443, in 0.016s
1 tree, 21 leaves, max depth = 10, train loss: 0.41229, val loss: 0.40246, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.41061, val loss: 0.40072, in 0.000s
1 tree, 39 leaves, max depth = 9, train loss: 0.40887, val loss: 0.39908, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.40726, val loss: 0.39741, in 0.000s
1 tree, 39 leaves, max depth = 9, train loss: 0.40560, val loss: 0.39586, in 0.016s
1 tree, 21 leaves, max depth = 7, train loss: 0.40392, val loss: 0.39402, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.40241, val loss: 0.39243, in 0.000s
1 tree, 39 leaves, max depth = 9, train loss: 0.40082, val loss: 0.39095, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.39937, val loss: 0.38944, in 0.016s
1 tree, 21 leaves, max depth = 10, train loss: 0.39776, val loss: 0.38767, in 0.000s
1 tree, 39 leaves, max depth = 9, train loss: 0.39625, val loss: 0.38626, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.39487, val loss: 0.38482, in 0.016s
1 tree, 39 leaves, max depth = 10, train loss: 0.39342, val loss: 0.38348, in 0.000s
1 tree, 21 leaves, max depth = 9, train loss: 0.39189, val loss: 0.38182, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.39058, val loss: 0.38044, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.38932, val loss: 0.37913, in 0.000s
1 tree, 39 leaves, max depth = 10, train loss: 0.38793, val loss: 0.37785, in 0.016s
1 tree, 21 leaves, max depth = 10, train loss: 0.38651, val loss: 0.37627, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.38530, val loss: 0.37501, in 0.016s
1 tree, 39 leaves, max depth = 10, train loss: 0.38398, val loss: 0.37379, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.38283, val loss: 0.37259, in 0.000s
1 tree, 21 leaves, max depth = 9, train loss: 0.38147, val loss: 0.37110, in 0.016s
Fit 80 trees in 1.095 s, (2059 total leaves)
Time spent computing histograms: 0.404s
Time spent finding best splits:  0.063s
Time spent applying splits:      0.053s
Time spent predicting:           0.000s
Trial 56, Fold 2: Log loss = 0.3832620626880714, Average precision = 0.9423748606646112, ROC-AUC = 0.9432571023274772, Elapsed Time = 1.0971567000015057 seconds
Trial 56, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 56, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
Binning 0.040 GB of training data: 0.174 s
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 21 leaves, max depth = 7, train loss: 0.68072, val loss: 0.68033, in 0.000s
1 tree, 21 leaves, max depth = 7, train loss: 0.66895, val loss: 0.66819, in 0.016s
1 tree, 21 leaves, max depth = 7, train loss: 0.65779, val loss: 0.65666, in 0.016s
1 tree, 21 leaves, max depth = 7, train loss: 0.64720, val loss: 0.64572, in 0.000s
1 tree, 21 leaves, max depth = 7, train loss: 0.63715, val loss: 0.63533, in 0.016s
1 tree, 21 leaves, max depth = 7, train loss: 0.62760, val loss: 0.62544, in 0.000s
1 tree, 21 leaves, max depth = 7, train loss: 0.61852, val loss: 0.61605, in 0.016s
1 tree, 21 leaves, max depth = 7, train loss: 0.60989, val loss: 0.60710, in 0.016s
1 tree, 21 leaves, max depth = 7, train loss: 0.60168, val loss: 0.59858, in 0.000s
1 tree, 21 leaves, max depth = 7, train loss: 0.59386, val loss: 0.59047, in 0.016s
1 tree, 21 leaves, max depth = 7, train loss: 0.58642, val loss: 0.58274, in 0.000s
1 tree, 21 leaves, max depth = 7, train loss: 0.57932, val loss: 0.57537, in 0.016s
1 tree, 21 leaves, max depth = 7, train loss: 0.57257, val loss: 0.56834, in 0.000s
1 tree, 21 leaves, max depth = 7, train loss: 0.56613, val loss: 0.56163, in 0.016s
1 tree, 21 leaves, max depth = 7, train loss: 0.55999, val loss: 0.55523, in 0.000s
1 tree, 41 leaves, max depth = 10, train loss: 0.55385, val loss: 0.54946, in 0.016s
1 tree, 21 leaves, max depth = 8, train loss: 0.54818, val loss: 0.54354, in 0.000s
1 tree, 41 leaves, max depth = 10, train loss: 0.54243, val loss: 0.53814, in 0.016s
1 tree, 21 leaves, max depth = 8, train loss: 0.53713, val loss: 0.53259, in 0.016s
1 tree, 41 leaves, max depth = 10, train loss: 0.53173, val loss: 0.52753, in 0.000s
1 tree, 21 leaves, max depth = 8, train loss: 0.52677, val loss: 0.52232, in 0.016s
1 tree, 41 leaves, max depth = 10, train loss: 0.52170, val loss: 0.51757, in 0.000s
1 tree, 41 leaves, max depth = 10, train loss: 0.51685, val loss: 0.51304, in 0.016s
1 tree, 21 leaves, max depth = 8, train loss: 0.51229, val loss: 0.50824, in 0.016s
[25/80] 1 tree, 41 leaves, max depth = 10, train loss: 0.50773, val loss: 0.50398, in 0.000s
1 tree, 21 leaves, max depth = 8, train loss: 0.50345, val loss: 0.49947, in 0.016s
1 tree, 41 leaves, max depth = 10, train loss: 0.49915, val loss: 0.49546, in 0.000s
1 tree, 21 leaves, max depth = 8, train loss: 0.49514, val loss: 0.49121, in 0.016s
1 tree, 41 leaves, max depth = 10, train loss: 0.49108, val loss: 0.48744, in 0.016s
1 tree, 21 leaves, max depth = 8, train loss: 0.48730, val loss: 0.48343, in 0.000s
1 tree, 41 leaves, max depth = 10, train loss: 0.48346, val loss: 0.47987, in 0.016s
1 tree, 21 leaves, max depth = 8, train loss: 0.47991, val loss: 0.47610, in 0.000s
1 tree, 41 leaves, max depth = 10, train loss: 0.47628, val loss: 0.47274, in 0.016s
1 tree, 41 leaves, max depth = 10, train loss: 0.47281, val loss: 0.46953, in 0.031s
1 tree, 21 leaves, max depth = 8, train loss: 0.46951, val loss: 0.46601, in 0.031s
1 tree, 41 leaves, max depth = 10, train loss: 0.46623, val loss: 0.46298, in 0.016s
1 tree, 21 leaves, max depth = 8, train loss: 0.46312, val loss: 0.45966, in 0.016s
1 tree, 41 leaves, max depth = 10, train loss: 0.46000, val loss: 0.45679, in 0.016s
1 tree, 20 leaves, max depth = 7, train loss: 0.45707, val loss: 0.45365, in 0.016s
1 tree, 41 leaves, max depth = 10, train loss: 0.45411, val loss: 0.45093, in 0.016s
1 tree, 21 leaves, max depth = 8, train loss: 0.45134, val loss: 0.44796, in 0.016s
1 tree, 41 leaves, max depth = 10, train loss: 0.44854, val loss: 0.44539, in 0.016s
1 tree, 41 leaves, max depth = 10, train loss: 0.44585, val loss: 0.44293, in 0.016s
1 tree, 21 leaves, max depth = 8, train loss: 0.44326, val loss: 0.44015, in 0.000s
1 tree, 41 leaves, max depth = 10, train loss: 0.44071, val loss: 0.43782, in 0.016s
1 tree, 21 leaves, max depth = 8, train loss: 0.43826, val loss: 0.43517, in 0.016s
1 tree, 40 leaves, max depth = 10, train loss: 0.43584, val loss: 0.43296, in 0.016s
1 tree, 19 leaves, max depth = 9, train loss: 0.43351, val loss: 0.43041, in 0.016s
1 tree, 41 leaves, max depth = 10, train loss: 0.43120, val loss: 0.42831, in 0.016s
1 tree, 21 leaves, max depth = 10, train loss: 0.42903, val loss: 0.42591, in 0.000s
1 tree, 41 leaves, max depth = 10, train loss: 0.42683, val loss: 0.42392, in 0.016s
1 tree, 21 leaves, max depth = 8, train loss: 0.42474, val loss: 0.42166, in 0.000s
1 tree, 41 leaves, max depth = 10, train loss: 0.42265, val loss: 0.41977, in 0.016s
1 tree, 41 leaves, max depth = 10, train loss: 0.42065, val loss: 0.41797, in 0.016s
1 tree, 20 leaves, max depth = 10, train loss: 0.41868, val loss: 0.41579, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.41683, val loss: 0.41407, in 0.000s
1 tree, 41 leaves, max depth = 10, train loss: 0.41493, val loss: 0.41236, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.41316, val loss: 0.41072, in 0.016s
1 tree, 20 leaves, max depth = 10, train loss: 0.41133, val loss: 0.40868, in 0.016s
1 tree, 41 leaves, max depth = 10, train loss: 0.40952, val loss: 0.40707, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.40785, val loss: 0.40552, in 0.016s
1 tree, 40 leaves, max depth = 10, train loss: 0.40613, val loss: 0.40398, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.40453, val loss: 0.40250, in 0.000s
1 tree, 20 leaves, max depth = 10, train loss: 0.40283, val loss: 0.40060, in 0.016s
1 tree, 41 leaves, max depth = 10, train loss: 0.40118, val loss: 0.39915, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.39967, val loss: 0.39774, in 0.000s
1 tree, 20 leaves, max depth = 10, train loss: 0.39806, val loss: 0.39594, in 0.016s
1 tree, 41 leaves, max depth = 10, train loss: 0.39649, val loss: 0.39456, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.39505, val loss: 0.39322, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.39367, val loss: 0.39194, in 0.016s
1 tree, 40 leaves, max depth = 9, train loss: 0.39216, val loss: 0.39062, in 0.000s
1 tree, 20 leaves, max depth = 8, train loss: 0.39063, val loss: 0.38890, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.38932, val loss: 0.38768, in 0.000s
1 tree, 40 leaves, max depth = 9, train loss: 0.38788, val loss: 0.38643, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.38662, val loss: 0.38526, in 0.016s
1 tree, 40 leaves, max depth = 9, train loss: 0.38525, val loss: 0.38408, in 0.000s
1 tree, 20 leaves, max depth = 7, train loss: 0.38380, val loss: 0.38245, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.38259, val loss: 0.38133, in 0.016s
1 tree, 41 leaves, max depth = 10, train loss: 0.38128, val loss: 0.38020, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.38012, val loss: 0.37913, in 0.016s
Fit 80 trees in 1.220 s, (2110 total leaves)
Time spent computing histograms: 0.450s
Time spent finding best splits:  0.082s
Time spent applying splits:      0.069s
Time spent predicting:           0.016s
Trial 56, Fold 3: Log loss = 0.3766158102262939, Average precision = 0.9477626147986153, ROC-AUC = 0.9475018298966589, Elapsed Time = 1.228315799999109 seconds
Trial 56, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 56, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
Binning 0.040 GB of training data: 0.174 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 20 leaves, max depth = 9, train loss: 0.68079, val loss: 0.68008, in 0.016s
1 tree, 20 leaves, max depth = 9, train loss: 0.66909, val loss: 0.66768, in 0.000s
1 tree, 20 leaves, max depth = 9, train loss: 0.65800, val loss: 0.65590, in 0.016s
1 tree, 20 leaves, max depth = 9, train loss: 0.64747, val loss: 0.64471, in 0.016s
1 tree, 20 leaves, max depth = 9, train loss: 0.63748, val loss: 0.63407, in 0.000s
1 tree, 20 leaves, max depth = 9, train loss: 0.62799, val loss: 0.62394, in 0.016s
1 tree, 20 leaves, max depth = 9, train loss: 0.61897, val loss: 0.61429, in 0.000s
1 tree, 20 leaves, max depth = 9, train loss: 0.61039, val loss: 0.60510, in 0.016s
1 tree, 20 leaves, max depth = 9, train loss: 0.60223, val loss: 0.59634, in 0.000s
1 tree, 20 leaves, max depth = 9, train loss: 0.59446, val loss: 0.58799, in 0.016s
1 tree, 20 leaves, max depth = 9, train loss: 0.58707, val loss: 0.58002, in 0.016s
1 tree, 20 leaves, max depth = 9, train loss: 0.58003, val loss: 0.57241, in 0.000s
1 tree, 20 leaves, max depth = 9, train loss: 0.57332, val loss: 0.56515, in 0.016s
1 tree, 20 leaves, max depth = 9, train loss: 0.56693, val loss: 0.55822, in 0.000s
1 tree, 20 leaves, max depth = 9, train loss: 0.56083, val loss: 0.55159, in 0.016s
1 tree, 20 leaves, max depth = 9, train loss: 0.55502, val loss: 0.54525, in 0.016s
1 tree, 38 leaves, max depth = 10, train loss: 0.54914, val loss: 0.53933, in 0.000s
1 tree, 21 leaves, max depth = 9, train loss: 0.54371, val loss: 0.53340, in 0.016s
1 tree, 21 leaves, max depth = 9, train loss: 0.53854, val loss: 0.52773, in 0.016s
1 tree, 38 leaves, max depth = 10, train loss: 0.53311, val loss: 0.52227, in 0.000s
1 tree, 38 leaves, max depth = 10, train loss: 0.52794, val loss: 0.51706, in 0.016s
1 tree, 38 leaves, max depth = 10, train loss: 0.52302, val loss: 0.51210, in 0.016s
1 tree, 21 leaves, max depth = 11, train loss: 0.51835, val loss: 0.50697, in 0.000s
1 tree, 38 leaves, max depth = 10, train loss: 0.51371, val loss: 0.50230, in 0.000s
1 tree, 21 leaves, max depth = 11, train loss: 0.50933, val loss: 0.49747, in 0.016s
1 tree, 38 leaves, max depth = 11, train loss: 0.50496, val loss: 0.49307, in 0.016s
1 tree, 21 leaves, max depth = 11, train loss: 0.50084, val loss: 0.48852, in 0.000s
1 tree, 38 leaves, max depth = 10, train loss: 0.49673, val loss: 0.48438, in 0.016s
1 tree, 21 leaves, max depth = 11, train loss: 0.49286, val loss: 0.48009, in 0.016s
1 tree, 38 leaves, max depth = 11, train loss: 0.48897, val loss: 0.47618, in 0.016s
1 tree, 21 leaves, max depth = 9, train loss: 0.48534, val loss: 0.47218, in 0.000s
1 tree, 38 leaves, max depth = 10, train loss: 0.48167, val loss: 0.46849, in 0.016s
1 tree, 38 leaves, max depth = 10, train loss: 0.47817, val loss: 0.46496, in 0.016s
1 tree, 21 leaves, max depth = 10, train loss: 0.47478, val loss: 0.46119, in 0.000s
1 tree, 38 leaves, max depth = 11, train loss: 0.47146, val loss: 0.45785, in 0.016s
1 tree, 21 leaves, max depth = 10, train loss: 0.46827, val loss: 0.45429, in 0.016s
1 tree, 38 leaves, max depth = 10, train loss: 0.46513, val loss: 0.45113, in 0.016s
1 tree, 20 leaves, max depth = 12, train loss: 0.46212, val loss: 0.44776, in 0.000s
1 tree, 38 leaves, max depth = 10, train loss: 0.45915, val loss: 0.44477, in 0.016s
1 tree, 21 leaves, max depth = 10, train loss: 0.45630, val loss: 0.44157, in 0.000s
1 tree, 20 leaves, max depth = 12, train loss: 0.45358, val loss: 0.43851, in 0.016s
1 tree, 38 leaves, max depth = 10, train loss: 0.45078, val loss: 0.43570, in 0.016s
1 tree, 38 leaves, max depth = 10, train loss: 0.44811, val loss: 0.43301, in 0.000s
1 tree, 38 leaves, max depth = 10, train loss: 0.44555, val loss: 0.43044, in 0.016s
1 tree, 21 leaves, max depth = 9, train loss: 0.44303, val loss: 0.42759, in 0.016s
1 tree, 38 leaves, max depth = 11, train loss: 0.44060, val loss: 0.42514, in 0.000s
1 tree, 21 leaves, max depth = 11, train loss: 0.43821, val loss: 0.42244, in 0.016s
1 tree, 38 leaves, max depth = 11, train loss: 0.43590, val loss: 0.42011, in 0.016s
1 tree, 20 leaves, max depth = 11, train loss: 0.43364, val loss: 0.41756, in 0.000s
1 tree, 38 leaves, max depth = 11, train loss: 0.43145, val loss: 0.41535, in 0.016s
1 tree, 21 leaves, max depth = 11, train loss: 0.42930, val loss: 0.41290, in 0.016s
1 tree, 38 leaves, max depth = 11, train loss: 0.42721, val loss: 0.41080, in 0.000s
1 tree, 21 leaves, max depth = 9, train loss: 0.42519, val loss: 0.40851, in 0.016s
1 tree, 38 leaves, max depth = 11, train loss: 0.42320, val loss: 0.40651, in 0.016s
1 tree, 20 leaves, max depth = 10, train loss: 0.42128, val loss: 0.40431, in 0.000s
1 tree, 38 leaves, max depth = 10, train loss: 0.41939, val loss: 0.40240, in 0.016s
1 tree, 38 leaves, max depth = 11, train loss: 0.41758, val loss: 0.40058, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.41580, val loss: 0.39866, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.41411, val loss: 0.39683, in 0.016s
1 tree, 20 leaves, max depth = 10, train loss: 0.41232, val loss: 0.39479, in 0.016s
1 tree, 38 leaves, max depth = 10, train loss: 0.41061, val loss: 0.39307, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.40900, val loss: 0.39134, in 0.016s
1 tree, 19 leaves, max depth = 10, train loss: 0.40732, val loss: 0.38941, in 0.000s
1 tree, 38 leaves, max depth = 10, train loss: 0.40569, val loss: 0.38777, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.40416, val loss: 0.38613, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.40270, val loss: 0.38455, in 0.000s
1 tree, 38 leaves, max depth = 10, train loss: 0.40115, val loss: 0.38300, in 0.016s
1 tree, 20 leaves, max depth = 9, train loss: 0.39957, val loss: 0.38119, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.39819, val loss: 0.37969, in 0.000s
1 tree, 38 leaves, max depth = 11, train loss: 0.39670, val loss: 0.37821, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.39537, val loss: 0.37678, in 0.000s
1 tree, 38 leaves, max depth = 11, train loss: 0.39395, val loss: 0.37536, in 0.016s
1 tree, 20 leaves, max depth = 9, train loss: 0.39247, val loss: 0.37367, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.39121, val loss: 0.37230, in 0.000s
1 tree, 38 leaves, max depth = 11, train loss: 0.38985, val loss: 0.37095, in 0.016s
1 tree, 20 leaves, max depth = 9, train loss: 0.38845, val loss: 0.36934, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.38724, val loss: 0.36803, in 0.000s
1 tree, 38 leaves, max depth = 11, train loss: 0.38593, val loss: 0.36674, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.38478, val loss: 0.36548, in 0.016s
1 tree, 21 leaves, max depth = 7, train loss: 0.38339, val loss: 0.36388, in 0.000s
Fit 80 trees in 1.158 s, (2004 total leaves)
Time spent computing histograms: 0.430s
Time spent finding best splits:  0.067s
Time spent applying splits:      0.056s
Time spent predicting:           0.000s
Trial 56, Fold 4: Log loss = 0.38315428635904075, Average precision = 0.945962665443955, ROC-AUC = 0.9426270026061598, Elapsed Time = 1.1737682000002678 seconds
Trial 56, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 56, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
Binning 0.040 GB of training data: 0.173 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 21 leaves, max depth = 8, train loss: 0.68073, val loss: 0.67990, in 0.000s
1 tree, 21 leaves, max depth = 8, train loss: 0.66897, val loss: 0.66733, in 0.016s
1 tree, 21 leaves, max depth = 9, train loss: 0.65776, val loss: 0.65533, in 0.000s
1 tree, 21 leaves, max depth = 9, train loss: 0.64712, val loss: 0.64392, in 0.016s
1 tree, 21 leaves, max depth = 8, train loss: 0.63707, val loss: 0.63313, in 0.000s
1 tree, 21 leaves, max depth = 9, train loss: 0.62748, val loss: 0.62280, in 0.016s
1 tree, 21 leaves, max depth = 9, train loss: 0.61836, val loss: 0.61297, in 0.000s
1 tree, 21 leaves, max depth = 8, train loss: 0.60973, val loss: 0.60365, in 0.016s
1 tree, 21 leaves, max depth = 9, train loss: 0.60147, val loss: 0.59471, in 0.000s
1 tree, 21 leaves, max depth = 9, train loss: 0.59361, val loss: 0.58620, in 0.016s
1 tree, 21 leaves, max depth = 8, train loss: 0.58617, val loss: 0.57811, in 0.016s
1 tree, 21 leaves, max depth = 9, train loss: 0.57904, val loss: 0.57035, in 0.000s
1 tree, 21 leaves, max depth = 9, train loss: 0.57225, val loss: 0.56294, in 0.016s
1 tree, 21 leaves, max depth = 8, train loss: 0.56581, val loss: 0.55590, in 0.000s
1 tree, 21 leaves, max depth = 9, train loss: 0.55964, val loss: 0.54914, in 0.016s
1 tree, 21 leaves, max depth = 9, train loss: 0.55375, val loss: 0.54268, in 0.000s
1 tree, 39 leaves, max depth = 10, train loss: 0.54790, val loss: 0.53706, in 0.016s
1 tree, 21 leaves, max depth = 8, train loss: 0.54242, val loss: 0.53103, in 0.000s
1 tree, 21 leaves, max depth = 8, train loss: 0.53720, val loss: 0.52527, in 0.016s
1 tree, 39 leaves, max depth = 10, train loss: 0.53179, val loss: 0.52010, in 0.000s
1 tree, 39 leaves, max depth = 10, train loss: 0.52664, val loss: 0.51518, in 0.016s
1 tree, 21 leaves, max depth = 8, train loss: 0.52184, val loss: 0.50986, in 0.016s
1 tree, 39 leaves, max depth = 10, train loss: 0.51699, val loss: 0.50525, in 0.000s
1 tree, 21 leaves, max depth = 8, train loss: 0.51249, val loss: 0.50025, in 0.016s
1 tree, 39 leaves, max depth = 10, train loss: 0.50792, val loss: 0.49592, in 0.016s
1 tree, 39 leaves, max depth = 10, train loss: 0.50357, val loss: 0.49179, in 0.000s
1 tree, 21 leaves, max depth = 8, train loss: 0.49941, val loss: 0.48716, in 0.016s
1 tree, 39 leaves, max depth = 10, train loss: 0.49530, val loss: 0.48328, in 0.016s
1 tree, 21 leaves, max depth = 8, train loss: 0.49139, val loss: 0.47892, in 0.000s
1 tree, 39 leaves, max depth = 11, train loss: 0.48751, val loss: 0.47527, in 0.016s
1 tree, 21 leaves, max depth = 8, train loss: 0.48383, val loss: 0.47115, in 0.016s
1 tree, 39 leaves, max depth = 11, train loss: 0.48017, val loss: 0.46771, in 0.000s
1 tree, 21 leaves, max depth = 8, train loss: 0.47670, val loss: 0.46382, in 0.016s
1 tree, 39 leaves, max depth = 11, train loss: 0.47324, val loss: 0.46058, in 0.016s
1 tree, 21 leaves, max depth = 8, train loss: 0.46996, val loss: 0.45690, in 0.000s
1 tree, 39 leaves, max depth = 10, train loss: 0.46668, val loss: 0.45384, in 0.000s
1 tree, 39 leaves, max depth = 10, train loss: 0.46355, val loss: 0.45093, in 0.016s
1 tree, 21 leaves, max depth = 7, train loss: 0.46050, val loss: 0.44749, in 0.016s
1 tree, 39 leaves, max depth = 9, train loss: 0.45752, val loss: 0.44473, in 0.000s
1 tree, 21 leaves, max depth = 7, train loss: 0.45464, val loss: 0.44147, in 0.016s
1 tree, 21 leaves, max depth = 7, train loss: 0.45189, val loss: 0.43835, in 0.016s
1 tree, 39 leaves, max depth = 11, train loss: 0.44909, val loss: 0.43577, in 0.000s
1 tree, 39 leaves, max depth = 9, train loss: 0.44641, val loss: 0.43330, in 0.016s
1 tree, 21 leaves, max depth = 7, train loss: 0.44383, val loss: 0.43037, in 0.016s
1 tree, 39 leaves, max depth = 11, train loss: 0.44129, val loss: 0.42804, in 0.000s
1 tree, 21 leaves, max depth = 7, train loss: 0.43885, val loss: 0.42526, in 0.016s
1 tree, 39 leaves, max depth = 10, train loss: 0.43643, val loss: 0.42305, in 0.016s
1 tree, 39 leaves, max depth = 10, train loss: 0.43411, val loss: 0.42094, in 0.016s
1 tree, 21 leaves, max depth = 7, train loss: 0.43182, val loss: 0.41832, in 0.000s
1 tree, 39 leaves, max depth = 10, train loss: 0.42962, val loss: 0.41633, in 0.016s
1 tree, 21 leaves, max depth = 7, train loss: 0.42745, val loss: 0.41384, in 0.016s
1 tree, 39 leaves, max depth = 10, train loss: 0.42535, val loss: 0.41194, in 0.000s
1 tree, 21 leaves, max depth = 8, train loss: 0.42330, val loss: 0.40958, in 0.016s
1 tree, 39 leaves, max depth = 10, train loss: 0.42130, val loss: 0.40779, in 0.016s
1 tree, 21 leaves, max depth = 7, train loss: 0.41935, val loss: 0.40553, in 0.000s
1 tree, 39 leaves, max depth = 10, train loss: 0.41744, val loss: 0.40382, in 0.016s
1 tree, 21 leaves, max depth = 7, train loss: 0.41558, val loss: 0.40167, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.41381, val loss: 0.39997, in 0.000s
1 tree, 39 leaves, max depth = 10, train loss: 0.41199, val loss: 0.39836, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.41030, val loss: 0.39674, in 0.016s
1 tree, 39 leaves, max depth = 9, train loss: 0.40857, val loss: 0.39521, in 0.000s
1 tree, 21 leaves, max depth = 9, train loss: 0.40686, val loss: 0.39322, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.40526, val loss: 0.39169, in 0.000s
1 tree, 39 leaves, max depth = 9, train loss: 0.40361, val loss: 0.39024, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.40208, val loss: 0.38878, in 0.016s
1 tree, 39 leaves, max depth = 9, train loss: 0.40050, val loss: 0.38741, in 0.016s
1 tree, 20 leaves, max depth = 10, train loss: 0.39889, val loss: 0.38550, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.39744, val loss: 0.38411, in 0.016s
1 tree, 38 leaves, max depth = 10, train loss: 0.39593, val loss: 0.38281, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.39455, val loss: 0.38148, in 0.000s
1 tree, 20 leaves, max depth = 10, train loss: 0.39303, val loss: 0.37968, in 0.016s
1 tree, 39 leaves, max depth = 10, train loss: 0.39159, val loss: 0.37844, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.39027, val loss: 0.37718, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.38900, val loss: 0.37597, in 0.000s
1 tree, 39 leaves, max depth = 10, train loss: 0.38763, val loss: 0.37480, in 0.016s
1 tree, 20 leaves, max depth = 9, train loss: 0.38620, val loss: 0.37310, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.38499, val loss: 0.37195, in 0.000s
1 tree, 39 leaves, max depth = 10, train loss: 0.38367, val loss: 0.37083, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.38251, val loss: 0.36973, in 0.016s
1 tree, 20 leaves, max depth = 8, train loss: 0.38116, val loss: 0.36811, in 0.000s
Fit 80 trees in 1.110 s, (2055 total leaves)
Time spent computing histograms: 0.422s
Time spent finding best splits:  0.065s
Time spent applying splits:      0.055s
Time spent predicting:           0.000s
Trial 56, Fold 5: Log loss = 0.3854740829711175, Average precision = 0.9450464468313988, ROC-AUC = 0.9415032978466454, Elapsed Time = 1.122230999999374 seconds
Optimization Progress:  57%|#####6    | 57/100 [11:13<08:49, 12.32s/it]
Trial 57, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 57, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
0.142 s 0.040 GB of training data: 
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 32 leaves, max depth = 12, train loss: 0.67075, val loss: 0.67008, in 0.000s
1 tree, 31 leaves, max depth = 12, train loss: 0.65045, val loss: 0.64913, in 0.016s
1 tree, 31 leaves, max depth = 12, train loss: 0.63199, val loss: 0.63006, in 0.000s
1 tree, 30 leaves, max depth = 13, train loss: 0.61534, val loss: 0.61293, in 0.000s
1 tree, 29 leaves, max depth = 10, train loss: 0.59998, val loss: 0.59701, in 0.016s
1 tree, 29 leaves, max depth = 10, train loss: 0.58594, val loss: 0.58243, in 0.000s
1 tree, 29 leaves, max depth = 10, train loss: 0.57308, val loss: 0.56906, in 0.016s
1 tree, 30 leaves, max depth = 10, train loss: 0.56130, val loss: 0.55678, in 0.000s
1 tree, 29 leaves, max depth = 10, train loss: 0.55050, val loss: 0.54550, in 0.016s
1 tree, 54 leaves, max depth = 10, train loss: 0.53979, val loss: 0.53552, in 0.000s
1 tree, 31 leaves, max depth = 12, train loss: 0.53024, val loss: 0.52551, in 0.016s
1 tree, 54 leaves, max depth = 10, train loss: 0.52071, val loss: 0.51669, in 0.000s
1 tree, 31 leaves, max depth = 11, train loss: 0.51237, val loss: 0.50788, in 0.016s
1 tree, 54 leaves, max depth = 10, train loss: 0.50383, val loss: 0.50000, in 0.000s
1 tree, 30 leaves, max depth = 10, train loss: 0.49628, val loss: 0.49203, in 0.016s
1 tree, 55 leaves, max depth = 10, train loss: 0.48860, val loss: 0.48495, in 0.000s
1 tree, 30 leaves, max depth = 10, train loss: 0.48186, val loss: 0.47782, in 0.000s
1 tree, 55 leaves, max depth = 10, train loss: 0.47494, val loss: 0.47147, in 0.000s
1 tree, 31 leaves, max depth = 10, train loss: 0.46899, val loss: 0.46512, in 0.016s
1 tree, 54 leaves, max depth = 11, train loss: 0.46272, val loss: 0.45937, in 0.016s
1 tree, 54 leaves, max depth = 11, train loss: 0.45694, val loss: 0.45410, in 0.000s
1 tree, 30 leaves, max depth = 11, train loss: 0.45169, val loss: 0.44850, in 0.016s
1 tree, 54 leaves, max depth = 11, train loss: 0.44645, val loss: 0.44376, in 0.000s
1 tree, 30 leaves, max depth = 9, train loss: 0.44177, val loss: 0.43878, in 0.016s
1 tree, 53 leaves, max depth = 10, train loss: 0.43700, val loss: 0.43450, in 0.000s
1 tree, 54 leaves, max depth = 10, train loss: 0.43259, val loss: 0.43056, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.42840, val loss: 0.42602, in 0.000s
1 tree, 54 leaves, max depth = 10, train loss: 0.42437, val loss: 0.42242, in 0.000s
1 tree, 31 leaves, max depth = 8, train loss: 0.42047, val loss: 0.41825, in 0.016s
1 tree, 53 leaves, max depth = 10, train loss: 0.41679, val loss: 0.41498, in 0.016s
1 tree, 30 leaves, max depth = 13, train loss: 0.41336, val loss: 0.41133, in 0.000s
1 tree, 3 leaves, max depth = 2, train loss: 0.41016, val loss: 0.40783, in 0.000s
1 tree, 54 leaves, max depth = 10, train loss: 0.40680, val loss: 0.40490, in 0.016s
1 tree, 3 leaves, max depth = 2, train loss: 0.40388, val loss: 0.40169, in 0.016s
1 tree, 54 leaves, max depth = 10, train loss: 0.40078, val loss: 0.39903, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.39774, val loss: 0.39575, in 0.000s
1 tree, 54 leaves, max depth = 10, train loss: 0.39490, val loss: 0.39333, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.39211, val loss: 0.39033, in 0.000s
1 tree, 3 leaves, max depth = 2, train loss: 0.38947, val loss: 0.38743, in 0.016s
1 tree, 3 leaves, max depth = 2, train loss: 0.38703, val loss: 0.38474, in 0.000s
1 tree, 3 leaves, max depth = 2, train loss: 0.38479, val loss: 0.38226, in 0.016s
1 tree, 53 leaves, max depth = 10, train loss: 0.38215, val loss: 0.38010, in 0.000s
1 tree, 31 leaves, max depth = 10, train loss: 0.37967, val loss: 0.37745, in 0.016s
1 tree, 3 leaves, max depth = 2, train loss: 0.37759, val loss: 0.37516, in 0.000s
1 tree, 55 leaves, max depth = 12, train loss: 0.37514, val loss: 0.37319, in 0.016s
1 tree, 2 leaves, max depth = 1, train loss: 0.37330, val loss: 0.37104, in 0.016s
1 tree, 30 leaves, max depth = 9, train loss: 0.37105, val loss: 0.36866, in 0.006s
1 tree, 55 leaves, max depth = 12, train loss: 0.36877, val loss: 0.36686, in 0.010s
1 tree, 3 leaves, max depth = 2, train loss: 0.36705, val loss: 0.36482, in 0.000s
1 tree, 30 leaves, max depth = 10, train loss: 0.36499, val loss: 0.36265, in 0.016s
1 tree, 55 leaves, max depth = 10, train loss: 0.36286, val loss: 0.36099, in 0.016s
1 tree, 3 leaves, max depth = 2, train loss: 0.36126, val loss: 0.35909, in 0.016s
1 tree, 30 leaves, max depth = 10, train loss: 0.35937, val loss: 0.35710, in 0.000s
1 tree, 57 leaves, max depth = 10, train loss: 0.35738, val loss: 0.35557, in 0.016s
1 tree, 30 leaves, max depth = 9, train loss: 0.35563, val loss: 0.35374, in 0.016s
1 tree, 2 leaves, max depth = 1, train loss: 0.35413, val loss: 0.35197, in 0.000s
1 tree, 30 leaves, max depth = 8, train loss: 0.35245, val loss: 0.35019, in 0.016s
1 tree, 3 leaves, max depth = 2, train loss: 0.35106, val loss: 0.34851, in 0.000s
1 tree, 55 leaves, max depth = 12, train loss: 0.34916, val loss: 0.34710, in 0.016s
1 tree, 3 leaves, max depth = 2, train loss: 0.34785, val loss: 0.34553, in 0.000s
1 tree, 38 leaves, max depth = 10, train loss: 0.34631, val loss: 0.34375, in 0.016s
1 tree, 55 leaves, max depth = 10, train loss: 0.34452, val loss: 0.34243, in 0.000s
1 tree, 31 leaves, max depth = 10, train loss: 0.34310, val loss: 0.34096, in 0.016s
1 tree, 2 leaves, max depth = 1, train loss: 0.34187, val loss: 0.33947, in 0.000s
1 tree, 39 leaves, max depth = 12, train loss: 0.34049, val loss: 0.33789, in 0.016s
1 tree, 3 leaves, max depth = 2, train loss: 0.33933, val loss: 0.33649, in 0.000s
1 tree, 55 leaves, max depth = 10, train loss: 0.33764, val loss: 0.33526, in 0.016s
1 tree, 30 leaves, max depth = 10, train loss: 0.33638, val loss: 0.33397, in 0.000s
1 tree, 53 leaves, max depth = 13, train loss: 0.33479, val loss: 0.33282, in 0.016s
1 tree, 3 leaves, max depth = 2, train loss: 0.33368, val loss: 0.33146, in 0.016s
1 tree, 39 leaves, max depth = 12, train loss: 0.33243, val loss: 0.33002, in 0.000s
Fit 71 trees in 0.877 s, (2340 total leaves)
Time spent computing histograms: 0.312s
Time spent finding best splits:  0.051s
Time spent applying splits:      0.047s
Time spent predicting:           0.016s
Trial 57, Fold 1: Log loss = 0.3367816199749105, Average precision = 0.9455012029625787, ROC-AUC = 0.9441351154031821, Elapsed Time = 0.8882864000006521 seconds
Trial 57, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 57, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
Binning 0.040 GB of training data: 0.157 s
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 30 leaves, max depth = 10, train loss: 0.67105, val loss: 0.67000, in 0.000s
1 tree, 30 leaves, max depth = 10, train loss: 0.65101, val loss: 0.64897, in 0.016s
1 tree, 30 leaves, max depth = 10, train loss: 0.63280, val loss: 0.62983, in 0.000s
1 tree, 31 leaves, max depth = 11, train loss: 0.61622, val loss: 0.61235, in 0.016s
1 tree, 32 leaves, max depth = 12, train loss: 0.60107, val loss: 0.59644, in 0.000s
1 tree, 32 leaves, max depth = 12, train loss: 0.58723, val loss: 0.58189, in 0.016s
1 tree, 30 leaves, max depth = 9, train loss: 0.57455, val loss: 0.56845, in 0.000s
1 tree, 32 leaves, max depth = 12, train loss: 0.56293, val loss: 0.55620, in 0.016s
1 tree, 56 leaves, max depth = 11, train loss: 0.55165, val loss: 0.54529, in 0.000s
1 tree, 32 leaves, max depth = 11, train loss: 0.54140, val loss: 0.53445, in 0.016s
1 tree, 55 leaves, max depth = 13, train loss: 0.53136, val loss: 0.52478, in 0.000s
1 tree, 28 leaves, max depth = 9, train loss: 0.52227, val loss: 0.51507, in 0.016s
1 tree, 55 leaves, max depth = 13, train loss: 0.51330, val loss: 0.50645, in 0.000s
1 tree, 32 leaves, max depth = 11, train loss: 0.50521, val loss: 0.49786, in 0.016s
1 tree, 29 leaves, max depth = 15, train loss: 0.49779, val loss: 0.48992, in 0.016s
1 tree, 55 leaves, max depth = 12, train loss: 0.48991, val loss: 0.48237, in 0.000s
1 tree, 29 leaves, max depth = 8, train loss: 0.48328, val loss: 0.47523, in 0.016s
1 tree, 54 leaves, max depth = 12, train loss: 0.47617, val loss: 0.46846, in 0.000s
1 tree, 32 leaves, max depth = 11, train loss: 0.47022, val loss: 0.46210, in 0.000s
1 tree, 54 leaves, max depth = 14, train loss: 0.46377, val loss: 0.45598, in 0.016s
1 tree, 55 leaves, max depth = 15, train loss: 0.45782, val loss: 0.45036, in 0.000s
1 tree, 54 leaves, max depth = 13, train loss: 0.45232, val loss: 0.44516, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.44724, val loss: 0.43969, in 0.000s
1 tree, 31 leaves, max depth = 15, train loss: 0.44259, val loss: 0.43465, in 0.016s
1 tree, 55 leaves, max depth = 10, train loss: 0.43764, val loss: 0.43001, in 0.000s
1 tree, 55 leaves, max depth = 10, train loss: 0.43307, val loss: 0.42574, in 0.016s
1 tree, 32 leaves, max depth = 11, train loss: 0.42891, val loss: 0.42125, in 0.016s
1 tree, 56 leaves, max depth = 13, train loss: 0.42471, val loss: 0.41733, in 0.000s
1 tree, 55 leaves, max depth = 13, train loss: 0.42084, val loss: 0.41372, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.41712, val loss: 0.40970, in 0.000s
1 tree, 32 leaves, max depth = 13, train loss: 0.41373, val loss: 0.40598, in 0.016s
1 tree, 56 leaves, max depth = 13, train loss: 0.41020, val loss: 0.40271, in 0.016s
1 tree, 3 leaves, max depth = 2, train loss: 0.40709, val loss: 0.39949, in 0.000s
1 tree, 56 leaves, max depth = 11, train loss: 0.40382, val loss: 0.39649, in 0.016s
1 tree, 3 leaves, max depth = 2, train loss: 0.40097, val loss: 0.39354, in 0.000s
1 tree, 56 leaves, max depth = 12, train loss: 0.39796, val loss: 0.39078, in 0.016s
1 tree, 31 leaves, max depth = 13, train loss: 0.39505, val loss: 0.38758, in 0.000s
1 tree, 56 leaves, max depth = 11, train loss: 0.39227, val loss: 0.38506, in 0.016s
1 tree, 3 leaves, max depth = 2, train loss: 0.38967, val loss: 0.38237, in 0.000s
1 tree, 3 leaves, max depth = 2, train loss: 0.38728, val loss: 0.37988, in 0.016s
1 tree, 30 leaves, max depth = 10, train loss: 0.38459, val loss: 0.37694, in 0.000s
1 tree, 55 leaves, max depth = 13, train loss: 0.38202, val loss: 0.37464, in 0.016s
1 tree, 30 leaves, max depth = 9, train loss: 0.37956, val loss: 0.37194, in 0.000s
1 tree, 2 leaves, max depth = 1, train loss: 0.37746, val loss: 0.36977, in 0.016s
1 tree, 55 leaves, max depth = 11, train loss: 0.37507, val loss: 0.36763, in 0.000s
1 tree, 2 leaves, max depth = 1, train loss: 0.37312, val loss: 0.36561, in 0.016s
1 tree, 55 leaves, max depth = 11, train loss: 0.37089, val loss: 0.36364, in 0.000s
1 tree, 32 leaves, max depth = 10, train loss: 0.36859, val loss: 0.36106, in 0.016s
1 tree, 2 leaves, max depth = 1, train loss: 0.36678, val loss: 0.35919, in 0.000s
1 tree, 2 leaves, max depth = 1, train loss: 0.36511, val loss: 0.35745, in 0.016s
1 tree, 30 leaves, max depth = 9, train loss: 0.36306, val loss: 0.35522, in 0.000s
1 tree, 56 leaves, max depth = 13, train loss: 0.36095, val loss: 0.35340, in 0.016s
1 tree, 2 leaves, max depth = 1, train loss: 0.35939, val loss: 0.35177, in 0.000s
1 tree, 33 leaves, max depth = 10, train loss: 0.35745, val loss: 0.34961, in 0.016s
1 tree, 54 leaves, max depth = 12, train loss: 0.35547, val loss: 0.34789, in 0.000s
1 tree, 3 leaves, max depth = 2, train loss: 0.35401, val loss: 0.34637, in 0.016s
1 tree, 30 leaves, max depth = 9, train loss: 0.35227, val loss: 0.34447, in 0.000s
1 tree, 55 leaves, max depth = 12, train loss: 0.35041, val loss: 0.34286, in 0.016s
1 tree, 3 leaves, max depth = 2, train loss: 0.34903, val loss: 0.34142, in 0.000s
1 tree, 29 leaves, max depth = 9, train loss: 0.34738, val loss: 0.33965, in 0.016s
1 tree, 40 leaves, max depth = 10, train loss: 0.34580, val loss: 0.33788, in 0.016s
1 tree, 3 leaves, max depth = 2, train loss: 0.34452, val loss: 0.33654, in 0.000s
1 tree, 55 leaves, max depth = 12, train loss: 0.34274, val loss: 0.33503, in 0.016s
1 tree, 40 leaves, max depth = 10, train loss: 0.34127, val loss: 0.33338, in 0.000s
1 tree, 2 leaves, max depth = 1, train loss: 0.34006, val loss: 0.33211, in 0.016s
1 tree, 32 leaves, max depth = 10, train loss: 0.33866, val loss: 0.33056, in 0.000s
1 tree, 57 leaves, max depth = 13, train loss: 0.33698, val loss: 0.32912, in 0.016s
1 tree, 3 leaves, max depth = 2, train loss: 0.33583, val loss: 0.32791, in 0.000s
1 tree, 39 leaves, max depth = 10, train loss: 0.33451, val loss: 0.32643, in 0.016s
1 tree, 56 leaves, max depth = 12, train loss: 0.33292, val loss: 0.32510, in 0.000s
1 tree, 3 leaves, max depth = 2, train loss: 0.33183, val loss: 0.32395, in 0.000s
Fit 71 trees in 0.907 s, (2402 total leaves)
Time spent computing histograms: 0.308s
Time spent finding best splits:  0.048s
Time spent applying splits:      0.046s
Time spent predicting:           0.016s
Trial 57, Fold 2: Log loss = 0.3348763084371926, Average precision = 0.9444874552828766, ROC-AUC = 0.9469416144325498, Elapsed Time = 0.906026099999508 seconds
Trial 57, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 57, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
Binning 0.040 GB of training data: 0.158 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 31 leaves, max depth = 10, train loss: 0.67125, val loss: 0.67053, in 0.016s
1 tree, 30 leaves, max depth = 8, train loss: 0.65119, val loss: 0.64980, in 0.000s
1 tree, 31 leaves, max depth = 10, train loss: 0.63312, val loss: 0.63109, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.61667, val loss: 0.61402, in 0.000s
1 tree, 30 leaves, max depth = 9, train loss: 0.60145, val loss: 0.59824, in 0.016s
1 tree, 30 leaves, max depth = 8, train loss: 0.58755, val loss: 0.58379, in 0.000s
1 tree, 30 leaves, max depth = 10, train loss: 0.57497, val loss: 0.57068, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.56345, val loss: 0.55866, in 0.000s
1 tree, 55 leaves, max depth = 12, train loss: 0.55209, val loss: 0.54807, in 0.016s
1 tree, 30 leaves, max depth = 9, train loss: 0.54177, val loss: 0.53728, in 0.000s
1 tree, 54 leaves, max depth = 11, train loss: 0.53168, val loss: 0.52789, in 0.016s
1 tree, 30 leaves, max depth = 10, train loss: 0.52266, val loss: 0.51841, in 0.016s
1 tree, 54 leaves, max depth = 11, train loss: 0.51365, val loss: 0.51005, in 0.000s
1 tree, 31 leaves, max depth = 10, train loss: 0.50564, val loss: 0.50159, in 0.016s
1 tree, 30 leaves, max depth = 8, train loss: 0.49810, val loss: 0.49364, in 0.000s
1 tree, 56 leaves, max depth = 13, train loss: 0.49018, val loss: 0.48632, in 0.016s
1 tree, 30 leaves, max depth = 8, train loss: 0.48346, val loss: 0.47920, in 0.000s
1 tree, 56 leaves, max depth = 12, train loss: 0.47632, val loss: 0.47261, in 0.016s
1 tree, 30 leaves, max depth = 10, train loss: 0.47045, val loss: 0.46636, in 0.016s
1 tree, 55 leaves, max depth = 12, train loss: 0.46399, val loss: 0.46041, in 0.000s
1 tree, 56 leaves, max depth = 12, train loss: 0.45802, val loss: 0.45494, in 0.016s
1 tree, 56 leaves, max depth = 12, train loss: 0.45251, val loss: 0.44990, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.44749, val loss: 0.44452, in 0.000s
1 tree, 32 leaves, max depth = 12, train loss: 0.44290, val loss: 0.43955, in 0.016s
1 tree, 54 leaves, max depth = 10, train loss: 0.43794, val loss: 0.43503, in 0.016s
1 tree, 53 leaves, max depth = 10, train loss: 0.43337, val loss: 0.43088, in 0.000s
1 tree, 54 leaves, max depth = 11, train loss: 0.42913, val loss: 0.42705, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.42511, val loss: 0.42268, in 0.000s
1 tree, 54 leaves, max depth = 13, train loss: 0.42122, val loss: 0.41918, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.41757, val loss: 0.41520, in 0.000s
1 tree, 30 leaves, max depth = 10, train loss: 0.41417, val loss: 0.41149, in 0.016s
1 tree, 54 leaves, max depth = 13, train loss: 0.41063, val loss: 0.40831, in 0.016s
1 tree, 3 leaves, max depth = 2, train loss: 0.40743, val loss: 0.40536, in 0.000s
1 tree, 52 leaves, max depth = 13, train loss: 0.40418, val loss: 0.40245, in 0.016s
1 tree, 3 leaves, max depth = 2, train loss: 0.40124, val loss: 0.39974, in 0.000s
1 tree, 3 leaves, max depth = 2, train loss: 0.39854, val loss: 0.39724, in 0.016s
1 tree, 54 leaves, max depth = 12, train loss: 0.39555, val loss: 0.39460, in 0.000s
1 tree, 30 leaves, max depth = 10, train loss: 0.39265, val loss: 0.39142, in 0.016s
1 tree, 3 leaves, max depth = 2, train loss: 0.39017, val loss: 0.38913, in 0.000s
1 tree, 53 leaves, max depth = 13, train loss: 0.38741, val loss: 0.38672, in 0.016s
1 tree, 3 leaves, max depth = 2, train loss: 0.38512, val loss: 0.38461, in 0.000s
1 tree, 53 leaves, max depth = 13, train loss: 0.38256, val loss: 0.38239, in 0.016s
1 tree, 30 leaves, max depth = 10, train loss: 0.37997, val loss: 0.37954, in 0.000s
1 tree, 3 leaves, max depth = 2, train loss: 0.37787, val loss: 0.37759, in 0.016s
1 tree, 29 leaves, max depth = 9, train loss: 0.37542, val loss: 0.37492, in 0.000s
1 tree, 54 leaves, max depth = 13, train loss: 0.37304, val loss: 0.37286, in 0.016s
1 tree, 53 leaves, max depth = 12, train loss: 0.37084, val loss: 0.37099, in 0.016s
1 tree, 29 leaves, max depth = 9, train loss: 0.36860, val loss: 0.36852, in 0.000s
1 tree, 3 leaves, max depth = 2, train loss: 0.36673, val loss: 0.36678, in 0.016s
1 tree, 3 leaves, max depth = 2, train loss: 0.36500, val loss: 0.36517, in 0.000s
1 tree, 30 leaves, max depth = 9, train loss: 0.36301, val loss: 0.36298, in 0.016s
1 tree, 3 leaves, max depth = 2, train loss: 0.36141, val loss: 0.36149, in 0.000s
1 tree, 53 leaves, max depth = 15, train loss: 0.35931, val loss: 0.35972, in 0.016s
1 tree, 30 leaves, max depth = 9, train loss: 0.35748, val loss: 0.35771, in 0.000s
1 tree, 54 leaves, max depth = 12, train loss: 0.35554, val loss: 0.35609, in 0.016s
1 tree, 2 leaves, max depth = 1, train loss: 0.35403, val loss: 0.35469, in 0.000s
1 tree, 29 leaves, max depth = 9, train loss: 0.35228, val loss: 0.35278, in 0.016s
1 tree, 3 leaves, max depth = 2, train loss: 0.35088, val loss: 0.35147, in 0.000s
1 tree, 29 leaves, max depth = 8, train loss: 0.34926, val loss: 0.34966, in 0.016s
[60/71] 1 tree, 3 leaves, max depth = 2, train loss: 0.34795, val loss: 0.34844, in 0.000s
1 tree, 39 leaves, max depth = 11, train loss: 0.34644, val loss: 0.34664, in 0.000s
1 tree, 54 leaves, max depth = 13, train loss: 0.34455, val loss: 0.34509, in 0.000s
1 tree, 54 leaves, max depth = 15, train loss: 0.34281, val loss: 0.34366, in 0.016s
1 tree, 2 leaves, max depth = 1, train loss: 0.34157, val loss: 0.34249, in 0.000s
1 tree, 39 leaves, max depth = 12, train loss: 0.34014, val loss: 0.34078, in 0.016s
1 tree, 3 leaves, max depth = 2, train loss: 0.33897, val loss: 0.33969, in 0.000s
1 tree, 30 leaves, max depth = 8, train loss: 0.33760, val loss: 0.33817, in 0.016s
1 tree, 55 leaves, max depth = 13, train loss: 0.33594, val loss: 0.33682, in 0.000s
1 tree, 3 leaves, max depth = 2, train loss: 0.33483, val loss: 0.33578, in 0.016s
1 tree, 40 leaves, max depth = 12, train loss: 0.33355, val loss: 0.33425, in 0.000s
1 tree, 55 leaves, max depth = 13, train loss: 0.33198, val loss: 0.33299, in 0.016s
Fit 71 trees in 0.908 s, (2362 total leaves)
Time spent computing histograms: 0.316s
Time spent finding best splits:  0.049s
Time spent applying splits:      0.046s
Time spent predicting:           0.000s
Trial 57, Fold 3: Log loss = 0.3302480489271217, Average precision = 0.9506856079438344, ROC-AUC = 0.950528232175362, Elapsed Time = 0.9109050000006391 seconds
Trial 57, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 57, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
Binning 0.040 GB of training data: 0.158 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 30 leaves, max depth = 11, train loss: 0.67097, val loss: 0.66966, in 0.016s
1 tree, 30 leaves, max depth = 11, train loss: 0.65086, val loss: 0.64830, in 0.000s
1 tree, 30 leaves, max depth = 11, train loss: 0.63258, val loss: 0.62882, in 0.016s
1 tree, 31 leaves, max depth = 12, train loss: 0.61611, val loss: 0.61125, in 0.000s
1 tree, 30 leaves, max depth = 11, train loss: 0.60089, val loss: 0.59495, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.58698, val loss: 0.57999, in 0.000s
1 tree, 31 leaves, max depth = 11, train loss: 0.57425, val loss: 0.56625, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.56259, val loss: 0.55362, in 0.000s
1 tree, 55 leaves, max depth = 10, train loss: 0.55122, val loss: 0.54241, in 0.016s
1 tree, 28 leaves, max depth = 10, train loss: 0.54094, val loss: 0.53123, in 0.000s
1 tree, 54 leaves, max depth = 12, train loss: 0.53084, val loss: 0.52128, in 0.016s
1 tree, 29 leaves, max depth = 10, train loss: 0.52175, val loss: 0.51134, in 0.000s
1 tree, 55 leaves, max depth = 11, train loss: 0.51272, val loss: 0.50248, in 0.016s
1 tree, 30 leaves, max depth = 10, train loss: 0.50465, val loss: 0.49360, in 0.000s
1 tree, 30 leaves, max depth = 10, train loss: 0.49723, val loss: 0.48540, in 0.016s
1 tree, 55 leaves, max depth = 12, train loss: 0.48931, val loss: 0.47763, in 0.000s
1 tree, 29 leaves, max depth = 12, train loss: 0.48274, val loss: 0.47035, in 0.016s
1 tree, 56 leaves, max depth = 13, train loss: 0.47558, val loss: 0.46336, in 0.000s
1 tree, 29 leaves, max depth = 12, train loss: 0.46971, val loss: 0.45683, in 0.016s
1 tree, 56 leaves, max depth = 13, train loss: 0.46321, val loss: 0.45050, in 0.000s
1 tree, 55 leaves, max depth = 14, train loss: 0.45721, val loss: 0.44468, in 0.016s
1 tree, 55 leaves, max depth = 13, train loss: 0.45166, val loss: 0.43927, in 0.016s
1 tree, 32 leaves, max depth = 12, train loss: 0.44662, val loss: 0.43364, in 0.000s
1 tree, 30 leaves, max depth = 14, train loss: 0.44200, val loss: 0.42845, in 0.016s
1 tree, 56 leaves, max depth = 11, train loss: 0.43701, val loss: 0.42362, in 0.000s
1 tree, 56 leaves, max depth = 11, train loss: 0.43240, val loss: 0.41917, in 0.016s
1 tree, 56 leaves, max depth = 9, train loss: 0.42812, val loss: 0.41505, in 0.016s
1 tree, 32 leaves, max depth = 11, train loss: 0.42410, val loss: 0.41050, in 0.000s
1 tree, 54 leaves, max depth = 12, train loss: 0.42017, val loss: 0.40670, in 0.016s
1 tree, 30 leaves, max depth = 11, train loss: 0.41652, val loss: 0.40254, in 0.000s
1 tree, 29 leaves, max depth = 11, train loss: 0.41317, val loss: 0.39871, in 0.016s
1 tree, 55 leaves, max depth = 15, train loss: 0.40958, val loss: 0.39526, in 0.016s
1 tree, 3 leaves, max depth = 2, train loss: 0.40649, val loss: 0.39198, in 0.000s
1 tree, 56 leaves, max depth = 11, train loss: 0.40318, val loss: 0.38882, in 0.000s
1 tree, 3 leaves, max depth = 2, train loss: 0.40035, val loss: 0.38581, in 0.016s
1 tree, 55 leaves, max depth = 11, train loss: 0.39729, val loss: 0.38290, in 0.016s
1 tree, 32 leaves, max depth = 11, train loss: 0.39439, val loss: 0.37957, in 0.000s
1 tree, 56 leaves, max depth = 11, train loss: 0.39156, val loss: 0.37686, in 0.016s
1 tree, 3 leaves, max depth = 2, train loss: 0.38898, val loss: 0.37412, in 0.000s
1 tree, 2 leaves, max depth = 1, train loss: 0.38669, val loss: 0.37169, in 0.000s
1 tree, 2 leaves, max depth = 1, train loss: 0.38458, val loss: 0.36944, in 0.016s
1 tree, 54 leaves, max depth = 12, train loss: 0.38194, val loss: 0.36693, in 0.000s
1 tree, 31 leaves, max depth = 11, train loss: 0.37938, val loss: 0.36400, in 0.016s
1 tree, 2 leaves, max depth = 1, train loss: 0.37742, val loss: 0.36193, in 0.000s
1 tree, 54 leaves, max depth = 12, train loss: 0.37496, val loss: 0.35961, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.37262, val loss: 0.35694, in 0.016s
1 tree, 54 leaves, max depth = 12, train loss: 0.37034, val loss: 0.35480, in 0.000s
1 tree, 30 leaves, max depth = 10, train loss: 0.36820, val loss: 0.35234, in 0.016s
1 tree, 2 leaves, max depth = 1, train loss: 0.36638, val loss: 0.35040, in 0.000s
1 tree, 2 leaves, max depth = 1, train loss: 0.36470, val loss: 0.34861, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.36274, val loss: 0.34636, in 0.000s
1 tree, 54 leaves, max depth = 12, train loss: 0.36058, val loss: 0.34435, in 0.016s
1 tree, 2 leaves, max depth = 1, train loss: 0.35900, val loss: 0.34268, in 0.000s
1 tree, 30 leaves, max depth = 10, train loss: 0.35721, val loss: 0.34062, in 0.016s
1 tree, 56 leaves, max depth = 14, train loss: 0.35517, val loss: 0.33874, in 0.000s
1 tree, 2 leaves, max depth = 1, train loss: 0.35370, val loss: 0.33717, in 0.016s
1 tree, 30 leaves, max depth = 10, train loss: 0.35205, val loss: 0.33527, in 0.000s
1 tree, 56 leaves, max depth = 14, train loss: 0.35013, val loss: 0.33352, in 0.016s
1 tree, 2 leaves, max depth = 1, train loss: 0.34875, val loss: 0.33204, in 0.000s
1 tree, 41 leaves, max depth = 13, train loss: 0.34717, val loss: 0.33037, in 0.016s
1 tree, 2 leaves, max depth = 1, train loss: 0.34588, val loss: 0.32900, in 0.000s
1 tree, 30 leaves, max depth = 10, train loss: 0.34436, val loss: 0.32731, in 0.016s
1 tree, 56 leaves, max depth = 14, train loss: 0.34252, val loss: 0.32564, in 0.000s
1 tree, 39 leaves, max depth = 12, train loss: 0.34108, val loss: 0.32414, in 0.016s
1 tree, 2 leaves, max depth = 1, train loss: 0.33986, val loss: 0.32284, in 0.000s
1 tree, 39 leaves, max depth = 12, train loss: 0.33853, val loss: 0.32147, in 0.016s
1 tree, 57 leaves, max depth = 13, train loss: 0.33677, val loss: 0.31988, in 0.000s
1 tree, 3 leaves, max depth = 2, train loss: 0.33561, val loss: 0.31862, in 0.016s
1 tree, 55 leaves, max depth = 11, train loss: 0.33397, val loss: 0.31716, in 0.000s
1 tree, 39 leaves, max depth = 14, train loss: 0.33270, val loss: 0.31588, in 0.016s
1 tree, 3 leaves, max depth = 2, train loss: 0.33160, val loss: 0.31468, in 0.016s
Fit 71 trees in 0.892 s, (2391 total leaves)
Time spent computing histograms: 0.307s
Time spent finding best splits:  0.048s
Time spent applying splits:      0.045s
Time spent predicting:           0.031s
Trial 57, Fold 4: Log loss = 0.33093443406721534, Average precision = 0.9528355563995237, ROC-AUC = 0.9508373451027798, Elapsed Time = 0.8867230000014388 seconds
Trial 57, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 57, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
Binning 0.040 GB of training data: 0.189 s
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 29 leaves, max depth = 8, train loss: 0.67074, val loss: 0.66927, in 0.000s
1 tree, 29 leaves, max depth = 8, train loss: 0.65042, val loss: 0.64756, in 0.016s
1 tree, 29 leaves, max depth = 8, train loss: 0.63195, val loss: 0.62776, in 0.016s
1 tree, 30 leaves, max depth = 12, train loss: 0.61530, val loss: 0.60976, in 0.000s
1 tree, 30 leaves, max depth = 8, train loss: 0.59992, val loss: 0.59317, in 0.016s
1 tree, 30 leaves, max depth = 9, train loss: 0.58586, val loss: 0.57796, in 0.000s
1 tree, 30 leaves, max depth = 9, train loss: 0.57299, val loss: 0.56398, in 0.016s
1 tree, 30 leaves, max depth = 11, train loss: 0.56133, val loss: 0.55119, in 0.000s
1 tree, 31 leaves, max depth = 9, train loss: 0.55050, val loss: 0.53934, in 0.016s
1 tree, 51 leaves, max depth = 14, train loss: 0.53975, val loss: 0.52903, in 0.000s
1 tree, 32 leaves, max depth = 12, train loss: 0.53028, val loss: 0.51863, in 0.016s
1 tree, 52 leaves, max depth = 13, train loss: 0.52069, val loss: 0.50948, in 0.016s
1 tree, 52 leaves, max depth = 13, train loss: 0.51189, val loss: 0.50110, in 0.000s
1 tree, 31 leaves, max depth = 11, train loss: 0.50376, val loss: 0.49211, in 0.016s
1 tree, 30 leaves, max depth = 11, train loss: 0.49627, val loss: 0.48372, in 0.000s
1 tree, 54 leaves, max depth = 12, train loss: 0.48853, val loss: 0.47639, in 0.016s
1 tree, 30 leaves, max depth = 11, train loss: 0.48185, val loss: 0.46893, in 0.016s
1 tree, 53 leaves, max depth = 14, train loss: 0.47487, val loss: 0.46235, in 0.000s
1 tree, 31 leaves, max depth = 10, train loss: 0.46889, val loss: 0.45564, in 0.016s
1 tree, 52 leaves, max depth = 13, train loss: 0.46256, val loss: 0.44971, in 0.000s
1 tree, 54 leaves, max depth = 13, train loss: 0.45672, val loss: 0.44424, in 0.016s
1 tree, 53 leaves, max depth = 13, train loss: 0.45132, val loss: 0.43922, in 0.016s
1 tree, 30 leaves, max depth = 10, train loss: 0.44619, val loss: 0.43344, in 0.000s
1 tree, 30 leaves, max depth = 13, train loss: 0.44148, val loss: 0.42809, in 0.016s
1 tree, 53 leaves, max depth = 10, train loss: 0.43662, val loss: 0.42362, in 0.000s
1 tree, 53 leaves, max depth = 10, train loss: 0.43214, val loss: 0.41948, in 0.016s
1 tree, 30 leaves, max depth = 9, train loss: 0.42794, val loss: 0.41469, in 0.000s
1 tree, 53 leaves, max depth = 13, train loss: 0.42383, val loss: 0.41096, in 0.000s
1 tree, 31 leaves, max depth = 9, train loss: 0.42002, val loss: 0.40659, in 0.016s
1 tree, 54 leaves, max depth = 12, train loss: 0.41625, val loss: 0.40317, in 0.000s
1 tree, 31 leaves, max depth = 13, train loss: 0.41281, val loss: 0.39919, in 0.016s
1 tree, 54 leaves, max depth = 12, train loss: 0.40934, val loss: 0.39608, in 0.016s
1 tree, 3 leaves, max depth = 2, train loss: 0.40623, val loss: 0.39308, in 0.000s
1 tree, 53 leaves, max depth = 12, train loss: 0.40304, val loss: 0.39024, in 0.016s
1 tree, 3 leaves, max depth = 2, train loss: 0.40019, val loss: 0.38748, in 0.000s
1 tree, 31 leaves, max depth = 10, train loss: 0.39715, val loss: 0.38394, in 0.016s
1 tree, 54 leaves, max depth = 12, train loss: 0.39421, val loss: 0.38134, in 0.000s
1 tree, 30 leaves, max depth = 9, train loss: 0.39144, val loss: 0.37811, in 0.016s
1 tree, 3 leaves, max depth = 2, train loss: 0.38887, val loss: 0.37563, in 0.000s
1 tree, 54 leaves, max depth = 12, train loss: 0.38615, val loss: 0.37327, in 0.016s
1 tree, 2 leaves, max depth = 1, train loss: 0.38388, val loss: 0.37101, in 0.000s
1 tree, 53 leaves, max depth = 13, train loss: 0.38136, val loss: 0.36883, in 0.016s
1 tree, 30 leaves, max depth = 11, train loss: 0.37889, val loss: 0.36590, in 0.000s
1 tree, 2 leaves, max depth = 1, train loss: 0.37680, val loss: 0.36382, in 0.016s
1 tree, 2 leaves, max depth = 1, train loss: 0.37487, val loss: 0.36191, in 0.000s
1 tree, 52 leaves, max depth = 12, train loss: 0.37251, val loss: 0.35990, in 0.016s
1 tree, 30 leaves, max depth = 10, train loss: 0.37027, val loss: 0.35724, in 0.000s
1 tree, 52 leaves, max depth = 12, train loss: 0.36808, val loss: 0.35539, in 0.016s
1 tree, 2 leaves, max depth = 1, train loss: 0.36628, val loss: 0.35360, in 0.000s
1 tree, 30 leaves, max depth = 10, train loss: 0.36422, val loss: 0.35116, in 0.016s
1 tree, 52 leaves, max depth = 12, train loss: 0.36218, val loss: 0.34945, in 0.000s
1 tree, 2 leaves, max depth = 1, train loss: 0.36050, val loss: 0.34778, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.35855, val loss: 0.34547, in 0.000s
1 tree, 54 leaves, max depth = 13, train loss: 0.35663, val loss: 0.34388, in 0.016s
1 tree, 30 leaves, max depth = 11, train loss: 0.35489, val loss: 0.34179, in 0.000s
1 tree, 2 leaves, max depth = 1, train loss: 0.35333, val loss: 0.34023, in 0.016s
1 tree, 38 leaves, max depth = 11, train loss: 0.35164, val loss: 0.33839, in 0.000s
1 tree, 2 leaves, max depth = 1, train loss: 0.35019, val loss: 0.33694, in 0.016s
1 tree, 54 leaves, max depth = 13, train loss: 0.34834, val loss: 0.33545, in 0.000s
1 tree, 2 leaves, max depth = 1, train loss: 0.34698, val loss: 0.33410, in 0.016s
1 tree, 30 leaves, max depth = 10, train loss: 0.34541, val loss: 0.33222, in 0.000s
1 tree, 2 leaves, max depth = 1, train loss: 0.34415, val loss: 0.33097, in 0.016s
1 tree, 38 leaves, max depth = 11, train loss: 0.34264, val loss: 0.32934, in 0.000s
1 tree, 52 leaves, max depth = 13, train loss: 0.34087, val loss: 0.32789, in 0.016s
1 tree, 2 leaves, max depth = 1, train loss: 0.33967, val loss: 0.32671, in 0.000s
1 tree, 39 leaves, max depth = 11, train loss: 0.33826, val loss: 0.32518, in 0.016s
1 tree, 54 leaves, max depth = 13, train loss: 0.33659, val loss: 0.32385, in 0.000s
1 tree, 30 leaves, max depth = 12, train loss: 0.33530, val loss: 0.32228, in 0.016s
1 tree, 2 leaves, max depth = 1, train loss: 0.33416, val loss: 0.32115, in 0.000s
1 tree, 38 leaves, max depth = 11, train loss: 0.33291, val loss: 0.31979, in 0.016s
1 tree, 53 leaves, max depth = 13, train loss: 0.33131, val loss: 0.31853, in 0.000s
Fit 71 trees in 0.939 s, (2355 total leaves)
Time spent computing histograms: 0.314s
Time spent finding best splits:  0.050s
Time spent applying splits:      0.047s
Time spent predicting:           0.000s
Trial 57, Fold 5: Log loss = 0.3377299161193195, Average precision = 0.9483662589185281, ROC-AUC = 0.9455798450476561, Elapsed Time = 0.9485564999995404 seconds
Optimization Progress:  58%|#####8    | 58/100 [11:25<08:37, 12.31s/it]
Trial 58, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 58, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
0.127 s 0.040 GB of training data: 
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 23 leaves, max depth = 7, train loss: 0.67353, val loss: 0.67345, in 0.016s
1 tree, 32 leaves, max depth = 12, train loss: 0.65484, val loss: 0.65486, in 0.016s
1 tree, 34 leaves, max depth = 12, train loss: 0.63738, val loss: 0.63749, in 0.016s
1 tree, 31 leaves, max depth = 12, train loss: 0.62104, val loss: 0.62122, in 0.016s
1 tree, 35 leaves, max depth = 12, train loss: 0.60571, val loss: 0.60596, in 0.016s
1 tree, 32 leaves, max depth = 12, train loss: 0.59129, val loss: 0.59158, in 0.016s
1 tree, 34 leaves, max depth = 10, train loss: 0.57818, val loss: 0.57836, in 0.016s
1 tree, 30 leaves, max depth = 7, train loss: 0.56553, val loss: 0.56565, in 0.016s
1 tree, 34 leaves, max depth = 12, train loss: 0.55345, val loss: 0.55357, in 0.016s
1 tree, 29 leaves, max depth = 10, train loss: 0.54220, val loss: 0.54218, in 0.016s
1 tree, 32 leaves, max depth = 11, train loss: 0.53143, val loss: 0.53152, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.52150, val loss: 0.52151, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.51159, val loss: 0.51154, in 0.016s
1 tree, 34 leaves, max depth = 10, train loss: 0.50220, val loss: 0.50209, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.49375, val loss: 0.49360, in 0.016s
1 tree, 33 leaves, max depth = 8, train loss: 0.48540, val loss: 0.48507, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.47740, val loss: 0.47702, in 0.031s
1 tree, 35 leaves, max depth = 10, train loss: 0.46976, val loss: 0.46936, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.46288, val loss: 0.46245, in 0.016s
1 tree, 35 leaves, max depth = 8, train loss: 0.45611, val loss: 0.45554, in 0.016s
1 tree, 32 leaves, max depth = 10, train loss: 0.44952, val loss: 0.44895, in 0.016s
Fit 21 trees in 0.642 s, (690 total leaves)
Time spent computing histograms: 0.136s
Time spent finding best splits:  0.024s
Time spent applying splits:      0.015s
Time spent predicting:           0.000s
Trial 58, Fold 1: Log loss = 0.4518877100166728, Average precision = 0.9206472773505876, ROC-AUC = 0.9320100443067922, Elapsed Time = 0.6472635999998602 seconds
Trial 58, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 58, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
Binning 0.040 GB of training data: 0.158 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 23 leaves, max depth = 6, train loss: 0.67342, val loss: 0.67295, in 0.016s
1 tree, 24 leaves, max depth = 7, train loss: 0.65472, val loss: 0.65388, in 0.016s
1 tree, 23 leaves, max depth = 6, train loss: 0.63803, val loss: 0.63696, in 0.016s
1 tree, 25 leaves, max depth = 8, train loss: 0.62176, val loss: 0.62029, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.60623, val loss: 0.60447, in 0.016s
1 tree, 30 leaves, max depth = 11, train loss: 0.59173, val loss: 0.58972, in 0.016s
1 tree, 24 leaves, max depth = 9, train loss: 0.57894, val loss: 0.57683, in 0.016s
1 tree, 30 leaves, max depth = 9, train loss: 0.56588, val loss: 0.56355, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.55357, val loss: 0.55098, in 0.016s
1 tree, 32 leaves, max depth = 9, train loss: 0.54220, val loss: 0.53936, in 0.016s
1 tree, 34 leaves, max depth = 9, train loss: 0.53111, val loss: 0.52815, in 0.016s
1 tree, 34 leaves, max depth = 9, train loss: 0.52134, val loss: 0.51830, in 0.031s
1 tree, 31 leaves, max depth = 9, train loss: 0.51139, val loss: 0.50822, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.50197, val loss: 0.49874, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.49368, val loss: 0.49039, in 0.016s
1 tree, 32 leaves, max depth = 7, train loss: 0.48530, val loss: 0.48200, in 0.016s
1 tree, 33 leaves, max depth = 10, train loss: 0.47725, val loss: 0.47387, in 0.031s
1 tree, 30 leaves, max depth = 7, train loss: 0.46977, val loss: 0.46631, in 0.016s
1 tree, 32 leaves, max depth = 10, train loss: 0.46252, val loss: 0.45897, in 0.016s
1 tree, 35 leaves, max depth = 12, train loss: 0.45290, val loss: 0.44948, in 0.016s
1 tree, 29 leaves, max depth = 10, train loss: 0.44631, val loss: 0.44279, in 0.016s
Fit 21 trees in 0.704 s, (629 total leaves)
Time spent computing histograms: 0.159s
Time spent finding best splits:  0.027s
Time spent applying splits:      0.015s
Time spent predicting:           0.000s
Trial 58, Fold 2: Log loss = 0.448487289569763, Average precision = 0.9484778573320208, ROC-AUC = 0.9477508094985616, Elapsed Time = 0.7175649000000703 seconds
Trial 58, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 58, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
Binning 0.040 GB of training data: 0.158 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 22 leaves, max depth = 6, train loss: 0.67356, val loss: 0.67346, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.65498, val loss: 0.65482, in 0.016s
1 tree, 33 leaves, max depth = 11, train loss: 0.63752, val loss: 0.63738, in 0.016s
1 tree, 32 leaves, max depth = 12, train loss: 0.62118, val loss: 0.62102, in 0.016s
1 tree, 35 leaves, max depth = 12, train loss: 0.60585, val loss: 0.60572, in 0.016s
1 tree, 30 leaves, max depth = 12, train loss: 0.59203, val loss: 0.59200, in 0.016s
1 tree, 34 leaves, max depth = 11, train loss: 0.57827, val loss: 0.57828, in 0.016s
1 tree, 24 leaves, max depth = 7, train loss: 0.56611, val loss: 0.56620, in 0.016s
1 tree, 32 leaves, max depth = 11, train loss: 0.55449, val loss: 0.55463, in 0.016s
1 tree, 29 leaves, max depth = 7, train loss: 0.54314, val loss: 0.54327, in 0.016s
1 tree, 33 leaves, max depth = 11, train loss: 0.53216, val loss: 0.53237, in 0.016s
1 tree, 31 leaves, max depth = 12, train loss: 0.52236, val loss: 0.52267, in 0.016s
1 tree, 33 leaves, max depth = 12, train loss: 0.51309, val loss: 0.51346, in 0.031s
1 tree, 33 leaves, max depth = 11, train loss: 0.50368, val loss: 0.50407, in 0.016s
1 tree, 32 leaves, max depth = 12, train loss: 0.49536, val loss: 0.49585, in 0.016s
1 tree, 35 leaves, max depth = 12, train loss: 0.48686, val loss: 0.48746, in 0.016s
1 tree, 35 leaves, max depth = 12, train loss: 0.47623, val loss: 0.47751, in 0.016s
1 tree, 35 leaves, max depth = 12, train loss: 0.46851, val loss: 0.46987, in 0.016s
1 tree, 35 leaves, max depth = 7, train loss: 0.46127, val loss: 0.46278, in 0.016s
1 tree, 35 leaves, max depth = 7, train loss: 0.45437, val loss: 0.45602, in 0.031s
1 tree, 32 leaves, max depth = 12, train loss: 0.44770, val loss: 0.44941, in 0.016s
Fit 21 trees in 0.721 s, (671 total leaves)
Time spent computing histograms: 0.151s
Time spent finding best splits:  0.026s
Time spent applying splits:      0.016s
Time spent predicting:           0.000s
Trial 58, Fold 3: Log loss = 0.4462454177332421, Average precision = 0.9515587752719641, ROC-AUC = 0.949697781129913, Elapsed Time = 0.7262883999992482 seconds
Trial 58, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 58, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
Binning 0.040 GB of training data: 0.173 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 21 leaves, max depth = 6, train loss: 0.67371, val loss: 0.67290, in 0.016s
1 tree, 30 leaves, max depth = 6, train loss: 0.65546, val loss: 0.65372, in 0.031s
1 tree, 31 leaves, max depth = 10, train loss: 0.63809, val loss: 0.63571, in 0.016s
1 tree, 32 leaves, max depth = 10, train loss: 0.62178, val loss: 0.61879, in 0.016s
1 tree, 28 leaves, max depth = 10, train loss: 0.60652, val loss: 0.60291, in 0.016s
1 tree, 33 leaves, max depth = 10, train loss: 0.59219, val loss: 0.58797, in 0.016s
1 tree, 29 leaves, max depth = 10, train loss: 0.57936, val loss: 0.57465, in 0.016s
1 tree, 30 leaves, max depth = 8, train loss: 0.56655, val loss: 0.56107, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.55430, val loss: 0.54834, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.54301, val loss: 0.53645, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.53205, val loss: 0.52505, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.52230, val loss: 0.51488, in 0.016s
1 tree, 32 leaves, max depth = 9, train loss: 0.51311, val loss: 0.50525, in 0.016s
1 tree, 32 leaves, max depth = 10, train loss: 0.50377, val loss: 0.49551, in 0.016s
1 tree, 33 leaves, max depth = 10, train loss: 0.49551, val loss: 0.48688, in 0.016s
1 tree, 34 leaves, max depth = 10, train loss: 0.48707, val loss: 0.47803, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.47654, val loss: 0.46733, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.46883, val loss: 0.45935, in 0.016s
1 tree, 31 leaves, max depth = 8, train loss: 0.46168, val loss: 0.45166, in 0.016s
1 tree, 33 leaves, max depth = 8, train loss: 0.45487, val loss: 0.44449, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.44828, val loss: 0.43758, in 0.016s
Fit 21 trees in 0.705 s, (662 total leaves)
Time spent computing histograms: 0.144s
Time spent finding best splits:  0.024s
Time spent applying splits:      0.015s
Time spent predicting:           0.000s
Trial 58, Fold 4: Log loss = 0.44771803535883925, Average precision = 0.9527280687118671, ROC-AUC = 0.9486615711829772, Elapsed Time = 0.7152146000007633 seconds
Trial 58, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 58, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
Binning 0.040 GB of training data: 0.190 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 22 leaves, max depth = 7, train loss: 0.67363, val loss: 0.67280, in 0.031s
1 tree, 24 leaves, max depth = 6, train loss: 0.65499, val loss: 0.65339, in 0.016s
1 tree, 29 leaves, max depth = 10, train loss: 0.63814, val loss: 0.63576, in 0.031s
1 tree, 22 leaves, max depth = 6, train loss: 0.62188, val loss: 0.61872, in 0.016s
1 tree, 30 leaves, max depth = 10, train loss: 0.60623, val loss: 0.60247, in 0.031s
1 tree, 32 leaves, max depth = 12, train loss: 0.59149, val loss: 0.58715, in 0.016s
1 tree, 22 leaves, max depth = 8, train loss: 0.57873, val loss: 0.57364, in 0.031s
1 tree, 32 leaves, max depth = 12, train loss: 0.56566, val loss: 0.56005, in 0.016s
1 tree, 32 leaves, max depth = 12, train loss: 0.55335, val loss: 0.54726, in 0.031s
1 tree, 26 leaves, max depth = 8, train loss: 0.54199, val loss: 0.53531, in 0.016s
1 tree, 32 leaves, max depth = 12, train loss: 0.53095, val loss: 0.52384, in 0.031s
1 tree, 30 leaves, max depth = 11, train loss: 0.52113, val loss: 0.51360, in 0.016s
1 tree, 31 leaves, max depth = 12, train loss: 0.51185, val loss: 0.50393, in 0.016s
1 tree, 32 leaves, max depth = 11, train loss: 0.50247, val loss: 0.49420, in 0.031s
1 tree, 30 leaves, max depth = 11, train loss: 0.49415, val loss: 0.48554, in 0.021s
1 tree, 31 leaves, max depth = 11, train loss: 0.48563, val loss: 0.47669, in 0.027s
1 tree, 32 leaves, max depth = 11, train loss: 0.47755, val loss: 0.46825, in 0.031s
1 tree, 32 leaves, max depth = 11, train loss: 0.46982, val loss: 0.46021, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.45987, val loss: 0.45024, in 0.016s
1 tree, 33 leaves, max depth = 7, train loss: 0.45299, val loss: 0.44307, in 0.031s
1 tree, 35 leaves, max depth = 11, train loss: 0.44388, val loss: 0.43396, in 0.016s
Fit 21 trees in 0.878 s, (624 total leaves)
Time spent computing histograms: 0.198s
Time spent finding best splits:  0.035s
Time spent applying splits:      0.021s
Time spent predicting:           0.000s
Trial 58, Fold 5: Log loss = 0.44844346568526616, Average precision = 0.9489391251785544, ROC-AUC = 0.9459536629322036, Elapsed Time = 0.8952907000002597 seconds
Optimization Progress:  59%|#####8    | 59/100 [11:35<07:59, 11.70s/it]
Trial 59, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 59, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
0.142 s 0.040 GB of training data: 
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 29 leaves, max depth = 11, train loss: 0.67087, val loss: 0.67019, in 0.016s
1 tree, 28 leaves, max depth = 11, train loss: 0.65066, val loss: 0.64935, in 0.000s
1 tree, 29 leaves, max depth = 11, train loss: 0.63229, val loss: 0.63037, in 0.000s
1 tree, 28 leaves, max depth = 11, train loss: 0.61556, val loss: 0.61305, in 0.015s
1 tree, 30 leaves, max depth = 11, train loss: 0.60022, val loss: 0.59717, in 0.000s
1 tree, 28 leaves, max depth = 11, train loss: 0.58627, val loss: 0.58267, in 0.016s
1 tree, 29 leaves, max depth = 13, train loss: 0.57353, val loss: 0.56955, in 0.000s
1 tree, 28 leaves, max depth = 11, train loss: 0.56189, val loss: 0.55740, in 0.016s
1 tree, 30 leaves, max depth = 12, train loss: 0.55112, val loss: 0.54615, in 0.000s
1 tree, 48 leaves, max depth = 10, train loss: 0.54034, val loss: 0.53591, in 0.000s
1 tree, 28 leaves, max depth = 10, train loss: 0.53088, val loss: 0.52601, in 0.016s
1 tree, 49 leaves, max depth = 10, train loss: 0.52127, val loss: 0.51690, in 0.000s
1 tree, 49 leaves, max depth = 10, train loss: 0.51244, val loss: 0.50855, in 0.016s
1 tree, 28 leaves, max depth = 10, train loss: 0.50432, val loss: 0.50000, in 0.000s
1 tree, 49 leaves, max depth = 11, train loss: 0.49640, val loss: 0.49253, in 0.016s
1 tree, 28 leaves, max depth = 10, train loss: 0.48916, val loss: 0.48487, in 0.000s
1 tree, 49 leaves, max depth = 10, train loss: 0.48201, val loss: 0.47815, in 0.016s
1 tree, 28 leaves, max depth = 11, train loss: 0.47553, val loss: 0.47126, in 0.000s
1 tree, 49 leaves, max depth = 10, train loss: 0.46907, val loss: 0.46521, in 0.016s
1 tree, 28 leaves, max depth = 10, train loss: 0.46326, val loss: 0.45901, in 0.000s
Fit 20 trees in 0.392 s, (692 total leaves)
Time spent computing histograms: 0.070s
Time spent finding best splits:  0.010s
Time spent applying splits:      0.011s
Time spent predicting:           0.000s
Trial 59, Fold 1: Log loss = 0.46407318827883015, Average precision = 0.9176346016864347, ROC-AUC = 0.9272196263078151, Elapsed Time = 0.39520360000096844 seconds
Trial 59, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 59, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
0.158 s 0.040 GB of training data: 
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 28 leaves, max depth = 11, train loss: 0.67095, val loss: 0.66977, in 0.016s
1 tree, 29 leaves, max depth = 11, train loss: 0.65082, val loss: 0.64853, in 0.016s
1 tree, 31 leaves, max depth = 13, train loss: 0.63265, val loss: 0.62950, in 0.000s
1 tree, 29 leaves, max depth = 10, train loss: 0.61595, val loss: 0.61185, in 0.016s
1 tree, 31 leaves, max depth = 13, train loss: 0.60083, val loss: 0.59597, in 0.000s
1 tree, 29 leaves, max depth = 10, train loss: 0.58688, val loss: 0.58117, in 0.016s
1 tree, 30 leaves, max depth = 13, train loss: 0.57423, val loss: 0.56784, in 0.000s
1 tree, 30 leaves, max depth = 10, train loss: 0.56250, val loss: 0.55536, in 0.016s
1 tree, 30 leaves, max depth = 13, train loss: 0.55187, val loss: 0.54413, in 0.000s
1 tree, 52 leaves, max depth = 12, train loss: 0.54106, val loss: 0.53375, in 0.016s
1 tree, 30 leaves, max depth = 13, train loss: 0.53166, val loss: 0.52380, in 0.000s
1 tree, 52 leaves, max depth = 12, train loss: 0.52202, val loss: 0.51455, in 0.016s
1 tree, 51 leaves, max depth = 12, train loss: 0.51318, val loss: 0.50608, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.50511, val loss: 0.49750, in 0.000s
1 tree, 52 leaves, max depth = 13, train loss: 0.49718, val loss: 0.48990, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.48998, val loss: 0.48223, in 0.000s
1 tree, 52 leaves, max depth = 12, train loss: 0.48283, val loss: 0.47541, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.47638, val loss: 0.46853, in 0.000s
1 tree, 51 leaves, max depth = 12, train loss: 0.46992, val loss: 0.46237, in 0.000s
1 tree, 31 leaves, max depth = 11, train loss: 0.46414, val loss: 0.45619, in 0.016s
Fit 20 trees in 0.471 s, (731 total leaves)
Time spent computing histograms: 0.095s
Time spent finding best splits:  0.013s
Time spent applying splits:      0.014s
Time spent predicting:           0.000s
Trial 59, Fold 2: Log loss = 0.46482889485721857, Average precision = 0.9129098439490244, ROC-AUC = 0.9281474994544667, Elapsed Time = 0.46732349999911094 seconds
Trial 59, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 59, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
Binning 0.040 GB of training data: 0.157 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 29 leaves, max depth = 8, train loss: 0.67108, val loss: 0.67035, in 0.000s
1 tree, 29 leaves, max depth = 8, train loss: 0.65108, val loss: 0.64965, in 0.016s
1 tree, 30 leaves, max depth = 12, train loss: 0.63301, val loss: 0.63105, in 0.000s
1 tree, 30 leaves, max depth = 8, train loss: 0.61633, val loss: 0.61377, in 0.016s
1 tree, 29 leaves, max depth = 8, train loss: 0.60119, val loss: 0.59803, in 0.000s
1 tree, 29 leaves, max depth = 9, train loss: 0.58738, val loss: 0.58360, in 0.016s
1 tree, 30 leaves, max depth = 12, train loss: 0.57480, val loss: 0.57056, in 0.000s
1 tree, 30 leaves, max depth = 12, train loss: 0.56326, val loss: 0.55859, in 0.016s
1 tree, 29 leaves, max depth = 10, train loss: 0.55256, val loss: 0.54743, in 0.000s
1 tree, 47 leaves, max depth = 12, train loss: 0.54179, val loss: 0.53742, in 0.016s
1 tree, 47 leaves, max depth = 11, train loss: 0.53193, val loss: 0.52828, in 0.000s
1 tree, 30 leaves, max depth = 12, train loss: 0.52288, val loss: 0.51881, in 0.016s
1 tree, 48 leaves, max depth = 13, train loss: 0.51407, val loss: 0.51067, in 0.016s
1 tree, 31 leaves, max depth = 12, train loss: 0.50602, val loss: 0.50223, in 0.000s
1 tree, 48 leaves, max depth = 13, train loss: 0.49811, val loss: 0.49495, in 0.000s
1 tree, 31 leaves, max depth = 12, train loss: 0.49092, val loss: 0.48739, in 0.016s
1 tree, 48 leaves, max depth = 12, train loss: 0.48380, val loss: 0.48085, in 0.016s
1 tree, 30 leaves, max depth = 11, train loss: 0.47737, val loss: 0.47407, in 0.000s
1 tree, 47 leaves, max depth = 11, train loss: 0.47093, val loss: 0.46817, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.46516, val loss: 0.46206, in 0.000s
Fit 20 trees in 0.454 s, (703 total leaves)
Time spent computing histograms: 0.090s
Time spent finding best splits:  0.013s
Time spent applying splits:      0.014s
Time spent predicting:           0.000s
Trial 59, Fold 3: Log loss = 0.4618078758230681, Average precision = 0.917900894446191, ROC-AUC = 0.9314134868199285, Elapsed Time = 0.46097200000076555 seconds
Trial 59, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 59, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
Binning 0.040 GB of training data: 0.158 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 30 leaves, max depth = 10, train loss: 0.67106, val loss: 0.66979, in 0.016s
1 tree, 30 leaves, max depth = 11, train loss: 0.65103, val loss: 0.64855, in 0.000s
1 tree, 30 leaves, max depth = 11, train loss: 0.63283, val loss: 0.62918, in 0.016s
1 tree, 30 leaves, max depth = 11, train loss: 0.61624, val loss: 0.61148, in 0.000s
1 tree, 31 leaves, max depth = 11, train loss: 0.60105, val loss: 0.59516, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.58716, val loss: 0.58020, in 0.000s
1 tree, 29 leaves, max depth = 13, train loss: 0.57453, val loss: 0.56658, in 0.016s
1 tree, 30 leaves, max depth = 12, train loss: 0.56291, val loss: 0.55401, in 0.000s
1 tree, 29 leaves, max depth = 13, train loss: 0.55229, val loss: 0.54247, in 0.016s
1 tree, 48 leaves, max depth = 10, train loss: 0.54158, val loss: 0.53196, in 0.000s
1 tree, 29 leaves, max depth = 13, train loss: 0.53219, val loss: 0.52171, in 0.016s
1 tree, 48 leaves, max depth = 11, train loss: 0.52264, val loss: 0.51235, in 0.000s
1 tree, 48 leaves, max depth = 11, train loss: 0.51388, val loss: 0.50377, in 0.016s
1 tree, 29 leaves, max depth = 14, train loss: 0.50581, val loss: 0.49494, in 0.000s
1 tree, 47 leaves, max depth = 12, train loss: 0.49796, val loss: 0.48726, in 0.016s
1 tree, 29 leaves, max depth = 13, train loss: 0.49076, val loss: 0.47932, in 0.000s
1 tree, 47 leaves, max depth = 12, train loss: 0.48369, val loss: 0.47241, in 0.016s
1 tree, 29 leaves, max depth = 13, train loss: 0.47725, val loss: 0.46528, in 0.000s
1 tree, 48 leaves, max depth = 12, train loss: 0.47086, val loss: 0.45904, in 0.016s
1 tree, 29 leaves, max depth = 13, train loss: 0.46508, val loss: 0.45261, in 0.000s
Fit 20 trees in 0.455 s, (701 total leaves)
Time spent computing histograms: 0.083s
Time spent finding best splits:  0.012s
Time spent applying splits:      0.013s
Time spent predicting:           0.000s
Trial 59, Fold 4: Log loss = 0.464658487404741, Average precision = 0.91684926447469, ROC-AUC = 0.9289775978740603, Elapsed Time = 0.46681159999934607 seconds
Trial 59, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 59, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
Binning 0.040 GB of training data: 0.158 s
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 29 leaves, max depth = 10, train loss: 0.67080, val loss: 0.66929, in 0.000s
1 tree, 29 leaves, max depth = 11, train loss: 0.65069, val loss: 0.64783, in 0.016s
1 tree, 30 leaves, max depth = 10, train loss: 0.63224, val loss: 0.62802, in 0.000s
1 tree, 29 leaves, max depth = 11, train loss: 0.61557, val loss: 0.61014, in 0.016s
1 tree, 30 leaves, max depth = 10, train loss: 0.60021, val loss: 0.59355, in 0.000s
1 tree, 29 leaves, max depth = 11, train loss: 0.58630, val loss: 0.57853, in 0.016s
1 tree, 29 leaves, max depth = 10, train loss: 0.57348, val loss: 0.56462, in 0.000s
1 tree, 29 leaves, max depth = 12, train loss: 0.56180, val loss: 0.55193, in 0.016s
1 tree, 29 leaves, max depth = 10, train loss: 0.55100, val loss: 0.54013, in 0.000s
1 tree, 48 leaves, max depth = 12, train loss: 0.54049, val loss: 0.53009, in 0.016s
1 tree, 29 leaves, max depth = 11, train loss: 0.53099, val loss: 0.51968, in 0.000s
1 tree, 48 leaves, max depth = 12, train loss: 0.52161, val loss: 0.51079, in 0.016s
1 tree, 29 leaves, max depth = 11, train loss: 0.51318, val loss: 0.50150, in 0.000s
1 tree, 48 leaves, max depth = 12, train loss: 0.50478, val loss: 0.49358, in 0.016s
1 tree, 30 leaves, max depth = 11, train loss: 0.49726, val loss: 0.48527, in 0.000s
1 tree, 47 leaves, max depth = 12, train loss: 0.48971, val loss: 0.47820, in 0.016s
1 tree, 46 leaves, max depth = 12, train loss: 0.48277, val loss: 0.47171, in 0.000s
1 tree, 29 leaves, max depth = 11, train loss: 0.47622, val loss: 0.46444, in 0.016s
1 tree, 47 leaves, max depth = 13, train loss: 0.46996, val loss: 0.45858, in 0.000s
1 tree, 29 leaves, max depth = 10, train loss: 0.46408, val loss: 0.45202, in 0.016s
Fit 20 trees in 0.471 s, (693 total leaves)
Time spent computing histograms: 0.082s
Time spent finding best splits:  0.012s
Time spent applying splits:      0.013s
Time spent predicting:           0.000s
Trial 59, Fold 5: Log loss = 0.4690791333260642, Average precision = 0.9139430069964957, ROC-AUC = 0.9255519016034037, Elapsed Time = 0.47083159999965574 seconds
Optimization Progress:  60%|######    | 60/100 [11:45<07:18, 10.97s/it]
Trial 60, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 60, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
0.157 s 0.040 GB of training data: 
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 20 leaves, max depth = 7, train loss: 0.66210, val loss: 0.66184, in 0.016s
1 tree, 19 leaves, max depth = 5, train loss: 0.63373, val loss: 0.63343, in 0.016s
1 tree, 18 leaves, max depth = 6, train loss: 0.60928, val loss: 0.60887, in 0.016s
1 tree, 20 leaves, max depth = 6, train loss: 0.58612, val loss: 0.58565, in 0.016s
1 tree, 22 leaves, max depth = 8, train loss: 0.56516, val loss: 0.56470, in 0.016s
1 tree, 19 leaves, max depth = 8, train loss: 0.54680, val loss: 0.54608, in 0.000s
1 tree, 21 leaves, max depth = 8, train loss: 0.52910, val loss: 0.52830, in 0.000s
1 tree, 22 leaves, max depth = 8, train loss: 0.51294, val loss: 0.51208, in 0.016s
1 tree, 20 leaves, max depth = 5, train loss: 0.49832, val loss: 0.49702, in 0.031s
1 tree, 23 leaves, max depth = 8, train loss: 0.48470, val loss: 0.48335, in 0.016s
1 tree, 23 leaves, max depth = 8, train loss: 0.47226, val loss: 0.47086, in 0.000s
1 tree, 24 leaves, max depth = 8, train loss: 0.46079, val loss: 0.45927, in 0.016s
1 tree, 24 leaves, max depth = 9, train loss: 0.45021, val loss: 0.44865, in 0.016s
1 tree, 25 leaves, max depth = 9, train loss: 0.44046, val loss: 0.43887, in 0.016s
1 tree, 21 leaves, max depth = 10, train loss: 0.42656, val loss: 0.42545, in 0.016s
1 tree, 20 leaves, max depth = 5, train loss: 0.41832, val loss: 0.41699, in 0.016s
1 tree, 19 leaves, max depth = 10, train loss: 0.40625, val loss: 0.40542, in 0.016s
1 tree, 25 leaves, max depth = 10, train loss: 0.39883, val loss: 0.39800, in 0.016s
1 tree, 18 leaves, max depth = 9, train loss: 0.38826, val loss: 0.38790, in 0.000s
1 tree, 25 leaves, max depth = 10, train loss: 0.38177, val loss: 0.38144, in 0.031s
1 tree, 24 leaves, max depth = 10, train loss: 0.37242, val loss: 0.37253, in 0.000s
1 tree, 23 leaves, max depth = 6, train loss: 0.36687, val loss: 0.36687, in 0.016s
1 tree, 26 leaves, max depth = 11, train loss: 0.36185, val loss: 0.36191, in 0.016s
1 tree, 12 leaves, max depth = 7, train loss: 0.35433, val loss: 0.35447, in 0.016s
1 tree, 13 leaves, max depth = 6, train loss: 0.34746, val loss: 0.34768, in 0.016s
1 tree, 20 leaves, max depth = 10, train loss: 0.34205, val loss: 0.34237, in 0.016s
1 tree, 15 leaves, max depth = 7, train loss: 0.33599, val loss: 0.33638, in 0.000s
Fit 27 trees in 0.673 s, (561 total leaves)
Time spent computing histograms: 0.162s
Time spent finding best splits:  0.016s
Time spent applying splits:      0.012s
Time spent predicting:           0.000s
Trial 60, Fold 1: Log loss = 0.3384298929201707, Average precision = 0.9562246653547312, ROC-AUC = 0.9504353608350898, Elapsed Time = 0.6907088000007207 seconds
Trial 60, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 60, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
Binning 0.040 GB of training data: 0.158 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 17 leaves, max depth = 6, train loss: 0.66211, val loss: 0.66122, in 0.016s
1 tree, 17 leaves, max depth = 5, train loss: 0.63339, val loss: 0.63185, in 0.016s
1 tree, 19 leaves, max depth = 6, train loss: 0.60759, val loss: 0.60548, in 0.016s
1 tree, 18 leaves, max depth = 5, train loss: 0.58433, val loss: 0.58174, in 0.016s
1 tree, 20 leaves, max depth = 6, train loss: 0.56326, val loss: 0.56007, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.54504, val loss: 0.54158, in 0.016s
1 tree, 21 leaves, max depth = 7, train loss: 0.52744, val loss: 0.52354, in 0.016s
1 tree, 21 leaves, max depth = 7, train loss: 0.51140, val loss: 0.50709, in 0.016s
1 tree, 19 leaves, max depth = 5, train loss: 0.49697, val loss: 0.49241, in 0.016s
1 tree, 19 leaves, max depth = 6, train loss: 0.48353, val loss: 0.47873, in 0.016s
1 tree, 23 leaves, max depth = 8, train loss: 0.47154, val loss: 0.46637, in 0.016s
1 tree, 22 leaves, max depth = 8, train loss: 0.46018, val loss: 0.45481, in 0.016s
1 tree, 20 leaves, max depth = 6, train loss: 0.44971, val loss: 0.44408, in 0.016s
1 tree, 19 leaves, max depth = 9, train loss: 0.43534, val loss: 0.42988, in 0.016s
1 tree, 21 leaves, max depth = 7, train loss: 0.42640, val loss: 0.42067, in 0.016s
1 tree, 23 leaves, max depth = 9, train loss: 0.41802, val loss: 0.41204, in 0.016s
1 tree, 18 leaves, max depth = 7, train loss: 0.40605, val loss: 0.40026, in 0.016s
1 tree, 27 leaves, max depth = 9, train loss: 0.39886, val loss: 0.39288, in 0.016s
1 tree, 18 leaves, max depth = 7, train loss: 0.38839, val loss: 0.38259, in 0.016s
1 tree, 23 leaves, max depth = 9, train loss: 0.37879, val loss: 0.37315, in 0.000s
1 tree, 25 leaves, max depth = 9, train loss: 0.37284, val loss: 0.36702, in 0.031s
1 tree, 24 leaves, max depth = 6, train loss: 0.36737, val loss: 0.36149, in 0.000s
1 tree, 23 leaves, max depth = 9, train loss: 0.35909, val loss: 0.35337, in 0.031s
1 tree, 25 leaves, max depth = 9, train loss: 0.35418, val loss: 0.34828, in 0.016s
1 tree, 11 leaves, max depth = 5, train loss: 0.34730, val loss: 0.34152, in 0.000s
1 tree, 11 leaves, max depth = 5, train loss: 0.34110, val loss: 0.33548, in 0.016s
1 tree, 30 leaves, max depth = 12, train loss: 0.33537, val loss: 0.33015, in 0.016s
Fit 27 trees in 0.736 s, (551 total leaves)
Time spent computing histograms: 0.178s
Time spent finding best splits:  0.017s
Time spent applying splits:      0.013s
Time spent predicting:           0.000s
Trial 60, Fold 2: Log loss = 0.336854622544885, Average precision = 0.9510914200412619, ROC-AUC = 0.9497730004877174, Elapsed Time = 0.747653000000355 seconds
Trial 60, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 60, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
Binning 0.040 GB of training data: 0.159 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 17 leaves, max depth = 6, train loss: 0.66187, val loss: 0.66194, in 0.000s
1 tree, 16 leaves, max depth = 6, train loss: 0.63335, val loss: 0.63344, in 0.016s
1 tree, 18 leaves, max depth = 6, train loss: 0.60791, val loss: 0.60781, in 0.016s
1 tree, 16 leaves, max depth = 6, train loss: 0.58479, val loss: 0.58460, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.56387, val loss: 0.56359, in 0.016s
1 tree, 22 leaves, max depth = 9, train loss: 0.54557, val loss: 0.54526, in 0.016s
1 tree, 24 leaves, max depth = 11, train loss: 0.52826, val loss: 0.52782, in 0.016s
1 tree, 18 leaves, max depth = 6, train loss: 0.51233, val loss: 0.51179, in 0.016s
1 tree, 24 leaves, max depth = 11, train loss: 0.49776, val loss: 0.49718, in 0.016s
1 tree, 23 leaves, max depth = 9, train loss: 0.48442, val loss: 0.48381, in 0.016s
1 tree, 20 leaves, max depth = 8, train loss: 0.47257, val loss: 0.47189, in 0.000s
1 tree, 26 leaves, max depth = 11, train loss: 0.46126, val loss: 0.46056, in 0.016s
1 tree, 24 leaves, max depth = 8, train loss: 0.45082, val loss: 0.45009, in 0.016s
1 tree, 23 leaves, max depth = 11, train loss: 0.43627, val loss: 0.43653, in 0.016s
1 tree, 25 leaves, max depth = 8, train loss: 0.42717, val loss: 0.42736, in 0.016s
1 tree, 21 leaves, max depth = 6, train loss: 0.41898, val loss: 0.41927, in 0.016s
1 tree, 25 leaves, max depth = 12, train loss: 0.40678, val loss: 0.40804, in 0.016s
1 tree, 23 leaves, max depth = 9, train loss: 0.39560, val loss: 0.39767, in 0.016s
1 tree, 26 leaves, max depth = 11, train loss: 0.38865, val loss: 0.39069, in 0.016s
1 tree, 22 leaves, max depth = 9, train loss: 0.38260, val loss: 0.38469, in 0.016s
1 tree, 23 leaves, max depth = 9, train loss: 0.37317, val loss: 0.37612, in 0.016s
1 tree, 25 leaves, max depth = 10, train loss: 0.36754, val loss: 0.37042, in 0.016s
1 tree, 7 leaves, max depth = 3, train loss: 0.35967, val loss: 0.36317, in 0.016s
1 tree, 24 leaves, max depth = 9, train loss: 0.35449, val loss: 0.35815, in 0.016s
1 tree, 12 leaves, max depth = 7, train loss: 0.34753, val loss: 0.35183, in 0.016s
1 tree, 33 leaves, max depth = 12, train loss: 0.34121, val loss: 0.34643, in 0.000s
1 tree, 16 leaves, max depth = 7, train loss: 0.33526, val loss: 0.34101, in 0.016s
Fit 27 trees in 0.737 s, (570 total leaves)
Time spent computing histograms: 0.167s
Time spent finding best splits:  0.016s
Time spent applying splits:      0.013s
Time spent predicting:           0.000s
Trial 60, Fold 3: Log loss = 0.33306986354968326, Average precision = 0.9541527565952485, ROC-AUC = 0.9513584574934268, Elapsed Time = 0.7431777999991027 seconds
Trial 60, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 60, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
Binning 0.040 GB of training data: 0.157 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 14 leaves, max depth = 5, train loss: 0.66229, val loss: 0.66093, in 0.016s
1 tree, 17 leaves, max depth = 5, train loss: 0.63426, val loss: 0.63166, in 0.016s
1 tree, 17 leaves, max depth = 5, train loss: 0.60898, val loss: 0.60531, in 0.000s
1 tree, 17 leaves, max depth = 5, train loss: 0.58617, val loss: 0.58139, in 0.016s
1 tree, 16 leaves, max depth = 6, train loss: 0.56517, val loss: 0.55927, in 0.016s
1 tree, 22 leaves, max depth = 7, train loss: 0.54704, val loss: 0.54015, in 0.016s
1 tree, 21 leaves, max depth = 8, train loss: 0.52951, val loss: 0.52178, in 0.016s
1 tree, 22 leaves, max depth = 8, train loss: 0.51356, val loss: 0.50489, in 0.031s
1 tree, 22 leaves, max depth = 7, train loss: 0.49915, val loss: 0.48959, in 0.016s
1 tree, 21 leaves, max depth = 6, train loss: 0.48578, val loss: 0.47544, in 0.000s
1 tree, 17 leaves, max depth = 6, train loss: 0.47385, val loss: 0.46259, in 0.016s
1 tree, 22 leaves, max depth = 8, train loss: 0.46248, val loss: 0.45058, in 0.016s
1 tree, 25 leaves, max depth = 8, train loss: 0.45207, val loss: 0.43952, in 0.016s
1 tree, 22 leaves, max depth = 7, train loss: 0.44245, val loss: 0.42932, in 0.016s
1 tree, 23 leaves, max depth = 8, train loss: 0.42867, val loss: 0.41532, in 0.016s
1 tree, 24 leaves, max depth = 7, train loss: 0.42054, val loss: 0.40653, in 0.016s
1 tree, 21 leaves, max depth = 7, train loss: 0.40856, val loss: 0.39438, in 0.000s
1 tree, 23 leaves, max depth = 8, train loss: 0.40127, val loss: 0.38659, in 0.031s
1 tree, 21 leaves, max depth = 7, train loss: 0.39078, val loss: 0.37596, in 0.016s
1 tree, 19 leaves, max depth = 6, train loss: 0.38466, val loss: 0.36932, in 0.016s
1 tree, 25 leaves, max depth = 9, train loss: 0.37535, val loss: 0.35991, in 0.016s
1 tree, 22 leaves, max depth = 9, train loss: 0.36688, val loss: 0.35132, in 0.016s
1 tree, 28 leaves, max depth = 10, train loss: 0.35915, val loss: 0.34351, in 0.000s
1 tree, 27 leaves, max depth = 7, train loss: 0.35426, val loss: 0.33812, in 0.016s
1 tree, 24 leaves, max depth = 9, train loss: 0.34870, val loss: 0.33239, in 0.016s
1 tree, 18 leaves, max depth = 8, train loss: 0.34244, val loss: 0.32578, in 0.016s
1 tree, 30 leaves, max depth = 9, train loss: 0.33671, val loss: 0.32033, in 0.016s
Fit 27 trees in 0.767 s, (580 total leaves)
Time spent computing histograms: 0.177s
Time spent finding best splits:  0.017s
Time spent applying splits:      0.014s
Time spent predicting:           0.000s
Trial 60, Fold 4: Log loss = 0.33538787395702674, Average precision = 0.9564025175831881, ROC-AUC = 0.9518338793631718, Elapsed Time = 0.7707864999993035 seconds
Trial 60, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 60, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
Binning 0.040 GB of training data: 0.157 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 16 leaves, max depth = 5, train loss: 0.66177, val loss: 0.66029, in 0.031s
1 tree, 17 leaves, max depth = 5, train loss: 0.63313, val loss: 0.63036, in 0.016s
1 tree, 17 leaves, max depth = 5, train loss: 0.60741, val loss: 0.60347, in 0.000s
1 tree, 19 leaves, max depth = 6, train loss: 0.58422, val loss: 0.57917, in 0.016s
1 tree, 20 leaves, max depth = 6, train loss: 0.56321, val loss: 0.55722, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.54497, val loss: 0.53804, in 0.016s
1 tree, 20 leaves, max depth = 6, train loss: 0.52748, val loss: 0.51976, in 0.016s
1 tree, 19 leaves, max depth = 6, train loss: 0.51150, val loss: 0.50304, in 0.016s
1 tree, 19 leaves, max depth = 6, train loss: 0.49689, val loss: 0.48779, in 0.016s
1 tree, 22 leaves, max depth = 8, train loss: 0.48349, val loss: 0.47377, in 0.016s
1 tree, 22 leaves, max depth = 9, train loss: 0.47159, val loss: 0.46111, in 0.016s
1 tree, 23 leaves, max depth = 8, train loss: 0.46024, val loss: 0.44924, in 0.016s
1 tree, 20 leaves, max depth = 6, train loss: 0.44972, val loss: 0.43830, in 0.016s
1 tree, 18 leaves, max depth = 8, train loss: 0.43530, val loss: 0.42382, in 0.016s
1 tree, 22 leaves, max depth = 8, train loss: 0.42627, val loss: 0.41443, in 0.016s
1 tree, 24 leaves, max depth = 9, train loss: 0.41792, val loss: 0.40570, in 0.016s
1 tree, 19 leaves, max depth = 8, train loss: 0.40586, val loss: 0.39363, in 0.016s
1 tree, 22 leaves, max depth = 9, train loss: 0.39860, val loss: 0.38611, in 0.000s
1 tree, 22 leaves, max depth = 9, train loss: 0.38803, val loss: 0.37560, in 0.016s
1 tree, 20 leaves, max depth = 8, train loss: 0.37842, val loss: 0.36595, in 0.016s
1 tree, 24 leaves, max depth = 8, train loss: 0.37239, val loss: 0.35972, in 0.016s
1 tree, 26 leaves, max depth = 9, train loss: 0.36680, val loss: 0.35396, in 0.016s
1 tree, 19 leaves, max depth = 9, train loss: 0.35862, val loss: 0.34580, in 0.016s
1 tree, 22 leaves, max depth = 8, train loss: 0.35371, val loss: 0.34078, in 0.016s
1 tree, 11 leaves, max depth = 6, train loss: 0.34674, val loss: 0.33363, in 0.000s
1 tree, 11 leaves, max depth = 6, train loss: 0.34045, val loss: 0.32705, in 0.016s
1 tree, 10 leaves, max depth = 6, train loss: 0.33469, val loss: 0.32115, in 0.016s
Fit 27 trees in 0.736 s, (521 total leaves)
Time spent computing histograms: 0.170s
Time spent finding best splits:  0.016s
Time spent applying splits:      0.013s
Time spent predicting:           0.000s
Trial 60, Fold 5: Log loss = 0.3408628390929188, Average precision = 0.9507184833691187, ROC-AUC = 0.9466764022815525, Elapsed Time = 0.7471552000006341 seconds
Optimization Progress:  61%|######1   | 61/100 [11:55<06:58, 10.73s/it]
Trial 61, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 61, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
0.141 s 0.040 GB of training data: 
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 75 leaves, max depth = 16, train loss: 0.67118, val loss: 0.67058, in 0.016s
1 tree, 75 leaves, max depth = 17, train loss: 0.65078, val loss: 0.64955, in 0.016s
1 tree, 75 leaves, max depth = 17, train loss: 0.63224, val loss: 0.63040, in 0.000s
1 tree, 75 leaves, max depth = 15, train loss: 0.61552, val loss: 0.61325, in 0.016s
1 tree, 75 leaves, max depth = 19, train loss: 0.60027, val loss: 0.59751, in 0.016s
1 tree, 75 leaves, max depth = 19, train loss: 0.58634, val loss: 0.58311, in 0.000s
1 tree, 75 leaves, max depth = 18, train loss: 0.57368, val loss: 0.57001, in 0.016s
1 tree, 75 leaves, max depth = 19, train loss: 0.56208, val loss: 0.55807, in 0.016s
1 tree, 75 leaves, max depth = 16, train loss: 0.55132, val loss: 0.54691, in 0.000s
1 tree, 75 leaves, max depth = 12, train loss: 0.54037, val loss: 0.53671, in 0.016s
1 tree, 75 leaves, max depth = 12, train loss: 0.53034, val loss: 0.52738, in 0.000s
1 tree, 75 leaves, max depth = 15, train loss: 0.52116, val loss: 0.51789, in 0.016s
1 tree, 75 leaves, max depth = 18, train loss: 0.51282, val loss: 0.50919, in 0.016s
1 tree, 75 leaves, max depth = 17, train loss: 0.50486, val loss: 0.50081, in 0.000s
1 tree, 75 leaves, max depth = 18, train loss: 0.49779, val loss: 0.49341, in 0.016s
1 tree, 75 leaves, max depth = 12, train loss: 0.48944, val loss: 0.48573, in 0.016s
1 tree, 75 leaves, max depth = 15, train loss: 0.48306, val loss: 0.47911, in 0.000s
1 tree, 75 leaves, max depth = 15, train loss: 0.47719, val loss: 0.47300, in 0.016s
1 tree, 75 leaves, max depth = 14, train loss: 0.47176, val loss: 0.46729, in 0.016s
1 tree, 75 leaves, max depth = 14, train loss: 0.46658, val loss: 0.46176, in 0.000s
1 tree, 75 leaves, max depth = 14, train loss: 0.46180, val loss: 0.45665, in 0.016s
1 tree, 75 leaves, max depth = 10, train loss: 0.45667, val loss: 0.45208, in 0.016s
1 tree, 75 leaves, max depth = 12, train loss: 0.44994, val loss: 0.44598, in 0.000s
1 tree, 75 leaves, max depth = 14, train loss: 0.44580, val loss: 0.44152, in 0.016s
1 tree, 75 leaves, max depth = 17, train loss: 0.44213, val loss: 0.43768, in 0.016s
1 tree, 75 leaves, max depth = 12, train loss: 0.43605, val loss: 0.43221, in 0.016s
1 tree, 75 leaves, max depth = 12, train loss: 0.43043, val loss: 0.42717, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.42712, val loss: 0.42351, in 0.016s
1 tree, 75 leaves, max depth = 12, train loss: 0.42195, val loss: 0.41892, in 0.016s
1 tree, 75 leaves, max depth = 12, train loss: 0.41718, val loss: 0.41468, in 0.000s
1 tree, 75 leaves, max depth = 14, train loss: 0.41414, val loss: 0.41147, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.41116, val loss: 0.40817, in 0.000s
1 tree, 75 leaves, max depth = 13, train loss: 0.40833, val loss: 0.40509, in 0.016s
1 tree, 75 leaves, max depth = 12, train loss: 0.40397, val loss: 0.40129, in 0.016s
1 tree, 75 leaves, max depth = 11, train loss: 0.40136, val loss: 0.39857, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.39868, val loss: 0.39559, in 0.016s
1 tree, 75 leaves, max depth = 14, train loss: 0.39640, val loss: 0.39320, in 0.016s
1 tree, 75 leaves, max depth = 12, train loss: 0.39241, val loss: 0.38978, in 0.000s
1 tree, 75 leaves, max depth = 14, train loss: 0.39023, val loss: 0.38755, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.38779, val loss: 0.38483, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.38554, val loss: 0.38232, in 0.016s
1 tree, 75 leaves, max depth = 13, train loss: 0.38186, val loss: 0.37921, in 0.016s
1 tree, 75 leaves, max depth = 20, train loss: 0.37987, val loss: 0.37744, in 0.000s
1 tree, 75 leaves, max depth = 13, train loss: 0.37659, val loss: 0.37469, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.37450, val loss: 0.37235, in 0.000s
1 tree, 75 leaves, max depth = 13, train loss: 0.37145, val loss: 0.36982, in 0.016s
1 tree, 75 leaves, max depth = 12, train loss: 0.36863, val loss: 0.36750, in 0.016s
1 tree, 75 leaves, max depth = 13, train loss: 0.36680, val loss: 0.36553, in 0.016s
1 tree, 75 leaves, max depth = 12, train loss: 0.36419, val loss: 0.36340, in 0.000s
1 tree, 75 leaves, max depth = 12, train loss: 0.36177, val loss: 0.36145, in 0.016s
1 tree, 75 leaves, max depth = 14, train loss: 0.36007, val loss: 0.35975, in 0.016s
1 tree, 75 leaves, max depth = 12, train loss: 0.35784, val loss: 0.35796, in 0.016s
1 tree, 75 leaves, max depth = 13, train loss: 0.35578, val loss: 0.35631, in 0.016s
1 tree, 75 leaves, max depth = 12, train loss: 0.35423, val loss: 0.35461, in 0.000s
1 tree, 75 leaves, max depth = 12, train loss: 0.35231, val loss: 0.35311, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.35034, val loss: 0.35088, in 0.016s
1 tree, 75 leaves, max depth = 13, train loss: 0.34857, val loss: 0.34952, in 0.000s
1 tree, 75 leaves, max depth = 15, train loss: 0.34712, val loss: 0.34809, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.34529, val loss: 0.34601, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.34360, val loss: 0.34408, in 0.016s
1 tree, 75 leaves, max depth = 13, train loss: 0.34223, val loss: 0.34255, in 0.000s
1 tree, 75 leaves, max depth = 12, train loss: 0.34053, val loss: 0.34127, in 0.016s
1 tree, 75 leaves, max depth = 12, train loss: 0.33897, val loss: 0.34012, in 0.016s
1 tree, 75 leaves, max depth = 15, train loss: 0.33757, val loss: 0.33848, in 0.016s
1 tree, 75 leaves, max depth = 12, train loss: 0.33611, val loss: 0.33742, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.33450, val loss: 0.33558, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.33301, val loss: 0.33386, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.33164, val loss: 0.33227, in 0.016s
1 tree, 75 leaves, max depth = 14, train loss: 0.33041, val loss: 0.33091, in 0.000s
1 tree, 75 leaves, max depth = 14, train loss: 0.32927, val loss: 0.32980, in 0.016s
1 tree, 75 leaves, max depth = 13, train loss: 0.32784, val loss: 0.32880, in 0.016s
1 tree, 75 leaves, max depth = 16, train loss: 0.32676, val loss: 0.32766, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.32545, val loss: 0.32614, in 0.000s
1 tree, 75 leaves, max depth = 14, train loss: 0.32444, val loss: 0.32516, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.32323, val loss: 0.32374, in 0.000s
1 tree, 75 leaves, max depth = 16, train loss: 0.32224, val loss: 0.32268, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.32111, val loss: 0.32134, in 0.000s
1 tree, 75 leaves, max depth = 15, train loss: 0.32018, val loss: 0.32053, in 0.016s
1 tree, 75 leaves, max depth = 17, train loss: 0.31921, val loss: 0.31945, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.31815, val loss: 0.31818, in 0.016s
1 tree, 50 leaves, max depth = 11, train loss: 0.31731, val loss: 0.31739, in 0.000s
1 tree, 75 leaves, max depth = 14, train loss: 0.31585, val loss: 0.31637, in 0.016s
1 tree, 75 leaves, max depth = 13, train loss: 0.31474, val loss: 0.31580, in 0.016s
1 tree, 75 leaves, max depth = 14, train loss: 0.31346, val loss: 0.31494, in 0.016s
1 tree, 75 leaves, max depth = 14, train loss: 0.31228, val loss: 0.31416, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.31123, val loss: 0.31291, in 0.000s
1 tree, 75 leaves, max depth = 15, train loss: 0.31013, val loss: 0.31219, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.30914, val loss: 0.31101, in 0.000s
1 tree, 75 leaves, max depth = 18, train loss: 0.30821, val loss: 0.30995, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.30728, val loss: 0.30884, in 0.000s
1 tree, 75 leaves, max depth = 18, train loss: 0.30642, val loss: 0.30786, in 0.016s
1 tree, 75 leaves, max depth = 14, train loss: 0.30535, val loss: 0.30719, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.30447, val loss: 0.30613, in 0.000s
1 tree, 75 leaves, max depth = 18, train loss: 0.30365, val loss: 0.30521, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.30283, val loss: 0.30420, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.30207, val loss: 0.30327, in 0.016s
1 tree, 75 leaves, max depth = 16, train loss: 0.30134, val loss: 0.30262, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.30062, val loss: 0.30174, in 0.000s
1 tree, 75 leaves, max depth = 14, train loss: 0.29954, val loss: 0.30108, in 0.016s
1 tree, 75 leaves, max depth = 18, train loss: 0.29878, val loss: 0.30025, in 0.000s
Fit 100 trees in 1.251 s, (5865 total leaves)
Time spent computing histograms: 0.465s
Time spent finding best splits:  0.097s
Time spent applying splits:      0.098s
Time spent predicting:           0.000s
Trial 61, Fold 1: Log loss = 0.3085742850973289, Average precision = 0.9500699490593552, ROC-AUC = 0.9481077755552687, Elapsed Time = 1.2683758000002854 seconds
Trial 61, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 61, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
Binning 0.040 GB of training data: 0.157 s
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 75 leaves, max depth = 20, train loss: 0.67109, val loss: 0.67018, in 0.016s
1 tree, 75 leaves, max depth = 17, train loss: 0.65104, val loss: 0.64923, in 0.000s
1 tree, 75 leaves, max depth = 16, train loss: 0.63257, val loss: 0.62980, in 0.016s
1 tree, 75 leaves, max depth = 14, train loss: 0.61599, val loss: 0.61250, in 0.016s
1 tree, 75 leaves, max depth = 16, train loss: 0.60083, val loss: 0.59659, in 0.000s
1 tree, 75 leaves, max depth = 11, train loss: 0.58688, val loss: 0.58184, in 0.016s
1 tree, 75 leaves, max depth = 17, train loss: 0.57420, val loss: 0.56855, in 0.016s
1 tree, 75 leaves, max depth = 23, train loss: 0.56258, val loss: 0.55628, in 0.016s
1 tree, 75 leaves, max depth = 11, train loss: 0.55182, val loss: 0.54484, in 0.000s
1 tree, 75 leaves, max depth = 16, train loss: 0.54094, val loss: 0.53435, in 0.016s
1 tree, 75 leaves, max depth = 16, train loss: 0.53097, val loss: 0.52476, in 0.016s
1 tree, 75 leaves, max depth = 15, train loss: 0.52180, val loss: 0.51505, in 0.016s
1 tree, 75 leaves, max depth = 11, train loss: 0.51338, val loss: 0.50603, in 0.000s
1 tree, 75 leaves, max depth = 13, train loss: 0.50546, val loss: 0.49756, in 0.016s
1 tree, 75 leaves, max depth = 14, train loss: 0.49839, val loss: 0.49008, in 0.016s
1 tree, 75 leaves, max depth = 14, train loss: 0.49009, val loss: 0.48213, in 0.000s
1 tree, 75 leaves, max depth = 14, train loss: 0.48375, val loss: 0.47540, in 0.016s
1 tree, 75 leaves, max depth = 14, train loss: 0.47789, val loss: 0.46913, in 0.016s
1 tree, 75 leaves, max depth = 20, train loss: 0.47253, val loss: 0.46339, in 0.000s
1 tree, 75 leaves, max depth = 13, train loss: 0.46739, val loss: 0.45783, in 0.016s
1 tree, 75 leaves, max depth = 13, train loss: 0.46265, val loss: 0.45270, in 0.016s
1 tree, 75 leaves, max depth = 13, train loss: 0.45829, val loss: 0.44795, in 0.000s
1 tree, 75 leaves, max depth = 15, train loss: 0.45131, val loss: 0.44134, in 0.016s
1 tree, 75 leaves, max depth = 13, train loss: 0.44743, val loss: 0.43711, in 0.016s
1 tree, 75 leaves, max depth = 13, train loss: 0.44307, val loss: 0.43329, in 0.016s
1 tree, 75 leaves, max depth = 14, train loss: 0.43702, val loss: 0.42759, in 0.000s
1 tree, 75 leaves, max depth = 14, train loss: 0.43144, val loss: 0.42233, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.42819, val loss: 0.41896, in 0.016s
1 tree, 75 leaves, max depth = 15, train loss: 0.42307, val loss: 0.41414, in 0.000s
1 tree, 75 leaves, max depth = 15, train loss: 0.41833, val loss: 0.40968, in 0.016s
1 tree, 75 leaves, max depth = 14, train loss: 0.41527, val loss: 0.40642, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.41236, val loss: 0.40339, in 0.000s
1 tree, 75 leaves, max depth = 13, train loss: 0.40944, val loss: 0.40035, in 0.016s
1 tree, 75 leaves, max depth = 15, train loss: 0.40515, val loss: 0.39634, in 0.000s
1 tree, 75 leaves, max depth = 12, train loss: 0.40266, val loss: 0.39371, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.40004, val loss: 0.39098, in 0.016s
1 tree, 75 leaves, max depth = 14, train loss: 0.39773, val loss: 0.38854, in 0.000s
1 tree, 75 leaves, max depth = 16, train loss: 0.39378, val loss: 0.38487, in 0.016s
1 tree, 75 leaves, max depth = 13, train loss: 0.39159, val loss: 0.38258, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.38921, val loss: 0.38010, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.38701, val loss: 0.37780, in 0.016s
1 tree, 75 leaves, max depth = 17, train loss: 0.38336, val loss: 0.37445, in 0.000s
1 tree, 75 leaves, max depth = 11, train loss: 0.38086, val loss: 0.37235, in 0.016s
1 tree, 75 leaves, max depth = 17, train loss: 0.37764, val loss: 0.36942, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.37561, val loss: 0.36729, in 0.000s
1 tree, 75 leaves, max depth = 17, train loss: 0.37263, val loss: 0.36457, in 0.016s
1 tree, 75 leaves, max depth = 18, train loss: 0.36988, val loss: 0.36206, in 0.016s
1 tree, 75 leaves, max depth = 14, train loss: 0.36806, val loss: 0.36007, in 0.016s
1 tree, 75 leaves, max depth = 18, train loss: 0.36551, val loss: 0.35776, in 0.000s
1 tree, 75 leaves, max depth = 17, train loss: 0.36316, val loss: 0.35565, in 0.016s
1 tree, 75 leaves, max depth = 14, train loss: 0.36147, val loss: 0.35389, in 0.016s
1 tree, 75 leaves, max depth = 16, train loss: 0.35929, val loss: 0.35196, in 0.016s
1 tree, 75 leaves, max depth = 17, train loss: 0.35729, val loss: 0.35017, in 0.000s
1 tree, 75 leaves, max depth = 15, train loss: 0.35574, val loss: 0.34846, in 0.016s
1 tree, 75 leaves, max depth = 18, train loss: 0.35388, val loss: 0.34681, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.35196, val loss: 0.34480, in 0.000s
1 tree, 75 leaves, max depth = 15, train loss: 0.35023, val loss: 0.34328, in 0.016s
1 tree, 75 leaves, max depth = 14, train loss: 0.34880, val loss: 0.34177, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.34702, val loss: 0.33989, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.34538, val loss: 0.33815, in 0.016s
1 tree, 75 leaves, max depth = 16, train loss: 0.34403, val loss: 0.33668, in 0.000s
1 tree, 75 leaves, max depth = 16, train loss: 0.34237, val loss: 0.33524, in 0.016s
1 tree, 75 leaves, max depth = 16, train loss: 0.34084, val loss: 0.33392, in 0.016s
1 tree, 75 leaves, max depth = 13, train loss: 0.33945, val loss: 0.33248, in 0.000s
1 tree, 75 leaves, max depth = 15, train loss: 0.33803, val loss: 0.33126, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.33646, val loss: 0.32960, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.33501, val loss: 0.32806, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.33367, val loss: 0.32663, in 0.000s
1 tree, 75 leaves, max depth = 14, train loss: 0.33239, val loss: 0.32535, in 0.016s
1 tree, 75 leaves, max depth = 16, train loss: 0.33102, val loss: 0.32419, in 0.016s
1 tree, 75 leaves, max depth = 14, train loss: 0.32975, val loss: 0.32288, in 0.016s
1 tree, 75 leaves, max depth = 13, train loss: 0.32864, val loss: 0.32174, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.32737, val loss: 0.32038, in 0.016s
1 tree, 75 leaves, max depth = 13, train loss: 0.32634, val loss: 0.31938, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.32515, val loss: 0.31811, in 0.000s
1 tree, 75 leaves, max depth = 14, train loss: 0.32413, val loss: 0.31703, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.32302, val loss: 0.31584, in 0.000s
1 tree, 75 leaves, max depth = 15, train loss: 0.32209, val loss: 0.31494, in 0.016s
1 tree, 75 leaves, max depth = 15, train loss: 0.32112, val loss: 0.31397, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.32007, val loss: 0.31286, in 0.000s
1 tree, 75 leaves, max depth = 15, train loss: 0.31923, val loss: 0.31198, in 0.016s
1 tree, 75 leaves, max depth = 15, train loss: 0.31780, val loss: 0.31076, in 0.016s
1 tree, 75 leaves, max depth = 19, train loss: 0.31655, val loss: 0.31002, in 0.016s
1 tree, 75 leaves, max depth = 15, train loss: 0.31529, val loss: 0.30896, in 0.000s
1 tree, 75 leaves, max depth = 15, train loss: 0.31414, val loss: 0.30800, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.31311, val loss: 0.30689, in 0.016s
1 tree, 75 leaves, max depth = 15, train loss: 0.31203, val loss: 0.30600, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.31106, val loss: 0.30495, in 0.000s
1 tree, 75 leaves, max depth = 15, train loss: 0.31014, val loss: 0.30403, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.30923, val loss: 0.30305, in 0.016s
1 tree, 75 leaves, max depth = 15, train loss: 0.30838, val loss: 0.30221, in 0.000s
1 tree, 75 leaves, max depth = 15, train loss: 0.30733, val loss: 0.30133, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.30647, val loss: 0.30040, in 0.016s
1 tree, 75 leaves, max depth = 13, train loss: 0.30568, val loss: 0.29969, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.30487, val loss: 0.29882, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.30412, val loss: 0.29800, in 0.016s
1 tree, 75 leaves, max depth = 15, train loss: 0.30334, val loss: 0.29723, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.30263, val loss: 0.29646, in 0.000s
1 tree, 75 leaves, max depth = 15, train loss: 0.30157, val loss: 0.29558, in 0.016s
1 tree, 75 leaves, max depth = 13, train loss: 0.30085, val loss: 0.29495, in 0.016s
Fit 100 trees in 1.392 s, (5890 total leaves)
Time spent computing histograms: 0.499s
Time spent finding best splits:  0.104s
Time spent applying splits:      0.104s
Time spent predicting:           0.000s
Trial 61, Fold 2: Log loss = 0.3076580276331425, Average precision = 0.9456636992142133, ROC-AUC = 0.9476590020883531, Elapsed Time = 1.3889223000005586 seconds
Trial 61, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 61, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
Binning 0.040 GB of training data: 0.173 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 75 leaves, max depth = 14, train loss: 0.67132, val loss: 0.67060, in 0.016s
1 tree, 75 leaves, max depth = 14, train loss: 0.65113, val loss: 0.64982, in 0.016s
1 tree, 75 leaves, max depth = 14, train loss: 0.63278, val loss: 0.63089, in 0.000s
1 tree, 75 leaves, max depth = 13, train loss: 0.61623, val loss: 0.61368, in 0.016s
1 tree, 75 leaves, max depth = 13, train loss: 0.60113, val loss: 0.59809, in 0.016s
1 tree, 75 leaves, max depth = 13, train loss: 0.58734, val loss: 0.58383, in 0.016s
1 tree, 75 leaves, max depth = 14, train loss: 0.57475, val loss: 0.57075, in 0.000s
1 tree, 75 leaves, max depth = 18, train loss: 0.56323, val loss: 0.55877, in 0.016s
1 tree, 75 leaves, max depth = 14, train loss: 0.55255, val loss: 0.54756, in 0.016s
1 tree, 75 leaves, max depth = 12, train loss: 0.54143, val loss: 0.53723, in 0.016s
1 tree, 75 leaves, max depth = 12, train loss: 0.53124, val loss: 0.52780, in 0.000s
1 tree, 75 leaves, max depth = 14, train loss: 0.52217, val loss: 0.51820, in 0.016s
1 tree, 75 leaves, max depth = 18, train loss: 0.51389, val loss: 0.50953, in 0.016s
1 tree, 75 leaves, max depth = 14, train loss: 0.50604, val loss: 0.50136, in 0.016s
1 tree, 75 leaves, max depth = 14, train loss: 0.49897, val loss: 0.49384, in 0.000s
1 tree, 75 leaves, max depth = 12, train loss: 0.49049, val loss: 0.48602, in 0.016s
1 tree, 75 leaves, max depth = 14, train loss: 0.48419, val loss: 0.47944, in 0.016s
1 tree, 75 leaves, max depth = 14, train loss: 0.47840, val loss: 0.47338, in 0.016s
1 tree, 75 leaves, max depth = 15, train loss: 0.47303, val loss: 0.46773, in 0.016s
1 tree, 75 leaves, max depth = 14, train loss: 0.46794, val loss: 0.46241, in 0.000s
1 tree, 75 leaves, max depth = 14, train loss: 0.46325, val loss: 0.45749, in 0.000s
1 tree, 75 leaves, max depth = 10, train loss: 0.45810, val loss: 0.45291, in 0.016s
1 tree, 75 leaves, max depth = 14, train loss: 0.45124, val loss: 0.44664, in 0.016s
1 tree, 75 leaves, max depth = 14, train loss: 0.44719, val loss: 0.44236, in 0.016s
1 tree, 75 leaves, max depth = 14, train loss: 0.44354, val loss: 0.43852, in 0.000s
1 tree, 75 leaves, max depth = 13, train loss: 0.43734, val loss: 0.43289, in 0.016s
1 tree, 75 leaves, max depth = 13, train loss: 0.43162, val loss: 0.42770, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.42827, val loss: 0.42461, in 0.000s
1 tree, 75 leaves, max depth = 13, train loss: 0.42300, val loss: 0.41985, in 0.016s
1 tree, 75 leaves, max depth = 12, train loss: 0.41813, val loss: 0.41547, in 0.016s
1 tree, 75 leaves, max depth = 15, train loss: 0.41516, val loss: 0.41228, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.41216, val loss: 0.40950, in 0.000s
1 tree, 75 leaves, max depth = 12, train loss: 0.40935, val loss: 0.40643, in 0.016s
1 tree, 75 leaves, max depth = 13, train loss: 0.40492, val loss: 0.40249, in 0.016s
1 tree, 75 leaves, max depth = 13, train loss: 0.40240, val loss: 0.39969, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.39970, val loss: 0.39718, in 0.000s
1 tree, 75 leaves, max depth = 14, train loss: 0.39740, val loss: 0.39473, in 0.016s
1 tree, 75 leaves, max depth = 12, train loss: 0.39334, val loss: 0.39117, in 0.016s
1 tree, 75 leaves, max depth = 12, train loss: 0.39124, val loss: 0.38885, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.38879, val loss: 0.38658, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.38653, val loss: 0.38448, in 0.000s
1 tree, 75 leaves, max depth = 12, train loss: 0.38277, val loss: 0.38119, in 0.016s
1 tree, 75 leaves, max depth = 22, train loss: 0.38070, val loss: 0.37961, in 0.016s
1 tree, 75 leaves, max depth = 13, train loss: 0.37734, val loss: 0.37670, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.37524, val loss: 0.37475, in 0.016s
1 tree, 75 leaves, max depth = 13, train loss: 0.37213, val loss: 0.37209, in 0.000s
1 tree, 75 leaves, max depth = 12, train loss: 0.36925, val loss: 0.36965, in 0.016s
1 tree, 75 leaves, max depth = 13, train loss: 0.36749, val loss: 0.36768, in 0.016s
1 tree, 75 leaves, max depth = 12, train loss: 0.36482, val loss: 0.36544, in 0.016s
1 tree, 75 leaves, max depth = 13, train loss: 0.36236, val loss: 0.36337, in 0.016s
1 tree, 75 leaves, max depth = 13, train loss: 0.36075, val loss: 0.36157, in 0.016s
1 tree, 75 leaves, max depth = 12, train loss: 0.35847, val loss: 0.35969, in 0.000s
1 tree, 75 leaves, max depth = 13, train loss: 0.35637, val loss: 0.35796, in 0.016s
1 tree, 75 leaves, max depth = 13, train loss: 0.35485, val loss: 0.35632, in 0.016s
1 tree, 75 leaves, max depth = 13, train loss: 0.35290, val loss: 0.35473, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.35092, val loss: 0.35288, in 0.000s
1 tree, 75 leaves, max depth = 13, train loss: 0.34909, val loss: 0.35141, in 0.016s
1 tree, 75 leaves, max depth = 15, train loss: 0.34773, val loss: 0.34988, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.34589, val loss: 0.34816, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.34419, val loss: 0.34657, in 0.016s
1 tree, 75 leaves, max depth = 14, train loss: 0.34289, val loss: 0.34505, in 0.016s
1 tree, 75 leaves, max depth = 13, train loss: 0.34115, val loss: 0.34367, in 0.000s
1 tree, 75 leaves, max depth = 13, train loss: 0.33954, val loss: 0.34241, in 0.016s
1 tree, 75 leaves, max depth = 14, train loss: 0.33822, val loss: 0.34077, in 0.016s
1 tree, 75 leaves, max depth = 12, train loss: 0.33672, val loss: 0.33960, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.33510, val loss: 0.33808, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.33360, val loss: 0.33668, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.33221, val loss: 0.33539, in 0.016s
1 tree, 75 leaves, max depth = 13, train loss: 0.33077, val loss: 0.33429, in 0.016s
1 tree, 75 leaves, max depth = 14, train loss: 0.32951, val loss: 0.33273, in 0.010s
1 tree, 75 leaves, max depth = 16, train loss: 0.32841, val loss: 0.33151, in 0.006s
1 tree, 75 leaves, max depth = 20, train loss: 0.32737, val loss: 0.33024, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.32605, val loss: 0.32900, in 0.000s
1 tree, 75 leaves, max depth = 12, train loss: 0.32508, val loss: 0.32798, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.32385, val loss: 0.32683, in 0.016s
1 tree, 75 leaves, max depth = 12, train loss: 0.32290, val loss: 0.32575, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.32176, val loss: 0.32468, in 0.016s
1 tree, 75 leaves, max depth = 17, train loss: 0.32089, val loss: 0.32373, in 0.016s
1 tree, 75 leaves, max depth = 19, train loss: 0.31966, val loss: 0.32304, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.31857, val loss: 0.32203, in 0.016s
1 tree, 75 leaves, max depth = 12, train loss: 0.31773, val loss: 0.32108, in 0.016s
1 tree, 75 leaves, max depth = 13, train loss: 0.31629, val loss: 0.32000, in 0.016s
1 tree, 75 leaves, max depth = 11, train loss: 0.31522, val loss: 0.31940, in 0.000s
1 tree, 75 leaves, max depth = 15, train loss: 0.31395, val loss: 0.31845, in 0.016s
1 tree, 75 leaves, max depth = 11, train loss: 0.31279, val loss: 0.31760, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.31172, val loss: 0.31660, in 0.016s
1 tree, 75 leaves, max depth = 13, train loss: 0.31062, val loss: 0.31582, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.30962, val loss: 0.31487, in 0.000s
1 tree, 75 leaves, max depth = 16, train loss: 0.30869, val loss: 0.31374, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.30775, val loss: 0.31285, in 0.016s
1 tree, 75 leaves, max depth = 16, train loss: 0.30690, val loss: 0.31181, in 0.000s
1 tree, 75 leaves, max depth = 15, train loss: 0.30582, val loss: 0.31099, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.30492, val loss: 0.31014, in 0.016s
1 tree, 75 leaves, max depth = 18, train loss: 0.30414, val loss: 0.30935, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.30331, val loss: 0.30857, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.30253, val loss: 0.30784, in 0.016s
1 tree, 75 leaves, max depth = 16, train loss: 0.30174, val loss: 0.30687, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.30101, val loss: 0.30619, in 0.000s
1 tree, 75 leaves, max depth = 17, train loss: 0.29992, val loss: 0.30539, in 0.016s
1 tree, 75 leaves, max depth = 18, train loss: 0.29921, val loss: 0.30469, in 0.016s
Fit 100 trees in 1.486 s, (5890 total leaves)
Time spent computing histograms: 0.540s
Time spent finding best splits:  0.113s
Time spent applying splits:      0.115s
Time spent predicting:           0.016s
Trial 61, Fold 3: Log loss = 0.30098675456426244, Average precision = 0.9532472784465278, ROC-AUC = 0.9525602300854272, Elapsed Time = 1.483257500000036 seconds
Trial 61, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 61, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
Binning 0.040 GB of training data: 0.158 s
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 75 leaves, max depth = 13, train loss: 0.67122, val loss: 0.67007, in 0.016s
1 tree, 75 leaves, max depth = 19, train loss: 0.65103, val loss: 0.64867, in 0.000s
1 tree, 75 leaves, max depth = 19, train loss: 0.63267, val loss: 0.62915, in 0.016s
1 tree, 75 leaves, max depth = 15, train loss: 0.61605, val loss: 0.61152, in 0.016s
1 tree, 75 leaves, max depth = 19, train loss: 0.60098, val loss: 0.59540, in 0.016s
1 tree, 75 leaves, max depth = 14, train loss: 0.58710, val loss: 0.58060, in 0.000s
1 tree, 75 leaves, max depth = 14, train loss: 0.57450, val loss: 0.56710, in 0.016s
1 tree, 75 leaves, max depth = 12, train loss: 0.56297, val loss: 0.55477, in 0.016s
1 tree, 75 leaves, max depth = 14, train loss: 0.55226, val loss: 0.54324, in 0.016s
1 tree, 75 leaves, max depth = 12, train loss: 0.54103, val loss: 0.53232, in 0.000s
1 tree, 75 leaves, max depth = 12, train loss: 0.53073, val loss: 0.52232, in 0.016s
1 tree, 75 leaves, max depth = 15, train loss: 0.52163, val loss: 0.51247, in 0.016s
1 tree, 75 leaves, max depth = 14, train loss: 0.51327, val loss: 0.50340, in 0.016s
1 tree, 75 leaves, max depth = 18, train loss: 0.50546, val loss: 0.49482, in 0.000s
1 tree, 75 leaves, max depth = 14, train loss: 0.49844, val loss: 0.48711, in 0.016s
1 tree, 75 leaves, max depth = 12, train loss: 0.48987, val loss: 0.47884, in 0.016s
1 tree, 75 leaves, max depth = 15, train loss: 0.48358, val loss: 0.47192, in 0.016s
1 tree, 75 leaves, max depth = 15, train loss: 0.47778, val loss: 0.46552, in 0.016s
1 tree, 75 leaves, max depth = 15, train loss: 0.47240, val loss: 0.45953, in 0.000s
1 tree, 75 leaves, max depth = 16, train loss: 0.46734, val loss: 0.45385, in 0.016s
1 tree, 75 leaves, max depth = 16, train loss: 0.46268, val loss: 0.44859, in 0.016s
1 tree, 75 leaves, max depth = 16, train loss: 0.45839, val loss: 0.44372, in 0.000s
1 tree, 75 leaves, max depth = 12, train loss: 0.45122, val loss: 0.43688, in 0.000s
1 tree, 75 leaves, max depth = 16, train loss: 0.44741, val loss: 0.43253, in 0.016s
1 tree, 75 leaves, max depth = 19, train loss: 0.44301, val loss: 0.42834, in 0.016s
1 tree, 75 leaves, max depth = 12, train loss: 0.43676, val loss: 0.42240, in 0.016s
1 tree, 75 leaves, max depth = 12, train loss: 0.43099, val loss: 0.41692, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.42775, val loss: 0.41344, in 0.000s
1 tree, 75 leaves, max depth = 12, train loss: 0.42243, val loss: 0.40840, in 0.016s
1 tree, 75 leaves, max depth = 13, train loss: 0.41749, val loss: 0.40373, in 0.016s
1 tree, 75 leaves, max depth = 15, train loss: 0.41452, val loss: 0.40032, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.41162, val loss: 0.39719, in 0.016s
1 tree, 75 leaves, max depth = 16, train loss: 0.40890, val loss: 0.39404, in 0.016s
1 tree, 75 leaves, max depth = 14, train loss: 0.40440, val loss: 0.38980, in 0.016s
1 tree, 75 leaves, max depth = 10, train loss: 0.40141, val loss: 0.38711, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.39879, val loss: 0.38428, in 0.016s
1 tree, 75 leaves, max depth = 14, train loss: 0.39643, val loss: 0.38155, in 0.000s
1 tree, 75 leaves, max depth = 13, train loss: 0.39244, val loss: 0.37783, in 0.016s
1 tree, 75 leaves, max depth = 12, train loss: 0.39018, val loss: 0.37528, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.38781, val loss: 0.37271, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.38561, val loss: 0.37033, in 0.016s
1 tree, 75 leaves, max depth = 13, train loss: 0.38192, val loss: 0.36692, in 0.016s
1 tree, 75 leaves, max depth = 10, train loss: 0.37958, val loss: 0.36487, in 0.000s
1 tree, 75 leaves, max depth = 13, train loss: 0.37630, val loss: 0.36183, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.37426, val loss: 0.35963, in 0.016s
1 tree, 75 leaves, max depth = 13, train loss: 0.37120, val loss: 0.35680, in 0.016s
1 tree, 75 leaves, max depth = 13, train loss: 0.36837, val loss: 0.35419, in 0.016s
1 tree, 75 leaves, max depth = 12, train loss: 0.36647, val loss: 0.35204, in 0.000s
1 tree, 75 leaves, max depth = 13, train loss: 0.36385, val loss: 0.34963, in 0.016s
1 tree, 75 leaves, max depth = 13, train loss: 0.36141, val loss: 0.34741, in 0.016s
1 tree, 75 leaves, max depth = 15, train loss: 0.35970, val loss: 0.34546, in 0.016s
1 tree, 75 leaves, max depth = 13, train loss: 0.35744, val loss: 0.34340, in 0.000s
1 tree, 75 leaves, max depth = 12, train loss: 0.35535, val loss: 0.34149, in 0.016s
1 tree, 75 leaves, max depth = 14, train loss: 0.35383, val loss: 0.33972, in 0.016s
1 tree, 75 leaves, max depth = 15, train loss: 0.35188, val loss: 0.33794, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.34995, val loss: 0.33583, in 0.000s
1 tree, 75 leaves, max depth = 14, train loss: 0.34813, val loss: 0.33418, in 0.016s
1 tree, 75 leaves, max depth = 14, train loss: 0.34666, val loss: 0.33264, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.34487, val loss: 0.33068, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.34321, val loss: 0.32887, in 0.000s
1 tree, 75 leaves, max depth = 15, train loss: 0.34180, val loss: 0.32746, in 0.016s
1 tree, 75 leaves, max depth = 13, train loss: 0.34004, val loss: 0.32589, in 0.016s
1 tree, 75 leaves, max depth = 12, train loss: 0.33842, val loss: 0.32442, in 0.016s
1 tree, 75 leaves, max depth = 13, train loss: 0.33700, val loss: 0.32296, in 0.000s
1 tree, 75 leaves, max depth = 13, train loss: 0.33548, val loss: 0.32161, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.33390, val loss: 0.31987, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.33243, val loss: 0.31826, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.33107, val loss: 0.31678, in 0.016s
1 tree, 75 leaves, max depth = 13, train loss: 0.32983, val loss: 0.31541, in 0.016s
1 tree, 75 leaves, max depth = 14, train loss: 0.32855, val loss: 0.31412, in 0.000s
1 tree, 75 leaves, max depth = 13, train loss: 0.32706, val loss: 0.31281, in 0.016s
1 tree, 75 leaves, max depth = 19, train loss: 0.32597, val loss: 0.31174, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.32468, val loss: 0.31033, in 0.000s
1 tree, 75 leaves, max depth = 12, train loss: 0.32365, val loss: 0.30918, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.32245, val loss: 0.30786, in 0.016s
1 tree, 75 leaves, max depth = 15, train loss: 0.32142, val loss: 0.30690, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.32030, val loss: 0.30567, in 0.000s
1 tree, 75 leaves, max depth = 13, train loss: 0.31936, val loss: 0.30467, in 0.016s
1 tree, 75 leaves, max depth = 13, train loss: 0.31840, val loss: 0.30374, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.31735, val loss: 0.30259, in 0.000s
1 tree, 51 leaves, max depth = 11, train loss: 0.31652, val loss: 0.30182, in 0.016s
1 tree, 75 leaves, max depth = 12, train loss: 0.31500, val loss: 0.30049, in 0.016s
1 tree, 75 leaves, max depth = 15, train loss: 0.31417, val loss: 0.29973, in 0.016s
1 tree, 75 leaves, max depth = 14, train loss: 0.31275, val loss: 0.29850, in 0.016s
1 tree, 75 leaves, max depth = 12, train loss: 0.31145, val loss: 0.29735, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.31041, val loss: 0.29621, in 0.016s
1 tree, 75 leaves, max depth = 14, train loss: 0.30919, val loss: 0.29515, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.30821, val loss: 0.29407, in 0.000s
1 tree, 75 leaves, max depth = 15, train loss: 0.30733, val loss: 0.29325, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.30642, val loss: 0.29224, in 0.000s
1 tree, 75 leaves, max depth = 15, train loss: 0.30560, val loss: 0.29149, in 0.016s
1 tree, 75 leaves, max depth = 14, train loss: 0.30441, val loss: 0.29047, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.30353, val loss: 0.28950, in 0.000s
1 tree, 75 leaves, max depth = 12, train loss: 0.30281, val loss: 0.28875, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.30200, val loss: 0.28785, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.30125, val loss: 0.28701, in 0.000s
1 tree, 75 leaves, max depth = 15, train loss: 0.30049, val loss: 0.28635, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.29978, val loss: 0.28557, in 0.000s
1 tree, 75 leaves, max depth = 14, train loss: 0.29857, val loss: 0.28455, in 0.016s
1 tree, 75 leaves, max depth = 14, train loss: 0.29745, val loss: 0.28379, in 0.016s
Fit 100 trees in 1.440 s, (5866 total leaves)
Time spent computing histograms: 0.521s
Time spent finding best splits:  0.112s
Time spent applying splits:      0.113s
Time spent predicting:           0.016s
Trial 61, Fold 4: Log loss = 0.30024239566538646, Average precision = 0.9538280617924989, ROC-AUC = 0.9534089310452537, Elapsed Time = 1.4443295000000944 seconds
Trial 61, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 61, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
Binning 0.040 GB of training data: 0.157 s
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 75 leaves, max depth = 14, train loss: 0.67094, val loss: 0.66952, in 0.000s
1 tree, 75 leaves, max depth = 15, train loss: 0.65052, val loss: 0.64772, in 0.016s
1 tree, 75 leaves, max depth = 15, train loss: 0.63196, val loss: 0.62785, in 0.016s
1 tree, 75 leaves, max depth = 13, train loss: 0.61527, val loss: 0.60999, in 0.016s
1 tree, 75 leaves, max depth = 14, train loss: 0.59998, val loss: 0.59356, in 0.000s
1 tree, 75 leaves, max depth = 14, train loss: 0.58600, val loss: 0.57849, in 0.016s
1 tree, 75 leaves, max depth = 16, train loss: 0.57323, val loss: 0.56476, in 0.016s
1 tree, 75 leaves, max depth = 14, train loss: 0.56153, val loss: 0.55212, in 0.000s
1 tree, 75 leaves, max depth = 14, train loss: 0.55072, val loss: 0.54028, in 0.016s
1 tree, 75 leaves, max depth = 18, train loss: 0.53968, val loss: 0.52988, in 0.016s
1 tree, 75 leaves, max depth = 16, train loss: 0.53016, val loss: 0.51947, in 0.016s
1 tree, 75 leaves, max depth = 13, train loss: 0.52135, val loss: 0.50979, in 0.000s
1 tree, 75 leaves, max depth = 16, train loss: 0.51332, val loss: 0.50101, in 0.016s
1 tree, 75 leaves, max depth = 15, train loss: 0.50571, val loss: 0.49258, in 0.016s
1 tree, 75 leaves, max depth = 13, train loss: 0.49892, val loss: 0.48498, in 0.016s
1 tree, 75 leaves, max depth = 14, train loss: 0.48996, val loss: 0.47668, in 0.016s
1 tree, 75 leaves, max depth = 13, train loss: 0.48386, val loss: 0.46985, in 0.000s
1 tree, 75 leaves, max depth = 13, train loss: 0.47825, val loss: 0.46354, in 0.016s
1 tree, 75 leaves, max depth = 14, train loss: 0.47311, val loss: 0.45781, in 0.016s
1 tree, 75 leaves, max depth = 15, train loss: 0.46818, val loss: 0.45222, in 0.016s
1 tree, 75 leaves, max depth = 15, train loss: 0.46364, val loss: 0.44705, in 0.000s
1 tree, 75 leaves, max depth = 11, train loss: 0.45834, val loss: 0.44256, in 0.016s
1 tree, 75 leaves, max depth = 15, train loss: 0.45106, val loss: 0.43596, in 0.016s
1 tree, 75 leaves, max depth = 14, train loss: 0.44715, val loss: 0.43147, in 0.016s
1 tree, 75 leaves, max depth = 15, train loss: 0.44277, val loss: 0.42734, in 0.000s
1 tree, 75 leaves, max depth = 15, train loss: 0.43644, val loss: 0.42165, in 0.016s
1 tree, 75 leaves, max depth = 15, train loss: 0.43058, val loss: 0.41641, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.42733, val loss: 0.41329, in 0.016s
1 tree, 75 leaves, max depth = 15, train loss: 0.42192, val loss: 0.40848, in 0.000s
1 tree, 75 leaves, max depth = 15, train loss: 0.41692, val loss: 0.40404, in 0.016s
1 tree, 75 leaves, max depth = 14, train loss: 0.41393, val loss: 0.40052, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.41101, val loss: 0.39772, in 0.000s
1 tree, 75 leaves, max depth = 15, train loss: 0.40821, val loss: 0.39446, in 0.016s
1 tree, 75 leaves, max depth = 15, train loss: 0.40363, val loss: 0.39045, in 0.016s
1 tree, 75 leaves, max depth = 15, train loss: 0.40111, val loss: 0.38749, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.39849, val loss: 0.38499, in 0.016s
1 tree, 75 leaves, max depth = 15, train loss: 0.39619, val loss: 0.38229, in 0.016s
1 tree, 75 leaves, max depth = 14, train loss: 0.39198, val loss: 0.37864, in 0.000s
1 tree, 75 leaves, max depth = 13, train loss: 0.38984, val loss: 0.37606, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.38746, val loss: 0.37379, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.38527, val loss: 0.37171, in 0.000s
1 tree, 75 leaves, max depth = 14, train loss: 0.38136, val loss: 0.36836, in 0.016s
1 tree, 75 leaves, max depth = 22, train loss: 0.37927, val loss: 0.36654, in 0.016s
1 tree, 75 leaves, max depth = 14, train loss: 0.37577, val loss: 0.36357, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.37373, val loss: 0.36164, in 0.000s
1 tree, 75 leaves, max depth = 14, train loss: 0.37047, val loss: 0.35889, in 0.016s
1 tree, 75 leaves, max depth = 14, train loss: 0.36746, val loss: 0.35637, in 0.016s
1 tree, 75 leaves, max depth = 13, train loss: 0.36566, val loss: 0.35424, in 0.016s
1 tree, 75 leaves, max depth = 14, train loss: 0.36286, val loss: 0.35191, in 0.016s
1 tree, 75 leaves, max depth = 15, train loss: 0.36028, val loss: 0.34979, in 0.000s
1 tree, 75 leaves, max depth = 13, train loss: 0.35865, val loss: 0.34779, in 0.016s
1 tree, 75 leaves, max depth = 15, train loss: 0.35625, val loss: 0.34583, in 0.016s
1 tree, 75 leaves, max depth = 15, train loss: 0.35403, val loss: 0.34403, in 0.016s
1 tree, 75 leaves, max depth = 17, train loss: 0.35254, val loss: 0.34241, in 0.000s
1 tree, 75 leaves, max depth = 14, train loss: 0.35047, val loss: 0.34074, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.34853, val loss: 0.33889, in 0.016s
1 tree, 75 leaves, max depth = 14, train loss: 0.34660, val loss: 0.33734, in 0.016s
1 tree, 75 leaves, max depth = 14, train loss: 0.34519, val loss: 0.33559, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.34340, val loss: 0.33387, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.34174, val loss: 0.33228, in 0.000s
1 tree, 75 leaves, max depth = 13, train loss: 0.34039, val loss: 0.33083, in 0.016s
1 tree, 75 leaves, max depth = 14, train loss: 0.33854, val loss: 0.32937, in 0.016s
1 tree, 75 leaves, max depth = 15, train loss: 0.33683, val loss: 0.32803, in 0.016s
1 tree, 75 leaves, max depth = 15, train loss: 0.33546, val loss: 0.32654, in 0.016s
1 tree, 75 leaves, max depth = 13, train loss: 0.33386, val loss: 0.32530, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.33228, val loss: 0.32377, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.33081, val loss: 0.32236, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.32946, val loss: 0.32106, in 0.016s
1 tree, 75 leaves, max depth = 13, train loss: 0.32824, val loss: 0.31976, in 0.016s
1 tree, 75 leaves, max depth = 16, train loss: 0.32669, val loss: 0.31856, in 0.000s
1 tree, 75 leaves, max depth = 15, train loss: 0.32548, val loss: 0.31724, in 0.016s
1 tree, 75 leaves, max depth = 13, train loss: 0.32440, val loss: 0.31593, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.32311, val loss: 0.31469, in 0.016s
1 tree, 75 leaves, max depth = 14, train loss: 0.32212, val loss: 0.31346, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.32092, val loss: 0.31231, in 0.016s
1 tree, 75 leaves, max depth = 13, train loss: 0.31996, val loss: 0.31129, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.31884, val loss: 0.31022, in 0.016s
1 tree, 75 leaves, max depth = 14, train loss: 0.31795, val loss: 0.30910, in 0.016s
1 tree, 75 leaves, max depth = 15, train loss: 0.31703, val loss: 0.30811, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.31598, val loss: 0.30710, in 0.016s
1 tree, 75 leaves, max depth = 13, train loss: 0.31519, val loss: 0.30627, in 0.000s
1 tree, 75 leaves, max depth = 16, train loss: 0.31358, val loss: 0.30505, in 0.016s
1 tree, 75 leaves, max depth = 13, train loss: 0.31235, val loss: 0.30412, in 0.016s
1 tree, 75 leaves, max depth = 16, train loss: 0.31093, val loss: 0.30307, in 0.016s
1 tree, 75 leaves, max depth = 16, train loss: 0.30962, val loss: 0.30210, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.30857, val loss: 0.30109, in 0.016s
1 tree, 75 leaves, max depth = 15, train loss: 0.30734, val loss: 0.30020, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.30636, val loss: 0.29924, in 0.016s
1 tree, 75 leaves, max depth = 18, train loss: 0.30547, val loss: 0.29829, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.30455, val loss: 0.29740, in 0.000s
1 tree, 75 leaves, max depth = 19, train loss: 0.30373, val loss: 0.29652, in 0.016s
1 tree, 75 leaves, max depth = 15, train loss: 0.30253, val loss: 0.29566, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.30165, val loss: 0.29481, in 0.000s
1 tree, 75 leaves, max depth = 18, train loss: 0.30087, val loss: 0.29398, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.30006, val loss: 0.29319, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.29930, val loss: 0.29247, in 0.016s
1 tree, 75 leaves, max depth = 14, train loss: 0.29859, val loss: 0.29158, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.29788, val loss: 0.29090, in 0.000s
1 tree, 75 leaves, max depth = 15, train loss: 0.29666, val loss: 0.29004, in 0.016s
1 tree, 75 leaves, max depth = 18, train loss: 0.29594, val loss: 0.28928, in 0.016s
Fit 100 trees in 1.423 s, (5890 total leaves)
Time spent computing histograms: 0.524s
Time spent finding best splits:  0.107s
Time spent applying splits:      0.109s
Time spent predicting:           0.000s
Trial 61, Fold 5: Log loss = 0.3061034647699614, Average precision = 0.9509200647523018, ROC-AUC = 0.949326496107612, Elapsed Time = 1.433862500000032 seconds
Optimization Progress:  62%|######2   | 62/100 [12:09<07:22, 11.65s/it]
Trial 62, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 62, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
0.143 s 0.040 GB of training data: 
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 49 leaves, max depth = 10, train loss: 0.65202, val loss: 0.65248, in 0.016s
1 tree, 49 leaves, max depth = 10, train loss: 0.61666, val loss: 0.61748, in 0.016s
1 tree, 49 leaves, max depth = 10, train loss: 0.58585, val loss: 0.58689, in 0.016s
1 tree, 50 leaves, max depth = 12, train loss: 0.55982, val loss: 0.56076, in 0.016s
1 tree, 50 leaves, max depth = 10, train loss: 0.53538, val loss: 0.53637, in 0.016s
1 tree, 51 leaves, max depth = 12, train loss: 0.51502, val loss: 0.51592, in 0.016s
1 tree, 54 leaves, max depth = 11, train loss: 0.49548, val loss: 0.49624, in 0.016s
1 tree, 53 leaves, max depth = 10, train loss: 0.47812, val loss: 0.47875, in 0.016s
1 tree, 54 leaves, max depth = 12, train loss: 0.46256, val loss: 0.46300, in 0.016s
1 tree, 53 leaves, max depth = 11, train loss: 0.44864, val loss: 0.44889, in 0.016s
1 tree, 55 leaves, max depth = 10, train loss: 0.43600, val loss: 0.43617, in 0.016s
1 tree, 52 leaves, max depth = 11, train loss: 0.41825, val loss: 0.41907, in 0.016s
1 tree, 53 leaves, max depth = 12, train loss: 0.40780, val loss: 0.40866, in 0.016s
1 tree, 52 leaves, max depth = 11, train loss: 0.39291, val loss: 0.39438, in 0.016s
1 tree, 55 leaves, max depth = 10, train loss: 0.38413, val loss: 0.38563, in 0.016s
1 tree, 52 leaves, max depth = 11, train loss: 0.37157, val loss: 0.37363, in 0.016s
1 tree, 55 leaves, max depth = 12, train loss: 0.36408, val loss: 0.36625, in 0.018s
1 tree, 29 leaves, max depth = 10, train loss: 0.35393, val loss: 0.35621, in 0.014s
1 tree, 30 leaves, max depth = 10, train loss: 0.34493, val loss: 0.34728, in 0.016s
1 tree, 55 leaves, max depth = 11, train loss: 0.33780, val loss: 0.34033, in 0.016s
1 tree, 45 leaves, max depth = 12, train loss: 0.32994, val loss: 0.33341, in 0.016s
1 tree, 29 leaves, max depth = 14, train loss: 0.32291, val loss: 0.32650, in 0.016s
1 tree, 54 leaves, max depth = 12, train loss: 0.31696, val loss: 0.32067, in 0.016s
1 tree, 45 leaves, max depth = 11, train loss: 0.31072, val loss: 0.31525, in 0.016s
1 tree, 29 leaves, max depth = 10, train loss: 0.30512, val loss: 0.30970, in 0.000s
1 tree, 62 leaves, max depth = 12, train loss: 0.29993, val loss: 0.30456, in 0.016s
1 tree, 45 leaves, max depth = 11, train loss: 0.29493, val loss: 0.30030, in 0.016s
1 tree, 28 leaves, max depth = 10, train loss: 0.29046, val loss: 0.29584, in 0.016s
1 tree, 53 leaves, max depth = 12, train loss: 0.28614, val loss: 0.29161, in 0.016s
1 tree, 54 leaves, max depth = 12, train loss: 0.28222, val loss: 0.28786, in 0.016s
1 tree, 45 leaves, max depth = 11, train loss: 0.27833, val loss: 0.28460, in 0.016s
1 tree, 60 leaves, max depth = 12, train loss: 0.27474, val loss: 0.28095, in 0.016s
1 tree, 28 leaves, max depth = 11, train loss: 0.27141, val loss: 0.27764, in 0.016s
1 tree, 45 leaves, max depth = 11, train loss: 0.26826, val loss: 0.27508, in 0.016s
1 tree, 28 leaves, max depth = 11, train loss: 0.26547, val loss: 0.27226, in 0.016s
1 tree, 44 leaves, max depth = 11, train loss: 0.26277, val loss: 0.27013, in 0.016s
1 tree, 28 leaves, max depth = 10, train loss: 0.26041, val loss: 0.26773, in 0.016s
1 tree, 59 leaves, max depth = 12, train loss: 0.25752, val loss: 0.26483, in 0.031s
1 tree, 45 leaves, max depth = 12, train loss: 0.25533, val loss: 0.26313, in 0.016s
1 tree, 35 leaves, max depth = 16, train loss: 0.25334, val loss: 0.26095, in 0.016s
1 tree, 46 leaves, max depth = 12, train loss: 0.25147, val loss: 0.25952, in 0.016s
1 tree, 29 leaves, max depth = 12, train loss: 0.24968, val loss: 0.25771, in 0.016s
1 tree, 59 leaves, max depth = 15, train loss: 0.24732, val loss: 0.25534, in 0.016s
1 tree, 49 leaves, max depth = 11, train loss: 0.24490, val loss: 0.25281, in 0.016s
1 tree, 28 leaves, max depth = 14, train loss: 0.24338, val loss: 0.25121, in 0.016s
1 tree, 49 leaves, max depth = 11, train loss: 0.24121, val loss: 0.24895, in 0.016s
1 tree, 32 leaves, max depth = 11, train loss: 0.23958, val loss: 0.24769, in 0.016s
1 tree, 36 leaves, max depth = 17, train loss: 0.23820, val loss: 0.24609, in 0.016s
1 tree, 65 leaves, max depth = 14, train loss: 0.23601, val loss: 0.24377, in 0.016s
1 tree, 58 leaves, max depth = 12, train loss: 0.23454, val loss: 0.24241, in 0.016s
1 tree, 52 leaves, max depth = 18, train loss: 0.23321, val loss: 0.24139, in 0.016s
1 tree, 61 leaves, max depth = 14, train loss: 0.23200, val loss: 0.23999, in 0.016s
1 tree, 62 leaves, max depth = 12, train loss: 0.23010, val loss: 0.23790, in 0.016s
1 tree, 33 leaves, max depth = 11, train loss: 0.22882, val loss: 0.23695, in 0.016s
1 tree, 60 leaves, max depth = 13, train loss: 0.22775, val loss: 0.23587, in 0.016s
1 tree, 52 leaves, max depth = 19, train loss: 0.22665, val loss: 0.23494, in 0.016s
1 tree, 66 leaves, max depth = 14, train loss: 0.22496, val loss: 0.23312, in 0.016s
1 tree, 55 leaves, max depth = 12, train loss: 0.22389, val loss: 0.23216, in 0.016s
1 tree, 37 leaves, max depth = 15, train loss: 0.22299, val loss: 0.23110, in 0.016s
1 tree, 60 leaves, max depth = 13, train loss: 0.22210, val loss: 0.23009, in 0.000s
1 tree, 61 leaves, max depth = 12, train loss: 0.22070, val loss: 0.22854, in 0.016s
1 tree, 60 leaves, max depth = 12, train loss: 0.21942, val loss: 0.22725, in 0.031s
1 tree, 33 leaves, max depth = 11, train loss: 0.21844, val loss: 0.22649, in 0.000s
1 tree, 58 leaves, max depth = 15, train loss: 0.21752, val loss: 0.22595, in 0.016s
Fit 64 trees in 1.315 s, (3062 total leaves)
Time spent computing histograms: 0.388s
Time spent finding best splits:  0.080s
Time spent applying splits:      0.059s
Time spent predicting:           0.000s
Trial 62, Fold 1: Log loss = 0.22921671941905178, Average precision = 0.9687370333075268, ROC-AUC = 0.9640845234510547, Elapsed Time = 1.327083500000299 seconds
Trial 62, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 62, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
Binning 0.040 GB of training data: 0.158 s
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 49 leaves, max depth = 11, train loss: 0.65203, val loss: 0.65130, in 0.016s
1 tree, 51 leaves, max depth = 11, train loss: 0.61660, val loss: 0.61514, in 0.016s
1 tree, 50 leaves, max depth = 11, train loss: 0.58569, val loss: 0.58361, in 0.016s
1 tree, 54 leaves, max depth = 12, train loss: 0.55961, val loss: 0.55727, in 0.016s
1 tree, 52 leaves, max depth = 10, train loss: 0.53489, val loss: 0.53223, in 0.016s
1 tree, 53 leaves, max depth = 12, train loss: 0.51451, val loss: 0.51166, in 0.031s
1 tree, 54 leaves, max depth = 10, train loss: 0.49491, val loss: 0.49176, in 0.016s
1 tree, 53 leaves, max depth = 11, train loss: 0.47736, val loss: 0.47402, in 0.016s
1 tree, 51 leaves, max depth = 11, train loss: 0.46184, val loss: 0.45820, in 0.016s
1 tree, 53 leaves, max depth = 10, train loss: 0.44775, val loss: 0.44393, in 0.016s
1 tree, 52 leaves, max depth = 10, train loss: 0.43516, val loss: 0.43111, in 0.016s
1 tree, 53 leaves, max depth = 12, train loss: 0.41764, val loss: 0.41398, in 0.016s
1 tree, 63 leaves, max depth = 11, train loss: 0.40729, val loss: 0.40362, in 0.016s
1 tree, 53 leaves, max depth = 14, train loss: 0.39258, val loss: 0.38927, in 0.031s
1 tree, 52 leaves, max depth = 11, train loss: 0.38378, val loss: 0.38042, in 0.016s
1 tree, 52 leaves, max depth = 12, train loss: 0.37138, val loss: 0.36833, in 0.016s
1 tree, 53 leaves, max depth = 11, train loss: 0.36453, val loss: 0.36152, in 0.016s
1 tree, 29 leaves, max depth = 11, train loss: 0.35449, val loss: 0.35166, in 0.016s
1 tree, 51 leaves, max depth = 13, train loss: 0.34695, val loss: 0.34416, in 0.016s
1 tree, 46 leaves, max depth = 12, train loss: 0.33834, val loss: 0.33627, in 0.016s
1 tree, 29 leaves, max depth = 13, train loss: 0.33052, val loss: 0.32866, in 0.016s
1 tree, 48 leaves, max depth = 13, train loss: 0.32422, val loss: 0.32242, in 0.016s
1 tree, 46 leaves, max depth = 11, train loss: 0.31737, val loss: 0.31613, in 0.016s
1 tree, 29 leaves, max depth = 13, train loss: 0.31121, val loss: 0.31014, in 0.016s
1 tree, 57 leaves, max depth = 13, train loss: 0.30570, val loss: 0.30483, in 0.016s
1 tree, 47 leaves, max depth = 11, train loss: 0.30025, val loss: 0.29988, in 0.016s
1 tree, 29 leaves, max depth = 13, train loss: 0.29531, val loss: 0.29504, in 0.016s
1 tree, 48 leaves, max depth = 11, train loss: 0.29074, val loss: 0.29092, in 0.016s
1 tree, 29 leaves, max depth = 13, train loss: 0.28663, val loss: 0.28685, in 0.016s
1 tree, 59 leaves, max depth = 13, train loss: 0.28227, val loss: 0.28267, in 0.016s
1 tree, 29 leaves, max depth = 11, train loss: 0.27875, val loss: 0.27926, in 0.000s
1 tree, 52 leaves, max depth = 14, train loss: 0.27500, val loss: 0.27565, in 0.031s
1 tree, 47 leaves, max depth = 13, train loss: 0.27165, val loss: 0.27267, in 0.016s
1 tree, 48 leaves, max depth = 12, train loss: 0.26865, val loss: 0.27003, in 0.016s
1 tree, 30 leaves, max depth = 16, train loss: 0.26596, val loss: 0.26741, in 0.016s
1 tree, 62 leaves, max depth = 13, train loss: 0.26270, val loss: 0.26441, in 0.016s
1 tree, 47 leaves, max depth = 14, train loss: 0.26024, val loss: 0.26222, in 0.000s
1 tree, 28 leaves, max depth = 11, train loss: 0.25798, val loss: 0.26003, in 0.016s
1 tree, 62 leaves, max depth = 13, train loss: 0.25516, val loss: 0.25743, in 0.031s
1 tree, 52 leaves, max depth = 14, train loss: 0.25268, val loss: 0.25504, in 0.016s
1 tree, 35 leaves, max depth = 12, train loss: 0.25034, val loss: 0.25322, in 0.016s
1 tree, 28 leaves, max depth = 16, train loss: 0.24859, val loss: 0.25154, in 0.016s
1 tree, 54 leaves, max depth = 13, train loss: 0.24680, val loss: 0.25025, in 0.016s
1 tree, 59 leaves, max depth = 14, train loss: 0.24460, val loss: 0.24825, in 0.016s
1 tree, 34 leaves, max depth = 11, train loss: 0.24280, val loss: 0.24682, in 0.016s
1 tree, 37 leaves, max depth = 11, train loss: 0.24132, val loss: 0.24533, in 0.016s
1 tree, 62 leaves, max depth = 13, train loss: 0.23991, val loss: 0.24421, in 0.016s
1 tree, 63 leaves, max depth = 14, train loss: 0.23773, val loss: 0.24222, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.23619, val loss: 0.24103, in 0.016s
1 tree, 60 leaves, max depth = 14, train loss: 0.23492, val loss: 0.24007, in 0.000s
1 tree, 55 leaves, max depth = 14, train loss: 0.23365, val loss: 0.23927, in 0.016s
1 tree, 61 leaves, max depth = 11, train loss: 0.23174, val loss: 0.23751, in 0.016s
1 tree, 34 leaves, max depth = 12, train loss: 0.23044, val loss: 0.23658, in 0.016s
1 tree, 60 leaves, max depth = 13, train loss: 0.22938, val loss: 0.23578, in 0.016s
1 tree, 36 leaves, max depth = 11, train loss: 0.22750, val loss: 0.23379, in 0.031s
1 tree, 36 leaves, max depth = 9, train loss: 0.22645, val loss: 0.23293, in 0.016s
1 tree, 36 leaves, max depth = 11, train loss: 0.22472, val loss: 0.23099, in 0.000s
1 tree, 63 leaves, max depth = 13, train loss: 0.22379, val loss: 0.23040, in 0.016s
1 tree, 60 leaves, max depth = 14, train loss: 0.22243, val loss: 0.22926, in 0.031s
1 tree, 48 leaves, max depth = 11, train loss: 0.22101, val loss: 0.22811, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.22009, val loss: 0.22747, in 0.016s
1 tree, 38 leaves, max depth = 13, train loss: 0.21869, val loss: 0.22588, in 0.016s
1 tree, 32 leaves, max depth = 8, train loss: 0.21793, val loss: 0.22523, in 0.016s
1 tree, 59 leaves, max depth = 14, train loss: 0.21723, val loss: 0.22479, in 0.016s
Fit 64 trees in 1.392 s, (3022 total leaves)
Time spent computing histograms: 0.393s
Time spent finding best splits:  0.081s
Time spent applying splits:      0.060s
Time spent predicting:           0.000s
Trial 62, Fold 2: Log loss = 0.22243597504614823, Average precision = 0.96841896352419, ROC-AUC = 0.9656550754626563, Elapsed Time = 1.4002233000010165 seconds
Trial 62, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 62, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
Binning 0.040 GB of training data: 0.158 s
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 52 leaves, max depth = 9, train loss: 0.65227, val loss: 0.65219, in 0.016s
1 tree, 52 leaves, max depth = 9, train loss: 0.61709, val loss: 0.61691, in 0.016s
1 tree, 54 leaves, max depth = 12, train loss: 0.58642, val loss: 0.58609, in 0.016s
1 tree, 56 leaves, max depth = 11, train loss: 0.56023, val loss: 0.56005, in 0.016s
1 tree, 54 leaves, max depth = 11, train loss: 0.53580, val loss: 0.53570, in 0.016s
1 tree, 55 leaves, max depth = 11, train loss: 0.51531, val loss: 0.51531, in 0.016s
1 tree, 54 leaves, max depth = 11, train loss: 0.49588, val loss: 0.49595, in 0.016s
1 tree, 52 leaves, max depth = 11, train loss: 0.47857, val loss: 0.47868, in 0.016s
1 tree, 55 leaves, max depth = 12, train loss: 0.46308, val loss: 0.46324, in 0.016s
1 tree, 53 leaves, max depth = 11, train loss: 0.44920, val loss: 0.44939, in 0.016s
1 tree, 54 leaves, max depth = 11, train loss: 0.43044, val loss: 0.43204, in 0.016s
1 tree, 55 leaves, max depth = 12, train loss: 0.41894, val loss: 0.42062, in 0.016s
1 tree, 55 leaves, max depth = 11, train loss: 0.40844, val loss: 0.41007, in 0.016s
1 tree, 51 leaves, max depth = 12, train loss: 0.39352, val loss: 0.39643, in 0.016s
1 tree, 55 leaves, max depth = 11, train loss: 0.38473, val loss: 0.38766, in 0.016s
1 tree, 54 leaves, max depth = 12, train loss: 0.37213, val loss: 0.37624, in 0.016s
1 tree, 56 leaves, max depth = 11, train loss: 0.36467, val loss: 0.36878, in 0.016s
1 tree, 29 leaves, max depth = 10, train loss: 0.35443, val loss: 0.35947, in 0.031s
1 tree, 29 leaves, max depth = 10, train loss: 0.34539, val loss: 0.35129, in 0.000s
1 tree, 51 leaves, max depth = 11, train loss: 0.33837, val loss: 0.34408, in 0.016s
1 tree, 48 leaves, max depth = 13, train loss: 0.33025, val loss: 0.33744, in 0.016s
1 tree, 28 leaves, max depth = 12, train loss: 0.32321, val loss: 0.33096, in 0.016s
1 tree, 51 leaves, max depth = 13, train loss: 0.31734, val loss: 0.32479, in 0.016s
1 tree, 48 leaves, max depth = 12, train loss: 0.31090, val loss: 0.31958, in 0.016s
1 tree, 29 leaves, max depth = 10, train loss: 0.30529, val loss: 0.31457, in 0.016s
1 tree, 51 leaves, max depth = 12, train loss: 0.30039, val loss: 0.30950, in 0.016s
1 tree, 46 leaves, max depth = 13, train loss: 0.29524, val loss: 0.30536, in 0.016s
1 tree, 29 leaves, max depth = 11, train loss: 0.29077, val loss: 0.30134, in 0.016s
1 tree, 48 leaves, max depth = 12, train loss: 0.28637, val loss: 0.29800, in 0.016s
1 tree, 28 leaves, max depth = 11, train loss: 0.28266, val loss: 0.29464, in 0.016s
1 tree, 52 leaves, max depth = 11, train loss: 0.27867, val loss: 0.29035, in 0.016s
1 tree, 45 leaves, max depth = 12, train loss: 0.27510, val loss: 0.28765, in 0.016s
1 tree, 47 leaves, max depth = 13, train loss: 0.27189, val loss: 0.28522, in 0.016s
1 tree, 28 leaves, max depth = 11, train loss: 0.26904, val loss: 0.28270, in 0.016s
1 tree, 52 leaves, max depth = 11, train loss: 0.26582, val loss: 0.27921, in 0.016s
1 tree, 46 leaves, max depth = 12, train loss: 0.26319, val loss: 0.27732, in 0.016s
1 tree, 28 leaves, max depth = 11, train loss: 0.26087, val loss: 0.27524, in 0.016s
1 tree, 63 leaves, max depth = 11, train loss: 0.25786, val loss: 0.27188, in 0.016s
1 tree, 45 leaves, max depth = 12, train loss: 0.25567, val loss: 0.27045, in 0.016s
1 tree, 33 leaves, max depth = 14, train loss: 0.25371, val loss: 0.26855, in 0.016s
1 tree, 62 leaves, max depth = 15, train loss: 0.25114, val loss: 0.26571, in 0.031s
1 tree, 28 leaves, max depth = 13, train loss: 0.24943, val loss: 0.26411, in 0.000s
1 tree, 61 leaves, max depth = 14, train loss: 0.24705, val loss: 0.26145, in 0.016s
1 tree, 34 leaves, max depth = 11, train loss: 0.24512, val loss: 0.26008, in 0.016s
1 tree, 32 leaves, max depth = 15, train loss: 0.24367, val loss: 0.25868, in 0.016s
1 tree, 60 leaves, max depth = 14, train loss: 0.24132, val loss: 0.25600, in 0.031s
1 tree, 59 leaves, max depth = 12, train loss: 0.23991, val loss: 0.25448, in 0.016s
1 tree, 59 leaves, max depth = 13, train loss: 0.23801, val loss: 0.25288, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.23634, val loss: 0.25161, in 0.016s
1 tree, 54 leaves, max depth = 16, train loss: 0.23498, val loss: 0.25083, in 0.016s
1 tree, 62 leaves, max depth = 12, train loss: 0.23301, val loss: 0.24862, in 0.031s
1 tree, 60 leaves, max depth = 11, train loss: 0.23188, val loss: 0.24743, in 0.016s
1 tree, 61 leaves, max depth = 12, train loss: 0.23014, val loss: 0.24548, in 0.016s
1 tree, 54 leaves, max depth = 16, train loss: 0.22887, val loss: 0.24477, in 0.031s
1 tree, 60 leaves, max depth = 12, train loss: 0.22729, val loss: 0.24301, in 0.016s
1 tree, 34 leaves, max depth = 11, train loss: 0.22605, val loss: 0.24216, in 0.016s
1 tree, 60 leaves, max depth = 12, train loss: 0.22511, val loss: 0.24123, in 0.016s
1 tree, 62 leaves, max depth = 14, train loss: 0.22424, val loss: 0.24047, in 0.031s
1 tree, 58 leaves, max depth = 13, train loss: 0.22307, val loss: 0.23983, in 0.016s
1 tree, 61 leaves, max depth = 13, train loss: 0.22172, val loss: 0.23830, in 0.016s
1 tree, 54 leaves, max depth = 15, train loss: 0.22074, val loss: 0.23782, in 0.031s
1 tree, 61 leaves, max depth = 13, train loss: 0.21950, val loss: 0.23635, in 0.016s
1 tree, 62 leaves, max depth = 13, train loss: 0.21873, val loss: 0.23583, in 0.031s
1 tree, 61 leaves, max depth = 12, train loss: 0.21763, val loss: 0.23451, in 0.016s
Fit 64 trees in 1.470 s, (3165 total leaves)
Time spent computing histograms: 0.402s
Time spent finding best splits:  0.090s
Time spent applying splits:      0.069s
Time spent predicting:           0.000s
Trial 62, Fold 3: Log loss = 0.22279210569114516, Average precision = 0.9677415351549978, ROC-AUC = 0.9644402623012396, Elapsed Time = 1.4759725999992952 seconds
Trial 62, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 62, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
Binning 0.040 GB of training data: 0.173 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 49 leaves, max depth = 10, train loss: 0.65255, val loss: 0.65082, in 0.016s
1 tree, 50 leaves, max depth = 10, train loss: 0.61746, val loss: 0.61419, in 0.031s
1 tree, 52 leaves, max depth = 11, train loss: 0.58655, val loss: 0.58175, in 0.016s
1 tree, 52 leaves, max depth = 11, train loss: 0.56076, val loss: 0.55467, in 0.016s
1 tree, 52 leaves, max depth = 10, train loss: 0.53636, val loss: 0.52918, in 0.016s
1 tree, 50 leaves, max depth = 10, train loss: 0.51632, val loss: 0.50806, in 0.016s
1 tree, 52 leaves, max depth = 11, train loss: 0.49689, val loss: 0.48771, in 0.031s
1 tree, 53 leaves, max depth = 10, train loss: 0.47962, val loss: 0.46944, in 0.016s
1 tree, 50 leaves, max depth = 10, train loss: 0.46417, val loss: 0.45303, in 0.016s
1 tree, 53 leaves, max depth = 10, train loss: 0.45022, val loss: 0.43821, in 0.016s
1 tree, 54 leaves, max depth = 11, train loss: 0.43776, val loss: 0.42501, in 0.016s
1 tree, 53 leaves, max depth = 13, train loss: 0.42023, val loss: 0.40725, in 0.031s
1 tree, 58 leaves, max depth = 11, train loss: 0.41003, val loss: 0.39632, in 0.016s
1 tree, 53 leaves, max depth = 13, train loss: 0.39531, val loss: 0.38140, in 0.016s
1 tree, 52 leaves, max depth = 12, train loss: 0.38654, val loss: 0.37194, in 0.016s
1 tree, 51 leaves, max depth = 12, train loss: 0.37410, val loss: 0.35932, in 0.016s
1 tree, 53 leaves, max depth = 14, train loss: 0.36731, val loss: 0.35213, in 0.016s
1 tree, 30 leaves, max depth = 15, train loss: 0.35720, val loss: 0.34169, in 0.016s
1 tree, 52 leaves, max depth = 12, train loss: 0.34971, val loss: 0.33410, in 0.016s
1 tree, 30 leaves, max depth = 15, train loss: 0.34115, val loss: 0.32526, in 0.016s
1 tree, 51 leaves, max depth = 11, train loss: 0.33465, val loss: 0.31863, in 0.016s
1 tree, 46 leaves, max depth = 11, train loss: 0.32705, val loss: 0.31153, in 0.016s
1 tree, 60 leaves, max depth = 11, train loss: 0.32125, val loss: 0.30556, in 0.016s
1 tree, 30 leaves, max depth = 14, train loss: 0.31476, val loss: 0.29885, in 0.016s
1 tree, 46 leaves, max depth = 11, train loss: 0.30873, val loss: 0.29326, in 0.031s
1 tree, 29 leaves, max depth = 10, train loss: 0.30339, val loss: 0.28771, in 0.016s
1 tree, 48 leaves, max depth = 11, train loss: 0.29837, val loss: 0.28309, in 0.016s
1 tree, 29 leaves, max depth = 10, train loss: 0.29394, val loss: 0.27846, in 0.016s
1 tree, 50 leaves, max depth = 11, train loss: 0.28961, val loss: 0.27408, in 0.016s
1 tree, 49 leaves, max depth = 11, train loss: 0.28557, val loss: 0.27040, in 0.016s
1 tree, 30 leaves, max depth = 11, train loss: 0.28202, val loss: 0.26665, in 0.016s
1 tree, 60 leaves, max depth = 13, train loss: 0.27811, val loss: 0.26245, in 0.016s
1 tree, 49 leaves, max depth = 11, train loss: 0.27485, val loss: 0.25950, in 0.016s
1 tree, 29 leaves, max depth = 11, train loss: 0.27193, val loss: 0.25642, in 0.016s
1 tree, 50 leaves, max depth = 11, train loss: 0.26874, val loss: 0.25317, in 0.016s
1 tree, 48 leaves, max depth = 11, train loss: 0.26610, val loss: 0.25077, in 0.016s
1 tree, 29 leaves, max depth = 11, train loss: 0.26373, val loss: 0.24827, in 0.016s
1 tree, 60 leaves, max depth = 13, train loss: 0.26079, val loss: 0.24509, in 0.016s
1 tree, 49 leaves, max depth = 12, train loss: 0.25854, val loss: 0.24291, in 0.016s
1 tree, 37 leaves, max depth = 14, train loss: 0.25651, val loss: 0.24086, in 0.016s
1 tree, 33 leaves, max depth = 11, train loss: 0.25425, val loss: 0.23896, in 0.031s
1 tree, 63 leaves, max depth = 14, train loss: 0.25153, val loss: 0.23632, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.24948, val loss: 0.23449, in 0.016s
1 tree, 37 leaves, max depth = 14, train loss: 0.24787, val loss: 0.23286, in 0.016s
1 tree, 60 leaves, max depth = 13, train loss: 0.24536, val loss: 0.23048, in 0.016s
1 tree, 61 leaves, max depth = 13, train loss: 0.24313, val loss: 0.22835, in 0.016s
1 tree, 60 leaves, max depth = 13, train loss: 0.24163, val loss: 0.22728, in 0.016s
1 tree, 60 leaves, max depth = 11, train loss: 0.23989, val loss: 0.22563, in 0.016s
1 tree, 34 leaves, max depth = 10, train loss: 0.23821, val loss: 0.22422, in 0.031s
1 tree, 34 leaves, max depth = 10, train loss: 0.23670, val loss: 0.22297, in 0.016s
1 tree, 60 leaves, max depth = 13, train loss: 0.23545, val loss: 0.22212, in 0.016s
1 tree, 59 leaves, max depth = 14, train loss: 0.23362, val loss: 0.22039, in 0.016s
1 tree, 53 leaves, max depth = 16, train loss: 0.23235, val loss: 0.21937, in 0.016s
1 tree, 63 leaves, max depth = 14, train loss: 0.23068, val loss: 0.21778, in 0.016s
1 tree, 59 leaves, max depth = 13, train loss: 0.22963, val loss: 0.21711, in 0.016s
1 tree, 57 leaves, max depth = 14, train loss: 0.22828, val loss: 0.21649, in 0.031s
1 tree, 57 leaves, max depth = 17, train loss: 0.22715, val loss: 0.21549, in 0.016s
1 tree, 36 leaves, max depth = 16, train loss: 0.22621, val loss: 0.21459, in 0.016s
1 tree, 59 leaves, max depth = 14, train loss: 0.22481, val loss: 0.21328, in 0.016s
1 tree, 60 leaves, max depth = 14, train loss: 0.22393, val loss: 0.21275, in 0.016s
1 tree, 60 leaves, max depth = 14, train loss: 0.22267, val loss: 0.21159, in 0.016s
1 tree, 60 leaves, max depth = 14, train loss: 0.22174, val loss: 0.21092, in 0.031s
1 tree, 49 leaves, max depth = 10, train loss: 0.22033, val loss: 0.20976, in 0.016s
1 tree, 56 leaves, max depth = 15, train loss: 0.21934, val loss: 0.20943, in 0.016s
Fit 64 trees in 1.486 s, (3143 total leaves)
Time spent computing histograms: 0.422s
Time spent finding best splits:  0.091s
Time spent applying splits:      0.069s
Time spent predicting:           0.000s
Trial 62, Fold 4: Log loss = 0.2271394765584005, Average precision = 0.96831361157216, ROC-AUC = 0.963736480396575, Elapsed Time = 1.4979230000008101 seconds
Trial 62, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 62, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
Binning 0.040 GB of training data: 0.158 s
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 50 leaves, max depth = 12, train loss: 0.65172, val loss: 0.65012, in 0.016s
1 tree, 50 leaves, max depth = 11, train loss: 0.61637, val loss: 0.61345, in 0.016s
1 tree, 52 leaves, max depth = 12, train loss: 0.58523, val loss: 0.58104, in 0.016s
1 tree, 54 leaves, max depth = 11, train loss: 0.55911, val loss: 0.55370, in 0.031s
1 tree, 53 leaves, max depth = 11, train loss: 0.53479, val loss: 0.52842, in 0.000s
1 tree, 52 leaves, max depth = 11, train loss: 0.51435, val loss: 0.50701, in 0.016s
1 tree, 54 leaves, max depth = 11, train loss: 0.49472, val loss: 0.48653, in 0.016s
1 tree, 54 leaves, max depth = 11, train loss: 0.47730, val loss: 0.46839, in 0.031s
1 tree, 52 leaves, max depth = 11, train loss: 0.46163, val loss: 0.45199, in 0.016s
1 tree, 54 leaves, max depth = 11, train loss: 0.44762, val loss: 0.43744, in 0.016s
1 tree, 52 leaves, max depth = 11, train loss: 0.43498, val loss: 0.42418, in 0.016s
1 tree, 52 leaves, max depth = 12, train loss: 0.41734, val loss: 0.40664, in 0.016s
1 tree, 62 leaves, max depth = 10, train loss: 0.40698, val loss: 0.39594, in 0.016s
1 tree, 51 leaves, max depth = 12, train loss: 0.39215, val loss: 0.38130, in 0.016s
1 tree, 53 leaves, max depth = 11, train loss: 0.38334, val loss: 0.37232, in 0.031s
1 tree, 52 leaves, max depth = 13, train loss: 0.37081, val loss: 0.35994, in 0.016s
1 tree, 54 leaves, max depth = 13, train loss: 0.36389, val loss: 0.35285, in 0.016s
1 tree, 28 leaves, max depth = 10, train loss: 0.35368, val loss: 0.34251, in 0.016s
1 tree, 52 leaves, max depth = 11, train loss: 0.34611, val loss: 0.33496, in 0.016s
1 tree, 30 leaves, max depth = 10, train loss: 0.33747, val loss: 0.32620, in 0.016s
1 tree, 52 leaves, max depth = 12, train loss: 0.33090, val loss: 0.31964, in 0.016s
1 tree, 45 leaves, max depth = 15, train loss: 0.32330, val loss: 0.31291, in 0.016s
1 tree, 60 leaves, max depth = 13, train loss: 0.31745, val loss: 0.30698, in 0.031s
1 tree, 28 leaves, max depth = 10, train loss: 0.31090, val loss: 0.30036, in 0.000s
1 tree, 46 leaves, max depth = 14, train loss: 0.30486, val loss: 0.29509, in 0.016s
1 tree, 29 leaves, max depth = 10, train loss: 0.29946, val loss: 0.28960, in 0.031s
1 tree, 46 leaves, max depth = 12, train loss: 0.29444, val loss: 0.28519, in 0.016s
1 tree, 28 leaves, max depth = 10, train loss: 0.28997, val loss: 0.28046, in 0.016s
1 tree, 52 leaves, max depth = 12, train loss: 0.28560, val loss: 0.27622, in 0.016s
1 tree, 29 leaves, max depth = 10, train loss: 0.28177, val loss: 0.27233, in 0.016s
1 tree, 60 leaves, max depth = 11, train loss: 0.27774, val loss: 0.26834, in 0.016s
1 tree, 46 leaves, max depth = 14, train loss: 0.27407, val loss: 0.26521, in 0.016s
1 tree, 45 leaves, max depth = 14, train loss: 0.27078, val loss: 0.26241, in 0.031s
1 tree, 29 leaves, max depth = 10, train loss: 0.26786, val loss: 0.25935, in 0.000s
1 tree, 54 leaves, max depth = 12, train loss: 0.26456, val loss: 0.25627, in 0.031s
1 tree, 45 leaves, max depth = 13, train loss: 0.26187, val loss: 0.25411, in 0.016s
1 tree, 29 leaves, max depth = 14, train loss: 0.25950, val loss: 0.25162, in 0.016s
1 tree, 43 leaves, max depth = 14, train loss: 0.25715, val loss: 0.24962, in 0.016s
1 tree, 34 leaves, max depth = 11, train loss: 0.25496, val loss: 0.24732, in 0.016s
1 tree, 63 leaves, max depth = 11, train loss: 0.25218, val loss: 0.24460, in 0.016s
1 tree, 44 leaves, max depth = 14, train loss: 0.25026, val loss: 0.24304, in 0.016s
1 tree, 28 leaves, max depth = 14, train loss: 0.24852, val loss: 0.24124, in 0.016s
1 tree, 49 leaves, max depth = 10, train loss: 0.24571, val loss: 0.23870, in 0.016s
1 tree, 51 leaves, max depth = 13, train loss: 0.24351, val loss: 0.23664, in 0.016s
1 tree, 34 leaves, max depth = 11, train loss: 0.24165, val loss: 0.23505, in 0.016s
1 tree, 61 leaves, max depth = 12, train loss: 0.24013, val loss: 0.23399, in 0.016s
1 tree, 35 leaves, max depth = 14, train loss: 0.23873, val loss: 0.23258, in 0.016s
1 tree, 59 leaves, max depth = 12, train loss: 0.23653, val loss: 0.23026, in 0.016s
1 tree, 49 leaves, max depth = 12, train loss: 0.23452, val loss: 0.22850, in 0.016s
1 tree, 44 leaves, max depth = 13, train loss: 0.23326, val loss: 0.22752, in 0.016s
1 tree, 60 leaves, max depth = 13, train loss: 0.23203, val loss: 0.22672, in 0.031s
1 tree, 34 leaves, max depth = 14, train loss: 0.23089, val loss: 0.22553, in 0.016s
1 tree, 58 leaves, max depth = 12, train loss: 0.22909, val loss: 0.22362, in 0.016s
1 tree, 48 leaves, max depth = 12, train loss: 0.22747, val loss: 0.22223, in 0.016s
1 tree, 61 leaves, max depth = 14, train loss: 0.22646, val loss: 0.22165, in 0.016s
1 tree, 56 leaves, max depth = 14, train loss: 0.22491, val loss: 0.22001, in 0.016s
1 tree, 34 leaves, max depth = 11, train loss: 0.22368, val loss: 0.21900, in 0.032s
1 tree, 55 leaves, max depth = 24, train loss: 0.22255, val loss: 0.21805, in 0.016s
1 tree, 57 leaves, max depth = 15, train loss: 0.22118, val loss: 0.21663, in 0.016s
1 tree, 61 leaves, max depth = 14, train loss: 0.22033, val loss: 0.21618, in 0.031s
1 tree, 34 leaves, max depth = 12, train loss: 0.21934, val loss: 0.21546, in 0.016s
1 tree, 54 leaves, max depth = 22, train loss: 0.21841, val loss: 0.21470, in 0.016s
1 tree, 62 leaves, max depth = 13, train loss: 0.21758, val loss: 0.21410, in 0.016s
1 tree, 59 leaves, max depth = 13, train loss: 0.21634, val loss: 0.21271, in 0.016s
Fit 64 trees in 1.470 s, (3061 total leaves)
Time spent computing histograms: 0.421s
Time spent finding best splits:  0.090s
Time spent applying splits:      0.067s
Time spent predicting:           0.000s
Trial 62, Fold 5: Log loss = 0.23154348837633418, Average precision = 0.9660934449123427, ROC-AUC = 0.962099102614124, Elapsed Time = 1.485750599998937 seconds
Optimization Progress:  63%|######3   | 63/100 [12:23<07:36, 12.33s/it]
Trial 63, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 63, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
0.127 s 0.040 GB of training data: 
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 17 leaves, max depth = 6, train loss: 0.67126, val loss: 0.67128, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.65074, val loss: 0.65091, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.63284, val loss: 0.63309, in 0.000s
1 tree, 17 leaves, max depth = 6, train loss: 0.61529, val loss: 0.61548, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.59926, val loss: 0.59925, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.58432, val loss: 0.58410, in 0.016s
1 tree, 17 leaves, max depth = 5, train loss: 0.57048, val loss: 0.57017, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.55672, val loss: 0.55634, in 0.016s
1 tree, 17 leaves, max depth = 5, train loss: 0.54387, val loss: 0.54358, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.53159, val loss: 0.53115, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.52039, val loss: 0.51964, in 0.016s
1 tree, 17 leaves, max depth = 5, train loss: 0.50980, val loss: 0.50867, in 0.000s
1 tree, 17 leaves, max depth = 5, train loss: 0.49953, val loss: 0.49822, in 0.016s
1 tree, 17 leaves, max depth = 5, train loss: 0.49006, val loss: 0.48852, in 0.016s
1 tree, 17 leaves, max depth = 5, train loss: 0.48094, val loss: 0.47922, in 0.016s
1 tree, 17 leaves, max depth = 5, train loss: 0.47240, val loss: 0.47062, in 0.016s
1 tree, 17 leaves, max depth = 5, train loss: 0.46457, val loss: 0.46251, in 0.016s
1 tree, 17 leaves, max depth = 8, train loss: 0.45380, val loss: 0.45208, in 0.016s
1 tree, 17 leaves, max depth = 5, train loss: 0.44687, val loss: 0.44507, in 0.000s
1 tree, 17 leaves, max depth = 7, train loss: 0.43993, val loss: 0.43803, in 0.016s
1 tree, 17 leaves, max depth = 5, train loss: 0.43335, val loss: 0.43140, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.42713, val loss: 0.42513, in 0.016s
1 tree, 17 leaves, max depth = 5, train loss: 0.42124, val loss: 0.41921, in 0.016s
1 tree, 17 leaves, max depth = 8, train loss: 0.41253, val loss: 0.41087, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.40731, val loss: 0.40553, in 0.016s
1 tree, 17 leaves, max depth = 5, train loss: 0.40223, val loss: 0.40044, in 0.016s
1 tree, 17 leaves, max depth = 8, train loss: 0.39447, val loss: 0.39306, in 0.000s
1 tree, 17 leaves, max depth = 6, train loss: 0.38716, val loss: 0.38607, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.38268, val loss: 0.38155, in 0.016s
1 tree, 17 leaves, max depth = 5, train loss: 0.37606, val loss: 0.37514, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.36973, val loss: 0.36920, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.36374, val loss: 0.36350, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.35981, val loss: 0.35960, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.35632, val loss: 0.35602, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.35130, val loss: 0.35104, in 0.016s
Fit 35 trees in 0.767 s, (595 total leaves)
Time spent computing histograms: 0.219s
Time spent finding best splits:  0.023s
Time spent applying splits:      0.014s
Time spent predicting:           0.000s
Trial 63, Fold 1: Log loss = 0.3543605450878688, Average precision = 0.9539285503411343, ROC-AUC = 0.9486214320366307, Elapsed Time = 0.7798387000002549 seconds
Trial 63, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 63, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
Binning 0.040 GB of training data: 0.157 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 17 leaves, max depth = 6, train loss: 0.67092, val loss: 0.67043, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.65043, val loss: 0.64946, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.63246, val loss: 0.63135, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.61431, val loss: 0.61282, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.59850, val loss: 0.59678, in 0.000s
1 tree, 17 leaves, max depth = 6, train loss: 0.58377, val loss: 0.58184, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.57002, val loss: 0.56784, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.55598, val loss: 0.55350, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.54284, val loss: 0.54008, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.53052, val loss: 0.52749, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.51927, val loss: 0.51591, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.50869, val loss: 0.50506, in 0.000s
1 tree, 17 leaves, max depth = 5, train loss: 0.49846, val loss: 0.49459, in 0.031s
1 tree, 17 leaves, max depth = 6, train loss: 0.48908, val loss: 0.48506, in 0.016s
1 tree, 17 leaves, max depth = 5, train loss: 0.47997, val loss: 0.47575, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.47135, val loss: 0.46703, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.46361, val loss: 0.45907, in 0.000s
1 tree, 17 leaves, max depth = 5, train loss: 0.45295, val loss: 0.44850, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.44294, val loss: 0.43857, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.43599, val loss: 0.43144, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.42943, val loss: 0.42474, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.42309, val loss: 0.41831, in 0.000s
1 tree, 17 leaves, max depth = 6, train loss: 0.41709, val loss: 0.41223, in 0.000s
1 tree, 17 leaves, max depth = 6, train loss: 0.40879, val loss: 0.40405, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.40349, val loss: 0.39874, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.39840, val loss: 0.39352, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.39102, val loss: 0.38625, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.38406, val loss: 0.37939, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.37954, val loss: 0.37479, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.37325, val loss: 0.36862, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.36918, val loss: 0.36447, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.36524, val loss: 0.36048, in 0.000s
1 tree, 17 leaves, max depth = 6, train loss: 0.36193, val loss: 0.35725, in 0.016s
1 tree, 17 leaves, max depth = 8, train loss: 0.35665, val loss: 0.35211, in 0.016s
1 tree, 17 leaves, max depth = 9, train loss: 0.35169, val loss: 0.34724, in 0.016s
Fit 35 trees in 0.814 s, (595 total leaves)
Time spent computing histograms: 0.216s
Time spent finding best splits:  0.024s
Time spent applying splits:      0.014s
Time spent predicting:           0.016s
Trial 63, Fold 2: Log loss = 0.35372807073401374, Average precision = 0.9503790214942958, ROC-AUC = 0.9492831586362245, Elapsed Time = 0.8227070000011736 seconds
Trial 63, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 63, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
Binning 0.040 GB of training data: 0.157 s
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 17 leaves, max depth = 6, train loss: 0.67125, val loss: 0.67116, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.65113, val loss: 0.65095, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.63313, val loss: 0.63296, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.61532, val loss: 0.61508, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.59943, val loss: 0.59917, in 0.000s
1 tree, 17 leaves, max depth = 6, train loss: 0.58467, val loss: 0.58442, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.57013, val loss: 0.56984, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.55622, val loss: 0.55590, in 0.031s
1 tree, 17 leaves, max depth = 6, train loss: 0.54320, val loss: 0.54285, in 0.000s
1 tree, 17 leaves, max depth = 6, train loss: 0.53100, val loss: 0.53063, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.51984, val loss: 0.51944, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.50985, val loss: 0.50939, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.49970, val loss: 0.49918, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.49023, val loss: 0.48976, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.48118, val loss: 0.48070, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.47267, val loss: 0.47215, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.46487, val loss: 0.46434, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.45726, val loss: 0.45673, in 0.016s
1 tree, 17 leaves, max depth = 9, train loss: 0.44674, val loss: 0.44694, in 0.000s
1 tree, 17 leaves, max depth = 7, train loss: 0.43983, val loss: 0.44008, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.43332, val loss: 0.43358, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.42715, val loss: 0.42743, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.42132, val loss: 0.42165, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.41260, val loss: 0.41354, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.40448, val loss: 0.40615, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.39935, val loss: 0.40106, in 0.016s
1 tree, 17 leaves, max depth = 9, train loss: 0.39187, val loss: 0.39419, in 0.000s
1 tree, 17 leaves, max depth = 9, train loss: 0.38479, val loss: 0.38774, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.38031, val loss: 0.38326, in 0.031s
1 tree, 17 leaves, max depth = 7, train loss: 0.37394, val loss: 0.37755, in 0.000s
1 tree, 17 leaves, max depth = 7, train loss: 0.36989, val loss: 0.37350, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.36598, val loss: 0.36962, in 0.031s
1 tree, 17 leaves, max depth = 9, train loss: 0.36051, val loss: 0.36454, in 0.000s
1 tree, 17 leaves, max depth = 8, train loss: 0.35535, val loss: 0.35981, in 0.016s
1 tree, 17 leaves, max depth = 8, train loss: 0.35049, val loss: 0.35540, in 0.016s
Fit 35 trees in 0.860 s, (595 total leaves)
Time spent computing histograms: 0.221s
Time spent finding best splits:  0.025s
Time spent applying splits:      0.015s
Time spent predicting:           0.000s
Trial 63, Fold 3: Log loss = 0.3476500742964973, Average precision = 0.9537392723874637, ROC-AUC = 0.9517430217131685, Elapsed Time = 0.8657454000003781 seconds
Trial 63, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 63, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
Binning 0.040 GB of training data: 0.157 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 17 leaves, max depth = 6, train loss: 0.67139, val loss: 0.67049, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.65096, val loss: 0.64924, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.63320, val loss: 0.63096, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.61575, val loss: 0.61260, in 0.016s
1 tree, 17 leaves, max depth = 5, train loss: 0.59954, val loss: 0.59559, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.58466, val loss: 0.57993, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.57079, val loss: 0.56533, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.55695, val loss: 0.55080, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.54418, val loss: 0.53732, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.53194, val loss: 0.52444, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.52075, val loss: 0.51259, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.50995, val loss: 0.50115, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.49997, val loss: 0.49052, in 0.000s
1 tree, 17 leaves, max depth = 6, train loss: 0.49068, val loss: 0.48061, in 0.000s
1 tree, 17 leaves, max depth = 6, train loss: 0.48165, val loss: 0.47103, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.47320, val loss: 0.46205, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.46541, val loss: 0.45378, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.45476, val loss: 0.44295, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.44472, val loss: 0.43274, in 0.000s
1 tree, 17 leaves, max depth = 6, train loss: 0.43793, val loss: 0.42559, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.43134, val loss: 0.41858, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.42510, val loss: 0.41195, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.41919, val loss: 0.40568, in 0.016s
1 tree, 17 leaves, max depth = 8, train loss: 0.41089, val loss: 0.39729, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.40567, val loss: 0.39163, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.40058, val loss: 0.38621, in 0.000s
1 tree, 17 leaves, max depth = 8, train loss: 0.39319, val loss: 0.37873, in 0.031s
1 tree, 17 leaves, max depth = 6, train loss: 0.38617, val loss: 0.37162, in 0.000s
1 tree, 17 leaves, max depth = 7, train loss: 0.38185, val loss: 0.36695, in 0.016s
1 tree, 17 leaves, max depth = 5, train loss: 0.37560, val loss: 0.36055, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.37148, val loss: 0.35616, in 0.016s
1 tree, 17 leaves, max depth = 5, train loss: 0.36751, val loss: 0.35194, in 0.000s
1 tree, 17 leaves, max depth = 6, train loss: 0.36419, val loss: 0.34834, in 0.000s
1 tree, 17 leaves, max depth = 8, train loss: 0.35887, val loss: 0.34283, in 0.016s
1 tree, 17 leaves, max depth = 8, train loss: 0.35389, val loss: 0.33767, in 0.016s
Fit 35 trees in 0.829 s, (595 total leaves)
Time spent computing histograms: 0.215s
Time spent finding best splits:  0.023s
Time spent applying splits:      0.014s
Time spent predicting:           0.016s
Trial 63, Fold 4: Log loss = 0.35249242395515334, Average precision = 0.955026329538689, ROC-AUC = 0.9499984799387684, Elapsed Time = 0.8395970000001398 seconds
Trial 63, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 63, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
Binning 0.040 GB of training data: 0.157 s
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 17 leaves, max depth = 6, train loss: 0.67099, val loss: 0.67000, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.65027, val loss: 0.64844, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.63218, val loss: 0.62985, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.61443, val loss: 0.61133, in 0.000s
1 tree, 17 leaves, max depth = 6, train loss: 0.59795, val loss: 0.59400, in 0.031s
1 tree, 17 leaves, max depth = 6, train loss: 0.58305, val loss: 0.57837, in 0.000s
1 tree, 17 leaves, max depth = 6, train loss: 0.56904, val loss: 0.56371, in 0.016s
1 tree, 17 leaves, max depth = 5, train loss: 0.55512, val loss: 0.54918, in 0.016s
1 tree, 17 leaves, max depth = 5, train loss: 0.54213, val loss: 0.53563, in 0.016s
1 tree, 17 leaves, max depth = 5, train loss: 0.52990, val loss: 0.52288, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.51884, val loss: 0.51119, in 0.000s
1 tree, 17 leaves, max depth = 6, train loss: 0.50835, val loss: 0.50010, in 0.016s
1 tree, 17 leaves, max depth = 5, train loss: 0.49818, val loss: 0.48952, in 0.016s
1 tree, 17 leaves, max depth = 5, train loss: 0.48876, val loss: 0.47961, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.47971, val loss: 0.47020, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.47116, val loss: 0.46129, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.46337, val loss: 0.45305, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.45575, val loss: 0.44509, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.44532, val loss: 0.43466, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.43846, val loss: 0.42751, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.43190, val loss: 0.42072, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.42570, val loss: 0.41431, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.41987, val loss: 0.40827, in 0.000s
1 tree, 17 leaves, max depth = 8, train loss: 0.41119, val loss: 0.39966, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.40319, val loss: 0.39165, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.39805, val loss: 0.38630, in 0.016s
1 tree, 17 leaves, max depth = 9, train loss: 0.39062, val loss: 0.37893, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.38361, val loss: 0.37199, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.37912, val loss: 0.36733, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.37285, val loss: 0.36107, in 0.000s
1 tree, 17 leaves, max depth = 6, train loss: 0.36870, val loss: 0.35680, in 0.016s
1 tree, 17 leaves, max depth = 9, train loss: 0.36282, val loss: 0.35099, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.35902, val loss: 0.34712, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.35566, val loss: 0.34367, in 0.016s
1 tree, 17 leaves, max depth = 10, train loss: 0.35064, val loss: 0.33854, in 0.016s
Fit 35 trees in 0.844 s, (595 total leaves)
Time spent computing histograms: 0.221s
Time spent finding best splits:  0.024s
Time spent applying splits:      0.014s
Time spent predicting:           0.000s
Trial 63, Fold 5: Log loss = 0.3572451824505063, Average precision = 0.9509240981302716, ROC-AUC = 0.9475750515578842, Elapsed Time = 0.846569699999236 seconds
Optimization Progress:  64%|######4   | 64/100 [12:33<07:06, 11.84s/it]
Trial 64, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 64, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
0.142 s 0.040 GB of training data: 
0.015 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 35 leaves, max depth = 8, train loss: 0.67357, val loss: 0.67362, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.65487, val loss: 0.65513, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.63740, val loss: 0.63785, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.62105, val loss: 0.62163, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.60634, val loss: 0.60689, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.59292, val loss: 0.59325, in 0.000s
1 tree, 35 leaves, max depth = 11, train loss: 0.58012, val loss: 0.58027, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.56701, val loss: 0.56709, in 0.031s
1 tree, 35 leaves, max depth = 7, train loss: 0.55542, val loss: 0.55543, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.54369, val loss: 0.54376, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.53336, val loss: 0.53340, in 0.000s
1 tree, 35 leaves, max depth = 11, train loss: 0.52284, val loss: 0.52287, in 0.016s
1 tree, 35 leaves, max depth = 8, train loss: 0.51305, val loss: 0.51299, in 0.031s
1 tree, 35 leaves, max depth = 10, train loss: 0.50361, val loss: 0.50362, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.49492, val loss: 0.49469, in 0.000s
1 tree, 35 leaves, max depth = 10, train loss: 0.48640, val loss: 0.48613, in 0.031s
1 tree, 35 leaves, max depth = 10, train loss: 0.47877, val loss: 0.47848, in 0.000s
1 tree, 35 leaves, max depth = 10, train loss: 0.47111, val loss: 0.47078, in 0.031s
1 tree, 35 leaves, max depth = 11, train loss: 0.46380, val loss: 0.46344, in 0.000s
1 tree, 35 leaves, max depth = 13, train loss: 0.45413, val loss: 0.45409, in 0.016s
1 tree, 35 leaves, max depth = 8, train loss: 0.44762, val loss: 0.44747, in 0.031s
1 tree, 35 leaves, max depth = 10, train loss: 0.44130, val loss: 0.44121, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.43529, val loss: 0.43521, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.42956, val loss: 0.42950, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.42146, val loss: 0.42161, in 0.016s
1 tree, 35 leaves, max depth = 8, train loss: 0.41667, val loss: 0.41668, in 0.016s
1 tree, 35 leaves, max depth = 8, train loss: 0.41189, val loss: 0.41179, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.40709, val loss: 0.40699, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.39985, val loss: 0.40010, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.39539, val loss: 0.39571, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.39139, val loss: 0.39174, in 0.016s
1 tree, 35 leaves, max depth = 13, train loss: 0.38488, val loss: 0.38559, in 0.016s
1 tree, 35 leaves, max depth = 13, train loss: 0.37874, val loss: 0.37981, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.37491, val loss: 0.37609, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.36920, val loss: 0.37072, in 0.016s
1 tree, 35 leaves, max depth = 12, train loss: 0.36566, val loss: 0.36729, in 0.016s
1 tree, 35 leaves, max depth = 12, train loss: 0.36038, val loss: 0.36233, in 0.016s
1 tree, 30 leaves, max depth = 10, train loss: 0.35564, val loss: 0.35762, in 0.016s
1 tree, 35 leaves, max depth = 13, train loss: 0.35193, val loss: 0.35394, in 0.016s
1 tree, 29 leaves, max depth = 10, train loss: 0.34758, val loss: 0.34966, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.34412, val loss: 0.34626, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.33998, val loss: 0.34259, in 0.016s
1 tree, 29 leaves, max depth = 10, train loss: 0.33611, val loss: 0.33878, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.33294, val loss: 0.33564, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.32924, val loss: 0.33238, in 0.016s
1 tree, 30 leaves, max depth = 10, train loss: 0.32577, val loss: 0.32895, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.32277, val loss: 0.32590, in 0.000s
1 tree, 35 leaves, max depth = 10, train loss: 0.31951, val loss: 0.32303, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.31638, val loss: 0.32030, in 0.016s
1 tree, 28 leaves, max depth = 13, train loss: 0.31340, val loss: 0.31738, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.31053, val loss: 0.31490, in 0.016s
1 tree, 30 leaves, max depth = 10, train loss: 0.30778, val loss: 0.31217, in 0.016s
1 tree, 35 leaves, max depth = 12, train loss: 0.30523, val loss: 0.30965, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.30265, val loss: 0.30742, in 0.016s
1 tree, 29 leaves, max depth = 10, train loss: 0.30020, val loss: 0.30499, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.29776, val loss: 0.30250, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.29543, val loss: 0.30052, in 0.000s
1 tree, 29 leaves, max depth = 15, train loss: 0.29323, val loss: 0.29836, in 0.016s
1 tree, 35 leaves, max depth = 12, train loss: 0.29104, val loss: 0.29622, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.28893, val loss: 0.29445, in 0.031s
1 tree, 30 leaves, max depth = 10, train loss: 0.28694, val loss: 0.29248, in 0.000s
1 tree, 35 leaves, max depth = 8, train loss: 0.28484, val loss: 0.29035, in 0.016s
1 tree, 35 leaves, max depth = 12, train loss: 0.28294, val loss: 0.28877, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.28097, val loss: 0.28680, in 0.031s
Fit 64 trees in 1.283 s, (2189 total leaves)
Time spent computing histograms: 0.399s
Time spent finding best splits:  0.068s
Time spent applying splits:      0.045s
Time spent predicting:           0.016s
Trial 64, Fold 1: Log loss = 0.28649361238491966, Average precision = 0.961671769787424, ROC-AUC = 0.9561228479899437, Elapsed Time = 1.288647299999866 seconds
Trial 64, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 64, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
Binning 0.040 GB of training data: 0.174 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 35 leaves, max depth = 10, train loss: 0.67341, val loss: 0.67298, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.65468, val loss: 0.65387, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.63711, val loss: 0.63596, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.62073, val loss: 0.61924, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.60531, val loss: 0.60350, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.59066, val loss: 0.58871, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.57762, val loss: 0.57553, in 0.016s
1 tree, 35 leaves, max depth = 8, train loss: 0.56480, val loss: 0.56253, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.55322, val loss: 0.55084, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.54179, val loss: 0.53918, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.53074, val loss: 0.52797, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.52097, val loss: 0.51811, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.51106, val loss: 0.50798, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.50169, val loss: 0.49854, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.49340, val loss: 0.49018, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.48489, val loss: 0.48157, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.47681, val loss: 0.47340, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.46919, val loss: 0.46567, in 0.016s
1 tree, 35 leaves, max depth = 8, train loss: 0.46217, val loss: 0.45851, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.45575, val loss: 0.45204, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.44919, val loss: 0.44544, in 0.031s
1 tree, 35 leaves, max depth = 10, train loss: 0.44298, val loss: 0.43912, in 0.000s
1 tree, 35 leaves, max depth = 11, train loss: 0.43703, val loss: 0.43302, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.42842, val loss: 0.42456, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.42031, val loss: 0.41659, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.41556, val loss: 0.41188, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.41053, val loss: 0.40672, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.40603, val loss: 0.40223, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.39892, val loss: 0.39529, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.39448, val loss: 0.39084, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.39055, val loss: 0.38692, in 0.016s
1 tree, 35 leaves, max depth = 12, train loss: 0.38415, val loss: 0.38066, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.37810, val loss: 0.37476, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.37430, val loss: 0.37097, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.36870, val loss: 0.36549, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.36518, val loss: 0.36189, in 0.016s
1 tree, 35 leaves, max depth = 12, train loss: 0.35994, val loss: 0.35677, in 0.031s
1 tree, 29 leaves, max depth = 11, train loss: 0.35528, val loss: 0.35220, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.35211, val loss: 0.34907, in 0.016s
1 tree, 35 leaves, max depth = 13, train loss: 0.34855, val loss: 0.34553, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.34429, val loss: 0.34163, in 0.016s
1 tree, 29 leaves, max depth = 11, train loss: 0.34023, val loss: 0.33766, in 0.016s
1 tree, 29 leaves, max depth = 11, train loss: 0.33641, val loss: 0.33394, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.33266, val loss: 0.33054, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.32911, val loss: 0.32727, in 0.016s
1 tree, 29 leaves, max depth = 11, train loss: 0.32575, val loss: 0.32398, in 0.016s
1 tree, 35 leaves, max depth = 12, train loss: 0.32272, val loss: 0.32099, in 0.016s
1 tree, 35 leaves, max depth = 12, train loss: 0.31960, val loss: 0.31810, in 0.016s
1 tree, 29 leaves, max depth = 11, train loss: 0.31660, val loss: 0.31517, in 0.016s
1 tree, 35 leaves, max depth = 8, train loss: 0.31372, val loss: 0.31237, in 0.000s
1 tree, 35 leaves, max depth = 10, train loss: 0.31086, val loss: 0.30979, in 0.031s
1 tree, 29 leaves, max depth = 11, train loss: 0.30817, val loss: 0.30715, in 0.000s
1 tree, 35 leaves, max depth = 11, train loss: 0.30559, val loss: 0.30461, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.30301, val loss: 0.30229, in 0.016s
1 tree, 29 leaves, max depth = 11, train loss: 0.30060, val loss: 0.29995, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.29813, val loss: 0.29757, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.29581, val loss: 0.29549, in 0.016s
1 tree, 28 leaves, max depth = 12, train loss: 0.29365, val loss: 0.29336, in 0.016s
1 tree, 35 leaves, max depth = 14, train loss: 0.29144, val loss: 0.29123, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.28925, val loss: 0.28913, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.28719, val loss: 0.28729, in 0.016s
1 tree, 29 leaves, max depth = 11, train loss: 0.28528, val loss: 0.28543, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.28338, val loss: 0.28375, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.28139, val loss: 0.28185, in 0.000s
Fit 64 trees in 1.347 s, (2185 total leaves)
Time spent computing histograms: 0.400s
Time spent finding best splits:  0.067s
Time spent applying splits:      0.046s
Time spent predicting:           0.000s
Trial 64, Fold 2: Log loss = 0.2846823637848209, Average precision = 0.9607167809910063, ROC-AUC = 0.9577754958283016, Elapsed Time = 1.3587115000009362 seconds
Trial 64, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 64, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
Binning 0.040 GB of training data: 0.158 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 35 leaves, max depth = 8, train loss: 0.67361, val loss: 0.67350, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.65501, val loss: 0.65486, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.63763, val loss: 0.63745, in 0.016s
1 tree, 35 leaves, max depth = 12, train loss: 0.62137, val loss: 0.62113, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.60612, val loss: 0.60583, in 0.016s
1 tree, 35 leaves, max depth = 8, train loss: 0.59222, val loss: 0.59199, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.57843, val loss: 0.57823, in 0.016s
1 tree, 35 leaves, max depth = 7, train loss: 0.56626, val loss: 0.56615, in 0.016s
1 tree, 35 leaves, max depth = 8, train loss: 0.55459, val loss: 0.55459, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.54324, val loss: 0.54321, in 0.016s
1 tree, 35 leaves, max depth = 8, train loss: 0.53223, val loss: 0.53227, in 0.016s
1 tree, 35 leaves, max depth = 8, train loss: 0.52243, val loss: 0.52253, in 0.016s
1 tree, 35 leaves, max depth = 8, train loss: 0.51255, val loss: 0.51271, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.50320, val loss: 0.50338, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.49488, val loss: 0.49513, in 0.031s
1 tree, 35 leaves, max depth = 9, train loss: 0.48658, val loss: 0.48687, in 0.016s
1 tree, 35 leaves, max depth = 12, train loss: 0.47859, val loss: 0.47889, in 0.016s
1 tree, 35 leaves, max depth = 8, train loss: 0.47113, val loss: 0.47148, in 0.016s
1 tree, 35 leaves, max depth = 12, train loss: 0.46394, val loss: 0.46430, in 0.016s
1 tree, 35 leaves, max depth = 12, train loss: 0.45420, val loss: 0.45523, in 0.016s
1 tree, 35 leaves, max depth = 12, train loss: 0.44758, val loss: 0.44872, in 0.031s
1 tree, 35 leaves, max depth = 7, train loss: 0.44141, val loss: 0.44267, in 0.016s
1 tree, 35 leaves, max depth = 12, train loss: 0.43546, val loss: 0.43675, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.42976, val loss: 0.43112, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.42162, val loss: 0.42371, in 0.016s
1 tree, 35 leaves, max depth = 7, train loss: 0.41653, val loss: 0.41874, in 0.016s
1 tree, 35 leaves, max depth = 12, train loss: 0.40892, val loss: 0.41179, in 0.016s
1 tree, 35 leaves, max depth = 8, train loss: 0.40425, val loss: 0.40724, in 0.031s
1 tree, 35 leaves, max depth = 12, train loss: 0.39723, val loss: 0.40086, in 0.016s
1 tree, 35 leaves, max depth = 12, train loss: 0.39282, val loss: 0.39652, in 0.016s
1 tree, 35 leaves, max depth = 12, train loss: 0.38881, val loss: 0.39264, in 0.016s
1 tree, 35 leaves, max depth = 12, train loss: 0.38253, val loss: 0.38692, in 0.016s
1 tree, 35 leaves, max depth = 12, train loss: 0.37654, val loss: 0.38153, in 0.031s
1 tree, 35 leaves, max depth = 12, train loss: 0.37305, val loss: 0.37814, in 0.016s
1 tree, 35 leaves, max depth = 7, train loss: 0.36950, val loss: 0.37462, in 0.016s
1 tree, 29 leaves, max depth = 10, train loss: 0.36435, val loss: 0.36994, in 0.016s
1 tree, 29 leaves, max depth = 10, train loss: 0.35950, val loss: 0.36553, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.35573, val loss: 0.36167, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.35112, val loss: 0.35783, in 0.016s
1 tree, 29 leaves, max depth = 13, train loss: 0.34684, val loss: 0.35392, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.34340, val loss: 0.35033, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.33928, val loss: 0.34693, in 0.016s
1 tree, 28 leaves, max depth = 10, train loss: 0.33545, val loss: 0.34344, in 0.016s
1 tree, 35 leaves, max depth = 13, train loss: 0.33230, val loss: 0.34019, in 0.031s
1 tree, 35 leaves, max depth = 11, train loss: 0.32860, val loss: 0.33717, in 0.016s
1 tree, 28 leaves, max depth = 10, train loss: 0.32518, val loss: 0.33409, in 0.016s
1 tree, 35 leaves, max depth = 15, train loss: 0.32229, val loss: 0.33111, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.31903, val loss: 0.32846, in 0.016s
1 tree, 29 leaves, max depth = 10, train loss: 0.31597, val loss: 0.32568, in 0.000s
1 tree, 35 leaves, max depth = 15, train loss: 0.31331, val loss: 0.32288, in 0.031s
1 tree, 35 leaves, max depth = 11, train loss: 0.31034, val loss: 0.32045, in 0.016s
1 tree, 29 leaves, max depth = 10, train loss: 0.30762, val loss: 0.31803, in 0.016s
1 tree, 35 leaves, max depth = 13, train loss: 0.30517, val loss: 0.31549, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.30249, val loss: 0.31333, in 0.016s
1 tree, 29 leaves, max depth = 11, train loss: 0.30005, val loss: 0.31112, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.29758, val loss: 0.30914, in 0.016s
1 tree, 29 leaves, max depth = 11, train loss: 0.29532, val loss: 0.30713, in 0.016s
1 tree, 35 leaves, max depth = 12, train loss: 0.29314, val loss: 0.30490, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.29092, val loss: 0.30313, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.28879, val loss: 0.30147, in 0.016s
1 tree, 29 leaves, max depth = 11, train loss: 0.28684, val loss: 0.29974, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.28480, val loss: 0.29760, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.28288, val loss: 0.29610, in 0.016s
1 tree, 35 leaves, max depth = 8, train loss: 0.28091, val loss: 0.29396, in 0.016s
Fit 64 trees in 1.455 s, (2178 total leaves)
Time spent computing histograms: 0.449s
Time spent finding best splits:  0.078s
Time spent applying splits:      0.050s
Time spent predicting:           0.000s
Trial 64, Fold 3: Log loss = 0.28106817183611726, Average precision = 0.962650523022376, ROC-AUC = 0.9594392721104487, Elapsed Time = 1.4647454000005382 seconds
Trial 64, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 64, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
Binning 0.040 GB of training data: 0.157 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 35 leaves, max depth = 8, train loss: 0.67371, val loss: 0.67286, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.65519, val loss: 0.65356, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.63791, val loss: 0.63547, in 0.016s
1 tree, 35 leaves, max depth = 7, train loss: 0.62194, val loss: 0.61865, in 0.016s
1 tree, 35 leaves, max depth = 8, train loss: 0.60657, val loss: 0.60256, in 0.024s
1 tree, 35 leaves, max depth = 9, train loss: 0.59283, val loss: 0.58818, in 0.008s
1 tree, 35 leaves, max depth = 8, train loss: 0.57919, val loss: 0.57384, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.56703, val loss: 0.56108, in 0.016s
1 tree, 35 leaves, max depth = 8, train loss: 0.55477, val loss: 0.54821, in 0.016s
1 tree, 35 leaves, max depth = 7, train loss: 0.54347, val loss: 0.53618, in 0.016s
1 tree, 35 leaves, max depth = 8, train loss: 0.53250, val loss: 0.52470, in 0.031s
1 tree, 35 leaves, max depth = 9, train loss: 0.52236, val loss: 0.51411, in 0.016s
1 tree, 35 leaves, max depth = 7, train loss: 0.51274, val loss: 0.50389, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.50352, val loss: 0.49421, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.49501, val loss: 0.48519, in 0.000s
1 tree, 35 leaves, max depth = 9, train loss: 0.48663, val loss: 0.47632, in 0.016s
1 tree, 35 leaves, max depth = 8, train loss: 0.47934, val loss: 0.46856, in 0.031s
1 tree, 35 leaves, max depth = 10, train loss: 0.47182, val loss: 0.46059, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.46462, val loss: 0.45294, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.45500, val loss: 0.44317, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.44866, val loss: 0.43636, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.44246, val loss: 0.42976, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.43658, val loss: 0.42350, in 0.031s
1 tree, 35 leaves, max depth = 9, train loss: 0.43099, val loss: 0.41756, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.42282, val loss: 0.40928, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.41514, val loss: 0.40148, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.41018, val loss: 0.39619, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.40546, val loss: 0.39115, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.40090, val loss: 0.38635, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.39415, val loss: 0.37953, in 0.016s
1 tree, 35 leaves, max depth = 8, train loss: 0.39022, val loss: 0.37528, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.38642, val loss: 0.37128, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.38031, val loss: 0.36511, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.37457, val loss: 0.35932, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.37109, val loss: 0.35562, in 0.031s
1 tree, 35 leaves, max depth = 12, train loss: 0.36576, val loss: 0.35022, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.36254, val loss: 0.34679, in 0.016s
1 tree, 30 leaves, max depth = 11, train loss: 0.35785, val loss: 0.34195, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.35343, val loss: 0.33732, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.34988, val loss: 0.33374, in 0.016s
1 tree, 29 leaves, max depth = 11, train loss: 0.34577, val loss: 0.32950, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.34181, val loss: 0.32578, in 0.016s
1 tree, 29 leaves, max depth = 11, train loss: 0.33808, val loss: 0.32192, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.33489, val loss: 0.31869, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.33135, val loss: 0.31538, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.32839, val loss: 0.31242, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.32511, val loss: 0.30929, in 0.016s
1 tree, 30 leaves, max depth = 12, train loss: 0.32201, val loss: 0.30603, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.31927, val loss: 0.30326, in 0.031s
1 tree, 35 leaves, max depth = 11, train loss: 0.31632, val loss: 0.30052, in 0.016s
1 tree, 35 leaves, max depth = 12, train loss: 0.31373, val loss: 0.29792, in 0.031s
1 tree, 30 leaves, max depth = 12, train loss: 0.31096, val loss: 0.29505, in 0.062s
1 tree, 35 leaves, max depth = 11, train loss: 0.30832, val loss: 0.29253, in 0.031s
1 tree, 30 leaves, max depth = 12, train loss: 0.30578, val loss: 0.28990, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.30334, val loss: 0.28764, in 0.016s
1 tree, 31 leaves, max depth = 12, train loss: 0.30102, val loss: 0.28516, in 0.031s
1 tree, 35 leaves, max depth = 9, train loss: 0.29868, val loss: 0.28268, in 0.016s
1 tree, 35 leaves, max depth = 13, train loss: 0.29651, val loss: 0.28049, in 0.031s
1 tree, 35 leaves, max depth = 11, train loss: 0.29436, val loss: 0.27846, in 0.016s
1 tree, 29 leaves, max depth = 11, train loss: 0.29230, val loss: 0.27632, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.29032, val loss: 0.27449, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.28843, val loss: 0.27247, in 0.031s
1 tree, 35 leaves, max depth = 9, train loss: 0.28643, val loss: 0.27037, in 0.016s
1 tree, 35 leaves, max depth = 13, train loss: 0.28456, val loss: 0.26849, in 0.016s
Fit 64 trees in 1.549 s, (2190 total leaves)
Time spent computing histograms: 0.481s
Time spent finding best splits:  0.091s
Time spent applying splits:      0.075s
Time spent predicting:           0.000s
Trial 64, Fold 4: Log loss = 0.2846748268270564, Average precision = 0.961753461255784, ROC-AUC = 0.9574160806979644, Elapsed Time = 1.5648371000006591 seconds
Trial 64, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 64, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
Binning 0.040 GB of training data: 0.156 s
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 35 leaves, max depth = 9, train loss: 0.67336, val loss: 0.67251, in 0.016s
1 tree, 35 leaves, max depth = 12, train loss: 0.65449, val loss: 0.65294, in 0.016s
1 tree, 35 leaves, max depth = 12, train loss: 0.63686, val loss: 0.63464, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.62050, val loss: 0.61768, in 0.016s
1 tree, 35 leaves, max depth = 12, train loss: 0.60502, val loss: 0.60161, in 0.016s
1 tree, 35 leaves, max depth = 12, train loss: 0.59048, val loss: 0.58651, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.57735, val loss: 0.57275, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.56455, val loss: 0.55937, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.55294, val loss: 0.54722, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.54156, val loss: 0.53519, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.53058, val loss: 0.52378, in 0.031s
1 tree, 35 leaves, max depth = 11, train loss: 0.52078, val loss: 0.51351, in 0.000s
1 tree, 35 leaves, max depth = 11, train loss: 0.51087, val loss: 0.50317, in 0.016s
1 tree, 35 leaves, max depth = 12, train loss: 0.50159, val loss: 0.49354, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.49327, val loss: 0.48483, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.48484, val loss: 0.47605, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.47680, val loss: 0.46769, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.46921, val loss: 0.45980, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.46224, val loss: 0.45240, in 0.031s
1 tree, 35 leaves, max depth = 11, train loss: 0.45581, val loss: 0.44566, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.44929, val loss: 0.43896, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.44306, val loss: 0.43249, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.43715, val loss: 0.42632, in 0.016s
1 tree, 35 leaves, max depth = 13, train loss: 0.42850, val loss: 0.41774, in 0.016s
1 tree, 35 leaves, max depth = 13, train loss: 0.42036, val loss: 0.40963, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.41563, val loss: 0.40467, in 0.016s
1 tree, 35 leaves, max depth = 7, train loss: 0.41076, val loss: 0.39963, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.40592, val loss: 0.39467, in 0.031s
1 tree, 35 leaves, max depth = 11, train loss: 0.39874, val loss: 0.38757, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.39432, val loss: 0.38297, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.39036, val loss: 0.37890, in 0.016s
1 tree, 35 leaves, max depth = 14, train loss: 0.38391, val loss: 0.37252, in 0.016s
1 tree, 35 leaves, max depth = 12, train loss: 0.37781, val loss: 0.36650, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.37432, val loss: 0.36300, in 0.016s
1 tree, 35 leaves, max depth = 8, train loss: 0.37078, val loss: 0.35933, in 0.016s
1 tree, 28 leaves, max depth = 10, train loss: 0.36546, val loss: 0.35393, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.36019, val loss: 0.34877, in 0.016s
1 tree, 29 leaves, max depth = 10, train loss: 0.35545, val loss: 0.34395, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.35233, val loss: 0.34085, in 0.016s
1 tree, 30 leaves, max depth = 10, train loss: 0.34795, val loss: 0.33641, in 0.016s
1 tree, 35 leaves, max depth = 12, train loss: 0.34444, val loss: 0.33296, in 0.031s
1 tree, 35 leaves, max depth = 13, train loss: 0.34027, val loss: 0.32921, in 0.016s
1 tree, 29 leaves, max depth = 10, train loss: 0.33641, val loss: 0.32521, in 0.016s
1 tree, 35 leaves, max depth = 12, train loss: 0.33318, val loss: 0.32201, in 0.016s
1 tree, 35 leaves, max depth = 13, train loss: 0.32945, val loss: 0.31868, in 0.016s
1 tree, 30 leaves, max depth = 10, train loss: 0.32598, val loss: 0.31515, in 0.016s
1 tree, 35 leaves, max depth = 12, train loss: 0.32300, val loss: 0.31225, in 0.016s
1 tree, 35 leaves, max depth = 13, train loss: 0.31972, val loss: 0.30933, in 0.016s
1 tree, 35 leaves, max depth = 13, train loss: 0.31655, val loss: 0.30652, in 0.016s
1 tree, 30 leaves, max depth = 13, train loss: 0.31356, val loss: 0.30349, in 0.016s
1 tree, 35 leaves, max depth = 13, train loss: 0.31089, val loss: 0.30084, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.30804, val loss: 0.29834, in 0.016s
1 tree, 35 leaves, max depth = 13, train loss: 0.30546, val loss: 0.29573, in 0.016s
1 tree, 30 leaves, max depth = 10, train loss: 0.30283, val loss: 0.29306, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.30026, val loss: 0.29082, in 0.016s
1 tree, 29 leaves, max depth = 13, train loss: 0.29787, val loss: 0.28839, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.29551, val loss: 0.28635, in 0.016s
1 tree, 30 leaves, max depth = 13, train loss: 0.29332, val loss: 0.28411, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.29109, val loss: 0.28195, in 0.031s
1 tree, 35 leaves, max depth = 12, train loss: 0.28942, val loss: 0.28039, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.28734, val loss: 0.27855, in 0.016s
1 tree, 28 leaves, max depth = 12, train loss: 0.28539, val loss: 0.27650, in 0.016s
1 tree, 35 leaves, max depth = 12, train loss: 0.28347, val loss: 0.27481, in 0.016s
1 tree, 29 leaves, max depth = 12, train loss: 0.28168, val loss: 0.27291, in 0.016s
Fit 64 trees in 1.422 s, (2177 total leaves)
Time spent computing histograms: 0.448s
Time spent finding best splits:  0.075s
Time spent applying splits:      0.049s
Time spent predicting:           0.000s
Trial 64, Fold 5: Log loss = 0.29058164076700016, Average precision = 0.9577833376287552, ROC-AUC = 0.9529413819370902, Elapsed Time = 1.4403724000003422 seconds
Optimization Progress:  65%|######5   | 65/100 [12:47<07:13, 12.38s/it]
Trial 65, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 65, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
0.157 s 0.040 GB of training data: 
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 115 leaves, max depth = 18, train loss: 0.67687, val loss: 0.67681, in 0.016s
1 tree, 115 leaves, max depth = 18, train loss: 0.66007, val loss: 0.65980, in 0.031s
1 tree, 115 leaves, max depth = 19, train loss: 0.64443, val loss: 0.64402, in 0.031s
1 tree, 115 leaves, max depth = 20, train loss: 0.63097, val loss: 0.63039, in 0.016s
1 tree, 115 leaves, max depth = 16, train loss: 0.61752, val loss: 0.61694, in 0.016s
1 tree, 72 leaves, max depth = 13, train loss: 0.60427, val loss: 0.60342, in 0.031s
1 tree, 115 leaves, max depth = 15, train loss: 0.59202, val loss: 0.59098, in 0.016s
1 tree, 115 leaves, max depth = 15, train loss: 0.58010, val loss: 0.57915, in 0.016s
1 tree, 115 leaves, max depth = 17, train loss: 0.56880, val loss: 0.56784, in 0.031s
1 tree, 115 leaves, max depth = 13, train loss: 0.55798, val loss: 0.55692, in 0.016s
1 tree, 115 leaves, max depth = 13, train loss: 0.54781, val loss: 0.54680, in 0.031s
1 tree, 115 leaves, max depth = 18, train loss: 0.53822, val loss: 0.53699, in 0.016s
1 tree, 115 leaves, max depth = 17, train loss: 0.52978, val loss: 0.52840, in 0.031s
1 tree, 115 leaves, max depth = 13, train loss: 0.52149, val loss: 0.52004, in 0.031s
1 tree, 115 leaves, max depth = 15, train loss: 0.51184, val loss: 0.51034, in 0.016s
1 tree, 115 leaves, max depth = 18, train loss: 0.50448, val loss: 0.50306, in 0.031s
1 tree, 115 leaves, max depth = 22, train loss: 0.49611, val loss: 0.49450, in 0.016s
1 tree, 28 leaves, max depth = 12, train loss: 0.48963, val loss: 0.48785, in 0.031s
1 tree, 115 leaves, max depth = 17, train loss: 0.48263, val loss: 0.48089, in 0.016s
1 tree, 89 leaves, max depth = 12, train loss: 0.47609, val loss: 0.47437, in 0.016s
1 tree, 115 leaves, max depth = 19, train loss: 0.47035, val loss: 0.46859, in 0.031s
1 tree, 115 leaves, max depth = 25, train loss: 0.46423, val loss: 0.46236, in 0.016s
1 tree, 115 leaves, max depth = 16, train loss: 0.45837, val loss: 0.45643, in 0.031s
1 tree, 115 leaves, max depth = 23, train loss: 0.45276, val loss: 0.45079, in 0.031s
1 tree, 115 leaves, max depth = 16, train loss: 0.44807, val loss: 0.44620, in 0.016s
1 tree, 115 leaves, max depth = 22, train loss: 0.44371, val loss: 0.44173, in 0.016s
1 tree, 115 leaves, max depth = 14, train loss: 0.43875, val loss: 0.43666, in 0.016s
1 tree, 115 leaves, max depth = 18, train loss: 0.43283, val loss: 0.43075, in 0.016s
1 tree, 115 leaves, max depth = 17, train loss: 0.42786, val loss: 0.42569, in 0.031s
1 tree, 115 leaves, max depth = 19, train loss: 0.42320, val loss: 0.42105, in 0.016s
1 tree, 115 leaves, max depth = 20, train loss: 0.41891, val loss: 0.41674, in 0.031s
1 tree, 115 leaves, max depth = 12, train loss: 0.41526, val loss: 0.41306, in 0.016s
1 tree, 115 leaves, max depth = 18, train loss: 0.41108, val loss: 0.40893, in 0.031s
1 tree, 100 leaves, max depth = 17, train loss: 0.40730, val loss: 0.40504, in 0.016s
1 tree, 115 leaves, max depth = 14, train loss: 0.40350, val loss: 0.40138, in 0.031s
1 tree, 115 leaves, max depth = 14, train loss: 0.40042, val loss: 0.39852, in 0.016s
1 tree, 115 leaves, max depth = 16, train loss: 0.39707, val loss: 0.39527, in 0.031s
1 tree, 11 leaves, max depth = 6, train loss: 0.39106, val loss: 0.38942, in 0.016s
1 tree, 9 leaves, max depth = 5, train loss: 0.38544, val loss: 0.38394, in 0.000s
1 tree, 115 leaves, max depth = 17, train loss: 0.38192, val loss: 0.38042, in 0.031s
1 tree, 55 leaves, max depth = 9, train loss: 0.37570, val loss: 0.37469, in 0.016s
1 tree, 26 leaves, max depth = 9, train loss: 0.37017, val loss: 0.36960, in 0.016s
1 tree, 22 leaves, max depth = 8, train loss: 0.36543, val loss: 0.36500, in 0.016s
1 tree, 17 leaves, max depth = 10, train loss: 0.36107, val loss: 0.36080, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.35653, val loss: 0.35650, in 0.016s
1 tree, 13 leaves, max depth = 5, train loss: 0.35239, val loss: 0.35246, in 0.016s
1 tree, 115 leaves, max depth = 14, train loss: 0.34975, val loss: 0.34997, in 0.031s
1 tree, 115 leaves, max depth = 18, train loss: 0.34743, val loss: 0.34773, in 0.016s
1 tree, 36 leaves, max depth = 9, train loss: 0.34548, val loss: 0.34567, in 0.016s
1 tree, 115 leaves, max depth = 21, train loss: 0.34175, val loss: 0.34196, in 0.031s
1 tree, 115 leaves, max depth = 17, train loss: 0.34007, val loss: 0.34033, in 0.016s
1 tree, 15 leaves, max depth = 5, train loss: 0.33643, val loss: 0.33681, in 0.016s
1 tree, 115 leaves, max depth = 14, train loss: 0.33359, val loss: 0.33411, in 0.031s
1 tree, 115 leaves, max depth = 12, train loss: 0.33150, val loss: 0.33211, in 0.016s
1 tree, 115 leaves, max depth = 14, train loss: 0.32837, val loss: 0.32916, in 0.031s
1 tree, 115 leaves, max depth = 15, train loss: 0.32616, val loss: 0.32688, in 0.031s
1 tree, 115 leaves, max depth = 17, train loss: 0.32435, val loss: 0.32515, in 0.016s
1 tree, 115 leaves, max depth = 13, train loss: 0.32154, val loss: 0.32258, in 0.016s
1 tree, 31 leaves, max depth = 13, train loss: 0.32024, val loss: 0.32131, in 0.016s
1 tree, 115 leaves, max depth = 17, train loss: 0.31797, val loss: 0.31915, in 0.031s
1 tree, 11 leaves, max depth = 6, train loss: 0.31683, val loss: 0.31798, in 0.016s
1 tree, 115 leaves, max depth = 17, train loss: 0.31440, val loss: 0.31568, in 0.016s
1 tree, 115 leaves, max depth = 13, train loss: 0.31289, val loss: 0.31414, in 0.031s
1 tree, 21 leaves, max depth = 6, train loss: 0.31018, val loss: 0.31167, in 0.016s
1 tree, 115 leaves, max depth = 12, train loss: 0.30856, val loss: 0.31020, in 0.016s
1 tree, 77 leaves, max depth = 14, train loss: 0.30728, val loss: 0.30893, in 0.031s
1 tree, 115 leaves, max depth = 23, train loss: 0.30541, val loss: 0.30701, in 0.016s
1 tree, 38 leaves, max depth = 10, train loss: 0.30426, val loss: 0.30589, in 0.016s
1 tree, 13 leaves, max depth = 6, train loss: 0.30154, val loss: 0.30336, in 0.016s
1 tree, 78 leaves, max depth = 16, train loss: 0.29875, val loss: 0.30082, in 0.016s
1 tree, 82 leaves, max depth = 11, train loss: 0.29594, val loss: 0.29845, in 0.016s
1 tree, 22 leaves, max depth = 9, train loss: 0.29468, val loss: 0.29721, in 0.031s
1 tree, 115 leaves, max depth = 12, train loss: 0.29335, val loss: 0.29599, in 0.016s
1 tree, 93 leaves, max depth = 14, train loss: 0.29186, val loss: 0.29438, in 0.016s
1 tree, 115 leaves, max depth = 17, train loss: 0.29075, val loss: 0.29335, in 0.031s
1 tree, 115 leaves, max depth = 15, train loss: 0.28945, val loss: 0.29215, in 0.031s
Fit 76 trees in 1.954 s, (6956 total leaves)
Time spent computing histograms: 0.599s
Time spent finding best splits:  0.140s
Time spent applying splits:      0.128s
Time spent predicting:           0.000s
Trial 65, Fold 1: Log loss = 0.300041432985021, Average precision = 0.961748428061056, ROC-AUC = 0.9546362463734865, Elapsed Time = 1.9627108999993652 seconds
Trial 65, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 65, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
Binning 0.040 GB of training data: 0.188 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 115 leaves, max depth = 16, train loss: 0.67707, val loss: 0.67651, in 0.031s
1 tree, 88 leaves, max depth = 12, train loss: 0.66096, val loss: 0.65998, in 0.031s
1 tree, 115 leaves, max depth = 25, train loss: 0.64653, val loss: 0.64512, in 0.016s
1 tree, 115 leaves, max depth = 21, train loss: 0.63142, val loss: 0.63009, in 0.031s
1 tree, 115 leaves, max depth = 15, train loss: 0.61789, val loss: 0.61627, in 0.031s
1 tree, 115 leaves, max depth = 22, train loss: 0.60439, val loss: 0.60271, in 0.031s
1 tree, 115 leaves, max depth = 17, train loss: 0.59069, val loss: 0.58892, in 0.016s
1 tree, 57 leaves, max depth = 10, train loss: 0.57942, val loss: 0.57720, in 0.016s
1 tree, 37 leaves, max depth = 11, train loss: 0.56878, val loss: 0.56631, in 0.031s
1 tree, 115 leaves, max depth = 24, train loss: 0.55812, val loss: 0.55562, in 0.016s
1 tree, 115 leaves, max depth = 18, train loss: 0.54770, val loss: 0.54533, in 0.031s
1 tree, 115 leaves, max depth = 20, train loss: 0.53832, val loss: 0.53583, in 0.016s
1 tree, 115 leaves, max depth = 19, train loss: 0.53004, val loss: 0.52742, in 0.031s
1 tree, 115 leaves, max depth = 15, train loss: 0.52158, val loss: 0.51886, in 0.031s
1 tree, 115 leaves, max depth = 19, train loss: 0.51199, val loss: 0.50913, in 0.016s
1 tree, 115 leaves, max depth = 20, train loss: 0.50468, val loss: 0.50165, in 0.031s
1 tree, 115 leaves, max depth = 17, train loss: 0.49718, val loss: 0.49402, in 0.031s
1 tree, 110 leaves, max depth = 15, train loss: 0.49132, val loss: 0.48778, in 0.016s
1 tree, 73 leaves, max depth = 14, train loss: 0.48463, val loss: 0.48094, in 0.016s
1 tree, 15 leaves, max depth = 6, train loss: 0.47554, val loss: 0.47188, in 0.016s
1 tree, 115 leaves, max depth = 18, train loss: 0.46928, val loss: 0.46563, in 0.031s
1 tree, 49 leaves, max depth = 11, train loss: 0.46414, val loss: 0.46020, in 0.016s
1 tree, 18 leaves, max depth = 8, train loss: 0.45613, val loss: 0.45224, in 0.016s
1 tree, 115 leaves, max depth = 17, train loss: 0.44977, val loss: 0.44581, in 0.031s
1 tree, 115 leaves, max depth = 13, train loss: 0.44458, val loss: 0.44071, in 0.016s
1 tree, 115 leaves, max depth = 15, train loss: 0.43903, val loss: 0.43527, in 0.031s
1 tree, 11 leaves, max depth = 5, train loss: 0.43219, val loss: 0.42845, in 0.016s
1 tree, 115 leaves, max depth = 16, train loss: 0.42594, val loss: 0.42229, in 0.016s
1 tree, 115 leaves, max depth = 19, train loss: 0.42097, val loss: 0.41745, in 0.031s
1 tree, 115 leaves, max depth = 13, train loss: 0.41692, val loss: 0.41331, in 0.016s
1 tree, 115 leaves, max depth = 11, train loss: 0.41201, val loss: 0.40849, in 0.031s
1 tree, 115 leaves, max depth = 14, train loss: 0.40791, val loss: 0.40454, in 0.016s
1 tree, 115 leaves, max depth = 16, train loss: 0.40409, val loss: 0.40080, in 0.031s
1 tree, 23 leaves, max depth = 9, train loss: 0.39816, val loss: 0.39500, in 0.016s
1 tree, 20 leaves, max depth = 7, train loss: 0.39222, val loss: 0.38920, in 0.016s
1 tree, 115 leaves, max depth = 20, train loss: 0.38828, val loss: 0.38551, in 0.016s
1 tree, 115 leaves, max depth = 16, train loss: 0.38479, val loss: 0.38204, in 0.031s
1 tree, 115 leaves, max depth = 16, train loss: 0.38104, val loss: 0.37849, in 0.031s
1 tree, 8 leaves, max depth = 4, train loss: 0.37631, val loss: 0.37381, in 0.000s
1 tree, 10 leaves, max depth = 5, train loss: 0.37181, val loss: 0.36936, in 0.016s
1 tree, 115 leaves, max depth = 18, train loss: 0.36942, val loss: 0.36701, in 0.031s
1 tree, 17 leaves, max depth = 6, train loss: 0.36535, val loss: 0.36303, in 0.016s
1 tree, 115 leaves, max depth = 16, train loss: 0.36301, val loss: 0.36074, in 0.031s
1 tree, 39 leaves, max depth = 10, train loss: 0.35886, val loss: 0.35699, in 0.000s
1 tree, 115 leaves, max depth = 15, train loss: 0.35488, val loss: 0.35320, in 0.016s
1 tree, 115 leaves, max depth = 20, train loss: 0.35108, val loss: 0.34946, in 0.016s
1 tree, 115 leaves, max depth = 17, train loss: 0.34741, val loss: 0.34594, in 0.031s
1 tree, 115 leaves, max depth = 13, train loss: 0.34497, val loss: 0.34350, in 0.031s
1 tree, 115 leaves, max depth = 28, train loss: 0.34319, val loss: 0.34181, in 0.031s
1 tree, 115 leaves, max depth = 15, train loss: 0.34008, val loss: 0.33890, in 0.016s
1 tree, 115 leaves, max depth = 19, train loss: 0.33787, val loss: 0.33679, in 0.031s
1 tree, 13 leaves, max depth = 5, train loss: 0.33444, val loss: 0.33343, in 0.016s
1 tree, 115 leaves, max depth = 18, train loss: 0.33182, val loss: 0.33094, in 0.016s
1 tree, 115 leaves, max depth = 16, train loss: 0.32892, val loss: 0.32820, in 0.031s
1 tree, 38 leaves, max depth = 15, train loss: 0.32730, val loss: 0.32664, in 0.016s
1 tree, 115 leaves, max depth = 17, train loss: 0.32476, val loss: 0.32422, in 0.031s
1 tree, 115 leaves, max depth = 20, train loss: 0.32288, val loss: 0.32241, in 0.031s
1 tree, 33 leaves, max depth = 8, train loss: 0.31900, val loss: 0.31882, in 0.016s
1 tree, 14 leaves, max depth = 5, train loss: 0.31549, val loss: 0.31553, in 0.016s
1 tree, 115 leaves, max depth = 21, train loss: 0.31430, val loss: 0.31435, in 0.016s
1 tree, 115 leaves, max depth = 17, train loss: 0.31255, val loss: 0.31266, in 0.031s
1 tree, 115 leaves, max depth = 15, train loss: 0.31004, val loss: 0.31036, in 0.016s
1 tree, 44 leaves, max depth = 11, train loss: 0.30876, val loss: 0.30911, in 0.016s
1 tree, 115 leaves, max depth = 15, train loss: 0.30668, val loss: 0.30726, in 0.016s
1 tree, 115 leaves, max depth = 22, train loss: 0.30471, val loss: 0.30549, in 0.031s
1 tree, 115 leaves, max depth = 16, train loss: 0.30320, val loss: 0.30422, in 0.031s
1 tree, 115 leaves, max depth = 14, train loss: 0.30182, val loss: 0.30299, in 0.016s
1 tree, 115 leaves, max depth = 21, train loss: 0.30042, val loss: 0.30185, in 0.047s
1 tree, 115 leaves, max depth = 18, train loss: 0.29909, val loss: 0.30073, in 0.031s
1 tree, 43 leaves, max depth = 13, train loss: 0.29584, val loss: 0.29779, in 0.016s
1 tree, 14 leaves, max depth = 6, train loss: 0.29373, val loss: 0.29584, in 0.016s
1 tree, 93 leaves, max depth = 15, train loss: 0.29251, val loss: 0.29477, in 0.031s
1 tree, 115 leaves, max depth = 18, train loss: 0.29055, val loss: 0.29304, in 0.031s
1 tree, 115 leaves, max depth = 15, train loss: 0.28844, val loss: 0.29103, in 0.031s
1 tree, 72 leaves, max depth = 18, train loss: 0.28595, val loss: 0.28876, in 0.016s
1 tree, 48 leaves, max depth = 12, train loss: 0.28407, val loss: 0.28700, in 0.031s
Fit 76 trees in 2.157 s, (6852 total leaves)
Time spent computing histograms: 0.655s
Time spent finding best splits:  0.155s
Time spent applying splits:      0.142s
Time spent predicting:           0.000s
Trial 65, Fold 2: Log loss = 0.2915758691707463, Average precision = 0.9600215453753159, ROC-AUC = 0.9564426191546374, Elapsed Time = 2.174914799999897 seconds
Trial 65, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 65, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
Binning 0.040 GB of training data: 0.158 s
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 115 leaves, max depth = 17, train loss: 0.67692, val loss: 0.67678, in 0.016s
1 tree, 94 leaves, max depth = 12, train loss: 0.66093, val loss: 0.66069, in 0.031s
1 tree, 115 leaves, max depth = 20, train loss: 0.64646, val loss: 0.64617, in 0.031s
1 tree, 115 leaves, max depth = 16, train loss: 0.63116, val loss: 0.63095, in 0.016s
1 tree, 115 leaves, max depth = 13, train loss: 0.61823, val loss: 0.61797, in 0.031s
1 tree, 115 leaves, max depth = 19, train loss: 0.60477, val loss: 0.60445, in 0.031s
1 tree, 24 leaves, max depth = 9, train loss: 0.59355, val loss: 0.59302, in 0.016s
1 tree, 115 leaves, max depth = 15, train loss: 0.58153, val loss: 0.58117, in 0.031s
1 tree, 98 leaves, max depth = 13, train loss: 0.57012, val loss: 0.57005, in 0.016s
1 tree, 115 leaves, max depth = 16, train loss: 0.55933, val loss: 0.55945, in 0.031s
1 tree, 115 leaves, max depth = 19, train loss: 0.55026, val loss: 0.55033, in 0.016s
1 tree, 115 leaves, max depth = 22, train loss: 0.54040, val loss: 0.54063, in 0.031s
1 tree, 104 leaves, max depth = 14, train loss: 0.53122, val loss: 0.53153, in 0.031s
1 tree, 115 leaves, max depth = 15, train loss: 0.52175, val loss: 0.52198, in 0.016s
1 tree, 29 leaves, max depth = 11, train loss: 0.51487, val loss: 0.51475, in 0.016s
1 tree, 115 leaves, max depth = 20, train loss: 0.50686, val loss: 0.50690, in 0.031s
1 tree, 115 leaves, max depth = 13, train loss: 0.49910, val loss: 0.49911, in 0.031s
1 tree, 115 leaves, max depth = 22, train loss: 0.49177, val loss: 0.49192, in 0.016s
1 tree, 115 leaves, max depth = 16, train loss: 0.48464, val loss: 0.48472, in 0.031s
1 tree, 20 leaves, max depth = 8, train loss: 0.47537, val loss: 0.47608, in 0.016s
1 tree, 115 leaves, max depth = 19, train loss: 0.46817, val loss: 0.46896, in 0.031s
1 tree, 115 leaves, max depth = 18, train loss: 0.46215, val loss: 0.46311, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.45388, val loss: 0.45540, in 0.016s
1 tree, 115 leaves, max depth = 20, train loss: 0.44750, val loss: 0.44911, in 0.031s
1 tree, 115 leaves, max depth = 18, train loss: 0.44111, val loss: 0.44285, in 0.016s
1 tree, 62 leaves, max depth = 17, train loss: 0.43617, val loss: 0.43780, in 0.031s
1 tree, 115 leaves, max depth = 16, train loss: 0.43013, val loss: 0.43160, in 0.016s
1 tree, 9 leaves, max depth = 5, train loss: 0.42574, val loss: 0.42711, in 0.016s
1 tree, 115 leaves, max depth = 19, train loss: 0.42021, val loss: 0.42167, in 0.031s
1 tree, 20 leaves, max depth = 9, train loss: 0.41348, val loss: 0.41550, in 0.016s
1 tree, 115 leaves, max depth = 23, train loss: 0.40917, val loss: 0.41138, in 0.031s
1 tree, 115 leaves, max depth = 15, train loss: 0.40524, val loss: 0.40755, in 0.031s
1 tree, 115 leaves, max depth = 18, train loss: 0.40107, val loss: 0.40341, in 0.016s
1 tree, 115 leaves, max depth = 19, train loss: 0.39649, val loss: 0.39889, in 0.031s
1 tree, 115 leaves, max depth = 18, train loss: 0.39173, val loss: 0.39406, in 0.031s
1 tree, 9 leaves, max depth = 4, train loss: 0.38630, val loss: 0.38895, in 0.016s
1 tree, 12 leaves, max depth = 7, train loss: 0.38108, val loss: 0.38420, in 0.016s
1 tree, 8 leaves, max depth = 4, train loss: 0.37639, val loss: 0.37970, in 0.000s
1 tree, 115 leaves, max depth = 16, train loss: 0.37357, val loss: 0.37692, in 0.031s
1 tree, 115 leaves, max depth = 17, train loss: 0.36944, val loss: 0.37270, in 0.031s
1 tree, 115 leaves, max depth = 16, train loss: 0.36522, val loss: 0.36839, in 0.031s
1 tree, 15 leaves, max depth = 5, train loss: 0.36091, val loss: 0.36449, in 0.016s
1 tree, 115 leaves, max depth = 16, train loss: 0.35772, val loss: 0.36139, in 0.031s
1 tree, 115 leaves, max depth = 20, train loss: 0.35563, val loss: 0.35948, in 0.031s
1 tree, 115 leaves, max depth = 13, train loss: 0.35305, val loss: 0.35703, in 0.016s
1 tree, 115 leaves, max depth = 17, train loss: 0.35105, val loss: 0.35521, in 0.031s
1 tree, 51 leaves, max depth = 12, train loss: 0.34614, val loss: 0.35104, in 0.016s
1 tree, 115 leaves, max depth = 14, train loss: 0.34375, val loss: 0.34896, in 0.031s
1 tree, 9 leaves, max depth = 4, train loss: 0.34035, val loss: 0.34576, in 0.016s
1 tree, 115 leaves, max depth = 12, train loss: 0.33811, val loss: 0.34385, in 0.031s
1 tree, 115 leaves, max depth = 15, train loss: 0.33609, val loss: 0.34204, in 0.016s
1 tree, 49 leaves, max depth = 12, train loss: 0.33317, val loss: 0.33938, in 0.031s
1 tree, 115 leaves, max depth = 24, train loss: 0.33123, val loss: 0.33764, in 0.016s
1 tree, 115 leaves, max depth = 15, train loss: 0.32828, val loss: 0.33469, in 0.031s
1 tree, 115 leaves, max depth = 23, train loss: 0.32690, val loss: 0.33343, in 0.016s
1 tree, 115 leaves, max depth = 17, train loss: 0.32501, val loss: 0.33152, in 0.031s
1 tree, 115 leaves, max depth = 16, train loss: 0.32243, val loss: 0.32881, in 0.016s
1 tree, 56 leaves, max depth = 11, train loss: 0.32085, val loss: 0.32720, in 0.031s
1 tree, 115 leaves, max depth = 16, train loss: 0.31956, val loss: 0.32597, in 0.031s
1 tree, 9 leaves, max depth = 4, train loss: 0.31638, val loss: 0.32318, in 0.016s
1 tree, 115 leaves, max depth = 22, train loss: 0.31384, val loss: 0.32059, in 0.031s
1 tree, 115 leaves, max depth = 15, train loss: 0.31154, val loss: 0.31833, in 0.016s
1 tree, 89 leaves, max depth = 15, train loss: 0.31009, val loss: 0.31705, in 0.031s
1 tree, 14 leaves, max depth = 6, train loss: 0.30725, val loss: 0.31474, in 0.016s
1 tree, 115 leaves, max depth = 16, train loss: 0.30550, val loss: 0.31328, in 0.016s
1 tree, 21 leaves, max depth = 7, train loss: 0.30286, val loss: 0.31112, in 0.016s
1 tree, 115 leaves, max depth = 17, train loss: 0.30095, val loss: 0.30913, in 0.031s
1 tree, 17 leaves, max depth = 9, train loss: 0.29840, val loss: 0.30703, in 0.016s
1 tree, 115 leaves, max depth = 17, train loss: 0.29698, val loss: 0.30573, in 0.031s
1 tree, 51 leaves, max depth = 11, train loss: 0.29378, val loss: 0.30311, in 0.016s
1 tree, 115 leaves, max depth = 14, train loss: 0.29221, val loss: 0.30159, in 0.031s
1 tree, 115 leaves, max depth = 12, train loss: 0.29104, val loss: 0.30044, in 0.031s
1 tree, 115 leaves, max depth = 19, train loss: 0.28962, val loss: 0.29914, in 0.016s
1 tree, 115 leaves, max depth = 20, train loss: 0.28821, val loss: 0.29791, in 0.047s
1 tree, 79 leaves, max depth = 16, train loss: 0.28519, val loss: 0.29536, in 0.016s
1 tree, 22 leaves, max depth = 8, train loss: 0.28289, val loss: 0.29348, in 0.016s
Fit 76 trees in 2.174 s, (6738 total leaves)
Time spent computing histograms: 0.666s
Time spent finding best splits:  0.156s
Time spent applying splits:      0.144s
Time spent predicting:           0.000s
Trial 65, Fold 3: Log loss = 0.2863868354714503, Average precision = 0.9625752619952467, ROC-AUC = 0.957905905991467, Elapsed Time = 2.191066100000171 seconds
Trial 65, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 65, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
Binning 0.040 GB of training data: 0.174 s
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 115 leaves, max depth = 16, train loss: 0.67700, val loss: 0.67664, in 0.031s
1 tree, 103 leaves, max depth = 15, train loss: 0.66083, val loss: 0.66018, in 0.016s
1 tree, 115 leaves, max depth = 18, train loss: 0.64673, val loss: 0.64585, in 0.031s
1 tree, 115 leaves, max depth = 18, train loss: 0.63219, val loss: 0.63065, in 0.016s
1 tree, 115 leaves, max depth = 17, train loss: 0.61877, val loss: 0.61671, in 0.031s
1 tree, 84 leaves, max depth = 18, train loss: 0.60697, val loss: 0.60409, in 0.016s
1 tree, 99 leaves, max depth = 15, train loss: 0.59452, val loss: 0.59094, in 0.031s
1 tree, 115 leaves, max depth = 13, train loss: 0.58223, val loss: 0.57821, in 0.016s
1 tree, 115 leaves, max depth = 16, train loss: 0.57096, val loss: 0.56675, in 0.031s
1 tree, 115 leaves, max depth = 15, train loss: 0.56004, val loss: 0.55525, in 0.031s
1 tree, 115 leaves, max depth = 20, train loss: 0.55078, val loss: 0.54576, in 0.016s
1 tree, 115 leaves, max depth = 13, train loss: 0.54119, val loss: 0.53597, in 0.031s
1 tree, 115 leaves, max depth = 17, train loss: 0.53304, val loss: 0.52757, in 0.031s
1 tree, 115 leaves, max depth = 15, train loss: 0.52468, val loss: 0.51881, in 0.016s
1 tree, 115 leaves, max depth = 14, train loss: 0.51619, val loss: 0.50992, in 0.031s
1 tree, 46 leaves, max depth = 9, train loss: 0.50827, val loss: 0.50141, in 0.016s
1 tree, 115 leaves, max depth = 16, train loss: 0.50160, val loss: 0.49462, in 0.016s
1 tree, 115 leaves, max depth = 16, train loss: 0.49462, val loss: 0.48719, in 0.031s
1 tree, 115 leaves, max depth = 15, train loss: 0.48746, val loss: 0.47977, in 0.031s
1 tree, 70 leaves, max depth = 15, train loss: 0.48087, val loss: 0.47305, in 0.016s
1 tree, 115 leaves, max depth = 17, train loss: 0.47341, val loss: 0.46540, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.46797, val loss: 0.45952, in 0.031s
1 tree, 115 leaves, max depth = 19, train loss: 0.46283, val loss: 0.45429, in 0.031s
1 tree, 10 leaves, max depth = 6, train loss: 0.45438, val loss: 0.44607, in 0.016s
1 tree, 15 leaves, max depth = 6, train loss: 0.44681, val loss: 0.43794, in 0.000s
1 tree, 14 leaves, max depth = 7, train loss: 0.43938, val loss: 0.43028, in 0.016s
1 tree, 115 leaves, max depth = 26, train loss: 0.43502, val loss: 0.42585, in 0.031s
1 tree, 115 leaves, max depth = 17, train loss: 0.42983, val loss: 0.42075, in 0.031s
1 tree, 115 leaves, max depth = 18, train loss: 0.42444, val loss: 0.41508, in 0.016s
1 tree, 115 leaves, max depth = 13, train loss: 0.42018, val loss: 0.41074, in 0.031s
1 tree, 115 leaves, max depth = 18, train loss: 0.41557, val loss: 0.40585, in 0.031s
1 tree, 115 leaves, max depth = 18, train loss: 0.41016, val loss: 0.40034, in 0.016s
1 tree, 45 leaves, max depth = 12, train loss: 0.40644, val loss: 0.39643, in 0.016s
1 tree, 115 leaves, max depth = 15, train loss: 0.40225, val loss: 0.39202, in 0.031s
1 tree, 67 leaves, max depth = 11, train loss: 0.39893, val loss: 0.38866, in 0.016s
1 tree, 115 leaves, max depth = 15, train loss: 0.39485, val loss: 0.38439, in 0.031s
1 tree, 115 leaves, max depth = 18, train loss: 0.39095, val loss: 0.38026, in 0.016s
1 tree, 115 leaves, max depth = 13, train loss: 0.38728, val loss: 0.37639, in 0.031s
1 tree, 15 leaves, max depth = 6, train loss: 0.38183, val loss: 0.37067, in 0.016s
1 tree, 115 leaves, max depth = 17, train loss: 0.37765, val loss: 0.36655, in 0.031s
1 tree, 115 leaves, max depth = 19, train loss: 0.37419, val loss: 0.36296, in 0.031s
1 tree, 11 leaves, max depth = 4, train loss: 0.36939, val loss: 0.35793, in 0.016s
1 tree, 115 leaves, max depth = 21, train loss: 0.36660, val loss: 0.35495, in 0.016s
1 tree, 21 leaves, max depth = 9, train loss: 0.36349, val loss: 0.35164, in 0.016s
1 tree, 115 leaves, max depth = 14, train loss: 0.36091, val loss: 0.34899, in 0.031s
1 tree, 115 leaves, max depth = 22, train loss: 0.35894, val loss: 0.34715, in 0.031s
1 tree, 115 leaves, max depth = 17, train loss: 0.35552, val loss: 0.34399, in 0.016s
1 tree, 97 leaves, max depth = 12, train loss: 0.35315, val loss: 0.34177, in 0.016s
1 tree, 115 leaves, max depth = 15, train loss: 0.35045, val loss: 0.33920, in 0.031s
1 tree, 115 leaves, max depth = 19, train loss: 0.34781, val loss: 0.33645, in 0.031s
1 tree, 115 leaves, max depth = 15, train loss: 0.34567, val loss: 0.33415, in 0.031s
1 tree, 115 leaves, max depth = 16, train loss: 0.34272, val loss: 0.33122, in 0.016s
1 tree, 97 leaves, max depth = 13, train loss: 0.34072, val loss: 0.32911, in 0.016s
1 tree, 75 leaves, max depth = 13, train loss: 0.33863, val loss: 0.32712, in 0.016s
1 tree, 41 leaves, max depth = 13, train loss: 0.33700, val loss: 0.32544, in 0.031s
1 tree, 115 leaves, max depth = 24, train loss: 0.33485, val loss: 0.32324, in 0.031s
1 tree, 115 leaves, max depth = 16, train loss: 0.33272, val loss: 0.32107, in 0.016s
1 tree, 115 leaves, max depth = 15, train loss: 0.33104, val loss: 0.31945, in 0.031s
1 tree, 115 leaves, max depth = 21, train loss: 0.32875, val loss: 0.31721, in 0.031s
1 tree, 115 leaves, max depth = 14, train loss: 0.32665, val loss: 0.31512, in 0.031s
1 tree, 115 leaves, max depth = 22, train loss: 0.32518, val loss: 0.31360, in 0.016s
1 tree, 19 leaves, max depth = 7, train loss: 0.32164, val loss: 0.31029, in 0.016s
1 tree, 111 leaves, max depth = 16, train loss: 0.32009, val loss: 0.30874, in 0.031s
1 tree, 38 leaves, max depth = 12, train loss: 0.31660, val loss: 0.30546, in 0.016s
1 tree, 115 leaves, max depth = 14, train loss: 0.31458, val loss: 0.30344, in 0.031s
1 tree, 115 leaves, max depth = 20, train loss: 0.31270, val loss: 0.30158, in 0.016s
1 tree, 115 leaves, max depth = 17, train loss: 0.31076, val loss: 0.29983, in 0.031s
1 tree, 115 leaves, max depth = 16, train loss: 0.30958, val loss: 0.29861, in 0.016s
1 tree, 79 leaves, max depth = 15, train loss: 0.30837, val loss: 0.29728, in 0.031s
1 tree, 13 leaves, max depth = 8, train loss: 0.30562, val loss: 0.29465, in 0.016s
1 tree, 115 leaves, max depth = 15, train loss: 0.30433, val loss: 0.29347, in 0.031s
1 tree, 13 leaves, max depth = 7, train loss: 0.30135, val loss: 0.29047, in 0.016s
1 tree, 111 leaves, max depth = 16, train loss: 0.30018, val loss: 0.28929, in 0.031s
1 tree, 115 leaves, max depth = 17, train loss: 0.29827, val loss: 0.28750, in 0.016s
1 tree, 40 leaves, max depth = 15, train loss: 0.29544, val loss: 0.28480, in 0.031s
1 tree, 115 leaves, max depth = 17, train loss: 0.29345, val loss: 0.28287, in 0.016s
Fit 76 trees in 2.206 s, (7119 total leaves)
Time spent computing histograms: 0.695s
Time spent finding best splits:  0.161s
Time spent applying splits:      0.148s
Time spent predicting:           0.031s
Trial 65, Fold 4: Log loss = 0.30077750899317207, Average precision = 0.9625300286328604, ROC-AUC = 0.9559317111001429, Elapsed Time = 2.2293044999987615 seconds
Trial 65, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 65, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
0.174 s 0.040 GB of training data: 
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 115 leaves, max depth = 16, train loss: 0.67682, val loss: 0.67611, in 0.031s
1 tree, 108 leaves, max depth = 13, train loss: 0.66060, val loss: 0.65933, in 0.016s
1 tree, 115 leaves, max depth = 16, train loss: 0.64577, val loss: 0.64381, in 0.031s
1 tree, 115 leaves, max depth = 18, train loss: 0.63060, val loss: 0.62798, in 0.031s
1 tree, 115 leaves, max depth = 14, train loss: 0.61703, val loss: 0.61361, in 0.016s
1 tree, 88 leaves, max depth = 14, train loss: 0.60372, val loss: 0.59954, in 0.031s
1 tree, 115 leaves, max depth = 18, train loss: 0.59027, val loss: 0.58544, in 0.031s
1 tree, 57 leaves, max depth = 10, train loss: 0.57904, val loss: 0.57387, in 0.016s
1 tree, 115 leaves, max depth = 13, train loss: 0.56793, val loss: 0.56215, in 0.031s
1 tree, 115 leaves, max depth = 15, train loss: 0.55718, val loss: 0.55096, in 0.031s
1 tree, 115 leaves, max depth = 14, train loss: 0.54702, val loss: 0.54074, in 0.016s
1 tree, 115 leaves, max depth = 17, train loss: 0.53633, val loss: 0.52978, in 0.031s
1 tree, 115 leaves, max depth = 16, train loss: 0.52723, val loss: 0.52053, in 0.031s
1 tree, 115 leaves, max depth = 14, train loss: 0.51887, val loss: 0.51178, in 0.016s
1 tree, 115 leaves, max depth = 17, train loss: 0.51078, val loss: 0.50331, in 0.031s
1 tree, 115 leaves, max depth = 17, train loss: 0.50347, val loss: 0.49572, in 0.016s
1 tree, 115 leaves, max depth = 15, train loss: 0.49610, val loss: 0.48797, in 0.031s
1 tree, 115 leaves, max depth = 19, train loss: 0.48971, val loss: 0.48121, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.48336, val loss: 0.47455, in 0.000s
1 tree, 10 leaves, max depth = 5, train loss: 0.47426, val loss: 0.46515, in 0.016s
1 tree, 115 leaves, max depth = 15, train loss: 0.46799, val loss: 0.45860, in 0.016s
1 tree, 115 leaves, max depth = 18, train loss: 0.46252, val loss: 0.45289, in 0.031s
1 tree, 49 leaves, max depth = 11, train loss: 0.45732, val loss: 0.44725, in 0.016s
1 tree, 115 leaves, max depth = 16, train loss: 0.45184, val loss: 0.44139, in 0.031s
1 tree, 115 leaves, max depth = 26, train loss: 0.44538, val loss: 0.43482, in 0.016s
1 tree, 115 leaves, max depth = 13, train loss: 0.43897, val loss: 0.42840, in 0.031s
1 tree, 115 leaves, max depth = 15, train loss: 0.43426, val loss: 0.42334, in 0.016s
1 tree, 115 leaves, max depth = 23, train loss: 0.43002, val loss: 0.41897, in 0.031s
1 tree, 22 leaves, max depth = 10, train loss: 0.42279, val loss: 0.41166, in 0.016s
1 tree, 17 leaves, max depth = 8, train loss: 0.41607, val loss: 0.40482, in 0.016s
1 tree, 115 leaves, max depth = 16, train loss: 0.41054, val loss: 0.39948, in 0.031s
1 tree, 63 leaves, max depth = 10, train loss: 0.40649, val loss: 0.39534, in 0.016s
1 tree, 115 leaves, max depth = 13, train loss: 0.40264, val loss: 0.39150, in 0.031s
1 tree, 115 leaves, max depth = 14, train loss: 0.39843, val loss: 0.38709, in 0.031s
1 tree, 115 leaves, max depth = 23, train loss: 0.39550, val loss: 0.38397, in 0.016s
1 tree, 115 leaves, max depth = 18, train loss: 0.39263, val loss: 0.38096, in 0.031s
1 tree, 115 leaves, max depth = 26, train loss: 0.38998, val loss: 0.37803, in 0.031s
1 tree, 115 leaves, max depth = 20, train loss: 0.38744, val loss: 0.37524, in 0.031s
1 tree, 115 leaves, max depth = 14, train loss: 0.38431, val loss: 0.37205, in 0.016s
1 tree, 9 leaves, max depth = 4, train loss: 0.37922, val loss: 0.36689, in 0.031s
1 tree, 12 leaves, max depth = 6, train loss: 0.37382, val loss: 0.36187, in 0.000s
1 tree, 14 leaves, max depth = 5, train loss: 0.36901, val loss: 0.35703, in 0.016s
1 tree, 115 leaves, max depth = 18, train loss: 0.36562, val loss: 0.35361, in 0.031s
1 tree, 115 leaves, max depth = 13, train loss: 0.36298, val loss: 0.35095, in 0.031s
1 tree, 115 leaves, max depth = 15, train loss: 0.35916, val loss: 0.34717, in 0.016s
1 tree, 115 leaves, max depth = 17, train loss: 0.35554, val loss: 0.34361, in 0.031s
1 tree, 115 leaves, max depth = 15, train loss: 0.35208, val loss: 0.34023, in 0.031s
1 tree, 115 leaves, max depth = 16, train loss: 0.34926, val loss: 0.33739, in 0.016s
1 tree, 103 leaves, max depth = 14, train loss: 0.34697, val loss: 0.33514, in 0.031s
1 tree, 115 leaves, max depth = 14, train loss: 0.34479, val loss: 0.33297, in 0.016s
1 tree, 115 leaves, max depth = 15, train loss: 0.34157, val loss: 0.32988, in 0.031s
1 tree, 29 leaves, max depth = 11, train loss: 0.33985, val loss: 0.32815, in 0.016s
1 tree, 115 leaves, max depth = 14, train loss: 0.33773, val loss: 0.32598, in 0.031s
1 tree, 115 leaves, max depth = 13, train loss: 0.33565, val loss: 0.32420, in 0.031s
1 tree, 115 leaves, max depth = 18, train loss: 0.33276, val loss: 0.32150, in 0.016s
1 tree, 115 leaves, max depth = 12, train loss: 0.33095, val loss: 0.31982, in 0.016s
1 tree, 29 leaves, max depth = 11, train loss: 0.32963, val loss: 0.31831, in 0.016s
1 tree, 105 leaves, max depth = 14, train loss: 0.32812, val loss: 0.31691, in 0.016s
1 tree, 115 leaves, max depth = 16, train loss: 0.32550, val loss: 0.31447, in 0.016s
1 tree, 115 leaves, max depth = 14, train loss: 0.32386, val loss: 0.31280, in 0.031s
1 tree, 71 leaves, max depth = 16, train loss: 0.32004, val loss: 0.30925, in 0.016s
1 tree, 26 leaves, max depth = 12, train loss: 0.31646, val loss: 0.30579, in 0.016s
1 tree, 12 leaves, max depth = 5, train loss: 0.31285, val loss: 0.30255, in 0.016s
1 tree, 115 leaves, max depth = 21, train loss: 0.31177, val loss: 0.30152, in 0.031s
1 tree, 115 leaves, max depth = 19, train loss: 0.31032, val loss: 0.30007, in 0.031s
1 tree, 115 leaves, max depth = 15, train loss: 0.30868, val loss: 0.29841, in 0.016s
1 tree, 115 leaves, max depth = 19, train loss: 0.30738, val loss: 0.29712, in 0.031s
1 tree, 13 leaves, max depth = 6, train loss: 0.30621, val loss: 0.29591, in 0.016s
1 tree, 14 leaves, max depth = 5, train loss: 0.30287, val loss: 0.29292, in 0.016s
1 tree, 115 leaves, max depth = 14, train loss: 0.30115, val loss: 0.29134, in 0.016s
1 tree, 115 leaves, max depth = 17, train loss: 0.29902, val loss: 0.28937, in 0.031s
1 tree, 95 leaves, max depth = 15, train loss: 0.29592, val loss: 0.28654, in 0.031s
1 tree, 115 leaves, max depth = 22, train loss: 0.29479, val loss: 0.28540, in 0.031s
1 tree, 93 leaves, max depth = 15, train loss: 0.29332, val loss: 0.28408, in 0.016s
1 tree, 115 leaves, max depth = 18, train loss: 0.29197, val loss: 0.28295, in 0.016s
1 tree, 16 leaves, max depth = 9, train loss: 0.29111, val loss: 0.28216, in 0.016s
Fit 76 trees in 2.142 s, (7070 total leaves)
Time spent computing histograms: 0.664s
Time spent finding best splits:  0.156s
Time spent applying splits:      0.144s
Time spent predicting:           0.016s
Trial 65, Fold 5: Log loss = 0.3062555495030684, Average precision = 0.9591346466491077, ROC-AUC = 0.9539096668710402, Elapsed Time = 2.1642406999999366 seconds
Optimization Progress:  66%|######6   | 66/100 [13:04<07:52, 13.90s/it]
Trial 66, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 66, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
0.206 s 0.040 GB of training data: 
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 64 leaves, max depth = 16, train loss: 0.65572, val loss: 0.65450, in 0.016s
1 tree, 64 leaves, max depth = 16, train loss: 0.62421, val loss: 0.62186, in 0.016s
1 tree, 68 leaves, max depth = 19, train loss: 0.59754, val loss: 0.59454, in 0.016s
1 tree, 68 leaves, max depth = 19, train loss: 0.57469, val loss: 0.57110, in 0.016s
1 tree, 68 leaves, max depth = 15, train loss: 0.55484, val loss: 0.55033, in 0.000s
1 tree, 107 leaves, max depth = 18, train loss: 0.53642, val loss: 0.53322, in 0.016s
1 tree, 67 leaves, max depth = 18, train loss: 0.52055, val loss: 0.51683, in 0.016s
1 tree, 106 leaves, max depth = 18, train loss: 0.50540, val loss: 0.50285, in 0.016s
1 tree, 67 leaves, max depth = 13, train loss: 0.49249, val loss: 0.48932, in 0.016s
1 tree, 107 leaves, max depth = 18, train loss: 0.47987, val loss: 0.47776, in 0.016s
1 tree, 67 leaves, max depth = 12, train loss: 0.46924, val loss: 0.46663, in 0.016s
1 tree, 107 leaves, max depth = 15, train loss: 0.45859, val loss: 0.45695, in 0.000s
1 tree, 107 leaves, max depth = 15, train loss: 0.44930, val loss: 0.44856, in 0.016s
1 tree, 68 leaves, max depth = 14, train loss: 0.44082, val loss: 0.43960, in 0.016s
1 tree, 68 leaves, max depth = 13, train loss: 0.43316, val loss: 0.43122, in 0.016s
1 tree, 107 leaves, max depth = 13, train loss: 0.42538, val loss: 0.42429, in 0.016s
1 tree, 67 leaves, max depth = 14, train loss: 0.41903, val loss: 0.41746, in 0.000s
1 tree, 107 leaves, max depth = 13, train loss: 0.41233, val loss: 0.41156, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.40685, val loss: 0.40550, in 0.016s
1 tree, 107 leaves, max depth = 15, train loss: 0.40102, val loss: 0.40048, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.39630, val loss: 0.39522, in 0.000s
1 tree, 107 leaves, max depth = 13, train loss: 0.39119, val loss: 0.39094, in 0.016s
1 tree, 67 leaves, max depth = 14, train loss: 0.38623, val loss: 0.38562, in 0.016s
1 tree, 67 leaves, max depth = 14, train loss: 0.38193, val loss: 0.38099, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.37791, val loss: 0.37649, in 0.000s
1 tree, 107 leaves, max depth = 14, train loss: 0.37343, val loss: 0.37285, in 0.016s
1 tree, 107 leaves, max depth = 14, train loss: 0.36951, val loss: 0.36970, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.36598, val loss: 0.36571, in 0.000s
1 tree, 68 leaves, max depth = 12, train loss: 0.36221, val loss: 0.36159, in 0.016s
1 tree, 106 leaves, max depth = 14, train loss: 0.35873, val loss: 0.35888, in 0.016s
1 tree, 67 leaves, max depth = 13, train loss: 0.35557, val loss: 0.35549, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.35248, val loss: 0.35198, in 0.000s
1 tree, 4 leaves, max depth = 3, train loss: 0.34978, val loss: 0.34889, in 0.016s
1 tree, 67 leaves, max depth = 13, train loss: 0.34705, val loss: 0.34604, in 0.000s
1 tree, 107 leaves, max depth = 14, train loss: 0.34384, val loss: 0.34366, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.34142, val loss: 0.34082, in 0.000s
1 tree, 86 leaves, max depth = 17, train loss: 0.33886, val loss: 0.33801, in 0.016s
1 tree, 107 leaves, max depth = 14, train loss: 0.33596, val loss: 0.33592, in 0.016s
1 tree, 86 leaves, max depth = 15, train loss: 0.33371, val loss: 0.33340, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.33152, val loss: 0.33086, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.32959, val loss: 0.32860, in 0.000s
1 tree, 107 leaves, max depth = 14, train loss: 0.32690, val loss: 0.32672, in 0.016s
1 tree, 85 leaves, max depth = 14, train loss: 0.32465, val loss: 0.32419, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.32288, val loss: 0.32207, in 0.000s
1 tree, 67 leaves, max depth = 14, train loss: 0.32097, val loss: 0.32013, in 0.016s
1 tree, 107 leaves, max depth = 14, train loss: 0.31851, val loss: 0.31845, in 0.016s
1 tree, 85 leaves, max depth = 15, train loss: 0.31671, val loss: 0.31651, in 0.016s
1 tree, 107 leaves, max depth = 14, train loss: 0.31454, val loss: 0.31507, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.31287, val loss: 0.31308, in 0.000s
1 tree, 67 leaves, max depth = 13, train loss: 0.31129, val loss: 0.31145, in 0.016s
1 tree, 107 leaves, max depth = 15, train loss: 0.30933, val loss: 0.31021, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.30780, val loss: 0.30834, in 0.016s
1 tree, 86 leaves, max depth = 15, train loss: 0.30623, val loss: 0.30665, in 0.000s
1 tree, 107 leaves, max depth = 14, train loss: 0.30443, val loss: 0.30553, in 0.016s
1 tree, 85 leaves, max depth = 19, train loss: 0.30294, val loss: 0.30382, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.30152, val loss: 0.30207, in 0.016s
1 tree, 107 leaves, max depth = 14, train loss: 0.29989, val loss: 0.30112, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.29861, val loss: 0.29952, in 0.000s
1 tree, 67 leaves, max depth = 15, train loss: 0.29723, val loss: 0.29826, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.29609, val loss: 0.29684, in 0.000s
1 tree, 85 leaves, max depth = 19, train loss: 0.29481, val loss: 0.29542, in 0.016s
1 tree, 107 leaves, max depth = 15, train loss: 0.29323, val loss: 0.29450, in 0.016s
1 tree, 40 leaves, max depth = 11, train loss: 0.29213, val loss: 0.29350, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.29104, val loss: 0.29212, in 0.000s
1 tree, 107 leaves, max depth = 15, train loss: 0.28966, val loss: 0.29138, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.28868, val loss: 0.29012, in 0.016s
1 tree, 85 leaves, max depth = 19, train loss: 0.28753, val loss: 0.28883, in 0.016s
1 tree, 132 leaves, max depth = 24, train loss: 0.28595, val loss: 0.28798, in 0.016s
1 tree, 64 leaves, max depth = 15, train loss: 0.28497, val loss: 0.28696, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.28404, val loss: 0.28575, in 0.000s
1 tree, 140 leaves, max depth = 18, train loss: 0.28243, val loss: 0.28485, in 0.016s
1 tree, 140 leaves, max depth = 20, train loss: 0.28082, val loss: 0.28362, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.27996, val loss: 0.28251, in 0.000s
1 tree, 83 leaves, max depth = 17, train loss: 0.27896, val loss: 0.28156, in 0.016s
1 tree, 140 leaves, max depth = 18, train loss: 0.27753, val loss: 0.28080, in 0.016s
1 tree, 131 leaves, max depth = 24, train loss: 0.27623, val loss: 0.28017, in 0.031s
1 tree, 4 leaves, max depth = 3, train loss: 0.27541, val loss: 0.27910, in 0.000s
1 tree, 40 leaves, max depth = 10, train loss: 0.27456, val loss: 0.27836, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.27383, val loss: 0.27738, in 0.016s
1 tree, 85 leaves, max depth = 17, train loss: 0.27293, val loss: 0.27635, in 0.016s
1 tree, 84 leaves, max depth = 13, train loss: 0.27138, val loss: 0.27547, in 0.016s
1 tree, 140 leaves, max depth = 17, train loss: 0.27018, val loss: 0.27490, in 0.031s
1 tree, 140 leaves, max depth = 17, train loss: 0.26887, val loss: 0.27393, in 0.016s
1 tree, 85 leaves, max depth = 14, train loss: 0.26804, val loss: 0.27291, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.26732, val loss: 0.27195, in 0.000s
Fit 85 trees in 1.393 s, (5840 total leaves)
Time spent computing histograms: 0.450s
Time spent finding best splits:  0.109s
Time spent applying splits:      0.125s
Time spent predicting:           0.000s
Trial 66, Fold 1: Log loss = 0.2807773485256574, Average precision = 0.9522178452405305, ROC-AUC = 0.9524693001008493, Elapsed Time = 1.4055117999996583 seconds
Trial 66, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 66, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
Binning 0.040 GB of training data: 0.173 s
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 65 leaves, max depth = 15, train loss: 0.65638, val loss: 0.65475, in 0.016s
1 tree, 68 leaves, max depth = 11, train loss: 0.62534, val loss: 0.62225, in 0.016s
1 tree, 67 leaves, max depth = 14, train loss: 0.59839, val loss: 0.59387, in 0.016s
1 tree, 67 leaves, max depth = 14, train loss: 0.57553, val loss: 0.56976, in 0.000s
1 tree, 67 leaves, max depth = 14, train loss: 0.55565, val loss: 0.54871, in 0.016s
1 tree, 107 leaves, max depth = 15, train loss: 0.53740, val loss: 0.53119, in 0.016s
1 tree, 67 leaves, max depth = 20, train loss: 0.52158, val loss: 0.51443, in 0.016s
1 tree, 107 leaves, max depth = 15, train loss: 0.50660, val loss: 0.50012, in 0.016s
1 tree, 68 leaves, max depth = 12, train loss: 0.49338, val loss: 0.48599, in 0.016s
1 tree, 107 leaves, max depth = 16, train loss: 0.48096, val loss: 0.47419, in 0.016s
1 tree, 68 leaves, max depth = 14, train loss: 0.47029, val loss: 0.46280, in 0.016s
1 tree, 107 leaves, max depth = 15, train loss: 0.45981, val loss: 0.45289, in 0.016s
1 tree, 68 leaves, max depth = 17, train loss: 0.45088, val loss: 0.44338, in 0.000s
1 tree, 68 leaves, max depth = 15, train loss: 0.44292, val loss: 0.43475, in 0.016s
1 tree, 68 leaves, max depth = 18, train loss: 0.43616, val loss: 0.42749, in 0.016s
1 tree, 107 leaves, max depth = 14, train loss: 0.42762, val loss: 0.41952, in 0.000s
1 tree, 4 leaves, max depth = 3, train loss: 0.42217, val loss: 0.41385, in 0.016s
1 tree, 106 leaves, max depth = 14, train loss: 0.41477, val loss: 0.40700, in 0.016s
1 tree, 68 leaves, max depth = 14, train loss: 0.40943, val loss: 0.40129, in 0.016s
1 tree, 107 leaves, max depth = 14, train loss: 0.40301, val loss: 0.39541, in 0.016s
1 tree, 107 leaves, max depth = 14, train loss: 0.39738, val loss: 0.39024, in 0.000s
1 tree, 106 leaves, max depth = 14, train loss: 0.39249, val loss: 0.38583, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.38798, val loss: 0.38112, in 0.016s
1 tree, 67 leaves, max depth = 12, train loss: 0.38358, val loss: 0.37632, in 0.000s
1 tree, 4 leaves, max depth = 3, train loss: 0.37970, val loss: 0.37226, in 0.016s
1 tree, 107 leaves, max depth = 15, train loss: 0.37539, val loss: 0.36839, in 0.016s
1 tree, 107 leaves, max depth = 15, train loss: 0.37166, val loss: 0.36510, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.36824, val loss: 0.36151, in 0.016s
1 tree, 67 leaves, max depth = 11, train loss: 0.36446, val loss: 0.35744, in 0.016s
1 tree, 107 leaves, max depth = 17, train loss: 0.36117, val loss: 0.35458, in 0.000s
1 tree, 67 leaves, max depth = 14, train loss: 0.35795, val loss: 0.35107, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.35495, val loss: 0.34791, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.35233, val loss: 0.34513, in 0.000s
1 tree, 67 leaves, max depth = 15, train loss: 0.34955, val loss: 0.34213, in 0.016s
1 tree, 107 leaves, max depth = 18, train loss: 0.34648, val loss: 0.33949, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.34413, val loss: 0.33700, in 0.000s
1 tree, 86 leaves, max depth = 16, train loss: 0.34166, val loss: 0.33427, in 0.016s
1 tree, 107 leaves, max depth = 18, train loss: 0.33894, val loss: 0.33197, in 0.016s
1 tree, 67 leaves, max depth = 14, train loss: 0.33661, val loss: 0.32957, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.33448, val loss: 0.32730, in 0.016s
1 tree, 83 leaves, max depth = 15, train loss: 0.33225, val loss: 0.32487, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.33036, val loss: 0.32284, in 0.000s
1 tree, 83 leaves, max depth = 15, train loss: 0.32843, val loss: 0.32074, in 0.016s
1 tree, 107 leaves, max depth = 17, train loss: 0.32585, val loss: 0.31858, in 0.029s
1 tree, 4 leaves, max depth = 3, train loss: 0.32412, val loss: 0.31673, in 0.007s
1 tree, 107 leaves, max depth = 18, train loss: 0.32184, val loss: 0.31484, in 0.019s
1 tree, 67 leaves, max depth = 13, train loss: 0.32009, val loss: 0.31302, in 0.014s
1 tree, 106 leaves, max depth = 17, train loss: 0.31808, val loss: 0.31138, in 0.014s
1 tree, 4 leaves, max depth = 3, train loss: 0.31646, val loss: 0.30964, in 0.000s
1 tree, 83 leaves, max depth = 16, train loss: 0.31474, val loss: 0.30775, in 0.016s
1 tree, 106 leaves, max depth = 16, train loss: 0.31286, val loss: 0.30621, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.31137, val loss: 0.30461, in 0.016s
1 tree, 86 leaves, max depth = 16, train loss: 0.30985, val loss: 0.30298, in 0.000s
1 tree, 107 leaves, max depth = 16, train loss: 0.30814, val loss: 0.30158, in 0.016s
1 tree, 67 leaves, max depth = 14, train loss: 0.30675, val loss: 0.30017, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.30537, val loss: 0.29869, in 0.016s
1 tree, 83 leaves, max depth = 16, train loss: 0.30406, val loss: 0.29725, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.30283, val loss: 0.29591, in 0.000s
1 tree, 107 leaves, max depth = 17, train loss: 0.30122, val loss: 0.29464, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.30010, val loss: 0.29341, in 0.016s
1 tree, 84 leaves, max depth = 14, train loss: 0.29813, val loss: 0.29203, in 0.016s
1 tree, 83 leaves, max depth = 16, train loss: 0.29687, val loss: 0.29064, in 0.016s
1 tree, 41 leaves, max depth = 10, train loss: 0.29578, val loss: 0.28960, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.29473, val loss: 0.28845, in 0.000s
1 tree, 106 leaves, max depth = 17, train loss: 0.29333, val loss: 0.28733, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.29238, val loss: 0.28628, in 0.016s
1 tree, 83 leaves, max depth = 16, train loss: 0.29129, val loss: 0.28509, in 0.016s
1 tree, 131 leaves, max depth = 21, train loss: 0.28970, val loss: 0.28436, in 0.016s
1 tree, 65 leaves, max depth = 18, train loss: 0.28867, val loss: 0.28342, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.28777, val loss: 0.28242, in 0.000s
1 tree, 106 leaves, max depth = 17, train loss: 0.28655, val loss: 0.28151, in 0.016s
1 tree, 140 leaves, max depth = 18, train loss: 0.28494, val loss: 0.28081, in 0.031s
1 tree, 4 leaves, max depth = 3, train loss: 0.28409, val loss: 0.27987, in 0.000s
1 tree, 83 leaves, max depth = 17, train loss: 0.28304, val loss: 0.27882, in 0.016s
1 tree, 84 leaves, max depth = 14, train loss: 0.28152, val loss: 0.27780, in 0.016s
1 tree, 86 leaves, max depth = 21, train loss: 0.28056, val loss: 0.27673, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.27976, val loss: 0.27584, in 0.000s
1 tree, 140 leaves, max depth = 17, train loss: 0.27823, val loss: 0.27522, in 0.031s
1 tree, 131 leaves, max depth = 21, train loss: 0.27700, val loss: 0.27475, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.27626, val loss: 0.27392, in 0.016s
1 tree, 41 leaves, max depth = 10, train loss: 0.27544, val loss: 0.27315, in 0.000s
1 tree, 140 leaves, max depth = 18, train loss: 0.27406, val loss: 0.27262, in 0.031s
1 tree, 86 leaves, max depth = 21, train loss: 0.27322, val loss: 0.27167, in 0.016s
1 tree, 140 leaves, max depth = 18, train loss: 0.27199, val loss: 0.27124, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.27128, val loss: 0.27043, in 0.016s
Fit 85 trees in 1.445 s, (5776 total leaves)
Time spent computing histograms: 0.489s
Time spent finding best splits:  0.110s
Time spent applying splits:      0.127s
Time spent predicting:           0.000s
Trial 66, Fold 2: Log loss = 0.2800543717383729, Average precision = 0.9508008089697524, ROC-AUC = 0.9531672190791882, Elapsed Time = 1.45176309999988 seconds
Trial 66, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 66, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
Binning 0.040 GB of training data: 0.173 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 65 leaves, max depth = 13, train loss: 0.65601, val loss: 0.65491, in 0.016s
1 tree, 65 leaves, max depth = 13, train loss: 0.62471, val loss: 0.62261, in 0.016s
1 tree, 65 leaves, max depth = 13, train loss: 0.59805, val loss: 0.59502, in 0.016s
1 tree, 67 leaves, max depth = 16, train loss: 0.57547, val loss: 0.57137, in 0.000s
1 tree, 66 leaves, max depth = 13, train loss: 0.55579, val loss: 0.55095, in 0.016s
1 tree, 106 leaves, max depth = 13, train loss: 0.53741, val loss: 0.53400, in 0.016s
1 tree, 66 leaves, max depth = 17, train loss: 0.52169, val loss: 0.51748, in 0.016s
1 tree, 106 leaves, max depth = 13, train loss: 0.50658, val loss: 0.50363, in 0.016s
1 tree, 66 leaves, max depth = 17, train loss: 0.49376, val loss: 0.49006, in 0.016s
1 tree, 106 leaves, max depth = 13, train loss: 0.48118, val loss: 0.47860, in 0.016s
1 tree, 66 leaves, max depth = 13, train loss: 0.47049, val loss: 0.46743, in 0.016s
1 tree, 106 leaves, max depth = 14, train loss: 0.45989, val loss: 0.45783, in 0.016s
1 tree, 67 leaves, max depth = 13, train loss: 0.45098, val loss: 0.44844, in 0.000s
1 tree, 67 leaves, max depth = 13, train loss: 0.44313, val loss: 0.44007, in 0.016s
1 tree, 67 leaves, max depth = 13, train loss: 0.43639, val loss: 0.43281, in 0.016s
1 tree, 106 leaves, max depth = 12, train loss: 0.42773, val loss: 0.42502, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.42206, val loss: 0.41978, in 0.000s
1 tree, 106 leaves, max depth = 12, train loss: 0.41456, val loss: 0.41311, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.40969, val loss: 0.40860, in 0.016s
1 tree, 106 leaves, max depth = 12, train loss: 0.40315, val loss: 0.40285, in 0.000s
1 tree, 106 leaves, max depth = 12, train loss: 0.39742, val loss: 0.39788, in 0.016s
1 tree, 106 leaves, max depth = 12, train loss: 0.39241, val loss: 0.39358, in 0.016s
1 tree, 67 leaves, max depth = 13, train loss: 0.38754, val loss: 0.38822, in 0.000s
1 tree, 67 leaves, max depth = 14, train loss: 0.38330, val loss: 0.38357, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.37922, val loss: 0.37978, in 0.016s
1 tree, 106 leaves, max depth = 15, train loss: 0.37483, val loss: 0.37605, in 0.016s
1 tree, 106 leaves, max depth = 15, train loss: 0.37099, val loss: 0.37285, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.36740, val loss: 0.36951, in 0.000s
1 tree, 67 leaves, max depth = 14, train loss: 0.36374, val loss: 0.36541, in 0.016s
1 tree, 106 leaves, max depth = 17, train loss: 0.36033, val loss: 0.36262, in 0.016s
1 tree, 66 leaves, max depth = 13, train loss: 0.35724, val loss: 0.35919, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.35410, val loss: 0.35626, in 0.000s
1 tree, 4 leaves, max depth = 3, train loss: 0.35135, val loss: 0.35370, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.34895, val loss: 0.35144, in 0.000s
1 tree, 67 leaves, max depth = 13, train loss: 0.34628, val loss: 0.34854, in 0.016s
1 tree, 105 leaves, max depth = 17, train loss: 0.34308, val loss: 0.34598, in 0.016s
1 tree, 85 leaves, max depth = 15, train loss: 0.34066, val loss: 0.34293, in 0.016s
1 tree, 106 leaves, max depth = 17, train loss: 0.33783, val loss: 0.34070, in 0.016s
1 tree, 67 leaves, max depth = 12, train loss: 0.33553, val loss: 0.33832, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.33330, val loss: 0.33623, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.33133, val loss: 0.33438, in 0.000s
1 tree, 83 leaves, max depth = 14, train loss: 0.32915, val loss: 0.33179, in 0.016s
1 tree, 83 leaves, max depth = 14, train loss: 0.32729, val loss: 0.32954, in 0.016s
1 tree, 103 leaves, max depth = 14, train loss: 0.32460, val loss: 0.32744, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.32278, val loss: 0.32572, in 0.016s
1 tree, 105 leaves, max depth = 14, train loss: 0.32039, val loss: 0.32389, in 0.016s
1 tree, 67 leaves, max depth = 13, train loss: 0.31874, val loss: 0.32211, in 0.000s
1 tree, 106 leaves, max depth = 15, train loss: 0.31662, val loss: 0.32052, in 0.031s
1 tree, 4 leaves, max depth = 3, train loss: 0.31492, val loss: 0.31893, in 0.000s
1 tree, 83 leaves, max depth = 15, train loss: 0.31326, val loss: 0.31689, in 0.016s
1 tree, 106 leaves, max depth = 15, train loss: 0.31133, val loss: 0.31547, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.30977, val loss: 0.31399, in 0.016s
1 tree, 67 leaves, max depth = 13, train loss: 0.30838, val loss: 0.31250, in 0.016s
1 tree, 106 leaves, max depth = 15, train loss: 0.30662, val loss: 0.31123, in 0.016s
1 tree, 83 leaves, max depth = 15, train loss: 0.30519, val loss: 0.30943, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.30374, val loss: 0.30805, in 0.000s
1 tree, 106 leaves, max depth = 13, train loss: 0.30213, val loss: 0.30692, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.30083, val loss: 0.30569, in 0.016s
1 tree, 83 leaves, max depth = 15, train loss: 0.29952, val loss: 0.30404, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.29835, val loss: 0.30292, in 0.000s
1 tree, 129 leaves, max depth = 19, train loss: 0.29654, val loss: 0.30210, in 0.016s
1 tree, 67 leaves, max depth = 14, train loss: 0.29534, val loss: 0.30085, in 0.016s
1 tree, 105 leaves, max depth = 15, train loss: 0.29392, val loss: 0.29988, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.29281, val loss: 0.29883, in 0.016s
1 tree, 83 leaves, max depth = 17, train loss: 0.29169, val loss: 0.29740, in 0.000s
1 tree, 4 leaves, max depth = 3, train loss: 0.29069, val loss: 0.29644, in 0.016s
1 tree, 106 leaves, max depth = 15, train loss: 0.28934, val loss: 0.29554, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.28843, val loss: 0.29467, in 0.016s
1 tree, 64 leaves, max depth = 16, train loss: 0.28739, val loss: 0.29361, in 0.016s
1 tree, 106 leaves, max depth = 15, train loss: 0.28617, val loss: 0.29282, in 0.016s
1 tree, 83 leaves, max depth = 17, train loss: 0.28518, val loss: 0.29153, in 0.000s
1 tree, 130 leaves, max depth = 20, train loss: 0.28374, val loss: 0.29104, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.28285, val loss: 0.29020, in 0.016s
1 tree, 40 leaves, max depth = 10, train loss: 0.28192, val loss: 0.28939, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.28112, val loss: 0.28862, in 0.000s
1 tree, 83 leaves, max depth = 13, train loss: 0.27949, val loss: 0.28740, in 0.016s
1 tree, 85 leaves, max depth = 15, train loss: 0.27847, val loss: 0.28630, in 0.016s
1 tree, 140 leaves, max depth = 15, train loss: 0.27708, val loss: 0.28525, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.27632, val loss: 0.28451, in 0.016s
1 tree, 140 leaves, max depth = 17, train loss: 0.27491, val loss: 0.28359, in 0.016s
1 tree, 85 leaves, max depth = 15, train loss: 0.27401, val loss: 0.28259, in 0.016s
1 tree, 140 leaves, max depth = 15, train loss: 0.27277, val loss: 0.28166, in 0.016s
1 tree, 131 leaves, max depth = 18, train loss: 0.27160, val loss: 0.28135, in 0.031s
1 tree, 83 leaves, max depth = 21, train loss: 0.27085, val loss: 0.28032, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.27010, val loss: 0.27961, in 0.016s
Fit 85 trees in 1.454 s, (5765 total leaves)
Time spent computing histograms: 0.497s
Time spent finding best splits:  0.108s
Time spent applying splits:      0.125s
Time spent predicting:           0.016s
Trial 66, Fold 3: Log loss = 0.2744742454659397, Average precision = 0.9557721692213328, ROC-AUC = 0.9553627860417409, Elapsed Time = 1.4555007999988447 seconds
Trial 66, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 66, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
Binning 0.040 GB of training data: 0.173 s
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 67 leaves, max depth = 13, train loss: 0.65633, val loss: 0.65431, in 0.016s
1 tree, 67 leaves, max depth = 13, train loss: 0.62527, val loss: 0.62136, in 0.000s
1 tree, 67 leaves, max depth = 15, train loss: 0.59856, val loss: 0.59275, in 0.016s
1 tree, 67 leaves, max depth = 13, train loss: 0.57590, val loss: 0.56845, in 0.016s
1 tree, 67 leaves, max depth = 15, train loss: 0.55621, val loss: 0.54711, in 0.000s
1 tree, 101 leaves, max depth = 14, train loss: 0.53808, val loss: 0.52926, in 0.016s
1 tree, 67 leaves, max depth = 15, train loss: 0.52227, val loss: 0.51210, in 0.016s
1 tree, 104 leaves, max depth = 15, train loss: 0.50739, val loss: 0.49748, in 0.016s
1 tree, 67 leaves, max depth = 14, train loss: 0.49449, val loss: 0.48334, in 0.016s
1 tree, 104 leaves, max depth = 15, train loss: 0.48211, val loss: 0.47120, in 0.000s
1 tree, 67 leaves, max depth = 14, train loss: 0.47129, val loss: 0.45916, in 0.016s
1 tree, 104 leaves, max depth = 15, train loss: 0.46088, val loss: 0.44898, in 0.031s
1 tree, 67 leaves, max depth = 13, train loss: 0.45197, val loss: 0.43904, in 0.000s
1 tree, 67 leaves, max depth = 14, train loss: 0.44410, val loss: 0.43012, in 0.016s
1 tree, 67 leaves, max depth = 13, train loss: 0.43733, val loss: 0.42244, in 0.016s
1 tree, 104 leaves, max depth = 14, train loss: 0.42888, val loss: 0.41423, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.42340, val loss: 0.40837, in 0.000s
1 tree, 104 leaves, max depth = 14, train loss: 0.41607, val loss: 0.40125, in 0.016s
1 tree, 68 leaves, max depth = 16, train loss: 0.41067, val loss: 0.39502, in 0.016s
1 tree, 104 leaves, max depth = 13, train loss: 0.40432, val loss: 0.38885, in 0.016s
1 tree, 104 leaves, max depth = 13, train loss: 0.39876, val loss: 0.38346, in 0.000s
1 tree, 104 leaves, max depth = 13, train loss: 0.39390, val loss: 0.37877, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.38935, val loss: 0.37387, in 0.016s
1 tree, 67 leaves, max depth = 13, train loss: 0.38507, val loss: 0.36895, in 0.000s
1 tree, 4 leaves, max depth = 3, train loss: 0.38117, val loss: 0.36474, in 0.016s
1 tree, 104 leaves, max depth = 14, train loss: 0.37690, val loss: 0.36070, in 0.016s
1 tree, 104 leaves, max depth = 14, train loss: 0.37317, val loss: 0.35713, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.36973, val loss: 0.35340, in 0.000s
1 tree, 67 leaves, max depth = 12, train loss: 0.36603, val loss: 0.34919, in 0.016s
1 tree, 104 leaves, max depth = 15, train loss: 0.36274, val loss: 0.34606, in 0.016s
1 tree, 67 leaves, max depth = 14, train loss: 0.35961, val loss: 0.34245, in 0.000s
1 tree, 4 leaves, max depth = 3, train loss: 0.35659, val loss: 0.33918, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.35395, val loss: 0.33632, in 0.000s
1 tree, 67 leaves, max depth = 14, train loss: 0.35126, val loss: 0.33324, in 0.016s
1 tree, 104 leaves, max depth = 14, train loss: 0.34821, val loss: 0.33045, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.34585, val loss: 0.32788, in 0.000s
1 tree, 86 leaves, max depth = 17, train loss: 0.34338, val loss: 0.32537, in 0.016s
1 tree, 104 leaves, max depth = 14, train loss: 0.34062, val loss: 0.32287, in 0.016s
1 tree, 67 leaves, max depth = 13, train loss: 0.33836, val loss: 0.32036, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.33622, val loss: 0.31803, in 0.016s
1 tree, 85 leaves, max depth = 15, train loss: 0.33404, val loss: 0.31583, in 0.000s
1 tree, 4 leaves, max depth = 3, train loss: 0.33214, val loss: 0.31374, in 0.016s
1 tree, 85 leaves, max depth = 15, train loss: 0.33025, val loss: 0.31185, in 0.016s
1 tree, 104 leaves, max depth = 14, train loss: 0.32763, val loss: 0.30951, in 0.000s
1 tree, 4 leaves, max depth = 3, train loss: 0.32590, val loss: 0.30762, in 0.016s
1 tree, 104 leaves, max depth = 14, train loss: 0.32355, val loss: 0.30553, in 0.016s
1 tree, 41 leaves, max depth = 11, train loss: 0.32190, val loss: 0.30399, in 0.000s
1 tree, 104 leaves, max depth = 14, train loss: 0.31988, val loss: 0.30222, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.31825, val loss: 0.30042, in 0.000s
1 tree, 85 leaves, max depth = 18, train loss: 0.31654, val loss: 0.29874, in 0.016s
1 tree, 64 leaves, max depth = 12, train loss: 0.31507, val loss: 0.29701, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.31360, val loss: 0.29541, in 0.000s
1 tree, 104 leaves, max depth = 16, train loss: 0.31167, val loss: 0.29378, in 0.016s
1 tree, 86 leaves, max depth = 15, train loss: 0.31021, val loss: 0.29233, in 0.016s
1 tree, 104 leaves, max depth = 15, train loss: 0.30851, val loss: 0.29084, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.30712, val loss: 0.28930, in 0.000s
1 tree, 85 leaves, max depth = 18, train loss: 0.30580, val loss: 0.28806, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.30456, val loss: 0.28669, in 0.000s
1 tree, 104 leaves, max depth = 15, train loss: 0.30295, val loss: 0.28536, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.30182, val loss: 0.28411, in 0.016s
1 tree, 67 leaves, max depth = 13, train loss: 0.30057, val loss: 0.28289, in 0.000s
1 tree, 83 leaves, max depth = 16, train loss: 0.29856, val loss: 0.28111, in 0.016s
[63/85] 1 tree, 85 leaves, max depth = 18, train loss: 0.29740, val loss: 0.28004, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.29635, val loss: 0.27889, in 0.000s
1 tree, 103 leaves, max depth = 15, train loss: 0.29495, val loss: 0.27771, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.29400, val loss: 0.27664, in 0.016s
1 tree, 41 leaves, max depth = 11, train loss: 0.29294, val loss: 0.27581, in 0.000s
1 tree, 140 leaves, max depth = 19, train loss: 0.29124, val loss: 0.27474, in 0.016s
1 tree, 85 leaves, max depth = 18, train loss: 0.29019, val loss: 0.27380, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.28928, val loss: 0.27279, in 0.016s
1 tree, 83 leaves, max depth = 13, train loss: 0.28759, val loss: 0.27131, in 0.000s
1 tree, 140 leaves, max depth = 18, train loss: 0.28592, val loss: 0.27038, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.28509, val loss: 0.26946, in 0.016s
1 tree, 86 leaves, max depth = 17, train loss: 0.28409, val loss: 0.26859, in 0.000s
1 tree, 129 leaves, max depth = 23, train loss: 0.28256, val loss: 0.26746, in 0.016s
1 tree, 64 leaves, max depth = 12, train loss: 0.28172, val loss: 0.26646, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.28093, val loss: 0.26559, in 0.000s
1 tree, 140 leaves, max depth = 20, train loss: 0.27946, val loss: 0.26469, in 0.016s
1 tree, 85 leaves, max depth = 17, train loss: 0.27862, val loss: 0.26397, in 0.016s
1 tree, 140 leaves, max depth = 18, train loss: 0.27731, val loss: 0.26317, in 0.016s
1 tree, 85 leaves, max depth = 17, train loss: 0.27655, val loss: 0.26251, in 0.016s
1 tree, 129 leaves, max depth = 22, train loss: 0.27523, val loss: 0.26151, in 0.016s
1 tree, 140 leaves, max depth = 18, train loss: 0.27410, val loss: 0.26084, in 0.016s
1 tree, 140 leaves, max depth = 20, train loss: 0.27280, val loss: 0.26014, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.27198, val loss: 0.25924, in 0.000s
Fit 85 trees in 1.299 s, (5883 total leaves)
Time spent computing histograms: 0.435s
Time spent finding best splits:  0.093s
Time spent applying splits:      0.107s
Time spent predicting:           0.016s
Trial 66, Fold 4: Log loss = 0.2763670443208256, Average precision = 0.9559006771424874, ROC-AUC = 0.9546079165981148, Elapsed Time = 1.2988741000008304 seconds
Trial 66, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 66, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
Binning 0.040 GB of training data: 0.158 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 64 leaves, max depth = 14, train loss: 0.65604, val loss: 0.65344, in 0.000s
1 tree, 67 leaves, max depth = 14, train loss: 0.62471, val loss: 0.61997, in 0.016s
1 tree, 67 leaves, max depth = 14, train loss: 0.59802, val loss: 0.59130, in 0.016s
1 tree, 66 leaves, max depth = 15, train loss: 0.57510, val loss: 0.56637, in 0.000s
1 tree, 67 leaves, max depth = 14, train loss: 0.55541, val loss: 0.54499, in 0.016s
1 tree, 105 leaves, max depth = 19, train loss: 0.53731, val loss: 0.52774, in 0.016s
1 tree, 68 leaves, max depth = 13, train loss: 0.52132, val loss: 0.51023, in 0.000s
1 tree, 105 leaves, max depth = 19, train loss: 0.50645, val loss: 0.49620, in 0.016s
1 tree, 67 leaves, max depth = 14, train loss: 0.49308, val loss: 0.48144, in 0.016s
1 tree, 105 leaves, max depth = 22, train loss: 0.48073, val loss: 0.46989, in 0.016s
1 tree, 68 leaves, max depth = 13, train loss: 0.46990, val loss: 0.45785, in 0.016s
1 tree, 105 leaves, max depth = 20, train loss: 0.45949, val loss: 0.44824, in 0.016s
1 tree, 68 leaves, max depth = 13, train loss: 0.45047, val loss: 0.43812, in 0.000s
1 tree, 67 leaves, max depth = 16, train loss: 0.44241, val loss: 0.42900, in 0.016s
1 tree, 67 leaves, max depth = 16, train loss: 0.43540, val loss: 0.42099, in 0.016s
1 tree, 105 leaves, max depth = 18, train loss: 0.42689, val loss: 0.41333, in 0.000s
1 tree, 67 leaves, max depth = 13, train loss: 0.42117, val loss: 0.40674, in 0.016s
1 tree, 105 leaves, max depth = 18, train loss: 0.41382, val loss: 0.40021, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.40852, val loss: 0.39513, in 0.016s
1 tree, 105 leaves, max depth = 18, train loss: 0.40214, val loss: 0.38953, in 0.000s
1 tree, 105 leaves, max depth = 18, train loss: 0.39658, val loss: 0.38469, in 0.016s
1 tree, 105 leaves, max depth = 18, train loss: 0.39173, val loss: 0.38051, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.38719, val loss: 0.37617, in 0.000s
1 tree, 67 leaves, max depth = 12, train loss: 0.38276, val loss: 0.37099, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.37885, val loss: 0.36726, in 0.000s
1 tree, 105 leaves, max depth = 18, train loss: 0.37460, val loss: 0.36370, in 0.016s
1 tree, 105 leaves, max depth = 19, train loss: 0.37089, val loss: 0.36062, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.36745, val loss: 0.35734, in 0.000s
1 tree, 67 leaves, max depth = 12, train loss: 0.36360, val loss: 0.35295, in 0.016s
1 tree, 67 leaves, max depth = 14, train loss: 0.36035, val loss: 0.34910, in 0.016s
1 tree, 67 leaves, max depth = 16, train loss: 0.35744, val loss: 0.34567, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.35447, val loss: 0.34284, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.35188, val loss: 0.34037, in 0.000s
1 tree, 105 leaves, max depth = 19, train loss: 0.34842, val loss: 0.33763, in 0.016s
1 tree, 105 leaves, max depth = 19, train loss: 0.34543, val loss: 0.33528, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.34307, val loss: 0.33303, in 0.000s
1 tree, 86 leaves, max depth = 13, train loss: 0.34050, val loss: 0.33033, in 0.016s
1 tree, 67 leaves, max depth = 14, train loss: 0.33823, val loss: 0.32759, in 0.000s
1 tree, 83 leaves, max depth = 15, train loss: 0.33619, val loss: 0.32532, in 0.016s
1 tree, 104 leaves, max depth = 18, train loss: 0.33341, val loss: 0.32319, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.33126, val loss: 0.32113, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.32937, val loss: 0.31933, in 0.016s
1 tree, 84 leaves, max depth = 14, train loss: 0.32726, val loss: 0.31710, in 0.000s
1 tree, 105 leaves, max depth = 18, train loss: 0.32470, val loss: 0.31517, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.32298, val loss: 0.31353, in 0.016s
1 tree, 104 leaves, max depth = 18, train loss: 0.32071, val loss: 0.31185, in 0.016s
1 tree, 67 leaves, max depth = 15, train loss: 0.31894, val loss: 0.30969, in 0.000s
1 tree, 86 leaves, max depth = 18, train loss: 0.31725, val loss: 0.30792, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.31566, val loss: 0.30640, in 0.016s
1 tree, 105 leaves, max depth = 17, train loss: 0.31357, val loss: 0.30488, in 0.000s
1 tree, 84 leaves, max depth = 15, train loss: 0.31193, val loss: 0.30315, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.31047, val loss: 0.30175, in 0.000s
1 tree, 105 leaves, max depth = 17, train loss: 0.30857, val loss: 0.30041, in 0.016s
1 tree, 67 leaves, max depth = 16, train loss: 0.30720, val loss: 0.29869, in 0.016s
1 tree, 105 leaves, max depth = 17, train loss: 0.30553, val loss: 0.29754, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.30415, val loss: 0.29622, in 0.000s
1 tree, 84 leaves, max depth = 15, train loss: 0.30272, val loss: 0.29471, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.30150, val loss: 0.29353, in 0.000s
1 tree, 105 leaves, max depth = 17, train loss: 0.29992, val loss: 0.29247, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.29881, val loss: 0.29140, in 0.016s
1 tree, 84 leaves, max depth = 16, train loss: 0.29749, val loss: 0.29003, in 0.016s
1 tree, 105 leaves, max depth = 18, train loss: 0.29607, val loss: 0.28907, in 0.000s
1 tree, 63 leaves, max depth = 13, train loss: 0.29491, val loss: 0.28767, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.29386, val loss: 0.28666, in 0.016s
1 tree, 105 leaves, max depth = 18, train loss: 0.29257, val loss: 0.28581, in 0.016s
1 tree, 84 leaves, max depth = 16, train loss: 0.29143, val loss: 0.28463, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.29046, val loss: 0.28369, in 0.016s
1 tree, 83 leaves, max depth = 14, train loss: 0.28862, val loss: 0.28234, in 0.016s
1 tree, 84 leaves, max depth = 16, train loss: 0.28758, val loss: 0.28125, in 0.000s
1 tree, 140 leaves, max depth = 21, train loss: 0.28593, val loss: 0.28015, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.28501, val loss: 0.27926, in 0.016s
1 tree, 128 leaves, max depth = 24, train loss: 0.28349, val loss: 0.27819, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.28266, val loss: 0.27738, in 0.000s
1 tree, 84 leaves, max depth = 19, train loss: 0.28166, val loss: 0.27645, in 0.016s
1 tree, 140 leaves, max depth = 18, train loss: 0.28017, val loss: 0.27547, in 0.016s
1 tree, 66 leaves, max depth = 13, train loss: 0.27930, val loss: 0.27437, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.27852, val loss: 0.27361, in 0.000s
1 tree, 140 leaves, max depth = 18, train loss: 0.27692, val loss: 0.27319, in 0.031s
1 tree, 140 leaves, max depth = 21, train loss: 0.27561, val loss: 0.27234, in 0.016s
1 tree, 83 leaves, max depth = 15, train loss: 0.27474, val loss: 0.27145, in 0.016s
1 tree, 82 leaves, max depth = 12, train loss: 0.27336, val loss: 0.27049, in 0.016s
1 tree, 129 leaves, max depth = 25, train loss: 0.27214, val loss: 0.26968, in 0.016s
1 tree, 39 leaves, max depth = 11, train loss: 0.27138, val loss: 0.26886, in 0.000s
1 tree, 140 leaves, max depth = 18, train loss: 0.27032, val loss: 0.26821, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.26952, val loss: 0.26741, in 0.016s
Fit 85 trees in 1.252 s, (5901 total leaves)
Time spent computing histograms: 0.417s
Time spent finding best splits:  0.093s
Time spent applying splits:      0.108s
Time spent predicting:           0.000s
Trial 66, Fold 5: Log loss = 0.2834021484954921, Average precision = 0.9528156004014571, ROC-AUC = 0.9512490199357152, Elapsed Time = 1.2532468999997946 seconds
Optimization Progress:  67%|######7   | 67/100 [13:19<07:45, 14.10s/it]
Trial 67, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 67, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
0.142 s 0.040 GB of training data: 
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 123 leaves, max depth = 13, train loss: 0.66207, val loss: 0.66244, in 0.016s
1 tree, 123 leaves, max depth = 12, train loss: 0.63449, val loss: 0.63467, in 0.016s
1 tree, 123 leaves, max depth = 14, train loss: 0.60773, val loss: 0.60835, in 0.031s
1 tree, 123 leaves, max depth = 14, train loss: 0.58359, val loss: 0.58395, in 0.016s
1 tree, 123 leaves, max depth = 13, train loss: 0.56303, val loss: 0.56339, in 0.016s
1 tree, 123 leaves, max depth = 12, train loss: 0.54270, val loss: 0.54312, in 0.016s
1 tree, 123 leaves, max depth = 14, train loss: 0.52582, val loss: 0.52646, in 0.031s
1 tree, 123 leaves, max depth = 15, train loss: 0.50930, val loss: 0.51002, in 0.016s
1 tree, 123 leaves, max depth = 14, train loss: 0.49468, val loss: 0.49593, in 0.016s
1 tree, 123 leaves, max depth = 14, train loss: 0.48021, val loss: 0.48142, in 0.016s
1 tree, 123 leaves, max depth = 13, train loss: 0.46803, val loss: 0.46919, in 0.031s
1 tree, 123 leaves, max depth = 14, train loss: 0.45569, val loss: 0.45687, in 0.016s
1 tree, 123 leaves, max depth = 11, train loss: 0.44522, val loss: 0.44654, in 0.016s
1 tree, 123 leaves, max depth = 16, train loss: 0.43531, val loss: 0.43667, in 0.031s
1 tree, 123 leaves, max depth = 16, train loss: 0.42583, val loss: 0.42725, in 0.016s
1 tree, 123 leaves, max depth = 13, train loss: 0.41772, val loss: 0.41933, in 0.016s
1 tree, 123 leaves, max depth = 14, train loss: 0.40947, val loss: 0.41105, in 0.031s
1 tree, 104 leaves, max depth = 11, train loss: 0.39700, val loss: 0.39893, in 0.016s
1 tree, 105 leaves, max depth = 11, train loss: 0.38572, val loss: 0.38798, in 0.016s
1 tree, 107 leaves, max depth = 13, train loss: 0.37518, val loss: 0.37794, in 0.016s
1 tree, 82 leaves, max depth = 11, train loss: 0.36601, val loss: 0.36909, in 0.016s
1 tree, 123 leaves, max depth = 15, train loss: 0.35880, val loss: 0.36205, in 0.031s
1 tree, 123 leaves, max depth = 14, train loss: 0.35203, val loss: 0.35549, in 0.016s
1 tree, 62 leaves, max depth = 12, train loss: 0.34422, val loss: 0.34772, in 0.016s
1 tree, 123 leaves, max depth = 16, train loss: 0.33818, val loss: 0.34185, in 0.031s
1 tree, 95 leaves, max depth = 13, train loss: 0.33114, val loss: 0.33547, in 0.016s
1 tree, 123 leaves, max depth = 12, train loss: 0.32573, val loss: 0.33026, in 0.031s
1 tree, 123 leaves, max depth = 12, train loss: 0.32070, val loss: 0.32544, in 0.016s
1 tree, 123 leaves, max depth = 17, train loss: 0.31428, val loss: 0.31945, in 0.016s
1 tree, 123 leaves, max depth = 16, train loss: 0.31043, val loss: 0.31588, in 0.031s
1 tree, 95 leaves, max depth = 13, train loss: 0.30478, val loss: 0.31085, in 0.016s
1 tree, 95 leaves, max depth = 12, train loss: 0.29960, val loss: 0.30626, in 0.016s
1 tree, 119 leaves, max depth = 12, train loss: 0.29464, val loss: 0.30153, in 0.016s
1 tree, 110 leaves, max depth = 13, train loss: 0.28987, val loss: 0.29714, in 0.031s
1 tree, 99 leaves, max depth = 14, train loss: 0.28567, val loss: 0.29347, in 0.016s
1 tree, 68 leaves, max depth = 11, train loss: 0.28188, val loss: 0.28973, in 0.016s
Fit 36 trees in 1.033 s, (4093 total leaves)
Time spent computing histograms: 0.265s
Time spent finding best splits:  0.084s
Time spent applying splits:      0.062s
Time spent predicting:           0.000s
Trial 67, Fold 1: Log loss = 0.2964510256821385, Average precision = 0.9613911346311143, ROC-AUC = 0.9561273288548583, Elapsed Time = 1.038752000000386 seconds
Trial 67, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 67, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
Binning 0.040 GB of training data: 0.126 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 123 leaves, max depth = 12, train loss: 0.66203, val loss: 0.66200, in 0.016s
1 tree, 123 leaves, max depth = 17, train loss: 0.63411, val loss: 0.63440, in 0.031s
1 tree, 123 leaves, max depth = 14, train loss: 0.60761, val loss: 0.60824, in 0.031s
1 tree, 123 leaves, max depth = 14, train loss: 0.58491, val loss: 0.58556, in 0.016s
1 tree, 123 leaves, max depth = 15, train loss: 0.56388, val loss: 0.56467, in 0.031s
1 tree, 123 leaves, max depth = 14, train loss: 0.54320, val loss: 0.54416, in 0.016s
1 tree, 123 leaves, max depth = 13, train loss: 0.52518, val loss: 0.52621, in 0.031s
1 tree, 123 leaves, max depth = 13, train loss: 0.50826, val loss: 0.50943, in 0.016s
1 tree, 123 leaves, max depth = 15, train loss: 0.49372, val loss: 0.49497, in 0.031s
1 tree, 123 leaves, max depth = 13, train loss: 0.47973, val loss: 0.48136, in 0.016s
1 tree, 123 leaves, max depth = 15, train loss: 0.46730, val loss: 0.46911, in 0.031s
1 tree, 123 leaves, max depth = 14, train loss: 0.45483, val loss: 0.45681, in 0.031s
1 tree, 123 leaves, max depth = 17, train loss: 0.44335, val loss: 0.44553, in 0.016s
1 tree, 123 leaves, max depth = 17, train loss: 0.43279, val loss: 0.43515, in 0.031s
1 tree, 123 leaves, max depth = 12, train loss: 0.42320, val loss: 0.42612, in 0.016s
1 tree, 109 leaves, max depth = 15, train loss: 0.40966, val loss: 0.41290, in 0.031s
1 tree, 123 leaves, max depth = 12, train loss: 0.40109, val loss: 0.40465, in 0.031s
1 tree, 99 leaves, max depth = 15, train loss: 0.38931, val loss: 0.39323, in 0.016s
1 tree, 100 leaves, max depth = 15, train loss: 0.37861, val loss: 0.38285, in 0.016s
1 tree, 123 leaves, max depth = 15, train loss: 0.37151, val loss: 0.37609, in 0.031s
1 tree, 123 leaves, max depth = 14, train loss: 0.36222, val loss: 0.36724, in 0.031s
1 tree, 123 leaves, max depth = 14, train loss: 0.35376, val loss: 0.35919, in 0.016s
1 tree, 123 leaves, max depth = 14, train loss: 0.34692, val loss: 0.35271, in 0.016s
1 tree, 123 leaves, max depth = 15, train loss: 0.34065, val loss: 0.34669, in 0.031s
1 tree, 123 leaves, max depth = 11, train loss: 0.33477, val loss: 0.34122, in 0.016s
1 tree, 103 leaves, max depth = 15, train loss: 0.32789, val loss: 0.33499, in 0.016s
1 tree, 70 leaves, max depth = 15, train loss: 0.32153, val loss: 0.32879, in 0.016s
1 tree, 123 leaves, max depth = 13, train loss: 0.31624, val loss: 0.32395, in 0.031s
1 tree, 123 leaves, max depth = 15, train loss: 0.31134, val loss: 0.31945, in 0.016s
1 tree, 123 leaves, max depth = 15, train loss: 0.30695, val loss: 0.31533, in 0.031s
1 tree, 71 leaves, max depth = 15, train loss: 0.30162, val loss: 0.31016, in 0.016s
1 tree, 123 leaves, max depth = 13, train loss: 0.29791, val loss: 0.30681, in 0.031s
1 tree, 104 leaves, max depth = 15, train loss: 0.29285, val loss: 0.30221, in 0.016s
1 tree, 123 leaves, max depth = 12, train loss: 0.28917, val loss: 0.29880, in 0.016s
1 tree, 103 leaves, max depth = 15, train loss: 0.28464, val loss: 0.29472, in 0.016s
1 tree, 102 leaves, max depth = 15, train loss: 0.28049, val loss: 0.29097, in 0.031s
Fit 36 trees in 1.158 s, (4182 total leaves)
Time spent computing histograms: 0.288s
Time spent finding best splits:  0.099s
Time spent applying splits:      0.074s
Time spent predicting:           0.000s
Trial 67, Fold 2: Log loss = 0.29018273364600816, Average precision = 0.9635144684162199, ROC-AUC = 0.9609087841026591, Elapsed Time = 1.16494640000019 seconds
Trial 67, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 67, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
Binning 0.040 GB of training data: 0.141 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 123 leaves, max depth = 13, train loss: 0.66171, val loss: 0.66191, in 0.016s
1 tree, 123 leaves, max depth = 12, train loss: 0.63371, val loss: 0.63444, in 0.016s
1 tree, 123 leaves, max depth = 15, train loss: 0.60740, val loss: 0.60833, in 0.031s
1 tree, 123 leaves, max depth = 15, train loss: 0.58465, val loss: 0.58606, in 0.016s
1 tree, 123 leaves, max depth = 15, train loss: 0.56365, val loss: 0.56558, in 0.016s
1 tree, 123 leaves, max depth = 15, train loss: 0.54340, val loss: 0.54561, in 0.031s
1 tree, 123 leaves, max depth = 14, train loss: 0.52618, val loss: 0.52882, in 0.016s
1 tree, 123 leaves, max depth = 14, train loss: 0.50951, val loss: 0.51254, in 0.031s
1 tree, 123 leaves, max depth = 14, train loss: 0.49496, val loss: 0.49841, in 0.016s
1 tree, 123 leaves, max depth = 15, train loss: 0.48059, val loss: 0.48437, in 0.016s
1 tree, 123 leaves, max depth = 13, train loss: 0.46811, val loss: 0.47231, in 0.031s
1 tree, 123 leaves, max depth = 13, train loss: 0.45585, val loss: 0.46034, in 0.016s
1 tree, 123 leaves, max depth = 12, train loss: 0.44493, val loss: 0.44967, in 0.031s
1 tree, 123 leaves, max depth = 15, train loss: 0.43462, val loss: 0.43979, in 0.016s
1 tree, 100 leaves, max depth = 19, train loss: 0.42047, val loss: 0.42671, in 0.016s
1 tree, 94 leaves, max depth = 13, train loss: 0.40758, val loss: 0.41484, in 0.031s
1 tree, 123 leaves, max depth = 14, train loss: 0.39909, val loss: 0.40653, in 0.016s
1 tree, 123 leaves, max depth = 13, train loss: 0.39109, val loss: 0.39888, in 0.016s
1 tree, 123 leaves, max depth = 13, train loss: 0.38368, val loss: 0.39186, in 0.031s
1 tree, 72 leaves, max depth = 12, train loss: 0.37343, val loss: 0.38254, in 0.016s
1 tree, 123 leaves, max depth = 13, train loss: 0.36748, val loss: 0.37696, in 0.016s
1 tree, 113 leaves, max depth = 17, train loss: 0.35843, val loss: 0.36889, in 0.031s
1 tree, 58 leaves, max depth = 12, train loss: 0.35048, val loss: 0.36154, in 0.016s
1 tree, 123 leaves, max depth = 13, train loss: 0.34415, val loss: 0.35510, in 0.016s
1 tree, 78 leaves, max depth = 13, train loss: 0.33696, val loss: 0.34898, in 0.016s
1 tree, 52 leaves, max depth = 8, train loss: 0.33028, val loss: 0.34283, in 0.016s
1 tree, 81 leaves, max depth = 13, train loss: 0.32409, val loss: 0.33759, in 0.016s
1 tree, 123 leaves, max depth = 13, train loss: 0.31884, val loss: 0.33219, in 0.031s
1 tree, 57 leaves, max depth = 10, train loss: 0.31333, val loss: 0.32719, in 0.016s
1 tree, 123 leaves, max depth = 15, train loss: 0.30845, val loss: 0.32230, in 0.031s
1 tree, 123 leaves, max depth = 13, train loss: 0.30394, val loss: 0.31774, in 0.016s
1 tree, 69 leaves, max depth = 11, train loss: 0.29919, val loss: 0.31350, in 0.016s
1 tree, 79 leaves, max depth = 13, train loss: 0.29456, val loss: 0.30967, in 0.031s
1 tree, 123 leaves, max depth = 13, train loss: 0.29132, val loss: 0.30675, in 0.016s
1 tree, 123 leaves, max depth = 13, train loss: 0.28832, val loss: 0.30402, in 0.016s
1 tree, 121 leaves, max depth = 16, train loss: 0.28397, val loss: 0.30020, in 0.031s
Fit 36 trees in 1.094 s, (3926 total leaves)
Time spent computing histograms: 0.266s
Time spent finding best splits:  0.089s
Time spent applying splits:      0.066s
Time spent predicting:           0.000s
Trial 67, Fold 3: Log loss = 0.2912220947073553, Average precision = 0.9607902861811848, ROC-AUC = 0.9576986882530265, Elapsed Time = 1.1027567000001 seconds
Trial 67, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 67, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
Binning 0.040 GB of training data: 0.174 s
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 123 leaves, max depth = 13, train loss: 0.66161, val loss: 0.66116, in 0.016s
1 tree, 123 leaves, max depth = 18, train loss: 0.63456, val loss: 0.63290, in 0.031s
1 tree, 123 leaves, max depth = 12, train loss: 0.60786, val loss: 0.60554, in 0.031s
1 tree, 123 leaves, max depth = 14, train loss: 0.58402, val loss: 0.58078, in 0.016s
1 tree, 123 leaves, max depth = 12, train loss: 0.56375, val loss: 0.55993, in 0.031s
1 tree, 123 leaves, max depth = 14, train loss: 0.54400, val loss: 0.53941, in 0.031s
1 tree, 123 leaves, max depth = 14, train loss: 0.52610, val loss: 0.52098, in 0.016s
1 tree, 123 leaves, max depth = 12, train loss: 0.50937, val loss: 0.50376, in 0.016s
1 tree, 123 leaves, max depth = 16, train loss: 0.49516, val loss: 0.48910, in 0.031s
1 tree, 123 leaves, max depth = 16, train loss: 0.48216, val loss: 0.47566, in 0.016s
1 tree, 123 leaves, max depth = 12, train loss: 0.47015, val loss: 0.46367, in 0.031s
1 tree, 123 leaves, max depth = 12, train loss: 0.45797, val loss: 0.45109, in 0.016s
1 tree, 123 leaves, max depth = 17, train loss: 0.44776, val loss: 0.44061, in 0.016s
1 tree, 98 leaves, max depth = 13, train loss: 0.43317, val loss: 0.42574, in 0.031s
1 tree, 113 leaves, max depth = 15, train loss: 0.41970, val loss: 0.41204, in 0.016s
1 tree, 123 leaves, max depth = 15, train loss: 0.41075, val loss: 0.40270, in 0.031s
1 tree, 123 leaves, max depth = 16, train loss: 0.40205, val loss: 0.39378, in 0.016s
1 tree, 113 leaves, max depth = 14, train loss: 0.39073, val loss: 0.38230, in 0.016s
1 tree, 115 leaves, max depth = 14, train loss: 0.38041, val loss: 0.37183, in 0.031s
1 tree, 123 leaves, max depth = 12, train loss: 0.37319, val loss: 0.36440, in 0.016s
1 tree, 123 leaves, max depth = 14, train loss: 0.36649, val loss: 0.35743, in 0.031s
1 tree, 123 leaves, max depth = 14, train loss: 0.36030, val loss: 0.35119, in 0.031s
1 tree, 108 leaves, max depth = 13, train loss: 0.35195, val loss: 0.34273, in 0.016s
1 tree, 123 leaves, max depth = 11, train loss: 0.34559, val loss: 0.33650, in 0.016s
1 tree, 64 leaves, max depth = 12, train loss: 0.33860, val loss: 0.32911, in 0.016s
1 tree, 89 leaves, max depth = 12, train loss: 0.33201, val loss: 0.32276, in 0.031s
1 tree, 68 leaves, max depth = 14, train loss: 0.32607, val loss: 0.31646, in 0.000s
1 tree, 123 leaves, max depth = 13, train loss: 0.32076, val loss: 0.31136, in 0.031s
1 tree, 123 leaves, max depth = 13, train loss: 0.31576, val loss: 0.30658, in 0.016s
1 tree, 123 leaves, max depth = 12, train loss: 0.31028, val loss: 0.30104, in 0.016s
1 tree, 66 leaves, max depth = 13, train loss: 0.30556, val loss: 0.29607, in 0.031s
1 tree, 66 leaves, max depth = 12, train loss: 0.30132, val loss: 0.29154, in 0.016s
1 tree, 99 leaves, max depth = 15, train loss: 0.29680, val loss: 0.28723, in 0.016s
1 tree, 123 leaves, max depth = 14, train loss: 0.29245, val loss: 0.28286, in 0.016s
1 tree, 123 leaves, max depth = 14, train loss: 0.28842, val loss: 0.27881, in 0.016s
1 tree, 123 leaves, max depth = 13, train loss: 0.28530, val loss: 0.27583, in 0.016s
Fit 36 trees in 1.174 s, (4074 total leaves)
Time spent computing histograms: 0.285s
Time spent finding best splits:  0.092s
Time spent applying splits:      0.068s
Time spent predicting:           0.000s
Trial 67, Fold 4: Log loss = 0.2932094147428679, Average precision = 0.9616622060698489, ROC-AUC = 0.9566776587957301, Elapsed Time = 1.1851515000016661 seconds
Trial 67, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 67, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
Binning 0.040 GB of training data: 0.158 s
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 123 leaves, max depth = 12, train loss: 0.66160, val loss: 0.66026, in 0.016s
1 tree, 123 leaves, max depth = 12, train loss: 0.63372, val loss: 0.63151, in 0.031s
1 tree, 123 leaves, max depth = 12, train loss: 0.60714, val loss: 0.60408, in 0.016s
1 tree, 123 leaves, max depth = 17, train loss: 0.58399, val loss: 0.58006, in 0.016s
1 tree, 123 leaves, max depth = 17, train loss: 0.56306, val loss: 0.55834, in 0.031s
1 tree, 123 leaves, max depth = 12, train loss: 0.54263, val loss: 0.53737, in 0.016s
1 tree, 123 leaves, max depth = 15, train loss: 0.52525, val loss: 0.51995, in 0.016s
1 tree, 123 leaves, max depth = 16, train loss: 0.50810, val loss: 0.50242, in 0.031s
1 tree, 123 leaves, max depth = 14, train loss: 0.49365, val loss: 0.48802, in 0.016s
1 tree, 123 leaves, max depth = 16, train loss: 0.47948, val loss: 0.47342, in 0.016s
1 tree, 123 leaves, max depth = 15, train loss: 0.46704, val loss: 0.46063, in 0.031s
1 tree, 123 leaves, max depth = 16, train loss: 0.45467, val loss: 0.44833, in 0.016s
1 tree, 123 leaves, max depth = 14, train loss: 0.44370, val loss: 0.43713, in 0.016s
1 tree, 123 leaves, max depth = 16, train loss: 0.43318, val loss: 0.42656, in 0.031s
1 tree, 123 leaves, max depth = 15, train loss: 0.42340, val loss: 0.41686, in 0.016s
1 tree, 84 leaves, max depth = 13, train loss: 0.40973, val loss: 0.40342, in 0.016s
1 tree, 123 leaves, max depth = 14, train loss: 0.40123, val loss: 0.39513, in 0.016s
1 tree, 109 leaves, max depth = 18, train loss: 0.38941, val loss: 0.38343, in 0.031s
1 tree, 123 leaves, max depth = 16, train loss: 0.38191, val loss: 0.37592, in 0.016s
1 tree, 111 leaves, max depth = 17, train loss: 0.37151, val loss: 0.36565, in 0.031s
1 tree, 123 leaves, max depth = 15, train loss: 0.36552, val loss: 0.35979, in 0.016s
1 tree, 50 leaves, max depth = 12, train loss: 0.35676, val loss: 0.35088, in 0.016s
1 tree, 123 leaves, max depth = 13, train loss: 0.35002, val loss: 0.34431, in 0.031s
1 tree, 50 leaves, max depth = 11, train loss: 0.34232, val loss: 0.33649, in 0.016s
1 tree, 69 leaves, max depth = 13, train loss: 0.33537, val loss: 0.32936, in 0.016s
1 tree, 94 leaves, max depth = 14, train loss: 0.32863, val loss: 0.32315, in 0.016s
1 tree, 123 leaves, max depth = 14, train loss: 0.32405, val loss: 0.31905, in 0.016s
1 tree, 123 leaves, max depth = 13, train loss: 0.31874, val loss: 0.31396, in 0.031s
1 tree, 91 leaves, max depth = 14, train loss: 0.31288, val loss: 0.30869, in 0.016s
1 tree, 99 leaves, max depth = 14, train loss: 0.30754, val loss: 0.30385, in 0.031s
1 tree, 123 leaves, max depth = 13, train loss: 0.30285, val loss: 0.29930, in 0.016s
1 tree, 62 leaves, max depth = 13, train loss: 0.29802, val loss: 0.29431, in 0.016s
1 tree, 123 leaves, max depth = 15, train loss: 0.29356, val loss: 0.28997, in 0.031s
1 tree, 86 leaves, max depth = 13, train loss: 0.28917, val loss: 0.28608, in 0.016s
1 tree, 69 leaves, max depth = 15, train loss: 0.28511, val loss: 0.28198, in 0.016s
1 tree, 63 leaves, max depth = 11, train loss: 0.28138, val loss: 0.27808, in 0.016s
Fit 36 trees in 1.096 s, (3866 total leaves)
Time spent computing histograms: 0.256s
Time spent finding best splits:  0.086s
Time spent applying splits:      0.065s
Time spent predicting:           0.000s
Trial 67, Fold 5: Log loss = 0.29708795787730236, Average precision = 0.9595966341554678, ROC-AUC = 0.9554665477583932, Elapsed Time = 1.1053009999995993 seconds
Optimization Progress:  68%|######8   | 68/100 [13:32<07:17, 13.68s/it]
Trial 68, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 68, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
0.205 s 0.040 GB of training data: 
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 29 leaves, max depth = 11, train loss: 0.68570, val loss: 0.68549, in 0.016s
1 tree, 31 leaves, max depth = 12, train loss: 0.67859, val loss: 0.67818, in 0.000s
1 tree, 28 leaves, max depth = 10, train loss: 0.67171, val loss: 0.67115, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.66511, val loss: 0.66436, in 0.000s
1 tree, 31 leaves, max depth = 12, train loss: 0.65863, val loss: 0.65769, in 0.016s
1 tree, 31 leaves, max depth = 12, train loss: 0.65235, val loss: 0.65121, in 0.000s
1 tree, 30 leaves, max depth = 12, train loss: 0.64632, val loss: 0.64499, in 0.016s
1 tree, 29 leaves, max depth = 10, train loss: 0.64047, val loss: 0.63894, in 0.000s
1 tree, 30 leaves, max depth = 12, train loss: 0.63476, val loss: 0.63302, in 0.016s
1 tree, 29 leaves, max depth = 10, train loss: 0.62910, val loss: 0.62719, in 0.000s
1 tree, 30 leaves, max depth = 11, train loss: 0.62373, val loss: 0.62162, in 0.016s
1 tree, 30 leaves, max depth = 11, train loss: 0.61854, val loss: 0.61624, in 0.000s
1 tree, 30 leaves, max depth = 12, train loss: 0.61345, val loss: 0.61097, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.60855, val loss: 0.60588, in 0.000s
1 tree, 30 leaves, max depth = 12, train loss: 0.60375, val loss: 0.60090, in 0.016s
1 tree, 30 leaves, max depth = 11, train loss: 0.59912, val loss: 0.59609, in 0.000s
1 tree, 28 leaves, max depth = 10, train loss: 0.59456, val loss: 0.59141, in 0.016s
1 tree, 28 leaves, max depth = 10, train loss: 0.59013, val loss: 0.58686, in 0.000s
1 tree, 6 leaves, max depth = 4, train loss: 0.58582, val loss: 0.58234, in 0.016s
1 tree, 29 leaves, max depth = 10, train loss: 0.58155, val loss: 0.57792, in 0.000s
1 tree, 29 leaves, max depth = 10, train loss: 0.57740, val loss: 0.57362, in 0.016s
1 tree, 29 leaves, max depth = 10, train loss: 0.57337, val loss: 0.56944, in 0.000s
1 tree, 29 leaves, max depth = 10, train loss: 0.56945, val loss: 0.56537, in 0.016s
1 tree, 29 leaves, max depth = 10, train loss: 0.56563, val loss: 0.56141, in 0.000s
1 tree, 28 leaves, max depth = 13, train loss: 0.56200, val loss: 0.55766, in 0.016s
1 tree, 29 leaves, max depth = 13, train loss: 0.55846, val loss: 0.55402, in 0.016s
1 tree, 30 leaves, max depth = 11, train loss: 0.55504, val loss: 0.55044, in 0.000s
1 tree, 29 leaves, max depth = 12, train loss: 0.55171, val loss: 0.54699, in 0.016s
1 tree, 30 leaves, max depth = 11, train loss: 0.54848, val loss: 0.54362, in 0.000s
1 tree, 51 leaves, max depth = 10, train loss: 0.54498, val loss: 0.54036, in 0.016s
1 tree, 29 leaves, max depth = 12, train loss: 0.54187, val loss: 0.53713, in 0.000s
1 tree, 31 leaves, max depth = 11, train loss: 0.53884, val loss: 0.53395, in 0.016s
1 tree, 28 leaves, max depth = 10, train loss: 0.53580, val loss: 0.53077, in 0.000s
1 tree, 51 leaves, max depth = 10, train loss: 0.53248, val loss: 0.52769, in 0.016s
1 tree, 28 leaves, max depth = 10, train loss: 0.52956, val loss: 0.52463, in 0.000s
1 tree, 28 leaves, max depth = 9, train loss: 0.52678, val loss: 0.52176, in 0.016s
1 tree, 28 leaves, max depth = 9, train loss: 0.52407, val loss: 0.51896, in 0.000s
1 tree, 51 leaves, max depth = 10, train loss: 0.52093, val loss: 0.51605, in 0.016s
1 tree, 28 leaves, max depth = 11, train loss: 0.51833, val loss: 0.51336, in 0.018s
1 tree, 31 leaves, max depth = 10, train loss: 0.51581, val loss: 0.51071, in 0.007s
1 tree, 28 leaves, max depth = 11, train loss: 0.51334, val loss: 0.50816, in 0.008s
1 tree, 28 leaves, max depth = 10, train loss: 0.51087, val loss: 0.50557, in 0.008s
1 tree, 28 leaves, max depth = 11, train loss: 0.50854, val loss: 0.50315, in 0.008s
1 tree, 51 leaves, max depth = 10, train loss: 0.50559, val loss: 0.50043, in 0.000s
1 tree, 32 leaves, max depth = 11, train loss: 0.50335, val loss: 0.49810, in 0.016s
1 tree, 28 leaves, max depth = 11, train loss: 0.50117, val loss: 0.49583, in 0.000s
1 tree, 51 leaves, max depth = 11, train loss: 0.49833, val loss: 0.49322, in 0.016s
1 tree, 28 leaves, max depth = 10, train loss: 0.49616, val loss: 0.49093, in 0.000s
1 tree, 28 leaves, max depth = 10, train loss: 0.49405, val loss: 0.48869, in 0.016s
1 tree, 51 leaves, max depth = 11, train loss: 0.49133, val loss: 0.48618, in 0.000s
1 tree, 31 leaves, max depth = 10, train loss: 0.48935, val loss: 0.48407, in 0.016s
1 tree, 51 leaves, max depth = 11, train loss: 0.48671, val loss: 0.48164, in 0.016s
1 tree, 51 leaves, max depth = 11, train loss: 0.48415, val loss: 0.47928, in 0.031s
1 tree, 30 leaves, max depth = 12, train loss: 0.48228, val loss: 0.47731, in 0.000s
1 tree, 52 leaves, max depth = 10, train loss: 0.47979, val loss: 0.47504, in 0.016s
1 tree, 52 leaves, max depth = 10, train loss: 0.47737, val loss: 0.47282, in 0.016s
1 tree, 6 leaves, max depth = 4, train loss: 0.47554, val loss: 0.47085, in 0.000s
1 tree, 32 leaves, max depth = 10, train loss: 0.47372, val loss: 0.46893, in 0.016s
1 tree, 52 leaves, max depth = 10, train loss: 0.47139, val loss: 0.46680, in 0.000s
1 tree, 30 leaves, max depth = 9, train loss: 0.46972, val loss: 0.46503, in 0.016s
1 tree, 30 leaves, max depth = 9, train loss: 0.46810, val loss: 0.46330, in 0.000s
1 tree, 52 leaves, max depth = 10, train loss: 0.46585, val loss: 0.46125, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.46425, val loss: 0.45954, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.46264, val loss: 0.45784, in 0.000s
1 tree, 50 leaves, max depth = 10, train loss: 0.46048, val loss: 0.45587, in 0.016s
1 tree, 30 leaves, max depth = 9, train loss: 0.45901, val loss: 0.45430, in 0.000s
1 tree, 32 leaves, max depth = 8, train loss: 0.45750, val loss: 0.45270, in 0.016s
1 tree, 30 leaves, max depth = 9, train loss: 0.45610, val loss: 0.45120, in 0.016s
1 tree, 50 leaves, max depth = 10, train loss: 0.45402, val loss: 0.44932, in 0.000s
1 tree, 51 leaves, max depth = 10, train loss: 0.45199, val loss: 0.44749, in 0.016s
1 tree, 28 leaves, max depth = 9, train loss: 0.45059, val loss: 0.44597, in 0.000s
Fit 71 trees in 0.957 s, (2372 total leaves)
Time spent computing histograms: 0.332s
Time spent finding best splits:  0.045s
Time spent applying splits:      0.052s
Time spent predicting:           0.000s
Trial 68, Fold 1: Log loss = 0.4511051676766809, Average precision = 0.9082653287694907, ROC-AUC = 0.915245410996879, Elapsed Time = 0.974603800001205 seconds
Trial 68, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 68, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
0.163 s 0.040 GB of training data: 
0.006 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 31 leaves, max depth = 12, train loss: 0.68579, val loss: 0.68545, in 0.007s
1 tree, 32 leaves, max depth = 12, train loss: 0.67871, val loss: 0.67806, in 0.009s
1 tree, 30 leaves, max depth = 10, train loss: 0.67182, val loss: 0.67084, in 0.008s
1 tree, 30 leaves, max depth = 9, train loss: 0.66519, val loss: 0.66391, in 0.008s
1 tree, 31 leaves, max depth = 17, train loss: 0.65876, val loss: 0.65716, in 0.009s
1 tree, 32 leaves, max depth = 11, train loss: 0.65251, val loss: 0.65062, in 0.007s
1 tree, 30 leaves, max depth = 14, train loss: 0.64645, val loss: 0.64428, in 0.010s
1 tree, 31 leaves, max depth = 17, train loss: 0.64058, val loss: 0.63811, in 0.010s
1 tree, 30 leaves, max depth = 10, train loss: 0.63485, val loss: 0.63207, in 0.009s
1 tree, 31 leaves, max depth = 10, train loss: 0.62923, val loss: 0.62612, in 0.009s
1 tree, 32 leaves, max depth = 11, train loss: 0.62384, val loss: 0.62048, in 0.011s
1 tree, 30 leaves, max depth = 14, train loss: 0.61863, val loss: 0.61498, in 0.009s
1 tree, 30 leaves, max depth = 10, train loss: 0.61354, val loss: 0.60959, in 0.008s
1 tree, 29 leaves, max depth = 9, train loss: 0.60864, val loss: 0.60443, in 0.008s
1 tree, 30 leaves, max depth = 10, train loss: 0.60383, val loss: 0.59933, in 0.009s
1 tree, 30 leaves, max depth = 14, train loss: 0.59918, val loss: 0.59444, in 0.009s
1 tree, 29 leaves, max depth = 8, train loss: 0.59462, val loss: 0.58962, in 0.008s
1 tree, 29 leaves, max depth = 8, train loss: 0.59019, val loss: 0.58494, in 0.009s
1 tree, 6 leaves, max depth = 4, train loss: 0.58587, val loss: 0.58039, in 0.007s
1 tree, 31 leaves, max depth = 10, train loss: 0.58163, val loss: 0.57589, in 0.008s
1 tree, 29 leaves, max depth = 8, train loss: 0.57756, val loss: 0.57158, in 0.008s
1 tree, 31 leaves, max depth = 10, train loss: 0.57355, val loss: 0.56733, in 0.008s
1 tree, 31 leaves, max depth = 10, train loss: 0.56965, val loss: 0.56318, in 0.009s
1 tree, 31 leaves, max depth = 10, train loss: 0.56586, val loss: 0.55915, in 0.009s
1 tree, 29 leaves, max depth = 8, train loss: 0.56222, val loss: 0.55529, in 0.010s
1 tree, 51 leaves, max depth = 11, train loss: 0.55854, val loss: 0.55174, in 0.012s
1 tree, 32 leaves, max depth = 11, train loss: 0.55506, val loss: 0.54807, in 0.010s
1 tree, 31 leaves, max depth = 11, train loss: 0.55169, val loss: 0.54448, in 0.008s
1 tree, 29 leaves, max depth = 14, train loss: 0.54841, val loss: 0.54100, in 0.008s
1 tree, 50 leaves, max depth = 13, train loss: 0.54493, val loss: 0.53765, in 0.011s
1 tree, 31 leaves, max depth = 11, train loss: 0.54178, val loss: 0.53430, in 0.009s
1 tree, 32 leaves, max depth = 11, train loss: 0.53870, val loss: 0.53104, in 0.010s
1 tree, 31 leaves, max depth = 10, train loss: 0.53564, val loss: 0.52777, in 0.009s
1 tree, 50 leaves, max depth = 13, train loss: 0.53235, val loss: 0.52460, in 0.011s
1 tree, 29 leaves, max depth = 8, train loss: 0.52945, val loss: 0.52151, in 0.009s
1 tree, 29 leaves, max depth = 8, train loss: 0.52663, val loss: 0.51850, in 0.009s
1 tree, 29 leaves, max depth = 8, train loss: 0.52388, val loss: 0.51557, in 0.009s
1 tree, 50 leaves, max depth = 12, train loss: 0.52076, val loss: 0.51257, in 0.010s
1 tree, 29 leaves, max depth = 8, train loss: 0.51812, val loss: 0.50974, in 0.009s
1 tree, 32 leaves, max depth = 11, train loss: 0.51556, val loss: 0.50703, in 0.008s
1 tree, 29 leaves, max depth = 8, train loss: 0.51306, val loss: 0.50435, in 0.008s
1 tree, 31 leaves, max depth = 10, train loss: 0.51058, val loss: 0.50168, in 0.009s
1 tree, 29 leaves, max depth = 8, train loss: 0.50821, val loss: 0.49914, in 0.009s
1 tree, 49 leaves, max depth = 12, train loss: 0.50528, val loss: 0.49634, in 0.010s
1 tree, 31 leaves, max depth = 10, train loss: 0.50302, val loss: 0.49393, in 0.010s
1 tree, 28 leaves, max depth = 8, train loss: 0.50080, val loss: 0.49154, in 0.008s
1 tree, 49 leaves, max depth = 12, train loss: 0.49799, val loss: 0.48885, in 0.010s
1 tree, 31 leaves, max depth = 9, train loss: 0.49580, val loss: 0.48650, in 0.008s
1 tree, 31 leaves, max depth = 9, train loss: 0.49367, val loss: 0.48420, in 0.009s
1 tree, 49 leaves, max depth = 14, train loss: 0.49097, val loss: 0.48162, in 0.012s
1 tree, 32 leaves, max depth = 12, train loss: 0.48897, val loss: 0.47950, in 0.007s
1 tree, 50 leaves, max depth = 9, train loss: 0.48636, val loss: 0.47700, in 0.010s
1 tree, 50 leaves, max depth = 15, train loss: 0.48381, val loss: 0.47458, in 0.012s
1 tree, 31 leaves, max depth = 11, train loss: 0.48191, val loss: 0.47253, in 0.010s
1 tree, 50 leaves, max depth = 9, train loss: 0.47945, val loss: 0.47018, in 0.011s
1 tree, 50 leaves, max depth = 9, train loss: 0.47705, val loss: 0.46789, in 0.008s
1 tree, 6 leaves, max depth = 4, train loss: 0.47520, val loss: 0.46590, in 0.006s
1 tree, 31 leaves, max depth = 11, train loss: 0.47343, val loss: 0.46401, in 0.008s
1 tree, 49 leaves, max depth = 10, train loss: 0.47112, val loss: 0.46181, in 0.010s
1 tree, 31 leaves, max depth = 15, train loss: 0.46943, val loss: 0.45999, in 0.011s
1 tree, 31 leaves, max depth = 15, train loss: 0.46778, val loss: 0.45820, in 0.011s
1 tree, 49 leaves, max depth = 10, train loss: 0.46555, val loss: 0.45609, in 0.011s
1 tree, 29 leaves, max depth = 9, train loss: 0.46394, val loss: 0.45435, in 0.009s
1 tree, 29 leaves, max depth = 10, train loss: 0.46238, val loss: 0.45265, in 0.010s
1 tree, 50 leaves, max depth = 12, train loss: 0.46022, val loss: 0.45060, in 0.011s
1 tree, 29 leaves, max depth = 9, train loss: 0.45872, val loss: 0.44896, in 0.008s
1 tree, 29 leaves, max depth = 9, train loss: 0.45725, val loss: 0.44737, in 0.009s
1 tree, 31 leaves, max depth = 15, train loss: 0.45584, val loss: 0.44583, in 0.009s
1 tree, 50 leaves, max depth = 12, train loss: 0.45377, val loss: 0.44386, in 0.010s
1 tree, 50 leaves, max depth = 12, train loss: 0.45175, val loss: 0.44195, in 0.017s
1 tree, 31 leaves, max depth = 10, train loss: 0.45034, val loss: 0.44041, in 0.011s
Fit 71 trees in 0.993 s, (2415 total leaves)
Time spent computing histograms: 0.344s
Time spent finding best splits:  0.048s
Time spent applying splits:      0.057s
Time spent predicting:           0.012s
Trial 68, Fold 2: Log loss = 0.4508887269438503, Average precision = 0.9074085799523012, ROC-AUC = 0.9224698560611556, Elapsed Time = 1.001351099999738 seconds
Trial 68, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 68, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
Binning 0.040 GB of training data: 0.151 s
0.005 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 29 leaves, max depth = 11, train loss: 0.68578, val loss: 0.68551, in 0.008s
1 tree, 30 leaves, max depth = 10, train loss: 0.67874, val loss: 0.67825, in 0.008s
1 tree, 31 leaves, max depth = 9, train loss: 0.67189, val loss: 0.67115, in 0.009s
1 tree, 28 leaves, max depth = 11, train loss: 0.66532, val loss: 0.66432, in 0.008s
1 tree, 32 leaves, max depth = 13, train loss: 0.65894, val loss: 0.65768, in 0.008s
1 tree, 30 leaves, max depth = 10, train loss: 0.65272, val loss: 0.65125, in 0.008s
1 tree, 31 leaves, max depth = 14, train loss: 0.64672, val loss: 0.64500, in 0.009s
1 tree, 31 leaves, max depth = 13, train loss: 0.64089, val loss: 0.63895, in 0.008s
1 tree, 30 leaves, max depth = 8, train loss: 0.63521, val loss: 0.63305, in 0.008s
1 tree, 30 leaves, max depth = 8, train loss: 0.62962, val loss: 0.62726, in 0.009s
1 tree, 30 leaves, max depth = 10, train loss: 0.62426, val loss: 0.62172, in 0.007s
1 tree, 31 leaves, max depth = 13, train loss: 0.61909, val loss: 0.61633, in 0.009s
1 tree, 31 leaves, max depth = 14, train loss: 0.61407, val loss: 0.61108, in 0.009s
1 tree, 30 leaves, max depth = 10, train loss: 0.60920, val loss: 0.60604, in 0.007s
1 tree, 28 leaves, max depth = 11, train loss: 0.60446, val loss: 0.60110, in 0.008s
1 tree, 31 leaves, max depth = 14, train loss: 0.59986, val loss: 0.59628, in 0.009s
1 tree, 30 leaves, max depth = 9, train loss: 0.59535, val loss: 0.59157, in 0.010s
1 tree, 29 leaves, max depth = 10, train loss: 0.59097, val loss: 0.58703, in 0.009s
1 tree, 6 leaves, max depth = 4, train loss: 0.58668, val loss: 0.58256, in 0.007s
1 tree, 30 leaves, max depth = 8, train loss: 0.58245, val loss: 0.57818, in 0.007s
1 tree, 30 leaves, max depth = 8, train loss: 0.57833, val loss: 0.57392, in 0.008s
1 tree, 30 leaves, max depth = 8, train loss: 0.57433, val loss: 0.56977, in 0.008s
1 tree, 30 leaves, max depth = 8, train loss: 0.57045, val loss: 0.56574, in 0.009s
1 tree, 30 leaves, max depth = 8, train loss: 0.56666, val loss: 0.56181, in 0.007s
1 tree, 29 leaves, max depth = 10, train loss: 0.56307, val loss: 0.55807, in 0.008s
1 tree, 52 leaves, max depth = 11, train loss: 0.55934, val loss: 0.55458, in 0.012s
1 tree, 29 leaves, max depth = 10, train loss: 0.55589, val loss: 0.55098, in 0.008s
1 tree, 29 leaves, max depth = 10, train loss: 0.55255, val loss: 0.54748, in 0.008s
1 tree, 28 leaves, max depth = 12, train loss: 0.54931, val loss: 0.54407, in 0.008s
1 tree, 51 leaves, max depth = 12, train loss: 0.54580, val loss: 0.54078, in 0.012s
1 tree, 29 leaves, max depth = 10, train loss: 0.54268, val loss: 0.53750, in 0.007s
1 tree, 29 leaves, max depth = 10, train loss: 0.53962, val loss: 0.53431, in 0.009s
1 tree, 30 leaves, max depth = 8, train loss: 0.53656, val loss: 0.53113, in 0.007s
1 tree, 51 leaves, max depth = 12, train loss: 0.53324, val loss: 0.52802, in 0.010s
1 tree, 30 leaves, max depth = 8, train loss: 0.53030, val loss: 0.52496, in 0.008s
1 tree, 30 leaves, max depth = 8, train loss: 0.52750, val loss: 0.52201, in 0.007s
1 tree, 31 leaves, max depth = 9, train loss: 0.52477, val loss: 0.51913, in 0.008s
1 tree, 51 leaves, max depth = 13, train loss: 0.52161, val loss: 0.51618, in 0.009s
1 tree, 30 leaves, max depth = 8, train loss: 0.51900, val loss: 0.51343, in 0.009s
1 tree, 30 leaves, max depth = 10, train loss: 0.51646, val loss: 0.51076, in 0.010s
1 tree, 31 leaves, max depth = 9, train loss: 0.51397, val loss: 0.50813, in 0.010s
1 tree, 30 leaves, max depth = 8, train loss: 0.51150, val loss: 0.50554, in 0.011s
1 tree, 31 leaves, max depth = 9, train loss: 0.50914, val loss: 0.50305, in 0.009s
1 tree, 53 leaves, max depth = 12, train loss: 0.50618, val loss: 0.50029, in 0.011s
1 tree, 28 leaves, max depth = 14, train loss: 0.50395, val loss: 0.49791, in 0.009s
1 tree, 31 leaves, max depth = 9, train loss: 0.50174, val loss: 0.49557, in 0.009s
[47/71] 1 tree, 53 leaves, max depth = 12, train loss: 0.49890, val loss: 0.49292, in 0.012s
1 tree, 30 leaves, max depth = 8, train loss: 0.49672, val loss: 0.49064, in 0.009s
1 tree, 30 leaves, max depth = 8, train loss: 0.49460, val loss: 0.48842, in 0.009s
1 tree, 52 leaves, max depth = 11, train loss: 0.49187, val loss: 0.48588, in 0.010s
1 tree, 30 leaves, max depth = 10, train loss: 0.48989, val loss: 0.48379, in 0.010s
1 tree, 52 leaves, max depth = 11, train loss: 0.48724, val loss: 0.48133, in 0.012s
1 tree, 52 leaves, max depth = 11, train loss: 0.48467, val loss: 0.47893, in 0.010s
1 tree, 29 leaves, max depth = 13, train loss: 0.48280, val loss: 0.47694, in 0.009s
1 tree, 52 leaves, max depth = 11, train loss: 0.48030, val loss: 0.47463, in 0.012s
1 tree, 52 leaves, max depth = 11, train loss: 0.47787, val loss: 0.47238, in 0.010s
1 tree, 6 leaves, max depth = 4, train loss: 0.47604, val loss: 0.47044, in 0.007s
1 tree, 29 leaves, max depth = 10, train loss: 0.47429, val loss: 0.46858, in 0.009s
1 tree, 52 leaves, max depth = 11, train loss: 0.47195, val loss: 0.46642, in 0.010s
1 tree, 31 leaves, max depth = 13, train loss: 0.47028, val loss: 0.46464, in 0.009s
1 tree, 31 leaves, max depth = 9, train loss: 0.46864, val loss: 0.46287, in 0.009s
1 tree, 52 leaves, max depth = 10, train loss: 0.46638, val loss: 0.46079, in 0.013s
1 tree, 29 leaves, max depth = 10, train loss: 0.46479, val loss: 0.45910, in 0.009s
1 tree, 31 leaves, max depth = 9, train loss: 0.46325, val loss: 0.45744, in 0.009s
1 tree, 52 leaves, max depth = 13, train loss: 0.46107, val loss: 0.45543, in 0.012s
1 tree, 31 leaves, max depth = 9, train loss: 0.45958, val loss: 0.45382, in 0.009s
1 tree, 29 leaves, max depth = 9, train loss: 0.45807, val loss: 0.45219, in 0.009s
1 tree, 31 leaves, max depth = 9, train loss: 0.45666, val loss: 0.45067, in 0.010s
1 tree, 51 leaves, max depth = 11, train loss: 0.45456, val loss: 0.44873, in 0.011s
1 tree, 52 leaves, max depth = 13, train loss: 0.45252, val loss: 0.44685, in 0.010s
1 tree, 30 leaves, max depth = 9, train loss: 0.45112, val loss: 0.44537, in 0.009s
Fit 71 trees in 0.947 s, (2430 total leaves)
Time spent computing histograms: 0.345s
Time spent finding best splits:  0.045s
Time spent applying splits:      0.055s
Time spent predicting:           0.006s
Trial 68, Fold 3: Log loss = 0.4462839684151748, Average precision = 0.9177517585201269, ROC-AUC = 0.9285210913648141, Elapsed Time = 0.9534422999986418 seconds
Trial 68, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 68, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
Binning 0.040 GB of training data: 0.176 s
0.006 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 31 leaves, max depth = 11, train loss: 0.68579, val loss: 0.68541, in 0.009s
1 tree, 31 leaves, max depth = 11, train loss: 0.67875, val loss: 0.67795, in 0.009s
1 tree, 29 leaves, max depth = 10, train loss: 0.67191, val loss: 0.67074, in 0.009s
1 tree, 30 leaves, max depth = 10, train loss: 0.66531, val loss: 0.66379, in 0.009s
1 tree, 29 leaves, max depth = 11, train loss: 0.65891, val loss: 0.65701, in 0.010s
1 tree, 31 leaves, max depth = 11, train loss: 0.65268, val loss: 0.65040, in 0.010s
1 tree, 30 leaves, max depth = 11, train loss: 0.64666, val loss: 0.64402, in 0.009s
1 tree, 30 leaves, max depth = 10, train loss: 0.64082, val loss: 0.63784, in 0.009s
1 tree, 29 leaves, max depth = 11, train loss: 0.63512, val loss: 0.63179, in 0.010s
1 tree, 30 leaves, max depth = 10, train loss: 0.62952, val loss: 0.62582, in 0.008s
1 tree, 31 leaves, max depth = 11, train loss: 0.62416, val loss: 0.62010, in 0.009s
1 tree, 29 leaves, max depth = 11, train loss: 0.61897, val loss: 0.61457, in 0.007s
1 tree, 29 leaves, max depth = 11, train loss: 0.61390, val loss: 0.60916, in 0.009s
1 tree, 29 leaves, max depth = 10, train loss: 0.60902, val loss: 0.60394, in 0.008s
1 tree, 30 leaves, max depth = 12, train loss: 0.60423, val loss: 0.59882, in 0.011s
1 tree, 30 leaves, max depth = 11, train loss: 0.59961, val loss: 0.59387, in 0.010s
1 tree, 29 leaves, max depth = 10, train loss: 0.59508, val loss: 0.58904, in 0.010s
1 tree, 29 leaves, max depth = 10, train loss: 0.59068, val loss: 0.58433, in 0.009s
1 tree, 6 leaves, max depth = 4, train loss: 0.58638, val loss: 0.57974, in 0.008s
1 tree, 29 leaves, max depth = 12, train loss: 0.58216, val loss: 0.57520, in 0.010s
1 tree, 29 leaves, max depth = 12, train loss: 0.57805, val loss: 0.57078, in 0.009s
1 tree, 29 leaves, max depth = 12, train loss: 0.57406, val loss: 0.56648, in 0.008s
1 tree, 29 leaves, max depth = 12, train loss: 0.57018, val loss: 0.56230, in 0.009s
1 tree, 29 leaves, max depth = 12, train loss: 0.56641, val loss: 0.55822, in 0.010s
1 tree, 29 leaves, max depth = 10, train loss: 0.56280, val loss: 0.55433, in 0.011s
1 tree, 50 leaves, max depth = 12, train loss: 0.55909, val loss: 0.55066, in 0.010s
1 tree, 30 leaves, max depth = 11, train loss: 0.55563, val loss: 0.54690, in 0.009s
1 tree, 30 leaves, max depth = 11, train loss: 0.55226, val loss: 0.54327, in 0.011s
1 tree, 30 leaves, max depth = 12, train loss: 0.54899, val loss: 0.53972, in 0.009s
1 tree, 50 leaves, max depth = 11, train loss: 0.54550, val loss: 0.53627, in 0.013s
1 tree, 31 leaves, max depth = 11, train loss: 0.54235, val loss: 0.53287, in 0.010s
1 tree, 31 leaves, max depth = 11, train loss: 0.53928, val loss: 0.52952, in 0.009s
1 tree, 29 leaves, max depth = 12, train loss: 0.53624, val loss: 0.52620, in 0.010s
1 tree, 51 leaves, max depth = 11, train loss: 0.53293, val loss: 0.52293, in 0.011s
1 tree, 29 leaves, max depth = 9, train loss: 0.53000, val loss: 0.51974, in 0.010s
1 tree, 29 leaves, max depth = 10, train loss: 0.52720, val loss: 0.51669, in 0.010s
1 tree, 29 leaves, max depth = 10, train loss: 0.52447, val loss: 0.51372, in 0.010s
1 tree, 51 leaves, max depth = 12, train loss: 0.52134, val loss: 0.51062, in 0.012s
1 tree, 29 leaves, max depth = 10, train loss: 0.51872, val loss: 0.50776, in 0.010s
1 tree, 30 leaves, max depth = 11, train loss: 0.51617, val loss: 0.50495, in 0.009s
1 tree, 29 leaves, max depth = 10, train loss: 0.51368, val loss: 0.50223, in 0.010s
1 tree, 29 leaves, max depth = 9, train loss: 0.51122, val loss: 0.49952, in 0.010s
1 tree, 29 leaves, max depth = 10, train loss: 0.50887, val loss: 0.49693, in 0.142s
1 tree, 52 leaves, max depth = 12, train loss: 0.50592, val loss: 0.49404, in 0.015s
1 tree, 31 leaves, max depth = 12, train loss: 0.50366, val loss: 0.49157, in 0.040s
1 tree, 29 leaves, max depth = 10, train loss: 0.50146, val loss: 0.48914, in 0.011s
1 tree, 52 leaves, max depth = 13, train loss: 0.49864, val loss: 0.48637, in 0.012s
1 tree, 30 leaves, max depth = 9, train loss: 0.49647, val loss: 0.48397, in 0.012s
1 tree, 30 leaves, max depth = 9, train loss: 0.49436, val loss: 0.48162, in 0.012s
1 tree, 52 leaves, max depth = 13, train loss: 0.49165, val loss: 0.47896, in 0.013s
1 tree, 30 leaves, max depth = 11, train loss: 0.48966, val loss: 0.47675, in 0.011s
1 tree, 52 leaves, max depth = 13, train loss: 0.48703, val loss: 0.47417, in 0.012s
1 tree, 52 leaves, max depth = 13, train loss: 0.48447, val loss: 0.47166, in 0.012s
1 tree, 29 leaves, max depth = 11, train loss: 0.48250, val loss: 0.46949, in 0.011s
1 tree, 52 leaves, max depth = 13, train loss: 0.48003, val loss: 0.46706, in 0.013s
1 tree, 52 leaves, max depth = 13, train loss: 0.47761, val loss: 0.46470, in 0.015s
1 tree, 6 leaves, max depth = 4, train loss: 0.47576, val loss: 0.46266, in 0.008s
1 tree, 31 leaves, max depth = 10, train loss: 0.47400, val loss: 0.46069, in 0.010s
1 tree, 53 leaves, max depth = 11, train loss: 0.47168, val loss: 0.45842, in 0.013s
1 tree, 30 leaves, max depth = 11, train loss: 0.46992, val loss: 0.45646, in 0.011s
1 tree, 30 leaves, max depth = 12, train loss: 0.46827, val loss: 0.45461, in 0.011s
1 tree, 52 leaves, max depth = 10, train loss: 0.46603, val loss: 0.45243, in 0.013s
1 tree, 31 leaves, max depth = 10, train loss: 0.46444, val loss: 0.45063, in 0.011s
1 tree, 30 leaves, max depth = 9, train loss: 0.46282, val loss: 0.44883, in 0.010s
1 tree, 52 leaves, max depth = 12, train loss: 0.46066, val loss: 0.44673, in 0.014s
1 tree, 30 leaves, max depth = 12, train loss: 0.45917, val loss: 0.44505, in 0.011s
1 tree, 30 leaves, max depth = 11, train loss: 0.45765, val loss: 0.44334, in 0.010s
1 tree, 30 leaves, max depth = 9, train loss: 0.45617, val loss: 0.44169, in 0.010s
1 tree, 51 leaves, max depth = 13, train loss: 0.45409, val loss: 0.43967, in 0.014s
1 tree, 52 leaves, max depth = 13, train loss: 0.45207, val loss: 0.43770, in 0.012s
1 tree, 30 leaves, max depth = 9, train loss: 0.45068, val loss: 0.43611, in 0.012s
Fit 71 trees in 1.244 s, (2413 total leaves)
Time spent computing histograms: 0.453s
Time spent finding best splits:  0.147s
Time spent applying splits:      0.067s
Time spent predicting:           0.007s
Trial 68, Fold 4: Log loss = 0.44999522667030717, Average precision = 0.9131729718295871, ROC-AUC = 0.9238388222446356, Elapsed Time = 1.253656900000351 seconds
Trial 68, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 68, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
Binning 0.040 GB of training data: 0.288 s
0.009 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 30 leaves, max depth = 10, train loss: 0.68566, val loss: 0.68519, in 0.014s
1 tree, 30 leaves, max depth = 11, train loss: 0.67855, val loss: 0.67757, in 0.020s
1 tree, 28 leaves, max depth = 10, train loss: 0.67162, val loss: 0.67019, in 0.015s
1 tree, 29 leaves, max depth = 11, train loss: 0.66493, val loss: 0.66307, in 0.013s
1 tree, 29 leaves, max depth = 13, train loss: 0.65846, val loss: 0.65615, in 0.016s
1 tree, 30 leaves, max depth = 11, train loss: 0.65216, val loss: 0.64938, in 0.014s
1 tree, 30 leaves, max depth = 10, train loss: 0.64607, val loss: 0.64282, in 0.011s
1 tree, 29 leaves, max depth = 13, train loss: 0.64016, val loss: 0.63648, in 0.010s
1 tree, 28 leaves, max depth = 10, train loss: 0.63439, val loss: 0.63030, in 0.013s
1 tree, 29 leaves, max depth = 8, train loss: 0.62874, val loss: 0.62423, in 0.012s
1 tree, 29 leaves, max depth = 11, train loss: 0.62332, val loss: 0.61836, in 0.013s
1 tree, 29 leaves, max depth = 13, train loss: 0.61807, val loss: 0.61271, in 0.010s
1 tree, 30 leaves, max depth = 10, train loss: 0.61297, val loss: 0.60718, in 0.013s
1 tree, 29 leaves, max depth = 11, train loss: 0.60804, val loss: 0.60183, in 0.015s
1 tree, 30 leaves, max depth = 11, train loss: 0.60322, val loss: 0.59665, in 0.013s
1 tree, 29 leaves, max depth = 11, train loss: 0.59854, val loss: 0.59160, in 0.044s
1 tree, 27 leaves, max depth = 10, train loss: 0.59397, val loss: 0.58665, in 0.033s
1 tree, 29 leaves, max depth = 11, train loss: 0.58954, val loss: 0.58181, in 0.021s
1 tree, 6 leaves, max depth = 4, train loss: 0.58520, val loss: 0.57711, in 0.011s
1 tree, 30 leaves, max depth = 8, train loss: 0.58094, val loss: 0.57249, in 0.012s
1 tree, 30 leaves, max depth = 8, train loss: 0.57680, val loss: 0.56798, in 0.013s
1 tree, 30 leaves, max depth = 8, train loss: 0.57278, val loss: 0.56360, in 0.013s
1 tree, 30 leaves, max depth = 8, train loss: 0.56887, val loss: 0.55934, in 0.010s
1 tree, 30 leaves, max depth = 8, train loss: 0.56507, val loss: 0.55518, in 0.017s
1 tree, 29 leaves, max depth = 11, train loss: 0.56142, val loss: 0.55118, in 0.015s
1 tree, 27 leaves, max depth = 9, train loss: 0.55787, val loss: 0.54728, in 0.014s
1 tree, 29 leaves, max depth = 11, train loss: 0.55443, val loss: 0.54348, in 0.012s
1 tree, 30 leaves, max depth = 11, train loss: 0.55107, val loss: 0.53981, in 0.011s
1 tree, 30 leaves, max depth = 12, train loss: 0.54781, val loss: 0.53623, in 0.013s
1 tree, 49 leaves, max depth = 14, train loss: 0.54429, val loss: 0.53288, in 0.013s
1 tree, 30 leaves, max depth = 11, train loss: 0.54116, val loss: 0.52941, in 0.013s
1 tree, 29 leaves, max depth = 11, train loss: 0.53811, val loss: 0.52602, in 0.012s
1 tree, 30 leaves, max depth = 8, train loss: 0.53508, val loss: 0.52268, in 0.012s
1 tree, 49 leaves, max depth = 13, train loss: 0.53175, val loss: 0.51952, in 0.015s
1 tree, 30 leaves, max depth = 8, train loss: 0.52884, val loss: 0.51630, in 0.013s
1 tree, 27 leaves, max depth = 9, train loss: 0.52605, val loss: 0.51321, in 0.013s
1 tree, 30 leaves, max depth = 11, train loss: 0.52334, val loss: 0.51021, in 0.012s
1 tree, 49 leaves, max depth = 14, train loss: 0.52019, val loss: 0.50722, in 0.018s
1 tree, 30 leaves, max depth = 11, train loss: 0.51758, val loss: 0.50434, in 0.015s
1 tree, 30 leaves, max depth = 11, train loss: 0.51505, val loss: 0.50152, in 0.014s
1 tree, 27 leaves, max depth = 9, train loss: 0.51257, val loss: 0.49877, in 0.011s
1 tree, 30 leaves, max depth = 8, train loss: 0.51011, val loss: 0.49603, in 0.014s
1 tree, 30 leaves, max depth = 11, train loss: 0.50777, val loss: 0.49342, in 0.011s
1 tree, 49 leaves, max depth = 13, train loss: 0.50480, val loss: 0.49063, in 0.017s
1 tree, 30 leaves, max depth = 11, train loss: 0.50255, val loss: 0.48812, in 0.014s
1 tree, 27 leaves, max depth = 9, train loss: 0.50035, val loss: 0.48566, in 0.012s
1 tree, 50 leaves, max depth = 13, train loss: 0.49750, val loss: 0.48298, in 0.018s
1 tree, 30 leaves, max depth = 8, train loss: 0.49534, val loss: 0.48055, in 0.013s
1 tree, 30 leaves, max depth = 8, train loss: 0.49324, val loss: 0.47819, in 0.010s
1 tree, 51 leaves, max depth = 13, train loss: 0.49050, val loss: 0.47562, in 0.015s
1 tree, 31 leaves, max depth = 10, train loss: 0.48852, val loss: 0.47339, in 0.011s
1 tree, 52 leaves, max depth = 12, train loss: 0.48587, val loss: 0.47090, in 0.016s
1 tree, 50 leaves, max depth = 13, train loss: 0.48329, val loss: 0.46849, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.48140, val loss: 0.46637, in 0.015s
1 tree, 52 leaves, max depth = 12, train loss: 0.47890, val loss: 0.46403, in 0.018s
1 tree, 52 leaves, max depth = 12, train loss: 0.47647, val loss: 0.46176, in 0.016s
1 tree, 6 leaves, max depth = 4, train loss: 0.47463, val loss: 0.45969, in 0.010s
1 tree, 30 leaves, max depth = 10, train loss: 0.47288, val loss: 0.45772, in 0.014s
1 tree, 51 leaves, max depth = 12, train loss: 0.47053, val loss: 0.45552, in 0.016s
1 tree, 29 leaves, max depth = 10, train loss: 0.46885, val loss: 0.45362, in 0.012s
1 tree, 29 leaves, max depth = 10, train loss: 0.46721, val loss: 0.45176, in 0.012s
1 tree, 53 leaves, max depth = 12, train loss: 0.46494, val loss: 0.44966, in 0.016s
1 tree, 30 leaves, max depth = 10, train loss: 0.46336, val loss: 0.44786, in 0.012s
1 tree, 29 leaves, max depth = 9, train loss: 0.46181, val loss: 0.44611, in 0.012s
1 tree, 53 leaves, max depth = 12, train loss: 0.45962, val loss: 0.44408, in 0.017s
1 tree, 29 leaves, max depth = 10, train loss: 0.45814, val loss: 0.44239, in 0.013s
1 tree, 30 leaves, max depth = 12, train loss: 0.45669, val loss: 0.44075, in 0.013s
1 tree, 29 leaves, max depth = 12, train loss: 0.45528, val loss: 0.43914, in 0.012s
1 tree, 52 leaves, max depth = 12, train loss: 0.45318, val loss: 0.43721, in 0.015s
1 tree, 51 leaves, max depth = 12, train loss: 0.45112, val loss: 0.43532, in 0.017s
1 tree, 30 leaves, max depth = 9, train loss: 0.44973, val loss: 0.43373, in 0.012s
Fit 71 trees in 1.562 s, (2361 total leaves)
Time spent computing histograms: 0.499s
Time spent finding best splits:  0.097s
Time spent applying splits:      0.103s
Time spent predicting:           0.010s
Trial 68, Fold 5: Log loss = 0.45655774610222777, Average precision = 0.9056527493890548, ROC-AUC = 0.9152489456180443, Elapsed Time = 1.5716627000001608 seconds
Optimization Progress:  69%|######9   | 69/100 [13:45<06:56, 13.42s/it]
Trial 69, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 69, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
0.210 s 0.040 GB of training data: 
0.006 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 81 leaves, max depth = 11, train loss: 0.63986, val loss: 0.63959, in 0.022s
1 tree, 81 leaves, max depth = 14, train loss: 0.59513, val loss: 0.59500, in 0.027s
1 tree, 81 leaves, max depth = 13, train loss: 0.55742, val loss: 0.55748, in 0.027s
1 tree, 81 leaves, max depth = 10, train loss: 0.52598, val loss: 0.52567, in 0.025s
1 tree, 81 leaves, max depth = 14, train loss: 0.49873, val loss: 0.49862, in 0.027s
1 tree, 81 leaves, max depth = 11, train loss: 0.47577, val loss: 0.47540, in 0.024s
1 tree, 81 leaves, max depth = 12, train loss: 0.45490, val loss: 0.45454, in 0.022s
1 tree, 81 leaves, max depth = 13, train loss: 0.42895, val loss: 0.42968, in 0.030s
1 tree, 81 leaves, max depth = 14, train loss: 0.41344, val loss: 0.41434, in 0.029s
1 tree, 81 leaves, max depth = 15, train loss: 0.39295, val loss: 0.39481, in 0.040s
1 tree, 81 leaves, max depth = 12, train loss: 0.38000, val loss: 0.38182, in 0.025s
1 tree, 81 leaves, max depth = 14, train loss: 0.36856, val loss: 0.37059, in 0.027s
1 tree, 81 leaves, max depth = 16, train loss: 0.35323, val loss: 0.35628, in 0.021s
1 tree, 81 leaves, max depth = 12, train loss: 0.34423, val loss: 0.34735, in 0.021s
1 tree, 81 leaves, max depth = 23, train loss: 0.33261, val loss: 0.33615, in 0.020s
1 tree, 81 leaves, max depth = 18, train loss: 0.32270, val loss: 0.32651, in 0.019s
1 tree, 81 leaves, max depth = 14, train loss: 0.31290, val loss: 0.31784, in 0.019s
1 tree, 81 leaves, max depth = 15, train loss: 0.30479, val loss: 0.30985, in 0.021s
1 tree, 81 leaves, max depth = 18, train loss: 0.29754, val loss: 0.30281, in 0.020s
1 tree, 81 leaves, max depth = 17, train loss: 0.29065, val loss: 0.29640, in 0.020s
1 tree, 81 leaves, max depth = 15, train loss: 0.28404, val loss: 0.28983, in 0.025s
1 tree, 81 leaves, max depth = 16, train loss: 0.27746, val loss: 0.28413, in 0.020s
1 tree, 81 leaves, max depth = 16, train loss: 0.27221, val loss: 0.27917, in 0.019s
1 tree, 81 leaves, max depth = 15, train loss: 0.26767, val loss: 0.27498, in 0.019s
1 tree, 81 leaves, max depth = 16, train loss: 0.26246, val loss: 0.27045, in 0.021s
1 tree, 81 leaves, max depth = 16, train loss: 0.25901, val loss: 0.26722, in 0.022s
1 tree, 81 leaves, max depth = 16, train loss: 0.25492, val loss: 0.26337, in 0.027s
1 tree, 81 leaves, max depth = 14, train loss: 0.25056, val loss: 0.25908, in 0.024s
1 tree, 81 leaves, max depth = 15, train loss: 0.24661, val loss: 0.25568, in 0.025s
1 tree, 81 leaves, max depth = 20, train loss: 0.24311, val loss: 0.25264, in 0.022s
1 tree, 81 leaves, max depth = 16, train loss: 0.23997, val loss: 0.24940, in 0.028s
1 tree, 81 leaves, max depth = 13, train loss: 0.23714, val loss: 0.24698, in 0.022s
1 tree, 81 leaves, max depth = 16, train loss: 0.23463, val loss: 0.24430, in 0.027s
1 tree, 81 leaves, max depth = 15, train loss: 0.23237, val loss: 0.24190, in 0.024s
1 tree, 81 leaves, max depth = 20, train loss: 0.23021, val loss: 0.23974, in 0.023s
1 tree, 81 leaves, max depth = 17, train loss: 0.22807, val loss: 0.23791, in 0.023s
1 tree, 81 leaves, max depth = 16, train loss: 0.22619, val loss: 0.23637, in 0.024s
1 tree, 81 leaves, max depth = 16, train loss: 0.22420, val loss: 0.23447, in 0.025s
1 tree, 81 leaves, max depth = 19, train loss: 0.22252, val loss: 0.23257, in 0.023s
1 tree, 81 leaves, max depth = 12, train loss: 0.21952, val loss: 0.22950, in 0.024s
1 tree, 81 leaves, max depth = 17, train loss: 0.21691, val loss: 0.22687, in 0.027s
1 tree, 81 leaves, max depth = 19, train loss: 0.21515, val loss: 0.22530, in 0.018s
1 tree, 81 leaves, max depth = 15, train loss: 0.21288, val loss: 0.22376, in 0.037s
1 tree, 81 leaves, max depth = 14, train loss: 0.21158, val loss: 0.22261, in 0.033s
1 tree, 81 leaves, max depth = 14, train loss: 0.20955, val loss: 0.22117, in 0.027s
1 tree, 81 leaves, max depth = 14, train loss: 0.20827, val loss: 0.22013, in 0.039s
1 tree, 81 leaves, max depth = 17, train loss: 0.20628, val loss: 0.21827, in 0.030s
1 tree, 81 leaves, max depth = 14, train loss: 0.20490, val loss: 0.21743, in 0.031s
1 tree, 81 leaves, max depth = 18, train loss: 0.20398, val loss: 0.21654, in 0.023s
1 tree, 81 leaves, max depth = 12, train loss: 0.20227, val loss: 0.21475, in 0.023s
1 tree, 81 leaves, max depth = 17, train loss: 0.20127, val loss: 0.21394, in 0.027s
1 tree, 81 leaves, max depth = 16, train loss: 0.19967, val loss: 0.21238, in 0.027s
1 tree, 81 leaves, max depth = 21, train loss: 0.19852, val loss: 0.21186, in 0.027s
1 tree, 81 leaves, max depth = 18, train loss: 0.19679, val loss: 0.21099, in 0.030s
1 tree, 81 leaves, max depth = 16, train loss: 0.19580, val loss: 0.21047, in 0.024s
1 tree, 81 leaves, max depth = 16, train loss: 0.19387, val loss: 0.20913, in 0.025s
1 tree, 81 leaves, max depth = 14, train loss: 0.19295, val loss: 0.20858, in 0.028s
1 tree, 30 leaves, max depth = 9, train loss: 0.19227, val loss: 0.20795, in 0.018s
1 tree, 81 leaves, max depth = 18, train loss: 0.19154, val loss: 0.20746, in 0.030s
1 tree, 81 leaves, max depth = 10, train loss: 0.19028, val loss: 0.20628, in 0.031s
1 tree, 30 leaves, max depth = 8, train loss: 0.18957, val loss: 0.20565, in 0.014s
1 tree, 81 leaves, max depth = 15, train loss: 0.18883, val loss: 0.20507, in 0.039s
1 tree, 81 leaves, max depth = 12, train loss: 0.18778, val loss: 0.20461, in 0.026s
1 tree, 81 leaves, max depth = 14, train loss: 0.18668, val loss: 0.20367, in 0.031s
Fit 64 trees in 2.035 s, (5082 total leaves)
Time spent computing histograms: 0.551s
Time spent finding best splits:  0.174s
Time spent applying splits:      0.132s
Time spent predicting:           0.006s
Trial 69, Fold 1: Log loss = 0.21489149959789555, Average precision = 0.9714608550870261, ROC-AUC = 0.9659503556014396, Elapsed Time = 2.049052999998821 seconds
Trial 69, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 69, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
Binning 0.040 GB of training data: 0.222 s
0.003 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 81 leaves, max depth = 14, train loss: 0.63988, val loss: 0.63921, in 0.037s
1 tree, 81 leaves, max depth = 13, train loss: 0.59507, val loss: 0.59392, in 0.032s
1 tree, 81 leaves, max depth = 10, train loss: 0.55679, val loss: 0.55522, in 0.033s
1 tree, 81 leaves, max depth = 11, train loss: 0.52476, val loss: 0.52314, in 0.034s
1 tree, 81 leaves, max depth = 11, train loss: 0.49713, val loss: 0.49523, in 0.023s
1 tree, 81 leaves, max depth = 11, train loss: 0.47379, val loss: 0.47188, in 0.031s
1 tree, 81 leaves, max depth = 13, train loss: 0.45414, val loss: 0.45221, in 0.031s
1 tree, 81 leaves, max depth = 11, train loss: 0.43616, val loss: 0.43414, in 0.031s
1 tree, 81 leaves, max depth = 16, train loss: 0.41255, val loss: 0.41119, in 0.031s
1 tree, 81 leaves, max depth = 15, train loss: 0.39259, val loss: 0.39175, in 0.047s
1 tree, 81 leaves, max depth = 13, train loss: 0.37987, val loss: 0.37892, in 0.016s
1 tree, 81 leaves, max depth = 11, train loss: 0.36864, val loss: 0.36777, in 0.031s
1 tree, 81 leaves, max depth = 17, train loss: 0.35364, val loss: 0.35340, in 0.031s
1 tree, 81 leaves, max depth = 10, train loss: 0.34474, val loss: 0.34508, in 0.031s
1 tree, 81 leaves, max depth = 21, train loss: 0.33334, val loss: 0.33414, in 0.031s
1 tree, 81 leaves, max depth = 23, train loss: 0.32359, val loss: 0.32474, in 0.016s
1 tree, 81 leaves, max depth = 13, train loss: 0.31404, val loss: 0.31600, in 0.031s
1 tree, 81 leaves, max depth = 16, train loss: 0.30572, val loss: 0.30832, in 0.031s
1 tree, 81 leaves, max depth = 16, train loss: 0.29856, val loss: 0.30171, in 0.031s
1 tree, 81 leaves, max depth = 14, train loss: 0.29125, val loss: 0.29508, in 0.031s
1 tree, 81 leaves, max depth = 20, train loss: 0.28513, val loss: 0.28928, in 0.016s
1 tree, 81 leaves, max depth = 15, train loss: 0.27927, val loss: 0.28393, in 0.031s
1 tree, 81 leaves, max depth = 12, train loss: 0.27337, val loss: 0.27867, in 0.031s
1 tree, 81 leaves, max depth = 16, train loss: 0.26848, val loss: 0.27419, in 0.031s
1 tree, 81 leaves, max depth = 14, train loss: 0.26391, val loss: 0.27003, in 0.031s
1 tree, 81 leaves, max depth = 18, train loss: 0.26033, val loss: 0.26694, in 0.031s
1 tree, 81 leaves, max depth = 19, train loss: 0.25651, val loss: 0.26342, in 0.031s
1 tree, 81 leaves, max depth = 16, train loss: 0.25359, val loss: 0.26103, in 0.016s
1 tree, 81 leaves, max depth = 13, train loss: 0.24960, val loss: 0.25753, in 0.031s
1 tree, 81 leaves, max depth = 15, train loss: 0.24629, val loss: 0.25437, in 0.031s
1 tree, 81 leaves, max depth = 14, train loss: 0.24297, val loss: 0.25137, in 0.031s
1 tree, 81 leaves, max depth = 18, train loss: 0.23972, val loss: 0.24892, in 0.016s
1 tree, 81 leaves, max depth = 19, train loss: 0.23699, val loss: 0.24639, in 0.031s
1 tree, 81 leaves, max depth = 12, train loss: 0.23396, val loss: 0.24376, in 0.031s
1 tree, 81 leaves, max depth = 16, train loss: 0.23131, val loss: 0.24219, in 0.031s
1 tree, 81 leaves, max depth = 18, train loss: 0.22825, val loss: 0.23950, in 0.016s
1 tree, 81 leaves, max depth = 17, train loss: 0.22577, val loss: 0.23760, in 0.031s
1 tree, 81 leaves, max depth = 18, train loss: 0.22390, val loss: 0.23597, in 0.031s
1 tree, 81 leaves, max depth = 17, train loss: 0.22187, val loss: 0.23433, in 0.031s
1 tree, 81 leaves, max depth = 15, train loss: 0.22006, val loss: 0.23325, in 0.016s
1 tree, 81 leaves, max depth = 19, train loss: 0.21850, val loss: 0.23180, in 0.034s
1 tree, 81 leaves, max depth = 17, train loss: 0.21685, val loss: 0.23056, in 0.033s
1 tree, 81 leaves, max depth = 14, train loss: 0.21446, val loss: 0.22850, in 0.026s
1 tree, 81 leaves, max depth = 15, train loss: 0.21299, val loss: 0.22776, in 0.029s
1 tree, 81 leaves, max depth = 16, train loss: 0.21151, val loss: 0.22703, in 0.028s
1 tree, 81 leaves, max depth = 18, train loss: 0.21039, val loss: 0.22598, in 0.033s
1 tree, 81 leaves, max depth = 13, train loss: 0.20842, val loss: 0.22441, in 0.030s
1 tree, 81 leaves, max depth = 13, train loss: 0.20726, val loss: 0.22383, in 0.028s
1 tree, 81 leaves, max depth = 15, train loss: 0.20551, val loss: 0.22234, in 0.023s
1 tree, 81 leaves, max depth = 17, train loss: 0.20365, val loss: 0.22172, in 0.023s
1 tree, 81 leaves, max depth = 17, train loss: 0.20250, val loss: 0.22130, in 0.027s
1 tree, 81 leaves, max depth = 15, train loss: 0.20148, val loss: 0.22055, in 0.024s
1 tree, 81 leaves, max depth = 17, train loss: 0.20064, val loss: 0.21992, in 0.024s
1 tree, 81 leaves, max depth = 16, train loss: 0.19963, val loss: 0.21960, in 0.025s
1 tree, 81 leaves, max depth = 13, train loss: 0.19879, val loss: 0.21923, in 0.024s
1 tree, 81 leaves, max depth = 15, train loss: 0.19732, val loss: 0.21807, in 0.029s
1 tree, 81 leaves, max depth = 14, train loss: 0.19537, val loss: 0.21622, in 0.028s
1 tree, 56 leaves, max depth = 9, train loss: 0.19453, val loss: 0.21554, in 0.025s
1 tree, 81 leaves, max depth = 15, train loss: 0.19318, val loss: 0.21534, in 0.028s
1 tree, 81 leaves, max depth = 18, train loss: 0.19241, val loss: 0.21489, in 0.032s
1 tree, 81 leaves, max depth = 16, train loss: 0.19155, val loss: 0.21452, in 0.030s
1 tree, 81 leaves, max depth = 17, train loss: 0.19096, val loss: 0.21420, in 0.021s
1 tree, 81 leaves, max depth = 17, train loss: 0.18936, val loss: 0.21371, in 0.030s
1 tree, 81 leaves, max depth = 17, train loss: 0.18869, val loss: 0.21347, in 0.033s
Fit 64 trees in 2.289 s, (5159 total leaves)
Time spent computing histograms: 0.620s
Time spent finding best splits:  0.196s
Time spent applying splits:      0.144s
Time spent predicting:           0.004s
Trial 69, Fold 2: Log loss = 0.2075985488626582, Average precision = 0.9717443660603746, ROC-AUC = 0.9680514764907778, Elapsed Time = 2.299709499999153 seconds
Trial 69, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 69, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
Binning 0.040 GB of training data: 0.194 s
0.007 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 81 leaves, max depth = 13, train loss: 0.63987, val loss: 0.63947, in 0.028s
1 tree, 81 leaves, max depth = 12, train loss: 0.59499, val loss: 0.59463, in 0.024s
1 tree, 81 leaves, max depth = 14, train loss: 0.55746, val loss: 0.55734, in 0.038s
1 tree, 81 leaves, max depth = 13, train loss: 0.52609, val loss: 0.52627, in 0.034s
1 tree, 81 leaves, max depth = 14, train loss: 0.49853, val loss: 0.49911, in 0.033s
1 tree, 81 leaves, max depth = 12, train loss: 0.47531, val loss: 0.47643, in 0.034s
1 tree, 81 leaves, max depth = 12, train loss: 0.45509, val loss: 0.45697, in 0.029s
1 tree, 81 leaves, max depth = 13, train loss: 0.42915, val loss: 0.43298, in 0.022s
1 tree, 81 leaves, max depth = 16, train loss: 0.40738, val loss: 0.41291, in 0.028s
1 tree, 81 leaves, max depth = 13, train loss: 0.39386, val loss: 0.39996, in 0.032s
1 tree, 81 leaves, max depth = 13, train loss: 0.38112, val loss: 0.38758, in 0.033s
1 tree, 81 leaves, max depth = 14, train loss: 0.36952, val loss: 0.37628, in 0.028s
1 tree, 81 leaves, max depth = 15, train loss: 0.35433, val loss: 0.36264, in 0.025s
1 tree, 81 leaves, max depth = 15, train loss: 0.34193, val loss: 0.35138, in 0.023s
1 tree, 81 leaves, max depth = 14, train loss: 0.33192, val loss: 0.34097, in 0.026s
1 tree, 81 leaves, max depth = 16, train loss: 0.32182, val loss: 0.33202, in 0.024s
1 tree, 81 leaves, max depth = 19, train loss: 0.31197, val loss: 0.32389, in 0.024s
1 tree, 81 leaves, max depth = 15, train loss: 0.30416, val loss: 0.31588, in 0.025s
1 tree, 81 leaves, max depth = 16, train loss: 0.29680, val loss: 0.30944, in 0.029s
1 tree, 81 leaves, max depth = 17, train loss: 0.29015, val loss: 0.30255, in 0.028s
1 tree, 81 leaves, max depth = 20, train loss: 0.28313, val loss: 0.29674, in 0.030s
1 tree, 81 leaves, max depth = 15, train loss: 0.27703, val loss: 0.29162, in 0.026s
1 tree, 81 leaves, max depth = 13, train loss: 0.27141, val loss: 0.28524, in 0.030s
1 tree, 81 leaves, max depth = 16, train loss: 0.26667, val loss: 0.28114, in 0.017s
1 tree, 81 leaves, max depth = 18, train loss: 0.26201, val loss: 0.27745, in 0.039s
1 tree, 81 leaves, max depth = 16, train loss: 0.25798, val loss: 0.27417, in 0.031s
1 tree, 81 leaves, max depth = 14, train loss: 0.25373, val loss: 0.26940, in 0.030s
1 tree, 81 leaves, max depth = 16, train loss: 0.25023, val loss: 0.26653, in 0.032s
1 tree, 81 leaves, max depth = 16, train loss: 0.24674, val loss: 0.26283, in 0.020s
1 tree, 81 leaves, max depth = 15, train loss: 0.24304, val loss: 0.25892, in 0.029s
1 tree, 81 leaves, max depth = 19, train loss: 0.23963, val loss: 0.25649, in 0.040s
1 tree, 81 leaves, max depth = 16, train loss: 0.23667, val loss: 0.25422, in 0.038s
1 tree, 81 leaves, max depth = 20, train loss: 0.23400, val loss: 0.25249, in 0.027s
1 tree, 81 leaves, max depth = 18, train loss: 0.23133, val loss: 0.24988, in 0.034s
1 tree, 81 leaves, max depth = 17, train loss: 0.22905, val loss: 0.24810, in 0.028s
1 tree, 81 leaves, max depth = 18, train loss: 0.22699, val loss: 0.24627, in 0.016s
1 tree, 71 leaves, max depth = 15, train loss: 0.22465, val loss: 0.24366, in 0.028s
1 tree, 81 leaves, max depth = 21, train loss: 0.22287, val loss: 0.24230, in 0.023s
1 tree, 81 leaves, max depth = 19, train loss: 0.22131, val loss: 0.24100, in 0.031s
1 tree, 81 leaves, max depth = 13, train loss: 0.21964, val loss: 0.23973, in 0.027s
1 tree, 81 leaves, max depth = 15, train loss: 0.21708, val loss: 0.23710, in 0.027s
1 tree, 81 leaves, max depth = 18, train loss: 0.21526, val loss: 0.23609, in 0.034s
1 tree, 81 leaves, max depth = 13, train loss: 0.21387, val loss: 0.23514, in 0.036s
1 tree, 81 leaves, max depth = 15, train loss: 0.21127, val loss: 0.23212, in 0.033s
1 tree, 81 leaves, max depth = 18, train loss: 0.20922, val loss: 0.23101, in 0.024s
1 tree, 81 leaves, max depth = 18, train loss: 0.20691, val loss: 0.22871, in 0.030s
1 tree, 81 leaves, max depth = 16, train loss: 0.20583, val loss: 0.22773, in 0.031s
1 tree, 81 leaves, max depth = 13, train loss: 0.20476, val loss: 0.22706, in 0.028s
1 tree, 81 leaves, max depth = 14, train loss: 0.20317, val loss: 0.22557, in 0.033s
1 tree, 81 leaves, max depth = 22, train loss: 0.20185, val loss: 0.22498, in 0.027s
1 tree, 81 leaves, max depth = 11, train loss: 0.20030, val loss: 0.22357, in 0.034s
1 tree, 81 leaves, max depth = 17, train loss: 0.19919, val loss: 0.22303, in 0.032s
1 tree, 81 leaves, max depth = 15, train loss: 0.19814, val loss: 0.22239, in 0.027s
1 tree, 35 leaves, max depth = 8, train loss: 0.19735, val loss: 0.22186, in 0.024s
1 tree, 81 leaves, max depth = 15, train loss: 0.19656, val loss: 0.22135, in 0.028s
1 tree, 81 leaves, max depth = 12, train loss: 0.19521, val loss: 0.22019, in 0.147s
1 tree, 81 leaves, max depth = 15, train loss: 0.19336, val loss: 0.21974, in 0.063s
1 tree, 81 leaves, max depth = 14, train loss: 0.19216, val loss: 0.21892, in 0.031s
1 tree, 81 leaves, max depth = 19, train loss: 0.19054, val loss: 0.21887, in 0.047s
1 tree, 81 leaves, max depth = 23, train loss: 0.18960, val loss: 0.21862, in 0.031s
1 tree, 81 leaves, max depth = 18, train loss: 0.18879, val loss: 0.21838, in 0.031s
1 tree, 81 leaves, max depth = 14, train loss: 0.18776, val loss: 0.21756, in 0.031s
1 tree, 53 leaves, max depth = 9, train loss: 0.18709, val loss: 0.21695, in 0.047s
1 tree, 81 leaves, max depth = 12, train loss: 0.18606, val loss: 0.21668, in 0.031s
Fit 64 trees in 2.523 s, (5100 total leaves)
Time spent computing histograms: 0.699s
Time spent finding best splits:  0.223s
Time spent applying splits:      0.220s
Time spent predicting:           0.021s
Trial 69, Fold 3: Log loss = 0.20688421374573782, Average precision = 0.9722179513099881, ROC-AUC = 0.9679817744700523, Elapsed Time = 2.536510100000669 seconds
Trial 69, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 69, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
Binning 0.040 GB of training data: 0.176 s
0.004 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 81 leaves, max depth = 12, train loss: 0.64013, val loss: 0.63816, in 0.021s
1 tree, 81 leaves, max depth = 13, train loss: 0.59548, val loss: 0.59200, in 0.024s
1 tree, 81 leaves, max depth = 15, train loss: 0.55797, val loss: 0.55333, in 0.023s
1 tree, 81 leaves, max depth = 11, train loss: 0.52682, val loss: 0.52062, in 0.020s
1 tree, 81 leaves, max depth = 11, train loss: 0.49932, val loss: 0.49204, in 0.019s
1 tree, 81 leaves, max depth = 13, train loss: 0.47554, val loss: 0.46745, in 0.024s
1 tree, 81 leaves, max depth = 11, train loss: 0.45514, val loss: 0.44620, in 0.023s
1 tree, 81 leaves, max depth = 15, train loss: 0.42986, val loss: 0.42069, in 0.022s
1 tree, 81 leaves, max depth = 14, train loss: 0.40805, val loss: 0.39863, in 0.023s
1 tree, 81 leaves, max depth = 17, train loss: 0.39410, val loss: 0.38447, in 0.022s
1 tree, 81 leaves, max depth = 11, train loss: 0.38141, val loss: 0.37133, in 0.024s
1 tree, 81 leaves, max depth = 12, train loss: 0.37016, val loss: 0.35979, in 0.020s
1 tree, 81 leaves, max depth = 13, train loss: 0.35523, val loss: 0.34493, in 0.021s
1 tree, 81 leaves, max depth = 13, train loss: 0.34655, val loss: 0.33580, in 0.025s
1 tree, 81 leaves, max depth = 16, train loss: 0.33509, val loss: 0.32404, in 0.022s
1 tree, 81 leaves, max depth = 16, train loss: 0.32458, val loss: 0.31422, in 0.024s
1 tree, 81 leaves, max depth = 14, train loss: 0.31574, val loss: 0.30489, in 0.024s
1 tree, 81 leaves, max depth = 18, train loss: 0.30767, val loss: 0.29685, in 0.021s
1 tree, 81 leaves, max depth = 12, train loss: 0.30169, val loss: 0.29087, in 0.028s
1 tree, 81 leaves, max depth = 14, train loss: 0.29413, val loss: 0.28403, in 0.020s
1 tree, 81 leaves, max depth = 14, train loss: 0.28754, val loss: 0.27801, in 0.020s
1 tree, 81 leaves, max depth = 15, train loss: 0.28177, val loss: 0.27206, in 0.025s
1 tree, 81 leaves, max depth = 18, train loss: 0.27651, val loss: 0.26732, in 0.026s
1 tree, 81 leaves, max depth = 15, train loss: 0.27194, val loss: 0.26260, in 0.020s
1 tree, 81 leaves, max depth = 12, train loss: 0.26621, val loss: 0.25690, in 0.025s
1 tree, 81 leaves, max depth = 16, train loss: 0.26250, val loss: 0.25309, in 0.027s
1 tree, 81 leaves, max depth = 12, train loss: 0.25767, val loss: 0.24828, in 0.020s
1 tree, 81 leaves, max depth = 15, train loss: 0.25414, val loss: 0.24517, in 0.020s
1 tree, 81 leaves, max depth = 20, train loss: 0.25106, val loss: 0.24234, in 0.024s
1 tree, 81 leaves, max depth = 14, train loss: 0.24816, val loss: 0.23982, in 0.024s
1 tree, 81 leaves, max depth = 14, train loss: 0.24521, val loss: 0.23772, in 0.020s
1 tree, 81 leaves, max depth = 14, train loss: 0.24270, val loss: 0.23559, in 0.021s
1 tree, 81 leaves, max depth = 15, train loss: 0.23787, val loss: 0.23138, in 0.026s
1 tree, 81 leaves, max depth = 16, train loss: 0.23379, val loss: 0.22801, in 0.022s
1 tree, 81 leaves, max depth = 18, train loss: 0.23169, val loss: 0.22607, in 0.022s
1 tree, 81 leaves, max depth = 12, train loss: 0.22965, val loss: 0.22473, in 0.023s
1 tree, 81 leaves, max depth = 21, train loss: 0.22772, val loss: 0.22313, in 0.020s
1 tree, 81 leaves, max depth = 15, train loss: 0.22468, val loss: 0.22049, in 0.020s
1 tree, 81 leaves, max depth = 14, train loss: 0.22302, val loss: 0.21942, in 0.020s
1 tree, 81 leaves, max depth = 22, train loss: 0.22104, val loss: 0.21805, in 0.021s
1 tree, 81 leaves, max depth = 15, train loss: 0.21846, val loss: 0.21560, in 0.021s
1 tree, 81 leaves, max depth = 14, train loss: 0.21666, val loss: 0.21421, in 0.019s
1 tree, 81 leaves, max depth = 13, train loss: 0.21530, val loss: 0.21346, in 0.020s
1 tree, 81 leaves, max depth = 14, train loss: 0.21313, val loss: 0.21137, in 0.021s
1 tree, 81 leaves, max depth = 24, train loss: 0.21162, val loss: 0.21031, in 0.026s
1 tree, 81 leaves, max depth = 20, train loss: 0.21024, val loss: 0.20949, in 0.025s
1 tree, 81 leaves, max depth = 13, train loss: 0.20834, val loss: 0.20770, in 0.022s
1 tree, 81 leaves, max depth = 13, train loss: 0.20694, val loss: 0.20664, in 0.022s
1 tree, 81 leaves, max depth = 13, train loss: 0.20569, val loss: 0.20631, in 0.019s
1 tree, 81 leaves, max depth = 21, train loss: 0.20445, val loss: 0.20555, in 0.023s
1 tree, 81 leaves, max depth = 15, train loss: 0.20286, val loss: 0.20421, in 0.020s
1 tree, 81 leaves, max depth = 16, train loss: 0.20194, val loss: 0.20386, in 0.022s
1 tree, 81 leaves, max depth = 19, train loss: 0.20099, val loss: 0.20321, in 0.025s
1 tree, 81 leaves, max depth = 15, train loss: 0.19999, val loss: 0.20291, in 0.024s
1 tree, 81 leaves, max depth = 13, train loss: 0.19860, val loss: 0.20164, in 0.020s
1 tree, 81 leaves, max depth = 14, train loss: 0.19761, val loss: 0.20094, in 0.028s
1 tree, 53 leaves, max depth = 8, train loss: 0.19671, val loss: 0.20033, in 0.019s
1 tree, 81 leaves, max depth = 13, train loss: 0.19599, val loss: 0.20006, in 0.019s
1 tree, 81 leaves, max depth = 13, train loss: 0.19485, val loss: 0.19904, in 0.020s
1 tree, 81 leaves, max depth = 19, train loss: 0.19356, val loss: 0.19818, in 0.026s
1 tree, 81 leaves, max depth = 14, train loss: 0.19205, val loss: 0.19753, in 0.024s
1 tree, 81 leaves, max depth = 14, train loss: 0.19033, val loss: 0.19751, in 0.026s
1 tree, 81 leaves, max depth = 20, train loss: 0.18934, val loss: 0.19672, in 0.023s
1 tree, 81 leaves, max depth = 24, train loss: 0.18852, val loss: 0.19627, in 0.022s
Fit 64 trees in 1.816 s, (5156 total leaves)
Time spent computing histograms: 0.491s
Time spent finding best splits:  0.147s
Time spent applying splits:      0.110s
Time spent predicting:           0.009s
Trial 69, Fold 4: Log loss = 0.21187220792729713, Average precision = 0.9714045250282974, ROC-AUC = 0.9661044973544972, Elapsed Time = 1.8256971999999223 seconds
Trial 69, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 69, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
Binning 0.040 GB of training data: 0.160 s
0.006 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 81 leaves, max depth = 12, train loss: 0.63928, val loss: 0.63710, in 0.022s
1 tree, 81 leaves, max depth = 15, train loss: 0.59393, val loss: 0.59046, in 0.023s
1 tree, 81 leaves, max depth = 16, train loss: 0.55585, val loss: 0.55107, in 0.023s
1 tree, 81 leaves, max depth = 11, train loss: 0.52421, val loss: 0.51861, in 0.023s
1 tree, 81 leaves, max depth = 16, train loss: 0.49657, val loss: 0.49014, in 0.025s
1 tree, 81 leaves, max depth = 11, train loss: 0.47331, val loss: 0.46640, in 0.024s
1 tree, 81 leaves, max depth = 15, train loss: 0.45361, val loss: 0.44604, in 0.024s
1 tree, 81 leaves, max depth = 15, train loss: 0.43550, val loss: 0.42753, in 0.012s
1 tree, 81 leaves, max depth = 19, train loss: 0.41168, val loss: 0.40405, in 0.034s
1 tree, 81 leaves, max depth = 13, train loss: 0.39753, val loss: 0.38956, in 0.027s
1 tree, 81 leaves, max depth = 16, train loss: 0.37903, val loss: 0.37142, in 0.023s
1 tree, 81 leaves, max depth = 14, train loss: 0.36771, val loss: 0.36019, in 0.027s
1 tree, 81 leaves, max depth = 17, train loss: 0.35250, val loss: 0.34534, in 0.026s
1 tree, 81 leaves, max depth = 12, train loss: 0.34387, val loss: 0.33703, in 0.027s
1 tree, 81 leaves, max depth = 16, train loss: 0.33226, val loss: 0.32538, in 0.020s
1 tree, 81 leaves, max depth = 17, train loss: 0.32308, val loss: 0.31651, in 0.020s
1 tree, 81 leaves, max depth = 17, train loss: 0.31296, val loss: 0.30761, in 0.023s
1 tree, 81 leaves, max depth = 18, train loss: 0.30456, val loss: 0.29907, in 0.021s
1 tree, 81 leaves, max depth = 15, train loss: 0.29730, val loss: 0.29187, in 0.024s
1 tree, 81 leaves, max depth = 13, train loss: 0.28990, val loss: 0.28469, in 0.026s
1 tree, 81 leaves, max depth = 13, train loss: 0.28360, val loss: 0.27901, in 0.021s
1 tree, 81 leaves, max depth = 16, train loss: 0.27702, val loss: 0.27318, in 0.012s
1 tree, 81 leaves, max depth = 15, train loss: 0.27144, val loss: 0.26784, in 0.035s
1 tree, 81 leaves, max depth = 18, train loss: 0.26602, val loss: 0.26314, in 0.023s
1 tree, 81 leaves, max depth = 13, train loss: 0.26151, val loss: 0.25900, in 0.022s
1 tree, 81 leaves, max depth = 15, train loss: 0.25723, val loss: 0.25492, in 0.022s
1 tree, 81 leaves, max depth = 13, train loss: 0.25302, val loss: 0.25158, in 0.011s
1 tree, 81 leaves, max depth = 21, train loss: 0.24793, val loss: 0.24690, in 0.040s
1 tree, 81 leaves, max depth = 17, train loss: 0.24435, val loss: 0.24390, in 0.025s
1 tree, 81 leaves, max depth = 14, train loss: 0.24120, val loss: 0.24083, in 0.025s
1 tree, 81 leaves, max depth = 17, train loss: 0.23831, val loss: 0.23855, in 0.022s
1 tree, 81 leaves, max depth = 18, train loss: 0.23579, val loss: 0.23619, in 0.023s
1 tree, 81 leaves, max depth = 13, train loss: 0.23344, val loss: 0.23381, in 0.020s
1 tree, 81 leaves, max depth = 11, train loss: 0.23032, val loss: 0.23083, in 0.026s
1 tree, 81 leaves, max depth = 15, train loss: 0.22802, val loss: 0.22963, in 0.026s
1 tree, 81 leaves, max depth = 18, train loss: 0.22587, val loss: 0.22783, in 0.028s
1 tree, 81 leaves, max depth = 15, train loss: 0.22397, val loss: 0.22687, in 0.023s
1 tree, 81 leaves, max depth = 15, train loss: 0.22150, val loss: 0.22485, in 0.024s
1 tree, 81 leaves, max depth = 13, train loss: 0.21941, val loss: 0.22347, in 0.024s
1 tree, 81 leaves, max depth = 19, train loss: 0.21783, val loss: 0.22193, in 0.025s
1 tree, 81 leaves, max depth = 22, train loss: 0.21644, val loss: 0.22093, in 0.022s
1 tree, 81 leaves, max depth = 15, train loss: 0.21492, val loss: 0.22032, in 0.022s
1 tree, 81 leaves, max depth = 13, train loss: 0.21273, val loss: 0.21836, in 0.031s
1 tree, 81 leaves, max depth = 13, train loss: 0.21047, val loss: 0.21671, in 0.021s
1 tree, 81 leaves, max depth = 26, train loss: 0.20886, val loss: 0.21569, in 0.027s
1 tree, 81 leaves, max depth = 15, train loss: 0.20679, val loss: 0.21467, in 0.023s
1 tree, 81 leaves, max depth = 19, train loss: 0.20570, val loss: 0.21368, in 0.027s
1 tree, 81 leaves, max depth = 17, train loss: 0.20363, val loss: 0.21201, in 0.026s
1 tree, 81 leaves, max depth = 13, train loss: 0.20257, val loss: 0.21179, in 0.027s
1 tree, 81 leaves, max depth = 13, train loss: 0.20149, val loss: 0.21103, in 0.024s
1 tree, 81 leaves, max depth = 15, train loss: 0.20053, val loss: 0.21078, in 0.022s
1 tree, 81 leaves, max depth = 18, train loss: 0.19876, val loss: 0.20915, in 0.027s
1 tree, 81 leaves, max depth = 12, train loss: 0.19783, val loss: 0.20852, in 0.023s
1 tree, 81 leaves, max depth = 18, train loss: 0.19679, val loss: 0.20808, in 0.022s
1 tree, 81 leaves, max depth = 16, train loss: 0.19527, val loss: 0.20672, in 0.023s
1 tree, 81 leaves, max depth = 20, train loss: 0.19429, val loss: 0.20622, in 0.026s
1 tree, 81 leaves, max depth = 16, train loss: 0.19327, val loss: 0.20577, in 0.024s
1 tree, 81 leaves, max depth = 20, train loss: 0.19191, val loss: 0.20489, in 0.028s
1 tree, 81 leaves, max depth = 16, train loss: 0.19065, val loss: 0.20367, in 0.025s
1 tree, 81 leaves, max depth = 13, train loss: 0.18912, val loss: 0.20332, in 0.024s
1 tree, 81 leaves, max depth = 20, train loss: 0.18829, val loss: 0.20310, in 0.023s
1 tree, 81 leaves, max depth = 15, train loss: 0.18746, val loss: 0.20285, in 0.023s
1 tree, 81 leaves, max depth = 17, train loss: 0.18682, val loss: 0.20251, in 0.027s
1 tree, 81 leaves, max depth = 14, train loss: 0.18526, val loss: 0.20241, in 0.029s
Fit 64 trees in 1.926 s, (5184 total leaves)
Time spent computing histograms: 0.525s
Time spent finding best splits:  0.164s
Time spent applying splits:      0.124s
Time spent predicting:           0.023s
Trial 69, Fold 5: Log loss = 0.21520133387638787, Average precision = 0.970336942697105, ROC-AUC = 0.9663292644408524, Elapsed Time = 1.9375400000008085 seconds
Optimization Progress:  70%|#######   | 70/100 [14:04<07:36, 15.20s/it]
Trial 70, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 70, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
0.166 s 0.040 GB of training data: 
0.004 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 36 leaves, max depth = 13, train loss: 0.67807, val loss: 0.67763, in 0.010s
1 tree, 36 leaves, max depth = 13, train loss: 0.66411, val loss: 0.66325, in 0.002s
1 tree, 36 leaves, max depth = 13, train loss: 0.65101, val loss: 0.64973, in 0.017s
1 tree, 36 leaves, max depth = 13, train loss: 0.63869, val loss: 0.63702, in 0.012s
1 tree, 36 leaves, max depth = 13, train loss: 0.62711, val loss: 0.62506, in 0.004s
1 tree, 36 leaves, max depth = 13, train loss: 0.61620, val loss: 0.61378, in 0.014s
1 tree, 36 leaves, max depth = 13, train loss: 0.60593, val loss: 0.60315, in 0.001s
1 tree, 36 leaves, max depth = 13, train loss: 0.59625, val loss: 0.59312, in 0.017s
1 tree, 36 leaves, max depth = 13, train loss: 0.58711, val loss: 0.58364, in 0.000s
1 tree, 36 leaves, max depth = 13, train loss: 0.57849, val loss: 0.57469, in 0.017s
1 tree, 36 leaves, max depth = 13, train loss: 0.57035, val loss: 0.56623, in 0.014s
1 tree, 36 leaves, max depth = 13, train loss: 0.56266, val loss: 0.55823, in 0.002s
1 tree, 35 leaves, max depth = 11, train loss: 0.55554, val loss: 0.55084, in 0.016s
1 tree, 48 leaves, max depth = 10, train loss: 0.54832, val loss: 0.54407, in 0.000s
1 tree, 36 leaves, max depth = 13, train loss: 0.54161, val loss: 0.53707, in 0.018s
1 tree, 48 leaves, max depth = 11, train loss: 0.53493, val loss: 0.53081, in 0.015s
1 tree, 36 leaves, max depth = 12, train loss: 0.52874, val loss: 0.52433, in 0.002s
1 tree, 48 leaves, max depth = 11, train loss: 0.52254, val loss: 0.51855, in 0.016s
1 tree, 35 leaves, max depth = 12, train loss: 0.51691, val loss: 0.51263, in 0.011s
1 tree, 48 leaves, max depth = 12, train loss: 0.51114, val loss: 0.50726, in 0.006s
1 tree, 36 leaves, max depth = 12, train loss: 0.50583, val loss: 0.50167, in 0.015s
1 tree, 48 leaves, max depth = 12, train loss: 0.50046, val loss: 0.49669, in 0.001s
1 tree, 37 leaves, max depth = 11, train loss: 0.49553, val loss: 0.49150, in 0.006s
1 tree, 37 leaves, max depth = 11, train loss: 0.49086, val loss: 0.48657, in 0.013s
1 tree, 48 leaves, max depth = 10, train loss: 0.48592, val loss: 0.48200, in 0.004s
1 tree, 48 leaves, max depth = 10, train loss: 0.48124, val loss: 0.47768, in 0.017s
1 tree, 48 leaves, max depth = 10, train loss: 0.47680, val loss: 0.47359, in 0.013s
1 tree, 33 leaves, max depth = 10, train loss: 0.47269, val loss: 0.46924, in 0.004s
1 tree, 48 leaves, max depth = 10, train loss: 0.46854, val loss: 0.46542, in 0.017s
1 tree, 36 leaves, max depth = 9, train loss: 0.46465, val loss: 0.46128, in 0.013s
1 tree, 30 leaves, max depth = 11, train loss: 0.46098, val loss: 0.45739, in 0.004s
1 tree, 48 leaves, max depth = 10, train loss: 0.45713, val loss: 0.45387, in 0.016s
1 tree, 48 leaves, max depth = 11, train loss: 0.45348, val loss: 0.45053, in 0.000s
1 tree, 48 leaves, max depth = 11, train loss: 0.45002, val loss: 0.44738, in 0.007s
1 tree, 31 leaves, max depth = 10, train loss: 0.44673, val loss: 0.44386, in 0.014s
1 tree, 48 leaves, max depth = 10, train loss: 0.44346, val loss: 0.44090, in 0.000s
1 tree, 33 leaves, max depth = 11, train loss: 0.44039, val loss: 0.43764, in 0.018s
1 tree, 48 leaves, max depth = 9, train loss: 0.43733, val loss: 0.43489, in 0.015s
1 tree, 34 leaves, max depth = 10, train loss: 0.43435, val loss: 0.43175, in 0.000s
1 tree, 48 leaves, max depth = 10, train loss: 0.43148, val loss: 0.42917, in 0.018s
1 tree, 32 leaves, max depth = 10, train loss: 0.42877, val loss: 0.42624, in 0.012s
1 tree, 48 leaves, max depth = 10, train loss: 0.42604, val loss: 0.42378, in 0.004s
1 tree, 29 leaves, max depth = 10, train loss: 0.42348, val loss: 0.42104, in 0.014s
1 tree, 48 leaves, max depth = 10, train loss: 0.42092, val loss: 0.41876, in 0.001s
1 tree, 48 leaves, max depth = 9, train loss: 0.41848, val loss: 0.41658, in 0.018s
1 tree, 32 leaves, max depth = 10, train loss: 0.41611, val loss: 0.41401, in 0.014s
1 tree, 48 leaves, max depth = 11, train loss: 0.41381, val loss: 0.41199, in 0.003s
1 tree, 33 leaves, max depth = 10, train loss: 0.41160, val loss: 0.40962, in 0.016s
1 tree, 3 leaves, max depth = 2, train loss: 0.40945, val loss: 0.40727, in 0.011s
Fit 49 trees in 0.799 s, (1922 total leaves)
Time spent computing histograms: 0.239s
Time spent finding best splits:  0.050s
Time spent applying splits:      0.044s
Time spent predicting:           0.000s
Trial 70, Fold 1: Log loss = 0.4112736509614114, Average precision = 0.9467322043099218, ROC-AUC = 0.9422860864962266, Elapsed Time = 0.8045333999998547 seconds
Trial 70, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 70, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
Binning 0.040 GB of training data: 0.160 s
0.005 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 34 leaves, max depth = 11, train loss: 0.67829, val loss: 0.67754, in 0.001s
1 tree, 35 leaves, max depth = 13, train loss: 0.66451, val loss: 0.66308, in 0.018s
1 tree, 34 leaves, max depth = 12, train loss: 0.65148, val loss: 0.64937, in 0.012s
1 tree, 35 leaves, max depth = 13, train loss: 0.63932, val loss: 0.63659, in 0.005s
1 tree, 34 leaves, max depth = 12, train loss: 0.62780, val loss: 0.62444, in 0.013s
1 tree, 34 leaves, max depth = 12, train loss: 0.61696, val loss: 0.61298, in 0.003s
1 tree, 32 leaves, max depth = 15, train loss: 0.60683, val loss: 0.60238, in 0.016s
1 tree, 34 leaves, max depth = 12, train loss: 0.59719, val loss: 0.59217, in 0.009s
1 tree, 33 leaves, max depth = 13, train loss: 0.58819, val loss: 0.58270, in 0.008s
1 tree, 33 leaves, max depth = 12, train loss: 0.57960, val loss: 0.57358, in 0.013s
1 tree, 32 leaves, max depth = 15, train loss: 0.57157, val loss: 0.56514, in 0.004s
1 tree, 35 leaves, max depth = 12, train loss: 0.56387, val loss: 0.55697, in 0.015s
1 tree, 32 leaves, max depth = 15, train loss: 0.55670, val loss: 0.54942, in 0.011s
1 tree, 48 leaves, max depth = 12, train loss: 0.54937, val loss: 0.54231, in 0.006s
1 tree, 35 leaves, max depth = 9, train loss: 0.54266, val loss: 0.53516, in 0.015s
1 tree, 48 leaves, max depth = 13, train loss: 0.53587, val loss: 0.52858, in 0.010s
1 tree, 32 leaves, max depth = 15, train loss: 0.52977, val loss: 0.52214, in 0.007s
1 tree, 48 leaves, max depth = 13, train loss: 0.52347, val loss: 0.51604, in 0.014s
1 tree, 32 leaves, max depth = 13, train loss: 0.51783, val loss: 0.51007, in 0.000s
1 tree, 48 leaves, max depth = 11, train loss: 0.51196, val loss: 0.50441, in 0.017s
1 tree, 48 leaves, max depth = 11, train loss: 0.50641, val loss: 0.49906, in 0.014s
1 tree, 35 leaves, max depth = 14, train loss: 0.50130, val loss: 0.49363, in 0.004s
1 tree, 48 leaves, max depth = 12, train loss: 0.49612, val loss: 0.48863, in 0.015s
1 tree, 35 leaves, max depth = 12, train loss: 0.49137, val loss: 0.48358, in 0.013s
1 tree, 48 leaves, max depth = 13, train loss: 0.48653, val loss: 0.47893, in 0.004s
1 tree, 48 leaves, max depth = 13, train loss: 0.48194, val loss: 0.47452, in 0.017s
1 tree, 34 leaves, max depth = 12, train loss: 0.47761, val loss: 0.46990, in 0.013s
1 tree, 48 leaves, max depth = 11, train loss: 0.47332, val loss: 0.46579, in 0.013s
1 tree, 34 leaves, max depth = 12, train loss: 0.46929, val loss: 0.46148, in 0.008s
1 tree, 48 leaves, max depth = 12, train loss: 0.46526, val loss: 0.45764, in 0.015s
1 tree, 35 leaves, max depth = 15, train loss: 0.46150, val loss: 0.45359, in 0.010s
1 tree, 48 leaves, max depth = 13, train loss: 0.45771, val loss: 0.44999, in 0.008s
1 tree, 34 leaves, max depth = 11, train loss: 0.45420, val loss: 0.44624, in 0.014s
1 tree, 48 leaves, max depth = 13, train loss: 0.45065, val loss: 0.44287, in 0.004s
1 tree, 48 leaves, max depth = 13, train loss: 0.44727, val loss: 0.43966, in 0.007s
1 tree, 33 leaves, max depth = 11, train loss: 0.44403, val loss: 0.43620, in 0.016s
1 tree, 48 leaves, max depth = 10, train loss: 0.44085, val loss: 0.43320, in 0.011s
1 tree, 33 leaves, max depth = 11, train loss: 0.43782, val loss: 0.42994, in 0.008s
1 tree, 48 leaves, max depth = 10, train loss: 0.43482, val loss: 0.42711, in 0.013s
1 tree, 34 leaves, max depth = 11, train loss: 0.43198, val loss: 0.42407, in 0.003s
1 tree, 35 leaves, max depth = 11, train loss: 0.42923, val loss: 0.42104, in 0.015s
1 tree, 48 leaves, max depth = 12, train loss: 0.42641, val loss: 0.41838, in 0.012s
1 tree, 33 leaves, max depth = 11, train loss: 0.42389, val loss: 0.41566, in 0.008s
1 tree, 48 leaves, max depth = 11, train loss: 0.42123, val loss: 0.41314, in 0.012s
1 tree, 48 leaves, max depth = 12, train loss: 0.41870, val loss: 0.41077, in 0.002s
1 tree, 48 leaves, max depth = 11, train loss: 0.41630, val loss: 0.40851, in 0.010s
1 tree, 34 leaves, max depth = 13, train loss: 0.41399, val loss: 0.40599, in 0.010s
1 tree, 48 leaves, max depth = 11, train loss: 0.41172, val loss: 0.40387, in 0.002s
1 tree, 3 leaves, max depth = 2, train loss: 0.40962, val loss: 0.40170, in 0.015s
Fit 49 trees in 0.820 s, (1908 total leaves)
Time spent computing histograms: 0.247s
Time spent finding best splits:  0.052s
Time spent applying splits:      0.044s
Time spent predicting:           0.012s
Trial 70, Fold 2: Log loss = 0.4112498194579391, Average precision = 0.9438457631997765, ROC-AUC = 0.9436594615806474, Elapsed Time = 0.8273012000008748 seconds
Trial 70, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 70, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
Binning 0.040 GB of training data: 0.178 s
0.005 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 33 leaves, max depth = 9, train loss: 0.67832, val loss: 0.67784, in 0.011s
1 tree, 33 leaves, max depth = 13, train loss: 0.66472, val loss: 0.66374, in 0.007s
1 tree, 34 leaves, max depth = 9, train loss: 0.65189, val loss: 0.65043, in 0.011s
1 tree, 36 leaves, max depth = 12, train loss: 0.63984, val loss: 0.63795, in 0.008s
1 tree, 32 leaves, max depth = 9, train loss: 0.62838, val loss: 0.62609, in 0.010s
1 tree, 33 leaves, max depth = 13, train loss: 0.61774, val loss: 0.61502, in 0.007s
1 tree, 34 leaves, max depth = 9, train loss: 0.60766, val loss: 0.60453, in 0.012s
1 tree, 33 leaves, max depth = 13, train loss: 0.59822, val loss: 0.59468, in 0.011s
1 tree, 34 leaves, max depth = 9, train loss: 0.58910, val loss: 0.58524, in 0.010s
1 tree, 32 leaves, max depth = 9, train loss: 0.58050, val loss: 0.57632, in 0.009s
1 tree, 33 leaves, max depth = 9, train loss: 0.57252, val loss: 0.56797, in 0.008s
1 tree, 34 leaves, max depth = 11, train loss: 0.56498, val loss: 0.56012, in 0.010s
1 tree, 35 leaves, max depth = 9, train loss: 0.55785, val loss: 0.55266, in 0.008s
1 tree, 48 leaves, max depth = 12, train loss: 0.55044, val loss: 0.54574, in 0.013s
1 tree, 32 leaves, max depth = 12, train loss: 0.54391, val loss: 0.53887, in 0.010s
1 tree, 48 leaves, max depth = 12, train loss: 0.53704, val loss: 0.53248, in 0.007s
1 tree, 33 leaves, max depth = 9, train loss: 0.53085, val loss: 0.52602, in 0.013s
1 tree, 48 leaves, max depth = 11, train loss: 0.52448, val loss: 0.52010, in 0.012s
1 tree, 35 leaves, max depth = 12, train loss: 0.51887, val loss: 0.51423, in 0.007s
1 tree, 48 leaves, max depth = 11, train loss: 0.51294, val loss: 0.50873, in 0.013s
1 tree, 48 leaves, max depth = 11, train loss: 0.50733, val loss: 0.50354, in 0.012s
1 tree, 35 leaves, max depth = 11, train loss: 0.50224, val loss: 0.49820, in 0.007s
1 tree, 48 leaves, max depth = 11, train loss: 0.49701, val loss: 0.49337, in 0.014s
1 tree, 35 leaves, max depth = 11, train loss: 0.49229, val loss: 0.48840, in 0.003s
1 tree, 48 leaves, max depth = 12, train loss: 0.48739, val loss: 0.48389, in 0.017s
1 tree, 48 leaves, max depth = 10, train loss: 0.48275, val loss: 0.47963, in 0.011s
1 tree, 35 leaves, max depth = 11, train loss: 0.47846, val loss: 0.47508, in 0.005s
1 tree, 48 leaves, max depth = 10, train loss: 0.47411, val loss: 0.47110, in 0.015s
1 tree, 35 leaves, max depth = 11, train loss: 0.47011, val loss: 0.46688, in 0.010s
1 tree, 48 leaves, max depth = 12, train loss: 0.46603, val loss: 0.46314, in 0.010s
1 tree, 35 leaves, max depth = 13, train loss: 0.46232, val loss: 0.45918, in 0.009s
1 tree, 48 leaves, max depth = 12, train loss: 0.45849, val loss: 0.45568, in 0.005s
1 tree, 48 leaves, max depth = 12, train loss: 0.45486, val loss: 0.45237, in 0.019s
1 tree, 35 leaves, max depth = 11, train loss: 0.45143, val loss: 0.44873, in 0.012s
1 tree, 48 leaves, max depth = 12, train loss: 0.44801, val loss: 0.44561, in 0.012s
1 tree, 35 leaves, max depth = 11, train loss: 0.44481, val loss: 0.44220, in 0.008s
1 tree, 48 leaves, max depth = 11, train loss: 0.44158, val loss: 0.43927, in 0.017s
[38/49] 1 tree, 35 leaves, max depth = 11, train loss: 0.43859, val loss: 0.43608, in 0.012s
1 tree, 48 leaves, max depth = 10, train loss: 0.43554, val loss: 0.43332, in 0.014s
1 tree, 35 leaves, max depth = 11, train loss: 0.43274, val loss: 0.43032, in 0.009s
1 tree, 35 leaves, max depth = 11, train loss: 0.43009, val loss: 0.42748, in 0.012s
1 tree, 48 leaves, max depth = 12, train loss: 0.42723, val loss: 0.42488, in 0.014s
1 tree, 35 leaves, max depth = 10, train loss: 0.42473, val loss: 0.42215, in 0.007s
1 tree, 48 leaves, max depth = 11, train loss: 0.42204, val loss: 0.41971, in 0.014s
1 tree, 48 leaves, max depth = 11, train loss: 0.41947, val loss: 0.41740, in 0.013s
1 tree, 48 leaves, max depth = 12, train loss: 0.41704, val loss: 0.41523, in 0.007s
1 tree, 34 leaves, max depth = 12, train loss: 0.41477, val loss: 0.41275, in 0.016s
1 tree, 48 leaves, max depth = 11, train loss: 0.41247, val loss: 0.41068, in 0.014s
1 tree, 3 leaves, max depth = 2, train loss: 0.41030, val loss: 0.40867, in 0.002s
Fit 49 trees in 0.842 s, (1918 total leaves)
Time spent computing histograms: 0.252s
Time spent finding best splits:  0.054s
Time spent applying splits:      0.047s
Time spent predicting:           0.005s
Trial 70, Fold 3: Log loss = 0.4076260721292932, Average precision = 0.9485255964042099, ROC-AUC = 0.9468514340611185, Elapsed Time = 0.8560482000011689 seconds
Trial 70, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 70, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
Binning 0.040 GB of training data: 0.179 s
0.006 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 34 leaves, max depth = 11, train loss: 0.67831, val loss: 0.67743, in 0.011s
1 tree, 34 leaves, max depth = 11, train loss: 0.66449, val loss: 0.66276, in 0.012s
1 tree, 34 leaves, max depth = 11, train loss: 0.65152, val loss: 0.64897, in 0.006s
1 tree, 34 leaves, max depth = 11, train loss: 0.63933, val loss: 0.63598, in 0.013s
1 tree, 34 leaves, max depth = 11, train loss: 0.62787, val loss: 0.62375, in 0.010s
1 tree, 34 leaves, max depth = 11, train loss: 0.61708, val loss: 0.61222, in 0.010s
1 tree, 34 leaves, max depth = 11, train loss: 0.60692, val loss: 0.60133, in 0.009s
1 tree, 34 leaves, max depth = 11, train loss: 0.59734, val loss: 0.59104, in 0.006s
1 tree, 34 leaves, max depth = 11, train loss: 0.58831, val loss: 0.58132, in 0.016s
1 tree, 34 leaves, max depth = 11, train loss: 0.57978, val loss: 0.57213, in 0.012s
1 tree, 34 leaves, max depth = 11, train loss: 0.57174, val loss: 0.56343, in 0.012s
1 tree, 33 leaves, max depth = 10, train loss: 0.56414, val loss: 0.55520, in 0.010s
1 tree, 35 leaves, max depth = 12, train loss: 0.55698, val loss: 0.54751, in 0.012s
1 tree, 48 leaves, max depth = 10, train loss: 0.54957, val loss: 0.54023, in 0.013s
1 tree, 33 leaves, max depth = 11, train loss: 0.54295, val loss: 0.53302, in 0.009s
1 tree, 48 leaves, max depth = 10, train loss: 0.53609, val loss: 0.52630, in 0.014s
1 tree, 34 leaves, max depth = 11, train loss: 0.53002, val loss: 0.51973, in 0.011s
1 tree, 48 leaves, max depth = 10, train loss: 0.52365, val loss: 0.51350, in 0.014s
1 tree, 34 leaves, max depth = 15, train loss: 0.51805, val loss: 0.50737, in 0.010s
1 tree, 48 leaves, max depth = 10, train loss: 0.51212, val loss: 0.50156, in 0.014s
1 tree, 48 leaves, max depth = 10, train loss: 0.50651, val loss: 0.49607, in 0.013s
1 tree, 34 leaves, max depth = 11, train loss: 0.50144, val loss: 0.49057, in 0.011s
1 tree, 48 leaves, max depth = 11, train loss: 0.49621, val loss: 0.48545, in 0.015s
1 tree, 34 leaves, max depth = 13, train loss: 0.49147, val loss: 0.48021, in 0.000s
1 tree, 48 leaves, max depth = 11, train loss: 0.48658, val loss: 0.47544, in 0.018s
1 tree, 48 leaves, max depth = 11, train loss: 0.48194, val loss: 0.47091, in 0.016s
1 tree, 34 leaves, max depth = 11, train loss: 0.47766, val loss: 0.46621, in 0.016s
1 tree, 48 leaves, max depth = 11, train loss: 0.47332, val loss: 0.46198, in 0.000s
1 tree, 35 leaves, max depth = 11, train loss: 0.46934, val loss: 0.45760, in 0.020s
1 tree, 48 leaves, max depth = 11, train loss: 0.46528, val loss: 0.45364, in 0.017s
1 tree, 32 leaves, max depth = 14, train loss: 0.46154, val loss: 0.44948, in 0.015s
1 tree, 48 leaves, max depth = 11, train loss: 0.45773, val loss: 0.44577, in 0.013s
1 tree, 48 leaves, max depth = 11, train loss: 0.45411, val loss: 0.44225, in 0.006s
1 tree, 34 leaves, max depth = 14, train loss: 0.45068, val loss: 0.43842, in 0.016s
1 tree, 48 leaves, max depth = 10, train loss: 0.44727, val loss: 0.43511, in 0.019s
1 tree, 34 leaves, max depth = 14, train loss: 0.44406, val loss: 0.43152, in 0.014s
1 tree, 48 leaves, max depth = 10, train loss: 0.44085, val loss: 0.42841, in 0.013s
1 tree, 32 leaves, max depth = 13, train loss: 0.43786, val loss: 0.42504, in 0.008s
1 tree, 48 leaves, max depth = 9, train loss: 0.43483, val loss: 0.42210, in 0.019s
1 tree, 35 leaves, max depth = 14, train loss: 0.43202, val loss: 0.41892, in 0.010s
1 tree, 33 leaves, max depth = 12, train loss: 0.42933, val loss: 0.41587, in 0.013s
1 tree, 48 leaves, max depth = 11, train loss: 0.42649, val loss: 0.41313, in 0.014s
1 tree, 32 leaves, max depth = 13, train loss: 0.42400, val loss: 0.41030, in 0.000s
1 tree, 48 leaves, max depth = 10, train loss: 0.42132, val loss: 0.40771, in 0.020s
1 tree, 48 leaves, max depth = 11, train loss: 0.41877, val loss: 0.40522, in 0.021s
1 tree, 48 leaves, max depth = 10, train loss: 0.41633, val loss: 0.40287, in 0.018s
1 tree, 31 leaves, max depth = 10, train loss: 0.41407, val loss: 0.40030, in 0.015s
1 tree, 48 leaves, max depth = 9, train loss: 0.41177, val loss: 0.39807, in 0.009s
1 tree, 3 leaves, max depth = 2, train loss: 0.40968, val loss: 0.39584, in 0.016s
Fit 49 trees in 0.948 s, (1906 total leaves)
Time spent computing histograms: 0.290s
Time spent finding best splits:  0.062s
Time spent applying splits:      0.055s
Time spent predicting:           0.003s
Trial 70, Fold 4: Log loss = 0.4089359936187368, Average precision = 0.9485610339411467, ROC-AUC = 0.9454596069062046, Elapsed Time = 0.9537225000003673 seconds
Trial 70, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 70, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
0.200 s 0.040 GB of training data: 
0.019 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 34 leaves, max depth = 10, train loss: 0.67829, val loss: 0.67728, in 0.010s
1 tree, 35 leaves, max depth = 12, train loss: 0.66450, val loss: 0.66250, in 0.012s
1 tree, 33 leaves, max depth = 11, train loss: 0.65137, val loss: 0.64845, in 0.007s
1 tree, 33 leaves, max depth = 11, train loss: 0.63903, val loss: 0.63522, in 0.022s
1 tree, 33 leaves, max depth = 11, train loss: 0.62742, val loss: 0.62276, in 0.013s
1 tree, 36 leaves, max depth = 12, train loss: 0.61664, val loss: 0.61110, in 0.011s
1 tree, 33 leaves, max depth = 11, train loss: 0.60633, val loss: 0.59999, in 0.012s
1 tree, 33 leaves, max depth = 11, train loss: 0.59662, val loss: 0.58949, in 0.010s
1 tree, 35 leaves, max depth = 12, train loss: 0.58758, val loss: 0.57966, in 0.014s
1 tree, 33 leaves, max depth = 11, train loss: 0.57892, val loss: 0.57027, in 0.005s
1 tree, 33 leaves, max depth = 11, train loss: 0.57075, val loss: 0.56137, in 0.019s
1 tree, 33 leaves, max depth = 11, train loss: 0.56303, val loss: 0.55295, in 0.000s
1 tree, 35 leaves, max depth = 12, train loss: 0.55582, val loss: 0.54504, in 0.023s
1 tree, 48 leaves, max depth = 12, train loss: 0.54855, val loss: 0.53805, in 0.014s
1 tree, 33 leaves, max depth = 11, train loss: 0.54181, val loss: 0.53067, in 0.013s
1 tree, 48 leaves, max depth = 12, train loss: 0.53507, val loss: 0.52421, in 0.008s
1 tree, 34 leaves, max depth = 10, train loss: 0.52893, val loss: 0.51749, in 0.019s
1 tree, 48 leaves, max depth = 12, train loss: 0.52266, val loss: 0.51151, in 0.015s
1 tree, 34 leaves, max depth = 10, train loss: 0.51698, val loss: 0.50528, in 0.013s
1 tree, 48 leaves, max depth = 12, train loss: 0.51115, val loss: 0.49973, in 0.015s
1 tree, 34 leaves, max depth = 12, train loss: 0.50590, val loss: 0.49394, in 0.010s
1 tree, 48 leaves, max depth = 12, train loss: 0.50045, val loss: 0.48877, in 0.008s
1 tree, 48 leaves, max depth = 12, train loss: 0.49529, val loss: 0.48389, in 0.019s
1 tree, 35 leaves, max depth = 10, train loss: 0.49051, val loss: 0.47859, in 0.017s
1 tree, 48 leaves, max depth = 12, train loss: 0.48569, val loss: 0.47403, in 0.014s
1 tree, 35 leaves, max depth = 10, train loss: 0.48124, val loss: 0.46910, in 0.014s
1 tree, 48 leaves, max depth = 13, train loss: 0.47673, val loss: 0.46486, in 0.014s
1 tree, 36 leaves, max depth = 10, train loss: 0.47260, val loss: 0.46021, in 0.004s
1 tree, 48 leaves, max depth = 12, train loss: 0.46836, val loss: 0.45625, in 0.021s
1 tree, 36 leaves, max depth = 10, train loss: 0.46451, val loss: 0.45193, in 0.013s
1 tree, 33 leaves, max depth = 10, train loss: 0.46079, val loss: 0.44774, in 0.013s
1 tree, 48 leaves, max depth = 12, train loss: 0.45686, val loss: 0.44408, in 0.013s
1 tree, 48 leaves, max depth = 12, train loss: 0.45312, val loss: 0.44062, in 0.003s
1 tree, 48 leaves, max depth = 12, train loss: 0.44957, val loss: 0.43733, in 0.016s
1 tree, 34 leaves, max depth = 9, train loss: 0.44627, val loss: 0.43360, in 0.011s
1 tree, 48 leaves, max depth = 12, train loss: 0.44292, val loss: 0.43050, in 0.015s
1 tree, 34 leaves, max depth = 12, train loss: 0.43985, val loss: 0.42702, in 0.012s
1 tree, 48 leaves, max depth = 13, train loss: 0.43670, val loss: 0.42411, in 0.012s
1 tree, 33 leaves, max depth = 9, train loss: 0.43381, val loss: 0.42083, in 0.013s
1 tree, 48 leaves, max depth = 12, train loss: 0.43083, val loss: 0.41810, in 0.016s
1 tree, 34 leaves, max depth = 9, train loss: 0.42813, val loss: 0.41500, in 0.014s
1 tree, 48 leaves, max depth = 12, train loss: 0.42531, val loss: 0.41244, in 0.007s
1 tree, 33 leaves, max depth = 9, train loss: 0.42277, val loss: 0.40953, in 0.023s
1 tree, 48 leaves, max depth = 13, train loss: 0.42011, val loss: 0.40711, in 0.015s
1 tree, 48 leaves, max depth = 12, train loss: 0.41758, val loss: 0.40481, in 0.015s
1 tree, 33 leaves, max depth = 9, train loss: 0.41522, val loss: 0.40210, in 0.012s
1 tree, 48 leaves, max depth = 13, train loss: 0.41282, val loss: 0.39994, in 0.016s
1 tree, 48 leaves, max depth = 12, train loss: 0.41054, val loss: 0.39788, in 0.001s
1 tree, 3 leaves, max depth = 2, train loss: 0.40843, val loss: 0.39584, in 0.016s
Fit 49 trees in 1.039 s, (1913 total leaves)
Time spent computing histograms: 0.307s
Time spent finding best splits:  0.070s
Time spent applying splits:      0.058s
Time spent predicting:           0.004s
Trial 70, Fold 5: Log loss = 0.41464192296993685, Average precision = 0.9462843445442741, ROC-AUC = 0.94220047191721, Elapsed Time = 1.0569494000010309 seconds
Optimization Progress:  71%|#######1  | 71/100 [14:16<06:52, 14.21s/it]
Trial 71, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 71, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
0.186 s 0.040 GB of training data: 
0.006 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 41 leaves, max depth = 14, train loss: 0.68366, val loss: 0.68339, in 0.011s
1 tree, 41 leaves, max depth = 14, train loss: 0.67456, val loss: 0.67401, in 0.012s
1 tree, 41 leaves, max depth = 14, train loss: 0.66581, val loss: 0.66499, in 0.012s
1 tree, 41 leaves, max depth = 14, train loss: 0.65740, val loss: 0.65632, in 0.011s
1 tree, 41 leaves, max depth = 14, train loss: 0.64931, val loss: 0.64798, in 0.011s
1 tree, 41 leaves, max depth = 14, train loss: 0.64153, val loss: 0.63996, in 0.011s
1 tree, 41 leaves, max depth = 17, train loss: 0.63405, val loss: 0.63223, in 0.005s
1 tree, 41 leaves, max depth = 14, train loss: 0.62685, val loss: 0.62479, in 0.015s
1 tree, 41 leaves, max depth = 13, train loss: 0.61991, val loss: 0.61762, in 0.011s
1 tree, 41 leaves, max depth = 13, train loss: 0.61324, val loss: 0.61071, in 0.011s
1 tree, 41 leaves, max depth = 13, train loss: 0.60680, val loss: 0.60405, in 0.010s
1 tree, 41 leaves, max depth = 13, train loss: 0.60060, val loss: 0.59763, in 0.010s
1 tree, 41 leaves, max depth = 17, train loss: 0.59463, val loss: 0.59144, in 0.010s
1 tree, 41 leaves, max depth = 17, train loss: 0.58887, val loss: 0.58546, in 0.012s
1 tree, 41 leaves, max depth = 17, train loss: 0.58332, val loss: 0.57970, in 0.002s
1 tree, 41 leaves, max depth = 17, train loss: 0.57796, val loss: 0.57414, in 0.020s
1 tree, 41 leaves, max depth = 16, train loss: 0.57279, val loss: 0.56876, in 0.012s
1 tree, 41 leaves, max depth = 13, train loss: 0.56781, val loss: 0.56358, in 0.011s
1 tree, 41 leaves, max depth = 17, train loss: 0.56300, val loss: 0.55858, in 0.015s
1 tree, 41 leaves, max depth = 17, train loss: 0.55836, val loss: 0.55374, in 0.010s
1 tree, 41 leaves, max depth = 8, train loss: 0.55361, val loss: 0.54929, in 0.016s
1 tree, 41 leaves, max depth = 11, train loss: 0.54920, val loss: 0.54469, in 0.015s
1 tree, 41 leaves, max depth = 8, train loss: 0.54468, val loss: 0.54046, in 0.013s
1 tree, 41 leaves, max depth = 18, train loss: 0.54050, val loss: 0.53609, in 0.011s
1 tree, 41 leaves, max depth = 8, train loss: 0.53619, val loss: 0.53206, in 0.009s
1 tree, 41 leaves, max depth = 18, train loss: 0.53221, val loss: 0.52790, in 0.010s
1 tree, 41 leaves, max depth = 10, train loss: 0.52811, val loss: 0.52406, in 0.010s
1 tree, 41 leaves, max depth = 13, train loss: 0.52432, val loss: 0.52010, in 0.012s
1 tree, 41 leaves, max depth = 10, train loss: 0.52040, val loss: 0.51643, in 0.012s
1 tree, 41 leaves, max depth = 9, train loss: 0.51661, val loss: 0.51290, in 0.009s
1 tree, 41 leaves, max depth = 15, train loss: 0.51306, val loss: 0.50917, in 0.011s
1 tree, 41 leaves, max depth = 9, train loss: 0.50944, val loss: 0.50581, in 0.011s
1 tree, 41 leaves, max depth = 15, train loss: 0.50605, val loss: 0.50225, in 0.009s
1 tree, 41 leaves, max depth = 9, train loss: 0.50260, val loss: 0.49903, in 0.012s
1 tree, 41 leaves, max depth = 15, train loss: 0.49937, val loss: 0.49563, in 0.010s
1 tree, 41 leaves, max depth = 9, train loss: 0.49606, val loss: 0.49256, in 0.025s
1 tree, 41 leaves, max depth = 15, train loss: 0.49298, val loss: 0.48931, in 0.042s
1 tree, 41 leaves, max depth = 9, train loss: 0.48981, val loss: 0.48639, in 0.016s
1 tree, 41 leaves, max depth = 9, train loss: 0.48675, val loss: 0.48354, in 0.016s
1 tree, 41 leaves, max depth = 13, train loss: 0.48387, val loss: 0.48049, in 0.016s
1 tree, 41 leaves, max depth = 9, train loss: 0.48093, val loss: 0.47778, in 0.016s
1 tree, 41 leaves, max depth = 13, train loss: 0.47818, val loss: 0.47487, in 0.016s
1 tree, 41 leaves, max depth = 9, train loss: 0.47536, val loss: 0.47226, in 0.016s
1 tree, 41 leaves, max depth = 13, train loss: 0.47274, val loss: 0.46948, in 0.016s
1 tree, 41 leaves, max depth = 9, train loss: 0.47003, val loss: 0.46699, in 0.000s
1 tree, 41 leaves, max depth = 13, train loss: 0.46752, val loss: 0.46432, in 0.016s
1 tree, 41 leaves, max depth = 9, train loss: 0.46492, val loss: 0.46194, in 0.016s
1 tree, 41 leaves, max depth = 9, train loss: 0.46241, val loss: 0.45963, in 0.016s
1 tree, 41 leaves, max depth = 13, train loss: 0.46003, val loss: 0.45710, in 0.031s
1 tree, 41 leaves, max depth = 10, train loss: 0.45761, val loss: 0.45490, in 0.016s
1 tree, 41 leaves, max depth = 13, train loss: 0.45534, val loss: 0.45247, in 0.016s
1 tree, 41 leaves, max depth = 9, train loss: 0.45302, val loss: 0.45035, in 0.016s
1 tree, 41 leaves, max depth = 13, train loss: 0.45085, val loss: 0.44806, in 0.020s
1 tree, 41 leaves, max depth = 10, train loss: 0.44862, val loss: 0.44605, in 0.013s
1 tree, 41 leaves, max depth = 14, train loss: 0.44652, val loss: 0.44383, in 0.013s
1 tree, 41 leaves, max depth = 10, train loss: 0.44437, val loss: 0.44189, in 0.003s
1 tree, 41 leaves, max depth = 10, train loss: 0.44229, val loss: 0.44001, in 0.016s
1 tree, 41 leaves, max depth = 12, train loss: 0.44031, val loss: 0.43788, in 0.016s
1 tree, 41 leaves, max depth = 10, train loss: 0.43831, val loss: 0.43608, in 0.016s
1 tree, 41 leaves, max depth = 15, train loss: 0.43639, val loss: 0.43406, in 0.016s
1 tree, 41 leaves, max depth = 10, train loss: 0.43446, val loss: 0.43233, in 0.016s
1 tree, 41 leaves, max depth = 11, train loss: 0.43263, val loss: 0.43036, in 0.000s
1 tree, 41 leaves, max depth = 10, train loss: 0.43078, val loss: 0.42869, in 0.000s
1 tree, 41 leaves, max depth = 11, train loss: 0.42902, val loss: 0.42680, in 0.016s
1 tree, 41 leaves, max depth = 10, train loss: 0.42724, val loss: 0.42518, in 0.016s
1 tree, 41 leaves, max depth = 10, train loss: 0.42551, val loss: 0.42364, in 0.016s
1 tree, 41 leaves, max depth = 15, train loss: 0.42382, val loss: 0.42185, in 0.016s
1 tree, 41 leaves, max depth = 10, train loss: 0.42216, val loss: 0.42035, in 0.043s
1 tree, 41 leaves, max depth = 11, train loss: 0.42054, val loss: 0.41861, in 0.016s
1 tree, 41 leaves, max depth = 10, train loss: 0.41893, val loss: 0.41718, in 0.031s
1 tree, 41 leaves, max depth = 10, train loss: 0.41739, val loss: 0.41551, in 0.000s
1 tree, 41 leaves, max depth = 12, train loss: 0.41584, val loss: 0.41411, in 0.016s
1 tree, 41 leaves, max depth = 15, train loss: 0.41434, val loss: 0.41253, in 0.016s
1 tree, 41 leaves, max depth = 12, train loss: 0.41284, val loss: 0.41120, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.41145, val loss: 0.40966, in 0.000s
1 tree, 41 leaves, max depth = 12, train loss: 0.41001, val loss: 0.40838, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.40867, val loss: 0.40689, in 0.016s
1 tree, 41 leaves, max depth = 16, train loss: 0.40726, val loss: 0.40540, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.40597, val loss: 0.40396, in 0.000s
1 tree, 41 leaves, max depth = 12, train loss: 0.40458, val loss: 0.40275, in 0.016s
Fit 80 trees in 1.451 s, (3172 total leaves)
Time spent computing histograms: 0.504s
Time spent finding best splits:  0.151s
Time spent applying splits:      0.113s
Time spent predicting:           0.002s
Trial 71, Fold 1: Log loss = 0.4071347963646409, Average precision = 0.9452412166113221, ROC-AUC = 0.9425571788235519, Elapsed Time = 1.457269799999267 seconds
Trial 71, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 71, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
Binning 0.040 GB of training data: 0.204 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 41 leaves, max depth = 9, train loss: 0.68378, val loss: 0.68335, in 0.016s
1 tree, 41 leaves, max depth = 9, train loss: 0.67479, val loss: 0.67395, in 0.016s
1 tree, 41 leaves, max depth = 9, train loss: 0.66615, val loss: 0.66490, in 0.000s
1 tree, 41 leaves, max depth = 10, train loss: 0.65784, val loss: 0.65620, in 0.016s
1 tree, 41 leaves, max depth = 9, train loss: 0.64986, val loss: 0.64783, in 0.016s
1 tree, 41 leaves, max depth = 10, train loss: 0.64218, val loss: 0.63978, in 0.016s
1 tree, 41 leaves, max depth = 9, train loss: 0.63479, val loss: 0.63202, in 0.000s
1 tree, 41 leaves, max depth = 9, train loss: 0.62768, val loss: 0.62455, in 0.016s
1 tree, 41 leaves, max depth = 9, train loss: 0.62083, val loss: 0.61736, in 0.016s
1 tree, 41 leaves, max depth = 9, train loss: 0.61424, val loss: 0.61042, in 0.016s
1 tree, 41 leaves, max depth = 9, train loss: 0.60789, val loss: 0.60373, in 0.000s
1 tree, 41 leaves, max depth = 9, train loss: 0.60178, val loss: 0.59729, in 0.016s
1 tree, 41 leaves, max depth = 9, train loss: 0.59588, val loss: 0.59107, in 0.016s
1 tree, 41 leaves, max depth = 9, train loss: 0.59020, val loss: 0.58507, in 0.016s
1 tree, 41 leaves, max depth = 9, train loss: 0.58472, val loss: 0.57928, in 0.000s
1 tree, 41 leaves, max depth = 10, train loss: 0.57944, val loss: 0.57369, in 0.016s
1 tree, 41 leaves, max depth = 9, train loss: 0.57435, val loss: 0.56830, in 0.016s
1 tree, 41 leaves, max depth = 9, train loss: 0.56943, val loss: 0.56309, in 0.016s
1 tree, 41 leaves, max depth = 9, train loss: 0.56469, val loss: 0.55807, in 0.000s
1 tree, 41 leaves, max depth = 9, train loss: 0.56012, val loss: 0.55321, in 0.016s
1 tree, 41 leaves, max depth = 11, train loss: 0.55543, val loss: 0.54868, in 0.016s
1 tree, 41 leaves, max depth = 10, train loss: 0.55109, val loss: 0.54406, in 0.016s
1 tree, 41 leaves, max depth = 11, train loss: 0.54663, val loss: 0.53973, in 0.000s
1 tree, 41 leaves, max depth = 13, train loss: 0.54248, val loss: 0.53535, in 0.016s
1 tree, 41 leaves, max depth = 11, train loss: 0.53822, val loss: 0.53124, in 0.016s
1 tree, 41 leaves, max depth = 13, train loss: 0.53428, val loss: 0.52706, in 0.016s
1 tree, 41 leaves, max depth = 11, train loss: 0.53022, val loss: 0.52315, in 0.016s
1 tree, 41 leaves, max depth = 12, train loss: 0.52646, val loss: 0.51917, in 0.000s
1 tree, 41 leaves, max depth = 11, train loss: 0.52259, val loss: 0.51544, in 0.016s
1 tree, 41 leaves, max depth = 12, train loss: 0.51901, val loss: 0.51165, in 0.016s
1 tree, 41 leaves, max depth = 10, train loss: 0.51531, val loss: 0.50808, in 0.016s
1 tree, 41 leaves, max depth = 10, train loss: 0.51174, val loss: 0.50465, in 0.000s
1 tree, 41 leaves, max depth = 12, train loss: 0.50838, val loss: 0.50108, in 0.016s
1 tree, 41 leaves, max depth = 11, train loss: 0.50496, val loss: 0.49780, in 0.016s
1 tree, 41 leaves, max depth = 12, train loss: 0.50175, val loss: 0.49439, in 0.016s
1 tree, 41 leaves, max depth = 11, train loss: 0.49848, val loss: 0.49126, in 0.000s
1 tree, 41 leaves, max depth = 12, train loss: 0.49542, val loss: 0.48800, in 0.016s
1 tree, 41 leaves, max depth = 11, train loss: 0.49229, val loss: 0.48500, in 0.016s
1 tree, 41 leaves, max depth = 12, train loss: 0.48937, val loss: 0.48189, in 0.016s
1 tree, 41 leaves, max depth = 11, train loss: 0.48636, val loss: 0.47902, in 0.016s
1 tree, 41 leaves, max depth = 11, train loss: 0.48346, val loss: 0.47624, in 0.000s
1 tree, 41 leaves, max depth = 12, train loss: 0.48070, val loss: 0.47330, in 0.016s
1 tree, 41 leaves, max depth = 11, train loss: 0.47792, val loss: 0.47064, in 0.016s
1 tree, 41 leaves, max depth = 12, train loss: 0.47528, val loss: 0.46782, in 0.000s
1 tree, 41 leaves, max depth = 11, train loss: 0.47261, val loss: 0.46527, in 0.016s
1 tree, 41 leaves, max depth = 12, train loss: 0.47009, val loss: 0.46258, in 0.016s
1 tree, 41 leaves, max depth = 13, train loss: 0.46752, val loss: 0.46014, in 0.016s
1 tree, 41 leaves, max depth = 12, train loss: 0.46511, val loss: 0.45756, in 0.016s
1 tree, 41 leaves, max depth = 12, train loss: 0.46265, val loss: 0.45521, in 0.000s
1 tree, 41 leaves, max depth = 12, train loss: 0.46027, val loss: 0.45294, in 0.016s
1 tree, 41 leaves, max depth = 12, train loss: 0.45798, val loss: 0.45050, in 0.016s
1 tree, 41 leaves, max depth = 11, train loss: 0.45569, val loss: 0.44831, in 0.016s
1 tree, 41 leaves, max depth = 15, train loss: 0.45351, val loss: 0.44596, in 0.000s
1 tree, 41 leaves, max depth = 11, train loss: 0.45130, val loss: 0.44387, in 0.016s
1 tree, 41 leaves, max depth = 12, train loss: 0.44920, val loss: 0.44162, in 0.016s
1 tree, 41 leaves, max depth = 10, train loss: 0.44708, val loss: 0.43961, in 0.016s
1 tree, 41 leaves, max depth = 12, train loss: 0.44507, val loss: 0.43745, in 0.000s
1 tree, 41 leaves, max depth = 10, train loss: 0.44303, val loss: 0.43551, in 0.000s
1 tree, 41 leaves, max depth = 12, train loss: 0.44110, val loss: 0.43344, in 0.016s
1 tree, 41 leaves, max depth = 10, train loss: 0.43914, val loss: 0.43158, in 0.016s
1 tree, 41 leaves, max depth = 10, train loss: 0.43724, val loss: 0.42978, in 0.016s
1 tree, 41 leaves, max depth = 12, train loss: 0.43540, val loss: 0.42781, in 0.016s
1 tree, 41 leaves, max depth = 10, train loss: 0.43357, val loss: 0.42609, in 0.016s
1 tree, 41 leaves, max depth = 12, train loss: 0.43180, val loss: 0.42419, in 0.000s
1 tree, 41 leaves, max depth = 12, train loss: 0.43003, val loss: 0.42252, in 0.016s
1 tree, 41 leaves, max depth = 12, train loss: 0.42834, val loss: 0.42070, in 0.016s
1 tree, 41 leaves, max depth = 12, train loss: 0.42664, val loss: 0.41910, in 0.016s
1 tree, 41 leaves, max depth = 12, train loss: 0.42501, val loss: 0.41735, in 0.000s
1 tree, 41 leaves, max depth = 12, train loss: 0.42337, val loss: 0.41580, in 0.016s
1 tree, 41 leaves, max depth = 12, train loss: 0.42177, val loss: 0.41431, in 0.016s
1 tree, 41 leaves, max depth = 11, train loss: 0.42022, val loss: 0.41263, in 0.016s
1 tree, 41 leaves, max depth = 12, train loss: 0.41868, val loss: 0.41120, in 0.016s
1 tree, 41 leaves, max depth = 11, train loss: 0.41719, val loss: 0.40959, in 0.016s
1 tree, 41 leaves, max depth = 12, train loss: 0.41571, val loss: 0.40820, in 0.000s
1 tree, 41 leaves, max depth = 11, train loss: 0.41427, val loss: 0.40665, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.41292, val loss: 0.40525, in 0.016s
1 tree, 41 leaves, max depth = 11, train loss: 0.41149, val loss: 0.40393, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.41019, val loss: 0.40258, in 0.000s
1 tree, 41 leaves, max depth = 10, train loss: 0.40881, val loss: 0.40129, in 0.016s
1 tree, 41 leaves, max depth = 12, train loss: 0.40745, val loss: 0.39983, in 0.016s
Fit 80 trees in 1.360 s, (3208 total leaves)
Time spent computing histograms: 0.462s
Time spent finding best splits:  0.115s
Time spent applying splits:      0.082s
Time spent predicting:           0.016s
Trial 71, Fold 2: Log loss = 0.4097584221749652, Average precision = 0.9431351239372479, ROC-AUC = 0.9423233982034884, Elapsed Time = 1.3551755999997113 seconds
Trial 71, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 71, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
Binning 0.040 GB of training data: 0.173 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 41 leaves, max depth = 13, train loss: 0.68386, val loss: 0.68359, in 0.016s
1 tree, 41 leaves, max depth = 12, train loss: 0.67498, val loss: 0.67438, in 0.016s
1 tree, 41 leaves, max depth = 10, train loss: 0.66631, val loss: 0.66543, in 0.016s
1 tree, 41 leaves, max depth = 10, train loss: 0.65797, val loss: 0.65681, in 0.000s
1 tree, 41 leaves, max depth = 10, train loss: 0.64995, val loss: 0.64852, in 0.016s
1 tree, 41 leaves, max depth = 12, train loss: 0.64236, val loss: 0.64064, in 0.016s
1 tree, 41 leaves, max depth = 10, train loss: 0.63494, val loss: 0.63296, in 0.000s
1 tree, 41 leaves, max depth = 10, train loss: 0.62779, val loss: 0.62556, in 0.016s
1 tree, 41 leaves, max depth = 10, train loss: 0.62091, val loss: 0.61844, in 0.016s
1 tree, 41 leaves, max depth = 10, train loss: 0.61428, val loss: 0.61157, in 0.000s
1 tree, 41 leaves, max depth = 13, train loss: 0.60800, val loss: 0.60502, in 0.016s
1 tree, 41 leaves, max depth = 10, train loss: 0.60184, val loss: 0.59864, in 0.016s
1 tree, 41 leaves, max depth = 10, train loss: 0.59591, val loss: 0.59248, in 0.016s
1 tree, 41 leaves, max depth = 10, train loss: 0.59019, val loss: 0.58655, in 0.000s
1 tree, 41 leaves, max depth = 10, train loss: 0.58468, val loss: 0.58082, in 0.016s
1 tree, 41 leaves, max depth = 13, train loss: 0.57945, val loss: 0.57535, in 0.016s
1 tree, 41 leaves, max depth = 10, train loss: 0.57431, val loss: 0.57001, in 0.000s
1 tree, 41 leaves, max depth = 10, train loss: 0.56936, val loss: 0.56486, in 0.016s
1 tree, 41 leaves, max depth = 10, train loss: 0.56458, val loss: 0.55988, in 0.016s
1 tree, 41 leaves, max depth = 13, train loss: 0.56004, val loss: 0.55512, in 0.016s
1 tree, 41 leaves, max depth = 12, train loss: 0.55532, val loss: 0.55073, in 0.016s
1 tree, 41 leaves, max depth = 10, train loss: 0.55094, val loss: 0.54616, in 0.016s
1 tree, 41 leaves, max depth = 12, train loss: 0.54644, val loss: 0.54198, in 0.016s
1 tree, 41 leaves, max depth = 13, train loss: 0.54234, val loss: 0.53767, in 0.016s
1 tree, 41 leaves, max depth = 12, train loss: 0.53806, val loss: 0.53368, in 0.016s
1 tree, 41 leaves, max depth = 13, train loss: 0.53417, val loss: 0.52958, in 0.000s
1 tree, 41 leaves, max depth = 12, train loss: 0.53008, val loss: 0.52579, in 0.016s
1 tree, 41 leaves, max depth = 12, train loss: 0.52614, val loss: 0.52212, in 0.016s
1 tree, 41 leaves, max depth = 13, train loss: 0.52249, val loss: 0.51827, in 0.016s
1 tree, 41 leaves, max depth = 12, train loss: 0.51873, val loss: 0.51478, in 0.016s
1 tree, 41 leaves, max depth = 13, train loss: 0.51523, val loss: 0.51113, in 0.000s
1 tree, 41 leaves, max depth = 12, train loss: 0.51163, val loss: 0.50780, in 0.016s
1 tree, 41 leaves, max depth = 11, train loss: 0.50829, val loss: 0.50432, in 0.016s
1 tree, 41 leaves, max depth = 12, train loss: 0.50485, val loss: 0.50114, in 0.016s
1 tree, 41 leaves, max depth = 11, train loss: 0.50163, val loss: 0.49774, in 0.000s
1 tree, 41 leaves, max depth = 12, train loss: 0.49833, val loss: 0.49470, in 0.016s
1 tree, 41 leaves, max depth = 11, train loss: 0.49533, val loss: 0.49151, in 0.016s
1 tree, 41 leaves, max depth = 12, train loss: 0.49217, val loss: 0.48860, in 0.016s
1 tree, 41 leaves, max depth = 12, train loss: 0.48912, val loss: 0.48580, in 0.016s
1 tree, 41 leaves, max depth = 11, train loss: 0.48629, val loss: 0.48278, in 0.000s
1 tree, 41 leaves, max depth = 13, train loss: 0.48337, val loss: 0.48010, in 0.016s
1 tree, 41 leaves, max depth = 11, train loss: 0.48063, val loss: 0.47723, in 0.016s
1 tree, 41 leaves, max depth = 13, train loss: 0.47783, val loss: 0.47467, in 0.016s
1 tree, 41 leaves, max depth = 11, train loss: 0.47522, val loss: 0.47193, in 0.031s
1 tree, 41 leaves, max depth = 13, train loss: 0.47253, val loss: 0.46948, in 0.016s
1 tree, 41 leaves, max depth = 11, train loss: 0.47006, val loss: 0.46683, in 0.000s
1 tree, 41 leaves, max depth = 12, train loss: 0.46747, val loss: 0.46449, in 0.016s
1 tree, 41 leaves, max depth = 12, train loss: 0.46497, val loss: 0.46222, in 0.016s
1 tree, 41 leaves, max depth = 11, train loss: 0.46261, val loss: 0.45972, in 0.016s
1 tree, 41 leaves, max depth = 12, train loss: 0.46021, val loss: 0.45754, in 0.016s
1 tree, 41 leaves, max depth = 11, train loss: 0.45797, val loss: 0.45514, in 0.016s
1 tree, 41 leaves, max depth = 12, train loss: 0.45566, val loss: 0.45304, in 0.000s
1 tree, 41 leaves, max depth = 13, train loss: 0.45352, val loss: 0.45073, in 0.016s
1 tree, 41 leaves, max depth = 12, train loss: 0.45130, val loss: 0.44873, in 0.016s
1 tree, 41 leaves, max depth = 13, train loss: 0.44921, val loss: 0.44653, in 0.016s
1 tree, 41 leaves, max depth = 13, train loss: 0.44708, val loss: 0.44462, in 0.000s
1 tree, 41 leaves, max depth = 13, train loss: 0.44500, val loss: 0.44275, in 0.016s
1 tree, 41 leaves, max depth = 13, train loss: 0.44303, val loss: 0.44065, in 0.016s
1 tree, 41 leaves, max depth = 12, train loss: 0.44103, val loss: 0.43886, in 0.000s
1 tree, 41 leaves, max depth = 13, train loss: 0.43913, val loss: 0.43684, in 0.016s
1 tree, 41 leaves, max depth = 14, train loss: 0.43722, val loss: 0.43513, in 0.016s
1 tree, 41 leaves, max depth = 13, train loss: 0.43543, val loss: 0.43318, in 0.000s
1 tree, 41 leaves, max depth = 14, train loss: 0.43358, val loss: 0.43154, in 0.016s
1 tree, 41 leaves, max depth = 13, train loss: 0.43183, val loss: 0.42968, in 0.016s
1 tree, 41 leaves, max depth = 14, train loss: 0.43006, val loss: 0.42810, in 0.016s
1 tree, 41 leaves, max depth = 14, train loss: 0.42833, val loss: 0.42658, in 0.000s
1 tree, 41 leaves, max depth = 14, train loss: 0.42670, val loss: 0.42479, in 0.016s
1 tree, 41 leaves, max depth = 12, train loss: 0.42503, val loss: 0.42330, in 0.016s
1 tree, 41 leaves, max depth = 10, train loss: 0.42343, val loss: 0.42156, in 0.000s
1 tree, 41 leaves, max depth = 12, train loss: 0.42183, val loss: 0.42013, in 0.016s
1 tree, 41 leaves, max depth = 13, train loss: 0.42028, val loss: 0.41848, in 0.016s
1 tree, 41 leaves, max depth = 12, train loss: 0.41873, val loss: 0.41710, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.41732, val loss: 0.41579, in 0.000s
1 tree, 41 leaves, max depth = 13, train loss: 0.41582, val loss: 0.41446, in 0.016s
1 tree, 41 leaves, max depth = 10, train loss: 0.41436, val loss: 0.41287, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.41301, val loss: 0.41161, in 0.016s
1 tree, 41 leaves, max depth = 12, train loss: 0.41157, val loss: 0.41034, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.41026, val loss: 0.40912, in 0.000s
1 tree, 41 leaves, max depth = 10, train loss: 0.40888, val loss: 0.40761, in 0.016s
[80/80] 1 tree, 41 leaves, max depth = 12, train loss: 0.40750, val loss: 0.40640, in 0.000s
Fit 80 trees in 1.283 s, (3172 total leaves)
Time spent computing histograms: 0.451s
Time spent finding best splits:  0.112s
Time spent applying splits:      0.083s
Time spent predicting:           0.047s
Trial 71, Fold 3: Log loss = 0.4058957659864714, Average precision = 0.9473637724315495, ROC-AUC = 0.9462533317361866, Elapsed Time = 1.2974756000003254 seconds
Trial 71, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 71, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
Binning 0.040 GB of training data: 0.174 s
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 41 leaves, max depth = 13, train loss: 0.68375, val loss: 0.68320, in 0.016s
1 tree, 41 leaves, max depth = 13, train loss: 0.67473, val loss: 0.67364, in 0.000s
1 tree, 41 leaves, max depth = 13, train loss: 0.66606, val loss: 0.66444, in 0.016s
1 tree, 41 leaves, max depth = 13, train loss: 0.65774, val loss: 0.65560, in 0.016s
1 tree, 41 leaves, max depth = 12, train loss: 0.64973, val loss: 0.64708, in 0.000s
1 tree, 41 leaves, max depth = 13, train loss: 0.64202, val loss: 0.63888, in 0.062s
1 tree, 41 leaves, max depth = 13, train loss: 0.63461, val loss: 0.63097, in 0.016s
1 tree, 41 leaves, max depth = 12, train loss: 0.62748, val loss: 0.62336, in 0.016s
1 tree, 41 leaves, max depth = 14, train loss: 0.62062, val loss: 0.61602, in 0.016s
1 tree, 41 leaves, max depth = 13, train loss: 0.61400, val loss: 0.60894, in 0.016s
1 tree, 41 leaves, max depth = 14, train loss: 0.60763, val loss: 0.60212, in 0.016s
1 tree, 41 leaves, max depth = 14, train loss: 0.60150, val loss: 0.59553, in 0.031s
1 tree, 41 leaves, max depth = 14, train loss: 0.59558, val loss: 0.58918, in 0.016s
1 tree, 41 leaves, max depth = 12, train loss: 0.58988, val loss: 0.58304, in 0.000s
1 tree, 41 leaves, max depth = 14, train loss: 0.58439, val loss: 0.57712, in 0.031s
1 tree, 41 leaves, max depth = 14, train loss: 0.57909, val loss: 0.57140, in 0.016s
1 tree, 41 leaves, max depth = 14, train loss: 0.57397, val loss: 0.56588, in 0.016s
1 tree, 41 leaves, max depth = 12, train loss: 0.56904, val loss: 0.56054, in 0.016s
1 tree, 41 leaves, max depth = 14, train loss: 0.56428, val loss: 0.55538, in 0.016s
1 tree, 41 leaves, max depth = 14, train loss: 0.55969, val loss: 0.55040, in 0.000s
1 tree, 41 leaves, max depth = 9, train loss: 0.55499, val loss: 0.54579, in 0.016s
1 tree, 41 leaves, max depth = 11, train loss: 0.55063, val loss: 0.54104, in 0.016s
1 tree, 41 leaves, max depth = 10, train loss: 0.54615, val loss: 0.53666, in 0.016s
1 tree, 41 leaves, max depth = 12, train loss: 0.54201, val loss: 0.53214, in 0.016s
1 tree, 41 leaves, max depth = 10, train loss: 0.53775, val loss: 0.52796, in 0.016s
1 tree, 41 leaves, max depth = 13, train loss: 0.53381, val loss: 0.52366, in 0.016s
1 tree, 41 leaves, max depth = 12, train loss: 0.52974, val loss: 0.51968, in 0.016s
1 tree, 41 leaves, max depth = 16, train loss: 0.52601, val loss: 0.51560, in 0.016s
1 tree, 41 leaves, max depth = 12, train loss: 0.52212, val loss: 0.51180, in 0.016s
1 tree, 41 leaves, max depth = 12, train loss: 0.51837, val loss: 0.50814, in 0.016s
1 tree, 41 leaves, max depth = 17, train loss: 0.51487, val loss: 0.50429, in 0.016s
1 tree, 41 leaves, max depth = 11, train loss: 0.51129, val loss: 0.50080, in 0.000s
1 tree, 41 leaves, max depth = 17, train loss: 0.50796, val loss: 0.49713, in 0.000s
1 tree, 41 leaves, max depth = 9, train loss: 0.50453, val loss: 0.49379, in 0.016s
1 tree, 41 leaves, max depth = 17, train loss: 0.50135, val loss: 0.49029, in 0.016s
1 tree, 41 leaves, max depth = 10, train loss: 0.49807, val loss: 0.48709, in 0.016s
1 tree, 41 leaves, max depth = 17, train loss: 0.49504, val loss: 0.48374, in 0.016s
1 tree, 41 leaves, max depth = 10, train loss: 0.49189, val loss: 0.48069, in 0.000s
1 tree, 41 leaves, max depth = 10, train loss: 0.48886, val loss: 0.47773, in 0.016s
1 tree, 41 leaves, max depth = 17, train loss: 0.48600, val loss: 0.47456, in 0.016s
1 tree, 41 leaves, max depth = 10, train loss: 0.48309, val loss: 0.47173, in 0.016s
1 tree, 41 leaves, max depth = 17, train loss: 0.48036, val loss: 0.46870, in 0.016s
1 tree, 41 leaves, max depth = 10, train loss: 0.47757, val loss: 0.46599, in 0.016s
1 tree, 41 leaves, max depth = 17, train loss: 0.47496, val loss: 0.46308, in 0.016s
1 tree, 41 leaves, max depth = 10, train loss: 0.47228, val loss: 0.46048, in 0.000s
1 tree, 41 leaves, max depth = 10, train loss: 0.46977, val loss: 0.45768, in 0.000s
1 tree, 41 leaves, max depth = 10, train loss: 0.46720, val loss: 0.45519, in 0.016s
1 tree, 41 leaves, max depth = 10, train loss: 0.46471, val loss: 0.45277, in 0.016s
1 tree, 41 leaves, max depth = 14, train loss: 0.46235, val loss: 0.45013, in 0.016s
1 tree, 41 leaves, max depth = 10, train loss: 0.45996, val loss: 0.44781, in 0.016s
1 tree, 41 leaves, max depth = 14, train loss: 0.45770, val loss: 0.44528, in 0.016s
1 tree, 41 leaves, max depth = 12, train loss: 0.45540, val loss: 0.44305, in 0.016s
1 tree, 41 leaves, max depth = 12, train loss: 0.45322, val loss: 0.44064, in 0.000s
1 tree, 41 leaves, max depth = 12, train loss: 0.45101, val loss: 0.43850, in 0.016s
1 tree, 41 leaves, max depth = 14, train loss: 0.44894, val loss: 0.43616, in 0.016s
1 tree, 41 leaves, max depth = 8, train loss: 0.44682, val loss: 0.43410, in 0.016s
1 tree, 41 leaves, max depth = 8, train loss: 0.44476, val loss: 0.43211, in 0.016s
1 tree, 41 leaves, max depth = 12, train loss: 0.44278, val loss: 0.42991, in 0.016s
1 tree, 41 leaves, max depth = 9, train loss: 0.44080, val loss: 0.42799, in 0.016s
1 tree, 41 leaves, max depth = 14, train loss: 0.43891, val loss: 0.42585, in 0.000s
1 tree, 41 leaves, max depth = 9, train loss: 0.43700, val loss: 0.42401, in 0.016s
1 tree, 41 leaves, max depth = 13, train loss: 0.43519, val loss: 0.42195, in 0.016s
1 tree, 41 leaves, max depth = 9, train loss: 0.43335, val loss: 0.42018, in 0.016s
1 tree, 41 leaves, max depth = 12, train loss: 0.43160, val loss: 0.41822, in 0.000s
1 tree, 41 leaves, max depth = 10, train loss: 0.42983, val loss: 0.41652, in 0.016s
1 tree, 41 leaves, max depth = 10, train loss: 0.42812, val loss: 0.41486, in 0.016s
1 tree, 41 leaves, max depth = 15, train loss: 0.42646, val loss: 0.41297, in 0.016s
1 tree, 41 leaves, max depth = 10, train loss: 0.42481, val loss: 0.41137, in 0.000s
1 tree, 41 leaves, max depth = 11, train loss: 0.42321, val loss: 0.40957, in 0.016s
1 tree, 41 leaves, max depth = 10, train loss: 0.42162, val loss: 0.40804, in 0.016s
1 tree, 41 leaves, max depth = 14, train loss: 0.42008, val loss: 0.40628, in 0.000s
1 tree, 41 leaves, max depth = 10, train loss: 0.41855, val loss: 0.40481, in 0.016s
1 tree, 41 leaves, max depth = 14, train loss: 0.41707, val loss: 0.40311, in 0.016s
1 tree, 41 leaves, max depth = 10, train loss: 0.41559, val loss: 0.40169, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.41424, val loss: 0.40024, in 0.000s
1 tree, 41 leaves, max depth = 9, train loss: 0.41280, val loss: 0.39887, in 0.016s
1 tree, 41 leaves, max depth = 11, train loss: 0.41140, val loss: 0.39728, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.41010, val loss: 0.39588, in 0.000s
1 tree, 41 leaves, max depth = 9, train loss: 0.40872, val loss: 0.39457, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.40747, val loss: 0.39322, in 0.016s
Fit 80 trees in 1.456 s, (3172 total leaves)
Time spent computing histograms: 0.502s
Time spent finding best splits:  0.142s
Time spent applying splits:      0.116s
Time spent predicting:           0.016s
Trial 71, Fold 4: Log loss = 0.40726675348936964, Average precision = 0.9476603377576511, ROC-AUC = 0.9445157531992818, Elapsed Time = 1.45156870000028 seconds
Trial 71, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 71, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
Binning 0.040 GB of training data: 0.173 s
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 41 leaves, max depth = 12, train loss: 0.68366, val loss: 0.68305, in 0.016s
1 tree, 41 leaves, max depth = 12, train loss: 0.67454, val loss: 0.67334, in 0.000s
1 tree, 41 leaves, max depth = 12, train loss: 0.66579, val loss: 0.66400, in 0.016s
1 tree, 41 leaves, max depth = 12, train loss: 0.65737, val loss: 0.65502, in 0.016s
1 tree, 41 leaves, max depth = 12, train loss: 0.64928, val loss: 0.64637, in 0.016s
1 tree, 41 leaves, max depth = 12, train loss: 0.64150, val loss: 0.63804, in 0.000s
1 tree, 41 leaves, max depth = 14, train loss: 0.63401, val loss: 0.63002, in 0.016s
1 tree, 41 leaves, max depth = 14, train loss: 0.62681, val loss: 0.62229, in 0.016s
1 tree, 41 leaves, max depth = 11, train loss: 0.61987, val loss: 0.61484, in 0.000s
1 tree, 41 leaves, max depth = 12, train loss: 0.61319, val loss: 0.60765, in 0.016s
1 tree, 41 leaves, max depth = 14, train loss: 0.60675, val loss: 0.60072, in 0.000s
1 tree, 41 leaves, max depth = 11, train loss: 0.60059, val loss: 0.59405, in 0.016s
1 tree, 41 leaves, max depth = 13, train loss: 0.59461, val loss: 0.58760, in 0.016s
1 tree, 41 leaves, max depth = 12, train loss: 0.58884, val loss: 0.58137, in 0.016s
1 tree, 41 leaves, max depth = 14, train loss: 0.58328, val loss: 0.57535, in 0.000s
1 tree, 41 leaves, max depth = 11, train loss: 0.57795, val loss: 0.56955, in 0.016s
1 tree, 41 leaves, max depth = 12, train loss: 0.57278, val loss: 0.56394, in 0.016s
1 tree, 41 leaves, max depth = 12, train loss: 0.56778, val loss: 0.55851, in 0.000s
1 tree, 41 leaves, max depth = 11, train loss: 0.56297, val loss: 0.55327, in 0.016s
1 tree, 41 leaves, max depth = 12, train loss: 0.55834, val loss: 0.54821, in 0.016s
1 tree, 41 leaves, max depth = 11, train loss: 0.55385, val loss: 0.54331, in 0.000s
1 tree, 41 leaves, max depth = 9, train loss: 0.54930, val loss: 0.53893, in 0.016s
1 tree, 41 leaves, max depth = 12, train loss: 0.54506, val loss: 0.53427, in 0.016s
1 tree, 41 leaves, max depth = 9, train loss: 0.54072, val loss: 0.53011, in 0.016s
1 tree, 41 leaves, max depth = 12, train loss: 0.53668, val loss: 0.52567, in 0.000s
1 tree, 41 leaves, max depth = 10, train loss: 0.53254, val loss: 0.52171, in 0.016s
1 tree, 41 leaves, max depth = 12, train loss: 0.52871, val loss: 0.51748, in 0.016s
1 tree, 41 leaves, max depth = 11, train loss: 0.52476, val loss: 0.51370, in 0.016s
1 tree, 41 leaves, max depth = 10, train loss: 0.52094, val loss: 0.51005, in 0.000s
1 tree, 41 leaves, max depth = 11, train loss: 0.51734, val loss: 0.50608, in 0.016s
1 tree, 41 leaves, max depth = 10, train loss: 0.51370, val loss: 0.50260, in 0.016s
1 tree, 41 leaves, max depth = 11, train loss: 0.51026, val loss: 0.49881, in 0.016s
1 tree, 41 leaves, max depth = 10, train loss: 0.50678, val loss: 0.49549, in 0.016s
1 tree, 41 leaves, max depth = 11, train loss: 0.50351, val loss: 0.49187, in 0.000s
1 tree, 41 leaves, max depth = 10, train loss: 0.50018, val loss: 0.48870, in 0.016s
1 tree, 41 leaves, max depth = 11, train loss: 0.49705, val loss: 0.48524, in 0.016s
1 tree, 41 leaves, max depth = 9, train loss: 0.49387, val loss: 0.48221, in 0.016s
1 tree, 41 leaves, max depth = 12, train loss: 0.49089, val loss: 0.47890, in 0.000s
1 tree, 41 leaves, max depth = 10, train loss: 0.48783, val loss: 0.47602, in 0.016s
1 tree, 41 leaves, max depth = 9, train loss: 0.48488, val loss: 0.47323, in 0.016s
1 tree, 41 leaves, max depth = 12, train loss: 0.48207, val loss: 0.47010, in 0.016s
1 tree, 41 leaves, max depth = 9, train loss: 0.47924, val loss: 0.46743, in 0.000s
1 tree, 41 leaves, max depth = 12, train loss: 0.47655, val loss: 0.46443, in 0.016s
1 tree, 41 leaves, max depth = 10, train loss: 0.47383, val loss: 0.46188, in 0.016s
1 tree, 41 leaves, max depth = 12, train loss: 0.47126, val loss: 0.45900, in 0.016s
1 tree, 41 leaves, max depth = 10, train loss: 0.46865, val loss: 0.45656, in 0.000s
1 tree, 41 leaves, max depth = 12, train loss: 0.46619, val loss: 0.45380, in 0.016s
1 tree, 41 leaves, max depth = 10, train loss: 0.46369, val loss: 0.45146, in 0.016s
1 tree, 41 leaves, max depth = 10, train loss: 0.46127, val loss: 0.44920, in 0.016s
1 tree, 41 leaves, max depth = 12, train loss: 0.45894, val loss: 0.44661, in 0.000s
1 tree, 41 leaves, max depth = 12, train loss: 0.45662, val loss: 0.44443, in 0.016s
1 tree, 41 leaves, max depth = 11, train loss: 0.45438, val loss: 0.44192, in 0.016s
1 tree, 41 leaves, max depth = 10, train loss: 0.45215, val loss: 0.43983, in 0.016s
1 tree, 41 leaves, max depth = 11, train loss: 0.45001, val loss: 0.43741, in 0.016s
1 tree, 41 leaves, max depth = 8, train loss: 0.44786, val loss: 0.43541, in 0.006s
1 tree, 41 leaves, max depth = 12, train loss: 0.44581, val loss: 0.43312, in 0.011s
1 tree, 41 leaves, max depth = 10, train loss: 0.44375, val loss: 0.43120, in 0.016s
1 tree, 41 leaves, max depth = 11, train loss: 0.44178, val loss: 0.42898, in 0.016s
1 tree, 41 leaves, max depth = 10, train loss: 0.43979, val loss: 0.42713, in 0.016s
1 tree, 41 leaves, max depth = 10, train loss: 0.43786, val loss: 0.42535, in 0.000s
1 tree, 41 leaves, max depth = 11, train loss: 0.43599, val loss: 0.42322, in 0.016s
1 tree, 41 leaves, max depth = 10, train loss: 0.43413, val loss: 0.42152, in 0.016s
1 tree, 41 leaves, max depth = 11, train loss: 0.43233, val loss: 0.41948, in 0.016s
1 tree, 41 leaves, max depth = 10, train loss: 0.43055, val loss: 0.41784, in 0.016s
1 tree, 41 leaves, max depth = 11, train loss: 0.42882, val loss: 0.41588, in 0.000s
1 tree, 41 leaves, max depth = 10, train loss: 0.42710, val loss: 0.41430, in 0.016s
1 tree, 41 leaves, max depth = 11, train loss: 0.42544, val loss: 0.41241, in 0.016s
1 tree, 41 leaves, max depth = 8, train loss: 0.42378, val loss: 0.41089, in 0.016s
1 tree, 41 leaves, max depth = 11, train loss: 0.42219, val loss: 0.40907, in 0.016s
1 tree, 41 leaves, max depth = 10, train loss: 0.42058, val loss: 0.40761, in 0.000s
1 tree, 41 leaves, max depth = 10, train loss: 0.41903, val loss: 0.40621, in 0.016s
1 tree, 41 leaves, max depth = 10, train loss: 0.41751, val loss: 0.40446, in 0.016s
1 tree, 41 leaves, max depth = 10, train loss: 0.41601, val loss: 0.40311, in 0.000s
1 tree, 41 leaves, max depth = 10, train loss: 0.41455, val loss: 0.40143, in 0.016s
1 tree, 41 leaves, max depth = 10, train loss: 0.41310, val loss: 0.40012, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.41175, val loss: 0.39882, in 0.000s
1 tree, 41 leaves, max depth = 10, train loss: 0.41035, val loss: 0.39721, in 0.000s
1 tree, 41 leaves, max depth = 10, train loss: 0.40896, val loss: 0.39596, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.40766, val loss: 0.39472, in 0.016s
1 tree, 41 leaves, max depth = 12, train loss: 0.40631, val loss: 0.39350, in 0.000s
Fit 80 trees in 1.252 s, (3208 total leaves)
Time spent computing histograms: 0.449s
Time spent finding best splits:  0.105s
Time spent applying splits:      0.077s
Time spent predicting:           0.016s
Trial 71, Fold 5: Log loss = 0.41181279095167456, Average precision = 0.9466656914645775, ROC-AUC = 0.9421432473106294, Elapsed Time = 1.260043800000858 seconds
Optimization Progress:  72%|#######2  | 72/100 [14:31<06:44, 14.46s/it]
Trial 72, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 72, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
0.174 s 0.040 GB of training data: 
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 31 leaves, max depth = 8, train loss: 0.67697, val loss: 0.67687, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.65917, val loss: 0.65903, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.64329, val loss: 0.64282, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.62763, val loss: 0.62691, in 0.016s
1 tree, 31 leaves, max depth = 8, train loss: 0.61362, val loss: 0.61266, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.60063, val loss: 0.59951, in 0.031s
1 tree, 31 leaves, max depth = 11, train loss: 0.58836, val loss: 0.58702, in 0.016s
1 tree, 31 leaves, max depth = 8, train loss: 0.57685, val loss: 0.57542, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.56646, val loss: 0.56492, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.55606, val loss: 0.55439, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.54542, val loss: 0.54376, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.53592, val loss: 0.53421, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.52668, val loss: 0.52488, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.51739, val loss: 0.51547, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.50938, val loss: 0.50740, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.50126, val loss: 0.49913, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.49289, val loss: 0.49065, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.48516, val loss: 0.48289, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.47817, val loss: 0.47575, in 0.016s
1 tree, 31 leaves, max depth = 8, train loss: 0.47160, val loss: 0.46908, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.46499, val loss: 0.46230, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.45913, val loss: 0.45651, in 0.016s
1 tree, 31 leaves, max depth = 8, train loss: 0.45384, val loss: 0.45113, in 0.016s
1 tree, 31 leaves, max depth = 12, train loss: 0.44845, val loss: 0.44584, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.44262, val loss: 0.44005, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.43719, val loss: 0.43446, in 0.016s
1 tree, 31 leaves, max depth = 13, train loss: 0.43284, val loss: 0.43001, in 0.016s
1 tree, 31 leaves, max depth = 8, train loss: 0.42792, val loss: 0.42508, in 0.031s
1 tree, 31 leaves, max depth = 12, train loss: 0.42329, val loss: 0.42046, in 0.031s
1 tree, 31 leaves, max depth = 9, train loss: 0.41898, val loss: 0.41610, in 0.016s
1 tree, 12 leaves, max depth = 6, train loss: 0.41219, val loss: 0.40948, in 0.016s
1 tree, 19 leaves, max depth = 8, train loss: 0.40554, val loss: 0.40305, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.40193, val loss: 0.39945, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.39788, val loss: 0.39530, in 0.016s
1 tree, 29 leaves, max depth = 9, train loss: 0.39181, val loss: 0.38928, in 0.016s
1 tree, 12 leaves, max depth = 5, train loss: 0.38608, val loss: 0.38364, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.38279, val loss: 0.38022, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.38020, val loss: 0.37756, in 0.016s
1 tree, 31 leaves, max depth = 8, train loss: 0.37624, val loss: 0.37367, in 0.016s
1 tree, 9 leaves, max depth = 4, train loss: 0.37110, val loss: 0.36872, in 0.000s
1 tree, 13 leaves, max depth = 5, train loss: 0.36653, val loss: 0.36431, in 0.000s
1 tree, 31 leaves, max depth = 8, train loss: 0.36350, val loss: 0.36140, in 0.016s
1 tree, 31 leaves, max depth = 8, train loss: 0.35978, val loss: 0.35774, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.35539, val loss: 0.35340, in 0.016s
1 tree, 31 leaves, max depth = 7, train loss: 0.35191, val loss: 0.35002, in 0.016s
1 tree, 28 leaves, max depth = 9, train loss: 0.34756, val loss: 0.34595, in 0.016s
1 tree, 12 leaves, max depth = 5, train loss: 0.34337, val loss: 0.34205, in 0.016s
1 tree, 13 leaves, max depth = 6, train loss: 0.33970, val loss: 0.33845, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.33604, val loss: 0.33497, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.33410, val loss: 0.33311, in 0.000s
1 tree, 31 leaves, max depth = 9, train loss: 0.33240, val loss: 0.33134, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.33050, val loss: 0.32953, in 0.016s
1 tree, 31 leaves, max depth = 6, train loss: 0.32764, val loss: 0.32686, in 0.000s
1 tree, 31 leaves, max depth = 11, train loss: 0.32615, val loss: 0.32539, in 0.016s
1 tree, 12 leaves, max depth = 5, train loss: 0.32309, val loss: 0.32252, in 0.016s
1 tree, 31 leaves, max depth = 7, train loss: 0.32040, val loss: 0.31991, in 0.016s
1 tree, 24 leaves, max depth = 7, train loss: 0.31752, val loss: 0.31711, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.31494, val loss: 0.31461, in 0.016s
1 tree, 14 leaves, max depth = 4, train loss: 0.31222, val loss: 0.31195, in 0.000s
1 tree, 31 leaves, max depth = 7, train loss: 0.30977, val loss: 0.30961, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.30706, val loss: 0.30715, in 0.016s
1 tree, 31 leaves, max depth = 7, train loss: 0.30468, val loss: 0.30474, in 0.016s
1 tree, 12 leaves, max depth = 5, train loss: 0.30229, val loss: 0.30265, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.30077, val loss: 0.30114, in 0.031s
1 tree, 31 leaves, max depth = 8, train loss: 0.29940, val loss: 0.29982, in 0.016s
1 tree, 31 leaves, max depth = 8, train loss: 0.29807, val loss: 0.29855, in 0.016s
1 tree, 31 leaves, max depth = 8, train loss: 0.29615, val loss: 0.29673, in 0.016s
1 tree, 25 leaves, max depth = 7, train loss: 0.29389, val loss: 0.29459, in 0.016s
1 tree, 31 leaves, max depth = 8, train loss: 0.29276, val loss: 0.29356, in 0.016s
1 tree, 31 leaves, max depth = 8, train loss: 0.29084, val loss: 0.29178, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.28918, val loss: 0.29010, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.28762, val loss: 0.28855, in 0.016s
Fit 72 trees in 1.518 s, (2004 total leaves)
Time spent computing histograms: 0.493s
Time spent finding best splits:  0.063s
Time spent applying splits:      0.048s
Time spent predicting:           0.000s
Trial 72, Fold 1: Log loss = 0.29415414771195725, Average precision = 0.961117044484153, ROC-AUC = 0.955106737189506, Elapsed Time = 1.520083399998839 seconds
Trial 72, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 72, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
Binning 0.040 GB of training data: 0.158 s
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 31 leaves, max depth = 12, train loss: 0.67712, val loss: 0.67641, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.66005, val loss: 0.65885, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.64408, val loss: 0.64277, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.62828, val loss: 0.62687, in 0.016s
1 tree, 31 leaves, max depth = 8, train loss: 0.61428, val loss: 0.61290, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.60131, val loss: 0.59976, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.58898, val loss: 0.58732, in 0.016s
1 tree, 31 leaves, max depth = 8, train loss: 0.57744, val loss: 0.57545, in 0.016s
1 tree, 22 leaves, max depth = 7, train loss: 0.56386, val loss: 0.56174, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.55307, val loss: 0.55081, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.54280, val loss: 0.54049, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.53334, val loss: 0.53082, in 0.016s
1 tree, 31 leaves, max depth = 8, train loss: 0.52391, val loss: 0.52131, in 0.016s
1 tree, 31 leaves, max depth = 12, train loss: 0.51428, val loss: 0.51165, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.50609, val loss: 0.50330, in 0.016s
1 tree, 31 leaves, max depth = 8, train loss: 0.49819, val loss: 0.49535, in 0.016s
1 tree, 31 leaves, max depth = 13, train loss: 0.49135, val loss: 0.48834, in 0.016s
1 tree, 11 leaves, max depth = 6, train loss: 0.48198, val loss: 0.47897, in 0.000s
1 tree, 31 leaves, max depth = 10, train loss: 0.47499, val loss: 0.47195, in 0.000s
1 tree, 31 leaves, max depth = 8, train loss: 0.46864, val loss: 0.46550, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.46255, val loss: 0.45939, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.45669, val loss: 0.45352, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.45090, val loss: 0.44779, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.44490, val loss: 0.44177, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.43918, val loss: 0.43596, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.43335, val loss: 0.43019, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.42869, val loss: 0.42539, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.42351, val loss: 0.42020, in 0.031s
1 tree, 31 leaves, max depth = 9, train loss: 0.41865, val loss: 0.41531, in 0.016s
1 tree, 31 leaves, max depth = 12, train loss: 0.41473, val loss: 0.41117, in 0.016s
1 tree, 17 leaves, max depth = 9, train loss: 0.40794, val loss: 0.40451, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.40284, val loss: 0.39943, in 0.031s
1 tree, 31 leaves, max depth = 8, train loss: 0.39809, val loss: 0.39475, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.39402, val loss: 0.39078, in 0.016s
1 tree, 31 leaves, max depth = 8, train loss: 0.38959, val loss: 0.38634, in 0.016s
1 tree, 13 leaves, max depth = 5, train loss: 0.38391, val loss: 0.38077, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.38071, val loss: 0.37760, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.37797, val loss: 0.37484, in 0.031s
1 tree, 31 leaves, max depth = 10, train loss: 0.37242, val loss: 0.36973, in 0.016s
1 tree, 12 leaves, max depth = 6, train loss: 0.36747, val loss: 0.36485, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.36423, val loss: 0.36155, in 0.031s
1 tree, 31 leaves, max depth = 11, train loss: 0.36126, val loss: 0.35872, in 0.016s
1 tree, 31 leaves, max depth = 8, train loss: 0.35811, val loss: 0.35565, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.35325, val loss: 0.35115, in 0.016s
1 tree, 31 leaves, max depth = 8, train loss: 0.34984, val loss: 0.34780, in 0.016s
1 tree, 28 leaves, max depth = 9, train loss: 0.34553, val loss: 0.34368, in 0.016s
1 tree, 31 leaves, max depth = 7, train loss: 0.34243, val loss: 0.34065, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.34025, val loss: 0.33850, in 0.016s
1 tree, 16 leaves, max depth = 9, train loss: 0.33640, val loss: 0.33477, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.33429, val loss: 0.33270, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.33209, val loss: 0.33059, in 0.031s
1 tree, 31 leaves, max depth = 9, train loss: 0.33039, val loss: 0.32883, in 0.016s
1 tree, 31 leaves, max depth = 8, train loss: 0.32773, val loss: 0.32623, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.32618, val loss: 0.32468, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.32291, val loss: 0.32154, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.32128, val loss: 0.31998, in 0.016s
1 tree, 18 leaves, max depth = 7, train loss: 0.31821, val loss: 0.31701, in 0.016s
1 tree, 31 leaves, max depth = 7, train loss: 0.31689, val loss: 0.31573, in 0.016s
1 tree, 19 leaves, max depth = 10, train loss: 0.31383, val loss: 0.31284, in 0.016s
1 tree, 10 leaves, max depth = 5, train loss: 0.31127, val loss: 0.31055, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.30995, val loss: 0.30924, in 0.016s
1 tree, 31 leaves, max depth = 8, train loss: 0.30830, val loss: 0.30766, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.30628, val loss: 0.30561, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.30413, val loss: 0.30355, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.30095, val loss: 0.30056, in 0.016s
1 tree, 26 leaves, max depth = 9, train loss: 0.29854, val loss: 0.29839, in 0.016s
1 tree, 22 leaves, max depth = 11, train loss: 0.29608, val loss: 0.29601, in 0.016s
1 tree, 27 leaves, max depth = 8, train loss: 0.29377, val loss: 0.29395, in 0.016s
1 tree, 16 leaves, max depth = 8, train loss: 0.29159, val loss: 0.29202, in 0.016s
1 tree, 31 leaves, max depth = 12, train loss: 0.29052, val loss: 0.29105, in 0.016s
1 tree, 14 leaves, max depth = 9, train loss: 0.28840, val loss: 0.28899, in 0.031s
1 tree, 31 leaves, max depth = 11, train loss: 0.28733, val loss: 0.28799, in 0.016s
Fit 72 trees in 1.565 s, (2024 total leaves)
Time spent computing histograms: 0.514s
Time spent finding best splits:  0.065s
Time spent applying splits:      0.048s
Time spent predicting:           0.000s
Trial 72, Fold 2: Log loss = 0.2910875895115242, Average precision = 0.9600590605027294, ROC-AUC = 0.9561806266030257, Elapsed Time = 1.5716917000008834 seconds
Trial 72, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 72, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
Binning 0.040 GB of training data: 0.141 s
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 31 leaves, max depth = 11, train loss: 0.67712, val loss: 0.67691, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.65932, val loss: 0.65910, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.64390, val loss: 0.64366, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.62823, val loss: 0.62796, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.61429, val loss: 0.61401, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.60112, val loss: 0.60090, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.59001, val loss: 0.58937, in 0.016s
1 tree, 31 leaves, max depth = 12, train loss: 0.57862, val loss: 0.57777, in 0.016s
1 tree, 23 leaves, max depth = 8, train loss: 0.56516, val loss: 0.56479, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.55414, val loss: 0.55369, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.54410, val loss: 0.54346, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.53448, val loss: 0.53385, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.52503, val loss: 0.52421, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.51582, val loss: 0.51505, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.50822, val loss: 0.50743, in 0.016s
1 tree, 31 leaves, max depth = 8, train loss: 0.50017, val loss: 0.49955, in 0.000s
1 tree, 31 leaves, max depth = 9, train loss: 0.49241, val loss: 0.49172, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.48508, val loss: 0.48451, in 0.031s
1 tree, 31 leaves, max depth = 9, train loss: 0.47734, val loss: 0.47684, in 0.016s
1 tree, 31 leaves, max depth = 12, train loss: 0.47086, val loss: 0.47041, in 0.016s
1 tree, 12 leaves, max depth = 5, train loss: 0.46221, val loss: 0.46222, in 0.000s
1 tree, 31 leaves, max depth = 11, train loss: 0.45627, val loss: 0.45636, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.44987, val loss: 0.45001, in 0.031s
1 tree, 9 leaves, max depth = 5, train loss: 0.44457, val loss: 0.44475, in 0.000s
1 tree, 31 leaves, max depth = 9, train loss: 0.43940, val loss: 0.43959, in 0.016s
1 tree, 8 leaves, max depth = 4, train loss: 0.43239, val loss: 0.43305, in 0.016s
1 tree, 31 leaves, max depth = 8, train loss: 0.42637, val loss: 0.42688, in 0.016s
1 tree, 31 leaves, max depth = 8, train loss: 0.42126, val loss: 0.42181, in 0.016s
1 tree, 9 leaves, max depth = 4, train loss: 0.41720, val loss: 0.41780, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.41356, val loss: 0.41415, in 0.016s
1 tree, 31 leaves, max depth = 8, train loss: 0.40887, val loss: 0.40949, in 0.000s
1 tree, 31 leaves, max depth = 7, train loss: 0.40381, val loss: 0.40428, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.39948, val loss: 0.40012, in 0.016s
1 tree, 15 leaves, max depth = 6, train loss: 0.39361, val loss: 0.39479, in 0.016s
1 tree, 31 leaves, max depth = 8, train loss: 0.38899, val loss: 0.38996, in 0.016s
1 tree, 31 leaves, max depth = 8, train loss: 0.38554, val loss: 0.38652, in 0.016s
1 tree, 31 leaves, max depth = 8, train loss: 0.38128, val loss: 0.38204, in 0.000s
1 tree, 31 leaves, max depth = 12, train loss: 0.37775, val loss: 0.37861, in 0.016s
1 tree, 31 leaves, max depth = 7, train loss: 0.37474, val loss: 0.37571, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.37147, val loss: 0.37236, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.36830, val loss: 0.36934, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.36503, val loss: 0.36616, in 0.031s
1 tree, 31 leaves, max depth = 10, train loss: 0.36256, val loss: 0.36362, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.35975, val loss: 0.36095, in 0.016s
1 tree, 10 leaves, max depth = 5, train loss: 0.35525, val loss: 0.35684, in 0.016s
1 tree, 11 leaves, max depth = 5, train loss: 0.35084, val loss: 0.35292, in 0.016s
1 tree, 30 leaves, max depth = 7, train loss: 0.34676, val loss: 0.34927, in 0.016s
1 tree, 22 leaves, max depth = 11, train loss: 0.34244, val loss: 0.34561, in 0.016s
1 tree, 31 leaves, max depth = 7, train loss: 0.33939, val loss: 0.34244, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.33726, val loss: 0.34041, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.33445, val loss: 0.33750, in 0.016s
1 tree, 14 leaves, max depth = 6, train loss: 0.33093, val loss: 0.33432, in 0.000s
1 tree, 14 leaves, max depth = 5, train loss: 0.32757, val loss: 0.33127, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.32594, val loss: 0.32977, in 0.016s
1 tree, 31 leaves, max depth = 12, train loss: 0.32387, val loss: 0.32789, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.32132, val loss: 0.32524, in 0.016s
1 tree, 12 leaves, max depth = 5, train loss: 0.31816, val loss: 0.32245, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.31573, val loss: 0.31988, in 0.016s
1 tree, 31 leaves, max depth = 8, train loss: 0.31407, val loss: 0.31835, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.31210, val loss: 0.31619, in 0.016s
1 tree, 12 leaves, max depth = 5, train loss: 0.30956, val loss: 0.31378, in 0.000s
1 tree, 31 leaves, max depth = 10, train loss: 0.30811, val loss: 0.31242, in 0.000s
1 tree, 31 leaves, max depth = 8, train loss: 0.30674, val loss: 0.31142, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.30570, val loss: 0.31046, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.30407, val loss: 0.30906, in 0.016s
1 tree, 31 leaves, max depth = 8, train loss: 0.30206, val loss: 0.30700, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.30100, val loss: 0.30585, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.29924, val loss: 0.30419, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.29655, val loss: 0.30200, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.29526, val loss: 0.30082, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.29414, val loss: 0.29998, in 0.016s
1 tree, 31 leaves, max depth = 7, train loss: 0.29122, val loss: 0.29764, in 0.016s
Fit 72 trees in 1.438 s, (1999 total leaves)
Time spent computing histograms: 0.464s
Time spent finding best splits:  0.057s
Time spent applying splits:      0.042s
Time spent predicting:           0.016s
Trial 72, Fold 3: Log loss = 0.29083142577126364, Average precision = 0.9623127350153663, ROC-AUC = 0.9575925963825833, Elapsed Time = 1.439354000000094 seconds
Trial 72, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 72, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
Binning 0.040 GB of training data: 0.158 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 31 leaves, max depth = 12, train loss: 0.67709, val loss: 0.67659, in 0.016s
1 tree, 31 leaves, max depth = 8, train loss: 0.65937, val loss: 0.65814, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.64409, val loss: 0.64209, in 0.028s
1 tree, 31 leaves, max depth = 11, train loss: 0.62851, val loss: 0.62581, in 0.004s
1 tree, 31 leaves, max depth = 7, train loss: 0.61465, val loss: 0.61120, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.60179, val loss: 0.59760, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.58943, val loss: 0.58453, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.57794, val loss: 0.57272, in 0.016s
1 tree, 29 leaves, max depth = 9, train loss: 0.56433, val loss: 0.55877, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.55333, val loss: 0.54733, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.54345, val loss: 0.53691, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.53302, val loss: 0.52598, in 0.016s
1 tree, 7 leaves, max depth = 3, train loss: 0.52488, val loss: 0.51730, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.51605, val loss: 0.50820, in 0.016s
1 tree, 31 leaves, max depth = 8, train loss: 0.50749, val loss: 0.49922, in 0.000s
1 tree, 31 leaves, max depth = 10, train loss: 0.49906, val loss: 0.49025, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.49157, val loss: 0.48232, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.48484, val loss: 0.47526, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.47795, val loss: 0.46807, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.47195, val loss: 0.46160, in 0.016s
1 tree, 31 leaves, max depth = 13, train loss: 0.46522, val loss: 0.45448, in 0.016s
1 tree, 7 leaves, max depth = 4, train loss: 0.45704, val loss: 0.44617, in 0.016s
1 tree, 31 leaves, max depth = 12, train loss: 0.45148, val loss: 0.44022, in 0.016s
1 tree, 31 leaves, max depth = 14, train loss: 0.44646, val loss: 0.43496, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.44141, val loss: 0.42975, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.43599, val loss: 0.42389, in 0.016s
1 tree, 31 leaves, max depth = 7, train loss: 0.43032, val loss: 0.41792, in 0.016s
1 tree, 17 leaves, max depth = 8, train loss: 0.42332, val loss: 0.41068, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.41902, val loss: 0.40591, in 0.016s
1 tree, 31 leaves, max depth = 8, train loss: 0.41363, val loss: 0.40045, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.40920, val loss: 0.39581, in 0.016s
1 tree, 31 leaves, max depth = 7, train loss: 0.40423, val loss: 0.39076, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.40008, val loss: 0.38631, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.39611, val loss: 0.38200, in 0.016s
1 tree, 10 leaves, max depth = 4, train loss: 0.39013, val loss: 0.37592, in 0.016s
1 tree, 21 leaves, max depth = 12, train loss: 0.38560, val loss: 0.37141, in 0.016s
1 tree, 31 leaves, max depth = 8, train loss: 0.38133, val loss: 0.36717, in 0.016s
1 tree, 31 leaves, max depth = 8, train loss: 0.37775, val loss: 0.36328, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.37464, val loss: 0.36004, in 0.016s
1 tree, 31 leaves, max depth = 8, train loss: 0.37136, val loss: 0.35652, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.36838, val loss: 0.35326, in 0.016s
1 tree, 27 leaves, max depth = 10, train loss: 0.36351, val loss: 0.34834, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.36105, val loss: 0.34581, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.35835, val loss: 0.34290, in 0.031s
1 tree, 12 leaves, max depth = 5, train loss: 0.35445, val loss: 0.33864, in 0.000s
1 tree, 8 leaves, max depth = 4, train loss: 0.35028, val loss: 0.33441, in 0.016s
1 tree, 31 leaves, max depth = 14, train loss: 0.34754, val loss: 0.33147, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.34515, val loss: 0.32891, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.34211, val loss: 0.32588, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.33955, val loss: 0.32326, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.33768, val loss: 0.32141, in 0.016s
1 tree, 31 leaves, max depth = 8, train loss: 0.33495, val loss: 0.31868, in 0.016s
1 tree, 14 leaves, max depth = 8, train loss: 0.33159, val loss: 0.31529, in 0.016s
1 tree, 8 leaves, max depth = 4, train loss: 0.32815, val loss: 0.31178, in 0.016s
1 tree, 31 leaves, max depth = 12, train loss: 0.32629, val loss: 0.30975, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.32417, val loss: 0.30756, in 0.016s
1 tree, 12 leaves, max depth = 5, train loss: 0.32101, val loss: 0.30431, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.31858, val loss: 0.30186, in 0.016s
1 tree, 19 leaves, max depth = 6, train loss: 0.31529, val loss: 0.29868, in 0.016s
1 tree, 31 leaves, max depth = 13, train loss: 0.31359, val loss: 0.29691, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.31037, val loss: 0.29386, in 0.016s
1 tree, 31 leaves, max depth = 8, train loss: 0.30728, val loss: 0.29096, in 0.000s
1 tree, 31 leaves, max depth = 9, train loss: 0.30571, val loss: 0.28932, in 0.031s
1 tree, 31 leaves, max depth = 10, train loss: 0.30427, val loss: 0.28771, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.30231, val loss: 0.28576, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.30096, val loss: 0.28442, in 0.016s
1 tree, 11 leaves, max depth = 6, train loss: 0.29857, val loss: 0.28195, in 0.016s
1 tree, 31 leaves, max depth = 8, train loss: 0.29675, val loss: 0.28018, in 0.016s
1 tree, 15 leaves, max depth = 6, train loss: 0.29412, val loss: 0.27754, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.29292, val loss: 0.27634, in 0.031s
1 tree, 31 leaves, max depth = 8, train loss: 0.29158, val loss: 0.27498, in 0.016s
1 tree, 23 leaves, max depth = 8, train loss: 0.28908, val loss: 0.27262, in 0.016s
Fit 72 trees in 1.471 s, (1976 total leaves)
Time spent computing histograms: 0.477s
Time spent finding best splits:  0.058s
Time spent applying splits:      0.043s
Time spent predicting:           0.000s
Trial 72, Fold 4: Log loss = 0.29002303537082896, Average precision = 0.961783253739625, ROC-AUC = 0.9564205151040437, Elapsed Time = 1.4727536999998847 seconds
Trial 72, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 72, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
Binning 0.040 GB of training data: 0.157 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 31 leaves, max depth = 9, train loss: 0.67688, val loss: 0.67615, in 0.000s
1 tree, 31 leaves, max depth = 10, train loss: 0.65903, val loss: 0.65764, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.64330, val loss: 0.64111, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.62756, val loss: 0.62463, in 0.031s
1 tree, 31 leaves, max depth = 9, train loss: 0.61339, val loss: 0.60985, in 0.016s
1 tree, 31 leaves, max depth = 12, train loss: 0.60040, val loss: 0.59614, in 0.016s
1 tree, 31 leaves, max depth = 8, train loss: 0.58793, val loss: 0.58305, in 0.016s
1 tree, 31 leaves, max depth = 8, train loss: 0.57600, val loss: 0.57038, in 0.000s
1 tree, 12 leaves, max depth = 8, train loss: 0.56271, val loss: 0.55670, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.55226, val loss: 0.54563, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.54236, val loss: 0.53534, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.53167, val loss: 0.52422, in 0.016s
1 tree, 7 leaves, max depth = 3, train loss: 0.52345, val loss: 0.51559, in 0.016s
1 tree, 31 leaves, max depth = 8, train loss: 0.51453, val loss: 0.50638, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.50615, val loss: 0.49729, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.49755, val loss: 0.48826, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.49016, val loss: 0.48036, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.48341, val loss: 0.47333, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.47628, val loss: 0.46573, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.46975, val loss: 0.45870, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.46301, val loss: 0.45159, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.45717, val loss: 0.44546, in 0.016s
1 tree, 31 leaves, max depth = 13, train loss: 0.45215, val loss: 0.44003, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.44590, val loss: 0.43360, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.44010, val loss: 0.42758, in 0.016s
1 tree, 31 leaves, max depth = 7, train loss: 0.43443, val loss: 0.42177, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.42927, val loss: 0.41637, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.42440, val loss: 0.41135, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.41972, val loss: 0.40648, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.41568, val loss: 0.40221, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.41051, val loss: 0.39697, in 0.016s
1 tree, 14 leaves, max depth = 5, train loss: 0.40393, val loss: 0.39034, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.39755, val loss: 0.38400, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.39356, val loss: 0.37991, in 0.016s
1 tree, 31 leaves, max depth = 8, train loss: 0.38906, val loss: 0.37536, in 0.016s
1 tree, 31 leaves, max depth = 8, train loss: 0.38553, val loss: 0.37194, in 0.016s
1 tree, 31 leaves, max depth = 7, train loss: 0.38139, val loss: 0.36773, in 0.016s
1 tree, 31 leaves, max depth = 13, train loss: 0.37885, val loss: 0.36498, in 0.016s
1 tree, 31 leaves, max depth = 8, train loss: 0.37322, val loss: 0.35978, in 0.016s
1 tree, 16 leaves, max depth = 6, train loss: 0.36834, val loss: 0.35483, in 0.016s
1 tree, 31 leaves, max depth = 8, train loss: 0.36464, val loss: 0.35110, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.36168, val loss: 0.34809, in 0.016s
1 tree, 31 leaves, max depth = 8, train loss: 0.35860, val loss: 0.34504, in 0.016s
1 tree, 16 leaves, max depth = 7, train loss: 0.35401, val loss: 0.34044, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.35067, val loss: 0.33723, in 0.016s
1 tree, 21 leaves, max depth = 8, train loss: 0.34625, val loss: 0.33289, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.34357, val loss: 0.33028, in 0.000s
1 tree, 31 leaves, max depth = 11, train loss: 0.34144, val loss: 0.32820, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.33947, val loss: 0.32617, in 0.016s
1 tree, 31 leaves, max depth = 8, train loss: 0.33723, val loss: 0.32400, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.33527, val loss: 0.32201, in 0.031s
1 tree, 31 leaves, max depth = 11, train loss: 0.33313, val loss: 0.31990, in 0.016s
1 tree, 31 leaves, max depth = 7, train loss: 0.33157, val loss: 0.31812, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.33008, val loss: 0.31666, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.32637, val loss: 0.31306, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.32485, val loss: 0.31142, in 0.016s
1 tree, 21 leaves, max depth = 7, train loss: 0.32156, val loss: 0.30811, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.31908, val loss: 0.30572, in 0.016s
1 tree, 11 leaves, max depth = 6, train loss: 0.31592, val loss: 0.30259, in 0.016s
1 tree, 11 leaves, max depth = 5, train loss: 0.31304, val loss: 0.29981, in 0.000s
1 tree, 31 leaves, max depth = 9, train loss: 0.31167, val loss: 0.29847, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.30940, val loss: 0.29626, in 0.031s
1 tree, 31 leaves, max depth = 8, train loss: 0.30764, val loss: 0.29474, in 0.016s
1 tree, 31 leaves, max depth = 8, train loss: 0.30617, val loss: 0.29335, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.30450, val loss: 0.29182, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.30324, val loss: 0.29062, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.30046, val loss: 0.28809, in 0.016s
1 tree, 16 leaves, max depth = 6, train loss: 0.29801, val loss: 0.28582, in 0.016s
1 tree, 20 leaves, max depth = 6, train loss: 0.29633, val loss: 0.28421, in 0.000s
1 tree, 31 leaves, max depth = 10, train loss: 0.29520, val loss: 0.28311, in 0.016s
1 tree, 31 leaves, max depth = 7, train loss: 0.29394, val loss: 0.28197, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.29297, val loss: 0.28096, in 0.016s
Fit 72 trees in 1.455 s, (2002 total leaves)
Time spent computing histograms: 0.480s
Time spent finding best splits:  0.057s
Time spent applying splits:      0.042s
Time spent predicting:           0.000s
Trial 72, Fold 5: Log loss = 0.3028119526249657, Average precision = 0.9591369312082461, ROC-AUC = 0.9534296490347993, Elapsed Time = 1.4715311999989353 seconds
Optimization Progress:  73%|#######3  | 73/100 [14:46<06:34, 14.60s/it]
Trial 73, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 73, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
0.142 s 0.040 GB of training data: 
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 50 leaves, max depth = 10, train loss: 0.68290, val loss: 0.68280, in 0.016s
1 tree, 75 leaves, max depth = 11, train loss: 0.67228, val loss: 0.67213, in 0.016s
1 tree, 83 leaves, max depth = 13, train loss: 0.66279, val loss: 0.66251, in 0.016s
1 tree, 76 leaves, max depth = 11, train loss: 0.65306, val loss: 0.65280, in 0.016s
1 tree, 76 leaves, max depth = 12, train loss: 0.64407, val loss: 0.64377, in 0.016s
1 tree, 77 leaves, max depth = 12, train loss: 0.63495, val loss: 0.63466, in 0.016s
1 tree, 75 leaves, max depth = 15, train loss: 0.62648, val loss: 0.62613, in 0.016s
1 tree, 76 leaves, max depth = 12, train loss: 0.61796, val loss: 0.61761, in 0.016s
1 tree, 77 leaves, max depth = 15, train loss: 0.61008, val loss: 0.60968, in 0.016s
1 tree, 82 leaves, max depth = 12, train loss: 0.60261, val loss: 0.60209, in 0.016s
1 tree, 77 leaves, max depth = 13, train loss: 0.59486, val loss: 0.59435, in 0.016s
1 tree, 77 leaves, max depth = 12, train loss: 0.58763, val loss: 0.58711, in 0.016s
1 tree, 76 leaves, max depth = 12, train loss: 0.58076, val loss: 0.58021, in 0.016s
1 tree, 59 leaves, max depth = 11, train loss: 0.57382, val loss: 0.57311, in 0.016s
1 tree, 77 leaves, max depth = 13, train loss: 0.56699, val loss: 0.56628, in 0.016s
1 tree, 78 leaves, max depth = 15, train loss: 0.56066, val loss: 0.55988, in 0.031s
1 tree, 83 leaves, max depth = 13, train loss: 0.55430, val loss: 0.55345, in 0.016s
1 tree, 82 leaves, max depth = 13, train loss: 0.54815, val loss: 0.54724, in 0.016s
1 tree, 80 leaves, max depth = 11, train loss: 0.54213, val loss: 0.54109, in 0.016s
1 tree, 75 leaves, max depth = 11, train loss: 0.53620, val loss: 0.53513, in 0.016s
1 tree, 76 leaves, max depth = 13, train loss: 0.53070, val loss: 0.52957, in 0.016s
1 tree, 76 leaves, max depth = 10, train loss: 0.52550, val loss: 0.52437, in 0.016s
1 tree, 78 leaves, max depth = 12, train loss: 0.52000, val loss: 0.51885, in 0.016s
1 tree, 76 leaves, max depth = 10, train loss: 0.51510, val loss: 0.51395, in 0.016s
1 tree, 78 leaves, max depth = 13, train loss: 0.51024, val loss: 0.50906, in 0.016s
1 tree, 79 leaves, max depth = 13, train loss: 0.50552, val loss: 0.50431, in 0.016s
1 tree, 81 leaves, max depth = 11, train loss: 0.50074, val loss: 0.49943, in 0.016s
1 tree, 78 leaves, max depth = 13, train loss: 0.49628, val loss: 0.49495, in 0.016s
1 tree, 76 leaves, max depth = 10, train loss: 0.49202, val loss: 0.49070, in 0.016s
1 tree, 78 leaves, max depth = 12, train loss: 0.48747, val loss: 0.48613, in 0.016s
1 tree, 78 leaves, max depth = 12, train loss: 0.48305, val loss: 0.48169, in 0.031s
1 tree, 80 leaves, max depth = 12, train loss: 0.47878, val loss: 0.47737, in 0.016s
1 tree, 78 leaves, max depth = 13, train loss: 0.47490, val loss: 0.47347, in 0.016s
1 tree, 77 leaves, max depth = 12, train loss: 0.47112, val loss: 0.46971, in 0.016s
1 tree, 83 leaves, max depth = 12, train loss: 0.46727, val loss: 0.46576, in 0.016s
1 tree, 78 leaves, max depth = 12, train loss: 0.46339, val loss: 0.46188, in 0.031s
1 tree, 79 leaves, max depth = 12, train loss: 0.45967, val loss: 0.45813, in 0.016s
1 tree, 57 leaves, max depth = 12, train loss: 0.45614, val loss: 0.45453, in 0.016s
1 tree, 57 leaves, max depth = 12, train loss: 0.45271, val loss: 0.45103, in 0.016s
1 tree, 82 leaves, max depth = 12, train loss: 0.44931, val loss: 0.44757, in 0.016s
1 tree, 78 leaves, max depth = 13, train loss: 0.44616, val loss: 0.44441, in 0.031s
1 tree, 78 leaves, max depth = 12, train loss: 0.44286, val loss: 0.44111, in 0.016s
1 tree, 83 leaves, max depth = 12, train loss: 0.43971, val loss: 0.43788, in 0.016s
1 tree, 83 leaves, max depth = 12, train loss: 0.43665, val loss: 0.43475, in 0.016s
1 tree, 57 leaves, max depth = 10, train loss: 0.43199, val loss: 0.43026, in 0.016s
1 tree, 61 leaves, max depth = 10, train loss: 0.42755, val loss: 0.42594, in 0.031s
1 tree, 77 leaves, max depth = 10, train loss: 0.42490, val loss: 0.42332, in 0.016s
1 tree, 83 leaves, max depth = 12, train loss: 0.42215, val loss: 0.42050, in 0.016s
1 tree, 61 leaves, max depth = 10, train loss: 0.41797, val loss: 0.41643, in 0.016s
1 tree, 60 leaves, max depth = 12, train loss: 0.41536, val loss: 0.41372, in 0.016s
1 tree, 57 leaves, max depth = 12, train loss: 0.41130, val loss: 0.40985, in 0.016s
1 tree, 81 leaves, max depth = 11, train loss: 0.40879, val loss: 0.40729, in 0.016s
1 tree, 80 leaves, max depth = 12, train loss: 0.40622, val loss: 0.40472, in 0.016s
1 tree, 77 leaves, max depth = 10, train loss: 0.40397, val loss: 0.40252, in 0.016s
1 tree, 57 leaves, max depth = 14, train loss: 0.40018, val loss: 0.39892, in 0.016s
1 tree, 57 leaves, max depth = 12, train loss: 0.39650, val loss: 0.39543, in 0.031s
1 tree, 58 leaves, max depth = 11, train loss: 0.39292, val loss: 0.39205, in 0.016s
1 tree, 57 leaves, max depth = 12, train loss: 0.38946, val loss: 0.38877, in 0.016s
1 tree, 77 leaves, max depth = 11, train loss: 0.38718, val loss: 0.38650, in 0.016s
Fit 59 trees in 1.314 s, (4358 total leaves)
Time spent computing histograms: 0.388s
Time spent finding best splits:  0.071s
Time spent applying splits:      0.075s
Time spent predicting:           0.000s
Trial 73, Fold 1: Log loss = 0.3897518471591272, Average precision = 0.9534496063506044, ROC-AUC = 0.9484036620017876, Elapsed Time = 1.3255394999996497 seconds
Trial 73, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 73, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
Binning 0.040 GB of training data: 0.142 s
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 51 leaves, max depth = 10, train loss: 0.68289, val loss: 0.68265, in 0.016s
1 tree, 76 leaves, max depth = 12, train loss: 0.67227, val loss: 0.67181, in 0.016s
1 tree, 80 leaves, max depth = 12, train loss: 0.66281, val loss: 0.66226, in 0.031s
1 tree, 75 leaves, max depth = 12, train loss: 0.65294, val loss: 0.65220, in 0.016s
1 tree, 81 leaves, max depth = 15, train loss: 0.64388, val loss: 0.64302, in 0.016s
1 tree, 79 leaves, max depth = 12, train loss: 0.63463, val loss: 0.63359, in 0.016s
1 tree, 81 leaves, max depth = 17, train loss: 0.62617, val loss: 0.62503, in 0.031s
1 tree, 80 leaves, max depth = 12, train loss: 0.61761, val loss: 0.61623, in 0.016s
1 tree, 79 leaves, max depth = 14, train loss: 0.60979, val loss: 0.60827, in 0.016s
1 tree, 85 leaves, max depth = 14, train loss: 0.60233, val loss: 0.60072, in 0.031s
1 tree, 77 leaves, max depth = 12, train loss: 0.59451, val loss: 0.59275, in 0.016s
1 tree, 77 leaves, max depth = 12, train loss: 0.58718, val loss: 0.58529, in 0.016s
1 tree, 80 leaves, max depth = 14, train loss: 0.58032, val loss: 0.57831, in 0.031s
1 tree, 58 leaves, max depth = 13, train loss: 0.57336, val loss: 0.57121, in 0.016s
1 tree, 78 leaves, max depth = 12, train loss: 0.56641, val loss: 0.56412, in 0.031s
1 tree, 80 leaves, max depth = 14, train loss: 0.56016, val loss: 0.55776, in 0.016s
1 tree, 83 leaves, max depth = 12, train loss: 0.55370, val loss: 0.55123, in 0.016s
1 tree, 83 leaves, max depth = 12, train loss: 0.54745, val loss: 0.54490, in 0.031s
1 tree, 57 leaves, max depth = 12, train loss: 0.54144, val loss: 0.53877, in 0.016s
1 tree, 83 leaves, max depth = 12, train loss: 0.53556, val loss: 0.53283, in 0.016s
1 tree, 80 leaves, max depth = 14, train loss: 0.53019, val loss: 0.52735, in 0.031s
1 tree, 75 leaves, max depth = 13, train loss: 0.52459, val loss: 0.52167, in 0.016s
1 tree, 83 leaves, max depth = 12, train loss: 0.51921, val loss: 0.51623, in 0.016s
1 tree, 77 leaves, max depth = 13, train loss: 0.51434, val loss: 0.51120, in 0.016s
1 tree, 77 leaves, max depth = 13, train loss: 0.50943, val loss: 0.50625, in 0.031s
1 tree, 77 leaves, max depth = 13, train loss: 0.50466, val loss: 0.50146, in 0.016s
1 tree, 82 leaves, max depth = 11, train loss: 0.49988, val loss: 0.49662, in 0.031s
1 tree, 80 leaves, max depth = 14, train loss: 0.49549, val loss: 0.49215, in 0.016s
1 tree, 74 leaves, max depth = 14, train loss: 0.49126, val loss: 0.48778, in 0.016s
1 tree, 80 leaves, max depth = 14, train loss: 0.48678, val loss: 0.48318, in 0.016s
1 tree, 78 leaves, max depth = 14, train loss: 0.48238, val loss: 0.47871, in 0.016s
1 tree, 80 leaves, max depth = 14, train loss: 0.47815, val loss: 0.47437, in 0.016s
1 tree, 77 leaves, max depth = 14, train loss: 0.47432, val loss: 0.47046, in 0.031s
1 tree, 77 leaves, max depth = 14, train loss: 0.47057, val loss: 0.46663, in 0.016s
1 tree, 83 leaves, max depth = 11, train loss: 0.46678, val loss: 0.46278, in 0.016s
1 tree, 78 leaves, max depth = 12, train loss: 0.46292, val loss: 0.45882, in 0.016s
1 tree, 78 leaves, max depth = 12, train loss: 0.45924, val loss: 0.45504, in 0.031s
1 tree, 58 leaves, max depth = 12, train loss: 0.45572, val loss: 0.45143, in 0.016s
1 tree, 58 leaves, max depth = 12, train loss: 0.45229, val loss: 0.44791, in 0.016s
1 tree, 84 leaves, max depth = 12, train loss: 0.44897, val loss: 0.44455, in 0.016s
1 tree, 81 leaves, max depth = 14, train loss: 0.44586, val loss: 0.44147, in 0.016s
1 tree, 80 leaves, max depth = 13, train loss: 0.44268, val loss: 0.43830, in 0.016s
1 tree, 59 leaves, max depth = 12, train loss: 0.43964, val loss: 0.43516, in 0.031s
1 tree, 79 leaves, max depth = 12, train loss: 0.43651, val loss: 0.43195, in 0.016s
1 tree, 79 leaves, max depth = 12, train loss: 0.43346, val loss: 0.42883, in 0.016s
1 tree, 62 leaves, max depth = 11, train loss: 0.42896, val loss: 0.42444, in 0.016s
1 tree, 83 leaves, max depth = 12, train loss: 0.42618, val loss: 0.42161, in 0.016s
1 tree, 83 leaves, max depth = 11, train loss: 0.42343, val loss: 0.41884, in 0.016s
1 tree, 57 leaves, max depth = 12, train loss: 0.41913, val loss: 0.41462, in 0.016s
1 tree, 59 leaves, max depth = 11, train loss: 0.41654, val loss: 0.41195, in 0.016s
1 tree, 58 leaves, max depth = 12, train loss: 0.41243, val loss: 0.40790, in 0.016s
1 tree, 59 leaves, max depth = 12, train loss: 0.41000, val loss: 0.40541, in 0.031s
1 tree, 80 leaves, max depth = 13, train loss: 0.40758, val loss: 0.40302, in 0.016s
1 tree, 78 leaves, max depth = 12, train loss: 0.40517, val loss: 0.40054, in 0.016s
1 tree, 58 leaves, max depth = 12, train loss: 0.40133, val loss: 0.39677, in 0.031s
1 tree, 57 leaves, max depth = 12, train loss: 0.39756, val loss: 0.39308, in 0.016s
1 tree, 58 leaves, max depth = 12, train loss: 0.39395, val loss: 0.38953, in 0.031s
1 tree, 58 leaves, max depth = 12, train loss: 0.39046, val loss: 0.38610, in 0.016s
1 tree, 79 leaves, max depth = 12, train loss: 0.38825, val loss: 0.38384, in 0.016s
Fit 59 trees in 1.548 s, (4366 total leaves)
Time spent computing histograms: 0.469s
Time spent finding best splits:  0.086s
Time spent applying splits:      0.089s
Time spent predicting:           0.000s
Trial 73, Fold 2: Log loss = 0.39035312161219826, Average precision = 0.9510917369187883, ROC-AUC = 0.9489883885629515, Elapsed Time = 1.5692022999992332 seconds
Trial 73, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 73, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
Binning 0.040 GB of training data: 0.173 s
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 54 leaves, max depth = 11, train loss: 0.68288, val loss: 0.68273, in 0.016s
1 tree, 76 leaves, max depth = 13, train loss: 0.67249, val loss: 0.67227, in 0.016s
1 tree, 80 leaves, max depth = 12, train loss: 0.66301, val loss: 0.66264, in 0.031s
1 tree, 77 leaves, max depth = 13, train loss: 0.65325, val loss: 0.65285, in 0.016s
1 tree, 77 leaves, max depth = 13, train loss: 0.64416, val loss: 0.64374, in 0.031s
1 tree, 79 leaves, max depth = 13, train loss: 0.63505, val loss: 0.63459, in 0.016s
1 tree, 79 leaves, max depth = 13, train loss: 0.62650, val loss: 0.62612, in 0.016s
1 tree, 78 leaves, max depth = 13, train loss: 0.61798, val loss: 0.61757, in 0.031s
1 tree, 79 leaves, max depth = 12, train loss: 0.61010, val loss: 0.60971, in 0.016s
1 tree, 80 leaves, max depth = 14, train loss: 0.60263, val loss: 0.60226, in 0.031s
1 tree, 78 leaves, max depth = 13, train loss: 0.59489, val loss: 0.59449, in 0.031s
1 tree, 82 leaves, max depth = 13, train loss: 0.58766, val loss: 0.58718, in 0.016s
1 tree, 79 leaves, max depth = 12, train loss: 0.58074, val loss: 0.58028, in 0.031s
1 tree, 58 leaves, max depth = 12, train loss: 0.57378, val loss: 0.57327, in 0.031s
1 tree, 80 leaves, max depth = 12, train loss: 0.56684, val loss: 0.56634, in 0.031s
1 tree, 80 leaves, max depth = 14, train loss: 0.56054, val loss: 0.56007, in 0.016s
1 tree, 83 leaves, max depth = 12, train loss: 0.55409, val loss: 0.55364, in 0.031s
1 tree, 83 leaves, max depth = 12, train loss: 0.54783, val loss: 0.54741, in 0.031s
1 tree, 82 leaves, max depth = 12, train loss: 0.54181, val loss: 0.54139, in 0.031s
1 tree, 78 leaves, max depth = 13, train loss: 0.53588, val loss: 0.53546, in 0.032s
1 tree, 77 leaves, max depth = 13, train loss: 0.53035, val loss: 0.53000, in 0.031s
1 tree, 77 leaves, max depth = 14, train loss: 0.52520, val loss: 0.52487, in 0.031s
1 tree, 76 leaves, max depth = 13, train loss: 0.51973, val loss: 0.51941, in 0.031s
1 tree, 77 leaves, max depth = 14, train loss: 0.51488, val loss: 0.51457, in 0.031s
1 tree, 79 leaves, max depth = 14, train loss: 0.51008, val loss: 0.50981, in 0.031s
1 tree, 76 leaves, max depth = 13, train loss: 0.50529, val loss: 0.50508, in 0.031s
1 tree, 82 leaves, max depth = 10, train loss: 0.50049, val loss: 0.50031, in 0.031s
1 tree, 80 leaves, max depth = 14, train loss: 0.49606, val loss: 0.49591, in 0.016s
1 tree, 77 leaves, max depth = 14, train loss: 0.49185, val loss: 0.49171, in 0.031s
1 tree, 76 leaves, max depth = 13, train loss: 0.48735, val loss: 0.48722, in 0.031s
1 tree, 76 leaves, max depth = 13, train loss: 0.48298, val loss: 0.48286, in 0.031s
1 tree, 75 leaves, max depth = 13, train loss: 0.47873, val loss: 0.47862, in 0.016s
1 tree, 80 leaves, max depth = 14, train loss: 0.47490, val loss: 0.47484, in 0.016s
1 tree, 78 leaves, max depth = 13, train loss: 0.47116, val loss: 0.47112, in 0.031s
1 tree, 81 leaves, max depth = 10, train loss: 0.46730, val loss: 0.46729, in 0.031s
1 tree, 77 leaves, max depth = 14, train loss: 0.46343, val loss: 0.46345, in 0.031s
1 tree, 77 leaves, max depth = 13, train loss: 0.45971, val loss: 0.45973, in 0.031s
1 tree, 55 leaves, max depth = 13, train loss: 0.45617, val loss: 0.45620, in 0.016s
1 tree, 56 leaves, max depth = 12, train loss: 0.45275, val loss: 0.45278, in 0.031s
1 tree, 81 leaves, max depth = 13, train loss: 0.44934, val loss: 0.44942, in 0.016s
1 tree, 83 leaves, max depth = 14, train loss: 0.44629, val loss: 0.44641, in 0.031s
1 tree, 83 leaves, max depth = 10, train loss: 0.44310, val loss: 0.44325, in 0.016s
1 tree, 57 leaves, max depth = 13, train loss: 0.44001, val loss: 0.44016, in 0.016s
1 tree, 58 leaves, max depth = 13, train loss: 0.43526, val loss: 0.43578, in 0.031s
1 tree, 79 leaves, max depth = 13, train loss: 0.43222, val loss: 0.43276, in 0.016s
1 tree, 84 leaves, max depth = 12, train loss: 0.42930, val loss: 0.42988, in 0.031s
1 tree, 57 leaves, max depth = 13, train loss: 0.42483, val loss: 0.42577, in 0.031s
1 tree, 58 leaves, max depth = 10, train loss: 0.42212, val loss: 0.42307, in 0.016s
1 tree, 58 leaves, max depth = 13, train loss: 0.41783, val loss: 0.41914, in 0.031s
1 tree, 84 leaves, max depth = 10, train loss: 0.41521, val loss: 0.41655, in 0.016s
1 tree, 76 leaves, max depth = 13, train loss: 0.41254, val loss: 0.41391, in 0.031s
1 tree, 79 leaves, max depth = 13, train loss: 0.40994, val loss: 0.41134, in 0.031s
1 tree, 83 leaves, max depth = 10, train loss: 0.40751, val loss: 0.40896, in 0.016s
1 tree, 78 leaves, max depth = 13, train loss: 0.40530, val loss: 0.40678, in 0.031s
1 tree, 83 leaves, max depth = 13, train loss: 0.40307, val loss: 0.40463, in 0.031s
1 tree, 57 leaves, max depth = 12, train loss: 0.40082, val loss: 0.40241, in 0.016s
1 tree, 58 leaves, max depth = 13, train loss: 0.39698, val loss: 0.39893, in 0.031s
1 tree, 58 leaves, max depth = 13, train loss: 0.39330, val loss: 0.39556, in 0.016s
1 tree, 75 leaves, max depth = 13, train loss: 0.39107, val loss: 0.39337, in 0.031s
Fit 59 trees in 1.924 s, (4403 total leaves)
Time spent computing histograms: 0.573s
Time spent finding best splits:  0.113s
Time spent applying splits:      0.115s
Time spent predicting:           0.000s
Trial 73, Fold 3: Log loss = 0.3888056946075886, Average precision = 0.9533842900230023, ROC-AUC = 0.9508368767516446, Elapsed Time = 1.926594299999124 seconds
Trial 73, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 73, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
Binning 0.040 GB of training data: 0.187 s
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 57 leaves, max depth = 12, train loss: 0.68285, val loss: 0.68242, in 0.016s
1 tree, 73 leaves, max depth = 11, train loss: 0.67245, val loss: 0.67155, in 0.016s
1 tree, 81 leaves, max depth = 12, train loss: 0.66300, val loss: 0.66154, in 0.016s
1 tree, 78 leaves, max depth = 12, train loss: 0.65329, val loss: 0.65139, in 0.032s
1 tree, 56 leaves, max depth = 14, train loss: 0.64452, val loss: 0.64220, in 0.016s
1 tree, 74 leaves, max depth = 11, train loss: 0.63547, val loss: 0.63273, in 0.031s
1 tree, 75 leaves, max depth = 11, train loss: 0.62672, val loss: 0.62358, in 0.016s
1 tree, 58 leaves, max depth = 11, train loss: 0.61845, val loss: 0.61489, in 0.031s
1 tree, 76 leaves, max depth = 16, train loss: 0.61065, val loss: 0.60675, in 0.016s
1 tree, 73 leaves, max depth = 11, train loss: 0.60273, val loss: 0.59845, in 0.016s
1 tree, 84 leaves, max depth = 14, train loss: 0.59550, val loss: 0.59081, in 0.016s
1 tree, 75 leaves, max depth = 14, train loss: 0.58842, val loss: 0.58342, in 0.031s
1 tree, 58 leaves, max depth = 12, train loss: 0.58140, val loss: 0.57597, in 0.016s
1 tree, 77 leaves, max depth = 12, train loss: 0.57426, val loss: 0.56851, in 0.016s
1 tree, 58 leaves, max depth = 11, train loss: 0.56756, val loss: 0.56143, in 0.016s
1 tree, 76 leaves, max depth = 12, train loss: 0.56130, val loss: 0.55482, in 0.031s
1 tree, 76 leaves, max depth = 12, train loss: 0.55479, val loss: 0.54802, in 0.016s
1 tree, 76 leaves, max depth = 12, train loss: 0.54887, val loss: 0.54181, in 0.031s
1 tree, 56 leaves, max depth = 11, train loss: 0.54308, val loss: 0.53560, in 0.016s
1 tree, 58 leaves, max depth = 11, train loss: 0.53743, val loss: 0.52957, in 0.031s
1 tree, 76 leaves, max depth = 12, train loss: 0.53170, val loss: 0.52352, in 0.016s
1 tree, 76 leaves, max depth = 12, train loss: 0.52625, val loss: 0.51771, in 0.031s
1 tree, 76 leaves, max depth = 12, train loss: 0.52096, val loss: 0.51208, in 0.016s
1 tree, 85 leaves, max depth = 11, train loss: 0.51576, val loss: 0.50658, in 0.031s
1 tree, 84 leaves, max depth = 11, train loss: 0.51079, val loss: 0.50132, in 0.016s
1 tree, 84 leaves, max depth = 11, train loss: 0.50589, val loss: 0.49613, in 0.031s
1 tree, 78 leaves, max depth = 11, train loss: 0.50104, val loss: 0.49104, in 0.016s
1 tree, 74 leaves, max depth = 11, train loss: 0.49639, val loss: 0.48612, in 0.031s
1 tree, 76 leaves, max depth = 13, train loss: 0.49210, val loss: 0.48169, in 0.031s
1 tree, 39 leaves, max depth = 10, train loss: 0.48646, val loss: 0.47593, in 0.016s
1 tree, 75 leaves, max depth = 11, train loss: 0.48215, val loss: 0.47136, in 0.016s
1 tree, 82 leaves, max depth = 11, train loss: 0.47805, val loss: 0.46696, in 0.031s
1 tree, 59 leaves, max depth = 11, train loss: 0.47406, val loss: 0.46274, in 0.016s
1 tree, 83 leaves, max depth = 13, train loss: 0.47040, val loss: 0.45892, in 0.031s
1 tree, 78 leaves, max depth = 11, train loss: 0.46648, val loss: 0.45479, in 0.016s
1 tree, 74 leaves, max depth = 15, train loss: 0.46291, val loss: 0.45104, in 0.031s
1 tree, 57 leaves, max depth = 11, train loss: 0.45781, val loss: 0.44585, in 0.016s
1 tree, 61 leaves, max depth = 11, train loss: 0.45430, val loss: 0.44212, in 0.031s
1 tree, 61 leaves, max depth = 11, train loss: 0.45090, val loss: 0.43850, in 0.016s
1 tree, 85 leaves, max depth = 11, train loss: 0.44754, val loss: 0.43491, in 0.031s
1 tree, 81 leaves, max depth = 15, train loss: 0.44449, val loss: 0.43166, in 0.031s
1 tree, 85 leaves, max depth = 11, train loss: 0.44130, val loss: 0.42826, in 0.016s
1 tree, 58 leaves, max depth = 12, train loss: 0.43824, val loss: 0.42501, in 0.031s
1 tree, 77 leaves, max depth = 12, train loss: 0.43513, val loss: 0.42172, in 0.016s
1 tree, 78 leaves, max depth = 12, train loss: 0.43210, val loss: 0.41852, in 0.031s
1 tree, 82 leaves, max depth = 12, train loss: 0.42924, val loss: 0.41545, in 0.031s
1 tree, 84 leaves, max depth = 11, train loss: 0.42649, val loss: 0.41248, in 0.016s
1 tree, 78 leaves, max depth = 12, train loss: 0.42368, val loss: 0.40952, in 0.031s
1 tree, 73 leaves, max depth = 11, train loss: 0.42103, val loss: 0.40675, in 0.016s
1 tree, 47 leaves, max depth = 12, train loss: 0.41695, val loss: 0.40260, in 0.031s
1 tree, 57 leaves, max depth = 12, train loss: 0.41289, val loss: 0.39850, in 0.016s
1 tree, 60 leaves, max depth = 11, train loss: 0.41044, val loss: 0.39589, in 0.031s
1 tree, 82 leaves, max depth = 11, train loss: 0.40805, val loss: 0.39330, in 0.031s
1 tree, 76 leaves, max depth = 10, train loss: 0.40583, val loss: 0.39100, in 0.031s
1 tree, 57 leaves, max depth = 10, train loss: 0.40205, val loss: 0.38719, in 0.016s
1 tree, 56 leaves, max depth = 13, train loss: 0.39840, val loss: 0.38350, in 0.016s
1 tree, 57 leaves, max depth = 12, train loss: 0.39483, val loss: 0.37990, in 0.031s
1 tree, 56 leaves, max depth = 13, train loss: 0.39140, val loss: 0.37643, in 0.016s
1 tree, 79 leaves, max depth = 11, train loss: 0.38917, val loss: 0.37406, in 0.031s
Fit 59 trees in 1.813 s, (4181 total leaves)
Time spent computing histograms: 0.544s
Time spent finding best splits:  0.102s
Time spent applying splits:      0.103s
Time spent predicting:           0.000s
Trial 73, Fold 4: Log loss = 0.38931738228668655, Average precision = 0.9534384907358634, ROC-AUC = 0.9483126277149486, Elapsed Time = 1.8212827000006655 seconds
Trial 73, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 73, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
Binning 0.040 GB of training data: 0.159 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 54 leaves, max depth = 10, train loss: 0.68275, val loss: 0.68220, in 0.016s
1 tree, 78 leaves, max depth = 12, train loss: 0.67212, val loss: 0.67113, in 0.031s
1 tree, 82 leaves, max depth = 11, train loss: 0.66268, val loss: 0.66117, in 0.016s
1 tree, 76 leaves, max depth = 12, train loss: 0.65277, val loss: 0.65085, in 0.016s
1 tree, 77 leaves, max depth = 14, train loss: 0.64361, val loss: 0.64126, in 0.016s
1 tree, 79 leaves, max depth = 12, train loss: 0.63436, val loss: 0.63162, in 0.016s
1 tree, 76 leaves, max depth = 12, train loss: 0.62582, val loss: 0.62266, in 0.031s
1 tree, 79 leaves, max depth = 12, train loss: 0.61717, val loss: 0.61365, in 0.016s
1 tree, 79 leaves, max depth = 12, train loss: 0.60930, val loss: 0.60541, in 0.031s
1 tree, 86 leaves, max depth = 12, train loss: 0.60181, val loss: 0.59755, in 0.016s
1 tree, 78 leaves, max depth = 12, train loss: 0.59400, val loss: 0.58943, in 0.016s
1 tree, 75 leaves, max depth = 15, train loss: 0.58665, val loss: 0.58168, in 0.016s
1 tree, 79 leaves, max depth = 13, train loss: 0.57974, val loss: 0.57444, in 0.016s
1 tree, 59 leaves, max depth = 10, train loss: 0.57282, val loss: 0.56718, in 0.031s
1 tree, 79 leaves, max depth = 13, train loss: 0.56587, val loss: 0.55994, in 0.016s
1 tree, 79 leaves, max depth = 13, train loss: 0.55959, val loss: 0.55336, in 0.016s
1 tree, 86 leaves, max depth = 11, train loss: 0.55318, val loss: 0.54671, in 0.016s
1 tree, 85 leaves, max depth = 12, train loss: 0.54695, val loss: 0.54022, in 0.016s
1 tree, 57 leaves, max depth = 12, train loss: 0.54096, val loss: 0.53393, in 0.016s
1 tree, 85 leaves, max depth = 12, train loss: 0.53510, val loss: 0.52782, in 0.016s
1 tree, 79 leaves, max depth = 13, train loss: 0.52970, val loss: 0.52215, in 0.016s
1 tree, 78 leaves, max depth = 13, train loss: 0.52411, val loss: 0.51634, in 0.031s
1 tree, 83 leaves, max depth = 10, train loss: 0.51877, val loss: 0.51080, in 0.016s
1 tree, 82 leaves, max depth = 10, train loss: 0.51391, val loss: 0.50582, in 0.016s
1 tree, 78 leaves, max depth = 13, train loss: 0.50876, val loss: 0.50046, in 0.016s
1 tree, 55 leaves, max depth = 10, train loss: 0.50392, val loss: 0.49539, in 0.016s
1 tree, 78 leaves, max depth = 13, train loss: 0.49903, val loss: 0.49029, in 0.031s
1 tree, 78 leaves, max depth = 13, train loss: 0.49429, val loss: 0.48534, in 0.016s
1 tree, 87 leaves, max depth = 12, train loss: 0.49009, val loss: 0.48093, in 0.031s
1 tree, 70 leaves, max depth = 13, train loss: 0.48590, val loss: 0.47661, in 0.016s
1 tree, 76 leaves, max depth = 13, train loss: 0.48174, val loss: 0.47224, in 0.031s
1 tree, 82 leaves, max depth = 10, train loss: 0.47761, val loss: 0.46793, in 0.016s
1 tree, 58 leaves, max depth = 12, train loss: 0.47360, val loss: 0.46367, in 0.016s
1 tree, 84 leaves, max depth = 10, train loss: 0.46992, val loss: 0.45991, in 0.031s
1 tree, 77 leaves, max depth = 12, train loss: 0.46597, val loss: 0.45579, in 0.016s
1 tree, 77 leaves, max depth = 12, train loss: 0.46213, val loss: 0.45179, in 0.016s
1 tree, 76 leaves, max depth = 12, train loss: 0.45842, val loss: 0.44793, in 0.016s
1 tree, 56 leaves, max depth = 12, train loss: 0.45494, val loss: 0.44423, in 0.016s
1 tree, 57 leaves, max depth = 12, train loss: 0.45155, val loss: 0.44063, in 0.016s
1 tree, 83 leaves, max depth = 12, train loss: 0.44821, val loss: 0.43713, in 0.031s
1 tree, 77 leaves, max depth = 12, train loss: 0.44500, val loss: 0.43377, in 0.016s
1 tree, 77 leaves, max depth = 12, train loss: 0.44172, val loss: 0.43034, in 0.031s
1 tree, 83 leaves, max depth = 10, train loss: 0.43866, val loss: 0.42716, in 0.016s
1 tree, 78 leaves, max depth = 12, train loss: 0.43555, val loss: 0.42392, in 0.016s
1 tree, 58 leaves, max depth = 12, train loss: 0.43288, val loss: 0.42107, in 0.031s
1 tree, 75 leaves, max depth = 12, train loss: 0.43012, val loss: 0.41827, in 0.016s
1 tree, 57 leaves, max depth = 12, train loss: 0.42561, val loss: 0.41380, in 0.031s
1 tree, 80 leaves, max depth = 12, train loss: 0.42277, val loss: 0.41084, in 0.016s
1 tree, 76 leaves, max depth = 13, train loss: 0.42006, val loss: 0.40799, in 0.016s
1 tree, 60 leaves, max depth = 12, train loss: 0.41750, val loss: 0.40526, in 0.031s
1 tree, 57 leaves, max depth = 12, train loss: 0.41328, val loss: 0.40107, in 0.016s
1 tree, 60 leaves, max depth = 12, train loss: 0.41083, val loss: 0.39845, in 0.031s
1 tree, 85 leaves, max depth = 10, train loss: 0.40844, val loss: 0.39597, in 0.016s
1 tree, 79 leaves, max depth = 13, train loss: 0.40620, val loss: 0.39369, in 0.016s
1 tree, 82 leaves, max depth = 12, train loss: 0.40400, val loss: 0.39138, in 0.031s
1 tree, 59 leaves, max depth = 12, train loss: 0.40180, val loss: 0.38902, in 0.016s
1 tree, 57 leaves, max depth = 12, train loss: 0.39793, val loss: 0.38519, in 0.016s
1 tree, 57 leaves, max depth = 12, train loss: 0.39420, val loss: 0.38151, in 0.031s
1 tree, 79 leaves, max depth = 12, train loss: 0.39198, val loss: 0.37922, in 0.016s
Fit 59 trees in 1.612 s, (4358 total leaves)
Time spent computing histograms: 0.474s
Time spent finding best splits:  0.085s
Time spent applying splits:      0.089s
Time spent predicting:           0.000s
Trial 73, Fold 5: Log loss = 0.39799173642544305, Average precision = 0.9482297461164175, ROC-AUC = 0.9457495122902849, Elapsed Time = 1.61448879999989 seconds
Optimization Progress:  74%|#######4  | 74/100 [15:01<06:24, 14.80s/it]
Trial 74, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 74, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
0.157 s 0.040 GB of training data: 
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 31 leaves, max depth = 7, train loss: 0.67330, val loss: 0.67326, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.65458, val loss: 0.65471, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.63709, val loss: 0.63734, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.62070, val loss: 0.62092, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.60535, val loss: 0.60569, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.59092, val loss: 0.59126, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.57792, val loss: 0.57821, in 0.016s
1 tree, 31 leaves, max depth = 6, train loss: 0.56526, val loss: 0.56537, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.55357, val loss: 0.55364, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.54238, val loss: 0.54229, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.53134, val loss: 0.53123, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.52148, val loss: 0.52133, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.51214, val loss: 0.51196, in 0.031s
1 tree, 31 leaves, max depth = 10, train loss: 0.50265, val loss: 0.50242, in 0.016s
1 tree, 31 leaves, max depth = 8, train loss: 0.49430, val loss: 0.49403, in 0.016s
1 tree, 31 leaves, max depth = 7, train loss: 0.48587, val loss: 0.48543, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.47779, val loss: 0.47734, in 0.000s
1 tree, 31 leaves, max depth = 10, train loss: 0.47006, val loss: 0.46957, in 0.031s
1 tree, 31 leaves, max depth = 10, train loss: 0.46002, val loss: 0.45979, in 0.016s
1 tree, 31 leaves, max depth = 8, train loss: 0.45315, val loss: 0.45278, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.44395, val loss: 0.44386, in 0.000s
1 tree, 31 leaves, max depth = 8, train loss: 0.43768, val loss: 0.43745, in 0.031s
1 tree, 31 leaves, max depth = 10, train loss: 0.43160, val loss: 0.43132, in 0.016s
1 tree, 31 leaves, max depth = 8, train loss: 0.42587, val loss: 0.42548, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.42033, val loss: 0.41994, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.41508, val loss: 0.41469, in 0.016s
1 tree, 31 leaves, max depth = 7, train loss: 0.41018, val loss: 0.40968, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.40275, val loss: 0.40253, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.39812, val loss: 0.39797, in 0.031s
1 tree, 31 leaves, max depth = 8, train loss: 0.39139, val loss: 0.39144, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.38713, val loss: 0.38719, in 0.016s
1 tree, 31 leaves, max depth = 13, train loss: 0.38081, val loss: 0.38121, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.37694, val loss: 0.37747, in 0.016s
1 tree, 31 leaves, max depth = 8, train loss: 0.37130, val loss: 0.37198, in 0.016s
1 tree, 31 leaves, max depth = 8, train loss: 0.36599, val loss: 0.36683, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.36251, val loss: 0.36348, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.35765, val loss: 0.35867, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.35308, val loss: 0.35415, in 0.016s
1 tree, 31 leaves, max depth = 13, train loss: 0.34934, val loss: 0.35048, in 0.016s
1 tree, 31 leaves, max depth = 13, train loss: 0.34577, val loss: 0.34696, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.34144, val loss: 0.34299, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.33745, val loss: 0.33907, in 0.016s
1 tree, 31 leaves, max depth = 13, train loss: 0.33418, val loss: 0.33590, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.33026, val loss: 0.33231, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.32671, val loss: 0.32882, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.32335, val loss: 0.32551, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.31987, val loss: 0.32237, in 0.016s
1 tree, 31 leaves, max depth = 8, train loss: 0.31677, val loss: 0.31920, in 0.016s
1 tree, 31 leaves, max depth = 13, train loss: 0.31400, val loss: 0.31652, in 0.016s
1 tree, 31 leaves, max depth = 8, train loss: 0.31115, val loss: 0.31359, in 0.031s
1 tree, 31 leaves, max depth = 9, train loss: 0.30826, val loss: 0.31076, in 0.016s
1 tree, 31 leaves, max depth = 13, train loss: 0.30524, val loss: 0.30805, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.30281, val loss: 0.30571, in 0.016s
1 tree, 31 leaves, max depth = 13, train loss: 0.30001, val loss: 0.30321, in 0.016s
1 tree, 31 leaves, max depth = 13, train loss: 0.29734, val loss: 0.30084, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.29492, val loss: 0.29833, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.29276, val loss: 0.29626, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.29043, val loss: 0.29397, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.28807, val loss: 0.29188, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.28633, val loss: 0.29023, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.28424, val loss: 0.28818, in 0.016s
1 tree, 31 leaves, max depth = 8, train loss: 0.28233, val loss: 0.28632, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.28033, val loss: 0.28424, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.27826, val loss: 0.28244, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.27641, val loss: 0.28062, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.27454, val loss: 0.27866, in 0.016s
1 tree, 31 leaves, max depth = 16, train loss: 0.27282, val loss: 0.27699, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.27105, val loss: 0.27515, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.26937, val loss: 0.27340, in 0.016s
1 tree, 31 leaves, max depth = 8, train loss: 0.26762, val loss: 0.27187, in 0.016s
1 tree, 31 leaves, max depth = 15, train loss: 0.26609, val loss: 0.27038, in 0.016s
1 tree, 31 leaves, max depth = 8, train loss: 0.26447, val loss: 0.26896, in 0.016s
1 tree, 31 leaves, max depth = 8, train loss: 0.26305, val loss: 0.26762, in 0.016s
1 tree, 31 leaves, max depth = 8, train loss: 0.26155, val loss: 0.26632, in 0.031s
1 tree, 31 leaves, max depth = 8, train loss: 0.26021, val loss: 0.26505, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.25889, val loss: 0.26374, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.25751, val loss: 0.26253, in 0.016s
1 tree, 31 leaves, max depth = 8, train loss: 0.25620, val loss: 0.26140, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.25484, val loss: 0.25996, in 0.031s
1 tree, 31 leaves, max depth = 11, train loss: 0.25367, val loss: 0.25878, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.25238, val loss: 0.25742, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.25122, val loss: 0.25644, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.25036, val loss: 0.25543, in 0.000s
1 tree, 31 leaves, max depth = 11, train loss: 0.24924, val loss: 0.25449, in 0.016s
1 tree, 31 leaves, max depth = 12, train loss: 0.24821, val loss: 0.25341, in 0.031s
1 tree, 31 leaves, max depth = 11, train loss: 0.24741, val loss: 0.25247, in 0.016s
1 tree, 31 leaves, max depth = 8, train loss: 0.24636, val loss: 0.25162, in 0.016s
1 tree, 31 leaves, max depth = 12, train loss: 0.24542, val loss: 0.25063, in 0.016s
Fit 88 trees in 1.767 s, (2728 total leaves)
Time spent computing histograms: 0.579s
Time spent finding best splits:  0.109s
Time spent applying splits:      0.065s
Time spent predicting:           0.000s
Trial 74, Fold 1: Log loss = 0.25379233630043185, Average precision = 0.9658598545976502, ROC-AUC = 0.9608860820751064, Elapsed Time = 1.7715054000000237 seconds
Trial 74, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 74, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
Binning 0.040 GB of training data: 0.189 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 31 leaves, max depth = 8, train loss: 0.67327, val loss: 0.67289, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.65441, val loss: 0.65373, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.63766, val loss: 0.63673, in 0.031s
1 tree, 31 leaves, max depth = 9, train loss: 0.62144, val loss: 0.62018, in 0.000s
1 tree, 31 leaves, max depth = 9, train loss: 0.60567, val loss: 0.60419, in 0.000s
1 tree, 31 leaves, max depth = 7, train loss: 0.59106, val loss: 0.58943, in 0.031s
1 tree, 31 leaves, max depth = 12, train loss: 0.57804, val loss: 0.57624, in 0.016s
1 tree, 31 leaves, max depth = 7, train loss: 0.56509, val loss: 0.56316, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.55273, val loss: 0.55058, in 0.016s
1 tree, 31 leaves, max depth = 8, train loss: 0.54132, val loss: 0.53894, in 0.016s
1 tree, 31 leaves, max depth = 8, train loss: 0.53024, val loss: 0.52770, in 0.016s
1 tree, 31 leaves, max depth = 12, train loss: 0.52047, val loss: 0.51781, in 0.016s
1 tree, 31 leaves, max depth = 8, train loss: 0.51056, val loss: 0.50776, in 0.016s
1 tree, 31 leaves, max depth = 8, train loss: 0.50119, val loss: 0.49827, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.49290, val loss: 0.48989, in 0.016s
1 tree, 31 leaves, max depth = 7, train loss: 0.48453, val loss: 0.48150, in 0.016s
1 tree, 31 leaves, max depth = 8, train loss: 0.47650, val loss: 0.47337, in 0.016s
1 tree, 31 leaves, max depth = 8, train loss: 0.46887, val loss: 0.46569, in 0.016s
1 tree, 31 leaves, max depth = 12, train loss: 0.45897, val loss: 0.45590, in 0.016s
1 tree, 31 leaves, max depth = 7, train loss: 0.45211, val loss: 0.44902, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.44547, val loss: 0.44235, in 0.031s
1 tree, 31 leaves, max depth = 9, train loss: 0.43914, val loss: 0.43587, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.43315, val loss: 0.42976, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.42744, val loss: 0.42394, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.42201, val loss: 0.41840, in 0.016s
1 tree, 31 leaves, max depth = 12, train loss: 0.41406, val loss: 0.41059, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.40908, val loss: 0.40553, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.40198, val loss: 0.39860, in 0.000s
1 tree, 31 leaves, max depth = 11, train loss: 0.39736, val loss: 0.39391, in 0.031s
1 tree, 31 leaves, max depth = 12, train loss: 0.39061, val loss: 0.38733, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.38661, val loss: 0.38337, in 0.016s
1 tree, 31 leaves, max depth = 12, train loss: 0.38038, val loss: 0.37725, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.37449, val loss: 0.37148, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.37068, val loss: 0.36760, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.36706, val loss: 0.36397, in 0.016s
1 tree, 31 leaves, max depth = 14, train loss: 0.36200, val loss: 0.35901, in 0.016s
1 tree, 31 leaves, max depth = 13, train loss: 0.35722, val loss: 0.35433, in 0.016s
1 tree, 31 leaves, max depth = 13, train loss: 0.35341, val loss: 0.35056, in 0.016s
1 tree, 31 leaves, max depth = 14, train loss: 0.34905, val loss: 0.34629, in 0.016s
1 tree, 31 leaves, max depth = 8, train loss: 0.34530, val loss: 0.34266, in 0.016s
1 tree, 31 leaves, max depth = 12, train loss: 0.34104, val loss: 0.33875, in 0.016s
1 tree, 31 leaves, max depth = 14, train loss: 0.33712, val loss: 0.33494, in 0.016s
1 tree, 31 leaves, max depth = 12, train loss: 0.33386, val loss: 0.33173, in 0.016s
1 tree, 31 leaves, max depth = 12, train loss: 0.32999, val loss: 0.32819, in 0.016s
1 tree, 31 leaves, max depth = 13, train loss: 0.32649, val loss: 0.32480, in 0.016s
1 tree, 31 leaves, max depth = 16, train loss: 0.32320, val loss: 0.32160, in 0.016s
1 tree, 31 leaves, max depth = 8, train loss: 0.32007, val loss: 0.31860, in 0.016s
1 tree, 31 leaves, max depth = 8, train loss: 0.31709, val loss: 0.31574, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.31440, val loss: 0.31310, in 0.031s
1 tree, 31 leaves, max depth = 12, train loss: 0.31117, val loss: 0.31019, in 0.016s
1 tree, 31 leaves, max depth = 12, train loss: 0.30811, val loss: 0.30744, in 0.016s
1 tree, 31 leaves, max depth = 12, train loss: 0.30520, val loss: 0.30480, in 0.016s
1 tree, 31 leaves, max depth = 14, train loss: 0.30255, val loss: 0.30224, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.29988, val loss: 0.29985, in 0.016s
1 tree, 31 leaves, max depth = 14, train loss: 0.29746, val loss: 0.29750, in 0.031s
1 tree, 31 leaves, max depth = 10, train loss: 0.29500, val loss: 0.29531, in 0.016s
1 tree, 31 leaves, max depth = 16, train loss: 0.29279, val loss: 0.29315, in 0.031s
1 tree, 31 leaves, max depth = 9, train loss: 0.29057, val loss: 0.29096, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.28833, val loss: 0.28894, in 0.016s
1 tree, 31 leaves, max depth = 14, train loss: 0.28634, val loss: 0.28704, in 0.016s
1 tree, 31 leaves, max depth = 8, train loss: 0.28418, val loss: 0.28497, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.28216, val loss: 0.28318, in 0.031s
1 tree, 31 leaves, max depth = 15, train loss: 0.28036, val loss: 0.28145, in 0.016s
1 tree, 31 leaves, max depth = 8, train loss: 0.27837, val loss: 0.27953, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.27654, val loss: 0.27793, in 0.031s
1 tree, 31 leaves, max depth = 13, train loss: 0.27491, val loss: 0.27639, in 0.016s
1 tree, 31 leaves, max depth = 8, train loss: 0.27306, val loss: 0.27463, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.27139, val loss: 0.27316, in 0.016s
1 tree, 31 leaves, max depth = 13, train loss: 0.26992, val loss: 0.27173, in 0.016s
1 tree, 31 leaves, max depth = 7, train loss: 0.26819, val loss: 0.27009, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.26665, val loss: 0.26873, in 0.016s
1 tree, 31 leaves, max depth = 17, train loss: 0.26533, val loss: 0.26743, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.26391, val loss: 0.26614, in 0.031s
1 tree, 31 leaves, max depth = 10, train loss: 0.26239, val loss: 0.26467, in 0.016s
1 tree, 31 leaves, max depth = 14, train loss: 0.26119, val loss: 0.26352, in 0.016s
1 tree, 31 leaves, max depth = 14, train loss: 0.26004, val loss: 0.26242, in 0.016s
1 tree, 31 leaves, max depth = 16, train loss: 0.25877, val loss: 0.26129, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.25730, val loss: 0.25990, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.25590, val loss: 0.25857, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.25472, val loss: 0.25757, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.25373, val loss: 0.25663, in 0.016s
1 tree, 31 leaves, max depth = 12, train loss: 0.25248, val loss: 0.25544, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.25139, val loss: 0.25452, in 0.031s
1 tree, 31 leaves, max depth = 10, train loss: 0.25045, val loss: 0.25366, in 0.016s
1 tree, 31 leaves, max depth = 16, train loss: 0.24945, val loss: 0.25277, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.24855, val loss: 0.25187, in 0.016s
1 tree, 31 leaves, max depth = 12, train loss: 0.24768, val loss: 0.25099, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.24656, val loss: 0.24991, in 0.016s
Fit 88 trees in 1.924 s, (2728 total leaves)
Time spent computing histograms: 0.620s
Time spent finding best splits:  0.115s
Time spent applying splits:      0.070s
Time spent predicting:           0.016s
Trial 74, Fold 2: Log loss = 0.25000810147832486, Average precision = 0.9661793912567854, ROC-AUC = 0.9633919848640689, Elapsed Time = 1.929179999999178 seconds
Trial 74, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 74, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
Binning 0.040 GB of training data: 0.158 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 31 leaves, max depth = 7, train loss: 0.67339, val loss: 0.67326, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.65476, val loss: 0.65461, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.63732, val loss: 0.63712, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.62100, val loss: 0.62075, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.60552, val loss: 0.60522, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.59113, val loss: 0.59081, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.57803, val loss: 0.57775, in 0.016s
1 tree, 31 leaves, max depth = 7, train loss: 0.56524, val loss: 0.56497, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.55353, val loss: 0.55332, in 0.031s
1 tree, 31 leaves, max depth = 10, train loss: 0.54227, val loss: 0.54194, in 0.000s
1 tree, 31 leaves, max depth = 11, train loss: 0.53130, val loss: 0.53103, in 0.016s
1 tree, 31 leaves, max depth = 12, train loss: 0.52147, val loss: 0.52127, in 0.016s
1 tree, 31 leaves, max depth = 12, train loss: 0.51219, val loss: 0.51203, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.50361, val loss: 0.50338, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.49525, val loss: 0.49508, in 0.016s
1 tree, 31 leaves, max depth = 7, train loss: 0.48691, val loss: 0.48681, in 0.016s
1 tree, 31 leaves, max depth = 12, train loss: 0.47885, val loss: 0.47884, in 0.031s
1 tree, 31 leaves, max depth = 11, train loss: 0.47120, val loss: 0.47125, in 0.016s
1 tree, 31 leaves, max depth = 12, train loss: 0.46117, val loss: 0.46190, in 0.016s
1 tree, 31 leaves, max depth = 7, train loss: 0.45430, val loss: 0.45516, in 0.016s
1 tree, 31 leaves, max depth = 12, train loss: 0.44767, val loss: 0.44861, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.44139, val loss: 0.44233, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.43536, val loss: 0.43637, in 0.016s
1 tree, 31 leaves, max depth = 12, train loss: 0.42967, val loss: 0.43074, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.42140, val loss: 0.42312, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.41612, val loss: 0.41794, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.40851, val loss: 0.41096, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.40152, val loss: 0.40454, in 0.031s
1 tree, 31 leaves, max depth = 12, train loss: 0.39683, val loss: 0.39994, in 0.016s
1 tree, 31 leaves, max depth = 12, train loss: 0.39236, val loss: 0.39554, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.38833, val loss: 0.39161, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.38200, val loss: 0.38588, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.37803, val loss: 0.38201, in 0.031s
1 tree, 31 leaves, max depth = 10, train loss: 0.37217, val loss: 0.37672, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.36664, val loss: 0.37175, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.36309, val loss: 0.36824, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.35820, val loss: 0.36380, in 0.031s
1 tree, 31 leaves, max depth = 11, train loss: 0.35358, val loss: 0.35962, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.34985, val loss: 0.35578, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.34561, val loss: 0.35193, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.34212, val loss: 0.34834, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.33802, val loss: 0.34484, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.33424, val loss: 0.34142, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.33107, val loss: 0.33812, in 0.016s
1 tree, 31 leaves, max depth = 8, train loss: 0.32736, val loss: 0.33502, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.32399, val loss: 0.33196, in 0.031s
1 tree, 31 leaves, max depth = 8, train loss: 0.32059, val loss: 0.32914, in 0.016s
1 tree, 31 leaves, max depth = 13, train loss: 0.31774, val loss: 0.32614, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.31475, val loss: 0.32346, in 0.016s
1 tree, 31 leaves, max depth = 13, train loss: 0.31209, val loss: 0.32068, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.30928, val loss: 0.31821, in 0.016s
1 tree, 31 leaves, max depth = 13, train loss: 0.30676, val loss: 0.31561, in 0.031s
1 tree, 31 leaves, max depth = 7, train loss: 0.30392, val loss: 0.31328, in 0.016s
1 tree, 31 leaves, max depth = 8, train loss: 0.30122, val loss: 0.31108, in 0.016s
1 tree, 31 leaves, max depth = 8, train loss: 0.29874, val loss: 0.30901, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.29631, val loss: 0.30705, in 0.016s
1 tree, 31 leaves, max depth = 12, train loss: 0.29405, val loss: 0.30499, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.29192, val loss: 0.30306, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.28967, val loss: 0.30059, in 0.016s
1 tree, 31 leaves, max depth = 8, train loss: 0.28754, val loss: 0.29888, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.28554, val loss: 0.29721, in 0.031s
1 tree, 31 leaves, max depth = 11, train loss: 0.28365, val loss: 0.29553, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.28162, val loss: 0.29328, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.27980, val loss: 0.29183, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.27809, val loss: 0.29031, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.27625, val loss: 0.28836, in 0.016s
1 tree, 31 leaves, max depth = 13, train loss: 0.27469, val loss: 0.28696, in 0.031s
1 tree, 31 leaves, max depth = 9, train loss: 0.27288, val loss: 0.28496, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.27122, val loss: 0.28320, in 0.016s
1 tree, 31 leaves, max depth = 8, train loss: 0.26967, val loss: 0.28196, in 0.016s
1 tree, 31 leaves, max depth = 12, train loss: 0.26828, val loss: 0.28072, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.26685, val loss: 0.27961, in 0.016s
1 tree, 31 leaves, max depth = 12, train loss: 0.26531, val loss: 0.27802, in 0.031s
1 tree, 31 leaves, max depth = 9, train loss: 0.26378, val loss: 0.27627, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.26248, val loss: 0.27524, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.26125, val loss: 0.27418, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.26003, val loss: 0.27324, in 0.016s
1 tree, 31 leaves, max depth = 15, train loss: 0.25890, val loss: 0.27223, in 0.031s
1 tree, 31 leaves, max depth = 11, train loss: 0.25776, val loss: 0.27140, in 0.016s
1 tree, 31 leaves, max depth = 15, train loss: 0.25671, val loss: 0.27043, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.25565, val loss: 0.26968, in 0.016s
1 tree, 31 leaves, max depth = 12, train loss: 0.25389, val loss: 0.26769, in 0.016s
1 tree, 31 leaves, max depth = 15, train loss: 0.25293, val loss: 0.26687, in 0.016s
1 tree, 31 leaves, max depth = 17, train loss: 0.25198, val loss: 0.26595, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.25071, val loss: 0.26451, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.24954, val loss: 0.26330, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.24865, val loss: 0.26257, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.24781, val loss: 0.26191, in 0.016s
Fit 88 trees in 1.923 s, (2728 total leaves)
Time spent computing histograms: 0.631s
Time spent finding best splits:  0.118s
Time spent applying splits:      0.071s
Time spent predicting:           0.000s
Trial 74, Fold 3: Log loss = 0.24984248592210537, Average precision = 0.9660236678991468, ROC-AUC = 0.9624181211536054, Elapsed Time = 1.928146400001424 seconds
Trial 74, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 74, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
Binning 0.040 GB of training data: 0.158 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 31 leaves, max depth = 8, train loss: 0.67358, val loss: 0.67269, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.65502, val loss: 0.65336, in 0.031s
1 tree, 31 leaves, max depth = 10, train loss: 0.63839, val loss: 0.63595, in 0.016s
1 tree, 31 leaves, max depth = 8, train loss: 0.62249, val loss: 0.61927, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.60716, val loss: 0.60328, in 0.016s
1 tree, 31 leaves, max depth = 6, train loss: 0.59294, val loss: 0.58834, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.57999, val loss: 0.57478, in 0.016s
1 tree, 31 leaves, max depth = 6, train loss: 0.56735, val loss: 0.56147, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.55506, val loss: 0.54864, in 0.016s
1 tree, 31 leaves, max depth = 8, train loss: 0.54374, val loss: 0.53667, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.53273, val loss: 0.52513, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.52299, val loss: 0.51489, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.51305, val loss: 0.50451, in 0.031s
1 tree, 31 leaves, max depth = 9, train loss: 0.50375, val loss: 0.49478, in 0.016s
1 tree, 31 leaves, max depth = 8, train loss: 0.49549, val loss: 0.48607, in 0.016s
1 tree, 31 leaves, max depth = 6, train loss: 0.48724, val loss: 0.47734, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.47923, val loss: 0.46890, in 0.016s
1 tree, 31 leaves, max depth = 7, train loss: 0.47174, val loss: 0.46096, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.46455, val loss: 0.45340, in 0.016s
1 tree, 31 leaves, max depth = 7, train loss: 0.45807, val loss: 0.44661, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.45199, val loss: 0.44016, in 0.031s
1 tree, 31 leaves, max depth = 8, train loss: 0.44568, val loss: 0.43350, in 0.016s
1 tree, 31 leaves, max depth = 8, train loss: 0.43966, val loss: 0.42719, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.43100, val loss: 0.41841, in 0.016s
1 tree, 31 leaves, max depth = 7, train loss: 0.42566, val loss: 0.41265, in 0.016s
1 tree, 31 leaves, max depth = 12, train loss: 0.41772, val loss: 0.40460, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.41268, val loss: 0.39926, in 0.031s
1 tree, 31 leaves, max depth = 10, train loss: 0.40552, val loss: 0.39195, in 0.016s
1 tree, 31 leaves, max depth = 8, train loss: 0.40101, val loss: 0.38719, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.39424, val loss: 0.38033, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.39030, val loss: 0.37620, in 0.031s
1 tree, 31 leaves, max depth = 7, train loss: 0.38619, val loss: 0.37182, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.38008, val loss: 0.36564, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.37426, val loss: 0.35973, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.37058, val loss: 0.35589, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.36550, val loss: 0.35065, in 0.031s
1 tree, 31 leaves, max depth = 11, train loss: 0.36070, val loss: 0.34558, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.35689, val loss: 0.34176, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.35252, val loss: 0.33715, in 0.031s
1 tree, 31 leaves, max depth = 11, train loss: 0.34896, val loss: 0.33359, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.34471, val loss: 0.32952, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.34075, val loss: 0.32540, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.33746, val loss: 0.32208, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.33366, val loss: 0.31843, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.33005, val loss: 0.31504, in 0.031s
1 tree, 31 leaves, max depth = 7, train loss: 0.32768, val loss: 0.31246, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.32478, val loss: 0.30953, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.32151, val loss: 0.30611, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.31832, val loss: 0.30312, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.31534, val loss: 0.30001, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.31241, val loss: 0.29728, in 0.016s
1 tree, 31 leaves, max depth = 13, train loss: 0.30971, val loss: 0.29439, in 0.016s
1 tree, 31 leaves, max depth = 8, train loss: 0.30710, val loss: 0.29169, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.30447, val loss: 0.28925, in 0.031s
1 tree, 31 leaves, max depth = 11, train loss: 0.30202, val loss: 0.28668, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.29961, val loss: 0.28445, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.29729, val loss: 0.28214, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.29514, val loss: 0.27985, in 0.016s
1 tree, 31 leaves, max depth = 8, train loss: 0.29291, val loss: 0.27753, in 0.031s
1 tree, 31 leaves, max depth = 11, train loss: 0.29086, val loss: 0.27538, in 0.016s
1 tree, 31 leaves, max depth = 8, train loss: 0.28876, val loss: 0.27320, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.28672, val loss: 0.27130, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.28479, val loss: 0.26955, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.28300, val loss: 0.26762, in 0.016s
1 tree, 31 leaves, max depth = 8, train loss: 0.28121, val loss: 0.26597, in 0.016s
1 tree, 31 leaves, max depth = 12, train loss: 0.27958, val loss: 0.26426, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.27773, val loss: 0.26234, in 0.016s
1 tree, 31 leaves, max depth = 8, train loss: 0.27597, val loss: 0.26059, in 0.031s
1 tree, 31 leaves, max depth = 9, train loss: 0.27468, val loss: 0.25921, in 0.016s
1 tree, 31 leaves, max depth = 8, train loss: 0.27312, val loss: 0.25780, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.27165, val loss: 0.25646, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.27001, val loss: 0.25472, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.26863, val loss: 0.25326, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.26728, val loss: 0.25205, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.26601, val loss: 0.25068, in 0.016s
1 tree, 31 leaves, max depth = 8, train loss: 0.26477, val loss: 0.24955, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.26328, val loss: 0.24793, in 0.031s
1 tree, 31 leaves, max depth = 10, train loss: 0.26213, val loss: 0.24669, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.26103, val loss: 0.24552, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.25962, val loss: 0.24398, in 0.016s
1 tree, 31 leaves, max depth = 8, train loss: 0.25833, val loss: 0.24270, in 0.016s
1 tree, 31 leaves, max depth = 8, train loss: 0.25724, val loss: 0.24170, in 0.031s
1 tree, 31 leaves, max depth = 15, train loss: 0.25622, val loss: 0.24067, in 0.016s
1 tree, 31 leaves, max depth = 8, train loss: 0.25525, val loss: 0.23977, in 0.016s
1 tree, 31 leaves, max depth = 15, train loss: 0.25430, val loss: 0.23881, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.25335, val loss: 0.23798, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.25249, val loss: 0.23704, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.25164, val loss: 0.23640, in 0.016s
Fit 88 trees in 1.924 s, (2728 total leaves)
Time spent computing histograms: 0.620s
Time spent finding best splits:  0.116s
Time spent applying splits:      0.072s
Time spent predicting:           0.000s
Trial 74, Fold 4: Log loss = 0.25293808784018346, Average precision = 0.9665306166848716, ROC-AUC = 0.9624423419911252, Elapsed Time = 1.941979100000026 seconds
Trial 74, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 74, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
Binning 0.040 GB of training data: 0.158 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 31 leaves, max depth = 8, train loss: 0.67313, val loss: 0.67229, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.65420, val loss: 0.65272, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.63646, val loss: 0.63431, in 0.016s
1 tree, 31 leaves, max depth = 12, train loss: 0.61988, val loss: 0.61715, in 0.016s
1 tree, 31 leaves, max depth = 12, train loss: 0.60432, val loss: 0.60101, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.58974, val loss: 0.58590, in 0.031s
1 tree, 31 leaves, max depth = 12, train loss: 0.57671, val loss: 0.57228, in 0.031s
1 tree, 31 leaves, max depth = 6, train loss: 0.56408, val loss: 0.55913, in 0.047s
1 tree, 31 leaves, max depth = 12, train loss: 0.55185, val loss: 0.54647, in 0.016s
1 tree, 31 leaves, max depth = 8, train loss: 0.54054, val loss: 0.53457, in 0.031s
1 tree, 31 leaves, max depth = 11, train loss: 0.52958, val loss: 0.52320, in 0.016s
1 tree, 31 leaves, max depth = 12, train loss: 0.51975, val loss: 0.51295, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.51048, val loss: 0.50328, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.50171, val loss: 0.49433, in 0.031s
1 tree, 31 leaves, max depth = 10, train loss: 0.49336, val loss: 0.48563, in 0.016s
1 tree, 31 leaves, max depth = 6, train loss: 0.48508, val loss: 0.47700, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.47696, val loss: 0.46855, in 0.031s
1 tree, 31 leaves, max depth = 12, train loss: 0.46926, val loss: 0.46053, in 0.016s
1 tree, 31 leaves, max depth = 12, train loss: 0.45928, val loss: 0.45052, in 0.016s
1 tree, 31 leaves, max depth = 7, train loss: 0.45252, val loss: 0.44346, in 0.031s
1 tree, 31 leaves, max depth = 11, train loss: 0.44338, val loss: 0.43430, in 0.016s
1 tree, 31 leaves, max depth = 7, train loss: 0.43721, val loss: 0.42788, in 0.016s
1 tree, 31 leaves, max depth = 12, train loss: 0.43116, val loss: 0.42166, in 0.016s
1 tree, 31 leaves, max depth = 7, train loss: 0.42556, val loss: 0.41586, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.42007, val loss: 0.41022, in 0.031s
1 tree, 31 leaves, max depth = 10, train loss: 0.41486, val loss: 0.40489, in 0.016s
1 tree, 31 leaves, max depth = 7, train loss: 0.40997, val loss: 0.39979, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.40257, val loss: 0.39246, in 0.016s
1 tree, 31 leaves, max depth = 8, train loss: 0.39798, val loss: 0.38776, in 0.016s
1 tree, 31 leaves, max depth = 7, train loss: 0.39395, val loss: 0.38369, in 0.016s
1 tree, 31 leaves, max depth = 12, train loss: 0.38728, val loss: 0.37711, in 0.031s
1 tree, 31 leaves, max depth = 9, train loss: 0.38094, val loss: 0.37081, in 0.016s
1 tree, 31 leaves, max depth = 8, train loss: 0.37703, val loss: 0.36680, in 0.016s
1 tree, 31 leaves, max depth = 12, train loss: 0.37145, val loss: 0.36118, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.36590, val loss: 0.35569, in 0.031s
1 tree, 31 leaves, max depth = 10, train loss: 0.36233, val loss: 0.35206, in 0.016s
1 tree, 31 leaves, max depth = 12, train loss: 0.35746, val loss: 0.34716, in 0.016s
1 tree, 31 leaves, max depth = 13, train loss: 0.35360, val loss: 0.34330, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.34917, val loss: 0.33878, in 0.016s
1 tree, 31 leaves, max depth = 13, train loss: 0.34560, val loss: 0.33520, in 0.031s
1 tree, 31 leaves, max depth = 11, train loss: 0.34141, val loss: 0.33095, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.33804, val loss: 0.32763, in 0.016s
1 tree, 31 leaves, max depth = 13, train loss: 0.33484, val loss: 0.32447, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.33088, val loss: 0.32089, in 0.016s
1 tree, 31 leaves, max depth = 13, train loss: 0.32722, val loss: 0.31717, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.32364, val loss: 0.31386, in 0.021s
1 tree, 31 leaves, max depth = 14, train loss: 0.32075, val loss: 0.31101, in 0.026s
1 tree, 31 leaves, max depth = 10, train loss: 0.31739, val loss: 0.30799, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.31423, val loss: 0.30478, in 0.016s
1 tree, 31 leaves, max depth = 7, train loss: 0.31202, val loss: 0.30265, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.30900, val loss: 0.29995, in 0.016s
1 tree, 31 leaves, max depth = 12, train loss: 0.30619, val loss: 0.29709, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.30342, val loss: 0.29463, in 0.016s
1 tree, 31 leaves, max depth = 12, train loss: 0.30084, val loss: 0.29200, in 0.031s
1 tree, 31 leaves, max depth = 8, train loss: 0.29829, val loss: 0.28975, in 0.016s
1 tree, 31 leaves, max depth = 12, train loss: 0.29594, val loss: 0.28733, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.29361, val loss: 0.28508, in 0.016s
1 tree, 31 leaves, max depth = 8, train loss: 0.29135, val loss: 0.28280, in 0.016s
1 tree, 31 leaves, max depth = 8, train loss: 0.28911, val loss: 0.28075, in 0.031s
1 tree, 31 leaves, max depth = 8, train loss: 0.28697, val loss: 0.27863, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.28493, val loss: 0.27655, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.28300, val loss: 0.27468, in 0.016s
1 tree, 31 leaves, max depth = 8, train loss: 0.28100, val loss: 0.27287, in 0.016s
1 tree, 31 leaves, max depth = 13, train loss: 0.27917, val loss: 0.27099, in 0.031s
1 tree, 31 leaves, max depth = 11, train loss: 0.27772, val loss: 0.26963, in 0.016s
1 tree, 31 leaves, max depth = 8, train loss: 0.27591, val loss: 0.26806, in 0.016s
1 tree, 31 leaves, max depth = 13, train loss: 0.27426, val loss: 0.26630, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.27254, val loss: 0.26463, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.27081, val loss: 0.26294, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.26914, val loss: 0.26128, in 0.031s
1 tree, 31 leaves, max depth = 8, train loss: 0.26755, val loss: 0.25986, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.26607, val loss: 0.25843, in 0.016s
1 tree, 31 leaves, max depth = 14, train loss: 0.26464, val loss: 0.25697, in 0.016s
1 tree, 31 leaves, max depth = 8, train loss: 0.26319, val loss: 0.25570, in 0.016s
1 tree, 31 leaves, max depth = 14, train loss: 0.26188, val loss: 0.25436, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.26040, val loss: 0.25290, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.25900, val loss: 0.25150, in 0.031s
1 tree, 31 leaves, max depth = 9, train loss: 0.25771, val loss: 0.25036, in 0.016s
1 tree, 31 leaves, max depth = 8, train loss: 0.25602, val loss: 0.24875, in 0.016s
1 tree, 31 leaves, max depth = 12, train loss: 0.25484, val loss: 0.24756, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.25366, val loss: 0.24653, in 0.016s
1 tree, 31 leaves, max depth = 16, train loss: 0.25255, val loss: 0.24542, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.25142, val loss: 0.24442, in 0.016s
1 tree, 31 leaves, max depth = 13, train loss: 0.25037, val loss: 0.24338, in 0.016s
1 tree, 31 leaves, max depth = 14, train loss: 0.24941, val loss: 0.24240, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.24840, val loss: 0.24150, in 0.016s
1 tree, 31 leaves, max depth = 12, train loss: 0.24748, val loss: 0.24063, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.24654, val loss: 0.23985, in 0.016s
Fit 88 trees in 2.002 s, (2728 total leaves)
Time spent computing histograms: 0.666s
Time spent finding best splits:  0.127s
Time spent applying splits:      0.079s
Time spent predicting:           0.031s
Trial 74, Fold 5: Log loss = 0.25782377744800283, Average precision = 0.963778447082425, ROC-AUC = 0.9593320327740928, Elapsed Time = 2.021543599999859 seconds
Optimization Progress:  75%|#######5  | 75/100 [15:18<06:27, 15.51s/it]
Trial 75, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 75, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
0.173 s 0.040 GB of training data: 
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 27 leaves, max depth = 8, train loss: 0.67221, val loss: 0.67213, in 0.016s
1 tree, 38 leaves, max depth = 11, train loss: 0.65231, val loss: 0.65222, in 0.016s
1 tree, 37 leaves, max depth = 9, train loss: 0.63380, val loss: 0.63384, in 0.016s
1 tree, 41 leaves, max depth = 11, train loss: 0.61653, val loss: 0.61666, in 0.031s
1 tree, 33 leaves, max depth = 9, train loss: 0.60101, val loss: 0.60101, in 0.016s
1 tree, 40 leaves, max depth = 12, train loss: 0.58590, val loss: 0.58598, in 0.016s
1 tree, 34 leaves, max depth = 7, train loss: 0.57252, val loss: 0.57234, in 0.016s
1 tree, 41 leaves, max depth = 11, train loss: 0.55908, val loss: 0.55882, in 0.031s
1 tree, 38 leaves, max depth = 12, train loss: 0.54714, val loss: 0.54684, in 0.016s
1 tree, 38 leaves, max depth = 12, train loss: 0.53593, val loss: 0.53559, in 0.016s
1 tree, 39 leaves, max depth = 12, train loss: 0.52466, val loss: 0.52433, in 0.016s
1 tree, 40 leaves, max depth = 12, train loss: 0.51472, val loss: 0.51434, in 0.016s
1 tree, 38 leaves, max depth = 11, train loss: 0.50466, val loss: 0.50433, in 0.031s
1 tree, 38 leaves, max depth = 11, train loss: 0.49495, val loss: 0.49458, in 0.016s
1 tree, 32 leaves, max depth = 9, train loss: 0.48608, val loss: 0.48554, in 0.031s
1 tree, 35 leaves, max depth = 7, train loss: 0.47756, val loss: 0.47681, in 0.016s
1 tree, 42 leaves, max depth = 10, train loss: 0.46934, val loss: 0.46859, in 0.031s
1 tree, 39 leaves, max depth = 11, train loss: 0.46160, val loss: 0.46082, in 0.016s
1 tree, 42 leaves, max depth = 10, train loss: 0.45427, val loss: 0.45348, in 0.016s
1 tree, 43 leaves, max depth = 10, train loss: 0.44735, val loss: 0.44655, in 0.031s
1 tree, 46 leaves, max depth = 11, train loss: 0.43763, val loss: 0.43720, in 0.016s
1 tree, 38 leaves, max depth = 8, train loss: 0.43135, val loss: 0.43082, in 0.031s
1 tree, 45 leaves, max depth = 11, train loss: 0.42528, val loss: 0.42480, in 0.016s
1 tree, 47 leaves, max depth = 11, train loss: 0.41953, val loss: 0.41910, in 0.016s
1 tree, 44 leaves, max depth = 11, train loss: 0.41121, val loss: 0.41102, in 0.016s
1 tree, 43 leaves, max depth = 12, train loss: 0.40601, val loss: 0.40585, in 0.016s
1 tree, 49 leaves, max depth = 12, train loss: 0.39828, val loss: 0.39846, in 0.031s
1 tree, 44 leaves, max depth = 10, train loss: 0.39346, val loss: 0.39374, in 0.016s
1 tree, 52 leaves, max depth = 12, train loss: 0.38637, val loss: 0.38701, in 0.031s
1 tree, 43 leaves, max depth = 10, train loss: 0.37974, val loss: 0.38077, in 0.016s
1 tree, 47 leaves, max depth = 11, train loss: 0.37344, val loss: 0.37480, in 0.016s
1 tree, 46 leaves, max depth = 13, train loss: 0.36937, val loss: 0.37080, in 0.016s
1 tree, 50 leaves, max depth = 14, train loss: 0.36572, val loss: 0.36725, in 0.016s
1 tree, 43 leaves, max depth = 8, train loss: 0.36211, val loss: 0.36360, in 0.031s
1 tree, 24 leaves, max depth = 10, train loss: 0.35687, val loss: 0.35843, in 0.016s
1 tree, 28 leaves, max depth = 10, train loss: 0.35196, val loss: 0.35359, in 0.016s
1 tree, 31 leaves, max depth = 13, train loss: 0.34735, val loss: 0.34907, in 0.016s
1 tree, 43 leaves, max depth = 13, train loss: 0.34352, val loss: 0.34529, in 0.031s
1 tree, 41 leaves, max depth = 13, train loss: 0.33988, val loss: 0.34172, in 0.016s
1 tree, 71 leaves, max depth = 16, train loss: 0.33543, val loss: 0.33774, in 0.016s
1 tree, 57 leaves, max depth = 12, train loss: 0.33109, val loss: 0.33377, in 0.031s
1 tree, 32 leaves, max depth = 15, train loss: 0.32734, val loss: 0.33011, in 0.016s
1 tree, 49 leaves, max depth = 14, train loss: 0.32389, val loss: 0.32662, in 0.016s
1 tree, 72 leaves, max depth = 15, train loss: 0.32009, val loss: 0.32325, in 0.016s
1 tree, 37 leaves, max depth = 12, train loss: 0.31674, val loss: 0.31996, in 0.016s
Fit 45 trees in 1.283 s, (1877 total leaves)
Time spent computing histograms: 0.363s
Time spent finding best splits:  0.072s
Time spent applying splits:      0.047s
Time spent predicting:           0.000s
Trial 75, Fold 1: Log loss = 0.3221012483428208, Average precision = 0.958427671986638, ROC-AUC = 0.9527340445362127, Elapsed Time = 1.290885200000048 seconds
Trial 75, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 75, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
Binning 0.040 GB of training data: 0.173 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 25 leaves, max depth = 7, train loss: 0.67199, val loss: 0.67151, in 0.016s
1 tree, 27 leaves, max depth = 7, train loss: 0.65196, val loss: 0.65107, in 0.016s
1 tree, 29 leaves, max depth = 8, train loss: 0.63425, val loss: 0.63317, in 0.031s
1 tree, 26 leaves, max depth = 6, train loss: 0.61718, val loss: 0.61572, in 0.016s
1 tree, 29 leaves, max depth = 6, train loss: 0.60087, val loss: 0.59909, in 0.016s
1 tree, 28 leaves, max depth = 7, train loss: 0.58555, val loss: 0.58349, in 0.016s
1 tree, 28 leaves, max depth = 10, train loss: 0.57237, val loss: 0.57017, in 0.016s
1 tree, 32 leaves, max depth = 9, train loss: 0.55885, val loss: 0.55651, in 0.016s
1 tree, 29 leaves, max depth = 7, train loss: 0.54694, val loss: 0.54439, in 0.031s
1 tree, 34 leaves, max depth = 10, train loss: 0.53509, val loss: 0.53228, in 0.016s
1 tree, 38 leaves, max depth = 10, train loss: 0.52378, val loss: 0.52076, in 0.016s
1 tree, 34 leaves, max depth = 9, train loss: 0.51382, val loss: 0.51084, in 0.016s
1 tree, 39 leaves, max depth = 10, train loss: 0.50354, val loss: 0.50043, in 0.031s
1 tree, 38 leaves, max depth = 10, train loss: 0.49384, val loss: 0.49062, in 0.016s
1 tree, 39 leaves, max depth = 10, train loss: 0.48543, val loss: 0.48212, in 0.016s
1 tree, 40 leaves, max depth = 10, train loss: 0.47674, val loss: 0.47332, in 0.016s
1 tree, 36 leaves, max depth = 11, train loss: 0.46590, val loss: 0.46258, in 0.031s
1 tree, 39 leaves, max depth = 10, train loss: 0.45874, val loss: 0.45537, in 0.016s
1 tree, 40 leaves, max depth = 10, train loss: 0.45137, val loss: 0.44806, in 0.016s
1 tree, 40 leaves, max depth = 10, train loss: 0.44426, val loss: 0.44085, in 0.016s
1 tree, 41 leaves, max depth = 11, train loss: 0.43753, val loss: 0.43403, in 0.016s
1 tree, 39 leaves, max depth = 12, train loss: 0.43111, val loss: 0.42754, in 0.031s
1 tree, 39 leaves, max depth = 13, train loss: 0.42506, val loss: 0.42147, in 0.016s
1 tree, 39 leaves, max depth = 13, train loss: 0.41930, val loss: 0.41569, in 0.016s
1 tree, 37 leaves, max depth = 13, train loss: 0.41100, val loss: 0.40755, in 0.031s
1 tree, 40 leaves, max depth = 13, train loss: 0.40574, val loss: 0.40218, in 0.016s
1 tree, 44 leaves, max depth = 13, train loss: 0.39807, val loss: 0.39464, in 0.016s
1 tree, 40 leaves, max depth = 13, train loss: 0.39328, val loss: 0.38985, in 0.016s
1 tree, 46 leaves, max depth = 10, train loss: 0.38641, val loss: 0.38324, in 0.031s
1 tree, 38 leaves, max depth = 12, train loss: 0.38197, val loss: 0.37877, in 0.016s
1 tree, 46 leaves, max depth = 10, train loss: 0.37567, val loss: 0.37272, in 0.016s
1 tree, 41 leaves, max depth = 13, train loss: 0.37166, val loss: 0.36871, in 0.016s
1 tree, 45 leaves, max depth = 12, train loss: 0.36811, val loss: 0.36518, in 0.016s
1 tree, 48 leaves, max depth = 10, train loss: 0.36244, val loss: 0.35977, in 0.016s
1 tree, 19 leaves, max depth = 11, train loss: 0.35727, val loss: 0.35469, in 0.016s
1 tree, 19 leaves, max depth = 11, train loss: 0.35242, val loss: 0.34991, in 0.016s
1 tree, 43 leaves, max depth = 15, train loss: 0.34846, val loss: 0.34609, in 0.016s
1 tree, 72 leaves, max depth = 17, train loss: 0.34377, val loss: 0.34176, in 0.016s
1 tree, 19 leaves, max depth = 11, train loss: 0.33950, val loss: 0.33755, in 0.016s
1 tree, 68 leaves, max depth = 16, train loss: 0.33520, val loss: 0.33361, in 0.016s
1 tree, 49 leaves, max depth = 13, train loss: 0.33221, val loss: 0.33060, in 0.031s
1 tree, 50 leaves, max depth = 17, train loss: 0.32874, val loss: 0.32719, in 0.031s
1 tree, 73 leaves, max depth = 15, train loss: 0.32487, val loss: 0.32367, in 0.016s
1 tree, 25 leaves, max depth = 10, train loss: 0.32132, val loss: 0.32022, in 0.016s
1 tree, 49 leaves, max depth = 12, train loss: 0.31874, val loss: 0.31763, in 0.016s
Fit 45 trees in 1.236 s, (1739 total leaves)
Time spent computing histograms: 0.354s
Time spent finding best splits:  0.065s
Time spent applying splits:      0.042s
Time spent predicting:           0.000s
Trial 75, Fold 2: Log loss = 0.322476233779029, Average precision = 0.9562086952003208, ROC-AUC = 0.9535934786085698, Elapsed Time = 1.2553203999996185 seconds
Trial 75, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 75, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
Binning 0.040 GB of training data: 0.157 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 26 leaves, max depth = 6, train loss: 0.67231, val loss: 0.67222, in 0.016s
1 tree, 37 leaves, max depth = 12, train loss: 0.65264, val loss: 0.65255, in 0.016s
1 tree, 40 leaves, max depth = 12, train loss: 0.63430, val loss: 0.63415, in 0.016s
1 tree, 37 leaves, max depth = 9, train loss: 0.61687, val loss: 0.61675, in 0.016s
1 tree, 40 leaves, max depth = 9, train loss: 0.60140, val loss: 0.60140, in 0.016s
1 tree, 39 leaves, max depth = 9, train loss: 0.58629, val loss: 0.58624, in 0.031s
1 tree, 31 leaves, max depth = 7, train loss: 0.57286, val loss: 0.57294, in 0.016s
1 tree, 37 leaves, max depth = 9, train loss: 0.55950, val loss: 0.55951, in 0.016s
1 tree, 37 leaves, max depth = 8, train loss: 0.54730, val loss: 0.54720, in 0.016s
1 tree, 37 leaves, max depth = 11, train loss: 0.53530, val loss: 0.53520, in 0.016s
1 tree, 39 leaves, max depth = 11, train loss: 0.52462, val loss: 0.52459, in 0.016s
1 tree, 39 leaves, max depth = 11, train loss: 0.51393, val loss: 0.51393, in 0.016s
1 tree, 39 leaves, max depth = 12, train loss: 0.50383, val loss: 0.50381, in 0.031s
1 tree, 41 leaves, max depth = 11, train loss: 0.49431, val loss: 0.49436, in 0.016s
1 tree, 43 leaves, max depth = 12, train loss: 0.48528, val loss: 0.48538, in 0.016s
1 tree, 35 leaves, max depth = 8, train loss: 0.47689, val loss: 0.47704, in 0.016s
1 tree, 43 leaves, max depth = 13, train loss: 0.46917, val loss: 0.46947, in 0.031s
1 tree, 43 leaves, max depth = 12, train loss: 0.46147, val loss: 0.46178, in 0.016s
1 tree, 45 leaves, max depth = 12, train loss: 0.45418, val loss: 0.45454, in 0.016s
1 tree, 43 leaves, max depth = 13, train loss: 0.44727, val loss: 0.44771, in 0.016s
1 tree, 38 leaves, max depth = 8, train loss: 0.44084, val loss: 0.44139, in 0.031s
1 tree, 47 leaves, max depth = 12, train loss: 0.43136, val loss: 0.43266, in 0.016s
1 tree, 43 leaves, max depth = 13, train loss: 0.42539, val loss: 0.42674, in 0.016s
1 tree, 46 leaves, max depth = 12, train loss: 0.41674, val loss: 0.41879, in 0.031s
1 tree, 42 leaves, max depth = 8, train loss: 0.41142, val loss: 0.41361, in 0.016s
1 tree, 51 leaves, max depth = 13, train loss: 0.40353, val loss: 0.40639, in 0.016s
1 tree, 42 leaves, max depth = 14, train loss: 0.39854, val loss: 0.40152, in 0.031s
1 tree, 45 leaves, max depth = 11, train loss: 0.39147, val loss: 0.39508, in 0.016s
1 tree, 41 leaves, max depth = 12, train loss: 0.38689, val loss: 0.39058, in 0.031s
1 tree, 45 leaves, max depth = 13, train loss: 0.38278, val loss: 0.38655, in 0.016s
1 tree, 49 leaves, max depth = 13, train loss: 0.37624, val loss: 0.38063, in 0.031s
1 tree, 45 leaves, max depth = 13, train loss: 0.37221, val loss: 0.37668, in 0.016s
1 tree, 50 leaves, max depth = 14, train loss: 0.36870, val loss: 0.37325, in 0.016s
1 tree, 59 leaves, max depth = 16, train loss: 0.36293, val loss: 0.36818, in 0.031s
1 tree, 29 leaves, max depth = 10, train loss: 0.35765, val loss: 0.36339, in 0.016s
1 tree, 29 leaves, max depth = 10, train loss: 0.35269, val loss: 0.35889, in 0.016s
1 tree, 71 leaves, max depth = 17, train loss: 0.34778, val loss: 0.35476, in 0.031s
1 tree, 29 leaves, max depth = 10, train loss: 0.34334, val loss: 0.35070, in 0.016s
1 tree, 41 leaves, max depth = 14, train loss: 0.33964, val loss: 0.34681, in 0.016s
1 tree, 67 leaves, max depth = 17, train loss: 0.33527, val loss: 0.34316, in 0.031s
1 tree, 41 leaves, max depth = 14, train loss: 0.33183, val loss: 0.33954, in 0.016s
1 tree, 33 leaves, max depth = 15, train loss: 0.32798, val loss: 0.33607, in 0.016s
1 tree, 65 leaves, max depth = 17, train loss: 0.32410, val loss: 0.33283, in 0.016s
1 tree, 32 leaves, max depth = 12, train loss: 0.32061, val loss: 0.32968, in 0.031s
1 tree, 40 leaves, max depth = 14, train loss: 0.31753, val loss: 0.32644, in 0.016s
Fit 45 trees in 1.267 s, (1891 total leaves)
Time spent computing histograms: 0.357s
Time spent finding best splits:  0.070s
Time spent applying splits:      0.047s
Time spent predicting:           0.016s
Trial 75, Fold 3: Log loss = 0.3175289258925027, Average precision = 0.9590985162914907, ROC-AUC = 0.9555419383421137, Elapsed Time = 1.26342300000033 seconds
Trial 75, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 75, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
Binning 0.040 GB of training data: 0.157 s
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 23 leaves, max depth = 8, train loss: 0.67229, val loss: 0.67146, in 0.016s
1 tree, 24 leaves, max depth = 8, train loss: 0.65289, val loss: 0.65121, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.63548, val loss: 0.63305, in 0.016s
1 tree, 24 leaves, max depth = 8, train loss: 0.61860, val loss: 0.61545, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.60247, val loss: 0.59873, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.58739, val loss: 0.58307, in 0.016s
1 tree, 22 leaves, max depth = 7, train loss: 0.57442, val loss: 0.56933, in 0.016s
1 tree, 36 leaves, max depth = 11, train loss: 0.56103, val loss: 0.55538, in 0.031s
1 tree, 32 leaves, max depth = 11, train loss: 0.54925, val loss: 0.54302, in 0.016s
1 tree, 36 leaves, max depth = 8, train loss: 0.53743, val loss: 0.53066, in 0.031s
1 tree, 40 leaves, max depth = 10, train loss: 0.52599, val loss: 0.51872, in 0.016s
1 tree, 36 leaves, max depth = 8, train loss: 0.51608, val loss: 0.50829, in 0.031s
1 tree, 42 leaves, max depth = 10, train loss: 0.50584, val loss: 0.49763, in 0.016s
1 tree, 39 leaves, max depth = 10, train loss: 0.49620, val loss: 0.48754, in 0.016s
1 tree, 40 leaves, max depth = 10, train loss: 0.48781, val loss: 0.47875, in 0.016s
1 tree, 44 leaves, max depth = 10, train loss: 0.47914, val loss: 0.46970, in 0.016s
1 tree, 47 leaves, max depth = 11, train loss: 0.46828, val loss: 0.45867, in 0.016s
1 tree, 39 leaves, max depth = 11, train loss: 0.46102, val loss: 0.45115, in 0.016s
1 tree, 44 leaves, max depth = 11, train loss: 0.45357, val loss: 0.44334, in 0.031s
1 tree, 47 leaves, max depth = 11, train loss: 0.44402, val loss: 0.43363, in 0.016s
1 tree, 45 leaves, max depth = 11, train loss: 0.43727, val loss: 0.42651, in 0.016s
1 tree, 44 leaves, max depth = 11, train loss: 0.43084, val loss: 0.41973, in 0.016s
1 tree, 47 leaves, max depth = 11, train loss: 0.42472, val loss: 0.41334, in 0.031s
1 tree, 46 leaves, max depth = 11, train loss: 0.41892, val loss: 0.40726, in 0.016s
1 tree, 30 leaves, max depth = 8, train loss: 0.41371, val loss: 0.40164, in 0.016s
1 tree, 42 leaves, max depth = 11, train loss: 0.40849, val loss: 0.39614, in 0.016s
1 tree, 49 leaves, max depth = 11, train loss: 0.40086, val loss: 0.38843, in 0.031s
1 tree, 44 leaves, max depth = 11, train loss: 0.39605, val loss: 0.38338, in 0.016s
1 tree, 51 leaves, max depth = 11, train loss: 0.38905, val loss: 0.37632, in 0.031s
1 tree, 51 leaves, max depth = 11, train loss: 0.38247, val loss: 0.36968, in 0.016s
1 tree, 57 leaves, max depth = 13, train loss: 0.37626, val loss: 0.36341, in 0.016s
1 tree, 29 leaves, max depth = 7, train loss: 0.37240, val loss: 0.35922, in 0.031s
1 tree, 50 leaves, max depth = 11, train loss: 0.36881, val loss: 0.35557, in 0.016s
1 tree, 53 leaves, max depth = 8, train loss: 0.36513, val loss: 0.35163, in 0.016s
1 tree, 28 leaves, max depth = 12, train loss: 0.35993, val loss: 0.34623, in 0.000s
1 tree, 33 leaves, max depth = 11, train loss: 0.35505, val loss: 0.34119, in 0.016s
1 tree, 42 leaves, max depth = 13, train loss: 0.35112, val loss: 0.33725, in 0.031s
1 tree, 65 leaves, max depth = 14, train loss: 0.34635, val loss: 0.33285, in 0.016s
1 tree, 29 leaves, max depth = 12, train loss: 0.34205, val loss: 0.32836, in 0.016s
1 tree, 42 leaves, max depth = 12, train loss: 0.33843, val loss: 0.32471, in 0.016s
1 tree, 35 leaves, max depth = 13, train loss: 0.33451, val loss: 0.32055, in 0.016s
1 tree, 63 leaves, max depth = 14, train loss: 0.33042, val loss: 0.31680, in 0.031s
1 tree, 48 leaves, max depth = 12, train loss: 0.32693, val loss: 0.31321, in 0.016s
1 tree, 40 leaves, max depth = 12, train loss: 0.32379, val loss: 0.31002, in 0.031s
1 tree, 50 leaves, max depth = 12, train loss: 0.32061, val loss: 0.30675, in 0.016s
Fit 45 trees in 1.235 s, (1829 total leaves)
Time spent computing histograms: 0.357s
Time spent finding best splits:  0.066s
Time spent applying splits:      0.045s
Time spent predicting:           0.000s
Trial 75, Fold 4: Log loss = 0.32137275120798536, Average precision = 0.9595918254177039, ROC-AUC = 0.9550634133779696, Elapsed Time = 1.250694600001225 seconds
Trial 75, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 75, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
Binning 0.040 GB of training data: 0.173 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 24 leaves, max depth = 6, train loss: 0.67198, val loss: 0.67111, in 0.016s
1 tree, 32 leaves, max depth = 9, train loss: 0.65189, val loss: 0.65033, in 0.016s
1 tree, 33 leaves, max depth = 9, train loss: 0.63318, val loss: 0.63101, in 0.031s
1 tree, 31 leaves, max depth = 7, train loss: 0.61603, val loss: 0.61320, in 0.016s
1 tree, 32 leaves, max depth = 10, train loss: 0.59974, val loss: 0.59635, in 0.016s
1 tree, 34 leaves, max depth = 10, train loss: 0.58526, val loss: 0.58123, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.57084, val loss: 0.56627, in 0.016s
1 tree, 34 leaves, max depth = 11, train loss: 0.55811, val loss: 0.55296, in 0.016s
1 tree, 38 leaves, max depth = 13, train loss: 0.54535, val loss: 0.53974, in 0.000s
1 tree, 28 leaves, max depth = 6, train loss: 0.53368, val loss: 0.52742, in 0.016s
1 tree, 39 leaves, max depth = 13, train loss: 0.52238, val loss: 0.51571, in 0.031s
1 tree, 39 leaves, max depth = 13, train loss: 0.51176, val loss: 0.50473, in 0.016s
1 tree, 34 leaves, max depth = 7, train loss: 0.50191, val loss: 0.49446, in 0.016s
1 tree, 39 leaves, max depth = 13, train loss: 0.49242, val loss: 0.48461, in 0.016s
1 tree, 39 leaves, max depth = 8, train loss: 0.48368, val loss: 0.47551, in 0.016s
1 tree, 38 leaves, max depth = 13, train loss: 0.47519, val loss: 0.46673, in 0.016s
1 tree, 44 leaves, max depth = 9, train loss: 0.46769, val loss: 0.45902, in 0.016s
1 tree, 39 leaves, max depth = 13, train loss: 0.46004, val loss: 0.45116, in 0.016s
1 tree, 41 leaves, max depth = 13, train loss: 0.45277, val loss: 0.44365, in 0.016s
1 tree, 40 leaves, max depth = 12, train loss: 0.44591, val loss: 0.43656, in 0.016s
1 tree, 37 leaves, max depth = 7, train loss: 0.43958, val loss: 0.42997, in 0.031s
1 tree, 39 leaves, max depth = 13, train loss: 0.43341, val loss: 0.42367, in 0.016s
1 tree, 38 leaves, max depth = 13, train loss: 0.42749, val loss: 0.41766, in 0.016s
1 tree, 34 leaves, max depth = 13, train loss: 0.41856, val loss: 0.40882, in 0.016s
1 tree, 40 leaves, max depth = 8, train loss: 0.41037, val loss: 0.40068, in 0.016s
1 tree, 38 leaves, max depth = 12, train loss: 0.40518, val loss: 0.39539, in 0.016s
1 tree, 37 leaves, max depth = 13, train loss: 0.39750, val loss: 0.38779, in 0.031s
1 tree, 47 leaves, max depth = 13, train loss: 0.39028, val loss: 0.38066, in 0.016s
1 tree, 44 leaves, max depth = 14, train loss: 0.38349, val loss: 0.37397, in 0.016s
1 tree, 39 leaves, max depth = 13, train loss: 0.37935, val loss: 0.36974, in 0.016s
1 tree, 43 leaves, max depth = 13, train loss: 0.37513, val loss: 0.36546, in 0.016s
1 tree, 47 leaves, max depth = 10, train loss: 0.37127, val loss: 0.36156, in 0.016s
1 tree, 57 leaves, max depth = 12, train loss: 0.36545, val loss: 0.35582, in 0.031s
1 tree, 49 leaves, max depth = 8, train loss: 0.36189, val loss: 0.35224, in 0.016s
1 tree, 30 leaves, max depth = 11, train loss: 0.35665, val loss: 0.34691, in 0.016s
1 tree, 30 leaves, max depth = 11, train loss: 0.35172, val loss: 0.34191, in 0.016s
1 tree, 44 leaves, max depth = 14, train loss: 0.34782, val loss: 0.33804, in 0.016s
1 tree, 30 leaves, max depth = 12, train loss: 0.34334, val loss: 0.33346, in 0.016s
1 tree, 42 leaves, max depth = 14, train loss: 0.33966, val loss: 0.32987, in 0.016s
1 tree, 72 leaves, max depth = 15, train loss: 0.33530, val loss: 0.32588, in 0.016s
1 tree, 30 leaves, max depth = 14, train loss: 0.33133, val loss: 0.32180, in 0.016s
1 tree, 42 leaves, max depth = 14, train loss: 0.32796, val loss: 0.31851, in 0.031s
1 tree, 48 leaves, max depth = 14, train loss: 0.32464, val loss: 0.31509, in 0.016s
1 tree, 72 leaves, max depth = 14, train loss: 0.32081, val loss: 0.31166, in 0.016s
1 tree, 52 leaves, max depth = 15, train loss: 0.31830, val loss: 0.30920, in 0.031s
Fit 45 trees in 1.174 s, (1794 total leaves)
Time spent computing histograms: 0.325s
Time spent finding best splits:  0.061s
Time spent applying splits:      0.041s
Time spent predicting:           0.000s
Trial 75, Fold 5: Log loss = 0.32681020784542797, Average precision = 0.956019732699627, ROC-AUC = 0.9517103840365644, Elapsed Time = 1.1787164000015764 seconds
Optimization Progress:  76%|#######6  | 76/100 [15:31<05:55, 14.81s/it]
Trial 76, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 76, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
0.141 s 0.040 GB of training data: 
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 17 leaves, max depth = 9, train loss: 0.68558, val loss: 0.68534, in 0.000s
1 tree, 6 leaves, max depth = 4, train loss: 0.68143, val loss: 0.68087, in 0.000s
1 tree, 17 leaves, max depth = 8, train loss: 0.67412, val loss: 0.67336, in 0.016s
1 tree, 6 leaves, max depth = 4, train loss: 0.66707, val loss: 0.66605, in 0.000s
1 tree, 17 leaves, max depth = 9, train loss: 0.66021, val loss: 0.65900, in 0.000s
1 tree, 17 leaves, max depth = 9, train loss: 0.65357, val loss: 0.65217, in 0.016s
1 tree, 6 leaves, max depth = 4, train loss: 0.64716, val loss: 0.64551, in 0.000s
1 tree, 17 leaves, max depth = 7, train loss: 0.64100, val loss: 0.63918, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.63732, val loss: 0.63523, in 0.000s
1 tree, 17 leaves, max depth = 9, train loss: 0.63129, val loss: 0.62903, in 0.000s
1 tree, 17 leaves, max depth = 7, train loss: 0.62557, val loss: 0.62311, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.62004, val loss: 0.61740, in 0.000s
1 tree, 17 leaves, max depth = 10, train loss: 0.61461, val loss: 0.61180, in 0.000s
1 tree, 17 leaves, max depth = 7, train loss: 0.60943, val loss: 0.60643, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.60439, val loss: 0.60124, in 0.000s
1 tree, 17 leaves, max depth = 8, train loss: 0.59949, val loss: 0.59618, in 0.016s
1 tree, 17 leaves, max depth = 10, train loss: 0.59496, val loss: 0.59147, in 0.000s
1 tree, 17 leaves, max depth = 8, train loss: 0.59031, val loss: 0.58665, in 0.000s
1 tree, 6 leaves, max depth = 4, train loss: 0.58579, val loss: 0.58192, in 0.000s
1 tree, 17 leaves, max depth = 8, train loss: 0.58130, val loss: 0.57726, in 0.000s
1 tree, 17 leaves, max depth = 8, train loss: 0.57693, val loss: 0.57275, in 0.016s
1 tree, 17 leaves, max depth = 8, train loss: 0.57270, val loss: 0.56836, in 0.000s
1 tree, 17 leaves, max depth = 8, train loss: 0.56892, val loss: 0.56444, in 0.016s
1 tree, 17 leaves, max depth = 8, train loss: 0.56491, val loss: 0.56028, in 0.000s
1 tree, 17 leaves, max depth = 8, train loss: 0.56112, val loss: 0.55633, in 0.000s
1 tree, 17 leaves, max depth = 8, train loss: 0.55741, val loss: 0.55248, in 0.016s
1 tree, 17 leaves, max depth = 8, train loss: 0.55383, val loss: 0.54875, in 0.000s
1 tree, 17 leaves, max depth = 10, train loss: 0.55037, val loss: 0.54517, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.54701, val loss: 0.54167, in 0.000s
1 tree, 17 leaves, max depth = 7, train loss: 0.54328, val loss: 0.53817, in 0.016s
1 tree, 17 leaves, max depth = 10, train loss: 0.54005, val loss: 0.53482, in 0.000s
1 tree, 6 leaves, max depth = 4, train loss: 0.53688, val loss: 0.53148, in 0.000s
1 tree, 17 leaves, max depth = 10, train loss: 0.53378, val loss: 0.52825, in 0.016s
1 tree, 6 leaves, max depth = 4, train loss: 0.53079, val loss: 0.52508, in 0.000s
1 tree, 17 leaves, max depth = 8, train loss: 0.52780, val loss: 0.52194, in 0.016s
1 tree, 17 leaves, max depth = 10, train loss: 0.52495, val loss: 0.51897, in 0.000s
1 tree, 17 leaves, max depth = 8, train loss: 0.52222, val loss: 0.51614, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.51882, val loss: 0.51297, in 0.000s
1 tree, 17 leaves, max depth = 8, train loss: 0.51616, val loss: 0.51020, in 0.000s
1 tree, 17 leaves, max depth = 8, train loss: 0.51362, val loss: 0.50756, in 0.016s
1 tree, 17 leaves, max depth = 8, train loss: 0.51110, val loss: 0.50493, in 0.000s
1 tree, 17 leaves, max depth = 7, train loss: 0.50868, val loss: 0.50237, in 0.016s
1 tree, 17 leaves, max depth = 9, train loss: 0.50635, val loss: 0.49993, in 0.000s
1 tree, 17 leaves, max depth = 7, train loss: 0.50317, val loss: 0.49698, in 0.016s
[45/70] 1 tree, 6 leaves, max depth = 4, train loss: 0.50089, val loss: 0.49455, in 0.000s
1 tree, 17 leaves, max depth = 8, train loss: 0.49866, val loss: 0.49222, in 0.000s
1 tree, 17 leaves, max depth = 7, train loss: 0.49561, val loss: 0.48939, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.49401, val loss: 0.48783, in 0.000s
1 tree, 17 leaves, max depth = 7, train loss: 0.49192, val loss: 0.48562, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.48901, val loss: 0.48293, in 0.000s
1 tree, 17 leaves, max depth = 9, train loss: 0.48702, val loss: 0.48084, in 0.000s
1 tree, 17 leaves, max depth = 7, train loss: 0.48420, val loss: 0.47824, in 0.016s
1 tree, 17 leaves, max depth = 8, train loss: 0.48229, val loss: 0.47624, in 0.000s
1 tree, 17 leaves, max depth = 8, train loss: 0.48043, val loss: 0.47430, in 0.016s
1 tree, 17 leaves, max depth = 8, train loss: 0.47858, val loss: 0.47235, in 0.000s
1 tree, 17 leaves, max depth = 7, train loss: 0.47589, val loss: 0.46987, in 0.016s
1 tree, 6 leaves, max depth = 4, train loss: 0.47411, val loss: 0.46796, in 0.000s
1 tree, 17 leaves, max depth = 9, train loss: 0.47238, val loss: 0.46613, in 0.000s
1 tree, 17 leaves, max depth = 7, train loss: 0.46979, val loss: 0.46375, in 0.016s
1 tree, 17 leaves, max depth = 9, train loss: 0.46817, val loss: 0.46204, in 0.000s
1 tree, 17 leaves, max depth = 8, train loss: 0.46655, val loss: 0.46033, in 0.016s
1 tree, 17 leaves, max depth = 8, train loss: 0.46497, val loss: 0.45866, in 0.000s
1 tree, 17 leaves, max depth = 9, train loss: 0.46344, val loss: 0.45705, in 0.016s
1 tree, 17 leaves, max depth = 8, train loss: 0.46190, val loss: 0.45543, in 0.000s
1 tree, 17 leaves, max depth = 8, train loss: 0.46046, val loss: 0.45388, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.45873, val loss: 0.45231, in 0.000s
1 tree, 17 leaves, max depth = 7, train loss: 0.45737, val loss: 0.45086, in 0.016s
1 tree, 17 leaves, max depth = 8, train loss: 0.45601, val loss: 0.44941, in 0.000s
1 tree, 17 leaves, max depth = 7, train loss: 0.45362, val loss: 0.44722, in 0.000s
1 tree, 17 leaves, max depth = 7, train loss: 0.45225, val loss: 0.44575, in 0.016s
Fit 70 trees in 0.704 s, (1102 total leaves)
Time spent computing histograms: 0.265s
Time spent finding best splits:  0.022s
Time spent applying splits:      0.022s
Time spent predicting:           0.000s
Trial 76, Fold 1: Log loss = 0.45140575375698305, Average precision = 0.9059768387995848, ROC-AUC = 0.9121660859465738, Elapsed Time = 0.7139700999996421 seconds
Trial 76, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 76, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
Binning 0.040 GB of training data: 0.142 s
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 17 leaves, max depth = 9, train loss: 0.68557, val loss: 0.68520, in 0.000s
1 tree, 6 leaves, max depth = 4, train loss: 0.68137, val loss: 0.68058, in 0.016s
1 tree, 17 leaves, max depth = 8, train loss: 0.67405, val loss: 0.67292, in 0.000s
1 tree, 6 leaves, max depth = 4, train loss: 0.66698, val loss: 0.66551, in 0.000s
1 tree, 6 leaves, max depth = 4, train loss: 0.66013, val loss: 0.65834, in 0.016s
1 tree, 17 leaves, max depth = 10, train loss: 0.65353, val loss: 0.65141, in 0.000s
1 tree, 6 leaves, max depth = 4, train loss: 0.64711, val loss: 0.64468, in 0.016s
1 tree, 17 leaves, max depth = 8, train loss: 0.64092, val loss: 0.63820, in 0.000s
1 tree, 17 leaves, max depth = 8, train loss: 0.63722, val loss: 0.63434, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.63122, val loss: 0.62801, in 0.000s
1 tree, 17 leaves, max depth = 10, train loss: 0.62550, val loss: 0.62199, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.61998, val loss: 0.61618, in 0.000s
1 tree, 17 leaves, max depth = 8, train loss: 0.61457, val loss: 0.61045, in 0.016s
1 tree, 17 leaves, max depth = 9, train loss: 0.60940, val loss: 0.60499, in 0.000s
1 tree, 17 leaves, max depth = 10, train loss: 0.60434, val loss: 0.59968, in 0.016s
1 tree, 17 leaves, max depth = 9, train loss: 0.59942, val loss: 0.59451, in 0.000s
1 tree, 17 leaves, max depth = 8, train loss: 0.59492, val loss: 0.58979, in 0.000s
1 tree, 17 leaves, max depth = 10, train loss: 0.59029, val loss: 0.58490, in 0.016s
1 tree, 6 leaves, max depth = 4, train loss: 0.58577, val loss: 0.58012, in 0.000s
1 tree, 17 leaves, max depth = 8, train loss: 0.58130, val loss: 0.57538, in 0.016s
1 tree, 17 leaves, max depth = 8, train loss: 0.57699, val loss: 0.57078, in 0.000s
1 tree, 17 leaves, max depth = 8, train loss: 0.57278, val loss: 0.56630, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.56899, val loss: 0.56233, in 0.000s
1 tree, 17 leaves, max depth = 8, train loss: 0.56501, val loss: 0.55809, in 0.000s
1 tree, 17 leaves, max depth = 10, train loss: 0.56124, val loss: 0.55407, in 0.016s
1 tree, 17 leaves, max depth = 8, train loss: 0.55752, val loss: 0.55013, in 0.000s
1 tree, 17 leaves, max depth = 10, train loss: 0.55395, val loss: 0.54635, in 0.016s
1 tree, 17 leaves, max depth = 12, train loss: 0.55049, val loss: 0.54269, in 0.000s
1 tree, 17 leaves, max depth = 9, train loss: 0.54712, val loss: 0.53911, in 0.000s
1 tree, 17 leaves, max depth = 7, train loss: 0.54346, val loss: 0.53557, in 0.016s
1 tree, 17 leaves, max depth = 9, train loss: 0.54023, val loss: 0.53214, in 0.000s
1 tree, 6 leaves, max depth = 4, train loss: 0.53707, val loss: 0.52878, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.53399, val loss: 0.52547, in 0.000s
1 tree, 6 leaves, max depth = 4, train loss: 0.53099, val loss: 0.52228, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.52804, val loss: 0.51910, in 0.000s
1 tree, 17 leaves, max depth = 7, train loss: 0.52521, val loss: 0.51606, in 0.016s
1 tree, 17 leaves, max depth = 10, train loss: 0.52249, val loss: 0.51317, in 0.000s
1 tree, 17 leaves, max depth = 6, train loss: 0.51911, val loss: 0.50990, in 0.016s
1 tree, 17 leaves, max depth = 8, train loss: 0.51646, val loss: 0.50706, in 0.000s
1 tree, 17 leaves, max depth = 9, train loss: 0.51392, val loss: 0.50434, in 0.016s
1 tree, 17 leaves, max depth = 8, train loss: 0.51142, val loss: 0.50166, in 0.000s
1 tree, 17 leaves, max depth = 9, train loss: 0.50900, val loss: 0.49908, in 0.016s
1 tree, 17 leaves, max depth = 9, train loss: 0.50667, val loss: 0.49658, in 0.000s
1 tree, 17 leaves, max depth = 7, train loss: 0.50351, val loss: 0.49353, in 0.000s
1 tree, 6 leaves, max depth = 4, train loss: 0.50124, val loss: 0.49109, in 0.016s
1 tree, 17 leaves, max depth = 8, train loss: 0.49903, val loss: 0.48872, in 0.000s
1 tree, 17 leaves, max depth = 6, train loss: 0.49601, val loss: 0.48580, in 0.016s
1 tree, 17 leaves, max depth = 8, train loss: 0.49432, val loss: 0.48427, in 0.000s
1 tree, 17 leaves, max depth = 10, train loss: 0.49224, val loss: 0.48203, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.48935, val loss: 0.47926, in 0.000s
1 tree, 17 leaves, max depth = 9, train loss: 0.48736, val loss: 0.47712, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.48457, val loss: 0.47444, in 0.000s
1 tree, 17 leaves, max depth = 9, train loss: 0.48265, val loss: 0.47238, in 0.016s
1 tree, 17 leaves, max depth = 11, train loss: 0.48079, val loss: 0.47037, in 0.000s
1 tree, 17 leaves, max depth = 8, train loss: 0.47895, val loss: 0.46838, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.47629, val loss: 0.46582, in 0.000s
1 tree, 6 leaves, max depth = 4, train loss: 0.47451, val loss: 0.46390, in 0.000s
1 tree, 17 leaves, max depth = 7, train loss: 0.47279, val loss: 0.46203, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.47023, val loss: 0.45955, in 0.000s
1 tree, 17 leaves, max depth = 10, train loss: 0.46860, val loss: 0.45781, in 0.016s
1 tree, 17 leaves, max depth = 9, train loss: 0.46702, val loss: 0.45610, in 0.000s
1 tree, 17 leaves, max depth = 8, train loss: 0.46545, val loss: 0.45440, in 0.016s
1 tree, 17 leaves, max depth = 8, train loss: 0.46393, val loss: 0.45275, in 0.000s
1 tree, 17 leaves, max depth = 6, train loss: 0.46241, val loss: 0.45112, in 0.016s
1 tree, 17 leaves, max depth = 9, train loss: 0.46097, val loss: 0.44955, in 0.000s
1 tree, 17 leaves, max depth = 6, train loss: 0.45927, val loss: 0.44798, in 0.016s
1 tree, 17 leaves, max depth = 9, train loss: 0.45791, val loss: 0.44649, in 0.000s
1 tree, 17 leaves, max depth = 10, train loss: 0.45658, val loss: 0.44506, in 0.016s
1 tree, 17 leaves, max depth = 5, train loss: 0.45421, val loss: 0.44279, in 0.000s
1 tree, 17 leaves, max depth = 7, train loss: 0.45286, val loss: 0.44131, in 0.016s
Fit 70 trees in 0.783 s, (1091 total leaves)
Time spent computing histograms: 0.287s
Time spent finding best splits:  0.025s
Time spent applying splits:      0.025s
Time spent predicting:           0.000s
Trial 76, Fold 2: Log loss = 0.4535027898029579, Average precision = 0.9029478982459576, ROC-AUC = 0.9163226291457116, Elapsed Time = 0.7876867999984825 seconds
Trial 76, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 76, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
Binning 0.040 GB of training data: 0.158 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 17 leaves, max depth = 10, train loss: 0.68561, val loss: 0.68537, in 0.000s
1 tree, 6 leaves, max depth = 4, train loss: 0.68139, val loss: 0.68104, in 0.000s
1 tree, 17 leaves, max depth = 7, train loss: 0.67412, val loss: 0.67350, in 0.016s
1 tree, 6 leaves, max depth = 4, train loss: 0.66709, val loss: 0.66623, in 0.000s
1 tree, 17 leaves, max depth = 8, train loss: 0.66028, val loss: 0.65919, in 0.016s
1 tree, 17 leaves, max depth = 8, train loss: 0.65369, val loss: 0.65237, in 0.000s
1 tree, 6 leaves, max depth = 4, train loss: 0.64730, val loss: 0.64574, in 0.016s
1 tree, 17 leaves, max depth = 9, train loss: 0.64116, val loss: 0.63936, in 0.000s
1 tree, 17 leaves, max depth = 7, train loss: 0.63744, val loss: 0.63559, in 0.016s
1 tree, 17 leaves, max depth = 8, train loss: 0.63147, val loss: 0.62942, in 0.000s
1 tree, 17 leaves, max depth = 9, train loss: 0.62578, val loss: 0.62352, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.62028, val loss: 0.61780, in 0.000s
1 tree, 17 leaves, max depth = 8, train loss: 0.61490, val loss: 0.61222, in 0.016s
1 tree, 17 leaves, max depth = 9, train loss: 0.60976, val loss: 0.60689, in 0.000s
1 tree, 17 leaves, max depth = 9, train loss: 0.60475, val loss: 0.60165, in 0.000s
1 tree, 17 leaves, max depth = 10, train loss: 0.59988, val loss: 0.59658, in 0.016s
1 tree, 17 leaves, max depth = 8, train loss: 0.59539, val loss: 0.59192, in 0.000s
1 tree, 17 leaves, max depth = 10, train loss: 0.59077, val loss: 0.58712, in 0.016s
1 tree, 6 leaves, max depth = 4, train loss: 0.58627, val loss: 0.58244, in 0.000s
1 tree, 17 leaves, max depth = 8, train loss: 0.58183, val loss: 0.57783, in 0.000s
1 tree, 17 leaves, max depth = 8, train loss: 0.57752, val loss: 0.57336, in 0.016s
1 tree, 17 leaves, max depth = 8, train loss: 0.57333, val loss: 0.56901, in 0.000s
1 tree, 17 leaves, max depth = 9, train loss: 0.56958, val loss: 0.56511, in 0.000s
1 tree, 17 leaves, max depth = 8, train loss: 0.56562, val loss: 0.56099, in 0.000s
1 tree, 17 leaves, max depth = 10, train loss: 0.56185, val loss: 0.55707, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.55817, val loss: 0.55322, in 0.000s
1 tree, 17 leaves, max depth = 10, train loss: 0.55462, val loss: 0.54952, in 0.016s
1 tree, 17 leaves, max depth = 9, train loss: 0.55118, val loss: 0.54590, in 0.000s
1 tree, 17 leaves, max depth = 10, train loss: 0.54785, val loss: 0.54241, in 0.016s
1 tree, 17 leaves, max depth = 8, train loss: 0.54415, val loss: 0.53893, in 0.000s
1 tree, 17 leaves, max depth = 9, train loss: 0.54095, val loss: 0.53556, in 0.016s
1 tree, 6 leaves, max depth = 4, train loss: 0.53780, val loss: 0.53227, in 0.000s
1 tree, 17 leaves, max depth = 9, train loss: 0.53474, val loss: 0.52906, in 0.016s
1 tree, 6 leaves, max depth = 4, train loss: 0.53176, val loss: 0.52594, in 0.000s
1 tree, 17 leaves, max depth = 8, train loss: 0.52881, val loss: 0.52287, in 0.016s
1 tree, 17 leaves, max depth = 9, train loss: 0.52600, val loss: 0.51992, in 0.000s
1 tree, 17 leaves, max depth = 9, train loss: 0.52330, val loss: 0.51707, in 0.016s
1 tree, 17 leaves, max depth = 8, train loss: 0.51992, val loss: 0.51390, in 0.000s
1 tree, 17 leaves, max depth = 7, train loss: 0.51729, val loss: 0.51114, in 0.016s
1 tree, 17 leaves, max depth = 9, train loss: 0.51478, val loss: 0.50848, in 0.000s
1 tree, 17 leaves, max depth = 7, train loss: 0.51230, val loss: 0.50586, in 0.000s
1 tree, 17 leaves, max depth = 8, train loss: 0.50991, val loss: 0.50334, in 0.000s
1 tree, 17 leaves, max depth = 10, train loss: 0.50760, val loss: 0.50090, in 0.000s
1 tree, 17 leaves, max depth = 7, train loss: 0.50443, val loss: 0.49794, in 0.016s
1 tree, 6 leaves, max depth = 4, train loss: 0.50217, val loss: 0.49556, in 0.000s
1 tree, 17 leaves, max depth = 7, train loss: 0.49997, val loss: 0.49323, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.49694, val loss: 0.49040, in 0.000s
1 tree, 17 leaves, max depth = 8, train loss: 0.49529, val loss: 0.48891, in 0.016s
[49/70] 1 tree, 17 leaves, max depth = 9, train loss: 0.49322, val loss: 0.48673, in 0.000s
1 tree, 17 leaves, max depth = 7, train loss: 0.49033, val loss: 0.48404, in 0.016s
1 tree, 17 leaves, max depth = 9, train loss: 0.48836, val loss: 0.48195, in 0.000s
1 tree, 17 leaves, max depth = 7, train loss: 0.48556, val loss: 0.47935, in 0.016s
1 tree, 17 leaves, max depth = 9, train loss: 0.48367, val loss: 0.47733, in 0.000s
1 tree, 17 leaves, max depth = 9, train loss: 0.48183, val loss: 0.47537, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.48000, val loss: 0.47343, in 0.000s
1 tree, 17 leaves, max depth = 8, train loss: 0.47732, val loss: 0.47094, in 0.016s
1 tree, 6 leaves, max depth = 4, train loss: 0.47556, val loss: 0.46907, in 0.000s
1 tree, 17 leaves, max depth = 9, train loss: 0.47385, val loss: 0.46726, in 0.016s
1 tree, 17 leaves, max depth = 8, train loss: 0.47128, val loss: 0.46488, in 0.000s
1 tree, 17 leaves, max depth = 9, train loss: 0.46967, val loss: 0.46316, in 0.016s
1 tree, 17 leaves, max depth = 9, train loss: 0.46808, val loss: 0.46146, in 0.000s
1 tree, 17 leaves, max depth = 8, train loss: 0.46653, val loss: 0.45980, in 0.000s
1 tree, 17 leaves, max depth = 8, train loss: 0.46502, val loss: 0.45818, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.46351, val loss: 0.45656, in 0.000s
1 tree, 17 leaves, max depth = 9, train loss: 0.46209, val loss: 0.45504, in 0.016s
1 tree, 17 leaves, max depth = 5, train loss: 0.46038, val loss: 0.45349, in 0.005s
1 tree, 17 leaves, max depth = 10, train loss: 0.45903, val loss: 0.45204, in 0.000s
1 tree, 17 leaves, max depth = 9, train loss: 0.45769, val loss: 0.45061, in 0.012s
1 tree, 17 leaves, max depth = 5, train loss: 0.45531, val loss: 0.44842, in 0.000s
1 tree, 17 leaves, max depth = 8, train loss: 0.45397, val loss: 0.44700, in 0.016s
Fit 70 trees in 0.815 s, (1102 total leaves)
Time spent computing histograms: 0.292s
Time spent finding best splits:  0.025s
Time spent applying splits:      0.025s
Time spent predicting:           0.016s
Trial 76, Fold 3: Log loss = 0.44810020855286986, Average precision = 0.912123486339555, ROC-AUC = 0.9214749358332289, Elapsed Time = 0.8128985000003013 seconds
Trial 76, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 76, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
Binning 0.040 GB of training data: 0.142 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 17 leaves, max depth = 9, train loss: 0.68560, val loss: 0.68517, in 0.000s
1 tree, 6 leaves, max depth = 4, train loss: 0.68135, val loss: 0.68064, in 0.016s
1 tree, 17 leaves, max depth = 9, train loss: 0.67408, val loss: 0.67298, in 0.000s
1 tree, 6 leaves, max depth = 4, train loss: 0.66705, val loss: 0.66557, in 0.000s
1 tree, 17 leaves, max depth = 8, train loss: 0.66023, val loss: 0.65837, in 0.016s
1 tree, 17 leaves, max depth = 8, train loss: 0.65363, val loss: 0.65139, in 0.000s
1 tree, 6 leaves, max depth = 4, train loss: 0.64724, val loss: 0.64464, in 0.016s
1 tree, 17 leaves, max depth = 9, train loss: 0.64107, val loss: 0.63812, in 0.000s
1 tree, 17 leaves, max depth = 7, train loss: 0.63732, val loss: 0.63425, in 0.016s
1 tree, 17 leaves, max depth = 8, train loss: 0.63137, val loss: 0.62789, in 0.000s
1 tree, 17 leaves, max depth = 11, train loss: 0.62567, val loss: 0.62181, in 0.000s
1 tree, 17 leaves, max depth = 6, train loss: 0.62015, val loss: 0.61596, in 0.016s
1 tree, 17 leaves, max depth = 9, train loss: 0.61476, val loss: 0.61022, in 0.000s
1 tree, 17 leaves, max depth = 9, train loss: 0.60960, val loss: 0.60470, in 0.016s
1 tree, 17 leaves, max depth = 9, train loss: 0.60455, val loss: 0.59931, in 0.000s
1 tree, 17 leaves, max depth = 9, train loss: 0.59966, val loss: 0.59409, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.59520, val loss: 0.58922, in 0.000s
1 tree, 17 leaves, max depth = 11, train loss: 0.59057, val loss: 0.58424, in 0.016s
1 tree, 6 leaves, max depth = 4, train loss: 0.58607, val loss: 0.57943, in 0.000s
1 tree, 17 leaves, max depth = 7, train loss: 0.58164, val loss: 0.57466, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.57734, val loss: 0.57002, in 0.000s
1 tree, 17 leaves, max depth = 7, train loss: 0.57316, val loss: 0.56551, in 0.000s
1 tree, 17 leaves, max depth = 8, train loss: 0.56936, val loss: 0.56142, in 0.016s
1 tree, 17 leaves, max depth = 8, train loss: 0.56541, val loss: 0.55714, in 0.000s
1 tree, 17 leaves, max depth = 11, train loss: 0.56164, val loss: 0.55305, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.55794, val loss: 0.54908, in 0.000s
1 tree, 17 leaves, max depth = 11, train loss: 0.55439, val loss: 0.54521, in 0.016s
1 tree, 17 leaves, max depth = 10, train loss: 0.55093, val loss: 0.54148, in 0.000s
1 tree, 17 leaves, max depth = 9, train loss: 0.54758, val loss: 0.53784, in 0.000s
1 tree, 17 leaves, max depth = 7, train loss: 0.54386, val loss: 0.53419, in 0.000s
1 tree, 17 leaves, max depth = 10, train loss: 0.54064, val loss: 0.53071, in 0.000s
1 tree, 6 leaves, max depth = 4, train loss: 0.53749, val loss: 0.52730, in 0.016s
1 tree, 17 leaves, max depth = 9, train loss: 0.53441, val loss: 0.52395, in 0.000s
1 tree, 6 leaves, max depth = 4, train loss: 0.53144, val loss: 0.52072, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.52850, val loss: 0.51749, in 0.000s
1 tree, 17 leaves, max depth = 9, train loss: 0.52567, val loss: 0.51440, in 0.016s
1 tree, 17 leaves, max depth = 10, train loss: 0.52296, val loss: 0.51144, in 0.000s
1 tree, 17 leaves, max depth = 7, train loss: 0.51956, val loss: 0.50811, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.51692, val loss: 0.50525, in 0.000s
1 tree, 17 leaves, max depth = 10, train loss: 0.51439, val loss: 0.50248, in 0.016s
1 tree, 17 leaves, max depth = 8, train loss: 0.51189, val loss: 0.49975, in 0.000s
1 tree, 17 leaves, max depth = 6, train loss: 0.50949, val loss: 0.49709, in 0.016s
1 tree, 17 leaves, max depth = 10, train loss: 0.50717, val loss: 0.49452, in 0.000s
1 tree, 17 leaves, max depth = 6, train loss: 0.50399, val loss: 0.49140, in 0.016s
1 tree, 6 leaves, max depth = 4, train loss: 0.50173, val loss: 0.48891, in 0.000s
1 tree, 17 leaves, max depth = 8, train loss: 0.49952, val loss: 0.48649, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.49647, val loss: 0.48349, in 0.000s
1 tree, 17 leaves, max depth = 8, train loss: 0.49482, val loss: 0.48186, in 0.016s
1 tree, 17 leaves, max depth = 11, train loss: 0.49275, val loss: 0.47954, in 0.000s
1 tree, 17 leaves, max depth = 7, train loss: 0.48984, val loss: 0.47669, in 0.016s
1 tree, 17 leaves, max depth = 10, train loss: 0.48786, val loss: 0.47449, in 0.000s
1 tree, 17 leaves, max depth = 6, train loss: 0.48505, val loss: 0.47174, in 0.000s
1 tree, 17 leaves, max depth = 9, train loss: 0.48315, val loss: 0.46962, in 0.016s
1 tree, 17 leaves, max depth = 9, train loss: 0.48129, val loss: 0.46756, in 0.000s
1 tree, 17 leaves, max depth = 8, train loss: 0.47946, val loss: 0.46553, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.47677, val loss: 0.46290, in 0.000s
1 tree, 6 leaves, max depth = 4, train loss: 0.47501, val loss: 0.46094, in 0.016s
1 tree, 17 leaves, max depth = 9, train loss: 0.47330, val loss: 0.45902, in 0.000s
1 tree, 17 leaves, max depth = 6, train loss: 0.47071, val loss: 0.45650, in 0.016s
1 tree, 17 leaves, max depth = 9, train loss: 0.46909, val loss: 0.45467, in 0.000s
1 tree, 17 leaves, max depth = 9, train loss: 0.46750, val loss: 0.45286, in 0.016s
1 tree, 17 leaves, max depth = 8, train loss: 0.46594, val loss: 0.45112, in 0.000s
1 tree, 17 leaves, max depth = 8, train loss: 0.46442, val loss: 0.44942, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.46289, val loss: 0.44770, in 0.000s
1 tree, 17 leaves, max depth = 7, train loss: 0.46147, val loss: 0.44607, in 0.000s
1 tree, 17 leaves, max depth = 6, train loss: 0.45979, val loss: 0.44445, in 0.016s
1 tree, 17 leaves, max depth = 9, train loss: 0.45843, val loss: 0.44290, in 0.000s
1 tree, 17 leaves, max depth = 9, train loss: 0.45709, val loss: 0.44137, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.45471, val loss: 0.43906, in 0.000s
1 tree, 17 leaves, max depth = 7, train loss: 0.45338, val loss: 0.43753, in 0.016s
Fit 70 trees in 0.799 s, (1102 total leaves)
Time spent computing histograms: 0.296s
Time spent finding best splits:  0.025s
Time spent applying splits:      0.025s
Time spent predicting:           0.000s
Trial 76, Fold 4: Log loss = 0.45288470105915773, Average precision = 0.9060052309612978, ROC-AUC = 0.9150917640101958, Elapsed Time = 0.8077116000004025 seconds
Trial 76, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 76, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
Binning 0.040 GB of training data: 0.173 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 17 leaves, max depth = 8, train loss: 0.68551, val loss: 0.68497, in 0.000s
1 tree, 6 leaves, max depth = 4, train loss: 0.68135, val loss: 0.68038, in 0.000s
1 tree, 17 leaves, max depth = 9, train loss: 0.67399, val loss: 0.67253, in 0.016s
1 tree, 6 leaves, max depth = 4, train loss: 0.66686, val loss: 0.66493, in 0.000s
1 tree, 6 leaves, max depth = 4, train loss: 0.65997, val loss: 0.65758, in 0.016s
1 tree, 17 leaves, max depth = 9, train loss: 0.65331, val loss: 0.65043, in 0.000s
1 tree, 6 leaves, max depth = 4, train loss: 0.64684, val loss: 0.64352, in 0.016s
1 tree, 17 leaves, max depth = 11, train loss: 0.64060, val loss: 0.63680, in 0.000s
1 tree, 17 leaves, max depth = 8, train loss: 0.63693, val loss: 0.63286, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.63092, val loss: 0.62638, in 0.000s
1 tree, 17 leaves, max depth = 9, train loss: 0.62516, val loss: 0.62017, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.61959, val loss: 0.61417, in 0.000s
1 tree, 17 leaves, max depth = 9, train loss: 0.61416, val loss: 0.60834, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.60895, val loss: 0.60267, in 0.000s
1 tree, 17 leaves, max depth = 11, train loss: 0.60384, val loss: 0.59713, in 0.016s
1 tree, 17 leaves, max depth = 8, train loss: 0.59889, val loss: 0.59174, in 0.000s
1 tree, 17 leaves, max depth = 8, train loss: 0.59436, val loss: 0.58683, in 0.016s
1 tree, 17 leaves, max depth = 9, train loss: 0.58968, val loss: 0.58174, in 0.000s
1 tree, 6 leaves, max depth = 4, train loss: 0.58512, val loss: 0.57681, in 0.000s
1 tree, 17 leaves, max depth = 7, train loss: 0.58065, val loss: 0.57194, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.57631, val loss: 0.56720, in 0.000s
1 tree, 17 leaves, max depth = 7, train loss: 0.57209, val loss: 0.56260, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.56826, val loss: 0.55842, in 0.000s
1 tree, 17 leaves, max depth = 7, train loss: 0.56427, val loss: 0.55405, in 0.016s
1 tree, 17 leaves, max depth = 9, train loss: 0.56046, val loss: 0.54987, in 0.000s
1 tree, 17 leaves, max depth = 9, train loss: 0.55674, val loss: 0.54579, in 0.016s
1 tree, 17 leaves, max depth = 9, train loss: 0.55314, val loss: 0.54183, in 0.000s
1 tree, 17 leaves, max depth = 11, train loss: 0.54964, val loss: 0.53800, in 0.016s
1 tree, 17 leaves, max depth = 9, train loss: 0.54624, val loss: 0.53425, in 0.000s
1 tree, 17 leaves, max depth = 7, train loss: 0.54261, val loss: 0.53077, in 0.016s
1 tree, 17 leaves, max depth = 11, train loss: 0.53935, val loss: 0.52719, in 0.000s
1 tree, 6 leaves, max depth = 4, train loss: 0.53616, val loss: 0.52369, in 0.016s
1 tree, 17 leaves, max depth = 9, train loss: 0.53308, val loss: 0.52027, in 0.000s
1 tree, 6 leaves, max depth = 4, train loss: 0.53006, val loss: 0.51694, in 0.000s
1 tree, 17 leaves, max depth = 7, train loss: 0.52709, val loss: 0.51365, in 0.016s
1 tree, 17 leaves, max depth = 11, train loss: 0.52426, val loss: 0.51050, in 0.000s
1 tree, 17 leaves, max depth = 11, train loss: 0.52151, val loss: 0.50745, in 0.016s
1 tree, 17 leaves, max depth = 8, train loss: 0.51819, val loss: 0.50430, in 0.000s
1 tree, 17 leaves, max depth = 9, train loss: 0.51552, val loss: 0.50136, in 0.016s
1 tree, 17 leaves, max depth = 10, train loss: 0.51296, val loss: 0.49851, in 0.000s
1 tree, 17 leaves, max depth = 9, train loss: 0.51044, val loss: 0.49572, in 0.000s
1 tree, 17 leaves, max depth = 6, train loss: 0.50801, val loss: 0.49298, in 0.000s
1 tree, 17 leaves, max depth = 9, train loss: 0.50565, val loss: 0.49033, in 0.000s
1 tree, 17 leaves, max depth = 8, train loss: 0.50255, val loss: 0.48739, in 0.016s
1 tree, 6 leaves, max depth = 4, train loss: 0.50025, val loss: 0.48483, in 0.000s
1 tree, 17 leaves, max depth = 8, train loss: 0.49803, val loss: 0.48235, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.49505, val loss: 0.47954, in 0.000s
1 tree, 17 leaves, max depth = 7, train loss: 0.49339, val loss: 0.47796, in 0.016s
1 tree, 17 leaves, max depth = 8, train loss: 0.49129, val loss: 0.47558, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.48845, val loss: 0.47291, in 0.000s
1 tree, 17 leaves, max depth = 9, train loss: 0.48644, val loss: 0.47063, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.48369, val loss: 0.46805, in 0.000s
1 tree, 17 leaves, max depth = 10, train loss: 0.48176, val loss: 0.46587, in 0.016s
1 tree, 17 leaves, max depth = 9, train loss: 0.47988, val loss: 0.46374, in 0.000s
1 tree, 17 leaves, max depth = 9, train loss: 0.47802, val loss: 0.46166, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.47539, val loss: 0.45922, in 0.000s
1 tree, 6 leaves, max depth = 4, train loss: 0.47360, val loss: 0.45719, in 0.000s
1 tree, 17 leaves, max depth = 8, train loss: 0.47187, val loss: 0.45524, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.46934, val loss: 0.45289, in 0.000s
1 tree, 17 leaves, max depth = 11, train loss: 0.46770, val loss: 0.45100, in 0.016s
1 tree, 17 leaves, max depth = 8, train loss: 0.46608, val loss: 0.44916, in 0.000s
1 tree, 17 leaves, max depth = 10, train loss: 0.46450, val loss: 0.44737, in 0.016s
1 tree, 17 leaves, max depth = 10, train loss: 0.46296, val loss: 0.44563, in 0.000s
1 tree, 17 leaves, max depth = 7, train loss: 0.46142, val loss: 0.44387, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.45998, val loss: 0.44220, in 0.000s
1 tree, 17 leaves, max depth = 6, train loss: 0.45832, val loss: 0.44076, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.45694, val loss: 0.43914, in 0.000s
1 tree, 17 leaves, max depth = 8, train loss: 0.45558, val loss: 0.43758, in 0.016s
1 tree, 17 leaves, max depth = 8, train loss: 0.45325, val loss: 0.43543, in 0.000s
1 tree, 17 leaves, max depth = 6, train loss: 0.45190, val loss: 0.43387, in 0.016s
Fit 70 trees in 0.845 s, (1091 total leaves)
Time spent computing histograms: 0.309s
Time spent finding best splits:  0.026s
Time spent applying splits:      0.026s
Time spent predicting:           0.000s
Trial 76, Fold 5: Log loss = 0.45875741501839024, Average precision = 0.9023385615786321, ROC-AUC = 0.910490608104342, Elapsed Time = 0.8457624999991822 seconds
Optimization Progress:  77%|#######7  | 77/100 [15:42<05:12, 13.58s/it]
Trial 77, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 77, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
0.143 s 0.040 GB of training data: 
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 215 leaves, max depth = 15, train loss: 0.67801, val loss: 0.67814, in 0.031s
1 tree, 239 leaves, max depth = 15, train loss: 0.66343, val loss: 0.66381, in 0.031s
1 tree, 239 leaves, max depth = 19, train loss: 0.65008, val loss: 0.65061, in 0.016s
1 tree, 239 leaves, max depth = 21, train loss: 0.63755, val loss: 0.63810, in 0.031s
1 tree, 239 leaves, max depth = 16, train loss: 0.62487, val loss: 0.62569, in 0.031s
1 tree, 239 leaves, max depth = 16, train loss: 0.61277, val loss: 0.61378, in 0.016s
1 tree, 239 leaves, max depth = 16, train loss: 0.60123, val loss: 0.60250, in 0.031s
1 tree, 239 leaves, max depth = 16, train loss: 0.59020, val loss: 0.59172, in 0.031s
1 tree, 239 leaves, max depth = 16, train loss: 0.57966, val loss: 0.58142, in 0.031s
1 tree, 239 leaves, max depth = 16, train loss: 0.56959, val loss: 0.57159, in 0.016s
1 tree, 239 leaves, max depth = 16, train loss: 0.55995, val loss: 0.56216, in 0.031s
1 tree, 239 leaves, max depth = 15, train loss: 0.55080, val loss: 0.55313, in 0.031s
1 tree, 239 leaves, max depth = 20, train loss: 0.54175, val loss: 0.54411, in 0.031s
1 tree, 239 leaves, max depth = 16, train loss: 0.53367, val loss: 0.53603, in 0.031s
1 tree, 239 leaves, max depth = 16, train loss: 0.52583, val loss: 0.52832, in 0.031s
1 tree, 239 leaves, max depth = 16, train loss: 0.51793, val loss: 0.52064, in 0.016s
1 tree, 239 leaves, max depth = 21, train loss: 0.51083, val loss: 0.51353, in 0.031s
1 tree, 239 leaves, max depth = 16, train loss: 0.50352, val loss: 0.50642, in 0.031s
1 tree, 239 leaves, max depth = 16, train loss: 0.49649, val loss: 0.49962, in 0.031s
1 tree, 239 leaves, max depth = 15, train loss: 0.48982, val loss: 0.49308, in 0.031s
1 tree, 239 leaves, max depth = 15, train loss: 0.48341, val loss: 0.48677, in 0.031s
1 tree, 239 leaves, max depth = 23, train loss: 0.47743, val loss: 0.48093, in 0.031s
1 tree, 239 leaves, max depth = 15, train loss: 0.47138, val loss: 0.47510, in 0.016s
1 tree, 239 leaves, max depth = 18, train loss: 0.46547, val loss: 0.46914, in 0.016s
1 tree, 197 leaves, max depth = 19, train loss: 0.45788, val loss: 0.46181, in 0.031s
1 tree, 239 leaves, max depth = 23, train loss: 0.45269, val loss: 0.45677, in 0.031s
1 tree, 239 leaves, max depth = 19, train loss: 0.44748, val loss: 0.45154, in 0.031s
1 tree, 239 leaves, max depth = 19, train loss: 0.44224, val loss: 0.44640, in 0.031s
1 tree, 239 leaves, max depth = 15, train loss: 0.43731, val loss: 0.44168, in 0.031s
1 tree, 239 leaves, max depth = 16, train loss: 0.43254, val loss: 0.43712, in 0.031s
1 tree, 239 leaves, max depth = 15, train loss: 0.42795, val loss: 0.43272, in 0.031s
1 tree, 197 leaves, max depth = 15, train loss: 0.42147, val loss: 0.42648, in 0.031s
1 tree, 239 leaves, max depth = 17, train loss: 0.41716, val loss: 0.42240, in 0.031s
1 tree, 239 leaves, max depth = 14, train loss: 0.41300, val loss: 0.41844, in 0.031s
1 tree, 239 leaves, max depth = 14, train loss: 0.40898, val loss: 0.41464, in 0.031s
1 tree, 197 leaves, max depth = 15, train loss: 0.40306, val loss: 0.40893, in 0.016s
1 tree, 239 leaves, max depth = 18, train loss: 0.39914, val loss: 0.40512, in 0.031s
1 tree, 201 leaves, max depth = 15, train loss: 0.39360, val loss: 0.39982, in 0.031s
1 tree, 196 leaves, max depth = 15, train loss: 0.38825, val loss: 0.39467, in 0.016s
1 tree, 239 leaves, max depth = 18, train loss: 0.38463, val loss: 0.39116, in 0.031s
1 tree, 195 leaves, max depth = 15, train loss: 0.37959, val loss: 0.38632, in 0.031s
1 tree, 239 leaves, max depth = 18, train loss: 0.37622, val loss: 0.38299, in 0.031s
1 tree, 239 leaves, max depth = 18, train loss: 0.37292, val loss: 0.37981, in 0.031s
1 tree, 231 leaves, max depth = 16, train loss: 0.36828, val loss: 0.37534, in 0.031s
1 tree, 197 leaves, max depth = 15, train loss: 0.36378, val loss: 0.37104, in 0.016s
1 tree, 239 leaves, max depth = 16, train loss: 0.36085, val loss: 0.36833, in 0.031s
1 tree, 234 leaves, max depth = 15, train loss: 0.35668, val loss: 0.36432, in 0.031s
1 tree, 239 leaves, max depth = 15, train loss: 0.35377, val loss: 0.36160, in 0.031s
1 tree, 102 leaves, max depth = 14, train loss: 0.35003, val loss: 0.35787, in 0.016s
1 tree, 102 leaves, max depth = 14, train loss: 0.34645, val loss: 0.35430, in 0.031s
1 tree, 239 leaves, max depth = 16, train loss: 0.34374, val loss: 0.35181, in 0.031s
1 tree, 98 leaves, max depth = 14, train loss: 0.34034, val loss: 0.34843, in 0.016s
1 tree, 106 leaves, max depth = 14, train loss: 0.33713, val loss: 0.34524, in 0.031s
1 tree, 239 leaves, max depth = 20, train loss: 0.33425, val loss: 0.34246, in 0.094s
1 tree, 148 leaves, max depth = 13, train loss: 0.33110, val loss: 0.33959, in 0.031s
1 tree, 99 leaves, max depth = 14, train loss: 0.32811, val loss: 0.33661, in 0.016s
1 tree, 239 leaves, max depth = 22, train loss: 0.32540, val loss: 0.33403, in 0.047s
1 tree, 149 leaves, max depth = 13, train loss: 0.32249, val loss: 0.33139, in 0.031s
1 tree, 149 leaves, max depth = 13, train loss: 0.31970, val loss: 0.32887, in 0.016s
1 tree, 103 leaves, max depth = 15, train loss: 0.31704, val loss: 0.32621, in 0.031s
1 tree, 239 leaves, max depth = 18, train loss: 0.31454, val loss: 0.32388, in 0.031s
1 tree, 239 leaves, max depth = 20, train loss: 0.31209, val loss: 0.32166, in 0.031s
1 tree, 146 leaves, max depth = 13, train loss: 0.30955, val loss: 0.31937, in 0.016s
1 tree, 101 leaves, max depth = 15, train loss: 0.30713, val loss: 0.31697, in 0.031s
1 tree, 145 leaves, max depth = 13, train loss: 0.30475, val loss: 0.31484, in 0.016s
1 tree, 100 leaves, max depth = 15, train loss: 0.30249, val loss: 0.31260, in 0.016s
1 tree, 239 leaves, max depth = 20, train loss: 0.30025, val loss: 0.31055, in 0.031s
1 tree, 146 leaves, max depth = 13, train loss: 0.29804, val loss: 0.30859, in 0.031s
1 tree, 101 leaves, max depth = 15, train loss: 0.29596, val loss: 0.30651, in 0.016s
1 tree, 239 leaves, max depth = 19, train loss: 0.29382, val loss: 0.30451, in 0.031s
1 tree, 100 leaves, max depth = 15, train loss: 0.29185, val loss: 0.30254, in 0.016s
1 tree, 239 leaves, max depth = 19, train loss: 0.28975, val loss: 0.30054, in 0.031s
1 tree, 145 leaves, max depth = 13, train loss: 0.28778, val loss: 0.29880, in 0.016s
1 tree, 239 leaves, max depth = 20, train loss: 0.28584, val loss: 0.29698, in 0.047s
1 tree, 101 leaves, max depth = 16, train loss: 0.28404, val loss: 0.29522, in 0.016s
1 tree, 138 leaves, max depth = 14, train loss: 0.28220, val loss: 0.29360, in 0.016s
1 tree, 139 leaves, max depth = 14, train loss: 0.28043, val loss: 0.29204, in 0.031s
1 tree, 239 leaves, max depth = 19, train loss: 0.27854, val loss: 0.29027, in 0.031s
1 tree, 142 leaves, max depth = 16, train loss: 0.27688, val loss: 0.28881, in 0.016s
1 tree, 96 leaves, max depth = 17, train loss: 0.27527, val loss: 0.28720, in 0.016s
1 tree, 103 leaves, max depth = 16, train loss: 0.27375, val loss: 0.28571, in 0.031s
1 tree, 137 leaves, max depth = 16, train loss: 0.27220, val loss: 0.28436, in 0.016s
Fit 82 trees in 2.581 s, (16428 total leaves)
Time spent computing histograms: 0.714s
Time spent finding best splits:  0.357s
Time spent applying splits:      0.265s
Time spent predicting:           0.016s
Trial 77, Fold 1: Log loss = 0.2906058467959607, Average precision = 0.9604337413844874, ROC-AUC = 0.9551242125626724, Elapsed Time = 2.6012752999995428 seconds
Trial 77, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 77, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
Binning 0.040 GB of training data: 0.158 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 227 leaves, max depth = 17, train loss: 0.67806, val loss: 0.67807, in 0.016s
1 tree, 239 leaves, max depth = 15, train loss: 0.66349, val loss: 0.66363, in 0.031s
1 tree, 239 leaves, max depth = 20, train loss: 0.65034, val loss: 0.65058, in 0.047s
1 tree, 239 leaves, max depth = 18, train loss: 0.63797, val loss: 0.63838, in 0.031s
1 tree, 239 leaves, max depth = 17, train loss: 0.62525, val loss: 0.62581, in 0.031s
1 tree, 239 leaves, max depth = 17, train loss: 0.61313, val loss: 0.61383, in 0.031s
1 tree, 239 leaves, max depth = 17, train loss: 0.60156, val loss: 0.60240, in 0.031s
1 tree, 239 leaves, max depth = 17, train loss: 0.59052, val loss: 0.59148, in 0.031s
1 tree, 239 leaves, max depth = 16, train loss: 0.57996, val loss: 0.58105, in 0.031s
1 tree, 239 leaves, max depth = 17, train loss: 0.56987, val loss: 0.57111, in 0.031s
1 tree, 239 leaves, max depth = 16, train loss: 0.56001, val loss: 0.56144, in 0.031s
1 tree, 239 leaves, max depth = 17, train loss: 0.55076, val loss: 0.55232, in 0.031s
1 tree, 239 leaves, max depth = 17, train loss: 0.54190, val loss: 0.54357, in 0.031s
1 tree, 239 leaves, max depth = 17, train loss: 0.53340, val loss: 0.53516, in 0.031s
1 tree, 239 leaves, max depth = 17, train loss: 0.52524, val loss: 0.52710, in 0.031s
1 tree, 239 leaves, max depth = 19, train loss: 0.51758, val loss: 0.51954, in 0.047s
1 tree, 239 leaves, max depth = 18, train loss: 0.51022, val loss: 0.51237, in 0.031s
1 tree, 239 leaves, max depth = 18, train loss: 0.50278, val loss: 0.50511, in 0.031s
1 tree, 239 leaves, max depth = 16, train loss: 0.49563, val loss: 0.49816, in 0.047s
1 tree, 239 leaves, max depth = 17, train loss: 0.48876, val loss: 0.49149, in 0.031s
1 tree, 239 leaves, max depth = 17, train loss: 0.48215, val loss: 0.48509, in 0.016s
1 tree, 239 leaves, max depth = 20, train loss: 0.47622, val loss: 0.47933, in 0.031s
1 tree, 239 leaves, max depth = 16, train loss: 0.47008, val loss: 0.47341, in 0.047s
1 tree, 239 leaves, max depth = 17, train loss: 0.46418, val loss: 0.46771, in 0.047s
1 tree, 239 leaves, max depth = 16, train loss: 0.45849, val loss: 0.46219, in 0.031s
1 tree, 239 leaves, max depth = 18, train loss: 0.45301, val loss: 0.45688, in 0.031s
1 tree, 239 leaves, max depth = 16, train loss: 0.44773, val loss: 0.45172, in 0.031s
1 tree, 239 leaves, max depth = 17, train loss: 0.44265, val loss: 0.44677, in 0.047s
1 tree, 239 leaves, max depth = 18, train loss: 0.43774, val loss: 0.44203, in 0.031s
1 tree, 193 leaves, max depth = 18, train loss: 0.43098, val loss: 0.43546, in 0.031s
1 tree, 239 leaves, max depth = 17, train loss: 0.42637, val loss: 0.43097, in 0.031s
1 tree, 193 leaves, max depth = 18, train loss: 0.42002, val loss: 0.42481, in 0.031s
1 tree, 239 leaves, max depth = 17, train loss: 0.41577, val loss: 0.42080, in 0.031s
1 tree, 239 leaves, max depth = 19, train loss: 0.41170, val loss: 0.41690, in 0.047s
1 tree, 197 leaves, max depth = 17, train loss: 0.40582, val loss: 0.41122, in 0.031s
1 tree, 239 leaves, max depth = 16, train loss: 0.40188, val loss: 0.40751, in 0.031s
1 tree, 194 leaves, max depth = 18, train loss: 0.39636, val loss: 0.40219, in 0.031s
1 tree, 239 leaves, max depth = 17, train loss: 0.39264, val loss: 0.39859, in 0.031s
1 tree, 239 leaves, max depth = 15, train loss: 0.38905, val loss: 0.39519, in 0.031s
1 tree, 197 leaves, max depth = 18, train loss: 0.38392, val loss: 0.39027, in 0.031s
1 tree, 196 leaves, max depth = 17, train loss: 0.37901, val loss: 0.38556, in 0.031s
1 tree, 239 leaves, max depth = 16, train loss: 0.37567, val loss: 0.38251, in 0.047s
1 tree, 200 leaves, max depth = 17, train loss: 0.37103, val loss: 0.37806, in 0.031s
1 tree, 239 leaves, max depth = 21, train loss: 0.36785, val loss: 0.37512, in 0.031s
1 tree, 239 leaves, max depth = 18, train loss: 0.36484, val loss: 0.37233, in 0.031s
1 tree, 202 leaves, max depth = 17, train loss: 0.36051, val loss: 0.36819, in 0.031s
1 tree, 239 leaves, max depth = 18, train loss: 0.35764, val loss: 0.36547, in 0.031s
1 tree, 202 leaves, max depth = 17, train loss: 0.35356, val loss: 0.36158, in 0.031s
1 tree, 93 leaves, max depth = 15, train loss: 0.34987, val loss: 0.35799, in 0.016s
1 tree, 92 leaves, max depth = 15, train loss: 0.34634, val loss: 0.35454, in 0.016s
1 tree, 95 leaves, max depth = 15, train loss: 0.34296, val loss: 0.35127, in 0.016s
1 tree, 239 leaves, max depth = 23, train loss: 0.33998, val loss: 0.34853, in 0.031s
1 tree, 239 leaves, max depth = 23, train loss: 0.33710, val loss: 0.34591, in 0.047s
1 tree, 98 leaves, max depth = 20, train loss: 0.33396, val loss: 0.34286, in 0.016s
1 tree, 96 leaves, max depth = 15, train loss: 0.33094, val loss: 0.33996, in 0.031s
1 tree, 239 leaves, max depth = 24, train loss: 0.32823, val loss: 0.33750, in 0.031s
1 tree, 142 leaves, max depth = 16, train loss: 0.32527, val loss: 0.33478, in 0.031s
1 tree, 239 leaves, max depth = 22, train loss: 0.32272, val loss: 0.33241, in 0.032s
1 tree, 239 leaves, max depth = 22, train loss: 0.32026, val loss: 0.33015, in 0.047s
1 tree, 142 leaves, max depth = 16, train loss: 0.31749, val loss: 0.32760, in 0.016s
1 tree, 141 leaves, max depth = 16, train loss: 0.31483, val loss: 0.32516, in 0.016s
1 tree, 98 leaves, max depth = 15, train loss: 0.31228, val loss: 0.32271, in 0.016s
1 tree, 143 leaves, max depth = 16, train loss: 0.30978, val loss: 0.32044, in 0.031s
1 tree, 94 leaves, max depth = 17, train loss: 0.30740, val loss: 0.31815, in 0.031s
1 tree, 239 leaves, max depth = 22, train loss: 0.30513, val loss: 0.31610, in 0.031s
1 tree, 93 leaves, max depth = 17, train loss: 0.30287, val loss: 0.31392, in 0.016s
1 tree, 143 leaves, max depth = 15, train loss: 0.30062, val loss: 0.31188, in 0.031s
1 tree, 141 leaves, max depth = 15, train loss: 0.29845, val loss: 0.30990, in 0.016s
1 tree, 95 leaves, max depth = 16, train loss: 0.29639, val loss: 0.30793, in 0.031s
1 tree, 144 leaves, max depth = 15, train loss: 0.29436, val loss: 0.30610, in 0.016s
1 tree, 96 leaves, max depth = 16, train loss: 0.29242, val loss: 0.30427, in 0.031s
1 tree, 144 leaves, max depth = 15, train loss: 0.29051, val loss: 0.30255, in 0.016s
1 tree, 239 leaves, max depth = 22, train loss: 0.28849, val loss: 0.30080, in 0.047s
1 tree, 239 leaves, max depth = 22, train loss: 0.28647, val loss: 0.29903, in 0.031s
1 tree, 95 leaves, max depth = 15, train loss: 0.28468, val loss: 0.29730, in 0.016s
1 tree, 144 leaves, max depth = 15, train loss: 0.28293, val loss: 0.29573, in 0.031s
1 tree, 97 leaves, max depth = 18, train loss: 0.28128, val loss: 0.29417, in 0.031s
1 tree, 239 leaves, max depth = 21, train loss: 0.27939, val loss: 0.29253, in 0.031s
1 tree, 98 leaves, max depth = 19, train loss: 0.27782, val loss: 0.29104, in 0.016s
1 tree, 239 leaves, max depth = 21, train loss: 0.27600, val loss: 0.28946, in 0.031s
1 tree, 239 leaves, max depth = 22, train loss: 0.27429, val loss: 0.28797, in 0.031s
1 tree, 144 leaves, max depth = 15, train loss: 0.27275, val loss: 0.28661, in 0.016s
Fit 82 trees in 2.893 s, (16380 total leaves)
Time spent computing histograms: 0.770s
Time spent finding best splits:  0.387s
Time spent applying splits:      0.297s
Time spent predicting:           0.016s
Trial 77, Fold 2: Log loss = 0.2863839142277258, Average precision = 0.9617003319210659, ROC-AUC = 0.9584315015048827, Elapsed Time = 2.906086900000446 seconds
Trial 77, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 77, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
Binning 0.040 GB of training data: 0.173 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 205 leaves, max depth = 17, train loss: 0.67810, val loss: 0.67818, in 0.031s
1 tree, 239 leaves, max depth = 15, train loss: 0.66363, val loss: 0.66398, in 0.031s
1 tree, 239 leaves, max depth = 20, train loss: 0.64983, val loss: 0.65037, in 0.047s
1 tree, 239 leaves, max depth = 14, train loss: 0.63677, val loss: 0.63750, in 0.016s
1 tree, 239 leaves, max depth = 16, train loss: 0.62483, val loss: 0.62579, in 0.031s
1 tree, 239 leaves, max depth = 14, train loss: 0.61269, val loss: 0.61381, in 0.031s
1 tree, 239 leaves, max depth = 14, train loss: 0.60112, val loss: 0.60237, in 0.031s
1 tree, 239 leaves, max depth = 14, train loss: 0.59015, val loss: 0.59158, in 0.031s
1 tree, 239 leaves, max depth = 16, train loss: 0.57957, val loss: 0.58115, in 0.031s
1 tree, 239 leaves, max depth = 16, train loss: 0.56945, val loss: 0.57120, in 0.016s
1 tree, 239 leaves, max depth = 17, train loss: 0.55977, val loss: 0.56168, in 0.031s
1 tree, 239 leaves, max depth = 21, train loss: 0.55094, val loss: 0.55307, in 0.031s
1 tree, 239 leaves, max depth = 16, train loss: 0.54203, val loss: 0.54431, in 0.047s
1 tree, 239 leaves, max depth = 18, train loss: 0.53345, val loss: 0.53595, in 0.031s
1 tree, 239 leaves, max depth = 17, train loss: 0.52524, val loss: 0.52788, in 0.031s
1 tree, 239 leaves, max depth = 16, train loss: 0.51736, val loss: 0.52031, in 0.031s
1 tree, 239 leaves, max depth = 15, train loss: 0.51018, val loss: 0.51340, in 0.016s
1 tree, 239 leaves, max depth = 16, train loss: 0.50288, val loss: 0.50621, in 0.047s
1 tree, 239 leaves, max depth = 16, train loss: 0.49587, val loss: 0.49932, in 0.031s
1 tree, 239 leaves, max depth = 19, train loss: 0.48947, val loss: 0.49322, in 0.031s
1 tree, 239 leaves, max depth = 15, train loss: 0.48301, val loss: 0.48699, in 0.031s
1 tree, 239 leaves, max depth = 16, train loss: 0.47675, val loss: 0.48088, in 0.031s
1 tree, 191 leaves, max depth = 17, train loss: 0.46881, val loss: 0.47342, in 0.016s
1 tree, 239 leaves, max depth = 16, train loss: 0.46295, val loss: 0.46772, in 0.031s
1 tree, 239 leaves, max depth = 16, train loss: 0.45731, val loss: 0.46222, in 0.031s
1 tree, 239 leaves, max depth = 15, train loss: 0.45188, val loss: 0.45693, in 0.031s
1 tree, 239 leaves, max depth = 17, train loss: 0.44665, val loss: 0.45186, in 0.031s
1 tree, 239 leaves, max depth = 17, train loss: 0.44161, val loss: 0.44695, in 0.031s
1 tree, 239 leaves, max depth = 16, train loss: 0.43674, val loss: 0.44223, in 0.031s
1 tree, 239 leaves, max depth = 16, train loss: 0.43206, val loss: 0.43770, in 0.047s
1 tree, 195 leaves, max depth = 17, train loss: 0.42551, val loss: 0.43161, in 0.031s
1 tree, 239 leaves, max depth = 16, train loss: 0.42111, val loss: 0.42735, in 0.031s
1 tree, 195 leaves, max depth = 17, train loss: 0.41494, val loss: 0.42166, in 0.016s
1 tree, 239 leaves, max depth = 16, train loss: 0.41080, val loss: 0.41772, in 0.047s
1 tree, 239 leaves, max depth = 14, train loss: 0.40681, val loss: 0.41400, in 0.031s
1 tree, 228 leaves, max depth = 16, train loss: 0.40115, val loss: 0.40883, in 0.031s
1 tree, 199 leaves, max depth = 19, train loss: 0.39570, val loss: 0.40379, in 0.031s
1 tree, 239 leaves, max depth = 16, train loss: 0.39201, val loss: 0.40036, in 0.031s
1 tree, 239 leaves, max depth = 21, train loss: 0.38835, val loss: 0.39693, in 0.031s
1 tree, 239 leaves, max depth = 16, train loss: 0.38491, val loss: 0.39369, in 0.031s
1 tree, 203 leaves, max depth = 18, train loss: 0.37992, val loss: 0.38914, in 0.031s
1 tree, 202 leaves, max depth = 19, train loss: 0.37513, val loss: 0.38475, in 0.031s
1 tree, 200 leaves, max depth = 19, train loss: 0.37053, val loss: 0.38055, in 0.031s
1 tree, 239 leaves, max depth = 18, train loss: 0.36736, val loss: 0.37762, in 0.031s
1 tree, 202 leaves, max depth = 21, train loss: 0.36305, val loss: 0.37369, in 0.031s
1 tree, 239 leaves, max depth = 19, train loss: 0.36008, val loss: 0.37087, in 0.047s
1 tree, 210 leaves, max depth = 20, train loss: 0.35598, val loss: 0.36718, in 0.031s
1 tree, 239 leaves, max depth = 18, train loss: 0.35319, val loss: 0.36477, in 0.031s
1 tree, 202 leaves, max depth = 19, train loss: 0.34930, val loss: 0.36125, in 0.031s
1 tree, 91 leaves, max depth = 20, train loss: 0.34581, val loss: 0.35802, in 0.016s
1 tree, 94 leaves, max depth = 13, train loss: 0.34244, val loss: 0.35493, in 0.031s
1 tree, 239 leaves, max depth = 17, train loss: 0.33951, val loss: 0.35203, in 0.031s
1 tree, 94 leaves, max depth = 13, train loss: 0.33633, val loss: 0.34912, in 0.031s
1 tree, 137 leaves, max depth = 17, train loss: 0.33321, val loss: 0.34654, in 0.016s
1 tree, 100 leaves, max depth = 13, train loss: 0.33026, val loss: 0.34386, in 0.031s
1 tree, 239 leaves, max depth = 21, train loss: 0.32761, val loss: 0.34126, in 0.031s
1 tree, 139 leaves, max depth = 17, train loss: 0.32473, val loss: 0.33889, in 0.016s
1 tree, 239 leaves, max depth = 18, train loss: 0.32215, val loss: 0.33635, in 0.047s
1 tree, 93 leaves, max depth = 16, train loss: 0.31946, val loss: 0.33389, in 0.016s
1 tree, 239 leaves, max depth = 18, train loss: 0.31700, val loss: 0.33148, in 0.031s
1 tree, 95 leaves, max depth = 16, train loss: 0.31445, val loss: 0.32918, in 0.031s
1 tree, 239 leaves, max depth = 18, train loss: 0.31211, val loss: 0.32687, in 0.016s
1 tree, 137 leaves, max depth = 17, train loss: 0.30959, val loss: 0.32483, in 0.031s
1 tree, 239 leaves, max depth = 18, train loss: 0.30735, val loss: 0.32263, in 0.031s
1 tree, 239 leaves, max depth = 17, train loss: 0.30519, val loss: 0.32056, in 0.031s
1 tree, 138 leaves, max depth = 17, train loss: 0.30284, val loss: 0.31866, in 0.031s
1 tree, 95 leaves, max depth = 22, train loss: 0.30062, val loss: 0.31665, in 0.016s
1 tree, 91 leaves, max depth = 13, train loss: 0.29847, val loss: 0.31469, in 0.031s
1 tree, 139 leaves, max depth = 16, train loss: 0.29632, val loss: 0.31296, in 0.031s
1 tree, 138 leaves, max depth = 16, train loss: 0.29425, val loss: 0.31131, in 0.016s
1 tree, 239 leaves, max depth = 18, train loss: 0.29228, val loss: 0.30941, in 0.031s
1 tree, 97 leaves, max depth = 15, train loss: 0.29035, val loss: 0.30767, in 0.031s
1 tree, 145 leaves, max depth = 17, train loss: 0.28843, val loss: 0.30616, in 0.031s
1 tree, 97 leaves, max depth = 15, train loss: 0.28662, val loss: 0.30454, in 0.016s
1 tree, 144 leaves, max depth = 16, train loss: 0.28481, val loss: 0.30312, in 0.031s
1 tree, 145 leaves, max depth = 16, train loss: 0.28306, val loss: 0.30176, in 0.031s
1 tree, 239 leaves, max depth = 22, train loss: 0.28142, val loss: 0.30027, in 0.031s
1 tree, 97 leaves, max depth = 15, train loss: 0.27979, val loss: 0.29881, in 0.016s
1 tree, 141 leaves, max depth = 17, train loss: 0.27816, val loss: 0.29755, in 0.031s
1 tree, 91 leaves, max depth = 13, train loss: 0.27661, val loss: 0.29614, in 0.016s
1 tree, 87 leaves, max depth = 13, train loss: 0.27513, val loss: 0.29478, in 0.031s
1 tree, 239 leaves, max depth = 18, train loss: 0.27343, val loss: 0.29310, in 0.031s
Fit 82 trees in 2.814 s, (16290 total leaves)
Time spent computing histograms: 0.755s
Time spent finding best splits:  0.373s
Time spent applying splits:      0.283s
Time spent predicting:           0.016s
Trial 77, Fold 3: Log loss = 0.28523170367129097, Average precision = 0.9624306571419454, ROC-AUC = 0.9585553236750103, Elapsed Time = 2.813362500000949 seconds
Trial 77, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 77, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
Binning 0.040 GB of training data: 0.158 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 229 leaves, max depth = 15, train loss: 0.67817, val loss: 0.67782, in 0.031s
1 tree, 239 leaves, max depth = 17, train loss: 0.66367, val loss: 0.66310, in 0.031s
1 tree, 239 leaves, max depth = 23, train loss: 0.65044, val loss: 0.64969, in 0.047s
1 tree, 239 leaves, max depth = 19, train loss: 0.63804, val loss: 0.63711, in 0.031s
1 tree, 239 leaves, max depth = 17, train loss: 0.62544, val loss: 0.62441, in 0.031s
1 tree, 239 leaves, max depth = 17, train loss: 0.61341, val loss: 0.61225, in 0.031s
1 tree, 239 leaves, max depth = 17, train loss: 0.60195, val loss: 0.60068, in 0.016s
1 tree, 239 leaves, max depth = 17, train loss: 0.59099, val loss: 0.58955, in 0.031s
1 tree, 239 leaves, max depth = 17, train loss: 0.58052, val loss: 0.57891, in 0.047s
1 tree, 239 leaves, max depth = 18, train loss: 0.57052, val loss: 0.56877, in 0.031s
1 tree, 239 leaves, max depth = 19, train loss: 0.56094, val loss: 0.55903, in 0.031s
1 tree, 239 leaves, max depth = 17, train loss: 0.55188, val loss: 0.54974, in 0.031s
1 tree, 239 leaves, max depth = 17, train loss: 0.54288, val loss: 0.54058, in 0.031s
1 tree, 239 leaves, max depth = 16, train loss: 0.53464, val loss: 0.53214, in 0.031s
1 tree, 239 leaves, max depth = 17, train loss: 0.52634, val loss: 0.52368, in 0.031s
1 tree, 222 leaves, max depth = 18, train loss: 0.51860, val loss: 0.51566, in 0.031s
1 tree, 239 leaves, max depth = 20, train loss: 0.51145, val loss: 0.50844, in 0.031s
1 tree, 239 leaves, max depth = 17, train loss: 0.50408, val loss: 0.50092, in 0.047s
1 tree, 239 leaves, max depth = 19, train loss: 0.49745, val loss: 0.49424, in 0.031s
1 tree, 239 leaves, max depth = 17, train loss: 0.49062, val loss: 0.48727, in 0.047s
1 tree, 239 leaves, max depth = 16, train loss: 0.48412, val loss: 0.48057, in 0.047s
1 tree, 239 leaves, max depth = 17, train loss: 0.47779, val loss: 0.47411, in 0.047s
1 tree, 195 leaves, max depth = 15, train loss: 0.46992, val loss: 0.46612, in 0.047s
1 tree, 239 leaves, max depth = 17, train loss: 0.46410, val loss: 0.46027, in 0.063s
1 tree, 239 leaves, max depth = 17, train loss: 0.45840, val loss: 0.45450, in 0.047s
1 tree, 239 leaves, max depth = 16, train loss: 0.45292, val loss: 0.44892, in 0.031s
1 tree, 239 leaves, max depth = 16, train loss: 0.44764, val loss: 0.44354, in 0.031s
1 tree, 226 leaves, max depth = 17, train loss: 0.44079, val loss: 0.43656, in 0.047s
1 tree, 239 leaves, max depth = 17, train loss: 0.43590, val loss: 0.43157, in 0.031s
1 tree, 239 leaves, max depth = 17, train loss: 0.43112, val loss: 0.42671, in 0.047s
1 tree, 239 leaves, max depth = 15, train loss: 0.42656, val loss: 0.42211, in 0.031s
1 tree, 239 leaves, max depth = 16, train loss: 0.42226, val loss: 0.41775, in 0.031s
1 tree, 239 leaves, max depth = 16, train loss: 0.41793, val loss: 0.41346, in 0.047s
1 tree, 201 leaves, max depth = 18, train loss: 0.41188, val loss: 0.40736, in 0.031s
1 tree, 198 leaves, max depth = 16, train loss: 0.40612, val loss: 0.40155, in 0.031s
1 tree, 239 leaves, max depth = 15, train loss: 0.40230, val loss: 0.39773, in 0.031s
1 tree, 239 leaves, max depth = 16, train loss: 0.39847, val loss: 0.39397, in 0.031s
1 tree, 198 leaves, max depth = 19, train loss: 0.39314, val loss: 0.38861, in 0.047s
1 tree, 239 leaves, max depth = 20, train loss: 0.38988, val loss: 0.38529, in 0.047s
1 tree, 239 leaves, max depth = 16, train loss: 0.38635, val loss: 0.38184, in 0.031s
1 tree, 198 leaves, max depth = 19, train loss: 0.38141, val loss: 0.37688, in 0.047s
1 tree, 239 leaves, max depth = 19, train loss: 0.37815, val loss: 0.37374, in 0.047s
1 tree, 202 leaves, max depth = 17, train loss: 0.37349, val loss: 0.36910, in 0.031s
1 tree, 239 leaves, max depth = 19, train loss: 0.37041, val loss: 0.36614, in 0.047s
1 tree, 239 leaves, max depth = 18, train loss: 0.36750, val loss: 0.36334, in 0.047s
1 tree, 199 leaves, max depth = 17, train loss: 0.36316, val loss: 0.35899, in 0.031s
1 tree, 236 leaves, max depth = 16, train loss: 0.35902, val loss: 0.35483, in 0.047s
1 tree, 237 leaves, max depth = 19, train loss: 0.35505, val loss: 0.35089, in 0.047s
1 tree, 239 leaves, max depth = 17, train loss: 0.35227, val loss: 0.34822, in 0.047s
1 tree, 96 leaves, max depth = 16, train loss: 0.34870, val loss: 0.34449, in 0.031s
1 tree, 95 leaves, max depth = 15, train loss: 0.34526, val loss: 0.34091, in 0.031s
1 tree, 239 leaves, max depth = 16, train loss: 0.34230, val loss: 0.33817, in 0.031s
1 tree, 97 leaves, max depth = 17, train loss: 0.33906, val loss: 0.33483, in 0.031s
1 tree, 239 leaves, max depth = 16, train loss: 0.33624, val loss: 0.33222, in 0.031s
1 tree, 239 leaves, max depth = 16, train loss: 0.33352, val loss: 0.32970, in 0.047s
1 tree, 144 leaves, max depth = 15, train loss: 0.33043, val loss: 0.32680, in 0.031s
1 tree, 96 leaves, max depth = 16, train loss: 0.32751, val loss: 0.32374, in 0.016s
1 tree, 239 leaves, max depth = 21, train loss: 0.32526, val loss: 0.32166, in 0.047s
1 tree, 144 leaves, max depth = 15, train loss: 0.32241, val loss: 0.31900, in 0.016s
1 tree, 99 leaves, max depth = 18, train loss: 0.31971, val loss: 0.31622, in 0.031s
1 tree, 239 leaves, max depth = 17, train loss: 0.31728, val loss: 0.31402, in 0.031s
1 tree, 142 leaves, max depth = 15, train loss: 0.31465, val loss: 0.31157, in 0.031s
1 tree, 140 leaves, max depth = 15, train loss: 0.31213, val loss: 0.30922, in 0.031s
1 tree, 239 leaves, max depth = 18, train loss: 0.30984, val loss: 0.30714, in 0.047s
1 tree, 104 leaves, max depth = 16, train loss: 0.30749, val loss: 0.30467, in 0.016s
1 tree, 239 leaves, max depth = 21, train loss: 0.30560, val loss: 0.30295, in 0.047s
1 tree, 140 leaves, max depth = 15, train loss: 0.30329, val loss: 0.30081, in 0.031s
1 tree, 94 leaves, max depth = 15, train loss: 0.30109, val loss: 0.29852, in 0.016s
1 tree, 140 leaves, max depth = 15, train loss: 0.29893, val loss: 0.29652, in 0.031s
1 tree, 95 leaves, max depth = 16, train loss: 0.29688, val loss: 0.29437, in 0.031s
1 tree, 135 leaves, max depth = 15, train loss: 0.29485, val loss: 0.29248, in 0.031s
1 tree, 239 leaves, max depth = 20, train loss: 0.29277, val loss: 0.29050, in 0.031s
1 tree, 92 leaves, max depth = 16, train loss: 0.29087, val loss: 0.28848, in 0.031s
1 tree, 143 leaves, max depth = 15, train loss: 0.28899, val loss: 0.28673, in 0.031s
1 tree, 100 leaves, max depth = 17, train loss: 0.28720, val loss: 0.28487, in 0.031s
1 tree, 136 leaves, max depth = 15, train loss: 0.28544, val loss: 0.28323, in 0.016s
1 tree, 239 leaves, max depth = 20, train loss: 0.28351, val loss: 0.28141, in 0.031s
1 tree, 93 leaves, max depth = 16, train loss: 0.28186, val loss: 0.27965, in 0.016s
1 tree, 140 leaves, max depth = 15, train loss: 0.28022, val loss: 0.27816, in 0.031s
1 tree, 96 leaves, max depth = 16, train loss: 0.27866, val loss: 0.27656, in 0.016s
1 tree, 239 leaves, max depth = 18, train loss: 0.27685, val loss: 0.27485, in 0.062s
1 tree, 100 leaves, max depth = 16, train loss: 0.27538, val loss: 0.27331, in 0.016s
Fit 82 trees in 3.237 s, (16435 total leaves)
Time spent computing histograms: 0.853s
Time spent finding best splits:  0.450s
Time spent applying splits:      0.337s
Time spent predicting:           0.000s
Trial 77, Fold 4: Log loss = 0.2879911332859797, Average precision = 0.9611189898196466, ROC-AUC = 0.9559277619214528, Elapsed Time = 3.2417513000000326 seconds
Trial 77, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 77, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
0.190 s 0.040 GB of training data: 
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 208 leaves, max depth = 18, train loss: 0.67793, val loss: 0.67750, in 0.047s
1 tree, 239 leaves, max depth = 16, train loss: 0.66321, val loss: 0.66250, in 0.031s
1 tree, 239 leaves, max depth = 17, train loss: 0.64977, val loss: 0.64872, in 0.031s
1 tree, 239 leaves, max depth = 16, train loss: 0.63720, val loss: 0.63584, in 0.031s
1 tree, 239 leaves, max depth = 16, train loss: 0.62440, val loss: 0.62281, in 0.031s
1 tree, 239 leaves, max depth = 16, train loss: 0.61278, val loss: 0.61096, in 0.031s
1 tree, 239 leaves, max depth = 16, train loss: 0.60110, val loss: 0.59907, in 0.031s
1 tree, 239 leaves, max depth = 18, train loss: 0.58997, val loss: 0.58771, in 0.031s
1 tree, 239 leaves, max depth = 18, train loss: 0.57933, val loss: 0.57687, in 0.031s
1 tree, 239 leaves, max depth = 18, train loss: 0.56931, val loss: 0.56661, in 0.031s
1 tree, 239 leaves, max depth = 15, train loss: 0.55970, val loss: 0.55686, in 0.047s
1 tree, 239 leaves, max depth = 23, train loss: 0.55089, val loss: 0.54786, in 0.047s
1 tree, 239 leaves, max depth = 18, train loss: 0.54188, val loss: 0.53865, in 0.047s
1 tree, 239 leaves, max depth = 18, train loss: 0.53324, val loss: 0.52981, in 0.031s
1 tree, 239 leaves, max depth = 23, train loss: 0.52546, val loss: 0.52186, in 0.047s
1 tree, 239 leaves, max depth = 15, train loss: 0.51795, val loss: 0.51439, in 0.047s
1 tree, 239 leaves, max depth = 17, train loss: 0.51026, val loss: 0.50656, in 0.031s
1 tree, 239 leaves, max depth = 18, train loss: 0.50288, val loss: 0.49903, in 0.047s
1 tree, 239 leaves, max depth = 18, train loss: 0.49593, val loss: 0.49189, in 0.031s
1 tree, 239 leaves, max depth = 18, train loss: 0.48910, val loss: 0.48493, in 0.047s
1 tree, 239 leaves, max depth = 18, train loss: 0.48245, val loss: 0.47822, in 0.047s
1 tree, 239 leaves, max depth = 18, train loss: 0.47626, val loss: 0.47188, in 0.047s
1 tree, 239 leaves, max depth = 18, train loss: 0.47009, val loss: 0.46567, in 0.031s
1 tree, 239 leaves, max depth = 18, train loss: 0.46419, val loss: 0.45982, in 0.047s
1 tree, 239 leaves, max depth = 18, train loss: 0.45846, val loss: 0.45407, in 0.031s
1 tree, 239 leaves, max depth = 21, train loss: 0.45302, val loss: 0.44871, in 0.047s
1 tree, 239 leaves, max depth = 18, train loss: 0.44770, val loss: 0.44337, in 0.031s
1 tree, 239 leaves, max depth = 18, train loss: 0.44257, val loss: 0.43823, in 0.047s
1 tree, 194 leaves, max depth = 18, train loss: 0.43561, val loss: 0.43133, in 0.031s
1 tree, 239 leaves, max depth = 15, train loss: 0.43094, val loss: 0.42677, in 0.031s
1 tree, 239 leaves, max depth = 18, train loss: 0.42629, val loss: 0.42214, in 0.031s
1 tree, 239 leaves, max depth = 18, train loss: 0.42181, val loss: 0.41768, in 0.031s
1 tree, 239 leaves, max depth = 21, train loss: 0.41753, val loss: 0.41353, in 0.047s
1 tree, 239 leaves, max depth = 15, train loss: 0.41345, val loss: 0.40950, in 0.047s
1 tree, 239 leaves, max depth = 20, train loss: 0.40941, val loss: 0.40552, in 0.047s
1 tree, 195 leaves, max depth = 20, train loss: 0.40343, val loss: 0.39967, in 0.031s
1 tree, 202 leaves, max depth = 20, train loss: 0.39769, val loss: 0.39404, in 0.047s
1 tree, 197 leaves, max depth = 20, train loss: 0.39220, val loss: 0.38862, in 0.031s
1 tree, 202 leaves, max depth = 20, train loss: 0.38693, val loss: 0.38346, in 0.031s
1 tree, 239 leaves, max depth = 19, train loss: 0.38340, val loss: 0.38000, in 0.047s
1 tree, 235 leaves, max depth = 17, train loss: 0.37851, val loss: 0.37524, in 0.047s
1 tree, 198 leaves, max depth = 20, train loss: 0.37373, val loss: 0.37053, in 0.031s
1 tree, 239 leaves, max depth = 19, train loss: 0.37048, val loss: 0.36736, in 0.047s
1 tree, 239 leaves, max depth = 19, train loss: 0.36733, val loss: 0.36431, in 0.031s
1 tree, 204 leaves, max depth = 20, train loss: 0.36290, val loss: 0.36000, in 0.047s
1 tree, 239 leaves, max depth = 19, train loss: 0.35994, val loss: 0.35713, in 0.047s
1 tree, 230 leaves, max depth = 17, train loss: 0.35581, val loss: 0.35313, in 0.031s
1 tree, 239 leaves, max depth = 19, train loss: 0.35300, val loss: 0.35040, in 0.047s
1 tree, 235 leaves, max depth = 17, train loss: 0.34911, val loss: 0.34666, in 0.047s
1 tree, 206 leaves, max depth = 20, train loss: 0.34529, val loss: 0.34297, in 0.031s
1 tree, 239 leaves, max depth = 21, train loss: 0.34269, val loss: 0.34056, in 0.047s
1 tree, 235 leaves, max depth = 17, train loss: 0.33915, val loss: 0.33717, in 0.031s
1 tree, 97 leaves, max depth = 18, train loss: 0.33589, val loss: 0.33391, in 0.031s
1 tree, 103 leaves, max depth = 16, train loss: 0.33277, val loss: 0.33070, in 0.016s
1 tree, 239 leaves, max depth = 19, train loss: 0.32995, val loss: 0.32810, in 0.047s
1 tree, 102 leaves, max depth = 16, train loss: 0.32699, val loss: 0.32512, in 0.016s
1 tree, 239 leaves, max depth = 19, train loss: 0.32430, val loss: 0.32263, in 0.062s
1 tree, 147 leaves, max depth = 19, train loss: 0.32146, val loss: 0.32001, in 0.016s
1 tree, 239 leaves, max depth = 19, train loss: 0.31887, val loss: 0.31765, in 0.047s
1 tree, 239 leaves, max depth = 19, train loss: 0.31637, val loss: 0.31536, in 0.031s
1 tree, 146 leaves, max depth = 19, train loss: 0.31371, val loss: 0.31296, in 0.031s
1 tree, 100 leaves, max depth = 16, train loss: 0.31113, val loss: 0.31038, in 0.031s
1 tree, 146 leaves, max depth = 17, train loss: 0.30864, val loss: 0.30814, in 0.016s
1 tree, 101 leaves, max depth = 16, train loss: 0.30623, val loss: 0.30573, in 0.031s
1 tree, 143 leaves, max depth = 17, train loss: 0.30390, val loss: 0.30364, in 0.031s
1 tree, 103 leaves, max depth = 16, train loss: 0.30165, val loss: 0.30133, in 0.031s
1 tree, 239 leaves, max depth = 19, train loss: 0.29942, val loss: 0.29931, in 0.031s
1 tree, 93 leaves, max depth = 15, train loss: 0.29727, val loss: 0.29715, in 0.016s
1 tree, 104 leaves, max depth = 16, train loss: 0.29522, val loss: 0.29505, in 0.031s
1 tree, 239 leaves, max depth = 18, train loss: 0.29309, val loss: 0.29303, in 0.031s
1 tree, 239 leaves, max depth = 18, train loss: 0.29105, val loss: 0.29120, in 0.031s
1 tree, 144 leaves, max depth = 18, train loss: 0.28905, val loss: 0.28943, in 0.031s
1 tree, 144 leaves, max depth = 18, train loss: 0.28713, val loss: 0.28774, in 0.031s
1 tree, 239 leaves, max depth = 20, train loss: 0.28520, val loss: 0.28603, in 0.031s
1 tree, 145 leaves, max depth = 16, train loss: 0.28338, val loss: 0.28443, in 0.031s
1 tree, 101 leaves, max depth = 15, train loss: 0.28162, val loss: 0.28263, in 0.031s
1 tree, 145 leaves, max depth = 16, train loss: 0.27991, val loss: 0.28113, in 0.016s
1 tree, 239 leaves, max depth = 21, train loss: 0.27837, val loss: 0.27987, in 0.047s
1 tree, 145 leaves, max depth = 16, train loss: 0.27674, val loss: 0.27841, in 0.031s
1 tree, 239 leaves, max depth = 17, train loss: 0.27495, val loss: 0.27671, in 0.031s
1 tree, 99 leaves, max depth = 14, train loss: 0.27338, val loss: 0.27511, in 0.031s
1 tree, 239 leaves, max depth = 17, train loss: 0.27195, val loss: 0.27404, in 0.031s
Fit 82 trees in 3.362 s, (16999 total leaves)
Time spent computing histograms: 0.884s
Time spent finding best splits:  0.470s
Time spent applying splits:      0.352s
Time spent predicting:           0.000s
Trial 77, Fold 5: Log loss = 0.29372377089913193, Average precision = 0.9590494070438371, ROC-AUC = 0.9544510711034315, Elapsed Time = 3.3814411999992444 seconds
Optimization Progress:  78%|#######8  | 78/100 [16:04<05:53, 16.07s/it]
Trial 78, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 78, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
0.158 s 0.040 GB of training data: 
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 29 leaves, max depth = 10, train loss: 0.67124, val loss: 0.67051, in 0.016s
1 tree, 29 leaves, max depth = 10, train loss: 0.65149, val loss: 0.65008, in 0.000s
1 tree, 29 leaves, max depth = 10, train loss: 0.63349, val loss: 0.63142, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.61737, val loss: 0.61467, in 0.000s
1 tree, 30 leaves, max depth = 11, train loss: 0.60230, val loss: 0.59900, in 0.016s
1 tree, 30 leaves, max depth = 11, train loss: 0.58849, val loss: 0.58462, in 0.000s
1 tree, 31 leaves, max depth = 9, train loss: 0.57593, val loss: 0.57164, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.56429, val loss: 0.55947, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.55358, val loss: 0.54825, in 0.000s
1 tree, 57 leaves, max depth = 11, train loss: 0.54300, val loss: 0.53831, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.53351, val loss: 0.52834, in 0.000s
1 tree, 57 leaves, max depth = 11, train loss: 0.52406, val loss: 0.51949, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.51578, val loss: 0.51077, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.50799, val loss: 0.50252, in 0.000s
1 tree, 31 leaves, max depth = 10, train loss: 0.50079, val loss: 0.49490, in 0.016s
1 tree, 57 leaves, max depth = 11, train loss: 0.49266, val loss: 0.48735, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.48639, val loss: 0.48068, in 0.000s
1 tree, 57 leaves, max depth = 11, train loss: 0.47902, val loss: 0.47387, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.47331, val loss: 0.46780, in 0.016s
1 tree, 57 leaves, max depth = 11, train loss: 0.46663, val loss: 0.46165, in 0.000s
1 tree, 57 leaves, max depth = 11, train loss: 0.46046, val loss: 0.45599, in 0.016s
1 tree, 57 leaves, max depth = 11, train loss: 0.45475, val loss: 0.45076, in 0.016s
1 tree, 57 leaves, max depth = 11, train loss: 0.44947, val loss: 0.44594, in 0.000s
1 tree, 31 leaves, max depth = 11, train loss: 0.44481, val loss: 0.44097, in 0.016s
1 tree, 57 leaves, max depth = 11, train loss: 0.43999, val loss: 0.43658, in 0.016s
1 tree, 57 leaves, max depth = 11, train loss: 0.43553, val loss: 0.43253, in 0.016s
1 tree, 30 leaves, max depth = 11, train loss: 0.43134, val loss: 0.42799, in 0.000s
1 tree, 31 leaves, max depth = 11, train loss: 0.42744, val loss: 0.42379, in 0.016s
1 tree, 57 leaves, max depth = 11, train loss: 0.42339, val loss: 0.42015, in 0.016s
1 tree, 57 leaves, max depth = 11, train loss: 0.41964, val loss: 0.41679, in 0.000s
1 tree, 31 leaves, max depth = 12, train loss: 0.41619, val loss: 0.41308, in 0.000s
1 tree, 57 leaves, max depth = 11, train loss: 0.41274, val loss: 0.41001, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.40952, val loss: 0.40644, in 0.000s
1 tree, 57 leaves, max depth = 11, train loss: 0.40634, val loss: 0.40364, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.40339, val loss: 0.40036, in 0.000s
1 tree, 31 leaves, max depth = 11, train loss: 0.40033, val loss: 0.39706, in 0.016s
1 tree, 57 leaves, max depth = 11, train loss: 0.39740, val loss: 0.39453, in 0.000s
1 tree, 31 leaves, max depth = 11, train loss: 0.39460, val loss: 0.39151, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.39201, val loss: 0.38865, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.38936, val loss: 0.38569, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.38692, val loss: 0.38295, in 0.000s
1 tree, 57 leaves, max depth = 11, train loss: 0.38421, val loss: 0.38066, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.38188, val loss: 0.37816, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.37963, val loss: 0.37563, in 0.016s
1 tree, 57 leaves, max depth = 11, train loss: 0.37711, val loss: 0.37353, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.37503, val loss: 0.37117, in 0.000s
1 tree, 57 leaves, max depth = 11, train loss: 0.37269, val loss: 0.36925, in 0.016s
1 tree, 57 leaves, max depth = 11, train loss: 0.37052, val loss: 0.36749, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.36857, val loss: 0.36527, in 0.000s
1 tree, 31 leaves, max depth = 10, train loss: 0.36648, val loss: 0.36306, in 0.016s
1 tree, 56 leaves, max depth = 11, train loss: 0.36446, val loss: 0.36144, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.36265, val loss: 0.35937, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.36066, val loss: 0.35724, in 0.000s
1 tree, 31 leaves, max depth = 9, train loss: 0.35882, val loss: 0.35527, in 0.016s
1 tree, 57 leaves, max depth = 11, train loss: 0.35692, val loss: 0.35377, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.35524, val loss: 0.35183, in 0.000s
1 tree, 31 leaves, max depth = 10, train loss: 0.35359, val loss: 0.35010, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.35203, val loss: 0.34830, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.35058, val loss: 0.34662, in 0.000s
1 tree, 31 leaves, max depth = 9, train loss: 0.34901, val loss: 0.34504, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.34767, val loss: 0.34347, in 0.016s
1 tree, 57 leaves, max depth = 12, train loss: 0.34582, val loss: 0.34206, in 0.000s
1 tree, 38 leaves, max depth = 12, train loss: 0.34434, val loss: 0.34034, in 0.016s
1 tree, 57 leaves, max depth = 12, train loss: 0.34262, val loss: 0.33904, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.34134, val loss: 0.33753, in 0.000s
1 tree, 39 leaves, max depth = 12, train loss: 0.33995, val loss: 0.33592, in 0.016s
1 tree, 57 leaves, max depth = 12, train loss: 0.33834, val loss: 0.33471, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.33712, val loss: 0.33328, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.33580, val loss: 0.33196, in 0.000s
1 tree, 38 leaves, max depth = 12, train loss: 0.33456, val loss: 0.33052, in 0.016s
1 tree, 57 leaves, max depth = 11, train loss: 0.33303, val loss: 0.32939, in 0.016s
1 tree, 39 leaves, max depth = 12, train loss: 0.33188, val loss: 0.32803, in 0.000s
Fit 72 trees in 0.970 s, (2538 total leaves)
Time spent computing histograms: 0.341s
Time spent finding best splits:  0.067s
Time spent applying splits:      0.053s
Time spent predicting:           0.000s
Trial 78, Fold 1: Log loss = 0.3356414306376848, Average precision = 0.9479986388867059, ROC-AUC = 0.9462670362484049, Elapsed Time = 0.9823266000003059 seconds
Trial 78, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 78, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
Binning 0.040 GB of training data: 0.174 s
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 32 leaves, max depth = 14, train loss: 0.67170, val loss: 0.67066, in 0.000s
1 tree, 32 leaves, max depth = 14, train loss: 0.65225, val loss: 0.65025, in 0.016s
1 tree, 32 leaves, max depth = 14, train loss: 0.63453, val loss: 0.63162, in 0.016s
1 tree, 32 leaves, max depth = 14, train loss: 0.61835, val loss: 0.61458, in 0.000s
1 tree, 32 leaves, max depth = 14, train loss: 0.60356, val loss: 0.59896, in 0.016s
1 tree, 32 leaves, max depth = 14, train loss: 0.59000, val loss: 0.58464, in 0.000s
1 tree, 32 leaves, max depth = 11, train loss: 0.57755, val loss: 0.57148, in 0.016s
1 tree, 32 leaves, max depth = 13, train loss: 0.56614, val loss: 0.55937, in 0.016s
1 tree, 31 leaves, max depth = 8, train loss: 0.55535, val loss: 0.54792, in 0.000s
1 tree, 57 leaves, max depth = 12, train loss: 0.54481, val loss: 0.53772, in 0.016s
1 tree, 31 leaves, max depth = 8, train loss: 0.53525, val loss: 0.52753, in 0.016s
1 tree, 57 leaves, max depth = 12, train loss: 0.52584, val loss: 0.51844, in 0.000s
1 tree, 57 leaves, max depth = 12, train loss: 0.51718, val loss: 0.51010, in 0.016s
1 tree, 32 leaves, max depth = 11, train loss: 0.50916, val loss: 0.50152, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.50154, val loss: 0.49336, in 0.000s
1 tree, 57 leaves, max depth = 12, train loss: 0.49392, val loss: 0.48605, in 0.016s
1 tree, 32 leaves, max depth = 11, train loss: 0.48731, val loss: 0.47894, in 0.016s
1 tree, 57 leaves, max depth = 12, train loss: 0.48041, val loss: 0.47233, in 0.000s
1 tree, 32 leaves, max depth = 11, train loss: 0.47447, val loss: 0.46593, in 0.016s
1 tree, 57 leaves, max depth = 12, train loss: 0.46821, val loss: 0.45995, in 0.016s
1 tree, 57 leaves, max depth = 12, train loss: 0.46243, val loss: 0.45444, in 0.000s
1 tree, 32 leaves, max depth = 9, train loss: 0.45721, val loss: 0.44880, in 0.016s
1 tree, 57 leaves, max depth = 11, train loss: 0.45194, val loss: 0.44378, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.44705, val loss: 0.43848, in 0.000s
1 tree, 57 leaves, max depth = 11, train loss: 0.44225, val loss: 0.43392, in 0.016s
1 tree, 57 leaves, max depth = 11, train loss: 0.43780, val loss: 0.42971, in 0.016s
1 tree, 32 leaves, max depth = 9, train loss: 0.43360, val loss: 0.42515, in 0.000s
1 tree, 32 leaves, max depth = 9, train loss: 0.42973, val loss: 0.42093, in 0.016s
1 tree, 57 leaves, max depth = 11, train loss: 0.42568, val loss: 0.41712, in 0.016s
1 tree, 56 leaves, max depth = 11, train loss: 0.42194, val loss: 0.41360, in 0.000s
1 tree, 32 leaves, max depth = 14, train loss: 0.41847, val loss: 0.40981, in 0.016s
1 tree, 57 leaves, max depth = 11, train loss: 0.41503, val loss: 0.40659, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.41191, val loss: 0.40336, in 0.000s
1 tree, 57 leaves, max depth = 11, train loss: 0.40874, val loss: 0.40040, in 0.016s
1 tree, 32 leaves, max depth = 10, train loss: 0.40565, val loss: 0.39702, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.40282, val loss: 0.39408, in 0.000s
1 tree, 57 leaves, max depth = 11, train loss: 0.39991, val loss: 0.39137, in 0.000s
1 tree, 31 leaves, max depth = 9, train loss: 0.39699, val loss: 0.38816, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.39430, val loss: 0.38526, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.39174, val loss: 0.38260, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.38938, val loss: 0.38013, in 0.016s
1 tree, 57 leaves, max depth = 12, train loss: 0.38670, val loss: 0.37767, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.38433, val loss: 0.37508, in 0.000s
1 tree, 57 leaves, max depth = 11, train loss: 0.38184, val loss: 0.37280, in 0.016s
[45/72] 1 tree, 5 leaves, max depth = 3, train loss: 0.37967, val loss: 0.37053, in 0.016s
1 tree, 57 leaves, max depth = 12, train loss: 0.37736, val loss: 0.36843, in 0.016s
1 tree, 57 leaves, max depth = 12, train loss: 0.37524, val loss: 0.36650, in 0.000s
1 tree, 31 leaves, max depth = 9, train loss: 0.37302, val loss: 0.36405, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.37100, val loss: 0.36194, in 0.016s
1 tree, 57 leaves, max depth = 12, train loss: 0.36902, val loss: 0.36014, in 0.000s
1 tree, 31 leaves, max depth = 9, train loss: 0.36699, val loss: 0.35790, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.36512, val loss: 0.35594, in 0.000s
1 tree, 57 leaves, max depth = 12, train loss: 0.36327, val loss: 0.35427, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.36144, val loss: 0.35228, in 0.000s
1 tree, 57 leaves, max depth = 13, train loss: 0.35971, val loss: 0.35074, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.35797, val loss: 0.34890, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.35628, val loss: 0.34706, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.35467, val loss: 0.34536, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.35317, val loss: 0.34378, in 0.000s
1 tree, 31 leaves, max depth = 9, train loss: 0.35158, val loss: 0.34208, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.35019, val loss: 0.34061, in 0.000s
1 tree, 39 leaves, max depth = 10, train loss: 0.34872, val loss: 0.33897, in 0.016s
1 tree, 57 leaves, max depth = 13, train loss: 0.34703, val loss: 0.33747, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.34572, val loss: 0.33608, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.34435, val loss: 0.33459, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.34313, val loss: 0.33330, in 0.016s
1 tree, 39 leaves, max depth = 10, train loss: 0.34181, val loss: 0.33182, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.34067, val loss: 0.33061, in 0.000s
1 tree, 57 leaves, max depth = 14, train loss: 0.33903, val loss: 0.32916, in 0.016s
1 tree, 39 leaves, max depth = 10, train loss: 0.33779, val loss: 0.32778, in 0.000s
1 tree, 57 leaves, max depth = 14, train loss: 0.33627, val loss: 0.32644, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.33508, val loss: 0.32517, in 0.016s
Fit 72 trees in 1.033 s, (2557 total leaves)
Time spent computing histograms: 0.360s
Time spent finding best splits:  0.071s
Time spent applying splits:      0.056s
Time spent predicting:           0.000s
Trial 78, Fold 2: Log loss = 0.3370147572962747, Average precision = 0.9455089027903654, ROC-AUC = 0.9465475254336876, Elapsed Time = 1.0398611000000528 seconds
Trial 78, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 78, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
Binning 0.040 GB of training data: 0.158 s
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 30 leaves, max depth = 10, train loss: 0.67146, val loss: 0.67075, in 0.000s
1 tree, 30 leaves, max depth = 10, train loss: 0.65187, val loss: 0.65048, in 0.016s
1 tree, 30 leaves, max depth = 10, train loss: 0.63402, val loss: 0.63199, in 0.000s
1 tree, 31 leaves, max depth = 11, train loss: 0.61795, val loss: 0.61537, in 0.016s
1 tree, 30 leaves, max depth = 10, train loss: 0.60301, val loss: 0.59986, in 0.000s
1 tree, 30 leaves, max depth = 10, train loss: 0.58933, val loss: 0.58563, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.57700, val loss: 0.57273, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.56545, val loss: 0.56069, in 0.000s
1 tree, 30 leaves, max depth = 9, train loss: 0.55483, val loss: 0.54961, in 0.016s
1 tree, 56 leaves, max depth = 11, train loss: 0.54438, val loss: 0.53992, in 0.000s
1 tree, 31 leaves, max depth = 9, train loss: 0.53497, val loss: 0.53005, in 0.016s
1 tree, 56 leaves, max depth = 11, train loss: 0.52563, val loss: 0.52142, in 0.000s
1 tree, 31 leaves, max depth = 11, train loss: 0.51739, val loss: 0.51279, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.50980, val loss: 0.50484, in 0.000s
1 tree, 31 leaves, max depth = 9, train loss: 0.50266, val loss: 0.49730, in 0.016s
1 tree, 56 leaves, max depth = 11, train loss: 0.49462, val loss: 0.48990, in 0.000s
1 tree, 31 leaves, max depth = 11, train loss: 0.48837, val loss: 0.48332, in 0.016s
1 tree, 56 leaves, max depth = 11, train loss: 0.48109, val loss: 0.47663, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.47550, val loss: 0.47073, in 0.000s
1 tree, 56 leaves, max depth = 10, train loss: 0.46887, val loss: 0.46467, in 0.016s
1 tree, 56 leaves, max depth = 10, train loss: 0.46275, val loss: 0.45910, in 0.000s
1 tree, 56 leaves, max depth = 10, train loss: 0.45708, val loss: 0.45396, in 0.016s
1 tree, 56 leaves, max depth = 10, train loss: 0.45184, val loss: 0.44923, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.44721, val loss: 0.44420, in 0.000s
1 tree, 56 leaves, max depth = 10, train loss: 0.44242, val loss: 0.43990, in 0.016s
1 tree, 56 leaves, max depth = 10, train loss: 0.43799, val loss: 0.43593, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.43382, val loss: 0.43141, in 0.000s
1 tree, 31 leaves, max depth = 11, train loss: 0.42997, val loss: 0.42729, in 0.016s
1 tree, 56 leaves, max depth = 11, train loss: 0.42594, val loss: 0.42371, in 0.000s
1 tree, 56 leaves, max depth = 11, train loss: 0.42221, val loss: 0.42040, in 0.000s
1 tree, 31 leaves, max depth = 11, train loss: 0.41878, val loss: 0.41666, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.41554, val loss: 0.41364, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.41254, val loss: 0.41086, in 0.016s
1 tree, 56 leaves, max depth = 10, train loss: 0.40914, val loss: 0.40788, in 0.000s
1 tree, 56 leaves, max depth = 10, train loss: 0.40598, val loss: 0.40514, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.40295, val loss: 0.40181, in 0.000s
1 tree, 56 leaves, max depth = 11, train loss: 0.40004, val loss: 0.39930, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.39727, val loss: 0.39624, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.39470, val loss: 0.39346, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.39204, val loss: 0.39098, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.38958, val loss: 0.38870, in 0.000s
1 tree, 56 leaves, max depth = 11, train loss: 0.38688, val loss: 0.38640, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.38459, val loss: 0.38385, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.38233, val loss: 0.38175, in 0.016s
1 tree, 56 leaves, max depth = 11, train loss: 0.37982, val loss: 0.37963, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.37772, val loss: 0.37768, in 0.016s
1 tree, 55 leaves, max depth = 11, train loss: 0.37538, val loss: 0.37573, in 0.000s
1 tree, 56 leaves, max depth = 11, train loss: 0.37322, val loss: 0.37394, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.37126, val loss: 0.37211, in 0.000s
1 tree, 31 leaves, max depth = 9, train loss: 0.36921, val loss: 0.36984, in 0.016s
1 tree, 31 leaves, max depth = 8, train loss: 0.36727, val loss: 0.36764, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.36547, val loss: 0.36595, in 0.000s
1 tree, 56 leaves, max depth = 11, train loss: 0.36343, val loss: 0.36429, in 0.016s
[54/72] 1 tree, 31 leaves, max depth = 9, train loss: 0.36169, val loss: 0.36235, in 0.000s
1 tree, 56 leaves, max depth = 11, train loss: 0.35978, val loss: 0.36081, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.35809, val loss: 0.35923, in 0.000s
1 tree, 31 leaves, max depth = 9, train loss: 0.35649, val loss: 0.35745, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.35492, val loss: 0.35598, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.35346, val loss: 0.35462, in 0.016s
1 tree, 31 leaves, max depth = 8, train loss: 0.35195, val loss: 0.35290, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.35059, val loss: 0.35163, in 0.016s
[62/72] 1 tree, 56 leaves, max depth = 11, train loss: 0.34874, val loss: 0.35016, in 0.000s
1 tree, 39 leaves, max depth = 11, train loss: 0.34731, val loss: 0.34842, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.34603, val loss: 0.34722, in 0.000s
1 tree, 56 leaves, max depth = 12, train loss: 0.34428, val loss: 0.34585, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.34293, val loss: 0.34436, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.34172, val loss: 0.34323, in 0.000s
1 tree, 39 leaves, max depth = 11, train loss: 0.34044, val loss: 0.34165, in 0.016s
1 tree, 55 leaves, max depth = 14, train loss: 0.33879, val loss: 0.34037, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.33764, val loss: 0.33930, in 0.016s
1 tree, 39 leaves, max depth = 11, train loss: 0.33644, val loss: 0.33782, in 0.016s
1 tree, 56 leaves, max depth = 14, train loss: 0.33488, val loss: 0.33661, in 0.000s
Fit 72 trees in 0.923 s, (2483 total leaves)
Time spent computing histograms: 0.324s
Time spent finding best splits:  0.062s
Time spent applying splits:      0.048s
Time spent predicting:           0.000s
Trial 78, Fold 3: Log loss = 0.33187436289576167, Average precision = 0.9506772581017511, ROC-AUC = 0.9500002558243684, Elapsed Time = 0.9412186999998085 seconds
Trial 78, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 78, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
Binning 0.040 GB of training data: 0.158 s
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 30 leaves, max depth = 11, train loss: 0.67153, val loss: 0.67024, in 0.000s
1 tree, 30 leaves, max depth = 11, train loss: 0.65196, val loss: 0.64946, in 0.016s
1 tree, 30 leaves, max depth = 11, train loss: 0.63413, val loss: 0.63046, in 0.000s
1 tree, 31 leaves, max depth = 15, train loss: 0.61804, val loss: 0.61331, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.60312, val loss: 0.59733, in 0.000s
1 tree, 31 leaves, max depth = 11, train loss: 0.58946, val loss: 0.58263, in 0.016s
1 tree, 31 leaves, max depth = 13, train loss: 0.57706, val loss: 0.56932, in 0.000s
1 tree, 29 leaves, max depth = 11, train loss: 0.56553, val loss: 0.55684, in 0.016s
1 tree, 29 leaves, max depth = 11, train loss: 0.55494, val loss: 0.54533, in 0.000s
1 tree, 56 leaves, max depth = 12, train loss: 0.54443, val loss: 0.53496, in 0.016s
1 tree, 30 leaves, max depth = 11, train loss: 0.53504, val loss: 0.52471, in 0.000s
1 tree, 56 leaves, max depth = 12, train loss: 0.52565, val loss: 0.51547, in 0.016s
1 tree, 56 leaves, max depth = 12, train loss: 0.51702, val loss: 0.50697, in 0.016s
1 tree, 31 leaves, max depth = 15, train loss: 0.50902, val loss: 0.49824, in 0.000s
1 tree, 31 leaves, max depth = 12, train loss: 0.50164, val loss: 0.49015, in 0.016s
1 tree, 56 leaves, max depth = 12, train loss: 0.49405, val loss: 0.48269, in 0.000s
1 tree, 31 leaves, max depth = 15, train loss: 0.48745, val loss: 0.47543, in 0.016s
1 tree, 56 leaves, max depth = 13, train loss: 0.48059, val loss: 0.46870, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.47464, val loss: 0.46208, in 0.000s
1 tree, 56 leaves, max depth = 13, train loss: 0.46842, val loss: 0.45600, in 0.016s
1 tree, 56 leaves, max depth = 13, train loss: 0.46268, val loss: 0.45038, in 0.000s
1 tree, 30 leaves, max depth = 11, train loss: 0.45747, val loss: 0.44458, in 0.016s
1 tree, 56 leaves, max depth = 12, train loss: 0.45224, val loss: 0.43947, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.44745, val loss: 0.43409, in 0.000s
1 tree, 56 leaves, max depth = 12, train loss: 0.44268, val loss: 0.42944, in 0.031s
1 tree, 56 leaves, max depth = 12, train loss: 0.43827, val loss: 0.42513, in 0.031s
1 tree, 31 leaves, max depth = 9, train loss: 0.43408, val loss: 0.42039, in 0.016s
1 tree, 31 leaves, max depth = 12, train loss: 0.43020, val loss: 0.41601, in 0.016s
1 tree, 56 leaves, max depth = 12, train loss: 0.42620, val loss: 0.41213, in 0.016s
1 tree, 56 leaves, max depth = 12, train loss: 0.42250, val loss: 0.40853, in 0.016s
1 tree, 31 leaves, max depth = 15, train loss: 0.41903, val loss: 0.40460, in 0.000s
1 tree, 56 leaves, max depth = 12, train loss: 0.41563, val loss: 0.40131, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.41252, val loss: 0.39795, in 0.000s
1 tree, 56 leaves, max depth = 12, train loss: 0.40939, val loss: 0.39492, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.40652, val loss: 0.39184, in 0.031s
1 tree, 31 leaves, max depth = 9, train loss: 0.40348, val loss: 0.38835, in 0.000s
1 tree, 56 leaves, max depth = 12, train loss: 0.40060, val loss: 0.38559, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.39782, val loss: 0.38238, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.39522, val loss: 0.37940, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.39266, val loss: 0.37663, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.39029, val loss: 0.37407, in 0.016s
1 tree, 56 leaves, max depth = 10, train loss: 0.38763, val loss: 0.37154, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.38532, val loss: 0.36886, in 0.016s
1 tree, 56 leaves, max depth = 10, train loss: 0.38286, val loss: 0.36652, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.38068, val loss: 0.36416, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.37866, val loss: 0.36197, in 0.016s
1 tree, 56 leaves, max depth = 10, train loss: 0.37637, val loss: 0.35981, in 0.031s
1 tree, 56 leaves, max depth = 10, train loss: 0.37425, val loss: 0.35781, in 0.031s
1 tree, 5 leaves, max depth = 3, train loss: 0.37236, val loss: 0.35576, in 0.016s
1 tree, 31 leaves, max depth = 8, train loss: 0.37029, val loss: 0.35337, in 0.016s
1 tree, 56 leaves, max depth = 10, train loss: 0.36831, val loss: 0.35152, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.36655, val loss: 0.34961, in 0.000s
1 tree, 31 leaves, max depth = 10, train loss: 0.36459, val loss: 0.34735, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.36278, val loss: 0.34525, in 0.016s
1 tree, 56 leaves, max depth = 11, train loss: 0.36092, val loss: 0.34352, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.35930, val loss: 0.34175, in 0.000s
1 tree, 31 leaves, max depth = 8, train loss: 0.35767, val loss: 0.33985, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.35616, val loss: 0.33821, in 0.016s
1 tree, 56 leaves, max depth = 11, train loss: 0.35439, val loss: 0.33658, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.35298, val loss: 0.33504, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.35141, val loss: 0.33327, in 0.000s
1 tree, 55 leaves, max depth = 11, train loss: 0.34976, val loss: 0.33176, in 0.000s
1 tree, 39 leaves, max depth = 11, train loss: 0.34831, val loss: 0.33017, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.34698, val loss: 0.32871, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.34574, val loss: 0.32736, in 0.000s
1 tree, 31 leaves, max depth = 10, train loss: 0.34436, val loss: 0.32581, in 0.016s
1 tree, 55 leaves, max depth = 11, train loss: 0.34278, val loss: 0.32437, in 0.016s
1 tree, 39 leaves, max depth = 10, train loss: 0.34148, val loss: 0.32296, in 0.000s
1 tree, 56 leaves, max depth = 11, train loss: 0.34001, val loss: 0.32163, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.33882, val loss: 0.32033, in 0.016s
1 tree, 39 leaves, max depth = 10, train loss: 0.33760, val loss: 0.31901, in 0.000s
1 tree, 56 leaves, max depth = 11, train loss: 0.33621, val loss: 0.31775, in 0.016s
Fit 72 trees in 1.142 s, (2531 total leaves)
Time spent computing histograms: 0.409s
Time spent finding best splits:  0.094s
Time spent applying splits:      0.076s
Time spent predicting:           0.016s
Trial 78, Fold 4: Log loss = 0.3340373016637059, Average precision = 0.9511456838640654, ROC-AUC = 0.9483110629460336, Elapsed Time = 1.1569469999994908 seconds
Trial 78, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 78, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
Binning 0.040 GB of training data: 0.174 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 31 leaves, max depth = 10, train loss: 0.67150, val loss: 0.67008, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.65194, val loss: 0.64918, in 0.000s
1 tree, 31 leaves, max depth = 10, train loss: 0.63411, val loss: 0.63007, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.61783, val loss: 0.61256, in 0.000s
1 tree, 31 leaves, max depth = 10, train loss: 0.60295, val loss: 0.59650, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.58906, val loss: 0.58146, in 0.000s
1 tree, 31 leaves, max depth = 10, train loss: 0.57654, val loss: 0.56791, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.56481, val loss: 0.55513, in 0.000s
1 tree, 31 leaves, max depth = 10, train loss: 0.55403, val loss: 0.54333, in 0.000s
1 tree, 58 leaves, max depth = 12, train loss: 0.54368, val loss: 0.53336, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.53411, val loss: 0.52285, in 0.000s
1 tree, 58 leaves, max depth = 13, train loss: 0.52486, val loss: 0.51398, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.51650, val loss: 0.50477, in 0.016s
1 tree, 31 leaves, max depth = 12, train loss: 0.50863, val loss: 0.49605, in 0.000s
1 tree, 31 leaves, max depth = 10, train loss: 0.50137, val loss: 0.48798, in 0.016s
1 tree, 57 leaves, max depth = 11, train loss: 0.49342, val loss: 0.48044, in 0.000s
1 tree, 31 leaves, max depth = 11, train loss: 0.48707, val loss: 0.47334, in 0.016s
1 tree, 58 leaves, max depth = 14, train loss: 0.47986, val loss: 0.46655, in 0.016s
1 tree, 30 leaves, max depth = 10, train loss: 0.47418, val loss: 0.46016, in 0.000s
1 tree, 58 leaves, max depth = 11, train loss: 0.46763, val loss: 0.45403, in 0.016s
1 tree, 58 leaves, max depth = 12, train loss: 0.46158, val loss: 0.44837, in 0.000s
1 tree, 58 leaves, max depth = 12, train loss: 0.45600, val loss: 0.44316, in 0.016s
1 tree, 58 leaves, max depth = 12, train loss: 0.45084, val loss: 0.43836, in 0.016s
1 tree, 31 leaves, max depth = 12, train loss: 0.44609, val loss: 0.43302, in 0.000s
1 tree, 58 leaves, max depth = 11, train loss: 0.44138, val loss: 0.42866, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.43705, val loss: 0.42375, in 0.016s
1 tree, 58 leaves, max depth = 11, train loss: 0.43274, val loss: 0.41978, in 0.000s
1 tree, 31 leaves, max depth = 10, train loss: 0.42881, val loss: 0.41530, in 0.016s
1 tree, 58 leaves, max depth = 11, train loss: 0.42485, val loss: 0.41168, in 0.016s
1 tree, 58 leaves, max depth = 11, train loss: 0.42118, val loss: 0.40834, in 0.000s
1 tree, 31 leaves, max depth = 10, train loss: 0.41766, val loss: 0.40432, in 0.016s
1 tree, 58 leaves, max depth = 11, train loss: 0.41429, val loss: 0.40127, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.41115, val loss: 0.39825, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.40798, val loss: 0.39459, in 0.000s
1 tree, 58 leaves, max depth = 11, train loss: 0.40488, val loss: 0.39182, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.40203, val loss: 0.38909, in 0.000s
1 tree, 58 leaves, max depth = 11, train loss: 0.39918, val loss: 0.38656, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.39631, val loss: 0.38322, in 0.000s
1 tree, 31 leaves, max depth = 9, train loss: 0.39368, val loss: 0.38015, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.39111, val loss: 0.37769, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.38873, val loss: 0.37542, in 0.016s
1 tree, 58 leaves, max depth = 11, train loss: 0.38608, val loss: 0.37311, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.38371, val loss: 0.37031, in 0.000s
1 tree, 58 leaves, max depth = 11, train loss: 0.38125, val loss: 0.36820, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.37906, val loss: 0.36610, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.37704, val loss: 0.36417, in 0.016s
1 tree, 57 leaves, max depth = 11, train loss: 0.37475, val loss: 0.36222, in 0.000s
1 tree, 31 leaves, max depth = 12, train loss: 0.37254, val loss: 0.35965, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.37067, val loss: 0.35786, in 0.000s
1 tree, 58 leaves, max depth = 11, train loss: 0.36853, val loss: 0.35606, in 0.016s
1 tree, 31 leaves, max depth = 12, train loss: 0.36651, val loss: 0.35370, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.36477, val loss: 0.35204, in 0.016s
1 tree, 58 leaves, max depth = 11, train loss: 0.36277, val loss: 0.35037, in 0.016s
1 tree, 31 leaves, max depth = 12, train loss: 0.36091, val loss: 0.34820, in 0.000s
1 tree, 58 leaves, max depth = 11, train loss: 0.35905, val loss: 0.34666, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.35742, val loss: 0.34511, in 0.000s
1 tree, 31 leaves, max depth = 9, train loss: 0.35573, val loss: 0.34309, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.35422, val loss: 0.34165, in 0.000s
1 tree, 57 leaves, max depth = 11, train loss: 0.35246, val loss: 0.34022, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.35105, val loss: 0.33887, in 0.000s
1 tree, 38 leaves, max depth = 10, train loss: 0.34945, val loss: 0.33717, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.34793, val loss: 0.33533, in 0.016s
1 tree, 58 leaves, max depth = 12, train loss: 0.34627, val loss: 0.33399, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.34494, val loss: 0.33272, in 0.016s
[65/72] 1 tree, 39 leaves, max depth = 10, train loss: 0.34352, val loss: 0.33121, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.34228, val loss: 0.33003, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.34094, val loss: 0.32839, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.33978, val loss: 0.32729, in 0.016s
1 tree, 58 leaves, max depth = 12, train loss: 0.33817, val loss: 0.32601, in 0.000s
1 tree, 39 leaves, max depth = 10, train loss: 0.33689, val loss: 0.32465, in 0.016s
1 tree, 58 leaves, max depth = 11, train loss: 0.33539, val loss: 0.32347, in 0.016s
1 tree, 39 leaves, max depth = 11, train loss: 0.33421, val loss: 0.32220, in 0.000s
Fit 72 trees in 0.940 s, (2570 total leaves)
Time spent computing histograms: 0.325s
Time spent finding best splits:  0.064s
Time spent applying splits:      0.051s
Time spent predicting:           0.000s
Trial 78, Fold 5: Log loss = 0.3390389330580322, Average precision = 0.9488684476965955, ROC-AUC = 0.9455521988740873, Elapsed Time = 0.9489164000005985 seconds
Optimization Progress:  79%|#######9  | 79/100 [16:17<05:15, 15.03s/it]
Trial 79, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 79, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
0.142 s 0.040 GB of training data: 
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 59 leaves, max depth = 13, train loss: 0.68276, val loss: 0.68246, in 0.000s
1 tree, 58 leaves, max depth = 13, train loss: 0.67273, val loss: 0.67213, in 0.016s
1 tree, 59 leaves, max depth = 11, train loss: 0.66312, val loss: 0.66230, in 0.016s
1 tree, 58 leaves, max depth = 12, train loss: 0.65404, val loss: 0.65296, in 0.000s
1 tree, 59 leaves, max depth = 13, train loss: 0.64525, val loss: 0.64390, in 0.016s
1 tree, 59 leaves, max depth = 13, train loss: 0.63684, val loss: 0.63521, in 0.016s
1 tree, 59 leaves, max depth = 15, train loss: 0.62886, val loss: 0.62696, in 0.000s
1 tree, 59 leaves, max depth = 13, train loss: 0.62123, val loss: 0.61907, in 0.016s
1 tree, 59 leaves, max depth = 13, train loss: 0.61382, val loss: 0.61141, in 0.000s
1 tree, 59 leaves, max depth = 12, train loss: 0.60659, val loss: 0.60391, in 0.016s
1 tree, 59 leaves, max depth = 15, train loss: 0.59982, val loss: 0.59685, in 0.000s
1 tree, 59 leaves, max depth = 12, train loss: 0.59336, val loss: 0.59016, in 0.000s
1 tree, 59 leaves, max depth = 13, train loss: 0.58707, val loss: 0.58364, in 0.016s
1 tree, 58 leaves, max depth = 13, train loss: 0.58111, val loss: 0.57743, in 0.000s
1 tree, 59 leaves, max depth = 13, train loss: 0.57530, val loss: 0.57140, in 0.016s
1 tree, 59 leaves, max depth = 13, train loss: 0.56972, val loss: 0.56561, in 0.016s
1 tree, 59 leaves, max depth = 13, train loss: 0.56435, val loss: 0.56007, in 0.000s
1 tree, 59 leaves, max depth = 13, train loss: 0.55918, val loss: 0.55475, in 0.016s
1 tree, 8 leaves, max depth = 6, train loss: 0.55423, val loss: 0.54953, in 0.000s
1 tree, 59 leaves, max depth = 12, train loss: 0.54937, val loss: 0.54445, in 0.016s
1 tree, 59 leaves, max depth = 12, train loss: 0.54469, val loss: 0.53955, in 0.000s
1 tree, 59 leaves, max depth = 12, train loss: 0.54019, val loss: 0.53484, in 0.016s
1 tree, 97 leaves, max depth = 12, train loss: 0.53516, val loss: 0.53018, in 0.016s
1 tree, 59 leaves, max depth = 12, train loss: 0.53091, val loss: 0.52573, in 0.000s
1 tree, 59 leaves, max depth = 13, train loss: 0.52690, val loss: 0.52159, in 0.016s
1 tree, 97 leaves, max depth = 12, train loss: 0.52219, val loss: 0.51723, in 0.016s
1 tree, 97 leaves, max depth = 12, train loss: 0.51765, val loss: 0.51305, in 0.000s
1 tree, 59 leaves, max depth = 15, train loss: 0.51400, val loss: 0.50921, in 0.016s
1 tree, 97 leaves, max depth = 12, train loss: 0.50968, val loss: 0.50523, in 0.016s
1 tree, 97 leaves, max depth = 12, train loss: 0.50554, val loss: 0.50142, in 0.000s
1 tree, 59 leaves, max depth = 15, train loss: 0.50213, val loss: 0.49785, in 0.016s
1 tree, 59 leaves, max depth = 13, train loss: 0.49882, val loss: 0.49433, in 0.016s
1 tree, 59 leaves, max depth = 12, train loss: 0.49554, val loss: 0.49085, in 0.000s
1 tree, 97 leaves, max depth = 12, train loss: 0.49167, val loss: 0.48730, in 0.016s
1 tree, 59 leaves, max depth = 11, train loss: 0.48856, val loss: 0.48400, in 0.016s
1 tree, 59 leaves, max depth = 15, train loss: 0.48563, val loss: 0.48096, in 0.000s
1 tree, 59 leaves, max depth = 15, train loss: 0.48282, val loss: 0.47803, in 0.016s
1 tree, 96 leaves, max depth = 12, train loss: 0.47919, val loss: 0.47472, in 0.016s
1 tree, 59 leaves, max depth = 15, train loss: 0.47653, val loss: 0.47194, in 0.016s
1 tree, 59 leaves, max depth = 13, train loss: 0.47398, val loss: 0.46919, in 0.000s
1 tree, 59 leaves, max depth = 15, train loss: 0.47151, val loss: 0.46661, in 0.016s
1 tree, 59 leaves, max depth = 11, train loss: 0.46906, val loss: 0.46399, in 0.016s
1 tree, 59 leaves, max depth = 15, train loss: 0.46677, val loss: 0.46159, in 0.000s
1 tree, 97 leaves, max depth = 12, train loss: 0.46340, val loss: 0.45853, in 0.016s
1 tree, 58 leaves, max depth = 13, train loss: 0.46121, val loss: 0.45620, in 0.016s
1 tree, 97 leaves, max depth = 12, train loss: 0.45799, val loss: 0.45327, in 0.016s
Fit 46 trees in 0.736 s, (3000 total leaves)
Time spent computing histograms: 0.204s
Time spent finding best splits:  0.062s
Time spent applying splits:      0.047s
Time spent predicting:           0.000s
Trial 79, Fold 1: Log loss = 0.45842813120494297, Average precision = 0.906537808073568, ROC-AUC = 0.9138198238243407, Elapsed Time = 0.7366600000004837 seconds
Trial 79, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 79, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
Binning 0.040 GB of training data: 0.158 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 58 leaves, max depth = 16, train loss: 0.68272, val loss: 0.68227, in 0.000s
1 tree, 59 leaves, max depth = 14, train loss: 0.67275, val loss: 0.67182, in 0.016s
1 tree, 59 leaves, max depth = 12, train loss: 0.66317, val loss: 0.66178, in 0.016s
1 tree, 59 leaves, max depth = 13, train loss: 0.65406, val loss: 0.65226, in 0.000s
1 tree, 59 leaves, max depth = 17, train loss: 0.64534, val loss: 0.64314, in 0.016s
1 tree, 58 leaves, max depth = 11, train loss: 0.63693, val loss: 0.63431, in 0.000s
1 tree, 59 leaves, max depth = 16, train loss: 0.62892, val loss: 0.62593, in 0.016s
1 tree, 59 leaves, max depth = 17, train loss: 0.62126, val loss: 0.61790, in 0.016s
1 tree, 59 leaves, max depth = 12, train loss: 0.61386, val loss: 0.61011, in 0.000s
1 tree, 59 leaves, max depth = 14, train loss: 0.60668, val loss: 0.60253, in 0.000s
1 tree, 59 leaves, max depth = 14, train loss: 0.59990, val loss: 0.59539, in 0.016s
1 tree, 59 leaves, max depth = 18, train loss: 0.59342, val loss: 0.58856, in 0.000s
1 tree, 59 leaves, max depth = 12, train loss: 0.58714, val loss: 0.58194, in 0.016s
1 tree, 58 leaves, max depth = 12, train loss: 0.58118, val loss: 0.57563, in 0.016s
1 tree, 59 leaves, max depth = 12, train loss: 0.57539, val loss: 0.56951, in 0.000s
1 tree, 59 leaves, max depth = 12, train loss: 0.56983, val loss: 0.56362, in 0.016s
1 tree, 59 leaves, max depth = 13, train loss: 0.56448, val loss: 0.55796, in 0.016s
1 tree, 59 leaves, max depth = 13, train loss: 0.55934, val loss: 0.55252, in 0.000s
1 tree, 8 leaves, max depth = 6, train loss: 0.55439, val loss: 0.54726, in 0.016s
1 tree, 59 leaves, max depth = 13, train loss: 0.54956, val loss: 0.54212, in 0.016s
1 tree, 59 leaves, max depth = 13, train loss: 0.54491, val loss: 0.53717, in 0.000s
1 tree, 59 leaves, max depth = 13, train loss: 0.54045, val loss: 0.53241, in 0.016s
1 tree, 96 leaves, max depth = 17, train loss: 0.53548, val loss: 0.52765, in 0.016s
1 tree, 59 leaves, max depth = 13, train loss: 0.53127, val loss: 0.52316, in 0.000s
1 tree, 59 leaves, max depth = 14, train loss: 0.52728, val loss: 0.51891, in 0.016s
1 tree, 96 leaves, max depth = 15, train loss: 0.52263, val loss: 0.51445, in 0.016s
1 tree, 96 leaves, max depth = 15, train loss: 0.51815, val loss: 0.51018, in 0.016s
1 tree, 59 leaves, max depth = 17, train loss: 0.51446, val loss: 0.50627, in 0.000s
1 tree, 96 leaves, max depth = 18, train loss: 0.51021, val loss: 0.50220, in 0.016s
1 tree, 96 leaves, max depth = 18, train loss: 0.50612, val loss: 0.49830, in 0.016s
1 tree, 59 leaves, max depth = 17, train loss: 0.50269, val loss: 0.49465, in 0.016s
1 tree, 59 leaves, max depth = 13, train loss: 0.49938, val loss: 0.49112, in 0.000s
1 tree, 59 leaves, max depth = 12, train loss: 0.49612, val loss: 0.48762, in 0.016s
1 tree, 96 leaves, max depth = 18, train loss: 0.49230, val loss: 0.48398, in 0.016s
1 tree, 59 leaves, max depth = 12, train loss: 0.48922, val loss: 0.48066, in 0.016s
1 tree, 59 leaves, max depth = 14, train loss: 0.48631, val loss: 0.47754, in 0.000s
1 tree, 59 leaves, max depth = 14, train loss: 0.48351, val loss: 0.47453, in 0.016s
1 tree, 96 leaves, max depth = 14, train loss: 0.47993, val loss: 0.47113, in 0.016s
1 tree, 59 leaves, max depth = 14, train loss: 0.47728, val loss: 0.46828, in 0.000s
1 tree, 59 leaves, max depth = 13, train loss: 0.47473, val loss: 0.46554, in 0.016s
1 tree, 59 leaves, max depth = 14, train loss: 0.47227, val loss: 0.46289, in 0.016s
1 tree, 59 leaves, max depth = 12, train loss: 0.46984, val loss: 0.46026, in 0.016s
1 tree, 59 leaves, max depth = 14, train loss: 0.46756, val loss: 0.45780, in 0.000s
1 tree, 96 leaves, max depth = 14, train loss: 0.46424, val loss: 0.45465, in 0.016s
1 tree, 59 leaves, max depth = 16, train loss: 0.46208, val loss: 0.45233, in 0.016s
1 tree, 96 leaves, max depth = 14, train loss: 0.45889, val loss: 0.44933, in 0.016s
Fit 46 trees in 0.799 s, (2993 total leaves)
Time spent computing histograms: 0.224s
Time spent finding best splits:  0.067s
Time spent applying splits:      0.051s
Time spent predicting:           0.000s
Trial 79, Fold 2: Log loss = 0.45980606153601994, Average precision = 0.9046649152467294, ROC-AUC = 0.9184656113721006, Elapsed Time = 0.8067893000006734 seconds
Trial 79, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 79, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
Binning 0.040 GB of training data: 0.158 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 58 leaves, max depth = 15, train loss: 0.68282, val loss: 0.68247, in 0.016s
1 tree, 58 leaves, max depth = 15, train loss: 0.67289, val loss: 0.67223, in 0.016s
1 tree, 58 leaves, max depth = 12, train loss: 0.66337, val loss: 0.66234, in 0.000s
1 tree, 58 leaves, max depth = 12, train loss: 0.65434, val loss: 0.65300, in 0.016s
1 tree, 58 leaves, max depth = 14, train loss: 0.64570, val loss: 0.64407, in 0.016s
1 tree, 58 leaves, max depth = 15, train loss: 0.63737, val loss: 0.63547, in 0.000s
1 tree, 58 leaves, max depth = 15, train loss: 0.62943, val loss: 0.62725, in 0.016s
1 tree, 58 leaves, max depth = 15, train loss: 0.62184, val loss: 0.61939, in 0.016s
1 tree, 58 leaves, max depth = 14, train loss: 0.61447, val loss: 0.61172, in 0.000s
1 tree, 58 leaves, max depth = 14, train loss: 0.60731, val loss: 0.60432, in 0.016s
1 tree, 58 leaves, max depth = 15, train loss: 0.60056, val loss: 0.59734, in 0.016s
1 tree, 58 leaves, max depth = 15, train loss: 0.59413, val loss: 0.59067, in 0.016s
1 tree, 58 leaves, max depth = 12, train loss: 0.58796, val loss: 0.58425, in 0.000s
1 tree, 58 leaves, max depth = 11, train loss: 0.58203, val loss: 0.57807, in 0.016s
1 tree, 58 leaves, max depth = 14, train loss: 0.57635, val loss: 0.57215, in 0.016s
1 tree, 58 leaves, max depth = 14, train loss: 0.57080, val loss: 0.56634, in 0.000s
1 tree, 58 leaves, max depth = 10, train loss: 0.56547, val loss: 0.56076, in 0.016s
1 tree, 58 leaves, max depth = 10, train loss: 0.56035, val loss: 0.55539, in 0.016s
1 tree, 8 leaves, max depth = 6, train loss: 0.55540, val loss: 0.55024, in 0.000s
1 tree, 58 leaves, max depth = 14, train loss: 0.55058, val loss: 0.54524, in 0.016s
1 tree, 58 leaves, max depth = 14, train loss: 0.54594, val loss: 0.54043, in 0.016s
1 tree, 58 leaves, max depth = 14, train loss: 0.54148, val loss: 0.53581, in 0.000s
1 tree, 95 leaves, max depth = 13, train loss: 0.53645, val loss: 0.53114, in 0.016s
1 tree, 58 leaves, max depth = 14, train loss: 0.53224, val loss: 0.52676, in 0.016s
1 tree, 58 leaves, max depth = 10, train loss: 0.52827, val loss: 0.52258, in 0.016s
1 tree, 95 leaves, max depth = 14, train loss: 0.52356, val loss: 0.51820, in 0.000s
1 tree, 95 leaves, max depth = 14, train loss: 0.51903, val loss: 0.51400, in 0.016s
1 tree, 58 leaves, max depth = 16, train loss: 0.51539, val loss: 0.51018, in 0.000s
1 tree, 95 leaves, max depth = 15, train loss: 0.51108, val loss: 0.50619, in 0.016s
1 tree, 95 leaves, max depth = 15, train loss: 0.50694, val loss: 0.50236, in 0.016s
1 tree, 58 leaves, max depth = 13, train loss: 0.50355, val loss: 0.49878, in 0.016s
1 tree, 58 leaves, max depth = 16, train loss: 0.50026, val loss: 0.49534, in 0.016s
1 tree, 58 leaves, max depth = 12, train loss: 0.49700, val loss: 0.49193, in 0.016s
1 tree, 95 leaves, max depth = 14, train loss: 0.49313, val loss: 0.48836, in 0.016s
1 tree, 58 leaves, max depth = 13, train loss: 0.49006, val loss: 0.48513, in 0.000s
1 tree, 58 leaves, max depth = 10, train loss: 0.48717, val loss: 0.48204, in 0.016s
1 tree, 58 leaves, max depth = 10, train loss: 0.48439, val loss: 0.47907, in 0.016s
1 tree, 95 leaves, max depth = 14, train loss: 0.48076, val loss: 0.47573, in 0.016s
1 tree, 58 leaves, max depth = 10, train loss: 0.47813, val loss: 0.47291, in 0.016s
1 tree, 58 leaves, max depth = 16, train loss: 0.47558, val loss: 0.47024, in 0.000s
1 tree, 58 leaves, max depth = 10, train loss: 0.47314, val loss: 0.46762, in 0.016s
1 tree, 58 leaves, max depth = 13, train loss: 0.47072, val loss: 0.46506, in 0.016s
1 tree, 58 leaves, max depth = 16, train loss: 0.46844, val loss: 0.46268, in 0.016s
1 tree, 95 leaves, max depth = 14, train loss: 0.46507, val loss: 0.45958, in 0.016s
1 tree, 58 leaves, max depth = 12, train loss: 0.46292, val loss: 0.45727, in 0.000s
1 tree, 95 leaves, max depth = 14, train loss: 0.45969, val loss: 0.45431, in 0.016s
Fit 46 trees in 0.861 s, (2951 total leaves)
Time spent computing histograms: 0.237s
Time spent finding best splits:  0.075s
Time spent applying splits:      0.056s
Time spent predicting:           0.031s
Trial 79, Fold 3: Log loss = 0.45530085571122997, Average precision = 0.9135555234024215, ROC-AUC = 0.9231098792749757, Elapsed Time = 0.8606184000000212 seconds
Trial 79, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 79, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
Binning 0.040 GB of training data: 0.173 s
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 58 leaves, max depth = 15, train loss: 0.68277, val loss: 0.68224, in 0.016s
1 tree, 59 leaves, max depth = 12, train loss: 0.67280, val loss: 0.67171, in 0.000s
1 tree, 59 leaves, max depth = 13, train loss: 0.66325, val loss: 0.66162, in 0.016s
1 tree, 59 leaves, max depth = 11, train loss: 0.65418, val loss: 0.65207, in 0.016s
1 tree, 58 leaves, max depth = 12, train loss: 0.64544, val loss: 0.64281, in 0.000s
1 tree, 59 leaves, max depth = 16, train loss: 0.63710, val loss: 0.63395, in 0.016s
1 tree, 59 leaves, max depth = 14, train loss: 0.62914, val loss: 0.62549, in 0.016s
1 tree, 59 leaves, max depth = 13, train loss: 0.62151, val loss: 0.61738, in 0.016s
1 tree, 59 leaves, max depth = 12, train loss: 0.61414, val loss: 0.60952, in 0.016s
1 tree, 59 leaves, max depth = 13, train loss: 0.60699, val loss: 0.60187, in 0.000s
1 tree, 59 leaves, max depth = 16, train loss: 0.60023, val loss: 0.59465, in 0.016s
1 tree, 59 leaves, max depth = 14, train loss: 0.59377, val loss: 0.58774, in 0.016s
1 tree, 59 leaves, max depth = 12, train loss: 0.58751, val loss: 0.58104, in 0.016s
1 tree, 58 leaves, max depth = 14, train loss: 0.58157, val loss: 0.57465, in 0.016s
1 tree, 59 leaves, max depth = 12, train loss: 0.57580, val loss: 0.56845, in 0.016s
1 tree, 59 leaves, max depth = 12, train loss: 0.57025, val loss: 0.56248, in 0.016s
1 tree, 59 leaves, max depth = 12, train loss: 0.56492, val loss: 0.55674, in 0.016s
1 tree, 59 leaves, max depth = 12, train loss: 0.55979, val loss: 0.55122, in 0.016s
1 tree, 8 leaves, max depth = 5, train loss: 0.55486, val loss: 0.54590, in 0.016s
1 tree, 59 leaves, max depth = 13, train loss: 0.55005, val loss: 0.54068, in 0.016s
1 tree, 59 leaves, max depth = 13, train loss: 0.54543, val loss: 0.53565, in 0.016s
1 tree, 59 leaves, max depth = 13, train loss: 0.54099, val loss: 0.53081, in 0.016s
1 tree, 95 leaves, max depth = 14, train loss: 0.53603, val loss: 0.52594, in 0.031s
1 tree, 59 leaves, max depth = 13, train loss: 0.53184, val loss: 0.52136, in 0.016s
1 tree, 59 leaves, max depth = 12, train loss: 0.52786, val loss: 0.51703, in 0.016s
1 tree, 95 leaves, max depth = 16, train loss: 0.52322, val loss: 0.51247, in 0.016s
1 tree, 95 leaves, max depth = 16, train loss: 0.51875, val loss: 0.50809, in 0.016s
1 tree, 59 leaves, max depth = 12, train loss: 0.51509, val loss: 0.50408, in 0.016s
1 tree, 95 leaves, max depth = 16, train loss: 0.51085, val loss: 0.49992, in 0.031s
1 tree, 95 leaves, max depth = 16, train loss: 0.50677, val loss: 0.49592, in 0.016s
1 tree, 59 leaves, max depth = 16, train loss: 0.50336, val loss: 0.49219, in 0.016s
1 tree, 59 leaves, max depth = 14, train loss: 0.50005, val loss: 0.48856, in 0.016s
1 tree, 59 leaves, max depth = 12, train loss: 0.49681, val loss: 0.48498, in 0.016s
1 tree, 94 leaves, max depth = 18, train loss: 0.49301, val loss: 0.48126, in 0.016s
1 tree, 59 leaves, max depth = 12, train loss: 0.48994, val loss: 0.47786, in 0.016s
1 tree, 59 leaves, max depth = 13, train loss: 0.48704, val loss: 0.47465, in 0.016s
1 tree, 59 leaves, max depth = 13, train loss: 0.48425, val loss: 0.47156, in 0.016s
1 tree, 95 leaves, max depth = 14, train loss: 0.48068, val loss: 0.46808, in 0.016s
1 tree, 59 leaves, max depth = 13, train loss: 0.47804, val loss: 0.46515, in 0.016s
1 tree, 59 leaves, max depth = 13, train loss: 0.47549, val loss: 0.46231, in 0.016s
1 tree, 59 leaves, max depth = 13, train loss: 0.47304, val loss: 0.45958, in 0.016s
1 tree, 59 leaves, max depth = 12, train loss: 0.47062, val loss: 0.45687, in 0.016s
1 tree, 59 leaves, max depth = 13, train loss: 0.46835, val loss: 0.45432, in 0.016s
1 tree, 95 leaves, max depth = 12, train loss: 0.46504, val loss: 0.45110, in 0.016s
1 tree, 58 leaves, max depth = 14, train loss: 0.46287, val loss: 0.44865, in 0.016s
1 tree, 95 leaves, max depth = 15, train loss: 0.45971, val loss: 0.44557, in 0.016s
Fit 46 trees in 1.033 s, (2982 total leaves)
Time spent computing histograms: 0.307s
Time spent finding best splits:  0.098s
Time spent applying splits:      0.076s
Time spent predicting:           0.000s
Trial 79, Fold 4: Log loss = 0.45939396332992505, Average precision = 0.9076412395230304, ROC-AUC = 0.9176424118543316, Elapsed Time = 1.0503874999994878 seconds
Trial 79, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 79, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
0.283 s 0.040 GB of training data: 
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 59 leaves, max depth = 11, train loss: 0.68265, val loss: 0.68201, in 0.016s
1 tree, 59 leaves, max depth = 16, train loss: 0.67260, val loss: 0.67133, in 0.016s
1 tree, 59 leaves, max depth = 13, train loss: 0.66300, val loss: 0.66111, in 0.016s
1 tree, 59 leaves, max depth = 15, train loss: 0.65381, val loss: 0.65130, in 0.016s
1 tree, 59 leaves, max depth = 14, train loss: 0.64502, val loss: 0.64191, in 0.000s
1 tree, 59 leaves, max depth = 15, train loss: 0.63659, val loss: 0.63289, in 0.016s
1 tree, 59 leaves, max depth = 13, train loss: 0.62853, val loss: 0.62427, in 0.016s
1 tree, 59 leaves, max depth = 14, train loss: 0.62081, val loss: 0.61598, in 0.016s
1 tree, 58 leaves, max depth = 12, train loss: 0.61337, val loss: 0.60795, in 0.016s
1 tree, 58 leaves, max depth = 13, train loss: 0.60614, val loss: 0.60017, in 0.016s
1 tree, 59 leaves, max depth = 15, train loss: 0.59931, val loss: 0.59281, in 0.016s
1 tree, 59 leaves, max depth = 14, train loss: 0.59277, val loss: 0.58576, in 0.016s
1 tree, 59 leaves, max depth = 13, train loss: 0.58650, val loss: 0.57898, in 0.016s
1 tree, 58 leaves, max depth = 11, train loss: 0.58050, val loss: 0.57243, in 0.016s
1 tree, 59 leaves, max depth = 15, train loss: 0.57471, val loss: 0.56613, in 0.016s
1 tree, 59 leaves, max depth = 13, train loss: 0.56915, val loss: 0.56010, in 0.000s
1 tree, 57 leaves, max depth = 14, train loss: 0.56374, val loss: 0.55423, in 0.016s
1 tree, 57 leaves, max depth = 14, train loss: 0.55855, val loss: 0.54858, in 0.016s
1 tree, 8 leaves, max depth = 6, train loss: 0.55356, val loss: 0.54312, in 0.016s
1 tree, 58 leaves, max depth = 11, train loss: 0.54870, val loss: 0.53780, in 0.016s
1 tree, 58 leaves, max depth = 11, train loss: 0.54402, val loss: 0.53267, in 0.016s
1 tree, 58 leaves, max depth = 11, train loss: 0.53953, val loss: 0.52773, in 0.016s
1 tree, 97 leaves, max depth = 15, train loss: 0.53462, val loss: 0.52307, in 0.016s
1 tree, 58 leaves, max depth = 12, train loss: 0.53038, val loss: 0.51839, in 0.016s
1 tree, 58 leaves, max depth = 14, train loss: 0.52635, val loss: 0.51396, in 0.016s
1 tree, 97 leaves, max depth = 15, train loss: 0.52175, val loss: 0.50961, in 0.016s
1 tree, 97 leaves, max depth = 15, train loss: 0.51734, val loss: 0.50543, in 0.016s
1 tree, 59 leaves, max depth = 14, train loss: 0.51363, val loss: 0.50134, in 0.016s
1 tree, 97 leaves, max depth = 14, train loss: 0.50943, val loss: 0.49738, in 0.016s
1 tree, 97 leaves, max depth = 14, train loss: 0.50539, val loss: 0.49357, in 0.016s
1 tree, 59 leaves, max depth = 12, train loss: 0.50194, val loss: 0.48976, in 0.016s
1 tree, 59 leaves, max depth = 14, train loss: 0.49860, val loss: 0.48607, in 0.016s
1 tree, 58 leaves, max depth = 13, train loss: 0.49531, val loss: 0.48241, in 0.016s
1 tree, 97 leaves, max depth = 18, train loss: 0.49154, val loss: 0.47887, in 0.016s
1 tree, 58 leaves, max depth = 13, train loss: 0.48842, val loss: 0.47539, in 0.016s
1 tree, 58 leaves, max depth = 13, train loss: 0.48547, val loss: 0.47210, in 0.016s
1 tree, 58 leaves, max depth = 13, train loss: 0.48264, val loss: 0.46892, in 0.016s
1 tree, 97 leaves, max depth = 16, train loss: 0.47910, val loss: 0.46563, in 0.016s
1 tree, 59 leaves, max depth = 13, train loss: 0.47644, val loss: 0.46266, in 0.016s
1 tree, 59 leaves, max depth = 14, train loss: 0.47385, val loss: 0.45976, in 0.016s
1 tree, 58 leaves, max depth = 13, train loss: 0.47136, val loss: 0.45696, in 0.016s
1 tree, 58 leaves, max depth = 13, train loss: 0.46890, val loss: 0.45418, in 0.016s
1 tree, 58 leaves, max depth = 13, train loss: 0.46659, val loss: 0.45157, in 0.016s
1 tree, 97 leaves, max depth = 17, train loss: 0.46330, val loss: 0.44853, in 0.016s
1 tree, 59 leaves, max depth = 16, train loss: 0.46113, val loss: 0.44605, in 0.016s
1 tree, 97 leaves, max depth = 17, train loss: 0.45798, val loss: 0.44315, in 0.016s
Fit 46 trees in 1.189 s, (2986 total leaves)
Time spent computing histograms: 0.311s
Time spent finding best splits:  0.101s
Time spent applying splits:      0.076s
Time spent predicting:           0.000s
Trial 79, Fold 5: Log loss = 0.46532086876685613, Average precision = 0.9022929032342281, ROC-AUC = 0.9129145532579009, Elapsed Time = 1.2088435999994545 seconds
Optimization Progress:  80%|########  | 80/100 [16:28<04:39, 13.97s/it]
Trial 80, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 80, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
0.236 s 0.040 GB of training data: 
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 35 leaves, max depth = 8, train loss: 0.68558, val loss: 0.68559, in 0.031s
1 tree, 35 leaves, max depth = 10, train loss: 0.67822, val loss: 0.67832, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.67105, val loss: 0.67123, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.66407, val loss: 0.66432, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.65741, val loss: 0.65768, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.65121, val loss: 0.65141, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.64510, val loss: 0.64521, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.63875, val loss: 0.63893, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.63260, val loss: 0.63281, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.62659, val loss: 0.62684, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.62095, val loss: 0.62120, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.61516, val loss: 0.61547, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.60951, val loss: 0.60987, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.60391, val loss: 0.60431, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.59866, val loss: 0.59903, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.59338, val loss: 0.59381, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.58846, val loss: 0.58888, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.58334, val loss: 0.58377, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.57833, val loss: 0.57879, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.57374, val loss: 0.57422, in 0.016s
1 tree, 35 leaves, max depth = 8, train loss: 0.56908, val loss: 0.56954, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.56438, val loss: 0.56486, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.55986, val loss: 0.56037, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.55543, val loss: 0.55598, in 0.031s
1 tree, 35 leaves, max depth = 10, train loss: 0.55128, val loss: 0.55181, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.54722, val loss: 0.54773, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.54306, val loss: 0.54360, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.53897, val loss: 0.53956, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.53497, val loss: 0.53559, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.53099, val loss: 0.53163, in 0.016s
1 tree, 35 leaves, max depth = 8, train loss: 0.52714, val loss: 0.52774, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.52350, val loss: 0.52407, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.51975, val loss: 0.52033, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.51608, val loss: 0.51663, in 0.031s
1 tree, 35 leaves, max depth = 10, train loss: 0.51246, val loss: 0.51298, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.50893, val loss: 0.50944, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.50570, val loss: 0.50618, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.50231, val loss: 0.50280, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.49917, val loss: 0.49958, in 0.016s
1 tree, 35 leaves, max depth = 8, train loss: 0.49610, val loss: 0.49644, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.49298, val loss: 0.49323, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.48982, val loss: 0.49005, in 0.016s
1 tree, 35 leaves, max depth = 8, train loss: 0.48692, val loss: 0.48707, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.48388, val loss: 0.48403, in 0.031s
1 tree, 35 leaves, max depth = 10, train loss: 0.48090, val loss: 0.48104, in 0.016s
1 tree, 35 leaves, max depth = 8, train loss: 0.47818, val loss: 0.47825, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.47531, val loss: 0.47537, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.47250, val loss: 0.47255, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.46974, val loss: 0.46978, in 0.016s
1 tree, 35 leaves, max depth = 8, train loss: 0.46708, val loss: 0.46707, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.46442, val loss: 0.46440, in 0.031s
1 tree, 35 leaves, max depth = 11, train loss: 0.46179, val loss: 0.46176, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.45924, val loss: 0.45918, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.45671, val loss: 0.45665, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.45425, val loss: 0.45418, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.45184, val loss: 0.45175, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.44944, val loss: 0.44935, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.44598, val loss: 0.44599, in 0.016s
1 tree, 35 leaves, max depth = 13, train loss: 0.44265, val loss: 0.44275, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.43934, val loss: 0.43956, in 0.016s
1 tree, 35 leaves, max depth = 7, train loss: 0.43716, val loss: 0.43733, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.43497, val loss: 0.43514, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.43282, val loss: 0.43299, in 0.016s
1 tree, 35 leaves, max depth = 7, train loss: 0.43076, val loss: 0.43088, in 0.016s
Fit 64 trees in 1.533 s, (2240 total leaves)
Time spent computing histograms: 0.473s
Time spent finding best splits:  0.073s
Time spent applying splits:      0.048s
Time spent predicting:           0.000s
Trial 80, Fold 1: Log loss = 0.43370266737972424, Average precision = 0.9503427459847125, ROC-AUC = 0.9456591322416752, Elapsed Time = 1.5502667000000656 seconds
Trial 80, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 80, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
Binning 0.040 GB of training data: 0.188 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 35 leaves, max depth = 10, train loss: 0.68564, val loss: 0.68548, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.67827, val loss: 0.67796, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.67106, val loss: 0.67061, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.66407, val loss: 0.66347, in 0.031s
1 tree, 35 leaves, max depth = 11, train loss: 0.65724, val loss: 0.65651, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.65055, val loss: 0.64969, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.64435, val loss: 0.64342, in 0.016s
1 tree, 35 leaves, max depth = 7, train loss: 0.63806, val loss: 0.63703, in 0.031s
1 tree, 35 leaves, max depth = 11, train loss: 0.63215, val loss: 0.63106, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.62622, val loss: 0.62498, in 0.031s
1 tree, 35 leaves, max depth = 11, train loss: 0.62027, val loss: 0.61892, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.61477, val loss: 0.61335, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.60909, val loss: 0.60755, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.60353, val loss: 0.60189, in 0.031s
1 tree, 35 leaves, max depth = 14, train loss: 0.59838, val loss: 0.59667, in 0.016s
1 tree, 35 leaves, max depth = 8, train loss: 0.59306, val loss: 0.59132, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.58777, val loss: 0.58596, in 0.031s
1 tree, 35 leaves, max depth = 10, train loss: 0.58260, val loss: 0.58073, in 0.016s
1 tree, 35 leaves, max depth = 14, train loss: 0.57789, val loss: 0.57597, in 0.031s
1 tree, 35 leaves, max depth = 8, train loss: 0.57301, val loss: 0.57103, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.56819, val loss: 0.56617, in 0.031s
1 tree, 35 leaves, max depth = 10, train loss: 0.56347, val loss: 0.56141, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.55883, val loss: 0.55672, in 0.031s
1 tree, 35 leaves, max depth = 10, train loss: 0.55435, val loss: 0.55213, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.54990, val loss: 0.54764, in 0.031s
1 tree, 35 leaves, max depth = 10, train loss: 0.54559, val loss: 0.54325, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.54134, val loss: 0.53895, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.53755, val loss: 0.53511, in 0.031s
1 tree, 35 leaves, max depth = 10, train loss: 0.53347, val loss: 0.53097, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.52946, val loss: 0.52692, in 0.031s
1 tree, 35 leaves, max depth = 10, train loss: 0.52579, val loss: 0.52322, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.52221, val loss: 0.51960, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.51845, val loss: 0.51578, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.51476, val loss: 0.51204, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.51115, val loss: 0.50838, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.50761, val loss: 0.50479, in 0.031s
1 tree, 35 leaves, max depth = 11, train loss: 0.50437, val loss: 0.50152, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.50096, val loss: 0.49806, in 0.016s
1 tree, 35 leaves, max depth = 8, train loss: 0.49782, val loss: 0.49490, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.49482, val loss: 0.49188, in 0.031s
1 tree, 35 leaves, max depth = 12, train loss: 0.49167, val loss: 0.48870, in 0.016s
1 tree, 35 leaves, max depth = 7, train loss: 0.48857, val loss: 0.48559, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.48572, val loss: 0.48273, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.48267, val loss: 0.47965, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.47968, val loss: 0.47662, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.47701, val loss: 0.47393, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.47414, val loss: 0.47104, in 0.031s
1 tree, 35 leaves, max depth = 9, train loss: 0.47131, val loss: 0.46817, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.46874, val loss: 0.46553, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.46602, val loss: 0.46277, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.46234, val loss: 0.45914, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.45972, val loss: 0.45650, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.45714, val loss: 0.45388, in 0.031s
1 tree, 35 leaves, max depth = 10, train loss: 0.45462, val loss: 0.45132, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.45214, val loss: 0.44881, in 0.016s
1 tree, 35 leaves, max depth = 7, train loss: 0.44974, val loss: 0.44642, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.44736, val loss: 0.44398, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.44399, val loss: 0.44066, in 0.031s
1 tree, 35 leaves, max depth = 10, train loss: 0.44068, val loss: 0.43739, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.43745, val loss: 0.43421, in 0.016s
1 tree, 35 leaves, max depth = 8, train loss: 0.43525, val loss: 0.43199, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.43303, val loss: 0.42975, in 0.031s
1 tree, 35 leaves, max depth = 10, train loss: 0.43085, val loss: 0.42752, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.42872, val loss: 0.42536, in 0.016s
Fit 64 trees in 1.642 s, (2240 total leaves)
Time spent computing histograms: 0.537s
Time spent finding best splits:  0.086s
Time spent applying splits:      0.059s
Time spent predicting:           0.000s
Trial 80, Fold 2: Log loss = 0.43118969439221216, Average precision = 0.9489102449125872, ROC-AUC = 0.947832373933251, Elapsed Time = 1.6524898000006942 seconds
Trial 80, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 80, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
Binning 0.040 GB of training data: 0.174 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 35 leaves, max depth = 8, train loss: 0.68564, val loss: 0.68560, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.67832, val loss: 0.67826, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.67119, val loss: 0.67112, in 0.016s
1 tree, 35 leaves, max depth = 12, train loss: 0.66424, val loss: 0.66415, in 0.031s
1 tree, 35 leaves, max depth = 9, train loss: 0.65747, val loss: 0.65735, in 0.000s
1 tree, 35 leaves, max depth = 12, train loss: 0.65115, val loss: 0.65106, in 0.031s
1 tree, 35 leaves, max depth = 9, train loss: 0.64469, val loss: 0.64458, in 0.016s
1 tree, 35 leaves, max depth = 7, train loss: 0.63875, val loss: 0.63867, in 0.000s
1 tree, 35 leaves, max depth = 12, train loss: 0.63288, val loss: 0.63282, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.62698, val loss: 0.62688, in 0.031s
1 tree, 35 leaves, max depth = 9, train loss: 0.62109, val loss: 0.62098, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.61556, val loss: 0.61547, in 0.016s
1 tree, 35 leaves, max depth = 8, train loss: 0.61014, val loss: 0.61008, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.60453, val loss: 0.60448, in 0.016s
1 tree, 35 leaves, max depth = 8, train loss: 0.59937, val loss: 0.59934, in 0.016s
1 tree, 35 leaves, max depth = 8, train loss: 0.59415, val loss: 0.59412, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.58900, val loss: 0.58896, in 0.016s
1 tree, 35 leaves, max depth = 7, train loss: 0.58402, val loss: 0.58398, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.57901, val loss: 0.57898, in 0.016s
1 tree, 35 leaves, max depth = 8, train loss: 0.57436, val loss: 0.57435, in 0.016s
1 tree, 35 leaves, max depth = 8, train loss: 0.56984, val loss: 0.56986, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.56512, val loss: 0.56515, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.56050, val loss: 0.56055, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.55599, val loss: 0.55606, in 0.016s
1 tree, 35 leaves, max depth = 8, train loss: 0.55162, val loss: 0.55172, in 0.016s
1 tree, 35 leaves, max depth = 8, train loss: 0.54729, val loss: 0.54741, in 0.016s
1 tree, 35 leaves, max depth = 8, train loss: 0.54306, val loss: 0.54320, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.53925, val loss: 0.53940, in 0.031s
1 tree, 35 leaves, max depth = 9, train loss: 0.53518, val loss: 0.53534, in 0.016s
1 tree, 35 leaves, max depth = 8, train loss: 0.53120, val loss: 0.53138, in 0.016s
1 tree, 35 leaves, max depth = 8, train loss: 0.52751, val loss: 0.52772, in 0.016s
1 tree, 35 leaves, max depth = 8, train loss: 0.52368, val loss: 0.52392, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.51993, val loss: 0.52018, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.51626, val loss: 0.51653, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.51266, val loss: 0.51295, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.50913, val loss: 0.50944, in 0.031s
1 tree, 35 leaves, max depth = 11, train loss: 0.50588, val loss: 0.50621, in 0.016s
1 tree, 35 leaves, max depth = 12, train loss: 0.50249, val loss: 0.50283, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.49830, val loss: 0.49887, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.49523, val loss: 0.49582, in 0.016s
1 tree, 35 leaves, max depth = 12, train loss: 0.49202, val loss: 0.49262, in 0.016s
1 tree, 35 leaves, max depth = 12, train loss: 0.48887, val loss: 0.48949, in 0.016s
1 tree, 35 leaves, max depth = 7, train loss: 0.48599, val loss: 0.48666, in 0.016s
1 tree, 35 leaves, max depth = 12, train loss: 0.48296, val loss: 0.48363, in 0.031s
1 tree, 35 leaves, max depth = 11, train loss: 0.47998, val loss: 0.48067, in 0.016s
1 tree, 35 leaves, max depth = 7, train loss: 0.47729, val loss: 0.47802, in 0.016s
1 tree, 35 leaves, max depth = 12, train loss: 0.47443, val loss: 0.47515, in 0.016s
1 tree, 35 leaves, max depth = 12, train loss: 0.47161, val loss: 0.47235, in 0.016s
1 tree, 35 leaves, max depth = 12, train loss: 0.46885, val loss: 0.46961, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.46618, val loss: 0.46696, in 0.031s
1 tree, 35 leaves, max depth = 12, train loss: 0.46352, val loss: 0.46430, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.46091, val loss: 0.46170, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.45835, val loss: 0.45916, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.45583, val loss: 0.45668, in 0.016s
1 tree, 35 leaves, max depth = 12, train loss: 0.45337, val loss: 0.45422, in 0.016s
1 tree, 35 leaves, max depth = 8, train loss: 0.45099, val loss: 0.45186, in 0.016s
1 tree, 35 leaves, max depth = 12, train loss: 0.44861, val loss: 0.44951, in 0.031s
1 tree, 35 leaves, max depth = 11, train loss: 0.44518, val loss: 0.44634, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.44190, val loss: 0.44329, in 0.016s
1 tree, 35 leaves, max depth = 8, train loss: 0.43967, val loss: 0.44111, in 0.016s
1 tree, 35 leaves, max depth = 12, train loss: 0.43745, val loss: 0.43891, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.43526, val loss: 0.43674, in 0.031s
1 tree, 35 leaves, max depth = 12, train loss: 0.43312, val loss: 0.43461, in 0.016s
1 tree, 35 leaves, max depth = 12, train loss: 0.43101, val loss: 0.43254, in 0.016s
Fit 64 trees in 1.472 s, (2240 total leaves)
Time spent computing histograms: 0.485s
Time spent finding best splits:  0.073s
Time spent applying splits:      0.050s
Time spent predicting:           0.000s
Trial 80, Fold 3: Log loss = 0.4285578476224689, Average precision = 0.9520868528689832, ROC-AUC = 0.9500523988500544, Elapsed Time = 1.4797304999992775 seconds
Trial 80, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 80, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
Binning 0.040 GB of training data: 0.159 s
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 35 leaves, max depth = 8, train loss: 0.68571, val loss: 0.68540, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.67843, val loss: 0.67781, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.67133, val loss: 0.67041, in 0.016s
1 tree, 35 leaves, max depth = 8, train loss: 0.66450, val loss: 0.66324, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.65775, val loss: 0.65620, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.65150, val loss: 0.64966, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.64506, val loss: 0.64294, in 0.016s
1 tree, 35 leaves, max depth = 7, train loss: 0.63912, val loss: 0.63671, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.63330, val loss: 0.63062, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.62743, val loss: 0.62446, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.62157, val loss: 0.61833, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.61614, val loss: 0.61265, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.61053, val loss: 0.60679, in 0.016s
1 tree, 35 leaves, max depth = 8, train loss: 0.60499, val loss: 0.60097, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.59988, val loss: 0.59562, in 0.016s
1 tree, 35 leaves, max depth = 7, train loss: 0.59465, val loss: 0.59008, in 0.016s
1 tree, 35 leaves, max depth = 8, train loss: 0.58942, val loss: 0.58461, in 0.016s
1 tree, 35 leaves, max depth = 8, train loss: 0.58438, val loss: 0.57929, in 0.016s
1 tree, 35 leaves, max depth = 8, train loss: 0.57942, val loss: 0.57407, in 0.016s
1 tree, 35 leaves, max depth = 8, train loss: 0.57484, val loss: 0.56936, in 0.016s
1 tree, 35 leaves, max depth = 8, train loss: 0.57009, val loss: 0.56437, in 0.016s
1 tree, 35 leaves, max depth = 7, train loss: 0.56547, val loss: 0.55949, in 0.016s
1 tree, 35 leaves, max depth = 8, train loss: 0.56089, val loss: 0.55468, in 0.016s
1 tree, 35 leaves, max depth = 8, train loss: 0.55643, val loss: 0.54999, in 0.031s
1 tree, 35 leaves, max depth = 8, train loss: 0.55233, val loss: 0.54576, in 0.016s
1 tree, 35 leaves, max depth = 8, train loss: 0.54837, val loss: 0.54155, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.54425, val loss: 0.53720, in 0.016s
1 tree, 35 leaves, max depth = 8, train loss: 0.54013, val loss: 0.53289, in 0.031s
1 tree, 35 leaves, max depth = 8, train loss: 0.53606, val loss: 0.52863, in 0.000s
1 tree, 35 leaves, max depth = 9, train loss: 0.53212, val loss: 0.52448, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.52846, val loss: 0.52064, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.52466, val loss: 0.51666, in 0.031s
1 tree, 35 leaves, max depth = 9, train loss: 0.52093, val loss: 0.51273, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.51726, val loss: 0.50888, in 0.016s
1 tree, 35 leaves, max depth = 8, train loss: 0.51368, val loss: 0.50510, in 0.031s
1 tree, 35 leaves, max depth = 9, train loss: 0.51015, val loss: 0.50140, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.50693, val loss: 0.49799, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.50354, val loss: 0.49443, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.50036, val loss: 0.49112, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.49738, val loss: 0.48793, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.49428, val loss: 0.48465, in 0.031s
1 tree, 35 leaves, max depth = 9, train loss: 0.49113, val loss: 0.48135, in 0.016s
1 tree, 35 leaves, max depth = 8, train loss: 0.48829, val loss: 0.47834, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.48529, val loss: 0.47516, in 0.016s
1 tree, 35 leaves, max depth = 8, train loss: 0.48237, val loss: 0.47206, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.47948, val loss: 0.46900, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.47669, val loss: 0.46602, in 0.031s
1 tree, 35 leaves, max depth = 9, train loss: 0.47398, val loss: 0.46314, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.47124, val loss: 0.46023, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.46854, val loss: 0.45738, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.46596, val loss: 0.45462, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.46337, val loss: 0.45187, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.46082, val loss: 0.44918, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.45833, val loss: 0.44652, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.45585, val loss: 0.44392, in 0.031s
1 tree, 35 leaves, max depth = 10, train loss: 0.45343, val loss: 0.44136, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.45111, val loss: 0.43890, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.44888, val loss: 0.43651, in 0.016s
1 tree, 35 leaves, max depth = 8, train loss: 0.44664, val loss: 0.43414, in 0.016s
1 tree, 35 leaves, max depth = 8, train loss: 0.44446, val loss: 0.43180, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.44113, val loss: 0.42842, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.43895, val loss: 0.42613, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.43682, val loss: 0.42387, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.43364, val loss: 0.42064, in 0.016s
Fit 64 trees in 1.472 s, (2240 total leaves)
Time spent computing histograms: 0.483s
Time spent finding best splits:  0.079s
Time spent applying splits:      0.052s
Time spent predicting:           0.000s
Trial 80, Fold 4: Log loss = 0.43292715360677114, Average precision = 0.9522821749291704, ROC-AUC = 0.9482136001964754, Elapsed Time = 1.4838823999998567 seconds
Trial 80, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 80, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
Binning 0.040 GB of training data: 0.158 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 35 leaves, max depth = 9, train loss: 0.68554, val loss: 0.68523, in 0.016s
1 tree, 35 leaves, max depth = 12, train loss: 0.67812, val loss: 0.67753, in 0.016s
1 tree, 35 leaves, max depth = 12, train loss: 0.67089, val loss: 0.67003, in 0.016s
1 tree, 35 leaves, max depth = 12, train loss: 0.66384, val loss: 0.66271, in 0.016s
1 tree, 35 leaves, max depth = 12, train loss: 0.65696, val loss: 0.65558, in 0.031s
1 tree, 35 leaves, max depth = 10, train loss: 0.65032, val loss: 0.64869, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.64413, val loss: 0.64223, in 0.016s
1 tree, 35 leaves, max depth = 8, train loss: 0.63782, val loss: 0.63567, in 0.016s
1 tree, 35 leaves, max depth = 12, train loss: 0.63186, val loss: 0.62942, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.62589, val loss: 0.62316, in 0.016s
1 tree, 35 leaves, max depth = 12, train loss: 0.61992, val loss: 0.61696, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.61440, val loss: 0.61117, in 0.016s
1 tree, 35 leaves, max depth = 12, train loss: 0.60869, val loss: 0.60525, in 0.016s
1 tree, 35 leaves, max depth = 12, train loss: 0.60312, val loss: 0.59947, in 0.016s
1 tree, 35 leaves, max depth = 12, train loss: 0.59793, val loss: 0.59403, in 0.016s
1 tree, 35 leaves, max depth = 8, train loss: 0.59268, val loss: 0.58857, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.58747, val loss: 0.58317, in 0.016s
1 tree, 35 leaves, max depth = 8, train loss: 0.58244, val loss: 0.57794, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.57746, val loss: 0.57278, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.57284, val loss: 0.56794, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.56804, val loss: 0.56294, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.56334, val loss: 0.55805, in 0.016s
1 tree, 35 leaves, max depth = 12, train loss: 0.55877, val loss: 0.55330, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.55429, val loss: 0.54866, in 0.016s
1 tree, 35 leaves, max depth = 8, train loss: 0.54995, val loss: 0.54414, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.54561, val loss: 0.53960, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.54136, val loss: 0.53516, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.53729, val loss: 0.53092, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.53322, val loss: 0.52666, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.52925, val loss: 0.52252, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.52555, val loss: 0.51865, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.52176, val loss: 0.51471, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.51805, val loss: 0.51086, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.51441, val loss: 0.50707, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.51082, val loss: 0.50334, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.50728, val loss: 0.49964, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.50403, val loss: 0.49623, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.50064, val loss: 0.49271, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.49751, val loss: 0.48938, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.49441, val loss: 0.48614, in 0.031s
1 tree, 35 leaves, max depth = 11, train loss: 0.49120, val loss: 0.48278, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.48806, val loss: 0.47952, in 0.016s
1 tree, 35 leaves, max depth = 7, train loss: 0.48520, val loss: 0.47651, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.48216, val loss: 0.47333, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.47920, val loss: 0.47025, in 0.016s
1 tree, 35 leaves, max depth = 16, train loss: 0.47530, val loss: 0.46632, in 0.031s
1 tree, 35 leaves, max depth = 10, train loss: 0.47261, val loss: 0.46350, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.46988, val loss: 0.46061, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.46712, val loss: 0.45774, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.46440, val loss: 0.45489, in 0.016s
1 tree, 35 leaves, max depth = 7, train loss: 0.46180, val loss: 0.45217, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.45917, val loss: 0.44943, in 0.031s
1 tree, 35 leaves, max depth = 11, train loss: 0.45661, val loss: 0.44677, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.45408, val loss: 0.44414, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.45162, val loss: 0.44157, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.44920, val loss: 0.43908, in 0.016s
1 tree, 35 leaves, max depth = 7, train loss: 0.44688, val loss: 0.43665, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.44454, val loss: 0.43422, in 0.031s
1 tree, 35 leaves, max depth = 11, train loss: 0.44224, val loss: 0.43183, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.43998, val loss: 0.42946, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.43777, val loss: 0.42716, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.43559, val loss: 0.42491, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.43235, val loss: 0.42168, in 0.031s
1 tree, 35 leaves, max depth = 11, train loss: 0.43025, val loss: 0.41950, in 0.000s
Fit 64 trees in 1.455 s, (2240 total leaves)
Time spent computing histograms: 0.473s
Time spent finding best splits:  0.072s
Time spent applying splits:      0.048s
Time spent predicting:           0.000s
Trial 80, Fold 5: Log loss = 0.43518271020566096, Average precision = 0.947309225647094, ROC-AUC = 0.9459746205153932, Elapsed Time = 1.4620661999997537 seconds
Optimization Progress:  81%|########1 | 81/100 [16:44<04:37, 14.61s/it]
Trial 81, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 81, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
0.147 s 0.040 GB of training data: 
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 7 leaves, max depth = 3, train loss: 0.68509, val loss: 0.68486, in 0.016s
1 tree, 8 leaves, max depth = 4, train loss: 0.67748, val loss: 0.67703, in 0.000s
1 tree, 8 leaves, max depth = 4, train loss: 0.67013, val loss: 0.66946, in 0.016s
1 tree, 7 leaves, max depth = 3, train loss: 0.66301, val loss: 0.66212, in 0.000s
1 tree, 8 leaves, max depth = 4, train loss: 0.65612, val loss: 0.65502, in 0.000s
1 tree, 8 leaves, max depth = 4, train loss: 0.64945, val loss: 0.64814, in 0.016s
1 tree, 8 leaves, max depth = 4, train loss: 0.64299, val loss: 0.64148, in 0.000s
1 tree, 7 leaves, max depth = 3, train loss: 0.63674, val loss: 0.63502, in 0.016s
1 tree, 8 leaves, max depth = 4, train loss: 0.63068, val loss: 0.62875, in 0.000s
1 tree, 8 leaves, max depth = 4, train loss: 0.62481, val loss: 0.62267, in 0.016s
1 tree, 8 leaves, max depth = 4, train loss: 0.61912, val loss: 0.61678, in 0.000s
Fit 11 trees in 0.331 s, (85 total leaves)
Time spent computing histograms: 0.047s
Time spent finding best splits:  0.004s
Time spent applying splits:      0.003s
Time spent predicting:           0.000s
Trial 81, Fold 1: Log loss = 0.6195250216480869, Average precision = 0.8109929655039347, ROC-AUC = 0.8611422531222668, Elapsed Time = 0.33699350000097184 seconds
Trial 81, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 81, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
Binning 0.040 GB of training data: 0.142 s
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 8 leaves, max depth = 4, train loss: 0.68524, val loss: 0.68485, in 0.000s
1 tree, 6 leaves, max depth = 4, train loss: 0.67772, val loss: 0.67695, in 0.016s
1 tree, 9 leaves, max depth = 5, train loss: 0.67039, val loss: 0.66926, in 0.000s
1 tree, 6 leaves, max depth = 4, train loss: 0.66336, val loss: 0.66185, in 0.016s
1 tree, 9 leaves, max depth = 5, train loss: 0.65649, val loss: 0.65464, in 0.000s
1 tree, 6 leaves, max depth = 4, train loss: 0.64990, val loss: 0.64769, in 0.000s
1 tree, 9 leaves, max depth = 5, train loss: 0.64346, val loss: 0.64092, in 0.016s
1 tree, 6 leaves, max depth = 4, train loss: 0.63729, val loss: 0.63440, in 0.000s
1 tree, 9 leaves, max depth = 5, train loss: 0.63124, val loss: 0.62804, in 0.016s
1 tree, 9 leaves, max depth = 5, train loss: 0.62539, val loss: 0.62187, in 0.000s
1 tree, 6 leaves, max depth = 4, train loss: 0.61977, val loss: 0.61593, in 0.000s
Fit 11 trees in 0.329 s, (83 total leaves)
Time spent computing histograms: 0.041s
Time spent finding best splits:  0.004s
Time spent applying splits:      0.002s
Time spent predicting:           0.016s
Trial 81, Fold 2: Log loss = 0.6193558844500675, Average precision = 0.8194662883517918, ROC-AUC = 0.8683016630646799, Elapsed Time = 0.34302349999961734 seconds
Trial 81, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 81, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
Binning 0.040 GB of training data: 0.158 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 8 leaves, max depth = 4, train loss: 0.68520, val loss: 0.68494, in 0.016s
1 tree, 7 leaves, max depth = 4, train loss: 0.67773, val loss: 0.67717, in 0.000s
1 tree, 8 leaves, max depth = 4, train loss: 0.67043, val loss: 0.66962, in 0.016s
1 tree, 7 leaves, max depth = 4, train loss: 0.66344, val loss: 0.66235, in 0.000s
1 tree, 8 leaves, max depth = 4, train loss: 0.65660, val loss: 0.65527, in 0.000s
1 tree, 8 leaves, max depth = 4, train loss: 0.64999, val loss: 0.64842, in 0.016s
1 tree, 7 leaves, max depth = 4, train loss: 0.64365, val loss: 0.64181, in 0.000s
1 tree, 8 leaves, max depth = 4, train loss: 0.63744, val loss: 0.63538, in 0.016s
1 tree, 7 leaves, max depth = 4, train loss: 0.63150, val loss: 0.62917, in 0.000s
1 tree, 9 leaves, max depth = 5, train loss: 0.62567, val loss: 0.62313, in 0.016s
1 tree, 9 leaves, max depth = 5, train loss: 0.62003, val loss: 0.61727, in 0.000s
Fit 11 trees in 0.345 s, (86 total leaves)
Time spent computing histograms: 0.045s
Time spent finding best splits:  0.004s
Time spent applying splits:      0.003s
Time spent predicting:           0.000s
Trial 81, Fold 3: Log loss = 0.6181173893012052, Average precision = 0.823732825492677, ROC-AUC = 0.8711087608710308, Elapsed Time = 0.3515162000003329 seconds
Trial 81, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 81, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
0.159 s 0.040 GB of training data: 
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 7 leaves, max depth = 4, train loss: 0.68524, val loss: 0.68478, in 0.016s
1 tree, 7 leaves, max depth = 4, train loss: 0.67769, val loss: 0.67678, in 0.000s
1 tree, 7 leaves, max depth = 4, train loss: 0.67040, val loss: 0.66904, in 0.016s
1 tree, 7 leaves, max depth = 4, train loss: 0.66334, val loss: 0.66154, in 0.000s
1 tree, 7 leaves, max depth = 4, train loss: 0.65651, val loss: 0.65428, in 0.000s
1 tree, 7 leaves, max depth = 4, train loss: 0.64990, val loss: 0.64724, in 0.000s
1 tree, 6 leaves, max depth = 3, train loss: 0.64349, val loss: 0.64042, in 0.000s
1 tree, 7 leaves, max depth = 4, train loss: 0.63729, val loss: 0.63381, in 0.016s
1 tree, 7 leaves, max depth = 4, train loss: 0.63129, val loss: 0.62740, in 0.000s
1 tree, 7 leaves, max depth = 4, train loss: 0.62547, val loss: 0.62119, in 0.016s
1 tree, 7 leaves, max depth = 4, train loss: 0.61983, val loss: 0.61516, in 0.000s
Fit 11 trees in 0.346 s, (76 total leaves)
Time spent computing histograms: 0.046s
Time spent finding best splits:  0.005s
Time spent applying splits:      0.002s
Time spent predicting:           0.000s
Trial 81, Fold 4: Log loss = 0.6188015647415763, Average precision = 0.8267691012009457, ROC-AUC = 0.8702258900405588, Elapsed Time = 0.3649242999999842 seconds
Trial 81, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 81, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
0.141 s 0.040 GB of training data: 
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 7 leaves, max depth = 5, train loss: 0.68522, val loss: 0.68468, in 0.000s
1 tree, 7 leaves, max depth = 5, train loss: 0.67770, val loss: 0.67662, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.67042, val loss: 0.66881, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.66338, val loss: 0.66124, in 0.000s
1 tree, 7 leaves, max depth = 5, train loss: 0.65656, val loss: 0.65391, in 0.016s
1 tree, 8 leaves, max depth = 5, train loss: 0.64997, val loss: 0.64681, in 0.000s
1 tree, 8 leaves, max depth = 5, train loss: 0.64358, val loss: 0.63992, in 0.016s
1 tree, 8 leaves, max depth = 5, train loss: 0.63730, val loss: 0.63319, in 0.000s
1 tree, 8 leaves, max depth = 5, train loss: 0.63121, val loss: 0.62666, in 0.000s
1 tree, 8 leaves, max depth = 5, train loss: 0.62532, val loss: 0.62034, in 0.016s
1 tree, 7 leaves, max depth = 5, train loss: 0.61969, val loss: 0.61424, in 0.000s
Fit 11 trees in 0.344 s, (78 total leaves)
Time spent computing histograms: 0.042s
Time spent finding best splits:  0.004s
Time spent applying splits:      0.002s
Time spent predicting:           0.000s
Trial 81, Fold 5: Log loss = 0.6219450285121826, Average precision = 0.8066400069708548, ROC-AUC = 0.8550896085316687, Elapsed Time = 0.359162999999171 seconds
Optimization Progress:  82%|########2 | 82/100 [16:52<03:48, 12.69s/it]
Trial 82, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 82, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
0.158 s 0.040 GB of training data: 
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 73 leaves, max depth = 10, train loss: 0.68500, val loss: 0.68496, in 0.016s
1 tree, 105 leaves, max depth = 15, train loss: 0.67689, val loss: 0.67690, in 0.016s
1 tree, 107 leaves, max depth = 15, train loss: 0.66899, val loss: 0.66908, in 0.016s
1 tree, 106 leaves, max depth = 15, train loss: 0.66132, val loss: 0.66148, in 0.031s
1 tree, 84 leaves, max depth = 14, train loss: 0.65406, val loss: 0.65418, in 0.016s
1 tree, 129 leaves, max depth = 12, train loss: 0.64690, val loss: 0.64701, in 0.016s
1 tree, 76 leaves, max depth = 10, train loss: 0.64000, val loss: 0.64006, in 0.031s
1 tree, 120 leaves, max depth = 13, train loss: 0.63308, val loss: 0.63320, in 0.016s
1 tree, 81 leaves, max depth = 10, train loss: 0.62654, val loss: 0.62661, in 0.016s
1 tree, 92 leaves, max depth = 10, train loss: 0.62017, val loss: 0.62015, in 0.016s
1 tree, 116 leaves, max depth = 15, train loss: 0.61377, val loss: 0.61380, in 0.031s
1 tree, 115 leaves, max depth = 14, train loss: 0.60753, val loss: 0.60760, in 0.016s
1 tree, 126 leaves, max depth = 15, train loss: 0.60162, val loss: 0.60173, in 0.031s
1 tree, 127 leaves, max depth = 14, train loss: 0.59583, val loss: 0.59589, in 0.016s
1 tree, 145 leaves, max depth = 15, train loss: 0.59011, val loss: 0.59012, in 0.016s
1 tree, 120 leaves, max depth = 15, train loss: 0.58446, val loss: 0.58451, in 0.031s
1 tree, 121 leaves, max depth = 15, train loss: 0.57896, val loss: 0.57904, in 0.031s
1 tree, 123 leaves, max depth = 15, train loss: 0.57379, val loss: 0.57389, in 0.016s
1 tree, 127 leaves, max depth = 15, train loss: 0.56851, val loss: 0.56860, in 0.031s
1 tree, 121 leaves, max depth = 15, train loss: 0.56338, val loss: 0.56351, in 0.016s
1 tree, 127 leaves, max depth = 15, train loss: 0.55834, val loss: 0.55848, in 0.031s
1 tree, 148 leaves, max depth = 14, train loss: 0.55340, val loss: 0.55346, in 0.031s
1 tree, 126 leaves, max depth = 15, train loss: 0.54880, val loss: 0.54886, in 0.031s
1 tree, 145 leaves, max depth = 14, train loss: 0.54428, val loss: 0.54429, in 0.031s
1 tree, 148 leaves, max depth = 15, train loss: 0.53965, val loss: 0.53959, in 0.016s
1 tree, 123 leaves, max depth = 14, train loss: 0.53517, val loss: 0.53516, in 0.031s
1 tree, 124 leaves, max depth = 14, train loss: 0.53079, val loss: 0.53081, in 0.016s
1 tree, 148 leaves, max depth = 15, train loss: 0.52646, val loss: 0.52642, in 0.016s
1 tree, 124 leaves, max depth = 14, train loss: 0.52226, val loss: 0.52226, in 0.031s
1 tree, 122 leaves, max depth = 14, train loss: 0.51833, val loss: 0.51834, in 0.031s
1 tree, 123 leaves, max depth = 14, train loss: 0.51432, val loss: 0.51438, in 0.016s
1 tree, 127 leaves, max depth = 14, train loss: 0.51050, val loss: 0.51056, in 0.031s
1 tree, 120 leaves, max depth = 15, train loss: 0.50665, val loss: 0.50673, in 0.031s
1 tree, 123 leaves, max depth = 14, train loss: 0.50289, val loss: 0.50300, in 0.016s
1 tree, 128 leaves, max depth = 13, train loss: 0.49912, val loss: 0.49922, in 0.031s
1 tree, 144 leaves, max depth = 14, train loss: 0.49557, val loss: 0.49567, in 0.031s
1 tree, 121 leaves, max depth = 14, train loss: 0.49204, val loss: 0.49219, in 0.016s
1 tree, 125 leaves, max depth = 14, train loss: 0.48858, val loss: 0.48876, in 0.031s
1 tree, 121 leaves, max depth = 15, train loss: 0.48533, val loss: 0.48554, in 0.031s
1 tree, 122 leaves, max depth = 15, train loss: 0.48203, val loss: 0.48228, in 0.031s
1 tree, 129 leaves, max depth = 15, train loss: 0.47868, val loss: 0.47893, in 0.016s
1 tree, 126 leaves, max depth = 13, train loss: 0.47542, val loss: 0.47567, in 0.031s
1 tree, 123 leaves, max depth = 14, train loss: 0.47243, val loss: 0.47272, in 0.031s
1 tree, 148 leaves, max depth = 14, train loss: 0.46934, val loss: 0.46958, in 0.031s
1 tree, 126 leaves, max depth = 16, train loss: 0.46643, val loss: 0.46667, in 0.016s
1 tree, 134 leaves, max depth = 15, train loss: 0.46234, val loss: 0.46267, in 0.031s
1 tree, 127 leaves, max depth = 13, train loss: 0.45936, val loss: 0.45968, in 0.031s
1 tree, 115 leaves, max depth = 15, train loss: 0.45537, val loss: 0.45581, in 0.016s
1 tree, 126 leaves, max depth = 13, train loss: 0.45267, val loss: 0.45310, in 0.031s
1 tree, 123 leaves, max depth = 14, train loss: 0.44995, val loss: 0.45043, in 0.031s
1 tree, 119 leaves, max depth = 16, train loss: 0.44613, val loss: 0.44675, in 0.031s
1 tree, 145 leaves, max depth = 15, train loss: 0.44346, val loss: 0.44405, in 0.016s
1 tree, 122 leaves, max depth = 16, train loss: 0.43977, val loss: 0.44052, in 0.031s
1 tree, 128 leaves, max depth = 15, train loss: 0.43715, val loss: 0.43790, in 0.016s
1 tree, 117 leaves, max depth = 15, train loss: 0.43360, val loss: 0.43447, in 0.031s
1 tree, 94 leaves, max depth = 13, train loss: 0.43119, val loss: 0.43201, in 0.016s
1 tree, 147 leaves, max depth = 14, train loss: 0.42876, val loss: 0.42955, in 0.031s
1 tree, 129 leaves, max depth = 13, train loss: 0.42633, val loss: 0.42714, in 0.031s
1 tree, 127 leaves, max depth = 13, train loss: 0.42395, val loss: 0.42476, in 0.016s
1 tree, 127 leaves, max depth = 13, train loss: 0.42162, val loss: 0.42243, in 0.031s
1 tree, 128 leaves, max depth = 13, train loss: 0.41934, val loss: 0.42015, in 0.031s
1 tree, 115 leaves, max depth = 15, train loss: 0.41608, val loss: 0.41702, in 0.016s
1 tree, 131 leaves, max depth = 13, train loss: 0.41387, val loss: 0.41483, in 0.031s
1 tree, 117 leaves, max depth = 15, train loss: 0.41072, val loss: 0.41182, in 0.016s
1 tree, 130 leaves, max depth = 13, train loss: 0.40859, val loss: 0.40971, in 0.031s
1 tree, 94 leaves, max depth = 12, train loss: 0.40662, val loss: 0.40769, in 0.031s
1 tree, 129 leaves, max depth = 12, train loss: 0.40456, val loss: 0.40566, in 0.016s
1 tree, 129 leaves, max depth = 12, train loss: 0.40255, val loss: 0.40368, in 0.031s
1 tree, 128 leaves, max depth = 12, train loss: 0.40058, val loss: 0.40173, in 0.016s
1 tree, 130 leaves, max depth = 14, train loss: 0.39864, val loss: 0.39981, in 0.016s
1 tree, 127 leaves, max depth = 15, train loss: 0.39684, val loss: 0.39802, in 0.031s
1 tree, 134 leaves, max depth = 15, train loss: 0.39400, val loss: 0.39529, in 0.031s
1 tree, 118 leaves, max depth = 15, train loss: 0.39119, val loss: 0.39262, in 0.016s
1 tree, 145 leaves, max depth = 14, train loss: 0.38942, val loss: 0.39084, in 0.031s
1 tree, 100 leaves, max depth = 15, train loss: 0.38784, val loss: 0.38921, in 0.016s
1 tree, 118 leaves, max depth = 15, train loss: 0.38514, val loss: 0.38664, in 0.031s
1 tree, 125 leaves, max depth = 13, train loss: 0.38341, val loss: 0.38494, in 0.031s
1 tree, 114 leaves, max depth = 18, train loss: 0.38081, val loss: 0.38251, in 0.016s
1 tree, 92 leaves, max depth = 15, train loss: 0.37836, val loss: 0.38012, in 0.031s
1 tree, 127 leaves, max depth = 14, train loss: 0.37671, val loss: 0.37848, in 0.031s
1 tree, 118 leaves, max depth = 15, train loss: 0.37423, val loss: 0.37614, in 0.016s
1 tree, 143 leaves, max depth = 14, train loss: 0.37266, val loss: 0.37458, in 0.031s
1 tree, 119 leaves, max depth = 15, train loss: 0.37026, val loss: 0.37232, in 0.031s
1 tree, 123 leaves, max depth = 13, train loss: 0.36880, val loss: 0.37089, in 0.016s
1 tree, 135 leaves, max depth = 15, train loss: 0.36651, val loss: 0.36871, in 0.031s
1 tree, 127 leaves, max depth = 13, train loss: 0.36499, val loss: 0.36723, in 0.031s
1 tree, 117 leaves, max depth = 15, train loss: 0.36273, val loss: 0.36510, in 0.016s
1 tree, 119 leaves, max depth = 17, train loss: 0.36052, val loss: 0.36305, in 0.031s
1 tree, 125 leaves, max depth = 15, train loss: 0.35907, val loss: 0.36162, in 0.031s
1 tree, 125 leaves, max depth = 15, train loss: 0.35764, val loss: 0.36022, in 0.016s
1 tree, 67 leaves, max depth = 14, train loss: 0.35565, val loss: 0.35824, in 0.016s
1 tree, 61 leaves, max depth = 15, train loss: 0.35371, val loss: 0.35633, in 0.031s
1 tree, 67 leaves, max depth = 14, train loss: 0.35181, val loss: 0.35445, in 0.016s
1 tree, 62 leaves, max depth = 16, train loss: 0.34996, val loss: 0.35266, in 0.016s
1 tree, 66 leaves, max depth = 14, train loss: 0.34815, val loss: 0.35087, in 0.031s
1 tree, 68 leaves, max depth = 14, train loss: 0.34638, val loss: 0.34912, in 0.016s
1 tree, 120 leaves, max depth = 17, train loss: 0.34456, val loss: 0.34750, in 0.016s
1 tree, 128 leaves, max depth = 18, train loss: 0.34306, val loss: 0.34607, in 0.031s
1 tree, 126 leaves, max depth = 17, train loss: 0.34158, val loss: 0.34465, in 0.016s
1 tree, 67 leaves, max depth = 14, train loss: 0.33991, val loss: 0.34300, in 0.016s
Fit 100 trees in 2.768 s, (11849 total leaves)
Time spent computing histograms: 0.865s
Time spent finding best splits:  0.237s
Time spent applying splits:      0.224s
Time spent predicting:           0.000s
Trial 82, Fold 1: Log loss = 0.34719319393428133, Average precision = 0.955178807528165, ROC-AUC = 0.9496816793564762, Elapsed Time = 2.782788900000014 seconds
Trial 82, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 82, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
Binning 0.040 GB of training data: 0.158 s
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 68 leaves, max depth = 12, train loss: 0.68493, val loss: 0.68479, in 0.016s
1 tree, 111 leaves, max depth = 14, train loss: 0.67671, val loss: 0.67650, in 0.016s
1 tree, 108 leaves, max depth = 16, train loss: 0.66879, val loss: 0.66848, in 0.031s
1 tree, 107 leaves, max depth = 16, train loss: 0.66110, val loss: 0.66068, in 0.031s
1 tree, 91 leaves, max depth = 12, train loss: 0.65366, val loss: 0.65311, in 0.016s
1 tree, 141 leaves, max depth = 12, train loss: 0.64641, val loss: 0.64582, in 0.031s
1 tree, 93 leaves, max depth = 12, train loss: 0.63943, val loss: 0.63873, in 0.031s
1 tree, 120 leaves, max depth = 14, train loss: 0.63240, val loss: 0.63162, in 0.016s
[9/100] 1 tree, 120 leaves, max depth = 14, train loss: 0.62556, val loss: 0.62469, in 0.031s
1 tree, 123 leaves, max depth = 17, train loss: 0.61931, val loss: 0.61836, in 0.016s
1 tree, 124 leaves, max depth = 14, train loss: 0.61280, val loss: 0.61177, in 0.031s
1 tree, 124 leaves, max depth = 25, train loss: 0.60688, val loss: 0.60580, in 0.016s
1 tree, 100 leaves, max depth = 14, train loss: 0.60083, val loss: 0.59964, in 0.031s
1 tree, 148 leaves, max depth = 14, train loss: 0.59485, val loss: 0.59362, in 0.016s
1 tree, 127 leaves, max depth = 25, train loss: 0.58933, val loss: 0.58807, in 0.031s
1 tree, 98 leaves, max depth = 17, train loss: 0.58409, val loss: 0.58274, in 0.031s
1 tree, 128 leaves, max depth = 15, train loss: 0.57850, val loss: 0.57711, in 0.016s
1 tree, 125 leaves, max depth = 14, train loss: 0.57300, val loss: 0.57153, in 0.031s
1 tree, 128 leaves, max depth = 25, train loss: 0.56799, val loss: 0.56650, in 0.031s
1 tree, 127 leaves, max depth = 17, train loss: 0.56300, val loss: 0.56150, in 0.016s
1 tree, 128 leaves, max depth = 16, train loss: 0.55791, val loss: 0.55638, in 0.031s
1 tree, 126 leaves, max depth = 16, train loss: 0.55294, val loss: 0.55139, in 0.016s
1 tree, 122 leaves, max depth = 14, train loss: 0.54804, val loss: 0.54644, in 0.031s
1 tree, 101 leaves, max depth = 17, train loss: 0.54370, val loss: 0.54205, in 0.016s
1 tree, 125 leaves, max depth = 17, train loss: 0.53934, val loss: 0.53766, in 0.031s
1 tree, 127 leaves, max depth = 15, train loss: 0.53476, val loss: 0.53305, in 0.031s
1 tree, 124 leaves, max depth = 15, train loss: 0.53027, val loss: 0.52852, in 0.031s
1 tree, 128 leaves, max depth = 15, train loss: 0.52589, val loss: 0.52412, in 0.016s
1 tree, 128 leaves, max depth = 15, train loss: 0.52161, val loss: 0.51981, in 0.016s
1 tree, 124 leaves, max depth = 18, train loss: 0.51772, val loss: 0.51589, in 0.016s
1 tree, 147 leaves, max depth = 14, train loss: 0.51365, val loss: 0.51183, in 0.031s
1 tree, 151 leaves, max depth = 15, train loss: 0.50969, val loss: 0.50787, in 0.031s
1 tree, 123 leaves, max depth = 14, train loss: 0.50575, val loss: 0.50391, in 0.016s
1 tree, 124 leaves, max depth = 14, train loss: 0.50189, val loss: 0.50004, in 0.031s
1 tree, 151 leaves, max depth = 14, train loss: 0.49819, val loss: 0.49634, in 0.031s
1 tree, 147 leaves, max depth = 14, train loss: 0.49455, val loss: 0.49271, in 0.031s
1 tree, 146 leaves, max depth = 14, train loss: 0.49099, val loss: 0.48914, in 0.031s
1 tree, 127 leaves, max depth = 14, train loss: 0.48754, val loss: 0.48571, in 0.031s
1 tree, 150 leaves, max depth = 14, train loss: 0.48412, val loss: 0.48230, in 0.031s
1 tree, 129 leaves, max depth = 14, train loss: 0.48074, val loss: 0.47889, in 0.031s
1 tree, 148 leaves, max depth = 14, train loss: 0.47748, val loss: 0.47561, in 0.031s
1 tree, 126 leaves, max depth = 14, train loss: 0.47422, val loss: 0.47233, in 0.031s
1 tree, 128 leaves, max depth = 14, train loss: 0.47104, val loss: 0.46912, in 0.031s
1 tree, 126 leaves, max depth = 14, train loss: 0.46792, val loss: 0.46599, in 0.031s
1 tree, 127 leaves, max depth = 14, train loss: 0.46487, val loss: 0.46291, in 0.031s
1 tree, 125 leaves, max depth = 14, train loss: 0.46189, val loss: 0.45991, in 0.016s
1 tree, 126 leaves, max depth = 14, train loss: 0.45897, val loss: 0.45697, in 0.031s
1 tree, 119 leaves, max depth = 18, train loss: 0.45499, val loss: 0.45306, in 0.031s
1 tree, 142 leaves, max depth = 18, train loss: 0.45115, val loss: 0.44930, in 0.031s
1 tree, 151 leaves, max depth = 14, train loss: 0.44843, val loss: 0.44659, in 0.031s
1 tree, 119 leaves, max depth = 22, train loss: 0.44467, val loss: 0.44293, in 0.031s
1 tree, 120 leaves, max depth = 15, train loss: 0.44098, val loss: 0.43928, in 0.016s
1 tree, 127 leaves, max depth = 14, train loss: 0.43834, val loss: 0.43664, in 0.031s
1 tree, 150 leaves, max depth = 14, train loss: 0.43580, val loss: 0.43412, in 0.016s
1 tree, 121 leaves, max depth = 18, train loss: 0.43230, val loss: 0.43070, in 0.031s
1 tree, 103 leaves, max depth = 13, train loss: 0.42994, val loss: 0.42830, in 0.031s
1 tree, 154 leaves, max depth = 13, train loss: 0.42754, val loss: 0.42593, in 0.031s
1 tree, 131 leaves, max depth = 14, train loss: 0.42513, val loss: 0.42353, in 0.016s
1 tree, 132 leaves, max depth = 14, train loss: 0.42277, val loss: 0.42119, in 0.031s
1 tree, 131 leaves, max depth = 14, train loss: 0.42046, val loss: 0.41889, in 0.016s
1 tree, 131 leaves, max depth = 14, train loss: 0.41821, val loss: 0.41664, in 0.047s
1 tree, 120 leaves, max depth = 16, train loss: 0.41500, val loss: 0.41352, in 0.031s
1 tree, 131 leaves, max depth = 13, train loss: 0.41285, val loss: 0.41134, in 0.031s
1 tree, 121 leaves, max depth = 17, train loss: 0.40975, val loss: 0.40832, in 0.047s
1 tree, 130 leaves, max depth = 13, train loss: 0.40764, val loss: 0.40622, in 0.016s
1 tree, 130 leaves, max depth = 13, train loss: 0.40557, val loss: 0.40417, in 0.031s
1 tree, 126 leaves, max depth = 13, train loss: 0.40354, val loss: 0.40215, in 0.031s
1 tree, 128 leaves, max depth = 13, train loss: 0.40155, val loss: 0.40015, in 0.031s
1 tree, 128 leaves, max depth = 13, train loss: 0.39960, val loss: 0.39820, in 0.031s
1 tree, 119 leaves, max depth = 17, train loss: 0.39672, val loss: 0.39540, in 0.031s
1 tree, 95 leaves, max depth = 15, train loss: 0.39398, val loss: 0.39271, in 0.031s
1 tree, 147 leaves, max depth = 13, train loss: 0.39216, val loss: 0.39093, in 0.031s
1 tree, 115 leaves, max depth = 23, train loss: 0.38943, val loss: 0.38828, in 0.031s
1 tree, 147 leaves, max depth = 14, train loss: 0.38769, val loss: 0.38657, in 0.031s
1 tree, 104 leaves, max depth = 16, train loss: 0.38613, val loss: 0.38502, in 0.016s
1 tree, 143 leaves, max depth = 17, train loss: 0.38355, val loss: 0.38255, in 0.031s
1 tree, 124 leaves, max depth = 14, train loss: 0.38184, val loss: 0.38085, in 0.031s
1 tree, 152 leaves, max depth = 18, train loss: 0.38025, val loss: 0.37933, in 0.031s
1 tree, 143 leaves, max depth = 17, train loss: 0.37778, val loss: 0.37696, in 0.031s
1 tree, 121 leaves, max depth = 15, train loss: 0.37530, val loss: 0.37455, in 0.031s
1 tree, 152 leaves, max depth = 15, train loss: 0.37373, val loss: 0.37302, in 0.031s
1 tree, 114 leaves, max depth = 23, train loss: 0.37134, val loss: 0.37073, in 0.031s
1 tree, 130 leaves, max depth = 15, train loss: 0.36977, val loss: 0.36918, in 0.031s
1 tree, 114 leaves, max depth = 23, train loss: 0.36746, val loss: 0.36696, in 0.031s
1 tree, 95 leaves, max depth = 15, train loss: 0.36527, val loss: 0.36483, in 0.016s
1 tree, 128 leaves, max depth = 15, train loss: 0.36377, val loss: 0.36334, in 0.032s
1 tree, 119 leaves, max depth = 17, train loss: 0.36158, val loss: 0.36122, in 0.031s
1 tree, 129 leaves, max depth = 14, train loss: 0.36013, val loss: 0.35979, in 0.031s
1 tree, 126 leaves, max depth = 16, train loss: 0.35870, val loss: 0.35838, in 0.031s
1 tree, 68 leaves, max depth = 16, train loss: 0.35672, val loss: 0.35645, in 0.016s
1 tree, 126 leaves, max depth = 16, train loss: 0.35533, val loss: 0.35507, in 0.031s
1 tree, 143 leaves, max depth = 17, train loss: 0.35333, val loss: 0.35317, in 0.031s
1 tree, 68 leaves, max depth = 16, train loss: 0.35146, val loss: 0.35134, in 0.016s
1 tree, 127 leaves, max depth = 14, train loss: 0.35013, val loss: 0.35005, in 0.031s
1 tree, 68 leaves, max depth = 18, train loss: 0.34831, val loss: 0.34829, in 0.016s
1 tree, 124 leaves, max depth = 17, train loss: 0.34647, val loss: 0.34659, in 0.031s
1 tree, 62 leaves, max depth = 19, train loss: 0.34472, val loss: 0.34489, in 0.016s
1 tree, 122 leaves, max depth = 19, train loss: 0.34288, val loss: 0.34312, in 0.031s
1 tree, 123 leaves, max depth = 20, train loss: 0.34141, val loss: 0.34173, in 0.031s
1 tree, 123 leaves, max depth = 20, train loss: 0.33997, val loss: 0.34036, in 0.031s
Fit 100 trees in 3.159 s, (12361 total leaves)
Time spent computing histograms: 0.969s
Time spent finding best splits:  0.275s
Time spent applying splits:      0.270s
Time spent predicting:           0.016s
Trial 82, Fold 2: Log loss = 0.344785585703399, Average precision = 0.9539294557132714, ROC-AUC = 0.9512520861526808, Elapsed Time = 3.155847199999698 seconds
Trial 82, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 82, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
Binning 0.040 GB of training data: 0.158 s
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 69 leaves, max depth = 11, train loss: 0.68499, val loss: 0.68497, in 0.016s
1 tree, 123 leaves, max depth = 17, train loss: 0.67725, val loss: 0.67731, in 0.031s
1 tree, 114 leaves, max depth = 15, train loss: 0.66935, val loss: 0.66944, in 0.031s
1 tree, 121 leaves, max depth = 13, train loss: 0.66160, val loss: 0.66178, in 0.016s
1 tree, 116 leaves, max depth = 15, train loss: 0.65413, val loss: 0.65433, in 0.031s
1 tree, 121 leaves, max depth = 14, train loss: 0.64731, val loss: 0.64754, in 0.016s
1 tree, 117 leaves, max depth = 16, train loss: 0.64022, val loss: 0.64049, in 0.031s
1 tree, 94 leaves, max depth = 16, train loss: 0.63348, val loss: 0.63369, in 0.016s
1 tree, 146 leaves, max depth = 15, train loss: 0.62712, val loss: 0.62741, in 0.031s
1 tree, 124 leaves, max depth = 18, train loss: 0.62084, val loss: 0.62124, in 0.016s
1 tree, 125 leaves, max depth = 16, train loss: 0.61443, val loss: 0.61484, in 0.031s
1 tree, 125 leaves, max depth = 16, train loss: 0.60818, val loss: 0.60861, in 0.031s
1 tree, 128 leaves, max depth = 17, train loss: 0.60236, val loss: 0.60273, in 0.016s
1 tree, 125 leaves, max depth = 18, train loss: 0.59668, val loss: 0.59715, in 0.031s
1 tree, 87 leaves, max depth = 16, train loss: 0.59094, val loss: 0.59143, in 0.016s
1 tree, 121 leaves, max depth = 16, train loss: 0.58553, val loss: 0.58608, in 0.031s
1 tree, 125 leaves, max depth = 15, train loss: 0.57998, val loss: 0.58055, in 0.016s
1 tree, 146 leaves, max depth = 15, train loss: 0.57455, val loss: 0.57522, in 0.031s
1 tree, 124 leaves, max depth = 18, train loss: 0.56952, val loss: 0.57025, in 0.031s
1 tree, 127 leaves, max depth = 13, train loss: 0.56430, val loss: 0.56503, in 0.016s
1 tree, 126 leaves, max depth = 15, train loss: 0.55917, val loss: 0.55994, in 0.031s
1 tree, 126 leaves, max depth = 15, train loss: 0.55417, val loss: 0.55498, in 0.031s
1 tree, 142 leaves, max depth = 16, train loss: 0.54963, val loss: 0.55050, in 0.031s
1 tree, 123 leaves, max depth = 14, train loss: 0.54519, val loss: 0.54608, in 0.031s
1 tree, 122 leaves, max depth = 15, train loss: 0.54052, val loss: 0.54145, in 0.016s
1 tree, 124 leaves, max depth = 16, train loss: 0.53595, val loss: 0.53693, in 0.016s
1 tree, 123 leaves, max depth = 16, train loss: 0.53175, val loss: 0.53277, in 0.031s
1 tree, 127 leaves, max depth = 18, train loss: 0.52754, val loss: 0.52866, in 0.016s
1 tree, 126 leaves, max depth = 14, train loss: 0.52361, val loss: 0.52476, in 0.031s
1 tree, 124 leaves, max depth = 16, train loss: 0.51941, val loss: 0.52061, in 0.031s
1 tree, 143 leaves, max depth = 16, train loss: 0.51535, val loss: 0.51662, in 0.016s
1 tree, 121 leaves, max depth = 14, train loss: 0.51134, val loss: 0.51262, in 0.031s
1 tree, 120 leaves, max depth = 15, train loss: 0.50741, val loss: 0.50875, in 0.031s
1 tree, 141 leaves, max depth = 14, train loss: 0.50391, val loss: 0.50535, in 0.016s
1 tree, 144 leaves, max depth = 16, train loss: 0.50019, val loss: 0.50171, in 0.016s
1 tree, 99 leaves, max depth = 15, train loss: 0.49662, val loss: 0.49812, in 0.031s
1 tree, 124 leaves, max depth = 15, train loss: 0.49302, val loss: 0.49453, in 0.016s
1 tree, 124 leaves, max depth = 14, train loss: 0.48949, val loss: 0.49105, in 0.031s
1 tree, 124 leaves, max depth = 14, train loss: 0.48603, val loss: 0.48764, in 0.031s
1 tree, 125 leaves, max depth = 14, train loss: 0.48265, val loss: 0.48429, in 0.016s
1 tree, 124 leaves, max depth = 14, train loss: 0.47933, val loss: 0.48100, in 0.031s
1 tree, 124 leaves, max depth = 15, train loss: 0.47609, val loss: 0.47778, in 0.016s
1 tree, 124 leaves, max depth = 15, train loss: 0.47293, val loss: 0.47463, in 0.031s
1 tree, 116 leaves, max depth = 15, train loss: 0.46870, val loss: 0.47070, in 0.031s
1 tree, 145 leaves, max depth = 15, train loss: 0.46568, val loss: 0.46777, in 0.031s
1 tree, 126 leaves, max depth = 15, train loss: 0.46267, val loss: 0.46481, in 0.016s
1 tree, 145 leaves, max depth = 15, train loss: 0.45978, val loss: 0.46200, in 0.031s
1 tree, 112 leaves, max depth = 17, train loss: 0.45583, val loss: 0.45831, in 0.016s
1 tree, 127 leaves, max depth = 15, train loss: 0.45300, val loss: 0.45554, in 0.047s
1 tree, 125 leaves, max depth = 17, train loss: 0.45030, val loss: 0.45293, in 0.016s
1 tree, 143 leaves, max depth = 15, train loss: 0.44762, val loss: 0.45033, in 0.031s
1 tree, 125 leaves, max depth = 15, train loss: 0.44496, val loss: 0.44769, in 0.031s
1 tree, 136 leaves, max depth = 16, train loss: 0.44128, val loss: 0.44435, in 0.031s
1 tree, 126 leaves, max depth = 16, train loss: 0.43871, val loss: 0.44183, in 0.016s
1 tree, 126 leaves, max depth = 15, train loss: 0.43618, val loss: 0.43934, in 0.031s
1 tree, 125 leaves, max depth = 15, train loss: 0.43371, val loss: 0.43692, in 0.031s
1 tree, 125 leaves, max depth = 14, train loss: 0.43129, val loss: 0.43453, in 0.016s
1 tree, 124 leaves, max depth = 15, train loss: 0.42892, val loss: 0.43218, in 0.031s
1 tree, 89 leaves, max depth = 14, train loss: 0.42670, val loss: 0.42998, in 0.031s
1 tree, 116 leaves, max depth = 16, train loss: 0.42329, val loss: 0.42686, in 0.031s
1 tree, 113 leaves, max depth = 17, train loss: 0.41999, val loss: 0.42382, in 0.016s
1 tree, 87 leaves, max depth = 14, train loss: 0.41787, val loss: 0.42172, in 0.031s
1 tree, 124 leaves, max depth = 14, train loss: 0.41570, val loss: 0.41960, in 0.016s
1 tree, 88 leaves, max depth = 15, train loss: 0.41366, val loss: 0.41757, in 0.016s
1 tree, 124 leaves, max depth = 15, train loss: 0.41157, val loss: 0.41551, in 0.031s
1 tree, 125 leaves, max depth = 16, train loss: 0.40952, val loss: 0.41351, in 0.031s
1 tree, 124 leaves, max depth = 15, train loss: 0.40753, val loss: 0.41154, in 0.031s
1 tree, 125 leaves, max depth = 14, train loss: 0.40556, val loss: 0.40961, in 0.016s
1 tree, 117 leaves, max depth = 16, train loss: 0.40252, val loss: 0.40684, in 0.031s
1 tree, 115 leaves, max depth = 19, train loss: 0.39957, val loss: 0.40415, in 0.031s
1 tree, 135 leaves, max depth = 19, train loss: 0.39672, val loss: 0.40161, in 0.031s
1 tree, 115 leaves, max depth = 19, train loss: 0.39390, val loss: 0.39904, in 0.016s
1 tree, 123 leaves, max depth = 15, train loss: 0.39207, val loss: 0.39724, in 0.031s
1 tree, 125 leaves, max depth = 16, train loss: 0.39027, val loss: 0.39549, in 0.031s
1 tree, 123 leaves, max depth = 16, train loss: 0.38861, val loss: 0.39389, in 0.016s
1 tree, 116 leaves, max depth = 17, train loss: 0.38591, val loss: 0.39146, in 0.031s
1 tree, 116 leaves, max depth = 18, train loss: 0.38327, val loss: 0.38909, in 0.031s
1 tree, 118 leaves, max depth = 16, train loss: 0.38070, val loss: 0.38677, in 0.016s
1 tree, 124 leaves, max depth = 14, train loss: 0.37914, val loss: 0.38526, in 0.031s
1 tree, 116 leaves, max depth = 16, train loss: 0.37665, val loss: 0.38302, in 0.031s
1 tree, 145 leaves, max depth = 14, train loss: 0.37506, val loss: 0.38152, in 0.031s
1 tree, 137 leaves, max depth = 15, train loss: 0.37267, val loss: 0.37944, in 0.031s
1 tree, 127 leaves, max depth = 15, train loss: 0.37110, val loss: 0.37794, in 0.031s
1 tree, 112 leaves, max depth = 14, train loss: 0.36878, val loss: 0.37584, in 0.016s
1 tree, 99 leaves, max depth = 13, train loss: 0.36735, val loss: 0.37443, in 0.031s
1 tree, 117 leaves, max depth = 16, train loss: 0.36508, val loss: 0.37239, in 0.016s
1 tree, 113 leaves, max depth = 14, train loss: 0.36287, val loss: 0.37041, in 0.031s
1 tree, 64 leaves, max depth = 13, train loss: 0.36081, val loss: 0.36855, in 0.031s
1 tree, 145 leaves, max depth = 13, train loss: 0.35940, val loss: 0.36723, in 0.031s
1 tree, 63 leaves, max depth = 14, train loss: 0.35741, val loss: 0.36543, in 0.016s
1 tree, 123 leaves, max depth = 19, train loss: 0.35581, val loss: 0.36379, in 0.016s
1 tree, 122 leaves, max depth = 19, train loss: 0.35425, val loss: 0.36219, in 0.031s
1 tree, 123 leaves, max depth = 19, train loss: 0.35271, val loss: 0.36061, in 0.016s
1 tree, 123 leaves, max depth = 19, train loss: 0.35120, val loss: 0.35906, in 0.031s
1 tree, 122 leaves, max depth = 19, train loss: 0.34973, val loss: 0.35755, in 0.016s
1 tree, 64 leaves, max depth = 14, train loss: 0.34786, val loss: 0.35587, in 0.016s
1 tree, 64 leaves, max depth = 14, train loss: 0.34604, val loss: 0.35424, in 0.031s
1 tree, 116 leaves, max depth = 15, train loss: 0.34413, val loss: 0.35257, in 0.016s
1 tree, 123 leaves, max depth = 18, train loss: 0.34271, val loss: 0.35112, in 0.031s
1 tree, 122 leaves, max depth = 18, train loss: 0.34132, val loss: 0.34969, in 0.016s
Fit 100 trees in 2.939 s, (11986 total leaves)
Time spent computing histograms: 0.927s
Time spent finding best splits:  0.250s
Time spent applying splits:      0.238s
Time spent predicting:           0.000s
Trial 82, Fold 3: Log loss = 0.342112439021262, Average precision = 0.9573984711296334, ROC-AUC = 0.9542221103283219, Elapsed Time = 2.9520584999991115 seconds
Trial 82, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 82, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
Binning 0.040 GB of training data: 0.158 s
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 79 leaves, max depth = 12, train loss: 0.68500, val loss: 0.68470, in 0.031s
1 tree, 113 leaves, max depth = 14, train loss: 0.67691, val loss: 0.67637, in 0.016s
1 tree, 136 leaves, max depth = 12, train loss: 0.66914, val loss: 0.66829, in 0.016s
1 tree, 135 leaves, max depth = 12, train loss: 0.66159, val loss: 0.66041, in 0.031s
1 tree, 125 leaves, max depth = 15, train loss: 0.65443, val loss: 0.65297, in 0.031s
1 tree, 128 leaves, max depth = 14, train loss: 0.64753, val loss: 0.64593, in 0.016s
1 tree, 123 leaves, max depth = 14, train loss: 0.64045, val loss: 0.63862, in 0.031s
1 tree, 99 leaves, max depth = 14, train loss: 0.63372, val loss: 0.63159, in 0.031s
1 tree, 145 leaves, max depth = 14, train loss: 0.62746, val loss: 0.62502, in 0.031s
1 tree, 126 leaves, max depth = 19, train loss: 0.62112, val loss: 0.61850, in 0.016s
1 tree, 126 leaves, max depth = 15, train loss: 0.61471, val loss: 0.61189, in 0.031s
1 tree, 127 leaves, max depth = 14, train loss: 0.60846, val loss: 0.60543, in 0.031s
1 tree, 126 leaves, max depth = 17, train loss: 0.60265, val loss: 0.59934, in 0.016s
1 tree, 122 leaves, max depth = 14, train loss: 0.59705, val loss: 0.59353, in 0.016s
1 tree, 148 leaves, max depth = 14, train loss: 0.59132, val loss: 0.58753, in 0.031s
1 tree, 126 leaves, max depth = 15, train loss: 0.58589, val loss: 0.58187, in 0.031s
1 tree, 125 leaves, max depth = 15, train loss: 0.58034, val loss: 0.57612, in 0.016s
1 tree, 126 leaves, max depth = 14, train loss: 0.57493, val loss: 0.57050, in 0.031s
1 tree, 91 leaves, max depth = 14, train loss: 0.56979, val loss: 0.56511, in 0.016s
1 tree, 145 leaves, max depth = 14, train loss: 0.56497, val loss: 0.56014, in 0.031s
1 tree, 125 leaves, max depth = 15, train loss: 0.55989, val loss: 0.55486, in 0.016s
1 tree, 127 leaves, max depth = 15, train loss: 0.55495, val loss: 0.54973, in 0.031s
1 tree, 146 leaves, max depth = 14, train loss: 0.55021, val loss: 0.54473, in 0.031s
1 tree, 146 leaves, max depth = 13, train loss: 0.54587, val loss: 0.54014, in 0.031s
1 tree, 127 leaves, max depth = 14, train loss: 0.54125, val loss: 0.53540, in 0.031s
1 tree, 125 leaves, max depth = 15, train loss: 0.53704, val loss: 0.53101, in 0.031s
1 tree, 125 leaves, max depth = 17, train loss: 0.53262, val loss: 0.52647, in 0.016s
1 tree, 124 leaves, max depth = 16, train loss: 0.52847, val loss: 0.52215, in 0.031s
1 tree, 147 leaves, max depth = 13, train loss: 0.52431, val loss: 0.51781, in 0.031s
1 tree, 146 leaves, max depth = 13, train loss: 0.52025, val loss: 0.51351, in 0.031s
1 tree, 125 leaves, max depth = 14, train loss: 0.51622, val loss: 0.50930, in 0.031s
1 tree, 144 leaves, max depth = 13, train loss: 0.51243, val loss: 0.50532, in 0.031s
1 tree, 148 leaves, max depth = 14, train loss: 0.50863, val loss: 0.50136, in 0.016s
1 tree, 124 leaves, max depth = 20, train loss: 0.50496, val loss: 0.49757, in 0.031s
1 tree, 126 leaves, max depth = 15, train loss: 0.50125, val loss: 0.49371, in 0.031s
1 tree, 131 leaves, max depth = 14, train loss: 0.49761, val loss: 0.48995, in 0.016s
1 tree, 149 leaves, max depth = 14, train loss: 0.49412, val loss: 0.48630, in 0.047s
1 tree, 127 leaves, max depth = 17, train loss: 0.49064, val loss: 0.48267, in 0.016s
1 tree, 126 leaves, max depth = 15, train loss: 0.48723, val loss: 0.47911, in 0.031s
1 tree, 148 leaves, max depth = 14, train loss: 0.48388, val loss: 0.47557, in 0.031s
1 tree, 128 leaves, max depth = 17, train loss: 0.48061, val loss: 0.47217, in 0.031s
1 tree, 150 leaves, max depth = 14, train loss: 0.47749, val loss: 0.46888, in 0.031s
1 tree, 120 leaves, max depth = 14, train loss: 0.47323, val loss: 0.46456, in 0.031s
1 tree, 130 leaves, max depth = 14, train loss: 0.47005, val loss: 0.46126, in 0.016s
1 tree, 124 leaves, max depth = 14, train loss: 0.46698, val loss: 0.45804, in 0.031s
1 tree, 125 leaves, max depth = 15, train loss: 0.46403, val loss: 0.45496, in 0.016s
1 tree, 149 leaves, max depth = 14, train loss: 0.46120, val loss: 0.45197, in 0.031s
1 tree, 131 leaves, max depth = 15, train loss: 0.45826, val loss: 0.44891, in 0.031s
1 tree, 92 leaves, max depth = 14, train loss: 0.45559, val loss: 0.44607, in 0.016s
1 tree, 141 leaves, max depth = 14, train loss: 0.45179, val loss: 0.44217, in 0.031s
1 tree, 124 leaves, max depth = 15, train loss: 0.44905, val loss: 0.43932, in 0.016s
1 tree, 125 leaves, max depth = 17, train loss: 0.44643, val loss: 0.43658, in 0.031s
1 tree, 125 leaves, max depth = 15, train loss: 0.44379, val loss: 0.43385, in 0.031s
1 tree, 131 leaves, max depth = 15, train loss: 0.44117, val loss: 0.43112, in 0.031s
1 tree, 130 leaves, max depth = 15, train loss: 0.43860, val loss: 0.42844, in 0.016s
1 tree, 130 leaves, max depth = 15, train loss: 0.43609, val loss: 0.42581, in 0.062s
1 tree, 114 leaves, max depth = 15, train loss: 0.43257, val loss: 0.42228, in 0.063s
1 tree, 129 leaves, max depth = 15, train loss: 0.43015, val loss: 0.41975, in 0.031s
1 tree, 131 leaves, max depth = 15, train loss: 0.42778, val loss: 0.41727, in 0.047s
1 tree, 112 leaves, max depth = 14, train loss: 0.42442, val loss: 0.41389, in 0.031s
1 tree, 127 leaves, max depth = 15, train loss: 0.42213, val loss: 0.41152, in 0.031s
1 tree, 124 leaves, max depth = 15, train loss: 0.42006, val loss: 0.40943, in 0.031s
1 tree, 146 leaves, max depth = 12, train loss: 0.41794, val loss: 0.40719, in 0.031s
1 tree, 128 leaves, max depth = 14, train loss: 0.41578, val loss: 0.40494, in 0.047s
1 tree, 117 leaves, max depth = 17, train loss: 0.41261, val loss: 0.40174, in 0.031s
1 tree, 149 leaves, max depth = 14, train loss: 0.41058, val loss: 0.39959, in 0.031s
1 tree, 127 leaves, max depth = 17, train loss: 0.40867, val loss: 0.39762, in 0.031s
1 tree, 138 leaves, max depth = 17, train loss: 0.40570, val loss: 0.39459, in 0.031s
1 tree, 148 leaves, max depth = 13, train loss: 0.40377, val loss: 0.39255, in 0.031s
1 tree, 117 leaves, max depth = 17, train loss: 0.40083, val loss: 0.38959, in 0.031s
1 tree, 147 leaves, max depth = 13, train loss: 0.39897, val loss: 0.38763, in 0.032s
1 tree, 139 leaves, max depth = 17, train loss: 0.39619, val loss: 0.38480, in 0.031s
1 tree, 119 leaves, max depth = 17, train loss: 0.39341, val loss: 0.38200, in 0.031s
1 tree, 119 leaves, max depth = 17, train loss: 0.39069, val loss: 0.37927, in 0.016s
1 tree, 128 leaves, max depth = 17, train loss: 0.38893, val loss: 0.37747, in 0.031s
1 tree, 145 leaves, max depth = 15, train loss: 0.38736, val loss: 0.37581, in 0.031s
1 tree, 150 leaves, max depth = 13, train loss: 0.38567, val loss: 0.37402, in 0.031s
1 tree, 118 leaves, max depth = 17, train loss: 0.38308, val loss: 0.37142, in 0.031s
1 tree, 129 leaves, max depth = 16, train loss: 0.38139, val loss: 0.36964, in 0.016s
1 tree, 119 leaves, max depth = 17, train loss: 0.37888, val loss: 0.36712, in 0.031s
1 tree, 149 leaves, max depth = 14, train loss: 0.37730, val loss: 0.36545, in 0.031s
1 tree, 117 leaves, max depth = 17, train loss: 0.37487, val loss: 0.36301, in 0.031s
1 tree, 112 leaves, max depth = 14, train loss: 0.37256, val loss: 0.36067, in 0.031s
1 tree, 129 leaves, max depth = 16, train loss: 0.37099, val loss: 0.35904, in 0.031s
1 tree, 128 leaves, max depth = 16, train loss: 0.36945, val loss: 0.35743, in 0.031s
1 tree, 113 leaves, max depth = 17, train loss: 0.36723, val loss: 0.35518, in 0.031s
1 tree, 117 leaves, max depth = 17, train loss: 0.36500, val loss: 0.35294, in 0.031s
1 tree, 123 leaves, max depth = 15, train loss: 0.36353, val loss: 0.35142, in 0.016s
1 tree, 67 leaves, max depth = 16, train loss: 0.36149, val loss: 0.34928, in 0.031s
1 tree, 62 leaves, max depth = 13, train loss: 0.35950, val loss: 0.34723, in 0.031s
1 tree, 124 leaves, max depth = 18, train loss: 0.35787, val loss: 0.34559, in 0.047s
1 tree, 67 leaves, max depth = 16, train loss: 0.35594, val loss: 0.34359, in 0.016s
1 tree, 68 leaves, max depth = 13, train loss: 0.35405, val loss: 0.34163, in 0.016s
1 tree, 123 leaves, max depth = 17, train loss: 0.35249, val loss: 0.34008, in 0.031s
1 tree, 124 leaves, max depth = 18, train loss: 0.35097, val loss: 0.33855, in 0.031s
1 tree, 123 leaves, max depth = 17, train loss: 0.34947, val loss: 0.33707, in 0.031s
1 tree, 127 leaves, max depth = 17, train loss: 0.34760, val loss: 0.33534, in 0.031s
1 tree, 61 leaves, max depth = 13, train loss: 0.34584, val loss: 0.33352, in 0.016s
1 tree, 127 leaves, max depth = 17, train loss: 0.34404, val loss: 0.33186, in 0.031s
1 tree, 66 leaves, max depth = 16, train loss: 0.34235, val loss: 0.33010, in 0.016s
Fit 100 trees in 3.252 s, (12459 total leaves)
Time spent computing histograms: 1.017s
Time spent finding best splits:  0.295s
Time spent applying splits:      0.285s
Time spent predicting:           0.000s
Trial 82, Fold 4: Log loss = 0.34486513459364354, Average precision = 0.9576524629502526, ROC-AUC = 0.952854257721315, Elapsed Time = 3.2568659000007756 seconds
Trial 82, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 82, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
Binning 0.040 GB of training data: 0.158 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 70 leaves, max depth = 12, train loss: 0.68497, val loss: 0.68470, in 0.031s
1 tree, 127 leaves, max depth = 14, train loss: 0.67712, val loss: 0.67656, in 0.016s
1 tree, 122 leaves, max depth = 15, train loss: 0.66919, val loss: 0.66845, in 0.031s
1 tree, 125 leaves, max depth = 14, train loss: 0.66149, val loss: 0.66050, in 0.031s
1 tree, 123 leaves, max depth = 15, train loss: 0.65400, val loss: 0.65283, in 0.016s
1 tree, 125 leaves, max depth = 15, train loss: 0.64693, val loss: 0.64545, in 0.031s
1 tree, 120 leaves, max depth = 15, train loss: 0.63982, val loss: 0.63818, in 0.031s
1 tree, 98 leaves, max depth = 14, train loss: 0.63294, val loss: 0.63099, in 0.016s
1 tree, 143 leaves, max depth = 16, train loss: 0.62649, val loss: 0.62426, in 0.031s
1 tree, 123 leaves, max depth = 14, train loss: 0.62020, val loss: 0.61778, in 0.031s
1 tree, 129 leaves, max depth = 14, train loss: 0.61365, val loss: 0.61100, in 0.031s
1 tree, 129 leaves, max depth = 14, train loss: 0.60727, val loss: 0.60441, in 0.016s
1 tree, 127 leaves, max depth = 17, train loss: 0.60143, val loss: 0.59835, in 0.031s
1 tree, 122 leaves, max depth = 14, train loss: 0.59575, val loss: 0.59248, in 0.031s
1 tree, 131 leaves, max depth = 14, train loss: 0.58981, val loss: 0.58635, in 0.016s
1 tree, 132 leaves, max depth = 14, train loss: 0.58401, val loss: 0.58038, in 0.031s
1 tree, 126 leaves, max depth = 15, train loss: 0.57839, val loss: 0.57458, in 0.016s
1 tree, 125 leaves, max depth = 15, train loss: 0.57289, val loss: 0.56894, in 0.031s
1 tree, 148 leaves, max depth = 13, train loss: 0.56758, val loss: 0.56345, in 0.031s
1 tree, 88 leaves, max depth = 14, train loss: 0.56282, val loss: 0.55855, in 0.031s
1 tree, 149 leaves, max depth = 13, train loss: 0.55775, val loss: 0.55331, in 0.016s
1 tree, 131 leaves, max depth = 14, train loss: 0.55273, val loss: 0.54814, in 0.031s
1 tree, 129 leaves, max depth = 14, train loss: 0.54782, val loss: 0.54307, in 0.031s
1 tree, 130 leaves, max depth = 14, train loss: 0.54302, val loss: 0.53814, in 0.016s
1 tree, 129 leaves, max depth = 14, train loss: 0.53834, val loss: 0.53333, in 0.031s
1 tree, 131 leaves, max depth = 14, train loss: 0.53375, val loss: 0.52859, in 0.031s
1 tree, 130 leaves, max depth = 14, train loss: 0.52928, val loss: 0.52400, in 0.016s
1 tree, 149 leaves, max depth = 13, train loss: 0.52498, val loss: 0.51955, in 0.031s
1 tree, 126 leaves, max depth = 14, train loss: 0.52072, val loss: 0.51518, in 0.016s
1 tree, 127 leaves, max depth = 14, train loss: 0.51655, val loss: 0.51089, in 0.031s
1 tree, 126 leaves, max depth = 14, train loss: 0.51246, val loss: 0.50669, in 0.031s
1 tree, 127 leaves, max depth = 14, train loss: 0.50847, val loss: 0.50258, in 0.031s
1 tree, 147 leaves, max depth = 13, train loss: 0.50462, val loss: 0.49862, in 0.031s
1 tree, 128 leaves, max depth = 14, train loss: 0.50080, val loss: 0.49468, in 0.031s
1 tree, 126 leaves, max depth = 14, train loss: 0.49706, val loss: 0.49085, in 0.016s
1 tree, 125 leaves, max depth = 14, train loss: 0.49361, val loss: 0.48730, in 0.031s
1 tree, 149 leaves, max depth = 13, train loss: 0.49007, val loss: 0.48363, in 0.031s
1 tree, 128 leaves, max depth = 14, train loss: 0.48654, val loss: 0.47999, in 0.016s
1 tree, 128 leaves, max depth = 14, train loss: 0.48309, val loss: 0.47642, in 0.031s
1 tree, 148 leaves, max depth = 14, train loss: 0.47977, val loss: 0.47298, in 0.031s
1 tree, 128 leaves, max depth = 14, train loss: 0.47646, val loss: 0.46958, in 0.016s
1 tree, 149 leaves, max depth = 13, train loss: 0.47328, val loss: 0.46630, in 0.031s
1 tree, 125 leaves, max depth = 15, train loss: 0.47029, val loss: 0.46322, in 0.031s
1 tree, 128 leaves, max depth = 14, train loss: 0.46718, val loss: 0.46000, in 0.031s
1 tree, 148 leaves, max depth = 14, train loss: 0.46419, val loss: 0.45690, in 0.032s
1 tree, 125 leaves, max depth = 17, train loss: 0.46120, val loss: 0.45384, in 0.016s
1 tree, 124 leaves, max depth = 17, train loss: 0.45828, val loss: 0.45085, in 0.031s
1 tree, 108 leaves, max depth = 19, train loss: 0.45428, val loss: 0.44688, in 0.031s
1 tree, 130 leaves, max depth = 14, train loss: 0.45145, val loss: 0.44398, in 0.016s
1 tree, 95 leaves, max depth = 13, train loss: 0.44882, val loss: 0.44122, in 0.031s
1 tree, 124 leaves, max depth = 14, train loss: 0.44612, val loss: 0.43845, in 0.031s
1 tree, 125 leaves, max depth = 16, train loss: 0.44361, val loss: 0.43588, in 0.031s
1 tree, 116 leaves, max depth = 14, train loss: 0.43986, val loss: 0.43217, in 0.016s
1 tree, 145 leaves, max depth = 15, train loss: 0.43736, val loss: 0.42960, in 0.047s
1 tree, 132 leaves, max depth = 14, train loss: 0.43484, val loss: 0.42702, in 0.016s
1 tree, 123 leaves, max depth = 15, train loss: 0.43238, val loss: 0.42450, in 0.031s
1 tree, 144 leaves, max depth = 13, train loss: 0.43002, val loss: 0.42207, in 0.031s
1 tree, 137 leaves, max depth = 13, train loss: 0.42657, val loss: 0.41865, in 0.016s
1 tree, 125 leaves, max depth = 14, train loss: 0.42423, val loss: 0.41626, in 0.031s
1 tree, 145 leaves, max depth = 13, train loss: 0.42200, val loss: 0.41396, in 0.031s
1 tree, 91 leaves, max depth = 13, train loss: 0.41876, val loss: 0.41069, in 0.031s
1 tree, 127 leaves, max depth = 14, train loss: 0.41652, val loss: 0.40840, in 0.031s
1 tree, 126 leaves, max depth = 16, train loss: 0.41435, val loss: 0.40618, in 0.031s
1 tree, 115 leaves, max depth = 14, train loss: 0.41113, val loss: 0.40301, in 0.031s
1 tree, 125 leaves, max depth = 16, train loss: 0.40904, val loss: 0.40087, in 0.016s
1 tree, 132 leaves, max depth = 19, train loss: 0.40600, val loss: 0.39787, in 0.038s
1 tree, 145 leaves, max depth = 12, train loss: 0.40402, val loss: 0.39585, in 0.025s
1 tree, 115 leaves, max depth = 16, train loss: 0.40101, val loss: 0.39289, in 0.031s
1 tree, 125 leaves, max depth = 14, train loss: 0.39905, val loss: 0.39091, in 0.016s
1 tree, 115 leaves, max depth = 16, train loss: 0.39613, val loss: 0.38804, in 0.031s
1 tree, 124 leaves, max depth = 15, train loss: 0.39424, val loss: 0.38615, in 0.031s
1 tree, 124 leaves, max depth = 14, train loss: 0.39238, val loss: 0.38426, in 0.016s
1 tree, 116 leaves, max depth = 14, train loss: 0.38958, val loss: 0.38153, in 0.031s
1 tree, 136 leaves, max depth = 14, train loss: 0.38690, val loss: 0.37890, in 0.031s
1 tree, 144 leaves, max depth = 12, train loss: 0.38517, val loss: 0.37715, in 0.031s
1 tree, 124 leaves, max depth = 14, train loss: 0.38342, val loss: 0.37536, in 0.016s
1 tree, 113 leaves, max depth = 20, train loss: 0.38081, val loss: 0.37281, in 0.047s
1 tree, 115 leaves, max depth = 14, train loss: 0.37825, val loss: 0.37030, in 0.016s
1 tree, 126 leaves, max depth = 14, train loss: 0.37657, val loss: 0.36861, in 0.031s
1 tree, 117 leaves, max depth = 16, train loss: 0.37407, val loss: 0.36619, in 0.031s
1 tree, 124 leaves, max depth = 14, train loss: 0.37258, val loss: 0.36472, in 0.031s
1 tree, 125 leaves, max depth = 14, train loss: 0.37100, val loss: 0.36312, in 0.016s
1 tree, 116 leaves, max depth = 14, train loss: 0.36861, val loss: 0.36079, in 0.031s
1 tree, 138 leaves, max depth = 14, train loss: 0.36632, val loss: 0.35857, in 0.031s
1 tree, 124 leaves, max depth = 14, train loss: 0.36480, val loss: 0.35705, in 0.031s
1 tree, 126 leaves, max depth = 14, train loss: 0.36331, val loss: 0.35554, in 0.031s
1 tree, 115 leaves, max depth = 14, train loss: 0.36107, val loss: 0.35336, in 0.031s
1 tree, 123 leaves, max depth = 16, train loss: 0.35962, val loss: 0.35193, in 0.031s
1 tree, 115 leaves, max depth = 13, train loss: 0.35744, val loss: 0.34982, in 0.031s
1 tree, 137 leaves, max depth = 13, train loss: 0.35536, val loss: 0.34780, in 0.031s
1 tree, 66 leaves, max depth = 12, train loss: 0.35340, val loss: 0.34582, in 0.016s
1 tree, 124 leaves, max depth = 16, train loss: 0.35203, val loss: 0.34447, in 0.031s
1 tree, 115 leaves, max depth = 14, train loss: 0.35001, val loss: 0.34252, in 0.016s
1 tree, 124 leaves, max depth = 16, train loss: 0.34868, val loss: 0.34122, in 0.031s
1 tree, 66 leaves, max depth = 12, train loss: 0.34683, val loss: 0.33936, in 0.016s
1 tree, 65 leaves, max depth = 12, train loss: 0.34504, val loss: 0.33754, in 0.031s
1 tree, 126 leaves, max depth = 16, train loss: 0.34353, val loss: 0.33609, in 0.031s
1 tree, 129 leaves, max depth = 16, train loss: 0.34204, val loss: 0.33464, in 0.016s
1 tree, 66 leaves, max depth = 12, train loss: 0.34031, val loss: 0.33290, in 0.031s
1 tree, 66 leaves, max depth = 12, train loss: 0.33862, val loss: 0.33120, in 0.016s
Fit 100 trees in 3.049 s, (12317 total leaves)
Time spent computing histograms: 0.946s
Time spent finding best splits:  0.266s
Time spent applying splits:      0.255s
Time spent predicting:           0.031s
Trial 82, Fold 5: Log loss = 0.34798127203454254, Average precision = 0.9529458646450839, ROC-AUC = 0.9496271854040095, Elapsed Time = 3.070813899999848 seconds
Optimization Progress:  83%|########2 | 83/100 [17:14<04:21, 15.41s/it]
Trial 83, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 83, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
0.143 s 0.040 GB of training data: 
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 67 leaves, max depth = 15, train loss: 0.66953, val loss: 0.66899, in 0.016s
1 tree, 93 leaves, max depth = 11, train loss: 0.64669, val loss: 0.64604, in 0.016s
1 tree, 88 leaves, max depth = 11, train loss: 0.62569, val loss: 0.62495, in 0.016s
1 tree, 97 leaves, max depth = 11, train loss: 0.60662, val loss: 0.60563, in 0.016s
1 tree, 92 leaves, max depth = 13, train loss: 0.58877, val loss: 0.58770, in 0.016s
1 tree, 91 leaves, max depth = 11, train loss: 0.57351, val loss: 0.57232, in 0.016s
1 tree, 93 leaves, max depth = 13, train loss: 0.55812, val loss: 0.55687, in 0.016s
1 tree, 99 leaves, max depth = 11, train loss: 0.54378, val loss: 0.54230, in 0.016s
1 tree, 102 leaves, max depth = 11, train loss: 0.53044, val loss: 0.52875, in 0.016s
1 tree, 74 leaves, max depth = 15, train loss: 0.51852, val loss: 0.51656, in 0.016s
1 tree, 91 leaves, max depth = 13, train loss: 0.50665, val loss: 0.50462, in 0.016s
1 tree, 80 leaves, max depth = 11, train loss: 0.49627, val loss: 0.49408, in 0.016s
1 tree, 94 leaves, max depth = 15, train loss: 0.48614, val loss: 0.48388, in 0.016s
1 tree, 58 leaves, max depth = 11, train loss: 0.47327, val loss: 0.47132, in 0.016s
1 tree, 94 leaves, max depth = 12, train loss: 0.46460, val loss: 0.46264, in 0.031s
1 tree, 95 leaves, max depth = 14, train loss: 0.45643, val loss: 0.45438, in 0.016s
1 tree, 39 leaves, max depth = 10, train loss: 0.44546, val loss: 0.44355, in 0.016s
1 tree, 101 leaves, max depth = 12, train loss: 0.43764, val loss: 0.43559, in 0.016s
1 tree, 74 leaves, max depth = 15, train loss: 0.43075, val loss: 0.42853, in 0.016s
1 tree, 73 leaves, max depth = 11, train loss: 0.42398, val loss: 0.42160, in 0.016s
1 tree, 74 leaves, max depth = 14, train loss: 0.41792, val loss: 0.41539, in 0.016s
1 tree, 75 leaves, max depth = 12, train loss: 0.41193, val loss: 0.40928, in 0.016s
1 tree, 91 leaves, max depth = 12, train loss: 0.40602, val loss: 0.40342, in 0.016s
1 tree, 89 leaves, max depth = 14, train loss: 0.40033, val loss: 0.39780, in 0.016s
1 tree, 93 leaves, max depth = 11, train loss: 0.39516, val loss: 0.39269, in 0.016s
1 tree, 92 leaves, max depth = 14, train loss: 0.39044, val loss: 0.38795, in 0.016s
1 tree, 96 leaves, max depth = 15, train loss: 0.38594, val loss: 0.38356, in 0.016s
1 tree, 94 leaves, max depth = 15, train loss: 0.38129, val loss: 0.37895, in 0.016s
1 tree, 29 leaves, max depth = 10, train loss: 0.37407, val loss: 0.37185, in 0.000s
1 tree, 29 leaves, max depth = 10, train loss: 0.36739, val loss: 0.36528, in 0.016s
1 tree, 91 leaves, max depth = 15, train loss: 0.36271, val loss: 0.36067, in 0.016s
1 tree, 29 leaves, max depth = 12, train loss: 0.35666, val loss: 0.35475, in 0.016s
1 tree, 43 leaves, max depth = 10, train loss: 0.35103, val loss: 0.34971, in 0.016s
1 tree, 66 leaves, max depth = 10, train loss: 0.34529, val loss: 0.34423, in 0.016s
1 tree, 83 leaves, max depth = 15, train loss: 0.34132, val loss: 0.34040, in 0.016s
1 tree, 42 leaves, max depth = 10, train loss: 0.33655, val loss: 0.33617, in 0.016s
1 tree, 89 leaves, max depth = 15, train loss: 0.33277, val loss: 0.33246, in 0.016s
1 tree, 28 leaves, max depth = 10, train loss: 0.32823, val loss: 0.32800, in 0.016s
1 tree, 30 leaves, max depth = 10, train loss: 0.32383, val loss: 0.32363, in 0.016s
1 tree, 43 leaves, max depth = 10, train loss: 0.31979, val loss: 0.32009, in 0.016s
1 tree, 43 leaves, max depth = 10, train loss: 0.31603, val loss: 0.31679, in 0.016s
1 tree, 29 leaves, max depth = 9, train loss: 0.31238, val loss: 0.31319, in 0.000s
1 tree, 93 leaves, max depth = 14, train loss: 0.30968, val loss: 0.31061, in 0.031s
1 tree, 88 leaves, max depth = 15, train loss: 0.30662, val loss: 0.30763, in 0.016s
1 tree, 91 leaves, max depth = 12, train loss: 0.30377, val loss: 0.30486, in 0.016s
1 tree, 41 leaves, max depth = 10, train loss: 0.30067, val loss: 0.30218, in 0.000s
1 tree, 28 leaves, max depth = 11, train loss: 0.29766, val loss: 0.29923, in 0.016s
1 tree, 28 leaves, max depth = 9, train loss: 0.29481, val loss: 0.29642, in 0.016s
1 tree, 41 leaves, max depth = 10, train loss: 0.29210, val loss: 0.29412, in 0.016s
1 tree, 122 leaves, max depth = 14, train loss: 0.28942, val loss: 0.29150, in 0.016s
1 tree, 27 leaves, max depth = 9, train loss: 0.28693, val loss: 0.28900, in 0.016s
1 tree, 90 leaves, max depth = 19, train loss: 0.28461, val loss: 0.28683, in 0.016s
1 tree, 91 leaves, max depth = 14, train loss: 0.28241, val loss: 0.28470, in 0.016s
1 tree, 49 leaves, max depth = 10, train loss: 0.28078, val loss: 0.28280, in 0.016s
1 tree, 64 leaves, max depth = 14, train loss: 0.27828, val loss: 0.28043, in 0.016s
1 tree, 26 leaves, max depth = 11, train loss: 0.27626, val loss: 0.27845, in 0.016s
1 tree, 42 leaves, max depth = 11, train loss: 0.27416, val loss: 0.27672, in 0.016s
1 tree, 43 leaves, max depth = 12, train loss: 0.27218, val loss: 0.27501, in 0.016s
1 tree, 42 leaves, max depth = 12, train loss: 0.27033, val loss: 0.27344, in 0.016s
1 tree, 90 leaves, max depth = 14, train loss: 0.26847, val loss: 0.27170, in 0.016s
1 tree, 119 leaves, max depth = 14, train loss: 0.26651, val loss: 0.26980, in 0.016s
1 tree, 41 leaves, max depth = 11, train loss: 0.26486, val loss: 0.26840, in 0.016s
1 tree, 91 leaves, max depth = 14, train loss: 0.26316, val loss: 0.26678, in 0.016s
1 tree, 91 leaves, max depth = 14, train loss: 0.26154, val loss: 0.26527, in 0.016s
1 tree, 118 leaves, max depth = 14, train loss: 0.25987, val loss: 0.26364, in 0.031s
1 tree, 24 leaves, max depth = 12, train loss: 0.25842, val loss: 0.26221, in 0.016s
1 tree, 119 leaves, max depth = 14, train loss: 0.25685, val loss: 0.26071, in 0.016s
1 tree, 33 leaves, max depth = 12, train loss: 0.25545, val loss: 0.25921, in 0.016s
1 tree, 43 leaves, max depth = 13, train loss: 0.25413, val loss: 0.25800, in 0.016s
1 tree, 28 leaves, max depth = 8, train loss: 0.25285, val loss: 0.25675, in 0.016s
1 tree, 40 leaves, max depth = 11, train loss: 0.25164, val loss: 0.25573, in 0.016s
1 tree, 42 leaves, max depth = 12, train loss: 0.25050, val loss: 0.25478, in 0.016s
1 tree, 110 leaves, max depth = 19, train loss: 0.24893, val loss: 0.25306, in 0.016s
1 tree, 110 leaves, max depth = 16, train loss: 0.24739, val loss: 0.25139, in 0.016s
1 tree, 27 leaves, max depth = 7, train loss: 0.24625, val loss: 0.25038, in 0.016s
1 tree, 23 leaves, max depth = 8, train loss: 0.24524, val loss: 0.24937, in 0.016s
1 tree, 119 leaves, max depth = 15, train loss: 0.24399, val loss: 0.24818, in 0.016s
1 tree, 108 leaves, max depth = 14, train loss: 0.24265, val loss: 0.24674, in 0.016s
1 tree, 44 leaves, max depth = 12, train loss: 0.24167, val loss: 0.24581, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.24062, val loss: 0.24497, in 0.016s
1 tree, 108 leaves, max depth = 14, train loss: 0.23969, val loss: 0.24422, in 0.016s
1 tree, 73 leaves, max depth = 13, train loss: 0.23801, val loss: 0.24243, in 0.016s
1 tree, 69 leaves, max depth = 13, train loss: 0.23731, val loss: 0.24169, in 0.016s
1 tree, 73 leaves, max depth = 16, train loss: 0.23577, val loss: 0.24005, in 0.016s
1 tree, 28 leaves, max depth = 10, train loss: 0.23489, val loss: 0.23935, in 0.016s
Fit 85 trees in 1.658 s, (5881 total leaves)
Time spent computing histograms: 0.504s
Time spent finding best splits:  0.102s
Time spent applying splits:      0.095s
Time spent predicting:           0.000s
Trial 83, Fold 1: Log loss = 0.2419060173779556, Average precision = 0.9677093062637296, ROC-AUC = 0.962729584582001, Elapsed Time = 1.6626142999994045 seconds
Trial 83, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 83, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
Binning 0.040 GB of training data: 0.158 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 67 leaves, max depth = 13, train loss: 0.66954, val loss: 0.66906, in 0.016s
1 tree, 88 leaves, max depth = 12, train loss: 0.64664, val loss: 0.64581, in 0.016s
1 tree, 88 leaves, max depth = 12, train loss: 0.62558, val loss: 0.62443, in 0.016s
1 tree, 91 leaves, max depth = 15, train loss: 0.60627, val loss: 0.60484, in 0.016s
1 tree, 97 leaves, max depth = 15, train loss: 0.58862, val loss: 0.58700, in 0.016s
1 tree, 83 leaves, max depth = 12, train loss: 0.57306, val loss: 0.57102, in 0.031s
1 tree, 92 leaves, max depth = 14, train loss: 0.55744, val loss: 0.55518, in 0.016s
1 tree, 100 leaves, max depth = 13, train loss: 0.54311, val loss: 0.54068, in 0.016s
1 tree, 74 leaves, max depth = 13, train loss: 0.53001, val loss: 0.52725, in 0.016s
1 tree, 93 leaves, max depth = 14, train loss: 0.51748, val loss: 0.51455, in 0.016s
1 tree, 96 leaves, max depth = 15, train loss: 0.50580, val loss: 0.50276, in 0.031s
1 tree, 93 leaves, max depth = 13, train loss: 0.49474, val loss: 0.49152, in 0.016s
1 tree, 59 leaves, max depth = 11, train loss: 0.48135, val loss: 0.47814, in 0.016s
1 tree, 77 leaves, max depth = 15, train loss: 0.47214, val loss: 0.46897, in 0.016s
1 tree, 96 leaves, max depth = 15, train loss: 0.46324, val loss: 0.46015, in 0.016s
1 tree, 74 leaves, max depth = 13, train loss: 0.45481, val loss: 0.45149, in 0.031s
1 tree, 73 leaves, max depth = 13, train loss: 0.44737, val loss: 0.44396, in 0.016s
1 tree, 40 leaves, max depth = 11, train loss: 0.43687, val loss: 0.43347, in 0.016s
1 tree, 71 leaves, max depth = 11, train loss: 0.43045, val loss: 0.42681, in 0.016s
1 tree, 99 leaves, max depth = 13, train loss: 0.42356, val loss: 0.41989, in 0.031s
1 tree, 91 leaves, max depth = 12, train loss: 0.41702, val loss: 0.41336, in 0.016s
1 tree, 96 leaves, max depth = 13, train loss: 0.41078, val loss: 0.40706, in 0.016s
1 tree, 97 leaves, max depth = 13, train loss: 0.40489, val loss: 0.40115, in 0.031s
1 tree, 81 leaves, max depth = 12, train loss: 0.39966, val loss: 0.39595, in 0.016s
1 tree, 85 leaves, max depth = 13, train loss: 0.39465, val loss: 0.39090, in 0.016s
1 tree, 58 leaves, max depth = 11, train loss: 0.38652, val loss: 0.38298, in 0.016s
1 tree, 92 leaves, max depth = 14, train loss: 0.38184, val loss: 0.37834, in 0.016s
1 tree, 29 leaves, max depth = 11, train loss: 0.37492, val loss: 0.37156, in 0.016s
1 tree, 58 leaves, max depth = 11, train loss: 0.36803, val loss: 0.36484, in 0.016s
1 tree, 29 leaves, max depth = 10, train loss: 0.36207, val loss: 0.35898, in 0.016s
1 tree, 29 leaves, max depth = 10, train loss: 0.35648, val loss: 0.35350, in 0.000s
1 tree, 92 leaves, max depth = 14, train loss: 0.35192, val loss: 0.34907, in 0.031s
1 tree, 92 leaves, max depth = 17, train loss: 0.34837, val loss: 0.34567, in 0.016s
1 tree, 28 leaves, max depth = 10, train loss: 0.34343, val loss: 0.34083, in 0.016s
1 tree, 28 leaves, max depth = 10, train loss: 0.33879, val loss: 0.33629, in 0.000s
1 tree, 94 leaves, max depth = 14, train loss: 0.33554, val loss: 0.33308, in 0.031s
1 tree, 92 leaves, max depth = 14, train loss: 0.33173, val loss: 0.32939, in 0.018s
1 tree, 42 leaves, max depth = 11, train loss: 0.32736, val loss: 0.32536, in 0.014s
1 tree, 93 leaves, max depth = 15, train loss: 0.32446, val loss: 0.32265, in 0.016s
1 tree, 42 leaves, max depth = 11, train loss: 0.32047, val loss: 0.31899, in 0.016s
1 tree, 91 leaves, max depth = 14, train loss: 0.31713, val loss: 0.31577, in 0.031s
1 tree, 42 leaves, max depth = 11, train loss: 0.31350, val loss: 0.31242, in 0.000s
1 tree, 42 leaves, max depth = 11, train loss: 0.31011, val loss: 0.30930, in 0.031s
1 tree, 28 leaves, max depth = 10, train loss: 0.30681, val loss: 0.30609, in 0.000s
1 tree, 93 leaves, max depth = 14, train loss: 0.30387, val loss: 0.30324, in 0.031s
1 tree, 28 leaves, max depth = 9, train loss: 0.30086, val loss: 0.30031, in 0.000s
1 tree, 42 leaves, max depth = 11, train loss: 0.29801, val loss: 0.29771, in 0.016s
1 tree, 43 leaves, max depth = 11, train loss: 0.29538, val loss: 0.29528, in 0.016s
1 tree, 118 leaves, max depth = 14, train loss: 0.29265, val loss: 0.29278, in 0.031s
1 tree, 28 leaves, max depth = 9, train loss: 0.29014, val loss: 0.29030, in 0.016s
1 tree, 95 leaves, max depth = 12, train loss: 0.28825, val loss: 0.28861, in 0.016s
1 tree, 85 leaves, max depth = 19, train loss: 0.28653, val loss: 0.28706, in 0.016s
1 tree, 28 leaves, max depth = 10, train loss: 0.28429, val loss: 0.28487, in 0.016s
1 tree, 89 leaves, max depth = 13, train loss: 0.28204, val loss: 0.28276, in 0.016s
1 tree, 42 leaves, max depth = 11, train loss: 0.27991, val loss: 0.28083, in 0.016s
1 tree, 48 leaves, max depth = 14, train loss: 0.27770, val loss: 0.27879, in 0.016s
1 tree, 111 leaves, max depth = 14, train loss: 0.27504, val loss: 0.27611, in 0.016s
1 tree, 121 leaves, max depth = 13, train loss: 0.27296, val loss: 0.27428, in 0.031s
1 tree, 41 leaves, max depth = 10, train loss: 0.27114, val loss: 0.27264, in 0.016s
1 tree, 94 leaves, max depth = 13, train loss: 0.26928, val loss: 0.27085, in 0.016s
1 tree, 42 leaves, max depth = 10, train loss: 0.26760, val loss: 0.26936, in 0.016s
1 tree, 94 leaves, max depth = 14, train loss: 0.26547, val loss: 0.26709, in 0.016s
1 tree, 27 leaves, max depth = 10, train loss: 0.26391, val loss: 0.26557, in 0.016s
1 tree, 32 leaves, max depth = 11, train loss: 0.26236, val loss: 0.26402, in 0.016s
1 tree, 76 leaves, max depth = 11, train loss: 0.26116, val loss: 0.26296, in 0.016s
1 tree, 43 leaves, max depth = 11, train loss: 0.25979, val loss: 0.26175, in 0.016s
1 tree, 41 leaves, max depth = 10, train loss: 0.25843, val loss: 0.26054, in 0.016s
1 tree, 93 leaves, max depth = 16, train loss: 0.25658, val loss: 0.25860, in 0.016s
1 tree, 26 leaves, max depth = 11, train loss: 0.25530, val loss: 0.25738, in 0.016s
1 tree, 32 leaves, max depth = 11, train loss: 0.25407, val loss: 0.25614, in 0.016s
1 tree, 111 leaves, max depth = 17, train loss: 0.25247, val loss: 0.25452, in 0.016s
1 tree, 91 leaves, max depth = 12, train loss: 0.25108, val loss: 0.25318, in 0.016s
1 tree, 45 leaves, max depth = 11, train loss: 0.24992, val loss: 0.25221, in 0.016s
1 tree, 43 leaves, max depth = 10, train loss: 0.24885, val loss: 0.25127, in 0.000s
1 tree, 25 leaves, max depth = 10, train loss: 0.24782, val loss: 0.25030, in 0.016s
1 tree, 108 leaves, max depth = 15, train loss: 0.24634, val loss: 0.24876, in 0.031s
1 tree, 123 leaves, max depth = 12, train loss: 0.24503, val loss: 0.24768, in 0.016s
1 tree, 72 leaves, max depth = 12, train loss: 0.24422, val loss: 0.24707, in 0.016s
1 tree, 63 leaves, max depth = 10, train loss: 0.24318, val loss: 0.24608, in 0.016s
1 tree, 111 leaves, max depth = 17, train loss: 0.24194, val loss: 0.24480, in 0.016s
1 tree, 26 leaves, max depth = 7, train loss: 0.24093, val loss: 0.24388, in 0.016s
1 tree, 32 leaves, max depth = 9, train loss: 0.23988, val loss: 0.24310, in 0.016s
1 tree, 43 leaves, max depth = 11, train loss: 0.23902, val loss: 0.24240, in 0.016s
1 tree, 32 leaves, max depth = 8, train loss: 0.23809, val loss: 0.24171, in 0.016s
1 tree, 42 leaves, max depth = 9, train loss: 0.23726, val loss: 0.24101, in 0.016s
Fit 85 trees in 1.799 s, (5781 total leaves)
Time spent computing histograms: 0.540s
Time spent finding best splits:  0.114s
Time spent applying splits:      0.105s
Time spent predicting:           0.016s
Trial 83, Fold 2: Log loss = 0.24140072050409492, Average precision = 0.9669439635515518, ROC-AUC = 0.9637237055231034, Elapsed Time = 1.8088112000004912 seconds
Trial 83, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 83, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
Binning 0.040 GB of training data: 0.143 s
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 70 leaves, max depth = 15, train loss: 0.66953, val loss: 0.66918, in 0.016s
1 tree, 101 leaves, max depth = 13, train loss: 0.64822, val loss: 0.64789, in 0.031s
1 tree, 93 leaves, max depth = 14, train loss: 0.62710, val loss: 0.62678, in 0.016s
1 tree, 92 leaves, max depth = 12, train loss: 0.60768, val loss: 0.60728, in 0.016s
1 tree, 99 leaves, max depth = 12, train loss: 0.58997, val loss: 0.58949, in 0.016s
1 tree, 101 leaves, max depth = 11, train loss: 0.57456, val loss: 0.57402, in 0.016s
1 tree, 73 leaves, max depth = 12, train loss: 0.55938, val loss: 0.55869, in 0.016s
1 tree, 102 leaves, max depth = 12, train loss: 0.54516, val loss: 0.54443, in 0.031s
1 tree, 100 leaves, max depth = 13, train loss: 0.53182, val loss: 0.53116, in 0.016s
1 tree, 75 leaves, max depth = 13, train loss: 0.52012, val loss: 0.51944, in 0.016s
1 tree, 95 leaves, max depth = 12, train loss: 0.50826, val loss: 0.50759, in 0.031s
1 tree, 65 leaves, max depth = 10, train loss: 0.49435, val loss: 0.49448, in 0.016s
1 tree, 73 leaves, max depth = 13, train loss: 0.48409, val loss: 0.48427, in 0.016s
1 tree, 89 leaves, max depth = 15, train loss: 0.47427, val loss: 0.47447, in 0.016s
1 tree, 95 leaves, max depth = 13, train loss: 0.46493, val loss: 0.46527, in 0.016s
1 tree, 92 leaves, max depth = 11, train loss: 0.45636, val loss: 0.45691, in 0.031s
1 tree, 97 leaves, max depth = 13, train loss: 0.44809, val loss: 0.44875, in 0.016s
1 tree, 91 leaves, max depth = 12, train loss: 0.44114, val loss: 0.44188, in 0.016s
1 tree, 59 leaves, max depth = 10, train loss: 0.43063, val loss: 0.43208, in 0.016s
1 tree, 76 leaves, max depth = 12, train loss: 0.42401, val loss: 0.42541, in 0.031s
1 tree, 65 leaves, max depth = 9, train loss: 0.41479, val loss: 0.41690, in 0.016s
1 tree, 97 leaves, max depth = 11, train loss: 0.40856, val loss: 0.41070, in 0.016s
1 tree, 59 leaves, max depth = 10, train loss: 0.39995, val loss: 0.40284, in 0.016s
1 tree, 101 leaves, max depth = 12, train loss: 0.39478, val loss: 0.39781, in 0.016s
1 tree, 99 leaves, max depth = 11, train loss: 0.38939, val loss: 0.39245, in 0.031s
1 tree, 96 leaves, max depth = 12, train loss: 0.38459, val loss: 0.38776, in 0.016s
1 tree, 94 leaves, max depth = 11, train loss: 0.38009, val loss: 0.38333, in 0.016s
1 tree, 97 leaves, max depth = 11, train loss: 0.37550, val loss: 0.37881, in 0.031s
1 tree, 93 leaves, max depth = 17, train loss: 0.37088, val loss: 0.37439, in 0.016s
1 tree, 59 leaves, max depth = 11, train loss: 0.36411, val loss: 0.36829, in 0.016s
1 tree, 94 leaves, max depth = 16, train loss: 0.35950, val loss: 0.36357, in 0.016s
1 tree, 38 leaves, max depth = 10, train loss: 0.35344, val loss: 0.35812, in 0.016s
1 tree, 43 leaves, max depth = 12, train loss: 0.34810, val loss: 0.35364, in 0.016s
1 tree, 92 leaves, max depth = 14, train loss: 0.34439, val loss: 0.35010, in 0.031s
1 tree, 27 leaves, max depth = 10, train loss: 0.33931, val loss: 0.34552, in 0.016s
1 tree, 27 leaves, max depth = 9, train loss: 0.33459, val loss: 0.34125, in 0.000s
1 tree, 43 leaves, max depth = 12, train loss: 0.33012, val loss: 0.33752, in 0.016s
1 tree, 91 leaves, max depth = 13, train loss: 0.32723, val loss: 0.33476, in 0.031s
1 tree, 43 leaves, max depth = 11, train loss: 0.32312, val loss: 0.33139, in 0.000s
1 tree, 27 leaves, max depth = 10, train loss: 0.31919, val loss: 0.32787, in 0.000s
1 tree, 80 leaves, max depth = 11, train loss: 0.31641, val loss: 0.32521, in 0.031s
1 tree, 59 leaves, max depth = 11, train loss: 0.31248, val loss: 0.32181, in 0.016s
1 tree, 91 leaves, max depth = 14, train loss: 0.30931, val loss: 0.31854, in 0.016s
1 tree, 27 leaves, max depth = 9, train loss: 0.30602, val loss: 0.31560, in 0.016s
1 tree, 27 leaves, max depth = 9, train loss: 0.30298, val loss: 0.31289, in 0.016s
1 tree, 90 leaves, max depth = 12, train loss: 0.30066, val loss: 0.31072, in 0.016s
1 tree, 93 leaves, max depth = 15, train loss: 0.29784, val loss: 0.30782, in 0.016s
1 tree, 94 leaves, max depth = 15, train loss: 0.29519, val loss: 0.30510, in 0.016s
1 tree, 58 leaves, max depth = 12, train loss: 0.29217, val loss: 0.30245, in 0.016s
1 tree, 116 leaves, max depth = 13, train loss: 0.28965, val loss: 0.29973, in 0.031s
1 tree, 43 leaves, max depth = 11, train loss: 0.28698, val loss: 0.29760, in 0.016s
1 tree, 93 leaves, max depth = 16, train loss: 0.28470, val loss: 0.29524, in 0.016s
1 tree, 118 leaves, max depth = 16, train loss: 0.28248, val loss: 0.29283, in 0.016s
1 tree, 41 leaves, max depth = 11, train loss: 0.28013, val loss: 0.29102, in 0.016s
1 tree, 27 leaves, max depth = 12, train loss: 0.27797, val loss: 0.28913, in 0.016s
1 tree, 43 leaves, max depth = 11, train loss: 0.27582, val loss: 0.28745, in 0.016s
1 tree, 94 leaves, max depth = 16, train loss: 0.27384, val loss: 0.28541, in 0.016s
1 tree, 27 leaves, max depth = 8, train loss: 0.27190, val loss: 0.28375, in 0.016s
1 tree, 45 leaves, max depth = 11, train loss: 0.27007, val loss: 0.28240, in 0.016s
1 tree, 116 leaves, max depth = 14, train loss: 0.26821, val loss: 0.28037, in 0.031s
1 tree, 95 leaves, max depth = 13, train loss: 0.26647, val loss: 0.27857, in 0.016s
1 tree, 95 leaves, max depth = 13, train loss: 0.26484, val loss: 0.27692, in 0.016s
1 tree, 43 leaves, max depth = 11, train loss: 0.26316, val loss: 0.27567, in 0.016s
1 tree, 47 leaves, max depth = 11, train loss: 0.26156, val loss: 0.27449, in 0.016s
1 tree, 32 leaves, max depth = 14, train loss: 0.25998, val loss: 0.27300, in 0.016s
1 tree, 44 leaves, max depth = 12, train loss: 0.25852, val loss: 0.27190, in 0.016s
1 tree, 44 leaves, max depth = 12, train loss: 0.25715, val loss: 0.27084, in 0.016s
1 tree, 114 leaves, max depth = 15, train loss: 0.25519, val loss: 0.26866, in 0.016s
1 tree, 27 leaves, max depth = 8, train loss: 0.25389, val loss: 0.26758, in 0.016s
1 tree, 85 leaves, max depth = 14, train loss: 0.25242, val loss: 0.26606, in 0.031s
1 tree, 44 leaves, max depth = 12, train loss: 0.25123, val loss: 0.26515, in 0.000s
1 tree, 26 leaves, max depth = 9, train loss: 0.25009, val loss: 0.26415, in 0.016s
1 tree, 41 leaves, max depth = 14, train loss: 0.24875, val loss: 0.26281, in 0.016s
1 tree, 92 leaves, max depth = 13, train loss: 0.24750, val loss: 0.26156, in 0.016s
1 tree, 100 leaves, max depth = 18, train loss: 0.24606, val loss: 0.25990, in 0.031s
1 tree, 47 leaves, max depth = 11, train loss: 0.24501, val loss: 0.25916, in 0.016s
1 tree, 49 leaves, max depth = 9, train loss: 0.24434, val loss: 0.25852, in 0.016s
1 tree, 30 leaves, max depth = 13, train loss: 0.24337, val loss: 0.25754, in 0.016s
1 tree, 46 leaves, max depth = 11, train loss: 0.24238, val loss: 0.25690, in 0.016s
1 tree, 45 leaves, max depth = 12, train loss: 0.24145, val loss: 0.25630, in 0.016s
1 tree, 101 leaves, max depth = 18, train loss: 0.24015, val loss: 0.25478, in 0.016s
1 tree, 47 leaves, max depth = 10, train loss: 0.23929, val loss: 0.25421, in 0.016s
1 tree, 109 leaves, max depth = 16, train loss: 0.23820, val loss: 0.25287, in 0.016s
1 tree, 93 leaves, max depth = 13, train loss: 0.23744, val loss: 0.25207, in 0.016s
1 tree, 93 leaves, max depth = 13, train loss: 0.23640, val loss: 0.25103, in 0.016s
Fit 85 trees in 1.846 s, (6094 total leaves)
Time spent computing histograms: 0.573s
Time spent finding best splits:  0.119s
Time spent applying splits:      0.111s
Time spent predicting:           0.000s
Trial 83, Fold 3: Log loss = 0.23801422688332105, Average precision = 0.9672258396658524, ROC-AUC = 0.9640686397848426, Elapsed Time = 1.8602890000001935 seconds
Trial 83, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 83, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
Binning 0.040 GB of training data: 0.158 s
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 70 leaves, max depth = 12, train loss: 0.66948, val loss: 0.66858, in 0.016s
1 tree, 96 leaves, max depth = 14, train loss: 0.64844, val loss: 0.64635, in 0.016s
1 tree, 98 leaves, max depth = 13, train loss: 0.62726, val loss: 0.62432, in 0.016s
1 tree, 93 leaves, max depth = 14, train loss: 0.60783, val loss: 0.60410, in 0.031s
1 tree, 77 leaves, max depth = 13, train loss: 0.59014, val loss: 0.58560, in 0.016s
1 tree, 102 leaves, max depth = 13, train loss: 0.57445, val loss: 0.56915, in 0.016s
1 tree, 98 leaves, max depth = 14, train loss: 0.55878, val loss: 0.55279, in 0.031s
1 tree, 103 leaves, max depth = 13, train loss: 0.54445, val loss: 0.53768, in 0.016s
1 tree, 71 leaves, max depth = 12, train loss: 0.53130, val loss: 0.52388, in 0.016s
1 tree, 99 leaves, max depth = 14, train loss: 0.51951, val loss: 0.51148, in 0.016s
1 tree, 97 leaves, max depth = 14, train loss: 0.50757, val loss: 0.49899, in 0.031s
1 tree, 98 leaves, max depth = 14, train loss: 0.49643, val loss: 0.48730, in 0.016s
1 tree, 58 leaves, max depth = 10, train loss: 0.48306, val loss: 0.47364, in 0.016s
1 tree, 94 leaves, max depth = 13, train loss: 0.47371, val loss: 0.46392, in 0.016s
1 tree, 96 leaves, max depth = 12, train loss: 0.46438, val loss: 0.45414, in 0.031s
1 tree, 96 leaves, max depth = 14, train loss: 0.45563, val loss: 0.44496, in 0.016s
1 tree, 93 leaves, max depth = 12, train loss: 0.44748, val loss: 0.43636, in 0.016s
1 tree, 75 leaves, max depth = 14, train loss: 0.44011, val loss: 0.42852, in 0.016s
1 tree, 100 leaves, max depth = 13, train loss: 0.43309, val loss: 0.42099, in 0.016s
1 tree, 57 leaves, max depth = 10, train loss: 0.42308, val loss: 0.41082, in 0.016s
1 tree, 94 leaves, max depth = 11, train loss: 0.41699, val loss: 0.40454, in 0.031s
1 tree, 58 leaves, max depth = 12, train loss: 0.40800, val loss: 0.39543, in 0.016s
1 tree, 57 leaves, max depth = 10, train loss: 0.39959, val loss: 0.38689, in 0.016s
1 tree, 100 leaves, max depth = 12, train loss: 0.39413, val loss: 0.38102, in 0.016s
1 tree, 71 leaves, max depth = 13, train loss: 0.38914, val loss: 0.37555, in 0.016s
1 tree, 102 leaves, max depth = 13, train loss: 0.38424, val loss: 0.37027, in 0.016s
1 tree, 49 leaves, max depth = 11, train loss: 0.37720, val loss: 0.36312, in 0.016s
1 tree, 92 leaves, max depth = 13, train loss: 0.37282, val loss: 0.35858, in 0.016s
1 tree, 103 leaves, max depth = 15, train loss: 0.36867, val loss: 0.35407, in 0.031s
1 tree, 81 leaves, max depth = 12, train loss: 0.36476, val loss: 0.34987, in 0.016s
1 tree, 28 leaves, max depth = 10, train loss: 0.35886, val loss: 0.34369, in 0.016s
1 tree, 91 leaves, max depth = 12, train loss: 0.35442, val loss: 0.33917, in 0.016s
1 tree, 43 leaves, max depth = 11, train loss: 0.34914, val loss: 0.33403, in 0.016s
1 tree, 59 leaves, max depth = 11, train loss: 0.34368, val loss: 0.32853, in 0.016s
1 tree, 84 leaves, max depth = 15, train loss: 0.33977, val loss: 0.32465, in 0.031s
1 tree, 28 leaves, max depth = 11, train loss: 0.33520, val loss: 0.31988, in 0.000s
1 tree, 42 leaves, max depth = 11, train loss: 0.33080, val loss: 0.31566, in 0.016s
1 tree, 27 leaves, max depth = 10, train loss: 0.32666, val loss: 0.31136, in 0.016s
1 tree, 40 leaves, max depth = 11, train loss: 0.32279, val loss: 0.30762, in 0.016s
1 tree, 92 leaves, max depth = 15, train loss: 0.31933, val loss: 0.30413, in 0.016s
1 tree, 123 leaves, max depth = 14, train loss: 0.31597, val loss: 0.30064, in 0.031s
1 tree, 59 leaves, max depth = 9, train loss: 0.31207, val loss: 0.29668, in 0.016s
1 tree, 42 leaves, max depth = 11, train loss: 0.30874, val loss: 0.29353, in 0.016s
1 tree, 90 leaves, max depth = 15, train loss: 0.30645, val loss: 0.29108, in 0.016s
1 tree, 27 leaves, max depth = 10, train loss: 0.30332, val loss: 0.28784, in 0.016s
1 tree, 29 leaves, max depth = 9, train loss: 0.30047, val loss: 0.28482, in 0.016s
1 tree, 29 leaves, max depth = 10, train loss: 0.29778, val loss: 0.28197, in 0.016s
1 tree, 93 leaves, max depth = 14, train loss: 0.29507, val loss: 0.27928, in 0.016s
1 tree, 44 leaves, max depth = 11, train loss: 0.29246, val loss: 0.27678, in 0.016s
1 tree, 92 leaves, max depth = 15, train loss: 0.28990, val loss: 0.27422, in 0.031s
1 tree, 27 leaves, max depth = 10, train loss: 0.28755, val loss: 0.27179, in 0.016s
1 tree, 125 leaves, max depth = 12, train loss: 0.28507, val loss: 0.26924, in 0.016s
1 tree, 124 leaves, max depth = 16, train loss: 0.28277, val loss: 0.26689, in 0.031s
1 tree, 100 leaves, max depth = 12, train loss: 0.28109, val loss: 0.26529, in 0.016s
1 tree, 48 leaves, max depth = 12, train loss: 0.27897, val loss: 0.26336, in 0.016s
1 tree, 85 leaves, max depth = 12, train loss: 0.27750, val loss: 0.26185, in 0.016s
1 tree, 121 leaves, max depth = 13, train loss: 0.27548, val loss: 0.25974, in 0.016s
1 tree, 94 leaves, max depth = 13, train loss: 0.27361, val loss: 0.25789, in 0.031s
1 tree, 42 leaves, max depth = 11, train loss: 0.27165, val loss: 0.25610, in 0.016s
1 tree, 26 leaves, max depth = 11, train loss: 0.26988, val loss: 0.25422, in 0.016s
1 tree, 123 leaves, max depth = 13, train loss: 0.26807, val loss: 0.25234, in 0.031s
1 tree, 49 leaves, max depth = 12, train loss: 0.26637, val loss: 0.25079, in 0.016s
1 tree, 119 leaves, max depth = 14, train loss: 0.26469, val loss: 0.24903, in 0.016s
1 tree, 41 leaves, max depth = 11, train loss: 0.26285, val loss: 0.24730, in 0.016s
1 tree, 42 leaves, max depth = 9, train loss: 0.26131, val loss: 0.24592, in 0.016s
1 tree, 88 leaves, max depth = 14, train loss: 0.25979, val loss: 0.24443, in 0.031s
1 tree, 45 leaves, max depth = 14, train loss: 0.25832, val loss: 0.24306, in 0.016s
1 tree, 122 leaves, max depth = 14, train loss: 0.25685, val loss: 0.24155, in 0.016s
1 tree, 89 leaves, max depth = 17, train loss: 0.25526, val loss: 0.24007, in 0.031s
1 tree, 46 leaves, max depth = 13, train loss: 0.25392, val loss: 0.23880, in 0.000s
1 tree, 91 leaves, max depth = 14, train loss: 0.25264, val loss: 0.23759, in 0.031s
1 tree, 74 leaves, max depth = 12, train loss: 0.25171, val loss: 0.23681, in 0.016s
1 tree, 39 leaves, max depth = 11, train loss: 0.25036, val loss: 0.23553, in 0.016s
1 tree, 111 leaves, max depth = 17, train loss: 0.24895, val loss: 0.23416, in 0.016s
1 tree, 89 leaves, max depth = 14, train loss: 0.24761, val loss: 0.23294, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.24633, val loss: 0.23188, in 0.016s
1 tree, 45 leaves, max depth = 12, train loss: 0.24519, val loss: 0.23085, in 0.016s
1 tree, 30 leaves, max depth = 9, train loss: 0.24401, val loss: 0.22981, in 0.016s
1 tree, 74 leaves, max depth = 12, train loss: 0.24324, val loss: 0.22919, in 0.016s
1 tree, 107 leaves, max depth = 13, train loss: 0.24195, val loss: 0.22797, in 0.016s
1 tree, 27 leaves, max depth = 7, train loss: 0.24091, val loss: 0.22711, in 0.000s
1 tree, 110 leaves, max depth = 15, train loss: 0.23975, val loss: 0.22598, in 0.031s
1 tree, 37 leaves, max depth = 8, train loss: 0.23894, val loss: 0.22530, in 0.016s
1 tree, 47 leaves, max depth = 12, train loss: 0.23801, val loss: 0.22451, in 0.016s
1 tree, 72 leaves, max depth = 15, train loss: 0.23640, val loss: 0.22303, in 0.016s
Fit 85 trees in 1.908 s, (6280 total leaves)
Time spent computing histograms: 0.574s
Time spent finding best splits:  0.121s
Time spent applying splits:      0.114s
Time spent predicting:           0.000s
Trial 83, Fold 4: Log loss = 0.2407239049784999, Average precision = 0.9676442621459418, ROC-AUC = 0.9632780776173069, Elapsed Time = 1.9141239000000496 seconds
Trial 83, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 83, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
Binning 0.040 GB of training data: 0.143 s
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 69 leaves, max depth = 12, train loss: 0.66934, val loss: 0.66800, in 0.016s
1 tree, 103 leaves, max depth = 13, train loss: 0.64809, val loss: 0.64580, in 0.016s
1 tree, 93 leaves, max depth = 14, train loss: 0.62700, val loss: 0.62397, in 0.016s
1 tree, 97 leaves, max depth = 14, train loss: 0.60752, val loss: 0.60378, in 0.016s
1 tree, 93 leaves, max depth = 13, train loss: 0.59071, val loss: 0.58659, in 0.016s
1 tree, 82 leaves, max depth = 12, train loss: 0.57489, val loss: 0.57017, in 0.031s
1 tree, 97 leaves, max depth = 12, train loss: 0.55910, val loss: 0.55387, in 0.016s
1 tree, 103 leaves, max depth = 12, train loss: 0.54464, val loss: 0.53891, in 0.016s
1 tree, 103 leaves, max depth = 13, train loss: 0.53119, val loss: 0.52498, in 0.016s
1 tree, 100 leaves, max depth = 15, train loss: 0.51919, val loss: 0.51243, in 0.031s
1 tree, 76 leaves, max depth = 14, train loss: 0.50758, val loss: 0.50027, in 0.016s
1 tree, 96 leaves, max depth = 15, train loss: 0.49681, val loss: 0.48898, in 0.016s
1 tree, 96 leaves, max depth = 14, train loss: 0.48697, val loss: 0.47872, in 0.016s
1 tree, 59 leaves, max depth = 10, train loss: 0.47409, val loss: 0.46573, in 0.016s
1 tree, 94 leaves, max depth = 12, train loss: 0.46530, val loss: 0.45680, in 0.016s
1 tree, 95 leaves, max depth = 15, train loss: 0.45686, val loss: 0.44800, in 0.031s
1 tree, 98 leaves, max depth = 13, train loss: 0.44842, val loss: 0.43934, in 0.016s
1 tree, 59 leaves, max depth = 10, train loss: 0.43771, val loss: 0.42861, in 0.016s
1 tree, 78 leaves, max depth = 13, train loss: 0.43051, val loss: 0.42100, in 0.016s
1 tree, 77 leaves, max depth = 13, train loss: 0.42368, val loss: 0.41387, in 0.016s
1 tree, 74 leaves, max depth = 14, train loss: 0.41785, val loss: 0.40760, in 0.031s
1 tree, 76 leaves, max depth = 15, train loss: 0.41184, val loss: 0.40126, in 0.016s
1 tree, 98 leaves, max depth = 14, train loss: 0.40583, val loss: 0.39514, in 0.016s
1 tree, 74 leaves, max depth = 13, train loss: 0.40045, val loss: 0.38950, in 0.016s
1 tree, 101 leaves, max depth = 13, train loss: 0.39525, val loss: 0.38418, in 0.016s
1 tree, 94 leaves, max depth = 12, train loss: 0.39060, val loss: 0.37938, in 0.031s
1 tree, 92 leaves, max depth = 12, train loss: 0.38614, val loss: 0.37491, in 0.016s
1 tree, 95 leaves, max depth = 13, train loss: 0.38154, val loss: 0.37025, in 0.016s
1 tree, 28 leaves, max depth = 11, train loss: 0.37429, val loss: 0.36297, in 0.016s
1 tree, 28 leaves, max depth = 9, train loss: 0.36758, val loss: 0.35622, in 0.016s
1 tree, 90 leaves, max depth = 15, train loss: 0.36284, val loss: 0.35153, in 0.016s
1 tree, 28 leaves, max depth = 9, train loss: 0.35673, val loss: 0.34538, in 0.016s
1 tree, 52 leaves, max depth = 12, train loss: 0.35067, val loss: 0.33988, in 0.000s
1 tree, 28 leaves, max depth = 9, train loss: 0.34533, val loss: 0.33439, in 0.016s
1 tree, 63 leaves, max depth = 11, train loss: 0.34021, val loss: 0.32932, in 0.031s
1 tree, 43 leaves, max depth = 11, train loss: 0.33544, val loss: 0.32503, in 0.016s
1 tree, 44 leaves, max depth = 11, train loss: 0.33105, val loss: 0.32114, in 0.047s
1 tree, 28 leaves, max depth = 10, train loss: 0.32682, val loss: 0.31684, in 0.016s
1 tree, 90 leaves, max depth = 13, train loss: 0.32313, val loss: 0.31321, in 0.031s
1 tree, 94 leaves, max depth = 13, train loss: 0.31961, val loss: 0.30974, in 0.016s
1 tree, 91 leaves, max depth = 14, train loss: 0.31625, val loss: 0.30648, in 0.031s
1 tree, 42 leaves, max depth = 11, train loss: 0.31252, val loss: 0.30326, in 0.016s
1 tree, 27 leaves, max depth = 10, train loss: 0.30899, val loss: 0.29968, in 0.016s
1 tree, 26 leaves, max depth = 10, train loss: 0.30573, val loss: 0.29641, in 0.016s
1 tree, 94 leaves, max depth = 14, train loss: 0.30279, val loss: 0.29358, in 0.016s
1 tree, 43 leaves, max depth = 11, train loss: 0.29971, val loss: 0.29091, in 0.031s
1 tree, 42 leaves, max depth = 11, train loss: 0.29677, val loss: 0.28843, in 0.016s
1 tree, 27 leaves, max depth = 10, train loss: 0.29400, val loss: 0.28553, in 0.016s
1 tree, 95 leaves, max depth = 13, train loss: 0.29137, val loss: 0.28297, in 0.031s
1 tree, 91 leaves, max depth = 14, train loss: 0.28885, val loss: 0.28053, in 0.016s
1 tree, 27 leaves, max depth = 11, train loss: 0.28638, val loss: 0.27807, in 0.016s
1 tree, 27 leaves, max depth = 13, train loss: 0.28409, val loss: 0.27568, in 0.016s
1 tree, 30 leaves, max depth = 11, train loss: 0.28178, val loss: 0.27327, in 0.016s
1 tree, 120 leaves, max depth = 14, train loss: 0.27931, val loss: 0.27094, in 0.031s
1 tree, 28 leaves, max depth = 11, train loss: 0.27733, val loss: 0.26890, in 0.016s
1 tree, 68 leaves, max depth = 11, train loss: 0.27526, val loss: 0.26697, in 0.016s
1 tree, 42 leaves, max depth = 10, train loss: 0.27321, val loss: 0.26533, in 0.016s
1 tree, 42 leaves, max depth = 10, train loss: 0.27130, val loss: 0.26380, in 0.016s
1 tree, 117 leaves, max depth = 14, train loss: 0.26913, val loss: 0.26175, in 0.016s
1 tree, 89 leaves, max depth = 11, train loss: 0.26721, val loss: 0.25992, in 0.031s
1 tree, 26 leaves, max depth = 12, train loss: 0.26564, val loss: 0.25829, in 0.016s
1 tree, 121 leaves, max depth = 15, train loss: 0.26370, val loss: 0.25648, in 0.016s
1 tree, 107 leaves, max depth = 15, train loss: 0.26172, val loss: 0.25436, in 0.031s
1 tree, 41 leaves, max depth = 10, train loss: 0.26014, val loss: 0.25306, in 0.016s
1 tree, 42 leaves, max depth = 10, train loss: 0.25863, val loss: 0.25184, in 0.016s
1 tree, 69 leaves, max depth = 11, train loss: 0.25683, val loss: 0.25015, in 0.016s
1 tree, 89 leaves, max depth = 15, train loss: 0.25527, val loss: 0.24874, in 0.016s
1 tree, 90 leaves, max depth = 15, train loss: 0.25385, val loss: 0.24742, in 0.031s
1 tree, 118 leaves, max depth = 15, train loss: 0.25236, val loss: 0.24604, in 0.016s
1 tree, 42 leaves, max depth = 11, train loss: 0.25107, val loss: 0.24494, in 0.031s
1 tree, 31 leaves, max depth = 9, train loss: 0.24967, val loss: 0.24367, in 0.016s
1 tree, 40 leaves, max depth = 13, train loss: 0.24824, val loss: 0.24236, in 0.016s
1 tree, 42 leaves, max depth = 11, train loss: 0.24715, val loss: 0.24150, in 0.000s
1 tree, 80 leaves, max depth = 13, train loss: 0.24605, val loss: 0.24056, in 0.031s
1 tree, 94 leaves, max depth = 18, train loss: 0.24438, val loss: 0.23895, in 0.016s
1 tree, 104 leaves, max depth = 16, train loss: 0.24296, val loss: 0.23746, in 0.016s
1 tree, 121 leaves, max depth = 14, train loss: 0.24180, val loss: 0.23640, in 0.031s
1 tree, 115 leaves, max depth = 15, train loss: 0.24016, val loss: 0.23497, in 0.016s
1 tree, 88 leaves, max depth = 13, train loss: 0.23917, val loss: 0.23412, in 0.031s
1 tree, 107 leaves, max depth = 14, train loss: 0.23802, val loss: 0.23292, in 0.016s
1 tree, 107 leaves, max depth = 15, train loss: 0.23689, val loss: 0.23174, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.23580, val loss: 0.23077, in 0.016s
1 tree, 47 leaves, max depth = 16, train loss: 0.23482, val loss: 0.22985, in 0.016s
1 tree, 41 leaves, max depth = 14, train loss: 0.23373, val loss: 0.22892, in 0.016s
1 tree, 34 leaves, max depth = 9, train loss: 0.23277, val loss: 0.22806, in 0.016s
Fit 85 trees in 2.018 s, (6083 total leaves)
Time spent computing histograms: 0.630s
Time spent finding best splits:  0.141s
Time spent applying splits:      0.131s
Time spent predicting:           0.000s
Trial 83, Fold 5: Log loss = 0.2437184623531028, Average precision = 0.9649102280878095, ROC-AUC = 0.9611272504319716, Elapsed Time = 2.023743600000671 seconds
Optimization Progress:  84%|########4 | 84/100 [17:30<04:08, 15.54s/it]
Trial 84, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 84, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
0.190 s 0.040 GB of training data: 
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 76 leaves, max depth = 19, train loss: 0.68533, val loss: 0.68510, in 0.016s
1 tree, 76 leaves, max depth = 19, train loss: 0.67777, val loss: 0.67731, in 0.016s
1 tree, 76 leaves, max depth = 19, train loss: 0.67046, val loss: 0.66978, in 0.000s
1 tree, 80 leaves, max depth = 15, train loss: 0.66345, val loss: 0.66261, in 0.016s
1 tree, 77 leaves, max depth = 19, train loss: 0.65659, val loss: 0.65554, in 0.016s
1 tree, 77 leaves, max depth = 19, train loss: 0.64996, val loss: 0.64869, in 0.016s
1 tree, 77 leaves, max depth = 19, train loss: 0.64353, val loss: 0.64205, in 0.016s
1 tree, 78 leaves, max depth = 19, train loss: 0.63730, val loss: 0.63562, in 0.000s
1 tree, 78 leaves, max depth = 19, train loss: 0.63126, val loss: 0.62938, in 0.016s
1 tree, 78 leaves, max depth = 19, train loss: 0.62541, val loss: 0.62333, in 0.016s
1 tree, 78 leaves, max depth = 19, train loss: 0.61974, val loss: 0.61746, in 0.016s
1 tree, 78 leaves, max depth = 19, train loss: 0.61424, val loss: 0.61177, in 0.000s
1 tree, 81 leaves, max depth = 17, train loss: 0.60900, val loss: 0.60636, in 0.016s
1 tree, 78 leaves, max depth = 19, train loss: 0.60382, val loss: 0.60099, in 0.016s
1 tree, 78 leaves, max depth = 19, train loss: 0.59880, val loss: 0.59579, in 0.000s
1 tree, 82 leaves, max depth = 21, train loss: 0.59397, val loss: 0.59080, in 0.016s
1 tree, 77 leaves, max depth = 19, train loss: 0.58924, val loss: 0.58589, in 0.016s
1 tree, 77 leaves, max depth = 19, train loss: 0.58465, val loss: 0.58112, in 0.000s
1 tree, 81 leaves, max depth = 17, train loss: 0.58027, val loss: 0.57659, in 0.016s
1 tree, 77 leaves, max depth = 19, train loss: 0.57594, val loss: 0.57209, in 0.016s
1 tree, 77 leaves, max depth = 19, train loss: 0.57173, val loss: 0.56772, in 0.000s
1 tree, 77 leaves, max depth = 19, train loss: 0.56765, val loss: 0.56347, in 0.016s
1 tree, 77 leaves, max depth = 19, train loss: 0.56369, val loss: 0.55934, in 0.016s
1 tree, 78 leaves, max depth = 19, train loss: 0.55983, val loss: 0.55533, in 0.000s
1 tree, 125 leaves, max depth = 14, train loss: 0.55587, val loss: 0.55165, in 0.016s
1 tree, 78 leaves, max depth = 19, train loss: 0.55218, val loss: 0.54780, in 0.016s
1 tree, 125 leaves, max depth = 15, train loss: 0.54837, val loss: 0.54428, in 0.000s
1 tree, 79 leaves, max depth = 18, train loss: 0.54489, val loss: 0.54069, in 0.016s
1 tree, 124 leaves, max depth = 15, train loss: 0.54123, val loss: 0.53731, in 0.016s
1 tree, 82 leaves, max depth = 19, train loss: 0.53783, val loss: 0.53376, in 0.016s
1 tree, 82 leaves, max depth = 19, train loss: 0.53453, val loss: 0.53030, in 0.000s
1 tree, 124 leaves, max depth = 15, train loss: 0.53105, val loss: 0.52708, in 0.016s
1 tree, 124 leaves, max depth = 15, train loss: 0.52766, val loss: 0.52396, in 0.016s
1 tree, 124 leaves, max depth = 15, train loss: 0.52437, val loss: 0.52093, in 0.016s
1 tree, 82 leaves, max depth = 19, train loss: 0.52128, val loss: 0.51769, in 0.000s
1 tree, 125 leaves, max depth = 15, train loss: 0.51811, val loss: 0.51477, in 0.016s
1 tree, 82 leaves, max depth = 19, train loss: 0.51515, val loss: 0.51165, in 0.016s
1 tree, 126 leaves, max depth = 15, train loss: 0.51210, val loss: 0.50885, in 0.016s
1 tree, 80 leaves, max depth = 17, train loss: 0.50929, val loss: 0.50592, in 0.000s
1 tree, 126 leaves, max depth = 15, train loss: 0.50635, val loss: 0.50321, in 0.016s
1 tree, 82 leaves, max depth = 17, train loss: 0.50361, val loss: 0.50032, in 0.016s
1 tree, 126 leaves, max depth = 16, train loss: 0.50077, val loss: 0.49772, in 0.016s
1 tree, 79 leaves, max depth = 15, train loss: 0.49818, val loss: 0.49501, in 0.000s
1 tree, 125 leaves, max depth = 14, train loss: 0.49544, val loss: 0.49250, in 0.016s
1 tree, 126 leaves, max depth = 15, train loss: 0.49277, val loss: 0.49006, in 0.016s
1 tree, 81 leaves, max depth = 14, train loss: 0.49026, val loss: 0.48741, in 0.000s
1 tree, 126 leaves, max depth = 14, train loss: 0.48769, val loss: 0.48506, in 0.016s
1 tree, 81 leaves, max depth = 14, train loss: 0.48528, val loss: 0.48250, in 0.016s
1 tree, 126 leaves, max depth = 14, train loss: 0.48279, val loss: 0.48024, in 0.016s
1 tree, 80 leaves, max depth = 19, train loss: 0.48050, val loss: 0.47784, in 0.016s
1 tree, 126 leaves, max depth = 14, train loss: 0.47810, val loss: 0.47566, in 0.000s
1 tree, 126 leaves, max depth = 14, train loss: 0.47577, val loss: 0.47353, in 0.016s
1 tree, 81 leaves, max depth = 13, train loss: 0.47355, val loss: 0.47118, in 0.016s
1 tree, 126 leaves, max depth = 13, train loss: 0.47129, val loss: 0.46912, in 0.000s
1 tree, 79 leaves, max depth = 19, train loss: 0.46918, val loss: 0.46687, in 0.000s
1 tree, 126 leaves, max depth = 13, train loss: 0.46700, val loss: 0.46489, in 0.016s
1 tree, 83 leaves, max depth = 13, train loss: 0.46493, val loss: 0.46269, in 0.016s
1 tree, 127 leaves, max depth = 16, train loss: 0.46282, val loss: 0.46078, in 0.016s
1 tree, 127 leaves, max depth = 16, train loss: 0.46076, val loss: 0.45892, in 0.016s
1 tree, 83 leaves, max depth = 13, train loss: 0.45879, val loss: 0.45681, in 0.016s
1 tree, 83 leaves, max depth = 13, train loss: 0.45688, val loss: 0.45476, in 0.016s
1 tree, 127 leaves, max depth = 16, train loss: 0.45490, val loss: 0.45298, in 0.016s
1 tree, 127 leaves, max depth = 16, train loss: 0.45297, val loss: 0.45124, in 0.016s
1 tree, 80 leaves, max depth = 17, train loss: 0.45115, val loss: 0.44929, in 0.000s
1 tree, 127 leaves, max depth = 17, train loss: 0.44928, val loss: 0.44761, in 0.016s
1 tree, 78 leaves, max depth = 13, train loss: 0.44748, val loss: 0.44573, in 0.016s
1 tree, 127 leaves, max depth = 17, train loss: 0.44567, val loss: 0.44410, in 0.016s
1 tree, 80 leaves, max depth = 19, train loss: 0.44399, val loss: 0.44233, in 0.016s
1 tree, 127 leaves, max depth = 17, train loss: 0.44223, val loss: 0.44075, in 0.016s
1 tree, 127 leaves, max depth = 17, train loss: 0.44052, val loss: 0.43922, in 0.016s
1 tree, 79 leaves, max depth = 15, train loss: 0.43890, val loss: 0.43747, in 0.016s
1 tree, 127 leaves, max depth = 17, train loss: 0.43724, val loss: 0.43599, in 0.000s
1 tree, 82 leaves, max depth = 16, train loss: 0.43564, val loss: 0.43430, in 0.016s
1 tree, 127 leaves, max depth = 17, train loss: 0.43403, val loss: 0.43286, in 0.016s
1 tree, 79 leaves, max depth = 15, train loss: 0.43252, val loss: 0.43122, in 0.016s
Fit 75 trees in 1.190 s, (7250 total leaves)
Time spent computing histograms: 0.366s
Time spent finding best splits:  0.101s
Time spent applying splits:      0.110s
Time spent predicting:           0.000s
Trial 84, Fold 1: Log loss = 0.43436533814762274, Average precision = 0.9183205254227605, ROC-AUC = 0.9288089890929774, Elapsed Time = 1.2009637000010116 seconds
Trial 84, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 84, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
Binning 0.040 GB of training data: 0.142 s
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 80 leaves, max depth = 17, train loss: 0.68537, val loss: 0.68499, in 0.000s
1 tree, 80 leaves, max depth = 16, train loss: 0.67785, val loss: 0.67710, in 0.016s
1 tree, 80 leaves, max depth = 16, train loss: 0.67058, val loss: 0.66946, in 0.016s
1 tree, 81 leaves, max depth = 15, train loss: 0.66359, val loss: 0.66214, in 0.016s
1 tree, 80 leaves, max depth = 16, train loss: 0.65677, val loss: 0.65498, in 0.000s
1 tree, 80 leaves, max depth = 16, train loss: 0.65016, val loss: 0.64803, in 0.016s
1 tree, 80 leaves, max depth = 16, train loss: 0.64377, val loss: 0.64130, in 0.016s
1 tree, 80 leaves, max depth = 16, train loss: 0.63757, val loss: 0.63478, in 0.000s
1 tree, 80 leaves, max depth = 16, train loss: 0.63157, val loss: 0.62846, in 0.016s
1 tree, 80 leaves, max depth = 16, train loss: 0.62575, val loss: 0.62233, in 0.016s
1 tree, 80 leaves, max depth = 16, train loss: 0.62011, val loss: 0.61638, in 0.000s
1 tree, 80 leaves, max depth = 16, train loss: 0.61464, val loss: 0.61061, in 0.016s
1 tree, 80 leaves, max depth = 17, train loss: 0.60937, val loss: 0.60510, in 0.016s
1 tree, 80 leaves, max depth = 16, train loss: 0.60422, val loss: 0.59966, in 0.016s
1 tree, 80 leaves, max depth = 16, train loss: 0.59923, val loss: 0.59439, in 0.016s
1 tree, 81 leaves, max depth = 18, train loss: 0.59441, val loss: 0.58935, in 0.000s
1 tree, 81 leaves, max depth = 18, train loss: 0.58974, val loss: 0.58446, in 0.016s
1 tree, 80 leaves, max depth = 16, train loss: 0.58517, val loss: 0.57962, in 0.016s
1 tree, 81 leaves, max depth = 19, train loss: 0.58077, val loss: 0.57501, in 0.000s
1 tree, 80 leaves, max depth = 16, train loss: 0.57646, val loss: 0.57044, in 0.016s
1 tree, 83 leaves, max depth = 17, train loss: 0.57230, val loss: 0.56609, in 0.016s
1 tree, 81 leaves, max depth = 18, train loss: 0.56827, val loss: 0.56186, in 0.000s
1 tree, 81 leaves, max depth = 17, train loss: 0.56432, val loss: 0.55767, in 0.016s
1 tree, 82 leaves, max depth = 17, train loss: 0.56051, val loss: 0.55367, in 0.016s
1 tree, 82 leaves, max depth = 18, train loss: 0.55682, val loss: 0.54979, in 0.016s
1 tree, 129 leaves, max depth = 18, train loss: 0.55292, val loss: 0.54600, in 0.016s
1 tree, 129 leaves, max depth = 18, train loss: 0.54913, val loss: 0.54231, in 0.016s
1 tree, 82 leaves, max depth = 18, train loss: 0.54564, val loss: 0.53864, in 0.000s
1 tree, 129 leaves, max depth = 18, train loss: 0.54200, val loss: 0.53511, in 0.016s
1 tree, 82 leaves, max depth = 18, train loss: 0.53865, val loss: 0.53158, in 0.016s
1 tree, 83 leaves, max depth = 16, train loss: 0.53536, val loss: 0.52807, in 0.016s
1 tree, 128 leaves, max depth = 16, train loss: 0.53189, val loss: 0.52470, in 0.016s
1 tree, 128 leaves, max depth = 16, train loss: 0.52852, val loss: 0.52144, in 0.000s
1 tree, 82 leaves, max depth = 18, train loss: 0.52543, val loss: 0.51818, in 0.016s
1 tree, 128 leaves, max depth = 16, train loss: 0.52219, val loss: 0.51504, in 0.016s
1 tree, 128 leaves, max depth = 16, train loss: 0.51904, val loss: 0.51199, in 0.016s
1 tree, 81 leaves, max depth = 20, train loss: 0.51611, val loss: 0.50890, in 0.016s
1 tree, 127 leaves, max depth = 16, train loss: 0.51307, val loss: 0.50596, in 0.016s
1 tree, 82 leaves, max depth = 18, train loss: 0.51026, val loss: 0.50299, in 0.016s
1 tree, 128 leaves, max depth = 16, train loss: 0.50733, val loss: 0.50016, in 0.016s
1 tree, 81 leaves, max depth = 16, train loss: 0.50463, val loss: 0.49730, in 0.000s
1 tree, 127 leaves, max depth = 17, train loss: 0.50181, val loss: 0.49458, in 0.016s
1 tree, 80 leaves, max depth = 15, train loss: 0.49920, val loss: 0.49182, in 0.016s
1 tree, 125 leaves, max depth = 16, train loss: 0.49648, val loss: 0.48920, in 0.016s
1 tree, 125 leaves, max depth = 16, train loss: 0.49384, val loss: 0.48664, in 0.016s
1 tree, 80 leaves, max depth = 15, train loss: 0.49136, val loss: 0.48402, in 0.016s
1 tree, 127 leaves, max depth = 18, train loss: 0.48880, val loss: 0.48156, in 0.016s
1 tree, 81 leaves, max depth = 20, train loss: 0.48641, val loss: 0.47903, in 0.000s
1 tree, 127 leaves, max depth = 18, train loss: 0.48395, val loss: 0.47666, in 0.016s
1 tree, 81 leaves, max depth = 20, train loss: 0.48165, val loss: 0.47422, in 0.016s
1 tree, 127 leaves, max depth = 18, train loss: 0.47927, val loss: 0.47193, in 0.016s
1 tree, 80 leaves, max depth = 16, train loss: 0.47705, val loss: 0.46958, in 0.016s
1 tree, 128 leaves, max depth = 18, train loss: 0.47475, val loss: 0.46737, in 0.016s
1 tree, 81 leaves, max depth = 16, train loss: 0.47262, val loss: 0.46511, in 0.016s
1 tree, 128 leaves, max depth = 15, train loss: 0.47039, val loss: 0.46297, in 0.000s
1 tree, 128 leaves, max depth = 15, train loss: 0.46822, val loss: 0.46090, in 0.016s
1 tree, 81 leaves, max depth = 17, train loss: 0.46619, val loss: 0.45873, in 0.016s
1 tree, 128 leaves, max depth = 17, train loss: 0.46409, val loss: 0.45673, in 0.016s
1 tree, 81 leaves, max depth = 17, train loss: 0.46213, val loss: 0.45464, in 0.016s
1 tree, 81 leaves, max depth = 17, train loss: 0.46022, val loss: 0.45261, in 0.016s
1 tree, 81 leaves, max depth = 16, train loss: 0.45835, val loss: 0.45063, in 0.000s
1 tree, 127 leaves, max depth = 17, train loss: 0.45635, val loss: 0.44871, in 0.016s
1 tree, 127 leaves, max depth = 17, train loss: 0.45440, val loss: 0.44685, in 0.016s
1 tree, 127 leaves, max depth = 17, train loss: 0.45250, val loss: 0.44504, in 0.016s
1 tree, 127 leaves, max depth = 17, train loss: 0.45066, val loss: 0.44327, in 0.016s
1 tree, 81 leaves, max depth = 14, train loss: 0.44890, val loss: 0.44141, in 0.016s
1 tree, 126 leaves, max depth = 17, train loss: 0.44711, val loss: 0.43970, in 0.016s
1 tree, 83 leaves, max depth = 18, train loss: 0.44542, val loss: 0.43790, in 0.000s
1 tree, 127 leaves, max depth = 17, train loss: 0.44369, val loss: 0.43625, in 0.016s
1 tree, 83 leaves, max depth = 18, train loss: 0.44206, val loss: 0.43451, in 0.016s
1 tree, 127 leaves, max depth = 17, train loss: 0.44038, val loss: 0.43291, in 0.016s
1 tree, 82 leaves, max depth = 18, train loss: 0.43880, val loss: 0.43123, in 0.000s
1 tree, 128 leaves, max depth = 16, train loss: 0.43717, val loss: 0.42969, in 0.016s
1 tree, 128 leaves, max depth = 16, train loss: 0.43559, val loss: 0.42818, in 0.016s
1 tree, 81 leaves, max depth = 13, train loss: 0.43407, val loss: 0.42656, in 0.016s
Fit 75 trees in 1.221 s, (7371 total leaves)
Time spent computing histograms: 0.386s
Time spent finding best splits:  0.108s
Time spent applying splits:      0.120s
Time spent predicting:           0.000s
Trial 84, Fold 2: Log loss = 0.43684396533371944, Average precision = 0.9113542035048336, ROC-AUC = 0.9271392871678127, Elapsed Time = 1.2313483999987511 seconds
Trial 84, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 84, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
Binning 0.040 GB of training data: 0.158 s
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 81 leaves, max depth = 12, train loss: 0.68547, val loss: 0.68519, in 0.016s
1 tree, 79 leaves, max depth = 14, train loss: 0.67798, val loss: 0.67748, in 0.016s
1 tree, 79 leaves, max depth = 14, train loss: 0.67073, val loss: 0.67002, in 0.000s
1 tree, 82 leaves, max depth = 12, train loss: 0.66377, val loss: 0.66280, in 0.016s
1 tree, 79 leaves, max depth = 14, train loss: 0.65698, val loss: 0.65580, in 0.016s
1 tree, 79 leaves, max depth = 14, train loss: 0.65039, val loss: 0.64903, in 0.000s
1 tree, 82 leaves, max depth = 12, train loss: 0.64408, val loss: 0.64246, in 0.016s
1 tree, 80 leaves, max depth = 14, train loss: 0.63790, val loss: 0.63610, in 0.016s
1 tree, 82 leaves, max depth = 12, train loss: 0.63198, val loss: 0.62993, in 0.016s
1 tree, 80 leaves, max depth = 14, train loss: 0.62617, val loss: 0.62394, in 0.000s
1 tree, 80 leaves, max depth = 14, train loss: 0.62055, val loss: 0.61814, in 0.016s
1 tree, 82 leaves, max depth = 13, train loss: 0.61515, val loss: 0.61251, in 0.016s
1 tree, 79 leaves, max depth = 15, train loss: 0.60991, val loss: 0.60711, in 0.016s
1 tree, 80 leaves, max depth = 14, train loss: 0.60478, val loss: 0.60181, in 0.000s
1 tree, 80 leaves, max depth = 14, train loss: 0.59980, val loss: 0.59667, in 0.016s
1 tree, 82 leaves, max depth = 13, train loss: 0.59502, val loss: 0.59167, in 0.016s
1 tree, 81 leaves, max depth = 14, train loss: 0.59033, val loss: 0.58682, in 0.000s
1 tree, 81 leaves, max depth = 14, train loss: 0.58577, val loss: 0.58212, in 0.016s
1 tree, 83 leaves, max depth = 13, train loss: 0.58140, val loss: 0.57754, in 0.016s
1 tree, 81 leaves, max depth = 14, train loss: 0.57711, val loss: 0.57310, in 0.016s
1 tree, 81 leaves, max depth = 14, train loss: 0.57294, val loss: 0.56879, in 0.016s
1 tree, 81 leaves, max depth = 14, train loss: 0.56890, val loss: 0.56460, in 0.000s
1 tree, 83 leaves, max depth = 13, train loss: 0.56501, val loss: 0.56053, in 0.016s
1 tree, 81 leaves, max depth = 14, train loss: 0.56120, val loss: 0.55657, in 0.016s
1 tree, 127 leaves, max depth = 14, train loss: 0.55722, val loss: 0.55290, in 0.016s
1 tree, 83 leaves, max depth = 13, train loss: 0.55360, val loss: 0.54911, in 0.000s
1 tree, 128 leaves, max depth = 14, train loss: 0.54978, val loss: 0.54558, in 0.031s
1 tree, 81 leaves, max depth = 15, train loss: 0.54630, val loss: 0.54198, in 0.000s
1 tree, 128 leaves, max depth = 15, train loss: 0.54263, val loss: 0.53860, in 0.016s
1 tree, 128 leaves, max depth = 15, train loss: 0.53906, val loss: 0.53532, in 0.016s
1 tree, 83 leaves, max depth = 13, train loss: 0.53579, val loss: 0.53187, in 0.016s
1 tree, 128 leaves, max depth = 15, train loss: 0.53235, val loss: 0.52872, in 0.016s
1 tree, 81 leaves, max depth = 16, train loss: 0.52919, val loss: 0.52543, in 0.016s
1 tree, 128 leaves, max depth = 15, train loss: 0.52589, val loss: 0.52240, in 0.000s
1 tree, 81 leaves, max depth = 16, train loss: 0.52285, val loss: 0.51925, in 0.016s
1 tree, 128 leaves, max depth = 15, train loss: 0.51967, val loss: 0.51634, in 0.016s
1 tree, 82 leaves, max depth = 13, train loss: 0.51678, val loss: 0.51326, in 0.016s
1 tree, 126 leaves, max depth = 16, train loss: 0.51371, val loss: 0.51046, in 0.016s
1 tree, 81 leaves, max depth = 15, train loss: 0.51091, val loss: 0.50754, in 0.000s
1 tree, 127 leaves, max depth = 16, train loss: 0.50796, val loss: 0.50485, in 0.000s
1 tree, 80 leaves, max depth = 15, train loss: 0.50526, val loss: 0.50204, in 0.016s
1 tree, 128 leaves, max depth = 16, train loss: 0.50242, val loss: 0.49944, in 0.016s
1 tree, 78 leaves, max depth = 15, train loss: 0.49982, val loss: 0.49674, in 0.016s
1 tree, 128 leaves, max depth = 16, train loss: 0.49708, val loss: 0.49423, in 0.016s
1 tree, 128 leaves, max depth = 16, train loss: 0.49441, val loss: 0.49180, in 0.016s
1 tree, 80 leaves, max depth = 16, train loss: 0.49194, val loss: 0.48923, in 0.000s
1 tree, 128 leaves, max depth = 15, train loss: 0.48936, val loss: 0.48688, in 0.016s
1 tree, 128 leaves, max depth = 15, train loss: 0.48685, val loss: 0.48461, in 0.016s
1 tree, 81 leaves, max depth = 15, train loss: 0.48450, val loss: 0.48215, in 0.016s
1 tree, 128 leaves, max depth = 15, train loss: 0.48208, val loss: 0.47995, in 0.016s
1 tree, 81 leaves, max depth = 15, train loss: 0.47982, val loss: 0.47758, in 0.000s
1 tree, 129 leaves, max depth = 15, train loss: 0.47748, val loss: 0.47546, in 0.031s
1 tree, 75 leaves, max depth = 18, train loss: 0.47531, val loss: 0.47315, in 0.000s
1 tree, 129 leaves, max depth = 16, train loss: 0.47305, val loss: 0.47111, in 0.016s
1 tree, 81 leaves, max depth = 15, train loss: 0.47095, val loss: 0.46890, in 0.016s
1 tree, 128 leaves, max depth = 16, train loss: 0.46876, val loss: 0.46693, in 0.016s
1 tree, 129 leaves, max depth = 16, train loss: 0.46663, val loss: 0.46502, in 0.016s
1 tree, 81 leaves, max depth = 15, train loss: 0.46462, val loss: 0.46290, in 0.016s
1 tree, 129 leaves, max depth = 16, train loss: 0.46256, val loss: 0.46106, in 0.016s
1 tree, 75 leaves, max depth = 18, train loss: 0.46064, val loss: 0.45900, in 0.000s
1 tree, 81 leaves, max depth = 14, train loss: 0.45876, val loss: 0.45701, in 0.016s
1 tree, 127 leaves, max depth = 15, train loss: 0.45678, val loss: 0.45524, in 0.016s
1 tree, 128 leaves, max depth = 15, train loss: 0.45485, val loss: 0.45352, in 0.016s
1 tree, 81 leaves, max depth = 14, train loss: 0.45304, val loss: 0.45162, in 0.016s
1 tree, 127 leaves, max depth = 15, train loss: 0.45117, val loss: 0.44996, in 0.016s
1 tree, 81 leaves, max depth = 14, train loss: 0.44943, val loss: 0.44812, in 0.000s
1 tree, 127 leaves, max depth = 15, train loss: 0.44762, val loss: 0.44651, in 0.016s
1 tree, 128 leaves, max depth = 15, train loss: 0.44586, val loss: 0.44495, in 0.016s
1 tree, 80 leaves, max depth = 15, train loss: 0.44419, val loss: 0.44319, in 0.016s
1 tree, 127 leaves, max depth = 14, train loss: 0.44248, val loss: 0.44167, in 0.016s
1 tree, 80 leaves, max depth = 15, train loss: 0.44087, val loss: 0.43997, in 0.000s
1 tree, 127 leaves, max depth = 14, train loss: 0.43921, val loss: 0.43851, in 0.031s
1 tree, 128 leaves, max depth = 14, train loss: 0.43760, val loss: 0.43708, in 0.000s
1 tree, 78 leaves, max depth = 15, train loss: 0.43605, val loss: 0.43544, in 0.016s
1 tree, 127 leaves, max depth = 14, train loss: 0.43449, val loss: 0.43407, in 0.016s
Fit 75 trees in 1.267 s, (7409 total leaves)
Time spent computing histograms: 0.387s
Time spent finding best splits:  0.107s
Time spent applying splits:      0.123s
Time spent predicting:           0.016s
Trial 84, Fold 3: Log loss = 0.43330142446967024, Average precision = 0.9190328847460254, ROC-AUC = 0.9310932549046047, Elapsed Time = 1.269082899998466 seconds
Trial 84, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 84, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
Binning 0.040 GB of training data: 0.173 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 74 leaves, max depth = 16, train loss: 0.68541, val loss: 0.68496, in 0.016s
1 tree, 74 leaves, max depth = 16, train loss: 0.67792, val loss: 0.67703, in 0.016s
1 tree, 75 leaves, max depth = 16, train loss: 0.67068, val loss: 0.66935, in 0.016s
1 tree, 78 leaves, max depth = 16, train loss: 0.66375, val loss: 0.66200, in 0.000s
1 tree, 76 leaves, max depth = 16, train loss: 0.65696, val loss: 0.65479, in 0.016s
1 tree, 76 leaves, max depth = 16, train loss: 0.65038, val loss: 0.64780, in 0.016s
1 tree, 76 leaves, max depth = 16, train loss: 0.64402, val loss: 0.64103, in 0.000s
1 tree, 76 leaves, max depth = 16, train loss: 0.63785, val loss: 0.63446, in 0.016s
1 tree, 76 leaves, max depth = 16, train loss: 0.63187, val loss: 0.62808, in 0.016s
1 tree, 76 leaves, max depth = 16, train loss: 0.62608, val loss: 0.62190, in 0.016s
1 tree, 76 leaves, max depth = 16, train loss: 0.62046, val loss: 0.61590, in 0.000s
1 tree, 77 leaves, max depth = 16, train loss: 0.61502, val loss: 0.61008, in 0.016s
1 tree, 82 leaves, max depth = 15, train loss: 0.60976, val loss: 0.60448, in 0.000s
1 tree, 75 leaves, max depth = 16, train loss: 0.60463, val loss: 0.59899, in 0.016s
1 tree, 75 leaves, max depth = 16, train loss: 0.59966, val loss: 0.59366, in 0.016s
1 tree, 82 leaves, max depth = 15, train loss: 0.59486, val loss: 0.58854, in 0.016s
1 tree, 75 leaves, max depth = 16, train loss: 0.59018, val loss: 0.58350, in 0.000s
1 tree, 75 leaves, max depth = 16, train loss: 0.58564, val loss: 0.57861, in 0.016s
1 tree, 79 leaves, max depth = 17, train loss: 0.58126, val loss: 0.57392, in 0.016s
1 tree, 75 leaves, max depth = 16, train loss: 0.57698, val loss: 0.56930, in 0.000s
1 tree, 75 leaves, max depth = 16, train loss: 0.57282, val loss: 0.56480, in 0.016s
1 tree, 75 leaves, max depth = 16, train loss: 0.56879, val loss: 0.56044, in 0.016s
1 tree, 75 leaves, max depth = 16, train loss: 0.56487, val loss: 0.55619, in 0.016s
1 tree, 75 leaves, max depth = 16, train loss: 0.56106, val loss: 0.55206, in 0.000s
1 tree, 125 leaves, max depth = 14, train loss: 0.55704, val loss: 0.54818, in 0.016s
1 tree, 79 leaves, max depth = 16, train loss: 0.55340, val loss: 0.54422, in 0.016s
1 tree, 126 leaves, max depth = 14, train loss: 0.54953, val loss: 0.54050, in 0.016s
1 tree, 79 leaves, max depth = 16, train loss: 0.54606, val loss: 0.53674, in 0.016s
1 tree, 126 leaves, max depth = 15, train loss: 0.54235, val loss: 0.53316, in 0.016s
1 tree, 126 leaves, max depth = 15, train loss: 0.53875, val loss: 0.52969, in 0.016s
1 tree, 79 leaves, max depth = 15, train loss: 0.53544, val loss: 0.52608, in 0.000s
1 tree, 126 leaves, max depth = 15, train loss: 0.53198, val loss: 0.52275, in 0.016s
1 tree, 81 leaves, max depth = 16, train loss: 0.52883, val loss: 0.51932, in 0.016s
1 tree, 126 leaves, max depth = 15, train loss: 0.52549, val loss: 0.51611, in 0.016s
1 tree, 81 leaves, max depth = 16, train loss: 0.52246, val loss: 0.51282, in 0.016s
1 tree, 126 leaves, max depth = 16, train loss: 0.51925, val loss: 0.50973, in 0.016s
1 tree, 78 leaves, max depth = 16, train loss: 0.51633, val loss: 0.50652, in 0.016s
1 tree, 127 leaves, max depth = 16, train loss: 0.51323, val loss: 0.50355, in 0.016s
1 tree, 81 leaves, max depth = 16, train loss: 0.51044, val loss: 0.50050, in 0.016s
1 tree, 125 leaves, max depth = 15, train loss: 0.50746, val loss: 0.49764, in 0.016s
1 tree, 81 leaves, max depth = 16, train loss: 0.50478, val loss: 0.49470, in 0.000s
1 tree, 128 leaves, max depth = 15, train loss: 0.50190, val loss: 0.49194, in 0.016s
1 tree, 81 leaves, max depth = 15, train loss: 0.49932, val loss: 0.48912, in 0.016s
1 tree, 129 leaves, max depth = 15, train loss: 0.49655, val loss: 0.48646, in 0.016s
1 tree, 129 leaves, max depth = 15, train loss: 0.49384, val loss: 0.48387, in 0.016s
1 tree, 129 leaves, max depth = 15, train loss: 0.49122, val loss: 0.48136, in 0.016s
1 tree, 79 leaves, max depth = 14, train loss: 0.48879, val loss: 0.47870, in 0.016s
1 tree, 129 leaves, max depth = 15, train loss: 0.48625, val loss: 0.47628, in 0.016s
1 tree, 80 leaves, max depth = 15, train loss: 0.48392, val loss: 0.47371, in 0.016s
1 tree, 130 leaves, max depth = 18, train loss: 0.48147, val loss: 0.47137, in 0.016s
1 tree, 79 leaves, max depth = 15, train loss: 0.47920, val loss: 0.46885, in 0.016s
1 tree, 130 leaves, max depth = 18, train loss: 0.47683, val loss: 0.46659, in 0.016s
1 tree, 80 leaves, max depth = 15, train loss: 0.47465, val loss: 0.46416, in 0.016s
1 tree, 130 leaves, max depth = 18, train loss: 0.47236, val loss: 0.46198, in 0.016s
1 tree, 130 leaves, max depth = 18, train loss: 0.47013, val loss: 0.45986, in 0.016s
1 tree, 79 leaves, max depth = 14, train loss: 0.46806, val loss: 0.45758, in 0.016s
1 tree, 129 leaves, max depth = 18, train loss: 0.46591, val loss: 0.45553, in 0.016s
1 tree, 80 leaves, max depth = 15, train loss: 0.46392, val loss: 0.45332, in 0.016s
1 tree, 129 leaves, max depth = 18, train loss: 0.46183, val loss: 0.45133, in 0.016s
1 tree, 81 leaves, max depth = 12, train loss: 0.45985, val loss: 0.44915, in 0.016s
1 tree, 80 leaves, max depth = 15, train loss: 0.45798, val loss: 0.44707, in 0.016s
1 tree, 130 leaves, max depth = 15, train loss: 0.45597, val loss: 0.44516, in 0.016s
1 tree, 130 leaves, max depth = 15, train loss: 0.45402, val loss: 0.44331, in 0.016s
1 tree, 130 leaves, max depth = 15, train loss: 0.45212, val loss: 0.44150, in 0.031s
1 tree, 81 leaves, max depth = 12, train loss: 0.45029, val loss: 0.43948, in 0.000s
1 tree, 80 leaves, max depth = 15, train loss: 0.44856, val loss: 0.43755, in 0.016s
1 tree, 130 leaves, max depth = 17, train loss: 0.44673, val loss: 0.43581, in 0.031s
1 tree, 130 leaves, max depth = 17, train loss: 0.44495, val loss: 0.43412, in 0.016s
1 tree, 80 leaves, max depth = 15, train loss: 0.44329, val loss: 0.43227, in 0.016s
1 tree, 130 leaves, max depth = 15, train loss: 0.44156, val loss: 0.43063, in 0.031s
1 tree, 130 leaves, max depth = 17, train loss: 0.43988, val loss: 0.42904, in 0.016s
1 tree, 77 leaves, max depth = 16, train loss: 0.43829, val loss: 0.42727, in 0.016s
1 tree, 130 leaves, max depth = 13, train loss: 0.43666, val loss: 0.42573, in 0.016s
1 tree, 79 leaves, max depth = 12, train loss: 0.43508, val loss: 0.42397, in 0.016s
1 tree, 130 leaves, max depth = 13, train loss: 0.43350, val loss: 0.42247, in 0.016s
Fit 75 trees in 1.361 s, (7308 total leaves)
Time spent computing histograms: 0.426s
Time spent finding best splits:  0.123s
Time spent applying splits:      0.136s
Time spent predicting:           0.031s
Trial 84, Fold 4: Log loss = 0.4347714117107563, Average precision = 0.9173394608907867, ROC-AUC = 0.9293842887663302, Elapsed Time = 1.3692844999986846 seconds
Trial 84, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 84, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
Binning 0.040 GB of training data: 0.157 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 79 leaves, max depth = 17, train loss: 0.68540, val loss: 0.68489, in 0.016s
1 tree, 78 leaves, max depth = 14, train loss: 0.67791, val loss: 0.67690, in 0.016s
1 tree, 80 leaves, max depth = 17, train loss: 0.67059, val loss: 0.66909, in 0.000s
1 tree, 78 leaves, max depth = 14, train loss: 0.66357, val loss: 0.66160, in 0.016s
1 tree, 80 leaves, max depth = 17, train loss: 0.65671, val loss: 0.65426, in 0.000s
1 tree, 80 leaves, max depth = 17, train loss: 0.65005, val loss: 0.64715, in 0.000s
1 tree, 80 leaves, max depth = 17, train loss: 0.64361, val loss: 0.64025, in 0.016s
1 tree, 80 leaves, max depth = 17, train loss: 0.63737, val loss: 0.63356, in 0.016s
1 tree, 80 leaves, max depth = 17, train loss: 0.63132, val loss: 0.62708, in 0.000s
1 tree, 79 leaves, max depth = 17, train loss: 0.62552, val loss: 0.62084, in 0.016s
1 tree, 80 leaves, max depth = 17, train loss: 0.61983, val loss: 0.61473, in 0.016s
1 tree, 76 leaves, max depth = 14, train loss: 0.61437, val loss: 0.60885, in 0.016s
1 tree, 76 leaves, max depth = 14, train loss: 0.60908, val loss: 0.60315, in 0.000s
1 tree, 80 leaves, max depth = 17, train loss: 0.60389, val loss: 0.59755, in 0.016s
1 tree, 80 leaves, max depth = 17, train loss: 0.59885, val loss: 0.59212, in 0.016s
1 tree, 79 leaves, max depth = 15, train loss: 0.59402, val loss: 0.58689, in 0.000s
1 tree, 80 leaves, max depth = 17, train loss: 0.58927, val loss: 0.58176, in 0.016s
1 tree, 80 leaves, max depth = 17, train loss: 0.58466, val loss: 0.57677, in 0.016s
1 tree, 78 leaves, max depth = 17, train loss: 0.58024, val loss: 0.57197, in 0.016s
1 tree, 80 leaves, max depth = 17, train loss: 0.57590, val loss: 0.56726, in 0.016s
1 tree, 80 leaves, max depth = 17, train loss: 0.57168, val loss: 0.56268, in 0.000s
1 tree, 80 leaves, max depth = 17, train loss: 0.56759, val loss: 0.55823, in 0.016s
1 tree, 80 leaves, max depth = 17, train loss: 0.56361, val loss: 0.55390, in 0.016s
1 tree, 80 leaves, max depth = 17, train loss: 0.55975, val loss: 0.54969, in 0.000s
1 tree, 78 leaves, max depth = 15, train loss: 0.55603, val loss: 0.54563, in 0.016s
1 tree, 127 leaves, max depth = 15, train loss: 0.55218, val loss: 0.54195, in 0.016s
1 tree, 78 leaves, max depth = 15, train loss: 0.54861, val loss: 0.53806, in 0.016s
1 tree, 128 leaves, max depth = 15, train loss: 0.54491, val loss: 0.53453, in 0.016s
1 tree, 80 leaves, max depth = 19, train loss: 0.54145, val loss: 0.53075, in 0.016s
1 tree, 127 leaves, max depth = 15, train loss: 0.53790, val loss: 0.52736, in 0.000s
1 tree, 79 leaves, max depth = 19, train loss: 0.53458, val loss: 0.52373, in 0.016s
1 tree, 125 leaves, max depth = 15, train loss: 0.53116, val loss: 0.52048, in 0.016s
1 tree, 79 leaves, max depth = 17, train loss: 0.52801, val loss: 0.51705, in 0.016s
1 tree, 124 leaves, max depth = 15, train loss: 0.52471, val loss: 0.51392, in 0.016s
1 tree, 80 leaves, max depth = 17, train loss: 0.52168, val loss: 0.51062, in 0.016s
1 tree, 125 leaves, max depth = 15, train loss: 0.51851, val loss: 0.50762, in 0.016s
1 tree, 125 leaves, max depth = 15, train loss: 0.51543, val loss: 0.50470, in 0.016s
1 tree, 81 leaves, max depth = 15, train loss: 0.51255, val loss: 0.50156, in 0.016s
1 tree, 81 leaves, max depth = 17, train loss: 0.50976, val loss: 0.49850, in 0.016s
1 tree, 127 leaves, max depth = 15, train loss: 0.50681, val loss: 0.49572, in 0.016s
1 tree, 81 leaves, max depth = 15, train loss: 0.50412, val loss: 0.49278, in 0.000s
1 tree, 126 leaves, max depth = 16, train loss: 0.50128, val loss: 0.49010, in 0.016s
1 tree, 81 leaves, max depth = 15, train loss: 0.49870, val loss: 0.48726, in 0.016s
1 tree, 126 leaves, max depth = 16, train loss: 0.49596, val loss: 0.48468, in 0.016s
1 tree, 127 leaves, max depth = 16, train loss: 0.49329, val loss: 0.48218, in 0.016s
1 tree, 127 leaves, max depth = 16, train loss: 0.49070, val loss: 0.47974, in 0.016s
1 tree, 80 leaves, max depth = 15, train loss: 0.48826, val loss: 0.47707, in 0.016s
1 tree, 126 leaves, max depth = 16, train loss: 0.48576, val loss: 0.47473, in 0.016s
1 tree, 83 leaves, max depth = 15, train loss: 0.48341, val loss: 0.47214, in 0.016s
1 tree, 126 leaves, max depth = 16, train loss: 0.48099, val loss: 0.46988, in 0.016s
1 tree, 83 leaves, max depth = 15, train loss: 0.47873, val loss: 0.46739, in 0.016s
1 tree, 126 leaves, max depth = 17, train loss: 0.47640, val loss: 0.46520, in 0.016s
1 tree, 80 leaves, max depth = 18, train loss: 0.47422, val loss: 0.46281, in 0.016s
1 tree, 126 leaves, max depth = 17, train loss: 0.47196, val loss: 0.46070, in 0.016s
1 tree, 126 leaves, max depth = 17, train loss: 0.46977, val loss: 0.45866, in 0.031s
1 tree, 80 leaves, max depth = 18, train loss: 0.46769, val loss: 0.45636, in 0.016s
1 tree, 125 leaves, max depth = 16, train loss: 0.46557, val loss: 0.45439, in 0.016s
1 tree, 82 leaves, max depth = 15, train loss: 0.46356, val loss: 0.45216, in 0.016s
1 tree, 125 leaves, max depth = 16, train loss: 0.46150, val loss: 0.45025, in 0.016s
1 tree, 80 leaves, max depth = 19, train loss: 0.45957, val loss: 0.44811, in 0.000s
1 tree, 82 leaves, max depth = 15, train loss: 0.45769, val loss: 0.44602, in 0.016s
1 tree, 125 leaves, max depth = 16, train loss: 0.45571, val loss: 0.44419, in 0.016s
1 tree, 125 leaves, max depth = 16, train loss: 0.45378, val loss: 0.44241, in 0.016s
1 tree, 125 leaves, max depth = 16, train loss: 0.45191, val loss: 0.44068, in 0.016s
1 tree, 82 leaves, max depth = 14, train loss: 0.45012, val loss: 0.43868, in 0.016s
1 tree, 82 leaves, max depth = 14, train loss: 0.44838, val loss: 0.43674, in 0.016s
1 tree, 126 leaves, max depth = 15, train loss: 0.44657, val loss: 0.43508, in 0.016s
1 tree, 126 leaves, max depth = 15, train loss: 0.44481, val loss: 0.43346, in 0.016s
1 tree, 82 leaves, max depth = 14, train loss: 0.44314, val loss: 0.43159, in 0.016s
1 tree, 126 leaves, max depth = 13, train loss: 0.44144, val loss: 0.43003, in 0.016s
1 tree, 82 leaves, max depth = 14, train loss: 0.43983, val loss: 0.42822, in 0.016s
1 tree, 126 leaves, max depth = 15, train loss: 0.43817, val loss: 0.42671, in 0.016s
1 tree, 126 leaves, max depth = 15, train loss: 0.43656, val loss: 0.42524, in 0.016s
1 tree, 83 leaves, max depth = 14, train loss: 0.43502, val loss: 0.42350, in 0.016s
1 tree, 125 leaves, max depth = 16, train loss: 0.43346, val loss: 0.42207, in 0.016s
Fit 75 trees in 1.345 s, (7286 total leaves)
Time spent computing histograms: 0.416s
Time spent finding best splits:  0.120s
Time spent applying splits:      0.134s
Time spent predicting:           0.016s
Trial 84, Fold 5: Log loss = 0.43985658498218044, Average precision = 0.9133008795843532, ROC-AUC = 0.9265036136967467, Elapsed Time = 1.3525437000007514 seconds
Optimization Progress:  85%|########5 | 85/100 [17:43<03:42, 14.82s/it]
Trial 85, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 85, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
0.142 s 0.040 GB of training data: 
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 71 leaves, max depth = 12, train loss: 0.67320, val loss: 0.67335, in 0.016s
1 tree, 71 leaves, max depth = 12, train loss: 0.65476, val loss: 0.65504, in 0.016s
1 tree, 71 leaves, max depth = 12, train loss: 0.63751, val loss: 0.63791, in 0.016s
1 tree, 75 leaves, max depth = 11, train loss: 0.62134, val loss: 0.62183, in 0.016s
1 tree, 70 leaves, max depth = 12, train loss: 0.60616, val loss: 0.60674, in 0.016s
1 tree, 73 leaves, max depth = 12, train loss: 0.59190, val loss: 0.59259, in 0.031s
1 tree, 73 leaves, max depth = 12, train loss: 0.57848, val loss: 0.57927, in 0.016s
1 tree, 76 leaves, max depth = 12, train loss: 0.56584, val loss: 0.56672, in 0.016s
1 tree, 71 leaves, max depth = 12, train loss: 0.55391, val loss: 0.55489, in 0.031s
1 tree, 76 leaves, max depth = 12, train loss: 0.54265, val loss: 0.54371, in 0.016s
1 tree, 88 leaves, max depth = 14, train loss: 0.53222, val loss: 0.53317, in 0.016s
1 tree, 83 leaves, max depth = 12, train loss: 0.52236, val loss: 0.52327, in 0.016s
1 tree, 80 leaves, max depth = 11, train loss: 0.51249, val loss: 0.51332, in 0.031s
1 tree, 78 leaves, max depth = 11, train loss: 0.50315, val loss: 0.50390, in 0.016s
1 tree, 82 leaves, max depth = 11, train loss: 0.49429, val loss: 0.49494, in 0.016s
1 tree, 80 leaves, max depth = 11, train loss: 0.48589, val loss: 0.48649, in 0.016s
1 tree, 83 leaves, max depth = 15, train loss: 0.47828, val loss: 0.47880, in 0.031s
1 tree, 80 leaves, max depth = 12, train loss: 0.47103, val loss: 0.47147, in 0.016s
1 tree, 78 leaves, max depth = 11, train loss: 0.46375, val loss: 0.46416, in 0.015s
1 tree, 78 leaves, max depth = 11, train loss: 0.45684, val loss: 0.45723, in 0.031s
1 tree, 80 leaves, max depth = 11, train loss: 0.45026, val loss: 0.45062, in 0.016s
1 tree, 78 leaves, max depth = 11, train loss: 0.44401, val loss: 0.44436, in 0.016s
1 tree, 78 leaves, max depth = 11, train loss: 0.43807, val loss: 0.43836, in 0.016s
1 tree, 79 leaves, max depth = 15, train loss: 0.42934, val loss: 0.42995, in 0.031s
1 tree, 94 leaves, max depth = 15, train loss: 0.42114, val loss: 0.42201, in 0.016s
1 tree, 83 leaves, max depth = 11, train loss: 0.41588, val loss: 0.41673, in 0.016s
1 tree, 81 leaves, max depth = 11, train loss: 0.41086, val loss: 0.41171, in 0.031s
1 tree, 78 leaves, max depth = 16, train loss: 0.40346, val loss: 0.40460, in 0.016s
1 tree, 83 leaves, max depth = 11, train loss: 0.39884, val loss: 0.39999, in 0.016s
1 tree, 80 leaves, max depth = 15, train loss: 0.39201, val loss: 0.39344, in 0.016s
1 tree, 77 leaves, max depth = 13, train loss: 0.38773, val loss: 0.38923, in 0.016s
1 tree, 80 leaves, max depth = 16, train loss: 0.38141, val loss: 0.38321, in 0.031s
1 tree, 83 leaves, max depth = 11, train loss: 0.37748, val loss: 0.37932, in 0.016s
1 tree, 80 leaves, max depth = 15, train loss: 0.37163, val loss: 0.37376, in 0.016s
1 tree, 74 leaves, max depth = 14, train loss: 0.36807, val loss: 0.37031, in 0.031s
1 tree, 81 leaves, max depth = 15, train loss: 0.36264, val loss: 0.36518, in 0.016s
Fit 36 trees in 1.017 s, (2826 total leaves)
Time spent computing histograms: 0.262s
Time spent finding best splits:  0.065s
Time spent applying splits:      0.052s
Time spent predicting:           0.016s
Trial 85, Fold 1: Log loss = 0.3676550507126004, Average precision = 0.952459670240728, ROC-AUC = 0.9474979298404095, Elapsed Time = 1.0264220000008208 seconds
Trial 85, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 85, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
Binning 0.040 GB of training data: 0.174 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 73 leaves, max depth = 12, train loss: 0.67321, val loss: 0.67284, in 0.016s
1 tree, 73 leaves, max depth = 12, train loss: 0.65466, val loss: 0.65394, in 0.031s
1 tree, 74 leaves, max depth = 11, train loss: 0.63740, val loss: 0.63633, in 0.016s
1 tree, 74 leaves, max depth = 11, train loss: 0.62122, val loss: 0.61983, in 0.031s
1 tree, 73 leaves, max depth = 12, train loss: 0.60595, val loss: 0.60430, in 0.016s
1 tree, 78 leaves, max depth = 11, train loss: 0.59142, val loss: 0.58955, in 0.016s
1 tree, 75 leaves, max depth = 11, train loss: 0.57773, val loss: 0.57560, in 0.031s
1 tree, 76 leaves, max depth = 11, train loss: 0.56483, val loss: 0.56249, in 0.016s
1 tree, 76 leaves, max depth = 11, train loss: 0.55268, val loss: 0.55015, in 0.016s
1 tree, 74 leaves, max depth = 11, train loss: 0.54118, val loss: 0.53843, in 0.031s
1 tree, 76 leaves, max depth = 11, train loss: 0.53033, val loss: 0.52741, in 0.016s
1 tree, 75 leaves, max depth = 13, train loss: 0.52002, val loss: 0.51689, in 0.031s
1 tree, 74 leaves, max depth = 11, train loss: 0.51026, val loss: 0.50694, in 0.016s
1 tree, 77 leaves, max depth = 14, train loss: 0.50102, val loss: 0.49756, in 0.016s
1 tree, 75 leaves, max depth = 11, train loss: 0.49230, val loss: 0.48869, in 0.016s
1 tree, 75 leaves, max depth = 13, train loss: 0.48398, val loss: 0.48019, in 0.031s
1 tree, 82 leaves, max depth = 12, train loss: 0.47645, val loss: 0.47261, in 0.016s
1 tree, 85 leaves, max depth = 14, train loss: 0.46930, val loss: 0.46542, in 0.016s
1 tree, 77 leaves, max depth = 11, train loss: 0.46211, val loss: 0.45811, in 0.031s
1 tree, 79 leaves, max depth = 13, train loss: 0.45528, val loss: 0.45118, in 0.016s
1 tree, 75 leaves, max depth = 11, train loss: 0.44878, val loss: 0.44454, in 0.031s
1 tree, 76 leaves, max depth = 14, train loss: 0.44260, val loss: 0.43824, in 0.016s
1 tree, 80 leaves, max depth = 14, train loss: 0.43379, val loss: 0.42960, in 0.031s
1 tree, 79 leaves, max depth = 15, train loss: 0.42813, val loss: 0.42387, in 0.016s
1 tree, 80 leaves, max depth = 14, train loss: 0.42003, val loss: 0.41595, in 0.016s
1 tree, 77 leaves, max depth = 14, train loss: 0.41484, val loss: 0.41071, in 0.031s
1 tree, 75 leaves, max depth = 12, train loss: 0.40989, val loss: 0.40566, in 0.016s
1 tree, 83 leaves, max depth = 14, train loss: 0.40260, val loss: 0.39856, in 0.031s
1 tree, 75 leaves, max depth = 12, train loss: 0.39804, val loss: 0.39395, in 0.016s
1 tree, 82 leaves, max depth = 14, train loss: 0.39131, val loss: 0.38741, in 0.031s
1 tree, 77 leaves, max depth = 14, train loss: 0.38711, val loss: 0.38320, in 0.016s
1 tree, 82 leaves, max depth = 14, train loss: 0.38090, val loss: 0.37717, in 0.016s
1 tree, 75 leaves, max depth = 12, train loss: 0.37702, val loss: 0.37326, in 0.031s
1 tree, 76 leaves, max depth = 13, train loss: 0.37330, val loss: 0.36950, in 0.016s
1 tree, 82 leaves, max depth = 14, train loss: 0.36762, val loss: 0.36399, in 0.031s
1 tree, 81 leaves, max depth = 14, train loss: 0.36229, val loss: 0.35882, in 0.016s
Fit 36 trees in 1.111 s, (2776 total leaves)
Time spent computing histograms: 0.299s
Time spent finding best splits:  0.073s
Time spent applying splits:      0.056s
Time spent predicting:           0.000s
Trial 85, Fold 2: Log loss = 0.36578762426480615, Average precision = 0.9499341777478105, ROC-AUC = 0.9485677437016323, Elapsed Time = 1.1277160000008735 seconds
Trial 85, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 85, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
Binning 0.040 GB of training data: 0.157 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 73 leaves, max depth = 12, train loss: 0.67328, val loss: 0.67336, in 0.016s
1 tree, 73 leaves, max depth = 12, train loss: 0.65488, val loss: 0.65500, in 0.031s
1 tree, 72 leaves, max depth = 13, train loss: 0.63766, val loss: 0.63781, in 0.016s
1 tree, 75 leaves, max depth = 12, train loss: 0.62149, val loss: 0.62161, in 0.016s
1 tree, 73 leaves, max depth = 11, train loss: 0.60634, val loss: 0.60650, in 0.031s
1 tree, 72 leaves, max depth = 12, train loss: 0.59211, val loss: 0.59226, in 0.016s
1 tree, 77 leaves, max depth = 14, train loss: 0.57849, val loss: 0.57872, in 0.016s
1 tree, 78 leaves, max depth = 14, train loss: 0.56565, val loss: 0.56589, in 0.031s
1 tree, 83 leaves, max depth = 15, train loss: 0.55406, val loss: 0.55440, in 0.016s
1 tree, 82 leaves, max depth = 14, train loss: 0.54257, val loss: 0.54293, in 0.031s
1 tree, 81 leaves, max depth = 14, train loss: 0.53172, val loss: 0.53217, in 0.016s
1 tree, 81 leaves, max depth = 14, train loss: 0.52145, val loss: 0.52193, in 0.016s
1 tree, 82 leaves, max depth = 14, train loss: 0.51174, val loss: 0.51229, in 0.031s
1 tree, 79 leaves, max depth = 14, train loss: 0.50253, val loss: 0.50314, in 0.016s
1 tree, 82 leaves, max depth = 13, train loss: 0.49380, val loss: 0.49445, in 0.031s
1 tree, 83 leaves, max depth = 13, train loss: 0.48552, val loss: 0.48619, in 0.016s
1 tree, 82 leaves, max depth = 13, train loss: 0.47767, val loss: 0.47836, in 0.016s
1 tree, 82 leaves, max depth = 12, train loss: 0.47020, val loss: 0.47093, in 0.031s
1 tree, 81 leaves, max depth = 14, train loss: 0.46311, val loss: 0.46385, in 0.016s
1 tree, 81 leaves, max depth = 13, train loss: 0.45637, val loss: 0.45718, in 0.016s
1 tree, 83 leaves, max depth = 13, train loss: 0.44995, val loss: 0.45080, in 0.016s
1 tree, 84 leaves, max depth = 15, train loss: 0.44076, val loss: 0.44230, in 0.031s
1 tree, 79 leaves, max depth = 13, train loss: 0.43485, val loss: 0.43643, in 0.031s
1 tree, 85 leaves, max depth = 15, train loss: 0.42641, val loss: 0.42864, in 0.016s
1 tree, 81 leaves, max depth = 15, train loss: 0.41844, val loss: 0.42130, in 0.016s
1 tree, 81 leaves, max depth = 13, train loss: 0.41323, val loss: 0.41612, in 0.031s
1 tree, 80 leaves, max depth = 13, train loss: 0.40823, val loss: 0.41118, in 0.016s
1 tree, 82 leaves, max depth = 15, train loss: 0.40107, val loss: 0.40461, in 0.031s
1 tree, 81 leaves, max depth = 17, train loss: 0.39650, val loss: 0.40011, in 0.016s
1 tree, 81 leaves, max depth = 15, train loss: 0.38988, val loss: 0.39407, in 0.016s
1 tree, 84 leaves, max depth = 14, train loss: 0.38566, val loss: 0.38990, in 0.031s
1 tree, 81 leaves, max depth = 15, train loss: 0.37954, val loss: 0.38433, in 0.016s
1 tree, 84 leaves, max depth = 16, train loss: 0.37562, val loss: 0.38048, in 0.016s
1 tree, 81 leaves, max depth = 15, train loss: 0.36995, val loss: 0.37533, in 0.031s
1 tree, 85 leaves, max depth = 12, train loss: 0.36635, val loss: 0.37180, in 0.016s
1 tree, 83 leaves, max depth = 15, train loss: 0.36110, val loss: 0.36705, in 0.016s
Fit 36 trees in 1.095 s, (2887 total leaves)
Time spent computing histograms: 0.287s
Time spent finding best splits:  0.072s
Time spent applying splits:      0.058s
Time spent predicting:           0.000s
Trial 85, Fold 3: Log loss = 0.36100000465176585, Average precision = 0.9513218767088868, ROC-AUC = 0.9504091986015737, Elapsed Time = 1.107314300001235 seconds
Trial 85, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 85, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
Binning 0.040 GB of training data: 0.173 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 77 leaves, max depth = 12, train loss: 0.67342, val loss: 0.67281, in 0.016s
1 tree, 76 leaves, max depth = 12, train loss: 0.65512, val loss: 0.65384, in 0.031s
1 tree, 74 leaves, max depth = 13, train loss: 0.63800, val loss: 0.63604, in 0.016s
1 tree, 96 leaves, max depth = 13, train loss: 0.62209, val loss: 0.61939, in 0.031s
1 tree, 73 leaves, max depth = 12, train loss: 0.60693, val loss: 0.60369, in 0.031s
1 tree, 74 leaves, max depth = 12, train loss: 0.59274, val loss: 0.58898, in 0.016s
1 tree, 78 leaves, max depth = 12, train loss: 0.57934, val loss: 0.57507, in 0.016s
1 tree, 74 leaves, max depth = 12, train loss: 0.56676, val loss: 0.56200, in 0.031s
1 tree, 77 leaves, max depth = 12, train loss: 0.55458, val loss: 0.54933, in 0.016s
1 tree, 74 leaves, max depth = 12, train loss: 0.54313, val loss: 0.53739, in 0.031s
1 tree, 77 leaves, max depth = 12, train loss: 0.53231, val loss: 0.52607, in 0.016s
1 tree, 77 leaves, max depth = 12, train loss: 0.52202, val loss: 0.51534, in 0.031s
1 tree, 77 leaves, max depth = 11, train loss: 0.51229, val loss: 0.50518, in 0.016s
1 tree, 76 leaves, max depth = 11, train loss: 0.50307, val loss: 0.49554, in 0.031s
1 tree, 77 leaves, max depth = 11, train loss: 0.49431, val loss: 0.48642, in 0.016s
1 tree, 79 leaves, max depth = 11, train loss: 0.48601, val loss: 0.47774, in 0.031s
1 tree, 76 leaves, max depth = 11, train loss: 0.47812, val loss: 0.46950, in 0.016s
1 tree, 75 leaves, max depth = 11, train loss: 0.47065, val loss: 0.46166, in 0.031s
1 tree, 76 leaves, max depth = 11, train loss: 0.46354, val loss: 0.45420, in 0.016s
1 tree, 78 leaves, max depth = 12, train loss: 0.45678, val loss: 0.44710, in 0.031s
1 tree, 76 leaves, max depth = 11, train loss: 0.45036, val loss: 0.44035, in 0.016s
1 tree, 76 leaves, max depth = 11, train loss: 0.44424, val loss: 0.43391, in 0.016s
1 tree, 78 leaves, max depth = 13, train loss: 0.43539, val loss: 0.42494, in 0.031s
1 tree, 76 leaves, max depth = 13, train loss: 0.42980, val loss: 0.41902, in 0.016s
1 tree, 78 leaves, max depth = 13, train loss: 0.42166, val loss: 0.41078, in 0.016s
1 tree, 78 leaves, max depth = 11, train loss: 0.41652, val loss: 0.40536, in 0.031s
1 tree, 79 leaves, max depth = 13, train loss: 0.40906, val loss: 0.39784, in 0.016s
1 tree, 81 leaves, max depth = 11, train loss: 0.40434, val loss: 0.39287, in 0.031s
1 tree, 79 leaves, max depth = 13, train loss: 0.39743, val loss: 0.38587, in 0.016s
1 tree, 81 leaves, max depth = 12, train loss: 0.39319, val loss: 0.38139, in 0.016s
1 tree, 77 leaves, max depth = 13, train loss: 0.38680, val loss: 0.37493, in 0.031s
1 tree, 81 leaves, max depth = 12, train loss: 0.38289, val loss: 0.37081, in 0.016s
1 tree, 77 leaves, max depth = 13, train loss: 0.37697, val loss: 0.36484, in 0.016s
1 tree, 82 leaves, max depth = 11, train loss: 0.37326, val loss: 0.36091, in 0.031s
1 tree, 76 leaves, max depth = 13, train loss: 0.36778, val loss: 0.35538, in 0.016s
1 tree, 83 leaves, max depth = 12, train loss: 0.36445, val loss: 0.35186, in 0.016s
Fit 36 trees in 1.158 s, (2799 total leaves)
Time spent computing histograms: 0.300s
Time spent finding best splits:  0.076s
Time spent applying splits:      0.059s
Time spent predicting:           0.000s
Trial 85, Fold 4: Log loss = 0.3651511463110123, Average precision = 0.9530434339767936, ROC-AUC = 0.9493210840242161, Elapsed Time = 1.1653210000004037 seconds
Trial 85, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 85, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
Binning 0.040 GB of training data: 0.157 s
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 77 leaves, max depth = 14, train loss: 0.67324, val loss: 0.67264, in 0.016s
1 tree, 74 leaves, max depth = 13, train loss: 0.65453, val loss: 0.65331, in 0.016s
1 tree, 75 leaves, max depth = 13, train loss: 0.63702, val loss: 0.63521, in 0.031s
1 tree, 73 leaves, max depth = 13, train loss: 0.62062, val loss: 0.61825, in 0.016s
1 tree, 90 leaves, max depth = 12, train loss: 0.60566, val loss: 0.60276, in 0.016s
1 tree, 75 leaves, max depth = 13, train loss: 0.59115, val loss: 0.58775, in 0.031s
1 tree, 75 leaves, max depth = 13, train loss: 0.57751, val loss: 0.57363, in 0.016s
1 tree, 77 leaves, max depth = 13, train loss: 0.56465, val loss: 0.56031, in 0.016s
1 tree, 80 leaves, max depth = 13, train loss: 0.55243, val loss: 0.54765, in 0.031s
1 tree, 79 leaves, max depth = 13, train loss: 0.54089, val loss: 0.53570, in 0.016s
1 tree, 79 leaves, max depth = 13, train loss: 0.52998, val loss: 0.52441, in 0.031s
1 tree, 82 leaves, max depth = 13, train loss: 0.52017, val loss: 0.51424, in 0.016s
1 tree, 82 leaves, max depth = 13, train loss: 0.51036, val loss: 0.50404, in 0.016s
1 tree, 80 leaves, max depth = 13, train loss: 0.50107, val loss: 0.49442, in 0.016s
1 tree, 74 leaves, max depth = 13, train loss: 0.49232, val loss: 0.48537, in 0.031s
1 tree, 81 leaves, max depth = 13, train loss: 0.48396, val loss: 0.47668, in 0.016s
1 tree, 83 leaves, max depth = 14, train loss: 0.47641, val loss: 0.46882, in 0.031s
1 tree, 84 leaves, max depth = 14, train loss: 0.46925, val loss: 0.46137, in 0.016s
1 tree, 81 leaves, max depth = 13, train loss: 0.46201, val loss: 0.45386, in 0.031s
1 tree, 80 leaves, max depth = 13, train loss: 0.45513, val loss: 0.44667, in 0.016s
1 tree, 79 leaves, max depth = 13, train loss: 0.44859, val loss: 0.43989, in 0.016s
1 tree, 79 leaves, max depth = 13, train loss: 0.44236, val loss: 0.43336, in 0.031s
1 tree, 78 leaves, max depth = 13, train loss: 0.43644, val loss: 0.42719, in 0.016s
1 tree, 80 leaves, max depth = 15, train loss: 0.42778, val loss: 0.41859, in 0.031s
1 tree, 96 leaves, max depth = 14, train loss: 0.41971, val loss: 0.41056, in 0.016s
1 tree, 84 leaves, max depth = 14, train loss: 0.41446, val loss: 0.40509, in 0.031s
1 tree, 82 leaves, max depth = 14, train loss: 0.40946, val loss: 0.39992, in 0.016s
1 tree, 80 leaves, max depth = 15, train loss: 0.40211, val loss: 0.39262, in 0.016s
1 tree, 77 leaves, max depth = 13, train loss: 0.39754, val loss: 0.38792, in 0.031s
1 tree, 79 leaves, max depth = 15, train loss: 0.39075, val loss: 0.38118, in 0.016s
1 tree, 79 leaves, max depth = 14, train loss: 0.38655, val loss: 0.37685, in 0.016s
1 tree, 77 leaves, max depth = 14, train loss: 0.38027, val loss: 0.37067, in 0.016s
1 tree, 82 leaves, max depth = 13, train loss: 0.37641, val loss: 0.36679, in 0.016s
1 tree, 81 leaves, max depth = 13, train loss: 0.37264, val loss: 0.36296, in 0.016s
1 tree, 79 leaves, max depth = 14, train loss: 0.36694, val loss: 0.35736, in 0.016s
1 tree, 81 leaves, max depth = 13, train loss: 0.36152, val loss: 0.35200, in 0.031s
Fit 36 trees in 1.142 s, (2874 total leaves)
Time spent computing histograms: 0.293s
Time spent finding best splits:  0.072s
Time spent applying splits:      0.058s
Time spent predicting:           0.000s
Trial 85, Fold 5: Log loss = 0.36914487007096985, Average precision = 0.9498696078413703, ROC-AUC = 0.9474313211823941, Elapsed Time = 1.1547817999999097 seconds
Optimization Progress:  86%|########6 | 86/100 [17:55<03:16, 14.02s/it]
Trial 86, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 86, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
0.142 s 0.040 GB of training data: 
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 27 leaves, max depth = 12, train loss: 0.67757, val loss: 0.67715, in 0.000s
1 tree, 28 leaves, max depth = 12, train loss: 0.66302, val loss: 0.66218, in 0.016s
1 tree, 29 leaves, max depth = 12, train loss: 0.64939, val loss: 0.64816, in 0.000s
1 tree, 29 leaves, max depth = 12, train loss: 0.63662, val loss: 0.63501, in 0.000s
1 tree, 29 leaves, max depth = 12, train loss: 0.62465, val loss: 0.62267, in 0.000s
1 tree, 29 leaves, max depth = 12, train loss: 0.61341, val loss: 0.61107, in 0.000s
1 tree, 29 leaves, max depth = 12, train loss: 0.60286, val loss: 0.60017, in 0.016s
1 tree, 29 leaves, max depth = 12, train loss: 0.59293, val loss: 0.58991, in 0.000s
1 tree, 30 leaves, max depth = 12, train loss: 0.58360, val loss: 0.58024, in 0.016s
1 tree, 31 leaves, max depth = 12, train loss: 0.57481, val loss: 0.57113, in 0.000s
1 tree, 28 leaves, max depth = 11, train loss: 0.56654, val loss: 0.56255, in 0.016s
1 tree, 28 leaves, max depth = 11, train loss: 0.55874, val loss: 0.55446, in 0.000s
1 tree, 31 leaves, max depth = 14, train loss: 0.55145, val loss: 0.54680, in 0.016s
Fit 13 trees in 0.361 s, (377 total leaves)
Time spent computing histograms: 0.051s
Time spent finding best splits:  0.007s
Time spent applying splits:      0.006s
Time spent predicting:           0.000s
Trial 86, Fold 1: Log loss = 0.5523366704044703, Average precision = 0.8140478710455946, ROC-AUC = 0.8619023571739245, Elapsed Time = 0.3603768000011769 seconds
Trial 86, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 86, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
Binning 0.040 GB of training data: 0.142 s
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 29 leaves, max depth = 9, train loss: 0.67775, val loss: 0.67702, in 0.000s
1 tree, 29 leaves, max depth = 9, train loss: 0.66336, val loss: 0.66191, in 0.016s
1 tree, 29 leaves, max depth = 9, train loss: 0.64990, val loss: 0.64776, in 0.000s
1 tree, 29 leaves, max depth = 9, train loss: 0.63728, val loss: 0.63449, in 0.016s
1 tree, 29 leaves, max depth = 9, train loss: 0.62545, val loss: 0.62202, in 0.000s
1 tree, 30 leaves, max depth = 11, train loss: 0.61434, val loss: 0.61036, in 0.016s
1 tree, 29 leaves, max depth = 9, train loss: 0.60390, val loss: 0.59934, in 0.000s
1 tree, 30 leaves, max depth = 11, train loss: 0.59408, val loss: 0.58902, in 0.000s
1 tree, 29 leaves, max depth = 9, train loss: 0.58485, val loss: 0.57923, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.57615, val loss: 0.57007, in 0.000s
1 tree, 31 leaves, max depth = 11, train loss: 0.56796, val loss: 0.56144, in 0.016s
1 tree, 28 leaves, max depth = 9, train loss: 0.56010, val loss: 0.55310, in 0.000s
1 tree, 54 leaves, max depth = 11, train loss: 0.55226, val loss: 0.54561, in 0.016s
Fit 13 trees in 0.376 s, (407 total leaves)
Time spent computing histograms: 0.046s
Time spent finding best splits:  0.007s
Time spent applying splits:      0.007s
Time spent predicting:           0.000s
Trial 86, Fold 2: Log loss = 0.5521814942210168, Average precision = 0.8878644760545134, ROC-AUC = 0.9063182770192243, Elapsed Time = 0.37138969999978144 seconds
Trial 86, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 86, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
0.158 s 0.040 GB of training data: 
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 28 leaves, max depth = 11, train loss: 0.67789, val loss: 0.67739, in 0.016s
1 tree, 28 leaves, max depth = 11, train loss: 0.66362, val loss: 0.66265, in 0.000s
1 tree, 21 leaves, max depth = 8, train loss: 0.65013, val loss: 0.64872, in 0.000s
1 tree, 28 leaves, max depth = 11, train loss: 0.63762, val loss: 0.63576, in 0.000s
1 tree, 21 leaves, max depth = 8, train loss: 0.62575, val loss: 0.62348, in 0.016s
1 tree, 28 leaves, max depth = 11, train loss: 0.61473, val loss: 0.61205, in 0.016s
1 tree, 20 leaves, max depth = 8, train loss: 0.60425, val loss: 0.60119, in 0.000s
1 tree, 28 leaves, max depth = 11, train loss: 0.59452, val loss: 0.59107, in 0.016s
1 tree, 21 leaves, max depth = 7, train loss: 0.58525, val loss: 0.58145, in 0.000s
1 tree, 29 leaves, max depth = 11, train loss: 0.57663, val loss: 0.57247, in 0.000s
1 tree, 22 leaves, max depth = 7, train loss: 0.56840, val loss: 0.56391, in 0.016s
1 tree, 29 leaves, max depth = 11, train loss: 0.56075, val loss: 0.55593, in 0.000s
1 tree, 54 leaves, max depth = 12, train loss: 0.55293, val loss: 0.54866, in 0.016s
Fit 13 trees in 0.377 s, (357 total leaves)
Time spent computing histograms: 0.057s
Time spent finding best splits:  0.007s
Time spent applying splits:      0.007s
Time spent predicting:           0.000s
Trial 86, Fold 3: Log loss = 0.5495966806223601, Average precision = 0.8902275823415812, ROC-AUC = 0.9079231666120907, Elapsed Time = 0.38836579999951937 seconds
Trial 86, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 86, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
0.142 s 0.040 GB of training data: 
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 27 leaves, max depth = 12, train loss: 0.67773, val loss: 0.67684, in 0.000s
1 tree, 27 leaves, max depth = 12, train loss: 0.66332, val loss: 0.66157, in 0.016s
1 tree, 27 leaves, max depth = 12, train loss: 0.64984, val loss: 0.64725, in 0.000s
1 tree, 27 leaves, max depth = 12, train loss: 0.63721, val loss: 0.63381, in 0.016s
1 tree, 27 leaves, max depth = 12, train loss: 0.62536, val loss: 0.62117, in 0.000s
1 tree, 27 leaves, max depth = 12, train loss: 0.61424, val loss: 0.60929, in 0.016s
1 tree, 29 leaves, max depth = 12, train loss: 0.60379, val loss: 0.59810, in 0.000s
1 tree, 32 leaves, max depth = 13, train loss: 0.59403, val loss: 0.58762, in 0.016s
1 tree, 29 leaves, max depth = 12, train loss: 0.58478, val loss: 0.57768, in 0.000s
1 tree, 32 leaves, max depth = 13, train loss: 0.57613, val loss: 0.56836, in 0.016s
1 tree, 30 leaves, max depth = 11, train loss: 0.56792, val loss: 0.55950, in 0.000s
1 tree, 33 leaves, max depth = 13, train loss: 0.56024, val loss: 0.55118, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.55295, val loss: 0.54326, in 0.000s
Fit 13 trees in 0.376 s, (378 total leaves)
Time spent computing histograms: 0.050s
Time spent finding best splits:  0.007s
Time spent applying splits:      0.007s
Time spent predicting:           0.000s
Trial 86, Fold 4: Log loss = 0.5517180277152086, Average precision = 0.8204335615774272, ROC-AUC = 0.8677278482370867, Elapsed Time = 0.3881531000006362 seconds
Trial 86, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 86, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
0.157 s 0.040 GB of training data: 
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 27 leaves, max depth = 10, train loss: 0.67760, val loss: 0.67656, in 0.000s
1 tree, 27 leaves, max depth = 10, train loss: 0.66306, val loss: 0.66102, in 0.016s
1 tree, 27 leaves, max depth = 11, train loss: 0.64952, val loss: 0.64655, in 0.000s
1 tree, 27 leaves, max depth = 10, train loss: 0.63675, val loss: 0.63285, in 0.016s
1 tree, 27 leaves, max depth = 12, train loss: 0.62485, val loss: 0.62008, in 0.000s
1 tree, 27 leaves, max depth = 10, train loss: 0.61360, val loss: 0.60796, in 0.016s
1 tree, 27 leaves, max depth = 12, train loss: 0.60309, val loss: 0.59664, in 0.000s
1 tree, 27 leaves, max depth = 10, train loss: 0.59314, val loss: 0.58587, in 0.016s
1 tree, 27 leaves, max depth = 12, train loss: 0.58385, val loss: 0.57582, in 0.000s
1 tree, 26 leaves, max depth = 12, train loss: 0.57510, val loss: 0.56633, in 0.016s
1 tree, 28 leaves, max depth = 10, train loss: 0.56678, val loss: 0.55726, in 0.000s
1 tree, 30 leaves, max depth = 12, train loss: 0.55901, val loss: 0.54880, in 0.016s
1 tree, 51 leaves, max depth = 11, train loss: 0.55133, val loss: 0.54145, in 0.000s
Fit 13 trees in 0.376 s, (378 total leaves)
Time spent computing histograms: 0.059s
Time spent finding best splits:  0.007s
Time spent applying splits:      0.007s
Time spent predicting:           0.000s
Trial 86, Fold 5: Log loss = 0.5551145966827552, Average precision = 0.8855353230294833, ROC-AUC = 0.9000196941827843, Elapsed Time = 0.39433289999942644 seconds
Optimization Progress:  87%|########7 | 87/100 [18:04<02:39, 12.30s/it]
Trial 87, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 87, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
0.143 s 0.040 GB of training data: 
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 47 leaves, max depth = 13, train loss: 0.68085, val loss: 0.68060, in 0.016s
1 tree, 40 leaves, max depth = 10, train loss: 0.66875, val loss: 0.66851, in 0.016s
1 tree, 44 leaves, max depth = 18, train loss: 0.65796, val loss: 0.65750, in 0.016s
1 tree, 42 leaves, max depth = 13, train loss: 0.64705, val loss: 0.64650, in 0.016s
1 tree, 46 leaves, max depth = 15, train loss: 0.63693, val loss: 0.63619, in 0.016s
1 tree, 49 leaves, max depth = 13, train loss: 0.62728, val loss: 0.62638, in 0.016s
1 tree, 41 leaves, max depth = 13, train loss: 0.61796, val loss: 0.61691, in 0.000s
1 tree, 59 leaves, max depth = 14, train loss: 0.60916, val loss: 0.60806, in 0.031s
1 tree, 53 leaves, max depth = 12, train loss: 0.60046, val loss: 0.59943, in 0.016s
1 tree, 48 leaves, max depth = 15, train loss: 0.59218, val loss: 0.59101, in 0.016s
1 tree, 55 leaves, max depth = 10, train loss: 0.58417, val loss: 0.58311, in 0.016s
1 tree, 28 leaves, max depth = 10, train loss: 0.57650, val loss: 0.57522, in 0.000s
1 tree, 46 leaves, max depth = 13, train loss: 0.56870, val loss: 0.56728, in 0.016s
1 tree, 49 leaves, max depth = 11, train loss: 0.56093, val loss: 0.55950, in 0.016s
1 tree, 44 leaves, max depth = 13, train loss: 0.55389, val loss: 0.55236, in 0.016s
1 tree, 49 leaves, max depth = 13, train loss: 0.54732, val loss: 0.54558, in 0.016s
1 tree, 50 leaves, max depth = 10, train loss: 0.54136, val loss: 0.53949, in 0.016s
1 tree, 30 leaves, max depth = 9, train loss: 0.53522, val loss: 0.53330, in 0.016s
1 tree, 49 leaves, max depth = 12, train loss: 0.52941, val loss: 0.52746, in 0.016s
1 tree, 47 leaves, max depth = 12, train loss: 0.52363, val loss: 0.52160, in 0.016s
1 tree, 49 leaves, max depth = 13, train loss: 0.51746, val loss: 0.51543, in 0.016s
1 tree, 45 leaves, max depth = 16, train loss: 0.51185, val loss: 0.50975, in 0.016s
1 tree, 46 leaves, max depth = 15, train loss: 0.50677, val loss: 0.50463, in 0.016s
1 tree, 44 leaves, max depth = 15, train loss: 0.50151, val loss: 0.49930, in 0.016s
1 tree, 53 leaves, max depth = 14, train loss: 0.49589, val loss: 0.49368, in 0.016s
1 tree, 21 leaves, max depth = 8, train loss: 0.49078, val loss: 0.48840, in 0.016s
1 tree, 53 leaves, max depth = 11, train loss: 0.48596, val loss: 0.48355, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.48137, val loss: 0.47883, in 0.016s
1 tree, 47 leaves, max depth = 12, train loss: 0.47672, val loss: 0.47407, in 0.016s
1 tree, 7 leaves, max depth = 3, train loss: 0.47080, val loss: 0.46825, in 0.016s
1 tree, 50 leaves, max depth = 14, train loss: 0.46649, val loss: 0.46387, in 0.016s
1 tree, 55 leaves, max depth = 13, train loss: 0.46211, val loss: 0.45940, in 0.016s
1 tree, 53 leaves, max depth = 12, train loss: 0.45856, val loss: 0.45580, in 0.016s
1 tree, 44 leaves, max depth = 14, train loss: 0.45455, val loss: 0.45174, in 0.016s
1 tree, 14 leaves, max depth = 7, train loss: 0.45113, val loss: 0.44829, in 0.016s
1 tree, 55 leaves, max depth = 12, train loss: 0.44707, val loss: 0.44424, in 0.016s
1 tree, 26 leaves, max depth = 9, train loss: 0.44362, val loss: 0.44063, in 0.000s
1 tree, 47 leaves, max depth = 14, train loss: 0.44017, val loss: 0.43710, in 0.000s
1 tree, 45 leaves, max depth = 11, train loss: 0.43653, val loss: 0.43350, in 0.016s
1 tree, 7 leaves, max depth = 5, train loss: 0.43339, val loss: 0.43011, in 0.031s
1 tree, 45 leaves, max depth = 10, train loss: 0.43047, val loss: 0.42717, in 0.000s
1 tree, 19 leaves, max depth = 8, train loss: 0.42730, val loss: 0.42393, in 0.016s
1 tree, 51 leaves, max depth = 14, train loss: 0.42447, val loss: 0.42104, in 0.016s
1 tree, 15 leaves, max depth = 7, train loss: 0.42156, val loss: 0.41800, in 0.016s
1 tree, 47 leaves, max depth = 11, train loss: 0.41842, val loss: 0.41493, in 0.016s
1 tree, 11 leaves, max depth = 6, train loss: 0.41563, val loss: 0.41195, in 0.016s
1 tree, 52 leaves, max depth = 13, train loss: 0.41309, val loss: 0.40942, in 0.016s
1 tree, 43 leaves, max depth = 11, train loss: 0.41054, val loss: 0.40681, in 0.016s
1 tree, 53 leaves, max depth = 11, train loss: 0.40787, val loss: 0.40416, in 0.016s
1 tree, 50 leaves, max depth = 13, train loss: 0.40530, val loss: 0.40162, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.40316, val loss: 0.39945, in 0.016s
1 tree, 49 leaves, max depth = 11, train loss: 0.40012, val loss: 0.39643, in 0.016s
Fit 52 trees in 1.096 s, (2178 total leaves)
Time spent computing histograms: 0.341s
Time spent finding best splits:  0.043s
Time spent applying splits:      0.038s
Time spent predicting:           0.000s
Trial 87, Fold 1: Log loss = 0.4021835494331139, Average precision = 0.9526312436607483, ROC-AUC = 0.9463635988873117, Elapsed Time = 1.0909823999991204 seconds
Trial 87, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 87, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
Binning 0.040 GB of training data: 0.141 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 43 leaves, max depth = 11, train loss: 0.68094, val loss: 0.68068, in 0.016s
1 tree, 41 leaves, max depth = 12, train loss: 0.66868, val loss: 0.66811, in 0.016s
1 tree, 44 leaves, max depth = 15, train loss: 0.65779, val loss: 0.65699, in 0.016s
1 tree, 43 leaves, max depth = 12, train loss: 0.64673, val loss: 0.64556, in 0.016s
1 tree, 45 leaves, max depth = 15, train loss: 0.63662, val loss: 0.63542, in 0.016s
1 tree, 44 leaves, max depth = 14, train loss: 0.62704, val loss: 0.62569, in 0.016s
1 tree, 46 leaves, max depth = 12, train loss: 0.61777, val loss: 0.61636, in 0.016s
1 tree, 48 leaves, max depth = 11, train loss: 0.60887, val loss: 0.60719, in 0.016s
1 tree, 52 leaves, max depth = 13, train loss: 0.60014, val loss: 0.59835, in 0.016s
1 tree, 45 leaves, max depth = 11, train loss: 0.59196, val loss: 0.58985, in 0.000s
1 tree, 50 leaves, max depth = 12, train loss: 0.58329, val loss: 0.58101, in 0.031s
1 tree, 51 leaves, max depth = 14, train loss: 0.57580, val loss: 0.57322, in 0.016s
1 tree, 47 leaves, max depth = 12, train loss: 0.56815, val loss: 0.56548, in 0.016s
1 tree, 42 leaves, max depth = 11, train loss: 0.56051, val loss: 0.55761, in 0.016s
1 tree, 48 leaves, max depth = 11, train loss: 0.55365, val loss: 0.55065, in 0.016s
1 tree, 54 leaves, max depth = 13, train loss: 0.54660, val loss: 0.54348, in 0.016s
1 tree, 54 leaves, max depth = 13, train loss: 0.53960, val loss: 0.53647, in 0.016s
1 tree, 46 leaves, max depth = 11, train loss: 0.53283, val loss: 0.52962, in 0.016s
1 tree, 44 leaves, max depth = 11, train loss: 0.52661, val loss: 0.52332, in 0.016s
1 tree, 46 leaves, max depth = 14, train loss: 0.52058, val loss: 0.51723, in 0.016s
1 tree, 48 leaves, max depth = 13, train loss: 0.51451, val loss: 0.51103, in 0.016s
1 tree, 46 leaves, max depth = 12, train loss: 0.50870, val loss: 0.50504, in 0.016s
1 tree, 48 leaves, max depth = 11, train loss: 0.50355, val loss: 0.49971, in 0.016s
1 tree, 46 leaves, max depth = 13, train loss: 0.49837, val loss: 0.49448, in 0.016s
1 tree, 47 leaves, max depth = 14, train loss: 0.49291, val loss: 0.48892, in 0.016s
1 tree, 42 leaves, max depth = 10, train loss: 0.48777, val loss: 0.48370, in 0.016s
1 tree, 53 leaves, max depth = 12, train loss: 0.48311, val loss: 0.47904, in 0.016s
1 tree, 50 leaves, max depth = 17, train loss: 0.47851, val loss: 0.47439, in 0.016s
1 tree, 44 leaves, max depth = 12, train loss: 0.47371, val loss: 0.46947, in 0.016s
1 tree, 6 leaves, max depth = 4, train loss: 0.46978, val loss: 0.46537, in 0.016s
1 tree, 46 leaves, max depth = 11, train loss: 0.46522, val loss: 0.46076, in 0.016s
1 tree, 49 leaves, max depth = 10, train loss: 0.46116, val loss: 0.45663, in 0.016s
1 tree, 51 leaves, max depth = 12, train loss: 0.45678, val loss: 0.45219, in 0.016s
1 tree, 49 leaves, max depth = 13, train loss: 0.45313, val loss: 0.44850, in 0.016s
1 tree, 56 leaves, max depth = 12, train loss: 0.44935, val loss: 0.44475, in 0.016s
1 tree, 45 leaves, max depth = 13, train loss: 0.44536, val loss: 0.44068, in 0.016s
1 tree, 49 leaves, max depth = 15, train loss: 0.44183, val loss: 0.43712, in 0.016s
1 tree, 10 leaves, max depth = 5, train loss: 0.43661, val loss: 0.43191, in 0.016s
1 tree, 49 leaves, max depth = 9, train loss: 0.43331, val loss: 0.42855, in 0.016s
1 tree, 8 leaves, max depth = 5, train loss: 0.43019, val loss: 0.42534, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.42720, val loss: 0.42222, in 0.016s
1 tree, 9 leaves, max depth = 5, train loss: 0.42411, val loss: 0.41907, in 0.016s
1 tree, 48 leaves, max depth = 13, train loss: 0.42076, val loss: 0.41569, in 0.016s
1 tree, 49 leaves, max depth = 13, train loss: 0.41762, val loss: 0.41257, in 0.016s
1 tree, 50 leaves, max depth = 15, train loss: 0.41465, val loss: 0.40949, in 0.016s
1 tree, 38 leaves, max depth = 9, train loss: 0.41218, val loss: 0.40697, in 0.016s
1 tree, 48 leaves, max depth = 17, train loss: 0.40924, val loss: 0.40395, in 0.016s
1 tree, 50 leaves, max depth = 11, train loss: 0.40672, val loss: 0.40142, in 0.016s
1 tree, 52 leaves, max depth = 15, train loss: 0.40415, val loss: 0.39886, in 0.016s
1 tree, 17 leaves, max depth = 8, train loss: 0.39974, val loss: 0.39456, in 0.016s
1 tree, 45 leaves, max depth = 14, train loss: 0.39752, val loss: 0.39236, in 0.016s
1 tree, 68 leaves, max depth = 11, train loss: 0.39453, val loss: 0.38944, in 0.016s
Fit 52 trees in 1.141 s, (2254 total leaves)
Time spent computing histograms: 0.341s
Time spent finding best splits:  0.047s
Time spent applying splits:      0.042s
Time spent predicting:           0.000s
Trial 87, Fold 2: Log loss = 0.396659992982574, Average precision = 0.9505285581424341, ROC-AUC = 0.9472481449591964, Elapsed Time = 1.1540588999996544 seconds
Trial 87, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 87, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
Binning 0.040 GB of training data: 0.157 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 40 leaves, max depth = 14, train loss: 0.68086, val loss: 0.68065, in 0.016s
1 tree, 40 leaves, max depth = 11, train loss: 0.66881, val loss: 0.66850, in 0.016s
1 tree, 50 leaves, max depth = 17, train loss: 0.65794, val loss: 0.65761, in 0.016s
1 tree, 34 leaves, max depth = 10, train loss: 0.64763, val loss: 0.64714, in 0.016s
1 tree, 42 leaves, max depth = 10, train loss: 0.63774, val loss: 0.63711, in 0.016s
1 tree, 43 leaves, max depth = 16, train loss: 0.62808, val loss: 0.62742, in 0.016s
1 tree, 46 leaves, max depth = 16, train loss: 0.61876, val loss: 0.61813, in 0.016s
1 tree, 58 leaves, max depth = 12, train loss: 0.60983, val loss: 0.60907, in 0.016s
1 tree, 55 leaves, max depth = 12, train loss: 0.60121, val loss: 0.60045, in 0.016s
1 tree, 51 leaves, max depth = 17, train loss: 0.59284, val loss: 0.59201, in 0.016s
1 tree, 60 leaves, max depth = 12, train loss: 0.58488, val loss: 0.58397, in 0.016s
1 tree, 38 leaves, max depth = 10, train loss: 0.57719, val loss: 0.57616, in 0.016s
1 tree, 49 leaves, max depth = 14, train loss: 0.56912, val loss: 0.56806, in 0.016s
1 tree, 44 leaves, max depth = 11, train loss: 0.56137, val loss: 0.56023, in 0.016s
1 tree, 48 leaves, max depth = 15, train loss: 0.55389, val loss: 0.55275, in 0.031s
1 tree, 40 leaves, max depth = 12, train loss: 0.54729, val loss: 0.54609, in 0.016s
1 tree, 56 leaves, max depth = 17, train loss: 0.54032, val loss: 0.53909, in 0.016s
1 tree, 49 leaves, max depth = 13, train loss: 0.53361, val loss: 0.53231, in 0.016s
1 tree, 45 leaves, max depth = 14, train loss: 0.52746, val loss: 0.52604, in 0.016s
1 tree, 57 leaves, max depth = 15, train loss: 0.52131, val loss: 0.51999, in 0.016s
1 tree, 49 leaves, max depth = 13, train loss: 0.51507, val loss: 0.51373, in 0.016s
1 tree, 45 leaves, max depth = 16, train loss: 0.50955, val loss: 0.50824, in 0.031s
1 tree, 49 leaves, max depth = 12, train loss: 0.50454, val loss: 0.50309, in 0.016s
1 tree, 48 leaves, max depth = 14, train loss: 0.49933, val loss: 0.49791, in 0.016s
1 tree, 58 leaves, max depth = 12, train loss: 0.49388, val loss: 0.49248, in 0.016s
1 tree, 50 leaves, max depth = 14, train loss: 0.48873, val loss: 0.48736, in 0.016s
1 tree, 49 leaves, max depth = 13, train loss: 0.48395, val loss: 0.48264, in 0.016s
1 tree, 56 leaves, max depth = 14, train loss: 0.47942, val loss: 0.47818, in 0.016s
1 tree, 45 leaves, max depth = 13, train loss: 0.47460, val loss: 0.47334, in 0.016s
1 tree, 6 leaves, max depth = 4, train loss: 0.47064, val loss: 0.46941, in 0.016s
1 tree, 49 leaves, max depth = 12, train loss: 0.46611, val loss: 0.46487, in 0.016s
1 tree, 45 leaves, max depth = 13, train loss: 0.46202, val loss: 0.46074, in 0.016s
1 tree, 54 leaves, max depth = 12, train loss: 0.45784, val loss: 0.45659, in 0.016s
1 tree, 46 leaves, max depth = 12, train loss: 0.45377, val loss: 0.45254, in 0.016s
1 tree, 41 leaves, max depth = 11, train loss: 0.45039, val loss: 0.44907, in 0.016s
1 tree, 51 leaves, max depth = 12, train loss: 0.44637, val loss: 0.44512, in 0.016s
1 tree, 23 leaves, max depth = 9, train loss: 0.44302, val loss: 0.44179, in 0.016s
1 tree, 46 leaves, max depth = 13, train loss: 0.43945, val loss: 0.43821, in 0.016s
1 tree, 26 leaves, max depth = 9, train loss: 0.43627, val loss: 0.43503, in 0.000s
1 tree, 45 leaves, max depth = 12, train loss: 0.43295, val loss: 0.43169, in 0.016s
1 tree, 41 leaves, max depth = 12, train loss: 0.43006, val loss: 0.42887, in 0.016s
1 tree, 44 leaves, max depth = 13, train loss: 0.42701, val loss: 0.42587, in 0.016s
1 tree, 53 leaves, max depth = 11, train loss: 0.42373, val loss: 0.42258, in 0.016s
1 tree, 24 leaves, max depth = 10, train loss: 0.41879, val loss: 0.41807, in 0.016s
1 tree, 45 leaves, max depth = 11, train loss: 0.41570, val loss: 0.41501, in 0.016s
1 tree, 10 leaves, max depth = 6, train loss: 0.41289, val loss: 0.41230, in 0.016s
1 tree, 50 leaves, max depth = 11, train loss: 0.41043, val loss: 0.40982, in 0.016s
1 tree, 48 leaves, max depth = 13, train loss: 0.40782, val loss: 0.40734, in 0.016s
1 tree, 65 leaves, max depth = 15, train loss: 0.40476, val loss: 0.40409, in 0.016s
1 tree, 11 leaves, max depth = 5, train loss: 0.40041, val loss: 0.40011, in 0.016s
1 tree, 49 leaves, max depth = 15, train loss: 0.39797, val loss: 0.39772, in 0.016s
1 tree, 46 leaves, max depth = 16, train loss: 0.39577, val loss: 0.39551, in 0.016s
Fit 52 trees in 1.204 s, (2312 total leaves)
Time spent computing histograms: 0.354s
Time spent finding best splits:  0.049s
Time spent applying splits:      0.044s
Time spent predicting:           0.000s
Trial 87, Fold 3: Log loss = 0.39246991409418885, Average precision = 0.9549524192568721, ROC-AUC = 0.9508018890071367, Elapsed Time = 1.2186887999996543 seconds
Trial 87, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 87, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
Binning 0.040 GB of training data: 0.157 s
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 43 leaves, max depth = 12, train loss: 0.68110, val loss: 0.68057, in 0.016s
1 tree, 43 leaves, max depth = 11, train loss: 0.66909, val loss: 0.66809, in 0.016s
1 tree, 50 leaves, max depth = 12, train loss: 0.65825, val loss: 0.65675, in 0.016s
1 tree, 43 leaves, max depth = 11, train loss: 0.64793, val loss: 0.64594, in 0.016s
1 tree, 46 leaves, max depth = 16, train loss: 0.63791, val loss: 0.63538, in 0.016s
1 tree, 45 leaves, max depth = 15, train loss: 0.62835, val loss: 0.62525, in 0.016s
1 tree, 45 leaves, max depth = 13, train loss: 0.61912, val loss: 0.61549, in 0.016s
1 tree, 48 leaves, max depth = 12, train loss: 0.61031, val loss: 0.60631, in 0.016s
1 tree, 47 leaves, max depth = 13, train loss: 0.60174, val loss: 0.59731, in 0.016s
1 tree, 53 leaves, max depth = 10, train loss: 0.59361, val loss: 0.58888, in 0.031s
1 tree, 57 leaves, max depth = 12, train loss: 0.58505, val loss: 0.57983, in 0.016s
1 tree, 48 leaves, max depth = 11, train loss: 0.57758, val loss: 0.57202, in 0.016s
1 tree, 45 leaves, max depth = 15, train loss: 0.57013, val loss: 0.56425, in 0.016s
1 tree, 49 leaves, max depth = 13, train loss: 0.56239, val loss: 0.55616, in 0.016s
1 tree, 49 leaves, max depth = 15, train loss: 0.55474, val loss: 0.54810, in 0.016s
1 tree, 20 leaves, max depth = 8, train loss: 0.54831, val loss: 0.54125, in 0.016s
1 tree, 58 leaves, max depth = 13, train loss: 0.54135, val loss: 0.53385, in 0.016s
1 tree, 41 leaves, max depth = 10, train loss: 0.53467, val loss: 0.52680, in 0.016s
1 tree, 47 leaves, max depth = 15, train loss: 0.52844, val loss: 0.52018, in 0.016s
1 tree, 47 leaves, max depth = 16, train loss: 0.52254, val loss: 0.51395, in 0.016s
1 tree, 49 leaves, max depth = 13, train loss: 0.51707, val loss: 0.50825, in 0.016s
1 tree, 44 leaves, max depth = 12, train loss: 0.51131, val loss: 0.50210, in 0.016s
1 tree, 46 leaves, max depth = 12, train loss: 0.50615, val loss: 0.49671, in 0.016s
1 tree, 46 leaves, max depth = 15, train loss: 0.50101, val loss: 0.49126, in 0.016s
1 tree, 54 leaves, max depth = 14, train loss: 0.49542, val loss: 0.48541, in 0.016s
1 tree, 34 leaves, max depth = 10, train loss: 0.49032, val loss: 0.48001, in 0.016s
1 tree, 58 leaves, max depth = 15, train loss: 0.48569, val loss: 0.47511, in 0.016s
1 tree, 52 leaves, max depth = 13, train loss: 0.48112, val loss: 0.47025, in 0.031s
1 tree, 45 leaves, max depth = 17, train loss: 0.47638, val loss: 0.46522, in 0.016s
1 tree, 6 leaves, max depth = 4, train loss: 0.47243, val loss: 0.46100, in 0.016s
1 tree, 47 leaves, max depth = 12, train loss: 0.46792, val loss: 0.45622, in 0.016s
1 tree, 47 leaves, max depth = 12, train loss: 0.46382, val loss: 0.45186, in 0.016s
1 tree, 53 leaves, max depth = 13, train loss: 0.45961, val loss: 0.44741, in 0.016s
1 tree, 45 leaves, max depth = 11, train loss: 0.45582, val loss: 0.44340, in 0.016s
1 tree, 49 leaves, max depth = 13, train loss: 0.45193, val loss: 0.43928, in 0.016s
1 tree, 47 leaves, max depth = 15, train loss: 0.44799, val loss: 0.43508, in 0.031s
1 tree, 54 leaves, max depth = 14, train loss: 0.44445, val loss: 0.43136, in 0.016s
1 tree, 6 leaves, max depth = 3, train loss: 0.44097, val loss: 0.42765, in 0.016s
1 tree, 53 leaves, max depth = 12, train loss: 0.43724, val loss: 0.42374, in 0.016s
1 tree, 15 leaves, max depth = 11, train loss: 0.43239, val loss: 0.41882, in 0.016s
1 tree, 48 leaves, max depth = 14, train loss: 0.42914, val loss: 0.41529, in 0.016s
1 tree, 7 leaves, max depth = 5, train loss: 0.42637, val loss: 0.41232, in 0.000s
1 tree, 45 leaves, max depth = 13, train loss: 0.42349, val loss: 0.40922, in 0.016s
1 tree, 20 leaves, max depth = 9, train loss: 0.41867, val loss: 0.40435, in 0.031s
1 tree, 45 leaves, max depth = 11, train loss: 0.41559, val loss: 0.40110, in 0.016s
1 tree, 10 leaves, max depth = 6, train loss: 0.41286, val loss: 0.39815, in 0.016s
1 tree, 48 leaves, max depth = 13, train loss: 0.41035, val loss: 0.39550, in 0.016s
1 tree, 46 leaves, max depth = 12, train loss: 0.40789, val loss: 0.39282, in 0.016s
1 tree, 60 leaves, max depth = 10, train loss: 0.40522, val loss: 0.39005, in 0.016s
1 tree, 53 leaves, max depth = 16, train loss: 0.40210, val loss: 0.38692, in 0.016s
1 tree, 46 leaves, max depth = 15, train loss: 0.39983, val loss: 0.38445, in 0.016s
1 tree, 70 leaves, max depth = 14, train loss: 0.39681, val loss: 0.38137, in 0.016s
Fit 52 trees in 1.220 s, (2272 total leaves)
Time spent computing histograms: 0.367s
Time spent finding best splits:  0.050s
Time spent applying splits:      0.045s
Time spent predicting:           0.000s
Trial 87, Fold 4: Log loss = 0.39664251896804825, Average precision = 0.9532641220458598, ROC-AUC = 0.9476337415442868, Elapsed Time = 1.2226099999988946 seconds
Trial 87, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 87, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
Binning 0.040 GB of training data: 0.158 s
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 43 leaves, max depth = 14, train loss: 0.68085, val loss: 0.68005, in 0.016s
1 tree, 42 leaves, max depth = 14, train loss: 0.66863, val loss: 0.66730, in 0.016s
1 tree, 46 leaves, max depth = 11, train loss: 0.65792, val loss: 0.65601, in 0.016s
1 tree, 41 leaves, max depth = 13, train loss: 0.64696, val loss: 0.64452, in 0.016s
1 tree, 49 leaves, max depth = 13, train loss: 0.63702, val loss: 0.63400, in 0.016s
1 tree, 45 leaves, max depth = 14, train loss: 0.62734, val loss: 0.62376, in 0.016s
1 tree, 46 leaves, max depth = 14, train loss: 0.61812, val loss: 0.61402, in 0.016s
1 tree, 49 leaves, max depth = 12, train loss: 0.60930, val loss: 0.60495, in 0.062s
1 tree, 51 leaves, max depth = 11, train loss: 0.60047, val loss: 0.59568, in 0.063s
1 tree, 48 leaves, max depth = 11, train loss: 0.59230, val loss: 0.58717, in 0.016s
1 tree, 33 leaves, max depth = 10, train loss: 0.58360, val loss: 0.57819, in 0.016s
1 tree, 25 leaves, max depth = 13, train loss: 0.57601, val loss: 0.57028, in 0.016s
1 tree, 52 leaves, max depth = 13, train loss: 0.56872, val loss: 0.56273, in 0.031s
1 tree, 43 leaves, max depth = 12, train loss: 0.56121, val loss: 0.55491, in 0.016s
1 tree, 13 leaves, max depth = 7, train loss: 0.55453, val loss: 0.54798, in 0.016s
1 tree, 28 leaves, max depth = 9, train loss: 0.54792, val loss: 0.54123, in 0.031s
1 tree, 28 leaves, max depth = 10, train loss: 0.54157, val loss: 0.53454, in 0.016s
1 tree, 48 leaves, max depth = 18, train loss: 0.53511, val loss: 0.52781, in 0.016s
1 tree, 45 leaves, max depth = 13, train loss: 0.52863, val loss: 0.52099, in 0.016s
1 tree, 43 leaves, max depth = 14, train loss: 0.52278, val loss: 0.51476, in 0.016s
1 tree, 52 leaves, max depth = 16, train loss: 0.51724, val loss: 0.50888, in 0.016s
1 tree, 42 leaves, max depth = 13, train loss: 0.51177, val loss: 0.50306, in 0.016s
1 tree, 46 leaves, max depth = 12, train loss: 0.50635, val loss: 0.49725, in 0.031s
1 tree, 50 leaves, max depth = 13, train loss: 0.50067, val loss: 0.49137, in 0.016s
1 tree, 45 leaves, max depth = 11, train loss: 0.49526, val loss: 0.48564, in 0.000s
1 tree, 62 leaves, max depth = 11, train loss: 0.48992, val loss: 0.48014, in 0.016s
1 tree, 38 leaves, max depth = 11, train loss: 0.48495, val loss: 0.47490, in 0.016s
1 tree, 43 leaves, max depth = 14, train loss: 0.48085, val loss: 0.47052, in 0.016s
1 tree, 46 leaves, max depth = 13, train loss: 0.47635, val loss: 0.46581, in 0.016s
1 tree, 50 leaves, max depth = 12, train loss: 0.47210, val loss: 0.46146, in 0.031s
1 tree, 50 leaves, max depth = 14, train loss: 0.46747, val loss: 0.45668, in 0.016s
1 tree, 7 leaves, max depth = 4, train loss: 0.46359, val loss: 0.45258, in 0.000s
1 tree, 9 leaves, max depth = 5, train loss: 0.45778, val loss: 0.44671, in 0.016s
1 tree, 39 leaves, max depth = 11, train loss: 0.45388, val loss: 0.44271, in 0.016s
1 tree, 8 leaves, max depth = 4, train loss: 0.44866, val loss: 0.43739, in 0.031s
1 tree, 50 leaves, max depth = 14, train loss: 0.44460, val loss: 0.43318, in 0.016s
1 tree, 51 leaves, max depth = 13, train loss: 0.44115, val loss: 0.42952, in 0.016s
1 tree, 43 leaves, max depth = 11, train loss: 0.43767, val loss: 0.42573, in 0.016s
1 tree, 50 leaves, max depth = 12, train loss: 0.43416, val loss: 0.42201, in 0.016s
1 tree, 45 leaves, max depth = 11, train loss: 0.43068, val loss: 0.41831, in 0.016s
1 tree, 44 leaves, max depth = 13, train loss: 0.42745, val loss: 0.41488, in 0.016s
1 tree, 28 leaves, max depth = 9, train loss: 0.42453, val loss: 0.41193, in 0.016s
1 tree, 62 leaves, max depth = 11, train loss: 0.42143, val loss: 0.40860, in 0.031s
1 tree, 51 leaves, max depth = 11, train loss: 0.41812, val loss: 0.40519, in 0.016s
1 tree, 47 leaves, max depth = 13, train loss: 0.41515, val loss: 0.40205, in 0.016s
1 tree, 39 leaves, max depth = 10, train loss: 0.41265, val loss: 0.39948, in 0.016s
1 tree, 46 leaves, max depth = 12, train loss: 0.40966, val loss: 0.39636, in 0.031s
1 tree, 46 leaves, max depth = 12, train loss: 0.40714, val loss: 0.39377, in 0.016s
1 tree, 52 leaves, max depth = 16, train loss: 0.40455, val loss: 0.39110, in 0.031s
1 tree, 5 leaves, max depth = 3, train loss: 0.40208, val loss: 0.38846, in 0.000s
1 tree, 32 leaves, max depth = 8, train loss: 0.39999, val loss: 0.38622, in 0.016s
1 tree, 47 leaves, max depth = 14, train loss: 0.39760, val loss: 0.38375, in 0.031s
Fit 52 trees in 1.377 s, (2143 total leaves)
Time spent computing histograms: 0.435s
Time spent finding best splits:  0.070s
Time spent applying splits:      0.060s
Time spent predicting:           0.016s
Trial 87, Fold 5: Log loss = 0.4032658384987923, Average precision = 0.9493148596627419, ROC-AUC = 0.9450400014863534, Elapsed Time = 1.3754774999997608 seconds
Optimization Progress:  88%|########8 | 88/100 [18:16<02:29, 12.42s/it]
Trial 88, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 88, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
0.127 s 0.040 GB of training data: 
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 48 leaves, max depth = 12, train loss: 0.66967, val loss: 0.66915, in 0.016s
1 tree, 66 leaves, max depth = 11, train loss: 0.64700, val loss: 0.64635, in 0.016s
1 tree, 71 leaves, max depth = 10, train loss: 0.62636, val loss: 0.62541, in 0.016s
1 tree, 68 leaves, max depth = 10, train loss: 0.60731, val loss: 0.60607, in 0.016s
1 tree, 65 leaves, max depth = 11, train loss: 0.58948, val loss: 0.58812, in 0.016s
1 tree, 65 leaves, max depth = 10, train loss: 0.57294, val loss: 0.57148, in 0.016s
1 tree, 45 leaves, max depth = 8, train loss: 0.55918, val loss: 0.55744, in 0.016s
1 tree, 47 leaves, max depth = 12, train loss: 0.54569, val loss: 0.54363, in 0.016s
1 tree, 66 leaves, max depth = 11, train loss: 0.53226, val loss: 0.53015, in 0.016s
1 tree, 62 leaves, max depth = 13, train loss: 0.52018, val loss: 0.51795, in 0.016s
1 tree, 65 leaves, max depth = 10, train loss: 0.50840, val loss: 0.50610, in 0.016s
1 tree, 72 leaves, max depth = 11, train loss: 0.49820, val loss: 0.49571, in 0.016s
Fit 12 trees in 0.486 s, (740 total leaves)
Time spent computing histograms: 0.079s
Time spent finding best splits:  0.013s
Time spent applying splits:      0.014s
Time spent predicting:           0.000s
Trial 88, Fold 1: Log loss = 0.49899187329362027, Average precision = 0.9206425128050846, ROC-AUC = 0.9312720458553791, Elapsed Time = 0.4972512000003917 seconds
Trial 88, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 88, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
Binning 0.040 GB of training data: 0.158 s
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 45 leaves, max depth = 11, train loss: 0.66964, val loss: 0.66913, in 0.016s
1 tree, 61 leaves, max depth = 12, train loss: 0.64686, val loss: 0.64596, in 0.016s
1 tree, 69 leaves, max depth = 12, train loss: 0.62617, val loss: 0.62496, in 0.031s
1 tree, 68 leaves, max depth = 11, train loss: 0.60709, val loss: 0.60558, in 0.016s
1 tree, 63 leaves, max depth = 12, train loss: 0.58917, val loss: 0.58735, in 0.016s
1 tree, 62 leaves, max depth = 11, train loss: 0.57257, val loss: 0.57046, in 0.016s
1 tree, 48 leaves, max depth = 10, train loss: 0.55850, val loss: 0.55589, in 0.032s
1 tree, 47 leaves, max depth = 10, train loss: 0.54504, val loss: 0.54229, in 0.016s
1 tree, 64 leaves, max depth = 11, train loss: 0.53151, val loss: 0.52855, in 0.016s
1 tree, 63 leaves, max depth = 12, train loss: 0.51949, val loss: 0.51648, in 0.016s
1 tree, 62 leaves, max depth = 11, train loss: 0.50784, val loss: 0.50457, in 0.031s
1 tree, 72 leaves, max depth = 10, train loss: 0.49764, val loss: 0.49416, in 0.016s
Fit 12 trees in 0.596 s, (724 total leaves)
Time spent computing histograms: 0.091s
Time spent finding best splits:  0.016s
Time spent applying splits:      0.016s
Time spent predicting:           0.000s
Trial 88, Fold 2: Log loss = 0.4988965466589309, Average precision = 0.914735125078572, ROC-AUC = 0.9307612791852722, Elapsed Time = 0.5944717000002129 seconds
Trial 88, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 88, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
Binning 0.040 GB of training data: 0.158 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 48 leaves, max depth = 12, train loss: 0.66970, val loss: 0.66945, in 0.031s
1 tree, 61 leaves, max depth = 13, train loss: 0.64701, val loss: 0.64678, in 0.016s
1 tree, 67 leaves, max depth = 12, train loss: 0.62636, val loss: 0.62616, in 0.016s
1 tree, 67 leaves, max depth = 12, train loss: 0.60731, val loss: 0.60711, in 0.016s
1 tree, 62 leaves, max depth = 12, train loss: 0.58943, val loss: 0.58927, in 0.031s
1 tree, 64 leaves, max depth = 12, train loss: 0.57287, val loss: 0.57272, in 0.016s
1 tree, 47 leaves, max depth = 11, train loss: 0.55883, val loss: 0.55854, in 0.016s
1 tree, 46 leaves, max depth = 10, train loss: 0.54520, val loss: 0.54491, in 0.016s
1 tree, 64 leaves, max depth = 10, train loss: 0.53216, val loss: 0.53198, in 0.016s
1 tree, 65 leaves, max depth = 10, train loss: 0.52017, val loss: 0.52003, in 0.016s
1 tree, 65 leaves, max depth = 10, train loss: 0.50836, val loss: 0.50821, in 0.016s
1 tree, 72 leaves, max depth = 12, train loss: 0.49817, val loss: 0.49802, in 0.031s
Fit 12 trees in 0.596 s, (728 total leaves)
Time spent computing histograms: 0.093s
Time spent finding best splits:  0.016s
Time spent applying splits:      0.017s
Time spent predicting:           0.000s
Trial 88, Fold 3: Log loss = 0.49575776300203145, Average precision = 0.9247529398996359, ROC-AUC = 0.9354105920919573, Elapsed Time = 0.59648659999948 seconds
Trial 88, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 88, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
Binning 0.040 GB of training data: 0.190 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 48 leaves, max depth = 11, train loss: 0.66968, val loss: 0.66863, in 0.016s
1 tree, 65 leaves, max depth = 12, train loss: 0.64698, val loss: 0.64491, in 0.016s
1 tree, 72 leaves, max depth = 12, train loss: 0.62635, val loss: 0.62322, in 0.031s
1 tree, 72 leaves, max depth = 12, train loss: 0.60732, val loss: 0.60316, in 0.016s
1 tree, 65 leaves, max depth = 12, train loss: 0.58945, val loss: 0.58443, in 0.031s
1 tree, 66 leaves, max depth = 12, train loss: 0.57289, val loss: 0.56707, in 0.016s
1 tree, 48 leaves, max depth = 10, train loss: 0.55877, val loss: 0.55225, in 0.016s
1 tree, 48 leaves, max depth = 10, train loss: 0.54537, val loss: 0.53821, in 0.031s
1 tree, 50 leaves, max depth = 13, train loss: 0.53306, val loss: 0.52515, in 0.016s
1 tree, 61 leaves, max depth = 15, train loss: 0.52117, val loss: 0.51254, in 0.016s
1 tree, 64 leaves, max depth = 13, train loss: 0.50999, val loss: 0.50074, in 0.016s
1 tree, 64 leaves, max depth = 10, train loss: 0.49954, val loss: 0.48980, in 0.032s
Fit 12 trees in 0.659 s, (723 total leaves)
Time spent computing histograms: 0.108s
Time spent finding best splits:  0.017s
Time spent applying splits:      0.017s
Time spent predicting:           0.000s
Trial 88, Fold 4: Log loss = 0.4991357854614416, Average precision = 0.9226061890411278, ROC-AUC = 0.9325586087922726, Elapsed Time = 0.669846900000266 seconds
Trial 88, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 88, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
Binning 0.040 GB of training data: 0.143 s
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 46 leaves, max depth = 10, train loss: 0.66951, val loss: 0.66803, in 0.016s
1 tree, 62 leaves, max depth = 11, train loss: 0.64671, val loss: 0.64448, in 0.016s
1 tree, 70 leaves, max depth = 11, train loss: 0.62602, val loss: 0.62305, in 0.016s
1 tree, 70 leaves, max depth = 11, train loss: 0.60694, val loss: 0.60327, in 0.016s
1 tree, 63 leaves, max depth = 11, train loss: 0.58901, val loss: 0.58468, in 0.031s
1 tree, 64 leaves, max depth = 12, train loss: 0.57239, val loss: 0.56749, in 0.016s
1 tree, 47 leaves, max depth = 13, train loss: 0.55828, val loss: 0.55268, in 0.016s
1 tree, 47 leaves, max depth = 11, train loss: 0.54483, val loss: 0.53841, in 0.016s
1 tree, 48 leaves, max depth = 12, train loss: 0.53237, val loss: 0.52522, in 0.016s
1 tree, 64 leaves, max depth = 13, train loss: 0.52030, val loss: 0.51262, in 0.031s
1 tree, 60 leaves, max depth = 10, train loss: 0.50915, val loss: 0.50088, in 0.016s
1 tree, 61 leaves, max depth = 12, train loss: 0.49859, val loss: 0.48986, in 0.016s
Fit 12 trees in 0.580 s, (702 total leaves)
Time spent computing histograms: 0.093s
Time spent finding best splits:  0.013s
Time spent applying splits:      0.014s
Time spent predicting:           0.000s
Trial 88, Fold 5: Log loss = 0.5017683253521471, Average precision = 0.9171203694323937, ROC-AUC = 0.9293585270237631, Elapsed Time = 0.5883169000007911 seconds
Optimization Progress:  89%|########9 | 89/100 [18:26<02:07, 11.58s/it]
Trial 89, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 89, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
0.158 s 0.040 GB of training data: 
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 76 leaves, max depth = 15, train loss: 0.64936, val loss: 0.64820, in 0.016s
1 tree, 89 leaves, max depth = 15, train loss: 0.61314, val loss: 0.61094, in 0.016s
1 tree, 87 leaves, max depth = 17, train loss: 0.58324, val loss: 0.58025, in 0.016s
1 tree, 79 leaves, max depth = 18, train loss: 0.55869, val loss: 0.55509, in 0.016s
1 tree, 128 leaves, max depth = 16, train loss: 0.53618, val loss: 0.53443, in 0.016s
1 tree, 89 leaves, max depth = 16, train loss: 0.51692, val loss: 0.51438, in 0.016s
1 tree, 128 leaves, max depth = 17, train loss: 0.49916, val loss: 0.49824, in 0.016s
1 tree, 78 leaves, max depth = 18, train loss: 0.48429, val loss: 0.48275, in 0.016s
1 tree, 89 leaves, max depth = 16, train loss: 0.47150, val loss: 0.46932, in 0.016s
1 tree, 127 leaves, max depth = 16, train loss: 0.45773, val loss: 0.45700, in 0.016s
1 tree, 127 leaves, max depth = 16, train loss: 0.44606, val loss: 0.44664, in 0.031s
1 tree, 78 leaves, max depth = 17, train loss: 0.43656, val loss: 0.43657, in 0.016s
1 tree, 85 leaves, max depth = 16, train loss: 0.42812, val loss: 0.42761, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.42134, val loss: 0.42011, in 0.000s
1 tree, 89 leaves, max depth = 18, train loss: 0.41459, val loss: 0.41301, in 0.016s
1 tree, 128 leaves, max depth = 14, train loss: 0.40536, val loss: 0.40511, in 0.031s
1 tree, 5 leaves, max depth = 3, train loss: 0.39984, val loss: 0.39897, in 0.000s
1 tree, 85 leaves, max depth = 12, train loss: 0.39429, val loss: 0.39318, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.38968, val loss: 0.38801, in 0.016s
1 tree, 91 leaves, max depth = 12, train loss: 0.38485, val loss: 0.38291, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.38100, val loss: 0.37855, in 0.016s
1 tree, 193 leaves, max depth = 14, train loss: 0.37530, val loss: 0.37433, in 0.031s
1 tree, 128 leaves, max depth = 15, train loss: 0.36812, val loss: 0.36851, in 0.016s
1 tree, 91 leaves, max depth = 16, train loss: 0.36431, val loss: 0.36448, in 0.031s
1 tree, 5 leaves, max depth = 3, train loss: 0.36100, val loss: 0.36070, in 0.000s
1 tree, 127 leaves, max depth = 16, train loss: 0.35480, val loss: 0.35582, in 0.016s
1 tree, 128 leaves, max depth = 16, train loss: 0.34954, val loss: 0.35175, in 0.031s
1 tree, 111 leaves, max depth = 15, train loss: 0.34641, val loss: 0.34804, in 0.016s
1 tree, 128 leaves, max depth = 15, train loss: 0.34188, val loss: 0.34459, in 0.016s
1 tree, 129 leaves, max depth = 15, train loss: 0.33804, val loss: 0.34174, in 0.016s
1 tree, 85 leaves, max depth = 13, train loss: 0.33528, val loss: 0.33889, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.33224, val loss: 0.33537, in 0.016s
1 tree, 113 leaves, max depth = 15, train loss: 0.32977, val loss: 0.33269, in 0.016s
1 tree, 129 leaves, max depth = 16, train loss: 0.32632, val loss: 0.33020, in 0.016s
1 tree, 91 leaves, max depth = 13, train loss: 0.32410, val loss: 0.32786, in 0.016s
1 tree, 111 leaves, max depth = 14, train loss: 0.32207, val loss: 0.32539, in 0.016s
1 tree, 162 leaves, max depth = 17, train loss: 0.31949, val loss: 0.32364, in 0.016s
1 tree, 128 leaves, max depth = 16, train loss: 0.31673, val loss: 0.32173, in 0.016s
1 tree, 110 leaves, max depth = 15, train loss: 0.31492, val loss: 0.31973, in 0.016s
1 tree, 162 leaves, max depth = 16, train loss: 0.31288, val loss: 0.31842, in 0.031s
1 tree, 5 leaves, max depth = 3, train loss: 0.31007, val loss: 0.31513, in 0.000s
1 tree, 111 leaves, max depth = 17, train loss: 0.30837, val loss: 0.31305, in 0.016s
1 tree, 146 leaves, max depth = 23, train loss: 0.30621, val loss: 0.31151, in 0.031s
1 tree, 128 leaves, max depth = 17, train loss: 0.30408, val loss: 0.31009, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.30157, val loss: 0.30714, in 0.000s
1 tree, 111 leaves, max depth = 15, train loss: 0.30005, val loss: 0.30528, in 0.031s
1 tree, 128 leaves, max depth = 17, train loss: 0.29813, val loss: 0.30408, in 0.016s
1 tree, 50 leaves, max depth = 15, train loss: 0.29674, val loss: 0.30265, in 0.016s
1 tree, 129 leaves, max depth = 16, train loss: 0.29514, val loss: 0.30164, in 0.016s
1 tree, 50 leaves, max depth = 12, train loss: 0.29394, val loss: 0.30040, in 0.016s
1 tree, 112 leaves, max depth = 17, train loss: 0.29267, val loss: 0.29906, in 0.016s
1 tree, 129 leaves, max depth = 17, train loss: 0.29124, val loss: 0.29821, in 0.016s
1 tree, 156 leaves, max depth = 20, train loss: 0.28954, val loss: 0.29709, in 0.016s
1 tree, 85 leaves, max depth = 13, train loss: 0.28831, val loss: 0.29573, in 0.000s
1 tree, 129 leaves, max depth = 16, train loss: 0.28708, val loss: 0.29500, in 0.031s
Fit 55 trees in 1.220 s, (5283 total leaves)
Time spent computing histograms: 0.345s
Time spent finding best splits:  0.101s
Time spent applying splits:      0.116s
Time spent predicting:           0.016s
Trial 89, Fold 1: Log loss = 0.2991288879401835, Average precision = 0.9549775656537438, ROC-AUC = 0.9511204850805122, Elapsed Time = 1.2206474000013259 seconds
Trial 89, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 89, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
Binning 0.040 GB of training data: 0.190 s
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 73 leaves, max depth = 18, train loss: 0.64921, val loss: 0.64738, in 0.016s
1 tree, 88 leaves, max depth = 14, train loss: 0.61322, val loss: 0.60940, in 0.016s
1 tree, 83 leaves, max depth = 17, train loss: 0.58345, val loss: 0.57803, in 0.016s
1 tree, 74 leaves, max depth = 17, train loss: 0.55882, val loss: 0.55233, in 0.016s
1 tree, 127 leaves, max depth = 15, train loss: 0.53656, val loss: 0.53099, in 0.031s
1 tree, 81 leaves, max depth = 14, train loss: 0.51743, val loss: 0.51072, in 0.016s
1 tree, 126 leaves, max depth = 15, train loss: 0.49984, val loss: 0.49397, in 0.031s
1 tree, 83 leaves, max depth = 23, train loss: 0.48489, val loss: 0.47802, in 0.016s
1 tree, 88 leaves, max depth = 14, train loss: 0.47215, val loss: 0.46429, in 0.016s
1 tree, 126 leaves, max depth = 16, train loss: 0.45852, val loss: 0.45145, in 0.031s
1 tree, 126 leaves, max depth = 15, train loss: 0.44697, val loss: 0.44064, in 0.016s
1 tree, 83 leaves, max depth = 17, train loss: 0.43745, val loss: 0.43035, in 0.031s
1 tree, 79 leaves, max depth = 22, train loss: 0.42935, val loss: 0.42165, in 0.016s
1 tree, 87 leaves, max depth = 16, train loss: 0.42203, val loss: 0.41366, in 0.016s
1 tree, 88 leaves, max depth = 13, train loss: 0.41603, val loss: 0.40721, in 0.016s
1 tree, 126 leaves, max depth = 14, train loss: 0.40693, val loss: 0.39888, in 0.031s
1 tree, 5 leaves, max depth = 3, train loss: 0.40077, val loss: 0.39249, in 0.000s
1 tree, 84 leaves, max depth = 19, train loss: 0.39617, val loss: 0.38743, in 0.031s
1 tree, 5 leaves, max depth = 3, train loss: 0.39109, val loss: 0.38213, in 0.000s
1 tree, 88 leaves, max depth = 17, train loss: 0.38700, val loss: 0.37764, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.38276, val loss: 0.37320, in 0.016s
1 tree, 196 leaves, max depth = 15, train loss: 0.37709, val loss: 0.36868, in 0.031s
1 tree, 126 leaves, max depth = 17, train loss: 0.36998, val loss: 0.36235, in 0.016s
1 tree, 88 leaves, max depth = 16, train loss: 0.36677, val loss: 0.35883, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.36316, val loss: 0.35503, in 0.000s
1 tree, 126 leaves, max depth = 17, train loss: 0.35700, val loss: 0.34962, in 0.031s
1 tree, 127 leaves, max depth = 17, train loss: 0.35176, val loss: 0.34507, in 0.031s
1 tree, 107 leaves, max depth = 15, train loss: 0.34889, val loss: 0.34207, in 0.016s
1 tree, 127 leaves, max depth = 17, train loss: 0.34439, val loss: 0.33821, in 0.031s
1 tree, 127 leaves, max depth = 17, train loss: 0.34058, val loss: 0.33497, in 0.031s
1 tree, 85 leaves, max depth = 14, train loss: 0.33807, val loss: 0.33250, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.33478, val loss: 0.32903, in 0.016s
1 tree, 145 leaves, max depth = 18, train loss: 0.33218, val loss: 0.32760, in 0.031s
1 tree, 127 leaves, max depth = 18, train loss: 0.32907, val loss: 0.32504, in 0.016s
1 tree, 113 leaves, max depth = 19, train loss: 0.32678, val loss: 0.32247, in 0.031s
1 tree, 107 leaves, max depth = 14, train loss: 0.32474, val loss: 0.32033, in 0.016s
1 tree, 163 leaves, max depth = 15, train loss: 0.32216, val loss: 0.31875, in 0.031s
1 tree, 126 leaves, max depth = 19, train loss: 0.31966, val loss: 0.31670, in 0.016s
1 tree, 7 leaves, max depth = 3, train loss: 0.31789, val loss: 0.31470, in 0.016s
1 tree, 107 leaves, max depth = 14, train loss: 0.31617, val loss: 0.31283, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.31313, val loss: 0.30963, in 0.000s
1 tree, 52 leaves, max depth = 11, train loss: 0.31178, val loss: 0.30830, in 0.016s
1 tree, 144 leaves, max depth = 19, train loss: 0.30982, val loss: 0.30740, in 0.031s
1 tree, 126 leaves, max depth = 21, train loss: 0.30769, val loss: 0.30572, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.30499, val loss: 0.30285, in 0.016s
1 tree, 127 leaves, max depth = 21, train loss: 0.30312, val loss: 0.30139, in 0.031s
1 tree, 113 leaves, max depth = 16, train loss: 0.30161, val loss: 0.29972, in 0.016s
1 tree, 108 leaves, max depth = 15, train loss: 0.29955, val loss: 0.29825, in 0.016s
1 tree, 109 leaves, max depth = 16, train loss: 0.29819, val loss: 0.29685, in 0.031s
1 tree, 127 leaves, max depth = 22, train loss: 0.29667, val loss: 0.29569, in 0.016s
1 tree, 52 leaves, max depth = 11, train loss: 0.29561, val loss: 0.29465, in 0.016s
1 tree, 159 leaves, max depth = 23, train loss: 0.29394, val loss: 0.29403, in 0.031s
1 tree, 87 leaves, max depth = 15, train loss: 0.29276, val loss: 0.29300, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.29191, val loss: 0.29231, in 0.016s
1 tree, 127 leaves, max depth = 23, train loss: 0.29058, val loss: 0.29128, in 0.016s
Fit 55 trees in 1.487 s, (5085 total leaves)
Time spent computing histograms: 0.429s
Time spent finding best splits:  0.119s
Time spent applying splits:      0.145s
Time spent predicting:           0.000s
Trial 89, Fold 2: Log loss = 0.3012319816631841, Average precision = 0.9527567543609047, ROC-AUC = 0.950794187210029, Elapsed Time = 1.4833385000001726 seconds
Trial 89, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 89, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
Binning 0.040 GB of training data: 0.206 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 77 leaves, max depth = 17, train loss: 0.64955, val loss: 0.64800, in 0.016s
1 tree, 89 leaves, max depth = 13, train loss: 0.61373, val loss: 0.61076, in 0.016s
1 tree, 86 leaves, max depth = 13, train loss: 0.58412, val loss: 0.57993, in 0.016s
1 tree, 77 leaves, max depth = 17, train loss: 0.55973, val loss: 0.55458, in 0.016s
1 tree, 126 leaves, max depth = 16, train loss: 0.53704, val loss: 0.53367, in 0.016s
1 tree, 89 leaves, max depth = 15, train loss: 0.51807, val loss: 0.51363, in 0.031s
1 tree, 126 leaves, max depth = 16, train loss: 0.50016, val loss: 0.49723, in 0.016s
1 tree, 76 leaves, max depth = 18, train loss: 0.48540, val loss: 0.48162, in 0.016s
1 tree, 88 leaves, max depth = 15, train loss: 0.47286, val loss: 0.46819, in 0.016s
1 tree, 125 leaves, max depth = 15, train loss: 0.45899, val loss: 0.45558, in 0.078s
1 tree, 125 leaves, max depth = 16, train loss: 0.44723, val loss: 0.44497, in 0.047s
1 tree, 76 leaves, max depth = 19, train loss: 0.43785, val loss: 0.43488, in 0.016s
1 tree, 86 leaves, max depth = 14, train loss: 0.42952, val loss: 0.42596, in 0.031s
1 tree, 5 leaves, max depth = 3, train loss: 0.42267, val loss: 0.41961, in 0.000s
1 tree, 89 leaves, max depth = 15, train loss: 0.41608, val loss: 0.41252, in 0.031s
1 tree, 125 leaves, max depth = 14, train loss: 0.40679, val loss: 0.40423, in 0.031s
1 tree, 5 leaves, max depth = 3, train loss: 0.40121, val loss: 0.39906, in 0.000s
1 tree, 80 leaves, max depth = 15, train loss: 0.39577, val loss: 0.39338, in 0.031s
1 tree, 5 leaves, max depth = 3, train loss: 0.39115, val loss: 0.38909, in 0.000s
1 tree, 88 leaves, max depth = 14, train loss: 0.38642, val loss: 0.38416, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.38254, val loss: 0.38055, in 0.000s
1 tree, 196 leaves, max depth = 13, train loss: 0.37681, val loss: 0.37618, in 0.031s
1 tree, 126 leaves, max depth = 16, train loss: 0.36953, val loss: 0.36980, in 0.031s
1 tree, 88 leaves, max depth = 14, train loss: 0.36581, val loss: 0.36587, in 0.031s
1 tree, 5 leaves, max depth = 3, train loss: 0.36248, val loss: 0.36277, in 0.000s
1 tree, 126 leaves, max depth = 15, train loss: 0.35619, val loss: 0.35730, in 0.031s
1 tree, 125 leaves, max depth = 15, train loss: 0.35083, val loss: 0.35272, in 0.031s
1 tree, 77 leaves, max depth = 13, train loss: 0.34795, val loss: 0.34963, in 0.031s
1 tree, 126 leaves, max depth = 15, train loss: 0.34338, val loss: 0.34577, in 0.016s
1 tree, 125 leaves, max depth = 15, train loss: 0.33949, val loss: 0.34254, in 0.031s
1 tree, 112 leaves, max depth = 15, train loss: 0.33676, val loss: 0.33920, in 0.031s
1 tree, 5 leaves, max depth = 3, train loss: 0.33369, val loss: 0.33632, in 0.000s
1 tree, 88 leaves, max depth = 16, train loss: 0.33131, val loss: 0.33380, in 0.016s
1 tree, 126 leaves, max depth = 19, train loss: 0.32781, val loss: 0.33092, in 0.031s
1 tree, 112 leaves, max depth = 15, train loss: 0.32565, val loss: 0.32827, in 0.031s
1 tree, 145 leaves, max depth = 20, train loss: 0.32301, val loss: 0.32680, in 0.031s
1 tree, 52 leaves, max depth = 13, train loss: 0.32126, val loss: 0.32503, in 0.016s
1 tree, 126 leaves, max depth = 17, train loss: 0.31847, val loss: 0.32274, in 0.031s
1 tree, 109 leaves, max depth = 25, train loss: 0.31665, val loss: 0.32035, in 0.031s
1 tree, 164 leaves, max depth = 16, train loss: 0.31439, val loss: 0.31881, in 0.031s
1 tree, 5 leaves, max depth = 3, train loss: 0.31154, val loss: 0.31613, in 0.016s
1 tree, 108 leaves, max depth = 16, train loss: 0.30971, val loss: 0.31396, in 0.031s
1 tree, 146 leaves, max depth = 23, train loss: 0.30761, val loss: 0.31301, in 0.031s
1 tree, 124 leaves, max depth = 20, train loss: 0.30546, val loss: 0.31129, in 0.031s
1 tree, 5 leaves, max depth = 3, train loss: 0.30291, val loss: 0.30890, in 0.016s
1 tree, 108 leaves, max depth = 16, train loss: 0.30128, val loss: 0.30695, in 0.031s
1 tree, 126 leaves, max depth = 18, train loss: 0.29934, val loss: 0.30552, in 0.031s
1 tree, 89 leaves, max depth = 16, train loss: 0.29786, val loss: 0.30388, in 0.016s
1 tree, 126 leaves, max depth = 20, train loss: 0.29616, val loss: 0.30259, in 0.031s
1 tree, 52 leaves, max depth = 12, train loss: 0.29505, val loss: 0.30145, in 0.016s
1 tree, 109 leaves, max depth = 23, train loss: 0.29383, val loss: 0.29982, in 0.031s
1 tree, 124 leaves, max depth = 20, train loss: 0.29234, val loss: 0.29869, in 0.031s
1 tree, 150 leaves, max depth = 18, train loss: 0.29072, val loss: 0.29763, in 0.031s
1 tree, 89 leaves, max depth = 13, train loss: 0.28960, val loss: 0.29650, in 0.016s
1 tree, 126 leaves, max depth = 18, train loss: 0.28829, val loss: 0.29551, in 0.031s
Fit 55 trees in 1.706 s, (5168 total leaves)
Time spent computing histograms: 0.495s
Time spent finding best splits:  0.153s
Time spent applying splits:      0.181s
Time spent predicting:           0.000s
Trial 89, Fold 3: Log loss = 0.2924300948581894, Average precision = 0.9579560866011733, ROC-AUC = 0.9552850605792104, Elapsed Time = 1.7165589000014734 seconds
Trial 89, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 89, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
0.205 s 0.040 GB of training data: 
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 81 leaves, max depth = 15, train loss: 0.64942, val loss: 0.64717, in 0.016s
1 tree, 89 leaves, max depth = 16, train loss: 0.61341, val loss: 0.60887, in 0.016s
1 tree, 91 leaves, max depth = 16, train loss: 0.58364, val loss: 0.57720, in 0.031s
1 tree, 80 leaves, max depth = 15, train loss: 0.55910, val loss: 0.55094, in 0.016s
1 tree, 131 leaves, max depth = 16, train loss: 0.53671, val loss: 0.52931, in 0.016s
1 tree, 89 leaves, max depth = 16, train loss: 0.51759, val loss: 0.50852, in 0.016s
1 tree, 131 leaves, max depth = 17, train loss: 0.49994, val loss: 0.49155, in 0.016s
1 tree, 85 leaves, max depth = 16, train loss: 0.48508, val loss: 0.47527, in 0.016s
1 tree, 89 leaves, max depth = 14, train loss: 0.47242, val loss: 0.46123, in 0.031s
1 tree, 131 leaves, max depth = 17, train loss: 0.45877, val loss: 0.44821, in 0.016s
1 tree, 131 leaves, max depth = 17, train loss: 0.44719, val loss: 0.43720, in 0.031s
1 tree, 85 leaves, max depth = 18, train loss: 0.43773, val loss: 0.42657, in 0.016s
1 tree, 89 leaves, max depth = 15, train loss: 0.42934, val loss: 0.41727, in 0.016s
1 tree, 88 leaves, max depth = 15, train loss: 0.42212, val loss: 0.40904, in 0.016s
1 tree, 87 leaves, max depth = 17, train loss: 0.41626, val loss: 0.40227, in 0.016s
1 tree, 131 leaves, max depth = 15, train loss: 0.40722, val loss: 0.39380, in 0.031s
1 tree, 4 leaves, max depth = 3, train loss: 0.40108, val loss: 0.38718, in 0.000s
1 tree, 91 leaves, max depth = 15, train loss: 0.39647, val loss: 0.38197, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.39140, val loss: 0.37650, in 0.016s
1 tree, 88 leaves, max depth = 14, train loss: 0.38740, val loss: 0.37186, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.38318, val loss: 0.36730, in 0.016s
1 tree, 195 leaves, max depth = 15, train loss: 0.37761, val loss: 0.36273, in 0.016s
1 tree, 130 leaves, max depth = 16, train loss: 0.37053, val loss: 0.35630, in 0.031s
1 tree, 88 leaves, max depth = 13, train loss: 0.36741, val loss: 0.35267, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.36381, val loss: 0.34877, in 0.000s
1 tree, 132 leaves, max depth = 14, train loss: 0.35766, val loss: 0.34327, in 0.031s
1 tree, 131 leaves, max depth = 14, train loss: 0.35244, val loss: 0.33861, in 0.031s
1 tree, 53 leaves, max depth = 11, train loss: 0.34997, val loss: 0.33613, in 0.000s
1 tree, 132 leaves, max depth = 16, train loss: 0.34555, val loss: 0.33222, in 0.031s
1 tree, 131 leaves, max depth = 16, train loss: 0.34180, val loss: 0.32892, in 0.016s
1 tree, 114 leaves, max depth = 16, train loss: 0.33919, val loss: 0.32622, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.33592, val loss: 0.32265, in 0.016s
1 tree, 114 leaves, max depth = 16, train loss: 0.33375, val loss: 0.32044, in 0.016s
1 tree, 130 leaves, max depth = 16, train loss: 0.33033, val loss: 0.31749, in 0.031s
1 tree, 83 leaves, max depth = 13, train loss: 0.32829, val loss: 0.31527, in 0.016s
1 tree, 145 leaves, max depth = 22, train loss: 0.32581, val loss: 0.31335, in 0.016s
1 tree, 164 leaves, max depth = 23, train loss: 0.32333, val loss: 0.31161, in 0.032s
1 tree, 132 leaves, max depth = 17, train loss: 0.32085, val loss: 0.30953, in 0.031s
1 tree, 113 leaves, max depth = 25, train loss: 0.31889, val loss: 0.30756, in 0.031s
1 tree, 105 leaves, max depth = 15, train loss: 0.31708, val loss: 0.30577, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.31406, val loss: 0.30247, in 0.000s
1 tree, 53 leaves, max depth = 11, train loss: 0.31269, val loss: 0.30116, in 0.016s
1 tree, 145 leaves, max depth = 23, train loss: 0.31071, val loss: 0.29969, in 0.031s
1 tree, 130 leaves, max depth = 16, train loss: 0.30859, val loss: 0.29794, in 0.016s
1 tree, 4 leaves, max depth = 3, train loss: 0.30590, val loss: 0.29499, in 0.016s
1 tree, 105 leaves, max depth = 17, train loss: 0.30436, val loss: 0.29356, in 0.016s
1 tree, 131 leaves, max depth = 17, train loss: 0.30244, val loss: 0.29199, in 0.031s
1 tree, 107 leaves, max depth = 17, train loss: 0.30108, val loss: 0.29093, in 0.016s
1 tree, 132 leaves, max depth = 17, train loss: 0.29938, val loss: 0.28958, in 0.016s
1 tree, 152 leaves, max depth = 20, train loss: 0.29757, val loss: 0.28869, in 0.016s
1 tree, 53 leaves, max depth = 12, train loss: 0.29650, val loss: 0.28769, in 0.016s
1 tree, 131 leaves, max depth = 17, train loss: 0.29506, val loss: 0.28656, in 0.016s
1 tree, 87 leaves, max depth = 15, train loss: 0.29390, val loss: 0.28527, in 0.031s
1 tree, 110 leaves, max depth = 16, train loss: 0.29196, val loss: 0.28370, in 0.016s
1 tree, 105 leaves, max depth = 18, train loss: 0.29079, val loss: 0.28261, in 0.031s
Fit 55 trees in 1.440 s, (5348 total leaves)
Time spent computing histograms: 0.405s
Time spent finding best splits:  0.114s
Time spent applying splits:      0.135s
Time spent predicting:           0.000s
Trial 89, Fold 4: Log loss = 0.2984179934391109, Average precision = 0.9564092380626489, ROC-AUC = 0.9529589482130042, Elapsed Time = 1.4476890999994794 seconds
Trial 89, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 89, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
Binning 0.040 GB of training data: 0.189 s
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 79 leaves, max depth = 15, train loss: 0.64887, val loss: 0.64617, in 0.016s
1 tree, 77 leaves, max depth = 15, train loss: 0.61270, val loss: 0.60729, in 0.016s
1 tree, 86 leaves, max depth = 16, train loss: 0.58265, val loss: 0.57491, in 0.016s
1 tree, 74 leaves, max depth = 14, train loss: 0.55783, val loss: 0.54778, in 0.016s
1 tree, 127 leaves, max depth = 20, train loss: 0.53545, val loss: 0.52651, in 0.031s
1 tree, 77 leaves, max depth = 15, train loss: 0.51620, val loss: 0.50536, in 0.031s
1 tree, 127 leaves, max depth = 18, train loss: 0.49852, val loss: 0.48872, in 0.016s
1 tree, 76 leaves, max depth = 15, train loss: 0.48349, val loss: 0.47216, in 0.016s
1 tree, 85 leaves, max depth = 15, train loss: 0.47065, val loss: 0.45794, in 0.016s
1 tree, 127 leaves, max depth = 16, train loss: 0.45695, val loss: 0.44527, in 0.016s
1 tree, 127 leaves, max depth = 17, train loss: 0.44533, val loss: 0.43459, in 0.031s
1 tree, 76 leaves, max depth = 15, train loss: 0.43576, val loss: 0.42380, in 0.016s
1 tree, 73 leaves, max depth = 16, train loss: 0.42766, val loss: 0.41459, in 0.016s
1 tree, 87 leaves, max depth = 15, train loss: 0.42034, val loss: 0.40623, in 0.016s
1 tree, 88 leaves, max depth = 17, train loss: 0.41437, val loss: 0.39923, in 0.016s
1 tree, 126 leaves, max depth = 19, train loss: 0.40521, val loss: 0.39111, in 0.031s
1 tree, 5 leaves, max depth = 3, train loss: 0.39901, val loss: 0.38516, in 0.000s
1 tree, 86 leaves, max depth = 15, train loss: 0.39435, val loss: 0.37976, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.38923, val loss: 0.37488, in 0.016s
1 tree, 87 leaves, max depth = 15, train loss: 0.38517, val loss: 0.37011, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.38090, val loss: 0.36607, in 0.000s
1 tree, 186 leaves, max depth = 13, train loss: 0.37521, val loss: 0.36211, in 0.031s
1 tree, 126 leaves, max depth = 19, train loss: 0.36803, val loss: 0.35594, in 0.016s
1 tree, 87 leaves, max depth = 14, train loss: 0.36484, val loss: 0.35216, in 0.031s
1 tree, 5 leaves, max depth = 3, train loss: 0.36121, val loss: 0.34873, in 0.000s
1 tree, 126 leaves, max depth = 17, train loss: 0.35498, val loss: 0.34345, in 0.031s
1 tree, 127 leaves, max depth = 19, train loss: 0.34968, val loss: 0.33902, in 0.016s
1 tree, 107 leaves, max depth = 16, train loss: 0.34672, val loss: 0.33593, in 0.016s
1 tree, 126 leaves, max depth = 18, train loss: 0.34217, val loss: 0.33217, in 0.031s
1 tree, 127 leaves, max depth = 17, train loss: 0.33831, val loss: 0.32899, in 0.016s
1 tree, 114 leaves, max depth = 16, train loss: 0.33576, val loss: 0.32640, in 0.031s
1 tree, 5 leaves, max depth = 3, train loss: 0.33244, val loss: 0.32323, in 0.000s
1 tree, 114 leaves, max depth = 14, train loss: 0.33032, val loss: 0.32110, in 0.016s
1 tree, 126 leaves, max depth = 20, train loss: 0.32682, val loss: 0.31826, in 0.031s
1 tree, 81 leaves, max depth = 13, train loss: 0.32475, val loss: 0.31589, in 0.016s
1 tree, 137 leaves, max depth = 26, train loss: 0.32233, val loss: 0.31414, in 0.031s
1 tree, 166 leaves, max depth = 16, train loss: 0.31982, val loss: 0.31227, in 0.016s
1 tree, 126 leaves, max depth = 22, train loss: 0.31729, val loss: 0.31027, in 0.016s
1 tree, 111 leaves, max depth = 16, train loss: 0.31540, val loss: 0.30833, in 0.016s
1 tree, 107 leaves, max depth = 17, train loss: 0.31365, val loss: 0.30654, in 0.031s
1 tree, 5 leaves, max depth = 3, train loss: 0.31058, val loss: 0.30358, in 0.000s
1 tree, 51 leaves, max depth = 12, train loss: 0.30922, val loss: 0.30198, in 0.016s
1 tree, 107 leaves, max depth = 14, train loss: 0.30673, val loss: 0.30020, in 0.031s
1 tree, 126 leaves, max depth = 23, train loss: 0.30460, val loss: 0.29857, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.30188, val loss: 0.29593, in 0.000s
1 tree, 107 leaves, max depth = 16, train loss: 0.30033, val loss: 0.29440, in 0.031s
1 tree, 126 leaves, max depth = 24, train loss: 0.29840, val loss: 0.29294, in 0.016s
1 tree, 81 leaves, max depth = 14, train loss: 0.29704, val loss: 0.29145, in 0.031s
1 tree, 127 leaves, max depth = 24, train loss: 0.29536, val loss: 0.29022, in 0.016s
1 tree, 159 leaves, max depth = 19, train loss: 0.29353, val loss: 0.28964, in 0.031s
1 tree, 111 leaves, max depth = 17, train loss: 0.29233, val loss: 0.28836, in 0.016s
1 tree, 126 leaves, max depth = 24, train loss: 0.29088, val loss: 0.28729, in 0.031s
1 tree, 89 leaves, max depth = 15, train loss: 0.28972, val loss: 0.28587, in 0.016s
1 tree, 106 leaves, max depth = 15, train loss: 0.28803, val loss: 0.28481, in 0.016s
1 tree, 107 leaves, max depth = 18, train loss: 0.28686, val loss: 0.28366, in 0.016s
Fit 55 trees in 1.424 s, (5237 total leaves)
Time spent computing histograms: 0.414s
Time spent finding best splits:  0.117s
Time spent applying splits:      0.142s
Time spent predicting:           0.016s
Trial 89, Fold 5: Log loss = 0.3030563569382536, Average precision = 0.9536670025897026, ROC-AUC = 0.950168441001059, Elapsed Time = 1.432476100000713 seconds
Optimization Progress:  90%|######### | 90/100 [18:41<02:07, 12.76s/it]
Trial 90, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 90, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
0.157 s 0.040 GB of training data: 
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 17 leaves, max depth = 7, train loss: 0.67072, val loss: 0.67079, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.65012, val loss: 0.65020, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.63101, val loss: 0.63108, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.61323, val loss: 0.61326, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.59667, val loss: 0.59655, in 0.016s
1 tree, 17 leaves, max depth = 5, train loss: 0.58126, val loss: 0.58117, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.56683, val loss: 0.56673, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.55333, val loss: 0.55314, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.54069, val loss: 0.54052, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.52882, val loss: 0.52857, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.51769, val loss: 0.51747, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.50720, val loss: 0.50691, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.49734, val loss: 0.49705, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.48805, val loss: 0.48774, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.47929, val loss: 0.47902, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.47075, val loss: 0.47045, in 0.031s
1 tree, 17 leaves, max depth = 7, train loss: 0.46295, val loss: 0.46265, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.45533, val loss: 0.45497, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.44813, val loss: 0.44774, in 0.016s
1 tree, 17 leaves, max depth = 5, train loss: 0.43793, val loss: 0.43786, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.43142, val loss: 0.43134, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.42527, val loss: 0.42520, in 0.016s
1 tree, 17 leaves, max depth = 5, train loss: 0.41625, val loss: 0.41651, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.40776, val loss: 0.40834, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.40240, val loss: 0.40297, in 0.016s
1 tree, 17 leaves, max depth = 5, train loss: 0.39469, val loss: 0.39558, in 0.000s
1 tree, 17 leaves, max depth = 9, train loss: 0.38993, val loss: 0.39080, in 0.031s
1 tree, 17 leaves, max depth = 7, train loss: 0.38531, val loss: 0.38614, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.37842, val loss: 0.37958, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.37420, val loss: 0.37536, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.36788, val loss: 0.36936, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.36401, val loss: 0.36551, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.35822, val loss: 0.36007, in 0.016s
1 tree, 17 leaves, max depth = 5, train loss: 0.35280, val loss: 0.35484, in 0.016s
1 tree, 17 leaves, max depth = 8, train loss: 0.34793, val loss: 0.35000, in 0.016s
1 tree, 17 leaves, max depth = 8, train loss: 0.34337, val loss: 0.34545, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.33947, val loss: 0.34158, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.33578, val loss: 0.33799, in 0.016s
1 tree, 17 leaves, max depth = 8, train loss: 0.33168, val loss: 0.33392, in 0.016s
1 tree, 17 leaves, max depth = 8, train loss: 0.32763, val loss: 0.33025, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.32391, val loss: 0.32656, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.32061, val loss: 0.32339, in 0.016s
1 tree, 17 leaves, max depth = 8, train loss: 0.31701, val loss: 0.32015, in 0.016s
1 tree, 17 leaves, max depth = 10, train loss: 0.31374, val loss: 0.31691, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.31071, val loss: 0.31395, in 0.016s
1 tree, 17 leaves, max depth = 8, train loss: 0.30750, val loss: 0.31108, in 0.016s
1 tree, 17 leaves, max depth = 8, train loss: 0.30461, val loss: 0.30820, in 0.000s
1 tree, 17 leaves, max depth = 6, train loss: 0.30185, val loss: 0.30555, in 0.031s
1 tree, 17 leaves, max depth = 8, train loss: 0.29903, val loss: 0.30307, in 0.000s
Fit 49 trees in 1.126 s, (833 total leaves)
Time spent computing histograms: 0.340s
Time spent finding best splits:  0.056s
Time spent applying splits:      0.022s
Time spent predicting:           0.000s
Trial 90, Fold 1: Log loss = 0.30327338909954293, Average precision = 0.9586633313997285, ROC-AUC = 0.9539470146685594, Elapsed Time = 1.1228356000010535 seconds
Trial 90, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 90, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
Binning 0.040 GB of training data: 0.174 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 17 leaves, max depth = 6, train loss: 0.67046, val loss: 0.67015, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.64968, val loss: 0.64902, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.63025, val loss: 0.62928, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.61220, val loss: 0.61085, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.59555, val loss: 0.59390, in 0.031s
1 tree, 17 leaves, max depth = 6, train loss: 0.58002, val loss: 0.57808, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.56539, val loss: 0.56326, in 0.000s
1 tree, 17 leaves, max depth = 7, train loss: 0.55181, val loss: 0.54936, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.53892, val loss: 0.53619, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.52680, val loss: 0.52386, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.51544, val loss: 0.51227, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.50475, val loss: 0.50133, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.49469, val loss: 0.49108, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.48523, val loss: 0.48137, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.47630, val loss: 0.47227, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.46789, val loss: 0.46371, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.45995, val loss: 0.45555, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.45255, val loss: 0.44796, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.44547, val loss: 0.44066, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.43543, val loss: 0.43076, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.42902, val loss: 0.42422, in 0.031s
1 tree, 17 leaves, max depth = 6, train loss: 0.41990, val loss: 0.41522, in 0.016s
1 tree, 17 leaves, max depth = 8, train loss: 0.41412, val loss: 0.40940, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.40582, val loss: 0.40124, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.40053, val loss: 0.39589, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.39295, val loss: 0.38841, in 0.016s
1 tree, 17 leaves, max depth = 8, train loss: 0.38817, val loss: 0.38366, in 0.016s
1 tree, 17 leaves, max depth = 9, train loss: 0.38122, val loss: 0.37687, in 0.016s
1 tree, 17 leaves, max depth = 8, train loss: 0.37686, val loss: 0.37253, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.37051, val loss: 0.36631, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.36646, val loss: 0.36223, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.36063, val loss: 0.35652, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.35692, val loss: 0.35273, in 0.016s
1 tree, 17 leaves, max depth = 5, train loss: 0.35183, val loss: 0.34779, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.34705, val loss: 0.34312, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.34258, val loss: 0.33873, in 0.016s
1 tree, 17 leaves, max depth = 5, train loss: 0.33875, val loss: 0.33501, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.33496, val loss: 0.33132, in 0.016s
1 tree, 17 leaves, max depth = 8, train loss: 0.33082, val loss: 0.32753, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.32696, val loss: 0.32381, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.32317, val loss: 0.32033, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.31969, val loss: 0.31697, in 0.000s
1 tree, 17 leaves, max depth = 6, train loss: 0.31644, val loss: 0.31386, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.31306, val loss: 0.31077, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.30999, val loss: 0.30780, in 0.016s
1 tree, 17 leaves, max depth = 5, train loss: 0.30703, val loss: 0.30491, in 0.016s
1 tree, 17 leaves, max depth = 8, train loss: 0.30402, val loss: 0.30215, in 0.000s
1 tree, 17 leaves, max depth = 6, train loss: 0.30125, val loss: 0.29950, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.29858, val loss: 0.29693, in 0.016s
Fit 49 trees in 1.127 s, (833 total leaves)
Time spent computing histograms: 0.338s
Time spent finding best splits:  0.055s
Time spent applying splits:      0.021s
Time spent predicting:           0.016s
Trial 90, Fold 2: Log loss = 0.30164434202133084, Average precision = 0.9563519071978452, ROC-AUC = 0.9546788546441348, Elapsed Time = 1.1341358000008768 seconds
Trial 90, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 90, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
Binning 0.040 GB of training data: 0.174 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 17 leaves, max depth = 6, train loss: 0.67083, val loss: 0.67081, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.65031, val loss: 0.65023, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.63125, val loss: 0.63107, in 0.031s
1 tree, 17 leaves, max depth = 6, train loss: 0.61357, val loss: 0.61331, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.59683, val loss: 0.59653, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.58121, val loss: 0.58088, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.56663, val loss: 0.56625, in 0.016s
1 tree, 17 leaves, max depth = 8, train loss: 0.55297, val loss: 0.55258, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.54020, val loss: 0.53972, in 0.016s
1 tree, 17 leaves, max depth = 8, train loss: 0.52827, val loss: 0.52765, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.51699, val loss: 0.51639, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.50638, val loss: 0.50574, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.49641, val loss: 0.49570, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.48700, val loss: 0.48624, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.47814, val loss: 0.47737, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.46978, val loss: 0.46896, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.46187, val loss: 0.46108, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.45437, val loss: 0.45355, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.44387, val loss: 0.44379, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.43405, val loss: 0.43462, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.42757, val loss: 0.42822, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.42146, val loss: 0.42214, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.41565, val loss: 0.41632, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.40726, val loss: 0.40857, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.40199, val loss: 0.40335, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.39427, val loss: 0.39622, in 0.016s
1 tree, 17 leaves, max depth = 8, train loss: 0.38943, val loss: 0.39133, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.38236, val loss: 0.38490, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.37797, val loss: 0.38057, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.37150, val loss: 0.37466, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.36744, val loss: 0.37059, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.36151, val loss: 0.36518, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.35595, val loss: 0.36009, in 0.031s
1 tree, 17 leaves, max depth = 7, train loss: 0.35241, val loss: 0.35666, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.34752, val loss: 0.35220, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.34293, val loss: 0.34803, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.33862, val loss: 0.34414, in 0.000s
1 tree, 17 leaves, max depth = 6, train loss: 0.33492, val loss: 0.34036, in 0.031s
1 tree, 17 leaves, max depth = 8, train loss: 0.33072, val loss: 0.33680, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.32693, val loss: 0.33338, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.32358, val loss: 0.32995, in 0.031s
1 tree, 17 leaves, max depth = 8, train loss: 0.31985, val loss: 0.32683, in 0.031s
1 tree, 17 leaves, max depth = 8, train loss: 0.31634, val loss: 0.32394, in 0.032s
1 tree, 17 leaves, max depth = 6, train loss: 0.31313, val loss: 0.32105, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.31012, val loss: 0.31791, in 0.016s
1 tree, 17 leaves, max depth = 8, train loss: 0.30699, val loss: 0.31528, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.30415, val loss: 0.31270, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.30140, val loss: 0.30982, in 0.016s
1 tree, 17 leaves, max depth = 8, train loss: 0.29861, val loss: 0.30743, in 0.016s
Fit 49 trees in 1.205 s, (833 total leaves)
Time spent computing histograms: 0.365s
Time spent finding best splits:  0.064s
Time spent applying splits:      0.038s
Time spent predicting:           0.016s
Trial 90, Fold 3: Log loss = 0.2972712315038747, Average precision = 0.9594648412397699, ROC-AUC = 0.9567885554410532, Elapsed Time = 1.2156603000003088 seconds
Trial 90, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 90, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
Binning 0.040 GB of training data: 0.174 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 17 leaves, max depth = 6, train loss: 0.67088, val loss: 0.67003, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.65037, val loss: 0.64870, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.63135, val loss: 0.62890, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.61368, val loss: 0.61051, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.59713, val loss: 0.59316, in 0.016s
1 tree, 17 leaves, max depth = 8, train loss: 0.58175, val loss: 0.57714, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.56740, val loss: 0.56212, in 0.016s
1 tree, 17 leaves, max depth = 5, train loss: 0.55397, val loss: 0.54806, in 0.031s
1 tree, 17 leaves, max depth = 7, train loss: 0.54137, val loss: 0.53483, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.52957, val loss: 0.52241, in 0.016s
1 tree, 17 leaves, max depth = 5, train loss: 0.51849, val loss: 0.51073, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.50803, val loss: 0.49970, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.49821, val loss: 0.48935, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.48888, val loss: 0.47961, in 0.016s
1 tree, 17 leaves, max depth = 9, train loss: 0.47995, val loss: 0.47018, in 0.016s
1 tree, 17 leaves, max depth = 9, train loss: 0.47153, val loss: 0.46130, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.46352, val loss: 0.45285, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.45595, val loss: 0.44488, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.44881, val loss: 0.43730, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.43875, val loss: 0.42708, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.43233, val loss: 0.42028, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.42319, val loss: 0.41099, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.41733, val loss: 0.40477, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.40905, val loss: 0.39637, in 0.016s
1 tree, 17 leaves, max depth = 8, train loss: 0.40383, val loss: 0.39080, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.39629, val loss: 0.38315, in 0.016s
1 tree, 17 leaves, max depth = 8, train loss: 0.39156, val loss: 0.37817, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.38460, val loss: 0.37110, in 0.016s
1 tree, 17 leaves, max depth = 8, train loss: 0.38029, val loss: 0.36653, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.37391, val loss: 0.36008, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.36993, val loss: 0.35577, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.36413, val loss: 0.34992, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.35865, val loss: 0.34435, in 0.016s
1 tree, 17 leaves, max depth = 8, train loss: 0.35526, val loss: 0.34067, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.35041, val loss: 0.33561, in 0.000s
1 tree, 17 leaves, max depth = 8, train loss: 0.34587, val loss: 0.33088, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.34160, val loss: 0.32640, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.33741, val loss: 0.32246, in 0.016s
1 tree, 17 leaves, max depth = 5, train loss: 0.33369, val loss: 0.31862, in 0.016s
1 tree, 17 leaves, max depth = 8, train loss: 0.32994, val loss: 0.31470, in 0.016s
1 tree, 17 leaves, max depth = 5, train loss: 0.32649, val loss: 0.31116, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.32285, val loss: 0.30775, in 0.016s
1 tree, 17 leaves, max depth = 5, train loss: 0.31965, val loss: 0.30446, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.31642, val loss: 0.30110, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.31319, val loss: 0.29810, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.31028, val loss: 0.29505, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.30741, val loss: 0.29209, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.30453, val loss: 0.28942, in 0.016s
1 tree, 17 leaves, max depth = 8, train loss: 0.30178, val loss: 0.28687, in 0.016s
Fit 49 trees in 1.127 s, (833 total leaves)
Time spent computing histograms: 0.335s
Time spent finding best splits:  0.056s
Time spent applying splits:      0.022s
Time spent predicting:           0.000s
Trial 90, Fold 4: Log loss = 0.30193565745199424, Average precision = 0.9581189580828368, ROC-AUC = 0.9541854289910847, Elapsed Time = 1.1225206999988586 seconds
Trial 90, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 90, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
Binning 0.040 GB of training data: 0.174 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 17 leaves, max depth = 7, train loss: 0.67063, val loss: 0.66979, in 0.000s
1 tree, 17 leaves, max depth = 7, train loss: 0.64993, val loss: 0.64834, in 0.031s
1 tree, 17 leaves, max depth = 7, train loss: 0.63076, val loss: 0.62843, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.61268, val loss: 0.60966, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.59584, val loss: 0.59217, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.58014, val loss: 0.57586, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.56547, val loss: 0.56059, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.55175, val loss: 0.54632, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.53889, val loss: 0.53292, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.52688, val loss: 0.52042, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.51555, val loss: 0.50867, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.50490, val loss: 0.49754, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.49488, val loss: 0.48707, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.48545, val loss: 0.47724, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.47652, val loss: 0.46794, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.46810, val loss: 0.45917, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.46016, val loss: 0.45091, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.45266, val loss: 0.44310, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.44557, val loss: 0.43574, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.43546, val loss: 0.42563, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.42905, val loss: 0.41897, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.41986, val loss: 0.40980, in 0.016s
1 tree, 17 leaves, max depth = 8, train loss: 0.41403, val loss: 0.40378, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.40567, val loss: 0.39548, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.40039, val loss: 0.38998, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.39274, val loss: 0.38237, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.38792, val loss: 0.37741, in 0.031s
1 tree, 17 leaves, max depth = 6, train loss: 0.38093, val loss: 0.37045, in 0.016s
1 tree, 17 leaves, max depth = 8, train loss: 0.37655, val loss: 0.36591, in 0.016s
1 tree, 17 leaves, max depth = 8, train loss: 0.37237, val loss: 0.36162, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.36611, val loss: 0.35542, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.36023, val loss: 0.34959, in 0.016s
1 tree, 17 leaves, max depth = 8, train loss: 0.35654, val loss: 0.34584, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.35124, val loss: 0.34050, in 0.016s
1 tree, 17 leaves, max depth = 8, train loss: 0.34640, val loss: 0.33558, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.34187, val loss: 0.33096, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.33797, val loss: 0.32712, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.33378, val loss: 0.32286, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.33015, val loss: 0.31931, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.32616, val loss: 0.31571, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.32248, val loss: 0.31199, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.31919, val loss: 0.30877, in 0.016s
1 tree, 17 leaves, max depth = 8, train loss: 0.31593, val loss: 0.30546, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.31245, val loss: 0.30234, in 0.016s
1 tree, 17 leaves, max depth = 5, train loss: 0.30929, val loss: 0.29914, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.30617, val loss: 0.29640, in 0.016s
1 tree, 17 leaves, max depth = 6, train loss: 0.30331, val loss: 0.29342, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.30043, val loss: 0.29080, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.29783, val loss: 0.28815, in 0.016s
Fit 49 trees in 1.143 s, (833 total leaves)
Time spent computing histograms: 0.346s
Time spent finding best splits:  0.060s
Time spent applying splits:      0.022s
Time spent predicting:           0.000s
Trial 90, Fold 5: Log loss = 0.3066667178325203, Average precision = 0.9558979910376308, ROC-AUC = 0.9514793303977854, Elapsed Time = 1.152712999999494 seconds
Optimization Progress:  91%|#########1| 91/100 [18:55<01:55, 12.88s/it]
Trial 91, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 91, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
0.143 s 0.040 GB of training data: 
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 25 leaves, max depth = 10, train loss: 0.66806, val loss: 0.66726, in 0.016s
1 tree, 6 leaves, max depth = 4, train loss: 0.65533, val loss: 0.65349, in 0.000s
1 tree, 25 leaves, max depth = 9, train loss: 0.63376, val loss: 0.63130, in 0.000s
1 tree, 6 leaves, max depth = 4, train loss: 0.61459, val loss: 0.61135, in 0.016s
1 tree, 6 leaves, max depth = 4, train loss: 0.59735, val loss: 0.59338, in 0.000s
1 tree, 24 leaves, max depth = 11, train loss: 0.58161, val loss: 0.57708, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.56757, val loss: 0.56237, in 0.000s
1 tree, 24 leaves, max depth = 9, train loss: 0.55498, val loss: 0.54928, in 0.000s
1 tree, 47 leaves, max depth = 8, train loss: 0.54589, val loss: 0.54077, in 0.016s
1 tree, 30 leaves, max depth = 11, train loss: 0.54088, val loss: 0.53491, in 0.000s
1 tree, 25 leaves, max depth = 10, train loss: 0.53026, val loss: 0.52387, in 0.016s
1 tree, 18 leaves, max depth = 6, train loss: 0.52507, val loss: 0.51845, in 0.000s
1 tree, 25 leaves, max depth = 10, train loss: 0.51565, val loss: 0.50865, in 0.016s
1 tree, 25 leaves, max depth = 10, train loss: 0.50706, val loss: 0.49964, in 0.000s
1 tree, 25 leaves, max depth = 11, train loss: 0.49931, val loss: 0.49152, in 0.016s
1 tree, 25 leaves, max depth = 10, train loss: 0.49226, val loss: 0.48410, in 0.000s
1 tree, 47 leaves, max depth = 14, train loss: 0.48762, val loss: 0.47935, in 0.016s
1 tree, 25 leaves, max depth = 10, train loss: 0.48125, val loss: 0.47261, in 0.000s
1 tree, 25 leaves, max depth = 10, train loss: 0.47544, val loss: 0.46647, in 0.016s
1 tree, 24 leaves, max depth = 11, train loss: 0.46981, val loss: 0.46050, in 0.000s
1 tree, 24 leaves, max depth = 11, train loss: 0.46469, val loss: 0.45506, in 0.016s
1 tree, 24 leaves, max depth = 11, train loss: 0.46003, val loss: 0.45009, in 0.000s
1 tree, 24 leaves, max depth = 7, train loss: 0.45609, val loss: 0.44590, in 0.016s
1 tree, 45 leaves, max depth = 9, train loss: 0.45153, val loss: 0.44177, in 0.000s
1 tree, 24 leaves, max depth = 10, train loss: 0.44798, val loss: 0.43795, in 0.016s
1 tree, 25 leaves, max depth = 10, train loss: 0.44464, val loss: 0.43440, in 0.000s
1 tree, 18 leaves, max depth = 7, train loss: 0.44129, val loss: 0.43103, in 0.000s
1 tree, 24 leaves, max depth = 10, train loss: 0.43845, val loss: 0.42800, in 0.016s
1 tree, 47 leaves, max depth = 8, train loss: 0.43352, val loss: 0.42369, in 0.016s
1 tree, 44 leaves, max depth = 11, train loss: 0.42609, val loss: 0.41700, in 0.000s
1 tree, 25 leaves, max depth = 11, train loss: 0.42372, val loss: 0.41446, in 0.016s
1 tree, 6 leaves, max depth = 4, train loss: 0.42142, val loss: 0.41189, in 0.000s
1 tree, 31 leaves, max depth = 9, train loss: 0.41786, val loss: 0.40850, in 0.016s
1 tree, 47 leaves, max depth = 12, train loss: 0.41479, val loss: 0.40553, in 0.000s
1 tree, 44 leaves, max depth = 9, train loss: 0.41173, val loss: 0.40287, in 0.016s
1 tree, 43 leaves, max depth = 9, train loss: 0.40895, val loss: 0.40048, in 0.000s
Fit 36 trees in 0.502 s, (957 total leaves)
Time spent computing histograms: 0.146s
Time spent finding best splits:  0.013s
Time spent applying splits:      0.017s
Time spent predicting:           0.000s
Trial 91, Fold 1: Log loss = 0.40882904263038683, Average precision = 0.9161066816081005, ROC-AUC = 0.9231154527704889, Elapsed Time = 0.5137892999991891 seconds
Trial 91, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 91, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
Binning 0.040 GB of training data: 0.143 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 25 leaves, max depth = 9, train loss: 0.66805, val loss: 0.66675, in 0.016s
1 tree, 6 leaves, max depth = 4, train loss: 0.65515, val loss: 0.65251, in 0.000s
1 tree, 25 leaves, max depth = 9, train loss: 0.63362, val loss: 0.62991, in 0.016s
1 tree, 6 leaves, max depth = 4, train loss: 0.61439, val loss: 0.60970, in 0.000s
1 tree, 6 leaves, max depth = 4, train loss: 0.59711, val loss: 0.59149, in 0.000s
1 tree, 24 leaves, max depth = 9, train loss: 0.58147, val loss: 0.57490, in 0.016s
1 tree, 6 leaves, max depth = 4, train loss: 0.56740, val loss: 0.56001, in 0.000s
1 tree, 25 leaves, max depth = 8, train loss: 0.55476, val loss: 0.54661, in 0.000s
1 tree, 47 leaves, max depth = 8, train loss: 0.54576, val loss: 0.53806, in 0.000s
1 tree, 31 leaves, max depth = 10, train loss: 0.54083, val loss: 0.53256, in 0.000s
1 tree, 25 leaves, max depth = 14, train loss: 0.53016, val loss: 0.52122, in 0.016s
1 tree, 32 leaves, max depth = 9, train loss: 0.52585, val loss: 0.51649, in 0.000s
1 tree, 25 leaves, max depth = 13, train loss: 0.51655, val loss: 0.50662, in 0.016s
1 tree, 24 leaves, max depth = 9, train loss: 0.50812, val loss: 0.49754, in 0.000s
1 tree, 25 leaves, max depth = 8, train loss: 0.50044, val loss: 0.48931, in 0.000s
1 tree, 24 leaves, max depth = 9, train loss: 0.49346, val loss: 0.48179, in 0.000s
1 tree, 47 leaves, max depth = 12, train loss: 0.48860, val loss: 0.47745, in 0.000s
1 tree, 24 leaves, max depth = 9, train loss: 0.48233, val loss: 0.47075, in 0.000s
1 tree, 24 leaves, max depth = 9, train loss: 0.47665, val loss: 0.46456, in 0.000s
1 tree, 25 leaves, max depth = 9, train loss: 0.47120, val loss: 0.45863, in 0.016s
1 tree, 25 leaves, max depth = 10, train loss: 0.46638, val loss: 0.45339, in 0.000s
1 tree, 25 leaves, max depth = 9, train loss: 0.46186, val loss: 0.44845, in 0.016s
1 tree, 24 leaves, max depth = 9, train loss: 0.45799, val loss: 0.44424, in 0.000s
1 tree, 45 leaves, max depth = 10, train loss: 0.45333, val loss: 0.43982, in 0.016s
1 tree, 25 leaves, max depth = 10, train loss: 0.44985, val loss: 0.43604, in 0.000s
1 tree, 25 leaves, max depth = 10, train loss: 0.44660, val loss: 0.43247, in 0.016s
1 tree, 18 leaves, max depth = 7, train loss: 0.44318, val loss: 0.42914, in 0.000s
1 tree, 25 leaves, max depth = 12, train loss: 0.44039, val loss: 0.42605, in 0.016s
1 tree, 47 leaves, max depth = 8, train loss: 0.43535, val loss: 0.42149, in 0.000s
1 tree, 43 leaves, max depth = 11, train loss: 0.42777, val loss: 0.41435, in 0.016s
1 tree, 25 leaves, max depth = 14, train loss: 0.42544, val loss: 0.41174, in 0.000s
1 tree, 6 leaves, max depth = 4, train loss: 0.42320, val loss: 0.40923, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.41916, val loss: 0.40576, in 0.000s
1 tree, 47 leaves, max depth = 11, train loss: 0.41594, val loss: 0.40311, in 0.016s
1 tree, 44 leaves, max depth = 9, train loss: 0.41283, val loss: 0.40025, in 0.000s
1 tree, 44 leaves, max depth = 9, train loss: 0.41001, val loss: 0.39767, in 0.016s
Fit 36 trees in 0.534 s, (975 total leaves)
Time spent computing histograms: 0.145s
Time spent finding best splits:  0.013s
Time spent applying splits:      0.018s
Time spent predicting:           0.000s
Trial 91, Fold 2: Log loss = 0.41045980898028744, Average precision = 0.9138552951984288, ROC-AUC = 0.9266183370202987, Elapsed Time = 0.5324492999989161 seconds
Trial 91, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 91, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
Binning 0.040 GB of training data: 0.158 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 25 leaves, max depth = 10, train loss: 0.66816, val loss: 0.66729, in 0.016s
1 tree, 6 leaves, max depth = 4, train loss: 0.65521, val loss: 0.65401, in 0.000s
1 tree, 24 leaves, max depth = 9, train loss: 0.63385, val loss: 0.63179, in 0.000s
1 tree, 6 leaves, max depth = 4, train loss: 0.61475, val loss: 0.61195, in 0.016s
1 tree, 6 leaves, max depth = 4, train loss: 0.59758, val loss: 0.59409, in 0.000s
1 tree, 25 leaves, max depth = 10, train loss: 0.58213, val loss: 0.57801, in 0.016s
1 tree, 6 leaves, max depth = 4, train loss: 0.56816, val loss: 0.56344, in 0.000s
1 tree, 24 leaves, max depth = 10, train loss: 0.55562, val loss: 0.55025, in 0.000s
1 tree, 47 leaves, max depth = 8, train loss: 0.54651, val loss: 0.54188, in 0.016s
1 tree, 30 leaves, max depth = 10, train loss: 0.54166, val loss: 0.53596, in 0.000s
1 tree, 25 leaves, max depth = 11, train loss: 0.53112, val loss: 0.52482, in 0.016s
1 tree, 18 leaves, max depth = 6, train loss: 0.52597, val loss: 0.51997, in 0.000s
1 tree, 25 leaves, max depth = 12, train loss: 0.51662, val loss: 0.51008, in 0.016s
1 tree, 25 leaves, max depth = 8, train loss: 0.50814, val loss: 0.50113, in 0.000s
1 tree, 25 leaves, max depth = 11, train loss: 0.50044, val loss: 0.49295, in 0.016s
1 tree, 25 leaves, max depth = 11, train loss: 0.49345, val loss: 0.48550, in 0.000s
1 tree, 47 leaves, max depth = 14, train loss: 0.48872, val loss: 0.48115, in 0.016s
1 tree, 25 leaves, max depth = 10, train loss: 0.48240, val loss: 0.47451, in 0.016s
1 tree, 25 leaves, max depth = 8, train loss: 0.47669, val loss: 0.46843, in 0.000s
1 tree, 23 leaves, max depth = 8, train loss: 0.47118, val loss: 0.46268, in 0.016s
1 tree, 23 leaves, max depth = 8, train loss: 0.46617, val loss: 0.45744, in 0.000s
1 tree, 23 leaves, max depth = 8, train loss: 0.46162, val loss: 0.45266, in 0.000s
1 tree, 24 leaves, max depth = 8, train loss: 0.45776, val loss: 0.44851, in 0.016s
1 tree, 44 leaves, max depth = 12, train loss: 0.45323, val loss: 0.44430, in 0.000s
1 tree, 25 leaves, max depth = 9, train loss: 0.44971, val loss: 0.44057, in 0.016s
1 tree, 25 leaves, max depth = 9, train loss: 0.44645, val loss: 0.43703, in 0.000s
1 tree, 18 leaves, max depth = 6, train loss: 0.44310, val loss: 0.43407, in 0.016s
1 tree, 25 leaves, max depth = 11, train loss: 0.44030, val loss: 0.43101, in 0.000s
1 tree, 47 leaves, max depth = 9, train loss: 0.43538, val loss: 0.42666, in 0.016s
1 tree, 42 leaves, max depth = 12, train loss: 0.42809, val loss: 0.42002, in 0.000s
1 tree, 25 leaves, max depth = 12, train loss: 0.42575, val loss: 0.41744, in 0.016s
1 tree, 6 leaves, max depth = 4, train loss: 0.42348, val loss: 0.41501, in 0.000s
1 tree, 31 leaves, max depth = 9, train loss: 0.41981, val loss: 0.41179, in 0.016s
1 tree, 47 leaves, max depth = 13, train loss: 0.41665, val loss: 0.40908, in 0.000s
1 tree, 43 leaves, max depth = 11, train loss: 0.41361, val loss: 0.40630, in 0.016s
1 tree, 43 leaves, max depth = 11, train loss: 0.41085, val loss: 0.40378, in 0.000s
Fit 36 trees in 0.549 s, (953 total leaves)
Time spent computing histograms: 0.146s
Time spent finding best splits:  0.014s
Time spent applying splits:      0.019s
Time spent predicting:           0.000s
Trial 91, Fold 3: Log loss = 0.40375749891006, Average precision = 0.9206012984117338, ROC-AUC = 0.9313995669645867, Elapsed Time = 0.5566916999996465 seconds
Trial 91, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 91, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
Binning 0.040 GB of training data: 0.158 s
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 25 leaves, max depth = 10, train loss: 0.66813, val loss: 0.66664, in 0.000s
1 tree, 6 leaves, max depth = 4, train loss: 0.65509, val loss: 0.65268, in 0.016s
1 tree, 25 leaves, max depth = 9, train loss: 0.63373, val loss: 0.62998, in 0.000s
1 tree, 6 leaves, max depth = 4, train loss: 0.61464, val loss: 0.60970, in 0.016s
1 tree, 6 leaves, max depth = 4, train loss: 0.59747, val loss: 0.59140, in 0.000s
1 tree, 24 leaves, max depth = 12, train loss: 0.58201, val loss: 0.57474, in 0.016s
1 tree, 6 leaves, max depth = 4, train loss: 0.56804, val loss: 0.55974, in 0.000s
1 tree, 25 leaves, max depth = 10, train loss: 0.55542, val loss: 0.54612, in 0.000s
1 tree, 47 leaves, max depth = 8, train loss: 0.54645, val loss: 0.53727, in 0.016s
1 tree, 30 leaves, max depth = 11, train loss: 0.54147, val loss: 0.53184, in 0.016s
1 tree, 25 leaves, max depth = 10, train loss: 0.53087, val loss: 0.52032, in 0.000s
1 tree, 18 leaves, max depth = 7, train loss: 0.52578, val loss: 0.51536, in 0.000s
1 tree, 25 leaves, max depth = 10, train loss: 0.51638, val loss: 0.50510, in 0.016s
1 tree, 24 leaves, max depth = 10, train loss: 0.50786, val loss: 0.49569, in 0.000s
1 tree, 25 leaves, max depth = 9, train loss: 0.50009, val loss: 0.48711, in 0.016s
1 tree, 25 leaves, max depth = 10, train loss: 0.49305, val loss: 0.47929, in 0.000s
1 tree, 47 leaves, max depth = 17, train loss: 0.48832, val loss: 0.47437, in 0.016s
1 tree, 25 leaves, max depth = 11, train loss: 0.48200, val loss: 0.46727, in 0.000s
1 tree, 25 leaves, max depth = 10, train loss: 0.47627, val loss: 0.46079, in 0.016s
1 tree, 25 leaves, max depth = 9, train loss: 0.47080, val loss: 0.45457, in 0.000s
1 tree, 25 leaves, max depth = 9, train loss: 0.46583, val loss: 0.44889, in 0.016s
1 tree, 25 leaves, max depth = 9, train loss: 0.46130, val loss: 0.44368, in 0.000s
1 tree, 24 leaves, max depth = 9, train loss: 0.45736, val loss: 0.43915, in 0.016s
1 tree, 45 leaves, max depth = 10, train loss: 0.45287, val loss: 0.43441, in 0.000s
1 tree, 25 leaves, max depth = 11, train loss: 0.44936, val loss: 0.43030, in 0.016s
1 tree, 25 leaves, max depth = 9, train loss: 0.44609, val loss: 0.42647, in 0.000s
1 tree, 25 leaves, max depth = 11, train loss: 0.44319, val loss: 0.42302, in 0.016s
1 tree, 25 leaves, max depth = 10, train loss: 0.44057, val loss: 0.41991, in 0.000s
1 tree, 47 leaves, max depth = 9, train loss: 0.43554, val loss: 0.41522, in 0.016s
1 tree, 43 leaves, max depth = 10, train loss: 0.42810, val loss: 0.40813, in 0.000s
1 tree, 25 leaves, max depth = 10, train loss: 0.42593, val loss: 0.40552, in 0.016s
1 tree, 24 leaves, max depth = 7, train loss: 0.42373, val loss: 0.40292, in 0.000s
1 tree, 30 leaves, max depth = 8, train loss: 0.41993, val loss: 0.39918, in 0.016s
1 tree, 47 leaves, max depth = 17, train loss: 0.41668, val loss: 0.39579, in 0.000s
1 tree, 44 leaves, max depth = 8, train loss: 0.41367, val loss: 0.39259, in 0.016s
1 tree, 44 leaves, max depth = 8, train loss: 0.41092, val loss: 0.38966, in 0.000s
Fit 36 trees in 0.564 s, (987 total leaves)
Time spent computing histograms: 0.153s
Time spent finding best splits:  0.014s
Time spent applying splits:      0.019s
Time spent predicting:           0.000s
Trial 91, Fold 4: Log loss = 0.4106653964918873, Average precision = 0.9136860523512662, ROC-AUC = 0.9226894622202193, Elapsed Time = 0.5814295000000129 seconds
Trial 91, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 91, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
Binning 0.040 GB of training data: 0.158 s
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 24 leaves, max depth = 11, train loss: 0.66782, val loss: 0.66597, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.65508, val loss: 0.65183, in 0.016s
1 tree, 24 leaves, max depth = 10, train loss: 0.63345, val loss: 0.62867, in 0.000s
1 tree, 6 leaves, max depth = 4, train loss: 0.61406, val loss: 0.60786, in 0.016s
1 tree, 6 leaves, max depth = 4, train loss: 0.59664, val loss: 0.58909, in 0.000s
1 tree, 25 leaves, max depth = 10, train loss: 0.58098, val loss: 0.57199, in 0.000s
1 tree, 6 leaves, max depth = 4, train loss: 0.56680, val loss: 0.55659, in 0.000s
1 tree, 24 leaves, max depth = 11, train loss: 0.55406, val loss: 0.54254, in 0.000s
1 tree, 47 leaves, max depth = 7, train loss: 0.54519, val loss: 0.53448, in 0.016s
1 tree, 31 leaves, max depth = 12, train loss: 0.54015, val loss: 0.52876, in 0.016s
1 tree, 25 leaves, max depth = 12, train loss: 0.52940, val loss: 0.51684, in 0.000s
1 tree, 31 leaves, max depth = 9, train loss: 0.52498, val loss: 0.51201, in 0.016s
1 tree, 25 leaves, max depth = 12, train loss: 0.51559, val loss: 0.50152, in 0.000s
1 tree, 25 leaves, max depth = 10, train loss: 0.50710, val loss: 0.49193, in 0.016s
1 tree, 25 leaves, max depth = 11, train loss: 0.49937, val loss: 0.48315, in 0.000s
1 tree, 25 leaves, max depth = 12, train loss: 0.49234, val loss: 0.47512, in 0.000s
1 tree, 47 leaves, max depth = 10, train loss: 0.48754, val loss: 0.47022, in 0.000s
1 tree, 25 leaves, max depth = 9, train loss: 0.48123, val loss: 0.46299, in 0.016s
1 tree, 25 leaves, max depth = 10, train loss: 0.47551, val loss: 0.45637, in 0.000s
1 tree, 25 leaves, max depth = 9, train loss: 0.47006, val loss: 0.45013, in 0.016s
1 tree, 25 leaves, max depth = 10, train loss: 0.46511, val loss: 0.44441, in 0.000s
1 tree, 25 leaves, max depth = 10, train loss: 0.46060, val loss: 0.43917, in 0.016s
1 tree, 24 leaves, max depth = 9, train loss: 0.45668, val loss: 0.43465, in 0.000s
1 tree, 45 leaves, max depth = 10, train loss: 0.45215, val loss: 0.42999, in 0.016s
1 tree, 25 leaves, max depth = 10, train loss: 0.44864, val loss: 0.42579, in 0.000s
1 tree, 25 leaves, max depth = 9, train loss: 0.44538, val loss: 0.42198, in 0.016s
1 tree, 18 leaves, max depth = 6, train loss: 0.44200, val loss: 0.41920, in 0.000s
1 tree, 25 leaves, max depth = 9, train loss: 0.43919, val loss: 0.41582, in 0.016s
1 tree, 47 leaves, max depth = 8, train loss: 0.43422, val loss: 0.41173, in 0.000s
1 tree, 43 leaves, max depth = 13, train loss: 0.42671, val loss: 0.40494, in 0.016s
1 tree, 25 leaves, max depth = 10, train loss: 0.42437, val loss: 0.40208, in 0.000s
1 tree, 6 leaves, max depth = 4, train loss: 0.42212, val loss: 0.39933, in 0.016s
1 tree, 30 leaves, max depth = 11, train loss: 0.41837, val loss: 0.39585, in 0.000s
1 tree, 47 leaves, max depth = 17, train loss: 0.41520, val loss: 0.39277, in 0.016s
1 tree, 44 leaves, max depth = 10, train loss: 0.41216, val loss: 0.38975, in 0.000s
1 tree, 43 leaves, max depth = 10, train loss: 0.40940, val loss: 0.38700, in 0.016s
Fit 36 trees in 0.596 s, (973 total leaves)
Time spent computing histograms: 0.161s
Time spent finding best splits:  0.015s
Time spent applying splits:      0.020s
Time spent predicting:           0.000s
Trial 91, Fold 5: Log loss = 0.4167221246317307, Average precision = 0.9144840675045608, ROC-AUC = 0.9236757520019322, Elapsed Time = 0.5988780999996379 seconds
Optimization Progress:  92%|#########2| 92/100 [19:04<01:34, 11.76s/it]
Trial 92, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 92, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
0.174 s 0.040 GB of training data: 
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 7 leaves, max depth = 4, train loss: 0.67285, val loss: 0.67218, in 0.016s
1 tree, 7 leaves, max depth = 4, train loss: 0.65443, val loss: 0.65314, in 0.000s
1 tree, 7 leaves, max depth = 4, train loss: 0.63752, val loss: 0.63563, in 0.016s
1 tree, 6 leaves, max depth = 5, train loss: 0.62230, val loss: 0.61982, in 0.000s
1 tree, 7 leaves, max depth = 4, train loss: 0.60796, val loss: 0.60494, in 0.000s
1 tree, 7 leaves, max depth = 4, train loss: 0.59474, val loss: 0.59119, in 0.016s
1 tree, 7 leaves, max depth = 4, train loss: 0.58254, val loss: 0.57848, in 0.000s
1 tree, 8 leaves, max depth = 5, train loss: 0.57127, val loss: 0.56672, in 0.016s
1 tree, 8 leaves, max depth = 5, train loss: 0.56084, val loss: 0.55583, in 0.000s
1 tree, 7 leaves, max depth = 4, train loss: 0.55119, val loss: 0.54572, in 0.016s
1 tree, 27 leaves, max depth = 10, train loss: 0.54164, val loss: 0.53685, in 0.000s
1 tree, 27 leaves, max depth = 9, train loss: 0.53282, val loss: 0.52867, in 0.016s
1 tree, 14 leaves, max depth = 7, train loss: 0.52461, val loss: 0.52005, in 0.000s
1 tree, 9 leaves, max depth = 5, train loss: 0.51683, val loss: 0.51185, in 0.016s
1 tree, 9 leaves, max depth = 5, train loss: 0.50961, val loss: 0.50422, in 0.000s
1 tree, 29 leaves, max depth = 10, train loss: 0.50197, val loss: 0.49720, in 0.016s
1 tree, 15 leaves, max depth = 8, train loss: 0.49561, val loss: 0.49047, in 0.000s
1 tree, 30 leaves, max depth = 9, train loss: 0.48865, val loss: 0.48410, in 0.016s
1 tree, 16 leaves, max depth = 8, train loss: 0.48291, val loss: 0.47800, in 0.000s
1 tree, 28 leaves, max depth = 9, train loss: 0.47656, val loss: 0.47222, in 0.016s
1 tree, 29 leaves, max depth = 9, train loss: 0.47066, val loss: 0.46686, in 0.016s
Fit 21 trees in 0.470 s, (304 total leaves)
Time spent computing histograms: 0.094s
Time spent finding best splits:  0.009s
Time spent applying splits:      0.008s
Time spent predicting:           0.000s
Trial 92, Fold 1: Log loss = 0.47138854252755547, Average precision = 0.9163772901012393, ROC-AUC = 0.9268395742819863, Elapsed Time = 0.4720883000009053 seconds
Trial 92, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 92, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
Binning 0.040 GB of training data: 0.174 s
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 8 leaves, max depth = 6, train loss: 0.67330, val loss: 0.67232, in 0.000s
1 tree, 8 leaves, max depth = 6, train loss: 0.65518, val loss: 0.65328, in 0.016s
1 tree, 8 leaves, max depth = 6, train loss: 0.63855, val loss: 0.63578, in 0.000s
1 tree, 8 leaves, max depth = 6, train loss: 0.62327, val loss: 0.61966, in 0.016s
1 tree, 7 leaves, max depth = 5, train loss: 0.60920, val loss: 0.60480, in 0.000s
1 tree, 8 leaves, max depth = 6, train loss: 0.59624, val loss: 0.59108, in 0.016s
1 tree, 10 leaves, max depth = 5, train loss: 0.58400, val loss: 0.57813, in 0.000s
1 tree, 8 leaves, max depth = 6, train loss: 0.57295, val loss: 0.56640, in 0.000s
1 tree, 9 leaves, max depth = 5, train loss: 0.56248, val loss: 0.55529, in 0.000s
1 tree, 11 leaves, max depth = 9, train loss: 0.55303, val loss: 0.54521, in 0.000s
1 tree, 24 leaves, max depth = 9, train loss: 0.54349, val loss: 0.53600, in 0.016s
1 tree, 24 leaves, max depth = 9, train loss: 0.53468, val loss: 0.52750, in 0.000s
1 tree, 10 leaves, max depth = 6, train loss: 0.52646, val loss: 0.51872, in 0.016s
1 tree, 10 leaves, max depth = 5, train loss: 0.51863, val loss: 0.51035, in 0.000s
1 tree, 10 leaves, max depth = 5, train loss: 0.51137, val loss: 0.50256, in 0.016s
1 tree, 27 leaves, max depth = 10, train loss: 0.50374, val loss: 0.49524, in 0.000s
1 tree, 13 leaves, max depth = 8, train loss: 0.49738, val loss: 0.48841, in 0.016s
1 tree, 28 leaves, max depth = 10, train loss: 0.49044, val loss: 0.48175, in 0.016s
1 tree, 11 leaves, max depth = 6, train loss: 0.48470, val loss: 0.47557, in 0.000s
1 tree, 26 leaves, max depth = 10, train loss: 0.47837, val loss: 0.46952, in 0.016s
1 tree, 27 leaves, max depth = 10, train loss: 0.47249, val loss: 0.46391, in 0.000s
Fit 21 trees in 0.486 s, (295 total leaves)
Time spent computing histograms: 0.096s
Time spent finding best splits:  0.008s
Time spent applying splits:      0.008s
Time spent predicting:           0.000s
Trial 92, Fold 2: Log loss = 0.4728401946769687, Average precision = 0.9123714330399207, ROC-AUC = 0.927864641582353, Elapsed Time = 0.5001008999988699 seconds
Trial 92, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 92, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
Binning 0.040 GB of training data: 0.173 s
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 6 leaves, max depth = 4, train loss: 0.67322, val loss: 0.67253, in 0.000s
1 tree, 8 leaves, max depth = 5, train loss: 0.65521, val loss: 0.65394, in 0.016s
1 tree, 7 leaves, max depth = 4, train loss: 0.63855, val loss: 0.63665, in 0.000s
1 tree, 8 leaves, max depth = 5, train loss: 0.62334, val loss: 0.62091, in 0.000s
1 tree, 8 leaves, max depth = 4, train loss: 0.60922, val loss: 0.60622, in 0.016s
1 tree, 8 leaves, max depth = 5, train loss: 0.59629, val loss: 0.59281, in 0.000s
1 tree, 5 leaves, max depth = 4, train loss: 0.58440, val loss: 0.58035, in 0.016s
1 tree, 8 leaves, max depth = 5, train loss: 0.57325, val loss: 0.56871, in 0.000s
1 tree, 7 leaves, max depth = 6, train loss: 0.56309, val loss: 0.55801, in 0.000s
1 tree, 8 leaves, max depth = 5, train loss: 0.55352, val loss: 0.54801, in 0.000s
1 tree, 31 leaves, max depth = 10, train loss: 0.54401, val loss: 0.53919, in 0.000s
1 tree, 10 leaves, max depth = 5, train loss: 0.53553, val loss: 0.53034, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.52695, val loss: 0.52241, in 0.000s
1 tree, 14 leaves, max depth = 8, train loss: 0.51933, val loss: 0.51444, in 0.016s
1 tree, 7 leaves, max depth = 6, train loss: 0.51232, val loss: 0.50697, in 0.000s
1 tree, 28 leaves, max depth = 10, train loss: 0.50470, val loss: 0.49996, in 0.016s
1 tree, 9 leaves, max depth = 5, train loss: 0.49834, val loss: 0.49328, in 0.016s
1 tree, 27 leaves, max depth = 10, train loss: 0.49139, val loss: 0.48691, in 0.000s
1 tree, 9 leaves, max depth = 5, train loss: 0.48565, val loss: 0.48086, in 0.016s
1 tree, 28 leaves, max depth = 10, train loss: 0.47931, val loss: 0.47507, in 0.000s
1 tree, 29 leaves, max depth = 10, train loss: 0.47342, val loss: 0.46972, in 0.000s
Fit 21 trees in 0.486 s, (296 total leaves)
Time spent computing histograms: 0.102s
Time spent finding best splits:  0.009s
Time spent applying splits:      0.009s
Time spent predicting:           0.000s
Trial 92, Fold 3: Log loss = 0.4695607971578864, Average precision = 0.9199735082968026, ROC-AUC = 0.9317574201105643, Elapsed Time = 0.4958059999989928 seconds
Trial 92, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 92, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
Binning 0.040 GB of training data: 0.188 s
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 8 leaves, max depth = 5, train loss: 0.67312, val loss: 0.67192, in 0.000s
1 tree, 8 leaves, max depth = 5, train loss: 0.65487, val loss: 0.65253, in 0.016s
1 tree, 8 leaves, max depth = 5, train loss: 0.63813, val loss: 0.63469, in 0.000s
1 tree, 9 leaves, max depth = 5, train loss: 0.62294, val loss: 0.61850, in 0.016s
1 tree, 8 leaves, max depth = 5, train loss: 0.60875, val loss: 0.60330, in 0.000s
1 tree, 8 leaves, max depth = 5, train loss: 0.59566, val loss: 0.58925, in 0.016s
1 tree, 8 leaves, max depth = 5, train loss: 0.58359, val loss: 0.57623, in 0.000s
1 tree, 8 leaves, max depth = 5, train loss: 0.57244, val loss: 0.56418, in 0.016s
1 tree, 8 leaves, max depth = 5, train loss: 0.56213, val loss: 0.55299, in 0.000s
1 tree, 26 leaves, max depth = 10, train loss: 0.55239, val loss: 0.54336, in 0.016s
1 tree, 20 leaves, max depth = 13, train loss: 0.54327, val loss: 0.53348, in 0.016s
1 tree, 28 leaves, max depth = 10, train loss: 0.53450, val loss: 0.52482, in 0.000s
1 tree, 19 leaves, max depth = 13, train loss: 0.52631, val loss: 0.51592, in 0.016s
1 tree, 9 leaves, max depth = 4, train loss: 0.51861, val loss: 0.50746, in 0.000s
1 tree, 19 leaves, max depth = 10, train loss: 0.51154, val loss: 0.49972, in 0.016s
1 tree, 23 leaves, max depth = 10, train loss: 0.50396, val loss: 0.49226, in 0.000s
1 tree, 8 leaves, max depth = 4, train loss: 0.49751, val loss: 0.48512, in 0.016s
1 tree, 23 leaves, max depth = 10, train loss: 0.49062, val loss: 0.47835, in 0.000s
1 tree, 12 leaves, max depth = 6, train loss: 0.48485, val loss: 0.47195, in 0.016s
1 tree, 29 leaves, max depth = 11, train loss: 0.47857, val loss: 0.46581, in 0.000s
1 tree, 31 leaves, max depth = 11, train loss: 0.47275, val loss: 0.46011, in 0.016s
Fit 21 trees in 0.516 s, (320 total leaves)
Time spent computing histograms: 0.105s
Time spent finding best splits:  0.009s
Time spent applying splits:      0.009s
Time spent predicting:           0.000s
Trial 92, Fold 4: Log loss = 0.47093547119013984, Average precision = 0.9171902347863797, ROC-AUC = 0.928863444256076, Elapsed Time = 0.5190452000006189 seconds
Trial 92, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 92, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
Binning 0.040 GB of training data: 0.158 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 11 leaves, max depth = 7, train loss: 0.67310, val loss: 0.67178, in 0.016s
1 tree, 11 leaves, max depth = 7, train loss: 0.65487, val loss: 0.65228, in 0.000s
1 tree, 11 leaves, max depth = 7, train loss: 0.63814, val loss: 0.63435, in 0.016s
1 tree, 14 leaves, max depth = 7, train loss: 0.62276, val loss: 0.61782, in 0.000s
1 tree, 14 leaves, max depth = 7, train loss: 0.60860, val loss: 0.60257, in 0.016s
1 tree, 7 leaves, max depth = 4, train loss: 0.59543, val loss: 0.58829, in 0.000s
1 tree, 9 leaves, max depth = 6, train loss: 0.58337, val loss: 0.57523, in 0.016s
1 tree, 14 leaves, max depth = 7, train loss: 0.57222, val loss: 0.56310, in 0.000s
1 tree, 8 leaves, max depth = 5, train loss: 0.56178, val loss: 0.55169, in 0.016s
1 tree, 13 leaves, max depth = 7, train loss: 0.55223, val loss: 0.54123, in 0.000s
1 tree, 30 leaves, max depth = 11, train loss: 0.54281, val loss: 0.53219, in 0.016s
1 tree, 8 leaves, max depth = 5, train loss: 0.53415, val loss: 0.52263, in 0.000s
1 tree, 30 leaves, max depth = 12, train loss: 0.52567, val loss: 0.51453, in 0.000s
1 tree, 14 leaves, max depth = 7, train loss: 0.51795, val loss: 0.50603, in 0.016s
1 tree, 15 leaves, max depth = 8, train loss: 0.51079, val loss: 0.49814, in 0.000s
1 tree, 29 leaves, max depth = 11, train loss: 0.50327, val loss: 0.49101, in 0.016s
1 tree, 15 leaves, max depth = 7, train loss: 0.49682, val loss: 0.48384, in 0.000s
1 tree, 30 leaves, max depth = 11, train loss: 0.48997, val loss: 0.47738, in 0.016s
1 tree, 16 leaves, max depth = 8, train loss: 0.48414, val loss: 0.47087, in 0.000s
1 tree, 27 leaves, max depth = 12, train loss: 0.47789, val loss: 0.46502, in 0.016s
1 tree, 31 leaves, max depth = 13, train loss: 0.47210, val loss: 0.45959, in 0.000s
Fit 21 trees in 0.454 s, (357 total leaves)
Time spent computing histograms: 0.095s
Time spent finding best splits:  0.008s
Time spent applying splits:      0.009s
Time spent predicting:           0.000s
Trial 92, Fold 5: Log loss = 0.47663555439002386, Average precision = 0.9146373133272797, ROC-AUC = 0.9261888783605522, Elapsed Time = 0.46562550000089686 seconds
Optimization Progress:  93%|#########3| 93/100 [19:13<01:17, 11.05s/it]
Trial 93, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 93, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
0.126 s 0.040 GB of training data: 
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 128 leaves, max depth = 12, train loss: 0.67794, val loss: 0.67791, in 0.016s
1 tree, 207 leaves, max depth = 16, train loss: 0.66330, val loss: 0.66346, in 0.016s
1 tree, 248 leaves, max depth = 17, train loss: 0.65023, val loss: 0.65032, in 0.031s
1 tree, 248 leaves, max depth = 16, train loss: 0.63690, val loss: 0.63718, in 0.031s
1 tree, 248 leaves, max depth = 14, train loss: 0.62439, val loss: 0.62466, in 0.016s
1 tree, 248 leaves, max depth = 16, train loss: 0.61230, val loss: 0.61272, in 0.031s
1 tree, 248 leaves, max depth = 24, train loss: 0.60136, val loss: 0.60184, in 0.031s
1 tree, 248 leaves, max depth = 24, train loss: 0.59107, val loss: 0.59145, in 0.031s
1 tree, 248 leaves, max depth = 16, train loss: 0.58047, val loss: 0.58095, in 0.031s
1 tree, 248 leaves, max depth = 17, train loss: 0.57033, val loss: 0.57087, in 0.016s
1 tree, 248 leaves, max depth = 16, train loss: 0.56077, val loss: 0.56131, in 0.031s
1 tree, 248 leaves, max depth = 23, train loss: 0.55199, val loss: 0.55260, in 0.031s
1 tree, 248 leaves, max depth = 16, train loss: 0.54321, val loss: 0.54378, in 0.031s
1 tree, 248 leaves, max depth = 17, train loss: 0.53463, val loss: 0.53532, in 0.031s
1 tree, 248 leaves, max depth = 16, train loss: 0.52627, val loss: 0.52701, in 0.031s
1 tree, 248 leaves, max depth = 20, train loss: 0.51876, val loss: 0.51962, in 0.031s
1 tree, 248 leaves, max depth = 17, train loss: 0.51117, val loss: 0.51211, in 0.016s
1 tree, 248 leaves, max depth = 16, train loss: 0.50388, val loss: 0.50495, in 0.031s
1 tree, 248 leaves, max depth = 17, train loss: 0.49732, val loss: 0.49858, in 0.031s
1 tree, 248 leaves, max depth = 15, train loss: 0.49055, val loss: 0.49176, in 0.031s
1 tree, 248 leaves, max depth = 16, train loss: 0.48405, val loss: 0.48539, in 0.031s
1 tree, 248 leaves, max depth = 20, train loss: 0.47809, val loss: 0.47955, in 0.031s
1 tree, 248 leaves, max depth = 16, train loss: 0.47194, val loss: 0.47349, in 0.031s
1 tree, 248 leaves, max depth = 19, train loss: 0.46625, val loss: 0.46779, in 0.031s
1 tree, 248 leaves, max depth = 20, train loss: 0.46088, val loss: 0.46239, in 0.031s
1 tree, 248 leaves, max depth = 15, train loss: 0.45535, val loss: 0.45691, in 0.031s
1 tree, 215 leaves, max depth = 15, train loss: 0.45023, val loss: 0.45168, in 0.016s
1 tree, 248 leaves, max depth = 16, train loss: 0.44510, val loss: 0.44662, in 0.047s
1 tree, 248 leaves, max depth = 15, train loss: 0.44026, val loss: 0.44176, in 0.031s
1 tree, 193 leaves, max depth = 15, train loss: 0.43340, val loss: 0.43525, in 0.016s
1 tree, 248 leaves, max depth = 16, train loss: 0.42884, val loss: 0.43081, in 0.047s
1 tree, 248 leaves, max depth = 17, train loss: 0.42437, val loss: 0.42642, in 0.031s
1 tree, 248 leaves, max depth = 15, train loss: 0.42013, val loss: 0.42221, in 0.031s
1 tree, 193 leaves, max depth = 18, train loss: 0.41388, val loss: 0.41627, in 0.031s
1 tree, 236 leaves, max depth = 16, train loss: 0.40801, val loss: 0.41059, in 0.031s
1 tree, 248 leaves, max depth = 15, train loss: 0.40415, val loss: 0.40676, in 0.031s
1 tree, 248 leaves, max depth = 15, train loss: 0.40043, val loss: 0.40310, in 0.031s
1 tree, 192 leaves, max depth = 16, train loss: 0.39489, val loss: 0.39786, in 0.031s
1 tree, 248 leaves, max depth = 17, train loss: 0.39127, val loss: 0.39437, in 0.031s
1 tree, 193 leaves, max depth = 16, train loss: 0.38605, val loss: 0.38944, in 0.016s
1 tree, 195 leaves, max depth = 21, train loss: 0.38101, val loss: 0.38470, in 0.031s
1 tree, 248 leaves, max depth = 16, train loss: 0.37775, val loss: 0.38163, in 0.031s
1 tree, 240 leaves, max depth = 21, train loss: 0.37309, val loss: 0.37719, in 0.031s
1 tree, 248 leaves, max depth = 16, train loss: 0.36990, val loss: 0.37412, in 0.031s
1 tree, 192 leaves, max depth = 19, train loss: 0.36546, val loss: 0.37002, in 0.031s
1 tree, 248 leaves, max depth = 16, train loss: 0.36244, val loss: 0.36711, in 0.031s
1 tree, 248 leaves, max depth = 16, train loss: 0.35983, val loss: 0.36472, in 0.031s
1 tree, 93 leaves, max depth = 17, train loss: 0.35594, val loss: 0.36089, in 0.016s
1 tree, 95 leaves, max depth = 16, train loss: 0.35223, val loss: 0.35728, in 0.016s
1 tree, 95 leaves, max depth = 18, train loss: 0.34871, val loss: 0.35386, in 0.016s
1 tree, 94 leaves, max depth = 15, train loss: 0.34531, val loss: 0.35049, in 0.031s
1 tree, 248 leaves, max depth = 20, train loss: 0.34239, val loss: 0.34774, in 0.016s
1 tree, 248 leaves, max depth = 20, train loss: 0.33956, val loss: 0.34511, in 0.031s
1 tree, 248 leaves, max depth = 20, train loss: 0.33683, val loss: 0.34249, in 0.031s
1 tree, 248 leaves, max depth = 20, train loss: 0.33419, val loss: 0.33995, in 0.031s
1 tree, 141 leaves, max depth = 18, train loss: 0.33093, val loss: 0.33704, in 0.016s
1 tree, 94 leaves, max depth = 21, train loss: 0.32796, val loss: 0.33416, in 0.016s
1 tree, 141 leaves, max depth = 18, train loss: 0.32494, val loss: 0.33148, in 0.016s
1 tree, 94 leaves, max depth = 21, train loss: 0.32218, val loss: 0.32879, in 0.016s
1 tree, 248 leaves, max depth = 19, train loss: 0.31977, val loss: 0.32648, in 0.031s
1 tree, 195 leaves, max depth = 18, train loss: 0.31682, val loss: 0.32381, in 0.031s
1 tree, 141 leaves, max depth = 19, train loss: 0.31409, val loss: 0.32140, in 0.016s
1 tree, 141 leaves, max depth = 19, train loss: 0.31147, val loss: 0.31910, in 0.031s
1 tree, 248 leaves, max depth = 25, train loss: 0.30926, val loss: 0.31699, in 0.016s
1 tree, 94 leaves, max depth = 17, train loss: 0.30690, val loss: 0.31466, in 0.031s
1 tree, 142 leaves, max depth = 19, train loss: 0.30447, val loss: 0.31253, in 0.016s
1 tree, 248 leaves, max depth = 22, train loss: 0.30238, val loss: 0.31055, in 0.031s
1 tree, 95 leaves, max depth = 17, train loss: 0.30021, val loss: 0.30842, in 0.016s
1 tree, 141 leaves, max depth = 18, train loss: 0.29797, val loss: 0.30646, in 0.016s
1 tree, 94 leaves, max depth = 19, train loss: 0.29593, val loss: 0.30449, in 0.016s
1 tree, 248 leaves, max depth = 24, train loss: 0.29387, val loss: 0.30251, in 0.031s
1 tree, 248 leaves, max depth = 21, train loss: 0.29186, val loss: 0.30061, in 0.031s
1 tree, 248 leaves, max depth = 16, train loss: 0.29031, val loss: 0.29925, in 0.031s
1 tree, 248 leaves, max depth = 17, train loss: 0.28880, val loss: 0.29783, in 0.031s
1 tree, 248 leaves, max depth = 21, train loss: 0.28693, val loss: 0.29605, in 0.031s
1 tree, 143 leaves, max depth = 18, train loss: 0.28494, val loss: 0.29433, in 0.016s
1 tree, 248 leaves, max depth = 21, train loss: 0.28315, val loss: 0.29261, in 0.031s
1 tree, 141 leaves, max depth = 18, train loss: 0.28127, val loss: 0.29099, in 0.016s
1 tree, 143 leaves, max depth = 18, train loss: 0.27944, val loss: 0.28941, in 0.031s
1 tree, 94 leaves, max depth = 15, train loss: 0.27777, val loss: 0.28776, in 0.016s
1 tree, 146 leaves, max depth = 18, train loss: 0.27603, val loss: 0.28624, in 0.031s
1 tree, 94 leaves, max depth = 15, train loss: 0.27446, val loss: 0.28469, in 0.016s
1 tree, 143 leaves, max depth = 18, train loss: 0.27285, val loss: 0.28328, in 0.031s
1 tree, 94 leaves, max depth = 15, train loss: 0.27138, val loss: 0.28182, in 0.016s
1 tree, 248 leaves, max depth = 22, train loss: 0.26979, val loss: 0.28031, in 0.031s
Fit 85 trees in 2.595 s, (17472 total leaves)
Time spent computing histograms: 0.715s
Time spent finding best splits:  0.314s
Time spent applying splits:      0.272s
Time spent predicting:           0.000s
Trial 93, Fold 1: Log loss = 0.28539871775659487, Average precision = 0.9620041346120233, ROC-AUC = 0.9569296277177939, Elapsed Time = 2.5979539999989356 seconds
Trial 93, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 93, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
Binning 0.040 GB of training data: 0.157 s
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 131 leaves, max depth = 14, train loss: 0.67803, val loss: 0.67785, in 0.016s
1 tree, 221 leaves, max depth = 15, train loss: 0.66342, val loss: 0.66318, in 0.031s
1 tree, 146 leaves, max depth = 15, train loss: 0.64986, val loss: 0.64952, in 0.031s
1 tree, 241 leaves, max depth = 17, train loss: 0.63663, val loss: 0.63622, in 0.031s
1 tree, 248 leaves, max depth = 21, train loss: 0.62447, val loss: 0.62408, in 0.031s
1 tree, 248 leaves, max depth = 20, train loss: 0.61300, val loss: 0.61259, in 0.031s
1 tree, 248 leaves, max depth = 16, train loss: 0.60148, val loss: 0.60097, in 0.031s
1 tree, 248 leaves, max depth = 20, train loss: 0.59032, val loss: 0.58978, in 0.031s
1 tree, 248 leaves, max depth = 14, train loss: 0.57991, val loss: 0.57940, in 0.031s
1 tree, 248 leaves, max depth = 16, train loss: 0.56985, val loss: 0.56930, in 0.031s
1 tree, 248 leaves, max depth = 20, train loss: 0.56008, val loss: 0.55952, in 0.031s
1 tree, 248 leaves, max depth = 16, train loss: 0.55086, val loss: 0.55029, in 0.031s
1 tree, 248 leaves, max depth = 14, train loss: 0.54213, val loss: 0.54163, in 0.031s
1 tree, 248 leaves, max depth = 17, train loss: 0.53404, val loss: 0.53351, in 0.016s
1 tree, 248 leaves, max depth = 20, train loss: 0.52579, val loss: 0.52525, in 0.047s
1 tree, 248 leaves, max depth = 20, train loss: 0.51785, val loss: 0.51732, in 0.031s
1 tree, 248 leaves, max depth = 20, train loss: 0.51023, val loss: 0.50974, in 0.031s
1 tree, 248 leaves, max depth = 20, train loss: 0.50293, val loss: 0.50246, in 0.031s
1 tree, 248 leaves, max depth = 20, train loss: 0.49591, val loss: 0.49548, in 0.031s
1 tree, 248 leaves, max depth = 20, train loss: 0.48916, val loss: 0.48877, in 0.031s
1 tree, 213 leaves, max depth = 16, train loss: 0.48323, val loss: 0.48288, in 0.031s
1 tree, 248 leaves, max depth = 20, train loss: 0.47697, val loss: 0.47665, in 0.031s
1 tree, 248 leaves, max depth = 19, train loss: 0.47130, val loss: 0.47105, in 0.031s
1 tree, 248 leaves, max depth = 14, train loss: 0.46554, val loss: 0.46541, in 0.031s
1 tree, 248 leaves, max depth = 20, train loss: 0.46046, val loss: 0.46027, in 0.031s
1 tree, 248 leaves, max depth = 20, train loss: 0.45505, val loss: 0.45491, in 0.031s
1 tree, 248 leaves, max depth = 21, train loss: 0.44983, val loss: 0.44973, in 0.031s
1 tree, 236 leaves, max depth = 19, train loss: 0.44285, val loss: 0.44300, in 0.031s
1 tree, 248 leaves, max depth = 19, train loss: 0.43797, val loss: 0.43817, in 0.031s
1 tree, 248 leaves, max depth = 21, train loss: 0.43324, val loss: 0.43356, in 0.031s
1 tree, 215 leaves, max depth = 17, train loss: 0.42888, val loss: 0.42919, in 0.031s
1 tree, 248 leaves, max depth = 21, train loss: 0.42450, val loss: 0.42486, in 0.031s
1 tree, 235 leaves, max depth = 17, train loss: 0.41829, val loss: 0.41895, in 0.031s
1 tree, 196 leaves, max depth = 21, train loss: 0.41231, val loss: 0.41316, in 0.031s
1 tree, 234 leaves, max depth = 18, train loss: 0.40664, val loss: 0.40775, in 0.031s
1 tree, 206 leaves, max depth = 21, train loss: 0.40299, val loss: 0.40424, in 0.031s
1 tree, 248 leaves, max depth = 21, train loss: 0.39921, val loss: 0.40050, in 0.031s
1 tree, 248 leaves, max depth = 21, train loss: 0.39587, val loss: 0.39721, in 0.031s
1 tree, 146 leaves, max depth = 17, train loss: 0.39083, val loss: 0.39235, in 0.031s
1 tree, 248 leaves, max depth = 22, train loss: 0.38739, val loss: 0.38896, in 0.031s
1 tree, 214 leaves, max depth = 16, train loss: 0.38422, val loss: 0.38585, in 0.031s
1 tree, 248 leaves, max depth = 20, train loss: 0.38123, val loss: 0.38307, in 0.047s
1 tree, 248 leaves, max depth = 21, train loss: 0.37811, val loss: 0.38005, in 0.031s
1 tree, 196 leaves, max depth = 23, train loss: 0.37344, val loss: 0.37556, in 0.031s
1 tree, 236 leaves, max depth = 17, train loss: 0.36900, val loss: 0.37142, in 0.032s
1 tree, 248 leaves, max depth = 20, train loss: 0.36629, val loss: 0.36886, in 0.031s
1 tree, 95 leaves, max depth = 18, train loss: 0.36231, val loss: 0.36501, in 0.031s
1 tree, 95 leaves, max depth = 16, train loss: 0.35851, val loss: 0.36133, in 0.016s
1 tree, 248 leaves, max depth = 23, train loss: 0.35540, val loss: 0.35840, in 0.031s
1 tree, 95 leaves, max depth = 16, train loss: 0.35180, val loss: 0.35492, in 0.016s
1 tree, 248 leaves, max depth = 23, train loss: 0.34886, val loss: 0.35215, in 0.031s
1 tree, 248 leaves, max depth = 23, train loss: 0.34601, val loss: 0.34942, in 0.031s
1 tree, 248 leaves, max depth = 23, train loss: 0.34324, val loss: 0.34676, in 0.047s
1 tree, 248 leaves, max depth = 22, train loss: 0.34046, val loss: 0.34419, in 0.031s
1 tree, 140 leaves, max depth = 17, train loss: 0.33710, val loss: 0.34109, in 0.031s
1 tree, 248 leaves, max depth = 23, train loss: 0.33459, val loss: 0.33875, in 0.031s
1 tree, 161 leaves, max depth = 18, train loss: 0.33129, val loss: 0.33570, in 0.031s
1 tree, 93 leaves, max depth = 16, train loss: 0.32827, val loss: 0.33282, in 0.031s
1 tree, 93 leaves, max depth = 19, train loss: 0.32540, val loss: 0.33007, in 0.016s
1 tree, 236 leaves, max depth = 20, train loss: 0.32242, val loss: 0.32734, in 0.047s
1 tree, 142 leaves, max depth = 17, train loss: 0.31961, val loss: 0.32477, in 0.031s
1 tree, 248 leaves, max depth = 17, train loss: 0.31764, val loss: 0.32299, in 0.031s
1 tree, 248 leaves, max depth = 22, train loss: 0.31542, val loss: 0.32094, in 0.031s
1 tree, 143 leaves, max depth = 17, train loss: 0.31280, val loss: 0.31852, in 0.031s
1 tree, 94 leaves, max depth = 16, train loss: 0.31035, val loss: 0.31619, in 0.016s
1 tree, 234 leaves, max depth = 17, train loss: 0.30781, val loss: 0.31391, in 0.031s
1 tree, 143 leaves, max depth = 17, train loss: 0.30541, val loss: 0.31171, in 0.031s
1 tree, 248 leaves, max depth = 21, train loss: 0.30326, val loss: 0.30976, in 0.031s
1 tree, 248 leaves, max depth = 24, train loss: 0.30118, val loss: 0.30788, in 0.031s
1 tree, 248 leaves, max depth = 24, train loss: 0.29916, val loss: 0.30607, in 0.031s
1 tree, 248 leaves, max depth = 22, train loss: 0.29724, val loss: 0.30433, in 0.047s
1 tree, 248 leaves, max depth = 18, train loss: 0.29576, val loss: 0.30300, in 0.031s
1 tree, 143 leaves, max depth = 18, train loss: 0.29361, val loss: 0.30103, in 0.031s
1 tree, 232 leaves, max depth = 19, train loss: 0.29215, val loss: 0.29972, in 0.031s
1 tree, 142 leaves, max depth = 18, train loss: 0.29010, val loss: 0.29784, in 0.016s
1 tree, 95 leaves, max depth = 16, train loss: 0.28817, val loss: 0.29603, in 0.016s
1 tree, 183 leaves, max depth = 22, train loss: 0.28618, val loss: 0.29418, in 0.031s
1 tree, 95 leaves, max depth = 16, train loss: 0.28440, val loss: 0.29252, in 0.016s
1 tree, 194 leaves, max depth = 20, train loss: 0.28246, val loss: 0.29070, in 0.031s
1 tree, 94 leaves, max depth = 17, train loss: 0.28082, val loss: 0.28917, in 0.016s
1 tree, 248 leaves, max depth = 23, train loss: 0.27925, val loss: 0.28773, in 0.047s
1 tree, 144 leaves, max depth = 19, train loss: 0.27751, val loss: 0.28615, in 0.031s
1 tree, 248 leaves, max depth = 25, train loss: 0.27590, val loss: 0.28477, in 0.031s
1 tree, 94 leaves, max depth = 16, train loss: 0.27442, val loss: 0.28336, in 0.016s
1 tree, 247 leaves, max depth = 16, train loss: 0.27320, val loss: 0.28237, in 0.031s
Fit 85 trees in 2.955 s, (17901 total leaves)
Time spent computing histograms: 0.797s
Time spent finding best splits:  0.366s
Time spent applying splits:      0.320s
Time spent predicting:           0.000s
Trial 93, Fold 2: Log loss = 0.2836545940853594, Average precision = 0.9613487091262867, ROC-AUC = 0.9583427290503834, Elapsed Time = 2.9765826999991987 seconds
Trial 93, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 93, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
Binning 0.040 GB of training data: 0.158 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 124 leaves, max depth = 15, train loss: 0.67811, val loss: 0.67812, in 0.016s
1 tree, 203 leaves, max depth = 17, train loss: 0.66365, val loss: 0.66372, in 0.031s
1 tree, 248 leaves, max depth = 20, train loss: 0.65048, val loss: 0.65067, in 0.031s
1 tree, 248 leaves, max depth = 17, train loss: 0.63734, val loss: 0.63760, in 0.016s
1 tree, 248 leaves, max depth = 17, train loss: 0.62499, val loss: 0.62538, in 0.016s
1 tree, 248 leaves, max depth = 17, train loss: 0.61305, val loss: 0.61351, in 0.031s
1 tree, 248 leaves, max depth = 20, train loss: 0.60199, val loss: 0.60253, in 0.031s
1 tree, 248 leaves, max depth = 21, train loss: 0.59159, val loss: 0.59225, in 0.031s
1 tree, 248 leaves, max depth = 19, train loss: 0.58107, val loss: 0.58184, in 0.031s
1 tree, 248 leaves, max depth = 21, train loss: 0.57087, val loss: 0.57173, in 0.031s
1 tree, 248 leaves, max depth = 18, train loss: 0.56145, val loss: 0.56240, in 0.031s
1 tree, 248 leaves, max depth = 18, train loss: 0.55264, val loss: 0.55374, in 0.031s
1 tree, 248 leaves, max depth = 17, train loss: 0.54383, val loss: 0.54496, in 0.031s
1 tree, 248 leaves, max depth = 20, train loss: 0.53555, val loss: 0.53686, in 0.031s
1 tree, 248 leaves, max depth = 21, train loss: 0.52726, val loss: 0.52863, in 0.031s
1 tree, 248 leaves, max depth = 17, train loss: 0.51936, val loss: 0.52083, in 0.031s
1 tree, 248 leaves, max depth = 18, train loss: 0.51221, val loss: 0.51382, in 0.031s
1 tree, 248 leaves, max depth = 22, train loss: 0.50532, val loss: 0.50720, in 0.031s
1 tree, 248 leaves, max depth = 22, train loss: 0.49823, val loss: 0.50018, in 0.031s
1 tree, 248 leaves, max depth = 17, train loss: 0.49141, val loss: 0.49345, in 0.031s
1 tree, 190 leaves, max depth = 16, train loss: 0.48310, val loss: 0.48566, in 0.031s
1 tree, 189 leaves, max depth = 17, train loss: 0.47509, val loss: 0.47816, in 0.031s
1 tree, 248 leaves, max depth = 17, train loss: 0.46905, val loss: 0.47229, in 0.031s
1 tree, 248 leaves, max depth = 17, train loss: 0.46355, val loss: 0.46695, in 0.031s
1 tree, 248 leaves, max depth = 22, train loss: 0.45808, val loss: 0.46169, in 0.031s
1 tree, 248 leaves, max depth = 17, train loss: 0.45254, val loss: 0.45624, in 0.031s
1 tree, 248 leaves, max depth = 17, train loss: 0.44719, val loss: 0.45099, in 0.031s
1 tree, 248 leaves, max depth = 17, train loss: 0.44213, val loss: 0.44610, in 0.031s
1 tree, 191 leaves, max depth = 16, train loss: 0.43547, val loss: 0.43993, in 0.016s
1 tree, 248 leaves, max depth = 17, train loss: 0.43065, val loss: 0.43519, in 0.031s
1 tree, 248 leaves, max depth = 17, train loss: 0.42600, val loss: 0.43067, in 0.031s
1 tree, 248 leaves, max depth = 17, train loss: 0.42160, val loss: 0.42650, in 0.031s
1 tree, 248 leaves, max depth = 17, train loss: 0.41727, val loss: 0.42228, in 0.031s
1 tree, 189 leaves, max depth = 17, train loss: 0.41133, val loss: 0.41684, in 0.031s
1 tree, 236 leaves, max depth = 17, train loss: 0.40572, val loss: 0.41179, in 0.047s
1 tree, 248 leaves, max depth = 17, train loss: 0.40184, val loss: 0.40810, in 0.016s
1 tree, 248 leaves, max depth = 18, train loss: 0.39801, val loss: 0.40438, in 0.031s
1 tree, 248 leaves, max depth = 19, train loss: 0.39432, val loss: 0.40083, in 0.047s
1 tree, 209 leaves, max depth = 19, train loss: 0.39089, val loss: 0.39746, in 0.016s
1 tree, 248 leaves, max depth = 19, train loss: 0.38744, val loss: 0.39416, in 0.031s
1 tree, 248 leaves, max depth = 20, train loss: 0.38410, val loss: 0.39098, in 0.031s
1 tree, 191 leaves, max depth = 16, train loss: 0.37921, val loss: 0.38657, in 0.031s
1 tree, 248 leaves, max depth = 21, train loss: 0.37616, val loss: 0.38373, in 0.031s
1 tree, 190 leaves, max depth = 17, train loss: 0.37151, val loss: 0.37956, in 0.031s
1 tree, 237 leaves, max depth = 17, train loss: 0.36713, val loss: 0.37571, in 0.031s
1 tree, 238 leaves, max depth = 17, train loss: 0.36293, val loss: 0.37202, in 0.047s
1 tree, 248 leaves, max depth = 19, train loss: 0.36007, val loss: 0.36934, in 0.031s
1 tree, 93 leaves, max depth = 14, train loss: 0.35626, val loss: 0.36589, in 0.016s
1 tree, 93 leaves, max depth = 14, train loss: 0.35262, val loss: 0.36259, in 0.016s
1 tree, 94 leaves, max depth = 18, train loss: 0.34914, val loss: 0.35945, in 0.016s
1 tree, 93 leaves, max depth = 14, train loss: 0.34581, val loss: 0.35642, in 0.031s
1 tree, 248 leaves, max depth = 20, train loss: 0.34289, val loss: 0.35343, in 0.031s
1 tree, 138 leaves, max depth = 16, train loss: 0.33956, val loss: 0.35063, in 0.016s
1 tree, 248 leaves, max depth = 17, train loss: 0.33723, val loss: 0.34851, in 0.031s
1 tree, 248 leaves, max depth = 20, train loss: 0.33455, val loss: 0.34574, in 0.031s
1 tree, 94 leaves, max depth = 14, train loss: 0.33157, val loss: 0.34309, in 0.031s
1 tree, 248 leaves, max depth = 21, train loss: 0.32900, val loss: 0.34050, in 0.031s
1 tree, 248 leaves, max depth = 21, train loss: 0.32650, val loss: 0.33795, in 0.031s
1 tree, 94 leaves, max depth = 19, train loss: 0.32374, val loss: 0.33549, in 0.026s
1 tree, 136 leaves, max depth = 17, train loss: 0.32087, val loss: 0.33309, in 0.022s
1 tree, 136 leaves, max depth = 17, train loss: 0.31812, val loss: 0.33078, in 0.016s
1 tree, 238 leaves, max depth = 17, train loss: 0.31543, val loss: 0.32851, in 0.047s
1 tree, 248 leaves, max depth = 19, train loss: 0.31346, val loss: 0.32673, in 0.031s
1 tree, 93 leaves, max depth = 13, train loss: 0.31106, val loss: 0.32459, in 0.016s
1 tree, 248 leaves, max depth = 18, train loss: 0.30920, val loss: 0.32286, in 0.047s
1 tree, 92 leaves, max depth = 14, train loss: 0.30695, val loss: 0.32086, in 0.016s
1 tree, 248 leaves, max depth = 20, train loss: 0.30484, val loss: 0.31869, in 0.031s
1 tree, 93 leaves, max depth = 14, train loss: 0.30271, val loss: 0.31683, in 0.016s
1 tree, 248 leaves, max depth = 18, train loss: 0.30110, val loss: 0.31549, in 0.047s
1 tree, 139 leaves, max depth = 15, train loss: 0.29883, val loss: 0.31362, in 0.016s
1 tree, 248 leaves, max depth = 20, train loss: 0.29688, val loss: 0.31162, in 0.031s
1 tree, 92 leaves, max depth = 14, train loss: 0.29494, val loss: 0.30993, in 0.031s
1 tree, 139 leaves, max depth = 15, train loss: 0.29283, val loss: 0.30820, in 0.016s
1 tree, 93 leaves, max depth = 19, train loss: 0.29103, val loss: 0.30663, in 0.016s
1 tree, 248 leaves, max depth = 19, train loss: 0.28920, val loss: 0.30481, in 0.016s
1 tree, 248 leaves, max depth = 21, train loss: 0.28744, val loss: 0.30305, in 0.047s
1 tree, 248 leaves, max depth = 19, train loss: 0.28573, val loss: 0.30127, in 0.031s
1 tree, 92 leaves, max depth = 14, train loss: 0.28405, val loss: 0.29983, in 0.016s
1 tree, 138 leaves, max depth = 15, train loss: 0.28218, val loss: 0.29827, in 0.016s
1 tree, 92 leaves, max depth = 14, train loss: 0.28062, val loss: 0.29692, in 0.031s
1 tree, 248 leaves, max depth = 17, train loss: 0.27936, val loss: 0.29585, in 0.032s
1 tree, 92 leaves, max depth = 23, train loss: 0.27788, val loss: 0.29458, in 0.016s
1 tree, 138 leaves, max depth = 15, train loss: 0.27618, val loss: 0.29317, in 0.031s
1 tree, 138 leaves, max depth = 15, train loss: 0.27454, val loss: 0.29180, in 0.016s
1 tree, 92 leaves, max depth = 14, train loss: 0.27318, val loss: 0.29064, in 0.031s
Fit 85 trees in 2.800 s, (17271 total leaves)
Time spent computing histograms: 0.760s
Time spent finding best splits:  0.339s
Time spent applying splits:      0.295s
Time spent predicting:           0.000s
Trial 93, Fold 3: Log loss = 0.28081531736409293, Average precision = 0.9617123144966295, ROC-AUC = 0.9583356909304542, Elapsed Time = 2.7998050000005605 seconds
Trial 93, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 93, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
Binning 0.040 GB of training data: 0.158 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 132 leaves, max depth = 14, train loss: 0.67813, val loss: 0.67759, in 0.016s
1 tree, 220 leaves, max depth = 18, train loss: 0.66367, val loss: 0.66276, in 0.031s
1 tree, 222 leaves, max depth = 17, train loss: 0.64993, val loss: 0.64865, in 0.031s
1 tree, 248 leaves, max depth = 18, train loss: 0.63756, val loss: 0.63597, in 0.016s
1 tree, 248 leaves, max depth = 18, train loss: 0.62585, val loss: 0.62386, in 0.031s
1 tree, 248 leaves, max depth = 17, train loss: 0.61386, val loss: 0.61152, in 0.031s
1 tree, 248 leaves, max depth = 16, train loss: 0.60227, val loss: 0.59964, in 0.031s
1 tree, 248 leaves, max depth = 19, train loss: 0.59126, val loss: 0.58832, in 0.047s
1 tree, 248 leaves, max depth = 17, train loss: 0.58094, val loss: 0.57756, in 0.016s
1 tree, 248 leaves, max depth = 16, train loss: 0.57097, val loss: 0.56727, in 0.031s
1 tree, 248 leaves, max depth = 17, train loss: 0.56155, val loss: 0.55747, in 0.031s
1 tree, 248 leaves, max depth = 16, train loss: 0.55239, val loss: 0.54796, in 0.031s
1 tree, 248 leaves, max depth = 16, train loss: 0.54347, val loss: 0.53879, in 0.031s
1 tree, 186 leaves, max depth = 16, train loss: 0.53517, val loss: 0.53014, in 0.031s
1 tree, 248 leaves, max depth = 16, train loss: 0.52699, val loss: 0.52175, in 0.031s
1 tree, 248 leaves, max depth = 16, train loss: 0.51913, val loss: 0.51366, in 0.016s
1 tree, 211 leaves, max depth = 16, train loss: 0.51180, val loss: 0.50603, in 0.031s
1 tree, 248 leaves, max depth = 16, train loss: 0.50456, val loss: 0.49856, in 0.031s
1 tree, 182 leaves, max depth = 16, train loss: 0.49783, val loss: 0.49156, in 0.031s
1 tree, 248 leaves, max depth = 17, train loss: 0.49113, val loss: 0.48471, in 0.031s
1 tree, 212 leaves, max depth = 21, train loss: 0.48527, val loss: 0.47863, in 0.016s
1 tree, 248 leaves, max depth = 16, train loss: 0.47913, val loss: 0.47226, in 0.016s
1 tree, 248 leaves, max depth = 17, train loss: 0.47315, val loss: 0.46614, in 0.047s
1 tree, 248 leaves, max depth = 16, train loss: 0.46741, val loss: 0.46022, in 0.016s
1 tree, 248 leaves, max depth = 16, train loss: 0.46186, val loss: 0.45456, in 0.016s
1 tree, 248 leaves, max depth = 17, train loss: 0.45653, val loss: 0.44911, in 0.031s
1 tree, 248 leaves, max depth = 16, train loss: 0.45140, val loss: 0.44386, in 0.016s
1 tree, 239 leaves, max depth = 19, train loss: 0.44439, val loss: 0.43676, in 0.047s
1 tree, 194 leaves, max depth = 16, train loss: 0.43765, val loss: 0.42998, in 0.016s
1 tree, 248 leaves, max depth = 17, train loss: 0.43296, val loss: 0.42520, in 0.031s
1 tree, 213 leaves, max depth = 17, train loss: 0.42865, val loss: 0.42073, in 0.031s
1 tree, 248 leaves, max depth = 16, train loss: 0.42429, val loss: 0.41627, in 0.031s
1 tree, 248 leaves, max depth = 24, train loss: 0.42031, val loss: 0.41228, in 0.047s
1 tree, 248 leaves, max depth = 18, train loss: 0.41621, val loss: 0.40807, in 0.031s
1 tree, 238 leaves, max depth = 20, train loss: 0.41038, val loss: 0.40220, in 0.031s
1 tree, 248 leaves, max depth = 18, train loss: 0.40667, val loss: 0.39838, in 0.032s
1 tree, 237 leaves, max depth = 20, train loss: 0.40118, val loss: 0.39286, in 0.031s
1 tree, 248 leaves, max depth = 17, train loss: 0.39766, val loss: 0.38924, in 0.031s
1 tree, 195 leaves, max depth = 17, train loss: 0.39241, val loss: 0.38405, in 0.031s
1 tree, 248 leaves, max depth = 25, train loss: 0.38921, val loss: 0.38088, in 0.031s
1 tree, 195 leaves, max depth = 17, train loss: 0.38428, val loss: 0.37597, in 0.031s
1 tree, 248 leaves, max depth = 18, train loss: 0.38111, val loss: 0.37273, in 0.031s
1 tree, 194 leaves, max depth = 20, train loss: 0.37644, val loss: 0.36811, in 0.031s
1 tree, 194 leaves, max depth = 17, train loss: 0.37196, val loss: 0.36366, in 0.047s
1 tree, 193 leaves, max depth = 17, train loss: 0.36765, val loss: 0.35939, in 0.016s
1 tree, 195 leaves, max depth = 17, train loss: 0.36348, val loss: 0.35522, in 0.031s
1 tree, 248 leaves, max depth = 17, train loss: 0.36070, val loss: 0.35235, in 0.031s
1 tree, 195 leaves, max depth = 17, train loss: 0.35676, val loss: 0.34841, in 0.031s
1 tree, 248 leaves, max depth = 16, train loss: 0.35421, val loss: 0.34578, in 0.031s
1 tree, 248 leaves, max depth = 18, train loss: 0.35163, val loss: 0.34319, in 0.031s
1 tree, 248 leaves, max depth = 18, train loss: 0.34923, val loss: 0.34072, in 0.031s
1 tree, 248 leaves, max depth = 24, train loss: 0.34640, val loss: 0.33795, in 0.031s
1 tree, 96 leaves, max depth = 19, train loss: 0.34310, val loss: 0.33455, in 0.016s
1 tree, 96 leaves, max depth = 15, train loss: 0.33994, val loss: 0.33133, in 0.016s
1 tree, 140 leaves, max depth = 15, train loss: 0.33683, val loss: 0.32844, in 0.031s
1 tree, 94 leaves, max depth = 14, train loss: 0.33393, val loss: 0.32541, in 0.016s
1 tree, 248 leaves, max depth = 21, train loss: 0.33138, val loss: 0.32291, in 0.047s
1 tree, 195 leaves, max depth = 17, train loss: 0.32837, val loss: 0.31995, in 0.031s
1 tree, 248 leaves, max depth = 21, train loss: 0.32592, val loss: 0.31755, in 0.031s
1 tree, 248 leaves, max depth = 20, train loss: 0.32346, val loss: 0.31509, in 0.031s
1 tree, 248 leaves, max depth = 19, train loss: 0.32116, val loss: 0.31286, in 0.031s
1 tree, 248 leaves, max depth = 21, train loss: 0.31893, val loss: 0.31071, in 0.031s
1 tree, 96 leaves, max depth = 14, train loss: 0.31642, val loss: 0.30812, in 0.016s
1 tree, 248 leaves, max depth = 20, train loss: 0.31429, val loss: 0.30607, in 0.031s
1 tree, 139 leaves, max depth = 15, train loss: 0.31179, val loss: 0.30374, in 0.016s
1 tree, 140 leaves, max depth = 15, train loss: 0.30937, val loss: 0.30152, in 0.031s
1 tree, 94 leaves, max depth = 17, train loss: 0.30711, val loss: 0.29917, in 0.031s
1 tree, 140 leaves, max depth = 15, train loss: 0.30485, val loss: 0.29710, in 0.016s
1 tree, 139 leaves, max depth = 15, train loss: 0.30269, val loss: 0.29510, in 0.016s
1 tree, 248 leaves, max depth = 19, train loss: 0.30068, val loss: 0.29308, in 0.047s
1 tree, 94 leaves, max depth = 17, train loss: 0.29864, val loss: 0.29099, in 0.016s
1 tree, 139 leaves, max depth = 15, train loss: 0.29663, val loss: 0.28916, in 0.031s
1 tree, 94 leaves, max depth = 15, train loss: 0.29473, val loss: 0.28720, in 0.016s
1 tree, 141 leaves, max depth = 15, train loss: 0.29282, val loss: 0.28544, in 0.031s
1 tree, 94 leaves, max depth = 14, train loss: 0.29105, val loss: 0.28361, in 0.016s
1 tree, 248 leaves, max depth = 25, train loss: 0.28930, val loss: 0.28193, in 0.031s
1 tree, 141 leaves, max depth = 16, train loss: 0.28753, val loss: 0.28030, in 0.031s
1 tree, 94 leaves, max depth = 15, train loss: 0.28588, val loss: 0.27858, in 0.016s
1 tree, 95 leaves, max depth = 16, train loss: 0.28429, val loss: 0.27688, in 0.016s
1 tree, 141 leaves, max depth = 16, train loss: 0.28266, val loss: 0.27539, in 0.031s
1 tree, 141 leaves, max depth = 16, train loss: 0.28110, val loss: 0.27397, in 0.016s
1 tree, 248 leaves, max depth = 19, train loss: 0.27941, val loss: 0.27228, in 0.047s
1 tree, 142 leaves, max depth = 16, train loss: 0.27793, val loss: 0.27091, in 0.016s
1 tree, 248 leaves, max depth = 19, train loss: 0.27632, val loss: 0.26930, in 0.031s
1 tree, 93 leaves, max depth = 16, train loss: 0.27492, val loss: 0.26785, in 0.031s
Fit 85 trees in 2.799 s, (17289 total leaves)
Time spent computing histograms: 0.763s
Time spent finding best splits:  0.335s
Time spent applying splits:      0.290s
Time spent predicting:           0.000s
Trial 93, Fold 4: Log loss = 0.2826723555976151, Average precision = 0.9625027075561599, ROC-AUC = 0.9576732243896507, Elapsed Time = 2.8056574999991426 seconds
Trial 93, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 93, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
Binning 0.040 GB of training data: 0.141 s
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 126 leaves, max depth = 15, train loss: 0.67788, val loss: 0.67727, in 0.016s
1 tree, 233 leaves, max depth = 17, train loss: 0.66319, val loss: 0.66213, in 0.031s
1 tree, 235 leaves, max depth = 16, train loss: 0.64925, val loss: 0.64774, in 0.031s
1 tree, 236 leaves, max depth = 16, train loss: 0.63600, val loss: 0.63405, in 0.031s
1 tree, 248 leaves, max depth = 16, train loss: 0.62332, val loss: 0.62094, in 0.016s
1 tree, 246 leaves, max depth = 16, train loss: 0.61124, val loss: 0.60844, in 0.031s
1 tree, 248 leaves, max depth = 18, train loss: 0.59977, val loss: 0.59659, in 0.031s
1 tree, 248 leaves, max depth = 19, train loss: 0.58879, val loss: 0.58526, in 0.031s
1 tree, 248 leaves, max depth = 16, train loss: 0.57827, val loss: 0.57436, in 0.031s
1 tree, 248 leaves, max depth = 16, train loss: 0.56822, val loss: 0.56394, in 0.031s
1 tree, 248 leaves, max depth = 20, train loss: 0.55889, val loss: 0.55428, in 0.031s
1 tree, 248 leaves, max depth = 19, train loss: 0.54967, val loss: 0.54478, in 0.031s
1 tree, 248 leaves, max depth = 15, train loss: 0.54091, val loss: 0.53575, in 0.031s
1 tree, 245 leaves, max depth = 20, train loss: 0.53279, val loss: 0.52751, in 0.031s
1 tree, 248 leaves, max depth = 19, train loss: 0.52465, val loss: 0.51912, in 0.031s
1 tree, 183 leaves, max depth = 17, train loss: 0.51705, val loss: 0.51118, in 0.031s
1 tree, 248 leaves, max depth = 15, train loss: 0.50960, val loss: 0.50352, in 0.031s
1 tree, 248 leaves, max depth = 18, train loss: 0.50270, val loss: 0.49645, in 0.031s
1 tree, 248 leaves, max depth = 16, train loss: 0.49572, val loss: 0.48924, in 0.031s
1 tree, 248 leaves, max depth = 16, train loss: 0.48940, val loss: 0.48293, in 0.031s
1 tree, 248 leaves, max depth = 16, train loss: 0.48294, val loss: 0.47626, in 0.031s
1 tree, 248 leaves, max depth = 19, train loss: 0.47675, val loss: 0.47000, in 0.031s
1 tree, 248 leaves, max depth = 17, train loss: 0.47107, val loss: 0.46417, in 0.016s
1 tree, 248 leaves, max depth = 16, train loss: 0.46538, val loss: 0.45835, in 0.031s
1 tree, 248 leaves, max depth = 18, train loss: 0.45983, val loss: 0.45265, in 0.031s
1 tree, 248 leaves, max depth = 18, train loss: 0.45448, val loss: 0.44714, in 0.031s
1 tree, 248 leaves, max depth = 18, train loss: 0.44934, val loss: 0.44190, in 0.031s
1 tree, 235 leaves, max depth = 18, train loss: 0.44230, val loss: 0.43494, in 0.031s
1 tree, 192 leaves, max depth = 19, train loss: 0.43545, val loss: 0.42814, in 0.031s
1 tree, 248 leaves, max depth = 18, train loss: 0.43076, val loss: 0.42333, in 0.016s
1 tree, 181 leaves, max depth = 21, train loss: 0.42647, val loss: 0.41895, in 0.031s
1 tree, 182 leaves, max depth = 18, train loss: 0.42232, val loss: 0.41469, in 0.031s
1 tree, 236 leaves, max depth = 18, train loss: 0.41627, val loss: 0.40874, in 0.031s
1 tree, 248 leaves, max depth = 22, train loss: 0.41222, val loss: 0.40464, in 0.031s
1 tree, 183 leaves, max depth = 17, train loss: 0.40866, val loss: 0.40098, in 0.016s
1 tree, 248 leaves, max depth = 19, train loss: 0.40482, val loss: 0.39707, in 0.016s
1 tree, 248 leaves, max depth = 21, train loss: 0.40109, val loss: 0.39330, in 0.031s
1 tree, 248 leaves, max depth = 18, train loss: 0.39749, val loss: 0.38971, in 0.031s
1 tree, 192 leaves, max depth = 18, train loss: 0.39206, val loss: 0.38440, in 0.031s
1 tree, 213 leaves, max depth = 15, train loss: 0.38885, val loss: 0.38100, in 0.031s
1 tree, 191 leaves, max depth = 18, train loss: 0.38378, val loss: 0.37604, in 0.031s
1 tree, 191 leaves, max depth = 18, train loss: 0.37891, val loss: 0.37127, in 0.016s
1 tree, 191 leaves, max depth = 19, train loss: 0.37424, val loss: 0.36669, in 0.016s
1 tree, 142 leaves, max depth = 16, train loss: 0.36990, val loss: 0.36233, in 0.031s
1 tree, 248 leaves, max depth = 14, train loss: 0.36699, val loss: 0.35946, in 0.031s
1 tree, 248 leaves, max depth = 18, train loss: 0.36410, val loss: 0.35657, in 0.031s
1 tree, 248 leaves, max depth = 16, train loss: 0.36136, val loss: 0.35380, in 0.016s
1 tree, 248 leaves, max depth = 19, train loss: 0.35875, val loss: 0.35130, in 0.031s
1 tree, 91 leaves, max depth = 16, train loss: 0.35495, val loss: 0.34742, in 0.031s
1 tree, 94 leaves, max depth = 16, train loss: 0.35129, val loss: 0.34374, in 0.016s
1 tree, 94 leaves, max depth = 16, train loss: 0.34779, val loss: 0.34022, in 0.016s
1 tree, 93 leaves, max depth = 16, train loss: 0.34444, val loss: 0.33684, in 0.016s
1 tree, 139 leaves, max depth = 18, train loss: 0.34117, val loss: 0.33387, in 0.031s
1 tree, 248 leaves, max depth = 18, train loss: 0.33834, val loss: 0.33115, in 0.031s
1 tree, 93 leaves, max depth = 17, train loss: 0.33528, val loss: 0.32806, in 0.016s
1 tree, 248 leaves, max depth = 19, train loss: 0.33257, val loss: 0.32547, in 0.031s
1 tree, 93 leaves, max depth = 16, train loss: 0.32967, val loss: 0.32254, in 0.022s
1 tree, 248 leaves, max depth = 18, train loss: 0.32714, val loss: 0.32006, in 0.025s
1 tree, 191 leaves, max depth = 18, train loss: 0.32413, val loss: 0.31721, in 0.031s
1 tree, 248 leaves, max depth = 18, train loss: 0.32169, val loss: 0.31485, in 0.031s
1 tree, 137 leaves, max depth = 17, train loss: 0.31899, val loss: 0.31239, in 0.031s
1 tree, 248 leaves, max depth = 18, train loss: 0.31666, val loss: 0.31015, in 0.031s
1 tree, 137 leaves, max depth = 17, train loss: 0.31410, val loss: 0.30786, in 0.016s
1 tree, 138 leaves, max depth = 17, train loss: 0.31163, val loss: 0.30566, in 0.031s
1 tree, 248 leaves, max depth = 15, train loss: 0.30984, val loss: 0.30400, in 0.031s
1 tree, 94 leaves, max depth = 16, train loss: 0.30751, val loss: 0.30166, in 0.016s
1 tree, 139 leaves, max depth = 17, train loss: 0.30520, val loss: 0.29959, in 0.031s
1 tree, 94 leaves, max depth = 22, train loss: 0.30303, val loss: 0.29741, in 0.016s
1 tree, 138 leaves, max depth = 17, train loss: 0.30088, val loss: 0.29549, in 0.016s
1 tree, 92 leaves, max depth = 16, train loss: 0.29884, val loss: 0.29340, in 0.031s
1 tree, 248 leaves, max depth = 18, train loss: 0.29686, val loss: 0.29151, in 0.031s
1 tree, 137 leaves, max depth = 17, train loss: 0.29488, val loss: 0.28971, in 0.031s
1 tree, 93 leaves, max depth = 15, train loss: 0.29299, val loss: 0.28781, in 0.016s
1 tree, 138 leaves, max depth = 17, train loss: 0.29112, val loss: 0.28616, in 0.078s
1 tree, 248 leaves, max depth = 19, train loss: 0.28924, val loss: 0.28439, in 0.062s
1 tree, 92 leaves, max depth = 15, train loss: 0.28752, val loss: 0.28264, in 0.016s
1 tree, 93 leaves, max depth = 15, train loss: 0.28584, val loss: 0.28096, in 0.016s
1 tree, 241 leaves, max depth = 18, train loss: 0.28432, val loss: 0.27972, in 0.047s
1 tree, 92 leaves, max depth = 15, train loss: 0.28274, val loss: 0.27810, in 0.031s
1 tree, 248 leaves, max depth = 18, train loss: 0.28104, val loss: 0.27651, in 0.031s
1 tree, 137 leaves, max depth = 17, train loss: 0.27942, val loss: 0.27507, in 0.031s
1 tree, 137 leaves, max depth = 17, train loss: 0.27787, val loss: 0.27368, in 0.031s
1 tree, 248 leaves, max depth = 19, train loss: 0.27616, val loss: 0.27204, in 0.047s
1 tree, 134 leaves, max depth = 17, train loss: 0.27470, val loss: 0.27080, in 0.031s
1 tree, 92 leaves, max depth = 15, train loss: 0.27332, val loss: 0.26939, in 0.016s
Fit 85 trees in 2.813 s, (16748 total leaves)
Time spent computing histograms: 0.778s
Time spent finding best splits:  0.348s
Time spent applying splits:      0.309s
Time spent predicting:           0.000s
Trial 93, Fold 5: Log loss = 0.2892328390065862, Average precision = 0.9588432364280189, ROC-AUC = 0.9540768073128588, Elapsed Time = 2.826665499998853 seconds
Optimization Progress:  94%|#########3| 94/100 [19:34<01:24, 14.08s/it]
Trial 94, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 94, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
0.188 s 0.040 GB of training data: 
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 25 leaves, max depth = 8, train loss: 0.68468, val loss: 0.68456, in 0.016s
1 tree, 25 leaves, max depth = 7, train loss: 0.67578, val loss: 0.67558, in 0.016s
1 tree, 25 leaves, max depth = 9, train loss: 0.66743, val loss: 0.66703, in 0.016s
1 tree, 25 leaves, max depth = 7, train loss: 0.65936, val loss: 0.65894, in 0.031s
1 tree, 25 leaves, max depth = 11, train loss: 0.65200, val loss: 0.65144, in 0.000s
1 tree, 25 leaves, max depth = 9, train loss: 0.64487, val loss: 0.64418, in 0.000s
1 tree, 25 leaves, max depth = 7, train loss: 0.63743, val loss: 0.63670, in 0.016s
1 tree, 18 leaves, max depth = 7, train loss: 0.63053, val loss: 0.62967, in 0.016s
1 tree, 25 leaves, max depth = 8, train loss: 0.62364, val loss: 0.62264, in 0.016s
1 tree, 25 leaves, max depth = 7, train loss: 0.61613, val loss: 0.61515, in 0.016s
1 tree, 25 leaves, max depth = 11, train loss: 0.60934, val loss: 0.60819, in 0.016s
1 tree, 25 leaves, max depth = 7, train loss: 0.60293, val loss: 0.60174, in 0.016s
1 tree, 25 leaves, max depth = 10, train loss: 0.59716, val loss: 0.59581, in 0.000s
1 tree, 25 leaves, max depth = 7, train loss: 0.59064, val loss: 0.58923, in 0.016s
1 tree, 25 leaves, max depth = 7, train loss: 0.58448, val loss: 0.58300, in 0.016s
1 tree, 25 leaves, max depth = 8, train loss: 0.57855, val loss: 0.57696, in 0.016s
1 tree, 15 leaves, max depth = 7, train loss: 0.57325, val loss: 0.57155, in 0.016s
1 tree, 25 leaves, max depth = 8, train loss: 0.56725, val loss: 0.56545, in 0.016s
1 tree, 4 leaves, max depth = 2, train loss: 0.56246, val loss: 0.56036, in 0.016s
1 tree, 25 leaves, max depth = 8, train loss: 0.55687, val loss: 0.55468, in 0.016s
1 tree, 25 leaves, max depth = 9, train loss: 0.55190, val loss: 0.54954, in 0.016s
1 tree, 25 leaves, max depth = 7, train loss: 0.54656, val loss: 0.54413, in 0.016s
1 tree, 25 leaves, max depth = 8, train loss: 0.54135, val loss: 0.53885, in 0.000s
1 tree, 25 leaves, max depth = 9, train loss: 0.53644, val loss: 0.53391, in 0.016s
1 tree, 9 leaves, max depth = 6, train loss: 0.53202, val loss: 0.52934, in 0.016s
1 tree, 20 leaves, max depth = 10, train loss: 0.52786, val loss: 0.52512, in 0.016s
1 tree, 25 leaves, max depth = 8, train loss: 0.52338, val loss: 0.52060, in 0.000s
1 tree, 25 leaves, max depth = 9, train loss: 0.51921, val loss: 0.51631, in 0.016s
1 tree, 25 leaves, max depth = 9, train loss: 0.51509, val loss: 0.51205, in 0.016s
1 tree, 25 leaves, max depth = 8, train loss: 0.51094, val loss: 0.50785, in 0.016s
1 tree, 25 leaves, max depth = 7, train loss: 0.50705, val loss: 0.50391, in 0.016s
1 tree, 25 leaves, max depth = 7, train loss: 0.50260, val loss: 0.49947, in 0.016s
1 tree, 25 leaves, max depth = 8, train loss: 0.49855, val loss: 0.49536, in 0.016s
1 tree, 25 leaves, max depth = 7, train loss: 0.49475, val loss: 0.49154, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.49139, val loss: 0.48799, in 0.016s
1 tree, 25 leaves, max depth = 8, train loss: 0.48805, val loss: 0.48460, in 0.016s
1 tree, 25 leaves, max depth = 10, train loss: 0.48484, val loss: 0.48143, in 0.016s
1 tree, 25 leaves, max depth = 11, train loss: 0.48134, val loss: 0.47786, in 0.016s
1 tree, 25 leaves, max depth = 8, train loss: 0.47804, val loss: 0.47448, in 0.000s
1 tree, 25 leaves, max depth = 10, train loss: 0.47457, val loss: 0.47095, in 0.016s
1 tree, 22 leaves, max depth = 9, train loss: 0.47139, val loss: 0.46764, in 0.016s
1 tree, 25 leaves, max depth = 10, train loss: 0.46821, val loss: 0.46439, in 0.016s
1 tree, 25 leaves, max depth = 8, train loss: 0.46513, val loss: 0.46133, in 0.016s
1 tree, 18 leaves, max depth = 9, train loss: 0.46249, val loss: 0.45861, in 0.016s
1 tree, 25 leaves, max depth = 8, train loss: 0.45958, val loss: 0.45562, in 0.016s
1 tree, 25 leaves, max depth = 8, train loss: 0.45694, val loss: 0.45288, in 0.016s
1 tree, 25 leaves, max depth = 10, train loss: 0.45454, val loss: 0.45042, in 0.016s
1 tree, 25 leaves, max depth = 9, train loss: 0.45156, val loss: 0.44738, in 0.000s
1 tree, 25 leaves, max depth = 9, train loss: 0.44869, val loss: 0.44444, in 0.016s
1 tree, 25 leaves, max depth = 8, train loss: 0.44557, val loss: 0.44137, in 0.016s
1 tree, 25 leaves, max depth = 9, train loss: 0.44324, val loss: 0.43905, in 0.016s
Fit 51 trees in 1.063 s, (1186 total leaves)
Time spent computing histograms: 0.315s
Time spent finding best splits:  0.028s
Time spent applying splits:      0.024s
Time spent predicting:           0.000s
Trial 94, Fold 1: Log loss = 0.44411788190088125, Average precision = 0.9523659075828259, ROC-AUC = 0.9449224780497363, Elapsed Time = 1.0696496999989904 seconds
Trial 94, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 94, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
Binning 0.040 GB of training data: 0.141 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 25 leaves, max depth = 9, train loss: 0.68484, val loss: 0.68446, in 0.016s
1 tree, 25 leaves, max depth = 10, train loss: 0.67619, val loss: 0.67571, in 0.016s
1 tree, 25 leaves, max depth = 13, train loss: 0.66761, val loss: 0.66698, in 0.016s
1 tree, 25 leaves, max depth = 7, train loss: 0.65949, val loss: 0.65852, in 0.016s
1 tree, 25 leaves, max depth = 12, train loss: 0.65215, val loss: 0.65083, in 0.000s
1 tree, 25 leaves, max depth = 10, train loss: 0.64512, val loss: 0.64348, in 0.016s
1 tree, 25 leaves, max depth = 8, train loss: 0.63755, val loss: 0.63570, in 0.016s
1 tree, 25 leaves, max depth = 7, train loss: 0.62996, val loss: 0.62795, in 0.016s
1 tree, 25 leaves, max depth = 9, train loss: 0.62318, val loss: 0.62092, in 0.016s
1 tree, 25 leaves, max depth = 7, train loss: 0.61637, val loss: 0.61390, in 0.016s
1 tree, 25 leaves, max depth = 11, train loss: 0.60940, val loss: 0.60668, in 0.016s
1 tree, 25 leaves, max depth = 8, train loss: 0.60298, val loss: 0.60015, in 0.016s
1 tree, 25 leaves, max depth = 10, train loss: 0.59723, val loss: 0.59411, in 0.016s
1 tree, 25 leaves, max depth = 9, train loss: 0.59049, val loss: 0.58729, in 0.016s
1 tree, 25 leaves, max depth = 8, train loss: 0.58412, val loss: 0.58078, in 0.016s
1 tree, 25 leaves, max depth = 10, train loss: 0.57831, val loss: 0.57494, in 0.016s
1 tree, 11 leaves, max depth = 6, train loss: 0.57297, val loss: 0.56941, in 0.000s
1 tree, 25 leaves, max depth = 8, train loss: 0.56723, val loss: 0.56362, in 0.016s
1 tree, 25 leaves, max depth = 11, train loss: 0.56161, val loss: 0.55790, in 0.016s
1 tree, 25 leaves, max depth = 7, train loss: 0.55603, val loss: 0.55222, in 0.016s
1 tree, 25 leaves, max depth = 7, train loss: 0.55103, val loss: 0.54714, in 0.016s
1 tree, 25 leaves, max depth = 7, train loss: 0.54546, val loss: 0.54147, in 0.016s
1 tree, 25 leaves, max depth = 6, train loss: 0.54017, val loss: 0.53608, in 0.016s
1 tree, 25 leaves, max depth = 7, train loss: 0.53566, val loss: 0.53147, in 0.016s
1 tree, 25 leaves, max depth = 8, train loss: 0.53139, val loss: 0.52700, in 0.000s
1 tree, 25 leaves, max depth = 9, train loss: 0.52703, val loss: 0.52248, in 0.000s
1 tree, 25 leaves, max depth = 11, train loss: 0.52254, val loss: 0.51798, in 0.016s
1 tree, 25 leaves, max depth = 8, train loss: 0.51861, val loss: 0.51396, in 0.016s
1 tree, 25 leaves, max depth = 7, train loss: 0.51455, val loss: 0.50971, in 0.016s
1 tree, 25 leaves, max depth = 8, train loss: 0.51075, val loss: 0.50574, in 0.016s
1 tree, 25 leaves, max depth = 8, train loss: 0.50645, val loss: 0.50136, in 0.016s
1 tree, 25 leaves, max depth = 8, train loss: 0.50265, val loss: 0.49742, in 0.000s
1 tree, 25 leaves, max depth = 10, train loss: 0.49838, val loss: 0.49308, in 0.016s
1 tree, 25 leaves, max depth = 11, train loss: 0.49468, val loss: 0.48934, in 0.015s
1 tree, 25 leaves, max depth = 8, train loss: 0.49102, val loss: 0.48564, in 0.016s
1 tree, 25 leaves, max depth = 9, train loss: 0.48764, val loss: 0.48216, in 0.016s
1 tree, 25 leaves, max depth = 6, train loss: 0.48413, val loss: 0.47865, in 0.016s
1 tree, 25 leaves, max depth = 8, train loss: 0.48061, val loss: 0.47505, in 0.016s
1 tree, 25 leaves, max depth = 8, train loss: 0.47729, val loss: 0.47165, in 0.000s
1 tree, 25 leaves, max depth = 9, train loss: 0.47375, val loss: 0.46806, in 0.000s
1 tree, 18 leaves, max depth = 9, train loss: 0.47058, val loss: 0.46488, in 0.031s
1 tree, 25 leaves, max depth = 8, train loss: 0.46746, val loss: 0.46169, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.46443, val loss: 0.45858, in 0.016s
1 tree, 11 leaves, max depth = 7, train loss: 0.46176, val loss: 0.45584, in 0.063s
1 tree, 25 leaves, max depth = 10, train loss: 0.45887, val loss: 0.45294, in 0.016s
1 tree, 25 leaves, max depth = 9, train loss: 0.45631, val loss: 0.45028, in 0.031s
1 tree, 25 leaves, max depth = 15, train loss: 0.45394, val loss: 0.44777, in 0.016s
1 tree, 25 leaves, max depth = 9, train loss: 0.45093, val loss: 0.44473, in 0.016s
1 tree, 25 leaves, max depth = 10, train loss: 0.44806, val loss: 0.44176, in 0.031s
1 tree, 25 leaves, max depth = 10, train loss: 0.44561, val loss: 0.43918, in 0.016s
1 tree, 25 leaves, max depth = 9, train loss: 0.44334, val loss: 0.43682, in 0.016s
Fit 51 trees in 1.079 s, (1220 total leaves)
Time spent computing histograms: 0.348s
Time spent finding best splits:  0.043s
Time spent applying splits:      0.033s
Time spent predicting:           0.000s
Trial 94, Fold 2: Log loss = 0.4446945884712925, Average precision = 0.9498656301917219, ROC-AUC = 0.9459203518363455, Elapsed Time = 1.083164300000135 seconds
Trial 94, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 94, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
Binning 0.040 GB of training data: 0.158 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 25 leaves, max depth = 8, train loss: 0.68478, val loss: 0.68467, in 0.000s
1 tree, 25 leaves, max depth = 8, train loss: 0.67572, val loss: 0.67551, in 0.016s
1 tree, 25 leaves, max depth = 11, train loss: 0.66732, val loss: 0.66711, in 0.031s
1 tree, 25 leaves, max depth = 7, train loss: 0.65930, val loss: 0.65893, in 0.000s
1 tree, 25 leaves, max depth = 10, train loss: 0.65199, val loss: 0.65142, in 0.016s
1 tree, 25 leaves, max depth = 10, train loss: 0.64489, val loss: 0.64418, in 0.016s
1 tree, 25 leaves, max depth = 7, train loss: 0.63753, val loss: 0.63672, in 0.078s
1 tree, 17 leaves, max depth = 8, train loss: 0.63067, val loss: 0.62969, in 0.047s
1 tree, 25 leaves, max depth = 8, train loss: 0.62376, val loss: 0.62266, in 0.016s
1 tree, 25 leaves, max depth = 9, train loss: 0.61627, val loss: 0.61513, in 0.016s
1 tree, 25 leaves, max depth = 9, train loss: 0.60948, val loss: 0.60833, in 0.016s
1 tree, 25 leaves, max depth = 6, train loss: 0.60310, val loss: 0.60185, in 0.016s
1 tree, 25 leaves, max depth = 11, train loss: 0.59735, val loss: 0.59599, in 0.016s
1 tree, 25 leaves, max depth = 9, train loss: 0.59066, val loss: 0.58928, in 0.016s
1 tree, 25 leaves, max depth = 7, train loss: 0.58451, val loss: 0.58306, in 0.016s
1 tree, 25 leaves, max depth = 8, train loss: 0.57861, val loss: 0.57716, in 0.016s
1 tree, 11 leaves, max depth = 6, train loss: 0.57330, val loss: 0.57169, in 0.016s
1 tree, 20 leaves, max depth = 7, train loss: 0.56758, val loss: 0.56598, in 0.016s
1 tree, 25 leaves, max depth = 9, train loss: 0.56204, val loss: 0.56032, in 0.016s
1 tree, 25 leaves, max depth = 8, train loss: 0.55660, val loss: 0.55471, in 0.016s
1 tree, 25 leaves, max depth = 8, train loss: 0.55159, val loss: 0.54960, in 0.016s
1 tree, 25 leaves, max depth = 7, train loss: 0.54613, val loss: 0.54411, in 0.016s
1 tree, 25 leaves, max depth = 8, train loss: 0.54099, val loss: 0.53896, in 0.016s
1 tree, 25 leaves, max depth = 8, train loss: 0.53649, val loss: 0.53441, in 0.017s
1 tree, 25 leaves, max depth = 12, train loss: 0.53170, val loss: 0.52963, in 0.015s
1 tree, 25 leaves, max depth = 8, train loss: 0.52737, val loss: 0.52521, in 0.016s
1 tree, 25 leaves, max depth = 13, train loss: 0.52288, val loss: 0.52075, in 0.016s
1 tree, 25 leaves, max depth = 8, train loss: 0.51878, val loss: 0.51665, in 0.016s
1 tree, 25 leaves, max depth = 8, train loss: 0.51472, val loss: 0.51256, in 0.016s
1 tree, 25 leaves, max depth = 9, train loss: 0.51085, val loss: 0.50865, in 0.016s
1 tree, 25 leaves, max depth = 11, train loss: 0.50668, val loss: 0.50443, in 0.016s
1 tree, 25 leaves, max depth = 7, train loss: 0.50289, val loss: 0.50062, in 0.016s
1 tree, 25 leaves, max depth = 8, train loss: 0.49911, val loss: 0.49674, in 0.016s
1 tree, 25 leaves, max depth = 9, train loss: 0.49541, val loss: 0.49306, in 0.016s
1 tree, 25 leaves, max depth = 8, train loss: 0.49140, val loss: 0.48906, in 0.016s
1 tree, 25 leaves, max depth = 8, train loss: 0.48797, val loss: 0.48562, in 0.000s
1 tree, 25 leaves, max depth = 7, train loss: 0.48462, val loss: 0.48225, in 0.016s
1 tree, 25 leaves, max depth = 7, train loss: 0.48114, val loss: 0.47876, in 0.016s
1 tree, 21 leaves, max depth = 9, train loss: 0.47652, val loss: 0.47446, in 0.016s
1 tree, 25 leaves, max depth = 9, train loss: 0.47320, val loss: 0.47115, in 0.016s
1 tree, 24 leaves, max depth = 9, train loss: 0.46995, val loss: 0.46796, in 0.016s
1 tree, 25 leaves, max depth = 11, train loss: 0.46683, val loss: 0.46481, in 0.016s
1 tree, 9 leaves, max depth = 5, train loss: 0.46249, val loss: 0.46073, in 0.016s
1 tree, 11 leaves, max depth = 6, train loss: 0.45990, val loss: 0.45800, in 0.016s
1 tree, 25 leaves, max depth = 7, train loss: 0.45698, val loss: 0.45511, in 0.016s
1 tree, 25 leaves, max depth = 7, train loss: 0.45443, val loss: 0.45254, in 0.016s
1 tree, 25 leaves, max depth = 12, train loss: 0.45207, val loss: 0.45012, in 0.016s
1 tree, 25 leaves, max depth = 10, train loss: 0.44910, val loss: 0.44718, in 0.016s
1 tree, 25 leaves, max depth = 9, train loss: 0.44620, val loss: 0.44427, in 0.016s
1 tree, 25 leaves, max depth = 8, train loss: 0.44367, val loss: 0.44168, in 0.016s
1 tree, 25 leaves, max depth = 11, train loss: 0.44139, val loss: 0.43935, in 0.000s
Fit 51 trees in 1.221 s, (1213 total leaves)
Time spent computing histograms: 0.394s
Time spent finding best splits:  0.060s
Time spent applying splits:      0.036s
Time spent predicting:           0.016s
Trial 94, Fold 3: Log loss = 0.43768270158311073, Average precision = 0.9558801258586747, ROC-AUC = 0.9505323705107338, Elapsed Time = 1.2125231999998505 seconds
Trial 94, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 94, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
Binning 0.040 GB of training data: 0.158 s
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 25 leaves, max depth = 11, train loss: 0.68484, val loss: 0.68458, in 0.016s
1 tree, 25 leaves, max depth = 8, train loss: 0.67602, val loss: 0.67530, in 0.016s
1 tree, 25 leaves, max depth = 10, train loss: 0.66758, val loss: 0.66643, in 0.016s
1 tree, 25 leaves, max depth = 9, train loss: 0.65952, val loss: 0.65806, in 0.016s
1 tree, 25 leaves, max depth = 10, train loss: 0.65219, val loss: 0.65047, in 0.000s
1 tree, 25 leaves, max depth = 12, train loss: 0.64518, val loss: 0.64319, in 0.016s
1 tree, 25 leaves, max depth = 8, train loss: 0.63776, val loss: 0.63553, in 0.016s
1 tree, 20 leaves, max depth = 8, train loss: 0.63090, val loss: 0.62832, in 0.016s
1 tree, 25 leaves, max depth = 8, train loss: 0.62390, val loss: 0.62100, in 0.016s
1 tree, 25 leaves, max depth = 8, train loss: 0.61642, val loss: 0.61322, in 0.016s
1 tree, 25 leaves, max depth = 11, train loss: 0.60971, val loss: 0.60610, in 0.016s
1 tree, 25 leaves, max depth = 7, train loss: 0.60332, val loss: 0.59945, in 0.016s
1 tree, 25 leaves, max depth = 11, train loss: 0.59756, val loss: 0.59346, in 0.016s
1 tree, 25 leaves, max depth = 10, train loss: 0.59106, val loss: 0.58661, in 0.016s
1 tree, 25 leaves, max depth = 8, train loss: 0.58487, val loss: 0.58014, in 0.000s
1 tree, 25 leaves, max depth = 7, train loss: 0.57906, val loss: 0.57398, in 0.031s
1 tree, 12 leaves, max depth = 6, train loss: 0.57376, val loss: 0.56837, in 0.000s
1 tree, 25 leaves, max depth = 9, train loss: 0.56814, val loss: 0.56241, in 0.016s
1 tree, 25 leaves, max depth = 10, train loss: 0.56234, val loss: 0.55630, in 0.016s
1 tree, 25 leaves, max depth = 8, train loss: 0.55690, val loss: 0.55054, in 0.016s
1 tree, 24 leaves, max depth = 9, train loss: 0.55194, val loss: 0.54525, in 0.016s
1 tree, 25 leaves, max depth = 9, train loss: 0.54663, val loss: 0.53963, in 0.016s
1 tree, 25 leaves, max depth = 8, train loss: 0.54173, val loss: 0.53445, in 0.016s
1 tree, 25 leaves, max depth = 8, train loss: 0.53701, val loss: 0.52943, in 0.016s
1 tree, 25 leaves, max depth = 9, train loss: 0.53289, val loss: 0.52509, in 0.016s
1 tree, 25 leaves, max depth = 7, train loss: 0.52851, val loss: 0.52053, in 0.016s
1 tree, 25 leaves, max depth = 13, train loss: 0.52410, val loss: 0.51590, in 0.016s
1 tree, 25 leaves, max depth = 10, train loss: 0.52019, val loss: 0.51176, in 0.016s
1 tree, 25 leaves, max depth = 9, train loss: 0.51613, val loss: 0.50751, in 0.016s
1 tree, 25 leaves, max depth = 9, train loss: 0.51228, val loss: 0.50349, in 0.016s
1 tree, 25 leaves, max depth = 10, train loss: 0.50806, val loss: 0.49898, in 0.000s
1 tree, 25 leaves, max depth = 8, train loss: 0.50423, val loss: 0.49497, in 0.000s
1 tree, 25 leaves, max depth = 9, train loss: 0.50044, val loss: 0.49087, in 0.031s
1 tree, 25 leaves, max depth = 9, train loss: 0.49681, val loss: 0.48698, in 0.000s
1 tree, 25 leaves, max depth = 8, train loss: 0.49290, val loss: 0.48280, in 0.016s
1 tree, 25 leaves, max depth = 8, train loss: 0.48949, val loss: 0.47922, in 0.016s
1 tree, 25 leaves, max depth = 7, train loss: 0.48608, val loss: 0.47562, in 0.016s
1 tree, 25 leaves, max depth = 7, train loss: 0.48258, val loss: 0.47195, in 0.016s
1 tree, 25 leaves, max depth = 8, train loss: 0.47935, val loss: 0.46847, in 0.016s
1 tree, 25 leaves, max depth = 11, train loss: 0.47622, val loss: 0.46507, in 0.000s
1 tree, 23 leaves, max depth = 9, train loss: 0.47298, val loss: 0.46163, in 0.031s
1 tree, 25 leaves, max depth = 11, train loss: 0.46988, val loss: 0.45825, in 0.000s
1 tree, 9 leaves, max depth = 5, train loss: 0.46554, val loss: 0.45384, in 0.016s
1 tree, 10 leaves, max depth = 7, train loss: 0.46293, val loss: 0.45101, in 0.016s
1 tree, 25 leaves, max depth = 9, train loss: 0.46008, val loss: 0.44798, in 0.016s
1 tree, 25 leaves, max depth = 7, train loss: 0.45753, val loss: 0.44518, in 0.016s
1 tree, 25 leaves, max depth = 12, train loss: 0.45515, val loss: 0.44270, in 0.016s
1 tree, 25 leaves, max depth = 11, train loss: 0.45217, val loss: 0.43953, in 0.016s
1 tree, 25 leaves, max depth = 8, train loss: 0.44895, val loss: 0.43628, in 0.016s
1 tree, 25 leaves, max depth = 8, train loss: 0.44640, val loss: 0.43350, in 0.016s
1 tree, 25 leaves, max depth = 10, train loss: 0.44411, val loss: 0.43116, in 0.016s
Fit 51 trees in 1.127 s, (1223 total leaves)
Time spent computing histograms: 0.337s
Time spent finding best splits:  0.031s
Time spent applying splits:      0.027s
Time spent predicting:           0.016s
Trial 94, Fold 4: Log loss = 0.4435109531285845, Average precision = 0.9534755101482932, ROC-AUC = 0.9469630517822271, Elapsed Time = 1.126024299999699 seconds
Trial 94, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 94, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
Binning 0.040 GB of training data: 0.157 s
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 25 leaves, max depth = 11, train loss: 0.68469, val loss: 0.68429, in 0.016s
1 tree, 25 leaves, max depth = 9, train loss: 0.67607, val loss: 0.67519, in 0.016s
1 tree, 25 leaves, max depth = 8, train loss: 0.66717, val loss: 0.66585, in 0.016s
1 tree, 25 leaves, max depth = 11, train loss: 0.65875, val loss: 0.65700, in 0.016s
1 tree, 22 leaves, max depth = 10, train loss: 0.65068, val loss: 0.64847, in 0.016s
1 tree, 25 leaves, max depth = 12, train loss: 0.64251, val loss: 0.63993, in 0.016s
1 tree, 25 leaves, max depth = 8, train loss: 0.63533, val loss: 0.63232, in 0.016s
1 tree, 25 leaves, max depth = 7, train loss: 0.62808, val loss: 0.62482, in 0.016s
1 tree, 25 leaves, max depth = 10, train loss: 0.62136, val loss: 0.61776, in 0.000s
1 tree, 25 leaves, max depth = 12, train loss: 0.61415, val loss: 0.61020, in 0.016s
1 tree, 25 leaves, max depth = 8, train loss: 0.60761, val loss: 0.60346, in 0.016s
1 tree, 25 leaves, max depth = 8, train loss: 0.60112, val loss: 0.59654, in 0.016s
1 tree, 25 leaves, max depth = 12, train loss: 0.59518, val loss: 0.59025, in 0.016s
1 tree, 25 leaves, max depth = 7, train loss: 0.58862, val loss: 0.58336, in 0.016s
1 tree, 25 leaves, max depth = 8, train loss: 0.58291, val loss: 0.57728, in 0.016s
1 tree, 25 leaves, max depth = 7, train loss: 0.57712, val loss: 0.57113, in 0.016s
1 tree, 25 leaves, max depth = 9, train loss: 0.57153, val loss: 0.56516, in 0.016s
1 tree, 8 leaves, max depth = 5, train loss: 0.56652, val loss: 0.55989, in 0.016s
1 tree, 25 leaves, max depth = 10, train loss: 0.56160, val loss: 0.55468, in 0.016s
1 tree, 25 leaves, max depth = 7, train loss: 0.55640, val loss: 0.54931, in 0.016s
1 tree, 25 leaves, max depth = 9, train loss: 0.55177, val loss: 0.54440, in 0.016s
1 tree, 25 leaves, max depth = 8, train loss: 0.54643, val loss: 0.53871, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.54063, val loss: 0.53273, in 0.016s
1 tree, 25 leaves, max depth = 8, train loss: 0.53542, val loss: 0.52722, in 0.016s
1 tree, 25 leaves, max depth = 8, train loss: 0.53095, val loss: 0.52242, in 0.016s
1 tree, 25 leaves, max depth = 8, train loss: 0.52643, val loss: 0.51761, in 0.016s
1 tree, 25 leaves, max depth = 7, train loss: 0.52247, val loss: 0.51340, in 0.016s
1 tree, 25 leaves, max depth = 9, train loss: 0.51872, val loss: 0.50936, in 0.016s
1 tree, 25 leaves, max depth = 8, train loss: 0.51412, val loss: 0.50448, in 0.016s
1 tree, 25 leaves, max depth = 11, train loss: 0.51011, val loss: 0.50021, in 0.016s
1 tree, 25 leaves, max depth = 10, train loss: 0.50623, val loss: 0.49609, in 0.016s
1 tree, 25 leaves, max depth = 8, train loss: 0.50249, val loss: 0.49213, in 0.016s
1 tree, 25 leaves, max depth = 8, train loss: 0.49911, val loss: 0.48846, in 0.016s
1 tree, 20 leaves, max depth = 7, train loss: 0.49508, val loss: 0.48421, in 0.016s
1 tree, 25 leaves, max depth = 8, train loss: 0.49163, val loss: 0.48063, in 0.031s
1 tree, 5 leaves, max depth = 3, train loss: 0.48703, val loss: 0.47589, in 0.016s
1 tree, 25 leaves, max depth = 7, train loss: 0.48356, val loss: 0.47218, in 0.016s
1 tree, 25 leaves, max depth = 10, train loss: 0.48027, val loss: 0.46860, in 0.000s
1 tree, 25 leaves, max depth = 10, train loss: 0.47701, val loss: 0.46509, in 0.016s
1 tree, 25 leaves, max depth = 9, train loss: 0.47325, val loss: 0.46117, in 0.016s
1 tree, 25 leaves, max depth = 8, train loss: 0.47026, val loss: 0.45807, in 0.016s
1 tree, 25 leaves, max depth = 7, train loss: 0.46715, val loss: 0.45470, in 0.016s
1 tree, 25 leaves, max depth = 10, train loss: 0.46450, val loss: 0.45178, in 0.000s
1 tree, 25 leaves, max depth = 10, train loss: 0.46127, val loss: 0.44837, in 0.016s
1 tree, 25 leaves, max depth = 6, train loss: 0.45842, val loss: 0.44541, in 0.031s
1 tree, 6 leaves, max depth = 4, train loss: 0.45573, val loss: 0.44255, in 0.016s
1 tree, 25 leaves, max depth = 11, train loss: 0.45276, val loss: 0.43939, in 0.016s
1 tree, 25 leaves, max depth = 8, train loss: 0.45020, val loss: 0.43673, in 0.016s
1 tree, 25 leaves, max depth = 7, train loss: 0.44763, val loss: 0.43397, in 0.016s
1 tree, 25 leaves, max depth = 9, train loss: 0.44448, val loss: 0.43077, in 0.000s
1 tree, 25 leaves, max depth = 9, train loss: 0.44229, val loss: 0.42841, in 0.031s
Fit 51 trees in 1.173 s, (1191 total leaves)
Time spent computing histograms: 0.362s
Time spent finding best splits:  0.033s
Time spent applying splits:      0.028s
Time spent predicting:           0.000s
Trial 94, Fold 5: Log loss = 0.4475411045755514, Average precision = 0.9518987120846267, ROC-AUC = 0.9456943685784887, Elapsed Time = 1.1737415000006877 seconds
Optimization Progress:  95%|#########5| 95/100 [19:47<01:07, 13.55s/it]
Trial 95, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 95, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
0.141 s 0.040 GB of training data: 
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 30 leaves, max depth = 12, train loss: 0.67525, val loss: 0.67497, in 0.016s
1 tree, 30 leaves, max depth = 8, train loss: 0.65881, val loss: 0.65818, in 0.016s
1 tree, 26 leaves, max depth = 10, train loss: 0.64364, val loss: 0.64269, in 0.016s
1 tree, 30 leaves, max depth = 11, train loss: 0.62939, val loss: 0.62833, in 0.000s
1 tree, 28 leaves, max depth = 10, train loss: 0.61602, val loss: 0.61486, in 0.016s
1 tree, 28 leaves, max depth = 12, train loss: 0.60305, val loss: 0.60166, in 0.016s
1 tree, 28 leaves, max depth = 10, train loss: 0.59104, val loss: 0.58948, in 0.016s
1 tree, 30 leaves, max depth = 8, train loss: 0.57859, val loss: 0.57686, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.56705, val loss: 0.56515, in 0.016s
1 tree, 32 leaves, max depth = 13, train loss: 0.55563, val loss: 0.55387, in 0.016s
1 tree, 25 leaves, max depth = 10, train loss: 0.54514, val loss: 0.54323, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.53559, val loss: 0.53375, in 0.031s
1 tree, 31 leaves, max depth = 9, train loss: 0.52677, val loss: 0.52488, in 0.016s
1 tree, 28 leaves, max depth = 10, train loss: 0.51777, val loss: 0.51574, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.50944, val loss: 0.50729, in 0.016s
1 tree, 30 leaves, max depth = 15, train loss: 0.50165, val loss: 0.49932, in 0.016s
1 tree, 28 leaves, max depth = 10, train loss: 0.49438, val loss: 0.49205, in 0.000s
1 tree, 31 leaves, max depth = 10, train loss: 0.48681, val loss: 0.48429, in 0.016s
1 tree, 4 leaves, max depth = 2, train loss: 0.48074, val loss: 0.47777, in 0.016s
1 tree, 30 leaves, max depth = 10, train loss: 0.47386, val loss: 0.47087, in 0.016s
1 tree, 18 leaves, max depth = 9, train loss: 0.46731, val loss: 0.46422, in 0.016s
1 tree, 30 leaves, max depth = 10, train loss: 0.46107, val loss: 0.45780, in 0.016s
1 tree, 29 leaves, max depth = 9, train loss: 0.45527, val loss: 0.45194, in 0.016s
1 tree, 23 leaves, max depth = 8, train loss: 0.45025, val loss: 0.44686, in 0.016s
1 tree, 22 leaves, max depth = 8, train loss: 0.44510, val loss: 0.44146, in 0.016s
1 tree, 33 leaves, max depth = 10, train loss: 0.44043, val loss: 0.43670, in 0.016s
1 tree, 31 leaves, max depth = 13, train loss: 0.43552, val loss: 0.43171, in 0.016s
1 tree, 25 leaves, max depth = 11, train loss: 0.43107, val loss: 0.42719, in 0.016s
1 tree, 27 leaves, max depth = 11, train loss: 0.42703, val loss: 0.42313, in 0.016s
1 tree, 6 leaves, max depth = 3, train loss: 0.41983, val loss: 0.41618, in 0.016s
1 tree, 30 leaves, max depth = 10, train loss: 0.41536, val loss: 0.41170, in 0.016s
1 tree, 28 leaves, max depth = 10, train loss: 0.41054, val loss: 0.40699, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.40713, val loss: 0.40354, in 0.016s
1 tree, 28 leaves, max depth = 9, train loss: 0.40360, val loss: 0.39988, in 0.016s
1 tree, 36 leaves, max depth = 11, train loss: 0.39991, val loss: 0.39623, in 0.000s
1 tree, 28 leaves, max depth = 11, train loss: 0.39565, val loss: 0.39201, in 0.016s
1 tree, 36 leaves, max depth = 11, train loss: 0.39155, val loss: 0.38800, in 0.016s
1 tree, 10 leaves, max depth = 6, train loss: 0.38581, val loss: 0.38236, in 0.016s
1 tree, 8 leaves, max depth = 4, train loss: 0.38020, val loss: 0.37700, in 0.016s
1 tree, 28 leaves, max depth = 10, train loss: 0.37714, val loss: 0.37388, in 0.016s
1 tree, 6 leaves, max depth = 3, train loss: 0.37202, val loss: 0.36888, in 0.016s
1 tree, 30 leaves, max depth = 15, train loss: 0.36887, val loss: 0.36581, in 0.016s
1 tree, 23 leaves, max depth = 9, train loss: 0.36634, val loss: 0.36312, in 0.016s
1 tree, 33 leaves, max depth = 11, train loss: 0.36364, val loss: 0.36050, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.36037, val loss: 0.35727, in 0.016s
1 tree, 27 leaves, max depth = 8, train loss: 0.35774, val loss: 0.35475, in 0.016s
1 tree, 6 leaves, max depth = 3, train loss: 0.35319, val loss: 0.35053, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.34939, val loss: 0.34664, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.34535, val loss: 0.34274, in 0.016s
1 tree, 12 leaves, max depth = 5, train loss: 0.34106, val loss: 0.33882, in 0.016s
1 tree, 31 leaves, max depth = 12, train loss: 0.33830, val loss: 0.33621, in 0.016s
1 tree, 34 leaves, max depth = 13, train loss: 0.33651, val loss: 0.33457, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.33337, val loss: 0.33134, in 0.016s
1 tree, 4 leaves, max depth = 2, train loss: 0.33048, val loss: 0.32836, in 0.016s
1 tree, 29 leaves, max depth = 15, train loss: 0.32861, val loss: 0.32654, in 0.016s
1 tree, 26 leaves, max depth = 8, train loss: 0.32609, val loss: 0.32415, in 0.016s
1 tree, 43 leaves, max depth = 12, train loss: 0.32383, val loss: 0.32183, in 0.000s
1 tree, 16 leaves, max depth = 8, train loss: 0.32076, val loss: 0.31884, in 0.016s
Fit 58 trees in 1.157 s, (1435 total leaves)
Time spent computing histograms: 0.371s
Time spent finding best splits:  0.038s
Time spent applying splits:      0.031s
Time spent predicting:           0.000s
Trial 95, Fold 1: Log loss = 0.3234746455634853, Average precision = 0.9569561453670864, ROC-AUC = 0.950871124948022, Elapsed Time = 1.1658042000017304 seconds
Trial 95, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 95, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
Binning 0.040 GB of training data: 0.126 s
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 30 leaves, max depth = 9, train loss: 0.67519, val loss: 0.67479, in 0.016s
1 tree, 28 leaves, max depth = 8, train loss: 0.65885, val loss: 0.65813, in 0.000s
1 tree, 23 leaves, max depth = 11, train loss: 0.64347, val loss: 0.64249, in 0.016s
1 tree, 36 leaves, max depth = 12, train loss: 0.62764, val loss: 0.62627, in 0.016s
1 tree, 27 leaves, max depth = 10, train loss: 0.61422, val loss: 0.61238, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.60100, val loss: 0.59891, in 0.016s
1 tree, 33 leaves, max depth = 10, train loss: 0.58904, val loss: 0.58647, in 0.031s
1 tree, 33 leaves, max depth = 8, train loss: 0.57722, val loss: 0.57436, in 0.016s
1 tree, 29 leaves, max depth = 11, train loss: 0.56625, val loss: 0.56315, in 0.016s
1 tree, 28 leaves, max depth = 8, train loss: 0.55608, val loss: 0.55262, in 0.000s
1 tree, 38 leaves, max depth = 12, train loss: 0.54560, val loss: 0.54188, in 0.016s
1 tree, 28 leaves, max depth = 9, train loss: 0.53641, val loss: 0.53246, in 0.016s
1 tree, 27 leaves, max depth = 10, train loss: 0.52767, val loss: 0.52340, in 0.016s
1 tree, 28 leaves, max depth = 11, train loss: 0.51860, val loss: 0.51409, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.51010, val loss: 0.50539, in 0.031s
1 tree, 15 leaves, max depth = 10, train loss: 0.50225, val loss: 0.49753, in 0.000s
1 tree, 28 leaves, max depth = 15, train loss: 0.49430, val loss: 0.48952, in 0.031s
1 tree, 33 leaves, max depth = 10, train loss: 0.48678, val loss: 0.48191, in 0.016s
1 tree, 37 leaves, max depth = 10, train loss: 0.48008, val loss: 0.47498, in 0.016s
1 tree, 31 leaves, max depth = 12, train loss: 0.47370, val loss: 0.46857, in 0.016s
1 tree, 36 leaves, max depth = 11, train loss: 0.46717, val loss: 0.46192, in 0.016s
1 tree, 28 leaves, max depth = 11, train loss: 0.46144, val loss: 0.45622, in 0.016s
1 tree, 7 leaves, max depth = 5, train loss: 0.45338, val loss: 0.44808, in 0.016s
1 tree, 27 leaves, max depth = 13, train loss: 0.44770, val loss: 0.44234, in 0.016s
1 tree, 29 leaves, max depth = 10, train loss: 0.44282, val loss: 0.43737, in 0.016s
1 tree, 37 leaves, max depth = 13, train loss: 0.43741, val loss: 0.43192, in 0.016s
1 tree, 29 leaves, max depth = 15, train loss: 0.43277, val loss: 0.42725, in 0.016s
1 tree, 32 leaves, max depth = 10, train loss: 0.42858, val loss: 0.42289, in 0.016s
1 tree, 33 leaves, max depth = 11, train loss: 0.42383, val loss: 0.41809, in 0.016s
1 tree, 34 leaves, max depth = 12, train loss: 0.41935, val loss: 0.41347, in 0.016s
1 tree, 22 leaves, max depth = 8, train loss: 0.41552, val loss: 0.40947, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.41068, val loss: 0.40467, in 0.016s
1 tree, 29 leaves, max depth = 9, train loss: 0.40717, val loss: 0.40103, in 0.016s
1 tree, 29 leaves, max depth = 11, train loss: 0.40331, val loss: 0.39717, in 0.016s
1 tree, 7 leaves, max depth = 4, train loss: 0.39758, val loss: 0.39144, in 0.016s
1 tree, 44 leaves, max depth = 11, train loss: 0.39340, val loss: 0.38734, in 0.016s
1 tree, 7 leaves, max depth = 4, train loss: 0.38823, val loss: 0.38214, in 0.016s
1 tree, 29 leaves, max depth = 10, train loss: 0.38497, val loss: 0.37878, in 0.016s
1 tree, 17 leaves, max depth = 8, train loss: 0.38183, val loss: 0.37562, in 0.016s
1 tree, 6 leaves, max depth = 3, train loss: 0.37666, val loss: 0.37056, in 0.016s
1 tree, 29 leaves, max depth = 11, train loss: 0.37406, val loss: 0.36800, in 0.016s
1 tree, 13 leaves, max depth = 6, train loss: 0.36861, val loss: 0.36288, in 0.016s
1 tree, 8 leaves, max depth = 5, train loss: 0.36399, val loss: 0.35850, in 0.000s
1 tree, 31 leaves, max depth = 11, train loss: 0.36056, val loss: 0.35514, in 0.000s
1 tree, 31 leaves, max depth = 11, train loss: 0.35825, val loss: 0.35289, in 0.031s
1 tree, 31 leaves, max depth = 10, train loss: 0.35510, val loss: 0.34978, in 0.016s
1 tree, 29 leaves, max depth = 10, train loss: 0.35199, val loss: 0.34675, in 0.016s
1 tree, 11 leaves, max depth = 6, train loss: 0.34913, val loss: 0.34393, in 0.016s
1 tree, 12 leaves, max depth = 6, train loss: 0.34631, val loss: 0.34117, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.34227, val loss: 0.33725, in 0.000s
1 tree, 43 leaves, max depth = 10, train loss: 0.33951, val loss: 0.33459, in 0.031s
1 tree, 36 leaves, max depth = 11, train loss: 0.33751, val loss: 0.33263, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.33381, val loss: 0.32908, in 0.000s
1 tree, 32 leaves, max depth = 8, train loss: 0.33200, val loss: 0.32725, in 0.031s
1 tree, 10 leaves, max depth = 6, train loss: 0.32818, val loss: 0.32372, in 0.016s
1 tree, 36 leaves, max depth = 9, train loss: 0.32661, val loss: 0.32214, in 0.016s
1 tree, 6 leaves, max depth = 3, train loss: 0.32372, val loss: 0.31925, in 0.016s
1 tree, 13 leaves, max depth = 6, train loss: 0.32035, val loss: 0.31615, in 0.016s
Fit 58 trees in 1.220 s, (1488 total leaves)
Time spent computing histograms: 0.403s
Time spent finding best splits:  0.043s
Time spent applying splits:      0.035s
Time spent predicting:           0.000s
Trial 95, Fold 2: Log loss = 0.3229884704946013, Average precision = 0.9535500219361897, ROC-AUC = 0.9504192943060286, Elapsed Time = 1.2325305000013032 seconds
Trial 95, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 95, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
Binning 0.040 GB of training data: 0.157 s
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 28 leaves, max depth = 10, train loss: 0.67539, val loss: 0.67508, in 0.016s
1 tree, 29 leaves, max depth = 11, train loss: 0.65870, val loss: 0.65806, in 0.016s
1 tree, 31 leaves, max depth = 12, train loss: 0.64326, val loss: 0.64260, in 0.016s
1 tree, 28 leaves, max depth = 9, train loss: 0.62912, val loss: 0.62821, in 0.016s
1 tree, 27 leaves, max depth = 9, train loss: 0.61589, val loss: 0.61469, in 0.016s
1 tree, 32 leaves, max depth = 10, train loss: 0.60287, val loss: 0.60152, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.59092, val loss: 0.58938, in 0.016s
1 tree, 30 leaves, max depth = 12, train loss: 0.57934, val loss: 0.57761, in 0.016s
1 tree, 29 leaves, max depth = 11, train loss: 0.56861, val loss: 0.56658, in 0.016s
1 tree, 29 leaves, max depth = 11, train loss: 0.55849, val loss: 0.55645, in 0.016s
1 tree, 39 leaves, max depth = 11, train loss: 0.54812, val loss: 0.54600, in 0.016s
1 tree, 29 leaves, max depth = 9, train loss: 0.53895, val loss: 0.53674, in 0.016s
1 tree, 30 leaves, max depth = 11, train loss: 0.53013, val loss: 0.52787, in 0.016s
1 tree, 27 leaves, max depth = 11, train loss: 0.52111, val loss: 0.51879, in 0.016s
1 tree, 30 leaves, max depth = 12, train loss: 0.51271, val loss: 0.51017, in 0.016s
1 tree, 26 leaves, max depth = 8, train loss: 0.50465, val loss: 0.50226, in 0.016s
1 tree, 28 leaves, max depth = 9, train loss: 0.49728, val loss: 0.49482, in 0.016s
1 tree, 29 leaves, max depth = 10, train loss: 0.48959, val loss: 0.48714, in 0.016s
1 tree, 4 leaves, max depth = 2, train loss: 0.48346, val loss: 0.48101, in 0.016s
1 tree, 8 leaves, max depth = 4, train loss: 0.47707, val loss: 0.47464, in 0.016s
1 tree, 28 leaves, max depth = 9, train loss: 0.47086, val loss: 0.46849, in 0.016s
1 tree, 34 leaves, max depth = 10, train loss: 0.46413, val loss: 0.46185, in 0.016s
1 tree, 31 leaves, max depth = 13, train loss: 0.45792, val loss: 0.45564, in 0.016s
1 tree, 34 leaves, max depth = 9, train loss: 0.45270, val loss: 0.45047, in 0.031s
1 tree, 28 leaves, max depth = 11, train loss: 0.44763, val loss: 0.44536, in 0.016s
1 tree, 29 leaves, max depth = 9, train loss: 0.44294, val loss: 0.44051, in 0.016s
1 tree, 29 leaves, max depth = 9, train loss: 0.43797, val loss: 0.43560, in 0.016s
1 tree, 26 leaves, max depth = 10, train loss: 0.43342, val loss: 0.43107, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.42934, val loss: 0.42691, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.42534, val loss: 0.42304, in 0.016s
1 tree, 17 leaves, max depth = 11, train loss: 0.42092, val loss: 0.41860, in 0.016s
1 tree, 29 leaves, max depth = 8, train loss: 0.41734, val loss: 0.41502, in 0.016s
1 tree, 30 leaves, max depth = 10, train loss: 0.41389, val loss: 0.41158, in 0.016s
1 tree, 27 leaves, max depth = 9, train loss: 0.41025, val loss: 0.40806, in 0.016s
1 tree, 30 leaves, max depth = 11, train loss: 0.40570, val loss: 0.40335, in 0.016s
1 tree, 29 leaves, max depth = 10, train loss: 0.40131, val loss: 0.39881, in 0.016s
1 tree, 20 leaves, max depth = 8, train loss: 0.39795, val loss: 0.39548, in 0.016s
1 tree, 24 leaves, max depth = 8, train loss: 0.39463, val loss: 0.39215, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.38880, val loss: 0.38702, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.38382, val loss: 0.38224, in 0.016s
1 tree, 33 leaves, max depth = 11, train loss: 0.38001, val loss: 0.37834, in 0.016s
1 tree, 6 leaves, max depth = 3, train loss: 0.37477, val loss: 0.37356, in 0.000s
1 tree, 9 leaves, max depth = 4, train loss: 0.36975, val loss: 0.36906, in 0.016s
1 tree, 29 leaves, max depth = 9, train loss: 0.36705, val loss: 0.36639, in 0.016s
1 tree, 32 leaves, max depth = 11, train loss: 0.36468, val loss: 0.36412, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.36017, val loss: 0.36014, in 0.016s
1 tree, 29 leaves, max depth = 10, train loss: 0.35836, val loss: 0.35834, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.35520, val loss: 0.35506, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.35145, val loss: 0.35151, in 0.016s
1 tree, 33 leaves, max depth = 8, train loss: 0.34854, val loss: 0.34843, in 0.016s
1 tree, 34 leaves, max depth = 10, train loss: 0.34631, val loss: 0.34639, in 0.016s
1 tree, 6 leaves, max depth = 4, train loss: 0.34294, val loss: 0.34318, in 0.016s
1 tree, 21 leaves, max depth = 8, train loss: 0.34128, val loss: 0.34161, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.33737, val loss: 0.33825, in 0.016s
1 tree, 29 leaves, max depth = 13, train loss: 0.33540, val loss: 0.33641, in 0.016s
1 tree, 29 leaves, max depth = 10, train loss: 0.33282, val loss: 0.33374, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.33112, val loss: 0.33204, in 0.016s
1 tree, 32 leaves, max depth = 12, train loss: 0.32960, val loss: 0.33055, in 0.016s
Fit 58 trees in 1.251 s, (1446 total leaves)
Time spent computing histograms: 0.400s
Time spent finding best splits:  0.043s
Time spent applying splits:      0.034s
Time spent predicting:           0.000s
Trial 95, Fold 3: Log loss = 0.3254918967385569, Average precision = 0.958430490667888, ROC-AUC = 0.9538103083375871, Elapsed Time = 1.2599082999986422 seconds
Trial 95, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 95, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
Binning 0.040 GB of training data: 0.157 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 30 leaves, max depth = 9, train loss: 0.67529, val loss: 0.67443, in 0.016s
1 tree, 40 leaves, max depth = 9, train loss: 0.65848, val loss: 0.65684, in 0.016s
1 tree, 27 leaves, max depth = 11, train loss: 0.64325, val loss: 0.64077, in 0.016s
1 tree, 27 leaves, max depth = 10, train loss: 0.62807, val loss: 0.62484, in 0.016s
1 tree, 30 leaves, max depth = 10, train loss: 0.61434, val loss: 0.61040, in 0.016s
1 tree, 30 leaves, max depth = 11, train loss: 0.60054, val loss: 0.59589, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.58826, val loss: 0.58314, in 0.016s
1 tree, 27 leaves, max depth = 10, train loss: 0.57694, val loss: 0.57130, in 0.016s
1 tree, 27 leaves, max depth = 14, train loss: 0.56579, val loss: 0.55964, in 0.016s
1 tree, 33 leaves, max depth = 11, train loss: 0.55463, val loss: 0.54804, in 0.016s
1 tree, 25 leaves, max depth = 17, train loss: 0.54476, val loss: 0.53755, in 0.031s
1 tree, 29 leaves, max depth = 12, train loss: 0.53524, val loss: 0.52751, in 0.016s
1 tree, 34 leaves, max depth = 10, train loss: 0.52566, val loss: 0.51755, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.51650, val loss: 0.50790, in 0.016s
1 tree, 32 leaves, max depth = 10, train loss: 0.50845, val loss: 0.49952, in 0.016s
1 tree, 28 leaves, max depth = 13, train loss: 0.50116, val loss: 0.49187, in 0.016s
1 tree, 29 leaves, max depth = 10, train loss: 0.49377, val loss: 0.48408, in 0.016s
1 tree, 33 leaves, max depth = 10, train loss: 0.48697, val loss: 0.47689, in 0.016s
1 tree, 29 leaves, max depth = 11, train loss: 0.48004, val loss: 0.46942, in 0.016s
1 tree, 34 leaves, max depth = 10, train loss: 0.47391, val loss: 0.46300, in 0.016s
1 tree, 30 leaves, max depth = 12, train loss: 0.46846, val loss: 0.45733, in 0.016s
1 tree, 7 leaves, max depth = 4, train loss: 0.46251, val loss: 0.45101, in 0.000s
1 tree, 28 leaves, max depth = 12, train loss: 0.45703, val loss: 0.44508, in 0.031s
1 tree, 28 leaves, max depth = 11, train loss: 0.45132, val loss: 0.43893, in 0.016s
1 tree, 29 leaves, max depth = 9, train loss: 0.44646, val loss: 0.43376, in 0.016s
1 tree, 38 leaves, max depth = 11, train loss: 0.44105, val loss: 0.42792, in 0.016s
1 tree, 27 leaves, max depth = 11, train loss: 0.43646, val loss: 0.42295, in 0.016s
1 tree, 28 leaves, max depth = 10, train loss: 0.43218, val loss: 0.41844, in 0.016s
1 tree, 31 leaves, max depth = 12, train loss: 0.42717, val loss: 0.41318, in 0.016s
1 tree, 32 leaves, max depth = 10, train loss: 0.42287, val loss: 0.40854, in 0.016s
1 tree, 15 leaves, max depth = 7, train loss: 0.41913, val loss: 0.40445, in 0.016s
1 tree, 27 leaves, max depth = 11, train loss: 0.41511, val loss: 0.40015, in 0.016s
1 tree, 8 leaves, max depth = 4, train loss: 0.40854, val loss: 0.39342, in 0.016s
1 tree, 31 leaves, max depth = 13, train loss: 0.40513, val loss: 0.38971, in 0.016s
1 tree, 14 leaves, max depth = 6, train loss: 0.40149, val loss: 0.38578, in 0.016s
1 tree, 26 leaves, max depth = 11, train loss: 0.39725, val loss: 0.38150, in 0.016s
1 tree, 10 leaves, max depth = 5, train loss: 0.39397, val loss: 0.37792, in 0.016s
1 tree, 32 leaves, max depth = 11, train loss: 0.39126, val loss: 0.37509, in 0.031s
1 tree, 34 leaves, max depth = 11, train loss: 0.38727, val loss: 0.37104, in 0.016s
1 tree, 27 leaves, max depth = 8, train loss: 0.38439, val loss: 0.36787, in 0.000s
1 tree, 27 leaves, max depth = 11, train loss: 0.38176, val loss: 0.36509, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.37895, val loss: 0.36224, in 0.031s
1 tree, 11 leaves, max depth = 6, train loss: 0.37671, val loss: 0.35986, in 0.000s
1 tree, 27 leaves, max depth = 10, train loss: 0.37438, val loss: 0.35732, in 0.016s
1 tree, 6 leaves, max depth = 3, train loss: 0.36930, val loss: 0.35212, in 0.000s
1 tree, 9 leaves, max depth = 6, train loss: 0.36513, val loss: 0.34756, in 0.016s
1 tree, 8 leaves, max depth = 5, train loss: 0.36074, val loss: 0.34314, in 0.016s
1 tree, 24 leaves, max depth = 10, train loss: 0.35847, val loss: 0.34073, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.35537, val loss: 0.33763, in 0.016s
1 tree, 9 leaves, max depth = 7, train loss: 0.35111, val loss: 0.33350, in 0.016s
1 tree, 33 leaves, max depth = 10, train loss: 0.34894, val loss: 0.33113, in 0.016s
1 tree, 7 leaves, max depth = 4, train loss: 0.34479, val loss: 0.32717, in 0.016s
1 tree, 6 leaves, max depth = 4, train loss: 0.34268, val loss: 0.32485, in 0.016s
1 tree, 29 leaves, max depth = 10, train loss: 0.34088, val loss: 0.32293, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.33916, val loss: 0.32107, in 0.016s
1 tree, 34 leaves, max depth = 9, train loss: 0.33700, val loss: 0.31885, in 0.016s
1 tree, 25 leaves, max depth = 7, train loss: 0.33523, val loss: 0.31695, in 0.016s
1 tree, 29 leaves, max depth = 11, train loss: 0.33322, val loss: 0.31487, in 0.016s
Fit 58 trees in 1.282 s, (1459 total leaves)
Time spent computing histograms: 0.406s
Time spent finding best splits:  0.044s
Time spent applying splits:      0.036s
Time spent predicting:           0.000s
Trial 95, Fold 4: Log loss = 0.33272664317341477, Average precision = 0.955768917238855, ROC-AUC = 0.9497438696824682, Elapsed Time = 1.2968270999990636 seconds
Trial 95, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 95, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
Binning 0.040 GB of training data: 0.173 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 28 leaves, max depth = 10, train loss: 0.67523, val loss: 0.67407, in 0.016s
1 tree, 27 leaves, max depth = 10, train loss: 0.65890, val loss: 0.65665, in 0.016s
1 tree, 28 leaves, max depth = 10, train loss: 0.64378, val loss: 0.64069, in 0.016s
1 tree, 27 leaves, max depth = 10, train loss: 0.62942, val loss: 0.62585, in 0.016s
1 tree, 30 leaves, max depth = 11, train loss: 0.61599, val loss: 0.61195, in 0.016s
1 tree, 28 leaves, max depth = 9, train loss: 0.60332, val loss: 0.59837, in 0.016s
1 tree, 29 leaves, max depth = 9, train loss: 0.59130, val loss: 0.58595, in 0.016s
1 tree, 28 leaves, max depth = 11, train loss: 0.57877, val loss: 0.57279, in 0.016s
1 tree, 28 leaves, max depth = 11, train loss: 0.56698, val loss: 0.56042, in 0.031s
1 tree, 30 leaves, max depth = 12, train loss: 0.55565, val loss: 0.54863, in 0.016s
1 tree, 28 leaves, max depth = 12, train loss: 0.54515, val loss: 0.53751, in 0.016s
1 tree, 27 leaves, max depth = 11, train loss: 0.53571, val loss: 0.52774, in 0.000s
1 tree, 24 leaves, max depth = 9, train loss: 0.52696, val loss: 0.51869, in 0.031s
1 tree, 29 leaves, max depth = 10, train loss: 0.51792, val loss: 0.50922, in 0.000s
1 tree, 29 leaves, max depth = 12, train loss: 0.50960, val loss: 0.50033, in 0.031s
1 tree, 16 leaves, max depth = 6, train loss: 0.50177, val loss: 0.49204, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.49398, val loss: 0.48391, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.48662, val loss: 0.47612, in 0.016s
1 tree, 4 leaves, max depth = 2, train loss: 0.48051, val loss: 0.46962, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.47364, val loss: 0.46229, in 0.016s
1 tree, 20 leaves, max depth = 6, train loss: 0.46704, val loss: 0.45536, in 0.016s
1 tree, 20 leaves, max depth = 8, train loss: 0.46089, val loss: 0.44883, in 0.016s
1 tree, 32 leaves, max depth = 11, train loss: 0.45502, val loss: 0.44255, in 0.016s
1 tree, 28 leaves, max depth = 7, train loss: 0.44991, val loss: 0.43733, in 0.016s
1 tree, 34 leaves, max depth = 10, train loss: 0.44468, val loss: 0.43174, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.43985, val loss: 0.42659, in 0.016s
1 tree, 33 leaves, max depth = 14, train loss: 0.43490, val loss: 0.42139, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.43052, val loss: 0.41686, in 0.016s
1 tree, 29 leaves, max depth = 10, train loss: 0.42647, val loss: 0.41265, in 0.031s
1 tree, 13 leaves, max depth = 5, train loss: 0.41927, val loss: 0.40547, in 0.000s
1 tree, 30 leaves, max depth = 12, train loss: 0.41504, val loss: 0.40092, in 0.016s
1 tree, 33 leaves, max depth = 11, train loss: 0.41016, val loss: 0.39594, in 0.031s
1 tree, 15 leaves, max depth = 6, train loss: 0.40343, val loss: 0.38932, in 0.016s
1 tree, 36 leaves, max depth = 10, train loss: 0.39988, val loss: 0.38561, in 0.016s
1 tree, 29 leaves, max depth = 9, train loss: 0.39546, val loss: 0.38109, in 0.016s
1 tree, 39 leaves, max depth = 11, train loss: 0.39123, val loss: 0.37682, in 0.016s
1 tree, 8 leaves, max depth = 5, train loss: 0.38795, val loss: 0.37348, in 0.016s
1 tree, 36 leaves, max depth = 10, train loss: 0.38404, val loss: 0.36961, in 0.000s
1 tree, 6 leaves, max depth = 4, train loss: 0.37901, val loss: 0.36439, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.37428, val loss: 0.35939, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.37067, val loss: 0.35579, in 0.016s
1 tree, 6 leaves, max depth = 3, train loss: 0.36582, val loss: 0.35085, in 0.016s
1 tree, 9 leaves, max depth = 5, train loss: 0.36116, val loss: 0.34613, in 0.016s
1 tree, 30 leaves, max depth = 8, train loss: 0.35856, val loss: 0.34337, in 0.016s
1 tree, 34 leaves, max depth = 11, train loss: 0.35631, val loss: 0.34113, in 0.016s
1 tree, 18 leaves, max depth = 6, train loss: 0.35422, val loss: 0.33906, in 0.016s
1 tree, 39 leaves, max depth = 10, train loss: 0.35208, val loss: 0.33690, in 0.031s
1 tree, 4 leaves, max depth = 2, train loss: 0.34805, val loss: 0.33322, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.34412, val loss: 0.32917, in 0.000s
1 tree, 7 leaves, max depth = 3, train loss: 0.34001, val loss: 0.32521, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.33656, val loss: 0.32173, in 0.016s
1 tree, 29 leaves, max depth = 13, train loss: 0.33465, val loss: 0.31966, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.33143, val loss: 0.31642, in 0.016s
1 tree, 36 leaves, max depth = 11, train loss: 0.32865, val loss: 0.31374, in 0.016s
1 tree, 34 leaves, max depth = 7, train loss: 0.32696, val loss: 0.31193, in 0.016s
1 tree, 28 leaves, max depth = 11, train loss: 0.32434, val loss: 0.30938, in 0.016s
1 tree, 33 leaves, max depth = 13, train loss: 0.32242, val loss: 0.30746, in 0.031s
1 tree, 34 leaves, max depth = 10, train loss: 0.32029, val loss: 0.30527, in 0.016s
Fit 58 trees in 1.298 s, (1422 total leaves)
Time spent computing histograms: 0.411s
Time spent finding best splits:  0.044s
Time spent applying splits:      0.035s
Time spent predicting:           0.000s
Trial 95, Fold 5: Log loss = 0.3282869013454593, Average precision = 0.9549812129532772, ROC-AUC = 0.9502090927670328, Elapsed Time = 1.3063646999999037 seconds
Optimization Progress:  96%|#########6| 96/100 [20:00<00:53, 13.40s/it]
Trial 96, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 96, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
0.142 s 0.040 GB of training data: 
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 101 leaves, max depth = 15, train loss: 0.68483, val loss: 0.68463, in 0.016s
1 tree, 103 leaves, max depth = 17, train loss: 0.67663, val loss: 0.67620, in 0.016s
1 tree, 103 leaves, max depth = 17, train loss: 0.66871, val loss: 0.66805, in 0.016s
1 tree, 106 leaves, max depth = 14, train loss: 0.66116, val loss: 0.66033, in 0.000s
1 tree, 105 leaves, max depth = 19, train loss: 0.65386, val loss: 0.65283, in 0.016s
1 tree, 105 leaves, max depth = 19, train loss: 0.64681, val loss: 0.64558, in 0.016s
1 tree, 104 leaves, max depth = 21, train loss: 0.64007, val loss: 0.63868, in 0.016s
1 tree, 103 leaves, max depth = 19, train loss: 0.63356, val loss: 0.63201, in 0.016s
1 tree, 105 leaves, max depth = 19, train loss: 0.62719, val loss: 0.62545, in 0.016s
1 tree, 105 leaves, max depth = 18, train loss: 0.62095, val loss: 0.61901, in 0.016s
1 tree, 104 leaves, max depth = 18, train loss: 0.61505, val loss: 0.61289, in 0.016s
1 tree, 105 leaves, max depth = 19, train loss: 0.60928, val loss: 0.60695, in 0.000s
1 tree, 105 leaves, max depth = 19, train loss: 0.60371, val loss: 0.60120, in 0.016s
1 tree, 104 leaves, max depth = 18, train loss: 0.59824, val loss: 0.59554, in 0.016s
1 tree, 105 leaves, max depth = 19, train loss: 0.59302, val loss: 0.59015, in 0.016s
1 tree, 105 leaves, max depth = 19, train loss: 0.58797, val loss: 0.58494, in 0.016s
1 tree, 105 leaves, max depth = 18, train loss: 0.58300, val loss: 0.57979, in 0.016s
1 tree, 105 leaves, max depth = 16, train loss: 0.57827, val loss: 0.57493, in 0.000s
1 tree, 103 leaves, max depth = 20, train loss: 0.57372, val loss: 0.57026, in 0.016s
1 tree, 105 leaves, max depth = 18, train loss: 0.56920, val loss: 0.56557, in 0.016s
1 tree, 105 leaves, max depth = 18, train loss: 0.56483, val loss: 0.56102, in 0.016s
1 tree, 105 leaves, max depth = 18, train loss: 0.56059, val loss: 0.55661, in 0.016s
1 tree, 105 leaves, max depth = 18, train loss: 0.55648, val loss: 0.55233, in 0.016s
1 tree, 105 leaves, max depth = 18, train loss: 0.55250, val loss: 0.54818, in 0.016s
1 tree, 105 leaves, max depth = 20, train loss: 0.54870, val loss: 0.54425, in 0.016s
1 tree, 163 leaves, max depth = 16, train loss: 0.54448, val loss: 0.54029, in 0.016s
1 tree, 163 leaves, max depth = 16, train loss: 0.54039, val loss: 0.53647, in 0.031s
1 tree, 106 leaves, max depth = 17, train loss: 0.53684, val loss: 0.53276, in 0.000s
1 tree, 163 leaves, max depth = 16, train loss: 0.53292, val loss: 0.52909, in 0.016s
1 tree, 163 leaves, max depth = 16, train loss: 0.52913, val loss: 0.52555, in 0.016s
1 tree, 104 leaves, max depth = 16, train loss: 0.52578, val loss: 0.52210, in 0.016s
1 tree, 106 leaves, max depth = 16, train loss: 0.52253, val loss: 0.51869, in 0.016s
1 tree, 105 leaves, max depth = 20, train loss: 0.51931, val loss: 0.51530, in 0.016s
1 tree, 163 leaves, max depth = 16, train loss: 0.51573, val loss: 0.51197, in 0.031s
1 tree, 163 leaves, max depth = 16, train loss: 0.51227, val loss: 0.50875, in 0.016s
1 tree, 104 leaves, max depth = 19, train loss: 0.50928, val loss: 0.50562, in 0.016s
1 tree, 105 leaves, max depth = 20, train loss: 0.50632, val loss: 0.50251, in 0.016s
1 tree, 163 leaves, max depth = 16, train loss: 0.50302, val loss: 0.49945, in 0.031s
1 tree, 105 leaves, max depth = 19, train loss: 0.50025, val loss: 0.49654, in 0.000s
1 tree, 106 leaves, max depth = 17, train loss: 0.49757, val loss: 0.49371, in 0.000s
1 tree, 106 leaves, max depth = 21, train loss: 0.49495, val loss: 0.49101, in 0.016s
1 tree, 163 leaves, max depth = 16, train loss: 0.49183, val loss: 0.48812, in 0.031s
1 tree, 105 leaves, max depth = 19, train loss: 0.48933, val loss: 0.48550, in 0.016s
1 tree, 163 leaves, max depth = 16, train loss: 0.48632, val loss: 0.48271, in 0.031s
1 tree, 163 leaves, max depth = 16, train loss: 0.48340, val loss: 0.48001, in 0.016s
1 tree, 163 leaves, max depth = 16, train loss: 0.48057, val loss: 0.47739, in 0.016s
1 tree, 163 leaves, max depth = 16, train loss: 0.47782, val loss: 0.47485, in 0.016s
1 tree, 105 leaves, max depth = 17, train loss: 0.47546, val loss: 0.47234, in 0.016s
1 tree, 163 leaves, max depth = 16, train loss: 0.47281, val loss: 0.46989, in 0.031s
1 tree, 163 leaves, max depth = 16, train loss: 0.47024, val loss: 0.46752, in 0.016s
1 tree, 105 leaves, max depth = 17, train loss: 0.46805, val loss: 0.46519, in 0.016s
1 tree, 163 leaves, max depth = 16, train loss: 0.46557, val loss: 0.46291, in 0.016s
1 tree, 163 leaves, max depth = 16, train loss: 0.46316, val loss: 0.46069, in 0.031s
1 tree, 105 leaves, max depth = 17, train loss: 0.46102, val loss: 0.45841, in 0.016s
1 tree, 163 leaves, max depth = 16, train loss: 0.45870, val loss: 0.45628, in 0.016s
1 tree, 163 leaves, max depth = 16, train loss: 0.45644, val loss: 0.45421, in 0.031s
1 tree, 163 leaves, max depth = 16, train loss: 0.45424, val loss: 0.45220, in 0.016s
1 tree, 106 leaves, max depth = 14, train loss: 0.45228, val loss: 0.45009, in 0.016s
1 tree, 163 leaves, max depth = 16, train loss: 0.45016, val loss: 0.44816, in 0.031s
1 tree, 104 leaves, max depth = 18, train loss: 0.44821, val loss: 0.44611, in 0.000s
1 tree, 106 leaves, max depth = 14, train loss: 0.44638, val loss: 0.44414, in 0.016s
1 tree, 163 leaves, max depth = 16, train loss: 0.44434, val loss: 0.44229, in 0.031s
1 tree, 163 leaves, max depth = 16, train loss: 0.44236, val loss: 0.44049, in 0.016s
1 tree, 104 leaves, max depth = 20, train loss: 0.44062, val loss: 0.43867, in 0.016s
1 tree, 163 leaves, max depth = 16, train loss: 0.43871, val loss: 0.43693, in 0.016s
1 tree, 103 leaves, max depth = 14, train loss: 0.43697, val loss: 0.43508, in 0.016s
1 tree, 106 leaves, max depth = 14, train loss: 0.43533, val loss: 0.43331, in 0.018s
1 tree, 104 leaves, max depth = 17, train loss: 0.43369, val loss: 0.43158, in 0.014s
1 tree, 163 leaves, max depth = 16, train loss: 0.43186, val loss: 0.42992, in 0.016s
1 tree, 163 leaves, max depth = 16, train loss: 0.43008, val loss: 0.42832, in 0.031s
1 tree, 163 leaves, max depth = 16, train loss: 0.42835, val loss: 0.42675, in 0.016s
1 tree, 163 leaves, max depth = 16, train loss: 0.42667, val loss: 0.42523, in 0.016s
1 tree, 106 leaves, max depth = 14, train loss: 0.42517, val loss: 0.42360, in 0.016s
1 tree, 104 leaves, max depth = 21, train loss: 0.42372, val loss: 0.42209, in 0.016s
1 tree, 104 leaves, max depth = 16, train loss: 0.42230, val loss: 0.42057, in 0.016s
1 tree, 105 leaves, max depth = 14, train loss: 0.42093, val loss: 0.41907, in 0.016s
1 tree, 105 leaves, max depth = 17, train loss: 0.41955, val loss: 0.41757, in 0.016s
1 tree, 104 leaves, max depth = 17, train loss: 0.41821, val loss: 0.41615, in 0.016s
1 tree, 104 leaves, max depth = 14, train loss: 0.41690, val loss: 0.41475, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.41565, val loss: 0.41337, in 0.016s
1 tree, 105 leaves, max depth = 17, train loss: 0.41439, val loss: 0.41200, in 0.016s
1 tree, 163 leaves, max depth = 16, train loss: 0.41280, val loss: 0.41058, in 0.016s
1 tree, 105 leaves, max depth = 17, train loss: 0.41159, val loss: 0.40925, in 0.016s
1 tree, 163 leaves, max depth = 16, train loss: 0.41005, val loss: 0.40788, in 0.016s
1 tree, 163 leaves, max depth = 16, train loss: 0.40856, val loss: 0.40655, in 0.031s
1 tree, 5 leaves, max depth = 3, train loss: 0.40737, val loss: 0.40524, in 0.000s
1 tree, 163 leaves, max depth = 16, train loss: 0.40592, val loss: 0.40395, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.40476, val loss: 0.40268, in 0.016s
1 tree, 105 leaves, max depth = 18, train loss: 0.40362, val loss: 0.40143, in 0.016s
1 tree, 163 leaves, max depth = 16, train loss: 0.40222, val loss: 0.40018, in 0.016s
1 tree, 105 leaves, max depth = 18, train loss: 0.40112, val loss: 0.39897, in 0.016s
Fit 91 trees in 1.799 s, (11094 total leaves)
Time spent computing histograms: 0.554s
Time spent finding best splits:  0.170s
Time spent applying splits:      0.207s
Time spent predicting:           0.000s
Trial 96, Fold 1: Log loss = 0.40533120527321725, Average precision = 0.9457526290398302, ROC-AUC = 0.9421818317058832, Elapsed Time = 1.8046297000000777 seconds
Trial 96, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 96, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
Binning 0.040 GB of training data: 0.158 s
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 105 leaves, max depth = 18, train loss: 0.68479, val loss: 0.68447, in 0.016s
1 tree, 105 leaves, max depth = 15, train loss: 0.67669, val loss: 0.67601, in 0.016s
1 tree, 105 leaves, max depth = 15, train loss: 0.66888, val loss: 0.66784, in 0.016s
1 tree, 105 leaves, max depth = 15, train loss: 0.66134, val loss: 0.65995, in 0.016s
1 tree, 105 leaves, max depth = 17, train loss: 0.65408, val loss: 0.65235, in 0.016s
1 tree, 105 leaves, max depth = 17, train loss: 0.64707, val loss: 0.64501, in 0.016s
1 tree, 105 leaves, max depth = 20, train loss: 0.64030, val loss: 0.63796, in 0.016s
1 tree, 104 leaves, max depth = 21, train loss: 0.63376, val loss: 0.63114, in 0.016s
1 tree, 8 leaves, max depth = 6, train loss: 0.62743, val loss: 0.62449, in 0.000s
1 tree, 105 leaves, max depth = 14, train loss: 0.62123, val loss: 0.61798, in 0.016s
1 tree, 104 leaves, max depth = 16, train loss: 0.61531, val loss: 0.61177, in 0.016s
1 tree, 106 leaves, max depth = 15, train loss: 0.60956, val loss: 0.60573, in 0.016s
1 tree, 104 leaves, max depth = 16, train loss: 0.60402, val loss: 0.59991, in 0.016s
1 tree, 106 leaves, max depth = 14, train loss: 0.59858, val loss: 0.59419, in 0.016s
1 tree, 104 leaves, max depth = 19, train loss: 0.59340, val loss: 0.58877, in 0.016s
1 tree, 106 leaves, max depth = 17, train loss: 0.58837, val loss: 0.58353, in 0.016s
1 tree, 106 leaves, max depth = 14, train loss: 0.58342, val loss: 0.57832, in 0.016s
1 tree, 106 leaves, max depth = 15, train loss: 0.57869, val loss: 0.57334, in 0.016s
1 tree, 104 leaves, max depth = 23, train loss: 0.57413, val loss: 0.56857, in 0.016s
1 tree, 106 leaves, max depth = 14, train loss: 0.56963, val loss: 0.56383, in 0.016s
1 tree, 106 leaves, max depth = 14, train loss: 0.56528, val loss: 0.55923, in 0.016s
1 tree, 106 leaves, max depth = 14, train loss: 0.56106, val loss: 0.55478, in 0.016s
1 tree, 106 leaves, max depth = 14, train loss: 0.55697, val loss: 0.55046, in 0.016s
1 tree, 106 leaves, max depth = 14, train loss: 0.55300, val loss: 0.54628, in 0.016s
1 tree, 106 leaves, max depth = 15, train loss: 0.54921, val loss: 0.54227, in 0.016s
1 tree, 161 leaves, max depth = 17, train loss: 0.54499, val loss: 0.53820, in 0.016s
1 tree, 161 leaves, max depth = 17, train loss: 0.54089, val loss: 0.53427, in 0.031s
1 tree, 104 leaves, max depth = 17, train loss: 0.53734, val loss: 0.53051, in 0.016s
1 tree, 161 leaves, max depth = 17, train loss: 0.53342, val loss: 0.52674, in 0.016s
1 tree, 161 leaves, max depth = 17, train loss: 0.52961, val loss: 0.52309, in 0.016s
1 tree, 106 leaves, max depth = 18, train loss: 0.52626, val loss: 0.51957, in 0.016s
1 tree, 104 leaves, max depth = 17, train loss: 0.52301, val loss: 0.51613, in 0.031s
1 tree, 106 leaves, max depth = 14, train loss: 0.51980, val loss: 0.51272, in 0.016s
1 tree, 161 leaves, max depth = 16, train loss: 0.51622, val loss: 0.50930, in 0.016s
1 tree, 161 leaves, max depth = 16, train loss: 0.51275, val loss: 0.50598, in 0.016s
1 tree, 106 leaves, max depth = 15, train loss: 0.50977, val loss: 0.50281, in 0.016s
1 tree, 105 leaves, max depth = 14, train loss: 0.50683, val loss: 0.49968, in 0.016s
1 tree, 161 leaves, max depth = 16, train loss: 0.50352, val loss: 0.49652, in 0.016s
1 tree, 106 leaves, max depth = 15, train loss: 0.50075, val loss: 0.49358, in 0.016s
1 tree, 105 leaves, max depth = 16, train loss: 0.49807, val loss: 0.49073, in 0.016s
1 tree, 106 leaves, max depth = 15, train loss: 0.49547, val loss: 0.48795, in 0.016s
1 tree, 161 leaves, max depth = 17, train loss: 0.49234, val loss: 0.48496, in 0.016s
1 tree, 106 leaves, max depth = 15, train loss: 0.48984, val loss: 0.48230, in 0.016s
1 tree, 161 leaves, max depth = 17, train loss: 0.48683, val loss: 0.47943, in 0.031s
1 tree, 161 leaves, max depth = 17, train loss: 0.48391, val loss: 0.47665, in 0.016s
1 tree, 161 leaves, max depth = 17, train loss: 0.48107, val loss: 0.47394, in 0.031s
1 tree, 161 leaves, max depth = 17, train loss: 0.47832, val loss: 0.47132, in 0.016s
1 tree, 106 leaves, max depth = 15, train loss: 0.47597, val loss: 0.46882, in 0.016s
1 tree, 161 leaves, max depth = 16, train loss: 0.47331, val loss: 0.46629, in 0.016s
1 tree, 161 leaves, max depth = 16, train loss: 0.47074, val loss: 0.46384, in 0.016s
1 tree, 105 leaves, max depth = 14, train loss: 0.46855, val loss: 0.46151, in 0.016s
1 tree, 161 leaves, max depth = 16, train loss: 0.46606, val loss: 0.45915, in 0.031s
1 tree, 161 leaves, max depth = 16, train loss: 0.46365, val loss: 0.45686, in 0.016s
1 tree, 106 leaves, max depth = 14, train loss: 0.46153, val loss: 0.45459, in 0.016s
1 tree, 161 leaves, max depth = 16, train loss: 0.45920, val loss: 0.45238, in 0.016s
1 tree, 161 leaves, max depth = 16, train loss: 0.45694, val loss: 0.45024, in 0.031s
1 tree, 161 leaves, max depth = 16, train loss: 0.45474, val loss: 0.44816, in 0.016s
1 tree, 105 leaves, max depth = 14, train loss: 0.45278, val loss: 0.44607, in 0.016s
1 tree, 161 leaves, max depth = 16, train loss: 0.45066, val loss: 0.44406, in 0.016s
1 tree, 106 leaves, max depth = 13, train loss: 0.44877, val loss: 0.44204, in 0.016s
1 tree, 105 leaves, max depth = 14, train loss: 0.44694, val loss: 0.44008, in 0.016s
1 tree, 161 leaves, max depth = 16, train loss: 0.44490, val loss: 0.43816, in 0.016s
1 tree, 161 leaves, max depth = 16, train loss: 0.44292, val loss: 0.43629, in 0.031s
1 tree, 105 leaves, max depth = 20, train loss: 0.44117, val loss: 0.43444, in 0.016s
1 tree, 161 leaves, max depth = 15, train loss: 0.43925, val loss: 0.43264, in 0.016s
1 tree, 105 leaves, max depth = 14, train loss: 0.43757, val loss: 0.43084, in 0.016s
1 tree, 105 leaves, max depth = 14, train loss: 0.43594, val loss: 0.42909, in 0.016s
1 tree, 104 leaves, max depth = 21, train loss: 0.43436, val loss: 0.42742, in 0.016s
1 tree, 161 leaves, max depth = 16, train loss: 0.43252, val loss: 0.42569, in 0.016s
1 tree, 161 leaves, max depth = 16, train loss: 0.43073, val loss: 0.42402, in 0.031s
1 tree, 161 leaves, max depth = 16, train loss: 0.42899, val loss: 0.42239, in 0.016s
1 tree, 161 leaves, max depth = 16, train loss: 0.42730, val loss: 0.42081, in 0.031s
1 tree, 105 leaves, max depth = 15, train loss: 0.42581, val loss: 0.41921, in 0.016s
1 tree, 105 leaves, max depth = 19, train loss: 0.42436, val loss: 0.41768, in 0.016s
1 tree, 105 leaves, max depth = 15, train loss: 0.42295, val loss: 0.41617, in 0.000s
1 tree, 105 leaves, max depth = 15, train loss: 0.42159, val loss: 0.41471, in 0.031s
1 tree, 106 leaves, max depth = 17, train loss: 0.42023, val loss: 0.41324, in 0.016s
1 tree, 106 leaves, max depth = 13, train loss: 0.41894, val loss: 0.41185, in 0.000s
1 tree, 106 leaves, max depth = 13, train loss: 0.41770, val loss: 0.41051, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.41648, val loss: 0.40924, in 0.016s
1 tree, 106 leaves, max depth = 18, train loss: 0.41524, val loss: 0.40790, in 0.016s
1 tree, 161 leaves, max depth = 15, train loss: 0.41364, val loss: 0.40642, in 0.016s
1 tree, 106 leaves, max depth = 18, train loss: 0.41245, val loss: 0.40512, in 0.016s
1 tree, 161 leaves, max depth = 15, train loss: 0.41089, val loss: 0.40368, in 0.016s
1 tree, 161 leaves, max depth = 15, train loss: 0.40939, val loss: 0.40228, in 0.031s
1 tree, 5 leaves, max depth = 3, train loss: 0.40822, val loss: 0.40108, in 0.016s
1 tree, 160 leaves, max depth = 15, train loss: 0.40676, val loss: 0.39972, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.40563, val loss: 0.39855, in 0.016s
1 tree, 105 leaves, max depth = 13, train loss: 0.40452, val loss: 0.39736, in 0.016s
1 tree, 160 leaves, max depth = 15, train loss: 0.40310, val loss: 0.39604, in 0.016s
1 tree, 105 leaves, max depth = 18, train loss: 0.40198, val loss: 0.39488, in 0.031s
Fit 91 trees in 1.877 s, (10965 total leaves)
Time spent computing histograms: 0.573s
Time spent finding best splits:  0.171s
Time spent applying splits:      0.208s
Time spent predicting:           0.000s
Trial 96, Fold 2: Log loss = 0.4061720059908957, Average precision = 0.9421895019687312, ROC-AUC = 0.9427703713055864, Elapsed Time = 1.8791829000001599 seconds
Trial 96, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 96, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
Binning 0.040 GB of training data: 0.172 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 99 leaves, max depth = 15, train loss: 0.68486, val loss: 0.68458, in 0.016s
1 tree, 101 leaves, max depth = 13, train loss: 0.67682, val loss: 0.67626, in 0.016s
1 tree, 101 leaves, max depth = 13, train loss: 0.66905, val loss: 0.66824, in 0.016s
1 tree, 101 leaves, max depth = 13, train loss: 0.66155, val loss: 0.66049, in 0.016s
1 tree, 102 leaves, max depth = 16, train loss: 0.65433, val loss: 0.65307, in 0.016s
1 tree, 102 leaves, max depth = 16, train loss: 0.64736, val loss: 0.64591, in 0.016s
1 tree, 100 leaves, max depth = 15, train loss: 0.64065, val loss: 0.63895, in 0.016s
1 tree, 103 leaves, max depth = 19, train loss: 0.63417, val loss: 0.63224, in 0.016s
1 tree, 104 leaves, max depth = 15, train loss: 0.62786, val loss: 0.62569, in 0.016s
1 tree, 103 leaves, max depth = 14, train loss: 0.62168, val loss: 0.61932, in 0.016s
1 tree, 102 leaves, max depth = 20, train loss: 0.61582, val loss: 0.61326, in 0.016s
1 tree, 104 leaves, max depth = 14, train loss: 0.61011, val loss: 0.60733, in 0.016s
1 tree, 103 leaves, max depth = 16, train loss: 0.60460, val loss: 0.60167, in 0.016s
1 tree, 105 leaves, max depth = 14, train loss: 0.59917, val loss: 0.59608, in 0.016s
1 tree, 102 leaves, max depth = 20, train loss: 0.59404, val loss: 0.59076, in 0.016s
1 tree, 105 leaves, max depth = 14, train loss: 0.58903, val loss: 0.58553, in 0.016s
1 tree, 104 leaves, max depth = 14, train loss: 0.58411, val loss: 0.58045, in 0.016s
1 tree, 103 leaves, max depth = 14, train loss: 0.57941, val loss: 0.57557, in 0.000s
1 tree, 103 leaves, max depth = 20, train loss: 0.57489, val loss: 0.57087, in 0.031s
1 tree, 104 leaves, max depth = 14, train loss: 0.57041, val loss: 0.56624, in 0.016s
1 tree, 104 leaves, max depth = 14, train loss: 0.56607, val loss: 0.56176, in 0.016s
1 tree, 104 leaves, max depth = 14, train loss: 0.56187, val loss: 0.55742, in 0.016s
1 tree, 163 leaves, max depth = 18, train loss: 0.55739, val loss: 0.55329, in 0.016s
1 tree, 104 leaves, max depth = 14, train loss: 0.55338, val loss: 0.54914, in 0.016s
1 tree, 105 leaves, max depth = 15, train loss: 0.54955, val loss: 0.54515, in 0.016s
1 tree, 162 leaves, max depth = 18, train loss: 0.54531, val loss: 0.54125, in 0.031s
1 tree, 162 leaves, max depth = 18, train loss: 0.54121, val loss: 0.53748, in 0.016s
1 tree, 105 leaves, max depth = 16, train loss: 0.53761, val loss: 0.53376, in 0.016s
1 tree, 163 leaves, max depth = 18, train loss: 0.53368, val loss: 0.53015, in 0.016s
1 tree, 163 leaves, max depth = 18, train loss: 0.52987, val loss: 0.52665, in 0.032s
1 tree, 100 leaves, max depth = 15, train loss: 0.52649, val loss: 0.52312, in 0.016s
1 tree, 105 leaves, max depth = 16, train loss: 0.52321, val loss: 0.51972, in 0.016s
1 tree, 104 leaves, max depth = 14, train loss: 0.51996, val loss: 0.51634, in 0.016s
1 tree, 163 leaves, max depth = 18, train loss: 0.51637, val loss: 0.51305, in 0.016s
1 tree, 163 leaves, max depth = 18, train loss: 0.51289, val loss: 0.50987, in 0.031s
1 tree, 104 leaves, max depth = 15, train loss: 0.50989, val loss: 0.50671, in 0.016s
1 tree, 105 leaves, max depth = 14, train loss: 0.50692, val loss: 0.50361, in 0.016s
1 tree, 162 leaves, max depth = 18, train loss: 0.50360, val loss: 0.50058, in 0.016s
1 tree, 104 leaves, max depth = 15, train loss: 0.50081, val loss: 0.49764, in 0.031s
1 tree, 105 leaves, max depth = 16, train loss: 0.49810, val loss: 0.49482, in 0.016s
1 tree, 104 leaves, max depth = 15, train loss: 0.49548, val loss: 0.49204, in 0.016s
1 tree, 163 leaves, max depth = 17, train loss: 0.49234, val loss: 0.48918, in 0.016s
1 tree, 104 leaves, max depth = 15, train loss: 0.48983, val loss: 0.48652, in 0.016s
1 tree, 163 leaves, max depth = 18, train loss: 0.48681, val loss: 0.48378, in 0.031s
1 tree, 163 leaves, max depth = 18, train loss: 0.48389, val loss: 0.48111, in 0.016s
1 tree, 163 leaves, max depth = 18, train loss: 0.48104, val loss: 0.47853, in 0.016s
1 tree, 163 leaves, max depth = 18, train loss: 0.47828, val loss: 0.47602, in 0.031s
1 tree, 105 leaves, max depth = 14, train loss: 0.47591, val loss: 0.47353, in 0.016s
1 tree, 163 leaves, max depth = 18, train loss: 0.47325, val loss: 0.47112, in 0.031s
1 tree, 163 leaves, max depth = 18, train loss: 0.47067, val loss: 0.46878, in 0.016s
1 tree, 104 leaves, max depth = 15, train loss: 0.46846, val loss: 0.46647, in 0.016s
1 tree, 162 leaves, max depth = 18, train loss: 0.46596, val loss: 0.46421, in 0.031s
1 tree, 162 leaves, max depth = 18, train loss: 0.46354, val loss: 0.46203, in 0.016s
1 tree, 105 leaves, max depth = 14, train loss: 0.46141, val loss: 0.45977, in 0.016s
1 tree, 162 leaves, max depth = 15, train loss: 0.45907, val loss: 0.45766, in 0.031s
1 tree, 163 leaves, max depth = 15, train loss: 0.45680, val loss: 0.45562, in 0.016s
1 tree, 163 leaves, max depth = 18, train loss: 0.45459, val loss: 0.45364, in 0.016s
1 tree, 104 leaves, max depth = 16, train loss: 0.45261, val loss: 0.45156, in 0.016s
1 tree, 163 leaves, max depth = 17, train loss: 0.45048, val loss: 0.44965, in 0.031s
1 tree, 105 leaves, max depth = 15, train loss: 0.44859, val loss: 0.44761, in 0.016s
1 tree, 105 leaves, max depth = 18, train loss: 0.44674, val loss: 0.44567, in 0.016s
1 tree, 162 leaves, max depth = 16, train loss: 0.44469, val loss: 0.44383, in 0.016s
1 tree, 162 leaves, max depth = 16, train loss: 0.44270, val loss: 0.44205, in 0.031s
1 tree, 105 leaves, max depth = 15, train loss: 0.44095, val loss: 0.44016, in 0.016s
1 tree, 163 leaves, max depth = 16, train loss: 0.43903, val loss: 0.43845, in 0.031s
1 tree, 105 leaves, max depth = 15, train loss: 0.43735, val loss: 0.43663, in 0.016s
1 tree, 105 leaves, max depth = 15, train loss: 0.43571, val loss: 0.43486, in 0.016s
1 tree, 104 leaves, max depth = 14, train loss: 0.43407, val loss: 0.43310, in 0.016s
1 tree, 162 leaves, max depth = 16, train loss: 0.43222, val loss: 0.43146, in 0.016s
1 tree, 162 leaves, max depth = 16, train loss: 0.43043, val loss: 0.42986, in 0.031s
1 tree, 162 leaves, max depth = 16, train loss: 0.42869, val loss: 0.42832, in 0.031s
1 tree, 163 leaves, max depth = 16, train loss: 0.42700, val loss: 0.42682, in 0.016s
1 tree, 104 leaves, max depth = 15, train loss: 0.42551, val loss: 0.42520, in 0.016s
1 tree, 104 leaves, max depth = 16, train loss: 0.42406, val loss: 0.42364, in 0.016s
1 tree, 103 leaves, max depth = 15, train loss: 0.42266, val loss: 0.42211, in 0.031s
1 tree, 102 leaves, max depth = 13, train loss: 0.42128, val loss: 0.42062, in 0.000s
1 tree, 5 leaves, max depth = 3, train loss: 0.42000, val loss: 0.41943, in 0.000s
1 tree, 104 leaves, max depth = 13, train loss: 0.41863, val loss: 0.41795, in 0.016s
1 tree, 104 leaves, max depth = 15, train loss: 0.41735, val loss: 0.41656, in 0.031s
1 tree, 5 leaves, max depth = 3, train loss: 0.41612, val loss: 0.41541, in 0.000s
1 tree, 104 leaves, max depth = 13, train loss: 0.41483, val loss: 0.41403, in 0.016s
1 tree, 162 leaves, max depth = 16, train loss: 0.41323, val loss: 0.41262, in 0.031s
1 tree, 105 leaves, max depth = 14, train loss: 0.41200, val loss: 0.41129, in 0.016s
1 tree, 162 leaves, max depth = 16, train loss: 0.41045, val loss: 0.40993, in 0.016s
1 tree, 162 leaves, max depth = 16, train loss: 0.40895, val loss: 0.40861, in 0.031s
1 tree, 5 leaves, max depth = 3, train loss: 0.40777, val loss: 0.40751, in 0.000s
1 tree, 163 leaves, max depth = 16, train loss: 0.40631, val loss: 0.40623, in 0.031s
1 tree, 5 leaves, max depth = 3, train loss: 0.40517, val loss: 0.40517, in 0.000s
1 tree, 104 leaves, max depth = 13, train loss: 0.40404, val loss: 0.40394, in 0.016s
1 tree, 163 leaves, max depth = 16, train loss: 0.40262, val loss: 0.40271, in 0.031s
1 tree, 103 leaves, max depth = 13, train loss: 0.40150, val loss: 0.40149, in 0.016s
Fit 91 trees in 1.986 s, (10977 total leaves)
Time spent computing histograms: 0.635s
Time spent finding best splits:  0.181s
Time spent applying splits:      0.218s
Time spent predicting:           0.016s
Trial 96, Fold 3: Log loss = 0.4005417862920448, Average precision = 0.9481568863042917, ROC-AUC = 0.9469454118952915, Elapsed Time = 1.9903591000002052 seconds
Trial 96, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 96, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
Binning 0.040 GB of training data: 0.142 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 96 leaves, max depth = 16, train loss: 0.68486, val loss: 0.68446, in 0.016s
1 tree, 99 leaves, max depth = 19, train loss: 0.67674, val loss: 0.67586, in 0.000s
1 tree, 99 leaves, max depth = 19, train loss: 0.66890, val loss: 0.66754, in 0.031s
1 tree, 103 leaves, max depth = 19, train loss: 0.66138, val loss: 0.65964, in 0.016s
1 tree, 103 leaves, max depth = 13, train loss: 0.65413, val loss: 0.65198, in 0.016s
1 tree, 102 leaves, max depth = 19, train loss: 0.64716, val loss: 0.64458, in 0.016s
1 tree, 102 leaves, max depth = 15, train loss: 0.64044, val loss: 0.63746, in 0.016s
1 tree, 102 leaves, max depth = 15, train loss: 0.63394, val loss: 0.63057, in 0.016s
1 tree, 103 leaves, max depth = 13, train loss: 0.62762, val loss: 0.62386, in 0.016s
1 tree, 104 leaves, max depth = 19, train loss: 0.62144, val loss: 0.61727, in 0.016s
1 tree, 103 leaves, max depth = 19, train loss: 0.61556, val loss: 0.61098, in 0.016s
1 tree, 103 leaves, max depth = 19, train loss: 0.60981, val loss: 0.60492, in 0.016s
1 tree, 104 leaves, max depth = 13, train loss: 0.60428, val loss: 0.59902, in 0.016s
1 tree, 103 leaves, max depth = 19, train loss: 0.59887, val loss: 0.59321, in 0.016s
1 tree, 103 leaves, max depth = 13, train loss: 0.59368, val loss: 0.58768, in 0.016s
1 tree, 103 leaves, max depth = 13, train loss: 0.58867, val loss: 0.58231, in 0.016s
1 tree, 103 leaves, max depth = 19, train loss: 0.58376, val loss: 0.57702, in 0.016s
1 tree, 103 leaves, max depth = 18, train loss: 0.57903, val loss: 0.57201, in 0.016s
1 tree, 104 leaves, max depth = 14, train loss: 0.57450, val loss: 0.56716, in 0.016s
1 tree, 104 leaves, max depth = 20, train loss: 0.57003, val loss: 0.56233, in 0.016s
1 tree, 104 leaves, max depth = 20, train loss: 0.56571, val loss: 0.55765, in 0.016s
1 tree, 104 leaves, max depth = 20, train loss: 0.56152, val loss: 0.55310, in 0.000s
1 tree, 104 leaves, max depth = 20, train loss: 0.55746, val loss: 0.54870, in 0.031s
1 tree, 105 leaves, max depth = 20, train loss: 0.55352, val loss: 0.54443, in 0.016s
1 tree, 103 leaves, max depth = 21, train loss: 0.54974, val loss: 0.54038, in 0.016s
1 tree, 161 leaves, max depth = 16, train loss: 0.54552, val loss: 0.53628, in 0.016s
1 tree, 161 leaves, max depth = 18, train loss: 0.54143, val loss: 0.53232, in 0.016s
1 tree, 103 leaves, max depth = 21, train loss: 0.53789, val loss: 0.52848, in 0.016s
1 tree, 161 leaves, max depth = 16, train loss: 0.53397, val loss: 0.52468, in 0.031s
1 tree, 161 leaves, max depth = 16, train loss: 0.53018, val loss: 0.52100, in 0.016s
1 tree, 102 leaves, max depth = 15, train loss: 0.52685, val loss: 0.51742, in 0.016s
1 tree, 103 leaves, max depth = 20, train loss: 0.52361, val loss: 0.51390, in 0.016s
1 tree, 104 leaves, max depth = 20, train loss: 0.52042, val loss: 0.51041, in 0.031s
1 tree, 161 leaves, max depth = 16, train loss: 0.51685, val loss: 0.50696, in 0.016s
1 tree, 161 leaves, max depth = 16, train loss: 0.51339, val loss: 0.50361, in 0.031s
1 tree, 104 leaves, max depth = 21, train loss: 0.51041, val loss: 0.50040, in 0.016s
1 tree, 105 leaves, max depth = 18, train loss: 0.50750, val loss: 0.49720, in 0.016s
1 tree, 160 leaves, max depth = 18, train loss: 0.50420, val loss: 0.49401, in 0.016s
1 tree, 103 leaves, max depth = 21, train loss: 0.50144, val loss: 0.49103, in 0.016s
1 tree, 103 leaves, max depth = 22, train loss: 0.49877, val loss: 0.48810, in 0.016s
1 tree, 103 leaves, max depth = 21, train loss: 0.49616, val loss: 0.48529, in 0.016s
1 tree, 160 leaves, max depth = 18, train loss: 0.49304, val loss: 0.48228, in 0.016s
1 tree, 104 leaves, max depth = 21, train loss: 0.49055, val loss: 0.47959, in 0.031s
1 tree, 161 leaves, max depth = 18, train loss: 0.48755, val loss: 0.47670, in 0.016s
1 tree, 161 leaves, max depth = 18, train loss: 0.48464, val loss: 0.47389, in 0.016s
1 tree, 160 leaves, max depth = 18, train loss: 0.48181, val loss: 0.47117, in 0.031s
1 tree, 161 leaves, max depth = 18, train loss: 0.47907, val loss: 0.46853, in 0.016s
1 tree, 105 leaves, max depth = 15, train loss: 0.47674, val loss: 0.46594, in 0.016s
1 tree, 160 leaves, max depth = 19, train loss: 0.47410, val loss: 0.46340, in 0.031s
1 tree, 161 leaves, max depth = 19, train loss: 0.47153, val loss: 0.46093, in 0.016s
1 tree, 103 leaves, max depth = 20, train loss: 0.46935, val loss: 0.45852, in 0.016s
1 tree, 160 leaves, max depth = 19, train loss: 0.46688, val loss: 0.45614, in 0.031s
1 tree, 161 leaves, max depth = 19, train loss: 0.46447, val loss: 0.45383, in 0.016s
1 tree, 104 leaves, max depth = 15, train loss: 0.46237, val loss: 0.45149, in 0.016s
1 tree, 160 leaves, max depth = 18, train loss: 0.46006, val loss: 0.44927, in 0.031s
1 tree, 161 leaves, max depth = 18, train loss: 0.45780, val loss: 0.44710, in 0.016s
1 tree, 161 leaves, max depth = 21, train loss: 0.45561, val loss: 0.44501, in 0.031s
1 tree, 104 leaves, max depth = 14, train loss: 0.45360, val loss: 0.44281, in 0.016s
1 tree, 160 leaves, max depth = 18, train loss: 0.45149, val loss: 0.44079, in 0.016s
1 tree, 104 leaves, max depth = 14, train loss: 0.44961, val loss: 0.43871, in 0.016s
1 tree, 103 leaves, max depth = 19, train loss: 0.44778, val loss: 0.43668, in 0.031s
1 tree, 161 leaves, max depth = 16, train loss: 0.44576, val loss: 0.43474, in 0.016s
1 tree, 161 leaves, max depth = 16, train loss: 0.44379, val loss: 0.43286, in 0.031s
1 tree, 104 leaves, max depth = 14, train loss: 0.44199, val loss: 0.43089, in 0.016s
1 tree, 161 leaves, max depth = 18, train loss: 0.44009, val loss: 0.42908, in 0.016s
1 tree, 102 leaves, max depth = 17, train loss: 0.43841, val loss: 0.42720, in 0.016s
1 tree, 103 leaves, max depth = 15, train loss: 0.43672, val loss: 0.42536, in 0.016s
1 tree, 104 leaves, max depth = 16, train loss: 0.43515, val loss: 0.42360, in 0.016s
1 tree, 161 leaves, max depth = 18, train loss: 0.43333, val loss: 0.42186, in 0.031s
1 tree, 161 leaves, max depth = 18, train loss: 0.43156, val loss: 0.42018, in 0.031s
1 tree, 161 leaves, max depth = 18, train loss: 0.42985, val loss: 0.41855, in 0.016s
1 tree, 161 leaves, max depth = 18, train loss: 0.42818, val loss: 0.41696, in 0.031s
1 tree, 103 leaves, max depth = 17, train loss: 0.42668, val loss: 0.41528, in 0.016s
1 tree, 104 leaves, max depth = 15, train loss: 0.42523, val loss: 0.41367, in 0.016s
1 tree, 103 leaves, max depth = 16, train loss: 0.42382, val loss: 0.41209, in 0.016s
1 tree, 104 leaves, max depth = 15, train loss: 0.42246, val loss: 0.41057, in 0.016s
1 tree, 105 leaves, max depth = 17, train loss: 0.42111, val loss: 0.40902, in 0.047s
1 tree, 104 leaves, max depth = 14, train loss: 0.41977, val loss: 0.40755, in 0.094s
1 tree, 103 leaves, max depth = 16, train loss: 0.41852, val loss: 0.40613, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.41730, val loss: 0.40482, in 0.016s
1 tree, 105 leaves, max depth = 17, train loss: 0.41607, val loss: 0.40341, in 0.016s
1 tree, 161 leaves, max depth = 17, train loss: 0.41449, val loss: 0.40192, in 0.031s
1 tree, 105 leaves, max depth = 17, train loss: 0.41331, val loss: 0.40056, in 0.031s
1 tree, 161 leaves, max depth = 17, train loss: 0.41178, val loss: 0.39911, in 0.016s
1 tree, 161 leaves, max depth = 17, train loss: 0.41029, val loss: 0.39771, in 0.031s
1 tree, 5 leaves, max depth = 3, train loss: 0.40913, val loss: 0.39646, in 0.016s
1 tree, 161 leaves, max depth = 17, train loss: 0.40769, val loss: 0.39510, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.40656, val loss: 0.39389, in 0.016s
1 tree, 105 leaves, max depth = 17, train loss: 0.40545, val loss: 0.39261, in 0.016s
1 tree, 161 leaves, max depth = 20, train loss: 0.40405, val loss: 0.39129, in 0.016s
1 tree, 105 leaves, max depth = 16, train loss: 0.40298, val loss: 0.39005, in 0.016s
Fit 91 trees in 2.126 s, (10942 total leaves)
Time spent computing histograms: 0.681s
Time spent finding best splits:  0.209s
Time spent applying splits:      0.268s
Time spent predicting:           0.031s
Trial 96, Fold 4: Log loss = 0.40511379728362673, Average precision = 0.9467197122543686, ROC-AUC = 0.9438916339406427, Elapsed Time = 2.1346135000003414 seconds
Trial 96, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 96, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
Binning 0.040 GB of training data: 0.158 s
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 102 leaves, max depth = 13, train loss: 0.68473, val loss: 0.68423, in 0.016s
1 tree, 99 leaves, max depth = 16, train loss: 0.67653, val loss: 0.67551, in 0.016s
1 tree, 99 leaves, max depth = 16, train loss: 0.66861, val loss: 0.66709, in 0.016s
1 tree, 101 leaves, max depth = 14, train loss: 0.66105, val loss: 0.65905, in 0.016s
1 tree, 105 leaves, max depth = 17, train loss: 0.65374, val loss: 0.65127, in 0.016s
1 tree, 105 leaves, max depth = 17, train loss: 0.64667, val loss: 0.64375, in 0.016s
1 tree, 99 leaves, max depth = 15, train loss: 0.63986, val loss: 0.63650, in 0.016s
1 tree, 103 leaves, max depth = 18, train loss: 0.63328, val loss: 0.62949, in 0.016s
1 tree, 104 leaves, max depth = 15, train loss: 0.62689, val loss: 0.62263, in 0.000s
1 tree, 104 leaves, max depth = 16, train loss: 0.62065, val loss: 0.61595, in 0.031s
1 tree, 105 leaves, max depth = 17, train loss: 0.61469, val loss: 0.60952, in 0.016s
1 tree, 104 leaves, max depth = 15, train loss: 0.60891, val loss: 0.60330, in 0.016s
1 tree, 105 leaves, max depth = 17, train loss: 0.60333, val loss: 0.59727, in 0.016s
1 tree, 104 leaves, max depth = 16, train loss: 0.59785, val loss: 0.59138, in 0.016s
1 tree, 99 leaves, max depth = 20, train loss: 0.59263, val loss: 0.58579, in 0.000s
1 tree, 104 leaves, max depth = 15, train loss: 0.58756, val loss: 0.58030, in 0.031s
1 tree, 105 leaves, max depth = 16, train loss: 0.58258, val loss: 0.57494, in 0.016s
1 tree, 105 leaves, max depth = 17, train loss: 0.57783, val loss: 0.56982, in 0.016s
1 tree, 103 leaves, max depth = 18, train loss: 0.57324, val loss: 0.56487, in 0.016s
1 tree, 105 leaves, max depth = 16, train loss: 0.56871, val loss: 0.55997, in 0.016s
1 tree, 105 leaves, max depth = 16, train loss: 0.56433, val loss: 0.55523, in 0.016s
1 tree, 105 leaves, max depth = 16, train loss: 0.56008, val loss: 0.55062, in 0.000s
1 tree, 105 leaves, max depth = 16, train loss: 0.55597, val loss: 0.54615, in 0.016s
1 tree, 105 leaves, max depth = 16, train loss: 0.55198, val loss: 0.54182, in 0.016s
1 tree, 105 leaves, max depth = 17, train loss: 0.54816, val loss: 0.53768, in 0.016s
1 tree, 158 leaves, max depth = 17, train loss: 0.54406, val loss: 0.53379, in 0.031s
1 tree, 158 leaves, max depth = 17, train loss: 0.54008, val loss: 0.53004, in 0.016s
1 tree, 105 leaves, max depth = 17, train loss: 0.53649, val loss: 0.52613, in 0.016s
1 tree, 158 leaves, max depth = 17, train loss: 0.53267, val loss: 0.52254, in 0.031s
1 tree, 158 leaves, max depth = 17, train loss: 0.52898, val loss: 0.51906, in 0.016s
1 tree, 100 leaves, max depth = 15, train loss: 0.52559, val loss: 0.51536, in 0.016s
1 tree, 105 leaves, max depth = 17, train loss: 0.52231, val loss: 0.51174, in 0.016s
1 tree, 105 leaves, max depth = 16, train loss: 0.51907, val loss: 0.50820, in 0.031s
1 tree, 159 leaves, max depth = 16, train loss: 0.51560, val loss: 0.50494, in 0.016s
1 tree, 159 leaves, max depth = 16, train loss: 0.51223, val loss: 0.50179, in 0.031s
1 tree, 103 leaves, max depth = 18, train loss: 0.50922, val loss: 0.49850, in 0.016s
1 tree, 105 leaves, max depth = 14, train loss: 0.50625, val loss: 0.49523, in 0.016s
1 tree, 159 leaves, max depth = 18, train loss: 0.50304, val loss: 0.49224, in 0.016s
1 tree, 105 leaves, max depth = 17, train loss: 0.50024, val loss: 0.48916, in 0.016s
1 tree, 105 leaves, max depth = 17, train loss: 0.49752, val loss: 0.48617, in 0.031s
1 tree, 102 leaves, max depth = 15, train loss: 0.49489, val loss: 0.48326, in 0.016s
1 tree, 159 leaves, max depth = 18, train loss: 0.49185, val loss: 0.48044, in 0.016s
1 tree, 105 leaves, max depth = 17, train loss: 0.48933, val loss: 0.47765, in 0.016s
1 tree, 158 leaves, max depth = 17, train loss: 0.48640, val loss: 0.47495, in 0.016s
1 tree, 158 leaves, max depth = 17, train loss: 0.48357, val loss: 0.47232, in 0.016s
1 tree, 159 leaves, max depth = 17, train loss: 0.48082, val loss: 0.46978, in 0.031s
1 tree, 159 leaves, max depth = 18, train loss: 0.47815, val loss: 0.46732, in 0.031s
1 tree, 105 leaves, max depth = 14, train loss: 0.47576, val loss: 0.46468, in 0.016s
1 tree, 159 leaves, max depth = 17, train loss: 0.47319, val loss: 0.46231, in 0.016s
1 tree, 159 leaves, max depth = 17, train loss: 0.47069, val loss: 0.46001, in 0.031s
1 tree, 105 leaves, max depth = 16, train loss: 0.46847, val loss: 0.45754, in 0.016s
1 tree, 159 leaves, max depth = 17, train loss: 0.46606, val loss: 0.45534, in 0.016s
1 tree, 159 leaves, max depth = 17, train loss: 0.46372, val loss: 0.45320, in 0.031s
1 tree, 105 leaves, max depth = 13, train loss: 0.46157, val loss: 0.45080, in 0.016s
1 tree, 159 leaves, max depth = 16, train loss: 0.45931, val loss: 0.44874, in 0.016s
1 tree, 159 leaves, max depth = 16, train loss: 0.45712, val loss: 0.44674, in 0.031s
1 tree, 159 leaves, max depth = 16, train loss: 0.45500, val loss: 0.44481, in 0.016s
1 tree, 105 leaves, max depth = 16, train loss: 0.45300, val loss: 0.44258, in 0.016s
1 tree, 159 leaves, max depth = 16, train loss: 0.45095, val loss: 0.44071, in 0.016s
1 tree, 103 leaves, max depth = 16, train loss: 0.44903, val loss: 0.43857, in 0.016s
1 tree, 105 leaves, max depth = 16, train loss: 0.44716, val loss: 0.43648, in 0.016s
1 tree, 158 leaves, max depth = 15, train loss: 0.44519, val loss: 0.43470, in 0.031s
1 tree, 158 leaves, max depth = 15, train loss: 0.44327, val loss: 0.43297, in 0.016s
1 tree, 99 leaves, max depth = 15, train loss: 0.44149, val loss: 0.43099, in 0.016s
1 tree, 159 leaves, max depth = 15, train loss: 0.43964, val loss: 0.42932, in 0.031s
1 tree, 105 leaves, max depth = 16, train loss: 0.43792, val loss: 0.42739, in 0.000s
1 tree, 105 leaves, max depth = 16, train loss: 0.43625, val loss: 0.42551, in 0.016s
1 tree, 103 leaves, max depth = 17, train loss: 0.43464, val loss: 0.42369, in 0.016s
1 tree, 159 leaves, max depth = 16, train loss: 0.43287, val loss: 0.42211, in 0.016s
1 tree, 159 leaves, max depth = 16, train loss: 0.43114, val loss: 0.42057, in 0.031s
1 tree, 159 leaves, max depth = 15, train loss: 0.42947, val loss: 0.41907, in 0.016s
1 tree, 159 leaves, max depth = 15, train loss: 0.42784, val loss: 0.41763, in 0.016s
1 tree, 105 leaves, max depth = 16, train loss: 0.42631, val loss: 0.41589, in 0.016s
1 tree, 100 leaves, max depth = 15, train loss: 0.42483, val loss: 0.41423, in 0.016s
1 tree, 105 leaves, max depth = 16, train loss: 0.42339, val loss: 0.41259, in 0.016s
1 tree, 105 leaves, max depth = 16, train loss: 0.42199, val loss: 0.41100, in 0.031s
1 tree, 105 leaves, max depth = 19, train loss: 0.42059, val loss: 0.40942, in 0.016s
1 tree, 101 leaves, max depth = 15, train loss: 0.41928, val loss: 0.40792, in 0.016s
1 tree, 105 leaves, max depth = 16, train loss: 0.41800, val loss: 0.40645, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.41678, val loss: 0.40527, in 0.000s
1 tree, 105 leaves, max depth = 19, train loss: 0.41550, val loss: 0.40382, in 0.016s
1 tree, 159 leaves, max depth = 17, train loss: 0.41397, val loss: 0.40247, in 0.031s
1 tree, 105 leaves, max depth = 19, train loss: 0.41274, val loss: 0.40107, in 0.016s
1 tree, 159 leaves, max depth = 19, train loss: 0.41124, val loss: 0.39977, in 0.016s
1 tree, 159 leaves, max depth = 17, train loss: 0.40980, val loss: 0.39850, in 0.031s
1 tree, 5 leaves, max depth = 3, train loss: 0.40863, val loss: 0.39738, in 0.000s
1 tree, 159 leaves, max depth = 17, train loss: 0.40723, val loss: 0.39616, in 0.016s
1 tree, 5 leaves, max depth = 3, train loss: 0.40610, val loss: 0.39508, in 0.016s
1 tree, 105 leaves, max depth = 19, train loss: 0.40494, val loss: 0.39375, in 0.016s
1 tree, 159 leaves, max depth = 19, train loss: 0.40358, val loss: 0.39258, in 0.016s
1 tree, 105 leaves, max depth = 19, train loss: 0.40246, val loss: 0.39130, in 0.016s
Fit 91 trees in 1.955 s, (10906 total leaves)
Time spent computing histograms: 0.610s
Time spent finding best splits:  0.180s
Time spent applying splits:      0.219s
Time spent predicting:           0.000s
Trial 96, Fold 5: Log loss = 0.4107916499987752, Average precision = 0.9446465371422839, ROC-AUC = 0.941807182802891, Elapsed Time = 1.9732861000011326 seconds
Optimization Progress:  97%|#########7| 97/100 [20:16<00:42, 14.27s/it]
Trial 97, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 97, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
0.142 s 0.040 GB of training data: 
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 35 leaves, max depth = 13, train loss: 0.67810, val loss: 0.67770, in 0.000s
1 tree, 35 leaves, max depth = 13, train loss: 0.66417, val loss: 0.66339, in 0.016s
1 tree, 35 leaves, max depth = 13, train loss: 0.65109, val loss: 0.64994, in 0.000s
1 tree, 31 leaves, max depth = 11, train loss: 0.63895, val loss: 0.63747, in 0.016s
1 tree, 35 leaves, max depth = 13, train loss: 0.62738, val loss: 0.62555, in 0.000s
1 tree, 35 leaves, max depth = 13, train loss: 0.61649, val loss: 0.61432, in 0.016s
1 tree, 35 leaves, max depth = 13, train loss: 0.60624, val loss: 0.60373, in 0.000s
1 tree, 35 leaves, max depth = 13, train loss: 0.59657, val loss: 0.59375, in 0.016s
1 tree, 35 leaves, max depth = 13, train loss: 0.58745, val loss: 0.58432, in 0.000s
1 tree, 35 leaves, max depth = 13, train loss: 0.57884, val loss: 0.57541, in 0.016s
1 tree, 35 leaves, max depth = 13, train loss: 0.57071, val loss: 0.56699, in 0.000s
1 tree, 34 leaves, max depth = 13, train loss: 0.56304, val loss: 0.55903, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.55586, val loss: 0.55154, in 0.000s
1 tree, 36 leaves, max depth = 13, train loss: 0.54899, val loss: 0.54440, in 0.016s
1 tree, 35 leaves, max depth = 13, train loss: 0.54249, val loss: 0.53764, in 0.000s
1 tree, 54 leaves, max depth = 11, train loss: 0.53560, val loss: 0.53120, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.52967, val loss: 0.52497, in 0.000s
1 tree, 54 leaves, max depth = 12, train loss: 0.52327, val loss: 0.51901, in 0.000s
1 tree, 35 leaves, max depth = 12, train loss: 0.51779, val loss: 0.51324, in 0.000s
1 tree, 54 leaves, max depth = 12, train loss: 0.51183, val loss: 0.50770, in 0.000s
1 tree, 54 leaves, max depth = 12, train loss: 0.50620, val loss: 0.50247, in 0.016s
1 tree, 54 leaves, max depth = 12, train loss: 0.50087, val loss: 0.49753, in 0.000s
1 tree, 32 leaves, max depth = 10, train loss: 0.49600, val loss: 0.49240, in 0.016s
1 tree, 33 leaves, max depth = 13, train loss: 0.49139, val loss: 0.48757, in 0.000s
1 tree, 56 leaves, max depth = 10, train loss: 0.48648, val loss: 0.48303, in 0.016s
1 tree, 56 leaves, max depth = 10, train loss: 0.48184, val loss: 0.47875, in 0.000s
1 tree, 56 leaves, max depth = 10, train loss: 0.47743, val loss: 0.47469, in 0.016s
1 tree, 33 leaves, max depth = 10, train loss: 0.47330, val loss: 0.47031, in 0.000s
1 tree, 56 leaves, max depth = 10, train loss: 0.46917, val loss: 0.46650, in 0.016s
1 tree, 32 leaves, max depth = 10, train loss: 0.46532, val loss: 0.46240, in 0.000s
1 tree, 31 leaves, max depth = 13, train loss: 0.46166, val loss: 0.45857, in 0.016s
1 tree, 54 leaves, max depth = 10, train loss: 0.45783, val loss: 0.45505, in 0.000s
1 tree, 32 leaves, max depth = 9, train loss: 0.45430, val loss: 0.45135, in 0.016s
1 tree, 54 leaves, max depth = 11, train loss: 0.45072, val loss: 0.44808, in 0.000s
1 tree, 54 leaves, max depth = 11, train loss: 0.44732, val loss: 0.44499, in 0.016s
1 tree, 54 leaves, max depth = 11, train loss: 0.44409, val loss: 0.44206, in 0.016s
1 tree, 31 leaves, max depth = 10, train loss: 0.44100, val loss: 0.43878, in 0.000s
1 tree, 52 leaves, max depth = 11, train loss: 0.43797, val loss: 0.43605, in 0.016s
1 tree, 33 leaves, max depth = 10, train loss: 0.43498, val loss: 0.43289, in 0.000s
1 tree, 53 leaves, max depth = 11, train loss: 0.43213, val loss: 0.43033, in 0.016s
1 tree, 33 leaves, max depth = 10, train loss: 0.42939, val loss: 0.42737, in 0.000s
1 tree, 53 leaves, max depth = 11, train loss: 0.42670, val loss: 0.42498, in 0.016s
1 tree, 32 leaves, max depth = 10, train loss: 0.42413, val loss: 0.42219, in 0.000s
1 tree, 54 leaves, max depth = 11, train loss: 0.42159, val loss: 0.41994, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.41916, val loss: 0.41733, in 0.000s
1 tree, 53 leaves, max depth = 10, train loss: 0.41677, val loss: 0.41522, in 0.016s
1 tree, 30 leaves, max depth = 12, train loss: 0.41452, val loss: 0.41282, in 0.016s
1 tree, 54 leaves, max depth = 11, train loss: 0.41225, val loss: 0.41082, in 0.000s
1 tree, 3 leaves, max depth = 2, train loss: 0.41011, val loss: 0.40847, in 0.000s
1 tree, 54 leaves, max depth = 10, train loss: 0.40796, val loss: 0.40660, in 0.016s
1 tree, 33 leaves, max depth = 10, train loss: 0.40581, val loss: 0.40432, in 0.016s
1 tree, 3 leaves, max depth = 2, train loss: 0.40380, val loss: 0.40212, in 0.000s
1 tree, 52 leaves, max depth = 10, train loss: 0.40178, val loss: 0.40038, in 0.000s
1 tree, 32 leaves, max depth = 11, train loss: 0.39982, val loss: 0.39826, in 0.016s
1 tree, 55 leaves, max depth = 11, train loss: 0.39790, val loss: 0.39665, in 0.000s
1 tree, 3 leaves, max depth = 2, train loss: 0.39601, val loss: 0.39457, in 0.016s
1 tree, 55 leaves, max depth = 10, train loss: 0.39420, val loss: 0.39302, in 0.000s
1 tree, 3 leaves, max depth = 2, train loss: 0.39241, val loss: 0.39106, in 0.016s
1 tree, 3 leaves, max depth = 2, train loss: 0.39071, val loss: 0.38919, in 0.000s
1 tree, 3 leaves, max depth = 2, train loss: 0.38911, val loss: 0.38742, in 0.000s
1 tree, 3 leaves, max depth = 2, train loss: 0.38758, val loss: 0.38574, in 0.016s
1 tree, 52 leaves, max depth = 11, train loss: 0.38586, val loss: 0.38434, in 0.000s
1 tree, 31 leaves, max depth = 10, train loss: 0.38408, val loss: 0.38245, in 0.016s
1 tree, 3 leaves, max depth = 2, train loss: 0.38263, val loss: 0.38085, in 0.000s
1 tree, 54 leaves, max depth = 11, train loss: 0.38099, val loss: 0.37953, in 0.016s
1 tree, 35 leaves, max depth = 9, train loss: 0.37929, val loss: 0.37773, in 0.000s
1 tree, 3 leaves, max depth = 2, train loss: 0.37791, val loss: 0.37621, in 0.000s
1 tree, 31 leaves, max depth = 9, train loss: 0.37633, val loss: 0.37455, in 0.016s
1 tree, 53 leaves, max depth = 11, train loss: 0.37477, val loss: 0.37330, in 0.000s
1 tree, 2 leaves, max depth = 1, train loss: 0.37353, val loss: 0.37184, in 0.016s
Fit 70 trees in 0.798 s, (2569 total leaves)
Time spent computing histograms: 0.274s
Time spent finding best splits:  0.038s
Time spent applying splits:      0.041s
Time spent predicting:           0.016s
Trial 97, Fold 1: Log loss = 0.3761059926294201, Average precision = 0.9467077987386714, ROC-AUC = 0.943012434698862, Elapsed Time = 0.7945130000007339 seconds
Trial 97, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 97, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
Binning 0.040 GB of training data: 0.142 s
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 34 leaves, max depth = 11, train loss: 0.67829, val loss: 0.67754, in 0.000s
1 tree, 35 leaves, max depth = 13, train loss: 0.66451, val loss: 0.66308, in 0.016s
1 tree, 34 leaves, max depth = 12, train loss: 0.65148, val loss: 0.64937, in 0.000s
1 tree, 35 leaves, max depth = 13, train loss: 0.63932, val loss: 0.63659, in 0.016s
1 tree, 34 leaves, max depth = 12, train loss: 0.62780, val loss: 0.62444, in 0.000s
1 tree, 34 leaves, max depth = 12, train loss: 0.61696, val loss: 0.61298, in 0.016s
1 tree, 32 leaves, max depth = 15, train loss: 0.60684, val loss: 0.60235, in 0.000s
1 tree, 34 leaves, max depth = 12, train loss: 0.59719, val loss: 0.59215, in 0.016s
1 tree, 31 leaves, max depth = 15, train loss: 0.58819, val loss: 0.58267, in 0.000s
1 tree, 34 leaves, max depth = 12, train loss: 0.57960, val loss: 0.57355, in 0.016s
1 tree, 31 leaves, max depth = 15, train loss: 0.57157, val loss: 0.56511, in 0.000s
1 tree, 34 leaves, max depth = 12, train loss: 0.56392, val loss: 0.55697, in 0.016s
1 tree, 31 leaves, max depth = 15, train loss: 0.55675, val loss: 0.54941, in 0.000s
1 tree, 34 leaves, max depth = 9, train loss: 0.54991, val loss: 0.54211, in 0.016s
1 tree, 32 leaves, max depth = 12, train loss: 0.54348, val loss: 0.53530, in 0.000s
1 tree, 55 leaves, max depth = 14, train loss: 0.53650, val loss: 0.52854, in 0.016s
1 tree, 31 leaves, max depth = 15, train loss: 0.53058, val loss: 0.52228, in 0.000s
1 tree, 55 leaves, max depth = 12, train loss: 0.52409, val loss: 0.51601, in 0.016s
1 tree, 31 leaves, max depth = 15, train loss: 0.51862, val loss: 0.51022, in 0.000s
1 tree, 55 leaves, max depth = 14, train loss: 0.51258, val loss: 0.50439, in 0.016s
1 tree, 55 leaves, max depth = 14, train loss: 0.50688, val loss: 0.49888, in 0.000s
1 tree, 55 leaves, max depth = 14, train loss: 0.50148, val loss: 0.49367, in 0.016s
1 tree, 55 leaves, max depth = 14, train loss: 0.49636, val loss: 0.48875, in 0.016s
1 tree, 33 leaves, max depth = 14, train loss: 0.49162, val loss: 0.48367, in 0.000s
1 tree, 56 leaves, max depth = 14, train loss: 0.48685, val loss: 0.47908, in 0.016s
1 tree, 33 leaves, max depth = 10, train loss: 0.48243, val loss: 0.47438, in 0.000s
1 tree, 53 leaves, max depth = 12, train loss: 0.47796, val loss: 0.47010, in 0.016s
1 tree, 54 leaves, max depth = 12, train loss: 0.47374, val loss: 0.46605, in 0.000s
1 tree, 33 leaves, max depth = 10, train loss: 0.46968, val loss: 0.46174, in 0.016s
1 tree, 53 leaves, max depth = 13, train loss: 0.46572, val loss: 0.45796, in 0.016s
1 tree, 33 leaves, max depth = 15, train loss: 0.46195, val loss: 0.45390, in 0.000s
1 tree, 54 leaves, max depth = 15, train loss: 0.45823, val loss: 0.45036, in 0.016s
1 tree, 33 leaves, max depth = 10, train loss: 0.45470, val loss: 0.44659, in 0.000s
1 tree, 54 leaves, max depth = 15, train loss: 0.45120, val loss: 0.44327, in 0.016s
1 tree, 33 leaves, max depth = 10, train loss: 0.44790, val loss: 0.43975, in 0.000s
1 tree, 55 leaves, max depth = 11, train loss: 0.44461, val loss: 0.43665, in 0.016s
1 tree, 33 leaves, max depth = 14, train loss: 0.44155, val loss: 0.43333, in 0.000s
1 tree, 55 leaves, max depth = 12, train loss: 0.43845, val loss: 0.43041, in 0.016s
1 tree, 33 leaves, max depth = 10, train loss: 0.43556, val loss: 0.42731, in 0.016s
1 tree, 56 leaves, max depth = 15, train loss: 0.43264, val loss: 0.42453, in 0.000s
1 tree, 34 leaves, max depth = 12, train loss: 0.42993, val loss: 0.42162, in 0.016s
1 tree, 57 leaves, max depth = 15, train loss: 0.42717, val loss: 0.41902, in 0.000s
1 tree, 33 leaves, max depth = 11, train loss: 0.42463, val loss: 0.41628, in 0.016s
1 tree, 55 leaves, max depth = 13, train loss: 0.42202, val loss: 0.41383, in 0.000s
1 tree, 55 leaves, max depth = 15, train loss: 0.41954, val loss: 0.41150, in 0.016s
1 tree, 34 leaves, max depth = 12, train loss: 0.41718, val loss: 0.40895, in 0.000s
1 tree, 55 leaves, max depth = 13, train loss: 0.41484, val loss: 0.40675, in 0.016s
1 tree, 56 leaves, max depth = 13, train loss: 0.41261, val loss: 0.40467, in 0.016s
1 tree, 3 leaves, max depth = 2, train loss: 0.41052, val loss: 0.40249, in 0.000s
1 tree, 32 leaves, max depth = 13, train loss: 0.40837, val loss: 0.40014, in 0.016s
1 tree, 55 leaves, max depth = 12, train loss: 0.40627, val loss: 0.39818, in 0.000s
1 tree, 3 leaves, max depth = 2, train loss: 0.40431, val loss: 0.39614, in 0.016s
[53/70] 1 tree, 32 leaves, max depth = 13, train loss: 0.40231, val loss: 0.39394, in 0.000s
1 tree, 55 leaves, max depth = 11, train loss: 0.40033, val loss: 0.39211, in 0.016s
1 tree, 56 leaves, max depth = 12, train loss: 0.39845, val loss: 0.39036, in 0.016s
1 tree, 3 leaves, max depth = 2, train loss: 0.39661, val loss: 0.38845, in 0.000s
1 tree, 33 leaves, max depth = 12, train loss: 0.39475, val loss: 0.38641, in 0.016s
1 tree, 3 leaves, max depth = 2, train loss: 0.39302, val loss: 0.38461, in 0.000s
1 tree, 3 leaves, max depth = 2, train loss: 0.39138, val loss: 0.38290, in 0.000s
1 tree, 3 leaves, max depth = 2, train loss: 0.38982, val loss: 0.38127, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.38799, val loss: 0.37930, in 0.000s
1 tree, 56 leaves, max depth = 13, train loss: 0.38621, val loss: 0.37769, in 0.016s
1 tree, 57 leaves, max depth = 13, train loss: 0.38452, val loss: 0.37615, in 0.016s
1 tree, 3 leaves, max depth = 2, train loss: 0.38304, val loss: 0.37461, in 0.000s
1 tree, 3 leaves, max depth = 2, train loss: 0.38164, val loss: 0.37314, in 0.000s
1 tree, 31 leaves, max depth = 8, train loss: 0.37994, val loss: 0.37132, in 0.016s
1 tree, 55 leaves, max depth = 12, train loss: 0.37834, val loss: 0.36987, in 0.016s
1 tree, 2 leaves, max depth = 1, train loss: 0.37706, val loss: 0.36855, in 0.000s
1 tree, 55 leaves, max depth = 12, train loss: 0.37553, val loss: 0.36717, in 0.016s
1 tree, 33 leaves, max depth = 8, train loss: 0.37397, val loss: 0.36546, in 0.000s
Fit 70 trees in 0.845 s, (2631 total leaves)
Time spent computing histograms: 0.306s
Time spent finding best splits:  0.044s
Time spent applying splits:      0.047s
Time spent predicting:           0.000s
Trial 97, Fold 2: Log loss = 0.3764287364805963, Average precision = 0.9427200995296994, ROC-AUC = 0.9438946706481238, Elapsed Time = 0.8514697000009619 seconds
Trial 97, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 97, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
Binning 0.040 GB of training data: 0.157 s
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 33 leaves, max depth = 9, train loss: 0.67832, val loss: 0.67784, in 0.000s
1 tree, 32 leaves, max depth = 9, train loss: 0.66474, val loss: 0.66375, in 0.016s
1 tree, 33 leaves, max depth = 9, train loss: 0.65192, val loss: 0.65044, in 0.000s
1 tree, 36 leaves, max depth = 12, train loss: 0.63986, val loss: 0.63796, in 0.016s
1 tree, 34 leaves, max depth = 9, train loss: 0.62851, val loss: 0.62616, in 0.000s
1 tree, 32 leaves, max depth = 9, train loss: 0.61790, val loss: 0.61511, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.60771, val loss: 0.60455, in 0.000s
1 tree, 31 leaves, max depth = 13, train loss: 0.59825, val loss: 0.59470, in 0.016s
1 tree, 34 leaves, max depth = 9, train loss: 0.58929, val loss: 0.58535, in 0.016s
1 tree, 34 leaves, max depth = 9, train loss: 0.58068, val loss: 0.57642, in 0.000s
1 tree, 35 leaves, max depth = 12, train loss: 0.57270, val loss: 0.56812, in 0.016s
1 tree, 34 leaves, max depth = 10, train loss: 0.56501, val loss: 0.56013, in 0.000s
1 tree, 35 leaves, max depth = 9, train loss: 0.55788, val loss: 0.55266, in 0.016s
1 tree, 33 leaves, max depth = 10, train loss: 0.55101, val loss: 0.54552, in 0.000s
1 tree, 34 leaves, max depth = 9, train loss: 0.54451, val loss: 0.53875, in 0.016s
1 tree, 52 leaves, max depth = 12, train loss: 0.53741, val loss: 0.53212, in 0.000s
1 tree, 31 leaves, max depth = 14, train loss: 0.53155, val loss: 0.52595, in 0.000s
1 tree, 52 leaves, max depth = 12, train loss: 0.52495, val loss: 0.51981, in 0.000s
1 tree, 34 leaves, max depth = 12, train loss: 0.51952, val loss: 0.51413, in 0.016s
1 tree, 52 leaves, max depth = 12, train loss: 0.51338, val loss: 0.50841, in 0.016s
1 tree, 53 leaves, max depth = 12, train loss: 0.50757, val loss: 0.50301, in 0.000s
1 tree, 52 leaves, max depth = 12, train loss: 0.50207, val loss: 0.49791, in 0.016s
1 tree, 51 leaves, max depth = 12, train loss: 0.49687, val loss: 0.49310, in 0.016s
1 tree, 31 leaves, max depth = 14, train loss: 0.49219, val loss: 0.48814, in 0.000s
1 tree, 50 leaves, max depth = 12, train loss: 0.48733, val loss: 0.48366, in 0.016s
1 tree, 52 leaves, max depth = 12, train loss: 0.48271, val loss: 0.47941, in 0.000s
1 tree, 34 leaves, max depth = 11, train loss: 0.47841, val loss: 0.47488, in 0.016s
1 tree, 52 leaves, max depth = 12, train loss: 0.47409, val loss: 0.47092, in 0.016s
1 tree, 34 leaves, max depth = 11, train loss: 0.47009, val loss: 0.46669, in 0.000s
1 tree, 53 leaves, max depth = 12, train loss: 0.46604, val loss: 0.46298, in 0.016s
1 tree, 31 leaves, max depth = 12, train loss: 0.46234, val loss: 0.45904, in 0.000s
1 tree, 51 leaves, max depth = 12, train loss: 0.45854, val loss: 0.45557, in 0.016s
1 tree, 34 leaves, max depth = 11, train loss: 0.45506, val loss: 0.45188, in 0.016s
1 tree, 53 leaves, max depth = 12, train loss: 0.45148, val loss: 0.44863, in 0.000s
1 tree, 54 leaves, max depth = 12, train loss: 0.44809, val loss: 0.44553, in 0.016s
1 tree, 34 leaves, max depth = 11, train loss: 0.44488, val loss: 0.44213, in 0.000s
1 tree, 53 leaves, max depth = 11, train loss: 0.44168, val loss: 0.43922, in 0.016s
1 tree, 34 leaves, max depth = 11, train loss: 0.43868, val loss: 0.43603, in 0.000s
1 tree, 34 leaves, max depth = 11, train loss: 0.43584, val loss: 0.43300, in 0.016s
1 tree, 53 leaves, max depth = 12, train loss: 0.43285, val loss: 0.43028, in 0.016s
1 tree, 32 leaves, max depth = 9, train loss: 0.43012, val loss: 0.42730, in 0.000s
1 tree, 53 leaves, max depth = 11, train loss: 0.42730, val loss: 0.42476, in 0.016s
1 tree, 33 leaves, max depth = 10, train loss: 0.42480, val loss: 0.42209, in 0.000s
1 tree, 53 leaves, max depth = 11, train loss: 0.42213, val loss: 0.41968, in 0.016s
1 tree, 54 leaves, max depth = 11, train loss: 0.41959, val loss: 0.41740, in 0.000s
1 tree, 55 leaves, max depth = 11, train loss: 0.41718, val loss: 0.41523, in 0.016s
1 tree, 31 leaves, max depth = 16, train loss: 0.41491, val loss: 0.41275, in 0.016s
1 tree, 54 leaves, max depth = 12, train loss: 0.41263, val loss: 0.41074, in 0.000s
1 tree, 3 leaves, max depth = 2, train loss: 0.41046, val loss: 0.40873, in 0.016s
1 tree, 54 leaves, max depth = 12, train loss: 0.40829, val loss: 0.40682, in 0.000s
1 tree, 34 leaves, max depth = 9, train loss: 0.40613, val loss: 0.40444, in 0.016s
1 tree, 3 leaves, max depth = 2, train loss: 0.40410, val loss: 0.40256, in 0.000s
1 tree, 54 leaves, max depth = 11, train loss: 0.40206, val loss: 0.40077, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.40007, val loss: 0.39862, in 0.016s
1 tree, 54 leaves, max depth = 12, train loss: 0.39813, val loss: 0.39692, in 0.000s
1 tree, 3 leaves, max depth = 2, train loss: 0.39623, val loss: 0.39516, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.39437, val loss: 0.39311, in 0.000s
1 tree, 3 leaves, max depth = 2, train loss: 0.39258, val loss: 0.39145, in 0.016s
1 tree, 3 leaves, max depth = 2, train loss: 0.39087, val loss: 0.38988, in 0.000s
1 tree, 3 leaves, max depth = 2, train loss: 0.38925, val loss: 0.38839, in 0.000s
1 tree, 3 leaves, max depth = 2, train loss: 0.38772, val loss: 0.38697, in 0.016s
1 tree, 54 leaves, max depth = 12, train loss: 0.38588, val loss: 0.38539, in 0.000s
1 tree, 56 leaves, max depth = 12, train loss: 0.38413, val loss: 0.38389, in 0.016s
1 tree, 3 leaves, max depth = 2, train loss: 0.38267, val loss: 0.38254, in 0.000s
1 tree, 56 leaves, max depth = 12, train loss: 0.38101, val loss: 0.38113, in 0.016s
1 tree, 3 leaves, max depth = 2, train loss: 0.37961, val loss: 0.37984, in 0.000s
1 tree, 35 leaves, max depth = 11, train loss: 0.37789, val loss: 0.37796, in 0.016s
1 tree, 2 leaves, max depth = 1, train loss: 0.37663, val loss: 0.37679, in 0.000s
1 tree, 34 leaves, max depth = 9, train loss: 0.37502, val loss: 0.37501, in 0.016s
1 tree, 55 leaves, max depth = 12, train loss: 0.37342, val loss: 0.37367, in 0.000s
Fit 70 trees in 0.907 s, (2565 total leaves)
Time spent computing histograms: 0.315s
Time spent finding best splits:  0.046s
Time spent applying splits:      0.050s
Time spent predicting:           0.016s
Trial 97, Fold 3: Log loss = 0.3713377352370851, Average precision = 0.9484391318896641, ROC-AUC = 0.9480855609114812, Elapsed Time = 0.9261010999998689 seconds
Trial 97, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 97, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
Binning 0.040 GB of training data: 0.158 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 33 leaves, max depth = 11, train loss: 0.67834, val loss: 0.67749, in 0.016s
1 tree, 33 leaves, max depth = 11, train loss: 0.66456, val loss: 0.66287, in 0.016s
1 tree, 33 leaves, max depth = 11, train loss: 0.65162, val loss: 0.64913, in 0.000s
1 tree, 32 leaves, max depth = 12, train loss: 0.63954, val loss: 0.63634, in 0.016s
1 tree, 33 leaves, max depth = 11, train loss: 0.62809, val loss: 0.62414, in 0.000s
1 tree, 33 leaves, max depth = 11, train loss: 0.61732, val loss: 0.61263, in 0.016s
1 tree, 33 leaves, max depth = 11, train loss: 0.60718, val loss: 0.60177, in 0.000s
1 tree, 31 leaves, max depth = 11, train loss: 0.59765, val loss: 0.59165, in 0.016s
1 tree, 32 leaves, max depth = 11, train loss: 0.58862, val loss: 0.58195, in 0.000s
1 tree, 34 leaves, max depth = 15, train loss: 0.58014, val loss: 0.57282, in 0.016s
1 tree, 33 leaves, max depth = 11, train loss: 0.57209, val loss: 0.56412, in 0.000s
1 tree, 31 leaves, max depth = 11, train loss: 0.56451, val loss: 0.55601, in 0.016s
1 tree, 31 leaves, max depth = 11, train loss: 0.55736, val loss: 0.54832, in 0.000s
1 tree, 31 leaves, max depth = 13, train loss: 0.55054, val loss: 0.54091, in 0.016s
1 tree, 31 leaves, max depth = 13, train loss: 0.54409, val loss: 0.53389, in 0.000s
1 tree, 56 leaves, max depth = 11, train loss: 0.53705, val loss: 0.52698, in 0.000s
1 tree, 31 leaves, max depth = 11, train loss: 0.53115, val loss: 0.52059, in 0.016s
1 tree, 56 leaves, max depth = 11, train loss: 0.52461, val loss: 0.51417, in 0.000s
1 tree, 34 leaves, max depth = 15, train loss: 0.51916, val loss: 0.50819, in 0.016s
1 tree, 57 leaves, max depth = 13, train loss: 0.51308, val loss: 0.50223, in 0.016s
1 tree, 56 leaves, max depth = 12, train loss: 0.50733, val loss: 0.49659, in 0.000s
1 tree, 54 leaves, max depth = 12, train loss: 0.50189, val loss: 0.49124, in 0.016s
1 tree, 56 leaves, max depth = 12, train loss: 0.49673, val loss: 0.48618, in 0.000s
1 tree, 32 leaves, max depth = 10, train loss: 0.49202, val loss: 0.48103, in 0.016s
[25/70] 1 tree, 56 leaves, max depth = 12, train loss: 0.48720, val loss: 0.47631, in 0.016s
1 tree, 55 leaves, max depth = 12, train loss: 0.48264, val loss: 0.47182, in 0.000s
1 tree, 34 leaves, max depth = 14, train loss: 0.47832, val loss: 0.46705, in 0.016s
1 tree, 56 leaves, max depth = 12, train loss: 0.47405, val loss: 0.46286, in 0.000s
1 tree, 34 leaves, max depth = 14, train loss: 0.47002, val loss: 0.45841, in 0.016s
1 tree, 56 leaves, max depth = 12, train loss: 0.46601, val loss: 0.45449, in 0.016s
1 tree, 32 leaves, max depth = 11, train loss: 0.46227, val loss: 0.45036, in 0.001s
1 tree, 57 leaves, max depth = 13, train loss: 0.45851, val loss: 0.44669, in 0.016s
1 tree, 33 leaves, max depth = 10, train loss: 0.45492, val loss: 0.44272, in 0.000s
1 tree, 57 leaves, max depth = 13, train loss: 0.45139, val loss: 0.43928, in 0.016s
1 tree, 34 leaves, max depth = 14, train loss: 0.44812, val loss: 0.43562, in 0.000s
1 tree, 57 leaves, max depth = 11, train loss: 0.44480, val loss: 0.43238, in 0.016s
1 tree, 56 leaves, max depth = 11, train loss: 0.44166, val loss: 0.42931, in 0.016s
1 tree, 35 leaves, max depth = 16, train loss: 0.43863, val loss: 0.42590, in 0.000s
1 tree, 33 leaves, max depth = 10, train loss: 0.43569, val loss: 0.42262, in 0.016s
1 tree, 56 leaves, max depth = 12, train loss: 0.43274, val loss: 0.41976, in 0.000s
1 tree, 34 leaves, max depth = 13, train loss: 0.43006, val loss: 0.41672, in 0.016s
1 tree, 56 leaves, max depth = 12, train loss: 0.42727, val loss: 0.41402, in 0.000s
1 tree, 35 leaves, max depth = 16, train loss: 0.42476, val loss: 0.41115, in 0.016s
1 tree, 57 leaves, max depth = 12, train loss: 0.42213, val loss: 0.40858, in 0.016s
1 tree, 55 leaves, max depth = 12, train loss: 0.41963, val loss: 0.40614, in 0.000s
1 tree, 56 leaves, max depth = 12, train loss: 0.41726, val loss: 0.40383, in 0.016s
1 tree, 32 leaves, max depth = 12, train loss: 0.41497, val loss: 0.40124, in 0.000s
1 tree, 58 leaves, max depth = 11, train loss: 0.41272, val loss: 0.39905, in 0.016s
1 tree, 2 leaves, max depth = 1, train loss: 0.41071, val loss: 0.39692, in 0.000s
1 tree, 32 leaves, max depth = 12, train loss: 0.40855, val loss: 0.39446, in 0.016s
1 tree, 58 leaves, max depth = 11, train loss: 0.40643, val loss: 0.39239, in 0.016s
1 tree, 2 leaves, max depth = 1, train loss: 0.40454, val loss: 0.39039, in 0.000s
1 tree, 58 leaves, max depth = 11, train loss: 0.40254, val loss: 0.38845, in 0.016s
1 tree, 33 leaves, max depth = 12, train loss: 0.40054, val loss: 0.38617, in 0.000s
1 tree, 58 leaves, max depth = 11, train loss: 0.39864, val loss: 0.38432, in 0.016s
1 tree, 2 leaves, max depth = 1, train loss: 0.39687, val loss: 0.38244, in 0.000s
1 tree, 33 leaves, max depth = 12, train loss: 0.39500, val loss: 0.38031, in 0.016s
1 tree, 2 leaves, max depth = 1, train loss: 0.39333, val loss: 0.37854, in 0.000s
1 tree, 2 leaves, max depth = 1, train loss: 0.39175, val loss: 0.37686, in 0.016s
1 tree, 2 leaves, max depth = 1, train loss: 0.39025, val loss: 0.37526, in 0.000s
1 tree, 2 leaves, max depth = 1, train loss: 0.38883, val loss: 0.37375, in 0.016s
1 tree, 56 leaves, max depth = 12, train loss: 0.38701, val loss: 0.37202, in 0.000s
1 tree, 56 leaves, max depth = 13, train loss: 0.38529, val loss: 0.37035, in 0.016s
1 tree, 2 leaves, max depth = 1, train loss: 0.38393, val loss: 0.36890, in 0.000s
1 tree, 33 leaves, max depth = 12, train loss: 0.38223, val loss: 0.36697, in 0.016s
1 tree, 2 leaves, max depth = 1, train loss: 0.38094, val loss: 0.36560, in 0.000s
1 tree, 56 leaves, max depth = 12, train loss: 0.37929, val loss: 0.36404, in 0.016s
1 tree, 33 leaves, max depth = 12, train loss: 0.37769, val loss: 0.36222, in 0.000s
1 tree, 57 leaves, max depth = 12, train loss: 0.37612, val loss: 0.36073, in 0.016s
1 tree, 2 leaves, max depth = 1, train loss: 0.37489, val loss: 0.35942, in 0.000s
Fit 70 trees in 0.908 s, (2623 total leaves)
Time spent computing histograms: 0.323s
Time spent finding best splits:  0.046s
Time spent applying splits:      0.050s
Time spent predicting:           0.000s
Trial 97, Fold 4: Log loss = 0.37434479667596815, Average precision = 0.9483072080684369, ROC-AUC = 0.9461314143603464, Elapsed Time = 0.9189717000008386 seconds
Trial 97, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 97, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
Binning 0.040 GB of training data: 0.173 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 34 leaves, max depth = 10, train loss: 0.67829, val loss: 0.67728, in 0.016s
1 tree, 31 leaves, max depth = 12, train loss: 0.66450, val loss: 0.66250, in 0.000s
1 tree, 32 leaves, max depth = 11, train loss: 0.65143, val loss: 0.64849, in 0.016s
1 tree, 34 leaves, max depth = 10, train loss: 0.63924, val loss: 0.63539, in 0.000s
1 tree, 32 leaves, max depth = 11, train loss: 0.62767, val loss: 0.62294, in 0.016s
1 tree, 33 leaves, max depth = 12, train loss: 0.61688, val loss: 0.61127, in 0.000s
1 tree, 32 leaves, max depth = 11, train loss: 0.60661, val loss: 0.60017, in 0.016s
1 tree, 34 leaves, max depth = 12, train loss: 0.59700, val loss: 0.58983, in 0.016s
1 tree, 32 leaves, max depth = 11, train loss: 0.58786, val loss: 0.57990, in 0.000s
1 tree, 34 leaves, max depth = 12, train loss: 0.57930, val loss: 0.57065, in 0.016s
1 tree, 34 leaves, max depth = 12, train loss: 0.57122, val loss: 0.56189, in 0.000s
1 tree, 32 leaves, max depth = 11, train loss: 0.56351, val loss: 0.55345, in 0.016s
1 tree, 33 leaves, max depth = 12, train loss: 0.55629, val loss: 0.54552, in 0.000s
1 tree, 32 leaves, max depth = 11, train loss: 0.54939, val loss: 0.53793, in 0.016s
1 tree, 34 leaves, max depth = 12, train loss: 0.54292, val loss: 0.53086, in 0.000s
1 tree, 54 leaves, max depth = 13, train loss: 0.53594, val loss: 0.52418, in 0.016s
1 tree, 34 leaves, max depth = 11, train loss: 0.52998, val loss: 0.51764, in 0.000s
1 tree, 54 leaves, max depth = 13, train loss: 0.52350, val loss: 0.51145, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.51799, val loss: 0.50537, in 0.016s
1 tree, 53 leaves, max depth = 13, train loss: 0.51196, val loss: 0.49963, in 0.000s
1 tree, 53 leaves, max depth = 13, train loss: 0.50625, val loss: 0.49420, in 0.016s
1 tree, 53 leaves, max depth = 13, train loss: 0.50085, val loss: 0.48907, in 0.016s
1 tree, 57 leaves, max depth = 13, train loss: 0.49572, val loss: 0.48422, in 0.000s
1 tree, 34 leaves, max depth = 12, train loss: 0.49095, val loss: 0.47892, in 0.016s
1 tree, 55 leaves, max depth = 13, train loss: 0.48616, val loss: 0.47440, in 0.000s
1 tree, 36 leaves, max depth = 10, train loss: 0.48171, val loss: 0.46944, in 0.016s
1 tree, 55 leaves, max depth = 13, train loss: 0.47723, val loss: 0.46524, in 0.016s
1 tree, 36 leaves, max depth = 10, train loss: 0.47308, val loss: 0.46061, in 0.000s
1 tree, 56 leaves, max depth = 14, train loss: 0.46888, val loss: 0.45668, in 0.016s
1 tree, 36 leaves, max depth = 10, train loss: 0.46502, val loss: 0.45236, in 0.000s
1 tree, 35 leaves, max depth = 13, train loss: 0.46137, val loss: 0.44826, in 0.016s
1 tree, 55 leaves, max depth = 13, train loss: 0.45747, val loss: 0.44463, in 0.000s
1 tree, 36 leaves, max depth = 10, train loss: 0.45405, val loss: 0.44078, in 0.016s
1 tree, 53 leaves, max depth = 13, train loss: 0.45038, val loss: 0.43737, in 0.016s
1 tree, 53 leaves, max depth = 13, train loss: 0.44690, val loss: 0.43413, in 0.000s
1 tree, 53 leaves, max depth = 13, train loss: 0.44359, val loss: 0.43107, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.44051, val loss: 0.42759, in 0.000s
1 tree, 54 leaves, max depth = 13, train loss: 0.43740, val loss: 0.42472, in 0.000s
1 tree, 34 leaves, max depth = 9, train loss: 0.43449, val loss: 0.42142, in 0.016s
1 tree, 53 leaves, max depth = 13, train loss: 0.43155, val loss: 0.41873, in 0.000s
1 tree, 33 leaves, max depth = 9, train loss: 0.42883, val loss: 0.41563, in 0.016s
1 tree, 54 leaves, max depth = 13, train loss: 0.42606, val loss: 0.41310, in 0.016s
1 tree, 33 leaves, max depth = 9, train loss: 0.42351, val loss: 0.41018, in 0.000s
1 tree, 53 leaves, max depth = 14, train loss: 0.42088, val loss: 0.40780, in 0.016s
1 tree, 55 leaves, max depth = 14, train loss: 0.41838, val loss: 0.40554, in 0.000s
1 tree, 36 leaves, max depth = 9, train loss: 0.41601, val loss: 0.40280, in 0.016s
1 tree, 54 leaves, max depth = 14, train loss: 0.41365, val loss: 0.40068, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.41144, val loss: 0.39814, in 0.000s
1 tree, 3 leaves, max depth = 2, train loss: 0.40935, val loss: 0.39611, in 0.016s
1 tree, 54 leaves, max depth = 12, train loss: 0.40712, val loss: 0.39412, in 0.000s
1 tree, 55 leaves, max depth = 12, train loss: 0.40499, val loss: 0.39223, in 0.016s
1 tree, 3 leaves, max depth = 2, train loss: 0.40302, val loss: 0.39033, in 0.000s
1 tree, 36 leaves, max depth = 12, train loss: 0.40100, val loss: 0.38799, in 0.016s
1 tree, 54 leaves, max depth = 13, train loss: 0.39900, val loss: 0.38622, in 0.000s
1 tree, 36 leaves, max depth = 9, train loss: 0.39707, val loss: 0.38397, in 0.016s
1 tree, 3 leaves, max depth = 2, train loss: 0.39523, val loss: 0.38220, in 0.016s
1 tree, 55 leaves, max depth = 12, train loss: 0.39333, val loss: 0.38053, in 0.000s
1 tree, 3 leaves, max depth = 2, train loss: 0.39159, val loss: 0.37885, in 0.016s
1 tree, 3 leaves, max depth = 2, train loss: 0.38994, val loss: 0.37727, in 0.000s
1 tree, 2 leaves, max depth = 1, train loss: 0.38844, val loss: 0.37578, in 0.000s
1 tree, 34 leaves, max depth = 11, train loss: 0.38661, val loss: 0.37367, in 0.016s
1 tree, 55 leaves, max depth = 14, train loss: 0.38480, val loss: 0.37210, in 0.016s
1 tree, 56 leaves, max depth = 13, train loss: 0.38308, val loss: 0.37061, in 0.000s
1 tree, 2 leaves, max depth = 1, train loss: 0.38166, val loss: 0.36920, in 0.016s
1 tree, 2 leaves, max depth = 1, train loss: 0.38032, val loss: 0.36787, in 0.000s
1 tree, 33 leaves, max depth = 11, train loss: 0.37866, val loss: 0.36598, in 0.016s
1 tree, 54 leaves, max depth = 13, train loss: 0.37702, val loss: 0.36457, in 0.000s
1 tree, 2 leaves, max depth = 1, train loss: 0.37574, val loss: 0.36331, in 0.016s
1 tree, 33 leaves, max depth = 10, train loss: 0.37414, val loss: 0.36145, in 0.000s
1 tree, 55 leaves, max depth = 13, train loss: 0.37257, val loss: 0.36011, in 0.016s
Fit 70 trees in 0.923 s, (2641 total leaves)
Time spent computing histograms: 0.330s
Time spent finding best splits:  0.047s
Time spent applying splits:      0.051s
Time spent predicting:           0.000s
Trial 97, Fold 5: Log loss = 0.37853788756599877, Average precision = 0.9464296262456326, ROC-AUC = 0.9434605280270516, Elapsed Time = 0.9309966000000713 seconds
Optimization Progress:  98%|#########8| 98/100 [20:27<00:26, 13.34s/it]
Trial 98, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 98, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
0.174 s 0.040 GB of training data: 
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 25 leaves, max depth = 10, train loss: 0.67807, val loss: 0.67763, in 0.000s
1 tree, 25 leaves, max depth = 10, train loss: 0.66412, val loss: 0.66325, in 0.000s
1 tree, 25 leaves, max depth = 10, train loss: 0.65101, val loss: 0.64974, in 0.016s
1 tree, 25 leaves, max depth = 10, train loss: 0.63882, val loss: 0.63720, in 0.000s
1 tree, 25 leaves, max depth = 10, train loss: 0.62723, val loss: 0.62523, in 0.016s
1 tree, 25 leaves, max depth = 10, train loss: 0.61632, val loss: 0.61395, in 0.000s
1 tree, 25 leaves, max depth = 11, train loss: 0.60616, val loss: 0.60346, in 0.016s
1 tree, 25 leaves, max depth = 10, train loss: 0.59647, val loss: 0.59341, in 0.000s
1 tree, 25 leaves, max depth = 9, train loss: 0.58732, val loss: 0.58393, in 0.016s
1 tree, 25 leaves, max depth = 10, train loss: 0.57869, val loss: 0.57497, in 0.000s
1 tree, 25 leaves, max depth = 9, train loss: 0.57054, val loss: 0.56650, in 0.016s
1 tree, 25 leaves, max depth = 10, train loss: 0.56284, val loss: 0.55849, in 0.000s
1 tree, 25 leaves, max depth = 9, train loss: 0.55572, val loss: 0.55108, in 0.016s
1 tree, 25 leaves, max depth = 10, train loss: 0.54883, val loss: 0.54390, in 0.000s
1 tree, 25 leaves, max depth = 9, train loss: 0.54232, val loss: 0.53710, in 0.016s
1 tree, 25 leaves, max depth = 7, train loss: 0.53540, val loss: 0.53061, in 0.000s
1 tree, 25 leaves, max depth = 10, train loss: 0.52939, val loss: 0.52432, in 0.016s
1 tree, 25 leaves, max depth = 8, train loss: 0.52298, val loss: 0.51831, in 0.000s
1 tree, 25 leaves, max depth = 10, train loss: 0.51754, val loss: 0.51259, in 0.016s
1 tree, 25 leaves, max depth = 8, train loss: 0.51156, val loss: 0.50701, in 0.000s
1 tree, 25 leaves, max depth = 8, train loss: 0.50591, val loss: 0.50174, in 0.016s
1 tree, 25 leaves, max depth = 8, train loss: 0.50057, val loss: 0.49676, in 0.016s
1 tree, 25 leaves, max depth = 9, train loss: 0.49564, val loss: 0.49157, in 0.000s
1 tree, 25 leaves, max depth = 9, train loss: 0.49098, val loss: 0.48664, in 0.016s
1 tree, 25 leaves, max depth = 8, train loss: 0.48606, val loss: 0.48207, in 0.000s
1 tree, 25 leaves, max depth = 9, train loss: 0.48140, val loss: 0.47777, in 0.016s
1 tree, 25 leaves, max depth = 8, train loss: 0.47700, val loss: 0.47371, in 0.000s
1 tree, 25 leaves, max depth = 10, train loss: 0.47289, val loss: 0.46936, in 0.016s
1 tree, 25 leaves, max depth = 8, train loss: 0.46876, val loss: 0.46554, in 0.000s
1 tree, 25 leaves, max depth = 8, train loss: 0.46487, val loss: 0.46140, in 0.016s
1 tree, 25 leaves, max depth = 9, train loss: 0.46120, val loss: 0.45751, in 0.000s
1 tree, 25 leaves, max depth = 7, train loss: 0.45738, val loss: 0.45400, in 0.016s
1 tree, 25 leaves, max depth = 8, train loss: 0.45393, val loss: 0.45032, in 0.000s
1 tree, 25 leaves, max depth = 8, train loss: 0.45033, val loss: 0.44703, in 0.016s
1 tree, 25 leaves, max depth = 9, train loss: 0.44692, val loss: 0.44393, in 0.000s
1 tree, 25 leaves, max depth = 7, train loss: 0.44368, val loss: 0.44095, in 0.016s
1 tree, 25 leaves, max depth = 9, train loss: 0.44062, val loss: 0.43770, in 0.016s
1 tree, 25 leaves, max depth = 8, train loss: 0.43757, val loss: 0.43493, in 0.000s
1 tree, 25 leaves, max depth = 8, train loss: 0.43460, val loss: 0.43181, in 0.016s
1 tree, 25 leaves, max depth = 8, train loss: 0.43174, val loss: 0.42922, in 0.000s
1 tree, 25 leaves, max depth = 10, train loss: 0.42903, val loss: 0.42629, in 0.016s
1 tree, 25 leaves, max depth = 9, train loss: 0.42632, val loss: 0.42386, in 0.000s
1 tree, 25 leaves, max depth = 9, train loss: 0.42377, val loss: 0.42112, in 0.016s
1 tree, 25 leaves, max depth = 8, train loss: 0.42122, val loss: 0.41884, in 0.000s
1 tree, 25 leaves, max depth = 7, train loss: 0.41880, val loss: 0.41665, in 0.016s
1 tree, 25 leaves, max depth = 10, train loss: 0.41643, val loss: 0.41407, in 0.000s
1 tree, 25 leaves, max depth = 8, train loss: 0.41414, val loss: 0.41204, in 0.016s
1 tree, 25 leaves, max depth = 9, train loss: 0.41193, val loss: 0.40968, in 0.000s
1 tree, 3 leaves, max depth = 2, train loss: 0.40979, val loss: 0.40733, in 0.016s
1 tree, 25 leaves, max depth = 8, train loss: 0.40762, val loss: 0.40541, in 0.000s
1 tree, 25 leaves, max depth = 8, train loss: 0.40549, val loss: 0.40316, in 0.016s
1 tree, 25 leaves, max depth = 9, train loss: 0.40346, val loss: 0.40137, in 0.000s
1 tree, 25 leaves, max depth = 12, train loss: 0.40153, val loss: 0.39931, in 0.016s
1 tree, 25 leaves, max depth = 10, train loss: 0.39966, val loss: 0.39730, in 0.000s
1 tree, 25 leaves, max depth = 8, train loss: 0.39774, val loss: 0.39563, in 0.016s
1 tree, 3 leaves, max depth = 2, train loss: 0.39576, val loss: 0.39346, in 0.000s
1 tree, 25 leaves, max depth = 8, train loss: 0.39394, val loss: 0.39188, in 0.016s
1 tree, 3 leaves, max depth = 2, train loss: 0.39207, val loss: 0.38984, in 0.000s
1 tree, 3 leaves, max depth = 2, train loss: 0.39030, val loss: 0.38789, in 0.016s
1 tree, 3 leaves, max depth = 2, train loss: 0.38862, val loss: 0.38605, in 0.000s
1 tree, 3 leaves, max depth = 2, train loss: 0.38703, val loss: 0.38430, in 0.016s
1 tree, 25 leaves, max depth = 8, train loss: 0.38528, val loss: 0.38282, in 0.000s
1 tree, 25 leaves, max depth = 8, train loss: 0.38364, val loss: 0.38145, in 0.016s
1 tree, 3 leaves, max depth = 2, train loss: 0.38213, val loss: 0.37978, in 0.000s
1 tree, 3 leaves, max depth = 2, train loss: 0.38069, val loss: 0.37820, in 0.000s
1 tree, 25 leaves, max depth = 7, train loss: 0.37900, val loss: 0.37642, in 0.016s
1 tree, 3 leaves, max depth = 2, train loss: 0.37764, val loss: 0.37492, in 0.000s
1 tree, 25 leaves, max depth = 9, train loss: 0.37607, val loss: 0.37328, in 0.016s
1 tree, 25 leaves, max depth = 8, train loss: 0.37449, val loss: 0.37198, in 0.000s
1 tree, 2 leaves, max depth = 1, train loss: 0.37326, val loss: 0.37054, in 0.016s
1 tree, 25 leaves, max depth = 8, train loss: 0.37175, val loss: 0.36894, in 0.000s
1 tree, 25 leaves, max depth = 8, train loss: 0.37025, val loss: 0.36774, in 0.016s
1 tree, 2 leaves, max depth = 1, train loss: 0.36907, val loss: 0.36637, in 0.000s
1 tree, 25 leaves, max depth = 8, train loss: 0.36768, val loss: 0.36491, in 0.016s
1 tree, 3 leaves, max depth = 2, train loss: 0.36656, val loss: 0.36359, in 0.000s
1 tree, 25 leaves, max depth = 9, train loss: 0.36524, val loss: 0.36221, in 0.016s
1 tree, 3 leaves, max depth = 2, train loss: 0.36417, val loss: 0.36095, in 0.000s
1 tree, 25 leaves, max depth = 8, train loss: 0.36289, val loss: 0.35967, in 0.016s
1 tree, 25 leaves, max depth = 7, train loss: 0.36168, val loss: 0.35841, in 0.000s
1 tree, 3 leaves, max depth = 2, train loss: 0.36066, val loss: 0.35721, in 0.016s
1 tree, 25 leaves, max depth = 9, train loss: 0.35951, val loss: 0.35599, in 0.000s
1 tree, 25 leaves, max depth = 8, train loss: 0.35803, val loss: 0.35484, in 0.016s
1 tree, 25 leaves, max depth = 8, train loss: 0.35694, val loss: 0.35368, in 0.000s
1 tree, 25 leaves, max depth = 8, train loss: 0.35553, val loss: 0.35259, in 0.016s
1 tree, 25 leaves, max depth = 9, train loss: 0.35420, val loss: 0.35155, in 0.016s
1 tree, 2 leaves, max depth = 1, train loss: 0.35322, val loss: 0.35039, in 0.000s
1 tree, 25 leaves, max depth = 8, train loss: 0.35194, val loss: 0.34940, in 0.016s
1 tree, 3 leaves, max depth = 2, train loss: 0.35100, val loss: 0.34827, in 0.000s
1 tree, 2 leaves, max depth = 1, train loss: 0.35011, val loss: 0.34721, in 0.000s
1 tree, 25 leaves, max depth = 8, train loss: 0.34907, val loss: 0.34618, in 0.016s
1 tree, 25 leaves, max depth = 9, train loss: 0.34807, val loss: 0.34503, in 0.016s
1 tree, 25 leaves, max depth = 9, train loss: 0.34684, val loss: 0.34409, in 0.000s
1 tree, 3 leaves, max depth = 2, train loss: 0.34597, val loss: 0.34306, in 0.016s
1 tree, 25 leaves, max depth = 8, train loss: 0.34479, val loss: 0.34214, in 0.000s
1 tree, 3 leaves, max depth = 2, train loss: 0.34396, val loss: 0.34114, in 0.016s
Fit 95 trees in 1.064 s, (1953 total leaves)
Time spent computing histograms: 0.433s
Time spent finding best splits:  0.051s
Time spent applying splits:      0.043s
Time spent predicting:           0.000s
Trial 98, Fold 1: Log loss = 0.34746178444472975, Average precision = 0.9454165208062872, ROC-AUC = 0.9436222057326394, Elapsed Time = 1.0571799000008468 seconds
Trial 98, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 98, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
Binning 0.040 GB of training data: 0.173 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 25 leaves, max depth = 10, train loss: 0.67829, val loss: 0.67754, in 0.016s
1 tree, 25 leaves, max depth = 10, train loss: 0.66451, val loss: 0.66308, in 0.000s
1 tree, 25 leaves, max depth = 12, train loss: 0.65148, val loss: 0.64937, in 0.016s
1 tree, 25 leaves, max depth = 11, train loss: 0.63932, val loss: 0.63659, in 0.000s
1 tree, 25 leaves, max depth = 12, train loss: 0.62780, val loss: 0.62443, in 0.016s
1 tree, 25 leaves, max depth = 12, train loss: 0.61696, val loss: 0.61298, in 0.000s
1 tree, 25 leaves, max depth = 14, train loss: 0.60685, val loss: 0.60237, in 0.016s
1 tree, 25 leaves, max depth = 12, train loss: 0.59721, val loss: 0.59216, in 0.016s
1 tree, 25 leaves, max depth = 12, train loss: 0.58812, val loss: 0.58252, in 0.000s
1 tree, 25 leaves, max depth = 12, train loss: 0.57961, val loss: 0.57360, in 0.016s
1 tree, 25 leaves, max depth = 12, train loss: 0.57159, val loss: 0.56516, in 0.000s
1 tree, 25 leaves, max depth = 12, train loss: 0.56389, val loss: 0.55699, in 0.016s
1 tree, 25 leaves, max depth = 12, train loss: 0.55673, val loss: 0.54945, in 0.000s
1 tree, 25 leaves, max depth = 10, train loss: 0.54985, val loss: 0.54212, in 0.016s
1 tree, 25 leaves, max depth = 12, train loss: 0.54334, val loss: 0.53517, in 0.000s
1 tree, 25 leaves, max depth = 8, train loss: 0.53631, val loss: 0.52836, in 0.016s
1 tree, 25 leaves, max depth = 12, train loss: 0.53041, val loss: 0.52212, in 0.016s
1 tree, 25 leaves, max depth = 8, train loss: 0.52387, val loss: 0.51581, in 0.000s
1 tree, 25 leaves, max depth = 12, train loss: 0.51842, val loss: 0.51004, in 0.016s
1 tree, 25 leaves, max depth = 8, train loss: 0.51234, val loss: 0.50417, in 0.016s
1 tree, 25 leaves, max depth = 8, train loss: 0.50659, val loss: 0.49862, in 0.016s
1 tree, 25 leaves, max depth = 8, train loss: 0.50115, val loss: 0.49337, in 0.000s
1 tree, 25 leaves, max depth = 8, train loss: 0.49599, val loss: 0.48841, in 0.016s
1 tree, 25 leaves, max depth = 12, train loss: 0.49126, val loss: 0.48334, in 0.016s
1 tree, 25 leaves, max depth = 8, train loss: 0.48645, val loss: 0.47871, in 0.016s
1 tree, 25 leaves, max depth = 8, train loss: 0.48189, val loss: 0.47433, in 0.000s
1 tree, 25 leaves, max depth = 12, train loss: 0.47757, val loss: 0.46972, in 0.016s
1 tree, 25 leaves, max depth = 12, train loss: 0.47347, val loss: 0.46535, in 0.016s
1 tree, 25 leaves, max depth = 8, train loss: 0.46925, val loss: 0.46131, in 0.000s
1 tree, 25 leaves, max depth = 8, train loss: 0.46524, val loss: 0.45748, in 0.016s
1 tree, 25 leaves, max depth = 14, train loss: 0.46149, val loss: 0.45343, in 0.000s
1 tree, 25 leaves, max depth = 8, train loss: 0.45773, val loss: 0.44985, in 0.016s
1 tree, 25 leaves, max depth = 10, train loss: 0.45422, val loss: 0.44610, in 0.000s
1 tree, 25 leaves, max depth = 8, train loss: 0.45069, val loss: 0.44274, in 0.016s
1 tree, 25 leaves, max depth = 10, train loss: 0.44741, val loss: 0.43923, in 0.000s
1 tree, 25 leaves, max depth = 8, train loss: 0.44408, val loss: 0.43607, in 0.016s
1 tree, 25 leaves, max depth = 8, train loss: 0.44092, val loss: 0.43309, in 0.016s
1 tree, 25 leaves, max depth = 10, train loss: 0.43790, val loss: 0.42984, in 0.000s
1 tree, 25 leaves, max depth = 10, train loss: 0.43503, val loss: 0.42676, in 0.000s
1 tree, 25 leaves, max depth = 8, train loss: 0.43207, val loss: 0.42395, in 0.016s
1 tree, 25 leaves, max depth = 11, train loss: 0.42938, val loss: 0.42106, in 0.016s
1 tree, 25 leaves, max depth = 8, train loss: 0.42658, val loss: 0.41841, in 0.000s
1 tree, 25 leaves, max depth = 11, train loss: 0.42407, val loss: 0.41569, in 0.016s
1 tree, 25 leaves, max depth = 8, train loss: 0.42143, val loss: 0.41319, in 0.000s
1 tree, 25 leaves, max depth = 8, train loss: 0.41891, val loss: 0.41082, in 0.016s
1 tree, 25 leaves, max depth = 8, train loss: 0.41652, val loss: 0.40859, in 0.000s
1 tree, 25 leaves, max depth = 11, train loss: 0.41423, val loss: 0.40607, in 0.016s
1 tree, 25 leaves, max depth = 8, train loss: 0.41197, val loss: 0.40395, in 0.000s
1 tree, 3 leaves, max depth = 2, train loss: 0.40987, val loss: 0.40178, in 0.016s
1 tree, 25 leaves, max depth = 17, train loss: 0.40773, val loss: 0.39943, in 0.000s
1 tree, 25 leaves, max depth = 9, train loss: 0.40560, val loss: 0.39744, in 0.016s
1 tree, 25 leaves, max depth = 11, train loss: 0.40359, val loss: 0.39524, in 0.000s
1 tree, 25 leaves, max depth = 9, train loss: 0.40157, val loss: 0.39336, in 0.016s
1 tree, 25 leaves, max depth = 10, train loss: 0.39966, val loss: 0.39127, in 0.000s
1 tree, 25 leaves, max depth = 9, train loss: 0.39774, val loss: 0.38949, in 0.016s
1 tree, 3 leaves, max depth = 2, train loss: 0.39582, val loss: 0.38750, in 0.000s
1 tree, 25 leaves, max depth = 9, train loss: 0.39400, val loss: 0.38582, in 0.016s
1 tree, 3 leaves, max depth = 2, train loss: 0.39219, val loss: 0.38394, in 0.000s
1 tree, 3 leaves, max depth = 2, train loss: 0.39046, val loss: 0.38215, in 0.016s
1 tree, 3 leaves, max depth = 2, train loss: 0.38883, val loss: 0.38046, in 0.000s
1 tree, 3 leaves, max depth = 2, train loss: 0.38728, val loss: 0.37884, in 0.016s
1 tree, 25 leaves, max depth = 9, train loss: 0.38555, val loss: 0.37726, in 0.000s
1 tree, 25 leaves, max depth = 9, train loss: 0.38380, val loss: 0.37535, in 0.016s
1 tree, 3 leaves, max depth = 2, train loss: 0.38233, val loss: 0.37382, in 0.000s
1 tree, 25 leaves, max depth = 9, train loss: 0.38069, val loss: 0.37233, in 0.016s
1 tree, 2 leaves, max depth = 1, train loss: 0.37935, val loss: 0.37094, in 0.000s
1 tree, 25 leaves, max depth = 9, train loss: 0.37778, val loss: 0.36952, in 0.016s
1 tree, 2 leaves, max depth = 1, train loss: 0.37651, val loss: 0.36819, in 0.000s
1 tree, 25 leaves, max depth = 8, train loss: 0.37486, val loss: 0.36642, in 0.000s
1 tree, 25 leaves, max depth = 8, train loss: 0.37332, val loss: 0.36473, in 0.016s
1 tree, 25 leaves, max depth = 9, train loss: 0.37182, val loss: 0.36339, in 0.000s
1 tree, 25 leaves, max depth = 8, train loss: 0.37034, val loss: 0.36179, in 0.016s
1 tree, 2 leaves, max depth = 1, train loss: 0.36913, val loss: 0.36054, in 0.000s
1 tree, 25 leaves, max depth = 9, train loss: 0.36771, val loss: 0.35926, in 0.016s
1 tree, 2 leaves, max depth = 1, train loss: 0.36655, val loss: 0.35806, in 0.000s
1 tree, 25 leaves, max depth = 8, train loss: 0.36518, val loss: 0.35656, in 0.016s
1 tree, 2 leaves, max depth = 1, train loss: 0.36408, val loss: 0.35542, in 0.000s
1 tree, 25 leaves, max depth = 7, train loss: 0.36276, val loss: 0.35399, in 0.016s
1 tree, 25 leaves, max depth = 8, train loss: 0.36149, val loss: 0.35263, in 0.000s
1 tree, 2 leaves, max depth = 1, train loss: 0.36045, val loss: 0.35154, in 0.016s
1 tree, 25 leaves, max depth = 12, train loss: 0.35924, val loss: 0.35019, in 0.000s
1 tree, 25 leaves, max depth = 10, train loss: 0.35786, val loss: 0.34895, in 0.016s
1 tree, 25 leaves, max depth = 12, train loss: 0.35672, val loss: 0.34767, in 0.000s
1 tree, 25 leaves, max depth = 10, train loss: 0.35540, val loss: 0.34650, in 0.016s
1 tree, 3 leaves, max depth = 2, train loss: 0.35439, val loss: 0.34546, in 0.016s
1 tree, 2 leaves, max depth = 1, train loss: 0.35344, val loss: 0.34447, in 0.000s
1 tree, 25 leaves, max depth = 10, train loss: 0.35217, val loss: 0.34335, in 0.000s
1 tree, 3 leaves, max depth = 2, train loss: 0.35125, val loss: 0.34239, in 0.016s
1 tree, 25 leaves, max depth = 8, train loss: 0.35020, val loss: 0.34124, in 0.000s
1 tree, 25 leaves, max depth = 10, train loss: 0.34898, val loss: 0.34016, in 0.016s
1 tree, 25 leaves, max depth = 9, train loss: 0.34794, val loss: 0.33898, in 0.000s
1 tree, 25 leaves, max depth = 9, train loss: 0.34677, val loss: 0.33796, in 0.016s
1 tree, 3 leaves, max depth = 2, train loss: 0.34588, val loss: 0.33703, in 0.000s
1 tree, 25 leaves, max depth = 9, train loss: 0.34489, val loss: 0.33590, in 0.016s
1 tree, 3 leaves, max depth = 2, train loss: 0.34404, val loss: 0.33502, in 0.000s
Fit 95 trees in 1.111 s, (1972 total leaves)
Time spent computing histograms: 0.440s
Time spent finding best splits:  0.059s
Time spent applying splits:      0.049s
Time spent predicting:           0.000s
Trial 98, Fold 2: Log loss = 0.34651320081061987, Average precision = 0.9441432964982537, ROC-AUC = 0.946327187649187, Elapsed Time = 1.1205337000010331 seconds
Trial 98, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 98, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
Binning 0.040 GB of training data: 0.158 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 25 leaves, max depth = 9, train loss: 0.67832, val loss: 0.67785, in 0.016s
1 tree, 25 leaves, max depth = 10, train loss: 0.66474, val loss: 0.66376, in 0.000s
1 tree, 25 leaves, max depth = 9, train loss: 0.65192, val loss: 0.65043, in 0.016s
1 tree, 25 leaves, max depth = 9, train loss: 0.63987, val loss: 0.63795, in 0.000s
1 tree, 25 leaves, max depth = 8, train loss: 0.62852, val loss: 0.62616, in 0.016s
1 tree, 25 leaves, max depth = 10, train loss: 0.61790, val loss: 0.61510, in 0.000s
1 tree, 25 leaves, max depth = 9, train loss: 0.60784, val loss: 0.60461, in 0.016s
1 tree, 25 leaves, max depth = 11, train loss: 0.59835, val loss: 0.59476, in 0.000s
1 tree, 25 leaves, max depth = 9, train loss: 0.58923, val loss: 0.58532, in 0.016s
1 tree, 25 leaves, max depth = 9, train loss: 0.58067, val loss: 0.57642, in 0.016s
1 tree, 25 leaves, max depth = 13, train loss: 0.57274, val loss: 0.56812, in 0.000s
1 tree, 25 leaves, max depth = 8, train loss: 0.56505, val loss: 0.56014, in 0.016s
1 tree, 25 leaves, max depth = 8, train loss: 0.55792, val loss: 0.55266, in 0.000s
1 tree, 25 leaves, max depth = 8, train loss: 0.55105, val loss: 0.54552, in 0.000s
1 tree, 25 leaves, max depth = 9, train loss: 0.54455, val loss: 0.53876, in 0.016s
1 tree, 25 leaves, max depth = 8, train loss: 0.53745, val loss: 0.53212, in 0.000s
1 tree, 25 leaves, max depth = 15, train loss: 0.53158, val loss: 0.52595, in 0.016s
1 tree, 25 leaves, max depth = 8, train loss: 0.52499, val loss: 0.51980, in 0.016s
1 tree, 25 leaves, max depth = 14, train loss: 0.51959, val loss: 0.51411, in 0.000s
1 tree, 25 leaves, max depth = 7, train loss: 0.51346, val loss: 0.50841, in 0.016s
1 tree, 25 leaves, max depth = 7, train loss: 0.50767, val loss: 0.50301, in 0.000s
1 tree, 25 leaves, max depth = 7, train loss: 0.50217, val loss: 0.49788, in 0.016s
1 tree, 25 leaves, max depth = 7, train loss: 0.49697, val loss: 0.49304, in 0.000s
1 tree, 25 leaves, max depth = 14, train loss: 0.49230, val loss: 0.48809, in 0.016s
1 tree, 25 leaves, max depth = 7, train loss: 0.48744, val loss: 0.48357, in 0.000s
1 tree, 25 leaves, max depth = 7, train loss: 0.48284, val loss: 0.47930, in 0.016s
1 tree, 25 leaves, max depth = 10, train loss: 0.47854, val loss: 0.47476, in 0.016s
1 tree, 25 leaves, max depth = 10, train loss: 0.47446, val loss: 0.47046, in 0.000s
1 tree, 25 leaves, max depth = 9, train loss: 0.47019, val loss: 0.46653, in 0.016s
1 tree, 25 leaves, max depth = 9, train loss: 0.46614, val loss: 0.46280, in 0.000s
1 tree, 25 leaves, max depth = 10, train loss: 0.46245, val loss: 0.45884, in 0.016s
1 tree, 25 leaves, max depth = 7, train loss: 0.45864, val loss: 0.45533, in 0.000s
1 tree, 25 leaves, max depth = 10, train loss: 0.45516, val loss: 0.45164, in 0.016s
1 tree, 25 leaves, max depth = 8, train loss: 0.45159, val loss: 0.44835, in 0.000s
1 tree, 25 leaves, max depth = 9, train loss: 0.44819, val loss: 0.44524, in 0.016s
1 tree, 25 leaves, max depth = 10, train loss: 0.44499, val loss: 0.44184, in 0.000s
1 tree, 25 leaves, max depth = 7, train loss: 0.44178, val loss: 0.43889, in 0.016s
1 tree, 25 leaves, max depth = 10, train loss: 0.43879, val loss: 0.43571, in 0.000s
1 tree, 25 leaves, max depth = 10, train loss: 0.43595, val loss: 0.43268, in 0.016s
1 tree, 25 leaves, max depth = 10, train loss: 0.43295, val loss: 0.42996, in 0.000s
1 tree, 25 leaves, max depth = 8, train loss: 0.43023, val loss: 0.42699, in 0.016s
1 tree, 25 leaves, max depth = 9, train loss: 0.42741, val loss: 0.42441, in 0.000s
1 tree, 25 leaves, max depth = 10, train loss: 0.42491, val loss: 0.42174, in 0.016s
1 tree, 25 leaves, max depth = 9, train loss: 0.42224, val loss: 0.41932, in 0.000s
1 tree, 25 leaves, max depth = 7, train loss: 0.41970, val loss: 0.41701, in 0.016s
1 tree, 25 leaves, max depth = 7, train loss: 0.41728, val loss: 0.41481, in 0.016s
1 tree, 25 leaves, max depth = 16, train loss: 0.41503, val loss: 0.41235, in 0.000s
1 tree, 25 leaves, max depth = 8, train loss: 0.41274, val loss: 0.41028, in 0.016s
1 tree, 3 leaves, max depth = 2, train loss: 0.41057, val loss: 0.40827, in 0.000s
1 tree, 25 leaves, max depth = 8, train loss: 0.40840, val loss: 0.40632, in 0.016s
1 tree, 25 leaves, max depth = 9, train loss: 0.40627, val loss: 0.40404, in 0.000s
1 tree, 25 leaves, max depth = 8, train loss: 0.40421, val loss: 0.40219, in 0.016s
1 tree, 25 leaves, max depth = 9, train loss: 0.40222, val loss: 0.39999, in 0.000s
1 tree, 25 leaves, max depth = 9, train loss: 0.40032, val loss: 0.39794, in 0.016s
1 tree, 25 leaves, max depth = 9, train loss: 0.39837, val loss: 0.39620, in 0.000s
1 tree, 3 leaves, max depth = 2, train loss: 0.39638, val loss: 0.39436, in 0.016s
1 tree, 25 leaves, max depth = 9, train loss: 0.39453, val loss: 0.39272, in 0.000s
1 tree, 3 leaves, max depth = 2, train loss: 0.39264, val loss: 0.39097, in 0.016s
1 tree, 3 leaves, max depth = 2, train loss: 0.39086, val loss: 0.38931, in 0.000s
1 tree, 3 leaves, max depth = 2, train loss: 0.38916, val loss: 0.38774, in 0.000s
1 tree, 3 leaves, max depth = 2, train loss: 0.38755, val loss: 0.38626, in 0.016s
1 tree, 25 leaves, max depth = 8, train loss: 0.38579, val loss: 0.38472, in 0.000s
1 tree, 25 leaves, max depth = 8, train loss: 0.38412, val loss: 0.38327, in 0.016s
1 tree, 3 leaves, max depth = 2, train loss: 0.38259, val loss: 0.38185, in 0.000s
1 tree, 3 leaves, max depth = 2, train loss: 0.38114, val loss: 0.38051, in 0.016s
1 tree, 3 leaves, max depth = 2, train loss: 0.37976, val loss: 0.37924, in 0.000s
1 tree, 25 leaves, max depth = 10, train loss: 0.37804, val loss: 0.37736, in 0.016s
1 tree, 25 leaves, max depth = 8, train loss: 0.37645, val loss: 0.37561, in 0.000s
1 tree, 25 leaves, max depth = 8, train loss: 0.37485, val loss: 0.37422, in 0.016s
1 tree, 2 leaves, max depth = 1, train loss: 0.37360, val loss: 0.37307, in 0.000s
1 tree, 25 leaves, max depth = 10, train loss: 0.37206, val loss: 0.37139, in 0.016s
1 tree, 25 leaves, max depth = 8, train loss: 0.37052, val loss: 0.37007, in 0.000s
1 tree, 3 leaves, max depth = 2, train loss: 0.36933, val loss: 0.36896, in 0.000s
1 tree, 25 leaves, max depth = 8, train loss: 0.36792, val loss: 0.36738, in 0.000s
1 tree, 3 leaves, max depth = 2, train loss: 0.36679, val loss: 0.36633, in 0.000s
1 tree, 25 leaves, max depth = 8, train loss: 0.36545, val loss: 0.36485, in 0.016s
1 tree, 3 leaves, max depth = 2, train loss: 0.36437, val loss: 0.36384, in 0.016s
1 tree, 25 leaves, max depth = 8, train loss: 0.36307, val loss: 0.36242, in 0.000s
1 tree, 25 leaves, max depth = 7, train loss: 0.36184, val loss: 0.36105, in 0.016s
1 tree, 2 leaves, max depth = 1, train loss: 0.36082, val loss: 0.36010, in 0.000s
1 tree, 25 leaves, max depth = 9, train loss: 0.35963, val loss: 0.35882, in 0.000s
1 tree, 25 leaves, max depth = 8, train loss: 0.35813, val loss: 0.35754, in 0.016s
1 tree, 25 leaves, max depth = 9, train loss: 0.35701, val loss: 0.35632, in 0.000s
1 tree, 25 leaves, max depth = 8, train loss: 0.35558, val loss: 0.35511, in 0.016s
1 tree, 25 leaves, max depth = 8, train loss: 0.35422, val loss: 0.35396, in 0.000s
1 tree, 2 leaves, max depth = 1, train loss: 0.35322, val loss: 0.35304, in 0.016s
1 tree, 25 leaves, max depth = 8, train loss: 0.35192, val loss: 0.35195, in 0.000s
1 tree, 3 leaves, max depth = 2, train loss: 0.35097, val loss: 0.35106, in 0.016s
1 tree, 3 leaves, max depth = 2, train loss: 0.35006, val loss: 0.35021, in 0.000s
1 tree, 25 leaves, max depth = 8, train loss: 0.34901, val loss: 0.34905, in 0.016s
1 tree, 25 leaves, max depth = 10, train loss: 0.34800, val loss: 0.34783, in 0.000s
1 tree, 25 leaves, max depth = 8, train loss: 0.34674, val loss: 0.34679, in 0.016s
1 tree, 3 leaves, max depth = 2, train loss: 0.34586, val loss: 0.34597, in 0.000s
1 tree, 25 leaves, max depth = 9, train loss: 0.34465, val loss: 0.34497, in 0.000s
1 tree, 2 leaves, max depth = 1, train loss: 0.34381, val loss: 0.34419, in 0.016s
Fit 95 trees in 1.064 s, (1953 total leaves)
Time spent computing histograms: 0.420s
Time spent finding best splits:  0.053s
Time spent applying splits:      0.044s
Time spent predicting:           0.000s
Trial 98, Fold 3: Log loss = 0.340985015897022, Average precision = 0.9499141215918279, ROC-AUC = 0.9504775939989021, Elapsed Time = 1.0630522000010387 seconds
Trial 98, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 98, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
Binning 0.040 GB of training data: 0.189 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 25 leaves, max depth = 9, train loss: 0.67834, val loss: 0.67748, in 0.016s
1 tree, 25 leaves, max depth = 9, train loss: 0.66456, val loss: 0.66287, in 0.016s
1 tree, 25 leaves, max depth = 13, train loss: 0.65162, val loss: 0.64913, in 0.000s
1 tree, 25 leaves, max depth = 12, train loss: 0.63954, val loss: 0.63634, in 0.016s
1 tree, 25 leaves, max depth = 9, train loss: 0.62810, val loss: 0.62414, in 0.000s
1 tree, 25 leaves, max depth = 12, train loss: 0.61733, val loss: 0.61263, in 0.016s
1 tree, 25 leaves, max depth = 10, train loss: 0.60723, val loss: 0.60192, in 0.016s
1 tree, 25 leaves, max depth = 11, train loss: 0.59766, val loss: 0.59164, in 0.000s
1 tree, 25 leaves, max depth = 11, train loss: 0.58863, val loss: 0.58194, in 0.016s
1 tree, 25 leaves, max depth = 15, train loss: 0.58015, val loss: 0.57280, in 0.000s
1 tree, 25 leaves, max depth = 9, train loss: 0.57214, val loss: 0.56422, in 0.016s
1 tree, 25 leaves, max depth = 13, train loss: 0.56452, val loss: 0.55598, in 0.016s
1 tree, 25 leaves, max depth = 15, train loss: 0.55737, val loss: 0.54822, in 0.000s
1 tree, 25 leaves, max depth = 9, train loss: 0.55056, val loss: 0.54081, in 0.016s
1 tree, 25 leaves, max depth = 9, train loss: 0.54415, val loss: 0.53389, in 0.016s
1 tree, 25 leaves, max depth = 9, train loss: 0.53706, val loss: 0.52691, in 0.000s
1 tree, 25 leaves, max depth = 13, train loss: 0.53112, val loss: 0.52042, in 0.016s
1 tree, 25 leaves, max depth = 9, train loss: 0.52453, val loss: 0.51395, in 0.016s
1 tree, 25 leaves, max depth = 15, train loss: 0.51909, val loss: 0.50797, in 0.000s
1 tree, 25 leaves, max depth = 7, train loss: 0.51296, val loss: 0.50194, in 0.016s
1 tree, 25 leaves, max depth = 8, train loss: 0.50716, val loss: 0.49623, in 0.016s
1 tree, 25 leaves, max depth = 7, train loss: 0.50167, val loss: 0.49081, in 0.000s
1 tree, 25 leaves, max depth = 7, train loss: 0.49646, val loss: 0.48569, in 0.016s
1 tree, 25 leaves, max depth = 10, train loss: 0.49176, val loss: 0.48056, in 0.016s
1 tree, 25 leaves, max depth = 7, train loss: 0.48689, val loss: 0.47577, in 0.000s
1 tree, 25 leaves, max depth = 7, train loss: 0.48228, val loss: 0.47124, in 0.016s
1 tree, 25 leaves, max depth = 14, train loss: 0.47798, val loss: 0.46649, in 0.000s
1 tree, 25 leaves, max depth = 14, train loss: 0.47391, val loss: 0.46198, in 0.016s
1 tree, 25 leaves, max depth = 9, train loss: 0.46963, val loss: 0.45778, in 0.016s
1 tree, 25 leaves, max depth = 9, train loss: 0.46558, val loss: 0.45380, in 0.000s
1 tree, 25 leaves, max depth = 10, train loss: 0.46185, val loss: 0.44970, in 0.016s
1 tree, 25 leaves, max depth = 9, train loss: 0.45805, val loss: 0.44595, in 0.000s
1 tree, 25 leaves, max depth = 8, train loss: 0.45447, val loss: 0.44199, in 0.016s
1 tree, 25 leaves, max depth = 8, train loss: 0.45090, val loss: 0.43848, in 0.016s
1 tree, 25 leaves, max depth = 8, train loss: 0.44750, val loss: 0.43514, in 0.000s
1 tree, 25 leaves, max depth = 11, train loss: 0.44430, val loss: 0.43155, in 0.016s
1 tree, 25 leaves, max depth = 8, train loss: 0.44110, val loss: 0.42845, in 0.016s
1 tree, 25 leaves, max depth = 15, train loss: 0.43811, val loss: 0.42506, in 0.016s
1 tree, 25 leaves, max depth = 9, train loss: 0.43518, val loss: 0.42180, in 0.000s
1 tree, 25 leaves, max depth = 8, train loss: 0.43220, val loss: 0.41890, in 0.016s
1 tree, 25 leaves, max depth = 12, train loss: 0.42954, val loss: 0.41589, in 0.016s
1 tree, 25 leaves, max depth = 9, train loss: 0.42672, val loss: 0.41315, in 0.000s
1 tree, 25 leaves, max depth = 12, train loss: 0.42422, val loss: 0.41032, in 0.016s
1 tree, 25 leaves, max depth = 9, train loss: 0.42155, val loss: 0.40772, in 0.016s
1 tree, 25 leaves, max depth = 8, train loss: 0.41902, val loss: 0.40526, in 0.000s
1 tree, 25 leaves, max depth = 8, train loss: 0.41661, val loss: 0.40292, in 0.016s
1 tree, 25 leaves, max depth = 12, train loss: 0.41434, val loss: 0.40036, in 0.016s
1 tree, 25 leaves, max depth = 7, train loss: 0.41206, val loss: 0.39812, in 0.000s
1 tree, 3 leaves, max depth = 2, train loss: 0.40996, val loss: 0.39590, in 0.016s
1 tree, 25 leaves, max depth = 7, train loss: 0.40779, val loss: 0.39377, in 0.000s
1 tree, 25 leaves, max depth = 12, train loss: 0.40567, val loss: 0.39134, in 0.016s
1 tree, 25 leaves, max depth = 7, train loss: 0.40361, val loss: 0.38933, in 0.016s
1 tree, 25 leaves, max depth = 12, train loss: 0.40163, val loss: 0.38705, in 0.000s
1 tree, 25 leaves, max depth = 10, train loss: 0.39973, val loss: 0.38485, in 0.016s
1 tree, 25 leaves, max depth = 7, train loss: 0.39779, val loss: 0.38295, in 0.000s
1 tree, 3 leaves, max depth = 2, train loss: 0.39586, val loss: 0.38091, in 0.000s
1 tree, 25 leaves, max depth = 8, train loss: 0.39401, val loss: 0.37911, in 0.016s
1 tree, 3 leaves, max depth = 2, train loss: 0.39219, val loss: 0.37718, in 0.000s
1 tree, 3 leaves, max depth = 2, train loss: 0.39047, val loss: 0.37535, in 0.016s
1 tree, 2 leaves, max depth = 1, train loss: 0.38890, val loss: 0.37368, in 0.000s
1 tree, 2 leaves, max depth = 1, train loss: 0.38741, val loss: 0.37209, in 0.016s
1 tree, 25 leaves, max depth = 9, train loss: 0.38564, val loss: 0.37044, in 0.000s
1 tree, 25 leaves, max depth = 8, train loss: 0.38396, val loss: 0.36885, in 0.016s
1 tree, 2 leaves, max depth = 1, train loss: 0.38254, val loss: 0.36734, in 0.016s
1 tree, 2 leaves, max depth = 1, train loss: 0.38119, val loss: 0.36591, in 0.000s
1 tree, 25 leaves, max depth = 9, train loss: 0.37948, val loss: 0.36399, in 0.016s
1 tree, 2 leaves, max depth = 1, train loss: 0.37820, val loss: 0.36263, in 0.000s
1 tree, 25 leaves, max depth = 9, train loss: 0.37662, val loss: 0.36083, in 0.016s
1 tree, 25 leaves, max depth = 9, train loss: 0.37501, val loss: 0.35933, in 0.016s
1 tree, 2 leaves, max depth = 1, train loss: 0.37379, val loss: 0.35804, in 0.000s
1 tree, 25 leaves, max depth = 11, train loss: 0.37227, val loss: 0.35630, in 0.016s
1 tree, 25 leaves, max depth = 8, train loss: 0.37073, val loss: 0.35488, in 0.016s
1 tree, 2 leaves, max depth = 1, train loss: 0.36957, val loss: 0.35364, in 0.000s
1 tree, 25 leaves, max depth = 9, train loss: 0.36817, val loss: 0.35204, in 0.016s
1 tree, 2 leaves, max depth = 1, train loss: 0.36707, val loss: 0.35087, in 0.000s
1 tree, 25 leaves, max depth = 9, train loss: 0.36574, val loss: 0.34934, in 0.016s
1 tree, 25 leaves, max depth = 9, train loss: 0.36445, val loss: 0.34786, in 0.016s
1 tree, 25 leaves, max depth = 8, train loss: 0.36321, val loss: 0.34646, in 0.000s
1 tree, 25 leaves, max depth = 9, train loss: 0.36204, val loss: 0.34514, in 0.016s
1 tree, 2 leaves, max depth = 1, train loss: 0.36100, val loss: 0.34402, in 0.016s
1 tree, 25 leaves, max depth = 9, train loss: 0.35990, val loss: 0.34275, in 0.000s
1 tree, 25 leaves, max depth = 8, train loss: 0.35839, val loss: 0.34137, in 0.016s
1 tree, 25 leaves, max depth = 9, train loss: 0.35735, val loss: 0.34016, in 0.000s
1 tree, 25 leaves, max depth = 9, train loss: 0.35591, val loss: 0.33886, in 0.016s
1 tree, 25 leaves, max depth = 8, train loss: 0.35454, val loss: 0.33760, in 0.016s
1 tree, 2 leaves, max depth = 1, train loss: 0.35353, val loss: 0.33652, in 0.000s
1 tree, 25 leaves, max depth = 9, train loss: 0.35222, val loss: 0.33533, in 0.016s
1 tree, 2 leaves, max depth = 1, train loss: 0.35125, val loss: 0.33429, in 0.000s
1 tree, 2 leaves, max depth = 1, train loss: 0.35033, val loss: 0.33330, in 0.016s
1 tree, 25 leaves, max depth = 8, train loss: 0.34907, val loss: 0.33215, in 0.000s
1 tree, 25 leaves, max depth = 11, train loss: 0.34804, val loss: 0.33108, in 0.016s
1 tree, 25 leaves, max depth = 8, train loss: 0.34683, val loss: 0.32998, in 0.016s
1 tree, 2 leaves, max depth = 1, train loss: 0.34594, val loss: 0.32902, in 0.000s
1 tree, 25 leaves, max depth = 11, train loss: 0.34496, val loss: 0.32800, in 0.016s
1 tree, 2 leaves, max depth = 1, train loss: 0.34411, val loss: 0.32709, in 0.000s
Fit 95 trees in 1.236 s, (1965 total leaves)
Time spent computing histograms: 0.507s
Time spent finding best splits:  0.066s
Time spent applying splits:      0.055s
Time spent predicting:           0.031s
Trial 98, Fold 4: Log loss = 0.34290472175752834, Average precision = 0.9498242747729755, ROC-AUC = 0.9485274481331263, Elapsed Time = 1.2537620999992214 seconds
Trial 98, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 98, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
Binning 0.040 GB of training data: 0.174 s
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 25 leaves, max depth = 9, train loss: 0.67829, val loss: 0.67729, in 0.000s
1 tree, 25 leaves, max depth = 11, train loss: 0.66451, val loss: 0.66249, in 0.016s
1 tree, 25 leaves, max depth = 9, train loss: 0.65143, val loss: 0.64848, in 0.000s
1 tree, 25 leaves, max depth = 9, train loss: 0.63925, val loss: 0.63538, in 0.016s
1 tree, 25 leaves, max depth = 9, train loss: 0.62768, val loss: 0.62294, in 0.016s
1 tree, 25 leaves, max depth = 12, train loss: 0.61688, val loss: 0.61135, in 0.000s
1 tree, 25 leaves, max depth = 12, train loss: 0.60671, val loss: 0.60033, in 0.016s
1 tree, 25 leaves, max depth = 9, train loss: 0.59701, val loss: 0.58983, in 0.000s
1 tree, 25 leaves, max depth = 9, train loss: 0.58787, val loss: 0.57991, in 0.016s
1 tree, 25 leaves, max depth = 12, train loss: 0.57932, val loss: 0.57066, in 0.016s
1 tree, 25 leaves, max depth = 12, train loss: 0.57124, val loss: 0.56191, in 0.000s
1 tree, 25 leaves, max depth = 9, train loss: 0.56353, val loss: 0.55348, in 0.016s
1 tree, 25 leaves, max depth = 12, train loss: 0.55631, val loss: 0.54555, in 0.016s
1 tree, 25 leaves, max depth = 9, train loss: 0.54941, val loss: 0.53796, in 0.000s
1 tree, 25 leaves, max depth = 12, train loss: 0.54295, val loss: 0.53088, in 0.016s
1 tree, 25 leaves, max depth = 9, train loss: 0.53595, val loss: 0.52417, in 0.000s
1 tree, 25 leaves, max depth = 9, train loss: 0.52999, val loss: 0.51764, in 0.016s
1 tree, 25 leaves, max depth = 9, train loss: 0.52349, val loss: 0.51143, in 0.016s
1 tree, 25 leaves, max depth = 9, train loss: 0.51799, val loss: 0.50538, in 0.000s
1 tree, 25 leaves, max depth = 9, train loss: 0.51193, val loss: 0.49961, in 0.016s
1 tree, 25 leaves, max depth = 9, train loss: 0.50620, val loss: 0.49417, in 0.000s
1 tree, 25 leaves, max depth = 9, train loss: 0.50077, val loss: 0.48900, in 0.000s
1 tree, 25 leaves, max depth = 9, train loss: 0.49563, val loss: 0.48411, in 0.016s
1 tree, 25 leaves, max depth = 11, train loss: 0.49086, val loss: 0.47883, in 0.000s
1 tree, 25 leaves, max depth = 9, train loss: 0.48605, val loss: 0.47427, in 0.016s
1 tree, 25 leaves, max depth = 10, train loss: 0.48161, val loss: 0.46933, in 0.000s
1 tree, 25 leaves, max depth = 10, train loss: 0.47710, val loss: 0.46509, in 0.024s
1 tree, 25 leaves, max depth = 10, train loss: 0.47297, val loss: 0.46047, in 0.008s
1 tree, 25 leaves, max depth = 10, train loss: 0.46875, val loss: 0.45648, in 0.000s
1 tree, 25 leaves, max depth = 10, train loss: 0.46490, val loss: 0.45216, in 0.016s
1 tree, 25 leaves, max depth = 13, train loss: 0.46125, val loss: 0.44807, in 0.000s
1 tree, 25 leaves, max depth = 10, train loss: 0.45732, val loss: 0.44438, in 0.016s
1 tree, 25 leaves, max depth = 8, train loss: 0.45386, val loss: 0.44045, in 0.016s
1 tree, 25 leaves, max depth = 10, train loss: 0.45017, val loss: 0.43700, in 0.016s
1 tree, 25 leaves, max depth = 10, train loss: 0.44666, val loss: 0.43372, in 0.000s
1 tree, 25 leaves, max depth = 10, train loss: 0.44334, val loss: 0.43062, in 0.016s
1 tree, 25 leaves, max depth = 10, train loss: 0.44027, val loss: 0.42714, in 0.016s
1 tree, 25 leaves, max depth = 9, train loss: 0.43713, val loss: 0.42424, in 0.000s
1 tree, 25 leaves, max depth = 9, train loss: 0.43424, val loss: 0.42095, in 0.016s
1 tree, 25 leaves, max depth = 9, train loss: 0.43127, val loss: 0.41823, in 0.000s
1 tree, 25 leaves, max depth = 9, train loss: 0.42856, val loss: 0.41514, in 0.016s
1 tree, 25 leaves, max depth = 10, train loss: 0.42576, val loss: 0.41257, in 0.016s
1 tree, 25 leaves, max depth = 8, train loss: 0.42322, val loss: 0.40966, in 0.000s
1 tree, 25 leaves, max depth = 10, train loss: 0.42057, val loss: 0.40726, in 0.016s
1 tree, 25 leaves, max depth = 10, train loss: 0.41805, val loss: 0.40495, in 0.016s
1 tree, 25 leaves, max depth = 9, train loss: 0.41569, val loss: 0.40223, in 0.000s
1 tree, 25 leaves, max depth = 10, train loss: 0.41331, val loss: 0.40008, in 0.016s
1 tree, 25 leaves, max depth = 10, train loss: 0.41104, val loss: 0.39804, in 0.000s
1 tree, 3 leaves, max depth = 2, train loss: 0.40894, val loss: 0.39601, in 0.016s
1 tree, 25 leaves, max depth = 13, train loss: 0.40678, val loss: 0.39352, in 0.000s
1 tree, 25 leaves, max depth = 10, train loss: 0.40463, val loss: 0.39160, in 0.000s
1 tree, 25 leaves, max depth = 11, train loss: 0.40260, val loss: 0.38926, in 0.016s
1 tree, 25 leaves, max depth = 10, train loss: 0.40057, val loss: 0.38744, in 0.000s
1 tree, 25 leaves, max depth = 10, train loss: 0.39863, val loss: 0.38518, in 0.016s
1 tree, 25 leaves, max depth = 10, train loss: 0.39669, val loss: 0.38346, in 0.016s
1 tree, 3 leaves, max depth = 2, train loss: 0.39477, val loss: 0.38160, in 0.000s
1 tree, 25 leaves, max depth = 10, train loss: 0.39293, val loss: 0.37998, in 0.016s
1 tree, 3 leaves, max depth = 2, train loss: 0.39110, val loss: 0.37822, in 0.016s
1 tree, 3 leaves, max depth = 2, train loss: 0.38937, val loss: 0.37655, in 0.000s
1 tree, 2 leaves, max depth = 1, train loss: 0.38781, val loss: 0.37500, in 0.000s
1 tree, 2 leaves, max depth = 1, train loss: 0.38632, val loss: 0.37352, in 0.016s
1 tree, 25 leaves, max depth = 10, train loss: 0.38457, val loss: 0.37199, in 0.016s
1 tree, 25 leaves, max depth = 10, train loss: 0.38282, val loss: 0.36995, in 0.000s
1 tree, 2 leaves, max depth = 1, train loss: 0.38141, val loss: 0.36855, in 0.016s
1 tree, 25 leaves, max depth = 10, train loss: 0.37974, val loss: 0.36711, in 0.016s
1 tree, 2 leaves, max depth = 1, train loss: 0.37840, val loss: 0.36578, in 0.000s
1 tree, 25 leaves, max depth = 8, train loss: 0.37673, val loss: 0.36384, in 0.016s
1 tree, 2 leaves, max depth = 1, train loss: 0.37546, val loss: 0.36259, in 0.000s
1 tree, 25 leaves, max depth = 10, train loss: 0.37386, val loss: 0.36121, in 0.016s
1 tree, 25 leaves, max depth = 8, train loss: 0.37228, val loss: 0.35938, in 0.000s
1 tree, 25 leaves, max depth = 10, train loss: 0.37075, val loss: 0.35808, in 0.000s
1 tree, 25 leaves, max depth = 9, train loss: 0.36926, val loss: 0.35631, in 0.016s
1 tree, 2 leaves, max depth = 1, train loss: 0.36805, val loss: 0.35512, in 0.000s
1 tree, 25 leaves, max depth = 10, train loss: 0.36659, val loss: 0.35388, in 0.016s
1 tree, 2 leaves, max depth = 1, train loss: 0.36544, val loss: 0.35274, in 0.016s
1 tree, 25 leaves, max depth = 9, train loss: 0.36406, val loss: 0.35111, in 0.000s
1 tree, 2 leaves, max depth = 1, train loss: 0.36297, val loss: 0.35003, in 0.016s
1 tree, 25 leaves, max depth = 9, train loss: 0.36163, val loss: 0.34845, in 0.016s
1 tree, 25 leaves, max depth = 8, train loss: 0.36036, val loss: 0.34696, in 0.000s
1 tree, 2 leaves, max depth = 1, train loss: 0.35932, val loss: 0.34593, in 0.016s
1 tree, 25 leaves, max depth = 7, train loss: 0.35811, val loss: 0.34451, in 0.000s
1 tree, 25 leaves, max depth = 10, train loss: 0.35669, val loss: 0.34332, in 0.016s
1 tree, 25 leaves, max depth = 8, train loss: 0.35554, val loss: 0.34197, in 0.016s
1 tree, 25 leaves, max depth = 10, train loss: 0.35419, val loss: 0.34083, in 0.000s
1 tree, 2 leaves, max depth = 1, train loss: 0.35319, val loss: 0.33984, in 0.016s
1 tree, 2 leaves, max depth = 1, train loss: 0.35224, val loss: 0.33890, in 0.000s
1 tree, 25 leaves, max depth = 10, train loss: 0.35093, val loss: 0.33781, in 0.016s
1 tree, 25 leaves, max depth = 10, train loss: 0.34985, val loss: 0.33665, in 0.000s
1 tree, 2 leaves, max depth = 1, train loss: 0.34894, val loss: 0.33575, in 0.016s
1 tree, 25 leaves, max depth = 10, train loss: 0.34768, val loss: 0.33471, in 0.016s
1 tree, 25 leaves, max depth = 9, train loss: 0.34660, val loss: 0.33352, in 0.000s
1 tree, 25 leaves, max depth = 10, train loss: 0.34540, val loss: 0.33252, in 0.016s
1 tree, 2 leaves, max depth = 1, train loss: 0.34452, val loss: 0.33165, in 0.000s
1 tree, 25 leaves, max depth = 9, train loss: 0.34348, val loss: 0.33052, in 0.016s
1 tree, 2 leaves, max depth = 1, train loss: 0.34264, val loss: 0.32969, in 0.016s
Fit 95 trees in 1.253 s, (1965 total leaves)
Time spent computing histograms: 0.505s
Time spent finding best splits:  0.065s
Time spent applying splits:      0.054s
Time spent predicting:           0.016s
Trial 98, Fold 5: Log loss = 0.34892619304748446, Average precision = 0.9478084946031027, ROC-AUC = 0.9457619233413225, Elapsed Time = 1.2518749000009848 seconds
Optimization Progress:  99%|#########9| 99/100 [20:40<00:13, 13.36s/it]
Trial 99, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 99, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
0.142 s 0.040 GB of training data: 
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 29 leaves, max depth = 9, train loss: 0.66366, val loss: 0.66376, in 0.016s
1 tree, 25 leaves, max depth = 8, train loss: 0.63715, val loss: 0.63719, in 0.016s
1 tree, 32 leaves, max depth = 8, train loss: 0.61314, val loss: 0.61315, in 0.016s
1 tree, 33 leaves, max depth = 9, train loss: 0.59124, val loss: 0.59133, in 0.016s
1 tree, 33 leaves, max depth = 8, train loss: 0.57128, val loss: 0.57141, in 0.016s
1 tree, 34 leaves, max depth = 9, train loss: 0.55304, val loss: 0.55316, in 0.016s
1 tree, 33 leaves, max depth = 9, train loss: 0.53633, val loss: 0.53644, in 0.016s
1 tree, 36 leaves, max depth = 9, train loss: 0.52098, val loss: 0.52109, in 0.016s
1 tree, 38 leaves, max depth = 9, train loss: 0.50687, val loss: 0.50702, in 0.016s
1 tree, 39 leaves, max depth = 9, train loss: 0.49385, val loss: 0.49405, in 0.016s
1 tree, 43 leaves, max depth = 9, train loss: 0.48181, val loss: 0.48197, in 0.016s
1 tree, 46 leaves, max depth = 9, train loss: 0.47067, val loss: 0.47087, in 0.016s
1 tree, 47 leaves, max depth = 9, train loss: 0.46035, val loss: 0.46062, in 0.016s
1 tree, 49 leaves, max depth = 10, train loss: 0.45077, val loss: 0.45113, in 0.016s
1 tree, 53 leaves, max depth = 10, train loss: 0.44161, val loss: 0.44177, in 0.016s
1 tree, 29 leaves, max depth = 9, train loss: 0.42857, val loss: 0.42919, in 0.016s
1 tree, 46 leaves, max depth = 9, train loss: 0.42078, val loss: 0.42149, in 0.031s
1 tree, 30 leaves, max depth = 8, train loss: 0.40928, val loss: 0.41041, in 0.016s
1 tree, 61 leaves, max depth = 11, train loss: 0.40240, val loss: 0.40375, in 0.000s
1 tree, 32 leaves, max depth = 8, train loss: 0.39223, val loss: 0.39399, in 0.031s
1 tree, 57 leaves, max depth = 11, train loss: 0.38616, val loss: 0.38807, in 0.000s
1 tree, 37 leaves, max depth = 10, train loss: 0.37719, val loss: 0.37951, in 0.016s
1 tree, 38 leaves, max depth = 10, train loss: 0.36892, val loss: 0.37163, in 0.016s
1 tree, 57 leaves, max depth = 10, train loss: 0.36379, val loss: 0.36668, in 0.016s
1 tree, 57 leaves, max depth = 11, train loss: 0.35900, val loss: 0.36210, in 0.016s
1 tree, 12 leaves, max depth = 5, train loss: 0.35222, val loss: 0.35538, in 0.016s
1 tree, 12 leaves, max depth = 5, train loss: 0.34591, val loss: 0.34917, in 0.016s
1 tree, 13 leaves, max depth = 5, train loss: 0.34012, val loss: 0.34346, in 0.000s
1 tree, 47 leaves, max depth = 10, train loss: 0.33510, val loss: 0.33830, in 0.031s
1 tree, 17 leaves, max depth = 7, train loss: 0.32998, val loss: 0.33318, in 0.000s
1 tree, 47 leaves, max depth = 10, train loss: 0.32543, val loss: 0.32854, in 0.031s
1 tree, 34 leaves, max depth = 11, train loss: 0.32066, val loss: 0.32428, in 0.000s
1 tree, 34 leaves, max depth = 11, train loss: 0.31625, val loss: 0.32036, in 0.000s
1 tree, 18 leaves, max depth = 7, train loss: 0.31212, val loss: 0.31622, in 0.016s
1 tree, 40 leaves, max depth = 10, train loss: 0.30821, val loss: 0.31237, in 0.031s
1 tree, 36 leaves, max depth = 11, train loss: 0.30447, val loss: 0.30907, in 0.000s
1 tree, 18 leaves, max depth = 7, train loss: 0.30094, val loss: 0.30553, in 0.016s
1 tree, 21 leaves, max depth = 7, train loss: 0.29771, val loss: 0.30228, in 0.016s
1 tree, 40 leaves, max depth = 11, train loss: 0.29431, val loss: 0.29896, in 0.000s
1 tree, 37 leaves, max depth = 10, train loss: 0.29118, val loss: 0.29624, in 0.031s
1 tree, 41 leaves, max depth = 10, train loss: 0.28829, val loss: 0.29372, in 0.000s
1 tree, 20 leaves, max depth = 7, train loss: 0.28562, val loss: 0.29102, in 0.016s
1 tree, 38 leaves, max depth = 11, train loss: 0.28265, val loss: 0.28801, in 0.016s
1 tree, 42 leaves, max depth = 10, train loss: 0.28018, val loss: 0.28592, in 0.016s
1 tree, 19 leaves, max depth = 7, train loss: 0.27789, val loss: 0.28352, in 0.016s
1 tree, 41 leaves, max depth = 11, train loss: 0.27522, val loss: 0.28092, in 0.016s
1 tree, 41 leaves, max depth = 11, train loss: 0.27311, val loss: 0.27918, in 0.016s
1 tree, 25 leaves, max depth = 9, train loss: 0.27114, val loss: 0.27712, in 0.000s
Fit 48 trees in 1.049 s, (1707 total leaves)
Time spent computing histograms: 0.295s
Time spent finding best splits:  0.050s
Time spent applying splits:      0.033s
Time spent predicting:           0.016s
Trial 99, Fold 1: Log loss = 0.27620585109547047, Average precision = 0.9619353206628051, ROC-AUC = 0.9561175456331283, Elapsed Time = 1.0621444000007614 seconds
Trial 99, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 99, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
Binning 0.040 GB of training data: 0.158 s
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 23 leaves, max depth = 9, train loss: 0.66346, val loss: 0.66287, in 0.016s
1 tree, 23 leaves, max depth = 9, train loss: 0.63670, val loss: 0.63553, in 0.016s
1 tree, 26 leaves, max depth = 9, train loss: 0.61216, val loss: 0.61065, in 0.016s
1 tree, 25 leaves, max depth = 9, train loss: 0.59016, val loss: 0.58822, in 0.016s
1 tree, 27 leaves, max depth = 9, train loss: 0.57012, val loss: 0.56771, in 0.016s
1 tree, 36 leaves, max depth = 10, train loss: 0.55204, val loss: 0.54935, in 0.016s
1 tree, 32 leaves, max depth = 10, train loss: 0.53522, val loss: 0.53221, in 0.016s
1 tree, 30 leaves, max depth = 8, train loss: 0.51978, val loss: 0.51646, in 0.016s
1 tree, 37 leaves, max depth = 9, train loss: 0.50525, val loss: 0.50186, in 0.016s
1 tree, 38 leaves, max depth = 9, train loss: 0.49186, val loss: 0.48843, in 0.016s
1 tree, 46 leaves, max depth = 9, train loss: 0.47949, val loss: 0.47606, in 0.016s
1 tree, 40 leaves, max depth = 8, train loss: 0.46807, val loss: 0.46459, in 0.016s
1 tree, 47 leaves, max depth = 9, train loss: 0.45747, val loss: 0.45402, in 0.016s
1 tree, 41 leaves, max depth = 9, train loss: 0.44768, val loss: 0.44418, in 0.016s
1 tree, 43 leaves, max depth = 10, train loss: 0.43855, val loss: 0.43513, in 0.016s
1 tree, 20 leaves, max depth = 6, train loss: 0.42572, val loss: 0.42248, in 0.016s
1 tree, 37 leaves, max depth = 9, train loss: 0.41774, val loss: 0.41447, in 0.016s
1 tree, 28 leaves, max depth = 9, train loss: 0.40641, val loss: 0.40340, in 0.016s
1 tree, 58 leaves, max depth = 13, train loss: 0.39950, val loss: 0.39646, in 0.016s
1 tree, 32 leaves, max depth = 9, train loss: 0.38957, val loss: 0.38688, in 0.016s
1 tree, 54 leaves, max depth = 11, train loss: 0.38348, val loss: 0.38072, in 0.016s
1 tree, 27 leaves, max depth = 7, train loss: 0.37450, val loss: 0.37191, in 0.016s
1 tree, 53 leaves, max depth = 10, train loss: 0.36912, val loss: 0.36648, in 0.016s
1 tree, 34 leaves, max depth = 11, train loss: 0.36118, val loss: 0.35875, in 0.016s
1 tree, 49 leaves, max depth = 11, train loss: 0.35623, val loss: 0.35389, in 0.016s
1 tree, 13 leaves, max depth = 6, train loss: 0.34953, val loss: 0.34731, in 0.016s
1 tree, 10 leaves, max depth = 4, train loss: 0.34340, val loss: 0.34125, in 0.016s
1 tree, 46 leaves, max depth = 11, train loss: 0.33828, val loss: 0.33634, in 0.016s
1 tree, 28 leaves, max depth = 11, train loss: 0.33283, val loss: 0.33130, in 0.016s
1 tree, 11 leaves, max depth = 6, train loss: 0.32767, val loss: 0.32622, in 0.016s
1 tree, 33 leaves, max depth = 12, train loss: 0.32281, val loss: 0.32180, in 0.016s
1 tree, 13 leaves, max depth = 7, train loss: 0.31831, val loss: 0.31734, in 0.016s
1 tree, 41 leaves, max depth = 10, train loss: 0.31406, val loss: 0.31326, in 0.016s
1 tree, 36 leaves, max depth = 12, train loss: 0.30987, val loss: 0.30947, in 0.016s
1 tree, 15 leaves, max depth = 7, train loss: 0.30604, val loss: 0.30567, in 0.016s
1 tree, 43 leaves, max depth = 10, train loss: 0.30227, val loss: 0.30207, in 0.016s
1 tree, 41 leaves, max depth = 12, train loss: 0.29870, val loss: 0.29886, in 0.016s
1 tree, 12 leaves, max depth = 4, train loss: 0.29544, val loss: 0.29563, in 0.016s
1 tree, 38 leaves, max depth = 10, train loss: 0.29199, val loss: 0.29243, in 0.000s
1 tree, 41 leaves, max depth = 12, train loss: 0.28892, val loss: 0.28968, in 0.000s
1 tree, 13 leaves, max depth = 5, train loss: 0.28613, val loss: 0.28692, in 0.016s
1 tree, 52 leaves, max depth = 13, train loss: 0.28310, val loss: 0.28405, in 0.031s
1 tree, 41 leaves, max depth = 12, train loss: 0.28046, val loss: 0.28172, in 0.016s
1 tree, 17 leaves, max depth = 5, train loss: 0.27806, val loss: 0.27932, in 0.016s
1 tree, 40 leaves, max depth = 9, train loss: 0.27526, val loss: 0.27675, in 0.016s
1 tree, 38 leaves, max depth = 12, train loss: 0.27298, val loss: 0.27474, in 0.016s
1 tree, 18 leaves, max depth = 5, train loss: 0.27092, val loss: 0.27270, in 0.016s
1 tree, 39 leaves, max depth = 12, train loss: 0.26889, val loss: 0.27094, in 0.016s
Fit 48 trees in 1.111 s, (1585 total leaves)
Time spent computing histograms: 0.298s
Time spent finding best splits:  0.053s
Time spent applying splits:      0.034s
Time spent predicting:           0.016s
Trial 99, Fold 2: Log loss = 0.27319669112073436, Average precision = 0.9622910927957704, ROC-AUC = 0.9599752317264558, Elapsed Time = 1.1303246000006766 seconds
Trial 99, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 99, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
Binning 0.040 GB of training data: 0.173 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 31 leaves, max depth = 9, train loss: 0.66374, val loss: 0.66367, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.63724, val loss: 0.63709, in 0.031s
1 tree, 36 leaves, max depth = 8, train loss: 0.61291, val loss: 0.61281, in 0.016s
1 tree, 37 leaves, max depth = 9, train loss: 0.59109, val loss: 0.59089, in 0.000s
1 tree, 37 leaves, max depth = 8, train loss: 0.57090, val loss: 0.57075, in 0.031s
1 tree, 31 leaves, max depth = 9, train loss: 0.55327, val loss: 0.55312, in 0.016s
1 tree, 39 leaves, max depth = 9, train loss: 0.53624, val loss: 0.53614, in 0.016s
1 tree, 44 leaves, max depth = 8, train loss: 0.52056, val loss: 0.52051, in 0.016s
1 tree, 45 leaves, max depth = 10, train loss: 0.50613, val loss: 0.50610, in 0.016s
1 tree, 36 leaves, max depth = 10, train loss: 0.49284, val loss: 0.49281, in 0.016s
1 tree, 42 leaves, max depth = 10, train loss: 0.48055, val loss: 0.48053, in 0.016s
1 tree, 43 leaves, max depth = 10, train loss: 0.46919, val loss: 0.46922, in 0.016s
1 tree, 44 leaves, max depth = 10, train loss: 0.45868, val loss: 0.45877, in 0.016s
1 tree, 44 leaves, max depth = 10, train loss: 0.44895, val loss: 0.44908, in 0.016s
1 tree, 19 leaves, max depth = 6, train loss: 0.43545, val loss: 0.43652, in 0.016s
1 tree, 55 leaves, max depth = 12, train loss: 0.42690, val loss: 0.42818, in 0.031s
1 tree, 53 leaves, max depth = 12, train loss: 0.41893, val loss: 0.42043, in 0.016s
1 tree, 28 leaves, max depth = 14, train loss: 0.40744, val loss: 0.40993, in 0.031s
1 tree, 53 leaves, max depth = 11, train loss: 0.40051, val loss: 0.40324, in 0.016s
1 tree, 36 leaves, max depth = 14, train loss: 0.39033, val loss: 0.39405, in 0.016s
1 tree, 32 leaves, max depth = 12, train loss: 0.38093, val loss: 0.38553, in 0.016s
1 tree, 56 leaves, max depth = 14, train loss: 0.37503, val loss: 0.37991, in 0.031s
1 tree, 58 leaves, max depth = 14, train loss: 0.36952, val loss: 0.37461, in 0.016s
1 tree, 40 leaves, max depth = 14, train loss: 0.36146, val loss: 0.36744, in 0.016s
1 tree, 63 leaves, max depth = 14, train loss: 0.35657, val loss: 0.36272, in 0.031s
1 tree, 9 leaves, max depth = 5, train loss: 0.34976, val loss: 0.35648, in 0.000s
1 tree, 13 leaves, max depth = 5, train loss: 0.34350, val loss: 0.35079, in 0.016s
1 tree, 13 leaves, max depth = 5, train loss: 0.33775, val loss: 0.34558, in 0.016s
1 tree, 37 leaves, max depth = 11, train loss: 0.33223, val loss: 0.34110, in 0.016s
1 tree, 53 leaves, max depth = 12, train loss: 0.32752, val loss: 0.33622, in 0.016s
1 tree, 15 leaves, max depth = 5, train loss: 0.32268, val loss: 0.33186, in 0.031s
1 tree, 62 leaves, max depth = 13, train loss: 0.31840, val loss: 0.32748, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.31385, val loss: 0.32382, in 0.016s
1 tree, 15 leaves, max depth = 5, train loss: 0.30975, val loss: 0.32009, in 0.016s
1 tree, 62 leaves, max depth = 13, train loss: 0.30594, val loss: 0.31612, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.30205, val loss: 0.31310, in 0.016s
1 tree, 37 leaves, max depth = 11, train loss: 0.29846, val loss: 0.31029, in 0.016s
1 tree, 16 leaves, max depth = 5, train loss: 0.29512, val loss: 0.30721, in 0.016s
1 tree, 36 leaves, max depth = 11, train loss: 0.29195, val loss: 0.30471, in 0.016s
1 tree, 16 leaves, max depth = 5, train loss: 0.28900, val loss: 0.30200, in 0.016s
1 tree, 57 leaves, max depth = 12, train loss: 0.28586, val loss: 0.29871, in 0.016s
1 tree, 34 leaves, max depth = 11, train loss: 0.28317, val loss: 0.29664, in 0.016s
1 tree, 18 leaves, max depth = 5, train loss: 0.28066, val loss: 0.29431, in 0.016s
1 tree, 55 leaves, max depth = 12, train loss: 0.27786, val loss: 0.29137, in 0.031s
1 tree, 35 leaves, max depth = 11, train loss: 0.27555, val loss: 0.28964, in 0.016s
1 tree, 19 leaves, max depth = 6, train loss: 0.27339, val loss: 0.28765, in 0.016s
1 tree, 60 leaves, max depth = 12, train loss: 0.27077, val loss: 0.28474, in 0.016s
1 tree, 40 leaves, max depth = 12, train loss: 0.26877, val loss: 0.28328, in 0.016s
Fit 48 trees in 1.235 s, (1805 total leaves)
Time spent computing histograms: 0.344s
Time spent finding best splits:  0.060s
Time spent applying splits:      0.040s
Time spent predicting:           0.000s
Trial 99, Fold 3: Log loss = 0.26933763869940724, Average precision = 0.9635613369023321, ROC-AUC = 0.9604339774489314, Elapsed Time = 1.2420631000004505 seconds
Trial 99, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 99, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
Binning 0.040 GB of training data: 0.158 s
0.000 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 35 leaves, max depth = 10, train loss: 0.66352, val loss: 0.66248, in 0.016s
1 tree, 33 leaves, max depth = 10, train loss: 0.63681, val loss: 0.63479, in 0.016s
1 tree, 34 leaves, max depth = 11, train loss: 0.61259, val loss: 0.60969, in 0.016s
1 tree, 29 leaves, max depth = 9, train loss: 0.59065, val loss: 0.58691, in 0.016s
1 tree, 35 leaves, max depth = 11, train loss: 0.57057, val loss: 0.56605, in 0.016s
1 tree, 34 leaves, max depth = 11, train loss: 0.55223, val loss: 0.54695, in 0.016s
1 tree, 33 leaves, max depth = 11, train loss: 0.53543, val loss: 0.52944, in 0.016s
1 tree, 40 leaves, max depth = 10, train loss: 0.52003, val loss: 0.51334, in 0.016s
1 tree, 39 leaves, max depth = 10, train loss: 0.50562, val loss: 0.49832, in 0.016s
1 tree, 39 leaves, max depth = 10, train loss: 0.49231, val loss: 0.48444, in 0.016s
1 tree, 35 leaves, max depth = 10, train loss: 0.48001, val loss: 0.47152, in 0.000s
1 tree, 37 leaves, max depth = 10, train loss: 0.46864, val loss: 0.45958, in 0.031s
1 tree, 39 leaves, max depth = 10, train loss: 0.45812, val loss: 0.44857, in 0.016s
1 tree, 38 leaves, max depth = 10, train loss: 0.44835, val loss: 0.43830, in 0.016s
1 tree, 41 leaves, max depth = 10, train loss: 0.43928, val loss: 0.42877, in 0.016s
1 tree, 26 leaves, max depth = 9, train loss: 0.42646, val loss: 0.41578, in 0.016s
1 tree, 57 leaves, max depth = 10, train loss: 0.41848, val loss: 0.40742, in 0.016s
1 tree, 33 leaves, max depth = 9, train loss: 0.40717, val loss: 0.39598, in 0.016s
1 tree, 54 leaves, max depth = 11, train loss: 0.40012, val loss: 0.38864, in 0.016s
1 tree, 34 leaves, max depth = 11, train loss: 0.39012, val loss: 0.37853, in 0.016s
1 tree, 58 leaves, max depth = 11, train loss: 0.38390, val loss: 0.37199, in 0.031s
1 tree, 57 leaves, max depth = 11, train loss: 0.37806, val loss: 0.36593, in 0.016s
1 tree, 39 leaves, max depth = 12, train loss: 0.36948, val loss: 0.35731, in 0.016s
1 tree, 43 leaves, max depth = 12, train loss: 0.36160, val loss: 0.34940, in 0.016s
1 tree, 38 leaves, max depth = 9, train loss: 0.35425, val loss: 0.34196, in 0.016s
1 tree, 55 leaves, max depth = 11, train loss: 0.34943, val loss: 0.33692, in 0.016s
1 tree, 13 leaves, max depth = 7, train loss: 0.34325, val loss: 0.33051, in 0.016s
1 tree, 13 leaves, max depth = 7, train loss: 0.33759, val loss: 0.32461, in 0.016s
1 tree, 41 leaves, max depth = 10, train loss: 0.33274, val loss: 0.31967, in 0.016s
1 tree, 41 leaves, max depth = 10, train loss: 0.32822, val loss: 0.31506, in 0.031s
1 tree, 33 leaves, max depth = 9, train loss: 0.32313, val loss: 0.31023, in 0.016s
1 tree, 16 leaves, max depth = 7, train loss: 0.31847, val loss: 0.30537, in 0.016s
1 tree, 36 leaves, max depth = 9, train loss: 0.31395, val loss: 0.30116, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.30988, val loss: 0.29691, in 0.000s
1 tree, 62 leaves, max depth = 13, train loss: 0.30588, val loss: 0.29271, in 0.031s
1 tree, 36 leaves, max depth = 9, train loss: 0.30203, val loss: 0.28913, in 0.016s
1 tree, 17 leaves, max depth = 7, train loss: 0.29856, val loss: 0.28552, in 0.016s
1 tree, 63 leaves, max depth = 13, train loss: 0.29500, val loss: 0.28177, in 0.016s
1 tree, 41 leaves, max depth = 10, train loss: 0.29170, val loss: 0.27874, in 0.016s
1 tree, 18 leaves, max depth = 7, train loss: 0.28875, val loss: 0.27563, in 0.016s
1 tree, 46 leaves, max depth = 12, train loss: 0.28572, val loss: 0.27255, in 0.016s
1 tree, 40 leaves, max depth = 10, train loss: 0.28288, val loss: 0.26995, in 0.016s
1 tree, 39 leaves, max depth = 9, train loss: 0.28025, val loss: 0.26751, in 0.016s
1 tree, 18 leaves, max depth = 7, train loss: 0.27783, val loss: 0.26495, in 0.016s
1 tree, 40 leaves, max depth = 9, train loss: 0.27549, val loss: 0.26281, in 0.016s
1 tree, 24 leaves, max depth = 8, train loss: 0.27335, val loss: 0.26051, in 0.016s
1 tree, 61 leaves, max depth = 12, train loss: 0.27065, val loss: 0.25761, in 0.016s
1 tree, 37 leaves, max depth = 9, train loss: 0.26862, val loss: 0.25578, in 0.016s
Fit 48 trees in 1.143 s, (1787 total leaves)
Time spent computing histograms: 0.334s
Time spent finding best splits:  0.057s
Time spent applying splits:      0.038s
Time spent predicting:           0.000s
Trial 99, Fold 4: Log loss = 0.26912938002939735, Average precision = 0.9637844512354639, ROC-AUC = 0.9605351867350514, Elapsed Time = 1.1649399999987509 seconds
Trial 99, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 99, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
Binning 0.040 GB of training data: 0.157 s
0.016 s 0.004 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 28 leaves, max depth = 9, train loss: 0.66315, val loss: 0.66194, in 0.016s
1 tree, 30 leaves, max depth = 9, train loss: 0.63607, val loss: 0.63374, in 0.016s
1 tree, 31 leaves, max depth = 9, train loss: 0.61158, val loss: 0.60823, in 0.016s
1 tree, 32 leaves, max depth = 10, train loss: 0.58934, val loss: 0.58506, in 0.016s
1 tree, 36 leaves, max depth = 10, train loss: 0.56905, val loss: 0.56390, in 0.016s
1 tree, 37 leaves, max depth = 10, train loss: 0.55051, val loss: 0.54460, in 0.016s
1 tree, 40 leaves, max depth = 10, train loss: 0.53347, val loss: 0.52680, in 0.016s
1 tree, 46 leaves, max depth = 11, train loss: 0.51779, val loss: 0.51051, in 0.016s
1 tree, 49 leaves, max depth = 11, train loss: 0.50336, val loss: 0.49553, in 0.016s
1 tree, 45 leaves, max depth = 10, train loss: 0.49010, val loss: 0.48174, in 0.016s
1 tree, 50 leaves, max depth = 11, train loss: 0.47781, val loss: 0.46896, in 0.016s
1 tree, 29 leaves, max depth = 9, train loss: 0.46695, val loss: 0.45763, in 0.016s
1 tree, 48 leaves, max depth = 10, train loss: 0.45638, val loss: 0.44664, in 0.016s
1 tree, 46 leaves, max depth = 11, train loss: 0.44656, val loss: 0.43644, in 0.016s
1 tree, 53 leaves, max depth = 10, train loss: 0.43745, val loss: 0.42701, in 0.016s
1 tree, 27 leaves, max depth = 9, train loss: 0.42455, val loss: 0.41420, in 0.016s
1 tree, 52 leaves, max depth = 11, train loss: 0.41654, val loss: 0.40594, in 0.016s
1 tree, 28 leaves, max depth = 8, train loss: 0.40517, val loss: 0.39463, in 0.016s
1 tree, 60 leaves, max depth = 12, train loss: 0.39826, val loss: 0.38739, in 0.016s
1 tree, 33 leaves, max depth = 12, train loss: 0.38820, val loss: 0.37740, in 0.016s
1 tree, 58 leaves, max depth = 13, train loss: 0.38194, val loss: 0.37098, in 0.016s
1 tree, 46 leaves, max depth = 10, train loss: 0.37605, val loss: 0.36496, in 0.016s
1 tree, 36 leaves, max depth = 13, train loss: 0.36738, val loss: 0.35645, in 0.016s
1 tree, 32 leaves, max depth = 8, train loss: 0.35942, val loss: 0.34862, in 0.016s
1 tree, 60 leaves, max depth = 12, train loss: 0.35440, val loss: 0.34353, in 0.031s
1 tree, 10 leaves, max depth = 4, train loss: 0.34762, val loss: 0.33662, in 0.000s
1 tree, 10 leaves, max depth = 4, train loss: 0.34141, val loss: 0.33028, in 0.016s
1 tree, 47 leaves, max depth = 11, train loss: 0.33624, val loss: 0.32518, in 0.016s
1 tree, 50 leaves, max depth = 13, train loss: 0.33030, val loss: 0.31946, in 0.016s
1 tree, 39 leaves, max depth = 12, train loss: 0.32498, val loss: 0.31465, in 0.031s
1 tree, 9 leaves, max depth = 4, train loss: 0.32018, val loss: 0.30972, in 0.000s
1 tree, 44 leaves, max depth = 11, train loss: 0.31567, val loss: 0.30519, in 0.031s
1 tree, 37 leaves, max depth = 10, train loss: 0.31114, val loss: 0.30113, in 0.016s
1 tree, 9 leaves, max depth = 4, train loss: 0.30707, val loss: 0.29694, in 0.016s
1 tree, 46 leaves, max depth = 12, train loss: 0.30307, val loss: 0.29292, in 0.016s
1 tree, 39 leaves, max depth = 11, train loss: 0.29921, val loss: 0.28948, in 0.016s
1 tree, 14 leaves, max depth = 5, train loss: 0.29576, val loss: 0.28583, in 0.016s
1 tree, 49 leaves, max depth = 12, train loss: 0.29230, val loss: 0.28248, in 0.016s
1 tree, 42 leaves, max depth = 12, train loss: 0.28900, val loss: 0.27947, in 0.016s
1 tree, 11 leaves, max depth = 4, train loss: 0.28605, val loss: 0.27643, in 0.016s
1 tree, 49 leaves, max depth = 10, train loss: 0.28284, val loss: 0.27322, in 0.016s
1 tree, 38 leaves, max depth = 11, train loss: 0.27998, val loss: 0.27067, in 0.016s
1 tree, 42 leaves, max depth = 11, train loss: 0.27734, val loss: 0.26830, in 0.016s
1 tree, 14 leaves, max depth = 5, train loss: 0.27492, val loss: 0.26581, in 0.016s
1 tree, 47 leaves, max depth = 13, train loss: 0.27215, val loss: 0.26320, in 0.016s
1 tree, 37 leaves, max depth = 11, train loss: 0.26986, val loss: 0.26118, in 0.000s
1 tree, 19 leaves, max depth = 8, train loss: 0.26777, val loss: 0.25908, in 0.016s
1 tree, 23 leaves, max depth = 9, train loss: 0.26578, val loss: 0.25709, in 0.016s
Fit 48 trees in 1.111 s, (1757 total leaves)
Time spent computing histograms: 0.302s
Time spent finding best splits:  0.053s
Time spent applying splits:      0.035s
Time spent predicting:           0.031s
Trial 99, Fold 5: Log loss = 0.2757542983872139, Average precision = 0.9617069684914026, ROC-AUC = 0.956636010627427, Elapsed Time = 1.1231758999983867 seconds
Optimization Progress: 100%|##########| 100/100 [20:54<00:00, 13.38s/it]
Optuna Optimization Elapsed Time: 1254.3631370999992 seconds
Optimization Progress: 100%|##########| 100/100 [20:54<00:00, 12.54s/it]

Training with Best Trial 69:

Full_params: {'loss': 'log_loss', 'verbose': 2, 'random_state': 42, 'categorical_features': 'from_dtype', 'max_depth': None, 'learning_rate': 0.09615350775493679, 'max_iter': 64, 'max_leaf_nodes': 81, 'min_samples_leaf': 47, 'l2_regularization': 1.1831955846640462e-05, 'class_weight': None, 'max_features': 0.7736302453809708, 'max_bins': 217, 'interaction_cst': 'pairwise', 'warm_start': False}
Binning 0.050 GB of training data: 0.236 s
0.016 s 0.006 GB of validation data: 
Fitting gradient boosted rounds:
1 tree, 81 leaves, max depth = 12, train loss: 0.63949, val loss: 0.64037, in 0.031s
1 tree, 81 leaves, max depth = 12, train loss: 0.59379, val loss: 0.59532, in 0.031s
1 tree, 81 leaves, max depth = 12, train loss: 0.55562, val loss: 0.55774, in 0.031s
1 tree, 81 leaves, max depth = 12, train loss: 0.52336, val loss: 0.52629, in 0.031s
1 tree, 81 leaves, max depth = 12, train loss: 0.49575, val loss: 0.49935, in 0.031s
1 tree, 81 leaves, max depth = 13, train loss: 0.47204, val loss: 0.47629, in 0.078s
1 tree, 81 leaves, max depth = 13, train loss: 0.45162, val loss: 0.45640, in 0.062s
1 tree, 81 leaves, max depth = 13, train loss: 0.43389, val loss: 0.43922, in 0.047s
1 tree, 81 leaves, max depth = 10, train loss: 0.41858, val loss: 0.42506, in 0.031s
1 tree, 81 leaves, max depth = 11, train loss: 0.39676, val loss: 0.40394, in 0.063s
1 tree, 81 leaves, max depth = 15, train loss: 0.37807, val loss: 0.38536, in 0.031s
1 tree, 81 leaves, max depth = 13, train loss: 0.36692, val loss: 0.37464, in 0.031s
1 tree, 81 leaves, max depth = 14, train loss: 0.35195, val loss: 0.35972, in 0.031s
1 tree, 81 leaves, max depth = 16, train loss: 0.33931, val loss: 0.34788, in 0.031s
1 tree, 81 leaves, max depth = 17, train loss: 0.32960, val loss: 0.33878, in 0.047s
1 tree, 81 leaves, max depth = 17, train loss: 0.31972, val loss: 0.32946, in 0.047s
1 tree, 81 leaves, max depth = 17, train loss: 0.31039, val loss: 0.31987, in 0.047s
1 tree, 81 leaves, max depth = 12, train loss: 0.30230, val loss: 0.31217, in 0.047s
1 tree, 81 leaves, max depth = 18, train loss: 0.29508, val loss: 0.30544, in 0.063s
1 tree, 81 leaves, max depth = 13, train loss: 0.28860, val loss: 0.29949, in 0.031s
1 tree, 81 leaves, max depth = 24, train loss: 0.28268, val loss: 0.29397, in 0.047s
1 tree, 81 leaves, max depth = 13, train loss: 0.27669, val loss: 0.28837, in 0.047s
1 tree, 81 leaves, max depth = 22, train loss: 0.27160, val loss: 0.28329, in 0.047s
1 tree, 81 leaves, max depth = 20, train loss: 0.26719, val loss: 0.27917, in 0.031s
1 tree, 81 leaves, max depth = 19, train loss: 0.26227, val loss: 0.27397, in 0.031s
1 tree, 81 leaves, max depth = 16, train loss: 0.25786, val loss: 0.26993, in 0.047s
1 tree, 81 leaves, max depth = 18, train loss: 0.25383, val loss: 0.26561, in 0.047s
1 tree, 81 leaves, max depth = 15, train loss: 0.24968, val loss: 0.26176, in 0.047s
1 tree, 81 leaves, max depth = 15, train loss: 0.24599, val loss: 0.25848, in 0.031s
1 tree, 81 leaves, max depth = 17, train loss: 0.24283, val loss: 0.25510, in 0.031s
1 tree, 81 leaves, max depth = 16, train loss: 0.23968, val loss: 0.25236, in 0.047s
1 tree, 81 leaves, max depth = 16, train loss: 0.23722, val loss: 0.25021, in 0.031s
1 tree, 81 leaves, max depth = 15, train loss: 0.23498, val loss: 0.24820, in 0.047s
1 tree, 81 leaves, max depth = 18, train loss: 0.23270, val loss: 0.24695, in 0.031s
1 tree, 81 leaves, max depth = 15, train loss: 0.22979, val loss: 0.24434, in 0.047s
1 tree, 81 leaves, max depth = 11, train loss: 0.22731, val loss: 0.24223, in 0.031s
1 tree, 81 leaves, max depth = 13, train loss: 0.22489, val loss: 0.24001, in 0.031s
1 tree, 81 leaves, max depth = 16, train loss: 0.22285, val loss: 0.23828, in 0.047s
1 tree, 81 leaves, max depth = 12, train loss: 0.22092, val loss: 0.23663, in 0.031s
1 tree, 81 leaves, max depth = 16, train loss: 0.21934, val loss: 0.23550, in 0.031s
1 tree, 81 leaves, max depth = 16, train loss: 0.21791, val loss: 0.23431, in 0.047s
1 tree, 81 leaves, max depth = 14, train loss: 0.21581, val loss: 0.23244, in 0.031s
1 tree, 81 leaves, max depth = 15, train loss: 0.21437, val loss: 0.23194, in 0.047s
1 tree, 81 leaves, max depth = 18, train loss: 0.21294, val loss: 0.23029, in 0.031s
1 tree, 81 leaves, max depth = 11, train loss: 0.21077, val loss: 0.22851, in 0.047s
1 tree, 81 leaves, max depth = 15, train loss: 0.20965, val loss: 0.22791, in 0.031s
1 tree, 81 leaves, max depth = 25, train loss: 0.20836, val loss: 0.22675, in 0.031s
1 tree, 81 leaves, max depth = 19, train loss: 0.20725, val loss: 0.22551, in 0.047s
1 tree, 81 leaves, max depth = 16, train loss: 0.20525, val loss: 0.22395, in 0.031s
1 tree, 81 leaves, max depth = 17, train loss: 0.20415, val loss: 0.22374, in 0.031s
1 tree, 81 leaves, max depth = 15, train loss: 0.20240, val loss: 0.22248, in 0.047s
1 tree, 81 leaves, max depth = 23, train loss: 0.20149, val loss: 0.22146, in 0.031s
1 tree, 81 leaves, max depth = 14, train loss: 0.20066, val loss: 0.22105, in 0.031s
1 tree, 81 leaves, max depth = 16, train loss: 0.19913, val loss: 0.21995, in 0.047s
1 tree, 81 leaves, max depth = 15, train loss: 0.19817, val loss: 0.21925, in 0.047s
1 tree, 81 leaves, max depth = 15, train loss: 0.19729, val loss: 0.21914, in 0.032s
1 tree, 81 leaves, max depth = 14, train loss: 0.19584, val loss: 0.21804, in 0.047s
1 tree, 81 leaves, max depth = 17, train loss: 0.19456, val loss: 0.21737, in 0.031s
1 tree, 36 leaves, max depth = 8, train loss: 0.19387, val loss: 0.21670, in 0.047s
1 tree, 81 leaves, max depth = 15, train loss: 0.19237, val loss: 0.21644, in 0.031s
1 tree, 81 leaves, max depth = 12, train loss: 0.19123, val loss: 0.21586, in 0.047s
1 tree, 81 leaves, max depth = 19, train loss: 0.18998, val loss: 0.21554, in 0.047s
1 tree, 81 leaves, max depth = 27, train loss: 0.18925, val loss: 0.21481, in 0.031s
1 tree, 81 leaves, max depth = 18, train loss: 0.18814, val loss: 0.21431, in 0.047s
Fit 64 trees in 3.112 s, (5139 total leaves)
Time spent computing histograms: 0.902s
Time spent finding best splits:  0.300s
Time spent applying splits:      0.238s
Time spent predicting:           0.031s
Training Elapsed Time: 3.1261192000001756 seconds
Log loss: (Train) 0.2112895608019953 vs (Test) 0.22135196393197867
PR-AUC:   (Train) 0.9714329280365582 vs (Test) 0.9678213476186919
ROC-AUC:  (Train) 0.9668834736715238 vs (Test) 0.9627144738851556
In [25]:
save_results(clf_name = "HistGradientBoostingClassifier",
             best_trials = best_trials_hgbc,
             exec_time = exec_time_hgbc,
             lloss_auc_train = lloss_auc_train_hgbc,
             lloss_auc_test = lloss_auc_test_hgbc,
             df_metrics = df_metrics_hgbc,
             cm_final = cm_final_hgbc,
             cm_all = cm_hgbc_all,
             cm_labels = cm_labels_hgbc_all)

Optuna with XGBoost¶

In [26]:
gc.collect();
In [27]:
X_df = clean_df.drop(columns = ["target", "anon_ssn"])

y_df = clean_df.target

anon_ssn = clean_df.anon_ssn;
In [28]:
# A single train-test split (80%-20%) using GroupShuffleSplit, ensuring that no anon_ssn (grouped by anon_ssn) appear in both sets
gss = GroupShuffleSplit(n_splits = 1, test_size = 0.2, random_state = seed)
train_idx, test_idx = next(gss.split(X_df, y_df, groups = anon_ssn))

X_train, X_test = X_df.iloc[train_idx], X_df.iloc[test_idx]
y_train, y_test = y_df.iloc[train_idx], y_df.iloc[test_idx]

anon_ssn_train = anon_ssn[train_idx]  # Keeping track of anon_ssn for cross-validation
In [29]:
# Estimate scale_pos_weight value to be used if data is imbalance only
estimate = Counter(y_train)[0] / Counter(y_train)[1]
In [30]:
del X_df, y_df, gss, train_idx, test_idx;
In [31]:
# Define the objective function
def objective(trial):
    
    # https://xgboost.readthedocs.io/en/stable/parameter.html#learning-task-parameters
    # https://xgboost.readthedocs.io/en/latest/treemethod.html
    # https://xgboost.readthedocs.io/en/stable/python/python_api.html#module-xgboost.training 
    # https://xgboost.readthedocs.io/en/stable/parameter.html#parameters-for-tree-booster
    param = {
        "objective": "binary:logistic",
        "booster": trial.suggest_categorical("booster", ["gbtree", "dart"]),
        "device": "cpu",
        "verbosity": 2,
        "validate_parameters": True,
        "eval_metric": ["logloss", "auc", "aucpr"],
        "seed": seed,
        "eta": trial.suggest_float("eta", 1e-2, 1e-1, log = True),
        "gamma": trial.suggest_float("gamma", 1e-8, 5e0, log = True),
        "max_depth": 0, # No limit on depth
        "min_child_weight": trial.suggest_float("min_child_weight", 1e-10, 1e1, log = True),
        "max_delta_step": trial.suggest_float("max_delta_step", 1, 100),
        "subsample": trial.suggest_float("subsample", 1e-1, 1.0),
        "sampling_method": "uniform",  # Only 'uniform' is supported on CPU
        "colsample_bytree": trial.suggest_float("colsample_bytree", 1e-1, 1e0),
        "colsample_bylevel": trial.suggest_float("colsample_bylevel", 1e-1, 1e0),
        "colsample_bynode": trial.suggest_float("colsample_bynode", 1e-1, 1e0),
        "lambda": trial.suggest_float("lambda", 1e-8, 1e1, log = True),
        "alpha": trial.suggest_float("alpha", 1e-8, 1e1, log = True),
        "tree_method": trial.suggest_categorical("tree_method", ["auto", "approx", "hist"]),  # CPU-supported methods
        "scale_pos_weight": trial.suggest_categorical("scale_pos_weight", [1, estimate]),
        "grow_policy": trial.suggest_categorical("grow_policy", ["depthwise", "lossguide"]),
        "max_leaves": trial.suggest_int("max_leaves", 2, 256),
        "max_bin": trial.suggest_int("max_bin", 40, 255),
        "num_parallel_tree": 1,
    }
   
    sgkf = StratifiedGroupKFold(n_splits = 5, shuffle = True, random_state = seed)
    lloss_scores, pr_auc_scores, roc_auc_scores = [], [], []
    
    for fold_idx, (train_index, valid_index) in enumerate(sgkf.split(X_train, y_train, groups = anon_ssn_train), start = 1):
  
        # Split data into training and validation sets
        X_train_fold, X_valid_fold = X_train.iloc[train_index], X_train.iloc[valid_index]
        y_train_fold, y_valid_fold = y_train.iloc[train_index], y_train.iloc[valid_index]       

        # Summarize the composition of classes in the train and validation sets
        train_0, train_1 = len(y_train_fold[y_train_fold == 0]), len(y_train_fold[y_train_fold == 1])
        valid_0, valid_1 = len(y_valid_fold[y_valid_fold == 0]), len(y_valid_fold[y_valid_fold == 1])

        print(f'Trial {trial.number}, Fold {fold_idx}: Train size = {len(train_index)} where 0 = {train_0}, 1 = {train_1}, 0/1 = {train_0/train_1}')
        print(f'Trial {trial.number}, Fold {fold_idx}: Validation size = {len(valid_index)} where  0 = {valid_0}, 1 = {valid_1}, 0/1 = {valid_0/valid_1}')

        # Create XGBoost datasets
        # https://xgboost.readthedocs.io/en/stable/python/python_api.html
        dtrain_fold = xgb.DMatrix(X_train_fold, label = y_train_fold, enable_categorical = True, nthread = -1)
        dvalid_fold = xgb.DMatrix(X_valid_fold, label = y_valid_fold, enable_categorical = True, nthread = -1)

        num_round = trial.suggest_int("num_boost_round", 5, 100)
         
        start_fold = time.perf_counter()
        # Train the model with the specified parameters and data
        # https://xgboost.readthedocs.io/en/stable/python/python_api.html#xgboost.train
        clf = xgb.train(param, 
                        dtrain_fold, 
                        num_boost_round = num_round,
                        evals = [(dvalid_fold, "validation")], # ValueError: Must have at least 1 validation dataset for early stopping
                        early_stopping_rounds = 50 
                       )
        end_fold = time.perf_counter()
        
        print(clf.attributes())  # Check model attributes
        
        y_prob_fold = clf.predict(dvalid_fold)   
        y_pred_fold = np.rint(y_prob_fold) # Set y_pred_fold = 1 if y_prob_fold => 0.5 and 0 if y_prob_fold < 0.5

        print(f'Trial {trial.number}, Fold {fold_idx}: '
              f'Log loss = {log_loss(y_valid_fold, y_prob_fold)}, '
              f'Average precision = {average_precision_score(y_valid_fold, y_prob_fold)}, '
              f'ROC-AUC = {roc_auc_score(y_valid_fold, y_prob_fold)}, '
              f'Elapsed Time = {end_fold - start_fold} seconds')   
        
        lloss_scores.append(log_loss(y_valid_fold, y_prob_fold))
        pr_auc_scores.append(average_precision_score(y_valid_fold, y_prob_fold))
        roc_auc_scores.append(roc_auc_score(y_valid_fold, y_prob_fold))

        del X_train_fold, X_valid_fold, y_train_fold, y_valid_fold, dtrain_fold, dvalid_fold, clf, start_fold, end_fold
        gc.collect()
        
    mean_lloss = np.mean(lloss_scores)
    mean_pr_auc = np.mean(pr_auc_scores)
    mean_roc_auc = np.mean(roc_auc_scores)

    del lloss_scores, pr_auc_scores, roc_auc_scores
    gc.collect()

    return mean_lloss, mean_pr_auc, mean_roc_auc
In [32]:
trial_progress = tqdm(total = n_trials, desc = "Optimization Progress", leave = True,
                      ascii = True,  # Plain text mode
                      dynamic_ncols = True   # Auto-fit width
                     )

def update_progress(study_xgb, trial):
    trial_progress.update(1)

# Disable Optuna's stdout handler so notebook isn’t spammed
optuna.logging.disable_default_handler()

# Enable propagation to Python’s logging
optuna.logging.enable_propagation()
optuna.logging.set_verbosity(optuna.logging.DEBUG)

# Configure Python logging
logging.basicConfig(filename = "optuna_debug_XGBClassifier.log", filemode = "w", level = logging.DEBUG, format="%(asctime)s %(levelname)s %(message)s")

study_xgb = optuna.create_study(study_name = "Optuna with XGBClassifier", 
                                directions=["minimize", "maximize", "maximize"],
                                sampler = module.AutoSampler(seed = seed))

start_optuna = time.perf_counter()
study_xgb.optimize(objective, n_trials = n_trials, n_jobs = 1, callbacks = [update_progress])
end_optuna = time.perf_counter()
print(f'Optuna Optimization Elapsed Time: {end_optuna - start_optuna} seconds')

gc.collect()

fig = plot_pareto_front(study_xgb, target_names = ["Log loss", "PR-AUC", "ROC-AUC"])
fig.update_layout(width = 900, height = 400)
fig.show()

trial_progress.close()

# Plot optimization history for each objective
metrics = ["Log loss", "PR-AUC", "ROC-AUC"]
for i, obj in enumerate(metrics):
    optuna.visualization.plot_optimization_history(study_xgb,
                                                   target = lambda t: t.values[i],  # Correctly target each objective
                                                   target_name = obj).show()

best_trials = study_xgb.best_trials
best_trials_xgb = {}

exec_time_xgb, lloss_auc_train_xgb, lloss_auc_test_xgb, all_metrics = [], [], [], []
cm_xgb_all, cm_labels_xgb_all = [], []

for i, trial in enumerate(best_trials):

    display(Markdown(f"<span style = 'font-size: 18px; font-weight: bold;'> Training with Best Trial {trial.number} </span>"))

    best_params = trial.params     

    # Non-optimized and best Optuna optimized parameters
    full_params = {"objective": "binary:logistic",
                   "device": "cpu",
                   "verbosity": 1,
                   "validate_parameters": True,
                   "eval_metric": ["logloss", "auc", "aucpr"],
                   "seed": seed,
                   "max_depth": 0,
                   "sampling_method": "uniform",  # Only 'uniform' is supported on CPU    
                   "num_parallel_tree": 1,
                   **best_params
                  }      
    print("Full_params:", full_params)         
    best_trials_xgb[trial.number] = full_params

    dtrain_all = xgb.DMatrix(X_train, label = y_train, enable_categorical = True, nthread = -1)
    dtest_all = xgb.DMatrix(X_test, label = y_test, enable_categorical = True, nthread = -1)
  
    # Extract `num_boost_round` separately (default to 10 if not found)
    num_boost_round = full_params.pop("num_boost_round", 10)

    start_train = time.perf_counter()
    # https://xgboost.readthedocs.io/en/stable/python/python_api.html#xgboost.Booster
    final_xgb = xgb.train(params = full_params, 
                          dtrain = dtrain_all, 
                          num_boost_round = num_boost_round,  # Pass explicitly
                          #early_stopping_rounds = 50 # ValueError: Must have at least 1 validation dataset for early stopping
                         )
    end_train = time.perf_counter()
   
    print(f'Training Elapsed Time: {end_train - start_train} seconds') 

    y_prob_all = final_xgb.predict(dtest_all)
    y_pred_all = np.rint(y_prob_all)
    
    print(f'Log loss: (Train) {trial.values[0]} vs (Test) {log_loss(y_test, y_prob_all)}') 
    print(f'PR-AUC:   (Train) {trial.values[1]} vs (Test) {average_precision_score(y_test, y_prob_all)}')
    print(f'ROC-AUC:  (Train) {trial.values[2]} vs (Test) {roc_auc_score(y_test, y_prob_all)}')

    exec_time_xgb.append({"Classifier": "XGBClassifier",
                           "Best Trial": trial.number,
                           "Optimization Elapsed Time (s)": end_optuna - start_optuna,
                           "Training Elapsed Time (s)": end_train - start_train})

    lloss_auc_train_xgb.append({"Classifier": "XGBClassifier",
                                "Best Trial": trial.number,
                                "Set": "Training",
                                "Log loss": trial.values[0], 
                                "PR-AUC": trial.values[1], 
                                "ROC-AUC": trial.values[2]})

    lloss_auc_test_xgb.append({"Classifier": "XGBClassifier",
                              "Best Trial": trial.number,
                              "Set": "Test",
                              "Log loss": log_loss(y_test, y_prob_all), 
                              "PR-AUC": average_precision_score(y_test, y_prob_all), 
                              "ROC-AUC": roc_auc_score(y_test, y_prob_all)})
    
    report = classification_report(y_test, y_pred_all, target_names = ["Safe", "Risky"], output_dict = True)
    all_metrics.append({"Classifier": "XGBClassifier",
                        "Trial": trial.number,
                        "Accuracy": accuracy_score(y_test, y_pred_all),
                        "Precision (Safe)": report["Safe"]["precision"],
                        "Recall (Safe)": report["Safe"]["recall"],
                        "F1-score (Safe)": report["Safe"]["f1-score"],
                        "Precision (Risky)": report["Risky"]["precision"],
                        "Recall (Risky)": report["Risky"]["recall"],
                        "F1-score (Risky)": report["Risky"]["f1-score"],
                        "Precision (Macro avg)": report["macro avg"]["precision"],
                        "Recall (Macro avg)": report["macro avg"]["recall"],
                        "F1-score (Macro avg)": report["macro avg"]["f1-score"],
                        "Precision (Weighted avg)": report["weighted avg"]["precision"],
                        "Recall (Weighted avg)": report["weighted avg"]["recall"],
                        "F1-score (Weighted avg)": report["weighted avg"]["f1-score"]})

    # Store confusion matrix
    cm_final_xgb = confusion_matrix(y_test, y_pred_all)
    cm_xgb_all.append(cm_final_xgb)
    cm_labels_xgb_all.append(f'XGBoostClassifier Confusion Matrix for Best Trial {trial.number}')  # Store label for subplots         

df_metrics_xgb = pd.DataFrame(all_metrics)

gc.collect();
Optimization Progress:   0%|          | 0/100 [00:00<?, ?it/s]
Trial 0, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 0, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[17:58:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.68714	validation-auc:0.91258	validation-aucpr:0.91700
[17:58:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.68097	validation-auc:0.93166	validation-aucpr:0.93703
[17:58:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.67488	validation-auc:0.93897	validation-aucpr:0.94349
[17:58:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.66831	validation-auc:0.95137	validation-aucpr:0.95887
[17:58:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.66292	validation-auc:0.94986	validation-aucpr:0.95779
[17:58:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5]	validation-logloss:0.65783	validation-auc:0.94954	validation-aucpr:0.95684
[17:58:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6]	validation-logloss:0.65216	validation-auc:0.94990	validation-aucpr:0.95706
[17:58:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7]	validation-logloss:0.64588	validation-auc:0.95255	validation-aucpr:0.95984
[17:58:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8]	validation-logloss:0.64091	validation-auc:0.95181	validation-aucpr:0.95890
[17:58:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9]	validation-logloss:0.63570	validation-auc:0.95195	validation-aucpr:0.95913
[17:58:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10]	validation-logloss:0.63098	validation-auc:0.95088	validation-aucpr:0.95800
[17:58:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11]	validation-logloss:0.62620	validation-auc:0.95117	validation-aucpr:0.95836
[17:58:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12]	validation-logloss:0.62129	validation-auc:0.95202	validation-aucpr:0.95919
[17:58:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13]	validation-logloss:0.61664	validation-auc:0.95205	validation-aucpr:0.95932
[17:58:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14]	validation-logloss:0.61216	validation-auc:0.95192	validation-aucpr:0.95920
[17:58:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15]	validation-logloss:0.60780	validation-auc:0.95137	validation-aucpr:0.95848
[17:58:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16]	validation-logloss:0.60433	validation-auc:0.95200	validation-aucpr:0.95910
[17:58:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17]	validation-logloss:0.59974	validation-auc:0.95261	validation-aucpr:0.95976
[17:58:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18]	validation-logloss:0.59532	validation-auc:0.95279	validation-aucpr:0.95995
[17:58:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19]	validation-logloss:0.59060	validation-auc:0.95323	validation-aucpr:0.96023
[17:58:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20]	validation-logloss:0.58650	validation-auc:0.95309	validation-aucpr:0.96014
[17:58:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21]	validation-logloss:0.58330	validation-auc:0.95252	validation-aucpr:0.95964
[17:58:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22]	validation-logloss:0.57924	validation-auc:0.95215	validation-aucpr:0.95931
[17:58:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23]	validation-logloss:0.57545	validation-auc:0.95220	validation-aucpr:0.95941
[17:58:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24]	validation-logloss:0.57116	validation-auc:0.95373	validation-aucpr:0.96117
[17:58:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25]	validation-logloss:0.56745	validation-auc:0.95357	validation-aucpr:0.96103
[17:58:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26]	validation-logloss:0.56402	validation-auc:0.95340	validation-aucpr:0.96080
[17:58:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27]	validation-logloss:0.55977	validation-auc:0.95381	validation-aucpr:0.96132
[17:58:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28]	validation-logloss:0.55635	validation-auc:0.95371	validation-aucpr:0.96118
[17:58:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29]	validation-logloss:0.55258	validation-auc:0.95400	validation-aucpr:0.96137
[17:58:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30]	validation-logloss:0.54912	validation-auc:0.95382	validation-aucpr:0.96123
[17:58:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31]	validation-logloss:0.54541	validation-auc:0.95372	validation-aucpr:0.96111
[17:58:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32]	validation-logloss:0.54087	validation-auc:0.95456	validation-aucpr:0.96196
[17:58:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33]	validation-logloss:0.53757	validation-auc:0.95440	validation-aucpr:0.96183
[17:58:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34]	validation-logloss:0.53419	validation-auc:0.95467	validation-aucpr:0.96203
[17:58:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35]	validation-logloss:0.53114	validation-auc:0.95437	validation-aucpr:0.96176
[17:58:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36]	validation-logloss:0.52807	validation-auc:0.95439	validation-aucpr:0.96174
[17:58:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37]	validation-logloss:0.52492	validation-auc:0.95448	validation-aucpr:0.96185
[17:58:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38]	validation-logloss:0.52220	validation-auc:0.95425	validation-aucpr:0.96162
[17:58:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39]	validation-logloss:0.51813	validation-auc:0.95462	validation-aucpr:0.96209
[17:58:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40]	validation-logloss:0.51428	validation-auc:0.95468	validation-aucpr:0.96223
[17:58:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41]	validation-logloss:0.51146	validation-auc:0.95466	validation-aucpr:0.96218
[17:59:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42]	validation-logloss:0.50875	validation-auc:0.95450	validation-aucpr:0.96200
[17:59:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43]	validation-logloss:0.50558	validation-auc:0.95482	validation-aucpr:0.96226
[17:59:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44]	validation-logloss:0.50312	validation-auc:0.95481	validation-aucpr:0.96221
[17:59:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45]	validation-logloss:0.50034	validation-auc:0.95478	validation-aucpr:0.96217
[17:59:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46]	validation-logloss:0.49764	validation-auc:0.95476	validation-aucpr:0.96213
[17:59:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[47]	validation-logloss:0.49491	validation-auc:0.95481	validation-aucpr:0.96211
[17:59:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[48]	validation-logloss:0.49231	validation-auc:0.95483	validation-aucpr:0.96215
[17:59:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[49]	validation-logloss:0.48995	validation-auc:0.95473	validation-aucpr:0.96202
[17:59:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[50]	validation-logloss:0.48707	validation-auc:0.95480	validation-aucpr:0.96210
[17:59:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[51]	validation-logloss:0.48439	validation-auc:0.95476	validation-aucpr:0.96203
[17:59:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[52]	validation-logloss:0.48239	validation-auc:0.95473	validation-aucpr:0.96201
[17:59:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[53]	validation-logloss:0.47984	validation-auc:0.95482	validation-aucpr:0.96206
[17:59:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[54]	validation-logloss:0.47721	validation-auc:0.95483	validation-aucpr:0.96201
[17:59:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[55]	validation-logloss:0.47483	validation-auc:0.95487	validation-aucpr:0.96206
[17:59:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[56]	validation-logloss:0.47276	validation-auc:0.95491	validation-aucpr:0.96208
[17:59:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[57]	validation-logloss:0.47081	validation-auc:0.95487	validation-aucpr:0.96208
[17:59:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[58]	validation-logloss:0.46760	validation-auc:0.95534	validation-aucpr:0.96263
[17:59:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[59]	validation-logloss:0.46544	validation-auc:0.95529	validation-aucpr:0.96259
[17:59:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[60]	validation-logloss:0.46320	validation-auc:0.95531	validation-aucpr:0.96259
[17:59:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[61]	validation-logloss:0.46132	validation-auc:0.95518	validation-aucpr:0.96247
[17:59:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[62]	validation-logloss:0.46009	validation-auc:0.95528	validation-aucpr:0.96254
[17:59:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[63]	validation-logloss:0.45799	validation-auc:0.95529	validation-aucpr:0.96252
[17:59:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[64]	validation-logloss:0.45624	validation-auc:0.95521	validation-aucpr:0.96243
[17:59:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[65]	validation-logloss:0.45419	validation-auc:0.95539	validation-aucpr:0.96258
[17:59:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[66]	validation-logloss:0.45232	validation-auc:0.95529	validation-aucpr:0.96249
[17:59:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[67]	validation-logloss:0.45039	validation-auc:0.95529	validation-aucpr:0.96247
[17:59:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[68]	validation-logloss:0.44726	validation-auc:0.95554	validation-aucpr:0.96277
[17:59:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[69]	validation-logloss:0.44505	validation-auc:0.95565	validation-aucpr:0.96283
[17:59:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[70]	validation-logloss:0.44314	validation-auc:0.95574	validation-aucpr:0.96293
[17:59:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[71]	validation-logloss:0.44129	validation-auc:0.95570	validation-aucpr:0.96291
[17:59:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[72]	validation-logloss:0.44014	validation-auc:0.95576	validation-aucpr:0.96295
[17:59:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[73]	validation-logloss:0.43846	validation-auc:0.95570	validation-aucpr:0.96289
[17:59:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[74]	validation-logloss:0.43680	validation-auc:0.95564	validation-aucpr:0.96285
[17:59:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[75]	validation-logloss:0.43506	validation-auc:0.95560	validation-aucpr:0.96280
[17:59:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[76]	validation-logloss:0.43289	validation-auc:0.95568	validation-aucpr:0.96284
[17:59:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[77]	validation-logloss:0.43186	validation-auc:0.95573	validation-aucpr:0.96286
[17:59:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[78]	validation-logloss:0.43003	validation-auc:0.95578	validation-aucpr:0.96291
[17:59:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[79]	validation-logloss:0.42846	validation-auc:0.95584	validation-aucpr:0.96295
[17:59:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[80]	validation-logloss:0.42673	validation-auc:0.95582	validation-aucpr:0.96295
[17:59:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[81]	validation-logloss:0.42521	validation-auc:0.95579	validation-aucpr:0.96291
[17:59:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[82]	validation-logloss:0.42352	validation-auc:0.95582	validation-aucpr:0.96295
[17:59:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[83]	validation-logloss:0.42182	validation-auc:0.95588	validation-aucpr:0.96302
[17:59:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[84]	validation-logloss:0.42030	validation-auc:0.95588	validation-aucpr:0.96301
[17:59:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[85]	validation-logloss:0.41853	validation-auc:0.95585	validation-aucpr:0.96301
[17:59:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[86]	validation-logloss:0.41705	validation-auc:0.95598	validation-aucpr:0.96309
[17:59:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[87]	validation-logloss:0.41571	validation-auc:0.95594	validation-aucpr:0.96305
[17:59:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[88]	validation-logloss:0.41413	validation-auc:0.95588	validation-aucpr:0.96303
[17:59:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[89]	validation-logloss:0.41265	validation-auc:0.95577	validation-aucpr:0.96292
[17:59:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[90]	validation-logloss:0.41126	validation-auc:0.95577	validation-aucpr:0.96289
[17:59:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[91]	validation-logloss:0.40991	validation-auc:0.95572	validation-aucpr:0.96284
[17:59:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[92]	validation-logloss:0.40839	validation-auc:0.95572	validation-aucpr:0.96288
{'best_iteration': '86', 'best_score': '0.9630916850735398'}
Trial 0, Fold 1: Log loss = 0.40839214376528166, Average precision = 0.9628826257850801, ROC-AUC = 0.9557242750558017, Elapsed Time = 16.0506733000002 seconds
Trial 0, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 0, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[17:59:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.68722	validation-auc:0.92238	validation-aucpr:0.91976
[17:59:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.68057	validation-auc:0.93376	validation-aucpr:0.93009
[17:59:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.67468	validation-auc:0.93503	validation-aucpr:0.93226
[17:59:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.66906	validation-auc:0.93381	validation-aucpr:0.93174
[17:59:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.66332	validation-auc:0.93762	validation-aucpr:0.93605
[17:59:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5]	validation-logloss:0.65765	validation-auc:0.94463	validation-aucpr:0.94623
[17:59:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6]	validation-logloss:0.65242	validation-auc:0.94435	validation-aucpr:0.94558
[17:59:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7]	validation-logloss:0.64731	validation-auc:0.94458	validation-aucpr:0.94603
[17:59:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8]	validation-logloss:0.64077	validation-auc:0.95344	validation-aucpr:0.95696
[17:59:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9]	validation-logloss:0.63520	validation-auc:0.95361	validation-aucpr:0.95701
[17:59:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10]	validation-logloss:0.63017	validation-auc:0.95375	validation-aucpr:0.95741
[17:59:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11]	validation-logloss:0.62523	validation-auc:0.95345	validation-aucpr:0.95719
[17:59:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12]	validation-logloss:0.62083	validation-auc:0.95319	validation-aucpr:0.95706
[17:59:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13]	validation-logloss:0.61576	validation-auc:0.95346	validation-aucpr:0.95717
[17:59:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14]	validation-logloss:0.61100	validation-auc:0.95345	validation-aucpr:0.95709
[17:59:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15]	validation-logloss:0.60754	validation-auc:0.95300	validation-aucpr:0.95655
[17:59:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16]	validation-logloss:0.60322	validation-auc:0.95284	validation-aucpr:0.95643
[17:59:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17]	validation-logloss:0.59908	validation-auc:0.95290	validation-aucpr:0.95649
[17:59:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18]	validation-logloss:0.59496	validation-auc:0.95294	validation-aucpr:0.95642
[17:59:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19]	validation-logloss:0.59093	validation-auc:0.95291	validation-aucpr:0.95655
[17:59:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20]	validation-logloss:0.58644	validation-auc:0.95290	validation-aucpr:0.95669
[17:59:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21]	validation-logloss:0.58271	validation-auc:0.95235	validation-aucpr:0.95609
[17:59:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22]	validation-logloss:0.57866	validation-auc:0.95271	validation-aucpr:0.95622
[17:59:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23]	validation-logloss:0.57452	validation-auc:0.95252	validation-aucpr:0.95595
[17:59:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24]	validation-logloss:0.56971	validation-auc:0.95413	validation-aucpr:0.95777
[17:59:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25]	validation-logloss:0.56637	validation-auc:0.95432	validation-aucpr:0.95801
[17:59:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26]	validation-logloss:0.56238	validation-auc:0.95442	validation-aucpr:0.95806
[17:59:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27]	validation-logloss:0.55875	validation-auc:0.95422	validation-aucpr:0.95785
[17:59:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28]	validation-logloss:0.55505	validation-auc:0.95399	validation-aucpr:0.95755
[17:59:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29]	validation-logloss:0.55035	validation-auc:0.95517	validation-aucpr:0.95909
[17:59:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30]	validation-logloss:0.54688	validation-auc:0.95529	validation-aucpr:0.95923
[17:59:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31]	validation-logloss:0.54329	validation-auc:0.95536	validation-aucpr:0.95934
[17:59:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32]	validation-logloss:0.53984	validation-auc:0.95520	validation-aucpr:0.95920
[17:59:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33]	validation-logloss:0.53650	validation-auc:0.95518	validation-aucpr:0.95918
[17:59:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34]	validation-logloss:0.53271	validation-auc:0.95553	validation-aucpr:0.95965
[17:59:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35]	validation-logloss:0.52927	validation-auc:0.95555	validation-aucpr:0.95974
[17:59:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36]	validation-logloss:0.52596	validation-auc:0.95544	validation-aucpr:0.95959
[17:59:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37]	validation-logloss:0.52265	validation-auc:0.95540	validation-aucpr:0.95949
[17:59:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38]	validation-logloss:0.51958	validation-auc:0.95541	validation-aucpr:0.95951
[17:59:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39]	validation-logloss:0.51728	validation-auc:0.95529	validation-aucpr:0.95943
[17:59:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40]	validation-logloss:0.51343	validation-auc:0.95596	validation-aucpr:0.96029
[17:59:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41]	validation-logloss:0.51084	validation-auc:0.95587	validation-aucpr:0.96015
[17:59:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42]	validation-logloss:0.50844	validation-auc:0.95598	validation-aucpr:0.96023
[17:59:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43]	validation-logloss:0.50514	validation-auc:0.95593	validation-aucpr:0.96008
[17:59:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44]	validation-logloss:0.50258	validation-auc:0.95596	validation-aucpr:0.96014
[17:59:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45]	validation-logloss:0.49968	validation-auc:0.95610	validation-aucpr:0.96021
[17:59:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46]	validation-logloss:0.49691	validation-auc:0.95609	validation-aucpr:0.96015
[17:59:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[47]	validation-logloss:0.49417	validation-auc:0.95607	validation-aucpr:0.96012
[17:59:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[48]	validation-logloss:0.49190	validation-auc:0.95602	validation-aucpr:0.96016
[17:59:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[49]	validation-logloss:0.48934	validation-auc:0.95621	validation-aucpr:0.96034
[17:59:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[50]	validation-logloss:0.48589	validation-auc:0.95663	validation-aucpr:0.96089
[17:59:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[51]	validation-logloss:0.48337	validation-auc:0.95666	validation-aucpr:0.96105
[17:59:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[52]	validation-logloss:0.48079	validation-auc:0.95662	validation-aucpr:0.96099
[17:59:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[53]	validation-logloss:0.47823	validation-auc:0.95668	validation-aucpr:0.96102
[17:59:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[54]	validation-logloss:0.47540	validation-auc:0.95658	validation-aucpr:0.96088
[17:59:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[55]	validation-logloss:0.47293	validation-auc:0.95650	validation-aucpr:0.96073
[17:59:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[56]	validation-logloss:0.47089	validation-auc:0.95642	validation-aucpr:0.96064
[17:59:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[57]	validation-logloss:0.46923	validation-auc:0.95647	validation-aucpr:0.96064
[17:59:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[58]	validation-logloss:0.46689	validation-auc:0.95647	validation-aucpr:0.96064
[17:59:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[59]	validation-logloss:0.46462	validation-auc:0.95658	validation-aucpr:0.96082
[17:59:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[60]	validation-logloss:0.46276	validation-auc:0.95661	validation-aucpr:0.96085
[17:59:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[61]	validation-logloss:0.46060	validation-auc:0.95648	validation-aucpr:0.96070
[17:59:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[62]	validation-logloss:0.45874	validation-auc:0.95646	validation-aucpr:0.96067
[17:59:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[63]	validation-logloss:0.45647	validation-auc:0.95647	validation-aucpr:0.96072
[17:59:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[64]	validation-logloss:0.45459	validation-auc:0.95642	validation-aucpr:0.96067
[17:59:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[65]	validation-logloss:0.45263	validation-auc:0.95639	validation-aucpr:0.96059
[17:59:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[66]	validation-logloss:0.45063	validation-auc:0.95639	validation-aucpr:0.96058
[17:59:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[67]	validation-logloss:0.44853	validation-auc:0.95635	validation-aucpr:0.96076
[17:59:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[68]	validation-logloss:0.44633	validation-auc:0.95640	validation-aucpr:0.96080
[17:59:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[69]	validation-logloss:0.44443	validation-auc:0.95620	validation-aucpr:0.96058
[17:59:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[70]	validation-logloss:0.44251	validation-auc:0.95616	validation-aucpr:0.96055
[17:59:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[71]	validation-logloss:0.44064	validation-auc:0.95600	validation-aucpr:0.96039
[17:59:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[72]	validation-logloss:0.43873	validation-auc:0.95605	validation-aucpr:0.96040
[17:59:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[73]	validation-logloss:0.43696	validation-auc:0.95604	validation-aucpr:0.96037
[17:59:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[74]	validation-logloss:0.43493	validation-auc:0.95604	validation-aucpr:0.96038
[17:59:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[75]	validation-logloss:0.43312	validation-auc:0.95597	validation-aucpr:0.96033
[17:59:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[76]	validation-logloss:0.43032	validation-auc:0.95652	validation-aucpr:0.96099
[17:59:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[77]	validation-logloss:0.42902	validation-auc:0.95647	validation-aucpr:0.96103
[17:59:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[78]	validation-logloss:0.42751	validation-auc:0.95635	validation-aucpr:0.96093
[17:59:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[79]	validation-logloss:0.42599	validation-auc:0.95623	validation-aucpr:0.96084
[17:59:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[80]	validation-logloss:0.42432	validation-auc:0.95618	validation-aucpr:0.96076
[17:59:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[81]	validation-logloss:0.42266	validation-auc:0.95616	validation-aucpr:0.96073
[17:59:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[82]	validation-logloss:0.42067	validation-auc:0.95620	validation-aucpr:0.96074
[17:59:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[83]	validation-logloss:0.41793	validation-auc:0.95660	validation-aucpr:0.96120
[17:59:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[84]	validation-logloss:0.41665	validation-auc:0.95659	validation-aucpr:0.96119
[17:59:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[85]	validation-logloss:0.41489	validation-auc:0.95673	validation-aucpr:0.96129
[17:59:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[86]	validation-logloss:0.41292	validation-auc:0.95690	validation-aucpr:0.96152
[17:59:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[87]	validation-logloss:0.41136	validation-auc:0.95689	validation-aucpr:0.96149
[17:59:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[88]	validation-logloss:0.40989	validation-auc:0.95687	validation-aucpr:0.96146
[17:59:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[89]	validation-logloss:0.40852	validation-auc:0.95683	validation-aucpr:0.96143
[17:59:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[90]	validation-logloss:0.40715	validation-auc:0.95677	validation-aucpr:0.96137
[17:59:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[91]	validation-logloss:0.40541	validation-auc:0.95673	validation-aucpr:0.96130
[17:59:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[92]	validation-logloss:0.40400	validation-auc:0.95674	validation-aucpr:0.96130
{'best_iteration': '86', 'best_score': '0.9615151857978635'}
Trial 0, Fold 2: Log loss = 0.40400137854890755, Average precision = 0.9613053529150961, ROC-AUC = 0.9567406518052979, Elapsed Time = 15.39552619999995 seconds
Trial 0, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 0, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[17:59:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.68770	validation-auc:0.89543	validation-aucpr:0.89711
[17:59:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.68144	validation-auc:0.93117	validation-aucpr:0.93039
[17:59:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.67547	validation-auc:0.93600	validation-aucpr:0.93526
[17:59:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.66957	validation-auc:0.93921	validation-aucpr:0.94207
[17:59:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.66413	validation-auc:0.94041	validation-aucpr:0.94403
[17:59:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5]	validation-logloss:0.65897	validation-auc:0.94172	validation-aucpr:0.94548
[17:59:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6]	validation-logloss:0.65351	validation-auc:0.94416	validation-aucpr:0.94793
[17:59:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7]	validation-logloss:0.64713	validation-auc:0.95457	validation-aucpr:0.95955
[17:59:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8]	validation-logloss:0.64069	validation-auc:0.95716	validation-aucpr:0.96258
[17:59:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9]	validation-logloss:0.63613	validation-auc:0.95675	validation-aucpr:0.96218
[17:59:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10]	validation-logloss:0.63105	validation-auc:0.95675	validation-aucpr:0.96225
[17:59:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11]	validation-logloss:0.62614	validation-auc:0.95662	validation-aucpr:0.96285
[17:59:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12]	validation-logloss:0.62136	validation-auc:0.95627	validation-aucpr:0.96246
[17:59:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13]	validation-logloss:0.61664	validation-auc:0.95638	validation-aucpr:0.96247
[17:59:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14]	validation-logloss:0.61182	validation-auc:0.95641	validation-aucpr:0.96236
[17:59:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15]	validation-logloss:0.60817	validation-auc:0.95643	validation-aucpr:0.96243
[17:59:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16]	validation-logloss:0.60358	validation-auc:0.95662	validation-aucpr:0.96268
[17:59:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17]	validation-logloss:0.59950	validation-auc:0.95638	validation-aucpr:0.96243
[17:59:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18]	validation-logloss:0.59486	validation-auc:0.95675	validation-aucpr:0.96272
[17:59:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19]	validation-logloss:0.59167	validation-auc:0.95675	validation-aucpr:0.96279
[17:59:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20]	validation-logloss:0.58842	validation-auc:0.95635	validation-aucpr:0.96225
[17:59:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21]	validation-logloss:0.58429	validation-auc:0.95622	validation-aucpr:0.96205
[17:59:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22]	validation-logloss:0.58023	validation-auc:0.95629	validation-aucpr:0.96204
[17:59:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23]	validation-logloss:0.57639	validation-auc:0.95604	validation-aucpr:0.96182
[17:59:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24]	validation-logloss:0.57262	validation-auc:0.95587	validation-aucpr:0.96165
[17:59:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25]	validation-logloss:0.56826	validation-auc:0.95614	validation-aucpr:0.96191
[17:59:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26]	validation-logloss:0.56421	validation-auc:0.95623	validation-aucpr:0.96200
[17:59:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27]	validation-logloss:0.56028	validation-auc:0.95642	validation-aucpr:0.96224
[17:59:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28]	validation-logloss:0.55638	validation-auc:0.95624	validation-aucpr:0.96202
[17:59:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29]	validation-logloss:0.55257	validation-auc:0.95632	validation-aucpr:0.96209
[17:59:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30]	validation-logloss:0.54904	validation-auc:0.95631	validation-aucpr:0.96207
[17:59:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31]	validation-logloss:0.54531	validation-auc:0.95648	validation-aucpr:0.96212
[17:59:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32]	validation-logloss:0.54160	validation-auc:0.95649	validation-aucpr:0.96209
[17:59:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33]	validation-logloss:0.53832	validation-auc:0.95624	validation-aucpr:0.96183
[17:59:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34]	validation-logloss:0.53525	validation-auc:0.95603	validation-aucpr:0.96162
[17:59:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35]	validation-logloss:0.53207	validation-auc:0.95611	validation-aucpr:0.96178
[17:59:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36]	validation-logloss:0.52874	validation-auc:0.95611	validation-aucpr:0.96181
[17:59:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37]	validation-logloss:0.52549	validation-auc:0.95616	validation-aucpr:0.96181
[17:59:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38]	validation-logloss:0.52114	validation-auc:0.95757	validation-aucpr:0.96337
[17:59:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39]	validation-logloss:0.51747	validation-auc:0.95767	validation-aucpr:0.96342
[17:59:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40]	validation-logloss:0.51414	validation-auc:0.95778	validation-aucpr:0.96351
[17:59:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41]	validation-logloss:0.51169	validation-auc:0.95772	validation-aucpr:0.96342
[17:59:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42]	validation-logloss:0.50916	validation-auc:0.95772	validation-aucpr:0.96336
[17:59:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43]	validation-logloss:0.50618	validation-auc:0.95787	validation-aucpr:0.96350
[17:59:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44]	validation-logloss:0.50318	validation-auc:0.95775	validation-aucpr:0.96339
[17:59:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45]	validation-logloss:0.50028	validation-auc:0.95764	validation-aucpr:0.96323
[17:59:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46]	validation-logloss:0.49787	validation-auc:0.95746	validation-aucpr:0.96304
[17:59:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[47]	validation-logloss:0.49413	validation-auc:0.95840	validation-aucpr:0.96416
[17:59:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[48]	validation-logloss:0.49162	validation-auc:0.95837	validation-aucpr:0.96413
[17:59:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[49]	validation-logloss:0.48898	validation-auc:0.95841	validation-aucpr:0.96418
[17:59:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[50]	validation-logloss:0.48659	validation-auc:0.95837	validation-aucpr:0.96417
[17:59:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[51]	validation-logloss:0.48449	validation-auc:0.95844	validation-aucpr:0.96416
[17:59:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[52]	validation-logloss:0.48238	validation-auc:0.95834	validation-aucpr:0.96410
[17:59:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[53]	validation-logloss:0.47999	validation-auc:0.95823	validation-aucpr:0.96396
[17:59:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[54]	validation-logloss:0.47735	validation-auc:0.95818	validation-aucpr:0.96388
[17:59:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[55]	validation-logloss:0.47486	validation-auc:0.95817	validation-aucpr:0.96384
[17:59:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[56]	validation-logloss:0.47259	validation-auc:0.95812	validation-aucpr:0.96379
[17:59:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[57]	validation-logloss:0.47017	validation-auc:0.95807	validation-aucpr:0.96371
[17:59:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[58]	validation-logloss:0.46814	validation-auc:0.95794	validation-aucpr:0.96356
[17:59:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[59]	validation-logloss:0.46575	validation-auc:0.95794	validation-aucpr:0.96352
[17:59:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[60]	validation-logloss:0.46349	validation-auc:0.95798	validation-aucpr:0.96354
[17:59:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[61]	validation-logloss:0.46206	validation-auc:0.95799	validation-aucpr:0.96355
[17:59:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[62]	validation-logloss:0.45945	validation-auc:0.95806	validation-aucpr:0.96358
[17:59:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[63]	validation-logloss:0.45742	validation-auc:0.95801	validation-aucpr:0.96353
[17:59:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[64]	validation-logloss:0.45519	validation-auc:0.95797	validation-aucpr:0.96348
[17:59:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[65]	validation-logloss:0.45255	validation-auc:0.95839	validation-aucpr:0.96394
[17:59:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[66]	validation-logloss:0.45061	validation-auc:0.95836	validation-aucpr:0.96391
[17:59:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[67]	validation-logloss:0.44853	validation-auc:0.95836	validation-aucpr:0.96390
[17:59:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[68]	validation-logloss:0.44627	validation-auc:0.95828	validation-aucpr:0.96377
[17:59:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[69]	validation-logloss:0.44439	validation-auc:0.95822	validation-aucpr:0.96366
[17:59:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[70]	validation-logloss:0.44215	validation-auc:0.95821	validation-aucpr:0.96362
[17:59:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[71]	validation-logloss:0.44033	validation-auc:0.95813	validation-aucpr:0.96354
[17:59:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[72]	validation-logloss:0.43840	validation-auc:0.95804	validation-aucpr:0.96345
[17:59:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[73]	validation-logloss:0.43687	validation-auc:0.95811	validation-aucpr:0.96348
[17:59:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[74]	validation-logloss:0.43507	validation-auc:0.95801	validation-aucpr:0.96338
[17:59:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[75]	validation-logloss:0.43297	validation-auc:0.95808	validation-aucpr:0.96346
[17:59:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[76]	validation-logloss:0.43071	validation-auc:0.95869	validation-aucpr:0.96418
[17:59:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[77]	validation-logloss:0.42900	validation-auc:0.95864	validation-aucpr:0.96411
[17:59:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[78]	validation-logloss:0.42733	validation-auc:0.95863	validation-aucpr:0.96410
[17:59:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[79]	validation-logloss:0.42552	validation-auc:0.95862	validation-aucpr:0.96407
[17:59:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[80]	validation-logloss:0.42382	validation-auc:0.95854	validation-aucpr:0.96399
[17:59:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[81]	validation-logloss:0.42118	validation-auc:0.95896	validation-aucpr:0.96451
[17:59:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[82]	validation-logloss:0.41929	validation-auc:0.95903	validation-aucpr:0.96455
[17:59:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[83]	validation-logloss:0.41760	validation-auc:0.95903	validation-aucpr:0.96455
[17:59:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[84]	validation-logloss:0.41632	validation-auc:0.95898	validation-aucpr:0.96447
[17:59:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[85]	validation-logloss:0.41392	validation-auc:0.95931	validation-aucpr:0.96487
[17:59:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[86]	validation-logloss:0.41240	validation-auc:0.95926	validation-aucpr:0.96483
[17:59:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[87]	validation-logloss:0.41087	validation-auc:0.95921	validation-aucpr:0.96478
[17:59:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[88]	validation-logloss:0.40843	validation-auc:0.95952	validation-aucpr:0.96515
[17:59:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[89]	validation-logloss:0.40713	validation-auc:0.95953	validation-aucpr:0.96514
[17:59:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[90]	validation-logloss:0.40576	validation-auc:0.95947	validation-aucpr:0.96509
[17:59:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[91]	validation-logloss:0.40446	validation-auc:0.95940	validation-aucpr:0.96502
[17:59:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[92]	validation-logloss:0.40206	validation-auc:0.95970	validation-aucpr:0.96535
{'best_iteration': '92', 'best_score': '0.9653474941847704'}
Trial 0, Fold 3: Log loss = 0.4020617406521426, Average precision = 0.9653535765256415, ROC-AUC = 0.9596951717213549, Elapsed Time = 15.429512600001544 seconds
Trial 0, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 0, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[17:59:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.68706	validation-auc:0.91637	validation-aucpr:0.91737
[17:59:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.68094	validation-auc:0.93038	validation-aucpr:0.93196
[17:59:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.67547	validation-auc:0.93114	validation-aucpr:0.93244
[17:59:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.66815	validation-auc:0.95085	validation-aucpr:0.95608
[17:59:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.66237	validation-auc:0.95106	validation-aucpr:0.95656
[17:59:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5]	validation-logloss:0.65672	validation-auc:0.95187	validation-aucpr:0.95764
[17:59:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6]	validation-logloss:0.65127	validation-auc:0.95158	validation-aucpr:0.95733
[17:59:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7]	validation-logloss:0.64621	validation-auc:0.95184	validation-aucpr:0.95763
[17:59:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8]	validation-logloss:0.64073	validation-auc:0.95207	validation-aucpr:0.95796
[17:59:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9]	validation-logloss:0.63549	validation-auc:0.95244	validation-aucpr:0.95862
[17:59:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10]	validation-logloss:0.63030	validation-auc:0.95252	validation-aucpr:0.95848
[17:59:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11]	validation-logloss:0.62549	validation-auc:0.95216	validation-aucpr:0.95814
[17:59:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12]	validation-logloss:0.62080	validation-auc:0.95190	validation-aucpr:0.95802
[17:59:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13]	validation-logloss:0.61613	validation-auc:0.95157	validation-aucpr:0.95759
[17:59:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14]	validation-logloss:0.61161	validation-auc:0.95102	validation-aucpr:0.95695
[17:59:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15]	validation-logloss:0.60661	validation-auc:0.95135	validation-aucpr:0.95713
[17:59:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16]	validation-logloss:0.60225	validation-auc:0.95106	validation-aucpr:0.95664
[17:59:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17]	validation-logloss:0.59829	validation-auc:0.95026	validation-aucpr:0.95568
[17:59:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18]	validation-logloss:0.59407	validation-auc:0.95008	validation-aucpr:0.95555
[17:59:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19]	validation-logloss:0.58988	validation-auc:0.94980	validation-aucpr:0.95523
[17:59:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20]	validation-logloss:0.58417	validation-auc:0.95299	validation-aucpr:0.95923
[17:59:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21]	validation-logloss:0.57998	validation-auc:0.95318	validation-aucpr:0.95940
[17:59:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22]	validation-logloss:0.57598	validation-auc:0.95300	validation-aucpr:0.95920
[17:59:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23]	validation-logloss:0.57200	validation-auc:0.95296	validation-aucpr:0.95918
[17:59:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24]	validation-logloss:0.56770	validation-auc:0.95286	validation-aucpr:0.95904
[17:59:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25]	validation-logloss:0.56367	validation-auc:0.95290	validation-aucpr:0.95904
[17:59:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26]	validation-logloss:0.56011	validation-auc:0.95311	validation-aucpr:0.95919
[17:59:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27]	validation-logloss:0.55663	validation-auc:0.95273	validation-aucpr:0.95873
[17:59:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28]	validation-logloss:0.55305	validation-auc:0.95267	validation-aucpr:0.95869
[17:59:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29]	validation-logloss:0.54972	validation-auc:0.95270	validation-aucpr:0.95876
[17:59:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30]	validation-logloss:0.54642	validation-auc:0.95251	validation-aucpr:0.95858
[17:59:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31]	validation-logloss:0.54329	validation-auc:0.95232	validation-aucpr:0.95841
[17:59:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32]	validation-logloss:0.53988	validation-auc:0.95186	validation-aucpr:0.95793
[17:59:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33]	validation-logloss:0.53667	validation-auc:0.95188	validation-aucpr:0.95794
[17:59:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34]	validation-logloss:0.53239	validation-auc:0.95344	validation-aucpr:0.95991
[17:59:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35]	validation-logloss:0.52889	validation-auc:0.95354	validation-aucpr:0.96005
[17:59:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36]	validation-logloss:0.52594	validation-auc:0.95348	validation-aucpr:0.95997
[17:59:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37]	validation-logloss:0.52259	validation-auc:0.95358	validation-aucpr:0.96007
[17:59:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38]	validation-logloss:0.51935	validation-auc:0.95370	validation-aucpr:0.96020
[17:59:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39]	validation-logloss:0.51657	validation-auc:0.95363	validation-aucpr:0.96009
[17:59:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40]	validation-logloss:0.51254	validation-auc:0.95470	validation-aucpr:0.96133
[17:59:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41]	validation-logloss:0.50989	validation-auc:0.95465	validation-aucpr:0.96126
[17:59:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42]	validation-logloss:0.50705	validation-auc:0.95464	validation-aucpr:0.96115
[17:59:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43]	validation-logloss:0.50419	validation-auc:0.95448	validation-aucpr:0.96105
[17:59:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44]	validation-logloss:0.50158	validation-auc:0.95437	validation-aucpr:0.96092
[17:59:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45]	validation-logloss:0.49795	validation-auc:0.95500	validation-aucpr:0.96172
[17:59:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46]	validation-logloss:0.49569	validation-auc:0.95496	validation-aucpr:0.96165
[17:59:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[47]	validation-logloss:0.49339	validation-auc:0.95492	validation-aucpr:0.96159
[17:59:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[48]	validation-logloss:0.49052	validation-auc:0.95500	validation-aucpr:0.96158
[17:59:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[49]	validation-logloss:0.48725	validation-auc:0.95533	validation-aucpr:0.96194
[17:59:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[50]	validation-logloss:0.48476	validation-auc:0.95521	validation-aucpr:0.96183
[17:59:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[51]	validation-logloss:0.48142	validation-auc:0.95563	validation-aucpr:0.96236
[17:59:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[52]	validation-logloss:0.47821	validation-auc:0.95585	validation-aucpr:0.96266
[17:59:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[53]	validation-logloss:0.47579	validation-auc:0.95588	validation-aucpr:0.96266
[17:59:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[54]	validation-logloss:0.47311	validation-auc:0.95590	validation-aucpr:0.96265
[17:59:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[55]	validation-logloss:0.47112	validation-auc:0.95571	validation-aucpr:0.96250
[17:59:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[56]	validation-logloss:0.46903	validation-auc:0.95563	validation-aucpr:0.96243
[17:59:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[57]	validation-logloss:0.46685	validation-auc:0.95568	validation-aucpr:0.96243
[17:59:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[58]	validation-logloss:0.46453	validation-auc:0.95568	validation-aucpr:0.96242
[17:59:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[59]	validation-logloss:0.46145	validation-auc:0.95597	validation-aucpr:0.96277
[17:59:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[60]	validation-logloss:0.45937	validation-auc:0.95591	validation-aucpr:0.96272
[17:59:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[61]	validation-logloss:0.45696	validation-auc:0.95594	validation-aucpr:0.96271
[17:59:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[62]	validation-logloss:0.45495	validation-auc:0.95598	validation-aucpr:0.96272
[17:59:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[63]	validation-logloss:0.45292	validation-auc:0.95600	validation-aucpr:0.96271
[17:59:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[64]	validation-logloss:0.45063	validation-auc:0.95605	validation-aucpr:0.96270
[17:59:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[65]	validation-logloss:0.44889	validation-auc:0.95599	validation-aucpr:0.96262
[17:59:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[66]	validation-logloss:0.44701	validation-auc:0.95597	validation-aucpr:0.96260
[17:59:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[67]	validation-logloss:0.44505	validation-auc:0.95600	validation-aucpr:0.96263
[17:59:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[68]	validation-logloss:0.44315	validation-auc:0.95605	validation-aucpr:0.96268
[17:59:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[69]	validation-logloss:0.44145	validation-auc:0.95607	validation-aucpr:0.96271
[17:59:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[70]	validation-logloss:0.43946	validation-auc:0.95610	validation-aucpr:0.96270
[17:59:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[71]	validation-logloss:0.43774	validation-auc:0.95601	validation-aucpr:0.96261
[17:59:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[72]	validation-logloss:0.43589	validation-auc:0.95609	validation-aucpr:0.96264
[17:59:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[73]	validation-logloss:0.43404	validation-auc:0.95609	validation-aucpr:0.96266
[17:59:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[74]	validation-logloss:0.43255	validation-auc:0.95604	validation-aucpr:0.96262
[17:59:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[75]	validation-logloss:0.43070	validation-auc:0.95604	validation-aucpr:0.96259
[17:59:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[76]	validation-logloss:0.42906	validation-auc:0.95600	validation-aucpr:0.96257
[17:59:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[77]	validation-logloss:0.42709	validation-auc:0.95601	validation-aucpr:0.96259
[17:59:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[78]	validation-logloss:0.42538	validation-auc:0.95600	validation-aucpr:0.96257
[17:59:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[79]	validation-logloss:0.42399	validation-auc:0.95598	validation-aucpr:0.96254
[17:59:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[80]	validation-logloss:0.42250	validation-auc:0.95595	validation-aucpr:0.96252
[17:59:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[81]	validation-logloss:0.41995	validation-auc:0.95634	validation-aucpr:0.96293
[17:59:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[82]	validation-logloss:0.41745	validation-auc:0.95655	validation-aucpr:0.96320
[17:59:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[83]	validation-logloss:0.41560	validation-auc:0.95658	validation-aucpr:0.96322
[17:59:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[84]	validation-logloss:0.41410	validation-auc:0.95652	validation-aucpr:0.96316
[17:59:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[85]	validation-logloss:0.41222	validation-auc:0.95659	validation-aucpr:0.96320
[17:59:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[86]	validation-logloss:0.41049	validation-auc:0.95647	validation-aucpr:0.96312
[17:59:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[87]	validation-logloss:0.40895	validation-auc:0.95647	validation-aucpr:0.96310
[17:59:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[88]	validation-logloss:0.40762	validation-auc:0.95650	validation-aucpr:0.96315
[17:59:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[89]	validation-logloss:0.40614	validation-auc:0.95646	validation-aucpr:0.96309
[17:59:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[90]	validation-logloss:0.40481	validation-auc:0.95641	validation-aucpr:0.96305
[17:59:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[91]	validation-logloss:0.40334	validation-auc:0.95642	validation-aucpr:0.96304
[17:59:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[92]	validation-logloss:0.40220	validation-auc:0.95632	validation-aucpr:0.96297
{'best_iteration': '83', 'best_score': '0.9632182894831647'}
Trial 0, Fold 4: Log loss = 0.4022018041340452, Average precision = 0.9629778336083984, ROC-AUC = 0.9563161226635765, Elapsed Time = 15.438011500000357 seconds
Trial 0, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 0, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[18:00:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.68753	validation-auc:0.89140	validation-aucpr:0.86380
[18:00:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.68146	validation-auc:0.92771	validation-aucpr:0.93034
[18:00:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.67620	validation-auc:0.92589	validation-aucpr:0.93015
[18:00:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.67043	validation-auc:0.93133	validation-aucpr:0.93622
[18:00:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.66431	validation-auc:0.94552	validation-aucpr:0.95293
[18:00:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5]	validation-logloss:0.66011	validation-auc:0.94665	validation-aucpr:0.95325
[18:00:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6]	validation-logloss:0.65412	validation-auc:0.94997	validation-aucpr:0.95638
[18:00:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7]	validation-logloss:0.64803	validation-auc:0.95180	validation-aucpr:0.95821
[18:00:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8]	validation-logloss:0.64283	validation-auc:0.95159	validation-aucpr:0.95799
[18:00:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9]	validation-logloss:0.63748	validation-auc:0.95198	validation-aucpr:0.95828
[18:00:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10]	validation-logloss:0.63273	validation-auc:0.95181	validation-aucpr:0.95816
[18:00:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11]	validation-logloss:0.62795	validation-auc:0.95149	validation-aucpr:0.95787
[18:00:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12]	validation-logloss:0.62317	validation-auc:0.95153	validation-aucpr:0.95789
[18:00:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13]	validation-logloss:0.61846	validation-auc:0.95144	validation-aucpr:0.95775
[18:00:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14]	validation-logloss:0.61508	validation-auc:0.95123	validation-aucpr:0.95739
[18:00:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15]	validation-logloss:0.61057	validation-auc:0.95109	validation-aucpr:0.95734
[18:00:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16]	validation-logloss:0.60444	validation-auc:0.95351	validation-aucpr:0.96023
[18:00:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17]	validation-logloss:0.60028	validation-auc:0.95374	validation-aucpr:0.96051
[18:00:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18]	validation-logloss:0.59644	validation-auc:0.95353	validation-aucpr:0.96025
[18:00:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19]	validation-logloss:0.59326	validation-auc:0.95312	validation-aucpr:0.95977
[18:00:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20]	validation-logloss:0.58928	validation-auc:0.95288	validation-aucpr:0.95952
[18:00:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21]	validation-logloss:0.58539	validation-auc:0.95273	validation-aucpr:0.95934
[18:00:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22]	validation-logloss:0.58042	validation-auc:0.95352	validation-aucpr:0.96027
[18:00:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23]	validation-logloss:0.57656	validation-auc:0.95350	validation-aucpr:0.96027
[18:00:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24]	validation-logloss:0.57352	validation-auc:0.95344	validation-aucpr:0.96021
[18:00:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25]	validation-logloss:0.56965	validation-auc:0.95341	validation-aucpr:0.96002
[18:00:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26]	validation-logloss:0.56559	validation-auc:0.95378	validation-aucpr:0.96048
[18:00:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27]	validation-logloss:0.56186	validation-auc:0.95373	validation-aucpr:0.96034
[18:00:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28]	validation-logloss:0.55839	validation-auc:0.95359	validation-aucpr:0.96008
[18:00:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29]	validation-logloss:0.55497	validation-auc:0.95371	validation-aucpr:0.96023
[18:00:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30]	validation-logloss:0.55070	validation-auc:0.95411	validation-aucpr:0.96057
[18:00:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31]	validation-logloss:0.54690	validation-auc:0.95416	validation-aucpr:0.96066
[18:00:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32]	validation-logloss:0.54243	validation-auc:0.95432	validation-aucpr:0.96094
[18:00:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33]	validation-logloss:0.53915	validation-auc:0.95438	validation-aucpr:0.96096
[18:00:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34]	validation-logloss:0.53610	validation-auc:0.95436	validation-aucpr:0.96095
[18:00:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35]	validation-logloss:0.53343	validation-auc:0.95426	validation-aucpr:0.96077
[18:00:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36]	validation-logloss:0.52925	validation-auc:0.95463	validation-aucpr:0.96114
[18:00:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37]	validation-logloss:0.52563	validation-auc:0.95476	validation-aucpr:0.96122
[18:00:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38]	validation-logloss:0.52269	validation-auc:0.95480	validation-aucpr:0.96118
[18:00:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39]	validation-logloss:0.51956	validation-auc:0.95483	validation-aucpr:0.96119
[18:00:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40]	validation-logloss:0.51683	validation-auc:0.95481	validation-aucpr:0.96109
[18:00:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41]	validation-logloss:0.51382	validation-auc:0.95477	validation-aucpr:0.96107
[18:00:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42]	validation-logloss:0.51128	validation-auc:0.95479	validation-aucpr:0.96111
[18:00:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43]	validation-logloss:0.50775	validation-auc:0.95485	validation-aucpr:0.96123
[18:00:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44]	validation-logloss:0.50486	validation-auc:0.95482	validation-aucpr:0.96119
[18:00:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45]	validation-logloss:0.50206	validation-auc:0.95476	validation-aucpr:0.96108
[18:00:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46]	validation-logloss:0.49940	validation-auc:0.95477	validation-aucpr:0.96111
[18:00:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[47]	validation-logloss:0.49656	validation-auc:0.95488	validation-aucpr:0.96121
[18:00:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[48]	validation-logloss:0.49429	validation-auc:0.95480	validation-aucpr:0.96113
[18:00:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[49]	validation-logloss:0.49157	validation-auc:0.95470	validation-aucpr:0.96107
[18:00:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[50]	validation-logloss:0.48906	validation-auc:0.95466	validation-aucpr:0.96099
[18:00:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[51]	validation-logloss:0.48614	validation-auc:0.95480	validation-aucpr:0.96114
[18:00:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[52]	validation-logloss:0.48386	validation-auc:0.95479	validation-aucpr:0.96108
[18:00:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[53]	validation-logloss:0.48147	validation-auc:0.95477	validation-aucpr:0.96110
[18:00:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[54]	validation-logloss:0.47917	validation-auc:0.95474	validation-aucpr:0.96109
[18:00:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[55]	validation-logloss:0.47708	validation-auc:0.95469	validation-aucpr:0.96106
[18:00:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[56]	validation-logloss:0.47488	validation-auc:0.95477	validation-aucpr:0.96111
[18:00:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[57]	validation-logloss:0.47260	validation-auc:0.95482	validation-aucpr:0.96112
[18:00:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[58]	validation-logloss:0.47038	validation-auc:0.95481	validation-aucpr:0.96108
[18:00:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[59]	validation-logloss:0.46774	validation-auc:0.95493	validation-aucpr:0.96115
[18:00:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[60]	validation-logloss:0.46438	validation-auc:0.95518	validation-aucpr:0.96139
[18:00:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[61]	validation-logloss:0.46225	validation-auc:0.95516	validation-aucpr:0.96139
[18:00:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[62]	validation-logloss:0.46016	validation-auc:0.95514	validation-aucpr:0.96135
[18:00:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[63]	validation-logloss:0.45807	validation-auc:0.95516	validation-aucpr:0.96137
[18:00:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[64]	validation-logloss:0.45603	validation-auc:0.95514	validation-aucpr:0.96143
[18:00:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[65]	validation-logloss:0.45402	validation-auc:0.95504	validation-aucpr:0.96136
[18:00:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[66]	validation-logloss:0.45191	validation-auc:0.95500	validation-aucpr:0.96129
[18:00:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[67]	validation-logloss:0.44887	validation-auc:0.95519	validation-aucpr:0.96144
[18:00:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[68]	validation-logloss:0.44610	validation-auc:0.95525	validation-aucpr:0.96155
[18:00:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[69]	validation-logloss:0.44427	validation-auc:0.95520	validation-aucpr:0.96151
[18:00:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[70]	validation-logloss:0.44184	validation-auc:0.95531	validation-aucpr:0.96159
[18:00:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[71]	validation-logloss:0.43987	validation-auc:0.95529	validation-aucpr:0.96156
[18:00:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[72]	validation-logloss:0.43790	validation-auc:0.95530	validation-aucpr:0.96160
[18:00:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[73]	validation-logloss:0.43610	validation-auc:0.95524	validation-aucpr:0.96159
[18:00:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[74]	validation-logloss:0.43426	validation-auc:0.95523	validation-aucpr:0.96157
[18:00:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[75]	validation-logloss:0.43247	validation-auc:0.95528	validation-aucpr:0.96160
[18:00:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[76]	validation-logloss:0.42998	validation-auc:0.95543	validation-aucpr:0.96173
[18:00:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[77]	validation-logloss:0.42864	validation-auc:0.95538	validation-aucpr:0.96167
[18:00:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[78]	validation-logloss:0.42688	validation-auc:0.95535	validation-aucpr:0.96162
[18:00:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[79]	validation-logloss:0.42583	validation-auc:0.95535	validation-aucpr:0.96159
[18:00:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[80]	validation-logloss:0.42420	validation-auc:0.95529	validation-aucpr:0.96152
[18:00:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[81]	validation-logloss:0.42243	validation-auc:0.95532	validation-aucpr:0.96155
[18:00:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[82]	validation-logloss:0.42065	validation-auc:0.95529	validation-aucpr:0.96160
[18:00:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[83]	validation-logloss:0.41924	validation-auc:0.95531	validation-aucpr:0.96161
[18:00:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[84]	validation-logloss:0.41796	validation-auc:0.95531	validation-aucpr:0.96161
[18:00:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[85]	validation-logloss:0.41638	validation-auc:0.95526	validation-aucpr:0.96157
[18:00:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[86]	validation-logloss:0.41508	validation-auc:0.95524	validation-aucpr:0.96153
[18:00:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[87]	validation-logloss:0.41381	validation-auc:0.95531	validation-aucpr:0.96161
[18:00:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[88]	validation-logloss:0.41228	validation-auc:0.95529	validation-aucpr:0.96163
[18:00:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[89]	validation-logloss:0.41099	validation-auc:0.95527	validation-aucpr:0.96169
[18:00:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[90]	validation-logloss:0.40904	validation-auc:0.95532	validation-aucpr:0.96178
[18:00:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[91]	validation-logloss:0.40759	validation-auc:0.95532	validation-aucpr:0.96178
[18:00:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[92]	validation-logloss:0.40635	validation-auc:0.95529	validation-aucpr:0.96174
{'best_iteration': '90', 'best_score': '0.9617848063106246'}
Trial 0, Fold 5: Log loss = 0.4063536315120741, Average precision = 0.961749182442062, ROC-AUC = 0.9552881110306002, Elapsed Time = 15.487014800000907 seconds
Optimization Progress:   1%|1         | 1/100 [01:28<2:25:19, 88.08s/it]
Trial 1, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 1, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[18:00:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.63392	validation-auc:0.94384	validation-aucpr:0.94999
[18:00:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.58832	validation-auc:0.94990	validation-aucpr:0.94299
[18:00:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.54919	validation-auc:0.95494	validation-aucpr:0.94824
[18:00:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.51654	validation-auc:0.95652	validation-aucpr:0.95592
[18:00:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.48251	validation-auc:0.95997	validation-aucpr:0.96125
{'best_iteration': '4', 'best_score': '0.9612493689860694'}
Trial 1, Fold 1: Log loss = 0.4825125440000191, Average precision = 0.9622372581737065, ROC-AUC = 0.9599684756217052, Elapsed Time = 1.3910605000000942 seconds
Trial 1, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 1, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[18:00:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.63285	validation-auc:0.93630	validation-aucpr:0.94394
[18:00:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.58116	validation-auc:0.95561	validation-aucpr:0.95181
[18:00:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.54115	validation-auc:0.96053	validation-aucpr:0.95841
[18:00:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.50679	validation-auc:0.96251	validation-aucpr:0.96537
[18:00:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.47790	validation-auc:0.96320	validation-aucpr:0.96723
{'best_iteration': '4', 'best_score': '0.9672330098919696'}
Trial 1, Fold 2: Log loss = 0.4778954946598143, Average precision = 0.9670471551250688, ROC-AUC = 0.963202831249482, Elapsed Time = 1.3718076999994082 seconds
Trial 1, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 1, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[18:00:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.63230	validation-auc:0.93998	validation-aucpr:0.95407
[18:00:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.58061	validation-auc:0.95589	validation-aucpr:0.94565
[18:00:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.54078	validation-auc:0.96280	validation-aucpr:0.96040
[18:00:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.50346	validation-auc:0.96485	validation-aucpr:0.96751
[18:00:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.46977	validation-auc:0.96618	validation-aucpr:0.97034
{'best_iteration': '4', 'best_score': '0.9703432338893858'}
Trial 1, Fold 3: Log loss = 0.4697678746716907, Average precision = 0.9706581052575195, ROC-AUC = 0.9661814480983523, Elapsed Time = 1.581263799998851 seconds
Trial 1, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 1, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[18:00:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.63334	validation-auc:0.92408	validation-aucpr:0.91700
[18:00:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.58359	validation-auc:0.94845	validation-aucpr:0.93471
[18:00:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.54465	validation-auc:0.95682	validation-aucpr:0.95295
[18:00:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.50549	validation-auc:0.96154	validation-aucpr:0.96622
[18:00:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.47218	validation-auc:0.96303	validation-aucpr:0.96621
{'best_iteration': '3', 'best_score': '0.966221599980624'}
Trial 1, Fold 4: Log loss = 0.4721788659488989, Average precision = 0.9668029424338604, ROC-AUC = 0.9630322598720525, Elapsed Time = 1.341411400000652 seconds
Trial 1, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 1, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[18:00:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.63208	validation-auc:0.93269	validation-aucpr:0.94766
[18:00:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.58183	validation-auc:0.95642	validation-aucpr:0.95017
[18:00:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.53918	validation-auc:0.95850	validation-aucpr:0.95651
[18:00:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.50252	validation-auc:0.96053	validation-aucpr:0.96324
[18:00:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.47027	validation-auc:0.96212	validation-aucpr:0.96623
{'best_iteration': '4', 'best_score': '0.9662340562252816'}
Trial 1, Fold 5: Log loss = 0.47026629801259107, Average precision = 0.9665882911261429, ROC-AUC = 0.962115898407744, Elapsed Time = 1.2950529999998253 seconds
Optimization Progress:   2%|2         | 2/100 [01:43<1:13:57, 45.28s/it]
Trial 2, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 2, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[0]	validation-logloss:0.68353	validation-auc:0.91262	validation-aucpr:0.91609
[1]	validation-logloss:0.67234	validation-auc:0.94708	validation-aucpr:0.95478
[2]	validation-logloss:0.66292	validation-auc:0.95054	validation-aucpr:0.95893
[3]	validation-logloss:0.65436	validation-auc:0.94975	validation-aucpr:0.95791
[4]	validation-logloss:0.64550	validation-auc:0.95050	validation-aucpr:0.95875
[5]	validation-logloss:0.63603	validation-auc:0.95166	validation-aucpr:0.95911
[6]	validation-logloss:0.62775	validation-auc:0.95168	validation-aucpr:0.95896
[7]	validation-logloss:0.62005	validation-auc:0.95170	validation-aucpr:0.95889
[8]	validation-logloss:0.61279	validation-auc:0.95124	validation-aucpr:0.95832
[9]	validation-logloss:0.60512	validation-auc:0.95178	validation-aucpr:0.95881
[10]	validation-logloss:0.59861	validation-auc:0.95132	validation-aucpr:0.95826
[11]	validation-logloss:0.59203	validation-auc:0.95058	validation-aucpr:0.95749
[12]	validation-logloss:0.58474	validation-auc:0.95131	validation-aucpr:0.95848
[13]	validation-logloss:0.57802	validation-auc:0.95106	validation-aucpr:0.95827
[14]	validation-logloss:0.57120	validation-auc:0.95149	validation-aucpr:0.95893
[15]	validation-logloss:0.56375	validation-auc:0.95204	validation-aucpr:0.95958
[16]	validation-logloss:0.55670	validation-auc:0.95233	validation-aucpr:0.96010
[17]	validation-logloss:0.55124	validation-auc:0.95205	validation-aucpr:0.95977
[18]	validation-logloss:0.54516	validation-auc:0.95276	validation-aucpr:0.96034
[19]	validation-logloss:0.53921	validation-auc:0.95289	validation-aucpr:0.96018
[20]	validation-logloss:0.53406	validation-auc:0.95284	validation-aucpr:0.96014
[21]	validation-logloss:0.52856	validation-auc:0.95288	validation-aucpr:0.96005
{'best_iteration': '18', 'best_score': '0.9603435077097092'}
Trial 2, Fold 1: Log loss = 0.5285589149145459, Average precision = 0.9599034058322928, ROC-AUC = 0.9528846762784207, Elapsed Time = 1.5365930999996635 seconds
Trial 2, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 2, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[0]	validation-logloss:0.68363	validation-auc:0.91583	validation-aucpr:0.91126
[1]	validation-logloss:0.67451	validation-auc:0.92725	validation-aucpr:0.92492
[2]	validation-logloss:0.66501	validation-auc:0.93310	validation-aucpr:0.93311
[3]	validation-logloss:0.65645	validation-auc:0.93276	validation-aucpr:0.93250
[4]	validation-logloss:0.64741	validation-auc:0.93954	validation-aucpr:0.94073
[5]	validation-logloss:0.63955	validation-auc:0.94139	validation-aucpr:0.94358
[6]	validation-logloss:0.63133	validation-auc:0.94242	validation-aucpr:0.94462
[7]	validation-logloss:0.62367	validation-auc:0.94334	validation-aucpr:0.94536
[8]	validation-logloss:0.61641	validation-auc:0.94318	validation-aucpr:0.94531
[9]	validation-logloss:0.60882	validation-auc:0.94351	validation-aucpr:0.94579
[10]	validation-logloss:0.60229	validation-auc:0.94309	validation-aucpr:0.94541
[11]	validation-logloss:0.59566	validation-auc:0.94290	validation-aucpr:0.94513
[12]	validation-logloss:0.58841	validation-auc:0.94387	validation-aucpr:0.94651
[13]	validation-logloss:0.58162	validation-auc:0.94394	validation-aucpr:0.94659
[14]	validation-logloss:0.57482	validation-auc:0.94452	validation-aucpr:0.94742
[15]	validation-logloss:0.56723	validation-auc:0.95083	validation-aucpr:0.95501
[16]	validation-logloss:0.56008	validation-auc:0.95258	validation-aucpr:0.95742
[17]	validation-logloss:0.55448	validation-auc:0.95270	validation-aucpr:0.95784
[18]	validation-logloss:0.54846	validation-auc:0.95332	validation-aucpr:0.95830
[19]	validation-logloss:0.54241	validation-auc:0.95380	validation-aucpr:0.95825
[20]	validation-logloss:0.53708	validation-auc:0.95368	validation-aucpr:0.95823
[21]	validation-logloss:0.53168	validation-auc:0.95359	validation-aucpr:0.95806
{'best_iteration': '18', 'best_score': '0.9582963343506642'}
Trial 2, Fold 2: Log loss = 0.531684667525449, Average precision = 0.957925966584543, ROC-AUC = 0.9535940855997118, Elapsed Time = 1.873345200001495 seconds
Trial 2, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 2, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[0]	validation-logloss:0.68134	validation-auc:0.94037	validation-aucpr:0.93717
[1]	validation-logloss:0.67031	validation-auc:0.95352	validation-aucpr:0.95412
[2]	validation-logloss:0.66074	validation-auc:0.95657	validation-aucpr:0.96157
[3]	validation-logloss:0.65196	validation-auc:0.95585	validation-aucpr:0.96089
[4]	validation-logloss:0.64278	validation-auc:0.95889	validation-aucpr:0.96347
[5]	validation-logloss:0.63391	validation-auc:0.95837	validation-aucpr:0.96310
[6]	validation-logloss:0.62601	validation-auc:0.95815	validation-aucpr:0.96281
[7]	validation-logloss:0.62166	validation-auc:0.95836	validation-aucpr:0.96298
[8]	validation-logloss:0.61422	validation-auc:0.95780	validation-aucpr:0.96302
[9]	validation-logloss:0.60690	validation-auc:0.95724	validation-aucpr:0.96235
[10]	validation-logloss:0.59931	validation-auc:0.95768	validation-aucpr:0.96253
[11]	validation-logloss:0.59238	validation-auc:0.95781	validation-aucpr:0.96246
[12]	validation-logloss:0.58582	validation-auc:0.95784	validation-aucpr:0.96261
[13]	validation-logloss:0.57913	validation-auc:0.95781	validation-aucpr:0.96244
[14]	validation-logloss:0.57286	validation-auc:0.95759	validation-aucpr:0.96229
[15]	validation-logloss:0.56661	validation-auc:0.95782	validation-aucpr:0.96254
[16]	validation-logloss:0.56078	validation-auc:0.95787	validation-aucpr:0.96279
[17]	validation-logloss:0.55301	validation-auc:0.95892	validation-aucpr:0.96404
[18]	validation-logloss:0.54702	validation-auc:0.95879	validation-aucpr:0.96390
[19]	validation-logloss:0.54167	validation-auc:0.95887	validation-aucpr:0.96411
[20]	validation-logloss:0.53642	validation-auc:0.95868	validation-aucpr:0.96390
[21]	validation-logloss:0.52959	validation-auc:0.95933	validation-aucpr:0.96467
{'best_iteration': '21', 'best_score': '0.9646701594022623'}
Trial 2, Fold 3: Log loss = 0.5295945618795187, Average precision = 0.9644928261022662, ROC-AUC = 0.9593264836609491, Elapsed Time = 1.8513464000006934 seconds
Trial 2, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 2, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[0]	validation-logloss:0.68152	validation-auc:0.94070	validation-aucpr:0.94479
[1]	validation-logloss:0.67048	validation-auc:0.94815	validation-aucpr:0.95350
[2]	validation-logloss:0.66102	validation-auc:0.95153	validation-aucpr:0.95926
[3]	validation-logloss:0.65216	validation-auc:0.95090	validation-aucpr:0.95798
[4]	validation-logloss:0.64325	validation-auc:0.95219	validation-aucpr:0.95918
[5]	validation-logloss:0.63529	validation-auc:0.95192	validation-aucpr:0.95929
[6]	validation-logloss:0.62694	validation-auc:0.95320	validation-aucpr:0.96008
[7]	validation-logloss:0.61915	validation-auc:0.95280	validation-aucpr:0.95979
[8]	validation-logloss:0.61180	validation-auc:0.95276	validation-aucpr:0.95947
[9]	validation-logloss:0.60462	validation-auc:0.95194	validation-aucpr:0.95871
[10]	validation-logloss:0.59717	validation-auc:0.95284	validation-aucpr:0.95903
[11]	validation-logloss:0.59054	validation-auc:0.95395	validation-aucpr:0.95991
[12]	validation-logloss:0.58411	validation-auc:0.95353	validation-aucpr:0.95980
[13]	validation-logloss:0.57772	validation-auc:0.95308	validation-aucpr:0.95942
[14]	validation-logloss:0.57136	validation-auc:0.95375	validation-aucpr:0.96010
[15]	validation-logloss:0.56518	validation-auc:0.95423	validation-aucpr:0.96066
[16]	validation-logloss:0.55945	validation-auc:0.95389	validation-aucpr:0.96035
[17]	validation-logloss:0.55176	validation-auc:0.95459	validation-aucpr:0.96128
[18]	validation-logloss:0.54599	validation-auc:0.95468	validation-aucpr:0.96129
[19]	validation-logloss:0.54079	validation-auc:0.95501	validation-aucpr:0.96150
[20]	validation-logloss:0.53568	validation-auc:0.95469	validation-aucpr:0.96121
[21]	validation-logloss:0.52909	validation-auc:0.95507	validation-aucpr:0.96164
{'best_iteration': '21', 'best_score': '0.9616435554777185'}
Trial 2, Fold 4: Log loss = 0.52909184951774, Average precision = 0.961437936932935, ROC-AUC = 0.9550681076847143, Elapsed Time = 1.8316604000010557 seconds
Trial 2, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 2, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[0]	validation-logloss:0.68143	validation-auc:0.93842	validation-aucpr:0.94365
[1]	validation-logloss:0.67050	validation-auc:0.94675	validation-aucpr:0.95040
[2]	validation-logloss:0.66113	validation-auc:0.94800	validation-aucpr:0.95419
[3]	validation-logloss:0.65252	validation-auc:0.94793	validation-aucpr:0.95343
[4]	validation-logloss:0.64373	validation-auc:0.95174	validation-aucpr:0.95681
[5]	validation-logloss:0.63496	validation-auc:0.95247	validation-aucpr:0.95770
[6]	validation-logloss:0.62714	validation-auc:0.95206	validation-aucpr:0.95746
[7]	validation-logloss:0.62275	validation-auc:0.95204	validation-aucpr:0.95729
[8]	validation-logloss:0.61510	validation-auc:0.95156	validation-aucpr:0.95661
[9]	validation-logloss:0.60807	validation-auc:0.95122	validation-aucpr:0.95616
[10]	validation-logloss:0.60071	validation-auc:0.95169	validation-aucpr:0.95680
[11]	validation-logloss:0.59420	validation-auc:0.95135	validation-aucpr:0.95662
[12]	validation-logloss:0.58782	validation-auc:0.95151	validation-aucpr:0.95707
[13]	validation-logloss:0.58148	validation-auc:0.95131	validation-aucpr:0.95696
[14]	validation-logloss:0.57524	validation-auc:0.95164	validation-aucpr:0.95725
[15]	validation-logloss:0.56916	validation-auc:0.95224	validation-aucpr:0.95787
[16]	validation-logloss:0.56337	validation-auc:0.95244	validation-aucpr:0.95802
[17]	validation-logloss:0.55568	validation-auc:0.95336	validation-aucpr:0.95948
[18]	validation-logloss:0.54982	validation-auc:0.95347	validation-aucpr:0.95946
[19]	validation-logloss:0.54483	validation-auc:0.95392	validation-aucpr:0.95977
[20]	validation-logloss:0.53981	validation-auc:0.95366	validation-aucpr:0.95942
[21]	validation-logloss:0.53325	validation-auc:0.95402	validation-aucpr:0.96005
{'best_iteration': '21', 'best_score': '0.9600526891124913'}
Trial 2, Fold 5: Log loss = 0.5332477529706764, Average precision = 0.9599245768391352, ROC-AUC = 0.9540233729074932, Elapsed Time = 1.8385123999996722 seconds
Optimization Progress:   3%|3         | 3/100 [02:00<52:27, 32.45s/it]  
Trial 3, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 3, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[0]	validation-logloss:0.68648	validation-auc:0.92196	validation-aucpr:0.92137
[1]	validation-logloss:0.67979	validation-auc:0.93993	validation-aucpr:0.94332
[2]	validation-logloss:0.67481	validation-auc:0.93782	validation-aucpr:0.94263
[3]	validation-logloss:0.67037	validation-auc:0.94815	validation-aucpr:0.95520
[4]	validation-logloss:0.66325	validation-auc:0.95313	validation-aucpr:0.96064
[5]	validation-logloss:0.65757	validation-auc:0.95367	validation-aucpr:0.96074
[6]	validation-logloss:0.65164	validation-auc:0.95371	validation-aucpr:0.96076
[7]	validation-logloss:0.64613	validation-auc:0.95296	validation-aucpr:0.95999
[8]	validation-logloss:0.63929	validation-auc:0.95503	validation-aucpr:0.96216
[9]	validation-logloss:0.63400	validation-auc:0.95465	validation-aucpr:0.96159
[10]	validation-logloss:0.62856	validation-auc:0.95467	validation-aucpr:0.96143
[11]	validation-logloss:0.62336	validation-auc:0.95547	validation-aucpr:0.96198
[12]	validation-logloss:0.61810	validation-auc:0.95542	validation-aucpr:0.96221
[13]	validation-logloss:0.61454	validation-auc:0.95524	validation-aucpr:0.96216
[14]	validation-logloss:0.60945	validation-auc:0.95545	validation-aucpr:0.96244
[15]	validation-logloss:0.60455	validation-auc:0.95551	validation-aucpr:0.96232
[16]	validation-logloss:0.59971	validation-auc:0.95561	validation-aucpr:0.96230
[17]	validation-logloss:0.59507	validation-auc:0.95588	validation-aucpr:0.96268
[18]	validation-logloss:0.59096	validation-auc:0.95576	validation-aucpr:0.96253
[19]	validation-logloss:0.58663	validation-auc:0.95559	validation-aucpr:0.96248
[20]	validation-logloss:0.58216	validation-auc:0.95558	validation-aucpr:0.96244
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{'best_iteration': '87', 'best_score': '0.9638543290754388'}
Trial 3, Fold 1: Log loss = 0.3938565476281746, Average precision = 0.9636843615734073, ROC-AUC = 0.957102141017001, Elapsed Time = 2.7186390999995638 seconds
Trial 3, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 3, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
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{'best_iteration': '54', 'best_score': '0.9628922856828451'}
Trial 3, Fold 2: Log loss = 0.3932079132355882, Average precision = 0.9617895947599515, ROC-AUC = 0.9576229134300127, Elapsed Time = 2.8691792999998142 seconds
Trial 3, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 3, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
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{'best_iteration': '75', 'best_score': '0.965536071811244'}
Trial 3, Fold 3: Log loss = 0.3920261647812823, Average precision = 0.9651939229642484, ROC-AUC = 0.9593688451666651, Elapsed Time = 2.9667830999987927 seconds
Trial 3, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 3, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
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{'best_iteration': '79', 'best_score': '0.9622923502065677'}
Trial 3, Fold 4: Log loss = 0.4003591014363517, Average precision = 0.9620560602631516, ROC-AUC = 0.9553464130131547, Elapsed Time = 2.7846226999990904 seconds
Trial 3, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 3, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
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[94]	validation-logloss:0.40052	validation-auc:0.95326	validation-aucpr:0.95957
{'best_iteration': '89', 'best_score': '0.959639811688674'}
Trial 3, Fold 5: Log loss = 0.4005184527327331, Average precision = 0.959576679824926, ROC-AUC = 0.9532623599576391, Elapsed Time = 2.835710700001073 seconds
Optimization Progress:   4%|4         | 4/100 [02:22<45:23, 28.37s/it]
Trial 4, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 4, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
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[3]	validation-logloss:0.55534	validation-auc:0.96160	validation-aucpr:0.96131
[4]	validation-logloss:0.52807	validation-auc:0.96369	validation-aucpr:0.96267
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[7]	validation-logloss:0.46084	validation-auc:0.96574	validation-aucpr:0.96729
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[9]	validation-logloss:0.42517	validation-auc:0.96669	validation-aucpr:0.96917
[10]	validation-logloss:0.40921	validation-auc:0.96710	validation-aucpr:0.96945
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[75]	validation-logloss:0.19696	validation-auc:0.97165	validation-aucpr:0.97568
[76]	validation-logloss:0.19691	validation-auc:0.97165	validation-aucpr:0.97564
[77]	validation-logloss:0.19696	validation-auc:0.97163	validation-aucpr:0.97562
[78]	validation-logloss:0.19695	validation-auc:0.97164	validation-aucpr:0.97572
[79]	validation-logloss:0.19706	validation-auc:0.97160	validation-aucpr:0.97568
[80]	validation-logloss:0.19694	validation-auc:0.97154	validation-aucpr:0.97564
[81]	validation-logloss:0.19649	validation-auc:0.97166	validation-aucpr:0.97570
[82]	validation-logloss:0.19645	validation-auc:0.97165	validation-aucpr:0.97566
[83]	validation-logloss:0.19653	validation-auc:0.97172	validation-aucpr:0.97571
[84]	validation-logloss:0.19673	validation-auc:0.97160	validation-aucpr:0.97561
[85]	validation-logloss:0.19689	validation-auc:0.97157	validation-aucpr:0.97563
[86]	validation-logloss:0.19673	validation-auc:0.97159	validation-aucpr:0.97564
[87]	validation-logloss:0.19684	validation-auc:0.97154	validation-aucpr:0.97560
[88]	validation-logloss:0.19681	validation-auc:0.97153	validation-aucpr:0.97551
{'best_iteration': '78', 'best_score': '0.97572330288339'}
Trial 4, Fold 1: Log loss = 0.1968148526228069, Average precision = 0.97551845493452, ROC-AUC = 0.9715284349712985, Elapsed Time = 2.971843600000284 seconds
Trial 4, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 4, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
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[4]	validation-logloss:0.53032	validation-auc:0.96465	validation-aucpr:0.96392
[5]	validation-logloss:0.50606	validation-auc:0.96637	validation-aucpr:0.96873
[6]	validation-logloss:0.48325	validation-auc:0.96693	validation-aucpr:0.97139
[7]	validation-logloss:0.46264	validation-auc:0.96691	validation-aucpr:0.97143
[8]	validation-logloss:0.44603	validation-auc:0.96767	validation-aucpr:0.97161
[9]	validation-logloss:0.42815	validation-auc:0.96824	validation-aucpr:0.97206
[10]	validation-logloss:0.41171	validation-auc:0.96856	validation-aucpr:0.97239
[11]	validation-logloss:0.39777	validation-auc:0.96910	validation-aucpr:0.97267
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[74]	validation-logloss:0.18348	validation-auc:0.97426	validation-aucpr:0.97657
[75]	validation-logloss:0.18344	validation-auc:0.97425	validation-aucpr:0.97666
[76]	validation-logloss:0.18301	validation-auc:0.97434	validation-aucpr:0.97672
[77]	validation-logloss:0.18272	validation-auc:0.97429	validation-aucpr:0.97667
[78]	validation-logloss:0.18223	validation-auc:0.97447	validation-aucpr:0.97680
[79]	validation-logloss:0.18196	validation-auc:0.97450	validation-aucpr:0.97681
[80]	validation-logloss:0.18192	validation-auc:0.97449	validation-aucpr:0.97682
[81]	validation-logloss:0.18180	validation-auc:0.97444	validation-aucpr:0.97674
[82]	validation-logloss:0.18151	validation-auc:0.97460	validation-aucpr:0.97692
[83]	validation-logloss:0.18131	validation-auc:0.97468	validation-aucpr:0.97695
[84]	validation-logloss:0.18141	validation-auc:0.97464	validation-aucpr:0.97697
[85]	validation-logloss:0.18153	validation-auc:0.97464	validation-aucpr:0.97717
[86]	validation-logloss:0.18159	validation-auc:0.97462	validation-aucpr:0.97715
[87]	validation-logloss:0.18154	validation-auc:0.97455	validation-aucpr:0.97707
[88]	validation-logloss:0.18135	validation-auc:0.97457	validation-aucpr:0.97706
{'best_iteration': '85', 'best_score': '0.9771727738107567'}
Trial 4, Fold 2: Log loss = 0.18135254688458874, Average precision = 0.9770647111537439, ROC-AUC = 0.974569954363371, Elapsed Time = 3.2144864999991114 seconds
Trial 4, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 4, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[0]	validation-logloss:0.65257	validation-auc:0.94874	validation-aucpr:0.95522
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[4]	validation-logloss:0.52565	validation-auc:0.96659	validation-aucpr:0.96425
[5]	validation-logloss:0.50078	validation-auc:0.96730	validation-aucpr:0.96627
[6]	validation-logloss:0.47832	validation-auc:0.96850	validation-aucpr:0.97182
[7]	validation-logloss:0.45804	validation-auc:0.96910	validation-aucpr:0.97369
[8]	validation-logloss:0.43904	validation-auc:0.96973	validation-aucpr:0.97414
[9]	validation-logloss:0.42171	validation-auc:0.96970	validation-aucpr:0.97278
[10]	validation-logloss:0.40580	validation-auc:0.96998	validation-aucpr:0.97426
[11]	validation-logloss:0.39125	validation-auc:0.96989	validation-aucpr:0.97399
[12]	validation-logloss:0.37749	validation-auc:0.97049	validation-aucpr:0.97433
[13]	validation-logloss:0.36449	validation-auc:0.97102	validation-aucpr:0.97474
[14]	validation-logloss:0.35295	validation-auc:0.97095	validation-aucpr:0.97468
[15]	validation-logloss:0.34236	validation-auc:0.97109	validation-aucpr:0.97499
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[77]	validation-logloss:0.18960	validation-auc:0.97313	validation-aucpr:0.97647
[78]	validation-logloss:0.18948	validation-auc:0.97305	validation-aucpr:0.97640
[79]	validation-logloss:0.18917	validation-auc:0.97310	validation-aucpr:0.97641
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[82]	validation-logloss:0.18941	validation-auc:0.97299	validation-aucpr:0.97619
[83]	validation-logloss:0.18964	validation-auc:0.97291	validation-aucpr:0.97616
[84]	validation-logloss:0.18960	validation-auc:0.97292	validation-aucpr:0.97622
[85]	validation-logloss:0.18950	validation-auc:0.97294	validation-aucpr:0.97617
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[88]	validation-logloss:0.18922	validation-auc:0.97301	validation-aucpr:0.97602
{'best_iteration': '76', 'best_score': '0.9765877426803053'}
Trial 4, Fold 3: Log loss = 0.18921685039483901, Average precision = 0.9760259892551311, ROC-AUC = 0.9730138397973631, Elapsed Time = 3.222346100001232 seconds
Trial 4, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 4, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
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[7]	validation-logloss:0.46334	validation-auc:0.96722	validation-aucpr:0.96986
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[9]	validation-logloss:0.42880	validation-auc:0.96722	validation-aucpr:0.96973
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[76]	validation-logloss:0.19033	validation-auc:0.97171	validation-aucpr:0.97611
[77]	validation-logloss:0.19027	validation-auc:0.97176	validation-aucpr:0.97615
[78]	validation-logloss:0.19046	validation-auc:0.97163	validation-aucpr:0.97606
[79]	validation-logloss:0.19064	validation-auc:0.97155	validation-aucpr:0.97598
[80]	validation-logloss:0.19064	validation-auc:0.97155	validation-aucpr:0.97596
[81]	validation-logloss:0.19054	validation-auc:0.97157	validation-aucpr:0.97598
[82]	validation-logloss:0.19054	validation-auc:0.97148	validation-aucpr:0.97592
[83]	validation-logloss:0.19041	validation-auc:0.97157	validation-aucpr:0.97598
[84]	validation-logloss:0.19043	validation-auc:0.97158	validation-aucpr:0.97599
[85]	validation-logloss:0.19021	validation-auc:0.97170	validation-aucpr:0.97608
[86]	validation-logloss:0.19001	validation-auc:0.97179	validation-aucpr:0.97614
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[88]	validation-logloss:0.18988	validation-auc:0.97183	validation-aucpr:0.97619
{'best_iteration': '39', 'best_score': '0.9769865175482205'}
Trial 4, Fold 4: Log loss = 0.1898793489588116, Average precision = 0.9761969319498418, ROC-AUC = 0.9718313280447124, Elapsed Time = 3.1994386999995186 seconds
Trial 4, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 4, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[0]	validation-logloss:0.65278	validation-auc:0.93567	validation-aucpr:0.93547
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[6]	validation-logloss:0.48488	validation-auc:0.96603	validation-aucpr:0.97029
[7]	validation-logloss:0.46434	validation-auc:0.96644	validation-aucpr:0.97096
[8]	validation-logloss:0.44581	validation-auc:0.96622	validation-aucpr:0.97089
[9]	validation-logloss:0.42847	validation-auc:0.96638	validation-aucpr:0.97110
[10]	validation-logloss:0.41269	validation-auc:0.96628	validation-aucpr:0.97105
[11]	validation-logloss:0.39811	validation-auc:0.96664	validation-aucpr:0.97119
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[84]	validation-logloss:0.19609	validation-auc:0.97172	validation-aucpr:0.97526
[85]	validation-logloss:0.19604	validation-auc:0.97172	validation-aucpr:0.97521
[86]	validation-logloss:0.19610	validation-auc:0.97166	validation-aucpr:0.97521
[87]	validation-logloss:0.19595	validation-auc:0.97169	validation-aucpr:0.97523
[88]	validation-logloss:0.19613	validation-auc:0.97160	validation-aucpr:0.97525
{'best_iteration': '82', 'best_score': '0.9754620734892333'}
Trial 4, Fold 5: Log loss = 0.19613147834100697, Average precision = 0.9752497992069998, ROC-AUC = 0.9716035895435038, Elapsed Time = 3.3549910000001546 seconds
Optimization Progress:   5%|5         | 5/100 [02:47<42:36, 26.91s/it]
Trial 5, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 5, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[0]	validation-logloss:0.68411	validation-auc:0.92495	validation-aucpr:0.92667
[1]	validation-logloss:0.67398	validation-auc:0.95069	validation-aucpr:0.95639
[2]	validation-logloss:0.66382	validation-auc:0.95533	validation-aucpr:0.96185
[3]	validation-logloss:0.65600	validation-auc:0.95352	validation-aucpr:0.95998
[4]	validation-logloss:0.64691	validation-auc:0.95499	validation-aucpr:0.96191
[5]	validation-logloss:0.63940	validation-auc:0.95475	validation-aucpr:0.96156
[6]	validation-logloss:0.63110	validation-auc:0.95566	validation-aucpr:0.96202
[7]	validation-logloss:0.62281	validation-auc:0.95646	validation-aucpr:0.96287
[8]	validation-logloss:0.61529	validation-auc:0.95652	validation-aucpr:0.96296
[9]	validation-logloss:0.60838	validation-auc:0.95570	validation-aucpr:0.96218
[10]	validation-logloss:0.60150	validation-auc:0.95552	validation-aucpr:0.96184
[11]	validation-logloss:0.59507	validation-auc:0.95605	validation-aucpr:0.96273
[12]	validation-logloss:0.58692	validation-auc:0.95727	validation-aucpr:0.96403
[13]	validation-logloss:0.57943	validation-auc:0.95806	validation-aucpr:0.96461
[14]	validation-logloss:0.57197	validation-auc:0.95849	validation-aucpr:0.96513
[15]	validation-logloss:0.56615	validation-auc:0.95846	validation-aucpr:0.96538
[16]	validation-logloss:0.55961	validation-auc:0.95860	validation-aucpr:0.96549
[17]	validation-logloss:0.55281	validation-auc:0.95902	validation-aucpr:0.96586
{'best_iteration': '17', 'best_score': '0.9658596269830234'}
Trial 5, Fold 1: Log loss = 0.552814188535648, Average precision = 0.9658570155340371, ROC-AUC = 0.9590218929085236, Elapsed Time = 2.8525215000008757 seconds
Trial 5, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 5, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[0]	validation-logloss:0.68338	validation-auc:0.93597	validation-aucpr:0.93907
[1]	validation-logloss:0.67327	validation-auc:0.95048	validation-aucpr:0.95611
[2]	validation-logloss:0.66397	validation-auc:0.95365	validation-aucpr:0.96019
[3]	validation-logloss:0.65405	validation-auc:0.95823	validation-aucpr:0.96382
[4]	validation-logloss:0.64544	validation-auc:0.95959	validation-aucpr:0.96459
[5]	validation-logloss:0.63633	validation-auc:0.96052	validation-aucpr:0.96537
[6]	validation-logloss:0.62716	validation-auc:0.96203	validation-aucpr:0.96648
[7]	validation-logloss:0.61860	validation-auc:0.96195	validation-aucpr:0.96676
[8]	validation-logloss:0.61064	validation-auc:0.96220	validation-aucpr:0.96676
[9]	validation-logloss:0.60334	validation-auc:0.96178	validation-aucpr:0.96629
[10]	validation-logloss:0.59626	validation-auc:0.96172	validation-aucpr:0.96615
[11]	validation-logloss:0.58932	validation-auc:0.96150	validation-aucpr:0.96585
[12]	validation-logloss:0.58274	validation-auc:0.96117	validation-aucpr:0.96546
[13]	validation-logloss:0.57548	validation-auc:0.96186	validation-aucpr:0.96596
[14]	validation-logloss:0.56897	validation-auc:0.96226	validation-aucpr:0.96625
[15]	validation-logloss:0.56278	validation-auc:0.96213	validation-aucpr:0.96607
[16]	validation-logloss:0.55681	validation-auc:0.96160	validation-aucpr:0.96556
[17]	validation-logloss:0.55075	validation-auc:0.96167	validation-aucpr:0.96557
{'best_iteration': '8', 'best_score': '0.9667615147131858'}
Trial 5, Fold 2: Log loss = 0.5507514087219968, Average precision = 0.9655134610344999, ROC-AUC = 0.9616703303640338, Elapsed Time = 3.293433200000436 seconds
Trial 5, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 5, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[0]	validation-logloss:0.68452	validation-auc:0.91430	validation-aucpr:0.89637
[1]	validation-logloss:0.67552	validation-auc:0.93844	validation-aucpr:0.94050
[2]	validation-logloss:0.66523	validation-auc:0.95468	validation-aucpr:0.95576
[3]	validation-logloss:0.65679	validation-auc:0.95555	validation-aucpr:0.95943
[4]	validation-logloss:0.64875	validation-auc:0.95470	validation-aucpr:0.95925
[5]	validation-logloss:0.63933	validation-auc:0.95731	validation-aucpr:0.96235
[6]	validation-logloss:0.63020	validation-auc:0.95787	validation-aucpr:0.96329
[7]	validation-logloss:0.62145	validation-auc:0.95855	validation-aucpr:0.96401
[8]	validation-logloss:0.61409	validation-auc:0.95906	validation-aucpr:0.96401
[9]	validation-logloss:0.60568	validation-auc:0.95958	validation-aucpr:0.96450
[10]	validation-logloss:0.59785	validation-auc:0.95937	validation-aucpr:0.96426
[11]	validation-logloss:0.59003	validation-auc:0.95997	validation-aucpr:0.96469
[12]	validation-logloss:0.58349	validation-auc:0.95958	validation-aucpr:0.96488
[13]	validation-logloss:0.57626	validation-auc:0.96036	validation-aucpr:0.96628
[14]	validation-logloss:0.56964	validation-auc:0.96067	validation-aucpr:0.96649
[15]	validation-logloss:0.56273	validation-auc:0.96066	validation-aucpr:0.96657
[16]	validation-logloss:0.55549	validation-auc:0.96143	validation-aucpr:0.96721
[17]	validation-logloss:0.54823	validation-auc:0.96246	validation-aucpr:0.96796
{'best_iteration': '17', 'best_score': '0.9679629925673398'}
Trial 5, Fold 3: Log loss = 0.5482264868254652, Average precision = 0.967963204762582, ROC-AUC = 0.9624570967485625, Elapsed Time = 3.393403899999612 seconds
Trial 5, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 5, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[0]	validation-logloss:0.68407	validation-auc:0.92159	validation-aucpr:0.89391
[1]	validation-logloss:0.67386	validation-auc:0.95333	validation-aucpr:0.95828
[2]	validation-logloss:0.66508	validation-auc:0.95452	validation-aucpr:0.96148
[3]	validation-logloss:0.65704	validation-auc:0.95389	validation-aucpr:0.96080
[4]	validation-logloss:0.64866	validation-auc:0.95435	validation-aucpr:0.96096
[5]	validation-logloss:0.63980	validation-auc:0.95546	validation-aucpr:0.96243
[6]	validation-logloss:0.63050	validation-auc:0.95681	validation-aucpr:0.96371
[7]	validation-logloss:0.62330	validation-auc:0.95603	validation-aucpr:0.96301
[8]	validation-logloss:0.61628	validation-auc:0.95542	validation-aucpr:0.96259
[9]	validation-logloss:0.60927	validation-auc:0.95504	validation-aucpr:0.96231
[10]	validation-logloss:0.60115	validation-auc:0.95644	validation-aucpr:0.96353
[11]	validation-logloss:0.59427	validation-auc:0.95678	validation-aucpr:0.96361
[12]	validation-logloss:0.58693	validation-auc:0.95685	validation-aucpr:0.96361
[13]	validation-logloss:0.57984	validation-auc:0.95708	validation-aucpr:0.96395
[14]	validation-logloss:0.57315	validation-auc:0.95754	validation-aucpr:0.96420
[15]	validation-logloss:0.56671	validation-auc:0.95756	validation-aucpr:0.96425
[16]	validation-logloss:0.55984	validation-auc:0.95781	validation-aucpr:0.96465
[17]	validation-logloss:0.55305	validation-auc:0.95809	validation-aucpr:0.96504
{'best_iteration': '17', 'best_score': '0.9650388062047341'}
Trial 5, Fold 4: Log loss = 0.5530506863842228, Average precision = 0.965038797513921, ROC-AUC = 0.9580878136393007, Elapsed Time = 3.3468467999991844 seconds
Trial 5, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 5, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[0]	validation-logloss:0.68377	validation-auc:0.92746	validation-aucpr:0.92197
[1]	validation-logloss:0.67479	validation-auc:0.94111	validation-aucpr:0.94618
[2]	validation-logloss:0.66475	validation-auc:0.95406	validation-aucpr:0.95977
[3]	validation-logloss:0.65642	validation-auc:0.95316	validation-aucpr:0.95912
[4]	validation-logloss:0.64750	validation-auc:0.95445	validation-aucpr:0.96017
[5]	validation-logloss:0.63949	validation-auc:0.95412	validation-aucpr:0.96001
[6]	validation-logloss:0.63138	validation-auc:0.95485	validation-aucpr:0.96028
[7]	validation-logloss:0.62322	validation-auc:0.95570	validation-aucpr:0.96099
[8]	validation-logloss:0.61643	validation-auc:0.95518	validation-aucpr:0.96037
[9]	validation-logloss:0.60766	validation-auc:0.95737	validation-aucpr:0.96278
[10]	validation-logloss:0.59931	validation-auc:0.95828	validation-aucpr:0.96403
[11]	validation-logloss:0.59182	validation-auc:0.95888	validation-aucpr:0.96353
[12]	validation-logloss:0.58400	validation-auc:0.95903	validation-aucpr:0.96385
[13]	validation-logloss:0.57641	validation-auc:0.95936	validation-aucpr:0.96457
[14]	validation-logloss:0.57065	validation-auc:0.95904	validation-aucpr:0.96416
[15]	validation-logloss:0.56348	validation-auc:0.95951	validation-aucpr:0.96463
[16]	validation-logloss:0.55800	validation-auc:0.95910	validation-aucpr:0.96442
[17]	validation-logloss:0.55198	validation-auc:0.95917	validation-aucpr:0.96378
{'best_iteration': '15', 'best_score': '0.9646284351019664'}
Trial 5, Fold 5: Log loss = 0.551975421982808, Average precision = 0.9641766244099718, ROC-AUC = 0.9591747022648309, Elapsed Time = 3.3459750000001804 seconds
Optimization Progress:   6%|6         | 6/100 [03:11<41:00, 26.18s/it]
Trial 6, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 6, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[0]	validation-logloss:0.66868	validation-auc:0.92656	validation-aucpr:0.89275
[1]	validation-logloss:0.64642	validation-auc:0.94301	validation-aucpr:0.93566
[2]	validation-logloss:0.62475	validation-auc:0.95046	validation-aucpr:0.95092
[3]	validation-logloss:0.60542	validation-auc:0.95212	validation-aucpr:0.95692
[4]	validation-logloss:0.58687	validation-auc:0.95403	validation-aucpr:0.95893
[5]	validation-logloss:0.56954	validation-auc:0.95452	validation-aucpr:0.95945
[6]	validation-logloss:0.54956	validation-auc:0.96044	validation-aucpr:0.96491
[7]	validation-logloss:0.53461	validation-auc:0.96030	validation-aucpr:0.96489
[8]	validation-logloss:0.51724	validation-auc:0.96261	validation-aucpr:0.96724
[9]	validation-logloss:0.50330	validation-auc:0.96294	validation-aucpr:0.96786
[10]	validation-logloss:0.49033	validation-auc:0.96300	validation-aucpr:0.96842
[11]	validation-logloss:0.47571	validation-auc:0.96388	validation-aucpr:0.96945
[12]	validation-logloss:0.46129	validation-auc:0.96477	validation-aucpr:0.97030
[13]	validation-logloss:0.45096	validation-auc:0.96456	validation-aucpr:0.97006
[14]	validation-logloss:0.43848	validation-auc:0.96512	validation-aucpr:0.97062
[15]	validation-logloss:0.42864	validation-auc:0.96520	validation-aucpr:0.97064
[16]	validation-logloss:0.41941	validation-auc:0.96511	validation-aucpr:0.97053
[17]	validation-logloss:0.40882	validation-auc:0.96548	validation-aucpr:0.97091
[18]	validation-logloss:0.39866	validation-auc:0.96566	validation-aucpr:0.97107
[19]	validation-logloss:0.39133	validation-auc:0.96546	validation-aucpr:0.97088
[20]	validation-logloss:0.38188	validation-auc:0.96588	validation-aucpr:0.97126
[21]	validation-logloss:0.37457	validation-auc:0.96611	validation-aucpr:0.97142
[22]	validation-logloss:0.36786	validation-auc:0.96617	validation-aucpr:0.97146
[23]	validation-logloss:0.36001	validation-auc:0.96653	validation-aucpr:0.97178
[24]	validation-logloss:0.35400	validation-auc:0.96645	validation-aucpr:0.97169
[25]	validation-logloss:0.34681	validation-auc:0.96670	validation-aucpr:0.97189
[26]	validation-logloss:0.34125	validation-auc:0.96685	validation-aucpr:0.97196
[27]	validation-logloss:0.33596	validation-auc:0.96682	validation-aucpr:0.97192
[28]	validation-logloss:0.33077	validation-auc:0.96693	validation-aucpr:0.97197
[29]	validation-logloss:0.32597	validation-auc:0.96695	validation-aucpr:0.97199
[30]	validation-logloss:0.31973	validation-auc:0.96723	validation-aucpr:0.97224
[31]	validation-logloss:0.31548	validation-auc:0.96742	validation-aucpr:0.97236
[32]	validation-logloss:0.30959	validation-auc:0.96778	validation-aucpr:0.97266
[33]	validation-logloss:0.30441	validation-auc:0.96795	validation-aucpr:0.97280
[34]	validation-logloss:0.30063	validation-auc:0.96797	validation-aucpr:0.97283
[35]	validation-logloss:0.29591	validation-auc:0.96806	validation-aucpr:0.97292
[36]	validation-logloss:0.29211	validation-auc:0.96818	validation-aucpr:0.97298
[37]	validation-logloss:0.28771	validation-auc:0.96837	validation-aucpr:0.97312
[38]	validation-logloss:0.28446	validation-auc:0.96833	validation-aucpr:0.97308
[39]	validation-logloss:0.28170	validation-auc:0.96830	validation-aucpr:0.97300
[40]	validation-logloss:0.27742	validation-auc:0.96855	validation-aucpr:0.97324
[41]	validation-logloss:0.27466	validation-auc:0.96849	validation-aucpr:0.97315
[42]	validation-logloss:0.27089	validation-auc:0.96868	validation-aucpr:0.97330
[43]	validation-logloss:0.26827	validation-auc:0.96872	validation-aucpr:0.97333
[44]	validation-logloss:0.26586	validation-auc:0.96876	validation-aucpr:0.97331
[45]	validation-logloss:0.26238	validation-auc:0.96897	validation-aucpr:0.97359
[46]	validation-logloss:0.25999	validation-auc:0.96908	validation-aucpr:0.97365
[47]	validation-logloss:0.25790	validation-auc:0.96897	validation-aucpr:0.97357
[48]	validation-logloss:0.25562	validation-auc:0.96914	validation-aucpr:0.97370
[49]	validation-logloss:0.25375	validation-auc:0.96907	validation-aucpr:0.97360
[50]	validation-logloss:0.25073	validation-auc:0.96929	validation-aucpr:0.97382
[51]	validation-logloss:0.24888	validation-auc:0.96934	validation-aucpr:0.97386
{'best_iteration': '51', 'best_score': '0.9738558070482932'}
Trial 6, Fold 1: Log loss = 0.24888475472946808, Average precision = 0.9738601882875679, ROC-AUC = 0.9693418475741195, Elapsed Time = 1.6845219000006182 seconds
Trial 6, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 6, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[0]	validation-logloss:0.66788	validation-auc:0.93467	validation-aucpr:0.91059
[1]	validation-logloss:0.64453	validation-auc:0.95338	validation-aucpr:0.95073
[2]	validation-logloss:0.62375	validation-auc:0.95511	validation-aucpr:0.95729
[3]	validation-logloss:0.60063	validation-auc:0.96283	validation-aucpr:0.96462
[4]	validation-logloss:0.57891	validation-auc:0.96523	validation-aucpr:0.96826
[5]	validation-logloss:0.56156	validation-auc:0.96566	validation-aucpr:0.96894
[6]	validation-logloss:0.54520	validation-auc:0.96588	validation-aucpr:0.96890
[7]	validation-logloss:0.52685	validation-auc:0.96672	validation-aucpr:0.96999
[8]	validation-logloss:0.51011	validation-auc:0.96704	validation-aucpr:0.97042
[9]	validation-logloss:0.49444	validation-auc:0.96748	validation-aucpr:0.97081
[10]	validation-logloss:0.47964	validation-auc:0.96765	validation-aucpr:0.97100
[11]	validation-logloss:0.46581	validation-auc:0.96809	validation-aucpr:0.97139
[12]	validation-logloss:0.45496	validation-auc:0.96793	validation-aucpr:0.97124
[13]	validation-logloss:0.44395	validation-auc:0.96799	validation-aucpr:0.97125
[14]	validation-logloss:0.43378	validation-auc:0.96798	validation-aucpr:0.97133
[15]	validation-logloss:0.42528	validation-auc:0.96748	validation-aucpr:0.97077
[16]	validation-logloss:0.41465	validation-auc:0.96762	validation-aucpr:0.97090
[17]	validation-logloss:0.40565	validation-auc:0.96757	validation-aucpr:0.97028
[18]	validation-logloss:0.39571	validation-auc:0.96767	validation-aucpr:0.97044
[19]	validation-logloss:0.38781	validation-auc:0.96774	validation-aucpr:0.97046
[20]	validation-logloss:0.38028	validation-auc:0.96781	validation-aucpr:0.97050
[21]	validation-logloss:0.37130	validation-auc:0.96824	validation-aucpr:0.97085
[22]	validation-logloss:0.36476	validation-auc:0.96815	validation-aucpr:0.97078
[23]	validation-logloss:0.35668	validation-auc:0.96842	validation-aucpr:0.97101
[24]	validation-logloss:0.35027	validation-auc:0.96858	validation-aucpr:0.97114
[25]	validation-logloss:0.34288	validation-auc:0.96874	validation-aucpr:0.97130
[26]	validation-logloss:0.33586	validation-auc:0.96892	validation-aucpr:0.97144
[27]	validation-logloss:0.33056	validation-auc:0.96895	validation-aucpr:0.97150
[28]	validation-logloss:0.32402	validation-auc:0.96926	validation-aucpr:0.97168
[29]	validation-logloss:0.31906	validation-auc:0.96920	validation-aucpr:0.97165
[30]	validation-logloss:0.31305	validation-auc:0.96948	validation-aucpr:0.97205
[31]	validation-logloss:0.30734	validation-auc:0.96974	validation-aucpr:0.97234
[32]	validation-logloss:0.30292	validation-auc:0.96984	validation-aucpr:0.97240
[33]	validation-logloss:0.29776	validation-auc:0.96992	validation-aucpr:0.97251
[34]	validation-logloss:0.29272	validation-auc:0.96999	validation-aucpr:0.97261
[35]	validation-logloss:0.28898	validation-auc:0.97005	validation-aucpr:0.97264
[36]	validation-logloss:0.28518	validation-auc:0.97010	validation-aucpr:0.97267
[37]	validation-logloss:0.28164	validation-auc:0.97020	validation-aucpr:0.97269
[38]	validation-logloss:0.27842	validation-auc:0.97032	validation-aucpr:0.97276
[39]	validation-logloss:0.27516	validation-auc:0.97038	validation-aucpr:0.97280
[40]	validation-logloss:0.27111	validation-auc:0.97049	validation-aucpr:0.97291
[41]	validation-logloss:0.26738	validation-auc:0.97060	validation-aucpr:0.97299
[42]	validation-logloss:0.26459	validation-auc:0.97062	validation-aucpr:0.97301
[43]	validation-logloss:0.26107	validation-auc:0.97082	validation-aucpr:0.97323
[44]	validation-logloss:0.25834	validation-auc:0.97089	validation-aucpr:0.97393
[45]	validation-logloss:0.25590	validation-auc:0.97095	validation-aucpr:0.97396
[46]	validation-logloss:0.25344	validation-auc:0.97103	validation-aucpr:0.97399
[47]	validation-logloss:0.25101	validation-auc:0.97109	validation-aucpr:0.97403
[48]	validation-logloss:0.24893	validation-auc:0.97107	validation-aucpr:0.97399
[49]	validation-logloss:0.24667	validation-auc:0.97108	validation-aucpr:0.97400
[50]	validation-logloss:0.24461	validation-auc:0.97118	validation-aucpr:0.97406
[51]	validation-logloss:0.24163	validation-auc:0.97143	validation-aucpr:0.97427
{'best_iteration': '51', 'best_score': '0.9742744916003644'}
Trial 6, Fold 2: Log loss = 0.2416254566667652, Average precision = 0.974289737756206, ROC-AUC = 0.9714251332573178, Elapsed Time = 1.8510905000002822 seconds
Trial 6, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 6, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[0]	validation-logloss:0.66832	validation-auc:0.92346	validation-aucpr:0.88843
[1]	validation-logloss:0.64380	validation-auc:0.95272	validation-aucpr:0.95000
[2]	validation-logloss:0.62157	validation-auc:0.95911	validation-aucpr:0.96307
[3]	validation-logloss:0.59860	validation-auc:0.96419	validation-aucpr:0.96711
[4]	validation-logloss:0.57704	validation-auc:0.96581	validation-aucpr:0.96937
[5]	validation-logloss:0.55930	validation-auc:0.96675	validation-aucpr:0.97002
[6]	validation-logloss:0.54293	validation-auc:0.96719	validation-aucpr:0.97030
[7]	validation-logloss:0.52767	validation-auc:0.96737	validation-aucpr:0.96963
[8]	validation-logloss:0.51299	validation-auc:0.96722	validation-aucpr:0.96950
[9]	validation-logloss:0.50055	validation-auc:0.96708	validation-aucpr:0.96921
[10]	validation-logloss:0.48829	validation-auc:0.96690	validation-aucpr:0.96913
[11]	validation-logloss:0.47310	validation-auc:0.96760	validation-aucpr:0.97226
[12]	validation-logloss:0.45907	validation-auc:0.96797	validation-aucpr:0.97263
[13]	validation-logloss:0.44667	validation-auc:0.96809	validation-aucpr:0.97279
[14]	validation-logloss:0.43562	validation-auc:0.96826	validation-aucpr:0.97294
[15]	validation-logloss:0.42385	validation-auc:0.96841	validation-aucpr:0.97310
[16]	validation-logloss:0.41436	validation-auc:0.96874	validation-aucpr:0.97338
[17]	validation-logloss:0.40374	validation-auc:0.96897	validation-aucpr:0.97358
[18]	validation-logloss:0.39549	validation-auc:0.96893	validation-aucpr:0.97354
[19]	validation-logloss:0.38716	validation-auc:0.96896	validation-aucpr:0.97357
[20]	validation-logloss:0.37922	validation-auc:0.96919	validation-aucpr:0.97372
[21]	validation-logloss:0.37037	validation-auc:0.96939	validation-aucpr:0.97387
[22]	validation-logloss:0.36176	validation-auc:0.96949	validation-aucpr:0.97397
[23]	validation-logloss:0.35367	validation-auc:0.96976	validation-aucpr:0.97421
[24]	validation-logloss:0.34586	validation-auc:0.96988	validation-aucpr:0.97433
[25]	validation-logloss:0.33841	validation-auc:0.97016	validation-aucpr:0.97457
[26]	validation-logloss:0.33269	validation-auc:0.97030	validation-aucpr:0.97463
[27]	validation-logloss:0.32732	validation-auc:0.97022	validation-aucpr:0.97457
[28]	validation-logloss:0.32193	validation-auc:0.97045	validation-aucpr:0.97475
[29]	validation-logloss:0.31622	validation-auc:0.97057	validation-aucpr:0.97485
[30]	validation-logloss:0.31038	validation-auc:0.97082	validation-aucpr:0.97507
[31]	validation-logloss:0.30544	validation-auc:0.97110	validation-aucpr:0.97523
[32]	validation-logloss:0.30030	validation-auc:0.97105	validation-aucpr:0.97521
[33]	validation-logloss:0.29628	validation-auc:0.97101	validation-aucpr:0.97515
[34]	validation-logloss:0.29245	validation-auc:0.97107	validation-aucpr:0.97520
[35]	validation-logloss:0.28883	validation-auc:0.97104	validation-aucpr:0.97519
[36]	validation-logloss:0.28389	validation-auc:0.97127	validation-aucpr:0.97537
[37]	validation-logloss:0.28063	validation-auc:0.97119	validation-aucpr:0.97530
[38]	validation-logloss:0.27750	validation-auc:0.97116	validation-aucpr:0.97526
[39]	validation-logloss:0.27325	validation-auc:0.97123	validation-aucpr:0.97533
[40]	validation-logloss:0.27030	validation-auc:0.97116	validation-aucpr:0.97527
[41]	validation-logloss:0.26646	validation-auc:0.97130	validation-aucpr:0.97537
[42]	validation-logloss:0.26342	validation-auc:0.97149	validation-aucpr:0.97549
[43]	validation-logloss:0.26088	validation-auc:0.97148	validation-aucpr:0.97547
[44]	validation-logloss:0.25833	validation-auc:0.97154	validation-aucpr:0.97552
[45]	validation-logloss:0.25584	validation-auc:0.97153	validation-aucpr:0.97548
[46]	validation-logloss:0.25349	validation-auc:0.97150	validation-aucpr:0.97543
[47]	validation-logloss:0.25019	validation-auc:0.97160	validation-aucpr:0.97553
[48]	validation-logloss:0.24808	validation-auc:0.97167	validation-aucpr:0.97563
[49]	validation-logloss:0.24598	validation-auc:0.97165	validation-aucpr:0.97560
[50]	validation-logloss:0.24396	validation-auc:0.97163	validation-aucpr:0.97559
[51]	validation-logloss:0.24117	validation-auc:0.97175	validation-aucpr:0.97569
{'best_iteration': '51', 'best_score': '0.9756914292499006'}
Trial 6, Fold 3: Log loss = 0.2411696065616846, Average precision = 0.9756956642097862, ROC-AUC = 0.9717455528695669, Elapsed Time = 1.7352725000000646 seconds
Trial 6, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 6, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[0]	validation-logloss:0.66848	validation-auc:0.92531	validation-aucpr:0.88966
[1]	validation-logloss:0.64595	validation-auc:0.94503	validation-aucpr:0.93222
[2]	validation-logloss:0.62460	validation-auc:0.95218	validation-aucpr:0.95507
[3]	validation-logloss:0.60120	validation-auc:0.96106	validation-aucpr:0.96514
[4]	validation-logloss:0.58273	validation-auc:0.96159	validation-aucpr:0.96633
[5]	validation-logloss:0.56187	validation-auc:0.96428	validation-aucpr:0.96930
[6]	validation-logloss:0.54578	validation-auc:0.96443	validation-aucpr:0.96933
[7]	validation-logloss:0.52969	validation-auc:0.96527	validation-aucpr:0.97000
[8]	validation-logloss:0.51239	validation-auc:0.96593	validation-aucpr:0.97084
[9]	validation-logloss:0.49680	validation-auc:0.96612	validation-aucpr:0.97112
[10]	validation-logloss:0.48098	validation-auc:0.96711	validation-aucpr:0.97201
[11]	validation-logloss:0.46852	validation-auc:0.96726	validation-aucpr:0.97218
[12]	validation-logloss:0.45710	validation-auc:0.96716	validation-aucpr:0.97206
[13]	validation-logloss:0.44662	validation-auc:0.96680	validation-aucpr:0.97177
[14]	validation-logloss:0.43433	validation-auc:0.96699	validation-aucpr:0.97206
[15]	validation-logloss:0.42454	validation-auc:0.96679	validation-aucpr:0.97190
[16]	validation-logloss:0.41541	validation-auc:0.96685	validation-aucpr:0.97198
[17]	validation-logloss:0.40681	validation-auc:0.96682	validation-aucpr:0.97196
[18]	validation-logloss:0.39676	validation-auc:0.96712	validation-aucpr:0.97225
[19]	validation-logloss:0.38865	validation-auc:0.96741	validation-aucpr:0.97245
[20]	validation-logloss:0.38153	validation-auc:0.96710	validation-aucpr:0.97219
[21]	validation-logloss:0.37415	validation-auc:0.96708	validation-aucpr:0.97212
[22]	validation-logloss:0.36591	validation-auc:0.96724	validation-aucpr:0.97234
[23]	validation-logloss:0.35958	validation-auc:0.96712	validation-aucpr:0.97222
[24]	validation-logloss:0.35205	validation-auc:0.96713	validation-aucpr:0.97230
[25]	validation-logloss:0.34635	validation-auc:0.96702	validation-aucpr:0.97222
[26]	validation-logloss:0.34088	validation-auc:0.96699	validation-aucpr:0.97220
[27]	validation-logloss:0.33484	validation-auc:0.96711	validation-aucpr:0.97231
[28]	validation-logloss:0.32844	validation-auc:0.96722	validation-aucpr:0.97246
[29]	validation-logloss:0.32361	validation-auc:0.96730	validation-aucpr:0.97246
[30]	validation-logloss:0.31878	validation-auc:0.96740	validation-aucpr:0.97252
[31]	validation-logloss:0.31279	validation-auc:0.96761	validation-aucpr:0.97270
[32]	validation-logloss:0.30723	validation-auc:0.96787	validation-aucpr:0.97293
[33]	validation-logloss:0.30309	validation-auc:0.96795	validation-aucpr:0.97296
[34]	validation-logloss:0.29896	validation-auc:0.96813	validation-aucpr:0.97308
[35]	validation-logloss:0.29486	validation-auc:0.96828	validation-aucpr:0.97318
[36]	validation-logloss:0.29129	validation-auc:0.96829	validation-aucpr:0.97317
[37]	validation-logloss:0.28664	validation-auc:0.96853	validation-aucpr:0.97340
[38]	validation-logloss:0.28238	validation-auc:0.96861	validation-aucpr:0.97350
[39]	validation-logloss:0.27820	validation-auc:0.96871	validation-aucpr:0.97360
[40]	validation-logloss:0.27520	validation-auc:0.96873	validation-aucpr:0.97359
[41]	validation-logloss:0.27256	validation-auc:0.96860	validation-aucpr:0.97348
[42]	validation-logloss:0.27001	validation-auc:0.96859	validation-aucpr:0.97349
[43]	validation-logloss:0.26621	validation-auc:0.96885	validation-aucpr:0.97370
[44]	validation-logloss:0.26271	validation-auc:0.96896	validation-aucpr:0.97380
[45]	validation-logloss:0.26011	validation-auc:0.96904	validation-aucpr:0.97385
[46]	validation-logloss:0.25675	validation-auc:0.96924	validation-aucpr:0.97402
[47]	validation-logloss:0.25437	validation-auc:0.96928	validation-aucpr:0.97407
[48]	validation-logloss:0.25136	validation-auc:0.96942	validation-aucpr:0.97420
[49]	validation-logloss:0.24929	validation-auc:0.96944	validation-aucpr:0.97421
[50]	validation-logloss:0.24666	validation-auc:0.96955	validation-aucpr:0.97432
[51]	validation-logloss:0.24464	validation-auc:0.96963	validation-aucpr:0.97439
{'best_iteration': '51', 'best_score': '0.9743919537729241'}
Trial 6, Fold 4: Log loss = 0.24464498648864635, Average precision = 0.9743956571983957, ROC-AUC = 0.9696342434625445, Elapsed Time = 1.8642994000001636 seconds
Trial 6, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 6, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[0]	validation-logloss:0.66837	validation-auc:0.92013	validation-aucpr:0.88776
[1]	validation-logloss:0.64569	validation-auc:0.94533	validation-aucpr:0.93629
[2]	validation-logloss:0.62488	validation-auc:0.94923	validation-aucpr:0.94806
[3]	validation-logloss:0.60140	validation-auc:0.95964	validation-aucpr:0.96366
[4]	validation-logloss:0.57992	validation-auc:0.96210	validation-aucpr:0.96599
[5]	validation-logloss:0.56293	validation-auc:0.96179	validation-aucpr:0.96558
[6]	validation-logloss:0.54669	validation-auc:0.96209	validation-aucpr:0.96568
[7]	validation-logloss:0.53101	validation-auc:0.96269	validation-aucpr:0.96581
[8]	validation-logloss:0.51657	validation-auc:0.96266	validation-aucpr:0.96579
[9]	validation-logloss:0.50308	validation-auc:0.96248	validation-aucpr:0.96588
[10]	validation-logloss:0.48987	validation-auc:0.96302	validation-aucpr:0.96623
[11]	validation-logloss:0.47787	validation-auc:0.96293	validation-aucpr:0.96608
[12]	validation-logloss:0.46411	validation-auc:0.96347	validation-aucpr:0.96622
[13]	validation-logloss:0.45101	validation-auc:0.96426	validation-aucpr:0.96697
[14]	validation-logloss:0.44053	validation-auc:0.96436	validation-aucpr:0.96700
[15]	validation-logloss:0.42948	validation-auc:0.96442	validation-aucpr:0.96680
[16]	validation-logloss:0.42044	validation-auc:0.96439	validation-aucpr:0.96674
[17]	validation-logloss:0.40995	validation-auc:0.96452	validation-aucpr:0.96692
[18]	validation-logloss:0.40207	validation-auc:0.96447	validation-aucpr:0.96670
[19]	validation-logloss:0.39202	validation-auc:0.96500	validation-aucpr:0.96742
[20]	validation-logloss:0.38485	validation-auc:0.96500	validation-aucpr:0.96747
[21]	validation-logloss:0.37770	validation-auc:0.96507	validation-aucpr:0.96747
[22]	validation-logloss:0.36904	validation-auc:0.96559	validation-aucpr:0.96790
[23]	validation-logloss:0.36094	validation-auc:0.96599	validation-aucpr:0.96941
[24]	validation-logloss:0.35346	validation-auc:0.96619	validation-aucpr:0.96930
[25]	validation-logloss:0.34712	validation-auc:0.96634	validation-aucpr:0.96941
[26]	validation-logloss:0.34005	validation-auc:0.96682	validation-aucpr:0.96981
[27]	validation-logloss:0.33429	validation-auc:0.96689	validation-aucpr:0.97002
[28]	validation-logloss:0.32918	validation-auc:0.96692	validation-aucpr:0.97000
[29]	validation-logloss:0.32433	validation-auc:0.96702	validation-aucpr:0.97116
[30]	validation-logloss:0.31817	validation-auc:0.96735	validation-aucpr:0.97145
[31]	validation-logloss:0.31389	validation-auc:0.96757	validation-aucpr:0.97154
[32]	validation-logloss:0.30980	validation-auc:0.96744	validation-aucpr:0.97142
[33]	validation-logloss:0.30554	validation-auc:0.96755	validation-aucpr:0.97149
[34]	validation-logloss:0.30047	validation-auc:0.96774	validation-aucpr:0.97166
[35]	validation-logloss:0.29599	validation-auc:0.96783	validation-aucpr:0.97181
[36]	validation-logloss:0.29258	validation-auc:0.96781	validation-aucpr:0.97175
[37]	validation-logloss:0.28796	validation-auc:0.96815	validation-aucpr:0.97205
[38]	validation-logloss:0.28344	validation-auc:0.96839	validation-aucpr:0.97225
[39]	validation-logloss:0.27925	validation-auc:0.96858	validation-aucpr:0.97243
[40]	validation-logloss:0.27545	validation-auc:0.96865	validation-aucpr:0.97249
[41]	validation-logloss:0.27171	validation-auc:0.96880	validation-aucpr:0.97265
[42]	validation-logloss:0.26805	validation-auc:0.96892	validation-aucpr:0.97276
[43]	validation-logloss:0.26589	validation-auc:0.96875	validation-aucpr:0.97263
[44]	validation-logloss:0.26353	validation-auc:0.96876	validation-aucpr:0.97262
[45]	validation-logloss:0.26108	validation-auc:0.96885	validation-aucpr:0.97268
[46]	validation-logloss:0.25851	validation-auc:0.96891	validation-aucpr:0.97274
[47]	validation-logloss:0.25536	validation-auc:0.96917	validation-aucpr:0.97297
[48]	validation-logloss:0.25252	validation-auc:0.96929	validation-aucpr:0.97306
[49]	validation-logloss:0.24971	validation-auc:0.96947	validation-aucpr:0.97316
[50]	validation-logloss:0.24772	validation-auc:0.96953	validation-aucpr:0.97317
[51]	validation-logloss:0.24576	validation-auc:0.96963	validation-aucpr:0.97323
{'best_iteration': '51', 'best_score': '0.9732328723738558'}
Trial 6, Fold 5: Log loss = 0.24575918207555797, Average precision = 0.9732383941377283, ROC-AUC = 0.9696304553815285, Elapsed Time = 1.744874300000447 seconds
Optimization Progress:   7%|7         | 7/100 [03:29<36:20, 23.45s/it]
Trial 7, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 7, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[0]	validation-logloss:0.68016	validation-auc:0.89251	validation-aucpr:0.87031
[1]	validation-logloss:0.66712	validation-auc:0.90934	validation-aucpr:0.90403
[2]	validation-logloss:0.65493	validation-auc:0.91940	validation-aucpr:0.91914
[3]	validation-logloss:0.64218	validation-auc:0.92620	validation-aucpr:0.93081
[4]	validation-logloss:0.63544	validation-auc:0.92475	validation-aucpr:0.93087
[5]	validation-logloss:0.62441	validation-auc:0.93475	validation-aucpr:0.94099
[6]	validation-logloss:0.61526	validation-auc:0.93665	validation-aucpr:0.94386
[7]	validation-logloss:0.60932	validation-auc:0.93241	validation-aucpr:0.94166
[8]	validation-logloss:0.59916	validation-auc:0.93500	validation-aucpr:0.94389
[9]	validation-logloss:0.58964	validation-auc:0.93711	validation-aucpr:0.94473
[10]	validation-logloss:0.58046	validation-auc:0.93881	validation-aucpr:0.94572
[11]	validation-logloss:0.57157	validation-auc:0.94023	validation-aucpr:0.94657
[12]	validation-logloss:0.56514	validation-auc:0.94241	validation-aucpr:0.94895
[13]	validation-logloss:0.55683	validation-auc:0.94293	validation-aucpr:0.94978
[14]	validation-logloss:0.54973	validation-auc:0.94335	validation-aucpr:0.95017
[15]	validation-logloss:0.54508	validation-auc:0.94246	validation-aucpr:0.94980
[16]	validation-logloss:0.53825	validation-auc:0.94314	validation-aucpr:0.95041
[17]	validation-logloss:0.53180	validation-auc:0.94337	validation-aucpr:0.95039
[18]	validation-logloss:0.52467	validation-auc:0.94405	validation-aucpr:0.95080
[19]	validation-logloss:0.51864	validation-auc:0.94388	validation-aucpr:0.95064
[20]	validation-logloss:0.51293	validation-auc:0.94378	validation-aucpr:0.95054
[21]	validation-logloss:0.50736	validation-auc:0.94372	validation-aucpr:0.95017
[22]	validation-logloss:0.50184	validation-auc:0.94344	validation-aucpr:0.94997
[23]	validation-logloss:0.49344	validation-auc:0.94956	validation-aucpr:0.95698
[24]	validation-logloss:0.48983	validation-auc:0.94934	validation-aucpr:0.95664
[25]	validation-logloss:0.48617	validation-auc:0.95008	validation-aucpr:0.95737
[26]	validation-logloss:0.48121	validation-auc:0.94982	validation-aucpr:0.95701
[27]	validation-logloss:0.47813	validation-auc:0.95035	validation-aucpr:0.95764
[28]	validation-logloss:0.47385	validation-auc:0.95076	validation-aucpr:0.95807
[29]	validation-logloss:0.46691	validation-auc:0.95212	validation-aucpr:0.95960
[30]	validation-logloss:0.46231	validation-auc:0.95230	validation-aucpr:0.95991
[31]	validation-logloss:0.45990	validation-auc:0.95229	validation-aucpr:0.96008
[32]	validation-logloss:0.45711	validation-auc:0.95246	validation-aucpr:0.96015
[33]	validation-logloss:0.45468	validation-auc:0.95230	validation-aucpr:0.95999
[34]	validation-logloss:0.45072	validation-auc:0.95221	validation-aucpr:0.95995
[35]	validation-logloss:0.44696	validation-auc:0.95222	validation-aucpr:0.95985
[36]	validation-logloss:0.44315	validation-auc:0.95207	validation-aucpr:0.95965
[37]	validation-logloss:0.44108	validation-auc:0.95219	validation-aucpr:0.95974
[38]	validation-logloss:0.43709	validation-auc:0.95222	validation-aucpr:0.95972
[39]	validation-logloss:0.43289	validation-auc:0.95231	validation-aucpr:0.95966
[40]	validation-logloss:0.42983	validation-auc:0.95198	validation-aucpr:0.95931
[41]	validation-logloss:0.42831	validation-auc:0.95212	validation-aucpr:0.95946
[42]	validation-logloss:0.42538	validation-auc:0.95195	validation-aucpr:0.95925
[43]	validation-logloss:0.42330	validation-auc:0.95198	validation-aucpr:0.95940
[44]	validation-logloss:0.41957	validation-auc:0.95204	validation-aucpr:0.95948
[45]	validation-logloss:0.41739	validation-auc:0.95186	validation-aucpr:0.95931
[46]	validation-logloss:0.41432	validation-auc:0.95198	validation-aucpr:0.95948
[47]	validation-logloss:0.41163	validation-auc:0.95179	validation-aucpr:0.95919
[48]	validation-logloss:0.41000	validation-auc:0.95197	validation-aucpr:0.95943
[49]	validation-logloss:0.40495	validation-auc:0.95289	validation-aucpr:0.96051
[50]	validation-logloss:0.40270	validation-auc:0.95280	validation-aucpr:0.96035
[51]	validation-logloss:0.40179	validation-auc:0.95277	validation-aucpr:0.96034
[52]	validation-logloss:0.39913	validation-auc:0.95276	validation-aucpr:0.96032
[53]	validation-logloss:0.39708	validation-auc:0.95267	validation-aucpr:0.96016
[54]	validation-logloss:0.39336	validation-auc:0.95303	validation-aucpr:0.96045
{'best_iteration': '49', 'best_score': '0.9605111595823358'}
Trial 7, Fold 1: Log loss = 0.39335688845107086, Average precision = 0.9604510688194967, ROC-AUC = 0.9530320220530248, Elapsed Time = 0.7336209999994026 seconds
Trial 7, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 7, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[0]	validation-logloss:0.67933	validation-auc:0.90882	validation-aucpr:0.89431
[1]	validation-logloss:0.66675	validation-auc:0.91501	validation-aucpr:0.89901
[2]	validation-logloss:0.65206	validation-auc:0.93963	validation-aucpr:0.93515
[3]	validation-logloss:0.63992	validation-auc:0.94320	validation-aucpr:0.94373
[4]	validation-logloss:0.62894	validation-auc:0.94160	validation-aucpr:0.94018
[5]	validation-logloss:0.61858	validation-auc:0.94205	validation-aucpr:0.94202
[6]	validation-logloss:0.60899	validation-auc:0.94013	validation-aucpr:0.94055
[7]	validation-logloss:0.59955	validation-auc:0.94683	validation-aucpr:0.95021
[8]	validation-logloss:0.58795	validation-auc:0.95064	validation-aucpr:0.95505
[9]	validation-logloss:0.57983	validation-auc:0.95104	validation-aucpr:0.95535
[10]	validation-logloss:0.57015	validation-auc:0.95200	validation-aucpr:0.95618
[11]	validation-logloss:0.56186	validation-auc:0.95137	validation-aucpr:0.95500
[12]	validation-logloss:0.55357	validation-auc:0.95168	validation-aucpr:0.95523
[13]	validation-logloss:0.54688	validation-auc:0.95224	validation-aucpr:0.95573
[14]	validation-logloss:0.54133	validation-auc:0.95203	validation-aucpr:0.95582
[15]	validation-logloss:0.53531	validation-auc:0.95226	validation-aucpr:0.95626
[16]	validation-logloss:0.52876	validation-auc:0.95188	validation-aucpr:0.95567
[17]	validation-logloss:0.52642	validation-auc:0.95163	validation-aucpr:0.95592
[18]	validation-logloss:0.52025	validation-auc:0.95145	validation-aucpr:0.95577
[19]	validation-logloss:0.51251	validation-auc:0.95244	validation-aucpr:0.95678
[20]	validation-logloss:0.50681	validation-auc:0.95245	validation-aucpr:0.95640
[21]	validation-logloss:0.49955	validation-auc:0.95320	validation-aucpr:0.95719
[22]	validation-logloss:0.49401	validation-auc:0.95304	validation-aucpr:0.95700
[23]	validation-logloss:0.49148	validation-auc:0.95294	validation-aucpr:0.95727
[24]	validation-logloss:0.48907	validation-auc:0.95270	validation-aucpr:0.95711
[25]	validation-logloss:0.48413	validation-auc:0.95268	validation-aucpr:0.95688
[26]	validation-logloss:0.47927	validation-auc:0.95270	validation-aucpr:0.95669
[27]	validation-logloss:0.47472	validation-auc:0.95235	validation-aucpr:0.95614
[28]	validation-logloss:0.46771	validation-auc:0.95332	validation-aucpr:0.95745
[29]	validation-logloss:0.46268	validation-auc:0.95332	validation-aucpr:0.95756
[30]	validation-logloss:0.45885	validation-auc:0.95332	validation-aucpr:0.95740
[31]	validation-logloss:0.45480	validation-auc:0.95336	validation-aucpr:0.95773
[32]	validation-logloss:0.45011	validation-auc:0.95325	validation-aucpr:0.95745
[33]	validation-logloss:0.44529	validation-auc:0.95321	validation-aucpr:0.95728
[34]	validation-logloss:0.44151	validation-auc:0.95325	validation-aucpr:0.95725
[35]	validation-logloss:0.43920	validation-auc:0.95310	validation-aucpr:0.95713
[36]	validation-logloss:0.43571	validation-auc:0.95319	validation-aucpr:0.95722
[37]	validation-logloss:0.43194	validation-auc:0.95306	validation-aucpr:0.95703
[38]	validation-logloss:0.42812	validation-auc:0.95290	validation-aucpr:0.95683
[39]	validation-logloss:0.42485	validation-auc:0.95285	validation-aucpr:0.95678
[40]	validation-logloss:0.42238	validation-auc:0.95290	validation-aucpr:0.95677
[41]	validation-logloss:0.41903	validation-auc:0.95285	validation-aucpr:0.95671
[42]	validation-logloss:0.41634	validation-auc:0.95306	validation-aucpr:0.95691
[43]	validation-logloss:0.41369	validation-auc:0.95307	validation-aucpr:0.95686
[44]	validation-logloss:0.41114	validation-auc:0.95309	validation-aucpr:0.95680
[45]	validation-logloss:0.40835	validation-auc:0.95294	validation-aucpr:0.95656
[46]	validation-logloss:0.40635	validation-auc:0.95315	validation-aucpr:0.95684
[47]	validation-logloss:0.40329	validation-auc:0.95309	validation-aucpr:0.95681
[48]	validation-logloss:0.40126	validation-auc:0.95306	validation-aucpr:0.95681
[49]	validation-logloss:0.39889	validation-auc:0.95332	validation-aucpr:0.95720
[50]	validation-logloss:0.39657	validation-auc:0.95334	validation-aucpr:0.95723
[51]	validation-logloss:0.39397	validation-auc:0.95336	validation-aucpr:0.95724
[52]	validation-logloss:0.39086	validation-auc:0.95348	validation-aucpr:0.95742
[53]	validation-logloss:0.38884	validation-auc:0.95351	validation-aucpr:0.95744
[54]	validation-logloss:0.38677	validation-auc:0.95351	validation-aucpr:0.95742
{'best_iteration': '31', 'best_score': '0.9577280678288306'}
Trial 7, Fold 2: Log loss = 0.38676745688883635, Average precision = 0.9574293626158382, ROC-AUC = 0.9535117624260951, Elapsed Time = 1.0004017999999633 seconds
Trial 7, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 7, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[0]	validation-logloss:0.67935	validation-auc:0.90470	validation-aucpr:0.89052
[1]	validation-logloss:0.66670	validation-auc:0.91107	validation-aucpr:0.89678
[2]	validation-logloss:0.65748	validation-auc:0.91284	validation-aucpr:0.90760
[3]	validation-logloss:0.64437	validation-auc:0.92801	validation-aucpr:0.92905
[4]	validation-logloss:0.63204	validation-auc:0.93349	validation-aucpr:0.93578
[5]	validation-logloss:0.62087	validation-auc:0.93476	validation-aucpr:0.93718
[6]	validation-logloss:0.61630	validation-auc:0.93486	validation-aucpr:0.93826
[7]	validation-logloss:0.61212	validation-auc:0.93174	validation-aucpr:0.93799
[8]	validation-logloss:0.60159	validation-auc:0.93401	validation-aucpr:0.93934
[9]	validation-logloss:0.59161	validation-auc:0.93668	validation-aucpr:0.94126
[10]	validation-logloss:0.58263	validation-auc:0.93978	validation-aucpr:0.94483
[11]	validation-logloss:0.57186	validation-auc:0.95108	validation-aucpr:0.95796
[12]	validation-logloss:0.56858	validation-auc:0.94955	validation-aucpr:0.95721
[13]	validation-logloss:0.56002	validation-auc:0.94974	validation-aucpr:0.95730
[14]	validation-logloss:0.55234	validation-auc:0.94992	validation-aucpr:0.95716
[15]	validation-logloss:0.54406	validation-auc:0.95155	validation-aucpr:0.95845
[16]	validation-logloss:0.53695	validation-auc:0.95150	validation-aucpr:0.95833
[17]	validation-logloss:0.52953	validation-auc:0.95245	validation-aucpr:0.95905
[18]	validation-logloss:0.52296	validation-auc:0.95230	validation-aucpr:0.95877
[19]	validation-logloss:0.51900	validation-auc:0.95238	validation-aucpr:0.95908
[20]	validation-logloss:0.51275	validation-auc:0.95292	validation-aucpr:0.95949
[21]	validation-logloss:0.50723	validation-auc:0.95304	validation-aucpr:0.95942
[22]	validation-logloss:0.50095	validation-auc:0.95318	validation-aucpr:0.95929
[23]	validation-logloss:0.49563	validation-auc:0.95320	validation-aucpr:0.95926
[24]	validation-logloss:0.49035	validation-auc:0.95288	validation-aucpr:0.95887
[25]	validation-logloss:0.48681	validation-auc:0.95267	validation-aucpr:0.95863
[26]	validation-logloss:0.48187	validation-auc:0.95262	validation-aucpr:0.95845
[27]	validation-logloss:0.47603	validation-auc:0.95327	validation-aucpr:0.95908
[28]	validation-logloss:0.47167	validation-auc:0.95319	validation-aucpr:0.95900
[29]	validation-logloss:0.46714	validation-auc:0.95359	validation-aucpr:0.95937
[30]	validation-logloss:0.46427	validation-auc:0.95360	validation-aucpr:0.95939
[31]	validation-logloss:0.46030	validation-auc:0.95307	validation-aucpr:0.95879
[32]	validation-logloss:0.45621	validation-auc:0.95313	validation-aucpr:0.95888
[33]	validation-logloss:0.45440	validation-auc:0.95291	validation-aucpr:0.95856
[34]	validation-logloss:0.45190	validation-auc:0.95242	validation-aucpr:0.95793
[35]	validation-logloss:0.44799	validation-auc:0.95217	validation-aucpr:0.95766
[36]	validation-logloss:0.44177	validation-auc:0.95468	validation-aucpr:0.96061
[37]	validation-logloss:0.43795	validation-auc:0.95492	validation-aucpr:0.96086
[38]	validation-logloss:0.43421	validation-auc:0.95481	validation-aucpr:0.96068
[39]	validation-logloss:0.43090	validation-auc:0.95464	validation-aucpr:0.96048
[40]	validation-logloss:0.42521	validation-auc:0.95571	validation-aucpr:0.96178
[41]	validation-logloss:0.42179	validation-auc:0.95555	validation-aucpr:0.96157
[42]	validation-logloss:0.41843	validation-auc:0.95546	validation-aucpr:0.96149
[43]	validation-logloss:0.41547	validation-auc:0.95558	validation-aucpr:0.96161
[44]	validation-logloss:0.41066	validation-auc:0.95638	validation-aucpr:0.96243
[45]	validation-logloss:0.40819	validation-auc:0.95618	validation-aucpr:0.96220
[46]	validation-logloss:0.40649	validation-auc:0.95614	validation-aucpr:0.96203
[47]	validation-logloss:0.40335	validation-auc:0.95655	validation-aucpr:0.96242
[48]	validation-logloss:0.40051	validation-auc:0.95652	validation-aucpr:0.96234
[49]	validation-logloss:0.39855	validation-auc:0.95658	validation-aucpr:0.96240
[50]	validation-logloss:0.39698	validation-auc:0.95674	validation-aucpr:0.96260
[51]	validation-logloss:0.39445	validation-auc:0.95666	validation-aucpr:0.96255
[52]	validation-logloss:0.39174	validation-auc:0.95661	validation-aucpr:0.96253
[53]	validation-logloss:0.38659	validation-auc:0.95728	validation-aucpr:0.96340
[54]	validation-logloss:0.38234	validation-auc:0.95745	validation-aucpr:0.96364
{'best_iteration': '54', 'best_score': '0.9636424415346243'}
Trial 7, Fold 3: Log loss = 0.38234323236366546, Average precision = 0.9636474357242567, ROC-AUC = 0.9574512910402482, Elapsed Time = 1.0445481999995536 seconds
Trial 7, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 7, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[0]	validation-logloss:0.67951	validation-auc:0.90264	validation-aucpr:0.89429
[1]	validation-logloss:0.66617	validation-auc:0.92506	validation-aucpr:0.92731
[2]	validation-logloss:0.65515	validation-auc:0.92324	validation-aucpr:0.92434
[3]	validation-logloss:0.64695	validation-auc:0.92645	validation-aucpr:0.92960
[4]	validation-logloss:0.63759	validation-auc:0.92542	validation-aucpr:0.92953
[5]	validation-logloss:0.62642	validation-auc:0.93200	validation-aucpr:0.93649
[6]	validation-logloss:0.61601	validation-auc:0.93403	validation-aucpr:0.93867
[7]	validation-logloss:0.60945	validation-auc:0.93368	validation-aucpr:0.93902
[8]	validation-logloss:0.59785	validation-auc:0.94521	validation-aucpr:0.95217
[9]	validation-logloss:0.58813	validation-auc:0.94676	validation-aucpr:0.95335
[10]	validation-logloss:0.57837	validation-auc:0.94708	validation-aucpr:0.95317
[11]	validation-logloss:0.57021	validation-auc:0.94647	validation-aucpr:0.95165
[12]	validation-logloss:0.56092	validation-auc:0.94800	validation-aucpr:0.95338
[13]	validation-logloss:0.55357	validation-auc:0.94763	validation-aucpr:0.95298
[14]	validation-logloss:0.54679	validation-auc:0.94728	validation-aucpr:0.95234
[15]	validation-logloss:0.54011	validation-auc:0.94694	validation-aucpr:0.95171
[16]	validation-logloss:0.53152	validation-auc:0.94949	validation-aucpr:0.95486
[17]	validation-logloss:0.52481	validation-auc:0.94942	validation-aucpr:0.95486
[18]	validation-logloss:0.51691	validation-auc:0.95087	validation-aucpr:0.95629
[19]	validation-logloss:0.51071	validation-auc:0.95076	validation-aucpr:0.95625
[20]	validation-logloss:0.50306	validation-auc:0.95188	validation-aucpr:0.95777
[21]	validation-logloss:0.49896	validation-auc:0.95225	validation-aucpr:0.95844
[22]	validation-logloss:0.49325	validation-auc:0.95265	validation-aucpr:0.95920
[23]	validation-logloss:0.48753	validation-auc:0.95283	validation-aucpr:0.95948
[24]	validation-logloss:0.48229	validation-auc:0.95292	validation-aucpr:0.95950
[25]	validation-logloss:0.47732	validation-auc:0.95277	validation-aucpr:0.95923
[26]	validation-logloss:0.47215	validation-auc:0.95295	validation-aucpr:0.95949
[27]	validation-logloss:0.46876	validation-auc:0.95282	validation-aucpr:0.95943
[28]	validation-logloss:0.46398	validation-auc:0.95274	validation-aucpr:0.95929
[29]	validation-logloss:0.46021	validation-auc:0.95252	validation-aucpr:0.95900
[30]	validation-logloss:0.45739	validation-auc:0.95277	validation-aucpr:0.95928
[31]	validation-logloss:0.45329	validation-auc:0.95253	validation-aucpr:0.95902
[32]	validation-logloss:0.44984	validation-auc:0.95281	validation-aucpr:0.95929
[33]	validation-logloss:0.44446	validation-auc:0.95361	validation-aucpr:0.96008
[34]	validation-logloss:0.44069	validation-auc:0.95327	validation-aucpr:0.95968
[35]	validation-logloss:0.43709	validation-auc:0.95317	validation-aucpr:0.95950
[36]	validation-logloss:0.43309	validation-auc:0.95322	validation-aucpr:0.95956
[37]	validation-logloss:0.42951	validation-auc:0.95313	validation-aucpr:0.95954
[38]	validation-logloss:0.42588	validation-auc:0.95328	validation-aucpr:0.95963
[39]	validation-logloss:0.42408	validation-auc:0.95332	validation-aucpr:0.95964
[40]	validation-logloss:0.42050	validation-auc:0.95307	validation-aucpr:0.95938
[41]	validation-logloss:0.41780	validation-auc:0.95288	validation-aucpr:0.95918
[42]	validation-logloss:0.41334	validation-auc:0.95339	validation-aucpr:0.95982
[43]	validation-logloss:0.41064	validation-auc:0.95358	validation-aucpr:0.96018
[44]	validation-logloss:0.40806	validation-auc:0.95328	validation-aucpr:0.95988
[45]	validation-logloss:0.40514	validation-auc:0.95336	validation-aucpr:0.95999
[46]	validation-logloss:0.39938	validation-auc:0.95438	validation-aucpr:0.96106
[47]	validation-logloss:0.39676	validation-auc:0.95454	validation-aucpr:0.96116
[48]	validation-logloss:0.39263	validation-auc:0.95484	validation-aucpr:0.96146
[49]	validation-logloss:0.39008	validation-auc:0.95483	validation-aucpr:0.96139
[50]	validation-logloss:0.38875	validation-auc:0.95489	validation-aucpr:0.96144
[51]	validation-logloss:0.38713	validation-auc:0.95470	validation-aucpr:0.96126
[52]	validation-logloss:0.38499	validation-auc:0.95483	validation-aucpr:0.96144
[53]	validation-logloss:0.38412	validation-auc:0.95494	validation-aucpr:0.96161
[54]	validation-logloss:0.38293	validation-auc:0.95493	validation-aucpr:0.96159
{'best_iteration': '53', 'best_score': '0.9616095000793439'}
Trial 7, Fold 4: Log loss = 0.3829302825616808, Average precision = 0.9615912528910233, ROC-AUC = 0.9549325688915593, Elapsed Time = 1.014082800000324 seconds
Trial 7, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 7, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[0]	validation-logloss:0.68005	validation-auc:0.89139	validation-aucpr:0.87633
[1]	validation-logloss:0.66708	validation-auc:0.91207	validation-aucpr:0.91324
[2]	validation-logloss:0.65532	validation-auc:0.92129	validation-aucpr:0.92471
[3]	validation-logloss:0.64280	validation-auc:0.92677	validation-aucpr:0.93153
[4]	validation-logloss:0.63319	validation-auc:0.92862	validation-aucpr:0.93410
[5]	validation-logloss:0.62247	validation-auc:0.93553	validation-aucpr:0.93941
[6]	validation-logloss:0.61248	validation-auc:0.93679	validation-aucpr:0.94078
[7]	validation-logloss:0.60286	validation-auc:0.93738	validation-aucpr:0.94164
[8]	validation-logloss:0.59772	validation-auc:0.93772	validation-aucpr:0.94272
[9]	validation-logloss:0.59146	validation-auc:0.93922	validation-aucpr:0.94386
[10]	validation-logloss:0.58172	validation-auc:0.94040	validation-aucpr:0.94490
[11]	validation-logloss:0.57129	validation-auc:0.94791	validation-aucpr:0.95330
[12]	validation-logloss:0.56417	validation-auc:0.94962	validation-aucpr:0.95564
[13]	validation-logloss:0.55580	validation-auc:0.95008	validation-aucpr:0.95600
[14]	validation-logloss:0.54835	validation-auc:0.95015	validation-aucpr:0.95582
[15]	validation-logloss:0.54062	validation-auc:0.95095	validation-aucpr:0.95681
[16]	validation-logloss:0.53470	validation-auc:0.95068	validation-aucpr:0.95659
[17]	validation-logloss:0.52802	validation-auc:0.95077	validation-aucpr:0.95659
[18]	validation-logloss:0.52146	validation-auc:0.95062	validation-aucpr:0.95642
[19]	validation-logloss:0.51544	validation-auc:0.95013	validation-aucpr:0.95571
[20]	validation-logloss:0.50931	validation-auc:0.95011	validation-aucpr:0.95570
[21]	validation-logloss:0.50313	validation-auc:0.94994	validation-aucpr:0.95555
[22]	validation-logloss:0.49776	validation-auc:0.95177	validation-aucpr:0.95766
[23]	validation-logloss:0.49294	validation-auc:0.95146	validation-aucpr:0.95734
[24]	validation-logloss:0.48717	validation-auc:0.95138	validation-aucpr:0.95731
[25]	validation-logloss:0.48162	validation-auc:0.95117	validation-aucpr:0.95692
[26]	validation-logloss:0.47693	validation-auc:0.95116	validation-aucpr:0.95705
[27]	validation-logloss:0.47241	validation-auc:0.95078	validation-aucpr:0.95657
[28]	validation-logloss:0.46850	validation-auc:0.95059	validation-aucpr:0.95630
[29]	validation-logloss:0.46486	validation-auc:0.95053	validation-aucpr:0.95640
[30]	validation-logloss:0.46017	validation-auc:0.95115	validation-aucpr:0.95714
[31]	validation-logloss:0.45638	validation-auc:0.95109	validation-aucpr:0.95702
[32]	validation-logloss:0.44950	validation-auc:0.95194	validation-aucpr:0.95815
[33]	validation-logloss:0.44490	validation-auc:0.95257	validation-aucpr:0.95875
[34]	validation-logloss:0.44167	validation-auc:0.95259	validation-aucpr:0.95883
[35]	validation-logloss:0.43810	validation-auc:0.95267	validation-aucpr:0.95885
[36]	validation-logloss:0.43474	validation-auc:0.95252	validation-aucpr:0.95874
[37]	validation-logloss:0.43075	validation-auc:0.95248	validation-aucpr:0.95859
[38]	validation-logloss:0.42759	validation-auc:0.95219	validation-aucpr:0.95824
[39]	validation-logloss:0.42523	validation-auc:0.95230	validation-aucpr:0.95827
[40]	validation-logloss:0.42350	validation-auc:0.95242	validation-aucpr:0.95846
[41]	validation-logloss:0.42053	validation-auc:0.95239	validation-aucpr:0.95845
[42]	validation-logloss:0.41733	validation-auc:0.95235	validation-aucpr:0.95830
[43]	validation-logloss:0.41358	validation-auc:0.95262	validation-aucpr:0.95879
[44]	validation-logloss:0.41037	validation-auc:0.95255	validation-aucpr:0.95862
[45]	validation-logloss:0.40747	validation-auc:0.95235	validation-aucpr:0.95837
[46]	validation-logloss:0.40442	validation-auc:0.95244	validation-aucpr:0.95849
[47]	validation-logloss:0.40226	validation-auc:0.95230	validation-aucpr:0.95827
[48]	validation-logloss:0.39997	validation-auc:0.95230	validation-aucpr:0.95836
[49]	validation-logloss:0.39775	validation-auc:0.95232	validation-aucpr:0.95838
[50]	validation-logloss:0.39405	validation-auc:0.95272	validation-aucpr:0.95875
[51]	validation-logloss:0.38953	validation-auc:0.95320	validation-aucpr:0.95935
[52]	validation-logloss:0.38533	validation-auc:0.95370	validation-aucpr:0.95999
[53]	validation-logloss:0.38300	validation-auc:0.95371	validation-aucpr:0.95999
[54]	validation-logloss:0.38231	validation-auc:0.95377	validation-aucpr:0.96003
{'best_iteration': '54', 'best_score': '0.9600339440211474'}
Trial 7, Fold 5: Log loss = 0.38230679109129057, Average precision = 0.9600364880023089, ROC-AUC = 0.9537678687549933, Elapsed Time = 1.1196113000005425 seconds
Optimization Progress:   8%|8         | 8/100 [03:43<31:07, 20.30s/it]
Trial 8, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 8, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[0]	validation-logloss:0.67257	validation-auc:0.92769	validation-aucpr:0.91050
[1]	validation-logloss:0.65027	validation-auc:0.95658	validation-aucpr:0.95645
[2]	validation-logloss:0.63157	validation-auc:0.95832	validation-aucpr:0.96167
[3]	validation-logloss:0.61415	validation-auc:0.95866	validation-aucpr:0.96361
[4]	validation-logloss:0.59720	validation-auc:0.95935	validation-aucpr:0.96394
[5]	validation-logloss:0.58111	validation-auc:0.95989	validation-aucpr:0.96423
[6]	validation-logloss:0.56754	validation-auc:0.95943	validation-aucpr:0.96397
[7]	validation-logloss:0.55336	validation-auc:0.95981	validation-aucpr:0.96470
[8]	validation-logloss:0.53679	validation-auc:0.96220	validation-aucpr:0.96740
[9]	validation-logloss:0.52404	validation-auc:0.96238	validation-aucpr:0.96753
[10]	validation-logloss:0.51220	validation-auc:0.96238	validation-aucpr:0.96753
[11]	validation-logloss:0.50134	validation-auc:0.96252	validation-aucpr:0.96761
[12]	validation-logloss:0.48980	validation-auc:0.96292	validation-aucpr:0.96809
[13]	validation-logloss:0.47960	validation-auc:0.96277	validation-aucpr:0.96795
[14]	validation-logloss:0.46999	validation-auc:0.96253	validation-aucpr:0.96735
[15]	validation-logloss:0.46119	validation-auc:0.96221	validation-aucpr:0.96701
[16]	validation-logloss:0.45209	validation-auc:0.96214	validation-aucpr:0.96691
[17]	validation-logloss:0.44408	validation-auc:0.96217	validation-aucpr:0.96738
[18]	validation-logloss:0.43600	validation-auc:0.96214	validation-aucpr:0.96728
{'best_iteration': '12', 'best_score': '0.968090333653044'}
Trial 8, Fold 1: Log loss = 0.43599817143202385, Average precision = 0.9672858086737529, ROC-AUC = 0.9621420685106321, Elapsed Time = 1.3593856999996206 seconds
Trial 8, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 8, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[0]	validation-logloss:0.67242	validation-auc:0.93086	validation-aucpr:0.90704
[1]	validation-logloss:0.64977	validation-auc:0.95785	validation-aucpr:0.95825
[2]	validation-logloss:0.63079	validation-auc:0.96015	validation-aucpr:0.96284
[3]	validation-logloss:0.61379	validation-auc:0.96007	validation-aucpr:0.96319
[4]	validation-logloss:0.59733	validation-auc:0.96125	validation-aucpr:0.96473
[5]	validation-logloss:0.58147	validation-auc:0.96153	validation-aucpr:0.96471
[6]	validation-logloss:0.56832	validation-auc:0.95991	validation-aucpr:0.96315
[7]	validation-logloss:0.55353	validation-auc:0.96076	validation-aucpr:0.96411
[8]	validation-logloss:0.53987	validation-auc:0.96089	validation-aucpr:0.96424
[9]	validation-logloss:0.52758	validation-auc:0.96092	validation-aucpr:0.96421
[10]	validation-logloss:0.51678	validation-auc:0.96033	validation-aucpr:0.96356
[11]	validation-logloss:0.50381	validation-auc:0.96201	validation-aucpr:0.96547
[12]	validation-logloss:0.49091	validation-auc:0.96305	validation-aucpr:0.96657
[13]	validation-logloss:0.48081	validation-auc:0.96311	validation-aucpr:0.96657
[14]	validation-logloss:0.47140	validation-auc:0.96313	validation-aucpr:0.96652
[15]	validation-logloss:0.46246	validation-auc:0.96288	validation-aucpr:0.96627
[16]	validation-logloss:0.45431	validation-auc:0.96259	validation-aucpr:0.96598
[17]	validation-logloss:0.44543	validation-auc:0.96309	validation-aucpr:0.96644
[18]	validation-logloss:0.43829	validation-auc:0.96283	validation-aucpr:0.96614
{'best_iteration': '12', 'best_score': '0.9665736485651384'}
Trial 8, Fold 2: Log loss = 0.43828518469363265, Average precision = 0.9661639868587157, ROC-AUC = 0.9628348428697207, Elapsed Time = 1.6450736000006145 seconds
Trial 8, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 8, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[0]	validation-logloss:0.67217	validation-auc:0.92719	validation-aucpr:0.90401
[1]	validation-logloss:0.65099	validation-auc:0.95515	validation-aucpr:0.95623
[2]	validation-logloss:0.63246	validation-auc:0.95664	validation-aucpr:0.95948
[3]	validation-logloss:0.61522	validation-auc:0.95661	validation-aucpr:0.95932
[4]	validation-logloss:0.59891	validation-auc:0.95667	validation-aucpr:0.96017
[5]	validation-logloss:0.58324	validation-auc:0.95751	validation-aucpr:0.96076
[6]	validation-logloss:0.56526	validation-auc:0.96327	validation-aucpr:0.96676
[7]	validation-logloss:0.55152	validation-auc:0.96316	validation-aucpr:0.96697
[8]	validation-logloss:0.53550	validation-auc:0.96447	validation-aucpr:0.96839
[9]	validation-logloss:0.52326	validation-auc:0.96469	validation-aucpr:0.96847
[10]	validation-logloss:0.51114	validation-auc:0.96436	validation-aucpr:0.96815
[11]	validation-logloss:0.50023	validation-auc:0.96424	validation-aucpr:0.96799
[12]	validation-logloss:0.48964	validation-auc:0.96447	validation-aucpr:0.96855
[13]	validation-logloss:0.47940	validation-auc:0.96487	validation-aucpr:0.96880
[14]	validation-logloss:0.46980	validation-auc:0.96490	validation-aucpr:0.96904
[15]	validation-logloss:0.45893	validation-auc:0.96545	validation-aucpr:0.96963
[16]	validation-logloss:0.44967	validation-auc:0.96560	validation-aucpr:0.96969
[17]	validation-logloss:0.43885	validation-auc:0.96601	validation-aucpr:0.97015
[18]	validation-logloss:0.43158	validation-auc:0.96593	validation-aucpr:0.97017
{'best_iteration': '18', 'best_score': '0.9701717611710687'}
Trial 8, Fold 3: Log loss = 0.431576279013927, Average precision = 0.9701762699819079, ROC-AUC = 0.9659336746732672, Elapsed Time = 1.5622930000008637 seconds
Trial 8, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 8, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[0]	validation-logloss:0.67264	validation-auc:0.92282	validation-aucpr:0.88926
[1]	validation-logloss:0.64939	validation-auc:0.95910	validation-aucpr:0.96190
[2]	validation-logloss:0.62990	validation-auc:0.96036	validation-aucpr:0.96612
[3]	validation-logloss:0.61286	validation-auc:0.96062	validation-aucpr:0.96666
[4]	validation-logloss:0.59632	validation-auc:0.96039	validation-aucpr:0.96651
[5]	validation-logloss:0.58025	validation-auc:0.96145	validation-aucpr:0.96740
[6]	validation-logloss:0.56505	validation-auc:0.96239	validation-aucpr:0.96798
[7]	validation-logloss:0.54853	validation-auc:0.96344	validation-aucpr:0.96900
[8]	validation-logloss:0.53268	validation-auc:0.96437	validation-aucpr:0.96997
[9]	validation-logloss:0.51970	validation-auc:0.96438	validation-aucpr:0.96997
[10]	validation-logloss:0.50779	validation-auc:0.96433	validation-aucpr:0.96981
[11]	validation-logloss:0.49657	validation-auc:0.96465	validation-aucpr:0.96999
[12]	validation-logloss:0.48608	validation-auc:0.96456	validation-aucpr:0.96987
[13]	validation-logloss:0.47570	validation-auc:0.96473	validation-aucpr:0.97002
[14]	validation-logloss:0.46410	validation-auc:0.96502	validation-aucpr:0.97034
[15]	validation-logloss:0.45355	validation-auc:0.96522	validation-aucpr:0.97061
[16]	validation-logloss:0.44299	validation-auc:0.96538	validation-aucpr:0.97085
[17]	validation-logloss:0.43307	validation-auc:0.96547	validation-aucpr:0.97101
[18]	validation-logloss:0.42449	validation-auc:0.96570	validation-aucpr:0.97117
{'best_iteration': '18', 'best_score': '0.9711688641698892'}
Trial 8, Fold 4: Log loss = 0.4244943068705463, Average precision = 0.9711685110879875, ROC-AUC = 0.965697731949422, Elapsed Time = 1.530232099999921 seconds
Trial 8, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 8, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[0]	validation-logloss:0.67267	validation-auc:0.91918	validation-aucpr:0.89168
[1]	validation-logloss:0.65222	validation-auc:0.94691	validation-aucpr:0.94239
[2]	validation-logloss:0.63352	validation-auc:0.95275	validation-aucpr:0.95577
[3]	validation-logloss:0.61526	validation-auc:0.95581	validation-aucpr:0.95973
[4]	validation-logloss:0.59935	validation-auc:0.95742	validation-aucpr:0.96176
[5]	validation-logloss:0.58507	validation-auc:0.95652	validation-aucpr:0.96055
[6]	validation-logloss:0.56745	validation-auc:0.96048	validation-aucpr:0.96483
[7]	validation-logloss:0.55348	validation-auc:0.96071	validation-aucpr:0.96524
[8]	validation-logloss:0.54116	validation-auc:0.95976	validation-aucpr:0.96425
[9]	validation-logloss:0.52659	validation-auc:0.96123	validation-aucpr:0.96573
[10]	validation-logloss:0.51486	validation-auc:0.96119	validation-aucpr:0.96572
[11]	validation-logloss:0.50349	validation-auc:0.96180	validation-aucpr:0.96615
[12]	validation-logloss:0.49053	validation-auc:0.96247	validation-aucpr:0.96682
[13]	validation-logloss:0.48153	validation-auc:0.96217	validation-aucpr:0.96645
[14]	validation-logloss:0.47185	validation-auc:0.96227	validation-aucpr:0.96528
[15]	validation-logloss:0.46302	validation-auc:0.96222	validation-aucpr:0.96533
[16]	validation-logloss:0.45214	validation-auc:0.96264	validation-aucpr:0.96645
[17]	validation-logloss:0.44099	validation-auc:0.96332	validation-aucpr:0.96712
[18]	validation-logloss:0.43070	validation-auc:0.96375	validation-aucpr:0.96754
{'best_iteration': '18', 'best_score': '0.9675360413317013'}
Trial 8, Fold 5: Log loss = 0.4307014960639307, Average precision = 0.9676823377355989, ROC-AUC = 0.9637460565185887, Elapsed Time = 1.5349166999985755 seconds
Optimization Progress:   9%|9         | 9/100 [03:59<28:51, 19.02s/it]
Trial 9, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 9, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[18:02:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.68501	validation-auc:0.94432	validation-aucpr:0.94674
[18:02:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.67707	validation-auc:0.94745	validation-aucpr:0.94975
[18:02:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.66938	validation-auc:0.95070	validation-aucpr:0.95346
[18:02:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.66194	validation-auc:0.95101	validation-aucpr:0.95378
[18:02:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.65456	validation-auc:0.95241	validation-aucpr:0.95538
[18:02:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5]	validation-logloss:0.64747	validation-auc:0.95343	validation-aucpr:0.95627
[18:02:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6]	validation-logloss:0.64064	validation-auc:0.95419	validation-aucpr:0.95732
[18:02:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7]	validation-logloss:0.63369	validation-auc:0.95621	validation-aucpr:0.95883
[18:02:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8]	validation-logloss:0.62576	validation-auc:0.95991	validation-aucpr:0.96309
[18:02:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9]	validation-logloss:0.61929	validation-auc:0.95976	validation-aucpr:0.96283
[18:02:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10]	validation-logloss:0.61323	validation-auc:0.95899	validation-aucpr:0.96439
[18:02:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11]	validation-logloss:0.60576	validation-auc:0.96066	validation-aucpr:0.96632
[18:02:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12]	validation-logloss:0.59948	validation-auc:0.96075	validation-aucpr:0.96633
[18:02:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13]	validation-logloss:0.59338	validation-auc:0.96074	validation-aucpr:0.96625
[18:02:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14]	validation-logloss:0.58762	validation-auc:0.96072	validation-aucpr:0.96622
[18:02:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15]	validation-logloss:0.58217	validation-auc:0.96056	validation-aucpr:0.96605
[18:02:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16]	validation-logloss:0.57660	validation-auc:0.96031	validation-aucpr:0.96572
[18:02:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17]	validation-logloss:0.57127	validation-auc:0.96009	validation-aucpr:0.96555
[18:02:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18]	validation-logloss:0.56595	validation-auc:0.95984	validation-aucpr:0.96519
[18:02:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19]	validation-logloss:0.56068	validation-auc:0.95990	validation-aucpr:0.96527
[18:02:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20]	validation-logloss:0.55583	validation-auc:0.95964	validation-aucpr:0.96508
[18:02:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21]	validation-logloss:0.54961	validation-auc:0.96057	validation-aucpr:0.96616
[18:02:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22]	validation-logloss:0.54489	validation-auc:0.96054	validation-aucpr:0.96619
[18:02:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23]	validation-logloss:0.54019	validation-auc:0.96063	validation-aucpr:0.96625
[18:02:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24]	validation-logloss:0.53530	validation-auc:0.96079	validation-aucpr:0.96633
[18:02:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25]	validation-logloss:0.53078	validation-auc:0.96078	validation-aucpr:0.96630
[18:02:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26]	validation-logloss:0.52624	validation-auc:0.96074	validation-aucpr:0.96624
[18:02:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27]	validation-logloss:0.52187	validation-auc:0.96077	validation-aucpr:0.96626
[18:02:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28]	validation-logloss:0.51647	validation-auc:0.96121	validation-aucpr:0.96682
[18:02:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29]	validation-logloss:0.51230	validation-auc:0.96126	validation-aucpr:0.96689
[18:02:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30]	validation-logloss:0.50817	validation-auc:0.96124	validation-aucpr:0.96683
[18:02:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31]	validation-logloss:0.50290	validation-auc:0.96195	validation-aucpr:0.96762
[18:02:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32]	validation-logloss:0.49898	validation-auc:0.96196	validation-aucpr:0.96761
[18:02:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33]	validation-logloss:0.49482	validation-auc:0.96199	validation-aucpr:0.96763
[18:02:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34]	validation-logloss:0.49060	validation-auc:0.96218	validation-aucpr:0.96777
[18:02:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35]	validation-logloss:0.48710	validation-auc:0.96207	validation-aucpr:0.96773
[18:02:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36]	validation-logloss:0.48225	validation-auc:0.96254	validation-aucpr:0.96822
[18:02:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37]	validation-logloss:0.47737	validation-auc:0.96288	validation-aucpr:0.96857
[18:02:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38]	validation-logloss:0.47302	validation-auc:0.96314	validation-aucpr:0.96887
[18:02:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39]	validation-logloss:0.46920	validation-auc:0.96312	validation-aucpr:0.96886
[18:02:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40]	validation-logloss:0.46567	validation-auc:0.96311	validation-aucpr:0.96884
[18:02:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41]	validation-logloss:0.46212	validation-auc:0.96317	validation-aucpr:0.96886
[18:02:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42]	validation-logloss:0.45886	validation-auc:0.96317	validation-aucpr:0.96885
[18:02:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43]	validation-logloss:0.45557	validation-auc:0.96314	validation-aucpr:0.96881
{'best_iteration': '38', 'best_score': '0.9688745689451286'}
Trial 9, Fold 1: Log loss = 0.45556608645567226, Average precision = 0.9688078257615644, ROC-AUC = 0.9631374926513816, Elapsed Time = 3.3502621999996336 seconds
Trial 9, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 9, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[18:02:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.68465	validation-auc:0.94008	validation-aucpr:0.93101
[18:02:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.67570	validation-auc:0.95996	validation-aucpr:0.96218
[18:02:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.66799	validation-auc:0.96088	validation-aucpr:0.96512
[18:02:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.66046	validation-auc:0.96114	validation-aucpr:0.96514
[18:02:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.65341	validation-auc:0.96049	validation-aucpr:0.96416
[18:02:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5]	validation-logloss:0.64647	validation-auc:0.95997	validation-aucpr:0.96386
[18:02:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6]	validation-logloss:0.63951	validation-auc:0.95993	validation-aucpr:0.96369
[18:02:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7]	validation-logloss:0.63254	validation-auc:0.95985	validation-aucpr:0.96364
[18:02:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8]	validation-logloss:0.62567	validation-auc:0.95979	validation-aucpr:0.96331
[18:02:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9]	validation-logloss:0.61934	validation-auc:0.95941	validation-aucpr:0.96278
[18:02:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10]	validation-logloss:0.61296	validation-auc:0.95898	validation-aucpr:0.96214
[18:02:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11]	validation-logloss:0.60568	validation-auc:0.96132	validation-aucpr:0.96500
[18:02:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12]	validation-logloss:0.59944	validation-auc:0.96086	validation-aucpr:0.96426
[18:02:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13]	validation-logloss:0.59360	validation-auc:0.96056	validation-aucpr:0.96395
[18:02:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14]	validation-logloss:0.58697	validation-auc:0.96170	validation-aucpr:0.96540
[18:02:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15]	validation-logloss:0.57996	validation-auc:0.96238	validation-aucpr:0.96532
[18:02:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16]	validation-logloss:0.57448	validation-auc:0.96241	validation-aucpr:0.96524
[18:02:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17]	validation-logloss:0.56913	validation-auc:0.96252	validation-aucpr:0.96523
[18:02:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18]	validation-logloss:0.56370	validation-auc:0.96231	validation-aucpr:0.96500
[18:02:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19]	validation-logloss:0.55846	validation-auc:0.96207	validation-aucpr:0.96474
[18:02:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20]	validation-logloss:0.55244	validation-auc:0.96242	validation-aucpr:0.96517
[18:02:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21]	validation-logloss:0.54729	validation-auc:0.96234	validation-aucpr:0.96510
[18:02:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22]	validation-logloss:0.54102	validation-auc:0.96296	validation-aucpr:0.96593
[18:02:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23]	validation-logloss:0.53594	validation-auc:0.96304	validation-aucpr:0.96594
[18:02:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24]	validation-logloss:0.53105	validation-auc:0.96306	validation-aucpr:0.96594
[18:02:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25]	validation-logloss:0.52511	validation-auc:0.96353	validation-aucpr:0.96644
[18:02:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26]	validation-logloss:0.52067	validation-auc:0.96357	validation-aucpr:0.96651
[18:02:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27]	validation-logloss:0.51522	validation-auc:0.96390	validation-aucpr:0.96686
[18:02:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28]	validation-logloss:0.51083	validation-auc:0.96392	validation-aucpr:0.96680
[18:02:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29]	validation-logloss:0.50692	validation-auc:0.96380	validation-aucpr:0.96657
[18:02:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30]	validation-logloss:0.50292	validation-auc:0.96373	validation-aucpr:0.96652
[18:02:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31]	validation-logloss:0.49876	validation-auc:0.96375	validation-aucpr:0.96651
[18:02:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32]	validation-logloss:0.49363	validation-auc:0.96398	validation-aucpr:0.96681
[18:03:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33]	validation-logloss:0.48974	validation-auc:0.96388	validation-aucpr:0.96789
[18:03:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34]	validation-logloss:0.48612	validation-auc:0.96376	validation-aucpr:0.96768
[18:03:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35]	validation-logloss:0.48118	validation-auc:0.96396	validation-aucpr:0.96792
[18:03:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36]	validation-logloss:0.47757	validation-auc:0.96384	validation-aucpr:0.96779
[18:03:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37]	validation-logloss:0.47382	validation-auc:0.96376	validation-aucpr:0.96771
[18:03:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38]	validation-logloss:0.47025	validation-auc:0.96382	validation-aucpr:0.96772
[18:03:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39]	validation-logloss:0.46678	validation-auc:0.96386	validation-aucpr:0.96775
[18:03:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40]	validation-logloss:0.46325	validation-auc:0.96390	validation-aucpr:0.96808
[18:03:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41]	validation-logloss:0.45910	validation-auc:0.96403	validation-aucpr:0.96820
[18:03:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42]	validation-logloss:0.45468	validation-auc:0.96429	validation-aucpr:0.96849
[18:03:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43]	validation-logloss:0.45118	validation-auc:0.96433	validation-aucpr:0.96849
{'best_iteration': '43', 'best_score': '0.968494347322956'}
Trial 9, Fold 2: Log loss = 0.4511816000390403, Average precision = 0.9684326083583713, ROC-AUC = 0.9643344144857222, Elapsed Time = 3.7610129000004235 seconds
Trial 9, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 9, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[18:03:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.68485	validation-auc:0.93746	validation-aucpr:0.93170
[18:03:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.67589	validation-auc:0.96026	validation-aucpr:0.96209
[18:03:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.66736	validation-auc:0.96233	validation-aucpr:0.96690
[18:03:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.65984	validation-auc:0.96268	validation-aucpr:0.96592
[18:03:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.65244	validation-auc:0.96265	validation-aucpr:0.96576
[18:03:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5]	validation-logloss:0.64516	validation-auc:0.96318	validation-aucpr:0.96805
[18:03:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6]	validation-logloss:0.63828	validation-auc:0.96350	validation-aucpr:0.96829
[18:03:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7]	validation-logloss:0.63102	validation-auc:0.96374	validation-aucpr:0.96851
[18:03:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8]	validation-logloss:0.62320	validation-auc:0.96444	validation-aucpr:0.96958
[18:03:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9]	validation-logloss:0.61684	validation-auc:0.96404	validation-aucpr:0.96919
[18:03:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10]	validation-logloss:0.61013	validation-auc:0.96431	validation-aucpr:0.96933
[18:03:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11]	validation-logloss:0.60364	validation-auc:0.96439	validation-aucpr:0.96935
[18:03:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12]	validation-logloss:0.59775	validation-auc:0.96442	validation-aucpr:0.96937
[18:03:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13]	validation-logloss:0.59040	validation-auc:0.96509	validation-aucpr:0.97008
[18:03:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14]	validation-logloss:0.58452	validation-auc:0.96505	validation-aucpr:0.96993
[18:03:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15]	validation-logloss:0.57766	validation-auc:0.96530	validation-aucpr:0.97024
[18:03:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16]	validation-logloss:0.57112	validation-auc:0.96545	validation-aucpr:0.97049
[18:03:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17]	validation-logloss:0.56568	validation-auc:0.96535	validation-aucpr:0.97046
[18:03:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18]	validation-logloss:0.55962	validation-auc:0.96550	validation-aucpr:0.97058
[18:03:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19]	validation-logloss:0.55413	validation-auc:0.96548	validation-aucpr:0.97052
[18:03:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20]	validation-logloss:0.54897	validation-auc:0.96533	validation-aucpr:0.97047
[18:03:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21]	validation-logloss:0.54385	validation-auc:0.96543	validation-aucpr:0.97051
[18:03:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22]	validation-logloss:0.53892	validation-auc:0.96548	validation-aucpr:0.97054
[18:03:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23]	validation-logloss:0.53407	validation-auc:0.96543	validation-aucpr:0.97046
[18:03:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24]	validation-logloss:0.52943	validation-auc:0.96538	validation-aucpr:0.97033
[18:03:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25]	validation-logloss:0.52382	validation-auc:0.96540	validation-aucpr:0.97040
[18:03:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26]	validation-logloss:0.51921	validation-auc:0.96546	validation-aucpr:0.97042
[18:03:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27]	validation-logloss:0.51482	validation-auc:0.96553	validation-aucpr:0.97044
[18:03:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28]	validation-logloss:0.51019	validation-auc:0.96557	validation-aucpr:0.97048
[18:03:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29]	validation-logloss:0.50575	validation-auc:0.96553	validation-aucpr:0.97041
[18:03:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30]	validation-logloss:0.50157	validation-auc:0.96546	validation-aucpr:0.97034
[18:03:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31]	validation-logloss:0.49742	validation-auc:0.96547	validation-aucpr:0.97030
[18:03:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32]	validation-logloss:0.49254	validation-auc:0.96563	validation-aucpr:0.97050
[18:03:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33]	validation-logloss:0.48860	validation-auc:0.96569	validation-aucpr:0.97050
[18:03:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34]	validation-logloss:0.48478	validation-auc:0.96560	validation-aucpr:0.97042
[18:03:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35]	validation-logloss:0.48097	validation-auc:0.96560	validation-aucpr:0.97037
[18:03:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36]	validation-logloss:0.47692	validation-auc:0.96568	validation-aucpr:0.97041
[18:03:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37]	validation-logloss:0.47330	validation-auc:0.96561	validation-aucpr:0.97035
[18:03:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38]	validation-logloss:0.46881	validation-auc:0.96583	validation-aucpr:0.97058
[18:03:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39]	validation-logloss:0.46512	validation-auc:0.96582	validation-aucpr:0.97057
[18:03:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40]	validation-logloss:0.46165	validation-auc:0.96572	validation-aucpr:0.97050
[18:03:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41]	validation-logloss:0.45812	validation-auc:0.96583	validation-aucpr:0.97057
[18:03:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42]	validation-logloss:0.45483	validation-auc:0.96578	validation-aucpr:0.97051
[18:03:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43]	validation-logloss:0.45114	validation-auc:0.96586	validation-aucpr:0.97059
{'best_iteration': '43', 'best_score': '0.9705907319091631'}
Trial 9, Fold 3: Log loss = 0.4511384331227501, Average precision = 0.9705902080277841, ROC-AUC = 0.9658637744267127, Elapsed Time = 3.7800318999998126 seconds
Trial 9, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 9, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[18:03:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.68470	validation-auc:0.93733	validation-aucpr:0.92477
[18:03:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.67582	validation-auc:0.95888	validation-aucpr:0.96391
[18:03:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.66734	validation-auc:0.96074	validation-aucpr:0.96742
[18:03:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.66009	validation-auc:0.95988	validation-aucpr:0.96691
[18:03:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.65142	validation-auc:0.96155	validation-aucpr:0.96835
[18:03:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5]	validation-logloss:0.64336	validation-auc:0.96232	validation-aucpr:0.96862
[18:03:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6]	validation-logloss:0.63653	validation-auc:0.96233	validation-aucpr:0.96885
[18:03:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7]	validation-logloss:0.62993	validation-auc:0.96176	validation-aucpr:0.96822
[18:03:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8]	validation-logloss:0.62347	validation-auc:0.96147	validation-aucpr:0.96783
[18:03:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9]	validation-logloss:0.61582	validation-auc:0.96180	validation-aucpr:0.96816
[18:03:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10]	validation-logloss:0.60928	validation-auc:0.96169	validation-aucpr:0.96808
[18:03:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11]	validation-logloss:0.60193	validation-auc:0.96192	validation-aucpr:0.96841
[18:03:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12]	validation-logloss:0.59594	validation-auc:0.96197	validation-aucpr:0.96842
[18:03:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13]	validation-logloss:0.58973	validation-auc:0.96194	validation-aucpr:0.96842
[18:03:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14]	validation-logloss:0.58288	validation-auc:0.96236	validation-aucpr:0.96885
[18:03:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15]	validation-logloss:0.57617	validation-auc:0.96261	validation-aucpr:0.96910
[18:03:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16]	validation-logloss:0.57105	validation-auc:0.96250	validation-aucpr:0.96894
[18:03:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17]	validation-logloss:0.56549	validation-auc:0.96264	validation-aucpr:0.96896
[18:03:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18]	validation-logloss:0.56007	validation-auc:0.96270	validation-aucpr:0.96891
[18:03:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19]	validation-logloss:0.55458	validation-auc:0.96291	validation-aucpr:0.96902
[18:03:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20]	validation-logloss:0.54950	validation-auc:0.96280	validation-aucpr:0.96888
[18:03:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21]	validation-logloss:0.54438	validation-auc:0.96281	validation-aucpr:0.96883
[18:03:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22]	validation-logloss:0.53832	validation-auc:0.96317	validation-aucpr:0.96919
[18:03:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23]	validation-logloss:0.53360	validation-auc:0.96311	validation-aucpr:0.96912
[18:03:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24]	validation-logloss:0.52847	validation-auc:0.96306	validation-aucpr:0.96912
[18:03:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25]	validation-logloss:0.52378	validation-auc:0.96298	validation-aucpr:0.96905
[18:03:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26]	validation-logloss:0.51926	validation-auc:0.96282	validation-aucpr:0.96895
[18:03:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27]	validation-logloss:0.51500	validation-auc:0.96270	validation-aucpr:0.96881
[18:03:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28]	validation-logloss:0.51068	validation-auc:0.96275	validation-aucpr:0.96880
[18:03:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29]	validation-logloss:0.50545	validation-auc:0.96291	validation-aucpr:0.96899
[18:03:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30]	validation-logloss:0.50115	validation-auc:0.96288	validation-aucpr:0.96891
[18:03:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31]	validation-logloss:0.49703	validation-auc:0.96291	validation-aucpr:0.96885
[18:03:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32]	validation-logloss:0.49225	validation-auc:0.96305	validation-aucpr:0.96902
[18:03:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33]	validation-logloss:0.48842	validation-auc:0.96293	validation-aucpr:0.96892
[18:03:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34]	validation-logloss:0.48453	validation-auc:0.96287	validation-aucpr:0.96884
[18:03:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35]	validation-logloss:0.48077	validation-auc:0.96280	validation-aucpr:0.96877
[18:03:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36]	validation-logloss:0.47676	validation-auc:0.96285	validation-aucpr:0.96879
[18:03:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37]	validation-logloss:0.47212	validation-auc:0.96302	validation-aucpr:0.96896
[18:03:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38]	validation-logloss:0.46775	validation-auc:0.96327	validation-aucpr:0.96921
[18:03:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39]	validation-logloss:0.46411	validation-auc:0.96327	validation-aucpr:0.96923
[18:03:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40]	validation-logloss:0.45991	validation-auc:0.96338	validation-aucpr:0.96935
[18:03:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41]	validation-logloss:0.45653	validation-auc:0.96335	validation-aucpr:0.96930
[18:03:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42]	validation-logloss:0.45237	validation-auc:0.96353	validation-aucpr:0.96947
[18:03:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43]	validation-logloss:0.44910	validation-auc:0.96354	validation-aucpr:0.96947
{'best_iteration': '43', 'best_score': '0.9694712773591828'}
Trial 9, Fold 4: Log loss = 0.44909951162714856, Average precision = 0.9694630808116731, ROC-AUC = 0.9635393940260997, Elapsed Time = 3.8774806000001263 seconds
Trial 9, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 9, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[18:03:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.68496	validation-auc:0.93507	validation-aucpr:0.91961
[18:03:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.67608	validation-auc:0.95637	validation-aucpr:0.95470
[18:03:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.66752	validation-auc:0.95770	validation-aucpr:0.96311
[18:03:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.66012	validation-auc:0.95890	validation-aucpr:0.96433
[18:03:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.65289	validation-auc:0.95894	validation-aucpr:0.96422
[18:03:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5]	validation-logloss:0.64577	validation-auc:0.95921	validation-aucpr:0.96419
[18:03:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6]	validation-logloss:0.63898	validation-auc:0.95908	validation-aucpr:0.96409
[18:03:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7]	validation-logloss:0.63252	validation-auc:0.95892	validation-aucpr:0.96428
[18:03:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8]	validation-logloss:0.62585	validation-auc:0.95907	validation-aucpr:0.96428
[18:03:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9]	validation-logloss:0.61939	validation-auc:0.95925	validation-aucpr:0.96438
[18:03:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10]	validation-logloss:0.61322	validation-auc:0.95923	validation-aucpr:0.96435
[18:03:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11]	validation-logloss:0.60581	validation-auc:0.95997	validation-aucpr:0.96513
[18:03:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12]	validation-logloss:0.59985	validation-auc:0.95981	validation-aucpr:0.96495
[18:03:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13]	validation-logloss:0.59410	validation-auc:0.95966	validation-aucpr:0.96477
[18:03:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14]	validation-logloss:0.58839	validation-auc:0.95953	validation-aucpr:0.96466
[18:03:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15]	validation-logloss:0.58279	validation-auc:0.95919	validation-aucpr:0.96439
[18:03:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16]	validation-logloss:0.57699	validation-auc:0.95944	validation-aucpr:0.96463
[18:03:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17]	validation-logloss:0.57138	validation-auc:0.95951	validation-aucpr:0.96466
[18:03:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18]	validation-logloss:0.56655	validation-auc:0.95930	validation-aucpr:0.96441
[18:03:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19]	validation-logloss:0.56020	validation-auc:0.95991	validation-aucpr:0.96499
[18:03:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20]	validation-logloss:0.55529	validation-auc:0.95978	validation-aucpr:0.96491
[18:03:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21]	validation-logloss:0.54914	validation-auc:0.96006	validation-aucpr:0.96524
[18:03:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22]	validation-logloss:0.54430	validation-auc:0.96008	validation-aucpr:0.96524
[18:03:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23]	validation-logloss:0.53950	validation-auc:0.96031	validation-aucpr:0.96534
[18:03:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24]	validation-logloss:0.53471	validation-auc:0.96036	validation-aucpr:0.96538
[18:03:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25]	validation-logloss:0.52985	validation-auc:0.96067	validation-aucpr:0.96561
[18:03:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26]	validation-logloss:0.52514	validation-auc:0.96069	validation-aucpr:0.96559
[18:03:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27]	validation-logloss:0.52092	validation-auc:0.96063	validation-aucpr:0.96551
[18:03:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28]	validation-logloss:0.51662	validation-auc:0.96059	validation-aucpr:0.96543
[18:03:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29]	validation-logloss:0.51124	validation-auc:0.96092	validation-aucpr:0.96580
[18:03:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30]	validation-logloss:0.50625	validation-auc:0.96117	validation-aucpr:0.96651
[18:03:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31]	validation-logloss:0.50111	validation-auc:0.96133	validation-aucpr:0.96665
[18:03:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32]	validation-logloss:0.49721	validation-auc:0.96123	validation-aucpr:0.96655
[18:03:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33]	validation-logloss:0.49206	validation-auc:0.96159	validation-aucpr:0.96689
[18:03:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34]	validation-logloss:0.48823	validation-auc:0.96150	validation-aucpr:0.96680
[18:03:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35]	validation-logloss:0.48387	validation-auc:0.96153	validation-aucpr:0.96689
[18:03:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36]	validation-logloss:0.47921	validation-auc:0.96168	validation-aucpr:0.96703
[18:03:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37]	validation-logloss:0.47458	validation-auc:0.96188	validation-aucpr:0.96722
[18:03:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38]	validation-logloss:0.47117	validation-auc:0.96183	validation-aucpr:0.96716
[18:03:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39]	validation-logloss:0.46784	validation-auc:0.96178	validation-aucpr:0.96709
[18:03:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40]	validation-logloss:0.46448	validation-auc:0.96165	validation-aucpr:0.96701
[18:03:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41]	validation-logloss:0.46101	validation-auc:0.96172	validation-aucpr:0.96705
[18:03:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42]	validation-logloss:0.45752	validation-auc:0.96177	validation-aucpr:0.96708
[18:03:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43]	validation-logloss:0.45425	validation-auc:0.96174	validation-aucpr:0.96706
{'best_iteration': '37', 'best_score': '0.9672193418676625'}
Trial 9, Fold 5: Log loss = 0.4542515284001841, Average precision = 0.9670490035762956, ROC-AUC = 0.96174490459469, Elapsed Time = 3.735684199999014 seconds
Optimization Progress:  10%|#         | 10/100 [04:26<32:11, 21.47s/it]
Trial 10, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 10, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[0]	validation-logloss:0.67854	validation-auc:0.91581	validation-aucpr:0.90795
[1]	validation-logloss:0.66452	validation-auc:0.92989	validation-aucpr:0.92944
[2]	validation-logloss:0.64978	validation-auc:0.94898	validation-aucpr:0.95368
[3]	validation-logloss:0.63678	validation-auc:0.95179	validation-aucpr:0.95817
[4]	validation-logloss:0.62092	validation-auc:0.95804	validation-aucpr:0.96407
[5]	validation-logloss:0.60858	validation-auc:0.95759	validation-aucpr:0.96377
[6]	validation-logloss:0.59778	validation-auc:0.95701	validation-aucpr:0.96330
[7]	validation-logloss:0.58770	validation-auc:0.95669	validation-aucpr:0.96292
[8]	validation-logloss:0.57914	validation-auc:0.95677	validation-aucpr:0.96342
[9]	validation-logloss:0.57009	validation-auc:0.95599	validation-aucpr:0.96237
[10]	validation-logloss:0.56079	validation-auc:0.95652	validation-aucpr:0.96275
[11]	validation-logloss:0.55168	validation-auc:0.95649	validation-aucpr:0.96271
[12]	validation-logloss:0.54383	validation-auc:0.95613	validation-aucpr:0.96226
[13]	validation-logloss:0.54043	validation-auc:0.95609	validation-aucpr:0.96140
[14]	validation-logloss:0.53295	validation-auc:0.95645	validation-aucpr:0.96288
[15]	validation-logloss:0.52513	validation-auc:0.95649	validation-aucpr:0.96301
[16]	validation-logloss:0.51814	validation-auc:0.95663	validation-aucpr:0.96321
[17]	validation-logloss:0.51001	validation-auc:0.95686	validation-aucpr:0.96336
[18]	validation-logloss:0.50350	validation-auc:0.95659	validation-aucpr:0.96309
[19]	validation-logloss:0.49708	validation-auc:0.95698	validation-aucpr:0.96351
[20]	validation-logloss:0.49048	validation-auc:0.95701	validation-aucpr:0.96349
[21]	validation-logloss:0.48409	validation-auc:0.95708	validation-aucpr:0.96362
[22]	validation-logloss:0.47753	validation-auc:0.95737	validation-aucpr:0.96386
[23]	validation-logloss:0.47173	validation-auc:0.95735	validation-aucpr:0.96386
[24]	validation-logloss:0.46604	validation-auc:0.95718	validation-aucpr:0.96367
[25]	validation-logloss:0.46019	validation-auc:0.95728	validation-aucpr:0.96374
[26]	validation-logloss:0.45554	validation-auc:0.95704	validation-aucpr:0.96347
{'best_iteration': '4', 'best_score': '0.9640740072621098'}
Trial 10, Fold 1: Log loss = 0.45554417321152957, Average precision = 0.9634563201492339, ROC-AUC = 0.9570427695568843, Elapsed Time = 1.0330285999989428 seconds
Trial 10, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 10, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[0]	validation-logloss:0.67846	validation-auc:0.91873	validation-aucpr:0.90311
[1]	validation-logloss:0.66542	validation-auc:0.92942	validation-aucpr:0.92406
[2]	validation-logloss:0.65286	validation-auc:0.93128	validation-aucpr:0.92687
[3]	validation-logloss:0.63966	validation-auc:0.94142	validation-aucpr:0.94299
[4]	validation-logloss:0.62541	validation-auc:0.95174	validation-aucpr:0.95526
[5]	validation-logloss:0.61327	validation-auc:0.95274	validation-aucpr:0.95579
[6]	validation-logloss:0.60389	validation-auc:0.95350	validation-aucpr:0.95655
[7]	validation-logloss:0.59448	validation-auc:0.95282	validation-aucpr:0.95564
[8]	validation-logloss:0.58427	validation-auc:0.95225	validation-aucpr:0.95570
[9]	validation-logloss:0.57413	validation-auc:0.95236	validation-aucpr:0.95580
[10]	validation-logloss:0.56522	validation-auc:0.95168	validation-aucpr:0.95499
[11]	validation-logloss:0.55616	validation-auc:0.95163	validation-aucpr:0.95484
[12]	validation-logloss:0.54743	validation-auc:0.95179	validation-aucpr:0.95475
[13]	validation-logloss:0.53862	validation-auc:0.95147	validation-aucpr:0.95429
[14]	validation-logloss:0.52693	validation-auc:0.95532	validation-aucpr:0.95882
[15]	validation-logloss:0.51872	validation-auc:0.95564	validation-aucpr:0.95889
[16]	validation-logloss:0.51264	validation-auc:0.95537	validation-aucpr:0.95863
[17]	validation-logloss:0.50538	validation-auc:0.95548	validation-aucpr:0.95895
[18]	validation-logloss:0.49840	validation-auc:0.95544	validation-aucpr:0.95900
[19]	validation-logloss:0.49135	validation-auc:0.95560	validation-aucpr:0.95919
[20]	validation-logloss:0.48570	validation-auc:0.95557	validation-aucpr:0.95908
[21]	validation-logloss:0.47965	validation-auc:0.95575	validation-aucpr:0.95911
[22]	validation-logloss:0.47324	validation-auc:0.95585	validation-aucpr:0.95914
[23]	validation-logloss:0.46620	validation-auc:0.95592	validation-aucpr:0.95905
[24]	validation-logloss:0.45986	validation-auc:0.95604	validation-aucpr:0.95914
[25]	validation-logloss:0.45508	validation-auc:0.95576	validation-aucpr:0.95883
[26]	validation-logloss:0.45213	validation-auc:0.95586	validation-aucpr:0.95888
{'best_iteration': '19', 'best_score': '0.9591881059143724'}
Trial 10, Fold 2: Log loss = 0.4521302113765439, Average precision = 0.9588842977089528, ROC-AUC = 0.9558649912092507, Elapsed Time = 1.3426713000008021 seconds
Trial 10, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 10, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[0]	validation-logloss:0.67796	validation-auc:0.92452	validation-aucpr:0.91870
[1]	validation-logloss:0.66517	validation-auc:0.92814	validation-aucpr:0.92500
[2]	validation-logloss:0.65165	validation-auc:0.93558	validation-aucpr:0.93512
[3]	validation-logloss:0.63856	validation-auc:0.93893	validation-aucpr:0.93850
[4]	validation-logloss:0.62334	validation-auc:0.95315	validation-aucpr:0.95637
[5]	validation-logloss:0.61199	validation-auc:0.95358	validation-aucpr:0.95676
[6]	validation-logloss:0.60083	validation-auc:0.95417	validation-aucpr:0.95926
[7]	validation-logloss:0.59101	validation-auc:0.95335	validation-aucpr:0.95878
[8]	validation-logloss:0.58087	validation-auc:0.95383	validation-aucpr:0.95922
[9]	validation-logloss:0.57160	validation-auc:0.95411	validation-aucpr:0.95874
[10]	validation-logloss:0.56290	validation-auc:0.95434	validation-aucpr:0.95876
[11]	validation-logloss:0.55485	validation-auc:0.95375	validation-aucpr:0.95838
[12]	validation-logloss:0.54498	validation-auc:0.95525	validation-aucpr:0.96000
[13]	validation-logloss:0.53740	validation-auc:0.95480	validation-aucpr:0.95952
[14]	validation-logloss:0.52926	validation-auc:0.95498	validation-aucpr:0.95972
[15]	validation-logloss:0.52178	validation-auc:0.95594	validation-aucpr:0.96058
[16]	validation-logloss:0.51499	validation-auc:0.95565	validation-aucpr:0.96027
[17]	validation-logloss:0.50855	validation-auc:0.95539	validation-aucpr:0.96004
[18]	validation-logloss:0.50149	validation-auc:0.95589	validation-aucpr:0.96079
[19]	validation-logloss:0.49457	validation-auc:0.95565	validation-aucpr:0.96060
[20]	validation-logloss:0.48845	validation-auc:0.95571	validation-aucpr:0.96059
[21]	validation-logloss:0.48232	validation-auc:0.95581	validation-aucpr:0.96062
[22]	validation-logloss:0.47589	validation-auc:0.95606	validation-aucpr:0.96076
[23]	validation-logloss:0.46998	validation-auc:0.95658	validation-aucpr:0.96112
[24]	validation-logloss:0.46504	validation-auc:0.95606	validation-aucpr:0.96074
[25]	validation-logloss:0.45942	validation-auc:0.95658	validation-aucpr:0.96120
[26]	validation-logloss:0.45406	validation-auc:0.95634	validation-aucpr:0.96099
{'best_iteration': '25', 'best_score': '0.9612033851979362'}
Trial 10, Fold 3: Log loss = 0.45405906982495015, Average precision = 0.96099287969551, ROC-AUC = 0.9563417657058105, Elapsed Time = 1.3540023999994446 seconds
Trial 10, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 10, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[0]	validation-logloss:0.67799	validation-auc:0.92305	validation-aucpr:0.91630
[1]	validation-logloss:0.66305	validation-auc:0.94321	validation-aucpr:0.94644
[2]	validation-logloss:0.64946	validation-auc:0.95125	validation-aucpr:0.95569
[3]	validation-logloss:0.64038	validation-auc:0.94774	validation-aucpr:0.95305
[4]	validation-logloss:0.62896	validation-auc:0.94848	validation-aucpr:0.95454
[5]	validation-logloss:0.61711	validation-auc:0.94967	validation-aucpr:0.95597
[6]	validation-logloss:0.60580	validation-auc:0.95091	validation-aucpr:0.95730
[7]	validation-logloss:0.59591	validation-auc:0.95098	validation-aucpr:0.95725
[8]	validation-logloss:0.58589	validation-auc:0.95133	validation-aucpr:0.95743
[9]	validation-logloss:0.57581	validation-auc:0.95200	validation-aucpr:0.95819
[10]	validation-logloss:0.56749	validation-auc:0.95131	validation-aucpr:0.95759
[11]	validation-logloss:0.55838	validation-auc:0.95126	validation-aucpr:0.95739
[12]	validation-logloss:0.55005	validation-auc:0.95090	validation-aucpr:0.95701
[13]	validation-logloss:0.54130	validation-auc:0.95096	validation-aucpr:0.95711
[14]	validation-logloss:0.53400	validation-auc:0.95069	validation-aucpr:0.95697
[15]	validation-logloss:0.52682	validation-auc:0.95051	validation-aucpr:0.95688
[16]	validation-logloss:0.52040	validation-auc:0.95115	validation-aucpr:0.95756
[17]	validation-logloss:0.51365	validation-auc:0.95147	validation-aucpr:0.95781
[18]	validation-logloss:0.50591	validation-auc:0.95208	validation-aucpr:0.95832
[19]	validation-logloss:0.50168	validation-auc:0.95202	validation-aucpr:0.95812
[20]	validation-logloss:0.49618	validation-auc:0.95213	validation-aucpr:0.95807
[21]	validation-logloss:0.48888	validation-auc:0.95337	validation-aucpr:0.95958
[22]	validation-logloss:0.48316	validation-auc:0.95353	validation-aucpr:0.95981
[23]	validation-logloss:0.47711	validation-auc:0.95381	validation-aucpr:0.95994
[24]	validation-logloss:0.47135	validation-auc:0.95404	validation-aucpr:0.96020
[25]	validation-logloss:0.46577	validation-auc:0.95380	validation-aucpr:0.95987
[26]	validation-logloss:0.46036	validation-auc:0.95368	validation-aucpr:0.95968
{'best_iteration': '24', 'best_score': '0.9601950676419457'}
Trial 10, Fold 4: Log loss = 0.46035901083769054, Average precision = 0.9596825375211024, ROC-AUC = 0.9536800086315633, Elapsed Time = 1.4137474999988626 seconds
Trial 10, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 10, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[0]	validation-logloss:0.67936	validation-auc:0.89905	validation-aucpr:0.87397
[1]	validation-logloss:0.66630	validation-auc:0.91514	validation-aucpr:0.90211
[2]	validation-logloss:0.65378	validation-auc:0.92234	validation-aucpr:0.91901
[3]	validation-logloss:0.64099	validation-auc:0.92810	validation-aucpr:0.92915
[4]	validation-logloss:0.63089	validation-auc:0.93158	validation-aucpr:0.93389
[5]	validation-logloss:0.62011	validation-auc:0.93258	validation-aucpr:0.93401
[6]	validation-logloss:0.60970	validation-auc:0.93295	validation-aucpr:0.93483
[7]	validation-logloss:0.59977	validation-auc:0.93444	validation-aucpr:0.93635
[8]	validation-logloss:0.59187	validation-auc:0.93689	validation-aucpr:0.93846
[9]	validation-logloss:0.58184	validation-auc:0.93884	validation-aucpr:0.94017
[10]	validation-logloss:0.57209	validation-auc:0.93993	validation-aucpr:0.94129
[11]	validation-logloss:0.56341	validation-auc:0.94012	validation-aucpr:0.94165
[12]	validation-logloss:0.55469	validation-auc:0.94069	validation-aucpr:0.94226
[13]	validation-logloss:0.54692	validation-auc:0.94100	validation-aucpr:0.94318
[14]	validation-logloss:0.53946	validation-auc:0.94182	validation-aucpr:0.94460
[15]	validation-logloss:0.52867	validation-auc:0.94978	validation-aucpr:0.95396
[16]	validation-logloss:0.51929	validation-auc:0.95258	validation-aucpr:0.95744
[17]	validation-logloss:0.51175	validation-auc:0.95309	validation-aucpr:0.95803
[18]	validation-logloss:0.50566	validation-auc:0.95257	validation-aucpr:0.95752
[19]	validation-logloss:0.50021	validation-auc:0.95318	validation-aucpr:0.95823
[20]	validation-logloss:0.49218	validation-auc:0.95354	validation-aucpr:0.95875
[21]	validation-logloss:0.48564	validation-auc:0.95378	validation-aucpr:0.95901
[22]	validation-logloss:0.47926	validation-auc:0.95465	validation-aucpr:0.95992
[23]	validation-logloss:0.47286	validation-auc:0.95483	validation-aucpr:0.96016
[24]	validation-logloss:0.46804	validation-auc:0.95459	validation-aucpr:0.95992
[25]	validation-logloss:0.46235	validation-auc:0.95459	validation-aucpr:0.95995
[26]	validation-logloss:0.45748	validation-auc:0.95450	validation-aucpr:0.95988
{'best_iteration': '23', 'best_score': '0.9601604895372757'}
Trial 10, Fold 5: Log loss = 0.4574784116641232, Average precision = 0.9598834535675733, ROC-AUC = 0.9544994519071773, Elapsed Time = 1.380747799999881 seconds
Optimization Progress:  11%|#1        | 11/100 [04:40<28:38, 19.30s/it]
Trial 11, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 11, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[18:03:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.68589	validation-auc:0.95478	validation-aucpr:0.95734
[18:03:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.67891	validation-auc:0.96195	validation-aucpr:0.96755
[18:03:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.67204	validation-auc:0.96271	validation-aucpr:0.96876
[18:03:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.66515	validation-auc:0.96429	validation-aucpr:0.97006
[18:03:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.65841	validation-auc:0.96486	validation-aucpr:0.97045
[18:03:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5]	validation-logloss:0.65255	validation-auc:0.96552	validation-aucpr:0.97108
[18:03:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6]	validation-logloss:0.64623	validation-auc:0.96555	validation-aucpr:0.97143
[18:03:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7]	validation-logloss:0.63986	validation-auc:0.96620	validation-aucpr:0.97186
[18:04:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8]	validation-logloss:0.63365	validation-auc:0.96602	validation-aucpr:0.97177
[18:04:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9]	validation-logloss:0.62839	validation-auc:0.96572	validation-aucpr:0.97153
[18:04:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10]	validation-logloss:0.62228	validation-auc:0.96636	validation-aucpr:0.97200
[18:04:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11]	validation-logloss:0.61638	validation-auc:0.96624	validation-aucpr:0.97198
[18:04:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12]	validation-logloss:0.61120	validation-auc:0.96622	validation-aucpr:0.97190
[18:04:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13]	validation-logloss:0.60609	validation-auc:0.96657	validation-aucpr:0.97210
[18:04:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14]	validation-logloss:0.60036	validation-auc:0.96679	validation-aucpr:0.97231
[18:04:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15]	validation-logloss:0.59477	validation-auc:0.96682	validation-aucpr:0.97233
[18:04:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16]	validation-logloss:0.58931	validation-auc:0.96673	validation-aucpr:0.97262
[18:04:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17]	validation-logloss:0.58387	validation-auc:0.96676	validation-aucpr:0.97268
[18:04:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18]	validation-logloss:0.57862	validation-auc:0.96697	validation-aucpr:0.97287
[18:04:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19]	validation-logloss:0.57395	validation-auc:0.96687	validation-aucpr:0.97279
[18:04:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20]	validation-logloss:0.56873	validation-auc:0.96706	validation-aucpr:0.97295
[18:04:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21]	validation-logloss:0.56362	validation-auc:0.96719	validation-aucpr:0.97304
[18:04:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22]	validation-logloss:0.55870	validation-auc:0.96739	validation-aucpr:0.97318
[18:04:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23]	validation-logloss:0.55431	validation-auc:0.96745	validation-aucpr:0.97317
[18:04:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24]	validation-logloss:0.55003	validation-auc:0.96727	validation-aucpr:0.97303
[18:04:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25]	validation-logloss:0.54538	validation-auc:0.96734	validation-aucpr:0.97309
[18:04:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26]	validation-logloss:0.54060	validation-auc:0.96750	validation-aucpr:0.97322
[18:04:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27]	validation-logloss:0.53602	validation-auc:0.96753	validation-aucpr:0.97333
[18:04:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28]	validation-logloss:0.53154	validation-auc:0.96757	validation-aucpr:0.97338
[18:05:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29]	validation-logloss:0.52722	validation-auc:0.96760	validation-aucpr:0.97338
[18:05:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30]	validation-logloss:0.52286	validation-auc:0.96775	validation-aucpr:0.97351
[18:05:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31]	validation-logloss:0.51868	validation-auc:0.96766	validation-aucpr:0.97343
[18:05:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32]	validation-logloss:0.51432	validation-auc:0.96785	validation-aucpr:0.97357
[18:05:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33]	validation-logloss:0.51021	validation-auc:0.96781	validation-aucpr:0.97353
[18:05:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34]	validation-logloss:0.50624	validation-auc:0.96780	validation-aucpr:0.97355
[18:05:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35]	validation-logloss:0.50223	validation-auc:0.96782	validation-aucpr:0.97358
[18:05:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36]	validation-logloss:0.49878	validation-auc:0.96778	validation-aucpr:0.97353
[18:05:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37]	validation-logloss:0.49536	validation-auc:0.96780	validation-aucpr:0.97346
[18:05:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38]	validation-logloss:0.49167	validation-auc:0.96770	validation-aucpr:0.97345
[18:05:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39]	validation-logloss:0.48823	validation-auc:0.96780	validation-aucpr:0.97353
[18:05:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40]	validation-logloss:0.48452	validation-auc:0.96785	validation-aucpr:0.97357
[18:05:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41]	validation-logloss:0.48094	validation-auc:0.96788	validation-aucpr:0.97363
[18:05:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42]	validation-logloss:0.47732	validation-auc:0.96793	validation-aucpr:0.97368
[18:05:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43]	validation-logloss:0.47374	validation-auc:0.96801	validation-aucpr:0.97372
[18:05:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44]	validation-logloss:0.47023	validation-auc:0.96806	validation-aucpr:0.97375
[18:05:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45]	validation-logloss:0.46676	validation-auc:0.96804	validation-aucpr:0.97375
[18:05:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46]	validation-logloss:0.46385	validation-auc:0.96791	validation-aucpr:0.97365
[18:05:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[47]	validation-logloss:0.46048	validation-auc:0.96796	validation-aucpr:0.97370
[18:06:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[48]	validation-logloss:0.45756	validation-auc:0.96801	validation-aucpr:0.97371
[18:06:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[49]	validation-logloss:0.45427	validation-auc:0.96808	validation-aucpr:0.97375
[18:06:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[50]	validation-logloss:0.45105	validation-auc:0.96803	validation-aucpr:0.97372
[18:06:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[51]	validation-logloss:0.44786	validation-auc:0.96808	validation-aucpr:0.97374
[18:06:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[52]	validation-logloss:0.44473	validation-auc:0.96808	validation-aucpr:0.97376
[18:06:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[53]	validation-logloss:0.44177	validation-auc:0.96808	validation-aucpr:0.97377
[18:06:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[54]	validation-logloss:0.43878	validation-auc:0.96804	validation-aucpr:0.97374
[18:06:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[55]	validation-logloss:0.43619	validation-auc:0.96797	validation-aucpr:0.97368
[18:06:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[56]	validation-logloss:0.43320	validation-auc:0.96793	validation-aucpr:0.97365
[18:06:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[57]	validation-logloss:0.43062	validation-auc:0.96795	validation-aucpr:0.97366
[18:06:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[58]	validation-logloss:0.42764	validation-auc:0.96805	validation-aucpr:0.97373
[18:06:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[59]	validation-logloss:0.42489	validation-auc:0.96799	validation-aucpr:0.97369
[18:06:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[60]	validation-logloss:0.42205	validation-auc:0.96802	validation-aucpr:0.97370
[18:06:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[61]	validation-logloss:0.41960	validation-auc:0.96799	validation-aucpr:0.97366
[18:06:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[62]	validation-logloss:0.41683	validation-auc:0.96801	validation-aucpr:0.97367
[18:06:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[63]	validation-logloss:0.41416	validation-auc:0.96809	validation-aucpr:0.97371
[18:06:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[64]	validation-logloss:0.41149	validation-auc:0.96818	validation-aucpr:0.97376
[18:06:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[65]	validation-logloss:0.40925	validation-auc:0.96812	validation-aucpr:0.97371
[18:06:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[66]	validation-logloss:0.40670	validation-auc:0.96812	validation-aucpr:0.97371
[18:06:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[67]	validation-logloss:0.40411	validation-auc:0.96812	validation-aucpr:0.97371
[18:06:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[68]	validation-logloss:0.40163	validation-auc:0.96822	validation-aucpr:0.97379
[18:07:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[69]	validation-logloss:0.39914	validation-auc:0.96816	validation-aucpr:0.97374
[18:07:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[70]	validation-logloss:0.39700	validation-auc:0.96808	validation-aucpr:0.97368
[18:07:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[71]	validation-logloss:0.39455	validation-auc:0.96812	validation-aucpr:0.97370
[18:07:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[72]	validation-logloss:0.39219	validation-auc:0.96811	validation-aucpr:0.97369
[18:07:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[73]	validation-logloss:0.38980	validation-auc:0.96818	validation-aucpr:0.97375
[18:07:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[74]	validation-logloss:0.38783	validation-auc:0.96823	validation-aucpr:0.97376
[18:07:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[75]	validation-logloss:0.38563	validation-auc:0.96821	validation-aucpr:0.97375
[18:07:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[76]	validation-logloss:0.38363	validation-auc:0.96823	validation-aucpr:0.97376
[18:07:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[77]	validation-logloss:0.38133	validation-auc:0.96831	validation-aucpr:0.97385
[18:07:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[78]	validation-logloss:0.37900	validation-auc:0.96842	validation-aucpr:0.97394
[18:07:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[79]	validation-logloss:0.37683	validation-auc:0.96847	validation-aucpr:0.97398
[18:07:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[80]	validation-logloss:0.37474	validation-auc:0.96841	validation-aucpr:0.97394
[18:07:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[81]	validation-logloss:0.37263	validation-auc:0.96851	validation-aucpr:0.97398
[18:07:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[82]	validation-logloss:0.37055	validation-auc:0.96847	validation-aucpr:0.97395
[18:07:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[83]	validation-logloss:0.36848	validation-auc:0.96849	validation-aucpr:0.97397
[18:07:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[84]	validation-logloss:0.36677	validation-auc:0.96848	validation-aucpr:0.97398
[18:07:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[85]	validation-logloss:0.36478	validation-auc:0.96849	validation-aucpr:0.97399
[18:07:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[86]	validation-logloss:0.36281	validation-auc:0.96850	validation-aucpr:0.97399
[18:07:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[87]	validation-logloss:0.36084	validation-auc:0.96849	validation-aucpr:0.97398
[18:07:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[88]	validation-logloss:0.35887	validation-auc:0.96856	validation-aucpr:0.97403
{'best_iteration': '88', 'best_score': '0.9740344937804971'}
Trial 11, Fold 1: Log loss = 0.3588683607750186, Average precision = 0.9740402771464329, ROC-AUC = 0.968557994938415, Elapsed Time = 261.71760129999893 seconds
Trial 11, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 11, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[18:08:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.68605	validation-auc:0.95120	validation-aucpr:0.95669
[18:08:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.67888	validation-auc:0.95967	validation-aucpr:0.96385
[18:08:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.67196	validation-auc:0.96205	validation-aucpr:0.96544
[18:08:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.66513	validation-auc:0.96373	validation-aucpr:0.96652
[18:08:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.65840	validation-auc:0.96515	validation-aucpr:0.96802
[18:08:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5]	validation-logloss:0.65198	validation-auc:0.96542	validation-aucpr:0.96811
[18:08:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6]	validation-logloss:0.64553	validation-auc:0.96568	validation-aucpr:0.96830
[18:08:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7]	validation-logloss:0.63912	validation-auc:0.96595	validation-aucpr:0.96854
[18:08:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8]	validation-logloss:0.63350	validation-auc:0.96573	validation-aucpr:0.96839
[18:08:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9]	validation-logloss:0.62746	validation-auc:0.96573	validation-aucpr:0.96841
[18:08:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10]	validation-logloss:0.62149	validation-auc:0.96630	validation-aucpr:0.96980
[18:08:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11]	validation-logloss:0.61558	validation-auc:0.96682	validation-aucpr:0.97041
[18:08:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12]	validation-logloss:0.60987	validation-auc:0.96690	validation-aucpr:0.97063
[18:08:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13]	validation-logloss:0.60397	validation-auc:0.96736	validation-aucpr:0.97100
[18:08:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14]	validation-logloss:0.59839	validation-auc:0.96785	validation-aucpr:0.97142
[18:08:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15]	validation-logloss:0.59283	validation-auc:0.96819	validation-aucpr:0.97176
[18:08:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16]	validation-logloss:0.58795	validation-auc:0.96816	validation-aucpr:0.97165
[18:08:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17]	validation-logloss:0.58260	validation-auc:0.96815	validation-aucpr:0.97161
[18:08:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18]	validation-logloss:0.57792	validation-auc:0.96839	validation-aucpr:0.97182
[18:08:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19]	validation-logloss:0.57335	validation-auc:0.96844	validation-aucpr:0.97209
[18:08:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20]	validation-logloss:0.56832	validation-auc:0.96839	validation-aucpr:0.97209
[18:09:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21]	validation-logloss:0.56393	validation-auc:0.96827	validation-aucpr:0.97198
[18:09:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22]	validation-logloss:0.55896	validation-auc:0.96843	validation-aucpr:0.97209
[18:09:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23]	validation-logloss:0.55416	validation-auc:0.96851	validation-aucpr:0.97220
[18:09:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24]	validation-logloss:0.54926	validation-auc:0.96872	validation-aucpr:0.97236
[18:09:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25]	validation-logloss:0.54457	validation-auc:0.96877	validation-aucpr:0.97237
[18:09:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26]	validation-logloss:0.53980	validation-auc:0.96895	validation-aucpr:0.97251
[18:09:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27]	validation-logloss:0.53522	validation-auc:0.96895	validation-aucpr:0.97249
[18:09:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28]	validation-logloss:0.53128	validation-auc:0.96899	validation-aucpr:0.97253
[18:09:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29]	validation-logloss:0.52734	validation-auc:0.96907	validation-aucpr:0.97260
[18:09:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30]	validation-logloss:0.52304	validation-auc:0.96913	validation-aucpr:0.97267
[18:09:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31]	validation-logloss:0.51917	validation-auc:0.96914	validation-aucpr:0.97266
[18:09:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32]	validation-logloss:0.51536	validation-auc:0.96912	validation-aucpr:0.97260
[18:09:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33]	validation-logloss:0.51167	validation-auc:0.96909	validation-aucpr:0.97255
[18:09:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34]	validation-logloss:0.50755	validation-auc:0.96922	validation-aucpr:0.97269
[18:09:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35]	validation-logloss:0.50343	validation-auc:0.96927	validation-aucpr:0.97274
[18:09:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36]	validation-logloss:0.49936	validation-auc:0.96927	validation-aucpr:0.97274
[18:09:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37]	validation-logloss:0.49548	validation-auc:0.96940	validation-aucpr:0.97289
[18:09:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38]	validation-logloss:0.49206	validation-auc:0.96928	validation-aucpr:0.97279
[18:09:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39]	validation-logloss:0.48823	validation-auc:0.96940	validation-aucpr:0.97287
[18:10:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40]	validation-logloss:0.48445	validation-auc:0.96935	validation-aucpr:0.97284
[18:10:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41]	validation-logloss:0.48077	validation-auc:0.96935	validation-aucpr:0.97282
[18:10:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42]	validation-logloss:0.47710	validation-auc:0.96940	validation-aucpr:0.97286
[18:10:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43]	validation-logloss:0.47347	validation-auc:0.96933	validation-aucpr:0.97281
[18:10:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44]	validation-logloss:0.47000	validation-auc:0.96948	validation-aucpr:0.97292
[18:10:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45]	validation-logloss:0.46645	validation-auc:0.96960	validation-aucpr:0.97301
[18:10:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46]	validation-logloss:0.46303	validation-auc:0.96970	validation-aucpr:0.97308
[18:10:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[47]	validation-logloss:0.45960	validation-auc:0.96966	validation-aucpr:0.97305
[18:10:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[48]	validation-logloss:0.45628	validation-auc:0.96968	validation-aucpr:0.97304
[18:10:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[49]	validation-logloss:0.45312	validation-auc:0.96964	validation-aucpr:0.97301
[18:10:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[50]	validation-logloss:0.44995	validation-auc:0.96958	validation-aucpr:0.97298
[18:10:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[51]	validation-logloss:0.44679	validation-auc:0.96962	validation-aucpr:0.97304
[18:10:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[52]	validation-logloss:0.44367	validation-auc:0.96973	validation-aucpr:0.97311
[18:10:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[53]	validation-logloss:0.44063	validation-auc:0.96960	validation-aucpr:0.97301
[18:10:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[54]	validation-logloss:0.43754	validation-auc:0.96967	validation-aucpr:0.97307
[18:10:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[55]	validation-logloss:0.43452	validation-auc:0.96971	validation-aucpr:0.97311
[18:10:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[56]	validation-logloss:0.43162	validation-auc:0.96971	validation-aucpr:0.97310
[18:10:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[57]	validation-logloss:0.42860	validation-auc:0.96975	validation-aucpr:0.97314
[18:10:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[58]	validation-logloss:0.42565	validation-auc:0.96980	validation-aucpr:0.97318
[18:10:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[59]	validation-logloss:0.42284	validation-auc:0.96977	validation-aucpr:0.97317
[18:10:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[60]	validation-logloss:0.42045	validation-auc:0.96976	validation-aucpr:0.97313
[18:10:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[61]	validation-logloss:0.41758	validation-auc:0.96981	validation-aucpr:0.97317
[18:11:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[62]	validation-logloss:0.41484	validation-auc:0.96981	validation-aucpr:0.97318
[18:11:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[63]	validation-logloss:0.41213	validation-auc:0.96985	validation-aucpr:0.97320
[18:11:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[64]	validation-logloss:0.40939	validation-auc:0.96990	validation-aucpr:0.97326
[18:11:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[65]	validation-logloss:0.40675	validation-auc:0.96998	validation-aucpr:0.97332
[18:11:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[66]	validation-logloss:0.40416	validation-auc:0.97004	validation-aucpr:0.97337
[18:11:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[67]	validation-logloss:0.40165	validation-auc:0.97004	validation-aucpr:0.97338
[18:11:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[68]	validation-logloss:0.39909	validation-auc:0.97003	validation-aucpr:0.97336
[18:11:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[69]	validation-logloss:0.39663	validation-auc:0.97011	validation-aucpr:0.97343
[18:11:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[70]	validation-logloss:0.39414	validation-auc:0.97016	validation-aucpr:0.97346
[18:11:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[71]	validation-logloss:0.39202	validation-auc:0.97019	validation-aucpr:0.97346
[18:11:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[72]	validation-logloss:0.38962	validation-auc:0.97026	validation-aucpr:0.97351
[18:11:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[73]	validation-logloss:0.38721	validation-auc:0.97030	validation-aucpr:0.97353
[18:11:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[74]	validation-logloss:0.38483	validation-auc:0.97031	validation-aucpr:0.97352
[18:11:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[75]	validation-logloss:0.38248	validation-auc:0.97033	validation-aucpr:0.97353
[18:11:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[76]	validation-logloss:0.38017	validation-auc:0.97036	validation-aucpr:0.97355
[18:11:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[77]	validation-logloss:0.37805	validation-auc:0.97027	validation-aucpr:0.97350
[18:11:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[78]	validation-logloss:0.37585	validation-auc:0.97032	validation-aucpr:0.97353
[18:11:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[79]	validation-logloss:0.37397	validation-auc:0.97035	validation-aucpr:0.97356
[18:11:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[80]	validation-logloss:0.37186	validation-auc:0.97032	validation-aucpr:0.97354
[18:11:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[81]	validation-logloss:0.36963	validation-auc:0.97033	validation-aucpr:0.97356
[18:12:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[82]	validation-logloss:0.36759	validation-auc:0.97034	validation-aucpr:0.97357
[18:12:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[83]	validation-logloss:0.36561	validation-auc:0.97030	validation-aucpr:0.97360
[18:12:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[84]	validation-logloss:0.36353	validation-auc:0.97035	validation-aucpr:0.97364
[18:12:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[85]	validation-logloss:0.36150	validation-auc:0.97038	validation-aucpr:0.97367
[18:12:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[86]	validation-logloss:0.35960	validation-auc:0.97042	validation-aucpr:0.97402
[18:12:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[87]	validation-logloss:0.35755	validation-auc:0.97044	validation-aucpr:0.97402
[18:12:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[88]	validation-logloss:0.35560	validation-auc:0.97039	validation-aucpr:0.97400
{'best_iteration': '87', 'best_score': '0.9740248478874732'}
Trial 11, Fold 2: Log loss = 0.3556043571620501, Average precision = 0.9739869661255756, ROC-AUC = 0.970393172442238, Elapsed Time = 260.57359840000026 seconds
Trial 11, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 11, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[18:12:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.68585	validation-auc:0.95527	validation-aucpr:0.95682
[18:12:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.67966	validation-auc:0.95976	validation-aucpr:0.96432
[18:12:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.67349	validation-auc:0.96038	validation-aucpr:0.96523
[18:12:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.66654	validation-auc:0.96394	validation-aucpr:0.96873
[18:12:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.65969	validation-auc:0.96518	validation-aucpr:0.96994
[18:12:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5]	validation-logloss:0.65297	validation-auc:0.96646	validation-aucpr:0.97094
[18:12:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6]	validation-logloss:0.64634	validation-auc:0.96700	validation-aucpr:0.97141
[18:12:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7]	validation-logloss:0.64009	validation-auc:0.96719	validation-aucpr:0.97153
[18:12:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8]	validation-logloss:0.63443	validation-auc:0.96754	validation-aucpr:0.97205
[18:12:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9]	validation-logloss:0.62826	validation-auc:0.96759	validation-aucpr:0.97219
[18:12:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10]	validation-logloss:0.62217	validation-auc:0.96747	validation-aucpr:0.97208
[18:12:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11]	validation-logloss:0.61698	validation-auc:0.96731	validation-aucpr:0.97195
[18:13:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12]	validation-logloss:0.61169	validation-auc:0.96739	validation-aucpr:0.97202
[18:13:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13]	validation-logloss:0.60665	validation-auc:0.96744	validation-aucpr:0.97203
[18:13:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14]	validation-logloss:0.60168	validation-auc:0.96733	validation-aucpr:0.97192
[18:13:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15]	validation-logloss:0.59610	validation-auc:0.96735	validation-aucpr:0.97197
[18:13:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16]	validation-logloss:0.59069	validation-auc:0.96750	validation-aucpr:0.97210
[18:13:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17]	validation-logloss:0.58588	validation-auc:0.96730	validation-aucpr:0.97193
[18:13:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18]	validation-logloss:0.58053	validation-auc:0.96760	validation-aucpr:0.97213
[18:13:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19]	validation-logloss:0.57605	validation-auc:0.96754	validation-aucpr:0.97208
[18:13:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20]	validation-logloss:0.57083	validation-auc:0.96780	validation-aucpr:0.97226
[18:13:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21]	validation-logloss:0.56577	validation-auc:0.96805	validation-aucpr:0.97254
[18:13:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22]	validation-logloss:0.56074	validation-auc:0.96822	validation-aucpr:0.97267
[18:13:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23]	validation-logloss:0.55597	validation-auc:0.96849	validation-aucpr:0.97326
[18:13:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24]	validation-logloss:0.55108	validation-auc:0.96868	validation-aucpr:0.97340
[18:13:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25]	validation-logloss:0.54651	validation-auc:0.96870	validation-aucpr:0.97343
[18:13:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26]	validation-logloss:0.54245	validation-auc:0.96864	validation-aucpr:0.97333
[18:13:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27]	validation-logloss:0.53792	validation-auc:0.96863	validation-aucpr:0.97337
[18:13:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28]	validation-logloss:0.53334	validation-auc:0.96878	validation-aucpr:0.97349
[18:13:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29]	validation-logloss:0.52938	validation-auc:0.96878	validation-aucpr:0.97347
[18:13:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30]	validation-logloss:0.52496	validation-auc:0.96885	validation-aucpr:0.97353
[18:13:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31]	validation-logloss:0.52056	validation-auc:0.96896	validation-aucpr:0.97359
[18:14:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32]	validation-logloss:0.51635	validation-auc:0.96894	validation-aucpr:0.97357
[18:14:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33]	validation-logloss:0.51217	validation-auc:0.96890	validation-aucpr:0.97355
[18:14:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34]	validation-logloss:0.50795	validation-auc:0.96893	validation-aucpr:0.97359
[18:14:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35]	validation-logloss:0.50385	validation-auc:0.96893	validation-aucpr:0.97361
[18:14:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36]	validation-logloss:0.49988	validation-auc:0.96900	validation-aucpr:0.97366
[18:14:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37]	validation-logloss:0.49635	validation-auc:0.96907	validation-aucpr:0.97368
[18:14:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38]	validation-logloss:0.49252	validation-auc:0.96912	validation-aucpr:0.97372
[18:14:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39]	validation-logloss:0.48855	validation-auc:0.96924	validation-aucpr:0.97383
[18:14:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40]	validation-logloss:0.48472	validation-auc:0.96938	validation-aucpr:0.97394
[18:14:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41]	validation-logloss:0.48109	validation-auc:0.96939	validation-aucpr:0.97395
[18:14:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42]	validation-logloss:0.47732	validation-auc:0.96939	validation-aucpr:0.97395
[18:14:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43]	validation-logloss:0.47373	validation-auc:0.96943	validation-aucpr:0.97399
[18:14:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44]	validation-logloss:0.47018	validation-auc:0.96941	validation-aucpr:0.97398
[18:14:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45]	validation-logloss:0.46656	validation-auc:0.96955	validation-aucpr:0.97409
[18:14:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46]	validation-logloss:0.46312	validation-auc:0.96969	validation-aucpr:0.97420
[18:14:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[47]	validation-logloss:0.45983	validation-auc:0.96959	validation-aucpr:0.97413
[18:14:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[48]	validation-logloss:0.45645	validation-auc:0.96963	validation-aucpr:0.97418
[18:14:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[49]	validation-logloss:0.45359	validation-auc:0.96963	validation-aucpr:0.97414
[18:14:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[50]	validation-logloss:0.45036	validation-auc:0.96967	validation-aucpr:0.97418
[18:14:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[51]	validation-logloss:0.44709	validation-auc:0.96969	validation-aucpr:0.97420
[18:14:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[52]	validation-logloss:0.44437	validation-auc:0.96971	validation-aucpr:0.97421
[18:14:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[53]	validation-logloss:0.44121	validation-auc:0.96974	validation-aucpr:0.97424
[18:15:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[54]	validation-logloss:0.43816	validation-auc:0.96978	validation-aucpr:0.97431
[18:15:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[55]	validation-logloss:0.43505	validation-auc:0.96987	validation-aucpr:0.97439
[18:15:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[56]	validation-logloss:0.43199	validation-auc:0.96992	validation-aucpr:0.97442
[18:15:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[57]	validation-logloss:0.42895	validation-auc:0.97004	validation-aucpr:0.97450
[18:15:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[58]	validation-logloss:0.42626	validation-auc:0.96999	validation-aucpr:0.97445
[18:15:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[59]	validation-logloss:0.42335	validation-auc:0.97000	validation-aucpr:0.97447
[18:15:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[60]	validation-logloss:0.42094	validation-auc:0.97001	validation-aucpr:0.97445
[18:15:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[61]	validation-logloss:0.41811	validation-auc:0.97004	validation-aucpr:0.97447
[18:15:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[62]	validation-logloss:0.41569	validation-auc:0.97011	validation-aucpr:0.97450
[18:15:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[63]	validation-logloss:0.41305	validation-auc:0.97012	validation-aucpr:0.97452
[18:15:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[64]	validation-logloss:0.41034	validation-auc:0.97018	validation-aucpr:0.97466
[18:15:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[65]	validation-logloss:0.40797	validation-auc:0.97022	validation-aucpr:0.97466
[18:15:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[66]	validation-logloss:0.40528	validation-auc:0.97021	validation-aucpr:0.97466
[18:15:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[67]	validation-logloss:0.40267	validation-auc:0.97019	validation-aucpr:0.97464
[18:15:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[68]	validation-logloss:0.40052	validation-auc:0.97022	validation-aucpr:0.97464
[18:15:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[69]	validation-logloss:0.39794	validation-auc:0.97026	validation-aucpr:0.97467
[18:15:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[70]	validation-logloss:0.39541	validation-auc:0.97030	validation-aucpr:0.97469
[18:15:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[71]	validation-logloss:0.39303	validation-auc:0.97031	validation-aucpr:0.97475
[18:15:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[72]	validation-logloss:0.39093	validation-auc:0.97022	validation-aucpr:0.97467
[18:15:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[73]	validation-logloss:0.38849	validation-auc:0.97022	validation-aucpr:0.97468
[18:15:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[74]	validation-logloss:0.38595	validation-auc:0.97032	validation-aucpr:0.97476
[18:16:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[75]	validation-logloss:0.38358	validation-auc:0.97035	validation-aucpr:0.97477
[18:16:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[76]	validation-logloss:0.38128	validation-auc:0.97037	validation-aucpr:0.97479
[18:16:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[77]	validation-logloss:0.37900	validation-auc:0.97037	validation-aucpr:0.97479
[18:16:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[78]	validation-logloss:0.37675	validation-auc:0.97041	validation-aucpr:0.97481
[18:16:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[79]	validation-logloss:0.37486	validation-auc:0.97044	validation-aucpr:0.97480
[18:16:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[80]	validation-logloss:0.37270	validation-auc:0.97050	validation-aucpr:0.97485
[18:16:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[81]	validation-logloss:0.37052	validation-auc:0.97054	validation-aucpr:0.97488
[18:16:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[82]	validation-logloss:0.36838	validation-auc:0.97055	validation-aucpr:0.97487
[18:16:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[83]	validation-logloss:0.36627	validation-auc:0.97057	validation-aucpr:0.97488
[18:16:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[84]	validation-logloss:0.36419	validation-auc:0.97060	validation-aucpr:0.97490
[18:16:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[85]	validation-logloss:0.36208	validation-auc:0.97062	validation-aucpr:0.97493
[18:16:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[86]	validation-logloss:0.36020	validation-auc:0.97063	validation-aucpr:0.97492
[18:16:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[87]	validation-logloss:0.35815	validation-auc:0.97064	validation-aucpr:0.97493
[18:16:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[88]	validation-logloss:0.35644	validation-auc:0.97065	validation-aucpr:0.97494
{'best_iteration': '88', 'best_score': '0.9749374341359066'}
Trial 11, Fold 3: Log loss = 0.35644221608984444, Average precision = 0.974941946430545, ROC-AUC = 0.9706546124230722, Elapsed Time = 259.26046209999913 seconds
Trial 11, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 11, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[18:16:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.68580	validation-auc:0.95624	validation-aucpr:0.96239
[18:16:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.67874	validation-auc:0.96047	validation-aucpr:0.96517
[18:16:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.67178	validation-auc:0.96229	validation-aucpr:0.96916
[18:16:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.66502	validation-auc:0.96249	validation-aucpr:0.96931
[18:16:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.65826	validation-auc:0.96430	validation-aucpr:0.97046
[18:16:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5]	validation-logloss:0.65177	validation-auc:0.96519	validation-aucpr:0.97103
[18:16:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6]	validation-logloss:0.64614	validation-auc:0.96584	validation-aucpr:0.97162
[18:17:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7]	validation-logloss:0.63979	validation-auc:0.96654	validation-aucpr:0.97205
[18:17:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8]	validation-logloss:0.63369	validation-auc:0.96690	validation-aucpr:0.97234
[18:17:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9]	validation-logloss:0.62759	validation-auc:0.96692	validation-aucpr:0.97240
[18:17:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10]	validation-logloss:0.62149	validation-auc:0.96723	validation-aucpr:0.97265
[18:17:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11]	validation-logloss:0.61549	validation-auc:0.96761	validation-aucpr:0.97290
[18:17:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12]	validation-logloss:0.60978	validation-auc:0.96769	validation-aucpr:0.97291
[18:17:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13]	validation-logloss:0.60410	validation-auc:0.96752	validation-aucpr:0.97279
[18:17:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14]	validation-logloss:0.59843	validation-auc:0.96795	validation-aucpr:0.97313
[18:17:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15]	validation-logloss:0.59280	validation-auc:0.96817	validation-aucpr:0.97332
[18:17:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16]	validation-logloss:0.58747	validation-auc:0.96810	validation-aucpr:0.97323
[18:17:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17]	validation-logloss:0.58209	validation-auc:0.96830	validation-aucpr:0.97331
[18:17:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18]	validation-logloss:0.57700	validation-auc:0.96816	validation-aucpr:0.97320
[18:17:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19]	validation-logloss:0.57189	validation-auc:0.96802	validation-aucpr:0.97311
[18:17:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20]	validation-logloss:0.56677	validation-auc:0.96811	validation-aucpr:0.97315
[18:17:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21]	validation-logloss:0.56177	validation-auc:0.96818	validation-aucpr:0.97325
[18:17:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22]	validation-logloss:0.55678	validation-auc:0.96857	validation-aucpr:0.97354
[18:17:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23]	validation-logloss:0.55201	validation-auc:0.96879	validation-aucpr:0.97368
[18:17:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24]	validation-logloss:0.54726	validation-auc:0.96885	validation-aucpr:0.97372
[18:17:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25]	validation-logloss:0.54265	validation-auc:0.96883	validation-aucpr:0.97375
[18:17:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26]	validation-logloss:0.53791	validation-auc:0.96906	validation-aucpr:0.97390
[18:17:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27]	validation-logloss:0.53336	validation-auc:0.96929	validation-aucpr:0.97408
[18:18:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28]	validation-logloss:0.52941	validation-auc:0.96919	validation-aucpr:0.97405
[18:18:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29]	validation-logloss:0.52496	validation-auc:0.96938	validation-aucpr:0.97419
[18:18:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30]	validation-logloss:0.52055	validation-auc:0.96943	validation-aucpr:0.97423
[18:18:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31]	validation-logloss:0.51628	validation-auc:0.96956	validation-aucpr:0.97432
[18:18:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32]	validation-logloss:0.51214	validation-auc:0.96953	validation-aucpr:0.97429
[18:18:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33]	validation-logloss:0.50799	validation-auc:0.96961	validation-aucpr:0.97436
[18:18:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34]	validation-logloss:0.50436	validation-auc:0.96956	validation-aucpr:0.97430
[18:18:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35]	validation-logloss:0.50038	validation-auc:0.96948	validation-aucpr:0.97424
[18:18:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36]	validation-logloss:0.49638	validation-auc:0.96962	validation-aucpr:0.97432
[18:18:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37]	validation-logloss:0.49243	validation-auc:0.96968	validation-aucpr:0.97437
[18:18:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38]	validation-logloss:0.48905	validation-auc:0.96969	validation-aucpr:0.97437
[18:18:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39]	validation-logloss:0.48535	validation-auc:0.96960	validation-aucpr:0.97431
[18:18:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40]	validation-logloss:0.48155	validation-auc:0.96967	validation-aucpr:0.97436
[18:18:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41]	validation-logloss:0.47826	validation-auc:0.96959	validation-aucpr:0.97429
[18:18:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42]	validation-logloss:0.47473	validation-auc:0.96965	validation-aucpr:0.97431
[18:18:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43]	validation-logloss:0.47116	validation-auc:0.96954	validation-aucpr:0.97423
[18:18:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44]	validation-logloss:0.46763	validation-auc:0.96961	validation-aucpr:0.97428
[18:18:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45]	validation-logloss:0.46451	validation-auc:0.96955	validation-aucpr:0.97422
[18:18:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46]	validation-logloss:0.46116	validation-auc:0.96948	validation-aucpr:0.97416
[18:18:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[47]	validation-logloss:0.45782	validation-auc:0.96953	validation-aucpr:0.97418
[18:18:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[48]	validation-logloss:0.45459	validation-auc:0.96962	validation-aucpr:0.97424
[18:19:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[49]	validation-logloss:0.45132	validation-auc:0.96958	validation-aucpr:0.97423
[18:19:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[50]	validation-logloss:0.44817	validation-auc:0.96958	validation-aucpr:0.97423
[18:19:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[51]	validation-logloss:0.44508	validation-auc:0.96953	validation-aucpr:0.97420
[18:19:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[52]	validation-logloss:0.44200	validation-auc:0.96942	validation-aucpr:0.97410
[18:19:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[53]	validation-logloss:0.43899	validation-auc:0.96946	validation-aucpr:0.97412
[18:19:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[54]	validation-logloss:0.43639	validation-auc:0.96932	validation-aucpr:0.97402
[18:19:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[55]	validation-logloss:0.43342	validation-auc:0.96930	validation-aucpr:0.97400
[18:19:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[56]	validation-logloss:0.43050	validation-auc:0.96931	validation-aucpr:0.97401
[18:19:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[57]	validation-logloss:0.42803	validation-auc:0.96926	validation-aucpr:0.97399
[18:19:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[58]	validation-logloss:0.42554	validation-auc:0.96918	validation-aucpr:0.97392
[18:19:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[59]	validation-logloss:0.42309	validation-auc:0.96925	validation-aucpr:0.97398
[18:19:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[60]	validation-logloss:0.42033	validation-auc:0.96923	validation-aucpr:0.97397
[18:19:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[61]	validation-logloss:0.41786	validation-auc:0.96913	validation-aucpr:0.97389
[18:19:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[62]	validation-logloss:0.41516	validation-auc:0.96921	validation-aucpr:0.97395
[18:19:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[63]	validation-logloss:0.41246	validation-auc:0.96916	validation-aucpr:0.97391
[18:19:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[64]	validation-logloss:0.40977	validation-auc:0.96918	validation-aucpr:0.97394
[18:19:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[65]	validation-logloss:0.40722	validation-auc:0.96916	validation-aucpr:0.97393
[18:19:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[66]	validation-logloss:0.40468	validation-auc:0.96922	validation-aucpr:0.97396
[18:19:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[67]	validation-logloss:0.40259	validation-auc:0.96913	validation-aucpr:0.97389
[18:19:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[68]	validation-logloss:0.40005	validation-auc:0.96921	validation-aucpr:0.97395
[18:20:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[69]	validation-logloss:0.39753	validation-auc:0.96932	validation-aucpr:0.97403
[18:20:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[70]	validation-logloss:0.39508	validation-auc:0.96932	validation-aucpr:0.97404
[18:20:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[71]	validation-logloss:0.39265	validation-auc:0.96933	validation-aucpr:0.97404
[18:20:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[72]	validation-logloss:0.39066	validation-auc:0.96924	validation-aucpr:0.97399
[18:20:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[73]	validation-logloss:0.38865	validation-auc:0.96917	validation-aucpr:0.97394
[18:20:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[74]	validation-logloss:0.38637	validation-auc:0.96913	validation-aucpr:0.97392
[18:20:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[75]	validation-logloss:0.38406	validation-auc:0.96917	validation-aucpr:0.97396
[18:20:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[76]	validation-logloss:0.38174	validation-auc:0.96923	validation-aucpr:0.97401
[18:20:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[77]	validation-logloss:0.37942	validation-auc:0.96932	validation-aucpr:0.97407
[18:20:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[78]	validation-logloss:0.37723	validation-auc:0.96938	validation-aucpr:0.97411
[18:20:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[79]	validation-logloss:0.37508	validation-auc:0.96946	validation-aucpr:0.97416
[18:20:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[80]	validation-logloss:0.37330	validation-auc:0.96939	validation-aucpr:0.97410
[18:20:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[81]	validation-logloss:0.37112	validation-auc:0.96945	validation-aucpr:0.97414
[18:20:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[82]	validation-logloss:0.36905	validation-auc:0.96940	validation-aucpr:0.97412
[18:20:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[83]	validation-logloss:0.36702	validation-auc:0.96939	validation-aucpr:0.97410
[18:20:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[84]	validation-logloss:0.36513	validation-auc:0.96938	validation-aucpr:0.97410
[18:20:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[85]	validation-logloss:0.36314	validation-auc:0.96951	validation-aucpr:0.97420
[18:20:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[86]	validation-logloss:0.36123	validation-auc:0.96946	validation-aucpr:0.97414
[18:20:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[87]	validation-logloss:0.35923	validation-auc:0.96951	validation-aucpr:0.97419
[18:20:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
{'best_iteration': '38', 'best_score': '0.9743741208236099'}
Trial 11, Fold 4: Log loss = 0.3572366804694858, Average precision = 0.9742509040078975, ROC-AUC = 0.9695805942426042, Elapsed Time = 257.6183670999999 seconds
Trial 11, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 11, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[18:20:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.68595	validation-auc:0.95108	validation-aucpr:0.95716
[18:21:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.67893	validation-auc:0.95631	validation-aucpr:0.96264
[18:21:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.67196	validation-auc:0.95890	validation-aucpr:0.96451
[18:21:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.66515	validation-auc:0.95991	validation-aucpr:0.96521
[18:21:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.65859	validation-auc:0.96067	validation-aucpr:0.96532
[18:21:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5]	validation-logloss:0.65196	validation-auc:0.96175	validation-aucpr:0.96615
[18:21:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6]	validation-logloss:0.64622	validation-auc:0.96169	validation-aucpr:0.96649
[18:21:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7]	validation-logloss:0.64001	validation-auc:0.96280	validation-aucpr:0.96741
[18:21:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8]	validation-logloss:0.63383	validation-auc:0.96339	validation-aucpr:0.96790
[18:21:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9]	validation-logloss:0.62765	validation-auc:0.96424	validation-aucpr:0.96853
[18:21:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10]	validation-logloss:0.62190	validation-auc:0.96418	validation-aucpr:0.96849
[18:21:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11]	validation-logloss:0.61609	validation-auc:0.96437	validation-aucpr:0.96869
[18:21:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12]	validation-logloss:0.61026	validation-auc:0.96433	validation-aucpr:0.96872
[18:21:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13]	validation-logloss:0.60456	validation-auc:0.96490	validation-aucpr:0.96917
[18:21:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14]	validation-logloss:0.59899	validation-auc:0.96494	validation-aucpr:0.96921
[18:21:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15]	validation-logloss:0.59346	validation-auc:0.96512	validation-aucpr:0.96929
[18:21:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16]	validation-logloss:0.58858	validation-auc:0.96519	validation-aucpr:0.96947
[18:21:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17]	validation-logloss:0.58324	validation-auc:0.96531	validation-aucpr:0.96953
[18:21:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18]	validation-logloss:0.57809	validation-auc:0.96556	validation-aucpr:0.96969
[18:21:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19]	validation-logloss:0.57299	validation-auc:0.96549	validation-aucpr:0.96962
[18:21:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20]	validation-logloss:0.56791	validation-auc:0.96553	validation-aucpr:0.96967
[18:21:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21]	validation-logloss:0.56309	validation-auc:0.96586	validation-aucpr:0.96984
[18:21:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22]	validation-logloss:0.55889	validation-auc:0.96575	validation-aucpr:0.96977
[18:22:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23]	validation-logloss:0.55406	validation-auc:0.96578	validation-aucpr:0.96980
[18:22:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24]	validation-logloss:0.54919	validation-auc:0.96611	validation-aucpr:0.97002
[18:22:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25]	validation-logloss:0.54460	validation-auc:0.96612	validation-aucpr:0.97005
[18:22:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26]	validation-logloss:0.54003	validation-auc:0.96605	validation-aucpr:0.97001
[18:22:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27]	validation-logloss:0.53542	validation-auc:0.96626	validation-aucpr:0.97018
[18:22:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28]	validation-logloss:0.53119	validation-auc:0.96621	validation-aucpr:0.97015
[18:22:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29]	validation-logloss:0.52677	validation-auc:0.96626	validation-aucpr:0.97057
[18:22:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30]	validation-logloss:0.52297	validation-auc:0.96637	validation-aucpr:0.97060
[18:22:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31]	validation-logloss:0.51876	validation-auc:0.96637	validation-aucpr:0.97062
[18:22:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32]	validation-logloss:0.51445	validation-auc:0.96656	validation-aucpr:0.97073
[18:22:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33]	validation-logloss:0.51036	validation-auc:0.96658	validation-aucpr:0.97075
[18:22:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34]	validation-logloss:0.50697	validation-auc:0.96648	validation-aucpr:0.97063
[18:22:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35]	validation-logloss:0.50303	validation-auc:0.96650	validation-aucpr:0.97063
[18:22:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36]	validation-logloss:0.49920	validation-auc:0.96635	validation-aucpr:0.97054
[18:22:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37]	validation-logloss:0.49585	validation-auc:0.96631	validation-aucpr:0.97047
[18:22:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38]	validation-logloss:0.49203	validation-auc:0.96638	validation-aucpr:0.97052
[18:22:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39]	validation-logloss:0.48826	validation-auc:0.96636	validation-aucpr:0.97045
[18:22:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40]	validation-logloss:0.48457	validation-auc:0.96640	validation-aucpr:0.97048
[18:22:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41]	validation-logloss:0.48087	validation-auc:0.96659	validation-aucpr:0.97066
[18:22:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42]	validation-logloss:0.47729	validation-auc:0.96670	validation-aucpr:0.97074
[18:22:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43]	validation-logloss:0.47381	validation-auc:0.96670	validation-aucpr:0.97075
[18:22:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44]	validation-logloss:0.47039	validation-auc:0.96667	validation-aucpr:0.96921
[18:23:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45]	validation-logloss:0.46701	validation-auc:0.96663	validation-aucpr:0.96918
[18:23:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46]	validation-logloss:0.46358	validation-auc:0.96676	validation-aucpr:0.96927
[18:23:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[47]	validation-logloss:0.46038	validation-auc:0.96672	validation-aucpr:0.96923
[18:23:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[48]	validation-logloss:0.45756	validation-auc:0.96664	validation-aucpr:0.96916
[18:23:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[49]	validation-logloss:0.45441	validation-auc:0.96667	validation-aucpr:0.96915
[18:23:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[50]	validation-logloss:0.45118	validation-auc:0.96667	validation-aucpr:0.96915
[18:23:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[51]	validation-logloss:0.44797	validation-auc:0.96678	validation-aucpr:0.96924
[18:23:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[52]	validation-logloss:0.44522	validation-auc:0.96683	validation-aucpr:0.96928
[18:23:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[53]	validation-logloss:0.44225	validation-auc:0.96676	validation-aucpr:0.96923
[18:23:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[54]	validation-logloss:0.43926	validation-auc:0.96695	validation-aucpr:0.96939
[18:23:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[55]	validation-logloss:0.43669	validation-auc:0.96685	validation-aucpr:0.96932
[18:23:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[56]	validation-logloss:0.43404	validation-auc:0.96683	validation-aucpr:0.96931
[18:23:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[57]	validation-logloss:0.43149	validation-auc:0.96680	validation-aucpr:0.96929
[18:23:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[58]	validation-logloss:0.42896	validation-auc:0.96668	validation-aucpr:0.96920
[18:23:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[59]	validation-logloss:0.42614	validation-auc:0.96672	validation-aucpr:0.96925
[18:23:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[60]	validation-logloss:0.42331	validation-auc:0.96681	validation-aucpr:0.96932
[18:23:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[61]	validation-logloss:0.42057	validation-auc:0.96685	validation-aucpr:0.96963
[18:23:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[62]	validation-logloss:0.41784	validation-auc:0.96689	validation-aucpr:0.96972
[18:23:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[63]	validation-logloss:0.41511	validation-auc:0.96699	validation-aucpr:0.96979
[18:23:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[64]	validation-logloss:0.41280	validation-auc:0.96698	validation-aucpr:0.96977
[18:23:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[65]	validation-logloss:0.41016	validation-auc:0.96697	validation-aucpr:0.96978
[18:24:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[66]	validation-logloss:0.40761	validation-auc:0.96701	validation-aucpr:0.96981
[18:24:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[67]	validation-logloss:0.40508	validation-auc:0.96705	validation-aucpr:0.96983
[18:24:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[68]	validation-logloss:0.40253	validation-auc:0.96706	validation-aucpr:0.96984
[18:24:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[69]	validation-logloss:0.40007	validation-auc:0.96702	validation-aucpr:0.96981
[18:24:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[70]	validation-logloss:0.39791	validation-auc:0.96703	validation-aucpr:0.96982
[18:24:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[71]	validation-logloss:0.39584	validation-auc:0.96701	validation-aucpr:0.96979
[18:24:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[72]	validation-logloss:0.39346	validation-auc:0.96707	validation-aucpr:0.96984
[18:24:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[73]	validation-logloss:0.39144	validation-auc:0.96709	validation-aucpr:0.96982
[18:24:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[74]	validation-logloss:0.38910	validation-auc:0.96708	validation-aucpr:0.96982
[18:24:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[75]	validation-logloss:0.38681	validation-auc:0.96710	validation-aucpr:0.96983
[18:24:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[76]	validation-logloss:0.38457	validation-auc:0.96707	validation-aucpr:0.96981
[18:24:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[77]	validation-logloss:0.38239	validation-auc:0.96709	validation-aucpr:0.96982
[18:24:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[78]	validation-logloss:0.38023	validation-auc:0.96715	validation-aucpr:0.96987
[18:24:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[79]	validation-logloss:0.37804	validation-auc:0.96719	validation-aucpr:0.96991
[18:24:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[80]	validation-logloss:0.37607	validation-auc:0.96724	validation-aucpr:0.96995
[18:24:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[81]	validation-logloss:0.37407	validation-auc:0.96726	validation-aucpr:0.96997
[18:24:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[82]	validation-logloss:0.37201	validation-auc:0.96724	validation-aucpr:0.96958
[18:24:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[83]	validation-logloss:0.36998	validation-auc:0.96716	validation-aucpr:0.96952
{'best_iteration': '33', 'best_score': '0.9707520012467902'}
Trial 11, Fold 5: Log loss = 0.3699763639546245, Average precision = 0.9705531018910141, ROC-AUC = 0.9671560485294391, Elapsed Time = 228.57969969999976 seconds
Optimization Progress:  12%|#2        | 12/100 [25:57<9:49:25, 401.88s/it]
Trial 12, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 12, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[18:24:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.66259	validation-auc:0.94294	validation-aucpr:0.93924
[18:24:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.63428	validation-auc:0.95463	validation-aucpr:0.95805
[18:24:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.60932	validation-auc:0.95643	validation-aucpr:0.96112
[18:24:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.58603	validation-auc:0.95797	validation-aucpr:0.96251
[18:25:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.56475	validation-auc:0.95805	validation-aucpr:0.96270
[18:25:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5]	validation-logloss:0.54351	validation-auc:0.95997	validation-aucpr:0.96492
[18:25:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6]	validation-logloss:0.52625	validation-auc:0.96042	validation-aucpr:0.96503
[18:25:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7]	validation-logloss:0.50972	validation-auc:0.96048	validation-aucpr:0.96531
[18:25:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8]	validation-logloss:0.48994	validation-auc:0.96277	validation-aucpr:0.96772
[18:25:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9]	validation-logloss:0.47233	validation-auc:0.96360	validation-aucpr:0.96885
[18:25:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10]	validation-logloss:0.45930	validation-auc:0.96345	validation-aucpr:0.96876
[18:25:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11]	validation-logloss:0.44865	validation-auc:0.96304	validation-aucpr:0.96829
[18:25:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12]	validation-logloss:0.43703	validation-auc:0.96304	validation-aucpr:0.96818
[18:25:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13]	validation-logloss:0.42596	validation-auc:0.96324	validation-aucpr:0.96849
[18:25:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14]	validation-logloss:0.41246	validation-auc:0.96400	validation-aucpr:0.96934
[18:25:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15]	validation-logloss:0.40000	validation-auc:0.96474	validation-aucpr:0.97009
[18:25:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16]	validation-logloss:0.39074	validation-auc:0.96472	validation-aucpr:0.97005
[18:25:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17]	validation-logloss:0.37932	validation-auc:0.96527	validation-aucpr:0.97058
[18:25:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18]	validation-logloss:0.36852	validation-auc:0.96575	validation-aucpr:0.97102
[18:25:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19]	validation-logloss:0.36046	validation-auc:0.96597	validation-aucpr:0.97116
[18:25:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20]	validation-logloss:0.35358	validation-auc:0.96593	validation-aucpr:0.97116
[18:25:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21]	validation-logloss:0.34714	validation-auc:0.96585	validation-aucpr:0.97111
[18:25:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22]	validation-logloss:0.34141	validation-auc:0.96571	validation-aucpr:0.97095
[18:25:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23]	validation-logloss:0.33502	validation-auc:0.96588	validation-aucpr:0.97107
[18:25:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24]	validation-logloss:0.32988	validation-auc:0.96588	validation-aucpr:0.97102
[18:25:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25]	validation-logloss:0.32296	validation-auc:0.96599	validation-aucpr:0.97115
[18:25:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26]	validation-logloss:0.31652	validation-auc:0.96616	validation-aucpr:0.97133
[18:25:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27]	validation-logloss:0.31225	validation-auc:0.96613	validation-aucpr:0.97130
[18:25:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28]	validation-logloss:0.30603	validation-auc:0.96639	validation-aucpr:0.97156
{'best_iteration': '28', 'best_score': '0.9715565489584634'}
Trial 12, Fold 1: Log loss = 0.30603410223586613, Average precision = 0.9715614797671308, ROC-AUC = 0.9663893310008937, Elapsed Time = 53.693495299999995 seconds
Trial 12, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 12, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[18:25:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.66405	validation-auc:0.93662	validation-aucpr:0.93297
[18:25:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.63717	validation-auc:0.94590	validation-aucpr:0.94476
[18:25:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.60878	validation-auc:0.95973	validation-aucpr:0.96166
[18:25:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.58238	validation-auc:0.96332	validation-aucpr:0.96699
[18:25:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.56224	validation-auc:0.96292	validation-aucpr:0.96663
[18:25:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5]	validation-logloss:0.54181	validation-auc:0.96346	validation-aucpr:0.96689
[18:25:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6]	validation-logloss:0.52407	validation-auc:0.96363	validation-aucpr:0.96678
[18:26:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7]	validation-logloss:0.50769	validation-auc:0.96352	validation-aucpr:0.96665
[18:26:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8]	validation-logloss:0.49264	validation-auc:0.96327	validation-aucpr:0.96630
[18:26:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9]	validation-logloss:0.47488	validation-auc:0.96469	validation-aucpr:0.96778
[18:26:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10]	validation-logloss:0.46040	validation-auc:0.96491	validation-aucpr:0.96777
[18:26:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11]	validation-logloss:0.44749	validation-auc:0.96491	validation-aucpr:0.96812
[18:26:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12]	validation-logloss:0.43521	validation-auc:0.96518	validation-aucpr:0.96834
[18:26:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13]	validation-logloss:0.42396	validation-auc:0.96546	validation-aucpr:0.96847
[18:26:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14]	validation-logloss:0.41358	validation-auc:0.96565	validation-aucpr:0.96863
[18:26:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15]	validation-logloss:0.40227	validation-auc:0.96600	validation-aucpr:0.96902
[18:26:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16]	validation-logloss:0.39100	validation-auc:0.96636	validation-aucpr:0.96935
[18:26:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17]	validation-logloss:0.38242	validation-auc:0.96643	validation-aucpr:0.96933
[18:26:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18]	validation-logloss:0.37471	validation-auc:0.96632	validation-aucpr:0.96921
[18:26:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19]	validation-logloss:0.36568	validation-auc:0.96664	validation-aucpr:0.96946
[18:26:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20]	validation-logloss:0.35859	validation-auc:0.96659	validation-aucpr:0.96938
[18:26:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21]	validation-logloss:0.35133	validation-auc:0.96676	validation-aucpr:0.96950
[18:26:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22]	validation-logloss:0.34315	validation-auc:0.96710	validation-aucpr:0.96979
[18:26:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23]	validation-logloss:0.33454	validation-auc:0.96747	validation-aucpr:0.97014
[18:26:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24]	validation-logloss:0.32775	validation-auc:0.96769	validation-aucpr:0.97030
[18:26:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25]	validation-logloss:0.32063	validation-auc:0.96793	validation-aucpr:0.97049
[18:26:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26]	validation-logloss:0.31557	validation-auc:0.96796	validation-aucpr:0.97048
[18:26:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27]	validation-logloss:0.30904	validation-auc:0.96819	validation-aucpr:0.97073
[18:26:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28]	validation-logloss:0.30284	validation-auc:0.96846	validation-aucpr:0.97135
{'best_iteration': '28', 'best_score': '0.9713477996479326'}
Trial 12, Fold 2: Log loss = 0.3028431411702671, Average precision = 0.971361292061751, ROC-AUC = 0.9684592986642249, Elapsed Time = 53.507002000000284 seconds
Trial 12, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 12, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[18:26:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.66239	validation-auc:0.94767	validation-aucpr:0.95016
[18:26:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.63434	validation-auc:0.95690	validation-aucpr:0.95905
[18:26:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.61081	validation-auc:0.95657	validation-aucpr:0.96020
[18:26:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.58871	validation-auc:0.95568	validation-aucpr:0.95868
[18:26:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.56640	validation-auc:0.96000	validation-aucpr:0.96273
[18:26:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5]	validation-logloss:0.54629	validation-auc:0.96183	validation-aucpr:0.96466
[18:26:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6]	validation-logloss:0.52812	validation-auc:0.96205	validation-aucpr:0.96552
[18:26:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7]	validation-logloss:0.51158	validation-auc:0.96192	validation-aucpr:0.96532
[18:26:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8]	validation-logloss:0.49587	validation-auc:0.96204	validation-aucpr:0.96580
[18:26:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9]	validation-logloss:0.47702	validation-auc:0.96506	validation-aucpr:0.96934
[18:26:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10]	validation-logloss:0.46401	validation-auc:0.96487	validation-aucpr:0.96903
[18:27:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11]	validation-logloss:0.44886	validation-auc:0.96570	validation-aucpr:0.96999
[18:27:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12]	validation-logloss:0.43399	validation-auc:0.96645	validation-aucpr:0.97098
[18:27:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13]	validation-logloss:0.42069	validation-auc:0.96685	validation-aucpr:0.97142
[18:27:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14]	validation-logloss:0.40991	validation-auc:0.96716	validation-aucpr:0.97171
[18:27:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15]	validation-logloss:0.39989	validation-auc:0.96705	validation-aucpr:0.97163
[18:27:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16]	validation-logloss:0.39035	validation-auc:0.96719	validation-aucpr:0.97173
[18:27:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17]	validation-logloss:0.37903	validation-auc:0.96745	validation-aucpr:0.97202
[18:27:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18]	validation-logloss:0.37144	validation-auc:0.96731	validation-aucpr:0.97186
[18:27:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19]	validation-logloss:0.36378	validation-auc:0.96719	validation-aucpr:0.97176
[18:27:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20]	validation-logloss:0.35450	validation-auc:0.96738	validation-aucpr:0.97199
[18:27:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21]	validation-logloss:0.34755	validation-auc:0.96757	validation-aucpr:0.97209
[18:27:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22]	validation-logloss:0.33920	validation-auc:0.96771	validation-aucpr:0.97225
[18:27:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23]	validation-logloss:0.33335	validation-auc:0.96767	validation-aucpr:0.97225
[18:27:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24]	validation-logloss:0.32542	validation-auc:0.96801	validation-aucpr:0.97254
[18:27:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25]	validation-logloss:0.31965	validation-auc:0.96814	validation-aucpr:0.97263
[18:27:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26]	validation-logloss:0.31198	validation-auc:0.96867	validation-aucpr:0.97311
[18:27:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27]	validation-logloss:0.30669	validation-auc:0.96896	validation-aucpr:0.97333
[18:27:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28]	validation-logloss:0.30179	validation-auc:0.96896	validation-aucpr:0.97333
{'best_iteration': '27', 'best_score': '0.9733342783045276'}
Trial 12, Fold 3: Log loss = 0.3017870133683276, Average precision = 0.9733305895492752, ROC-AUC = 0.9689629361654997, Elapsed Time = 52.86153060000106 seconds
Trial 12, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 12, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[18:27:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.65952	validation-auc:0.94855	validation-aucpr:0.94885
[18:27:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.63334	validation-auc:0.95400	validation-aucpr:0.96011
[18:27:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.60796	validation-auc:0.95741	validation-aucpr:0.96390
[18:27:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.58166	validation-auc:0.96230	validation-aucpr:0.96786
[18:27:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.56142	validation-auc:0.96212	validation-aucpr:0.96781
[18:27:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5]	validation-logloss:0.54217	validation-auc:0.96172	validation-aucpr:0.96766
[18:27:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6]	validation-logloss:0.52369	validation-auc:0.96255	validation-aucpr:0.96829
[18:27:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7]	validation-logloss:0.50683	validation-auc:0.96240	validation-aucpr:0.96800
[18:27:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8]	validation-logloss:0.49166	validation-auc:0.96181	validation-aucpr:0.96748
[18:27:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9]	validation-logloss:0.47382	validation-auc:0.96299	validation-aucpr:0.96874
[18:27:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10]	validation-logloss:0.45801	validation-auc:0.96368	validation-aucpr:0.96947
[18:27:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11]	validation-logloss:0.44566	validation-auc:0.96360	validation-aucpr:0.96937
[18:27:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12]	validation-logloss:0.43217	validation-auc:0.96402	validation-aucpr:0.96983
[18:27:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13]	validation-logloss:0.42100	validation-auc:0.96409	validation-aucpr:0.96984
[18:28:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14]	validation-logloss:0.40792	validation-auc:0.96486	validation-aucpr:0.97057
[18:28:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15]	validation-logloss:0.39699	validation-auc:0.96519	validation-aucpr:0.97090
[18:28:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16]	validation-logloss:0.38804	validation-auc:0.96508	validation-aucpr:0.97077
[18:28:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17]	validation-logloss:0.37930	validation-auc:0.96500	validation-aucpr:0.97066
[18:28:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18]	validation-logloss:0.36885	validation-auc:0.96538	validation-aucpr:0.97105
[18:28:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19]	validation-logloss:0.36095	validation-auc:0.96549	validation-aucpr:0.97112
[18:28:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20]	validation-logloss:0.35323	validation-auc:0.96555	validation-aucpr:0.97118
[18:28:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21]	validation-logloss:0.34611	validation-auc:0.96571	validation-aucpr:0.97128
[18:28:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22]	validation-logloss:0.33928	validation-auc:0.96608	validation-aucpr:0.97152
[18:28:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23]	validation-logloss:0.33346	validation-auc:0.96604	validation-aucpr:0.97148
[18:28:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24]	validation-logloss:0.32800	validation-auc:0.96606	validation-aucpr:0.97149
[18:28:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25]	validation-logloss:0.32263	validation-auc:0.96615	validation-aucpr:0.97152
[18:28:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26]	validation-logloss:0.31779	validation-auc:0.96603	validation-aucpr:0.97137
[18:28:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27]	validation-logloss:0.31307	validation-auc:0.96601	validation-aucpr:0.97135
[18:28:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28]	validation-logloss:0.30850	validation-auc:0.96609	validation-aucpr:0.97139
{'best_iteration': '22', 'best_score': '0.9715216628471062'}
Trial 12, Fold 4: Log loss = 0.3085016825154917, Average precision = 0.9713912806763556, ROC-AUC = 0.9660949597153969, Elapsed Time = 53.205132799999774 seconds
Trial 12, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 12, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[18:28:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.66283	validation-auc:0.94372	validation-aucpr:0.94356
[18:28:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.63360	validation-auc:0.95657	validation-aucpr:0.95950
[18:28:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.60862	validation-auc:0.95788	validation-aucpr:0.96140
[18:28:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.58644	validation-auc:0.95707	validation-aucpr:0.96084
[18:28:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.56481	validation-auc:0.95857	validation-aucpr:0.96289
[18:28:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5]	validation-logloss:0.54563	validation-auc:0.95935	validation-aucpr:0.96323
[18:28:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6]	validation-logloss:0.52717	validation-auc:0.96000	validation-aucpr:0.96396
[18:28:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7]	validation-logloss:0.51130	validation-auc:0.95970	validation-aucpr:0.96365
[18:28:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8]	validation-logloss:0.49270	validation-auc:0.96179	validation-aucpr:0.96598
[18:28:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9]	validation-logloss:0.47617	validation-auc:0.96227	validation-aucpr:0.96667
[18:28:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10]	validation-logloss:0.46293	validation-auc:0.96210	validation-aucpr:0.96650
[18:28:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11]	validation-logloss:0.44688	validation-auc:0.96312	validation-aucpr:0.96744
[18:28:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12]	validation-logloss:0.43315	validation-auc:0.96336	validation-aucpr:0.96809
[18:28:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13]	validation-logloss:0.42298	validation-auc:0.96317	validation-aucpr:0.96784
[18:28:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14]	validation-logloss:0.41097	validation-auc:0.96328	validation-aucpr:0.96801
[18:28:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15]	validation-logloss:0.40214	validation-auc:0.96300	validation-aucpr:0.96767
[18:28:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16]	validation-logloss:0.39274	validation-auc:0.96279	validation-aucpr:0.96753
[18:28:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17]	validation-logloss:0.38429	validation-auc:0.96301	validation-aucpr:0.96765
[18:29:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18]	validation-logloss:0.37446	validation-auc:0.96312	validation-aucpr:0.96781
[18:29:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19]	validation-logloss:0.36749	validation-auc:0.96309	validation-aucpr:0.96781
[18:29:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20]	validation-logloss:0.35817	validation-auc:0.96325	validation-aucpr:0.96804
[18:29:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21]	validation-logloss:0.35118	validation-auc:0.96347	validation-aucpr:0.96821
[18:29:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22]	validation-logloss:0.34505	validation-auc:0.96344	validation-aucpr:0.96811
[18:29:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23]	validation-logloss:0.33953	validation-auc:0.96322	validation-aucpr:0.96794
[18:29:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24]	validation-logloss:0.33427	validation-auc:0.96332	validation-aucpr:0.96796
[18:29:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25]	validation-logloss:0.32729	validation-auc:0.96351	validation-aucpr:0.96826
[18:29:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26]	validation-logloss:0.32167	validation-auc:0.96378	validation-aucpr:0.96858
[18:29:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27]	validation-logloss:0.31664	validation-auc:0.96398	validation-aucpr:0.96874
[18:29:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28]	validation-logloss:0.31166	validation-auc:0.96412	validation-aucpr:0.96891
{'best_iteration': '28', 'best_score': '0.9689149458783484'}
Trial 12, Fold 5: Log loss = 0.31166072012417156, Average precision = 0.9689226422619246, ROC-AUC = 0.9641158612489085, Elapsed Time = 53.00868619999892 seconds
Optimization Progress:  13%|#3        | 13/100 [30:31<8:46:37, 363.19s/it]
Trial 13, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 13, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[18:29:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.68199	validation-auc:0.91934	validation-aucpr:0.89907
[18:29:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.67118	validation-auc:0.94339	validation-aucpr:0.94700
[18:29:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.65875	validation-auc:0.96044	validation-aucpr:0.96344
[18:29:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.64739	validation-auc:0.96276	validation-aucpr:0.96840
[18:29:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.63735	validation-auc:0.96386	validation-aucpr:0.96919
[18:29:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5]	validation-logloss:0.62614	validation-auc:0.96518	validation-aucpr:0.97051
[18:29:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6]	validation-logloss:0.61654	validation-auc:0.96538	validation-aucpr:0.97058
[18:29:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7]	validation-logloss:0.60583	validation-auc:0.96625	validation-aucpr:0.97137
[18:29:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8]	validation-logloss:0.59564	validation-auc:0.96707	validation-aucpr:0.97211
[18:29:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9]	validation-logloss:0.58711	validation-auc:0.96692	validation-aucpr:0.97190
[18:29:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10]	validation-logloss:0.57876	validation-auc:0.96711	validation-aucpr:0.97180
[18:29:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11]	validation-logloss:0.56969	validation-auc:0.96741	validation-aucpr:0.97215
[18:29:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12]	validation-logloss:0.56191	validation-auc:0.96713	validation-aucpr:0.97187
[18:29:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13]	validation-logloss:0.55418	validation-auc:0.96706	validation-aucpr:0.97146
{'best_iteration': '11', 'best_score': '0.9721533735935723'}
Trial 13, Fold 1: Log loss = 0.5541760171341904, Average precision = 0.9715588809569982, ROC-AUC = 0.9670561583812487, Elapsed Time = 8.191645800001425 seconds
Trial 13, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 13, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[18:29:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.68205	validation-auc:0.92486	validation-aucpr:0.89257
[18:29:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.67062	validation-auc:0.95107	validation-aucpr:0.95183
[18:29:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.65970	validation-auc:0.95674	validation-aucpr:0.95885
[18:29:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.64923	validation-auc:0.95845	validation-aucpr:0.96106
[18:29:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.63893	validation-auc:0.95881	validation-aucpr:0.96155
[18:29:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5]	validation-logloss:0.62746	validation-auc:0.96518	validation-aucpr:0.96826
[18:29:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6]	validation-logloss:0.61787	validation-auc:0.96484	validation-aucpr:0.96790
[18:29:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7]	validation-logloss:0.60742	validation-auc:0.96667	validation-aucpr:0.96988
[18:29:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8]	validation-logloss:0.59728	validation-auc:0.96769	validation-aucpr:0.97111
[18:29:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9]	validation-logloss:0.58740	validation-auc:0.96849	validation-aucpr:0.97196
[18:29:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10]	validation-logloss:0.57921	validation-auc:0.96842	validation-aucpr:0.97186
[18:29:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11]	validation-logloss:0.57127	validation-auc:0.96836	validation-aucpr:0.97166
[18:29:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12]	validation-logloss:0.56223	validation-auc:0.96892	validation-aucpr:0.97213
[18:29:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13]	validation-logloss:0.55369	validation-auc:0.96885	validation-aucpr:0.97222
{'best_iteration': '13', 'best_score': '0.9722168506448429'}
Trial 13, Fold 2: Log loss = 0.5536905013799721, Average precision = 0.9722019875011716, ROC-AUC = 0.9688539187803363, Elapsed Time = 8.690538099999685 seconds
Trial 13, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 13, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[18:29:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.68175	validation-auc:0.92494	validation-aucpr:0.89388
[18:29:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.67063	validation-auc:0.94710	validation-aucpr:0.94695
[18:29:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.66008	validation-auc:0.95246	validation-aucpr:0.95365
[18:29:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.64965	validation-auc:0.95434	validation-aucpr:0.95691
[18:29:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.63927	validation-auc:0.95700	validation-aucpr:0.95967
[18:29:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5]	validation-logloss:0.62977	validation-auc:0.95784	validation-aucpr:0.96078
[18:29:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6]	validation-logloss:0.62003	validation-auc:0.95862	validation-aucpr:0.96179
[18:29:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7]	validation-logloss:0.61077	validation-auc:0.95928	validation-aucpr:0.96292
[18:29:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8]	validation-logloss:0.60170	validation-auc:0.95979	validation-aucpr:0.96332
[18:29:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9]	validation-logloss:0.59295	validation-auc:0.95998	validation-aucpr:0.96332
[18:29:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10]	validation-logloss:0.58471	validation-auc:0.96018	validation-aucpr:0.96353
[18:29:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11]	validation-logloss:0.57661	validation-auc:0.96033	validation-aucpr:0.96365
[18:29:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12]	validation-logloss:0.56744	validation-auc:0.96288	validation-aucpr:0.96652
[18:29:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13]	validation-logloss:0.55985	validation-auc:0.96291	validation-aucpr:0.96644
{'best_iteration': '12', 'best_score': '0.966518950749605'}
Trial 13, Fold 3: Log loss = 0.5598497446494258, Average precision = 0.9669748866732013, ROC-AUC = 0.9629090029711743, Elapsed Time = 7.858257300000332 seconds
Trial 13, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 13, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[18:29:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.68161	validation-auc:0.92751	validation-aucpr:0.90009
[18:29:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.67016	validation-auc:0.94928	validation-aucpr:0.94291
[18:29:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.65784	validation-auc:0.96084	validation-aucpr:0.96151
[18:29:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.64629	validation-auc:0.96445	validation-aucpr:0.96949
[18:29:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.63616	validation-auc:0.96470	validation-aucpr:0.96962
[18:29:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5]	validation-logloss:0.62502	validation-auc:0.96526	validation-aucpr:0.97043
[18:29:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6]	validation-logloss:0.61541	validation-auc:0.96534	validation-aucpr:0.97049
[18:29:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7]	validation-logloss:0.60513	validation-auc:0.96583	validation-aucpr:0.97104
[18:29:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8]	validation-logloss:0.59581	validation-auc:0.96634	validation-aucpr:0.97149
[18:29:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9]	validation-logloss:0.58747	validation-auc:0.96614	validation-aucpr:0.97126
[18:30:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10]	validation-logloss:0.57793	validation-auc:0.96669	validation-aucpr:0.97190
[18:30:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11]	validation-logloss:0.56905	validation-auc:0.96678	validation-aucpr:0.97209
[18:30:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12]	validation-logloss:0.56007	validation-auc:0.96690	validation-aucpr:0.97232
[18:30:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13]	validation-logloss:0.55237	validation-auc:0.96658	validation-aucpr:0.97213
{'best_iteration': '12', 'best_score': '0.972317800443495'}
Trial 13, Fold 4: Log loss = 0.5523687742806942, Average precision = 0.972130547603141, ROC-AUC = 0.966584583360158, Elapsed Time = 8.161774599999262 seconds
Trial 13, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 13, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[18:30:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.68195	validation-auc:0.92479	validation-aucpr:0.90042
[18:30:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.66939	validation-auc:0.95639	validation-aucpr:0.95941
[18:30:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.65735	validation-auc:0.96104	validation-aucpr:0.96365
[18:30:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.64698	validation-auc:0.96273	validation-aucpr:0.96809
[18:30:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.63647	validation-auc:0.96369	validation-aucpr:0.96885
[18:30:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5]	validation-logloss:0.62690	validation-auc:0.96339	validation-aucpr:0.96840
[18:30:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6]	validation-logloss:0.61614	validation-auc:0.96454	validation-aucpr:0.96974
[18:30:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7]	validation-logloss:0.60590	validation-auc:0.96481	validation-aucpr:0.97010
[18:30:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8]	validation-logloss:0.59584	validation-auc:0.96517	validation-aucpr:0.97051
[18:30:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9]	validation-logloss:0.58610	validation-auc:0.96540	validation-aucpr:0.97072
[18:30:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10]	validation-logloss:0.57786	validation-auc:0.96531	validation-aucpr:0.97044
[18:30:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11]	validation-logloss:0.56890	validation-auc:0.96535	validation-aucpr:0.97056
[18:30:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12]	validation-logloss:0.56080	validation-auc:0.96556	validation-aucpr:0.97075
[18:30:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13]	validation-logloss:0.55342	validation-auc:0.96535	validation-aucpr:0.97050
{'best_iteration': '12', 'best_score': '0.9707524225232216'}
Trial 13, Fold 5: Log loss = 0.5534183540087733, Average precision = 0.9705031427069372, ROC-AUC = 0.9653473793731304, Elapsed Time = 8.009748399999808 seconds
Optimization Progress:  14%|#4        | 14/100 [31:20<6:24:32, 268.29s/it]
Trial 14, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 14, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[18:30:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.67300	validation-auc:0.93535	validation-aucpr:0.91414
[18:30:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.65352	validation-auc:0.95997	validation-aucpr:0.94955
[18:30:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.63501	validation-auc:0.96534	validation-aucpr:0.96582
[18:30:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.61730	validation-auc:0.96661	validation-aucpr:0.96679
[18:30:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.60074	validation-auc:0.96710	validation-aucpr:0.96875
{'best_iteration': '4', 'best_score': '0.9687460586423775'}
Trial 14, Fold 1: Log loss = 0.6007382625759794, Average precision = 0.9697246970219232, ROC-AUC = 0.9671048504466526, Elapsed Time = 9.356031400000575 seconds
Trial 14, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 14, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[18:30:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.67308	validation-auc:0.93724	validation-aucpr:0.90542
[18:30:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.65404	validation-auc:0.95966	validation-aucpr:0.95575
[18:30:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.63566	validation-auc:0.96497	validation-aucpr:0.96601
[18:30:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.61960	validation-auc:0.96509	validation-aucpr:0.96872
[18:30:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.60307	validation-auc:0.96593	validation-aucpr:0.96962
{'best_iteration': '4', 'best_score': '0.9696233259644926'}
Trial 14, Fold 2: Log loss = 0.6030703772186887, Average precision = 0.969516017292491, ROC-AUC = 0.9659294354587835, Elapsed Time = 9.748138599999947 seconds
Trial 14, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 14, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[18:30:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.67279	validation-auc:0.94268	validation-aucpr:0.92792
[18:30:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.65354	validation-auc:0.96313	validation-aucpr:0.96026
[18:30:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.63494	validation-auc:0.96576	validation-aucpr:0.96564
[18:30:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.61735	validation-auc:0.96730	validation-aucpr:0.96911
[18:30:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.60225	validation-auc:0.96770	validation-aucpr:0.96910
{'best_iteration': '3', 'best_score': '0.9691086296590194'}
Trial 14, Fold 3: Log loss = 0.6022545490642423, Average precision = 0.9701395087255767, ROC-AUC = 0.9677014963016826, Elapsed Time = 9.65295689999948 seconds
Trial 14, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 14, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[18:30:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.67389	validation-auc:0.93183	validation-aucpr:0.91325
[18:30:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.65492	validation-auc:0.95639	validation-aucpr:0.95643
[18:30:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.63656	validation-auc:0.96061	validation-aucpr:0.96473
[18:30:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.61956	validation-auc:0.96282	validation-aucpr:0.96665
[18:30:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.60402	validation-auc:0.96411	validation-aucpr:0.96995
{'best_iteration': '4', 'best_score': '0.9699524125024998'}
Trial 14, Fold 4: Log loss = 0.6040156415532489, Average precision = 0.969877524685255, ROC-AUC = 0.964110087603215, Elapsed Time = 9.464738699998634 seconds
Trial 14, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 14, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[18:31:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.67263	validation-auc:0.93698	validation-aucpr:0.92727
[18:31:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.65439	validation-auc:0.95989	validation-aucpr:0.96219
[18:31:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.63584	validation-auc:0.96387	validation-aucpr:0.96903
[18:31:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.61880	validation-auc:0.96427	validation-aucpr:0.96953
[18:31:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.60297	validation-auc:0.96474	validation-aucpr:0.96920
{'best_iteration': '3', 'best_score': '0.9695329455063676'}
Trial 14, Fold 5: Log loss = 0.6029668724141746, Average precision = 0.969375726079295, ROC-AUC = 0.964739832413652, Elapsed Time = 9.787574100000711 seconds
Optimization Progress:  15%|#5        | 15/100 [32:16<4:49:20, 204.24s/it]
Trial 15, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 15, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[18:31:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.65835	validation-auc:0.95231	validation-aucpr:0.95569
[18:31:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.62991	validation-auc:0.95311	validation-aucpr:0.95398
[18:31:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.60171	validation-auc:0.95713	validation-aucpr:0.96099
[18:31:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.57811	validation-auc:0.95772	validation-aucpr:0.96117
[18:31:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.55292	validation-auc:0.96075	validation-aucpr:0.96445
[18:31:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5]	validation-logloss:0.53003	validation-auc:0.96176	validation-aucpr:0.96522
[18:31:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6]	validation-logloss:0.51238	validation-auc:0.96237	validation-aucpr:0.96548
[18:31:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7]	validation-logloss:0.49493	validation-auc:0.96314	validation-aucpr:0.96610
[18:31:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8]	validation-logloss:0.47646	validation-auc:0.96344	validation-aucpr:0.96656
[18:31:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9]	validation-logloss:0.46201	validation-auc:0.96369	validation-aucpr:0.96711
[18:31:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10]	validation-logloss:0.44873	validation-auc:0.96369	validation-aucpr:0.96715
[18:31:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11]	validation-logloss:0.43649	validation-auc:0.96322	validation-aucpr:0.96663
[18:31:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12]	validation-logloss:0.42217	validation-auc:0.96347	validation-aucpr:0.96688
[18:31:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13]	validation-logloss:0.40893	validation-auc:0.96388	validation-aucpr:0.96714
[18:31:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14]	validation-logloss:0.39879	validation-auc:0.96403	validation-aucpr:0.96723
[18:31:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15]	validation-logloss:0.38702	validation-auc:0.96432	validation-aucpr:0.96744
[18:31:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16]	validation-logloss:0.37732	validation-auc:0.96432	validation-aucpr:0.96751
[18:31:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17]	validation-logloss:0.36847	validation-auc:0.96437	validation-aucpr:0.96740
[18:31:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18]	validation-logloss:0.35939	validation-auc:0.96526	validation-aucpr:0.96962
[18:31:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19]	validation-logloss:0.35032	validation-auc:0.96539	validation-aucpr:0.96985
[18:31:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20]	validation-logloss:0.34340	validation-auc:0.96534	validation-aucpr:0.96979
[18:31:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21]	validation-logloss:0.33562	validation-auc:0.96536	validation-aucpr:0.96982
[18:31:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22]	validation-logloss:0.32751	validation-auc:0.96575	validation-aucpr:0.97020
[18:31:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23]	validation-logloss:0.32026	validation-auc:0.96570	validation-aucpr:0.97044
[18:31:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24]	validation-logloss:0.31355	validation-auc:0.96578	validation-aucpr:0.97053
[18:31:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25]	validation-logloss:0.30706	validation-auc:0.96605	validation-aucpr:0.97075
[18:31:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26]	validation-logloss:0.30049	validation-auc:0.96622	validation-aucpr:0.97095
[18:31:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27]	validation-logloss:0.29473	validation-auc:0.96671	validation-aucpr:0.97226
[18:31:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28]	validation-logloss:0.28924	validation-auc:0.96678	validation-aucpr:0.97238
[18:31:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29]	validation-logloss:0.28438	validation-auc:0.96677	validation-aucpr:0.97234
[18:31:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30]	validation-logloss:0.27997	validation-auc:0.96665	validation-aucpr:0.97225
[18:31:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31]	validation-logloss:0.27614	validation-auc:0.96680	validation-aucpr:0.97236
[18:31:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32]	validation-logloss:0.27256	validation-auc:0.96680	validation-aucpr:0.97229
[18:31:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33]	validation-logloss:0.26822	validation-auc:0.96689	validation-aucpr:0.97235
[18:31:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34]	validation-logloss:0.26427	validation-auc:0.96699	validation-aucpr:0.97243
[18:31:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35]	validation-logloss:0.26029	validation-auc:0.96722	validation-aucpr:0.97264
[18:31:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36]	validation-logloss:0.25666	validation-auc:0.96740	validation-aucpr:0.97314
[18:31:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37]	validation-logloss:0.25326	validation-auc:0.96755	validation-aucpr:0.97327
[18:31:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38]	validation-logloss:0.25075	validation-auc:0.96779	validation-aucpr:0.97341
{'best_iteration': '38', 'best_score': '0.9734073712231963'}
Trial 15, Fold 1: Log loss = 0.2507485350769062, Average precision = 0.9734117928534539, ROC-AUC = 0.9677870621298805, Elapsed Time = 5.148564700000861 seconds
Trial 15, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 15, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[18:31:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.65842	validation-auc:0.95149	validation-aucpr:0.95086
[18:31:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.62647	validation-auc:0.95926	validation-aucpr:0.95947
[18:31:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.59846	validation-auc:0.96257	validation-aucpr:0.96585
[18:31:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.57212	validation-auc:0.96383	validation-aucpr:0.96792
[18:31:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.54773	validation-auc:0.96486	validation-aucpr:0.96906
[18:31:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5]	validation-logloss:0.52494	validation-auc:0.96559	validation-aucpr:0.96983
[18:31:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6]	validation-logloss:0.50660	validation-auc:0.96684	validation-aucpr:0.97087
[18:31:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7]	validation-logloss:0.48925	validation-auc:0.96658	validation-aucpr:0.97100
[18:31:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8]	validation-logloss:0.47305	validation-auc:0.96670	validation-aucpr:0.97108
[18:31:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9]	validation-logloss:0.45704	validation-auc:0.96662	validation-aucpr:0.97116
[18:31:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10]	validation-logloss:0.44329	validation-auc:0.96684	validation-aucpr:0.97120
[18:31:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11]	validation-logloss:0.42896	validation-auc:0.96697	validation-aucpr:0.97131
[18:31:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12]	validation-logloss:0.41478	validation-auc:0.96729	validation-aucpr:0.97162
[18:31:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13]	validation-logloss:0.40140	validation-auc:0.96790	validation-aucpr:0.97209
[18:31:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14]	validation-logloss:0.38867	validation-auc:0.96862	validation-aucpr:0.97262
[18:31:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15]	validation-logloss:0.37852	validation-auc:0.96859	validation-aucpr:0.97257
[18:31:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16]	validation-logloss:0.36953	validation-auc:0.96876	validation-aucpr:0.97262
[18:31:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17]	validation-logloss:0.35897	validation-auc:0.96898	validation-aucpr:0.97284
[18:31:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18]	validation-logloss:0.35111	validation-auc:0.96894	validation-aucpr:0.97274
[18:31:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19]	validation-logloss:0.34180	validation-auc:0.96937	validation-aucpr:0.97309
[18:31:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20]	validation-logloss:0.33334	validation-auc:0.96950	validation-aucpr:0.97318
[18:31:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21]	validation-logloss:0.32668	validation-auc:0.96942	validation-aucpr:0.97278
[18:31:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22]	validation-logloss:0.31899	validation-auc:0.96946	validation-aucpr:0.97287
[18:31:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23]	validation-logloss:0.31209	validation-auc:0.96957	validation-aucpr:0.97299
[18:31:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24]	validation-logloss:0.30523	validation-auc:0.96952	validation-aucpr:0.97302
[18:31:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25]	validation-logloss:0.29872	validation-auc:0.96960	validation-aucpr:0.97310
[18:31:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26]	validation-logloss:0.29299	validation-auc:0.96954	validation-aucpr:0.97299
[18:31:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27]	validation-logloss:0.28731	validation-auc:0.96943	validation-aucpr:0.97293
[18:31:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28]	validation-logloss:0.28294	validation-auc:0.96945	validation-aucpr:0.97295
[18:31:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29]	validation-logloss:0.27739	validation-auc:0.96971	validation-aucpr:0.97320
[18:31:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30]	validation-logloss:0.27383	validation-auc:0.96964	validation-aucpr:0.97312
[18:31:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31]	validation-logloss:0.26917	validation-auc:0.96979	validation-aucpr:0.97328
[18:31:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32]	validation-logloss:0.26477	validation-auc:0.96995	validation-aucpr:0.97342
[18:31:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33]	validation-logloss:0.26067	validation-auc:0.97011	validation-aucpr:0.97350
[18:31:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34]	validation-logloss:0.25691	validation-auc:0.97002	validation-aucpr:0.97349
[18:31:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35]	validation-logloss:0.25396	validation-auc:0.96996	validation-aucpr:0.97344
[18:31:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36]	validation-logloss:0.25028	validation-auc:0.97007	validation-aucpr:0.97352
[18:31:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37]	validation-logloss:0.24758	validation-auc:0.97012	validation-aucpr:0.97351
[18:31:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38]	validation-logloss:0.24442	validation-auc:0.97013	validation-aucpr:0.97353
{'best_iteration': '38', 'best_score': '0.9735321248399427'}
Trial 15, Fold 2: Log loss = 0.24442360141281588, Average precision = 0.9735330060498169, ROC-AUC = 0.9701298141605572, Elapsed Time = 5.283658000000287 seconds
Trial 15, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 15, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[18:31:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.65832	validation-auc:0.94735	validation-aucpr:0.94594
[18:31:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.62628	validation-auc:0.95822	validation-aucpr:0.95927
[18:31:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.59758	validation-auc:0.96044	validation-aucpr:0.96377
[18:31:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.57100	validation-auc:0.96076	validation-aucpr:0.96423
[18:31:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.54634	validation-auc:0.96319	validation-aucpr:0.96671
[18:31:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5]	validation-logloss:0.52340	validation-auc:0.96474	validation-aucpr:0.96794
[18:31:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6]	validation-logloss:0.50501	validation-auc:0.96504	validation-aucpr:0.96844
[18:31:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7]	validation-logloss:0.48776	validation-auc:0.96524	validation-aucpr:0.96854
[18:31:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8]	validation-logloss:0.47153	validation-auc:0.96642	validation-aucpr:0.97066
[18:31:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9]	validation-logloss:0.45753	validation-auc:0.96637	validation-aucpr:0.97066
[18:31:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10]	validation-logloss:0.44218	validation-auc:0.96647	validation-aucpr:0.97062
[18:31:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11]	validation-logloss:0.42917	validation-auc:0.96658	validation-aucpr:0.97066
[18:31:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12]	validation-logloss:0.41731	validation-auc:0.96651	validation-aucpr:0.97079
[18:31:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13]	validation-logloss:0.40377	validation-auc:0.96742	validation-aucpr:0.97225
[18:31:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14]	validation-logloss:0.39155	validation-auc:0.96740	validation-aucpr:0.97226
[18:31:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15]	validation-logloss:0.37977	validation-auc:0.96761	validation-aucpr:0.97247
[18:31:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16]	validation-logloss:0.36887	validation-auc:0.96791	validation-aucpr:0.97275
[18:31:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17]	validation-logloss:0.36036	validation-auc:0.96783	validation-aucpr:0.97260
[18:31:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18]	validation-logloss:0.35255	validation-auc:0.96780	validation-aucpr:0.97270
[18:31:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19]	validation-logloss:0.34339	validation-auc:0.96796	validation-aucpr:0.97288
[18:31:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20]	validation-logloss:0.33573	validation-auc:0.96816	validation-aucpr:0.97303
[18:31:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21]	validation-logloss:0.32893	validation-auc:0.96816	validation-aucpr:0.97295
[18:31:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22]	validation-logloss:0.32092	validation-auc:0.96831	validation-aucpr:0.97308
[18:31:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23]	validation-logloss:0.31510	validation-auc:0.96818	validation-aucpr:0.97299
[18:31:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24]	validation-logloss:0.30834	validation-auc:0.96797	validation-aucpr:0.97177
[18:31:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25]	validation-logloss:0.30204	validation-auc:0.96786	validation-aucpr:0.97175
[18:31:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26]	validation-logloss:0.29599	validation-auc:0.96784	validation-aucpr:0.97112
[18:31:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27]	validation-logloss:0.29120	validation-auc:0.96772	validation-aucpr:0.97089
[18:31:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28]	validation-logloss:0.28621	validation-auc:0.96756	validation-aucpr:0.97044
[18:31:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29]	validation-logloss:0.28159	validation-auc:0.96804	validation-aucpr:0.97077
[18:31:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30]	validation-logloss:0.27739	validation-auc:0.96810	validation-aucpr:0.97078
[18:31:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31]	validation-logloss:0.27294	validation-auc:0.96838	validation-aucpr:0.97240
[18:31:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32]	validation-logloss:0.26865	validation-auc:0.96840	validation-aucpr:0.97250
[18:31:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33]	validation-logloss:0.26559	validation-auc:0.96834	validation-aucpr:0.97245
[18:31:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34]	validation-logloss:0.26225	validation-auc:0.96839	validation-aucpr:0.97248
[18:31:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35]	validation-logloss:0.25853	validation-auc:0.96839	validation-aucpr:0.97246
[18:31:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36]	validation-logloss:0.25559	validation-auc:0.96832	validation-aucpr:0.97241
[18:31:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37]	validation-logloss:0.25227	validation-auc:0.96829	validation-aucpr:0.97240
[18:31:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38]	validation-logloss:0.24930	validation-auc:0.96855	validation-aucpr:0.97255
{'best_iteration': '22', 'best_score': '0.9730788157534537'}
Trial 15, Fold 3: Log loss = 0.24930300686625795, Average precision = 0.9727403636924389, ROC-AUC = 0.9685533162061426, Elapsed Time = 5.271522000000914 seconds
Trial 15, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 15, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[18:31:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.65897	validation-auc:0.94989	validation-aucpr:0.95155
[18:31:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.63117	validation-auc:0.95327	validation-aucpr:0.95725
[18:31:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.60142	validation-auc:0.95973	validation-aucpr:0.96495
[18:31:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.57778	validation-auc:0.96069	validation-aucpr:0.96648
[18:31:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.55610	validation-auc:0.96158	validation-aucpr:0.96718
[18:31:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5]	validation-logloss:0.53242	validation-auc:0.96334	validation-aucpr:0.96905
[18:31:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6]	validation-logloss:0.51115	validation-auc:0.96442	validation-aucpr:0.97006
[18:31:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7]	validation-logloss:0.49378	validation-auc:0.96480	validation-aucpr:0.97024
[18:31:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8]	validation-logloss:0.47537	validation-auc:0.96499	validation-aucpr:0.97057
[18:31:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9]	validation-logloss:0.46020	validation-auc:0.96500	validation-aucpr:0.97046
[18:31:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10]	validation-logloss:0.44416	validation-auc:0.96576	validation-aucpr:0.97118
[18:31:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11]	validation-logloss:0.43254	validation-auc:0.96503	validation-aucpr:0.97053
[18:31:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12]	validation-logloss:0.41816	validation-auc:0.96583	validation-aucpr:0.97121
[18:31:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13]	validation-logloss:0.40543	validation-auc:0.96575	validation-aucpr:0.97129
[18:31:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14]	validation-logloss:0.39301	validation-auc:0.96582	validation-aucpr:0.97145
[18:31:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15]	validation-logloss:0.38135	validation-auc:0.96622	validation-aucpr:0.97176
[18:31:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16]	validation-logloss:0.37215	validation-auc:0.96609	validation-aucpr:0.97171
[18:31:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17]	validation-logloss:0.36297	validation-auc:0.96645	validation-aucpr:0.97197
[18:31:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18]	validation-logloss:0.35502	validation-auc:0.96627	validation-aucpr:0.97178
[18:31:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19]	validation-logloss:0.34777	validation-auc:0.96637	validation-aucpr:0.97183
[18:31:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20]	validation-logloss:0.33934	validation-auc:0.96656	validation-aucpr:0.97198
[18:31:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21]	validation-logloss:0.33112	validation-auc:0.96669	validation-aucpr:0.97211
[18:31:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22]	validation-logloss:0.32464	validation-auc:0.96700	validation-aucpr:0.97231
[18:31:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23]	validation-logloss:0.31879	validation-auc:0.96692	validation-aucpr:0.97226
[18:31:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24]	validation-logloss:0.31187	validation-auc:0.96711	validation-aucpr:0.97238
[18:31:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25]	validation-logloss:0.30665	validation-auc:0.96710	validation-aucpr:0.97238
[18:31:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26]	validation-logloss:0.30039	validation-auc:0.96737	validation-aucpr:0.97256
[18:31:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27]	validation-logloss:0.29538	validation-auc:0.96741	validation-aucpr:0.97257
[18:31:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28]	validation-logloss:0.28964	validation-auc:0.96762	validation-aucpr:0.97277
[18:31:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29]	validation-logloss:0.28405	validation-auc:0.96786	validation-aucpr:0.97299
[18:31:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30]	validation-logloss:0.27887	validation-auc:0.96815	validation-aucpr:0.97320
[18:31:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31]	validation-logloss:0.27356	validation-auc:0.96849	validation-aucpr:0.97349
[18:31:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32]	validation-logloss:0.26936	validation-auc:0.96871	validation-aucpr:0.97368
[18:31:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33]	validation-logloss:0.26520	validation-auc:0.96894	validation-aucpr:0.97386
[18:31:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34]	validation-logloss:0.26242	validation-auc:0.96878	validation-aucpr:0.97373
[18:31:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35]	validation-logloss:0.25923	validation-auc:0.96891	validation-aucpr:0.97381
[18:31:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36]	validation-logloss:0.25575	validation-auc:0.96926	validation-aucpr:0.97406
[18:31:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37]	validation-logloss:0.25193	validation-auc:0.96949	validation-aucpr:0.97423
[18:31:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38]	validation-logloss:0.24844	validation-auc:0.96965	validation-aucpr:0.97438
{'best_iteration': '38', 'best_score': '0.9743752728524171'}
Trial 15, Fold 4: Log loss = 0.24843866113024915, Average precision = 0.9743756236521505, ROC-AUC = 0.9696492950492499, Elapsed Time = 5.2661658999986685 seconds
Trial 15, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 15, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[18:31:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.65847	validation-auc:0.95169	validation-aucpr:0.95688
[18:31:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.62713	validation-auc:0.95678	validation-aucpr:0.96259
[18:31:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.59869	validation-auc:0.95860	validation-aucpr:0.96373
[18:31:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.57244	validation-auc:0.95982	validation-aucpr:0.96521
[18:31:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.54877	validation-auc:0.96048	validation-aucpr:0.96587
[18:31:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5]	validation-logloss:0.52637	validation-auc:0.96112	validation-aucpr:0.96637
[18:31:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6]	validation-logloss:0.50830	validation-auc:0.96140	validation-aucpr:0.96640
[18:31:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7]	validation-logloss:0.49137	validation-auc:0.96139	validation-aucpr:0.96649
[18:31:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8]	validation-logloss:0.47365	validation-auc:0.96215	validation-aucpr:0.96633
[18:31:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9]	validation-logloss:0.45713	validation-auc:0.96227	validation-aucpr:0.96673
[18:31:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10]	validation-logloss:0.44398	validation-auc:0.96289	validation-aucpr:0.96704
[18:31:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11]	validation-logloss:0.43102	validation-auc:0.96309	validation-aucpr:0.96726
[18:31:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12]	validation-logloss:0.41859	validation-auc:0.96285	validation-aucpr:0.96713
[18:31:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13]	validation-logloss:0.40581	validation-auc:0.96322	validation-aucpr:0.96747
[18:31:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14]	validation-logloss:0.39400	validation-auc:0.96304	validation-aucpr:0.96752
[18:31:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15]	validation-logloss:0.38263	validation-auc:0.96334	validation-aucpr:0.96759
[18:31:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16]	validation-logloss:0.37368	validation-auc:0.96337	validation-aucpr:0.96750
[18:31:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17]	validation-logloss:0.36339	validation-auc:0.96368	validation-aucpr:0.96773
[18:31:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18]	validation-logloss:0.35640	validation-auc:0.96333	validation-aucpr:0.96855
[18:31:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19]	validation-logloss:0.34906	validation-auc:0.96349	validation-aucpr:0.96862
[18:31:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20]	validation-logloss:0.34069	validation-auc:0.96379	validation-aucpr:0.96817
[18:31:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21]	validation-logloss:0.33412	validation-auc:0.96386	validation-aucpr:0.96819
[18:31:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22]	validation-logloss:0.32766	validation-auc:0.96402	validation-aucpr:0.96839
[18:31:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23]	validation-logloss:0.32099	validation-auc:0.96407	validation-aucpr:0.96847
[18:31:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24]	validation-logloss:0.31375	validation-auc:0.96444	validation-aucpr:0.96873
[18:31:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25]	validation-logloss:0.30850	validation-auc:0.96438	validation-aucpr:0.96865
[18:31:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26]	validation-logloss:0.30351	validation-auc:0.96451	validation-aucpr:0.96873
[18:31:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27]	validation-logloss:0.29911	validation-auc:0.96426	validation-aucpr:0.96856
[18:31:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28]	validation-logloss:0.29369	validation-auc:0.96454	validation-aucpr:0.96886
[18:31:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29]	validation-logloss:0.28841	validation-auc:0.96467	validation-aucpr:0.96901
[18:31:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30]	validation-logloss:0.28476	validation-auc:0.96471	validation-aucpr:0.96910
[18:31:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31]	validation-logloss:0.28098	validation-auc:0.96472	validation-aucpr:0.96904
[18:31:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32]	validation-logloss:0.27654	validation-auc:0.96476	validation-aucpr:0.96908
[18:31:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33]	validation-logloss:0.27246	validation-auc:0.96484	validation-aucpr:0.96914
[18:31:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34]	validation-logloss:0.26870	validation-auc:0.96529	validation-aucpr:0.96950
[18:31:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35]	validation-logloss:0.26591	validation-auc:0.96539	validation-aucpr:0.96955
[18:31:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36]	validation-logloss:0.26305	validation-auc:0.96538	validation-aucpr:0.96951
[18:31:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37]	validation-logloss:0.25959	validation-auc:0.96554	validation-aucpr:0.96980
[18:31:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38]	validation-logloss:0.25668	validation-auc:0.96559	validation-aucpr:0.96999
{'best_iteration': '38', 'best_score': '0.969985383677907'}
Trial 15, Fold 5: Log loss = 0.25668126798836727, Average precision = 0.9701177753994549, ROC-AUC = 0.9655906211099343, Elapsed Time = 6.848654200000965 seconds
Optimization Progress:  16%|#6        | 16/100 [32:52<3:35:03, 153.61s/it]
Trial 16, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 16, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[0]	validation-logloss:0.66463	validation-auc:0.95925	validation-aucpr:0.96434
[1]	validation-logloss:0.64010	validation-auc:0.96240	validation-aucpr:0.96801
[2]	validation-logloss:0.61513	validation-auc:0.96566	validation-aucpr:0.97118
[3]	validation-logloss:0.59295	validation-auc:0.96626	validation-aucpr:0.97152
[4]	validation-logloss:0.57390	validation-auc:0.96643	validation-aucpr:0.97140
[5]	validation-logloss:0.55627	validation-auc:0.96664	validation-aucpr:0.97154
[6]	validation-logloss:0.54065	validation-auc:0.96639	validation-aucpr:0.97129
[7]	validation-logloss:0.52491	validation-auc:0.96629	validation-aucpr:0.97117
[8]	validation-logloss:0.51049	validation-auc:0.96623	validation-aucpr:0.97103
[9]	validation-logloss:0.49658	validation-auc:0.96627	validation-aucpr:0.97104
[10]	validation-logloss:0.48341	validation-auc:0.96618	validation-aucpr:0.97090
[11]	validation-logloss:0.47140	validation-auc:0.96601	validation-aucpr:0.97074
[12]	validation-logloss:0.45757	validation-auc:0.96618	validation-aucpr:0.97104
[13]	validation-logloss:0.44432	validation-auc:0.96663	validation-aucpr:0.97148
[14]	validation-logloss:0.43193	validation-auc:0.96704	validation-aucpr:0.97187
[15]	validation-logloss:0.42049	validation-auc:0.96731	validation-aucpr:0.97216
[16]	validation-logloss:0.40985	validation-auc:0.96739	validation-aucpr:0.97221
[17]	validation-logloss:0.39931	validation-auc:0.96771	validation-aucpr:0.97255
[18]	validation-logloss:0.39106	validation-auc:0.96761	validation-aucpr:0.97246
[19]	validation-logloss:0.38149	validation-auc:0.96782	validation-aucpr:0.97265
[20]	validation-logloss:0.37406	validation-auc:0.96785	validation-aucpr:0.97269
[21]	validation-logloss:0.36550	validation-auc:0.96811	validation-aucpr:0.97293
[22]	validation-logloss:0.35742	validation-auc:0.96838	validation-aucpr:0.97314
[23]	validation-logloss:0.35108	validation-auc:0.96834	validation-aucpr:0.97322
[24]	validation-logloss:0.34373	validation-auc:0.96851	validation-aucpr:0.97339
[25]	validation-logloss:0.33679	validation-auc:0.96875	validation-aucpr:0.97360
[26]	validation-logloss:0.33135	validation-auc:0.96880	validation-aucpr:0.97366
[27]	validation-logloss:0.32601	validation-auc:0.96882	validation-aucpr:0.97366
[28]	validation-logloss:0.32013	validation-auc:0.96894	validation-aucpr:0.97375
[29]	validation-logloss:0.31433	validation-auc:0.96902	validation-aucpr:0.97402
[30]	validation-logloss:0.30993	validation-auc:0.96904	validation-aucpr:0.97402
[31]	validation-logloss:0.30471	validation-auc:0.96920	validation-aucpr:0.97417
[32]	validation-logloss:0.29986	validation-auc:0.96945	validation-aucpr:0.97437
[33]	validation-logloss:0.29584	validation-auc:0.96948	validation-aucpr:0.97437
[34]	validation-logloss:0.29097	validation-auc:0.96967	validation-aucpr:0.97451
[35]	validation-logloss:0.28652	validation-auc:0.96988	validation-aucpr:0.97472
[36]	validation-logloss:0.28229	validation-auc:0.97004	validation-aucpr:0.97488
[37]	validation-logloss:0.27841	validation-auc:0.97008	validation-aucpr:0.97492
[38]	validation-logloss:0.27517	validation-auc:0.97004	validation-aucpr:0.97489
[39]	validation-logloss:0.27166	validation-auc:0.97003	validation-aucpr:0.97494
[40]	validation-logloss:0.26821	validation-auc:0.97001	validation-aucpr:0.97493
[41]	validation-logloss:0.26478	validation-auc:0.97009	validation-aucpr:0.97500
[42]	validation-logloss:0.26168	validation-auc:0.97012	validation-aucpr:0.97503
[43]	validation-logloss:0.25852	validation-auc:0.97033	validation-aucpr:0.97517
[44]	validation-logloss:0.25582	validation-auc:0.97031	validation-aucpr:0.97521
[45]	validation-logloss:0.25351	validation-auc:0.97036	validation-aucpr:0.97522
[46]	validation-logloss:0.25072	validation-auc:0.97054	validation-aucpr:0.97533
[47]	validation-logloss:0.24813	validation-auc:0.97061	validation-aucpr:0.97538
[48]	validation-logloss:0.24560	validation-auc:0.97069	validation-aucpr:0.97552
[49]	validation-logloss:0.24375	validation-auc:0.97077	validation-aucpr:0.97556
[50]	validation-logloss:0.24169	validation-auc:0.97067	validation-aucpr:0.97547
[51]	validation-logloss:0.23988	validation-auc:0.97073	validation-aucpr:0.97555
[52]	validation-logloss:0.23763	validation-auc:0.97087	validation-aucpr:0.97565
[53]	validation-logloss:0.23565	validation-auc:0.97099	validation-aucpr:0.97578
[54]	validation-logloss:0.23405	validation-auc:0.97109	validation-aucpr:0.97584
[55]	validation-logloss:0.23195	validation-auc:0.97126	validation-aucpr:0.97598
[56]	validation-logloss:0.22980	validation-auc:0.97153	validation-aucpr:0.97620
{'best_iteration': '56', 'best_score': '0.9761988403450483'}
Trial 16, Fold 1: Log loss = 0.22979919630740303, Average precision = 0.9762028387089069, ROC-AUC = 0.9715299285929367, Elapsed Time = 9.99813069999982 seconds
Trial 16, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 16, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[0]	validation-logloss:0.66419	validation-auc:0.95790	validation-aucpr:0.96292
[1]	validation-logloss:0.63789	validation-auc:0.96261	validation-aucpr:0.96685
[2]	validation-logloss:0.61439	validation-auc:0.96572	validation-aucpr:0.96950
[3]	validation-logloss:0.59173	validation-auc:0.96657	validation-aucpr:0.97030
[4]	validation-logloss:0.57056	validation-auc:0.96751	validation-aucpr:0.97112
[5]	validation-logloss:0.55304	validation-auc:0.96809	validation-aucpr:0.97158
[6]	validation-logloss:0.53432	validation-auc:0.96892	validation-aucpr:0.97226
[7]	validation-logloss:0.51646	validation-auc:0.96955	validation-aucpr:0.97297
[8]	validation-logloss:0.50081	validation-auc:0.96990	validation-aucpr:0.97310
[9]	validation-logloss:0.48717	validation-auc:0.96988	validation-aucpr:0.97308
[10]	validation-logloss:0.47244	validation-auc:0.97001	validation-aucpr:0.97348
[11]	validation-logloss:0.45849	validation-auc:0.97022	validation-aucpr:0.97385
[12]	validation-logloss:0.44562	validation-auc:0.97023	validation-aucpr:0.97389
[13]	validation-logloss:0.43322	validation-auc:0.97028	validation-aucpr:0.97402
[14]	validation-logloss:0.42152	validation-auc:0.97040	validation-aucpr:0.97410
[15]	validation-logloss:0.41179	validation-auc:0.97046	validation-aucpr:0.97411
[16]	validation-logloss:0.40120	validation-auc:0.97069	validation-aucpr:0.97428
[17]	validation-logloss:0.39138	validation-auc:0.97063	validation-aucpr:0.97421
[18]	validation-logloss:0.38181	validation-auc:0.97084	validation-aucpr:0.97434
[19]	validation-logloss:0.37394	validation-auc:0.97099	validation-aucpr:0.97436
[20]	validation-logloss:0.36524	validation-auc:0.97122	validation-aucpr:0.97454
[21]	validation-logloss:0.35732	validation-auc:0.97116	validation-aucpr:0.97447
[22]	validation-logloss:0.34972	validation-auc:0.97118	validation-aucpr:0.97452
[23]	validation-logloss:0.34235	validation-auc:0.97119	validation-aucpr:0.97442
[24]	validation-logloss:0.33543	validation-auc:0.97119	validation-aucpr:0.97436
[25]	validation-logloss:0.32968	validation-auc:0.97098	validation-aucpr:0.97420
[26]	validation-logloss:0.32337	validation-auc:0.97095	validation-aucpr:0.97417
[27]	validation-logloss:0.31815	validation-auc:0.97101	validation-aucpr:0.97422
[28]	validation-logloss:0.31238	validation-auc:0.97104	validation-aucpr:0.97423
[29]	validation-logloss:0.30744	validation-auc:0.97117	validation-aucpr:0.97430
[30]	validation-logloss:0.30200	validation-auc:0.97137	validation-aucpr:0.97443
[31]	validation-logloss:0.29760	validation-auc:0.97139	validation-aucpr:0.97446
[32]	validation-logloss:0.29271	validation-auc:0.97146	validation-aucpr:0.97453
[33]	validation-logloss:0.28795	validation-auc:0.97150	validation-aucpr:0.97458
[34]	validation-logloss:0.28347	validation-auc:0.97161	validation-aucpr:0.97467
[35]	validation-logloss:0.27992	validation-auc:0.97172	validation-aucpr:0.97472
[36]	validation-logloss:0.27589	validation-auc:0.97160	validation-aucpr:0.97465
[37]	validation-logloss:0.27179	validation-auc:0.97166	validation-aucpr:0.97471
[38]	validation-logloss:0.26864	validation-auc:0.97177	validation-aucpr:0.97478
[39]	validation-logloss:0.26511	validation-auc:0.97173	validation-aucpr:0.97475
[40]	validation-logloss:0.26180	validation-auc:0.97169	validation-aucpr:0.97467
[41]	validation-logloss:0.25870	validation-auc:0.97168	validation-aucpr:0.97464
[42]	validation-logloss:0.25612	validation-auc:0.97170	validation-aucpr:0.97462
[43]	validation-logloss:0.25370	validation-auc:0.97176	validation-aucpr:0.97465
[44]	validation-logloss:0.25091	validation-auc:0.97166	validation-aucpr:0.97458
[45]	validation-logloss:0.24821	validation-auc:0.97178	validation-aucpr:0.97467
[46]	validation-logloss:0.24562	validation-auc:0.97183	validation-aucpr:0.97472
[47]	validation-logloss:0.24340	validation-auc:0.97181	validation-aucpr:0.97451
[48]	validation-logloss:0.24090	validation-auc:0.97195	validation-aucpr:0.97459
[49]	validation-logloss:0.23853	validation-auc:0.97189	validation-aucpr:0.97448
[50]	validation-logloss:0.23603	validation-auc:0.97200	validation-aucpr:0.97455
[51]	validation-logloss:0.23419	validation-auc:0.97201	validation-aucpr:0.97456
[52]	validation-logloss:0.23208	validation-auc:0.97208	validation-aucpr:0.97458
[53]	validation-logloss:0.23013	validation-auc:0.97218	validation-aucpr:0.97495
[54]	validation-logloss:0.22815	validation-auc:0.97217	validation-aucpr:0.97493
[55]	validation-logloss:0.22616	validation-auc:0.97229	validation-aucpr:0.97501
[56]	validation-logloss:0.22446	validation-auc:0.97222	validation-aucpr:0.97496
{'best_iteration': '55', 'best_score': '0.9750061463455643'}
Trial 16, Fold 2: Log loss = 0.22445777337154255, Average precision = 0.9749343967660432, ROC-AUC = 0.9722240094739177, Elapsed Time = 10.321142300001156 seconds
Trial 16, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 16, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[0]	validation-logloss:0.66447	validation-auc:0.95985	validation-aucpr:0.96127
[1]	validation-logloss:0.63915	validation-auc:0.96178	validation-aucpr:0.96616
[2]	validation-logloss:0.61443	validation-auc:0.96493	validation-aucpr:0.96916
[3]	validation-logloss:0.59137	validation-auc:0.96618	validation-aucpr:0.97034
[4]	validation-logloss:0.56991	validation-auc:0.96786	validation-aucpr:0.97140
[5]	validation-logloss:0.55269	validation-auc:0.96819	validation-aucpr:0.97203
[6]	validation-logloss:0.53402	validation-auc:0.96886	validation-aucpr:0.97213
[7]	validation-logloss:0.51946	validation-auc:0.96807	validation-aucpr:0.97146
[8]	validation-logloss:0.50278	validation-auc:0.96827	validation-aucpr:0.97156
[9]	validation-logloss:0.48702	validation-auc:0.96851	validation-aucpr:0.97163
[10]	validation-logloss:0.47267	validation-auc:0.96812	validation-aucpr:0.97122
[11]	validation-logloss:0.46006	validation-auc:0.96846	validation-aucpr:0.97149
[12]	validation-logloss:0.44725	validation-auc:0.96851	validation-aucpr:0.97154
[13]	validation-logloss:0.43562	validation-auc:0.96812	validation-aucpr:0.97101
[14]	validation-logloss:0.42499	validation-auc:0.96814	validation-aucpr:0.97125
[15]	validation-logloss:0.41379	validation-auc:0.96825	validation-aucpr:0.97191
[16]	validation-logloss:0.40429	validation-auc:0.96851	validation-aucpr:0.97209
[17]	validation-logloss:0.39424	validation-auc:0.96858	validation-aucpr:0.97211
[18]	validation-logloss:0.38442	validation-auc:0.96930	validation-aucpr:0.97270
[19]	validation-logloss:0.37713	validation-auc:0.96914	validation-aucpr:0.97340
[20]	validation-logloss:0.36878	validation-auc:0.96926	validation-aucpr:0.97347
[21]	validation-logloss:0.36044	validation-auc:0.96933	validation-aucpr:0.97355
[22]	validation-logloss:0.35362	validation-auc:0.96937	validation-aucpr:0.97299
[23]	validation-logloss:0.34595	validation-auc:0.96949	validation-aucpr:0.97303
[24]	validation-logloss:0.33970	validation-auc:0.96955	validation-aucpr:0.97307
[25]	validation-logloss:0.33371	validation-auc:0.96962	validation-aucpr:0.97309
[26]	validation-logloss:0.32707	validation-auc:0.96997	validation-aucpr:0.97437
[27]	validation-logloss:0.32064	validation-auc:0.97014	validation-aucpr:0.97449
[28]	validation-logloss:0.31453	validation-auc:0.97027	validation-aucpr:0.97456
[29]	validation-logloss:0.30974	validation-auc:0.97025	validation-aucpr:0.97454
[30]	validation-logloss:0.30411	validation-auc:0.97055	validation-aucpr:0.97480
[31]	validation-logloss:0.29880	validation-auc:0.97075	validation-aucpr:0.97496
[32]	validation-logloss:0.29386	validation-auc:0.97082	validation-aucpr:0.97503
[33]	validation-logloss:0.28936	validation-auc:0.97073	validation-aucpr:0.97497
[34]	validation-logloss:0.28503	validation-auc:0.97063	validation-aucpr:0.97488
[35]	validation-logloss:0.28132	validation-auc:0.97075	validation-aucpr:0.97502
[36]	validation-logloss:0.27758	validation-auc:0.97083	validation-aucpr:0.97508
[37]	validation-logloss:0.27393	validation-auc:0.97087	validation-aucpr:0.97513
[38]	validation-logloss:0.26992	validation-auc:0.97103	validation-aucpr:0.97529
[39]	validation-logloss:0.26630	validation-auc:0.97099	validation-aucpr:0.97526
[40]	validation-logloss:0.26290	validation-auc:0.97116	validation-aucpr:0.97535
[41]	validation-logloss:0.25957	validation-auc:0.97125	validation-aucpr:0.97539
[42]	validation-logloss:0.25693	validation-auc:0.97123	validation-aucpr:0.97533
[43]	validation-logloss:0.25399	validation-auc:0.97126	validation-aucpr:0.97538
[44]	validation-logloss:0.25100	validation-auc:0.97127	validation-aucpr:0.97540
[45]	validation-logloss:0.24815	validation-auc:0.97127	validation-aucpr:0.97540
[46]	validation-logloss:0.24530	validation-auc:0.97138	validation-aucpr:0.97551
[47]	validation-logloss:0.24273	validation-auc:0.97148	validation-aucpr:0.97557
[48]	validation-logloss:0.24018	validation-auc:0.97158	validation-aucpr:0.97571
[49]	validation-logloss:0.23780	validation-auc:0.97166	validation-aucpr:0.97574
[50]	validation-logloss:0.23546	validation-auc:0.97173	validation-aucpr:0.97578
[51]	validation-logloss:0.23339	validation-auc:0.97169	validation-aucpr:0.97576
[52]	validation-logloss:0.23175	validation-auc:0.97174	validation-aucpr:0.97576
[53]	validation-logloss:0.22975	validation-auc:0.97176	validation-aucpr:0.97578
[54]	validation-logloss:0.22805	validation-auc:0.97162	validation-aucpr:0.97565
[55]	validation-logloss:0.22612	validation-auc:0.97168	validation-aucpr:0.97566
[56]	validation-logloss:0.22442	validation-auc:0.97168	validation-aucpr:0.97564
{'best_iteration': '53', 'best_score': '0.975780860731931'}
Trial 16, Fold 3: Log loss = 0.2244200578116086, Average precision = 0.9756433442739769, ROC-AUC = 0.971681897747301, Elapsed Time = 10.35664270000052 seconds
Trial 16, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 16, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[0]	validation-logloss:0.66455	validation-auc:0.95339	validation-aucpr:0.95766
[1]	validation-logloss:0.63835	validation-auc:0.96090	validation-aucpr:0.96545
[2]	validation-logloss:0.61414	validation-auc:0.96464	validation-aucpr:0.97065
[3]	validation-logloss:0.59135	validation-auc:0.96593	validation-aucpr:0.97133
[4]	validation-logloss:0.56988	validation-auc:0.96776	validation-aucpr:0.97271
[5]	validation-logloss:0.55220	validation-auc:0.96762	validation-aucpr:0.97266
[6]	validation-logloss:0.53339	validation-auc:0.96837	validation-aucpr:0.97336
[7]	validation-logloss:0.51833	validation-auc:0.96785	validation-aucpr:0.97304
[8]	validation-logloss:0.50365	validation-auc:0.96721	validation-aucpr:0.97262
[9]	validation-logloss:0.49009	validation-auc:0.96684	validation-aucpr:0.97219
[10]	validation-logloss:0.47590	validation-auc:0.96720	validation-aucpr:0.97252
[11]	validation-logloss:0.46173	validation-auc:0.96766	validation-aucpr:0.97290
[12]	validation-logloss:0.44827	validation-auc:0.96819	validation-aucpr:0.97330
[13]	validation-logloss:0.43581	validation-auc:0.96834	validation-aucpr:0.97343
[14]	validation-logloss:0.42541	validation-auc:0.96822	validation-aucpr:0.97335
[15]	validation-logloss:0.41455	validation-auc:0.96829	validation-aucpr:0.97344
[16]	validation-logloss:0.40524	validation-auc:0.96836	validation-aucpr:0.97350
[17]	validation-logloss:0.39523	validation-auc:0.96859	validation-aucpr:0.97363
[18]	validation-logloss:0.38584	validation-auc:0.96867	validation-aucpr:0.97375
[19]	validation-logloss:0.37822	validation-auc:0.96863	validation-aucpr:0.97370
[20]	validation-logloss:0.37001	validation-auc:0.96881	validation-aucpr:0.97380
[21]	validation-logloss:0.36181	validation-auc:0.96876	validation-aucpr:0.97377
[22]	validation-logloss:0.35551	validation-auc:0.96847	validation-aucpr:0.97355
[23]	validation-logloss:0.34919	validation-auc:0.96849	validation-aucpr:0.97354
[24]	validation-logloss:0.34206	validation-auc:0.96845	validation-aucpr:0.97350
[25]	validation-logloss:0.33537	validation-auc:0.96871	validation-aucpr:0.97373
[26]	validation-logloss:0.32975	validation-auc:0.96874	validation-aucpr:0.97373
[27]	validation-logloss:0.32358	validation-auc:0.96892	validation-aucpr:0.97389
[28]	validation-logloss:0.31850	validation-auc:0.96893	validation-aucpr:0.97385
[29]	validation-logloss:0.31382	validation-auc:0.96893	validation-aucpr:0.97384
[30]	validation-logloss:0.30812	validation-auc:0.96924	validation-aucpr:0.97408
[31]	validation-logloss:0.30291	validation-auc:0.96936	validation-aucpr:0.97418
[32]	validation-logloss:0.29794	validation-auc:0.96949	validation-aucpr:0.97428
[33]	validation-logloss:0.29327	validation-auc:0.96954	validation-aucpr:0.97432
[34]	validation-logloss:0.28948	validation-auc:0.96951	validation-aucpr:0.97431
[35]	validation-logloss:0.28507	validation-auc:0.96971	validation-aucpr:0.97449
[36]	validation-logloss:0.28154	validation-auc:0.96975	validation-aucpr:0.97454
[37]	validation-logloss:0.27824	validation-auc:0.96977	validation-aucpr:0.97453
[38]	validation-logloss:0.27507	validation-auc:0.96974	validation-aucpr:0.97451
[39]	validation-logloss:0.27112	validation-auc:0.96987	validation-aucpr:0.97463
[40]	validation-logloss:0.26738	validation-auc:0.97009	validation-aucpr:0.97481
[41]	validation-logloss:0.26399	validation-auc:0.97015	validation-aucpr:0.97483
[42]	validation-logloss:0.26129	validation-auc:0.97012	validation-aucpr:0.97480
[43]	validation-logloss:0.25790	validation-auc:0.97028	validation-aucpr:0.97494
[44]	validation-logloss:0.25550	validation-auc:0.97027	validation-aucpr:0.97492
[45]	validation-logloss:0.25343	validation-auc:0.97015	validation-aucpr:0.97481
[46]	validation-logloss:0.25110	validation-auc:0.97027	validation-aucpr:0.97490
[47]	validation-logloss:0.24838	validation-auc:0.97026	validation-aucpr:0.97487
[48]	validation-logloss:0.24595	validation-auc:0.97024	validation-aucpr:0.97487
[49]	validation-logloss:0.24405	validation-auc:0.97022	validation-aucpr:0.97485
[50]	validation-logloss:0.24209	validation-auc:0.97026	validation-aucpr:0.97487
[51]	validation-logloss:0.23972	validation-auc:0.97037	validation-aucpr:0.97499
[52]	validation-logloss:0.23755	validation-auc:0.97040	validation-aucpr:0.97501
[53]	validation-logloss:0.23605	validation-auc:0.97037	validation-aucpr:0.97497
[54]	validation-logloss:0.23395	validation-auc:0.97042	validation-aucpr:0.97502
[55]	validation-logloss:0.23193	validation-auc:0.97056	validation-aucpr:0.97513
[56]	validation-logloss:0.23063	validation-auc:0.97041	validation-aucpr:0.97505
{'best_iteration': '55', 'best_score': '0.9751346047427522'}
Trial 16, Fold 4: Log loss = 0.23063088965856088, Average precision = 0.9750531428006062, ROC-AUC = 0.9704149141254818, Elapsed Time = 10.488736199999039 seconds
Trial 16, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 16, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[0]	validation-logloss:0.66464	validation-auc:0.95664	validation-aucpr:0.96172
[1]	validation-logloss:0.63920	validation-auc:0.96086	validation-aucpr:0.96353
[2]	validation-logloss:0.61741	validation-auc:0.96288	validation-aucpr:0.96780
[3]	validation-logloss:0.59442	validation-auc:0.96538	validation-aucpr:0.96991
[4]	validation-logloss:0.57322	validation-auc:0.96664	validation-aucpr:0.97098
[5]	validation-logloss:0.55315	validation-auc:0.96683	validation-aucpr:0.97100
[6]	validation-logloss:0.53423	validation-auc:0.96745	validation-aucpr:0.97135
[7]	validation-logloss:0.51695	validation-auc:0.96775	validation-aucpr:0.97159
[8]	validation-logloss:0.50077	validation-auc:0.96790	validation-aucpr:0.97196
[9]	validation-logloss:0.48736	validation-auc:0.96769	validation-aucpr:0.97183
[10]	validation-logloss:0.47464	validation-auc:0.96733	validation-aucpr:0.97158
[11]	validation-logloss:0.46247	validation-auc:0.96770	validation-aucpr:0.97182
[12]	validation-logloss:0.44934	validation-auc:0.96799	validation-aucpr:0.97209
[13]	validation-logloss:0.43861	validation-auc:0.96775	validation-aucpr:0.97182
[14]	validation-logloss:0.42687	validation-auc:0.96785	validation-aucpr:0.97194
[15]	validation-logloss:0.41571	validation-auc:0.96822	validation-aucpr:0.97221
[16]	validation-logloss:0.40617	validation-auc:0.96863	validation-aucpr:0.97259
[17]	validation-logloss:0.39660	validation-auc:0.96855	validation-aucpr:0.97258
[18]	validation-logloss:0.38839	validation-auc:0.96838	validation-aucpr:0.97236
[19]	validation-logloss:0.38059	validation-auc:0.96827	validation-aucpr:0.97220
[20]	validation-logloss:0.37184	validation-auc:0.96862	validation-aucpr:0.97248
[21]	validation-logloss:0.36528	validation-auc:0.96836	validation-aucpr:0.97245
[22]	validation-logloss:0.35720	validation-auc:0.96876	validation-aucpr:0.97278
[23]	validation-logloss:0.35115	validation-auc:0.96860	validation-aucpr:0.97267
[24]	validation-logloss:0.34421	validation-auc:0.96888	validation-aucpr:0.97288
[25]	validation-logloss:0.33875	validation-auc:0.96877	validation-aucpr:0.97269
[26]	validation-logloss:0.33201	validation-auc:0.96903	validation-aucpr:0.97289
[27]	validation-logloss:0.32570	validation-auc:0.96919	validation-aucpr:0.97235
[28]	validation-logloss:0.32022	validation-auc:0.96948	validation-aucpr:0.97256
[29]	validation-logloss:0.31535	validation-auc:0.96959	validation-aucpr:0.97263
[30]	validation-logloss:0.30983	validation-auc:0.96979	validation-aucpr:0.97347
[31]	validation-logloss:0.30550	validation-auc:0.96967	validation-aucpr:0.97335
[32]	validation-logloss:0.30025	validation-auc:0.96988	validation-aucpr:0.97362
[33]	validation-logloss:0.29548	validation-auc:0.96998	validation-aucpr:0.97370
[34]	validation-logloss:0.29102	validation-auc:0.97001	validation-aucpr:0.97372
[35]	validation-logloss:0.28702	validation-auc:0.96997	validation-aucpr:0.97368
[36]	validation-logloss:0.28293	validation-auc:0.96998	validation-aucpr:0.97373
[37]	validation-logloss:0.27897	validation-auc:0.97017	validation-aucpr:0.97385
[38]	validation-logloss:0.27514	validation-auc:0.97033	validation-aucpr:0.97399
[39]	validation-logloss:0.27204	validation-auc:0.97021	validation-aucpr:0.97392
[40]	validation-logloss:0.26864	validation-auc:0.97019	validation-aucpr:0.97389
[41]	validation-logloss:0.26541	validation-auc:0.97032	validation-aucpr:0.97397
[42]	validation-logloss:0.26282	validation-auc:0.97023	validation-aucpr:0.97386
[43]	validation-logloss:0.26046	validation-auc:0.97017	validation-aucpr:0.97390
[44]	validation-logloss:0.25802	validation-auc:0.97015	validation-aucpr:0.97380
[45]	validation-logloss:0.25511	validation-auc:0.97030	validation-aucpr:0.97385
[46]	validation-logloss:0.25307	validation-auc:0.97022	validation-aucpr:0.97370
[47]	validation-logloss:0.25054	validation-auc:0.97020	validation-aucpr:0.97369
[48]	validation-logloss:0.24860	validation-auc:0.97032	validation-aucpr:0.97376
[49]	validation-logloss:0.24612	validation-auc:0.97043	validation-aucpr:0.97383
[50]	validation-logloss:0.24360	validation-auc:0.97058	validation-aucpr:0.97392
[51]	validation-logloss:0.24130	validation-auc:0.97067	validation-aucpr:0.97400
[52]	validation-logloss:0.23920	validation-auc:0.97071	validation-aucpr:0.97388
[53]	validation-logloss:0.23752	validation-auc:0.97072	validation-aucpr:0.97386
[54]	validation-logloss:0.23620	validation-auc:0.97050	validation-aucpr:0.97369
[55]	validation-logloss:0.23413	validation-auc:0.97077	validation-aucpr:0.97386
[56]	validation-logloss:0.23209	validation-auc:0.97098	validation-aucpr:0.97403
{'best_iteration': '56', 'best_score': '0.9740272707349765'}
Trial 16, Fold 5: Log loss = 0.23209462109948828, Average precision = 0.9740309172331549, ROC-AUC = 0.970983482897646, Elapsed Time = 10.367234399998779 seconds
Optimization Progress:  17%|#7        | 17/100 [33:52<2:53:29, 125.41s/it]
Trial 17, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 17, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[0]	validation-logloss:0.68070	validation-auc:0.94898	validation-aucpr:0.92672
[1]	validation-logloss:0.67016	validation-auc:0.96222	validation-aucpr:0.96208
[2]	validation-logloss:0.65893	validation-auc:0.96484	validation-aucpr:0.96820
[3]	validation-logloss:0.64785	validation-auc:0.96631	validation-aucpr:0.96911
[4]	validation-logloss:0.63708	validation-auc:0.96744	validation-aucpr:0.97010
[5]	validation-logloss:0.62658	validation-auc:0.96754	validation-aucpr:0.96975
[6]	validation-logloss:0.61637	validation-auc:0.96832	validation-aucpr:0.97309
[7]	validation-logloss:0.60679	validation-auc:0.96827	validation-aucpr:0.97310
[8]	validation-logloss:0.59797	validation-auc:0.96876	validation-aucpr:0.97353
[9]	validation-logloss:0.58967	validation-auc:0.96903	validation-aucpr:0.97361
[10]	validation-logloss:0.58167	validation-auc:0.96863	validation-aucpr:0.97336
[11]	validation-logloss:0.57302	validation-auc:0.96858	validation-aucpr:0.97330
[12]	validation-logloss:0.56516	validation-auc:0.96856	validation-aucpr:0.97329
[13]	validation-logloss:0.55678	validation-auc:0.96853	validation-aucpr:0.97329
[14]	validation-logloss:0.54872	validation-auc:0.96874	validation-aucpr:0.97356
[15]	validation-logloss:0.54084	validation-auc:0.96906	validation-aucpr:0.97380
[16]	validation-logloss:0.53407	validation-auc:0.96879	validation-aucpr:0.97383
[17]	validation-logloss:0.52748	validation-auc:0.96864	validation-aucpr:0.97363
[18]	validation-logloss:0.52008	validation-auc:0.96877	validation-aucpr:0.97371
[19]	validation-logloss:0.51293	validation-auc:0.96881	validation-aucpr:0.97380
[20]	validation-logloss:0.50591	validation-auc:0.96900	validation-aucpr:0.97396
[21]	validation-logloss:0.49994	validation-auc:0.96878	validation-aucpr:0.97374
[22]	validation-logloss:0.49338	validation-auc:0.96887	validation-aucpr:0.97380
[23]	validation-logloss:0.48702	validation-auc:0.96894	validation-aucpr:0.97389
[24]	validation-logloss:0.48086	validation-auc:0.96901	validation-aucpr:0.97392
[25]	validation-logloss:0.47488	validation-auc:0.96927	validation-aucpr:0.97420
[26]	validation-logloss:0.46956	validation-auc:0.96922	validation-aucpr:0.97409
[27]	validation-logloss:0.46372	validation-auc:0.96940	validation-aucpr:0.97424
[28]	validation-logloss:0.45803	validation-auc:0.96941	validation-aucpr:0.97423
[29]	validation-logloss:0.45247	validation-auc:0.96961	validation-aucpr:0.97440
[30]	validation-logloss:0.44764	validation-auc:0.96951	validation-aucpr:0.97427
[31]	validation-logloss:0.44230	validation-auc:0.96968	validation-aucpr:0.97441
[32]	validation-logloss:0.43704	validation-auc:0.96977	validation-aucpr:0.97450
[33]	validation-logloss:0.43202	validation-auc:0.96985	validation-aucpr:0.97456
[34]	validation-logloss:0.42703	validation-auc:0.96993	validation-aucpr:0.97464
[35]	validation-logloss:0.42260	validation-auc:0.96977	validation-aucpr:0.97447
[36]	validation-logloss:0.41841	validation-auc:0.96968	validation-aucpr:0.97443
[37]	validation-logloss:0.41366	validation-auc:0.96981	validation-aucpr:0.97453
[38]	validation-logloss:0.40921	validation-auc:0.96978	validation-aucpr:0.97454
[39]	validation-logloss:0.40466	validation-auc:0.97000	validation-aucpr:0.97469
[40]	validation-logloss:0.40024	validation-auc:0.97011	validation-aucpr:0.97478
[41]	validation-logloss:0.39648	validation-auc:0.97007	validation-aucpr:0.97473
{'best_iteration': '40', 'best_score': '0.9747813685044032'}
Trial 17, Fold 1: Log loss = 0.39647990137081396, Average precision = 0.9747384831870218, ROC-AUC = 0.9700692413119016, Elapsed Time = 1.3540717999985645 seconds
Trial 17, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 17, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[0]	validation-logloss:0.68110	validation-auc:0.94313	validation-aucpr:0.91708
[1]	validation-logloss:0.66937	validation-auc:0.96204	validation-aucpr:0.95203
[2]	validation-logloss:0.65910	validation-auc:0.96507	validation-aucpr:0.96812
[3]	validation-logloss:0.64823	validation-auc:0.96729	validation-aucpr:0.97087
[4]	validation-logloss:0.63878	validation-auc:0.96777	validation-aucpr:0.97130
[5]	validation-logloss:0.62843	validation-auc:0.96854	validation-aucpr:0.97194
[6]	validation-logloss:0.61821	validation-auc:0.96897	validation-aucpr:0.97236
[7]	validation-logloss:0.60838	validation-auc:0.96928	validation-aucpr:0.97272
[8]	validation-logloss:0.59990	validation-auc:0.96885	validation-aucpr:0.97233
[9]	validation-logloss:0.59051	validation-auc:0.96896	validation-aucpr:0.97245
[10]	validation-logloss:0.58142	validation-auc:0.96913	validation-aucpr:0.97257
[11]	validation-logloss:0.57258	validation-auc:0.96918	validation-aucpr:0.97277
[12]	validation-logloss:0.56387	validation-auc:0.96969	validation-aucpr:0.97312
[13]	validation-logloss:0.55546	validation-auc:0.96968	validation-aucpr:0.97313
[14]	validation-logloss:0.54733	validation-auc:0.96969	validation-aucpr:0.97310
[15]	validation-logloss:0.53945	validation-auc:0.96966	validation-aucpr:0.97309
[16]	validation-logloss:0.53180	validation-auc:0.96947	validation-aucpr:0.97296
[17]	validation-logloss:0.52440	validation-auc:0.96937	validation-aucpr:0.97287
[18]	validation-logloss:0.51788	validation-auc:0.96949	validation-aucpr:0.97296
[19]	validation-logloss:0.51148	validation-auc:0.96967	validation-aucpr:0.97322
[20]	validation-logloss:0.50458	validation-auc:0.96966	validation-aucpr:0.97322
[21]	validation-logloss:0.49870	validation-auc:0.96943	validation-aucpr:0.97305
[22]	validation-logloss:0.49200	validation-auc:0.96967	validation-aucpr:0.97324
[23]	validation-logloss:0.48550	validation-auc:0.96977	validation-aucpr:0.97331
[24]	validation-logloss:0.47928	validation-auc:0.96974	validation-aucpr:0.97327
[25]	validation-logloss:0.47337	validation-auc:0.96981	validation-aucpr:0.97331
[26]	validation-logloss:0.46733	validation-auc:0.96997	validation-aucpr:0.97342
[27]	validation-logloss:0.46157	validation-auc:0.96992	validation-aucpr:0.97337
[28]	validation-logloss:0.45655	validation-auc:0.96992	validation-aucpr:0.97334
[29]	validation-logloss:0.45179	validation-auc:0.96981	validation-aucpr:0.97318
[30]	validation-logloss:0.44636	validation-auc:0.96979	validation-aucpr:0.97318
[31]	validation-logloss:0.44101	validation-auc:0.96987	validation-aucpr:0.97325
[32]	validation-logloss:0.43584	validation-auc:0.96994	validation-aucpr:0.97328
[33]	validation-logloss:0.43143	validation-auc:0.96988	validation-aucpr:0.97324
[34]	validation-logloss:0.42635	validation-auc:0.97007	validation-aucpr:0.97337
[35]	validation-logloss:0.42213	validation-auc:0.97010	validation-aucpr:0.97337
[36]	validation-logloss:0.41756	validation-auc:0.97010	validation-aucpr:0.97338
[37]	validation-logloss:0.41344	validation-auc:0.97023	validation-aucpr:0.97350
[38]	validation-logloss:0.40897	validation-auc:0.97027	validation-aucpr:0.97351
[39]	validation-logloss:0.40460	validation-auc:0.97027	validation-aucpr:0.97352
[40]	validation-logloss:0.40021	validation-auc:0.97025	validation-aucpr:0.97353
[41]	validation-logloss:0.39626	validation-auc:0.97022	validation-aucpr:0.97344
{'best_iteration': '40', 'best_score': '0.9735317036055212'}
Trial 17, Fold 2: Log loss = 0.3962613455256332, Average precision = 0.9732858310430789, ROC-AUC = 0.9702157034071321, Elapsed Time = 1.8218703000002279 seconds
Trial 17, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 17, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[0]	validation-logloss:0.68088	validation-auc:0.94306	validation-aucpr:0.92744
[1]	validation-logloss:0.66888	validation-auc:0.96486	validation-aucpr:0.96352
[2]	validation-logloss:0.65766	validation-auc:0.96609	validation-aucpr:0.96783
[3]	validation-logloss:0.64664	validation-auc:0.96827	validation-aucpr:0.97263
[4]	validation-logloss:0.63599	validation-auc:0.96896	validation-aucpr:0.97321
[5]	validation-logloss:0.62545	validation-auc:0.96974	validation-aucpr:0.97372
[6]	validation-logloss:0.61527	validation-auc:0.97027	validation-aucpr:0.97416
[7]	validation-logloss:0.60568	validation-auc:0.97077	validation-aucpr:0.97456
[8]	validation-logloss:0.59627	validation-auc:0.97097	validation-aucpr:0.97504
[9]	validation-logloss:0.58727	validation-auc:0.97070	validation-aucpr:0.97513
[10]	validation-logloss:0.57901	validation-auc:0.97081	validation-aucpr:0.97512
[11]	validation-logloss:0.57003	validation-auc:0.97109	validation-aucpr:0.97536
[12]	validation-logloss:0.56155	validation-auc:0.97099	validation-aucpr:0.97527
[13]	validation-logloss:0.55330	validation-auc:0.97100	validation-aucpr:0.97527
[14]	validation-logloss:0.54592	validation-auc:0.97135	validation-aucpr:0.97552
[15]	validation-logloss:0.53882	validation-auc:0.97127	validation-aucpr:0.97536
[16]	validation-logloss:0.53199	validation-auc:0.97140	validation-aucpr:0.97535
[17]	validation-logloss:0.52456	validation-auc:0.97143	validation-aucpr:0.97535
[18]	validation-logloss:0.51724	validation-auc:0.97160	validation-aucpr:0.97546
[19]	validation-logloss:0.51002	validation-auc:0.97166	validation-aucpr:0.97553
[20]	validation-logloss:0.50296	validation-auc:0.97178	validation-aucpr:0.97564
[21]	validation-logloss:0.49611	validation-auc:0.97192	validation-aucpr:0.97576
[22]	validation-logloss:0.48946	validation-auc:0.97188	validation-aucpr:0.97570
[23]	validation-logloss:0.48352	validation-auc:0.97213	validation-aucpr:0.97602
[24]	validation-logloss:0.47729	validation-auc:0.97199	validation-aucpr:0.97592
[25]	validation-logloss:0.47103	validation-auc:0.97206	validation-aucpr:0.97600
[26]	validation-logloss:0.46580	validation-auc:0.97180	validation-aucpr:0.97570
[27]	validation-logloss:0.46000	validation-auc:0.97189	validation-aucpr:0.97576
[28]	validation-logloss:0.45497	validation-auc:0.97183	validation-aucpr:0.97574
[29]	validation-logloss:0.44959	validation-auc:0.97174	validation-aucpr:0.97565
[30]	validation-logloss:0.44409	validation-auc:0.97185	validation-aucpr:0.97574
[31]	validation-logloss:0.43876	validation-auc:0.97192	validation-aucpr:0.97580
[32]	validation-logloss:0.43422	validation-auc:0.97198	validation-aucpr:0.97591
[33]	validation-logloss:0.42923	validation-auc:0.97186	validation-aucpr:0.97582
[34]	validation-logloss:0.42490	validation-auc:0.97185	validation-aucpr:0.97577
[35]	validation-logloss:0.42002	validation-auc:0.97185	validation-aucpr:0.97580
[36]	validation-logloss:0.41534	validation-auc:0.97189	validation-aucpr:0.97585
[37]	validation-logloss:0.41061	validation-auc:0.97190	validation-aucpr:0.97586
[38]	validation-logloss:0.40599	validation-auc:0.97198	validation-aucpr:0.97594
[39]	validation-logloss:0.40156	validation-auc:0.97198	validation-aucpr:0.97592
[40]	validation-logloss:0.39710	validation-auc:0.97205	validation-aucpr:0.97598
[41]	validation-logloss:0.39307	validation-auc:0.97211	validation-aucpr:0.97601
{'best_iteration': '23', 'best_score': '0.9760153538535357'}
Trial 17, Fold 3: Log loss = 0.3930701404115779, Average precision = 0.9760085592503406, ROC-AUC = 0.9721139399601276, Elapsed Time = 1.6774910000003729 seconds
Trial 17, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 17, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[0]	validation-logloss:0.68092	validation-auc:0.93976	validation-aucpr:0.92820
[1]	validation-logloss:0.67073	validation-auc:0.95822	validation-aucpr:0.96040
[2]	validation-logloss:0.65918	validation-auc:0.96465	validation-aucpr:0.96808
[3]	validation-logloss:0.64802	validation-auc:0.96658	validation-aucpr:0.96915
[4]	validation-logloss:0.63726	validation-auc:0.96750	validation-aucpr:0.97281
[5]	validation-logloss:0.62668	validation-auc:0.96890	validation-aucpr:0.97381
[6]	validation-logloss:0.61663	validation-auc:0.96905	validation-aucpr:0.97402
[7]	validation-logloss:0.60789	validation-auc:0.96827	validation-aucpr:0.97340
[8]	validation-logloss:0.59942	validation-auc:0.96815	validation-aucpr:0.97325
[9]	validation-logloss:0.59114	validation-auc:0.96793	validation-aucpr:0.97318
[10]	validation-logloss:0.58205	validation-auc:0.96853	validation-aucpr:0.97369
[11]	validation-logloss:0.57315	validation-auc:0.96898	validation-aucpr:0.97402
[12]	validation-logloss:0.56464	validation-auc:0.96914	validation-aucpr:0.97417
[13]	validation-logloss:0.55623	validation-auc:0.96938	validation-aucpr:0.97436
[14]	validation-logloss:0.54800	validation-auc:0.96940	validation-aucpr:0.97441
[15]	validation-logloss:0.54018	validation-auc:0.96933	validation-aucpr:0.97439
[16]	validation-logloss:0.53338	validation-auc:0.96898	validation-aucpr:0.97412
[17]	validation-logloss:0.52569	validation-auc:0.96941	validation-aucpr:0.97445
[18]	validation-logloss:0.51920	validation-auc:0.96929	validation-aucpr:0.97431
[19]	validation-logloss:0.51193	validation-auc:0.96982	validation-aucpr:0.97471
[20]	validation-logloss:0.50543	validation-auc:0.96971	validation-aucpr:0.97461
[21]	validation-logloss:0.49858	validation-auc:0.96983	validation-aucpr:0.97468
[22]	validation-logloss:0.49190	validation-auc:0.96999	validation-aucpr:0.97483
[23]	validation-logloss:0.48616	validation-auc:0.96996	validation-aucpr:0.97480
[24]	validation-logloss:0.47977	validation-auc:0.97019	validation-aucpr:0.97498
[25]	validation-logloss:0.47382	validation-auc:0.97009	validation-aucpr:0.97493
[26]	validation-logloss:0.46781	validation-auc:0.97027	validation-aucpr:0.97507
[27]	validation-logloss:0.46200	validation-auc:0.97030	validation-aucpr:0.97507
[28]	validation-logloss:0.45656	validation-auc:0.97029	validation-aucpr:0.97505
[29]	validation-logloss:0.45101	validation-auc:0.97024	validation-aucpr:0.97501
[30]	validation-logloss:0.44545	validation-auc:0.97033	validation-aucpr:0.97508
[31]	validation-logloss:0.44058	validation-auc:0.97036	validation-aucpr:0.97509
[32]	validation-logloss:0.43539	validation-auc:0.97038	validation-aucpr:0.97510
[33]	validation-logloss:0.43028	validation-auc:0.97064	validation-aucpr:0.97533
[34]	validation-logloss:0.42604	validation-auc:0.97053	validation-aucpr:0.97524
[35]	validation-logloss:0.42182	validation-auc:0.97046	validation-aucpr:0.97517
[36]	validation-logloss:0.41776	validation-auc:0.97021	validation-aucpr:0.97499
[37]	validation-logloss:0.41362	validation-auc:0.97016	validation-aucpr:0.97495
[38]	validation-logloss:0.40902	validation-auc:0.97026	validation-aucpr:0.97504
[39]	validation-logloss:0.40513	validation-auc:0.97013	validation-aucpr:0.97494
[40]	validation-logloss:0.40081	validation-auc:0.97021	validation-aucpr:0.97498
[41]	validation-logloss:0.39658	validation-auc:0.97035	validation-aucpr:0.97509
{'best_iteration': '33', 'best_score': '0.9753345323573815'}
Trial 17, Fold 4: Log loss = 0.3965803346159523, Average precision = 0.9750878709584755, ROC-AUC = 0.9703468839340855, Elapsed Time = 1.805470499999501 seconds
Trial 17, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 17, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[0]	validation-logloss:0.68100	validation-auc:0.93722	validation-aucpr:0.91276
[1]	validation-logloss:0.66927	validation-auc:0.96019	validation-aucpr:0.95802
[2]	validation-logloss:0.65798	validation-auc:0.96497	validation-aucpr:0.96872
[3]	validation-logloss:0.64696	validation-auc:0.96467	validation-aucpr:0.96643
[4]	validation-logloss:0.63619	validation-auc:0.96601	validation-aucpr:0.96870
[5]	validation-logloss:0.62584	validation-auc:0.96626	validation-aucpr:0.96794
[6]	validation-logloss:0.61580	validation-auc:0.96677	validation-aucpr:0.97012
[7]	validation-logloss:0.60706	validation-auc:0.96667	validation-aucpr:0.97029
[8]	validation-logloss:0.59752	validation-auc:0.96678	validation-aucpr:0.97033
[9]	validation-logloss:0.58826	validation-auc:0.96721	validation-aucpr:0.97089
[10]	validation-logloss:0.58063	validation-auc:0.96742	validation-aucpr:0.97213
[11]	validation-logloss:0.57225	validation-auc:0.96760	validation-aucpr:0.97233
[12]	validation-logloss:0.56365	validation-auc:0.96795	validation-aucpr:0.97264
[13]	validation-logloss:0.55542	validation-auc:0.96841	validation-aucpr:0.97295
[14]	validation-logloss:0.54815	validation-auc:0.96841	validation-aucpr:0.97288
[15]	validation-logloss:0.54032	validation-auc:0.96868	validation-aucpr:0.97296
[16]	validation-logloss:0.53282	validation-auc:0.96860	validation-aucpr:0.97287
[17]	validation-logloss:0.52628	validation-auc:0.96821	validation-aucpr:0.97256
[18]	validation-logloss:0.51910	validation-auc:0.96824	validation-aucpr:0.97258
[19]	validation-logloss:0.51287	validation-auc:0.96801	validation-aucpr:0.97236
[20]	validation-logloss:0.50587	validation-auc:0.96827	validation-aucpr:0.97257
[21]	validation-logloss:0.49936	validation-auc:0.96818	validation-aucpr:0.97250
[22]	validation-logloss:0.49279	validation-auc:0.96820	validation-aucpr:0.97249
[23]	validation-logloss:0.48645	validation-auc:0.96830	validation-aucpr:0.97257
[24]	validation-logloss:0.48025	validation-auc:0.96848	validation-aucpr:0.97269
[25]	validation-logloss:0.47529	validation-auc:0.96823	validation-aucpr:0.97267
[26]	validation-logloss:0.47006	validation-auc:0.96811	validation-aucpr:0.97254
[27]	validation-logloss:0.46480	validation-auc:0.96812	validation-aucpr:0.97252
[28]	validation-logloss:0.45980	validation-auc:0.96807	validation-aucpr:0.97247
[29]	validation-logloss:0.45426	validation-auc:0.96822	validation-aucpr:0.97259
[30]	validation-logloss:0.44943	validation-auc:0.96834	validation-aucpr:0.97268
[31]	validation-logloss:0.44406	validation-auc:0.96843	validation-aucpr:0.97275
[32]	validation-logloss:0.43966	validation-auc:0.96825	validation-aucpr:0.97260
[33]	validation-logloss:0.43455	validation-auc:0.96834	validation-aucpr:0.97267
[34]	validation-logloss:0.42951	validation-auc:0.96852	validation-aucpr:0.97279
[35]	validation-logloss:0.42534	validation-auc:0.96848	validation-aucpr:0.97272
[36]	validation-logloss:0.42052	validation-auc:0.96860	validation-aucpr:0.97285
[37]	validation-logloss:0.41637	validation-auc:0.96858	validation-aucpr:0.97283
[38]	validation-logloss:0.41174	validation-auc:0.96879	validation-aucpr:0.97298
[39]	validation-logloss:0.40778	validation-auc:0.96887	validation-aucpr:0.97306
[40]	validation-logloss:0.40396	validation-auc:0.96886	validation-aucpr:0.97303
[41]	validation-logloss:0.39983	validation-auc:0.96884	validation-aucpr:0.97300
{'best_iteration': '39', 'best_score': '0.9730560035632158'}
Trial 17, Fold 5: Log loss = 0.3998339284718829, Average precision = 0.9729822569312114, ROC-AUC = 0.9688389721866116, Elapsed Time = 1.7565878000004886 seconds
Optimization Progress:  18%|#8        | 18/100 [34:09<2:06:46, 92.76s/it] 
Trial 18, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 18, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[0]	validation-logloss:0.66415	validation-auc:0.90117	validation-aucpr:0.89825
[1]	validation-logloss:0.63802	validation-auc:0.91492	validation-aucpr:0.91811
[2]	validation-logloss:0.61375	validation-auc:0.92827	validation-aucpr:0.93063
[3]	validation-logloss:0.58648	validation-auc:0.94237	validation-aucpr:0.94863
[4]	validation-logloss:0.56778	validation-auc:0.94191	validation-aucpr:0.95001
[5]	validation-logloss:0.54988	validation-auc:0.94233	validation-aucpr:0.95046
[6]	validation-logloss:0.52795	validation-auc:0.94457	validation-aucpr:0.95350
[7]	validation-logloss:0.50871	validation-auc:0.94546	validation-aucpr:0.95459
[8]	validation-logloss:0.49224	validation-auc:0.94577	validation-aucpr:0.95474
[9]	validation-logloss:0.47824	validation-auc:0.94599	validation-aucpr:0.95491
[10]	validation-logloss:0.46174	validation-auc:0.94668	validation-aucpr:0.95575
[11]	validation-logloss:0.44955	validation-auc:0.94784	validation-aucpr:0.95607
[12]	validation-logloss:0.44034	validation-auc:0.94804	validation-aucpr:0.95650
[13]	validation-logloss:0.42811	validation-auc:0.94834	validation-aucpr:0.95684
[14]	validation-logloss:0.42058	validation-auc:0.94795	validation-aucpr:0.95659
[15]	validation-logloss:0.41008	validation-auc:0.94862	validation-aucpr:0.95689
[16]	validation-logloss:0.40055	validation-auc:0.95022	validation-aucpr:0.95758
[17]	validation-logloss:0.39381	validation-auc:0.95082	validation-aucpr:0.95788
[18]	validation-logloss:0.38767	validation-auc:0.95060	validation-aucpr:0.95790
[19]	validation-logloss:0.37982	validation-auc:0.95092	validation-aucpr:0.95826
[20]	validation-logloss:0.37431	validation-auc:0.95039	validation-aucpr:0.95791
[21]	validation-logloss:0.36926	validation-auc:0.94992	validation-aucpr:0.95775
[22]	validation-logloss:0.36471	validation-auc:0.94962	validation-aucpr:0.95792
[23]	validation-logloss:0.36034	validation-auc:0.94948	validation-aucpr:0.95794
[24]	validation-logloss:0.35334	validation-auc:0.95030	validation-aucpr:0.95890
[25]	validation-logloss:0.34727	validation-auc:0.95183	validation-aucpr:0.95980
{'best_iteration': '25', 'best_score': '0.9597999490308322'}
Trial 18, Fold 1: Log loss = 0.34726644783133376, Average precision = 0.9578838551170721, ROC-AUC = 0.9518273415207698, Elapsed Time = 1.8354823999998189 seconds
Trial 18, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 18, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[0]	validation-logloss:0.66396	validation-auc:0.90215	validation-aucpr:0.89116
[1]	validation-logloss:0.63839	validation-auc:0.91495	validation-aucpr:0.90589
[2]	validation-logloss:0.60834	validation-auc:0.94015	validation-aucpr:0.93914
[3]	validation-logloss:0.58667	validation-auc:0.93817	validation-aucpr:0.93790
[4]	validation-logloss:0.56317	validation-auc:0.94234	validation-aucpr:0.94322
[5]	validation-logloss:0.54371	validation-auc:0.94296	validation-aucpr:0.94273
[6]	validation-logloss:0.52768	validation-auc:0.94360	validation-aucpr:0.94403
[7]	validation-logloss:0.51333	validation-auc:0.94263	validation-aucpr:0.94271
[8]	validation-logloss:0.49840	validation-auc:0.94278	validation-aucpr:0.94299
[9]	validation-logloss:0.48582	validation-auc:0.94358	validation-aucpr:0.94625
[10]	validation-logloss:0.47275	validation-auc:0.94369	validation-aucpr:0.94599
[11]	validation-logloss:0.45827	validation-auc:0.94465	validation-aucpr:0.94708
[12]	validation-logloss:0.44856	validation-auc:0.94487	validation-aucpr:0.94704
[13]	validation-logloss:0.43952	validation-auc:0.94487	validation-aucpr:0.94714
[14]	validation-logloss:0.42983	validation-auc:0.94437	validation-aucpr:0.94624
[15]	validation-logloss:0.42206	validation-auc:0.94433	validation-aucpr:0.94602
[16]	validation-logloss:0.41155	validation-auc:0.94514	validation-aucpr:0.94630
[17]	validation-logloss:0.40252	validation-auc:0.94613	validation-aucpr:0.95024
[18]	validation-logloss:0.39595	validation-auc:0.94658	validation-aucpr:0.95086
[19]	validation-logloss:0.38673	validation-auc:0.94734	validation-aucpr:0.95204
[20]	validation-logloss:0.37807	validation-auc:0.94778	validation-aucpr:0.95261
[21]	validation-logloss:0.37049	validation-auc:0.94806	validation-aucpr:0.95308
[22]	validation-logloss:0.36365	validation-auc:0.94825	validation-aucpr:0.95353
[23]	validation-logloss:0.35933	validation-auc:0.94777	validation-aucpr:0.95301
[24]	validation-logloss:0.35311	validation-auc:0.94848	validation-aucpr:0.95338
[25]	validation-logloss:0.34717	validation-auc:0.94943	validation-aucpr:0.95398
{'best_iteration': '25', 'best_score': '0.9539815995939754'}
Trial 18, Fold 2: Log loss = 0.34717386262269745, Average precision = 0.9518878146326276, ROC-AUC = 0.9494315679704128, Elapsed Time = 2.105443700000251 seconds
Trial 18, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 18, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[0]	validation-logloss:0.66343	validation-auc:0.90742	validation-aucpr:0.90215
[1]	validation-logloss:0.63726	validation-auc:0.92023	validation-aucpr:0.91689
[2]	validation-logloss:0.61301	validation-auc:0.92445	validation-aucpr:0.92579
[3]	validation-logloss:0.58562	validation-auc:0.94587	validation-aucpr:0.95187
[4]	validation-logloss:0.56645	validation-auc:0.94688	validation-aucpr:0.95475
[5]	validation-logloss:0.54810	validation-auc:0.94739	validation-aucpr:0.95548
[6]	validation-logloss:0.52958	validation-auc:0.94960	validation-aucpr:0.95656
[7]	validation-logloss:0.51453	validation-auc:0.95045	validation-aucpr:0.95695
[8]	validation-logloss:0.49960	validation-auc:0.94924	validation-aucpr:0.95620
[9]	validation-logloss:0.48648	validation-auc:0.94946	validation-aucpr:0.95648
[10]	validation-logloss:0.47340	validation-auc:0.94926	validation-aucpr:0.95610
[11]	validation-logloss:0.45838	validation-auc:0.95025	validation-aucpr:0.95725
[12]	validation-logloss:0.44830	validation-auc:0.95108	validation-aucpr:0.95767
[13]	validation-logloss:0.43866	validation-auc:0.95080	validation-aucpr:0.95763
[14]	validation-logloss:0.42867	validation-auc:0.95312	validation-aucpr:0.95869
[15]	validation-logloss:0.41645	validation-auc:0.95364	validation-aucpr:0.95942
[16]	validation-logloss:0.40939	validation-auc:0.95233	validation-aucpr:0.95869
[17]	validation-logloss:0.40296	validation-auc:0.95232	validation-aucpr:0.95871
[18]	validation-logloss:0.39653	validation-auc:0.95205	validation-aucpr:0.95858
[19]	validation-logloss:0.38636	validation-auc:0.95208	validation-aucpr:0.95869
[20]	validation-logloss:0.37795	validation-auc:0.95269	validation-aucpr:0.95915
[21]	validation-logloss:0.37281	validation-auc:0.95225	validation-aucpr:0.95900
[22]	validation-logloss:0.36516	validation-auc:0.95242	validation-aucpr:0.95941
[23]	validation-logloss:0.35774	validation-auc:0.95275	validation-aucpr:0.95993
[24]	validation-logloss:0.35191	validation-auc:0.95430	validation-aucpr:0.96066
[25]	validation-logloss:0.34656	validation-auc:0.95432	validation-aucpr:0.96064
{'best_iteration': '24', 'best_score': '0.9606556106886179'}
Trial 18, Fold 3: Log loss = 0.346561476570241, Average precision = 0.9575752170927161, ROC-AUC = 0.9543186464061794, Elapsed Time = 2.0811799000002793 seconds
Trial 18, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 18, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[0]	validation-logloss:0.66376	validation-auc:0.90244	validation-aucpr:0.89434
[1]	validation-logloss:0.63805	validation-auc:0.91080	validation-aucpr:0.90586
[2]	validation-logloss:0.60809	validation-auc:0.93520	validation-aucpr:0.94036
[3]	validation-logloss:0.58689	validation-auc:0.93533	validation-aucpr:0.94122
[4]	validation-logloss:0.56326	validation-auc:0.93942	validation-aucpr:0.94666
[5]	validation-logloss:0.54330	validation-auc:0.94206	validation-aucpr:0.94668
[6]	validation-logloss:0.52710	validation-auc:0.94376	validation-aucpr:0.94962
[7]	validation-logloss:0.51238	validation-auc:0.94520	validation-aucpr:0.95045
[8]	validation-logloss:0.49729	validation-auc:0.94402	validation-aucpr:0.94893
[9]	validation-logloss:0.48228	validation-auc:0.94413	validation-aucpr:0.94771
[10]	validation-logloss:0.46944	validation-auc:0.94460	validation-aucpr:0.94825
[11]	validation-logloss:0.45466	validation-auc:0.94543	validation-aucpr:0.94882
[12]	validation-logloss:0.44470	validation-auc:0.94546	validation-aucpr:0.94878
[13]	validation-logloss:0.43541	validation-auc:0.94536	validation-aucpr:0.95016
[14]	validation-logloss:0.42562	validation-auc:0.94677	validation-aucpr:0.95065
[15]	validation-logloss:0.41795	validation-auc:0.94678	validation-aucpr:0.95062
[16]	validation-logloss:0.40750	validation-auc:0.94818	validation-aucpr:0.95163
[17]	validation-logloss:0.39838	validation-auc:0.95058	validation-aucpr:0.95601
[18]	validation-logloss:0.39167	validation-auc:0.95084	validation-aucpr:0.95647
[19]	validation-logloss:0.38258	validation-auc:0.95178	validation-aucpr:0.95771
[20]	validation-logloss:0.37439	validation-auc:0.95193	validation-aucpr:0.95781
[21]	validation-logloss:0.36659	validation-auc:0.95239	validation-aucpr:0.95863
[22]	validation-logloss:0.35969	validation-auc:0.95268	validation-aucpr:0.95894
[23]	validation-logloss:0.35511	validation-auc:0.95094	validation-aucpr:0.95747
[24]	validation-logloss:0.34843	validation-auc:0.95201	validation-aucpr:0.95902
[25]	validation-logloss:0.34473	validation-auc:0.95119	validation-aucpr:0.95824
{'best_iteration': '24', 'best_score': '0.9590165483824703'}
Trial 18, Fold 4: Log loss = 0.34472849653115334, Average precision = 0.9551434753025292, ROC-AUC = 0.9511935908257451, Elapsed Time = 2.024505199999112 seconds
Trial 18, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 18, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[0]	validation-logloss:0.66397	validation-auc:0.90168	validation-aucpr:0.89628
[1]	validation-logloss:0.63881	validation-auc:0.90722	validation-aucpr:0.90642
[2]	validation-logloss:0.60858	validation-auc:0.93393	validation-aucpr:0.94129
[3]	validation-logloss:0.58734	validation-auc:0.93425	validation-aucpr:0.94134
[4]	validation-logloss:0.56411	validation-auc:0.93753	validation-aucpr:0.94571
[5]	validation-logloss:0.54450	validation-auc:0.94128	validation-aucpr:0.94725
[6]	validation-logloss:0.52910	validation-auc:0.94241	validation-aucpr:0.94920
[7]	validation-logloss:0.51425	validation-auc:0.94460	validation-aucpr:0.95063
[8]	validation-logloss:0.49960	validation-auc:0.94427	validation-aucpr:0.95206
[9]	validation-logloss:0.48696	validation-auc:0.94469	validation-aucpr:0.95235
[10]	validation-logloss:0.47403	validation-auc:0.94451	validation-aucpr:0.95195
[11]	validation-logloss:0.46377	validation-auc:0.94506	validation-aucpr:0.95277
[12]	validation-logloss:0.45471	validation-auc:0.94534	validation-aucpr:0.95304
[13]	validation-logloss:0.44440	validation-auc:0.94527	validation-aucpr:0.95292
[14]	validation-logloss:0.43473	validation-auc:0.94494	validation-aucpr:0.95256
[15]	validation-logloss:0.42731	validation-auc:0.94605	validation-aucpr:0.95298
[16]	validation-logloss:0.41653	validation-auc:0.94699	validation-aucpr:0.95416
[17]	validation-logloss:0.41046	validation-auc:0.94688	validation-aucpr:0.95400
[18]	validation-logloss:0.40393	validation-auc:0.94721	validation-aucpr:0.95437
[19]	validation-logloss:0.39421	validation-auc:0.94785	validation-aucpr:0.95517
[20]	validation-logloss:0.38551	validation-auc:0.94795	validation-aucpr:0.95517
[21]	validation-logloss:0.37731	validation-auc:0.94846	validation-aucpr:0.95576
[22]	validation-logloss:0.37014	validation-auc:0.94856	validation-aucpr:0.95583
[23]	validation-logloss:0.36571	validation-auc:0.94839	validation-aucpr:0.95603
[24]	validation-logloss:0.35931	validation-auc:0.94862	validation-aucpr:0.95636
[25]	validation-logloss:0.35366	validation-auc:0.94931	validation-aucpr:0.95701
{'best_iteration': '25', 'best_score': '0.9570113734803808'}
Trial 18, Fold 5: Log loss = 0.35366369208012155, Average precision = 0.9551741061597449, ROC-AUC = 0.9493108893967263, Elapsed Time = 2.0203742000012426 seconds
Optimization Progress:  19%|#9        | 19/100 [34:27<1:35:04, 70.43s/it]
Trial 19, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 19, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[0]	validation-logloss:0.66687	validation-auc:0.95022	validation-aucpr:0.94356
[1]	validation-logloss:0.64264	validation-auc:0.96143	validation-aucpr:0.96320
[2]	validation-logloss:0.62001	validation-auc:0.96411	validation-aucpr:0.96722
[3]	validation-logloss:0.60135	validation-auc:0.96370	validation-aucpr:0.96627
[4]	validation-logloss:0.58193	validation-auc:0.96485	validation-aucpr:0.96795
[5]	validation-logloss:0.56536	validation-auc:0.96431	validation-aucpr:0.97006
[6]	validation-logloss:0.55004	validation-auc:0.96422	validation-aucpr:0.96988
[7]	validation-logloss:0.53504	validation-auc:0.96411	validation-aucpr:0.96973
[8]	validation-logloss:0.52199	validation-auc:0.96384	validation-aucpr:0.96953
[9]	validation-logloss:0.50660	validation-auc:0.96426	validation-aucpr:0.96991
[10]	validation-logloss:0.49394	validation-auc:0.96475	validation-aucpr:0.97020
[11]	validation-logloss:0.48017	validation-auc:0.96500	validation-aucpr:0.97073
[12]	validation-logloss:0.46709	validation-auc:0.96545	validation-aucpr:0.97107
{'best_iteration': '12', 'best_score': '0.9710723987838434'}
Trial 19, Fold 1: Log loss = 0.4670901924666118, Average precision = 0.9709924434484682, ROC-AUC = 0.9654467810661351, Elapsed Time = 0.4070884999982809 seconds
Trial 19, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 19, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[0]	validation-logloss:0.66707	validation-auc:0.95571	validation-aucpr:0.94803
[1]	validation-logloss:0.64327	validation-auc:0.96257	validation-aucpr:0.96563
[2]	validation-logloss:0.62058	validation-auc:0.96486	validation-aucpr:0.96956
[3]	validation-logloss:0.59939	validation-auc:0.96540	validation-aucpr:0.97003
[4]	validation-logloss:0.58172	validation-auc:0.96602	validation-aucpr:0.97049
[5]	validation-logloss:0.56536	validation-auc:0.96716	validation-aucpr:0.97131
[6]	validation-logloss:0.54724	validation-auc:0.96808	validation-aucpr:0.97207
[7]	validation-logloss:0.53238	validation-auc:0.96789	validation-aucpr:0.97190
[8]	validation-logloss:0.51629	validation-auc:0.96784	validation-aucpr:0.97193
[9]	validation-logloss:0.50115	validation-auc:0.96809	validation-aucpr:0.97212
[10]	validation-logloss:0.48679	validation-auc:0.96844	validation-aucpr:0.97242
[11]	validation-logloss:0.47526	validation-auc:0.96839	validation-aucpr:0.97235
[12]	validation-logloss:0.46397	validation-auc:0.96831	validation-aucpr:0.97227
{'best_iteration': '10', 'best_score': '0.9724188374268375'}
Trial 19, Fold 2: Log loss = 0.46397375509965866, Average precision = 0.9720977182252877, ROC-AUC = 0.9683108134561439, Elapsed Time = 0.4283958000014536 seconds
Trial 19, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 19, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[0]	validation-logloss:0.66689	validation-auc:0.95270	validation-aucpr:0.93948
[1]	validation-logloss:0.64245	validation-auc:0.96317	validation-aucpr:0.96668
[2]	validation-logloss:0.62016	validation-auc:0.96576	validation-aucpr:0.97077
[3]	validation-logloss:0.60098	validation-auc:0.96729	validation-aucpr:0.97206
[4]	validation-logloss:0.58192	validation-auc:0.96756	validation-aucpr:0.97243
[5]	validation-logloss:0.56555	validation-auc:0.96766	validation-aucpr:0.97257
[6]	validation-logloss:0.54955	validation-auc:0.96798	validation-aucpr:0.97310
[7]	validation-logloss:0.53546	validation-auc:0.96799	validation-aucpr:0.97279
[8]	validation-logloss:0.52131	validation-auc:0.96808	validation-aucpr:0.97284
[9]	validation-logloss:0.50854	validation-auc:0.96782	validation-aucpr:0.97257
[10]	validation-logloss:0.49354	validation-auc:0.96826	validation-aucpr:0.97303
[11]	validation-logloss:0.47939	validation-auc:0.96866	validation-aucpr:0.97336
[12]	validation-logloss:0.46681	validation-auc:0.96887	validation-aucpr:0.97360
{'best_iteration': '12', 'best_score': '0.9735955391456544'}
Trial 19, Fold 3: Log loss = 0.4668126306623334, Average precision = 0.9733190556635031, ROC-AUC = 0.9688660991177973, Elapsed Time = 0.5241953000004287 seconds
Trial 19, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 19, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[0]	validation-logloss:0.66686	validation-auc:0.95712	validation-aucpr:0.95982
[1]	validation-logloss:0.64275	validation-auc:0.96260	validation-aucpr:0.96550
[2]	validation-logloss:0.61980	validation-auc:0.96674	validation-aucpr:0.97187
[3]	validation-logloss:0.60129	validation-auc:0.96496	validation-aucpr:0.97056
[4]	validation-logloss:0.58343	validation-auc:0.96491	validation-aucpr:0.97047
[5]	validation-logloss:0.56449	validation-auc:0.96523	validation-aucpr:0.97089
[6]	validation-logloss:0.54837	validation-auc:0.96564	validation-aucpr:0.97117
[7]	validation-logloss:0.53171	validation-auc:0.96572	validation-aucpr:0.97123
[8]	validation-logloss:0.51810	validation-auc:0.96609	validation-aucpr:0.97154
[9]	validation-logloss:0.50495	validation-auc:0.96592	validation-aucpr:0.97137
[10]	validation-logloss:0.49034	validation-auc:0.96640	validation-aucpr:0.97185
[11]	validation-logloss:0.47681	validation-auc:0.96642	validation-aucpr:0.97190
[12]	validation-logloss:0.46528	validation-auc:0.96672	validation-aucpr:0.97205
{'best_iteration': '12', 'best_score': '0.9720525847713024'}
Trial 19, Fold 4: Log loss = 0.46527833454358125, Average precision = 0.9719904271170401, ROC-AUC = 0.9667181848203704, Elapsed Time = 0.5389599999998609 seconds
Trial 19, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 19, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[0]	validation-logloss:0.66701	validation-auc:0.95208	validation-aucpr:0.94348
[1]	validation-logloss:0.64323	validation-auc:0.96138	validation-aucpr:0.96223
[2]	validation-logloss:0.62083	validation-auc:0.96242	validation-aucpr:0.96299
[3]	validation-logloss:0.60224	validation-auc:0.96229	validation-aucpr:0.96498
[4]	validation-logloss:0.58241	validation-auc:0.96338	validation-aucpr:0.96563
[5]	validation-logloss:0.56594	validation-auc:0.96345	validation-aucpr:0.96828
[6]	validation-logloss:0.54808	validation-auc:0.96453	validation-aucpr:0.96897
[7]	validation-logloss:0.53136	validation-auc:0.96520	validation-aucpr:0.96955
[8]	validation-logloss:0.51735	validation-auc:0.96536	validation-aucpr:0.96962
[9]	validation-logloss:0.50206	validation-auc:0.96621	validation-aucpr:0.97033
[10]	validation-logloss:0.48779	validation-auc:0.96686	validation-aucpr:0.97107
[11]	validation-logloss:0.47446	validation-auc:0.96715	validation-aucpr:0.97126
[12]	validation-logloss:0.46176	validation-auc:0.96744	validation-aucpr:0.97144
{'best_iteration': '12', 'best_score': '0.9714434035775554'}
Trial 19, Fold 5: Log loss = 0.46175643038908776, Average precision = 0.9713472304358971, ROC-AUC = 0.9674423945153559, Elapsed Time = 0.4952616999999009 seconds
Optimization Progress:  20%|##        | 20/100 [34:37<1:09:46, 52.33s/it]
Trial 20, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 20, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[18:33:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.66078	validation-auc:0.93642	validation-aucpr:0.93490
[18:33:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.63086	validation-auc:0.94541	validation-aucpr:0.94612
[18:33:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.60533	validation-auc:0.94667	validation-aucpr:0.94864
[18:33:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.57494	validation-auc:0.96064	validation-aucpr:0.96468
[18:33:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.55402	validation-auc:0.95961	validation-aucpr:0.96464
[18:33:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5]	validation-logloss:0.53391	validation-auc:0.95974	validation-aucpr:0.96486
[18:33:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6]	validation-logloss:0.51407	validation-auc:0.96001	validation-aucpr:0.96500
[18:33:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7]	validation-logloss:0.49586	validation-auc:0.96079	validation-aucpr:0.96561
[18:33:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8]	validation-logloss:0.48017	validation-auc:0.96084	validation-aucpr:0.96560
[18:33:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9]	validation-logloss:0.46509	validation-auc:0.96108	validation-aucpr:0.96617
[18:33:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10]	validation-logloss:0.45107	validation-auc:0.96107	validation-aucpr:0.96614
[18:33:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11]	validation-logloss:0.43329	validation-auc:0.96356	validation-aucpr:0.96881
[18:33:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12]	validation-logloss:0.42068	validation-auc:0.96355	validation-aucpr:0.96875
[18:33:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13]	validation-logloss:0.41054	validation-auc:0.96313	validation-aucpr:0.96844
[18:33:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14]	validation-logloss:0.40002	validation-auc:0.96317	validation-aucpr:0.96835
[18:33:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15]	validation-logloss:0.39087	validation-auc:0.96302	validation-aucpr:0.96811
[18:33:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16]	validation-logloss:0.38129	validation-auc:0.96306	validation-aucpr:0.96809
[18:33:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17]	validation-logloss:0.37278	validation-auc:0.96305	validation-aucpr:0.96805
[18:33:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18]	validation-logloss:0.36113	validation-auc:0.96437	validation-aucpr:0.96946
[18:33:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19]	validation-logloss:0.35383	validation-auc:0.96429	validation-aucpr:0.96925
[18:33:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20]	validation-logloss:0.34689	validation-auc:0.96440	validation-aucpr:0.96929
[18:33:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21]	validation-logloss:0.34052	validation-auc:0.96455	validation-aucpr:0.96938
[18:33:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22]	validation-logloss:0.33421	validation-auc:0.96457	validation-aucpr:0.96940
[18:33:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23]	validation-logloss:0.32819	validation-auc:0.96479	validation-aucpr:0.96961
[18:33:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24]	validation-logloss:0.32325	validation-auc:0.96472	validation-aucpr:0.96951
[18:33:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25]	validation-logloss:0.31785	validation-auc:0.96483	validation-aucpr:0.96957
[18:33:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26]	validation-logloss:0.31041	validation-auc:0.96546	validation-aucpr:0.97028
[18:33:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27]	validation-logloss:0.30611	validation-auc:0.96549	validation-aucpr:0.97029
[18:33:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28]	validation-logloss:0.30186	validation-auc:0.96545	validation-aucpr:0.97023
[18:33:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29]	validation-logloss:0.29810	validation-auc:0.96541	validation-aucpr:0.97024
[18:33:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30]	validation-logloss:0.29178	validation-auc:0.96594	validation-aucpr:0.97077
[18:33:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31]	validation-logloss:0.28543	validation-auc:0.96636	validation-aucpr:0.97122
[18:33:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32]	validation-logloss:0.28206	validation-auc:0.96644	validation-aucpr:0.97130
[18:33:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33]	validation-logloss:0.27896	validation-auc:0.96642	validation-aucpr:0.97130
[18:33:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34]	validation-logloss:0.27338	validation-auc:0.96681	validation-aucpr:0.97168
[18:33:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35]	validation-logloss:0.27077	validation-auc:0.96678	validation-aucpr:0.97166
[18:33:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36]	validation-logloss:0.26814	validation-auc:0.96688	validation-aucpr:0.97174
[18:33:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37]	validation-logloss:0.26585	validation-auc:0.96692	validation-aucpr:0.97175
[18:33:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38]	validation-logloss:0.26370	validation-auc:0.96694	validation-aucpr:0.97174
[18:33:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39]	validation-logloss:0.26158	validation-auc:0.96692	validation-aucpr:0.97173
[18:33:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40]	validation-logloss:0.25936	validation-auc:0.96701	validation-aucpr:0.97177
[18:33:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41]	validation-logloss:0.25734	validation-auc:0.96702	validation-aucpr:0.97172
[18:33:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42]	validation-logloss:0.25586	validation-auc:0.96699	validation-aucpr:0.97169
[18:33:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43]	validation-logloss:0.25194	validation-auc:0.96735	validation-aucpr:0.97211
[18:33:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44]	validation-logloss:0.25013	validation-auc:0.96746	validation-aucpr:0.97234
[18:33:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45]	validation-logloss:0.24669	validation-auc:0.96770	validation-aucpr:0.97263
[18:33:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46]	validation-logloss:0.24553	validation-auc:0.96762	validation-aucpr:0.97256
{'best_iteration': '45', 'best_score': '0.9726338681129421'}
Trial 20, Fold 1: Log loss = 0.24552598115549665, Average precision = 0.9725694283267494, ROC-AUC = 0.9676181335226051, Elapsed Time = 6.571905600001628 seconds
Trial 20, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 20, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[18:33:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.66057	validation-auc:0.93451	validation-aucpr:0.93073
[18:33:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.63085	validation-auc:0.94389	validation-aucpr:0.94285
[18:33:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.60271	validation-auc:0.95107	validation-aucpr:0.95059
[18:33:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.57773	validation-auc:0.95352	validation-aucpr:0.95378
[18:33:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.55508	validation-auc:0.95537	validation-aucpr:0.95697
[18:33:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5]	validation-logloss:0.53442	validation-auc:0.95650	validation-aucpr:0.95855
[18:33:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6]	validation-logloss:0.51557	validation-auc:0.95651	validation-aucpr:0.95845
[18:33:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7]	validation-logloss:0.49883	validation-auc:0.95678	validation-aucpr:0.95940
[18:33:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8]	validation-logloss:0.47874	validation-auc:0.96128	validation-aucpr:0.96481
[18:33:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9]	validation-logloss:0.45925	validation-auc:0.96369	validation-aucpr:0.96760
[18:33:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10]	validation-logloss:0.44154	validation-auc:0.96490	validation-aucpr:0.96880
[18:33:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11]	validation-logloss:0.42830	validation-auc:0.96492	validation-aucpr:0.96885
[18:33:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12]	validation-logloss:0.41591	validation-auc:0.96504	validation-aucpr:0.96889
[18:33:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13]	validation-logloss:0.40452	validation-auc:0.96509	validation-aucpr:0.96886
[18:33:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14]	validation-logloss:0.39408	validation-auc:0.96521	validation-aucpr:0.96893
[18:33:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15]	validation-logloss:0.38451	validation-auc:0.96535	validation-aucpr:0.96893
[18:33:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16]	validation-logloss:0.37482	validation-auc:0.96550	validation-aucpr:0.96910
[18:33:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17]	validation-logloss:0.36309	validation-auc:0.96622	validation-aucpr:0.96992
[18:33:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18]	validation-logloss:0.35479	validation-auc:0.96626	validation-aucpr:0.96988
[18:33:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19]	validation-logloss:0.34711	validation-auc:0.96620	validation-aucpr:0.96991
[18:33:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20]	validation-logloss:0.33973	validation-auc:0.96637	validation-aucpr:0.97002
[18:33:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21]	validation-logloss:0.33404	validation-auc:0.96625	validation-aucpr:0.96975
[18:33:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22]	validation-logloss:0.32785	validation-auc:0.96626	validation-aucpr:0.96971
[18:33:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23]	validation-logloss:0.31884	validation-auc:0.96690	validation-aucpr:0.97035
[18:33:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24]	validation-logloss:0.31320	validation-auc:0.96702	validation-aucpr:0.97050
[18:33:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25]	validation-logloss:0.30801	validation-auc:0.96707	validation-aucpr:0.97050
[18:33:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26]	validation-logloss:0.30336	validation-auc:0.96715	validation-aucpr:0.97077
[18:33:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27]	validation-logloss:0.29657	validation-auc:0.96759	validation-aucpr:0.97115
[18:33:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28]	validation-logloss:0.29018	validation-auc:0.96785	validation-aucpr:0.97144
[18:33:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29]	validation-logloss:0.28390	validation-auc:0.96818	validation-aucpr:0.97173
[18:33:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30]	validation-logloss:0.27846	validation-auc:0.96848	validation-aucpr:0.97198
[18:33:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31]	validation-logloss:0.27462	validation-auc:0.96870	validation-aucpr:0.97214
[18:33:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32]	validation-logloss:0.27158	validation-auc:0.96864	validation-aucpr:0.97203
[18:33:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33]	validation-logloss:0.26840	validation-auc:0.96888	validation-aucpr:0.97220
[18:33:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34]	validation-logloss:0.26537	validation-auc:0.96899	validation-aucpr:0.97226
[18:33:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35]	validation-logloss:0.26253	validation-auc:0.96903	validation-aucpr:0.97228
[18:33:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36]	validation-logloss:0.26018	validation-auc:0.96891	validation-aucpr:0.97220
[18:33:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37]	validation-logloss:0.25560	validation-auc:0.96918	validation-aucpr:0.97246
[18:33:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38]	validation-logloss:0.25108	validation-auc:0.96952	validation-aucpr:0.97278
[18:33:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39]	validation-logloss:0.24915	validation-auc:0.96949	validation-aucpr:0.97273
[18:33:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40]	validation-logloss:0.24581	validation-auc:0.96956	validation-aucpr:0.97279
[18:33:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41]	validation-logloss:0.24229	validation-auc:0.96974	validation-aucpr:0.97296
[18:33:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42]	validation-logloss:0.23889	validation-auc:0.96996	validation-aucpr:0.97312
[18:33:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43]	validation-logloss:0.23603	validation-auc:0.97014	validation-aucpr:0.97337
[18:33:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44]	validation-logloss:0.23436	validation-auc:0.97013	validation-aucpr:0.97335
[18:33:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45]	validation-logloss:0.23255	validation-auc:0.97022	validation-aucpr:0.97340
[18:33:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46]	validation-logloss:0.23098	validation-auc:0.97024	validation-aucpr:0.97340
{'best_iteration': '45', 'best_score': '0.9734037572532945'}
Trial 20, Fold 2: Log loss = 0.2309817435022436, Average precision = 0.973403500713181, ROC-AUC = 0.9702374792143471, Elapsed Time = 8.763767099999313 seconds
Trial 20, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 20, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[18:33:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.66043	validation-auc:0.93767	validation-aucpr:0.93605
[18:33:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.63148	validation-auc:0.94698	validation-aucpr:0.94963
[18:33:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.59849	validation-auc:0.96370	validation-aucpr:0.96786
[18:33:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.57263	validation-auc:0.96510	validation-aucpr:0.96889
[18:33:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.55136	validation-auc:0.96347	validation-aucpr:0.96780
[18:33:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5]	validation-logloss:0.53029	validation-auc:0.96312	validation-aucpr:0.96742
[18:33:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6]	validation-logloss:0.50595	validation-auc:0.96597	validation-aucpr:0.97052
[18:33:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7]	validation-logloss:0.48783	validation-auc:0.96639	validation-aucpr:0.97070
[18:33:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8]	validation-logloss:0.47152	validation-auc:0.96622	validation-aucpr:0.97036
[18:33:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9]	validation-logloss:0.45396	validation-auc:0.96672	validation-aucpr:0.97116
[18:33:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10]	validation-logloss:0.43662	validation-auc:0.96727	validation-aucpr:0.97180
[18:33:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11]	validation-logloss:0.42007	validation-auc:0.96788	validation-aucpr:0.97239
[18:33:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12]	validation-logloss:0.40847	validation-auc:0.96765	validation-aucpr:0.97220
[18:33:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13]	validation-logloss:0.39838	validation-auc:0.96727	validation-aucpr:0.97187
[18:33:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14]	validation-logloss:0.38811	validation-auc:0.96719	validation-aucpr:0.97180
[18:33:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15]	validation-logloss:0.37846	validation-auc:0.96699	validation-aucpr:0.97162
[18:33:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16]	validation-logloss:0.36716	validation-auc:0.96707	validation-aucpr:0.97180
[18:33:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17]	validation-logloss:0.35929	validation-auc:0.96701	validation-aucpr:0.97173
[18:33:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18]	validation-logloss:0.34863	validation-auc:0.96734	validation-aucpr:0.97201
[18:33:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19]	validation-logloss:0.34035	validation-auc:0.96759	validation-aucpr:0.97219
[18:33:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20]	validation-logloss:0.33364	validation-auc:0.96764	validation-aucpr:0.97218
[18:33:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21]	validation-logloss:0.32737	validation-auc:0.96765	validation-aucpr:0.97230
[18:33:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22]	validation-logloss:0.31893	validation-auc:0.96801	validation-aucpr:0.97264
[18:33:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23]	validation-logloss:0.31297	validation-auc:0.96805	validation-aucpr:0.97266
[18:33:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24]	validation-logloss:0.30825	validation-auc:0.96797	validation-aucpr:0.97262
[18:33:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25]	validation-logloss:0.30167	validation-auc:0.96795	validation-aucpr:0.97262
[18:33:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26]	validation-logloss:0.29690	validation-auc:0.96803	validation-aucpr:0.97268
[18:33:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27]	validation-logloss:0.29274	validation-auc:0.96804	validation-aucpr:0.97270
[18:33:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28]	validation-logloss:0.28832	validation-auc:0.96809	validation-aucpr:0.97272
[18:33:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29]	validation-logloss:0.28454	validation-auc:0.96806	validation-aucpr:0.97268
[18:33:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30]	validation-logloss:0.27905	validation-auc:0.96818	validation-aucpr:0.97282
[18:33:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31]	validation-logloss:0.27555	validation-auc:0.96825	validation-aucpr:0.97283
[18:33:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32]	validation-logloss:0.27025	validation-auc:0.96855	validation-aucpr:0.97309
[18:33:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33]	validation-logloss:0.26758	validation-auc:0.96851	validation-aucpr:0.97301
[18:33:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34]	validation-logloss:0.26318	validation-auc:0.96859	validation-aucpr:0.97307
[18:33:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35]	validation-logloss:0.25998	validation-auc:0.96870	validation-aucpr:0.97314
[18:33:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36]	validation-logloss:0.25737	validation-auc:0.96874	validation-aucpr:0.97318
[18:33:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37]	validation-logloss:0.25359	validation-auc:0.96880	validation-aucpr:0.97327
[18:33:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38]	validation-logloss:0.25041	validation-auc:0.96878	validation-aucpr:0.97328
[18:33:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39]	validation-logloss:0.24807	validation-auc:0.96887	validation-aucpr:0.97334
[18:33:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40]	validation-logloss:0.24425	validation-auc:0.96910	validation-aucpr:0.97353
[18:33:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41]	validation-logloss:0.24156	validation-auc:0.96916	validation-aucpr:0.97360
[18:33:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42]	validation-logloss:0.23941	validation-auc:0.96928	validation-aucpr:0.97367
[18:33:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43]	validation-logloss:0.23749	validation-auc:0.96935	validation-aucpr:0.97370
[18:33:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44]	validation-logloss:0.23571	validation-auc:0.96933	validation-aucpr:0.97367
[18:33:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45]	validation-logloss:0.23298	validation-auc:0.96947	validation-aucpr:0.97382
[18:33:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46]	validation-logloss:0.23158	validation-auc:0.96949	validation-aucpr:0.97380
{'best_iteration': '45', 'best_score': '0.9738152691478276'}
Trial 20, Fold 3: Log loss = 0.23158353629015746, Average precision = 0.9738055826676788, ROC-AUC = 0.9694872256358891, Elapsed Time = 7.577756599999702 seconds
Trial 20, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 20, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[18:33:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.66059	validation-auc:0.93175	validation-aucpr:0.93407
[18:33:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.62531	validation-auc:0.95875	validation-aucpr:0.96446
[18:33:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.59299	validation-auc:0.96314	validation-aucpr:0.96873
[18:33:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.56817	validation-auc:0.96340	validation-aucpr:0.96892
[18:33:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.54561	validation-auc:0.96322	validation-aucpr:0.96887
[18:33:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5]	validation-logloss:0.52424	validation-auc:0.96346	validation-aucpr:0.96901
[18:33:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6]	validation-logloss:0.50568	validation-auc:0.96356	validation-aucpr:0.96921
[18:33:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7]	validation-logloss:0.48917	validation-auc:0.96354	validation-aucpr:0.96910
[18:33:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8]	validation-logloss:0.47320	validation-auc:0.96330	validation-aucpr:0.96888
[18:33:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9]	validation-logloss:0.45895	validation-auc:0.96316	validation-aucpr:0.96866
[18:33:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10]	validation-logloss:0.44456	validation-auc:0.96317	validation-aucpr:0.96864
[18:33:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11]	validation-logloss:0.43174	validation-auc:0.96308	validation-aucpr:0.96849
[18:33:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12]	validation-logloss:0.42149	validation-auc:0.96246	validation-aucpr:0.96796
[18:33:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13]	validation-logloss:0.41152	validation-auc:0.96204	validation-aucpr:0.96767
[18:33:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14]	validation-logloss:0.40075	validation-auc:0.96221	validation-aucpr:0.96784
[18:33:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15]	validation-logloss:0.39058	validation-auc:0.96221	validation-aucpr:0.96788
[18:33:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16]	validation-logloss:0.38161	validation-auc:0.96208	validation-aucpr:0.96775
[18:33:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17]	validation-logloss:0.36959	validation-auc:0.96324	validation-aucpr:0.96900
[18:33:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18]	validation-logloss:0.36153	validation-auc:0.96324	validation-aucpr:0.96899
[18:33:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19]	validation-logloss:0.35090	validation-auc:0.96381	validation-aucpr:0.96958
[18:33:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20]	validation-logloss:0.34386	validation-auc:0.96377	validation-aucpr:0.96956
[18:33:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21]	validation-logloss:0.33707	validation-auc:0.96389	validation-aucpr:0.96965
[18:34:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22]	validation-logloss:0.33066	validation-auc:0.96409	validation-aucpr:0.96982
[18:34:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23]	validation-logloss:0.32469	validation-auc:0.96423	validation-aucpr:0.96990
[18:34:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24]	validation-logloss:0.31629	validation-auc:0.96472	validation-aucpr:0.97041
[18:34:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25]	validation-logloss:0.30867	validation-auc:0.96517	validation-aucpr:0.97084
[18:34:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26]	validation-logloss:0.30353	validation-auc:0.96524	validation-aucpr:0.97085
[18:34:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27]	validation-logloss:0.29901	validation-auc:0.96515	validation-aucpr:0.97078
[18:34:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28]	validation-logloss:0.29504	validation-auc:0.96524	validation-aucpr:0.97082
[18:34:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29]	validation-logloss:0.29110	validation-auc:0.96534	validation-aucpr:0.97088
[18:34:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30]	validation-logloss:0.28529	validation-auc:0.96578	validation-aucpr:0.97128
[18:34:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31]	validation-logloss:0.28177	validation-auc:0.96574	validation-aucpr:0.97123
[18:34:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32]	validation-logloss:0.27887	validation-auc:0.96558	validation-aucpr:0.97111
[18:34:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33]	validation-logloss:0.27570	validation-auc:0.96552	validation-aucpr:0.97107
[18:34:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34]	validation-logloss:0.27340	validation-auc:0.96548	validation-aucpr:0.97102
[18:34:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35]	validation-logloss:0.26824	validation-auc:0.96583	validation-aucpr:0.97134
[18:34:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36]	validation-logloss:0.26525	validation-auc:0.96590	validation-aucpr:0.97140
[18:34:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37]	validation-logloss:0.26079	validation-auc:0.96616	validation-aucpr:0.97165
[18:34:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38]	validation-logloss:0.25639	validation-auc:0.96644	validation-aucpr:0.97192
[18:34:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39]	validation-logloss:0.25242	validation-auc:0.96668	validation-aucpr:0.97213
[18:34:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40]	validation-logloss:0.25020	validation-auc:0.96679	validation-aucpr:0.97220
[18:34:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41]	validation-logloss:0.24687	validation-auc:0.96694	validation-aucpr:0.97234
[18:34:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42]	validation-logloss:0.24522	validation-auc:0.96700	validation-aucpr:0.97237
[18:34:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43]	validation-logloss:0.24170	validation-auc:0.96721	validation-aucpr:0.97256
[18:34:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44]	validation-logloss:0.23974	validation-auc:0.96736	validation-aucpr:0.97267
[18:34:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45]	validation-logloss:0.23700	validation-auc:0.96738	validation-aucpr:0.97271
[18:34:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46]	validation-logloss:0.23554	validation-auc:0.96747	validation-aucpr:0.97278
{'best_iteration': '46', 'best_score': '0.9727819744692342'}
Trial 20, Fold 4: Log loss = 0.23554353868065628, Average precision = 0.9727860480148764, ROC-AUC = 0.9674674110793984, Elapsed Time = 6.664979299999686 seconds
Trial 20, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 20, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[18:34:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.66165	validation-auc:0.92788	validation-aucpr:0.92757
[18:34:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.63181	validation-auc:0.94335	validation-aucpr:0.94601
[18:34:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.60371	validation-auc:0.95083	validation-aucpr:0.95442
[18:34:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.57927	validation-auc:0.95115	validation-aucpr:0.95461
[18:34:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.55855	validation-auc:0.95127	validation-aucpr:0.95499
[18:34:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5]	validation-logloss:0.53778	validation-auc:0.95283	validation-aucpr:0.95735
[18:34:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6]	validation-logloss:0.51329	validation-auc:0.95969	validation-aucpr:0.96498
[18:34:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7]	validation-logloss:0.49186	validation-auc:0.96111	validation-aucpr:0.96654
[18:34:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8]	validation-logloss:0.47589	validation-auc:0.96102	validation-aucpr:0.96644
[18:34:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9]	validation-logloss:0.45758	validation-auc:0.96195	validation-aucpr:0.96740
[18:34:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10]	validation-logloss:0.44409	validation-auc:0.96202	validation-aucpr:0.96750
[18:34:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11]	validation-logloss:0.43087	validation-auc:0.96250	validation-aucpr:0.96791
[18:34:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12]	validation-logloss:0.41892	validation-auc:0.96251	validation-aucpr:0.96787
[18:34:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13]	validation-logloss:0.40452	validation-auc:0.96316	validation-aucpr:0.96852
[18:34:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14]	validation-logloss:0.39440	validation-auc:0.96327	validation-aucpr:0.96863
[18:34:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15]	validation-logloss:0.38193	validation-auc:0.96360	validation-aucpr:0.96896
[18:34:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16]	validation-logloss:0.37133	validation-auc:0.96382	validation-aucpr:0.96920
[18:34:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17]	validation-logloss:0.36336	validation-auc:0.96378	validation-aucpr:0.96918
[18:34:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18]	validation-logloss:0.35554	validation-auc:0.96403	validation-aucpr:0.96933
[18:34:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19]	validation-logloss:0.34756	validation-auc:0.96426	validation-aucpr:0.96949
[18:34:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20]	validation-logloss:0.34072	validation-auc:0.96419	validation-aucpr:0.96946
[18:34:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21]	validation-logloss:0.33458	validation-auc:0.96436	validation-aucpr:0.96956
[18:34:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22]	validation-logloss:0.32850	validation-auc:0.96446	validation-aucpr:0.96964
[18:34:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23]	validation-logloss:0.32289	validation-auc:0.96463	validation-aucpr:0.96972
[18:34:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24]	validation-logloss:0.31751	validation-auc:0.96462	validation-aucpr:0.96967
[18:34:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25]	validation-logloss:0.31247	validation-auc:0.96470	validation-aucpr:0.96967
[18:34:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26]	validation-logloss:0.30759	validation-auc:0.96487	validation-aucpr:0.96979
[18:34:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27]	validation-logloss:0.30344	validation-auc:0.96486	validation-aucpr:0.96973
[18:34:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28]	validation-logloss:0.29648	validation-auc:0.96529	validation-aucpr:0.97014
[18:34:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29]	validation-logloss:0.29227	validation-auc:0.96542	validation-aucpr:0.97023
[18:34:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30]	validation-logloss:0.28858	validation-auc:0.96542	validation-aucpr:0.97022
[18:34:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31]	validation-logloss:0.28453	validation-auc:0.96559	validation-aucpr:0.97033
[18:34:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32]	validation-logloss:0.28165	validation-auc:0.96552	validation-aucpr:0.97025
[18:34:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33]	validation-logloss:0.27877	validation-auc:0.96554	validation-aucpr:0.97021
[18:34:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34]	validation-logloss:0.27603	validation-auc:0.96544	validation-aucpr:0.97016
[18:34:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35]	validation-logloss:0.27345	validation-auc:0.96547	validation-aucpr:0.97021
[18:34:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36]	validation-logloss:0.27088	validation-auc:0.96554	validation-aucpr:0.97023
[18:34:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37]	validation-logloss:0.26844	validation-auc:0.96558	validation-aucpr:0.97026
[18:34:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38]	validation-logloss:0.26578	validation-auc:0.96571	validation-aucpr:0.97035
[18:34:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39]	validation-logloss:0.26146	validation-auc:0.96602	validation-aucpr:0.97064
[18:34:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40]	validation-logloss:0.25919	validation-auc:0.96609	validation-aucpr:0.97069
[18:34:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41]	validation-logloss:0.25703	validation-auc:0.96620	validation-aucpr:0.97075
[18:34:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42]	validation-logloss:0.25508	validation-auc:0.96626	validation-aucpr:0.97081
[18:34:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43]	validation-logloss:0.25343	validation-auc:0.96620	validation-aucpr:0.97075
[18:34:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44]	validation-logloss:0.25158	validation-auc:0.96636	validation-aucpr:0.97083
[18:34:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45]	validation-logloss:0.24978	validation-auc:0.96649	validation-aucpr:0.97099
[18:34:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46]	validation-logloss:0.24816	validation-auc:0.96653	validation-aucpr:0.97102
{'best_iteration': '46', 'best_score': '0.971019754688506'}
Trial 20, Fold 5: Log loss = 0.248159576737433, Average precision = 0.9710249427718667, ROC-AUC = 0.9665340096241384, Elapsed Time = 6.658546999999089 seconds
Optimization Progress:  21%|##1       | 21/100 [35:22<1:05:56, 50.08s/it]
Trial 21, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 21, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[18:34:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.68447	validation-auc:0.93569	validation-aucpr:0.89227
[18:34:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.67583	validation-auc:0.96004	validation-aucpr:0.95021
[18:34:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.66753	validation-auc:0.96484	validation-aucpr:0.96664
[18:34:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.65918	validation-auc:0.96775	validation-aucpr:0.97129
[18:34:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.65118	validation-auc:0.96849	validation-aucpr:0.97242
[18:34:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5]	validation-logloss:0.64333	validation-auc:0.96874	validation-aucpr:0.97260
[18:34:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6]	validation-logloss:0.63576	validation-auc:0.96896	validation-aucpr:0.97379
[18:34:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7]	validation-logloss:0.62881	validation-auc:0.96941	validation-aucpr:0.97434
[18:34:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8]	validation-logloss:0.62216	validation-auc:0.96939	validation-aucpr:0.97411
[18:34:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9]	validation-logloss:0.61564	validation-auc:0.96902	validation-aucpr:0.97375
[18:34:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10]	validation-logloss:0.60868	validation-auc:0.96906	validation-aucpr:0.97392
[18:34:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11]	validation-logloss:0.60235	validation-auc:0.96916	validation-aucpr:0.97391
[18:34:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12]	validation-logloss:0.59550	validation-auc:0.96952	validation-aucpr:0.97423
[18:34:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13]	validation-logloss:0.58884	validation-auc:0.96978	validation-aucpr:0.97450
[18:34:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14]	validation-logloss:0.58253	validation-auc:0.96952	validation-aucpr:0.97434
[18:34:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15]	validation-logloss:0.57612	validation-auc:0.96983	validation-aucpr:0.97456
[18:34:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16]	validation-logloss:0.56985	validation-auc:0.96983	validation-aucpr:0.97459
[18:34:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17]	validation-logloss:0.56425	validation-auc:0.96978	validation-aucpr:0.97452
[18:34:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18]	validation-logloss:0.55814	validation-auc:0.96992	validation-aucpr:0.97464
[18:34:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19]	validation-logloss:0.55218	validation-auc:0.97001	validation-aucpr:0.97472
[18:34:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20]	validation-logloss:0.54643	validation-auc:0.97003	validation-aucpr:0.97392
[18:34:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21]	validation-logloss:0.54079	validation-auc:0.96997	validation-aucpr:0.97389
[18:34:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22]	validation-logloss:0.53520	validation-auc:0.97004	validation-aucpr:0.97397
[18:34:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23]	validation-logloss:0.52974	validation-auc:0.97006	validation-aucpr:0.97399
[18:34:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24]	validation-logloss:0.52433	validation-auc:0.97013	validation-aucpr:0.97403
[18:34:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25]	validation-logloss:0.51911	validation-auc:0.97011	validation-aucpr:0.97404
[18:34:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26]	validation-logloss:0.51389	validation-auc:0.97023	validation-aucpr:0.97413
[18:34:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27]	validation-logloss:0.50880	validation-auc:0.97035	validation-aucpr:0.97422
[18:34:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28]	validation-logloss:0.50380	validation-auc:0.97037	validation-aucpr:0.97424
[18:34:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29]	validation-logloss:0.49944	validation-auc:0.97027	validation-aucpr:0.97411
[18:34:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30]	validation-logloss:0.49464	validation-auc:0.97032	validation-aucpr:0.97412
[18:34:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31]	validation-logloss:0.49041	validation-auc:0.97018	validation-aucpr:0.97397
[18:34:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32]	validation-logloss:0.48578	validation-auc:0.97031	validation-aucpr:0.97405
[18:34:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33]	validation-logloss:0.48130	validation-auc:0.97036	validation-aucpr:0.97411
[18:34:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34]	validation-logloss:0.47681	validation-auc:0.97037	validation-aucpr:0.97411
[18:34:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35]	validation-logloss:0.47235	validation-auc:0.97051	validation-aucpr:0.97422
[18:34:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36]	validation-logloss:0.46809	validation-auc:0.97049	validation-aucpr:0.97422
[18:34:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37]	validation-logloss:0.46380	validation-auc:0.97063	validation-aucpr:0.97431
[18:34:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38]	validation-logloss:0.45963	validation-auc:0.97066	validation-aucpr:0.97433
[18:34:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39]	validation-logloss:0.45590	validation-auc:0.97063	validation-aucpr:0.97429
[18:34:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40]	validation-logloss:0.45189	validation-auc:0.97077	validation-aucpr:0.97438
[18:34:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41]	validation-logloss:0.44832	validation-auc:0.97081	validation-aucpr:0.97437
[18:34:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42]	validation-logloss:0.44441	validation-auc:0.97084	validation-aucpr:0.97436
[18:34:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43]	validation-logloss:0.44054	validation-auc:0.97106	validation-aucpr:0.97451
[18:34:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44]	validation-logloss:0.43721	validation-auc:0.97099	validation-aucpr:0.97444
[18:34:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45]	validation-logloss:0.43346	validation-auc:0.97117	validation-aucpr:0.97454
[18:34:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46]	validation-logloss:0.42980	validation-auc:0.97119	validation-aucpr:0.97453
[18:34:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[47]	validation-logloss:0.42626	validation-auc:0.97116	validation-aucpr:0.97444
[18:34:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[48]	validation-logloss:0.42313	validation-auc:0.97103	validation-aucpr:0.97430
[18:34:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[49]	validation-logloss:0.41966	validation-auc:0.97098	validation-aucpr:0.97417
[18:34:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[50]	validation-logloss:0.41624	validation-auc:0.97106	validation-aucpr:0.97426
[18:34:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[51]	validation-logloss:0.41332	validation-auc:0.97087	validation-aucpr:0.97407
[18:34:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[52]	validation-logloss:0.41005	validation-auc:0.97081	validation-aucpr:0.97403
[18:34:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[53]	validation-logloss:0.40678	validation-auc:0.97095	validation-aucpr:0.97419
[18:34:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[54]	validation-logloss:0.40399	validation-auc:0.97110	validation-aucpr:0.97560
[18:34:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[55]	validation-logloss:0.40091	validation-auc:0.97108	validation-aucpr:0.97558
[18:34:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[56]	validation-logloss:0.39777	validation-auc:0.97117	validation-aucpr:0.97564
[18:34:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[57]	validation-logloss:0.39504	validation-auc:0.97116	validation-aucpr:0.97563
[18:34:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[58]	validation-logloss:0.39201	validation-auc:0.97124	validation-aucpr:0.97568
[18:34:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[59]	validation-logloss:0.38909	validation-auc:0.97125	validation-aucpr:0.97569
[18:34:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[60]	validation-logloss:0.38626	validation-auc:0.97125	validation-aucpr:0.97568
[18:34:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[61]	validation-logloss:0.38344	validation-auc:0.97123	validation-aucpr:0.97567
[18:34:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[62]	validation-logloss:0.38094	validation-auc:0.97122	validation-aucpr:0.97562
[18:34:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[63]	validation-logloss:0.37848	validation-auc:0.97118	validation-aucpr:0.97559
[18:34:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[64]	validation-logloss:0.37578	validation-auc:0.97120	validation-aucpr:0.97561
[18:34:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[65]	validation-logloss:0.37306	validation-auc:0.97122	validation-aucpr:0.97562
[18:34:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[66]	validation-logloss:0.37039	validation-auc:0.97122	validation-aucpr:0.97563
[18:34:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[67]	validation-logloss:0.36808	validation-auc:0.97121	validation-aucpr:0.97561
[18:34:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[68]	validation-logloss:0.36556	validation-auc:0.97126	validation-aucpr:0.97568
[18:34:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[69]	validation-logloss:0.36310	validation-auc:0.97125	validation-aucpr:0.97566
[18:34:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[70]	validation-logloss:0.36093	validation-auc:0.97119	validation-aucpr:0.97561
[18:34:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[71]	validation-logloss:0.35874	validation-auc:0.97115	validation-aucpr:0.97559
[18:34:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[72]	validation-logloss:0.35659	validation-auc:0.97114	validation-aucpr:0.97557
[18:34:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[73]	validation-logloss:0.35413	validation-auc:0.97124	validation-aucpr:0.97564
[18:34:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[74]	validation-logloss:0.35180	validation-auc:0.97129	validation-aucpr:0.97567
[18:34:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[75]	validation-logloss:0.34980	validation-auc:0.97123	validation-aucpr:0.97560
[18:34:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[76]	validation-logloss:0.34749	validation-auc:0.97127	validation-aucpr:0.97563
[18:34:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[77]	validation-logloss:0.34521	validation-auc:0.97132	validation-aucpr:0.97568
[18:34:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[78]	validation-logloss:0.34302	validation-auc:0.97137	validation-aucpr:0.97577
[18:34:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[79]	validation-logloss:0.34084	validation-auc:0.97142	validation-aucpr:0.97581
[18:34:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[80]	validation-logloss:0.33860	validation-auc:0.97153	validation-aucpr:0.97589
[18:34:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[81]	validation-logloss:0.33656	validation-auc:0.97153	validation-aucpr:0.97589
[18:34:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[82]	validation-logloss:0.33451	validation-auc:0.97151	validation-aucpr:0.97588
{'best_iteration': '81', 'best_score': '0.9758924405030649'}
Trial 21, Fold 1: Log loss = 0.3345103914095373, Average precision = 0.9758799882601042, ROC-AUC = 0.9715121544954426, Elapsed Time = 20.817922200001703 seconds
Trial 21, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 21, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[18:34:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.68394	validation-auc:0.94208	validation-aucpr:0.90150
[18:34:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.67606	validation-auc:0.96278	validation-aucpr:0.95869
[18:34:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.66762	validation-auc:0.96656	validation-aucpr:0.96660
[18:34:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.65946	validation-auc:0.96733	validation-aucpr:0.96997
[18:34:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.65137	validation-auc:0.96827	validation-aucpr:0.96890
[18:34:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5]	validation-logloss:0.64416	validation-auc:0.96852	validation-aucpr:0.97179
[18:34:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6]	validation-logloss:0.63640	validation-auc:0.96892	validation-aucpr:0.97223
[18:34:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7]	validation-logloss:0.62881	validation-auc:0.96970	validation-aucpr:0.97305
[18:34:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8]	validation-logloss:0.62141	validation-auc:0.96982	validation-aucpr:0.97320
[18:34:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9]	validation-logloss:0.61492	validation-auc:0.96983	validation-aucpr:0.97316
[18:34:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10]	validation-logloss:0.60849	validation-auc:0.96991	validation-aucpr:0.97323
[18:34:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11]	validation-logloss:0.60151	validation-auc:0.97008	validation-aucpr:0.97344
[18:34:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12]	validation-logloss:0.59474	validation-auc:0.97022	validation-aucpr:0.97362
[18:34:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13]	validation-logloss:0.58863	validation-auc:0.97044	validation-aucpr:0.97388
[18:34:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14]	validation-logloss:0.58269	validation-auc:0.97054	validation-aucpr:0.97384
[18:34:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15]	validation-logloss:0.57633	validation-auc:0.97070	validation-aucpr:0.97401
[18:34:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16]	validation-logloss:0.57004	validation-auc:0.97084	validation-aucpr:0.97414
[18:34:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17]	validation-logloss:0.56461	validation-auc:0.97048	validation-aucpr:0.97384
[18:34:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18]	validation-logloss:0.55849	validation-auc:0.97070	validation-aucpr:0.97402
[18:34:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19]	validation-logloss:0.55252	validation-auc:0.97074	validation-aucpr:0.97405
[18:34:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20]	validation-logloss:0.54668	validation-auc:0.97077	validation-aucpr:0.97401
[18:34:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21]	validation-logloss:0.54098	validation-auc:0.97078	validation-aucpr:0.97400
[18:34:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22]	validation-logloss:0.53592	validation-auc:0.97083	validation-aucpr:0.97405
[18:34:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23]	validation-logloss:0.53042	validation-auc:0.97095	validation-aucpr:0.97416
[18:34:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24]	validation-logloss:0.52549	validation-auc:0.97092	validation-aucpr:0.97417
[18:34:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25]	validation-logloss:0.52009	validation-auc:0.97104	validation-aucpr:0.97424
[18:34:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26]	validation-logloss:0.51538	validation-auc:0.97117	validation-aucpr:0.97431
[18:34:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27]	validation-logloss:0.51025	validation-auc:0.97115	validation-aucpr:0.97427
[18:34:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28]	validation-logloss:0.50525	validation-auc:0.97110	validation-aucpr:0.97424
[18:34:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29]	validation-logloss:0.50021	validation-auc:0.97111	validation-aucpr:0.97429
[18:34:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30]	validation-logloss:0.49532	validation-auc:0.97131	validation-aucpr:0.97445
[18:34:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31]	validation-logloss:0.49062	validation-auc:0.97137	validation-aucpr:0.97449
[18:34:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32]	validation-logloss:0.48591	validation-auc:0.97155	validation-aucpr:0.97461
[18:34:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33]	validation-logloss:0.48126	validation-auc:0.97171	validation-aucpr:0.97474
[18:34:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34]	validation-logloss:0.47681	validation-auc:0.97171	validation-aucpr:0.97468
[18:34:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35]	validation-logloss:0.47233	validation-auc:0.97175	validation-aucpr:0.97472
[18:34:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36]	validation-logloss:0.46848	validation-auc:0.97165	validation-aucpr:0.97463
[18:34:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37]	validation-logloss:0.46416	validation-auc:0.97188	validation-aucpr:0.97480
[18:34:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38]	validation-logloss:0.45987	validation-auc:0.97187	validation-aucpr:0.97481
[18:34:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39]	validation-logloss:0.45577	validation-auc:0.97186	validation-aucpr:0.97484
[18:34:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40]	validation-logloss:0.45162	validation-auc:0.97200	validation-aucpr:0.97494
[18:34:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41]	validation-logloss:0.44806	validation-auc:0.97199	validation-aucpr:0.97496
[18:34:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42]	validation-logloss:0.44410	validation-auc:0.97212	validation-aucpr:0.97501
[18:34:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43]	validation-logloss:0.44033	validation-auc:0.97214	validation-aucpr:0.97498
[18:34:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44]	validation-logloss:0.43662	validation-auc:0.97213	validation-aucpr:0.97497
[18:34:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45]	validation-logloss:0.43288	validation-auc:0.97216	validation-aucpr:0.97500
[18:34:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46]	validation-logloss:0.42916	validation-auc:0.97227	validation-aucpr:0.97509
[18:34:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[47]	validation-logloss:0.42550	validation-auc:0.97233	validation-aucpr:0.97514
[18:34:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[48]	validation-logloss:0.42187	validation-auc:0.97243	validation-aucpr:0.97520
[18:34:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[49]	validation-logloss:0.41841	validation-auc:0.97234	validation-aucpr:0.97514
[18:34:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[50]	validation-logloss:0.41496	validation-auc:0.97239	validation-aucpr:0.97516
[18:34:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[51]	validation-logloss:0.41202	validation-auc:0.97236	validation-aucpr:0.97513
[18:34:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[52]	validation-logloss:0.40883	validation-auc:0.97234	validation-aucpr:0.97511
[18:34:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[53]	validation-logloss:0.40555	validation-auc:0.97237	validation-aucpr:0.97512
[18:34:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[54]	validation-logloss:0.40232	validation-auc:0.97238	validation-aucpr:0.97512
[18:34:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[55]	validation-logloss:0.39918	validation-auc:0.97233	validation-aucpr:0.97509
[18:34:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[56]	validation-logloss:0.39605	validation-auc:0.97240	validation-aucpr:0.97514
[18:34:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[57]	validation-logloss:0.39331	validation-auc:0.97237	validation-aucpr:0.97512
[18:34:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[58]	validation-logloss:0.39032	validation-auc:0.97236	validation-aucpr:0.97510
[18:34:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[59]	validation-logloss:0.38765	validation-auc:0.97228	validation-aucpr:0.97509
[18:34:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[60]	validation-logloss:0.38461	validation-auc:0.97232	validation-aucpr:0.97512
[18:34:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[61]	validation-logloss:0.38171	validation-auc:0.97238	validation-aucpr:0.97518
[18:34:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[62]	validation-logloss:0.37916	validation-auc:0.97260	validation-aucpr:0.97529
[18:34:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[63]	validation-logloss:0.37660	validation-auc:0.97261	validation-aucpr:0.97530
[18:34:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[64]	validation-logloss:0.37384	validation-auc:0.97260	validation-aucpr:0.97530
[18:34:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[65]	validation-logloss:0.37116	validation-auc:0.97254	validation-aucpr:0.97525
[18:34:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[66]	validation-logloss:0.36843	validation-auc:0.97259	validation-aucpr:0.97529
[18:34:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[67]	validation-logloss:0.36576	validation-auc:0.97265	validation-aucpr:0.97533
[18:34:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[68]	validation-logloss:0.36308	validation-auc:0.97270	validation-aucpr:0.97538
[18:34:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[69]	validation-logloss:0.36044	validation-auc:0.97271	validation-aucpr:0.97539
[18:34:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[70]	validation-logloss:0.35792	validation-auc:0.97277	validation-aucpr:0.97540
[18:34:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[71]	validation-logloss:0.35550	validation-auc:0.97279	validation-aucpr:0.97540
[18:34:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[72]	validation-logloss:0.35310	validation-auc:0.97274	validation-aucpr:0.97536
[18:34:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[73]	validation-logloss:0.35071	validation-auc:0.97271	validation-aucpr:0.97533
[18:34:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[74]	validation-logloss:0.34860	validation-auc:0.97271	validation-aucpr:0.97533
[18:34:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[75]	validation-logloss:0.34624	validation-auc:0.97275	validation-aucpr:0.97537
[18:34:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[76]	validation-logloss:0.34393	validation-auc:0.97272	validation-aucpr:0.97536
[18:34:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[77]	validation-logloss:0.34165	validation-auc:0.97278	validation-aucpr:0.97538
[18:34:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[78]	validation-logloss:0.33966	validation-auc:0.97277	validation-aucpr:0.97538
[18:34:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[79]	validation-logloss:0.33742	validation-auc:0.97282	validation-aucpr:0.97542
[18:34:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[80]	validation-logloss:0.33518	validation-auc:0.97288	validation-aucpr:0.97547
[18:34:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[81]	validation-logloss:0.33304	validation-auc:0.97289	validation-aucpr:0.97549
[18:34:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[82]	validation-logloss:0.33097	validation-auc:0.97285	validation-aucpr:0.97546
{'best_iteration': '81', 'best_score': '0.9754875814965706'}
Trial 21, Fold 2: Log loss = 0.33096566923356896, Average precision = 0.9754165941955653, ROC-AUC = 0.9728503484584398, Elapsed Time = 20.76533600000039 seconds
Trial 21, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 21, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[18:35:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.68430	validation-auc:0.94102	validation-aucpr:0.90218
[18:35:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.67563	validation-auc:0.96315	validation-aucpr:0.95918
[18:35:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.66714	validation-auc:0.96681	validation-aucpr:0.97083
[18:35:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.65887	validation-auc:0.96832	validation-aucpr:0.97210
[18:35:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.65096	validation-auc:0.96840	validation-aucpr:0.97242
[18:35:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5]	validation-logloss:0.64380	validation-auc:0.96784	validation-aucpr:0.97210
[18:35:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6]	validation-logloss:0.63613	validation-auc:0.96834	validation-aucpr:0.97243
[18:35:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7]	validation-logloss:0.62850	validation-auc:0.96869	validation-aucpr:0.97160
[18:35:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8]	validation-logloss:0.62184	validation-auc:0.96856	validation-aucpr:0.97116
[18:35:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9]	validation-logloss:0.61458	validation-auc:0.96856	validation-aucpr:0.97116
[18:35:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10]	validation-logloss:0.60746	validation-auc:0.96889	validation-aucpr:0.97046
[18:35:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11]	validation-logloss:0.60071	validation-auc:0.96879	validation-aucpr:0.97022
[18:35:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12]	validation-logloss:0.59390	validation-auc:0.96895	validation-aucpr:0.97031
[18:35:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13]	validation-logloss:0.58719	validation-auc:0.96929	validation-aucpr:0.97153
[18:35:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14]	validation-logloss:0.58066	validation-auc:0.96955	validation-aucpr:0.97105
[18:35:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15]	validation-logloss:0.57427	validation-auc:0.96955	validation-aucpr:0.97112
[18:35:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16]	validation-logloss:0.56804	validation-auc:0.96953	validation-aucpr:0.97139
[18:35:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17]	validation-logloss:0.56194	validation-auc:0.96952	validation-aucpr:0.97144
[18:35:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18]	validation-logloss:0.55637	validation-auc:0.96974	validation-aucpr:0.97158
[18:35:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19]	validation-logloss:0.55046	validation-auc:0.96977	validation-aucpr:0.97160
[18:35:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20]	validation-logloss:0.54453	validation-auc:0.97014	validation-aucpr:0.97054
[18:35:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21]	validation-logloss:0.53874	validation-auc:0.97037	validation-aucpr:0.97073
[18:35:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22]	validation-logloss:0.53317	validation-auc:0.97074	validation-aucpr:0.97212
[18:35:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23]	validation-logloss:0.52771	validation-auc:0.97069	validation-aucpr:0.97211
[18:35:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24]	validation-logloss:0.52231	validation-auc:0.97083	validation-aucpr:0.97221
[18:35:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25]	validation-logloss:0.51742	validation-auc:0.97108	validation-aucpr:0.97324
[18:35:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26]	validation-logloss:0.51269	validation-auc:0.97112	validation-aucpr:0.97326
[18:35:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27]	validation-logloss:0.50772	validation-auc:0.97110	validation-aucpr:0.97326
[18:35:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28]	validation-logloss:0.50273	validation-auc:0.97116	validation-aucpr:0.97328
[18:35:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29]	validation-logloss:0.49823	validation-auc:0.97117	validation-aucpr:0.97329
[18:35:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30]	validation-logloss:0.49333	validation-auc:0.97126	validation-aucpr:0.97336
[18:35:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31]	validation-logloss:0.48849	validation-auc:0.97146	validation-aucpr:0.97351
[18:35:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32]	validation-logloss:0.48392	validation-auc:0.97127	validation-aucpr:0.97461
[18:35:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33]	validation-logloss:0.47930	validation-auc:0.97130	validation-aucpr:0.97463
[18:35:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34]	validation-logloss:0.47483	validation-auc:0.97155	validation-aucpr:0.97474
[18:35:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35]	validation-logloss:0.47039	validation-auc:0.97163	validation-aucpr:0.97480
[18:35:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36]	validation-logloss:0.46603	validation-auc:0.97178	validation-aucpr:0.97489
[18:35:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37]	validation-logloss:0.46183	validation-auc:0.97176	validation-aucpr:0.97488
[18:35:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38]	validation-logloss:0.45805	validation-auc:0.97168	validation-aucpr:0.97487
[18:35:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39]	validation-logloss:0.45388	validation-auc:0.97169	validation-aucpr:0.97487
[18:35:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40]	validation-logloss:0.44983	validation-auc:0.97177	validation-aucpr:0.97493
[18:35:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41]	validation-logloss:0.44592	validation-auc:0.97171	validation-aucpr:0.97477
[18:35:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42]	validation-logloss:0.44212	validation-auc:0.97169	validation-aucpr:0.97474
[18:35:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43]	validation-logloss:0.43839	validation-auc:0.97181	validation-aucpr:0.97415
[18:35:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44]	validation-logloss:0.43465	validation-auc:0.97174	validation-aucpr:0.97411
[18:35:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45]	validation-logloss:0.43133	validation-auc:0.97174	validation-aucpr:0.97410
[18:35:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46]	validation-logloss:0.42770	validation-auc:0.97175	validation-aucpr:0.97409
[18:35:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[47]	validation-logloss:0.42413	validation-auc:0.97181	validation-aucpr:0.97415
[18:35:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[48]	validation-logloss:0.42054	validation-auc:0.97185	validation-aucpr:0.97418
[18:35:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[49]	validation-logloss:0.41732	validation-auc:0.97190	validation-aucpr:0.97420
[18:35:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[50]	validation-logloss:0.41397	validation-auc:0.97192	validation-aucpr:0.97420
[18:35:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[51]	validation-logloss:0.41057	validation-auc:0.97202	validation-aucpr:0.97426
[18:35:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[52]	validation-logloss:0.40757	validation-auc:0.97228	validation-aucpr:0.97606
[18:35:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[53]	validation-logloss:0.40459	validation-auc:0.97231	validation-aucpr:0.97608
[18:35:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[54]	validation-logloss:0.40134	validation-auc:0.97233	validation-aucpr:0.97610
[18:35:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[55]	validation-logloss:0.39842	validation-auc:0.97240	validation-aucpr:0.97616
[18:35:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[56]	validation-logloss:0.39537	validation-auc:0.97235	validation-aucpr:0.97613
[18:35:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[57]	validation-logloss:0.39235	validation-auc:0.97234	validation-aucpr:0.97612
[18:35:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[58]	validation-logloss:0.38932	validation-auc:0.97237	validation-aucpr:0.97615
[18:35:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[59]	validation-logloss:0.38632	validation-auc:0.97242	validation-aucpr:0.97618
[18:35:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[60]	validation-logloss:0.38338	validation-auc:0.97246	validation-aucpr:0.97621
[18:35:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[61]	validation-logloss:0.38080	validation-auc:0.97244	validation-aucpr:0.97620
[18:35:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[62]	validation-logloss:0.37805	validation-auc:0.97234	validation-aucpr:0.97611
[18:35:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[63]	validation-logloss:0.37553	validation-auc:0.97243	validation-aucpr:0.97617
[18:35:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[64]	validation-logloss:0.37273	validation-auc:0.97247	validation-aucpr:0.97620
[18:35:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[65]	validation-logloss:0.36993	validation-auc:0.97255	validation-aucpr:0.97627
[18:35:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[66]	validation-logloss:0.36762	validation-auc:0.97258	validation-aucpr:0.97636
[18:35:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[67]	validation-logloss:0.36521	validation-auc:0.97261	validation-aucpr:0.97640
[18:35:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[68]	validation-logloss:0.36268	validation-auc:0.97254	validation-aucpr:0.97636
[18:35:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[69]	validation-logloss:0.36022	validation-auc:0.97257	validation-aucpr:0.97637
[18:35:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[70]	validation-logloss:0.35800	validation-auc:0.97254	validation-aucpr:0.97633
[18:35:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[71]	validation-logloss:0.35559	validation-auc:0.97249	validation-aucpr:0.97630
[18:35:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[72]	validation-logloss:0.35345	validation-auc:0.97248	validation-aucpr:0.97631
[18:35:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[73]	validation-logloss:0.35130	validation-auc:0.97249	validation-aucpr:0.97630
[18:35:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[74]	validation-logloss:0.34893	validation-auc:0.97254	validation-aucpr:0.97633
[18:35:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[75]	validation-logloss:0.34663	validation-auc:0.97254	validation-aucpr:0.97633
[18:35:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[76]	validation-logloss:0.34437	validation-auc:0.97258	validation-aucpr:0.97645
[18:35:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[77]	validation-logloss:0.34242	validation-auc:0.97250	validation-aucpr:0.97637
[18:35:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[78]	validation-logloss:0.34014	validation-auc:0.97250	validation-aucpr:0.97637
[18:35:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[79]	validation-logloss:0.33798	validation-auc:0.97248	validation-aucpr:0.97636
[18:35:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[80]	validation-logloss:0.33602	validation-auc:0.97250	validation-aucpr:0.97638
[18:35:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[81]	validation-logloss:0.33396	validation-auc:0.97248	validation-aucpr:0.97635
[18:35:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[82]	validation-logloss:0.33180	validation-auc:0.97250	validation-aucpr:0.97637
{'best_iteration': '76', 'best_score': '0.9764509129277438'}
Trial 21, Fold 3: Log loss = 0.33180345818438056, Average precision = 0.976378138082869, ROC-AUC = 0.9724998585441726, Elapsed Time = 20.840254299999287 seconds
Trial 21, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 21, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[18:35:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.68401	validation-auc:0.94302	validation-aucpr:0.91190
[18:35:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.67538	validation-auc:0.96397	validation-aucpr:0.97035
[18:35:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.66763	validation-auc:0.96476	validation-aucpr:0.96867
[18:35:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.65936	validation-auc:0.96725	validation-aucpr:0.97103
[18:35:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.65188	validation-auc:0.96816	validation-aucpr:0.97317
[18:35:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5]	validation-logloss:0.64394	validation-auc:0.96913	validation-aucpr:0.97399
[18:35:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6]	validation-logloss:0.63623	validation-auc:0.96976	validation-aucpr:0.97445
[18:35:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7]	validation-logloss:0.62873	validation-auc:0.97004	validation-aucpr:0.97469
[18:35:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8]	validation-logloss:0.62138	validation-auc:0.97019	validation-aucpr:0.97478
[18:35:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9]	validation-logloss:0.61477	validation-auc:0.97024	validation-aucpr:0.97478
[18:35:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10]	validation-logloss:0.60773	validation-auc:0.97016	validation-aucpr:0.97478
[18:35:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11]	validation-logloss:0.60144	validation-auc:0.96995	validation-aucpr:0.97459
[18:35:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12]	validation-logloss:0.59474	validation-auc:0.97002	validation-aucpr:0.97464
[18:35:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13]	validation-logloss:0.58800	validation-auc:0.97028	validation-aucpr:0.97489
[18:35:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14]	validation-logloss:0.58163	validation-auc:0.97026	validation-aucpr:0.97492
[18:35:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15]	validation-logloss:0.57525	validation-auc:0.97031	validation-aucpr:0.97499
[18:35:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16]	validation-logloss:0.56893	validation-auc:0.97055	validation-aucpr:0.97517
[18:35:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17]	validation-logloss:0.56329	validation-auc:0.97047	validation-aucpr:0.97504
[18:35:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18]	validation-logloss:0.55719	validation-auc:0.97069	validation-aucpr:0.97525
[18:35:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19]	validation-logloss:0.55137	validation-auc:0.97063	validation-aucpr:0.97523
[18:35:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20]	validation-logloss:0.54614	validation-auc:0.97055	validation-aucpr:0.97517
[18:35:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21]	validation-logloss:0.54051	validation-auc:0.97048	validation-aucpr:0.97513
[18:35:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22]	validation-logloss:0.53494	validation-auc:0.97065	validation-aucpr:0.97524
[18:35:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23]	validation-logloss:0.52948	validation-auc:0.97067	validation-aucpr:0.97525
[18:35:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24]	validation-logloss:0.52458	validation-auc:0.97065	validation-aucpr:0.97525
[18:35:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25]	validation-logloss:0.51921	validation-auc:0.97093	validation-aucpr:0.97545
[18:35:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26]	validation-logloss:0.51449	validation-auc:0.97098	validation-aucpr:0.97547
[18:35:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27]	validation-logloss:0.50941	validation-auc:0.97097	validation-aucpr:0.97549
[18:35:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28]	validation-logloss:0.50491	validation-auc:0.97082	validation-aucpr:0.97536
[18:35:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29]	validation-logloss:0.50007	validation-auc:0.97078	validation-aucpr:0.97536
[18:35:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30]	validation-logloss:0.49537	validation-auc:0.97065	validation-aucpr:0.97526
[18:35:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31]	validation-logloss:0.49069	validation-auc:0.97064	validation-aucpr:0.97527
[18:35:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32]	validation-logloss:0.48613	validation-auc:0.97056	validation-aucpr:0.97523
[18:35:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33]	validation-logloss:0.48142	validation-auc:0.97080	validation-aucpr:0.97542
[18:35:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34]	validation-logloss:0.47684	validation-auc:0.97095	validation-aucpr:0.97554
[18:35:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35]	validation-logloss:0.47241	validation-auc:0.97100	validation-aucpr:0.97558
[18:35:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36]	validation-logloss:0.46807	validation-auc:0.97109	validation-aucpr:0.97565
[18:35:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37]	validation-logloss:0.46389	validation-auc:0.97108	validation-aucpr:0.97562
[18:35:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38]	validation-logloss:0.46010	validation-auc:0.97102	validation-aucpr:0.97557
[18:35:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39]	validation-logloss:0.45598	validation-auc:0.97109	validation-aucpr:0.97563
[18:35:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40]	validation-logloss:0.45198	validation-auc:0.97100	validation-aucpr:0.97559
[18:35:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41]	validation-logloss:0.44804	validation-auc:0.97092	validation-aucpr:0.97553
[18:35:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42]	validation-logloss:0.44415	validation-auc:0.97091	validation-aucpr:0.97552
[18:35:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43]	validation-logloss:0.44026	validation-auc:0.97101	validation-aucpr:0.97559
[18:35:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44]	validation-logloss:0.43692	validation-auc:0.97092	validation-aucpr:0.97553
[18:35:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45]	validation-logloss:0.43322	validation-auc:0.97094	validation-aucpr:0.97557
[18:35:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46]	validation-logloss:0.42954	validation-auc:0.97103	validation-aucpr:0.97564
[18:35:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[47]	validation-logloss:0.42597	validation-auc:0.97112	validation-aucpr:0.97569
[18:35:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[48]	validation-logloss:0.42274	validation-auc:0.97117	validation-aucpr:0.97572
[18:35:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[49]	validation-logloss:0.41921	validation-auc:0.97116	validation-aucpr:0.97573
[18:35:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[50]	validation-logloss:0.41577	validation-auc:0.97119	validation-aucpr:0.97578
[18:35:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[51]	validation-logloss:0.41243	validation-auc:0.97125	validation-aucpr:0.97583
[18:35:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[52]	validation-logloss:0.40941	validation-auc:0.97120	validation-aucpr:0.97578
[18:35:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[53]	validation-logloss:0.40643	validation-auc:0.97129	validation-aucpr:0.97584
[18:35:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[54]	validation-logloss:0.40325	validation-auc:0.97121	validation-aucpr:0.97580
[18:35:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[55]	validation-logloss:0.40018	validation-auc:0.97122	validation-aucpr:0.97581
[18:35:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[56]	validation-logloss:0.39746	validation-auc:0.97106	validation-aucpr:0.97570
[18:35:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[57]	validation-logloss:0.39470	validation-auc:0.97108	validation-aucpr:0.97571
[18:35:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[58]	validation-logloss:0.39168	validation-auc:0.97114	validation-aucpr:0.97574
[18:35:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[59]	validation-logloss:0.38911	validation-auc:0.97105	validation-aucpr:0.97569
[18:35:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[60]	validation-logloss:0.38625	validation-auc:0.97108	validation-aucpr:0.97572
[18:35:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[61]	validation-logloss:0.38333	validation-auc:0.97109	validation-aucpr:0.97574
[18:35:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[62]	validation-logloss:0.38052	validation-auc:0.97110	validation-aucpr:0.97576
[18:35:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[63]	validation-logloss:0.37775	validation-auc:0.97111	validation-aucpr:0.97576
[18:35:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[64]	validation-logloss:0.37492	validation-auc:0.97116	validation-aucpr:0.97579
[18:35:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[65]	validation-logloss:0.37221	validation-auc:0.97123	validation-aucpr:0.97585
[18:35:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[66]	validation-logloss:0.36953	validation-auc:0.97124	validation-aucpr:0.97585
[18:35:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[67]	validation-logloss:0.36694	validation-auc:0.97127	validation-aucpr:0.97588
[18:35:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[68]	validation-logloss:0.36434	validation-auc:0.97133	validation-aucpr:0.97593
[18:35:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[69]	validation-logloss:0.36181	validation-auc:0.97140	validation-aucpr:0.97599
[18:35:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[70]	validation-logloss:0.35930	validation-auc:0.97140	validation-aucpr:0.97598
[18:35:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[71]	validation-logloss:0.35676	validation-auc:0.97146	validation-aucpr:0.97602
[18:35:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[72]	validation-logloss:0.35438	validation-auc:0.97140	validation-aucpr:0.97598
[18:35:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[73]	validation-logloss:0.35235	validation-auc:0.97130	validation-aucpr:0.97591
[18:35:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[74]	validation-logloss:0.35002	validation-auc:0.97129	validation-aucpr:0.97592
[18:35:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[75]	validation-logloss:0.34775	validation-auc:0.97130	validation-aucpr:0.97592
[18:35:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[76]	validation-logloss:0.34539	validation-auc:0.97141	validation-aucpr:0.97601
[18:35:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[77]	validation-logloss:0.34339	validation-auc:0.97138	validation-aucpr:0.97598
[18:35:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[78]	validation-logloss:0.34112	validation-auc:0.97142	validation-aucpr:0.97602
[18:35:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[79]	validation-logloss:0.33922	validation-auc:0.97139	validation-aucpr:0.97600
[18:35:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[80]	validation-logloss:0.33704	validation-auc:0.97142	validation-aucpr:0.97604
[18:35:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[81]	validation-logloss:0.33512	validation-auc:0.97138	validation-aucpr:0.97600
[18:35:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[82]	validation-logloss:0.33327	validation-auc:0.97135	validation-aucpr:0.97596
{'best_iteration': '80', 'best_score': '0.976037216196178'}
Trial 21, Fold 4: Log loss = 0.33326755809500186, Average precision = 0.9759524422770561, ROC-AUC = 0.9713480379884164, Elapsed Time = 20.94706589999987 seconds
Trial 21, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 21, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[18:35:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.68434	validation-auc:0.93097	validation-aucpr:0.87197
[18:35:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.67656	validation-auc:0.95917	validation-aucpr:0.95191
[18:35:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.66816	validation-auc:0.96334	validation-aucpr:0.96412
[18:35:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.66002	validation-auc:0.96502	validation-aucpr:0.97028
[18:35:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.65268	validation-auc:0.96527	validation-aucpr:0.97029
[18:35:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5]	validation-logloss:0.64495	validation-auc:0.96578	validation-aucpr:0.97074
[18:35:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6]	validation-logloss:0.63785	validation-auc:0.96613	validation-aucpr:0.97096
[18:35:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7]	validation-logloss:0.63045	validation-auc:0.96691	validation-aucpr:0.97153
[18:35:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8]	validation-logloss:0.62302	validation-auc:0.96766	validation-aucpr:0.97203
[18:35:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9]	validation-logloss:0.61574	validation-auc:0.96799	validation-aucpr:0.97229
[18:35:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10]	validation-logloss:0.60869	validation-auc:0.96824	validation-aucpr:0.97244
[18:35:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11]	validation-logloss:0.60172	validation-auc:0.96851	validation-aucpr:0.97266
[18:35:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12]	validation-logloss:0.59497	validation-auc:0.96852	validation-aucpr:0.97276
[18:35:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13]	validation-logloss:0.58847	validation-auc:0.96822	validation-aucpr:0.97264
[18:35:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14]	validation-logloss:0.58202	validation-auc:0.96847	validation-aucpr:0.97283
[18:35:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15]	validation-logloss:0.57573	validation-auc:0.96874	validation-aucpr:0.97309
[18:35:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16]	validation-logloss:0.56960	validation-auc:0.96891	validation-aucpr:0.97318
[18:35:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17]	validation-logloss:0.56358	validation-auc:0.96899	validation-aucpr:0.97328
[18:35:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18]	validation-logloss:0.55769	validation-auc:0.96880	validation-aucpr:0.97310
[18:35:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19]	validation-logloss:0.55193	validation-auc:0.96892	validation-aucpr:0.97317
[18:35:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20]	validation-logloss:0.54612	validation-auc:0.96895	validation-aucpr:0.97320
[18:35:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21]	validation-logloss:0.54043	validation-auc:0.96923	validation-aucpr:0.97339
[18:35:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22]	validation-logloss:0.53557	validation-auc:0.96918	validation-aucpr:0.97326
[18:35:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23]	validation-logloss:0.53008	validation-auc:0.96932	validation-aucpr:0.97337
[18:35:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24]	validation-logloss:0.52523	validation-auc:0.96923	validation-aucpr:0.97331
[18:35:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25]	validation-logloss:0.52036	validation-auc:0.96928	validation-aucpr:0.97332
[18:35:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26]	validation-logloss:0.51517	validation-auc:0.96940	validation-aucpr:0.97344
[18:35:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27]	validation-logloss:0.51029	validation-auc:0.96944	validation-aucpr:0.97350
[18:35:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28]	validation-logloss:0.50529	validation-auc:0.96955	validation-aucpr:0.97359
[18:35:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29]	validation-logloss:0.50040	validation-auc:0.96962	validation-aucpr:0.97358
[18:35:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30]	validation-logloss:0.49574	validation-auc:0.96958	validation-aucpr:0.97360
[18:35:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31]	validation-logloss:0.49100	validation-auc:0.96961	validation-aucpr:0.97361
[18:35:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32]	validation-logloss:0.48692	validation-auc:0.96962	validation-aucpr:0.97364
[18:35:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33]	validation-logloss:0.48234	validation-auc:0.96970	validation-aucpr:0.97370
[18:35:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34]	validation-logloss:0.47788	validation-auc:0.96973	validation-aucpr:0.97374
[18:35:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35]	validation-logloss:0.47356	validation-auc:0.96974	validation-aucpr:0.97368
[18:35:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36]	validation-logloss:0.46928	validation-auc:0.96973	validation-aucpr:0.97362
[18:35:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37]	validation-logloss:0.46503	validation-auc:0.96981	validation-aucpr:0.97373
[18:35:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38]	validation-logloss:0.46093	validation-auc:0.96991	validation-aucpr:0.97376
[18:35:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39]	validation-logloss:0.45739	validation-auc:0.96983	validation-aucpr:0.97370
[18:35:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40]	validation-logloss:0.45344	validation-auc:0.96972	validation-aucpr:0.97366
[18:35:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41]	validation-logloss:0.44946	validation-auc:0.96980	validation-aucpr:0.97373
[18:35:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42]	validation-logloss:0.44564	validation-auc:0.96980	validation-aucpr:0.97374
[18:35:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43]	validation-logloss:0.44177	validation-auc:0.96992	validation-aucpr:0.97383
[18:35:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44]	validation-logloss:0.43836	validation-auc:0.96992	validation-aucpr:0.97382
[18:35:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45]	validation-logloss:0.43469	validation-auc:0.96993	validation-aucpr:0.97383
[18:35:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46]	validation-logloss:0.43142	validation-auc:0.96994	validation-aucpr:0.97383
[18:35:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[47]	validation-logloss:0.42792	validation-auc:0.97010	validation-aucpr:0.97388
[18:35:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[48]	validation-logloss:0.42446	validation-auc:0.97005	validation-aucpr:0.97386
[18:35:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[49]	validation-logloss:0.42105	validation-auc:0.97003	validation-aucpr:0.97386
[18:35:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[50]	validation-logloss:0.41782	validation-auc:0.96991	validation-aucpr:0.97378
[18:35:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[51]	validation-logloss:0.41486	validation-auc:0.96999	validation-aucpr:0.97382
[18:35:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[52]	validation-logloss:0.41170	validation-auc:0.96997	validation-aucpr:0.97380
[18:35:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[53]	validation-logloss:0.40846	validation-auc:0.97005	validation-aucpr:0.97384
[18:35:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[54]	validation-logloss:0.40520	validation-auc:0.97017	validation-aucpr:0.97396
[18:35:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[55]	validation-logloss:0.40202	validation-auc:0.97030	validation-aucpr:0.97406
[18:35:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[56]	validation-logloss:0.39904	validation-auc:0.97031	validation-aucpr:0.97406
[18:35:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[57]	validation-logloss:0.39636	validation-auc:0.97025	validation-aucpr:0.97398
[18:35:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[58]	validation-logloss:0.39333	validation-auc:0.97029	validation-aucpr:0.97401
[18:35:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[59]	validation-logloss:0.39036	validation-auc:0.97039	validation-aucpr:0.97409
[18:35:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[60]	validation-logloss:0.38775	validation-auc:0.97028	validation-aucpr:0.97274
[18:35:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[61]	validation-logloss:0.38493	validation-auc:0.97023	validation-aucpr:0.97274
[18:35:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[62]	validation-logloss:0.38216	validation-auc:0.97027	validation-aucpr:0.97277
[18:35:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[63]	validation-logloss:0.37940	validation-auc:0.97025	validation-aucpr:0.97276
[18:36:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[64]	validation-logloss:0.37695	validation-auc:0.97023	validation-aucpr:0.97266
[18:36:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[65]	validation-logloss:0.37424	validation-auc:0.97030	validation-aucpr:0.97267
[18:36:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[66]	validation-logloss:0.37192	validation-auc:0.97034	validation-aucpr:0.97270
[18:36:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[67]	validation-logloss:0.36929	validation-auc:0.97043	validation-aucpr:0.97274
[18:36:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[68]	validation-logloss:0.36670	validation-auc:0.97048	validation-aucpr:0.97280
[18:36:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[69]	validation-logloss:0.36455	validation-auc:0.97044	validation-aucpr:0.97296
[18:36:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[70]	validation-logloss:0.36233	validation-auc:0.97038	validation-aucpr:0.97290
[18:36:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[71]	validation-logloss:0.35991	validation-auc:0.97044	validation-aucpr:0.97295
[18:36:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[72]	validation-logloss:0.35777	validation-auc:0.97038	validation-aucpr:0.97290
[18:36:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[73]	validation-logloss:0.35537	validation-auc:0.97045	validation-aucpr:0.97295
[18:36:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[74]	validation-logloss:0.35309	validation-auc:0.97051	validation-aucpr:0.97304
[18:36:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[75]	validation-logloss:0.35070	validation-auc:0.97061	validation-aucpr:0.97315
[18:36:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[76]	validation-logloss:0.34845	validation-auc:0.97058	validation-aucpr:0.97313
[18:36:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[77]	validation-logloss:0.34644	validation-auc:0.97058	validation-aucpr:0.97312
[18:36:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[78]	validation-logloss:0.34419	validation-auc:0.97066	validation-aucpr:0.97317
[18:36:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[79]	validation-logloss:0.34202	validation-auc:0.97070	validation-aucpr:0.97318
[18:36:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[80]	validation-logloss:0.34013	validation-auc:0.97066	validation-aucpr:0.97295
[18:36:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[81]	validation-logloss:0.33804	validation-auc:0.97068	validation-aucpr:0.97309
[18:36:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[82]	validation-logloss:0.33600	validation-auc:0.97074	validation-aucpr:0.97312
{'best_iteration': '59', 'best_score': '0.9740903400979094'}
Trial 21, Fold 5: Log loss = 0.33599993079141377, Average precision = 0.974024744362116, ROC-AUC = 0.9707432881853483, Elapsed Time = 22.729992100001255 seconds
Optimization Progress:  22%|##2       | 22/100 [37:17<1:30:15, 69.43s/it]
Trial 22, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 22, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[18:36:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.66534	validation-auc:0.82859	validation-aucpr:0.81740
[18:36:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.63069	validation-auc:0.84888	validation-aucpr:0.82950
[18:36:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.61584	validation-auc:0.86316	validation-aucpr:0.85073
[18:36:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.61219	validation-auc:0.85958	validation-aucpr:0.85603
[18:36:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.61037	validation-auc:0.85655	validation-aucpr:0.84991
[18:36:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5]	validation-logloss:0.60284	validation-auc:0.85753	validation-aucpr:0.83697
[18:36:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6]	validation-logloss:0.59989	validation-auc:0.84902	validation-aucpr:0.83168
[18:36:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7]	validation-logloss:0.58183	validation-auc:0.85358	validation-aucpr:0.84506
[18:36:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8]	validation-logloss:0.58032	validation-auc:0.84760	validation-aucpr:0.83470
[18:36:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9]	validation-logloss:0.55682	validation-auc:0.86457	validation-aucpr:0.85791
[18:36:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10]	validation-logloss:0.53023	validation-auc:0.90063	validation-aucpr:0.90279
[18:36:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11]	validation-logloss:0.51509	validation-auc:0.91001	validation-aucpr:0.91155
[18:36:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12]	validation-logloss:0.51445	validation-auc:0.90667	validation-aucpr:0.90656
{'best_iteration': '11', 'best_score': '0.9115515191694321'}
Trial 22, Fold 1: Log loss = 0.5144471949959329, Average precision = 0.9039888838431559, ROC-AUC = 0.9066697076803218, Elapsed Time = 0.4393142999997508 seconds
Trial 22, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 22, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[18:36:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.66534	validation-auc:0.82738	validation-aucpr:0.82150
[18:36:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.65950	validation-auc:0.82413	validation-aucpr:0.82233
[18:36:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.64338	validation-auc:0.82202	validation-aucpr:0.78106
[18:36:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.64025	validation-auc:0.80404	validation-aucpr:0.75031
[18:36:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.61070	validation-auc:0.84332	validation-aucpr:0.79836
[18:36:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5]	validation-logloss:0.60712	validation-auc:0.83594	validation-aucpr:0.79079
[18:36:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6]	validation-logloss:0.60595	validation-auc:0.82719	validation-aucpr:0.77069
[18:36:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7]	validation-logloss:0.60434	validation-auc:0.82479	validation-aucpr:0.76080
[18:36:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8]	validation-logloss:0.60269	validation-auc:0.82752	validation-aucpr:0.76604
[18:36:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9]	validation-logloss:0.60190	validation-auc:0.82646	validation-aucpr:0.76179
[18:36:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10]	validation-logloss:0.59018	validation-auc:0.83292	validation-aucpr:0.76908
[18:36:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11]	validation-logloss:0.55934	validation-auc:0.86031	validation-aucpr:0.82476
[18:36:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12]	validation-logloss:0.55321	validation-auc:0.85851	validation-aucpr:0.82720
{'best_iteration': '12', 'best_score': '0.82720031891643'}
Trial 22, Fold 2: Log loss = 0.553212866445822, Average precision = 0.8235345231859007, ROC-AUC = 0.858507178125496, Elapsed Time = 0.4681982000001881 seconds
Trial 22, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 22, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[18:36:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.66482	validation-auc:0.82983	validation-aucpr:0.81626
[18:36:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.66482	validation-auc:0.82983	validation-aucpr:0.81626
[18:36:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.65871	validation-auc:0.81927	validation-aucpr:0.80524
[18:36:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.65577	validation-auc:0.79766	validation-aucpr:0.73470
[18:36:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.65339	validation-auc:0.79757	validation-aucpr:0.73619
[18:36:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5]	validation-logloss:0.64140	validation-auc:0.79833	validation-aucpr:0.73805
[18:36:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6]	validation-logloss:0.64140	validation-auc:0.79833	validation-aucpr:0.73805
[18:36:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7]	validation-logloss:0.63474	validation-auc:0.80063	validation-aucpr:0.75471
[18:36:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8]	validation-logloss:0.62018	validation-auc:0.81373	validation-aucpr:0.76437
[18:36:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9]	validation-logloss:0.60200	validation-auc:0.84022	validation-aucpr:0.79799
[18:36:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10]	validation-logloss:0.57422	validation-auc:0.86227	validation-aucpr:0.83985
[18:36:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11]	validation-logloss:0.55015	validation-auc:0.87818	validation-aucpr:0.87099
[18:36:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12]	validation-logloss:0.54954	validation-auc:0.87116	validation-aucpr:0.85570
{'best_iteration': '11', 'best_score': '0.8709905104598027'}
Trial 22, Fold 3: Log loss = 0.5495449233834574, Average precision = 0.8494831472255703, ROC-AUC = 0.8711569912887287, Elapsed Time = 0.5590210000009392 seconds
Trial 22, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 22, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[18:36:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.66513	validation-auc:0.82575	validation-aucpr:0.80961
[18:36:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.65613	validation-auc:0.81019	validation-aucpr:0.79149
[18:36:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.62651	validation-auc:0.85436	validation-aucpr:0.85019
[18:36:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.61667	validation-auc:0.85176	validation-aucpr:0.84246
[18:36:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.60990	validation-auc:0.84522	validation-aucpr:0.83211
[18:36:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5]	validation-logloss:0.58277	validation-auc:0.86419	validation-aucpr:0.85499
[18:36:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6]	validation-logloss:0.58013	validation-auc:0.86915	validation-aucpr:0.86664
[18:36:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7]	validation-logloss:0.57805	validation-auc:0.85937	validation-aucpr:0.85414
[18:36:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8]	validation-logloss:0.55521	validation-auc:0.87155	validation-aucpr:0.86867
[18:36:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9]	validation-logloss:0.55454	validation-auc:0.86622	validation-aucpr:0.86019
[18:36:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10]	validation-logloss:0.55367	validation-auc:0.86482	validation-aucpr:0.85700
[18:36:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11]	validation-logloss:0.55322	validation-auc:0.85839	validation-aucpr:0.84371
[18:36:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12]	validation-logloss:0.55183	validation-auc:0.85330	validation-aucpr:0.83146
{'best_iteration': '8', 'best_score': '0.8686678124859214'}
Trial 22, Fold 4: Log loss = 0.5518270399325328, Average precision = 0.8193038375097094, ROC-AUC = 0.8533011259182958, Elapsed Time = 0.5348350000003848 seconds
Trial 22, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 22, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[18:36:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.66578	validation-auc:0.81850	validation-aucpr:0.82146
[18:36:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.65403	validation-auc:0.81988	validation-aucpr:0.82139
[18:36:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.64183	validation-auc:0.85871	validation-aucpr:0.85673
[18:36:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.63956	validation-auc:0.84010	validation-aucpr:0.80902
[18:36:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.62942	validation-auc:0.82334	validation-aucpr:0.78522
[18:36:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5]	validation-logloss:0.62667	validation-auc:0.81153	validation-aucpr:0.74412
[18:36:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6]	validation-logloss:0.61833	validation-auc:0.80627	validation-aucpr:0.74120
[18:36:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7]	validation-logloss:0.59032	validation-auc:0.84637	validation-aucpr:0.83656
[18:36:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8]	validation-logloss:0.56555	validation-auc:0.86916	validation-aucpr:0.87463
[18:36:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9]	validation-logloss:0.54567	validation-auc:0.87825	validation-aucpr:0.88727
[18:36:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10]	validation-logloss:0.52925	validation-auc:0.88512	validation-aucpr:0.89739
[18:36:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11]	validation-logloss:0.51573	validation-auc:0.90352	validation-aucpr:0.92329
[18:36:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12]	validation-logloss:0.51248	validation-auc:0.90250	validation-aucpr:0.92071
{'best_iteration': '11', 'best_score': '0.9232917725587191'}
Trial 22, Fold 5: Log loss = 0.5124814976954526, Average precision = 0.9202401278312367, ROC-AUC = 0.902498931683481, Elapsed Time = 0.5430703000001813 seconds
Optimization Progress:  23%|##3       | 23/100 [37:27<1:06:26, 51.77s/it]
Trial 23, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 23, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[0]	validation-logloss:0.65142	validation-auc:0.94745	validation-aucpr:0.95243
[1]	validation-logloss:0.62137	validation-auc:0.94927	validation-aucpr:0.95089
[2]	validation-logloss:0.59166	validation-auc:0.95627	validation-aucpr:0.95819
[3]	validation-logloss:0.56790	validation-auc:0.95636	validation-aucpr:0.96088
[4]	validation-logloss:0.54506	validation-auc:0.95734	validation-aucpr:0.96296
[5]	validation-logloss:0.52566	validation-auc:0.95732	validation-aucpr:0.96323
[6]	validation-logloss:0.50700	validation-auc:0.95756	validation-aucpr:0.96351
[7]	validation-logloss:0.49095	validation-auc:0.95730	validation-aucpr:0.96324
[8]	validation-logloss:0.47611	validation-auc:0.95725	validation-aucpr:0.96301
[9]	validation-logloss:0.46227	validation-auc:0.95769	validation-aucpr:0.96355
[10]	validation-logloss:0.44962	validation-auc:0.95778	validation-aucpr:0.96382
[11]	validation-logloss:0.43869	validation-auc:0.95727	validation-aucpr:0.96319
[12]	validation-logloss:0.42806	validation-auc:0.95693	validation-aucpr:0.96267
[13]	validation-logloss:0.41717	validation-auc:0.95729	validation-aucpr:0.96305
[14]	validation-logloss:0.40813	validation-auc:0.95732	validation-aucpr:0.96313
[15]	validation-logloss:0.39866	validation-auc:0.95750	validation-aucpr:0.96324
[16]	validation-logloss:0.39102	validation-auc:0.95731	validation-aucpr:0.96300
[17]	validation-logloss:0.38266	validation-auc:0.95769	validation-aucpr:0.96357
[18]	validation-logloss:0.37561	validation-auc:0.95784	validation-aucpr:0.96363
[19]	validation-logloss:0.36781	validation-auc:0.95813	validation-aucpr:0.96379
[20]	validation-logloss:0.35548	validation-auc:0.95962	validation-aucpr:0.96542
[21]	validation-logloss:0.34964	validation-auc:0.95965	validation-aucpr:0.96545
[22]	validation-logloss:0.34032	validation-auc:0.96010	validation-aucpr:0.96593
[23]	validation-logloss:0.33570	validation-auc:0.96010	validation-aucpr:0.96595
[24]	validation-logloss:0.33073	validation-auc:0.96006	validation-aucpr:0.96597
[25]	validation-logloss:0.32643	validation-auc:0.96002	validation-aucpr:0.96592
[26]	validation-logloss:0.32250	validation-auc:0.96016	validation-aucpr:0.96612
[27]	validation-logloss:0.31781	validation-auc:0.96024	validation-aucpr:0.96620
[28]	validation-logloss:0.31404	validation-auc:0.96027	validation-aucpr:0.96624
[29]	validation-logloss:0.31084	validation-auc:0.96014	validation-aucpr:0.96610
[30]	validation-logloss:0.30620	validation-auc:0.96034	validation-aucpr:0.96620
[31]	validation-logloss:0.30307	validation-auc:0.96037	validation-aucpr:0.96618
[32]	validation-logloss:0.30012	validation-auc:0.96037	validation-aucpr:0.96621
[33]	validation-logloss:0.29406	validation-auc:0.96078	validation-aucpr:0.96672
[34]	validation-logloss:0.29074	validation-auc:0.96098	validation-aucpr:0.96685
[35]	validation-logloss:0.28843	validation-auc:0.96086	validation-aucpr:0.96678
[36]	validation-logloss:0.28256	validation-auc:0.96131	validation-aucpr:0.96724
[37]	validation-logloss:0.28047	validation-auc:0.96140	validation-aucpr:0.96734
[38]	validation-logloss:0.27881	validation-auc:0.96137	validation-aucpr:0.96733
[39]	validation-logloss:0.27380	validation-auc:0.96173	validation-aucpr:0.96775
[40]	validation-logloss:0.27198	validation-auc:0.96188	validation-aucpr:0.96784
[41]	validation-logloss:0.26990	validation-auc:0.96197	validation-aucpr:0.96797
[42]	validation-logloss:0.26509	validation-auc:0.96240	validation-aucpr:0.96838
[43]	validation-logloss:0.26108	validation-auc:0.96266	validation-aucpr:0.96871
[44]	validation-logloss:0.25987	validation-auc:0.96274	validation-aucpr:0.96882
[45]	validation-logloss:0.25749	validation-auc:0.96296	validation-aucpr:0.96896
[46]	validation-logloss:0.25402	validation-auc:0.96326	validation-aucpr:0.96924
[47]	validation-logloss:0.25303	validation-auc:0.96322	validation-aucpr:0.96920
[48]	validation-logloss:0.24918	validation-auc:0.96356	validation-aucpr:0.96953
[49]	validation-logloss:0.24767	validation-auc:0.96370	validation-aucpr:0.96968
{'best_iteration': '49', 'best_score': '0.9696847725863038'}
Trial 23, Fold 1: Log loss = 0.24766681338758925, Average precision = 0.9696768625925263, ROC-AUC = 0.9637044714252259, Elapsed Time = 0.7275594000002457 seconds
Trial 23, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 23, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[0]	validation-logloss:0.65076	validation-auc:0.95547	validation-aucpr:0.95878
[1]	validation-logloss:0.62079	validation-auc:0.95520	validation-aucpr:0.95925
[2]	validation-logloss:0.59094	validation-auc:0.96035	validation-aucpr:0.96300
[3]	validation-logloss:0.56574	validation-auc:0.95943	validation-aucpr:0.96253
[4]	validation-logloss:0.54324	validation-auc:0.95905	validation-aucpr:0.96279
[5]	validation-logloss:0.52416	validation-auc:0.95799	validation-aucpr:0.96168
[6]	validation-logloss:0.50594	validation-auc:0.95773	validation-aucpr:0.96156
[7]	validation-logloss:0.48921	validation-auc:0.95809	validation-aucpr:0.96176
[8]	validation-logloss:0.47411	validation-auc:0.95814	validation-aucpr:0.96165
[9]	validation-logloss:0.46001	validation-auc:0.95822	validation-aucpr:0.96180
[10]	validation-logloss:0.44764	validation-auc:0.95859	validation-aucpr:0.96215
[11]	validation-logloss:0.43637	validation-auc:0.95820	validation-aucpr:0.96164
[12]	validation-logloss:0.42511	validation-auc:0.95776	validation-aucpr:0.96100
[13]	validation-logloss:0.41542	validation-auc:0.95809	validation-aucpr:0.96139
[14]	validation-logloss:0.40547	validation-auc:0.95825	validation-aucpr:0.96143
[15]	validation-logloss:0.39649	validation-auc:0.95792	validation-aucpr:0.96112
[16]	validation-logloss:0.38883	validation-auc:0.95767	validation-aucpr:0.96081
[17]	validation-logloss:0.38098	validation-auc:0.95788	validation-aucpr:0.96105
[18]	validation-logloss:0.37365	validation-auc:0.95801	validation-aucpr:0.96114
[19]	validation-logloss:0.36631	validation-auc:0.95787	validation-aucpr:0.96084
[20]	validation-logloss:0.35361	validation-auc:0.96002	validation-aucpr:0.96324
[21]	validation-logloss:0.34797	validation-auc:0.96005	validation-aucpr:0.96396
[22]	validation-logloss:0.33817	validation-auc:0.96124	validation-aucpr:0.96533
[23]	validation-logloss:0.33317	validation-auc:0.96124	validation-aucpr:0.96524
[24]	validation-logloss:0.32859	validation-auc:0.96103	validation-aucpr:0.96492
[25]	validation-logloss:0.32413	validation-auc:0.96115	validation-aucpr:0.96500
[26]	validation-logloss:0.32020	validation-auc:0.96120	validation-aucpr:0.96508
[27]	validation-logloss:0.31554	validation-auc:0.96137	validation-aucpr:0.96520
[28]	validation-logloss:0.31202	validation-auc:0.96132	validation-aucpr:0.96507
[29]	validation-logloss:0.30877	validation-auc:0.96123	validation-aucpr:0.96495
[30]	validation-logloss:0.30487	validation-auc:0.96133	validation-aucpr:0.96507
[31]	validation-logloss:0.30191	validation-auc:0.96120	validation-aucpr:0.96481
[32]	validation-logloss:0.29842	validation-auc:0.96149	validation-aucpr:0.96495
[33]	validation-logloss:0.29569	validation-auc:0.96156	validation-aucpr:0.96495
[34]	validation-logloss:0.29351	validation-auc:0.96147	validation-aucpr:0.96481
[35]	validation-logloss:0.29049	validation-auc:0.96158	validation-aucpr:0.96485
[36]	validation-logloss:0.28788	validation-auc:0.96178	validation-aucpr:0.96504
[37]	validation-logloss:0.28599	validation-auc:0.96173	validation-aucpr:0.96522
[38]	validation-logloss:0.28160	validation-auc:0.96221	validation-aucpr:0.96574
[39]	validation-logloss:0.27990	validation-auc:0.96224	validation-aucpr:0.96577
[40]	validation-logloss:0.27426	validation-auc:0.96297	validation-aucpr:0.96660
[41]	validation-logloss:0.27208	validation-auc:0.96303	validation-aucpr:0.96658
[42]	validation-logloss:0.27032	validation-auc:0.96320	validation-aucpr:0.96666
[43]	validation-logloss:0.26775	validation-auc:0.96356	validation-aucpr:0.96697
[44]	validation-logloss:0.26599	validation-auc:0.96380	validation-aucpr:0.96717
[45]	validation-logloss:0.26078	validation-auc:0.96424	validation-aucpr:0.96769
[46]	validation-logloss:0.25623	validation-auc:0.96467	validation-aucpr:0.96822
[47]	validation-logloss:0.25439	validation-auc:0.96492	validation-aucpr:0.96840
[48]	validation-logloss:0.25253	validation-auc:0.96509	validation-aucpr:0.96856
[49]	validation-logloss:0.25123	validation-auc:0.96519	validation-aucpr:0.96855
{'best_iteration': '48', 'best_score': '0.9685554995351819'}
Trial 23, Fold 2: Log loss = 0.2512295796579628, Average precision = 0.9685552808996968, ROC-AUC = 0.9651874646617346, Elapsed Time = 0.9321792999999161 seconds
Trial 23, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 23, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[0]	validation-logloss:0.65094	validation-auc:0.95011	validation-aucpr:0.95493
[1]	validation-logloss:0.62074	validation-auc:0.95393	validation-aucpr:0.95924
[2]	validation-logloss:0.59084	validation-auc:0.95915	validation-aucpr:0.96202
[3]	validation-logloss:0.56659	validation-auc:0.95978	validation-aucpr:0.96225
[4]	validation-logloss:0.54377	validation-auc:0.96088	validation-aucpr:0.96407
[5]	validation-logloss:0.52402	validation-auc:0.96068	validation-aucpr:0.96381
[6]	validation-logloss:0.50510	validation-auc:0.96063	validation-aucpr:0.96433
[7]	validation-logloss:0.48890	validation-auc:0.96084	validation-aucpr:0.96447
[8]	validation-logloss:0.47362	validation-auc:0.96101	validation-aucpr:0.96516
[9]	validation-logloss:0.45971	validation-auc:0.96097	validation-aucpr:0.96512
[10]	validation-logloss:0.44767	validation-auc:0.96091	validation-aucpr:0.96561
[11]	validation-logloss:0.43616	validation-auc:0.96086	validation-aucpr:0.96555
[12]	validation-logloss:0.42504	validation-auc:0.96080	validation-aucpr:0.96553
[13]	validation-logloss:0.41525	validation-auc:0.96058	validation-aucpr:0.96536
[14]	validation-logloss:0.40553	validation-auc:0.96068	validation-aucpr:0.96557
[15]	validation-logloss:0.39607	validation-auc:0.96051	validation-aucpr:0.96532
[16]	validation-logloss:0.38830	validation-auc:0.96043	validation-aucpr:0.96513
[17]	validation-logloss:0.38025	validation-auc:0.96041	validation-aucpr:0.96517
[18]	validation-logloss:0.37246	validation-auc:0.96107	validation-aucpr:0.96578
[19]	validation-logloss:0.36432	validation-auc:0.96102	validation-aucpr:0.96567
[20]	validation-logloss:0.35163	validation-auc:0.96277	validation-aucpr:0.96762
[21]	validation-logloss:0.34558	validation-auc:0.96282	validation-aucpr:0.96765
[22]	validation-logloss:0.34035	validation-auc:0.96282	validation-aucpr:0.96757
[23]	validation-logloss:0.33545	validation-auc:0.96275	validation-aucpr:0.96748
[24]	validation-logloss:0.33083	validation-auc:0.96262	validation-aucpr:0.96742
[25]	validation-logloss:0.32681	validation-auc:0.96253	validation-aucpr:0.96728
[26]	validation-logloss:0.32234	validation-auc:0.96271	validation-aucpr:0.96742
[27]	validation-logloss:0.31705	validation-auc:0.96295	validation-aucpr:0.96766
[28]	validation-logloss:0.30917	validation-auc:0.96358	validation-aucpr:0.96839
[29]	validation-logloss:0.30557	validation-auc:0.96361	validation-aucpr:0.96839
[30]	validation-logloss:0.30173	validation-auc:0.96373	validation-aucpr:0.96850
[31]	validation-logloss:0.29417	validation-auc:0.96419	validation-aucpr:0.96906
[32]	validation-logloss:0.29089	validation-auc:0.96424	validation-aucpr:0.96905
[33]	validation-logloss:0.28750	validation-auc:0.96454	validation-aucpr:0.96928
[34]	validation-logloss:0.28470	validation-auc:0.96464	validation-aucpr:0.96931
[35]	validation-logloss:0.28159	validation-auc:0.96483	validation-aucpr:0.96945
[36]	validation-logloss:0.27894	validation-auc:0.96503	validation-aucpr:0.96960
[37]	validation-logloss:0.27702	validation-auc:0.96496	validation-aucpr:0.96950
[38]	validation-logloss:0.27312	validation-auc:0.96528	validation-aucpr:0.96985
[39]	validation-logloss:0.27116	validation-auc:0.96537	validation-aucpr:0.96993
[40]	validation-logloss:0.26910	validation-auc:0.96543	validation-aucpr:0.96994
[41]	validation-logloss:0.26757	validation-auc:0.96546	validation-aucpr:0.96990
[42]	validation-logloss:0.26571	validation-auc:0.96558	validation-aucpr:0.96999
[43]	validation-logloss:0.26388	validation-auc:0.96563	validation-aucpr:0.97009
[44]	validation-logloss:0.26222	validation-auc:0.96565	validation-aucpr:0.97012
[45]	validation-logloss:0.26045	validation-auc:0.96573	validation-aucpr:0.97019
[46]	validation-logloss:0.25509	validation-auc:0.96613	validation-aucpr:0.97062
[47]	validation-logloss:0.25374	validation-auc:0.96619	validation-aucpr:0.97069
[48]	validation-logloss:0.24965	validation-auc:0.96642	validation-aucpr:0.97098
[49]	validation-logloss:0.24819	validation-auc:0.96651	validation-aucpr:0.97104
{'best_iteration': '49', 'best_score': '0.9710446138361724'}
Trial 23, Fold 3: Log loss = 0.2481948596089837, Average precision = 0.9710479627303537, ROC-AUC = 0.9665101071693425, Elapsed Time = 0.9626014999994368 seconds
Trial 23, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 23, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[0]	validation-logloss:0.65127	validation-auc:0.95231	validation-aucpr:0.95618
[1]	validation-logloss:0.62100	validation-auc:0.95361	validation-aucpr:0.95967
[2]	validation-logloss:0.59108	validation-auc:0.95902	validation-aucpr:0.96362
[3]	validation-logloss:0.56568	validation-auc:0.95876	validation-aucpr:0.96329
[4]	validation-logloss:0.54216	validation-auc:0.95913	validation-aucpr:0.96411
[5]	validation-logloss:0.52275	validation-auc:0.95806	validation-aucpr:0.96311
[6]	validation-logloss:0.50436	validation-auc:0.95771	validation-aucpr:0.96312
[7]	validation-logloss:0.48816	validation-auc:0.95754	validation-aucpr:0.96301
[8]	validation-logloss:0.46874	validation-auc:0.95865	validation-aucpr:0.96445
[9]	validation-logloss:0.45442	validation-auc:0.95822	validation-aucpr:0.96400
[10]	validation-logloss:0.44039	validation-auc:0.95868	validation-aucpr:0.96439
[11]	validation-logloss:0.42853	validation-auc:0.95842	validation-aucpr:0.96421
[12]	validation-logloss:0.41335	validation-auc:0.95939	validation-aucpr:0.96542
[13]	validation-logloss:0.40389	validation-auc:0.95910	validation-aucpr:0.96502
[14]	validation-logloss:0.39455	validation-auc:0.95928	validation-aucpr:0.96531
[15]	validation-logloss:0.38641	validation-auc:0.95885	validation-aucpr:0.96474
[16]	validation-logloss:0.37758	validation-auc:0.95947	validation-aucpr:0.96527
[17]	validation-logloss:0.37064	validation-auc:0.95915	validation-aucpr:0.96510
[18]	validation-logloss:0.36283	validation-auc:0.95939	validation-aucpr:0.96515
[19]	validation-logloss:0.35623	validation-auc:0.95959	validation-aucpr:0.96535
[20]	validation-logloss:0.34765	validation-auc:0.95990	validation-aucpr:0.96570
[21]	validation-logloss:0.33788	validation-auc:0.96041	validation-aucpr:0.96618
[22]	validation-logloss:0.33286	validation-auc:0.96047	validation-aucpr:0.96629
[23]	validation-logloss:0.32806	validation-auc:0.96056	validation-aucpr:0.96638
[24]	validation-logloss:0.32353	validation-auc:0.96055	validation-aucpr:0.96643
[25]	validation-logloss:0.31975	validation-auc:0.96031	validation-aucpr:0.96618
[26]	validation-logloss:0.31514	validation-auc:0.96067	validation-aucpr:0.96655
[27]	validation-logloss:0.31032	validation-auc:0.96126	validation-aucpr:0.96711
[28]	validation-logloss:0.30632	validation-auc:0.96135	validation-aucpr:0.96723
[29]	validation-logloss:0.30231	validation-auc:0.96148	validation-aucpr:0.96732
[30]	validation-logloss:0.29866	validation-auc:0.96169	validation-aucpr:0.96746
[31]	validation-logloss:0.29123	validation-auc:0.96245	validation-aucpr:0.96823
[32]	validation-logloss:0.28793	validation-auc:0.96271	validation-aucpr:0.96844
[33]	validation-logloss:0.28510	validation-auc:0.96289	validation-aucpr:0.96852
[34]	validation-logloss:0.28273	validation-auc:0.96278	validation-aucpr:0.96849
[35]	validation-logloss:0.27988	validation-auc:0.96291	validation-aucpr:0.96864
[36]	validation-logloss:0.27747	validation-auc:0.96300	validation-aucpr:0.96868
[37]	validation-logloss:0.27322	validation-auc:0.96308	validation-aucpr:0.96878
[38]	validation-logloss:0.27006	validation-auc:0.96338	validation-aucpr:0.96904
[39]	validation-logloss:0.26814	validation-auc:0.96341	validation-aucpr:0.96907
[40]	validation-logloss:0.26349	validation-auc:0.96377	validation-aucpr:0.96947
[41]	validation-logloss:0.26134	validation-auc:0.96377	validation-aucpr:0.96947
[42]	validation-logloss:0.25983	validation-auc:0.96387	validation-aucpr:0.96953
[43]	validation-logloss:0.25777	validation-auc:0.96407	validation-aucpr:0.96974
[44]	validation-logloss:0.25627	validation-auc:0.96414	validation-aucpr:0.96980
[45]	validation-logloss:0.25499	validation-auc:0.96418	validation-aucpr:0.96982
[46]	validation-logloss:0.25342	validation-auc:0.96421	validation-aucpr:0.96983
[47]	validation-logloss:0.25134	validation-auc:0.96452	validation-aucpr:0.97008
[48]	validation-logloss:0.24962	validation-auc:0.96460	validation-aucpr:0.97014
[49]	validation-logloss:0.24831	validation-auc:0.96464	validation-aucpr:0.97017
{'best_iteration': '49', 'best_score': '0.9701724688367994'}
Trial 23, Fold 4: Log loss = 0.24831006230194794, Average precision = 0.9701685420356017, ROC-AUC = 0.9646393520604877, Elapsed Time = 0.9430483999985881 seconds
Trial 23, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 23, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[0]	validation-logloss:0.65341	validation-auc:0.94036	validation-aucpr:0.94665
[1]	validation-logloss:0.62349	validation-auc:0.94719	validation-aucpr:0.95075
[2]	validation-logloss:0.59392	validation-auc:0.95151	validation-aucpr:0.95509
[3]	validation-logloss:0.56876	validation-auc:0.95169	validation-aucpr:0.95491
[4]	validation-logloss:0.54627	validation-auc:0.95179	validation-aucpr:0.95553
[5]	validation-logloss:0.52703	validation-auc:0.95119	validation-aucpr:0.95475
[6]	validation-logloss:0.50902	validation-auc:0.95145	validation-aucpr:0.95485
[7]	validation-logloss:0.49313	validation-auc:0.95188	validation-aucpr:0.95539
[8]	validation-logloss:0.47810	validation-auc:0.95206	validation-aucpr:0.95544
[9]	validation-logloss:0.46450	validation-auc:0.95245	validation-aucpr:0.95590
[10]	validation-logloss:0.45314	validation-auc:0.95285	validation-aucpr:0.95732
[11]	validation-logloss:0.44223	validation-auc:0.95201	validation-aucpr:0.95620
[12]	validation-logloss:0.43136	validation-auc:0.95245	validation-aucpr:0.95666
[13]	validation-logloss:0.42132	validation-auc:0.95300	validation-aucpr:0.95781
[14]	validation-logloss:0.41218	validation-auc:0.95364	validation-aucpr:0.95852
[15]	validation-logloss:0.40326	validation-auc:0.95349	validation-aucpr:0.95822
[16]	validation-logloss:0.39550	validation-auc:0.95354	validation-aucpr:0.95821
[17]	validation-logloss:0.38717	validation-auc:0.95365	validation-aucpr:0.95878
[18]	validation-logloss:0.37973	validation-auc:0.95397	validation-aucpr:0.95907
[19]	validation-logloss:0.37334	validation-auc:0.95384	validation-aucpr:0.95888
[20]	validation-logloss:0.36369	validation-auc:0.95533	validation-aucpr:0.96088
[21]	validation-logloss:0.35262	validation-auc:0.95690	validation-aucpr:0.96268
[22]	validation-logloss:0.34747	validation-auc:0.95699	validation-aucpr:0.96275
[23]	validation-logloss:0.34284	validation-auc:0.95703	validation-aucpr:0.96275
[24]	validation-logloss:0.33856	validation-auc:0.95686	validation-aucpr:0.96256
[25]	validation-logloss:0.33481	validation-auc:0.95681	validation-aucpr:0.96247
[26]	validation-logloss:0.33103	validation-auc:0.95679	validation-aucpr:0.96240
[27]	validation-logloss:0.32584	validation-auc:0.95730	validation-aucpr:0.96285
[28]	validation-logloss:0.32212	validation-auc:0.95731	validation-aucpr:0.96282
[29]	validation-logloss:0.31790	validation-auc:0.95754	validation-aucpr:0.96303
[30]	validation-logloss:0.31422	validation-auc:0.95769	validation-aucpr:0.96313
[31]	validation-logloss:0.30608	validation-auc:0.95884	validation-aucpr:0.96436
[32]	validation-logloss:0.30309	validation-auc:0.95884	validation-aucpr:0.96434
[33]	validation-logloss:0.30032	validation-auc:0.95909	validation-aucpr:0.96449
[34]	validation-logloss:0.29777	validation-auc:0.95917	validation-aucpr:0.96453
[35]	validation-logloss:0.29496	validation-auc:0.95929	validation-aucpr:0.96457
[36]	validation-logloss:0.29258	validation-auc:0.95940	validation-aucpr:0.96467
[37]	validation-logloss:0.28761	validation-auc:0.95986	validation-aucpr:0.96524
[38]	validation-logloss:0.28418	validation-auc:0.96020	validation-aucpr:0.96553
[39]	validation-logloss:0.28254	validation-auc:0.96024	validation-aucpr:0.96552
[40]	validation-logloss:0.27701	validation-auc:0.96075	validation-aucpr:0.96604
[41]	validation-logloss:0.27492	validation-auc:0.96083	validation-aucpr:0.96608
[42]	validation-logloss:0.27313	validation-auc:0.96102	validation-aucpr:0.96621
[43]	validation-logloss:0.27094	validation-auc:0.96121	validation-aucpr:0.96640
[44]	validation-logloss:0.26934	validation-auc:0.96140	validation-aucpr:0.96654
[45]	validation-logloss:0.26473	validation-auc:0.96188	validation-aucpr:0.96703
[46]	validation-logloss:0.26296	validation-auc:0.96204	validation-aucpr:0.96714
[47]	validation-logloss:0.26193	validation-auc:0.96202	validation-aucpr:0.96708
[48]	validation-logloss:0.25814	validation-auc:0.96221	validation-aucpr:0.96730
[49]	validation-logloss:0.25625	validation-auc:0.96249	validation-aucpr:0.96753
{'best_iteration': '49', 'best_score': '0.9675298542241902'}
Trial 23, Fold 5: Log loss = 0.2562486586560138, Average precision = 0.9675302529511657, ROC-AUC = 0.9624879326681902, Elapsed Time = 0.9584814000008919 seconds
Optimization Progress:  24%|##4       | 24/100 [37:40<50:37, 39.97s/it]  
Trial 24, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 24, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[18:36:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.67754	validation-auc:0.94648	validation-aucpr:0.95313
[18:36:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.66446	validation-auc:0.95291	validation-aucpr:0.94587
[18:36:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.65146	validation-auc:0.95806	validation-aucpr:0.95520
[18:36:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.63909	validation-auc:0.96182	validation-aucpr:0.96495
[18:36:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.62699	validation-auc:0.96247	validation-aucpr:0.96495
[18:36:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5]	validation-logloss:0.61423	validation-auc:0.96446	validation-aucpr:0.96677
[18:36:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6]	validation-logloss:0.60175	validation-auc:0.96548	validation-aucpr:0.96685
[18:36:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7]	validation-logloss:0.58963	validation-auc:0.96624	validation-aucpr:0.96807
[18:36:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8]	validation-logloss:0.57821	validation-auc:0.96716	validation-aucpr:0.97022
[18:36:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9]	validation-logloss:0.56892	validation-auc:0.96625	validation-aucpr:0.96933
[18:36:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10]	validation-logloss:0.55941	validation-auc:0.96601	validation-aucpr:0.96871
[18:36:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11]	validation-logloss:0.55067	validation-auc:0.96578	validation-aucpr:0.96819
[18:36:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12]	validation-logloss:0.54168	validation-auc:0.96579	validation-aucpr:0.96820
[18:36:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13]	validation-logloss:0.53233	validation-auc:0.96591	validation-aucpr:0.96825
[18:36:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14]	validation-logloss:0.52405	validation-auc:0.96611	validation-aucpr:0.96853
[18:36:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15]	validation-logloss:0.51486	validation-auc:0.96676	validation-aucpr:0.97094
[18:36:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16]	validation-logloss:0.50584	validation-auc:0.96697	validation-aucpr:0.97092
[18:36:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17]	validation-logloss:0.49724	validation-auc:0.96733	validation-aucpr:0.97127
[18:36:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18]	validation-logloss:0.48909	validation-auc:0.96750	validation-aucpr:0.97149
[18:36:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19]	validation-logloss:0.48088	validation-auc:0.96805	validation-aucpr:0.97265
[18:36:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20]	validation-logloss:0.47322	validation-auc:0.96806	validation-aucpr:0.97261
[18:36:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21]	validation-logloss:0.46678	validation-auc:0.96827	validation-aucpr:0.97284
[18:36:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22]	validation-logloss:0.45953	validation-auc:0.96831	validation-aucpr:0.97290
[18:36:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23]	validation-logloss:0.45338	validation-auc:0.96823	validation-aucpr:0.97294
[18:36:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24]	validation-logloss:0.44648	validation-auc:0.96838	validation-aucpr:0.97315
[18:36:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25]	validation-logloss:0.44021	validation-auc:0.96859	validation-aucpr:0.97336
[18:36:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26]	validation-logloss:0.43359	validation-auc:0.96883	validation-aucpr:0.97356
[18:36:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27]	validation-logloss:0.42834	validation-auc:0.96874	validation-aucpr:0.97337
[18:36:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28]	validation-logloss:0.42314	validation-auc:0.96859	validation-aucpr:0.97323
[18:36:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29]	validation-logloss:0.41808	validation-auc:0.96843	validation-aucpr:0.97308
[18:36:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30]	validation-logloss:0.41306	validation-auc:0.96843	validation-aucpr:0.97304
[18:36:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31]	validation-logloss:0.40727	validation-auc:0.96855	validation-aucpr:0.97313
[18:36:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32]	validation-logloss:0.40193	validation-auc:0.96857	validation-aucpr:0.97325
[18:36:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33]	validation-logloss:0.39664	validation-auc:0.96855	validation-aucpr:0.97326
[18:36:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34]	validation-logloss:0.39140	validation-auc:0.96859	validation-aucpr:0.97331
[18:36:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35]	validation-logloss:0.38723	validation-auc:0.96852	validation-aucpr:0.97318
[18:36:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36]	validation-logloss:0.38311	validation-auc:0.96843	validation-aucpr:0.97305
[18:36:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37]	validation-logloss:0.37909	validation-auc:0.96848	validation-aucpr:0.97308
[18:36:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38]	validation-logloss:0.37509	validation-auc:0.96848	validation-aucpr:0.97306
[18:36:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39]	validation-logloss:0.37125	validation-auc:0.96835	validation-aucpr:0.97298
[18:36:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40]	validation-logloss:0.36685	validation-auc:0.96846	validation-aucpr:0.97302
[18:36:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41]	validation-logloss:0.36304	validation-auc:0.96848	validation-aucpr:0.97304
[18:36:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42]	validation-logloss:0.35967	validation-auc:0.96834	validation-aucpr:0.97288
[18:36:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43]	validation-logloss:0.35625	validation-auc:0.96830	validation-aucpr:0.97281
{'best_iteration': '26', 'best_score': '0.9735624594295109'}
Trial 24, Fold 1: Log loss = 0.3562467255542872, Average precision = 0.9728185954572157, ROC-AUC = 0.9682985528598671, Elapsed Time = 4.798161999999138 seconds
Trial 24, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 24, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[18:36:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.67795	validation-auc:0.95003	validation-aucpr:0.95522
[18:36:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.66306	validation-auc:0.95893	validation-aucpr:0.96415
[18:36:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.64936	validation-auc:0.96179	validation-aucpr:0.96638
[18:36:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.63579	validation-auc:0.96512	validation-aucpr:0.96811
[18:36:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.62415	validation-auc:0.96673	validation-aucpr:0.97068
[18:36:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5]	validation-logloss:0.61271	validation-auc:0.96744	validation-aucpr:0.97100
[18:36:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6]	validation-logloss:0.60174	validation-auc:0.96816	validation-aucpr:0.97156
[18:36:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7]	validation-logloss:0.59079	validation-auc:0.96877	validation-aucpr:0.97198
[18:36:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8]	validation-logloss:0.57919	validation-auc:0.96929	validation-aucpr:0.97251
[18:36:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9]	validation-logloss:0.56807	validation-auc:0.96969	validation-aucpr:0.97285
[18:36:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10]	validation-logloss:0.55900	validation-auc:0.96921	validation-aucpr:0.97237
[18:36:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11]	validation-logloss:0.54858	validation-auc:0.96939	validation-aucpr:0.97284
[18:36:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12]	validation-logloss:0.53997	validation-auc:0.96886	validation-aucpr:0.97237
[18:36:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13]	validation-logloss:0.53013	validation-auc:0.96942	validation-aucpr:0.97277
[18:36:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14]	validation-logloss:0.52183	validation-auc:0.96954	validation-aucpr:0.97283
[18:36:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15]	validation-logloss:0.51258	validation-auc:0.96997	validation-aucpr:0.97327
[18:36:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16]	validation-logloss:0.50491	validation-auc:0.96963	validation-aucpr:0.97299
[18:36:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17]	validation-logloss:0.49735	validation-auc:0.96973	validation-aucpr:0.97301
[18:36:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18]	validation-logloss:0.48889	validation-auc:0.97011	validation-aucpr:0.97329
[18:36:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19]	validation-logloss:0.48145	validation-auc:0.97019	validation-aucpr:0.97343
[18:36:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20]	validation-logloss:0.47475	validation-auc:0.97019	validation-aucpr:0.97342
[18:36:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21]	validation-logloss:0.46701	validation-auc:0.97026	validation-aucpr:0.97344
[18:36:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22]	validation-logloss:0.46030	validation-auc:0.97013	validation-aucpr:0.97329
[18:36:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23]	validation-logloss:0.45406	validation-auc:0.97007	validation-aucpr:0.97314
[18:36:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24]	validation-logloss:0.44778	validation-auc:0.97021	validation-aucpr:0.97322
[18:36:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25]	validation-logloss:0.44202	validation-auc:0.97008	validation-aucpr:0.97314
[18:36:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26]	validation-logloss:0.43536	validation-auc:0.97028	validation-aucpr:0.97352
[18:36:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27]	validation-logloss:0.42986	validation-auc:0.97028	validation-aucpr:0.97349
[18:36:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28]	validation-logloss:0.42455	validation-auc:0.97021	validation-aucpr:0.97343
[18:36:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29]	validation-logloss:0.41942	validation-auc:0.97009	validation-aucpr:0.97327
[18:36:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30]	validation-logloss:0.41425	validation-auc:0.97005	validation-aucpr:0.97326
[18:36:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31]	validation-logloss:0.40943	validation-auc:0.97011	validation-aucpr:0.97340
[18:36:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32]	validation-logloss:0.40441	validation-auc:0.97025	validation-aucpr:0.97342
[18:36:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33]	validation-logloss:0.39989	validation-auc:0.97015	validation-aucpr:0.97330
[18:36:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34]	validation-logloss:0.39579	validation-auc:0.96989	validation-aucpr:0.97309
[18:36:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35]	validation-logloss:0.39141	validation-auc:0.96986	validation-aucpr:0.97305
[18:36:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36]	validation-logloss:0.38702	validation-auc:0.96988	validation-aucpr:0.97308
[18:36:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37]	validation-logloss:0.38202	validation-auc:0.97007	validation-aucpr:0.97317
[18:36:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38]	validation-logloss:0.37813	validation-auc:0.96996	validation-aucpr:0.97311
[18:36:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39]	validation-logloss:0.37420	validation-auc:0.96994	validation-aucpr:0.97310
[18:36:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40]	validation-logloss:0.36930	validation-auc:0.97010	validation-aucpr:0.97325
[18:36:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41]	validation-logloss:0.36546	validation-auc:0.97011	validation-aucpr:0.97321
[18:36:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42]	validation-logloss:0.36082	validation-auc:0.97021	validation-aucpr:0.97330
[18:36:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43]	validation-logloss:0.35630	validation-auc:0.97040	validation-aucpr:0.97347
{'best_iteration': '26', 'best_score': '0.9735159251236497'}
Trial 24, Fold 2: Log loss = 0.3563022562597658, Average precision = 0.9734075077131078, ROC-AUC = 0.9704006080837259, Elapsed Time = 4.943635200001154 seconds
Trial 24, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 24, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[18:36:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.67755	validation-auc:0.95357	validation-aucpr:0.95829
[18:36:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.66279	validation-auc:0.95870	validation-aucpr:0.95148
[18:36:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.65024	validation-auc:0.96146	validation-aucpr:0.96566
[18:36:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.63640	validation-auc:0.96460	validation-aucpr:0.96668
[18:36:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.62300	validation-auc:0.96692	validation-aucpr:0.97016
[18:36:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5]	validation-logloss:0.61010	validation-auc:0.96815	validation-aucpr:0.97122
[18:36:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6]	validation-logloss:0.59754	validation-auc:0.96919	validation-aucpr:0.97155
[18:36:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7]	validation-logloss:0.58724	validation-auc:0.96904	validation-aucpr:0.97005
[18:36:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8]	validation-logloss:0.57719	validation-auc:0.96887	validation-aucpr:0.97008
[18:36:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9]	validation-logloss:0.56767	validation-auc:0.96880	validation-aucpr:0.97016
[18:36:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10]	validation-logloss:0.55707	validation-auc:0.96861	validation-aucpr:0.96992
[18:36:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11]	validation-logloss:0.54784	validation-auc:0.96922	validation-aucpr:0.97178
[18:36:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12]	validation-logloss:0.53779	validation-auc:0.96940	validation-aucpr:0.97197
[18:36:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13]	validation-logloss:0.52927	validation-auc:0.96929	validation-aucpr:0.97188
[18:36:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14]	validation-logloss:0.52018	validation-auc:0.96919	validation-aucpr:0.97178
[18:36:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15]	validation-logloss:0.51237	validation-auc:0.96890	validation-aucpr:0.97051
[18:36:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16]	validation-logloss:0.50398	validation-auc:0.96914	validation-aucpr:0.97070
[18:36:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17]	validation-logloss:0.49556	validation-auc:0.96931	validation-aucpr:0.97286
[18:36:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18]	validation-logloss:0.48843	validation-auc:0.96912	validation-aucpr:0.97268
[18:36:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19]	validation-logloss:0.48123	validation-auc:0.96913	validation-aucpr:0.97277
[18:36:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20]	validation-logloss:0.47312	validation-auc:0.96936	validation-aucpr:0.97300
[18:36:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21]	validation-logloss:0.46636	validation-auc:0.96941	validation-aucpr:0.97295
[18:36:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22]	validation-logloss:0.45988	validation-auc:0.96946	validation-aucpr:0.97285
[18:36:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23]	validation-logloss:0.45264	validation-auc:0.96969	validation-aucpr:0.97298
[18:36:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24]	validation-logloss:0.44636	validation-auc:0.96988	validation-aucpr:0.97317
[18:36:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25]	validation-logloss:0.44054	validation-auc:0.96984	validation-aucpr:0.97388
[18:36:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26]	validation-logloss:0.43450	validation-auc:0.96997	validation-aucpr:0.97396
[18:36:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27]	validation-logloss:0.42909	validation-auc:0.96992	validation-aucpr:0.97319
[18:36:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28]	validation-logloss:0.42290	validation-auc:0.96994	validation-aucpr:0.97301
[18:36:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29]	validation-logloss:0.41751	validation-auc:0.97006	validation-aucpr:0.97400
[18:36:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30]	validation-logloss:0.41235	validation-auc:0.97015	validation-aucpr:0.97403
[18:36:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31]	validation-logloss:0.40740	validation-auc:0.97010	validation-aucpr:0.97387
[18:36:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32]	validation-logloss:0.40163	validation-auc:0.97019	validation-aucpr:0.97399
[18:36:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33]	validation-logloss:0.39602	validation-auc:0.97028	validation-aucpr:0.97405
[18:36:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34]	validation-logloss:0.39162	validation-auc:0.97026	validation-aucpr:0.97399
[18:36:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35]	validation-logloss:0.38718	validation-auc:0.97029	validation-aucpr:0.97348
[18:36:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36]	validation-logloss:0.38219	validation-auc:0.97034	validation-aucpr:0.97349
[18:36:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37]	validation-logloss:0.37710	validation-auc:0.97052	validation-aucpr:0.97364
[18:36:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38]	validation-logloss:0.37232	validation-auc:0.97062	validation-aucpr:0.97377
[18:36:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39]	validation-logloss:0.36849	validation-auc:0.97060	validation-aucpr:0.97372
[18:36:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40]	validation-logloss:0.36458	validation-auc:0.97061	validation-aucpr:0.97373
[18:36:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41]	validation-logloss:0.36018	validation-auc:0.97062	validation-aucpr:0.97376
[18:36:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42]	validation-logloss:0.35658	validation-auc:0.97057	validation-aucpr:0.97367
[18:36:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43]	validation-logloss:0.35295	validation-auc:0.97065	validation-aucpr:0.97374
{'best_iteration': '33', 'best_score': '0.9740539892541278'}
Trial 24, Fold 3: Log loss = 0.3529549688708266, Average precision = 0.9737998160380797, ROC-AUC = 0.9706531828163073, Elapsed Time = 5.05997539999953 seconds
Trial 24, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 24, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[18:36:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.67756	validation-auc:0.94669	validation-aucpr:0.95494
[18:36:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.66441	validation-auc:0.95335	validation-aucpr:0.95834
[18:36:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.65023	validation-auc:0.95958	validation-aucpr:0.96440
[18:36:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.63669	validation-auc:0.96288	validation-aucpr:0.96940
[18:36:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.62349	validation-auc:0.96425	validation-aucpr:0.97044
[18:36:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5]	validation-logloss:0.61083	validation-auc:0.96541	validation-aucpr:0.97136
[18:36:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6]	validation-logloss:0.59887	validation-auc:0.96571	validation-aucpr:0.97077
[18:36:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7]	validation-logloss:0.58734	validation-auc:0.96569	validation-aucpr:0.96967
[18:36:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8]	validation-logloss:0.57621	validation-auc:0.96647	validation-aucpr:0.97228
[18:36:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9]	validation-logloss:0.56590	validation-auc:0.96718	validation-aucpr:0.97268
[18:36:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10]	validation-logloss:0.55677	validation-auc:0.96684	validation-aucpr:0.97236
[18:36:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11]	validation-logloss:0.54668	validation-auc:0.96687	validation-aucpr:0.97244
[18:36:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12]	validation-logloss:0.53704	validation-auc:0.96701	validation-aucpr:0.97253
[18:36:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13]	validation-logloss:0.52735	validation-auc:0.96733	validation-aucpr:0.97280
[18:36:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14]	validation-logloss:0.51940	validation-auc:0.96715	validation-aucpr:0.97261
[18:36:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15]	validation-logloss:0.51013	validation-auc:0.96780	validation-aucpr:0.97311
[18:36:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16]	validation-logloss:0.50230	validation-auc:0.96802	validation-aucpr:0.97324
[18:36:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17]	validation-logloss:0.49392	validation-auc:0.96819	validation-aucpr:0.97338
[18:36:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18]	validation-logloss:0.48580	validation-auc:0.96831	validation-aucpr:0.97351
[18:36:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19]	validation-logloss:0.47778	validation-auc:0.96863	validation-aucpr:0.97379
[18:36:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20]	validation-logloss:0.47021	validation-auc:0.96845	validation-aucpr:0.97370
[18:36:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21]	validation-logloss:0.46338	validation-auc:0.96858	validation-aucpr:0.97382
[18:36:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22]	validation-logloss:0.45681	validation-auc:0.96876	validation-aucpr:0.97393
[18:36:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23]	validation-logloss:0.45086	validation-auc:0.96878	validation-aucpr:0.97392
[18:36:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24]	validation-logloss:0.44483	validation-auc:0.96885	validation-aucpr:0.97394
[18:36:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25]	validation-logloss:0.43821	validation-auc:0.96900	validation-aucpr:0.97407
[18:36:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26]	validation-logloss:0.43285	validation-auc:0.96875	validation-aucpr:0.97388
[18:36:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27]	validation-logloss:0.42632	validation-auc:0.96907	validation-aucpr:0.97414
[18:36:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28]	validation-logloss:0.41998	validation-auc:0.96926	validation-aucpr:0.97433
[18:36:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29]	validation-logloss:0.41465	validation-auc:0.96925	validation-aucpr:0.97431
[18:36:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30]	validation-logloss:0.40952	validation-auc:0.96923	validation-aucpr:0.97429
[18:36:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31]	validation-logloss:0.40380	validation-auc:0.96929	validation-aucpr:0.97438
[18:36:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32]	validation-logloss:0.39826	validation-auc:0.96935	validation-aucpr:0.97446
[18:36:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33]	validation-logloss:0.39361	validation-auc:0.96944	validation-aucpr:0.97452
[18:36:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34]	validation-logloss:0.38847	validation-auc:0.96949	validation-aucpr:0.97457
[18:36:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35]	validation-logloss:0.38346	validation-auc:0.96936	validation-aucpr:0.97448
[18:36:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36]	validation-logloss:0.37917	validation-auc:0.96933	validation-aucpr:0.97443
[18:36:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37]	validation-logloss:0.37502	validation-auc:0.96928	validation-aucpr:0.97437
[18:36:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38]	validation-logloss:0.37039	validation-auc:0.96931	validation-aucpr:0.97439
[18:36:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39]	validation-logloss:0.36575	validation-auc:0.96944	validation-aucpr:0.97449
[18:36:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40]	validation-logloss:0.36123	validation-auc:0.96952	validation-aucpr:0.97457
[18:36:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41]	validation-logloss:0.35742	validation-auc:0.96958	validation-aucpr:0.97458
[18:36:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42]	validation-logloss:0.35381	validation-auc:0.96965	validation-aucpr:0.97463
[18:36:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43]	validation-logloss:0.34956	validation-auc:0.96986	validation-aucpr:0.97478
{'best_iteration': '43', 'best_score': '0.9747828782362944'}
Trial 24, Fold 4: Log loss = 0.3495584092387853, Average precision = 0.9747869080590805, ROC-AUC = 0.9698566641868804, Elapsed Time = 5.095767300001171 seconds
Trial 24, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 24, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[18:36:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.67775	validation-auc:0.94775	validation-aucpr:0.94986
[18:36:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.66501	validation-auc:0.95325	validation-aucpr:0.95732
[18:36:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.65082	validation-auc:0.95775	validation-aucpr:0.96329
[18:36:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.63701	validation-auc:0.96022	validation-aucpr:0.96463
[18:36:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.62379	validation-auc:0.96338	validation-aucpr:0.96490
[18:36:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5]	validation-logloss:0.61170	validation-auc:0.96486	validation-aucpr:0.96984
[18:36:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6]	validation-logloss:0.60074	validation-auc:0.96507	validation-aucpr:0.96969
[18:36:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7]	validation-logloss:0.58885	validation-auc:0.96609	validation-aucpr:0.97045
[18:36:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8]	validation-logloss:0.57880	validation-auc:0.96583	validation-aucpr:0.97030
[18:36:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9]	validation-logloss:0.56903	validation-auc:0.96595	validation-aucpr:0.96921
[18:36:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10]	validation-logloss:0.55854	validation-auc:0.96601	validation-aucpr:0.96933
[18:36:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11]	validation-logloss:0.54847	validation-auc:0.96635	validation-aucpr:0.96978
[18:36:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12]	validation-logloss:0.53900	validation-auc:0.96689	validation-aucpr:0.97000
[18:36:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13]	validation-logloss:0.52926	validation-auc:0.96753	validation-aucpr:0.97040
[18:36:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14]	validation-logloss:0.51993	validation-auc:0.96773	validation-aucpr:0.97061
[18:36:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15]	validation-logloss:0.51115	validation-auc:0.96794	validation-aucpr:0.97066
[18:36:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16]	validation-logloss:0.50249	validation-auc:0.96813	validation-aucpr:0.97088
[18:36:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17]	validation-logloss:0.49523	validation-auc:0.96791	validation-aucpr:0.97068
[18:36:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18]	validation-logloss:0.48731	validation-auc:0.96792	validation-aucpr:0.97040
[18:36:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19]	validation-logloss:0.47950	validation-auc:0.96803	validation-aucpr:0.97050
[18:36:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20]	validation-logloss:0.47286	validation-auc:0.96786	validation-aucpr:0.97029
[18:36:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21]	validation-logloss:0.46656	validation-auc:0.96778	validation-aucpr:0.97237
[18:36:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22]	validation-logloss:0.46020	validation-auc:0.96780	validation-aucpr:0.97238
[18:36:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23]	validation-logloss:0.45411	validation-auc:0.96763	validation-aucpr:0.97172
[18:36:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24]	validation-logloss:0.44855	validation-auc:0.96737	validation-aucpr:0.97138
[18:36:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25]	validation-logloss:0.44177	validation-auc:0.96758	validation-aucpr:0.97156
[18:36:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26]	validation-logloss:0.43605	validation-auc:0.96754	validation-aucpr:0.97149
[18:36:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27]	validation-logloss:0.42990	validation-auc:0.96774	validation-aucpr:0.97165
[18:36:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28]	validation-logloss:0.42369	validation-auc:0.96795	validation-aucpr:0.97186
[18:36:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29]	validation-logloss:0.41855	validation-auc:0.96788	validation-aucpr:0.97178
[18:36:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30]	validation-logloss:0.41256	validation-auc:0.96815	validation-aucpr:0.97200
[18:36:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31]	validation-logloss:0.40683	validation-auc:0.96837	validation-aucpr:0.97216
[18:36:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32]	validation-logloss:0.40206	validation-auc:0.96829	validation-aucpr:0.97135
[18:37:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33]	validation-logloss:0.39745	validation-auc:0.96819	validation-aucpr:0.97127
[18:37:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34]	validation-logloss:0.39293	validation-auc:0.96818	validation-aucpr:0.97126
[18:37:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35]	validation-logloss:0.38786	validation-auc:0.96840	validation-aucpr:0.97142
[18:37:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36]	validation-logloss:0.38283	validation-auc:0.96864	validation-aucpr:0.97164
[18:37:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37]	validation-logloss:0.37795	validation-auc:0.96886	validation-aucpr:0.97183
[18:37:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38]	validation-logloss:0.37389	validation-auc:0.96879	validation-aucpr:0.97177
[18:37:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39]	validation-logloss:0.36923	validation-auc:0.96898	validation-aucpr:0.97186
[18:37:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40]	validation-logloss:0.36563	validation-auc:0.96889	validation-aucpr:0.97170
[18:37:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41]	validation-logloss:0.36143	validation-auc:0.96905	validation-aucpr:0.97340
[18:37:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42]	validation-logloss:0.35713	validation-auc:0.96925	validation-aucpr:0.97368
[18:37:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43]	validation-logloss:0.35370	validation-auc:0.96913	validation-aucpr:0.97357
{'best_iteration': '42', 'best_score': '0.973677385463933'}
Trial 24, Fold 5: Log loss = 0.3536975849183317, Average precision = 0.9735737443153737, ROC-AUC = 0.969126804525946, Elapsed Time = 5.088518399999884 seconds
Optimization Progress:  25%|##5       | 25/100 [38:13<47:30, 38.01s/it]
Trial 25, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 25, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[0]	validation-logloss:0.67136	validation-auc:0.92645	validation-aucpr:0.92797
[1]	validation-logloss:0.65045	validation-auc:0.93927	validation-aucpr:0.94577
[2]	validation-logloss:0.62847	validation-auc:0.95043	validation-aucpr:0.95863
[3]	validation-logloss:0.60879	validation-auc:0.95524	validation-aucpr:0.96105
[4]	validation-logloss:0.59011	validation-auc:0.95747	validation-aucpr:0.96265
[5]	validation-logloss:0.57531	validation-auc:0.95568	validation-aucpr:0.96092
[6]	validation-logloss:0.55744	validation-auc:0.95778	validation-aucpr:0.96344
[7]	validation-logloss:0.54321	validation-auc:0.95790	validation-aucpr:0.96430
[8]	validation-logloss:0.53138	validation-auc:0.95740	validation-aucpr:0.96381
[9]	validation-logloss:0.51916	validation-auc:0.95736	validation-aucpr:0.96369
[10]	validation-logloss:0.50745	validation-auc:0.95717	validation-aucpr:0.96353
[11]	validation-logloss:0.49565	validation-auc:0.95745	validation-aucpr:0.96393
[12]	validation-logloss:0.48441	validation-auc:0.95766	validation-aucpr:0.96421
[13]	validation-logloss:0.47120	validation-auc:0.95912	validation-aucpr:0.96579
[14]	validation-logloss:0.46180	validation-auc:0.95924	validation-aucpr:0.96585
[15]	validation-logloss:0.45253	validation-auc:0.95952	validation-aucpr:0.96603
[16]	validation-logloss:0.44349	validation-auc:0.95957	validation-aucpr:0.96604
[17]	validation-logloss:0.43490	validation-auc:0.95954	validation-aucpr:0.96600
[18]	validation-logloss:0.42815	validation-auc:0.95919	validation-aucpr:0.96572
[19]	validation-logloss:0.41788	validation-auc:0.96025	validation-aucpr:0.96671
[20]	validation-logloss:0.41213	validation-auc:0.95990	validation-aucpr:0.96630
[21]	validation-logloss:0.40480	validation-auc:0.96029	validation-aucpr:0.96658
[22]	validation-logloss:0.39603	validation-auc:0.96066	validation-aucpr:0.96701
[23]	validation-logloss:0.38666	validation-auc:0.96145	validation-aucpr:0.96774
[24]	validation-logloss:0.38092	validation-auc:0.96136	validation-aucpr:0.96762
[25]	validation-logloss:0.37635	validation-auc:0.96106	validation-aucpr:0.96740
[26]	validation-logloss:0.37070	validation-auc:0.96120	validation-aucpr:0.96743
[27]	validation-logloss:0.36546	validation-auc:0.96135	validation-aucpr:0.96751
[28]	validation-logloss:0.35802	validation-auc:0.96189	validation-aucpr:0.96804
[29]	validation-logloss:0.35351	validation-auc:0.96196	validation-aucpr:0.96805
[30]	validation-logloss:0.34984	validation-auc:0.96183	validation-aucpr:0.96795
[31]	validation-logloss:0.34655	validation-auc:0.96146	validation-aucpr:0.96765
[32]	validation-logloss:0.34241	validation-auc:0.96152	validation-aucpr:0.96768
[33]	validation-logloss:0.33647	validation-auc:0.96179	validation-aucpr:0.96796
[34]	validation-logloss:0.33313	validation-auc:0.96163	validation-aucpr:0.96780
[35]	validation-logloss:0.32905	validation-auc:0.96190	validation-aucpr:0.96800
[36]	validation-logloss:0.32565	validation-auc:0.96200	validation-aucpr:0.96806
[37]	validation-logloss:0.32215	validation-auc:0.96225	validation-aucpr:0.96826
[38]	validation-logloss:0.31689	validation-auc:0.96252	validation-aucpr:0.96858
[39]	validation-logloss:0.31322	validation-auc:0.96275	validation-aucpr:0.96874
[40]	validation-logloss:0.31011	validation-auc:0.96282	validation-aucpr:0.96879
[41]	validation-logloss:0.30612	validation-auc:0.96292	validation-aucpr:0.96890
[42]	validation-logloss:0.30268	validation-auc:0.96329	validation-aucpr:0.96918
[43]	validation-logloss:0.29816	validation-auc:0.96382	validation-aucpr:0.96962
[44]	validation-logloss:0.29539	validation-auc:0.96402	validation-aucpr:0.96977
[45]	validation-logloss:0.29267	validation-auc:0.96414	validation-aucpr:0.96985
[46]	validation-logloss:0.28872	validation-auc:0.96439	validation-aucpr:0.97012
[47]	validation-logloss:0.28674	validation-auc:0.96434	validation-aucpr:0.97007
[48]	validation-logloss:0.28442	validation-auc:0.96440	validation-aucpr:0.97011
[49]	validation-logloss:0.28251	validation-auc:0.96447	validation-aucpr:0.97017
[50]	validation-logloss:0.27902	validation-auc:0.96474	validation-aucpr:0.97045
[51]	validation-logloss:0.27705	validation-auc:0.96482	validation-aucpr:0.97051
[52]	validation-logloss:0.27542	validation-auc:0.96481	validation-aucpr:0.97048
[53]	validation-logloss:0.27344	validation-auc:0.96483	validation-aucpr:0.97048
[54]	validation-logloss:0.27018	validation-auc:0.96504	validation-aucpr:0.97068
[55]	validation-logloss:0.26907	validation-auc:0.96497	validation-aucpr:0.97063
{'best_iteration': '54', 'best_score': '0.9706754928377431'}
Trial 25, Fold 1: Log loss = 0.2690666545589348, Average precision = 0.9706323993273999, ROC-AUC = 0.9649702410824813, Elapsed Time = 1.1947266000006493 seconds
Trial 25, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 25, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[0]	validation-logloss:0.67061	validation-auc:0.93136	validation-aucpr:0.92698
[1]	validation-logloss:0.65091	validation-auc:0.93686	validation-aucpr:0.93478
[2]	validation-logloss:0.63111	validation-auc:0.94713	validation-aucpr:0.94825
[3]	validation-logloss:0.61210	validation-auc:0.95005	validation-aucpr:0.95175
[4]	validation-logloss:0.59208	validation-auc:0.95885	validation-aucpr:0.96273
[5]	validation-logloss:0.57508	validation-auc:0.95946	validation-aucpr:0.96335
[6]	validation-logloss:0.56160	validation-auc:0.95778	validation-aucpr:0.96170
[7]	validation-logloss:0.54381	validation-auc:0.96128	validation-aucpr:0.96557
[8]	validation-logloss:0.52942	validation-auc:0.96143	validation-aucpr:0.96536
[9]	validation-logloss:0.51655	validation-auc:0.96130	validation-aucpr:0.96531
[10]	validation-logloss:0.50556	validation-auc:0.96053	validation-aucpr:0.96458
[11]	validation-logloss:0.49411	validation-auc:0.96059	validation-aucpr:0.96455
[12]	validation-logloss:0.48347	validation-auc:0.96034	validation-aucpr:0.96424
[13]	validation-logloss:0.47336	validation-auc:0.96002	validation-aucpr:0.96377
[14]	validation-logloss:0.46312	validation-auc:0.96023	validation-aucpr:0.96376
[15]	validation-logloss:0.45392	validation-auc:0.95998	validation-aucpr:0.96359
[16]	validation-logloss:0.44160	validation-auc:0.96168	validation-aucpr:0.96537
[17]	validation-logloss:0.43318	validation-auc:0.96156	validation-aucpr:0.96514
[18]	validation-logloss:0.42520	validation-auc:0.96141	validation-aucpr:0.96508
[19]	validation-logloss:0.41774	validation-auc:0.96124	validation-aucpr:0.96498
[20]	validation-logloss:0.41024	validation-auc:0.96158	validation-aucpr:0.96511
[21]	validation-logloss:0.40411	validation-auc:0.96119	validation-aucpr:0.96459
[22]	validation-logloss:0.39732	validation-auc:0.96121	validation-aucpr:0.96456
[23]	validation-logloss:0.39068	validation-auc:0.96113	validation-aucpr:0.96447
[24]	validation-logloss:0.38439	validation-auc:0.96126	validation-aucpr:0.96462
[25]	validation-logloss:0.37872	validation-auc:0.96112	validation-aucpr:0.96450
[26]	validation-logloss:0.37376	validation-auc:0.96101	validation-aucpr:0.96437
[27]	validation-logloss:0.36871	validation-auc:0.96090	validation-aucpr:0.96400
[28]	validation-logloss:0.36333	validation-auc:0.96102	validation-aucpr:0.96405
[29]	validation-logloss:0.35622	validation-auc:0.96191	validation-aucpr:0.96506
[30]	validation-logloss:0.35142	validation-auc:0.96203	validation-aucpr:0.96515
[31]	validation-logloss:0.34752	validation-auc:0.96186	validation-aucpr:0.96499
[32]	validation-logloss:0.34069	validation-auc:0.96271	validation-aucpr:0.96592
[33]	validation-logloss:0.33605	validation-auc:0.96297	validation-aucpr:0.96608
[34]	validation-logloss:0.32954	validation-auc:0.96360	validation-aucpr:0.96679
[35]	validation-logloss:0.32570	validation-auc:0.96368	validation-aucpr:0.96686
[36]	validation-logloss:0.32188	validation-auc:0.96382	validation-aucpr:0.96696
[37]	validation-logloss:0.31872	validation-auc:0.96378	validation-aucpr:0.96688
[38]	validation-logloss:0.31593	validation-auc:0.96377	validation-aucpr:0.96681
[39]	validation-logloss:0.31042	validation-auc:0.96424	validation-aucpr:0.96734
[40]	validation-logloss:0.30741	validation-auc:0.96427	validation-aucpr:0.96769
[41]	validation-logloss:0.30497	validation-auc:0.96420	validation-aucpr:0.96758
[42]	validation-logloss:0.30223	validation-auc:0.96427	validation-aucpr:0.96762
[43]	validation-logloss:0.29758	validation-auc:0.96469	validation-aucpr:0.96810
[44]	validation-logloss:0.29477	validation-auc:0.96480	validation-aucpr:0.96814
[45]	validation-logloss:0.29080	validation-auc:0.96493	validation-aucpr:0.96825
[46]	validation-logloss:0.28693	validation-auc:0.96519	validation-aucpr:0.96852
[47]	validation-logloss:0.28472	validation-auc:0.96517	validation-aucpr:0.96851
[48]	validation-logloss:0.28120	validation-auc:0.96550	validation-aucpr:0.96883
[49]	validation-logloss:0.27714	validation-auc:0.96588	validation-aucpr:0.96919
[50]	validation-logloss:0.27505	validation-auc:0.96593	validation-aucpr:0.96919
[51]	validation-logloss:0.27112	validation-auc:0.96628	validation-aucpr:0.96950
[52]	validation-logloss:0.26929	validation-auc:0.96635	validation-aucpr:0.96956
[53]	validation-logloss:0.26728	validation-auc:0.96646	validation-aucpr:0.96964
[54]	validation-logloss:0.26555	validation-auc:0.96646	validation-aucpr:0.96961
[55]	validation-logloss:0.26345	validation-auc:0.96671	validation-aucpr:0.96979
{'best_iteration': '55', 'best_score': '0.9697896161344084'}
Trial 25, Fold 2: Log loss = 0.2634486405045629, Average precision = 0.9697925827319714, ROC-AUC = 0.9667127575273731, Elapsed Time = 1.5920014000002993 seconds
Trial 25, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 25, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[0]	validation-logloss:0.67096	validation-auc:0.92747	validation-aucpr:0.92769
[1]	validation-logloss:0.64721	validation-auc:0.95369	validation-aucpr:0.95988
[2]	validation-logloss:0.62642	validation-auc:0.95662	validation-aucpr:0.96262
[3]	validation-logloss:0.60830	validation-auc:0.96139	validation-aucpr:0.96630
[4]	validation-logloss:0.58983	validation-auc:0.96198	validation-aucpr:0.96652
[5]	validation-logloss:0.57351	validation-auc:0.96141	validation-aucpr:0.96608
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[18]	validation-logloss:0.42107	validation-auc:0.96422	validation-aucpr:0.96890
[19]	validation-logloss:0.41281	validation-auc:0.96424	validation-aucpr:0.96888
[20]	validation-logloss:0.40561	validation-auc:0.96420	validation-aucpr:0.96878
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[24]	validation-logloss:0.37768	validation-auc:0.96487	validation-aucpr:0.96951
[25]	validation-logloss:0.37250	validation-auc:0.96474	validation-aucpr:0.96954
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[28]	validation-logloss:0.35680	validation-auc:0.96436	validation-aucpr:0.96934
[29]	validation-logloss:0.34959	validation-auc:0.96493	validation-aucpr:0.96989
[30]	validation-logloss:0.34517	validation-auc:0.96481	validation-aucpr:0.96977
[31]	validation-logloss:0.34129	validation-auc:0.96485	validation-aucpr:0.96984
[32]	validation-logloss:0.33800	validation-auc:0.96458	validation-aucpr:0.96962
[33]	validation-logloss:0.33220	validation-auc:0.96485	validation-aucpr:0.96994
[34]	validation-logloss:0.32840	validation-auc:0.96484	validation-aucpr:0.96992
[35]	validation-logloss:0.32387	validation-auc:0.96506	validation-aucpr:0.97009
[36]	validation-logloss:0.32003	validation-auc:0.96517	validation-aucpr:0.97012
[37]	validation-logloss:0.31686	validation-auc:0.96522	validation-aucpr:0.97014
[38]	validation-logloss:0.31350	validation-auc:0.96523	validation-aucpr:0.97016
[39]	validation-logloss:0.31083	validation-auc:0.96506	validation-aucpr:0.96999
[40]	validation-logloss:0.30749	validation-auc:0.96522	validation-aucpr:0.97009
[41]	validation-logloss:0.30229	validation-auc:0.96560	validation-aucpr:0.97045
[42]	validation-logloss:0.29765	validation-auc:0.96576	validation-aucpr:0.97062
[43]	validation-logloss:0.29495	validation-auc:0.96580	validation-aucpr:0.97063
[44]	validation-logloss:0.29224	validation-auc:0.96583	validation-aucpr:0.97072
[45]	validation-logloss:0.28999	validation-auc:0.96581	validation-aucpr:0.97069
[46]	validation-logloss:0.28784	validation-auc:0.96577	validation-aucpr:0.97067
[47]	validation-logloss:0.28551	validation-auc:0.96595	validation-aucpr:0.97078
[48]	validation-logloss:0.28290	validation-auc:0.96608	validation-aucpr:0.97087
[49]	validation-logloss:0.28053	validation-auc:0.96615	validation-aucpr:0.97090
[50]	validation-logloss:0.27852	validation-auc:0.96617	validation-aucpr:0.97093
[51]	validation-logloss:0.27661	validation-auc:0.96618	validation-aucpr:0.97092
[52]	validation-logloss:0.27287	validation-auc:0.96649	validation-aucpr:0.97123
[53]	validation-logloss:0.27084	validation-auc:0.96663	validation-aucpr:0.97132
[54]	validation-logloss:0.26952	validation-auc:0.96652	validation-aucpr:0.97122
[55]	validation-logloss:0.26764	validation-auc:0.96661	validation-aucpr:0.97131
{'best_iteration': '53', 'best_score': '0.9713172950178219'}
Trial 25, Fold 3: Log loss = 0.267635464730996, Average precision = 0.9713168943918258, ROC-AUC = 0.966612060704414, Elapsed Time = 1.624384500000815 seconds
Trial 25, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 25, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[0]	validation-logloss:0.67115	validation-auc:0.91979	validation-aucpr:0.91803
[1]	validation-logloss:0.64973	validation-auc:0.94236	validation-aucpr:0.94599
[2]	validation-logloss:0.62750	validation-auc:0.95848	validation-aucpr:0.96492
[3]	validation-logloss:0.60959	validation-auc:0.95865	validation-aucpr:0.96487
[4]	validation-logloss:0.59150	validation-auc:0.95936	validation-aucpr:0.96547
[5]	validation-logloss:0.57424	validation-auc:0.96145	validation-aucpr:0.96776
[6]	validation-logloss:0.55902	validation-auc:0.96092	validation-aucpr:0.96714
[7]	validation-logloss:0.54229	validation-auc:0.96169	validation-aucpr:0.96796
[8]	validation-logloss:0.52964	validation-auc:0.96073	validation-aucpr:0.96701
[9]	validation-logloss:0.51694	validation-auc:0.96048	validation-aucpr:0.96675
[10]	validation-logloss:0.50214	validation-auc:0.96103	validation-aucpr:0.96738
[11]	validation-logloss:0.48849	validation-auc:0.96105	validation-aucpr:0.96757
[12]	validation-logloss:0.47751	validation-auc:0.96054	validation-aucpr:0.96709
[13]	validation-logloss:0.46514	validation-auc:0.96040	validation-aucpr:0.96720
[14]	validation-logloss:0.45366	validation-auc:0.96050	validation-aucpr:0.96739
[15]	validation-logloss:0.44469	validation-auc:0.96029	validation-aucpr:0.96710
[16]	validation-logloss:0.43588	validation-auc:0.96056	validation-aucpr:0.96721
[17]	validation-logloss:0.42686	validation-auc:0.96145	validation-aucpr:0.96782
[18]	validation-logloss:0.41644	validation-auc:0.96199	validation-aucpr:0.96829
[19]	validation-logloss:0.40871	validation-auc:0.96197	validation-aucpr:0.96831
[20]	validation-logloss:0.39951	validation-auc:0.96221	validation-aucpr:0.96857
[21]	validation-logloss:0.39327	validation-auc:0.96203	validation-aucpr:0.96836
[22]	validation-logloss:0.38503	validation-auc:0.96215	validation-aucpr:0.96855
[23]	validation-logloss:0.37917	validation-auc:0.96189	validation-aucpr:0.96831
[24]	validation-logloss:0.37403	validation-auc:0.96182	validation-aucpr:0.96832
[25]	validation-logloss:0.36834	validation-auc:0.96178	validation-aucpr:0.96824
[26]	validation-logloss:0.36365	validation-auc:0.96169	validation-aucpr:0.96808
[27]	validation-logloss:0.35854	validation-auc:0.96175	validation-aucpr:0.96809
[28]	validation-logloss:0.35411	validation-auc:0.96161	validation-aucpr:0.96802
[29]	validation-logloss:0.34907	validation-auc:0.96160	validation-aucpr:0.96804
[30]	validation-logloss:0.34378	validation-auc:0.96202	validation-aucpr:0.96837
[31]	validation-logloss:0.34033	validation-auc:0.96183	validation-aucpr:0.96821
[32]	validation-logloss:0.33436	validation-auc:0.96221	validation-aucpr:0.96856
[33]	validation-logloss:0.32862	validation-auc:0.96257	validation-aucpr:0.96887
[34]	validation-logloss:0.32490	validation-auc:0.96244	validation-aucpr:0.96875
[35]	validation-logloss:0.31972	validation-auc:0.96266	validation-aucpr:0.96895
[36]	validation-logloss:0.31625	validation-auc:0.96255	validation-aucpr:0.96888
[37]	validation-logloss:0.31301	validation-auc:0.96248	validation-aucpr:0.96885
[38]	validation-logloss:0.30935	validation-auc:0.96266	validation-aucpr:0.96901
[39]	validation-logloss:0.30444	validation-auc:0.96302	validation-aucpr:0.96932
[40]	validation-logloss:0.30201	validation-auc:0.96302	validation-aucpr:0.96930
[41]	validation-logloss:0.29797	validation-auc:0.96305	validation-aucpr:0.96935
[42]	validation-logloss:0.29481	validation-auc:0.96324	validation-aucpr:0.96947
[43]	validation-logloss:0.29174	validation-auc:0.96337	validation-aucpr:0.96953
[44]	validation-logloss:0.28847	validation-auc:0.96369	validation-aucpr:0.96977
[45]	validation-logloss:0.28598	validation-auc:0.96377	validation-aucpr:0.96979
[46]	validation-logloss:0.28323	validation-auc:0.96394	validation-aucpr:0.96997
[47]	validation-logloss:0.28073	validation-auc:0.96399	validation-aucpr:0.96999
[48]	validation-logloss:0.27912	validation-auc:0.96382	validation-aucpr:0.96987
[49]	validation-logloss:0.27658	validation-auc:0.96396	validation-aucpr:0.96997
[50]	validation-logloss:0.27350	validation-auc:0.96415	validation-aucpr:0.97018
[51]	validation-logloss:0.27128	validation-auc:0.96431	validation-aucpr:0.97030
[52]	validation-logloss:0.26785	validation-auc:0.96457	validation-aucpr:0.97053
[53]	validation-logloss:0.26509	validation-auc:0.96475	validation-aucpr:0.97072
[54]	validation-logloss:0.26379	validation-auc:0.96470	validation-aucpr:0.97068
[55]	validation-logloss:0.26189	validation-auc:0.96481	validation-aucpr:0.97079
{'best_iteration': '55', 'best_score': '0.9707935271000794'}
Trial 25, Fold 4: Log loss = 0.2618905262310492, Average precision = 0.9707982250800647, ROC-AUC = 0.9648084216161052, Elapsed Time = 1.4710076999999728 seconds
Trial 25, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 25, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[0]	validation-logloss:0.67141	validation-auc:0.92058	validation-aucpr:0.91798
[1]	validation-logloss:0.65048	validation-auc:0.93603	validation-aucpr:0.94189
[2]	validation-logloss:0.63149	validation-auc:0.93869	validation-aucpr:0.94458
[3]	validation-logloss:0.61234	validation-auc:0.94543	validation-aucpr:0.95018
[4]	validation-logloss:0.59490	validation-auc:0.94715	validation-aucpr:0.95168
[5]	validation-logloss:0.57953	validation-auc:0.94706	validation-aucpr:0.95158
[6]	validation-logloss:0.56517	validation-auc:0.94737	validation-aucpr:0.95219
[7]	validation-logloss:0.55121	validation-auc:0.94698	validation-aucpr:0.95191
[8]	validation-logloss:0.53763	validation-auc:0.94742	validation-aucpr:0.95207
[9]	validation-logloss:0.52568	validation-auc:0.94794	validation-aucpr:0.95275
[10]	validation-logloss:0.51307	validation-auc:0.94890	validation-aucpr:0.95359
[11]	validation-logloss:0.49836	validation-auc:0.95373	validation-aucpr:0.95902
[12]	validation-logloss:0.48738	validation-auc:0.95364	validation-aucpr:0.95888
[13]	validation-logloss:0.47660	validation-auc:0.95439	validation-aucpr:0.95938
[14]	validation-logloss:0.46781	validation-auc:0.95397	validation-aucpr:0.95900
[15]	validation-logloss:0.45839	validation-auc:0.95450	validation-aucpr:0.95940
[16]	validation-logloss:0.44985	validation-auc:0.95474	validation-aucpr:0.95986
[17]	validation-logloss:0.44229	validation-auc:0.95460	validation-aucpr:0.95956
[18]	validation-logloss:0.43107	validation-auc:0.95681	validation-aucpr:0.96190
[19]	validation-logloss:0.42367	validation-auc:0.95680	validation-aucpr:0.96189
[20]	validation-logloss:0.41378	validation-auc:0.95810	validation-aucpr:0.96328
[21]	validation-logloss:0.40640	validation-auc:0.95824	validation-aucpr:0.96338
[22]	validation-logloss:0.39977	validation-auc:0.95841	validation-aucpr:0.96354
[23]	validation-logloss:0.39354	validation-auc:0.95841	validation-aucpr:0.96362
[24]	validation-logloss:0.38856	validation-auc:0.95824	validation-aucpr:0.96368
[25]	validation-logloss:0.38284	validation-auc:0.95849	validation-aucpr:0.96379
[26]	validation-logloss:0.37762	validation-auc:0.95856	validation-aucpr:0.96386
[27]	validation-logloss:0.37176	validation-auc:0.95869	validation-aucpr:0.96393
[28]	validation-logloss:0.36403	validation-auc:0.95952	validation-aucpr:0.96484
[29]	validation-logloss:0.35744	validation-auc:0.96003	validation-aucpr:0.96544
[30]	validation-logloss:0.35060	validation-auc:0.96051	validation-aucpr:0.96594
[31]	validation-logloss:0.34648	validation-auc:0.96054	validation-aucpr:0.96592
[32]	validation-logloss:0.34298	validation-auc:0.96039	validation-aucpr:0.96584
[33]	validation-logloss:0.33881	validation-auc:0.96044	validation-aucpr:0.96587
[34]	validation-logloss:0.33522	validation-auc:0.96040	validation-aucpr:0.96587
[35]	validation-logloss:0.33207	validation-auc:0.96034	validation-aucpr:0.96579
[36]	validation-logloss:0.32875	validation-auc:0.96039	validation-aucpr:0.96580
[37]	validation-logloss:0.32504	validation-auc:0.96064	validation-aucpr:0.96604
[38]	validation-logloss:0.32146	validation-auc:0.96109	validation-aucpr:0.96638
[39]	validation-logloss:0.31822	validation-auc:0.96111	validation-aucpr:0.96641
[40]	validation-logloss:0.31327	validation-auc:0.96150	validation-aucpr:0.96678
[41]	validation-logloss:0.30825	validation-auc:0.96191	validation-aucpr:0.96721
[42]	validation-logloss:0.30370	validation-auc:0.96225	validation-aucpr:0.96760
[43]	validation-logloss:0.30148	validation-auc:0.96214	validation-aucpr:0.96748
[44]	validation-logloss:0.29898	validation-auc:0.96219	validation-aucpr:0.96748
[45]	validation-logloss:0.29511	validation-auc:0.96242	validation-aucpr:0.96770
[46]	validation-logloss:0.29080	validation-auc:0.96270	validation-aucpr:0.96800
[47]	validation-logloss:0.28917	validation-auc:0.96262	validation-aucpr:0.96791
[48]	validation-logloss:0.28535	validation-auc:0.96288	validation-aucpr:0.96819
[49]	validation-logloss:0.28317	validation-auc:0.96302	validation-aucpr:0.96834
[50]	validation-logloss:0.28201	validation-auc:0.96284	validation-aucpr:0.96822
[51]	validation-logloss:0.28024	validation-auc:0.96289	validation-aucpr:0.96825
[52]	validation-logloss:0.27846	validation-auc:0.96286	validation-aucpr:0.96822
[53]	validation-logloss:0.27703	validation-auc:0.96293	validation-aucpr:0.96831
[54]	validation-logloss:0.27502	validation-auc:0.96304	validation-aucpr:0.96838
[55]	validation-logloss:0.27291	validation-auc:0.96330	validation-aucpr:0.96859
{'best_iteration': '55', 'best_score': '0.968587545474199'}
Trial 25, Fold 5: Log loss = 0.2729147602555443, Average precision = 0.9685929768466469, ROC-AUC = 0.9633032718354607, Elapsed Time = 1.555640200000198 seconds
Optimization Progress:  26%|##6       | 26/100 [38:28<38:27, 31.18s/it]
Trial 26, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 26, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[18:37:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.68542	validation-auc:0.95577	validation-aucpr:0.96213
[18:37:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.67867	validation-auc:0.96068	validation-aucpr:0.96692
[18:37:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.67120	validation-auc:0.96338	validation-aucpr:0.96919
[18:37:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.66384	validation-auc:0.96580	validation-aucpr:0.97120
[18:37:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.65661	validation-auc:0.96626	validation-aucpr:0.97158
[18:37:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5]	validation-logloss:0.64959	validation-auc:0.96711	validation-aucpr:0.97239
[18:37:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6]	validation-logloss:0.64273	validation-auc:0.96780	validation-aucpr:0.97295
[18:37:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7]	validation-logloss:0.63604	validation-auc:0.96813	validation-aucpr:0.97354
[18:37:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8]	validation-logloss:0.62933	validation-auc:0.96837	validation-aucpr:0.97372
[18:37:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9]	validation-logloss:0.62285	validation-auc:0.96863	validation-aucpr:0.97386
[18:37:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10]	validation-logloss:0.61637	validation-auc:0.96883	validation-aucpr:0.97397
[18:37:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11]	validation-logloss:0.61024	validation-auc:0.96871	validation-aucpr:0.97390
[18:37:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12]	validation-logloss:0.60476	validation-auc:0.96863	validation-aucpr:0.97377
[18:37:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13]	validation-logloss:0.59939	validation-auc:0.96873	validation-aucpr:0.97378
[18:37:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14]	validation-logloss:0.59348	validation-auc:0.96873	validation-aucpr:0.97380
[18:37:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15]	validation-logloss:0.58817	validation-auc:0.96866	validation-aucpr:0.97373
[18:37:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16]	validation-logloss:0.58251	validation-auc:0.96856	validation-aucpr:0.97369
[18:37:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17]	validation-logloss:0.57693	validation-auc:0.96862	validation-aucpr:0.97373
[18:37:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18]	validation-logloss:0.57188	validation-auc:0.96873	validation-aucpr:0.97378
[18:37:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19]	validation-logloss:0.56651	validation-auc:0.96867	validation-aucpr:0.97374
[18:37:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20]	validation-logloss:0.56116	validation-auc:0.96893	validation-aucpr:0.97395
[18:37:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21]	validation-logloss:0.55587	validation-auc:0.96895	validation-aucpr:0.97398
[18:37:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22]	validation-logloss:0.55064	validation-auc:0.96917	validation-aucpr:0.97416
[18:37:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23]	validation-logloss:0.54581	validation-auc:0.96929	validation-aucpr:0.97421
[18:37:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24]	validation-logloss:0.54080	validation-auc:0.96946	validation-aucpr:0.97436
[18:37:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25]	validation-logloss:0.53591	validation-auc:0.96953	validation-aucpr:0.97440
[18:37:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26]	validation-logloss:0.53108	validation-auc:0.96968	validation-aucpr:0.97451
[18:37:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27]	validation-logloss:0.52627	validation-auc:0.96982	validation-aucpr:0.97475
[18:38:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28]	validation-logloss:0.52151	validation-auc:0.97000	validation-aucpr:0.97491
[18:38:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29]	validation-logloss:0.51692	validation-auc:0.97016	validation-aucpr:0.97500
[18:38:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30]	validation-logloss:0.51234	validation-auc:0.97020	validation-aucpr:0.97510
[18:38:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31]	validation-logloss:0.50786	validation-auc:0.97023	validation-aucpr:0.97514
[18:38:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32]	validation-logloss:0.50350	validation-auc:0.97024	validation-aucpr:0.97514
[18:38:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33]	validation-logloss:0.49918	validation-auc:0.97031	validation-aucpr:0.97518
[18:38:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34]	validation-logloss:0.49495	validation-auc:0.97030	validation-aucpr:0.97520
[18:38:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35]	validation-logloss:0.49079	validation-auc:0.97036	validation-aucpr:0.97530
[18:38:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36]	validation-logloss:0.48670	validation-auc:0.97038	validation-aucpr:0.97533
[18:38:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37]	validation-logloss:0.48266	validation-auc:0.97049	validation-aucpr:0.97546
[18:38:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38]	validation-logloss:0.47908	validation-auc:0.97044	validation-aucpr:0.97542
[18:38:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39]	validation-logloss:0.47512	validation-auc:0.97053	validation-aucpr:0.97548
[18:38:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40]	validation-logloss:0.47126	validation-auc:0.97055	validation-aucpr:0.97550
[18:38:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41]	validation-logloss:0.46742	validation-auc:0.97074	validation-aucpr:0.97563
[18:38:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42]	validation-logloss:0.46389	validation-auc:0.97079	validation-aucpr:0.97565
[18:38:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43]	validation-logloss:0.46026	validation-auc:0.97071	validation-aucpr:0.97560
[18:38:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44]	validation-logloss:0.45700	validation-auc:0.97078	validation-aucpr:0.97562
[18:38:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45]	validation-logloss:0.45345	validation-auc:0.97081	validation-aucpr:0.97564
[18:38:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46]	validation-logloss:0.44989	validation-auc:0.97086	validation-aucpr:0.97569
[18:38:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[47]	validation-logloss:0.44645	validation-auc:0.97091	validation-aucpr:0.97572
[18:38:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[48]	validation-logloss:0.44304	validation-auc:0.97100	validation-aucpr:0.97579
[18:38:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[49]	validation-logloss:0.43971	validation-auc:0.97098	validation-aucpr:0.97577
[18:38:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[50]	validation-logloss:0.43650	validation-auc:0.97090	validation-aucpr:0.97571
[18:38:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[51]	validation-logloss:0.43330	validation-auc:0.97078	validation-aucpr:0.97562
[18:38:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[52]	validation-logloss:0.43045	validation-auc:0.97074	validation-aucpr:0.97558
[18:38:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[53]	validation-logloss:0.42735	validation-auc:0.97078	validation-aucpr:0.97562
[18:38:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[54]	validation-logloss:0.42425	validation-auc:0.97079	validation-aucpr:0.97564
[18:38:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[55]	validation-logloss:0.42154	validation-auc:0.97069	validation-aucpr:0.97556
[18:38:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[56]	validation-logloss:0.41848	validation-auc:0.97075	validation-aucpr:0.97560
[18:38:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[57]	validation-logloss:0.41589	validation-auc:0.97070	validation-aucpr:0.97554
[18:38:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[58]	validation-logloss:0.41295	validation-auc:0.97078	validation-aucpr:0.97560
[18:38:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[59]	validation-logloss:0.41013	validation-auc:0.97081	validation-aucpr:0.97562
[18:38:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[60]	validation-logloss:0.40731	validation-auc:0.97080	validation-aucpr:0.97561
[18:38:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[61]	validation-logloss:0.40452	validation-auc:0.97075	validation-aucpr:0.97558
[18:38:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[62]	validation-logloss:0.40178	validation-auc:0.97081	validation-aucpr:0.97561
[18:38:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[63]	validation-logloss:0.39898	validation-auc:0.97087	validation-aucpr:0.97565
[18:38:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[64]	validation-logloss:0.39632	validation-auc:0.97086	validation-aucpr:0.97564
[18:38:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[65]	validation-logloss:0.39370	validation-auc:0.97080	validation-aucpr:0.97561
[18:38:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[66]	validation-logloss:0.39130	validation-auc:0.97077	validation-aucpr:0.97558
[18:38:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[67]	validation-logloss:0.38866	validation-auc:0.97083	validation-aucpr:0.97563
[18:38:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[68]	validation-logloss:0.38611	validation-auc:0.97099	validation-aucpr:0.97579
[18:38:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[69]	validation-logloss:0.38363	validation-auc:0.97100	validation-aucpr:0.97580
[18:38:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[70]	validation-logloss:0.38118	validation-auc:0.97102	validation-aucpr:0.97582
[18:39:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[71]	validation-logloss:0.37876	validation-auc:0.97108	validation-aucpr:0.97582
[18:39:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[72]	validation-logloss:0.37638	validation-auc:0.97119	validation-aucpr:0.97590
[18:39:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[73]	validation-logloss:0.37400	validation-auc:0.97125	validation-aucpr:0.97593
[18:39:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[74]	validation-logloss:0.37194	validation-auc:0.97117	validation-aucpr:0.97585
[18:39:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[75]	validation-logloss:0.36966	validation-auc:0.97121	validation-aucpr:0.97587
[18:39:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[76]	validation-logloss:0.36740	validation-auc:0.97124	validation-aucpr:0.97589
[18:39:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[77]	validation-logloss:0.36525	validation-auc:0.97126	validation-aucpr:0.97590
[18:39:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[78]	validation-logloss:0.36308	validation-auc:0.97137	validation-aucpr:0.97594
{'best_iteration': '78', 'best_score': '0.9759377173694493'}
Trial 26, Fold 1: Log loss = 0.3630798792810747, Average precision = 0.9759361280515932, ROC-AUC = 0.9713674972397873, Elapsed Time = 108.3564650999997 seconds
Trial 26, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 26, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[18:39:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.68565	validation-auc:0.95899	validation-aucpr:0.96175
[18:39:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.67799	validation-auc:0.96605	validation-aucpr:0.96878
[18:39:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.67063	validation-auc:0.96761	validation-aucpr:0.97093
[18:39:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.66335	validation-auc:0.96813	validation-aucpr:0.97149
[18:39:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.65622	validation-auc:0.96847	validation-aucpr:0.97178
[18:39:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5]	validation-logloss:0.64919	validation-auc:0.96838	validation-aucpr:0.97186
[18:39:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6]	validation-logloss:0.64298	validation-auc:0.96890	validation-aucpr:0.97236
[18:39:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7]	validation-logloss:0.63628	validation-auc:0.96883	validation-aucpr:0.97227
[18:39:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8]	validation-logloss:0.62956	validation-auc:0.96938	validation-aucpr:0.97272
[18:39:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9]	validation-logloss:0.62302	validation-auc:0.96977	validation-aucpr:0.97307
[18:39:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10]	validation-logloss:0.61658	validation-auc:0.96992	validation-aucpr:0.97313
[18:39:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11]	validation-logloss:0.61043	validation-auc:0.96967	validation-aucpr:0.97291
[18:39:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12]	validation-logloss:0.60430	validation-auc:0.96955	validation-aucpr:0.97286
[18:39:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13]	validation-logloss:0.59878	validation-auc:0.96985	validation-aucpr:0.97309
[18:39:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14]	validation-logloss:0.59294	validation-auc:0.97001	validation-aucpr:0.97322
[18:39:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15]	validation-logloss:0.58713	validation-auc:0.97023	validation-aucpr:0.97346
[18:39:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16]	validation-logloss:0.58188	validation-auc:0.97024	validation-aucpr:0.97348
[18:39:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17]	validation-logloss:0.57628	validation-auc:0.97036	validation-aucpr:0.97357
[18:39:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18]	validation-logloss:0.57078	validation-auc:0.97024	validation-aucpr:0.97347
[18:39:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19]	validation-logloss:0.56535	validation-auc:0.97032	validation-aucpr:0.97353
[18:39:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20]	validation-logloss:0.56005	validation-auc:0.97046	validation-aucpr:0.97359
[18:39:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21]	validation-logloss:0.55473	validation-auc:0.97073	validation-aucpr:0.97378
[18:39:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22]	validation-logloss:0.54972	validation-auc:0.97071	validation-aucpr:0.97379
[18:39:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23]	validation-logloss:0.54465	validation-auc:0.97079	validation-aucpr:0.97383
[18:39:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24]	validation-logloss:0.53975	validation-auc:0.97067	validation-aucpr:0.97375
[18:39:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25]	validation-logloss:0.53487	validation-auc:0.97086	validation-aucpr:0.97391
[18:39:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26]	validation-logloss:0.52995	validation-auc:0.97095	validation-aucpr:0.97398
[18:39:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27]	validation-logloss:0.52513	validation-auc:0.97103	validation-aucpr:0.97406
[18:39:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28]	validation-logloss:0.52056	validation-auc:0.97107	validation-aucpr:0.97410
[18:39:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29]	validation-logloss:0.51591	validation-auc:0.97119	validation-aucpr:0.97418
[18:39:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30]	validation-logloss:0.51190	validation-auc:0.97112	validation-aucpr:0.97412
[18:39:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31]	validation-logloss:0.50740	validation-auc:0.97136	validation-aucpr:0.97430
[18:39:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32]	validation-logloss:0.50296	validation-auc:0.97148	validation-aucpr:0.97438
[18:39:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33]	validation-logloss:0.49867	validation-auc:0.97148	validation-aucpr:0.97439
[18:39:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34]	validation-logloss:0.49439	validation-auc:0.97167	validation-aucpr:0.97461
[18:39:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35]	validation-logloss:0.49023	validation-auc:0.97165	validation-aucpr:0.97458
[18:40:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36]	validation-logloss:0.48605	validation-auc:0.97172	validation-aucpr:0.97463
[18:40:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37]	validation-logloss:0.48204	validation-auc:0.97177	validation-aucpr:0.97471
[18:40:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38]	validation-logloss:0.47833	validation-auc:0.97185	validation-aucpr:0.97478
[18:40:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39]	validation-logloss:0.47440	validation-auc:0.97202	validation-aucpr:0.97491
[18:40:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40]	validation-logloss:0.47053	validation-auc:0.97202	validation-aucpr:0.97492
[18:40:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41]	validation-logloss:0.46671	validation-auc:0.97199	validation-aucpr:0.97489
[18:40:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42]	validation-logloss:0.46314	validation-auc:0.97191	validation-aucpr:0.97483
[18:40:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43]	validation-logloss:0.45947	validation-auc:0.97188	validation-aucpr:0.97480
[18:40:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44]	validation-logloss:0.45579	validation-auc:0.97189	validation-aucpr:0.97478
[18:40:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45]	validation-logloss:0.45221	validation-auc:0.97188	validation-aucpr:0.97476
[18:40:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46]	validation-logloss:0.44871	validation-auc:0.97182	validation-aucpr:0.97470
[18:40:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[47]	validation-logloss:0.44527	validation-auc:0.97177	validation-aucpr:0.97466
[18:40:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[48]	validation-logloss:0.44188	validation-auc:0.97171	validation-aucpr:0.97461
[18:40:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[49]	validation-logloss:0.43856	validation-auc:0.97177	validation-aucpr:0.97465
[18:40:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[50]	validation-logloss:0.43520	validation-auc:0.97184	validation-aucpr:0.97468
[18:40:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[51]	validation-logloss:0.43198	validation-auc:0.97180	validation-aucpr:0.97467
[18:40:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[52]	validation-logloss:0.42871	validation-auc:0.97185	validation-aucpr:0.97470
[18:40:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[53]	validation-logloss:0.42569	validation-auc:0.97195	validation-aucpr:0.97476
[18:40:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[54]	validation-logloss:0.42256	validation-auc:0.97192	validation-aucpr:0.97474
[18:40:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[55]	validation-logloss:0.41984	validation-auc:0.97196	validation-aucpr:0.97478
[18:40:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[56]	validation-logloss:0.41678	validation-auc:0.97201	validation-aucpr:0.97484
[18:40:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[57]	validation-logloss:0.41372	validation-auc:0.97205	validation-aucpr:0.97486
[18:40:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[58]	validation-logloss:0.41072	validation-auc:0.97204	validation-aucpr:0.97484
[18:40:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[59]	validation-logloss:0.40776	validation-auc:0.97211	validation-aucpr:0.97491
[18:40:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[60]	validation-logloss:0.40493	validation-auc:0.97212	validation-aucpr:0.97488
[18:40:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[61]	validation-logloss:0.40216	validation-auc:0.97206	validation-aucpr:0.97485
[18:40:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[62]	validation-logloss:0.39939	validation-auc:0.97210	validation-aucpr:0.97488
[18:40:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[63]	validation-logloss:0.39663	validation-auc:0.97214	validation-aucpr:0.97489
[18:40:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[64]	validation-logloss:0.39414	validation-auc:0.97212	validation-aucpr:0.97486
[18:40:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[65]	validation-logloss:0.39150	validation-auc:0.97218	validation-aucpr:0.97489
[18:40:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[66]	validation-logloss:0.38886	validation-auc:0.97220	validation-aucpr:0.97490
[18:40:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[67]	validation-logloss:0.38616	validation-auc:0.97234	validation-aucpr:0.97500
[18:40:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[68]	validation-logloss:0.38368	validation-auc:0.97228	validation-aucpr:0.97495
[18:40:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[69]	validation-logloss:0.38117	validation-auc:0.97227	validation-aucpr:0.97498
[18:40:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[70]	validation-logloss:0.37881	validation-auc:0.97220	validation-aucpr:0.97493
[18:40:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[71]	validation-logloss:0.37636	validation-auc:0.97219	validation-aucpr:0.97492
[18:40:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[72]	validation-logloss:0.37391	validation-auc:0.97222	validation-aucpr:0.97495
[18:40:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[73]	validation-logloss:0.37175	validation-auc:0.97224	validation-aucpr:0.97493
[18:40:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[74]	validation-logloss:0.36945	validation-auc:0.97225	validation-aucpr:0.97494
[18:40:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[75]	validation-logloss:0.36742	validation-auc:0.97218	validation-aucpr:0.97486
[18:40:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[76]	validation-logloss:0.36514	validation-auc:0.97216	validation-aucpr:0.97485
[18:40:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[77]	validation-logloss:0.36302	validation-auc:0.97213	validation-aucpr:0.97481
[18:40:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[78]	validation-logloss:0.36082	validation-auc:0.97213	validation-aucpr:0.97482
{'best_iteration': '67', 'best_score': '0.975003136856076'}
Trial 26, Fold 2: Log loss = 0.3608162098978818, Average precision = 0.9747282694988629, ROC-AUC = 0.97213250555928, Elapsed Time = 107.27267639999991 seconds
Trial 26, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 26, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[18:41:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.68550	validation-auc:0.95309	validation-aucpr:0.95686
[18:41:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.67793	validation-auc:0.96434	validation-aucpr:0.96972
[18:41:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.67041	validation-auc:0.96710	validation-aucpr:0.97192
[18:41:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.66312	validation-auc:0.96727	validation-aucpr:0.97218
[18:41:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.65591	validation-auc:0.96767	validation-aucpr:0.97246
[18:41:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5]	validation-logloss:0.64882	validation-auc:0.96821	validation-aucpr:0.97276
[18:41:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6]	validation-logloss:0.64191	validation-auc:0.96833	validation-aucpr:0.97306
[18:41:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7]	validation-logloss:0.63514	validation-auc:0.96850	validation-aucpr:0.97316
[18:41:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8]	validation-logloss:0.62849	validation-auc:0.96875	validation-aucpr:0.97341
[18:41:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9]	validation-logloss:0.62193	validation-auc:0.96921	validation-aucpr:0.97375
[18:41:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10]	validation-logloss:0.61557	validation-auc:0.96961	validation-aucpr:0.97400
[18:41:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11]	validation-logloss:0.60924	validation-auc:0.96968	validation-aucpr:0.97411
[18:41:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12]	validation-logloss:0.60306	validation-auc:0.97005	validation-aucpr:0.97422
[18:41:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13]	validation-logloss:0.59755	validation-auc:0.97063	validation-aucpr:0.97486
[18:41:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14]	validation-logloss:0.59155	validation-auc:0.97064	validation-aucpr:0.97486
[18:41:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15]	validation-logloss:0.58571	validation-auc:0.97075	validation-aucpr:0.97496
[18:41:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16]	validation-logloss:0.58045	validation-auc:0.97084	validation-aucpr:0.97500
[18:41:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17]	validation-logloss:0.57488	validation-auc:0.97090	validation-aucpr:0.97506
[18:41:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18]	validation-logloss:0.56931	validation-auc:0.97097	validation-aucpr:0.97510
[18:41:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19]	validation-logloss:0.56431	validation-auc:0.97124	validation-aucpr:0.97530
[18:41:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20]	validation-logloss:0.55881	validation-auc:0.97153	validation-aucpr:0.97553
[18:41:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21]	validation-logloss:0.55353	validation-auc:0.97168	validation-aucpr:0.97566
[18:41:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22]	validation-logloss:0.54825	validation-auc:0.97175	validation-aucpr:0.97574
[18:41:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23]	validation-logloss:0.54318	validation-auc:0.97173	validation-aucpr:0.97571
[18:41:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24]	validation-logloss:0.53871	validation-auc:0.97168	validation-aucpr:0.97567
[18:41:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25]	validation-logloss:0.53371	validation-auc:0.97157	validation-aucpr:0.97561
[18:41:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26]	validation-logloss:0.52889	validation-auc:0.97138	validation-aucpr:0.97550
[18:41:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27]	validation-logloss:0.52414	validation-auc:0.97131	validation-aucpr:0.97545
[18:41:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28]	validation-logloss:0.51943	validation-auc:0.97140	validation-aucpr:0.97551
[18:41:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29]	validation-logloss:0.51524	validation-auc:0.97146	validation-aucpr:0.97563
[18:41:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30]	validation-logloss:0.51064	validation-auc:0.97149	validation-aucpr:0.97565
[18:41:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31]	validation-logloss:0.50617	validation-auc:0.97147	validation-aucpr:0.97564
[18:41:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32]	validation-logloss:0.50169	validation-auc:0.97157	validation-aucpr:0.97570
[18:41:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33]	validation-logloss:0.49756	validation-auc:0.97149	validation-aucpr:0.97562
[18:41:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34]	validation-logloss:0.49343	validation-auc:0.97148	validation-aucpr:0.97562
[18:41:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35]	validation-logloss:0.48979	validation-auc:0.97148	validation-aucpr:0.97561
[18:41:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36]	validation-logloss:0.48575	validation-auc:0.97153	validation-aucpr:0.97565
[18:41:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37]	validation-logloss:0.48177	validation-auc:0.97149	validation-aucpr:0.97563
[18:41:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38]	validation-logloss:0.47778	validation-auc:0.97159	validation-aucpr:0.97570
[18:41:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39]	validation-logloss:0.47390	validation-auc:0.97145	validation-aucpr:0.97560
[18:41:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40]	validation-logloss:0.47002	validation-auc:0.97147	validation-aucpr:0.97563
[18:41:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41]	validation-logloss:0.46649	validation-auc:0.97152	validation-aucpr:0.97569
[18:41:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42]	validation-logloss:0.46281	validation-auc:0.97145	validation-aucpr:0.97562
[18:41:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43]	validation-logloss:0.45941	validation-auc:0.97148	validation-aucpr:0.97564
[18:41:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44]	validation-logloss:0.45611	validation-auc:0.97149	validation-aucpr:0.97559
[18:42:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45]	validation-logloss:0.45262	validation-auc:0.97146	validation-aucpr:0.97557
[18:42:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46]	validation-logloss:0.44905	validation-auc:0.97147	validation-aucpr:0.97559
[18:42:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[47]	validation-logloss:0.44559	validation-auc:0.97152	validation-aucpr:0.97562
[18:42:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[48]	validation-logloss:0.44253	validation-auc:0.97149	validation-aucpr:0.97561
[18:42:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[49]	validation-logloss:0.43921	validation-auc:0.97147	validation-aucpr:0.97560
[18:42:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[50]	validation-logloss:0.43588	validation-auc:0.97149	validation-aucpr:0.97562
[18:42:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[51]	validation-logloss:0.43259	validation-auc:0.97151	validation-aucpr:0.97564
[18:42:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[52]	validation-logloss:0.42979	validation-auc:0.97151	validation-aucpr:0.97566
[18:42:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[53]	validation-logloss:0.42665	validation-auc:0.97152	validation-aucpr:0.97565
[18:42:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[54]	validation-logloss:0.42356	validation-auc:0.97155	validation-aucpr:0.97568
[18:42:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[55]	validation-logloss:0.42044	validation-auc:0.97162	validation-aucpr:0.97573
[18:42:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[56]	validation-logloss:0.41741	validation-auc:0.97163	validation-aucpr:0.97575
[18:42:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[57]	validation-logloss:0.41439	validation-auc:0.97172	validation-aucpr:0.97581
[18:42:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[58]	validation-logloss:0.41144	validation-auc:0.97183	validation-aucpr:0.97591
[18:42:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[59]	validation-logloss:0.40850	validation-auc:0.97188	validation-aucpr:0.97595
[18:42:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[60]	validation-logloss:0.40560	validation-auc:0.97188	validation-aucpr:0.97595
[18:42:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[61]	validation-logloss:0.40292	validation-auc:0.97187	validation-aucpr:0.97596
[18:42:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[62]	validation-logloss:0.40015	validation-auc:0.97190	validation-aucpr:0.97596
[18:42:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[63]	validation-logloss:0.39739	validation-auc:0.97198	validation-aucpr:0.97603
[18:42:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[64]	validation-logloss:0.39464	validation-auc:0.97197	validation-aucpr:0.97602
[18:42:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[65]	validation-logloss:0.39198	validation-auc:0.97194	validation-aucpr:0.97601
[18:42:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[66]	validation-logloss:0.38960	validation-auc:0.97196	validation-aucpr:0.97597
[18:42:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[67]	validation-logloss:0.38708	validation-auc:0.97191	validation-aucpr:0.97596
[18:42:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[68]	validation-logloss:0.38457	validation-auc:0.97186	validation-aucpr:0.97596
[18:42:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[69]	validation-logloss:0.38206	validation-auc:0.97188	validation-aucpr:0.97598
[18:42:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[70]	validation-logloss:0.37962	validation-auc:0.97189	validation-aucpr:0.97600
[18:42:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[71]	validation-logloss:0.37739	validation-auc:0.97189	validation-aucpr:0.97599
[18:42:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[72]	validation-logloss:0.37494	validation-auc:0.97191	validation-aucpr:0.97600
[18:42:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[73]	validation-logloss:0.37256	validation-auc:0.97186	validation-aucpr:0.97597
[18:42:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[74]	validation-logloss:0.37027	validation-auc:0.97188	validation-aucpr:0.97598
[18:42:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[75]	validation-logloss:0.36795	validation-auc:0.97186	validation-aucpr:0.97596
[18:42:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[76]	validation-logloss:0.36573	validation-auc:0.97190	validation-aucpr:0.97599
[18:42:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[77]	validation-logloss:0.36345	validation-auc:0.97188	validation-aucpr:0.97598
[18:42:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[78]	validation-logloss:0.36119	validation-auc:0.97194	validation-aucpr:0.97609
{'best_iteration': '78', 'best_score': '0.9760926971288632'}
Trial 26, Fold 3: Log loss = 0.3611888171930082, Average precision = 0.9760961494961098, ROC-AUC = 0.9719414089963498, Elapsed Time = 108.23821640000097 seconds
Trial 26, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 26, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[18:42:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.68536	validation-auc:0.95745	validation-aucpr:0.95606
[18:42:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.67782	validation-auc:0.96335	validation-aucpr:0.96994
[18:42:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.67038	validation-auc:0.96625	validation-aucpr:0.97207
[18:42:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.66315	validation-auc:0.96726	validation-aucpr:0.97260
[18:42:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.65595	validation-auc:0.96774	validation-aucpr:0.97297
[18:42:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5]	validation-logloss:0.64891	validation-auc:0.96819	validation-aucpr:0.97336
[18:42:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6]	validation-logloss:0.64203	validation-auc:0.96809	validation-aucpr:0.97333
[18:42:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7]	validation-logloss:0.63593	validation-auc:0.96831	validation-aucpr:0.97367
[18:43:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8]	validation-logloss:0.62940	validation-auc:0.96854	validation-aucpr:0.97389
[18:43:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9]	validation-logloss:0.62296	validation-auc:0.96900	validation-aucpr:0.97411
[18:43:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10]	validation-logloss:0.61661	validation-auc:0.96910	validation-aucpr:0.97427
[18:43:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11]	validation-logloss:0.61030	validation-auc:0.96943	validation-aucpr:0.97451
[18:43:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12]	validation-logloss:0.60414	validation-auc:0.96957	validation-aucpr:0.97456
[18:43:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13]	validation-logloss:0.59819	validation-auc:0.96934	validation-aucpr:0.97445
[18:43:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14]	validation-logloss:0.59229	validation-auc:0.96928	validation-aucpr:0.97439
[18:43:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15]	validation-logloss:0.58695	validation-auc:0.96913	validation-aucpr:0.97426
[18:43:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16]	validation-logloss:0.58134	validation-auc:0.96899	validation-aucpr:0.97415
[18:43:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17]	validation-logloss:0.57585	validation-auc:0.96900	validation-aucpr:0.97418
[18:43:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18]	validation-logloss:0.57028	validation-auc:0.96931	validation-aucpr:0.97441
[18:43:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19]	validation-logloss:0.56480	validation-auc:0.96962	validation-aucpr:0.97464
[18:43:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20]	validation-logloss:0.55947	validation-auc:0.96972	validation-aucpr:0.97474
[18:43:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21]	validation-logloss:0.55442	validation-auc:0.96973	validation-aucpr:0.97474
[18:43:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22]	validation-logloss:0.54934	validation-auc:0.96977	validation-aucpr:0.97478
[18:43:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23]	validation-logloss:0.54420	validation-auc:0.97004	validation-aucpr:0.97494
[18:43:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24]	validation-logloss:0.53924	validation-auc:0.96999	validation-aucpr:0.97493
[18:43:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25]	validation-logloss:0.53436	validation-auc:0.97005	validation-aucpr:0.97496
[18:43:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26]	validation-logloss:0.52954	validation-auc:0.97005	validation-aucpr:0.97498
[18:43:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27]	validation-logloss:0.52479	validation-auc:0.97022	validation-aucpr:0.97507
[18:43:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28]	validation-logloss:0.52029	validation-auc:0.97014	validation-aucpr:0.97503
[18:43:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29]	validation-logloss:0.51609	validation-auc:0.97022	validation-aucpr:0.97510
[18:43:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30]	validation-logloss:0.51163	validation-auc:0.97027	validation-aucpr:0.97513
[18:43:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31]	validation-logloss:0.50728	validation-auc:0.97026	validation-aucpr:0.97513
[18:43:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32]	validation-logloss:0.50290	validation-auc:0.97021	validation-aucpr:0.97510
[18:43:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33]	validation-logloss:0.49872	validation-auc:0.97032	validation-aucpr:0.97516
[18:43:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34]	validation-logloss:0.49453	validation-auc:0.97036	validation-aucpr:0.97518
[18:43:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35]	validation-logloss:0.49031	validation-auc:0.97050	validation-aucpr:0.97529
[18:43:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36]	validation-logloss:0.48618	validation-auc:0.97067	validation-aucpr:0.97543
[18:43:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37]	validation-logloss:0.48219	validation-auc:0.97068	validation-aucpr:0.97542
[18:43:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38]	validation-logloss:0.47821	validation-auc:0.97066	validation-aucpr:0.97542
[18:43:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39]	validation-logloss:0.47442	validation-auc:0.97054	validation-aucpr:0.97531
[18:43:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40]	validation-logloss:0.47059	validation-auc:0.97067	validation-aucpr:0.97539
[18:43:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41]	validation-logloss:0.46678	validation-auc:0.97077	validation-aucpr:0.97546
[18:43:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42]	validation-logloss:0.46341	validation-auc:0.97077	validation-aucpr:0.97547
[18:43:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43]	validation-logloss:0.45974	validation-auc:0.97080	validation-aucpr:0.97550
[18:43:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44]	validation-logloss:0.45612	validation-auc:0.97087	validation-aucpr:0.97552
[18:43:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45]	validation-logloss:0.45254	validation-auc:0.97090	validation-aucpr:0.97556
[18:43:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46]	validation-logloss:0.44907	validation-auc:0.97091	validation-aucpr:0.97556
[18:43:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[47]	validation-logloss:0.44595	validation-auc:0.97088	validation-aucpr:0.97555
[18:43:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[48]	validation-logloss:0.44254	validation-auc:0.97089	validation-aucpr:0.97555
[18:43:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[49]	validation-logloss:0.43918	validation-auc:0.97096	validation-aucpr:0.97561
[18:43:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[50]	validation-logloss:0.43591	validation-auc:0.97093	validation-aucpr:0.97559
[18:43:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[51]	validation-logloss:0.43276	validation-auc:0.97086	validation-aucpr:0.97552
[18:43:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[52]	validation-logloss:0.42987	validation-auc:0.97084	validation-aucpr:0.97552
[18:43:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[53]	validation-logloss:0.42669	validation-auc:0.97088	validation-aucpr:0.97556
[18:44:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[54]	validation-logloss:0.42356	validation-auc:0.97083	validation-aucpr:0.97554
[18:44:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[55]	validation-logloss:0.42076	validation-auc:0.97093	validation-aucpr:0.97562
[18:44:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[56]	validation-logloss:0.41774	validation-auc:0.97097	validation-aucpr:0.97565
[18:44:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[57]	validation-logloss:0.41474	validation-auc:0.97106	validation-aucpr:0.97570
[18:44:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[58]	validation-logloss:0.41179	validation-auc:0.97116	validation-aucpr:0.97576
[18:44:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[59]	validation-logloss:0.40919	validation-auc:0.97110	validation-aucpr:0.97573
[18:44:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[60]	validation-logloss:0.40635	validation-auc:0.97112	validation-aucpr:0.97575
[18:44:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[61]	validation-logloss:0.40354	validation-auc:0.97111	validation-aucpr:0.97575
[18:44:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[62]	validation-logloss:0.40077	validation-auc:0.97113	validation-aucpr:0.97575
[18:44:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[63]	validation-logloss:0.39801	validation-auc:0.97116	validation-aucpr:0.97577
[18:44:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[64]	validation-logloss:0.39527	validation-auc:0.97128	validation-aucpr:0.97585
[18:44:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[65]	validation-logloss:0.39259	validation-auc:0.97132	validation-aucpr:0.97587
[18:44:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[66]	validation-logloss:0.38997	validation-auc:0.97133	validation-aucpr:0.97587
[18:44:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[67]	validation-logloss:0.38740	validation-auc:0.97134	validation-aucpr:0.97587
[18:44:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[68]	validation-logloss:0.38504	validation-auc:0.97136	validation-aucpr:0.97589
[18:44:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[69]	validation-logloss:0.38248	validation-auc:0.97144	validation-aucpr:0.97595
[18:44:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[70]	validation-logloss:0.38005	validation-auc:0.97137	validation-aucpr:0.97589
[18:44:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[71]	validation-logloss:0.37764	validation-auc:0.97133	validation-aucpr:0.97587
[18:44:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[72]	validation-logloss:0.37520	validation-auc:0.97137	validation-aucpr:0.97589
[18:44:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[73]	validation-logloss:0.37287	validation-auc:0.97136	validation-aucpr:0.97588
[18:44:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[74]	validation-logloss:0.37051	validation-auc:0.97134	validation-aucpr:0.97587
[18:44:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[75]	validation-logloss:0.36822	validation-auc:0.97135	validation-aucpr:0.97587
[18:44:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[76]	validation-logloss:0.36598	validation-auc:0.97128	validation-aucpr:0.97582
[18:44:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[77]	validation-logloss:0.36374	validation-auc:0.97130	validation-aucpr:0.97582
[18:44:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[78]	validation-logloss:0.36154	validation-auc:0.97130	validation-aucpr:0.97581
{'best_iteration': '69', 'best_score': '0.975951096302995'}
Trial 26, Fold 4: Log loss = 0.3615393712953612, Average precision = 0.9757995200171711, ROC-AUC = 0.9712955064605582, Elapsed Time = 106.7030864999997 seconds
Trial 26, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 26, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[18:44:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.68543	validation-auc:0.95258	validation-aucpr:0.95746
[18:44:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.67787	validation-auc:0.96072	validation-aucpr:0.96581
[18:44:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.67039	validation-auc:0.96449	validation-aucpr:0.96857
[18:44:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.66323	validation-auc:0.96455	validation-aucpr:0.96869
[18:44:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.65615	validation-auc:0.96421	validation-aucpr:0.96853
[18:44:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5]	validation-logloss:0.64925	validation-auc:0.96447	validation-aucpr:0.96876
[18:44:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6]	validation-logloss:0.64256	validation-auc:0.96455	validation-aucpr:0.96888
[18:44:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7]	validation-logloss:0.63584	validation-auc:0.96545	validation-aucpr:0.96957
[18:44:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8]	validation-logloss:0.62918	validation-auc:0.96607	validation-aucpr:0.97059
[18:44:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9]	validation-logloss:0.62271	validation-auc:0.96637	validation-aucpr:0.97081
[18:44:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10]	validation-logloss:0.61636	validation-auc:0.96662	validation-aucpr:0.97096
[18:44:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11]	validation-logloss:0.61018	validation-auc:0.96715	validation-aucpr:0.97132
[18:44:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12]	validation-logloss:0.60411	validation-auc:0.96724	validation-aucpr:0.97144
[18:44:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13]	validation-logloss:0.59812	validation-auc:0.96736	validation-aucpr:0.97155
[18:44:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14]	validation-logloss:0.59224	validation-auc:0.96766	validation-aucpr:0.97171
[18:44:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15]	validation-logloss:0.58642	validation-auc:0.96799	validation-aucpr:0.97197
[18:44:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16]	validation-logloss:0.58076	validation-auc:0.96798	validation-aucpr:0.97198
[18:44:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17]	validation-logloss:0.57584	validation-auc:0.96763	validation-aucpr:0.97198
[18:44:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18]	validation-logloss:0.57035	validation-auc:0.96790	validation-aucpr:0.97225
[18:45:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19]	validation-logloss:0.56497	validation-auc:0.96780	validation-aucpr:0.97217
[18:45:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20]	validation-logloss:0.55974	validation-auc:0.96792	validation-aucpr:0.97227
[18:45:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21]	validation-logloss:0.55452	validation-auc:0.96822	validation-aucpr:0.97251
[18:45:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22]	validation-logloss:0.54943	validation-auc:0.96838	validation-aucpr:0.97259
[18:45:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23]	validation-logloss:0.54438	validation-auc:0.96831	validation-aucpr:0.97259
[18:45:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24]	validation-logloss:0.53941	validation-auc:0.96838	validation-aucpr:0.97258
[18:45:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25]	validation-logloss:0.53458	validation-auc:0.96833	validation-aucpr:0.97255
[18:45:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26]	validation-logloss:0.52980	validation-auc:0.96848	validation-aucpr:0.97264
[18:45:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27]	validation-logloss:0.52507	validation-auc:0.96876	validation-aucpr:0.97286
[18:45:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28]	validation-logloss:0.52047	validation-auc:0.96887	validation-aucpr:0.97292
[18:45:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29]	validation-logloss:0.51591	validation-auc:0.96893	validation-aucpr:0.97294
[18:45:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30]	validation-logloss:0.51145	validation-auc:0.96893	validation-aucpr:0.97294
[18:45:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31]	validation-logloss:0.50705	validation-auc:0.96899	validation-aucpr:0.97302
[18:45:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32]	validation-logloss:0.50263	validation-auc:0.96919	validation-aucpr:0.97315
[18:45:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33]	validation-logloss:0.49843	validation-auc:0.96911	validation-aucpr:0.97316
[18:45:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34]	validation-logloss:0.49427	validation-auc:0.96909	validation-aucpr:0.97314
[18:45:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35]	validation-logloss:0.49015	validation-auc:0.96903	validation-aucpr:0.97310
[18:45:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36]	validation-logloss:0.48611	validation-auc:0.96889	validation-aucpr:0.97302
[18:45:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37]	validation-logloss:0.48250	validation-auc:0.96891	validation-aucpr:0.97306
[18:45:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38]	validation-logloss:0.47857	validation-auc:0.96905	validation-aucpr:0.97316
[18:45:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39]	validation-logloss:0.47471	validation-auc:0.96915	validation-aucpr:0.97322
[18:45:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40]	validation-logloss:0.47101	validation-auc:0.96915	validation-aucpr:0.97320
[18:45:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41]	validation-logloss:0.46727	validation-auc:0.96910	validation-aucpr:0.97318
[18:45:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42]	validation-logloss:0.46361	validation-auc:0.96910	validation-aucpr:0.97319
[18:45:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43]	validation-logloss:0.46007	validation-auc:0.96901	validation-aucpr:0.97309
[18:45:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44]	validation-logloss:0.45655	validation-auc:0.96902	validation-aucpr:0.97310
[18:45:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45]	validation-logloss:0.45303	validation-auc:0.96903	validation-aucpr:0.97311
[18:45:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46]	validation-logloss:0.44954	validation-auc:0.96921	validation-aucpr:0.97321
[18:45:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[47]	validation-logloss:0.44622	validation-auc:0.96946	validation-aucpr:0.97331
[18:45:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[48]	validation-logloss:0.44288	validation-auc:0.96947	validation-aucpr:0.97333
[18:45:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[49]	validation-logloss:0.43968	validation-auc:0.96942	validation-aucpr:0.97329
[18:45:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[50]	validation-logloss:0.43647	validation-auc:0.96934	validation-aucpr:0.97323
[18:45:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[51]	validation-logloss:0.43355	validation-auc:0.96944	validation-aucpr:0.97334
[18:45:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[52]	validation-logloss:0.43046	validation-auc:0.96952	validation-aucpr:0.97338
[18:45:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[53]	validation-logloss:0.42742	validation-auc:0.96945	validation-aucpr:0.97332
[18:45:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[54]	validation-logloss:0.42434	validation-auc:0.96938	validation-aucpr:0.97328
[18:45:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[55]	validation-logloss:0.42145	validation-auc:0.96935	validation-aucpr:0.97330
[18:45:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[56]	validation-logloss:0.41844	validation-auc:0.96941	validation-aucpr:0.97335
[18:45:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[57]	validation-logloss:0.41583	validation-auc:0.96940	validation-aucpr:0.97334
[18:45:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[58]	validation-logloss:0.41295	validation-auc:0.96936	validation-aucpr:0.97322
[18:45:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[59]	validation-logloss:0.41020	validation-auc:0.96929	validation-aucpr:0.97317
[18:45:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[60]	validation-logloss:0.40739	validation-auc:0.96935	validation-aucpr:0.97322
[18:45:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[61]	validation-logloss:0.40456	validation-auc:0.96946	validation-aucpr:0.97329
[18:46:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[62]	validation-logloss:0.40182	validation-auc:0.96947	validation-aucpr:0.97329
[18:46:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[63]	validation-logloss:0.39915	validation-auc:0.96950	validation-aucpr:0.97332
[18:46:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[64]	validation-logloss:0.39648	validation-auc:0.96963	validation-aucpr:0.97340
[18:46:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[65]	validation-logloss:0.39391	validation-auc:0.96963	validation-aucpr:0.97345
[18:46:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[66]	validation-logloss:0.39134	validation-auc:0.96965	validation-aucpr:0.97345
[18:46:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[67]	validation-logloss:0.38885	validation-auc:0.96965	validation-aucpr:0.97345
[18:46:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[68]	validation-logloss:0.38633	validation-auc:0.96970	validation-aucpr:0.97349
[18:46:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[69]	validation-logloss:0.38384	validation-auc:0.96980	validation-aucpr:0.97357
[18:46:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[70]	validation-logloss:0.38163	validation-auc:0.96983	validation-aucpr:0.97357
[18:46:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[71]	validation-logloss:0.37923	validation-auc:0.96986	validation-aucpr:0.97359
[18:46:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[72]	validation-logloss:0.37683	validation-auc:0.96994	validation-aucpr:0.97365
[18:46:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[73]	validation-logloss:0.37483	validation-auc:0.96993	validation-aucpr:0.97369
[18:46:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[74]	validation-logloss:0.37254	validation-auc:0.96997	validation-aucpr:0.97371
[18:46:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[75]	validation-logloss:0.37030	validation-auc:0.96994	validation-aucpr:0.97369
[18:46:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[76]	validation-logloss:0.36812	validation-auc:0.96992	validation-aucpr:0.97367
[18:46:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[77]	validation-logloss:0.36581	validation-auc:0.97007	validation-aucpr:0.97379
[18:46:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[78]	validation-logloss:0.36361	validation-auc:0.97009	validation-aucpr:0.97379
{'best_iteration': '78', 'best_score': '0.9737867965658782'}
Trial 26, Fold 5: Log loss = 0.36361108693323485, Average precision = 0.9737929935568028, ROC-AUC = 0.9700917451647066, Elapsed Time = 107.7331622999991 seconds
Optimization Progress:  27%|##7       | 27/100 [47:35<3:45:59, 185.75s/it]
Trial 27, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 27, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[0]	validation-logloss:0.67710	validation-auc:0.81462	validation-aucpr:0.74736
[1]	validation-logloss:0.66225	validation-auc:0.89137	validation-aucpr:0.83793
[2]	validation-logloss:0.64776	validation-auc:0.93149	validation-aucpr:0.91845
[3]	validation-logloss:0.63378	validation-auc:0.94311	validation-aucpr:0.93988
[4]	validation-logloss:0.62062	validation-auc:0.94563	validation-aucpr:0.94713
[5]	validation-logloss:0.60765	validation-auc:0.95090	validation-aucpr:0.95911
[6]	validation-logloss:0.59496	validation-auc:0.95299	validation-aucpr:0.96198
[7]	validation-logloss:0.58297	validation-auc:0.95449	validation-aucpr:0.96269
[8]	validation-logloss:0.57165	validation-auc:0.95677	validation-aucpr:0.96310
[9]	validation-logloss:0.56193	validation-auc:0.95746	validation-aucpr:0.96273
[10]	validation-logloss:0.55161	validation-auc:0.95838	validation-aucpr:0.96322
[11]	validation-logloss:0.54112	validation-auc:0.95946	validation-aucpr:0.96455
[12]	validation-logloss:0.53139	validation-auc:0.95968	validation-aucpr:0.96460
[13]	validation-logloss:0.52203	validation-auc:0.95959	validation-aucpr:0.96460
[14]	validation-logloss:0.51310	validation-auc:0.95997	validation-aucpr:0.96617
[15]	validation-logloss:0.50506	validation-auc:0.96112	validation-aucpr:0.96647
[16]	validation-logloss:0.49662	validation-auc:0.96170	validation-aucpr:0.96664
[17]	validation-logloss:0.48829	validation-auc:0.96233	validation-aucpr:0.96854
[18]	validation-logloss:0.48007	validation-auc:0.96307	validation-aucpr:0.96891
[19]	validation-logloss:0.47225	validation-auc:0.96365	validation-aucpr:0.96924
[20]	validation-logloss:0.46451	validation-auc:0.96405	validation-aucpr:0.96949
[21]	validation-logloss:0.45703	validation-auc:0.96409	validation-aucpr:0.96953
[22]	validation-logloss:0.45006	validation-auc:0.96443	validation-aucpr:0.96987
[23]	validation-logloss:0.44294	validation-auc:0.96507	validation-aucpr:0.97022
[24]	validation-logloss:0.43665	validation-auc:0.96504	validation-aucpr:0.97031
[25]	validation-logloss:0.43005	validation-auc:0.96519	validation-aucpr:0.97046
[26]	validation-logloss:0.42423	validation-auc:0.96519	validation-aucpr:0.97050
[27]	validation-logloss:0.41800	validation-auc:0.96516	validation-aucpr:0.97058
[28]	validation-logloss:0.41203	validation-auc:0.96521	validation-aucpr:0.97053
[29]	validation-logloss:0.40632	validation-auc:0.96535	validation-aucpr:0.97093
[30]	validation-logloss:0.40140	validation-auc:0.96548	validation-aucpr:0.97140
[31]	validation-logloss:0.39606	validation-auc:0.96556	validation-aucpr:0.97154
[32]	validation-logloss:0.39086	validation-auc:0.96572	validation-aucpr:0.97159
[33]	validation-logloss:0.38553	validation-auc:0.96590	validation-aucpr:0.97172
[34]	validation-logloss:0.38032	validation-auc:0.96626	validation-aucpr:0.97195
[35]	validation-logloss:0.37585	validation-auc:0.96632	validation-aucpr:0.97193
[36]	validation-logloss:0.37079	validation-auc:0.96668	validation-aucpr:0.97225
[37]	validation-logloss:0.36636	validation-auc:0.96657	validation-aucpr:0.97215
[38]	validation-logloss:0.36203	validation-auc:0.96658	validation-aucpr:0.97219
[39]	validation-logloss:0.35778	validation-auc:0.96654	validation-aucpr:0.97216
[40]	validation-logloss:0.35345	validation-auc:0.96657	validation-aucpr:0.97225
[41]	validation-logloss:0.34948	validation-auc:0.96656	validation-aucpr:0.97228
[42]	validation-logloss:0.34556	validation-auc:0.96673	validation-aucpr:0.97238
[43]	validation-logloss:0.34156	validation-auc:0.96685	validation-aucpr:0.97245
[44]	validation-logloss:0.33776	validation-auc:0.96694	validation-aucpr:0.97253
[45]	validation-logloss:0.33408	validation-auc:0.96711	validation-aucpr:0.97261
[46]	validation-logloss:0.33041	validation-auc:0.96730	validation-aucpr:0.97277
[47]	validation-logloss:0.32697	validation-auc:0.96738	validation-aucpr:0.97283
{'best_iteration': '47', 'best_score': '0.9728271886681858'}
Trial 27, Fold 1: Log loss = 0.32696823726928415, Average precision = 0.9729045792862018, ROC-AUC = 0.9673756440496504, Elapsed Time = 14.147009500000422 seconds
Trial 27, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 27, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[0]	validation-logloss:0.67726	validation-auc:0.80978	validation-aucpr:0.71893
[1]	validation-logloss:0.66200	validation-auc:0.91014	validation-aucpr:0.84861
[2]	validation-logloss:0.64775	validation-auc:0.93518	validation-aucpr:0.90438
[3]	validation-logloss:0.63508	validation-auc:0.94685	validation-aucpr:0.92445
[4]	validation-logloss:0.62172	validation-auc:0.95331	validation-aucpr:0.94448
[5]	validation-logloss:0.60906	validation-auc:0.95586	validation-aucpr:0.95279
[6]	validation-logloss:0.59662	validation-auc:0.95748	validation-aucpr:0.95485
[7]	validation-logloss:0.58487	validation-auc:0.95926	validation-aucpr:0.95898
[8]	validation-logloss:0.57492	validation-auc:0.96014	validation-aucpr:0.95980
[9]	validation-logloss:0.56374	validation-auc:0.96172	validation-aucpr:0.96498
[10]	validation-logloss:0.55299	validation-auc:0.96246	validation-aucpr:0.96516
[11]	validation-logloss:0.54246	validation-auc:0.96412	validation-aucpr:0.96612
[12]	validation-logloss:0.53330	validation-auc:0.96480	validation-aucpr:0.96605
[13]	validation-logloss:0.52388	validation-auc:0.96465	validation-aucpr:0.96558
[14]	validation-logloss:0.51450	validation-auc:0.96550	validation-aucpr:0.96785
[15]	validation-logloss:0.50569	validation-auc:0.96554	validation-aucpr:0.96792
[16]	validation-logloss:0.49715	validation-auc:0.96630	validation-aucpr:0.97021
[17]	validation-logloss:0.48826	validation-auc:0.96708	validation-aucpr:0.97074
[18]	validation-logloss:0.47989	validation-auc:0.96695	validation-aucpr:0.97086
[19]	validation-logloss:0.47190	validation-auc:0.96713	validation-aucpr:0.97106
[20]	validation-logloss:0.46442	validation-auc:0.96726	validation-aucpr:0.97105
[21]	validation-logloss:0.45706	validation-auc:0.96753	validation-aucpr:0.97114
[22]	validation-logloss:0.44998	validation-auc:0.96764	validation-aucpr:0.97132
[23]	validation-logloss:0.44282	validation-auc:0.96802	validation-aucpr:0.97113
[24]	validation-logloss:0.43649	validation-auc:0.96814	validation-aucpr:0.97107
[25]	validation-logloss:0.43020	validation-auc:0.96796	validation-aucpr:0.97091
[26]	validation-logloss:0.42387	validation-auc:0.96802	validation-aucpr:0.97103
[27]	validation-logloss:0.41766	validation-auc:0.96795	validation-aucpr:0.97100
[28]	validation-logloss:0.41159	validation-auc:0.96798	validation-aucpr:0.97106
[29]	validation-logloss:0.40563	validation-auc:0.96810	validation-aucpr:0.97115
[30]	validation-logloss:0.39992	validation-auc:0.96815	validation-aucpr:0.97115
[31]	validation-logloss:0.39418	validation-auc:0.96835	validation-aucpr:0.97128
[32]	validation-logloss:0.38869	validation-auc:0.96843	validation-aucpr:0.97141
[33]	validation-logloss:0.38334	validation-auc:0.96862	validation-aucpr:0.97151
[34]	validation-logloss:0.37817	validation-auc:0.96869	validation-aucpr:0.97163
[35]	validation-logloss:0.37329	validation-auc:0.96860	validation-aucpr:0.97176
[36]	validation-logloss:0.36878	validation-auc:0.96867	validation-aucpr:0.97194
[37]	validation-logloss:0.36402	validation-auc:0.96870	validation-aucpr:0.97254
[38]	validation-logloss:0.35944	validation-auc:0.96894	validation-aucpr:0.97267
[39]	validation-logloss:0.35510	validation-auc:0.96898	validation-aucpr:0.97272
[40]	validation-logloss:0.35111	validation-auc:0.96912	validation-aucpr:0.97285
[41]	validation-logloss:0.34704	validation-auc:0.96928	validation-aucpr:0.97290
[42]	validation-logloss:0.34361	validation-auc:0.96923	validation-aucpr:0.97276
[43]	validation-logloss:0.33974	validation-auc:0.96917	validation-aucpr:0.97271
[44]	validation-logloss:0.33586	validation-auc:0.96932	validation-aucpr:0.97283
[45]	validation-logloss:0.33198	validation-auc:0.96959	validation-aucpr:0.97309
[46]	validation-logloss:0.32831	validation-auc:0.96960	validation-aucpr:0.97319
[47]	validation-logloss:0.32496	validation-auc:0.96970	validation-aucpr:0.97324
{'best_iteration': '47', 'best_score': '0.9732447278228306'}
Trial 27, Fold 2: Log loss = 0.3249584929680322, Average precision = 0.9733388508949556, ROC-AUC = 0.9697001403060024, Elapsed Time = 13.547955099998944 seconds
Trial 27, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 27, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[0]	validation-logloss:0.67718	validation-auc:0.87853	validation-aucpr:0.90062
[1]	validation-logloss:0.66183	validation-auc:0.91449	validation-aucpr:0.86609
[2]	validation-logloss:0.64756	validation-auc:0.94079	validation-aucpr:0.92209
[3]	validation-logloss:0.63387	validation-auc:0.94707	validation-aucpr:0.93535
[4]	validation-logloss:0.62057	validation-auc:0.95179	validation-aucpr:0.94525
[5]	validation-logloss:0.60746	validation-auc:0.95617	validation-aucpr:0.95559
[6]	validation-logloss:0.59525	validation-auc:0.95750	validation-aucpr:0.96035
[7]	validation-logloss:0.58461	validation-auc:0.95938	validation-aucpr:0.96129
[8]	validation-logloss:0.57274	validation-auc:0.96218	validation-aucpr:0.96318
[9]	validation-logloss:0.56155	validation-auc:0.96345	validation-aucpr:0.96621
[10]	validation-logloss:0.55080	validation-auc:0.96390	validation-aucpr:0.96731
[11]	validation-logloss:0.54138	validation-auc:0.96404	validation-aucpr:0.96578
[12]	validation-logloss:0.53124	validation-auc:0.96421	validation-aucpr:0.96611
[13]	validation-logloss:0.52160	validation-auc:0.96478	validation-aucpr:0.96638
[14]	validation-logloss:0.51210	validation-auc:0.96517	validation-aucpr:0.96665
[15]	validation-logloss:0.50331	validation-auc:0.96502	validation-aucpr:0.96572
[16]	validation-logloss:0.49436	validation-auc:0.96576	validation-aucpr:0.96754
[17]	validation-logloss:0.48630	validation-auc:0.96633	validation-aucpr:0.96846
[18]	validation-logloss:0.47781	validation-auc:0.96683	validation-aucpr:0.96874
[19]	validation-logloss:0.47007	validation-auc:0.96665	validation-aucpr:0.96851
[20]	validation-logloss:0.46244	validation-auc:0.96713	validation-aucpr:0.96862
[21]	validation-logloss:0.45500	validation-auc:0.96730	validation-aucpr:0.96869
[22]	validation-logloss:0.44776	validation-auc:0.96763	validation-aucpr:0.96879
[23]	validation-logloss:0.44062	validation-auc:0.96761	validation-aucpr:0.96838
[24]	validation-logloss:0.43370	validation-auc:0.96753	validation-aucpr:0.96834
[25]	validation-logloss:0.42684	validation-auc:0.96787	validation-aucpr:0.96810
[26]	validation-logloss:0.42062	validation-auc:0.96746	validation-aucpr:0.96747
[27]	validation-logloss:0.41452	validation-auc:0.96760	validation-aucpr:0.96795
[28]	validation-logloss:0.40846	validation-auc:0.96793	validation-aucpr:0.96826
[29]	validation-logloss:0.40279	validation-auc:0.96821	validation-aucpr:0.96937
[30]	validation-logloss:0.39739	validation-auc:0.96808	validation-aucpr:0.96911
[31]	validation-logloss:0.39189	validation-auc:0.96822	validation-aucpr:0.96921
[32]	validation-logloss:0.38682	validation-auc:0.96814	validation-aucpr:0.96926
[33]	validation-logloss:0.38192	validation-auc:0.96790	validation-aucpr:0.96913
[34]	validation-logloss:0.37662	validation-auc:0.96807	validation-aucpr:0.96910
[35]	validation-logloss:0.37144	validation-auc:0.96820	validation-aucpr:0.96921
[36]	validation-logloss:0.36705	validation-auc:0.96808	validation-aucpr:0.96938
[37]	validation-logloss:0.36291	validation-auc:0.96775	validation-aucpr:0.96821
[38]	validation-logloss:0.35819	validation-auc:0.96787	validation-aucpr:0.96800
[39]	validation-logloss:0.35408	validation-auc:0.96774	validation-aucpr:0.96779
[40]	validation-logloss:0.34975	validation-auc:0.96796	validation-aucpr:0.96775
[41]	validation-logloss:0.34598	validation-auc:0.96781	validation-aucpr:0.96721
[42]	validation-logloss:0.34184	validation-auc:0.96805	validation-aucpr:0.96921
[43]	validation-logloss:0.33816	validation-auc:0.96820	validation-aucpr:0.97004
[44]	validation-logloss:0.33485	validation-auc:0.96802	validation-aucpr:0.96986
[45]	validation-logloss:0.33116	validation-auc:0.96802	validation-aucpr:0.96992
[46]	validation-logloss:0.32810	validation-auc:0.96807	validation-aucpr:0.96954
[47]	validation-logloss:0.32490	validation-auc:0.96811	validation-aucpr:0.96906
{'best_iteration': '43', 'best_score': '0.9700397161982565'}
Trial 27, Fold 3: Log loss = 0.32490451688075633, Average precision = 0.9693429881943123, ROC-AUC = 0.9681149536265663, Elapsed Time = 13.689646200000425 seconds
Trial 27, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 27, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[0]	validation-logloss:0.67743	validation-auc:0.81076	validation-aucpr:0.71757
[1]	validation-logloss:0.66216	validation-auc:0.89878	validation-aucpr:0.83078
[2]	validation-logloss:0.64765	validation-auc:0.93143	validation-aucpr:0.90569
[3]	validation-logloss:0.63401	validation-auc:0.94414	validation-aucpr:0.93825
[4]	validation-logloss:0.62057	validation-auc:0.95023	validation-aucpr:0.94818
[5]	validation-logloss:0.60790	validation-auc:0.95327	validation-aucpr:0.94992
[6]	validation-logloss:0.59554	validation-auc:0.95592	validation-aucpr:0.95984
[7]	validation-logloss:0.58397	validation-auc:0.95867	validation-aucpr:0.96543
[8]	validation-logloss:0.57317	validation-auc:0.95943	validation-aucpr:0.96475
[9]	validation-logloss:0.56250	validation-auc:0.96107	validation-aucpr:0.96689
[10]	validation-logloss:0.55227	validation-auc:0.96284	validation-aucpr:0.96913
[11]	validation-logloss:0.54184	validation-auc:0.96332	validation-aucpr:0.96981
[12]	validation-logloss:0.53188	validation-auc:0.96431	validation-aucpr:0.97036
[13]	validation-logloss:0.52222	validation-auc:0.96476	validation-aucpr:0.97071
[14]	validation-logloss:0.51292	validation-auc:0.96536	validation-aucpr:0.97109
[15]	validation-logloss:0.50398	validation-auc:0.96553	validation-aucpr:0.97130
[16]	validation-logloss:0.49560	validation-auc:0.96553	validation-aucpr:0.97135
[17]	validation-logloss:0.48745	validation-auc:0.96538	validation-aucpr:0.97099
[18]	validation-logloss:0.47929	validation-auc:0.96561	validation-aucpr:0.97158
[19]	validation-logloss:0.47114	validation-auc:0.96582	validation-aucpr:0.97170
[20]	validation-logloss:0.46362	validation-auc:0.96625	validation-aucpr:0.97191
[21]	validation-logloss:0.45683	validation-auc:0.96628	validation-aucpr:0.97182
[22]	validation-logloss:0.44954	validation-auc:0.96640	validation-aucpr:0.97198
[23]	validation-logloss:0.44249	validation-auc:0.96643	validation-aucpr:0.97197
[24]	validation-logloss:0.43555	validation-auc:0.96692	validation-aucpr:0.97235
[25]	validation-logloss:0.42903	validation-auc:0.96713	validation-aucpr:0.97245
[26]	validation-logloss:0.42291	validation-auc:0.96725	validation-aucpr:0.97252
[27]	validation-logloss:0.41705	validation-auc:0.96701	validation-aucpr:0.97235
[28]	validation-logloss:0.41130	validation-auc:0.96701	validation-aucpr:0.97239
[29]	validation-logloss:0.40564	validation-auc:0.96711	validation-aucpr:0.97246
[30]	validation-logloss:0.40002	validation-auc:0.96723	validation-aucpr:0.97256
[31]	validation-logloss:0.39462	validation-auc:0.96709	validation-aucpr:0.97251
[32]	validation-logloss:0.38943	validation-auc:0.96708	validation-aucpr:0.97249
[33]	validation-logloss:0.38404	validation-auc:0.96728	validation-aucpr:0.97264
[34]	validation-logloss:0.37898	validation-auc:0.96734	validation-aucpr:0.97288
[35]	validation-logloss:0.37418	validation-auc:0.96724	validation-aucpr:0.97285
[36]	validation-logloss:0.36968	validation-auc:0.96734	validation-aucpr:0.97287
[37]	validation-logloss:0.36536	validation-auc:0.96737	validation-aucpr:0.97289
[38]	validation-logloss:0.36079	validation-auc:0.96746	validation-aucpr:0.97294
[39]	validation-logloss:0.35652	validation-auc:0.96745	validation-aucpr:0.97293
[40]	validation-logloss:0.35209	validation-auc:0.96760	validation-aucpr:0.97305
[41]	validation-logloss:0.34816	validation-auc:0.96741	validation-aucpr:0.97290
[42]	validation-logloss:0.34414	validation-auc:0.96743	validation-aucpr:0.97290
[43]	validation-logloss:0.34052	validation-auc:0.96740	validation-aucpr:0.97289
[44]	validation-logloss:0.33680	validation-auc:0.96750	validation-aucpr:0.97296
[45]	validation-logloss:0.33305	validation-auc:0.96747	validation-aucpr:0.97292
[46]	validation-logloss:0.32903	validation-auc:0.96780	validation-aucpr:0.97315
[47]	validation-logloss:0.32529	validation-auc:0.96809	validation-aucpr:0.97334
{'best_iteration': '47', 'best_score': '0.9733443455114036'}
Trial 27, Fold 4: Log loss = 0.32529408301928064, Average precision = 0.973338719744536, ROC-AUC = 0.9680886988514298, Elapsed Time = 13.645755100000315 seconds
Trial 27, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 27, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[0]	validation-logloss:0.67738	validation-auc:0.81385	validation-aucpr:0.73173
[1]	validation-logloss:0.66237	validation-auc:0.90118	validation-aucpr:0.84005
[2]	validation-logloss:0.64846	validation-auc:0.92205	validation-aucpr:0.89053
[3]	validation-logloss:0.63505	validation-auc:0.93435	validation-aucpr:0.92671
[4]	validation-logloss:0.62169	validation-auc:0.93970	validation-aucpr:0.93789
[5]	validation-logloss:0.60894	validation-auc:0.94506	validation-aucpr:0.94859
[6]	validation-logloss:0.59665	validation-auc:0.95013	validation-aucpr:0.95372
[7]	validation-logloss:0.58482	validation-auc:0.95344	validation-aucpr:0.95548
[8]	validation-logloss:0.57389	validation-auc:0.95436	validation-aucpr:0.95889
[9]	validation-logloss:0.56268	validation-auc:0.95629	validation-aucpr:0.96026
[10]	validation-logloss:0.55208	validation-auc:0.95708	validation-aucpr:0.96116
[11]	validation-logloss:0.54197	validation-auc:0.95843	validation-aucpr:0.96321
[12]	validation-logloss:0.53196	validation-auc:0.95966	validation-aucpr:0.96381
[13]	validation-logloss:0.52262	validation-auc:0.96025	validation-aucpr:0.96402
[14]	validation-logloss:0.51390	validation-auc:0.96038	validation-aucpr:0.96556
[15]	validation-logloss:0.50478	validation-auc:0.96132	validation-aucpr:0.96608
[16]	validation-logloss:0.49603	validation-auc:0.96212	validation-aucpr:0.96658
[17]	validation-logloss:0.48764	validation-auc:0.96282	validation-aucpr:0.96701
[18]	validation-logloss:0.47971	validation-auc:0.96276	validation-aucpr:0.96692
[19]	validation-logloss:0.47242	validation-auc:0.96261	validation-aucpr:0.96620
[20]	validation-logloss:0.46483	validation-auc:0.96338	validation-aucpr:0.96802
[21]	validation-logloss:0.45752	validation-auc:0.96341	validation-aucpr:0.96798
[22]	validation-logloss:0.45059	validation-auc:0.96360	validation-aucpr:0.96803
[23]	validation-logloss:0.44397	validation-auc:0.96331	validation-aucpr:0.96787
[24]	validation-logloss:0.43755	validation-auc:0.96321	validation-aucpr:0.96770
[25]	validation-logloss:0.43091	validation-auc:0.96369	validation-aucpr:0.96790
[26]	validation-logloss:0.42472	validation-auc:0.96361	validation-aucpr:0.96895
[27]	validation-logloss:0.41870	validation-auc:0.96364	validation-aucpr:0.96895
[28]	validation-logloss:0.41276	validation-auc:0.96368	validation-aucpr:0.96899
[29]	validation-logloss:0.40704	validation-auc:0.96383	validation-aucpr:0.96911
[30]	validation-logloss:0.40219	validation-auc:0.96372	validation-aucpr:0.96847
[31]	validation-logloss:0.39687	validation-auc:0.96387	validation-aucpr:0.96860
[32]	validation-logloss:0.39155	validation-auc:0.96408	validation-aucpr:0.96882
[33]	validation-logloss:0.38672	validation-auc:0.96430	validation-aucpr:0.96881
[34]	validation-logloss:0.38164	validation-auc:0.96450	validation-aucpr:0.96895
[35]	validation-logloss:0.37686	validation-auc:0.96452	validation-aucpr:0.96902
[36]	validation-logloss:0.37210	validation-auc:0.96465	validation-aucpr:0.96915
[37]	validation-logloss:0.36770	validation-auc:0.96477	validation-aucpr:0.96914
[38]	validation-logloss:0.36353	validation-auc:0.96478	validation-aucpr:0.96915
[39]	validation-logloss:0.35957	validation-auc:0.96477	validation-aucpr:0.96868
[40]	validation-logloss:0.35559	validation-auc:0.96487	validation-aucpr:0.96852
[41]	validation-logloss:0.35143	validation-auc:0.96494	validation-aucpr:0.96859
[42]	validation-logloss:0.34795	validation-auc:0.96491	validation-aucpr:0.96830
[43]	validation-logloss:0.34400	validation-auc:0.96510	validation-aucpr:0.96823
[44]	validation-logloss:0.34021	validation-auc:0.96519	validation-aucpr:0.96818
[45]	validation-logloss:0.33661	validation-auc:0.96519	validation-aucpr:0.96822
[46]	validation-logloss:0.33316	validation-auc:0.96532	validation-aucpr:0.96853
[47]	validation-logloss:0.32985	validation-auc:0.96533	validation-aucpr:0.96866
{'best_iteration': '38', 'best_score': '0.9691535642346878'}
Trial 27, Fold 5: Log loss = 0.32985425076024333, Average precision = 0.9688231578351952, ROC-AUC = 0.9653324415212828, Elapsed Time = 14.10808200000065 seconds
Optimization Progress:  28%|##8       | 28/100 [48:52<3:03:46, 153.15s/it]
Trial 28, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 28, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[0]	validation-logloss:0.67453	validation-auc:0.92966	validation-aucpr:0.92412
[1]	validation-logloss:0.65734	validation-auc:0.94220	validation-aucpr:0.94079
[2]	validation-logloss:0.64190	validation-auc:0.94381	validation-aucpr:0.94793
[3]	validation-logloss:0.62513	validation-auc:0.95034	validation-aucpr:0.95588
[4]	validation-logloss:0.61047	validation-auc:0.95093	validation-aucpr:0.95709
[5]	validation-logloss:0.59542	validation-auc:0.95285	validation-aucpr:0.95884
[6]	validation-logloss:0.58235	validation-auc:0.95329	validation-aucpr:0.95917
[7]	validation-logloss:0.56761	validation-auc:0.95724	validation-aucpr:0.96378
[8]	validation-logloss:0.55330	validation-auc:0.95864	validation-aucpr:0.96548
[9]	validation-logloss:0.54248	validation-auc:0.95811	validation-aucpr:0.96489
[10]	validation-logloss:0.53158	validation-auc:0.95876	validation-aucpr:0.96530
[11]	validation-logloss:0.51977	validation-auc:0.95895	validation-aucpr:0.96559
[12]	validation-logloss:0.50953	validation-auc:0.95900	validation-aucpr:0.96570
[13]	validation-logloss:0.50093	validation-auc:0.95857	validation-aucpr:0.96533
[14]	validation-logloss:0.49037	validation-auc:0.95885	validation-aucpr:0.96570
[15]	validation-logloss:0.48179	validation-auc:0.95892	validation-aucpr:0.96568
[16]	validation-logloss:0.47358	validation-auc:0.95888	validation-aucpr:0.96558
[17]	validation-logloss:0.46601	validation-auc:0.95864	validation-aucpr:0.96541
[18]	validation-logloss:0.45761	validation-auc:0.95848	validation-aucpr:0.96536
[19]	validation-logloss:0.44815	validation-auc:0.95907	validation-aucpr:0.96591
[20]	validation-logloss:0.43952	validation-auc:0.95952	validation-aucpr:0.96641
[21]	validation-logloss:0.43300	validation-auc:0.95957	validation-aucpr:0.96643
[22]	validation-logloss:0.42651	validation-auc:0.95975	validation-aucpr:0.96658
[23]	validation-logloss:0.41861	validation-auc:0.95982	validation-aucpr:0.96674
[24]	validation-logloss:0.41035	validation-auc:0.96038	validation-aucpr:0.96730
{'best_iteration': '24', 'best_score': '0.967297137732974'}
Trial 28, Fold 1: Log loss = 0.41034771616691573, Average precision = 0.9673015836050309, ROC-AUC = 0.9603838517992763, Elapsed Time = 0.9977183999999397 seconds
Trial 28, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 28, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[0]	validation-logloss:0.67493	validation-auc:0.92659	validation-aucpr:0.91603
[1]	validation-logloss:0.65440	validation-auc:0.95558	validation-aucpr:0.96034
[2]	validation-logloss:0.63804	validation-auc:0.95563	validation-aucpr:0.96028
[3]	validation-logloss:0.62193	validation-auc:0.95796	validation-aucpr:0.96183
[4]	validation-logloss:0.60719	validation-auc:0.95712	validation-aucpr:0.96109
[5]	validation-logloss:0.59324	validation-auc:0.95693	validation-aucpr:0.96071
[6]	validation-logloss:0.57882	validation-auc:0.95797	validation-aucpr:0.96157
[7]	validation-logloss:0.56650	validation-auc:0.95736	validation-aucpr:0.96121
[8]	validation-logloss:0.55490	validation-auc:0.95750	validation-aucpr:0.96111
[9]	validation-logloss:0.54254	validation-auc:0.95863	validation-aucpr:0.96241
[10]	validation-logloss:0.53208	validation-auc:0.95793	validation-aucpr:0.96168
[11]	validation-logloss:0.51964	validation-auc:0.95966	validation-aucpr:0.96375
[12]	validation-logloss:0.50705	validation-auc:0.96118	validation-aucpr:0.96578
[13]	validation-logloss:0.49555	validation-auc:0.96162	validation-aucpr:0.96616
[14]	validation-logloss:0.48666	validation-auc:0.96141	validation-aucpr:0.96585
[15]	validation-logloss:0.47513	validation-auc:0.96223	validation-aucpr:0.96669
[16]	validation-logloss:0.46474	validation-auc:0.96290	validation-aucpr:0.96736
[17]	validation-logloss:0.45657	validation-auc:0.96301	validation-aucpr:0.96737
[18]	validation-logloss:0.44932	validation-auc:0.96257	validation-aucpr:0.96691
[19]	validation-logloss:0.44048	validation-auc:0.96280	validation-aucpr:0.96714
[20]	validation-logloss:0.43353	validation-auc:0.96272	validation-aucpr:0.96704
[21]	validation-logloss:0.42738	validation-auc:0.96251	validation-aucpr:0.96683
[22]	validation-logloss:0.42151	validation-auc:0.96242	validation-aucpr:0.96672
[23]	validation-logloss:0.41597	validation-auc:0.96235	validation-aucpr:0.96664
[24]	validation-logloss:0.41013	validation-auc:0.96245	validation-aucpr:0.96669
{'best_iteration': '17', 'best_score': '0.9673667641165824'}
Trial 28, Fold 2: Log loss = 0.4101304550323175, Average precision = 0.9666330371412952, ROC-AUC = 0.9624493276207374, Elapsed Time = 1.2822998999999982 seconds
Trial 28, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 28, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[0]	validation-logloss:0.67449	validation-auc:0.93199	validation-aucpr:0.92687
[1]	validation-logloss:0.65477	validation-auc:0.95745	validation-aucpr:0.96278
[2]	validation-logloss:0.63839	validation-auc:0.95725	validation-aucpr:0.96317
[3]	validation-logloss:0.62300	validation-auc:0.95666	validation-aucpr:0.96261
[4]	validation-logloss:0.60752	validation-auc:0.95706	validation-aucpr:0.96320
[5]	validation-logloss:0.59387	validation-auc:0.95680	validation-aucpr:0.96336
[6]	validation-logloss:0.58044	validation-auc:0.95810	validation-aucpr:0.96379
[7]	validation-logloss:0.56757	validation-auc:0.95870	validation-aucpr:0.96417
[8]	validation-logloss:0.55673	validation-auc:0.95799	validation-aucpr:0.96354
[9]	validation-logloss:0.54440	validation-auc:0.95855	validation-aucpr:0.96395
[10]	validation-logloss:0.53330	validation-auc:0.95814	validation-aucpr:0.96352
[11]	validation-logloss:0.52290	validation-auc:0.95761	validation-aucpr:0.96295
[12]	validation-logloss:0.51239	validation-auc:0.95829	validation-aucpr:0.96379
[13]	validation-logloss:0.50305	validation-auc:0.95823	validation-aucpr:0.96375
[14]	validation-logloss:0.49428	validation-auc:0.95778	validation-aucpr:0.96334
[15]	validation-logloss:0.48541	validation-auc:0.95800	validation-aucpr:0.96341
[16]	validation-logloss:0.47637	validation-auc:0.95854	validation-aucpr:0.96369
[17]	validation-logloss:0.46880	validation-auc:0.95807	validation-aucpr:0.96322
[18]	validation-logloss:0.46171	validation-auc:0.95788	validation-aucpr:0.96306
[19]	validation-logloss:0.45486	validation-auc:0.95767	validation-aucpr:0.96290
[20]	validation-logloss:0.44828	validation-auc:0.95755	validation-aucpr:0.96278
[21]	validation-logloss:0.43986	validation-auc:0.95857	validation-aucpr:0.96410
[22]	validation-logloss:0.43406	validation-auc:0.95835	validation-aucpr:0.96395
[23]	validation-logloss:0.42816	validation-auc:0.95807	validation-aucpr:0.96371
[24]	validation-logloss:0.42208	validation-auc:0.95836	validation-aucpr:0.96387
{'best_iteration': '7', 'best_score': '0.9641735778234024'}
Trial 28, Fold 3: Log loss = 0.42207755835198346, Average precision = 0.9638774698876118, ROC-AUC = 0.9583618753069894, Elapsed Time = 1.3180508999994345 seconds
Trial 28, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 28, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[0]	validation-logloss:0.67487	validation-auc:0.92848	validation-aucpr:0.92519
[1]	validation-logloss:0.65758	validation-auc:0.93797	validation-aucpr:0.93847
[2]	validation-logloss:0.64007	validation-auc:0.95223	validation-aucpr:0.95651
[3]	validation-logloss:0.62384	validation-auc:0.95442	validation-aucpr:0.96065
[4]	validation-logloss:0.60735	validation-auc:0.95637	validation-aucpr:0.96316
[5]	validation-logloss:0.59402	validation-auc:0.95596	validation-aucpr:0.96283
[6]	validation-logloss:0.58079	validation-auc:0.95533	validation-aucpr:0.96204
[7]	validation-logloss:0.56572	validation-auc:0.95708	validation-aucpr:0.96393
[8]	validation-logloss:0.55312	validation-auc:0.95708	validation-aucpr:0.96397
[9]	validation-logloss:0.53975	validation-auc:0.95803	validation-aucpr:0.96481
[10]	validation-logloss:0.52881	validation-auc:0.95869	validation-aucpr:0.96522
[11]	validation-logloss:0.51717	validation-auc:0.95914	validation-aucpr:0.96566
[12]	validation-logloss:0.50687	validation-auc:0.95932	validation-aucpr:0.96581
[13]	validation-logloss:0.49786	validation-auc:0.95874	validation-aucpr:0.96533
[14]	validation-logloss:0.48824	validation-auc:0.95920	validation-aucpr:0.96564
[15]	validation-logloss:0.47747	validation-auc:0.96011	validation-aucpr:0.96646
[16]	validation-logloss:0.46911	validation-auc:0.96021	validation-aucpr:0.96650
[17]	validation-logloss:0.46103	validation-auc:0.96070	validation-aucpr:0.96690
[18]	validation-logloss:0.45382	validation-auc:0.96053	validation-aucpr:0.96667
[19]	validation-logloss:0.44491	validation-auc:0.96111	validation-aucpr:0.96724
[20]	validation-logloss:0.43808	validation-auc:0.96107	validation-aucpr:0.96717
[21]	validation-logloss:0.42941	validation-auc:0.96140	validation-aucpr:0.96752
[22]	validation-logloss:0.42274	validation-auc:0.96152	validation-aucpr:0.96760
[23]	validation-logloss:0.41489	validation-auc:0.96189	validation-aucpr:0.96801
[24]	validation-logloss:0.40935	validation-auc:0.96188	validation-aucpr:0.96799
{'best_iteration': '23', 'best_score': '0.9680064618656231'}
Trial 28, Fold 4: Log loss = 0.409350621791954, Average precision = 0.9679871158277571, ROC-AUC = 0.9618803664122502, Elapsed Time = 1.2848967999998422 seconds
Trial 28, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 28, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[0]	validation-logloss:0.67495	validation-auc:0.91969	validation-aucpr:0.90610
[1]	validation-logloss:0.65813	validation-auc:0.93588	validation-aucpr:0.93828
[2]	validation-logloss:0.64248	validation-auc:0.93797	validation-aucpr:0.94127
[3]	validation-logloss:0.62754	validation-auc:0.93957	validation-aucpr:0.94455
[4]	validation-logloss:0.61008	validation-auc:0.94994	validation-aucpr:0.95687
[5]	validation-logloss:0.59344	validation-auc:0.95363	validation-aucpr:0.96018
[6]	validation-logloss:0.58120	validation-auc:0.95313	validation-aucpr:0.96021
[7]	validation-logloss:0.56863	validation-auc:0.95286	validation-aucpr:0.96003
[8]	validation-logloss:0.55453	validation-auc:0.95443	validation-aucpr:0.96131
[9]	validation-logloss:0.54319	validation-auc:0.95455	validation-aucpr:0.96132
[10]	validation-logloss:0.53230	validation-auc:0.95490	validation-aucpr:0.96160
[11]	validation-logloss:0.52226	validation-auc:0.95486	validation-aucpr:0.96151
[12]	validation-logloss:0.51321	validation-auc:0.95474	validation-aucpr:0.96134
[13]	validation-logloss:0.50445	validation-auc:0.95453	validation-aucpr:0.96117
[14]	validation-logloss:0.49649	validation-auc:0.95444	validation-aucpr:0.96145
[15]	validation-logloss:0.48890	validation-auc:0.95432	validation-aucpr:0.96129
[16]	validation-logloss:0.48053	validation-auc:0.95437	validation-aucpr:0.96124
[17]	validation-logloss:0.47301	validation-auc:0.95430	validation-aucpr:0.96114
[18]	validation-logloss:0.46379	validation-auc:0.95513	validation-aucpr:0.96189
[19]	validation-logloss:0.45706	validation-auc:0.95482	validation-aucpr:0.96159
[20]	validation-logloss:0.44790	validation-auc:0.95561	validation-aucpr:0.96244
[21]	validation-logloss:0.44038	validation-auc:0.95607	validation-aucpr:0.96272
[22]	validation-logloss:0.43436	validation-auc:0.95596	validation-aucpr:0.96254
[23]	validation-logloss:0.42855	validation-auc:0.95596	validation-aucpr:0.96258
[24]	validation-logloss:0.42321	validation-auc:0.95581	validation-aucpr:0.96247
{'best_iteration': '21', 'best_score': '0.9627214154090976'}
Trial 28, Fold 5: Log loss = 0.42320609848369695, Average precision = 0.9624687066772707, ROC-AUC = 0.9558063281496758, Elapsed Time = 1.2925592999999935 seconds
Optimization Progress:  29%|##9       | 29/100 [49:06<2:11:52, 111.45s/it]
Trial 29, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 29, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[18:48:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.64541	validation-auc:0.91169	validation-aucpr:0.91761
[18:48:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.60390	validation-auc:0.93151	validation-aucpr:0.93490
[18:48:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.56258	validation-auc:0.94565	validation-aucpr:0.95033
[18:48:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.53228	validation-auc:0.94971	validation-aucpr:0.95664
[18:48:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.50397	validation-auc:0.95238	validation-aucpr:0.95835
[18:48:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5]	validation-logloss:0.48025	validation-auc:0.95444	validation-aucpr:0.95879
[18:48:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6]	validation-logloss:0.45484	validation-auc:0.95602	validation-aucpr:0.96062
[18:48:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7]	validation-logloss:0.43727	validation-auc:0.95625	validation-aucpr:0.96108
[18:48:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8]	validation-logloss:0.41788	validation-auc:0.95818	validation-aucpr:0.96269
[18:48:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9]	validation-logloss:0.40385	validation-auc:0.95779	validation-aucpr:0.96240
[18:48:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10]	validation-logloss:0.38933	validation-auc:0.95868	validation-aucpr:0.96319
[18:48:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11]	validation-logloss:0.37717	validation-auc:0.95881	validation-aucpr:0.96220
[18:48:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12]	validation-logloss:0.36023	validation-auc:0.96053	validation-aucpr:0.96401
[18:48:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13]	validation-logloss:0.35017	validation-auc:0.96065	validation-aucpr:0.96393
[18:48:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14]	validation-logloss:0.34203	validation-auc:0.96039	validation-aucpr:0.96372
[18:48:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15]	validation-logloss:0.33393	validation-auc:0.96071	validation-aucpr:0.96352
[18:48:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16]	validation-logloss:0.32278	validation-auc:0.96130	validation-aucpr:0.96421
[18:48:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17]	validation-logloss:0.31714	validation-auc:0.96100	validation-aucpr:0.96305
[18:48:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18]	validation-logloss:0.31066	validation-auc:0.96119	validation-aucpr:0.96318
[18:48:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19]	validation-logloss:0.30438	validation-auc:0.96131	validation-aucpr:0.96372
[18:48:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20]	validation-logloss:0.29466	validation-auc:0.96206	validation-aucpr:0.96483
[18:48:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21]	validation-logloss:0.28839	validation-auc:0.96270	validation-aucpr:0.96702
[18:48:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22]	validation-logloss:0.28440	validation-auc:0.96293	validation-aucpr:0.96716
[18:48:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23]	validation-logloss:0.27877	validation-auc:0.96347	validation-aucpr:0.96743
[18:48:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24]	validation-logloss:0.27457	validation-auc:0.96363	validation-aucpr:0.96750
[18:48:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25]	validation-logloss:0.27128	validation-auc:0.96368	validation-aucpr:0.96756
[18:48:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26]	validation-logloss:0.26828	validation-auc:0.96387	validation-aucpr:0.96876
[18:48:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27]	validation-logloss:0.26574	validation-auc:0.96382	validation-aucpr:0.96772
[18:48:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28]	validation-logloss:0.26306	validation-auc:0.96390	validation-aucpr:0.96766
[18:48:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29]	validation-logloss:0.26011	validation-auc:0.96420	validation-aucpr:0.96892
[18:48:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30]	validation-logloss:0.25810	validation-auc:0.96391	validation-aucpr:0.96855
[18:48:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31]	validation-logloss:0.25614	validation-auc:0.96390	validation-aucpr:0.96878
[18:48:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32]	validation-logloss:0.25481	validation-auc:0.96400	validation-aucpr:0.96956
[18:48:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33]	validation-logloss:0.25265	validation-auc:0.96420	validation-aucpr:0.96960
[18:48:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34]	validation-logloss:0.24764	validation-auc:0.96488	validation-aucpr:0.97028
[18:48:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35]	validation-logloss:0.24315	validation-auc:0.96533	validation-aucpr:0.97079
[18:48:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36]	validation-logloss:0.24109	validation-auc:0.96570	validation-aucpr:0.97108
[18:48:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37]	validation-logloss:0.23990	validation-auc:0.96591	validation-aucpr:0.97124
[18:48:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38]	validation-logloss:0.23887	validation-auc:0.96590	validation-aucpr:0.97122
[18:48:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39]	validation-logloss:0.23697	validation-auc:0.96600	validation-aucpr:0.97138
[18:48:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40]	validation-logloss:0.23386	validation-auc:0.96620	validation-aucpr:0.97163
[18:48:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41]	validation-logloss:0.23076	validation-auc:0.96660	validation-aucpr:0.97203
[18:48:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42]	validation-logloss:0.22987	validation-auc:0.96673	validation-aucpr:0.97209
[18:48:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43]	validation-logloss:0.22815	validation-auc:0.96679	validation-aucpr:0.97213
[18:48:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44]	validation-logloss:0.22617	validation-auc:0.96694	validation-aucpr:0.97227
[18:48:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45]	validation-logloss:0.22627	validation-auc:0.96680	validation-aucpr:0.97210
[18:48:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46]	validation-logloss:0.22582	validation-auc:0.96678	validation-aucpr:0.97208
[18:48:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[47]	validation-logloss:0.22572	validation-auc:0.96665	validation-aucpr:0.97185
[18:48:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[48]	validation-logloss:0.22484	validation-auc:0.96671	validation-aucpr:0.97197
[18:48:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[49]	validation-logloss:0.22269	validation-auc:0.96704	validation-aucpr:0.97232
[18:48:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[50]	validation-logloss:0.22300	validation-auc:0.96678	validation-aucpr:0.97210
[18:48:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[51]	validation-logloss:0.22248	validation-auc:0.96691	validation-aucpr:0.97215
[18:48:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[52]	validation-logloss:0.22211	validation-auc:0.96701	validation-aucpr:0.97218
[18:48:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[53]	validation-logloss:0.22179	validation-auc:0.96700	validation-aucpr:0.97213
{'best_iteration': '49', 'best_score': '0.9723227413480227'}
Trial 29, Fold 1: Log loss = 0.2217877447091866, Average precision = 0.972137945326637, ROC-AUC = 0.9670048524779781, Elapsed Time = 6.752285800001118 seconds
Trial 29, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 29, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[18:48:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.64490	validation-auc:0.91025	validation-aucpr:0.89715
[18:48:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.59718	validation-auc:0.94638	validation-aucpr:0.94511
[18:48:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.56146	validation-auc:0.95100	validation-aucpr:0.95479
[18:48:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.53075	validation-auc:0.95366	validation-aucpr:0.95701
[18:48:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.50243	validation-auc:0.95516	validation-aucpr:0.95991
[18:48:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5]	validation-logloss:0.47286	validation-auc:0.95755	validation-aucpr:0.96278
[18:48:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6]	validation-logloss:0.45099	validation-auc:0.95856	validation-aucpr:0.96356
[18:48:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7]	validation-logloss:0.43191	validation-auc:0.95870	validation-aucpr:0.96129
[18:48:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8]	validation-logloss:0.40901	validation-auc:0.96055	validation-aucpr:0.96300
[18:48:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9]	validation-logloss:0.39460	validation-auc:0.96081	validation-aucpr:0.96304
[18:48:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10]	validation-logloss:0.38144	validation-auc:0.96124	validation-aucpr:0.96586
[18:48:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11]	validation-logloss:0.36900	validation-auc:0.96137	validation-aucpr:0.96592
[18:48:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12]	validation-logloss:0.35892	validation-auc:0.96110	validation-aucpr:0.96563
[18:48:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13]	validation-logloss:0.34433	validation-auc:0.96190	validation-aucpr:0.96650
[18:48:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14]	validation-logloss:0.33530	validation-auc:0.96207	validation-aucpr:0.96607
[18:48:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15]	validation-logloss:0.32704	validation-auc:0.96184	validation-aucpr:0.96605
[18:48:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16]	validation-logloss:0.31647	validation-auc:0.96227	validation-aucpr:0.96651
[18:48:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17]	validation-logloss:0.30982	validation-auc:0.96262	validation-aucpr:0.96681
[18:48:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18]	validation-logloss:0.30323	validation-auc:0.96276	validation-aucpr:0.96685
[18:48:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19]	validation-logloss:0.29368	validation-auc:0.96338	validation-aucpr:0.96740
[18:48:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20]	validation-logloss:0.28486	validation-auc:0.96413	validation-aucpr:0.96818
[18:48:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21]	validation-logloss:0.27980	validation-auc:0.96427	validation-aucpr:0.96827
[18:48:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22]	validation-logloss:0.27518	validation-auc:0.96453	validation-aucpr:0.96854
[18:48:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23]	validation-logloss:0.26847	validation-auc:0.96491	validation-aucpr:0.96882
[18:48:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24]	validation-logloss:0.26388	validation-auc:0.96527	validation-aucpr:0.96918
[18:48:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25]	validation-logloss:0.26109	validation-auc:0.96506	validation-aucpr:0.96895
[18:48:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26]	validation-logloss:0.25792	validation-auc:0.96526	validation-aucpr:0.96906
[18:48:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27]	validation-logloss:0.25419	validation-auc:0.96561	validation-aucpr:0.96941
[18:48:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28]	validation-logloss:0.24922	validation-auc:0.96584	validation-aucpr:0.96966
[18:48:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29]	validation-logloss:0.24651	validation-auc:0.96613	validation-aucpr:0.96981
[18:48:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30]	validation-logloss:0.24519	validation-auc:0.96592	validation-aucpr:0.96970
[18:48:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31]	validation-logloss:0.24185	validation-auc:0.96668	validation-aucpr:0.97060
[18:48:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32]	validation-logloss:0.23883	validation-auc:0.96671	validation-aucpr:0.97075
[18:48:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33]	validation-logloss:0.23528	validation-auc:0.96686	validation-aucpr:0.97077
[18:48:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34]	validation-logloss:0.23331	validation-auc:0.96702	validation-aucpr:0.97079
[18:48:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35]	validation-logloss:0.22974	validation-auc:0.96736	validation-aucpr:0.97121
[18:48:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36]	validation-logloss:0.22777	validation-auc:0.96776	validation-aucpr:0.97112
[18:48:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37]	validation-logloss:0.22637	validation-auc:0.96798	validation-aucpr:0.97108
[18:48:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38]	validation-logloss:0.22523	validation-auc:0.96796	validation-aucpr:0.97093
[18:48:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39]	validation-logloss:0.22371	validation-auc:0.96822	validation-aucpr:0.97094
[18:48:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40]	validation-logloss:0.22283	validation-auc:0.96808	validation-aucpr:0.97074
[18:48:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41]	validation-logloss:0.22280	validation-auc:0.96778	validation-aucpr:0.97048
[18:48:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42]	validation-logloss:0.22129	validation-auc:0.96768	validation-aucpr:0.97051
[18:48:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43]	validation-logloss:0.21993	validation-auc:0.96750	validation-aucpr:0.97077
[18:48:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44]	validation-logloss:0.21782	validation-auc:0.96776	validation-aucpr:0.97092
[18:48:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45]	validation-logloss:0.21496	validation-auc:0.96826	validation-aucpr:0.97128
[18:48:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46]	validation-logloss:0.21431	validation-auc:0.96839	validation-aucpr:0.97142
[18:48:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[47]	validation-logloss:0.21447	validation-auc:0.96830	validation-aucpr:0.97150
[18:48:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[48]	validation-logloss:0.21373	validation-auc:0.96839	validation-aucpr:0.97154
[18:48:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[49]	validation-logloss:0.21232	validation-auc:0.96859	validation-aucpr:0.97166
[18:48:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[50]	validation-logloss:0.21266	validation-auc:0.96828	validation-aucpr:0.97137
[18:48:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[51]	validation-logloss:0.21169	validation-auc:0.96818	validation-aucpr:0.97115
[18:48:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[52]	validation-logloss:0.21162	validation-auc:0.96829	validation-aucpr:0.97087
[18:48:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[53]	validation-logloss:0.21039	validation-auc:0.96847	validation-aucpr:0.97084
{'best_iteration': '49', 'best_score': '0.9716618943586721'}
Trial 29, Fold 2: Log loss = 0.21039004558810057, Average precision = 0.970847171830053, ROC-AUC = 0.968472955964917, Elapsed Time = 6.9477287999998225 seconds
Trial 29, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 29, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[18:48:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.64386	validation-auc:0.92319	validation-aucpr:0.92384
[18:48:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.60188	validation-auc:0.93882	validation-aucpr:0.93939
[18:48:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.56659	validation-auc:0.94351	validation-aucpr:0.94409
[18:48:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.53346	validation-auc:0.94853	validation-aucpr:0.95234
[18:48:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.50650	validation-auc:0.94981	validation-aucpr:0.95381
[18:48:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5]	validation-logloss:0.48344	validation-auc:0.95081	validation-aucpr:0.95484
[18:48:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6]	validation-logloss:0.46198	validation-auc:0.95191	validation-aucpr:0.95633
[18:48:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7]	validation-logloss:0.44093	validation-auc:0.95335	validation-aucpr:0.95589
[18:48:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8]	validation-logloss:0.41706	validation-auc:0.95852	validation-aucpr:0.96101
[18:48:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9]	validation-logloss:0.39609	validation-auc:0.96103	validation-aucpr:0.96550
[18:48:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10]	validation-logloss:0.37858	validation-auc:0.96179	validation-aucpr:0.96620
[18:48:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11]	validation-logloss:0.36437	validation-auc:0.96261	validation-aucpr:0.96691
[18:48:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12]	validation-logloss:0.35270	validation-auc:0.96273	validation-aucpr:0.96611
[18:48:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13]	validation-logloss:0.34169	validation-auc:0.96298	validation-aucpr:0.96487
[18:48:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14]	validation-logloss:0.33360	validation-auc:0.96263	validation-aucpr:0.96502
[18:48:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15]	validation-logloss:0.32453	validation-auc:0.96290	validation-aucpr:0.96526
[18:48:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16]	validation-logloss:0.31707	validation-auc:0.96315	validation-aucpr:0.96400
[18:48:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17]	validation-logloss:0.31041	validation-auc:0.96328	validation-aucpr:0.96391
[18:48:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18]	validation-logloss:0.30402	validation-auc:0.96327	validation-aucpr:0.96390
[18:48:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19]	validation-logloss:0.29938	validation-auc:0.96318	validation-aucpr:0.96362
[18:48:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20]	validation-logloss:0.29085	validation-auc:0.96370	validation-aucpr:0.96493
[18:48:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21]	validation-logloss:0.28595	validation-auc:0.96380	validation-aucpr:0.96560
[18:48:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22]	validation-logloss:0.28230	validation-auc:0.96380	validation-aucpr:0.96590
[18:48:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23]	validation-logloss:0.27932	validation-auc:0.96386	validation-aucpr:0.96738
[18:48:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24]	validation-logloss:0.27579	validation-auc:0.96382	validation-aucpr:0.96731
[18:48:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25]	validation-logloss:0.27278	validation-auc:0.96373	validation-aucpr:0.96769
[18:48:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26]	validation-logloss:0.27058	validation-auc:0.96360	validation-aucpr:0.96757
[18:48:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27]	validation-logloss:0.26561	validation-auc:0.96404	validation-aucpr:0.96790
[18:48:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28]	validation-logloss:0.26381	validation-auc:0.96376	validation-aucpr:0.96706
[18:48:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29]	validation-logloss:0.26137	validation-auc:0.96371	validation-aucpr:0.96715
[18:48:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30]	validation-logloss:0.25954	validation-auc:0.96344	validation-aucpr:0.96668
[18:48:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31]	validation-logloss:0.25729	validation-auc:0.96355	validation-aucpr:0.96677
[18:48:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32]	validation-logloss:0.25236	validation-auc:0.96430	validation-aucpr:0.96910
[18:48:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33]	validation-logloss:0.25129	validation-auc:0.96413	validation-aucpr:0.96895
[18:48:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34]	validation-logloss:0.24862	validation-auc:0.96433	validation-aucpr:0.96923
[18:48:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35]	validation-logloss:0.24413	validation-auc:0.96474	validation-aucpr:0.96958
[18:48:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36]	validation-logloss:0.24228	validation-auc:0.96499	validation-aucpr:0.96976
[18:48:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37]	validation-logloss:0.24063	validation-auc:0.96532	validation-aucpr:0.96993
[18:48:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38]	validation-logloss:0.23938	validation-auc:0.96534	validation-aucpr:0.97006
[18:48:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39]	validation-logloss:0.23800	validation-auc:0.96548	validation-aucpr:0.97012
[18:48:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40]	validation-logloss:0.23678	validation-auc:0.96573	validation-aucpr:0.97037
[18:48:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41]	validation-logloss:0.23572	validation-auc:0.96585	validation-aucpr:0.97055
[18:48:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42]	validation-logloss:0.23442	validation-auc:0.96585	validation-aucpr:0.97056
[18:48:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43]	validation-logloss:0.23143	validation-auc:0.96616	validation-aucpr:0.97085
[18:48:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44]	validation-logloss:0.23070	validation-auc:0.96622	validation-aucpr:0.97088
[18:48:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45]	validation-logloss:0.22768	validation-auc:0.96647	validation-aucpr:0.97095
[18:48:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46]	validation-logloss:0.22714	validation-auc:0.96647	validation-aucpr:0.97094
[18:48:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[47]	validation-logloss:0.22464	validation-auc:0.96678	validation-aucpr:0.97116
[18:48:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[48]	validation-logloss:0.22358	validation-auc:0.96696	validation-aucpr:0.97143
[18:48:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[49]	validation-logloss:0.22294	validation-auc:0.96704	validation-aucpr:0.97100
[18:48:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[50]	validation-logloss:0.22254	validation-auc:0.96717	validation-aucpr:0.97115
[18:48:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[51]	validation-logloss:0.22258	validation-auc:0.96711	validation-aucpr:0.97139
[18:48:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[52]	validation-logloss:0.22249	validation-auc:0.96704	validation-aucpr:0.97134
[18:48:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[53]	validation-logloss:0.22047	validation-auc:0.96717	validation-aucpr:0.97147
{'best_iteration': '53', 'best_score': '0.9714691673218635'}
Trial 29, Fold 3: Log loss = 0.220469281968947, Average precision = 0.9714746681701518, ROC-AUC = 0.9671723160713082, Elapsed Time = 7.089355300000534 seconds
Trial 29, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 29, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[18:48:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.64628	validation-auc:0.89556	validation-aucpr:0.91012
[18:48:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.59900	validation-auc:0.94428	validation-aucpr:0.94372
[18:48:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.56390	validation-auc:0.94788	validation-aucpr:0.94820
[18:48:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.53289	validation-auc:0.95223	validation-aucpr:0.95777
[18:48:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.50459	validation-auc:0.95366	validation-aucpr:0.95973
[18:48:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5]	validation-logloss:0.47957	validation-auc:0.95403	validation-aucpr:0.96028
[18:48:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6]	validation-logloss:0.45126	validation-auc:0.95756	validation-aucpr:0.96386
[18:48:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7]	validation-logloss:0.43384	validation-auc:0.95720	validation-aucpr:0.96336
[18:48:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8]	validation-logloss:0.41201	validation-auc:0.95891	validation-aucpr:0.96527
[18:48:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9]	validation-logloss:0.39652	validation-auc:0.95881	validation-aucpr:0.96527
[18:48:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10]	validation-logloss:0.38243	validation-auc:0.95865	validation-aucpr:0.96514
[18:48:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11]	validation-logloss:0.36925	validation-auc:0.95956	validation-aucpr:0.96582
[18:48:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12]	validation-logloss:0.35757	validation-auc:0.95997	validation-aucpr:0.96634
[18:48:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13]	validation-logloss:0.34414	validation-auc:0.96084	validation-aucpr:0.96712
[18:48:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14]	validation-logloss:0.33558	validation-auc:0.96098	validation-aucpr:0.96723
[18:48:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15]	validation-logloss:0.32237	validation-auc:0.96205	validation-aucpr:0.96822
[18:48:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16]	validation-logloss:0.31110	validation-auc:0.96301	validation-aucpr:0.96898
[18:48:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17]	validation-logloss:0.30415	validation-auc:0.96278	validation-aucpr:0.96879
[18:48:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18]	validation-logloss:0.29423	validation-auc:0.96340	validation-aucpr:0.96939
[18:48:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19]	validation-logloss:0.28877	validation-auc:0.96338	validation-aucpr:0.96944
[18:48:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20]	validation-logloss:0.28283	validation-auc:0.96365	validation-aucpr:0.96962
[18:48:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21]	validation-logloss:0.27796	validation-auc:0.96396	validation-aucpr:0.96987
[18:48:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22]	validation-logloss:0.27411	validation-auc:0.96405	validation-aucpr:0.96989
[18:48:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23]	validation-logloss:0.26754	validation-auc:0.96453	validation-aucpr:0.97037
[18:48:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24]	validation-logloss:0.26421	validation-auc:0.96453	validation-aucpr:0.97036
[18:48:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25]	validation-logloss:0.26074	validation-auc:0.96448	validation-aucpr:0.97029
[18:48:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26]	validation-logloss:0.25768	validation-auc:0.96467	validation-aucpr:0.97043
[18:48:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27]	validation-logloss:0.25188	validation-auc:0.96511	validation-aucpr:0.97088
[18:48:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28]	validation-logloss:0.24964	validation-auc:0.96517	validation-aucpr:0.97089
[18:48:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29]	validation-logloss:0.24743	validation-auc:0.96530	validation-aucpr:0.97096
[18:48:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30]	validation-logloss:0.24537	validation-auc:0.96529	validation-aucpr:0.97096
[18:48:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31]	validation-logloss:0.24115	validation-auc:0.96558	validation-aucpr:0.97128
[18:48:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32]	validation-logloss:0.23780	validation-auc:0.96582	validation-aucpr:0.97150
[18:48:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33]	validation-logloss:0.23609	validation-auc:0.96586	validation-aucpr:0.97151
[18:48:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34]	validation-logloss:0.23350	validation-auc:0.96578	validation-aucpr:0.97135
[18:48:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35]	validation-logloss:0.23237	validation-auc:0.96575	validation-aucpr:0.97140
[18:48:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36]	validation-logloss:0.23065	validation-auc:0.96601	validation-aucpr:0.97159
[18:48:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37]	validation-logloss:0.22776	validation-auc:0.96638	validation-aucpr:0.97196
[18:48:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38]	validation-logloss:0.22684	validation-auc:0.96641	validation-aucpr:0.97199
[18:48:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39]	validation-logloss:0.22497	validation-auc:0.96644	validation-aucpr:0.97203
[18:48:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40]	validation-logloss:0.22292	validation-auc:0.96694	validation-aucpr:0.97233
[18:48:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41]	validation-logloss:0.22095	validation-auc:0.96721	validation-aucpr:0.97254
[18:48:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42]	validation-logloss:0.22040	validation-auc:0.96711	validation-aucpr:0.97249
[18:48:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43]	validation-logloss:0.21913	validation-auc:0.96738	validation-aucpr:0.97269
[18:48:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44]	validation-logloss:0.21883	validation-auc:0.96733	validation-aucpr:0.97264
[18:48:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45]	validation-logloss:0.21750	validation-auc:0.96736	validation-aucpr:0.97269
[18:48:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46]	validation-logloss:0.21688	validation-auc:0.96748	validation-aucpr:0.97276
[18:48:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[47]	validation-logloss:0.21571	validation-auc:0.96783	validation-aucpr:0.97300
[18:48:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[48]	validation-logloss:0.21368	validation-auc:0.96821	validation-aucpr:0.97328
[18:48:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[49]	validation-logloss:0.21324	validation-auc:0.96831	validation-aucpr:0.97339
[18:48:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[50]	validation-logloss:0.21172	validation-auc:0.96869	validation-aucpr:0.97368
[18:48:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[51]	validation-logloss:0.21151	validation-auc:0.96865	validation-aucpr:0.97367
[18:48:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[52]	validation-logloss:0.21170	validation-auc:0.96838	validation-aucpr:0.97348
[18:48:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[53]	validation-logloss:0.21202	validation-auc:0.96815	validation-aucpr:0.97323
{'best_iteration': '50', 'best_score': '0.9736780566332014'}
Trial 29, Fold 4: Log loss = 0.21202301627995604, Average precision = 0.9732314089812211, ROC-AUC = 0.968145179557978, Elapsed Time = 9.093965699999899 seconds
Trial 29, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 29, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[18:48:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.64529	validation-auc:0.90132	validation-aucpr:0.91196
[18:48:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.60198	validation-auc:0.93659	validation-aucpr:0.93723
[18:48:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.56527	validation-auc:0.94311	validation-aucpr:0.94273
[18:48:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.53389	validation-auc:0.94624	validation-aucpr:0.94825
[18:48:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.50769	validation-auc:0.94717	validation-aucpr:0.95187
[18:48:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5]	validation-logloss:0.48347	validation-auc:0.95008	validation-aucpr:0.95406
[18:48:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6]	validation-logloss:0.46231	validation-auc:0.95062	validation-aucpr:0.95503
[18:48:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7]	validation-logloss:0.44375	validation-auc:0.95115	validation-aucpr:0.95522
[18:48:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8]	validation-logloss:0.42109	validation-auc:0.95512	validation-aucpr:0.95981
[18:48:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9]	validation-logloss:0.40650	validation-auc:0.95518	validation-aucpr:0.96009
[18:48:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10]	validation-logloss:0.39462	validation-auc:0.95529	validation-aucpr:0.96068
[18:48:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11]	validation-logloss:0.37714	validation-auc:0.95733	validation-aucpr:0.96316
[18:48:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12]	validation-logloss:0.36635	validation-auc:0.95690	validation-aucpr:0.96276
[18:48:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13]	validation-logloss:0.35676	validation-auc:0.95711	validation-aucpr:0.96284
[18:48:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14]	validation-logloss:0.34751	validation-auc:0.95718	validation-aucpr:0.96300
[18:48:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15]	validation-logloss:0.33915	validation-auc:0.95730	validation-aucpr:0.96305
[18:48:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16]	validation-logloss:0.33291	validation-auc:0.95688	validation-aucpr:0.96274
[18:48:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17]	validation-logloss:0.32761	validation-auc:0.95670	validation-aucpr:0.96259
[18:48:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18]	validation-logloss:0.31691	validation-auc:0.95790	validation-aucpr:0.96297
[18:48:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19]	validation-logloss:0.31259	validation-auc:0.95757	validation-aucpr:0.96298
[18:48:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20]	validation-logloss:0.30643	validation-auc:0.95817	validation-aucpr:0.96298
[18:48:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21]	validation-logloss:0.30059	validation-auc:0.95866	validation-aucpr:0.96323
[18:48:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22]	validation-logloss:0.29786	validation-auc:0.95810	validation-aucpr:0.96202
[18:48:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23]	validation-logloss:0.28972	validation-auc:0.95917	validation-aucpr:0.96395
[18:48:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24]	validation-logloss:0.28543	validation-auc:0.95940	validation-aucpr:0.96381
[18:48:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25]	validation-logloss:0.28207	validation-auc:0.95944	validation-aucpr:0.96413
[18:48:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26]	validation-logloss:0.27893	validation-auc:0.95961	validation-aucpr:0.96416
[18:48:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27]	validation-logloss:0.27676	validation-auc:0.95941	validation-aucpr:0.96401
[18:48:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28]	validation-logloss:0.27162	validation-auc:0.95997	validation-aucpr:0.96443
[18:48:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29]	validation-logloss:0.26585	validation-auc:0.96053	validation-aucpr:0.96585
[18:48:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30]	validation-logloss:0.26275	validation-auc:0.96089	validation-aucpr:0.96607
[18:48:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31]	validation-logloss:0.26138	validation-auc:0.96084	validation-aucpr:0.96527
[18:48:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32]	validation-logloss:0.25685	validation-auc:0.96118	validation-aucpr:0.96511
[18:48:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33]	validation-logloss:0.25283	validation-auc:0.96169	validation-aucpr:0.96662
[18:48:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34]	validation-logloss:0.24905	validation-auc:0.96202	validation-aucpr:0.96667
[18:48:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35]	validation-logloss:0.24578	validation-auc:0.96216	validation-aucpr:0.96671
[18:48:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36]	validation-logloss:0.24385	validation-auc:0.96239	validation-aucpr:0.96697
[18:48:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37]	validation-logloss:0.24066	validation-auc:0.96285	validation-aucpr:0.96735
[18:48:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38]	validation-logloss:0.23951	validation-auc:0.96292	validation-aucpr:0.96729
[18:48:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39]	validation-logloss:0.23658	validation-auc:0.96348	validation-aucpr:0.96777
[18:48:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40]	validation-logloss:0.23554	validation-auc:0.96363	validation-aucpr:0.96805
[18:48:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41]	validation-logloss:0.23492	validation-auc:0.96364	validation-aucpr:0.96807
[18:48:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42]	validation-logloss:0.23292	validation-auc:0.96398	validation-aucpr:0.96795
[18:48:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43]	validation-logloss:0.23274	validation-auc:0.96380	validation-aucpr:0.96787
[18:48:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44]	validation-logloss:0.23152	validation-auc:0.96393	validation-aucpr:0.96792
[18:48:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45]	validation-logloss:0.23065	validation-auc:0.96412	validation-aucpr:0.96807
[18:48:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46]	validation-logloss:0.23042	validation-auc:0.96413	validation-aucpr:0.96830
[18:48:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[47]	validation-logloss:0.22993	validation-auc:0.96429	validation-aucpr:0.96835
[18:48:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[48]	validation-logloss:0.22977	validation-auc:0.96417	validation-aucpr:0.96822
[18:48:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[49]	validation-logloss:0.22903	validation-auc:0.96428	validation-aucpr:0.96845
[18:48:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[50]	validation-logloss:0.22735	validation-auc:0.96465	validation-aucpr:0.96932
[18:48:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[51]	validation-logloss:0.22616	validation-auc:0.96502	validation-aucpr:0.96959
[18:48:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[52]	validation-logloss:0.22491	validation-auc:0.96514	validation-aucpr:0.96973
[18:48:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[53]	validation-logloss:0.22447	validation-auc:0.96525	validation-aucpr:0.96987
{'best_iteration': '53', 'best_score': '0.969869479613832'}
Trial 29, Fold 5: Log loss = 0.2244748179461408, Average precision = 0.9698752741540864, ROC-AUC = 0.9652471248351076, Elapsed Time = 9.2844757999992 seconds
Optimization Progress:  30%|###       | 30/100 [49:53<1:47:34, 92.21s/it] 
Trial 30, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 30, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[0]	validation-logloss:0.68429	validation-auc:0.94592	validation-aucpr:0.95150
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[2]	validation-logloss:0.66675	validation-auc:0.94673	validation-aucpr:0.94831
[3]	validation-logloss:0.65861	validation-auc:0.95024	validation-aucpr:0.95688
[4]	validation-logloss:0.65139	validation-auc:0.95150	validation-aucpr:0.95454
[5]	validation-logloss:0.64332	validation-auc:0.95423	validation-aucpr:0.96099
[6]	validation-logloss:0.63530	validation-auc:0.95723	validation-aucpr:0.96523
[7]	validation-logloss:0.62740	validation-auc:0.95863	validation-aucpr:0.96553
[8]	validation-logloss:0.61993	validation-auc:0.95944	validation-aucpr:0.96701
[9]	validation-logloss:0.61339	validation-auc:0.95979	validation-aucpr:0.96727
[10]	validation-logloss:0.60710	validation-auc:0.96040	validation-aucpr:0.96584
[11]	validation-logloss:0.60038	validation-auc:0.96130	validation-aucpr:0.96669
[12]	validation-logloss:0.59437	validation-auc:0.96237	validation-aucpr:0.96906
[13]	validation-logloss:0.58734	validation-auc:0.96312	validation-aucpr:0.96844
[14]	validation-logloss:0.58055	validation-auc:0.96386	validation-aucpr:0.96908
[15]	validation-logloss:0.57401	validation-auc:0.96429	validation-aucpr:0.96993
[16]	validation-logloss:0.56841	validation-auc:0.96440	validation-aucpr:0.97014
[17]	validation-logloss:0.56276	validation-auc:0.96505	validation-aucpr:0.97077
[18]	validation-logloss:0.55744	validation-auc:0.96532	validation-aucpr:0.97094
[19]	validation-logloss:0.55132	validation-auc:0.96545	validation-aucpr:0.97093
[20]	validation-logloss:0.54604	validation-auc:0.96553	validation-aucpr:0.97092
[21]	validation-logloss:0.54070	validation-auc:0.96584	validation-aucpr:0.97118
[22]	validation-logloss:0.53573	validation-auc:0.96563	validation-aucpr:0.97098
[23]	validation-logloss:0.53025	validation-auc:0.96542	validation-aucpr:0.97088
[24]	validation-logloss:0.52468	validation-auc:0.96565	validation-aucpr:0.97105
[25]	validation-logloss:0.51943	validation-auc:0.96566	validation-aucpr:0.97112
[26]	validation-logloss:0.51424	validation-auc:0.96584	validation-aucpr:0.97123
[27]	validation-logloss:0.50921	validation-auc:0.96563	validation-aucpr:0.97109
[28]	validation-logloss:0.50410	validation-auc:0.96567	validation-aucpr:0.97114
[29]	validation-logloss:0.50007	validation-auc:0.96536	validation-aucpr:0.97089
[30]	validation-logloss:0.49528	validation-auc:0.96537	validation-aucpr:0.97086
[31]	validation-logloss:0.49038	validation-auc:0.96559	validation-aucpr:0.97102
[32]	validation-logloss:0.48545	validation-auc:0.96580	validation-aucpr:0.97126
[33]	validation-logloss:0.48088	validation-auc:0.96576	validation-aucpr:0.97130
[34]	validation-logloss:0.47632	validation-auc:0.96618	validation-aucpr:0.97157
[35]	validation-logloss:0.47214	validation-auc:0.96644	validation-aucpr:0.97172
[36]	validation-logloss:0.46841	validation-auc:0.96627	validation-aucpr:0.97160
[37]	validation-logloss:0.46417	validation-auc:0.96637	validation-aucpr:0.97169
[38]	validation-logloss:0.46038	validation-auc:0.96639	validation-aucpr:0.97168
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[40]	validation-logloss:0.45191	validation-auc:0.96643	validation-aucpr:0.97156
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[42]	validation-logloss:0.44494	validation-auc:0.96667	validation-aucpr:0.97172
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[44]	validation-logloss:0.43765	validation-auc:0.96676	validation-aucpr:0.97183
[45]	validation-logloss:0.43384	validation-auc:0.96688	validation-aucpr:0.97194
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[55]	validation-logloss:0.40372	validation-auc:0.96691	validation-aucpr:0.97204
[56]	validation-logloss:0.40052	validation-auc:0.96683	validation-aucpr:0.97201
[57]	validation-logloss:0.39801	validation-auc:0.96671	validation-aucpr:0.97183
[58]	validation-logloss:0.39534	validation-auc:0.96668	validation-aucpr:0.97181
[59]	validation-logloss:0.39227	validation-auc:0.96675	validation-aucpr:0.97157
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[75]	validation-logloss:0.35178	validation-auc:0.96737	validation-aucpr:0.97193
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[77]	validation-logloss:0.34744	validation-auc:0.96738	validation-aucpr:0.97193
[78]	validation-logloss:0.34553	validation-auc:0.96737	validation-aucpr:0.97192
[79]	validation-logloss:0.34330	validation-auc:0.96739	validation-aucpr:0.97194
[80]	validation-logloss:0.34108	validation-auc:0.96743	validation-aucpr:0.97198
[81]	validation-logloss:0.33942	validation-auc:0.96744	validation-aucpr:0.97189
[82]	validation-logloss:0.33732	validation-auc:0.96746	validation-aucpr:0.97191
[83]	validation-logloss:0.33519	validation-auc:0.96746	validation-aucpr:0.97195
[84]	validation-logloss:0.33347	validation-auc:0.96745	validation-aucpr:0.97195
[85]	validation-logloss:0.33180	validation-auc:0.96749	validation-aucpr:0.97199
[86]	validation-logloss:0.33005	validation-auc:0.96758	validation-aucpr:0.97196
[87]	validation-logloss:0.32842	validation-auc:0.96757	validation-aucpr:0.97191
[88]	validation-logloss:0.32691	validation-auc:0.96755	validation-aucpr:0.97186
[89]	validation-logloss:0.32524	validation-auc:0.96766	validation-aucpr:0.97191
[90]	validation-logloss:0.32311	validation-auc:0.96779	validation-aucpr:0.97203
[91]	validation-logloss:0.32119	validation-auc:0.96784	validation-aucpr:0.97207
[92]	validation-logloss:0.31967	validation-auc:0.96783	validation-aucpr:0.97206
[93]	validation-logloss:0.31793	validation-auc:0.96785	validation-aucpr:0.97211
{'best_iteration': '93', 'best_score': '0.9721051971930318'}
Trial 30, Fold 1: Log loss = 0.31792534158671387, Average precision = 0.9721165311715719, ROC-AUC = 0.9678542751035975, Elapsed Time = 2.6847978999994666 seconds
Trial 30, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 30, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[0]	validation-logloss:0.68365	validation-auc:0.94604	validation-aucpr:0.95049
[1]	validation-logloss:0.67572	validation-auc:0.94530	validation-aucpr:0.94160
[2]	validation-logloss:0.66691	validation-auc:0.95222	validation-aucpr:0.94586
[3]	validation-logloss:0.65860	validation-auc:0.95589	validation-aucpr:0.94929
[4]	validation-logloss:0.65023	validation-auc:0.95917	validation-aucpr:0.95637
[5]	validation-logloss:0.64297	validation-auc:0.96098	validation-aucpr:0.96079
[6]	validation-logloss:0.63576	validation-auc:0.96114	validation-aucpr:0.95928
[7]	validation-logloss:0.62904	validation-auc:0.96144	validation-aucpr:0.96299
[8]	validation-logloss:0.62169	validation-auc:0.96206	validation-aucpr:0.96346
[9]	validation-logloss:0.61536	validation-auc:0.96258	validation-aucpr:0.96540
[10]	validation-logloss:0.60804	validation-auc:0.96356	validation-aucpr:0.96631
[11]	validation-logloss:0.60181	validation-auc:0.96407	validation-aucpr:0.96660
[12]	validation-logloss:0.59564	validation-auc:0.96426	validation-aucpr:0.96675
[13]	validation-logloss:0.58906	validation-auc:0.96467	validation-aucpr:0.96698
[14]	validation-logloss:0.58317	validation-auc:0.96439	validation-aucpr:0.96662
[15]	validation-logloss:0.57749	validation-auc:0.96410	validation-aucpr:0.96610
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[23]	validation-logloss:0.53114	validation-auc:0.96660	validation-aucpr:0.97059
[24]	validation-logloss:0.52581	validation-auc:0.96666	validation-aucpr:0.97070
[25]	validation-logloss:0.52097	validation-auc:0.96651	validation-aucpr:0.97060
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{'best_iteration': '92', 'best_score': '0.9735212751789194'}
Trial 30, Fold 2: Log loss = 0.3151728598347162, Average precision = 0.97351458863413, ROC-AUC = 0.9698109920632872, Elapsed Time = 2.9415685000003577 seconds
Trial 30, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 30, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
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[60]	validation-logloss:0.38366	validation-auc:0.96883	validation-aucpr:0.97134
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[62]	validation-logloss:0.37849	validation-auc:0.96880	validation-aucpr:0.97158
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[68]	validation-logloss:0.36332	validation-auc:0.96906	validation-aucpr:0.97205
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[71]	validation-logloss:0.35635	validation-auc:0.96926	validation-aucpr:0.97251
{'best_iteration': '21', 'best_score': '0.9728873063164621'}
Trial 30, Fold 3: Log loss = 0.35634576391738043, Average precision = 0.9725254290879971, ROC-AUC = 0.9692625516464255, Elapsed Time = 2.331017199998314 seconds
Trial 30, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 30, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
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[4]	validation-logloss:0.65255	validation-auc:0.95388	validation-aucpr:0.95359
[5]	validation-logloss:0.64522	validation-auc:0.95537	validation-aucpr:0.95826
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[88]	validation-logloss:0.32873	validation-auc:0.96735	validation-aucpr:0.97266
[89]	validation-logloss:0.32663	validation-auc:0.96743	validation-aucpr:0.97272
[90]	validation-logloss:0.32507	validation-auc:0.96744	validation-aucpr:0.97273
[91]	validation-logloss:0.32368	validation-auc:0.96751	validation-aucpr:0.97276
[92]	validation-logloss:0.32184	validation-auc:0.96751	validation-aucpr:0.97277
[93]	validation-logloss:0.32051	validation-auc:0.96739	validation-aucpr:0.97268
{'best_iteration': '92', 'best_score': '0.9727730022275809'}
Trial 30, Fold 4: Log loss = 0.3205108791725433, Average precision = 0.9726860324319256, ROC-AUC = 0.9673858195574059, Elapsed Time = 2.7896488000005775 seconds
Trial 30, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 30, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[0]	validation-logloss:0.68389	validation-auc:0.94408	validation-aucpr:0.95018
[1]	validation-logloss:0.67593	validation-auc:0.94630	validation-aucpr:0.94872
[2]	validation-logloss:0.66727	validation-auc:0.95062	validation-aucpr:0.95591
[3]	validation-logloss:0.65885	validation-auc:0.95609	validation-aucpr:0.95779
[4]	validation-logloss:0.65153	validation-auc:0.95795	validation-aucpr:0.95871
[5]	validation-logloss:0.64346	validation-auc:0.95900	validation-aucpr:0.96111
[6]	validation-logloss:0.63639	validation-auc:0.96002	validation-aucpr:0.96209
[7]	validation-logloss:0.62865	validation-auc:0.96094	validation-aucpr:0.96319
[8]	validation-logloss:0.62186	validation-auc:0.96195	validation-aucpr:0.96294
[9]	validation-logloss:0.61413	validation-auc:0.96361	validation-aucpr:0.96569
[10]	validation-logloss:0.60685	validation-auc:0.96372	validation-aucpr:0.96641
[11]	validation-logloss:0.59983	validation-auc:0.96387	validation-aucpr:0.96739
[12]	validation-logloss:0.59367	validation-auc:0.96366	validation-aucpr:0.96683
[13]	validation-logloss:0.58787	validation-auc:0.96348	validation-aucpr:0.96616
[14]	validation-logloss:0.58193	validation-auc:0.96344	validation-aucpr:0.96696
[15]	validation-logloss:0.57639	validation-auc:0.96299	validation-aucpr:0.96664
[16]	validation-logloss:0.56976	validation-auc:0.96373	validation-aucpr:0.96727
[17]	validation-logloss:0.56419	validation-auc:0.96385	validation-aucpr:0.96726
[18]	validation-logloss:0.55887	validation-auc:0.96372	validation-aucpr:0.96708
[19]	validation-logloss:0.55282	validation-auc:0.96387	validation-aucpr:0.96720
[20]	validation-logloss:0.54697	validation-auc:0.96419	validation-aucpr:0.96745
[21]	validation-logloss:0.54168	validation-auc:0.96421	validation-aucpr:0.96748
[22]	validation-logloss:0.53664	validation-auc:0.96444	validation-aucpr:0.96754
[23]	validation-logloss:0.53165	validation-auc:0.96457	validation-aucpr:0.96811
[24]	validation-logloss:0.52697	validation-auc:0.96463	validation-aucpr:0.96805
[25]	validation-logloss:0.52145	validation-auc:0.96501	validation-aucpr:0.96835
[26]	validation-logloss:0.51586	validation-auc:0.96520	validation-aucpr:0.96882
[27]	validation-logloss:0.51060	validation-auc:0.96526	validation-aucpr:0.96894
[28]	validation-logloss:0.50598	validation-auc:0.96516	validation-aucpr:0.96883
[29]	validation-logloss:0.50148	validation-auc:0.96516	validation-aucpr:0.96883
[30]	validation-logloss:0.49719	validation-auc:0.96503	validation-aucpr:0.96871
[31]	validation-logloss:0.49243	validation-auc:0.96515	validation-aucpr:0.96879
[32]	validation-logloss:0.48826	validation-auc:0.96527	validation-aucpr:0.96877
[33]	validation-logloss:0.48420	validation-auc:0.96508	validation-aucpr:0.96865
[34]	validation-logloss:0.47947	validation-auc:0.96535	validation-aucpr:0.96884
[35]	validation-logloss:0.47481	validation-auc:0.96546	validation-aucpr:0.96896
[36]	validation-logloss:0.47024	validation-auc:0.96562	validation-aucpr:0.96916
[37]	validation-logloss:0.46582	validation-auc:0.96589	validation-aucpr:0.96938
[38]	validation-logloss:0.46197	validation-auc:0.96607	validation-aucpr:0.96938
[39]	validation-logloss:0.45774	validation-auc:0.96611	validation-aucpr:0.96936
[40]	validation-logloss:0.45422	validation-auc:0.96600	validation-aucpr:0.96922
[41]	validation-logloss:0.45013	validation-auc:0.96619	validation-aucpr:0.97111
[42]	validation-logloss:0.44669	validation-auc:0.96600	validation-aucpr:0.97095
[43]	validation-logloss:0.44339	validation-auc:0.96590	validation-aucpr:0.97086
[44]	validation-logloss:0.43950	validation-auc:0.96602	validation-aucpr:0.97102
[45]	validation-logloss:0.43635	validation-auc:0.96585	validation-aucpr:0.97089
[46]	validation-logloss:0.43302	validation-auc:0.96593	validation-aucpr:0.97089
[47]	validation-logloss:0.42971	validation-auc:0.96595	validation-aucpr:0.97091
[48]	validation-logloss:0.42652	validation-auc:0.96591	validation-aucpr:0.97085
[49]	validation-logloss:0.42299	validation-auc:0.96591	validation-aucpr:0.97085
[50]	validation-logloss:0.41946	validation-auc:0.96594	validation-aucpr:0.97089
[51]	validation-logloss:0.41602	validation-auc:0.96601	validation-aucpr:0.97104
[52]	validation-logloss:0.41316	validation-auc:0.96593	validation-aucpr:0.97095
[53]	validation-logloss:0.40987	validation-auc:0.96589	validation-aucpr:0.97093
[54]	validation-logloss:0.40726	validation-auc:0.96585	validation-aucpr:0.97083
[55]	validation-logloss:0.40409	validation-auc:0.96587	validation-aucpr:0.97085
[56]	validation-logloss:0.40149	validation-auc:0.96583	validation-aucpr:0.97076
[57]	validation-logloss:0.39861	validation-auc:0.96587	validation-aucpr:0.97076
[58]	validation-logloss:0.39560	validation-auc:0.96590	validation-aucpr:0.97083
[59]	validation-logloss:0.39256	validation-auc:0.96592	validation-aucpr:0.97085
[60]	validation-logloss:0.38969	validation-auc:0.96604	validation-aucpr:0.97098
[61]	validation-logloss:0.38714	validation-auc:0.96606	validation-aucpr:0.97099
[62]	validation-logloss:0.38429	validation-auc:0.96611	validation-aucpr:0.97106
[63]	validation-logloss:0.38189	validation-auc:0.96613	validation-aucpr:0.97144
[64]	validation-logloss:0.37956	validation-auc:0.96613	validation-aucpr:0.97129
[65]	validation-logloss:0.37674	validation-auc:0.96623	validation-aucpr:0.97139
[66]	validation-logloss:0.37406	validation-auc:0.96631	validation-aucpr:0.97144
[67]	validation-logloss:0.37136	validation-auc:0.96635	validation-aucpr:0.97146
[68]	validation-logloss:0.36898	validation-auc:0.96637	validation-aucpr:0.97148
[69]	validation-logloss:0.36681	validation-auc:0.96639	validation-aucpr:0.97148
[70]	validation-logloss:0.36432	validation-auc:0.96647	validation-aucpr:0.97155
[71]	validation-logloss:0.36216	validation-auc:0.96642	validation-aucpr:0.97151
[72]	validation-logloss:0.36015	validation-auc:0.96633	validation-aucpr:0.97129
[73]	validation-logloss:0.35765	validation-auc:0.96644	validation-aucpr:0.97136
[74]	validation-logloss:0.35565	validation-auc:0.96635	validation-aucpr:0.97129
[75]	validation-logloss:0.35367	validation-auc:0.96625	validation-aucpr:0.97119
[76]	validation-logloss:0.35145	validation-auc:0.96624	validation-aucpr:0.97121
[77]	validation-logloss:0.34941	validation-auc:0.96630	validation-aucpr:0.97126
[78]	validation-logloss:0.34759	validation-auc:0.96626	validation-aucpr:0.97123
[79]	validation-logloss:0.34539	validation-auc:0.96626	validation-aucpr:0.97122
[80]	validation-logloss:0.34306	validation-auc:0.96640	validation-aucpr:0.97132
[81]	validation-logloss:0.34139	validation-auc:0.96634	validation-aucpr:0.97128
[82]	validation-logloss:0.33913	validation-auc:0.96646	validation-aucpr:0.97139
[83]	validation-logloss:0.33745	validation-auc:0.96637	validation-aucpr:0.97129
[84]	validation-logloss:0.33536	validation-auc:0.96636	validation-aucpr:0.97129
[85]	validation-logloss:0.33366	validation-auc:0.96635	validation-aucpr:0.97126
[86]	validation-logloss:0.33201	validation-auc:0.96634	validation-aucpr:0.97123
[87]	validation-logloss:0.33025	validation-auc:0.96644	validation-aucpr:0.97128
[88]	validation-logloss:0.32833	validation-auc:0.96647	validation-aucpr:0.97132
[89]	validation-logloss:0.32614	validation-auc:0.96663	validation-aucpr:0.97144
[90]	validation-logloss:0.32447	validation-auc:0.96673	validation-aucpr:0.97150
[91]	validation-logloss:0.32296	validation-auc:0.96674	validation-aucpr:0.97150
[92]	validation-logloss:0.32118	validation-auc:0.96676	validation-aucpr:0.97146
[93]	validation-logloss:0.31976	validation-auc:0.96669	validation-aucpr:0.97140
{'best_iteration': '70', 'best_score': '0.9715477136630459'}
Trial 30, Fold 5: Log loss = 0.3197561209559464, Average precision = 0.9714092461106858, ROC-AUC = 0.9666948330639318, Elapsed Time = 2.8984010000003764 seconds
Optimization Progress:  31%|###1      | 31/100 [50:17<1:22:15, 71.52s/it]
Trial 31, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 31, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[0]	validation-logloss:0.66183	validation-auc:0.95646	validation-aucpr:0.95706
[1]	validation-logloss:0.63297	validation-auc:0.96248	validation-aucpr:0.96496
[2]	validation-logloss:0.60646	validation-auc:0.96382	validation-aucpr:0.96777
[3]	validation-logloss:0.58199	validation-auc:0.96545	validation-aucpr:0.97079
[4]	validation-logloss:0.55933	validation-auc:0.96640	validation-aucpr:0.97186
[5]	validation-logloss:0.53802	validation-auc:0.96680	validation-aucpr:0.97221
[6]	validation-logloss:0.51885	validation-auc:0.96668	validation-aucpr:0.97209
[7]	validation-logloss:0.50046	validation-auc:0.96686	validation-aucpr:0.97228
[8]	validation-logloss:0.48402	validation-auc:0.96716	validation-aucpr:0.97265
[9]	validation-logloss:0.46789	validation-auc:0.96732	validation-aucpr:0.97279
[10]	validation-logloss:0.45298	validation-auc:0.96740	validation-aucpr:0.97285
[11]	validation-logloss:0.43896	validation-auc:0.96748	validation-aucpr:0.97287
[12]	validation-logloss:0.42568	validation-auc:0.96771	validation-aucpr:0.97302
[13]	validation-logloss:0.41462	validation-auc:0.96823	validation-aucpr:0.97348
[14]	validation-logloss:0.40427	validation-auc:0.96817	validation-aucpr:0.97349
[15]	validation-logloss:0.39318	validation-auc:0.96815	validation-aucpr:0.97348
[16]	validation-logloss:0.38312	validation-auc:0.96809	validation-aucpr:0.97343
[17]	validation-logloss:0.37376	validation-auc:0.96805	validation-aucpr:0.97338
[18]	validation-logloss:0.36441	validation-auc:0.96819	validation-aucpr:0.97346
[19]	validation-logloss:0.35571	validation-auc:0.96843	validation-aucpr:0.97361
[20]	validation-logloss:0.34753	validation-auc:0.96842	validation-aucpr:0.97358
[21]	validation-logloss:0.33995	validation-auc:0.96861	validation-aucpr:0.97403
[22]	validation-logloss:0.33281	validation-auc:0.96855	validation-aucpr:0.97398
[23]	validation-logloss:0.32569	validation-auc:0.96871	validation-aucpr:0.97410
[24]	validation-logloss:0.31917	validation-auc:0.96888	validation-aucpr:0.97422
[25]	validation-logloss:0.31279	validation-auc:0.96915	validation-aucpr:0.97443
[26]	validation-logloss:0.30690	validation-auc:0.96927	validation-aucpr:0.97452
[27]	validation-logloss:0.30141	validation-auc:0.96953	validation-aucpr:0.97463
[28]	validation-logloss:0.29670	validation-auc:0.96967	validation-aucpr:0.97471
[29]	validation-logloss:0.29209	validation-auc:0.96985	validation-aucpr:0.97484
[30]	validation-logloss:0.28713	validation-auc:0.97002	validation-aucpr:0.97495
[31]	validation-logloss:0.28249	validation-auc:0.97013	validation-aucpr:0.97502
[32]	validation-logloss:0.27809	validation-auc:0.97021	validation-aucpr:0.97505
[33]	validation-logloss:0.27414	validation-auc:0.97021	validation-aucpr:0.97509
[34]	validation-logloss:0.27030	validation-auc:0.97021	validation-aucpr:0.97509
[35]	validation-logloss:0.26659	validation-auc:0.97032	validation-aucpr:0.97517
[36]	validation-logloss:0.26306	validation-auc:0.97037	validation-aucpr:0.97522
[37]	validation-logloss:0.25957	validation-auc:0.97042	validation-aucpr:0.97526
[38]	validation-logloss:0.25630	validation-auc:0.97057	validation-aucpr:0.97535
[39]	validation-logloss:0.25326	validation-auc:0.97067	validation-aucpr:0.97541
[40]	validation-logloss:0.25033	validation-auc:0.97084	validation-aucpr:0.97561
[41]	validation-logloss:0.24780	validation-auc:0.97093	validation-aucpr:0.97564
[42]	validation-logloss:0.24514	validation-auc:0.97099	validation-aucpr:0.97567
{'best_iteration': '42', 'best_score': '0.975667931459085'}
Trial 31, Fold 1: Log loss = 0.24514445785594513, Average precision = 0.9756598868388844, ROC-AUC = 0.9709922248032004, Elapsed Time = 1.0101247000002331 seconds
Trial 31, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 31, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[0]	validation-logloss:0.66193	validation-auc:0.95879	validation-aucpr:0.95950
[1]	validation-logloss:0.63288	validation-auc:0.96351	validation-aucpr:0.96590
[2]	validation-logloss:0.60870	validation-auc:0.96519	validation-aucpr:0.96779
[3]	validation-logloss:0.58404	validation-auc:0.96680	validation-aucpr:0.96988
[4]	validation-logloss:0.56373	validation-auc:0.96679	validation-aucpr:0.97063
[5]	validation-logloss:0.54220	validation-auc:0.96742	validation-aucpr:0.97119
[6]	validation-logloss:0.52272	validation-auc:0.96782	validation-aucpr:0.97166
[7]	validation-logloss:0.50640	validation-auc:0.96780	validation-aucpr:0.97158
[8]	validation-logloss:0.49092	validation-auc:0.96778	validation-aucpr:0.97148
[9]	validation-logloss:0.47483	validation-auc:0.96831	validation-aucpr:0.97197
[10]	validation-logloss:0.45941	validation-auc:0.96870	validation-aucpr:0.97224
[11]	validation-logloss:0.44531	validation-auc:0.96861	validation-aucpr:0.97214
[12]	validation-logloss:0.43158	validation-auc:0.96909	validation-aucpr:0.97255
[13]	validation-logloss:0.41885	validation-auc:0.96938	validation-aucpr:0.97280
[14]	validation-logloss:0.40668	validation-auc:0.96967	validation-aucpr:0.97309
[15]	validation-logloss:0.39536	validation-auc:0.96979	validation-aucpr:0.97316
[16]	validation-logloss:0.38460	validation-auc:0.96995	validation-aucpr:0.97328
[17]	validation-logloss:0.37501	validation-auc:0.96990	validation-aucpr:0.97323
[18]	validation-logloss:0.36567	validation-auc:0.96984	validation-aucpr:0.97320
[19]	validation-logloss:0.35660	validation-auc:0.96994	validation-aucpr:0.97329
[20]	validation-logloss:0.34823	validation-auc:0.96999	validation-aucpr:0.97324
[21]	validation-logloss:0.34131	validation-auc:0.97012	validation-aucpr:0.97333
[22]	validation-logloss:0.33377	validation-auc:0.97014	validation-aucpr:0.97332
[23]	validation-logloss:0.32677	validation-auc:0.97004	validation-aucpr:0.97324
[24]	validation-logloss:0.31991	validation-auc:0.97007	validation-aucpr:0.97326
[25]	validation-logloss:0.31332	validation-auc:0.97008	validation-aucpr:0.97327
[26]	validation-logloss:0.30730	validation-auc:0.97026	validation-aucpr:0.97338
[27]	validation-logloss:0.30129	validation-auc:0.97039	validation-aucpr:0.97348
[28]	validation-logloss:0.29576	validation-auc:0.97044	validation-aucpr:0.97348
[29]	validation-logloss:0.29055	validation-auc:0.97046	validation-aucpr:0.97350
[30]	validation-logloss:0.28568	validation-auc:0.97061	validation-aucpr:0.97359
[31]	validation-logloss:0.28150	validation-auc:0.97068	validation-aucpr:0.97367
[32]	validation-logloss:0.27683	validation-auc:0.97074	validation-aucpr:0.97372
[33]	validation-logloss:0.27249	validation-auc:0.97075	validation-aucpr:0.97374
[34]	validation-logloss:0.26876	validation-auc:0.97091	validation-aucpr:0.97387
[35]	validation-logloss:0.26493	validation-auc:0.97096	validation-aucpr:0.97389
[36]	validation-logloss:0.26150	validation-auc:0.97079	validation-aucpr:0.97377
[37]	validation-logloss:0.25798	validation-auc:0.97081	validation-aucpr:0.97377
[38]	validation-logloss:0.25468	validation-auc:0.97086	validation-aucpr:0.97413
[39]	validation-logloss:0.25148	validation-auc:0.97095	validation-aucpr:0.97421
[40]	validation-logloss:0.24849	validation-auc:0.97098	validation-aucpr:0.97423
[41]	validation-logloss:0.24613	validation-auc:0.97100	validation-aucpr:0.97425
[42]	validation-logloss:0.24319	validation-auc:0.97103	validation-aucpr:0.97428
{'best_iteration': '42', 'best_score': '0.9742822625501332'}
Trial 31, Fold 2: Log loss = 0.2431944995877271, Average precision = 0.9742661387249827, ROC-AUC = 0.9710278575549607, Elapsed Time = 1.205074200001036 seconds
Trial 31, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 31, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[0]	validation-logloss:0.66155	validation-auc:0.96269	validation-aucpr:0.96502
[1]	validation-logloss:0.63567	validation-auc:0.96477	validation-aucpr:0.96781
[2]	validation-logloss:0.60879	validation-auc:0.96584	validation-aucpr:0.97005
[3]	validation-logloss:0.58748	validation-auc:0.96561	validation-aucpr:0.96964
[4]	validation-logloss:0.56424	validation-auc:0.96632	validation-aucpr:0.97021
[5]	validation-logloss:0.54594	validation-auc:0.96677	validation-aucpr:0.97070
[6]	validation-logloss:0.52830	validation-auc:0.96649	validation-aucpr:0.97038
[7]	validation-logloss:0.50908	validation-auc:0.96715	validation-aucpr:0.97099
[8]	validation-logloss:0.49107	validation-auc:0.96772	validation-aucpr:0.97144
[9]	validation-logloss:0.47687	validation-auc:0.96786	validation-aucpr:0.97150
[10]	validation-logloss:0.46118	validation-auc:0.96811	validation-aucpr:0.97178
[11]	validation-logloss:0.44636	validation-auc:0.96854	validation-aucpr:0.97242
[12]	validation-logloss:0.43416	validation-auc:0.96864	validation-aucpr:0.97249
[13]	validation-logloss:0.42113	validation-auc:0.96879	validation-aucpr:0.97262
[14]	validation-logloss:0.41051	validation-auc:0.96881	validation-aucpr:0.97259
[15]	validation-logloss:0.40051	validation-auc:0.96874	validation-aucpr:0.97252
[16]	validation-logloss:0.38962	validation-auc:0.96897	validation-aucpr:0.97278
[17]	validation-logloss:0.37920	validation-auc:0.96910	validation-aucpr:0.97290
[18]	validation-logloss:0.36940	validation-auc:0.96930	validation-aucpr:0.97337
[19]	validation-logloss:0.36014	validation-auc:0.96948	validation-aucpr:0.97351
[20]	validation-logloss:0.35126	validation-auc:0.96980	validation-aucpr:0.97381
[21]	validation-logloss:0.34316	validation-auc:0.96990	validation-aucpr:0.97390
[22]	validation-logloss:0.33556	validation-auc:0.96992	validation-aucpr:0.97393
[23]	validation-logloss:0.32804	validation-auc:0.97010	validation-aucpr:0.97409
[24]	validation-logloss:0.32107	validation-auc:0.97012	validation-aucpr:0.97412
[25]	validation-logloss:0.31442	validation-auc:0.97027	validation-aucpr:0.97425
[26]	validation-logloss:0.30908	validation-auc:0.97041	validation-aucpr:0.97433
[27]	validation-logloss:0.30309	validation-auc:0.97062	validation-aucpr:0.97446
[28]	validation-logloss:0.29742	validation-auc:0.97069	validation-aucpr:0.97450
[29]	validation-logloss:0.29303	validation-auc:0.97067	validation-aucpr:0.97447
[30]	validation-logloss:0.28786	validation-auc:0.97070	validation-aucpr:0.97452
[31]	validation-logloss:0.28300	validation-auc:0.97083	validation-aucpr:0.97463
[32]	validation-logloss:0.27842	validation-auc:0.97087	validation-aucpr:0.97465
[33]	validation-logloss:0.27487	validation-auc:0.97086	validation-aucpr:0.97463
[34]	validation-logloss:0.27081	validation-auc:0.97095	validation-aucpr:0.97472
[35]	validation-logloss:0.26673	validation-auc:0.97105	validation-aucpr:0.97480
[36]	validation-logloss:0.26310	validation-auc:0.97109	validation-aucpr:0.97483
[37]	validation-logloss:0.25937	validation-auc:0.97118	validation-aucpr:0.97490
[38]	validation-logloss:0.25586	validation-auc:0.97121	validation-aucpr:0.97492
[39]	validation-logloss:0.25267	validation-auc:0.97129	validation-aucpr:0.97497
[40]	validation-logloss:0.24958	validation-auc:0.97127	validation-aucpr:0.97493
[41]	validation-logloss:0.24677	validation-auc:0.97125	validation-aucpr:0.97490
[42]	validation-logloss:0.24382	validation-auc:0.97133	validation-aucpr:0.97495
{'best_iteration': '39', 'best_score': '0.9749729169827692'}
Trial 31, Fold 3: Log loss = 0.24381864069012466, Average precision = 0.9749243711198644, ROC-AUC = 0.9713275057545434, Elapsed Time = 1.1850333000002138 seconds
Trial 31, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 31, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[0]	validation-logloss:0.66180	validation-auc:0.96164	validation-aucpr:0.96373
[1]	validation-logloss:0.63291	validation-auc:0.96602	validation-aucpr:0.96790
[2]	validation-logloss:0.60627	validation-auc:0.96732	validation-aucpr:0.96907
[3]	validation-logloss:0.58496	validation-auc:0.96733	validation-aucpr:0.97206
[4]	validation-logloss:0.56435	validation-auc:0.96676	validation-aucpr:0.97179
[5]	validation-logloss:0.54289	validation-auc:0.96763	validation-aucpr:0.97264
[6]	validation-logloss:0.52277	validation-auc:0.96805	validation-aucpr:0.97296
[7]	validation-logloss:0.50653	validation-auc:0.96741	validation-aucpr:0.97257
[8]	validation-logloss:0.49114	validation-auc:0.96726	validation-aucpr:0.97242
[9]	validation-logloss:0.47453	validation-auc:0.96751	validation-aucpr:0.97262
[10]	validation-logloss:0.45930	validation-auc:0.96778	validation-aucpr:0.97281
[11]	validation-logloss:0.44644	validation-auc:0.96761	validation-aucpr:0.97271
[12]	validation-logloss:0.43268	validation-auc:0.96792	validation-aucpr:0.97297
[13]	validation-logloss:0.41970	validation-auc:0.96842	validation-aucpr:0.97337
[14]	validation-logloss:0.40759	validation-auc:0.96896	validation-aucpr:0.97379
[15]	validation-logloss:0.39630	validation-auc:0.96909	validation-aucpr:0.97391
[16]	validation-logloss:0.38556	validation-auc:0.96949	validation-aucpr:0.97420
[17]	validation-logloss:0.37557	validation-auc:0.96959	validation-aucpr:0.97426
[18]	validation-logloss:0.36609	validation-auc:0.96971	validation-aucpr:0.97437
[19]	validation-logloss:0.35856	validation-auc:0.96950	validation-aucpr:0.97418
[20]	validation-logloss:0.35117	validation-auc:0.96946	validation-aucpr:0.97416
[21]	validation-logloss:0.34290	validation-auc:0.96984	validation-aucpr:0.97448
[22]	validation-logloss:0.33518	validation-auc:0.96988	validation-aucpr:0.97455
[23]	validation-logloss:0.32880	validation-auc:0.96980	validation-aucpr:0.97448
[24]	validation-logloss:0.32177	validation-auc:0.97003	validation-aucpr:0.97465
[25]	validation-logloss:0.31535	validation-auc:0.97016	validation-aucpr:0.97475
[26]	validation-logloss:0.30905	validation-auc:0.97040	validation-aucpr:0.97499
[27]	validation-logloss:0.30311	validation-auc:0.97059	validation-aucpr:0.97513
[28]	validation-logloss:0.29748	validation-auc:0.97073	validation-aucpr:0.97525
[29]	validation-logloss:0.29216	validation-auc:0.97082	validation-aucpr:0.97534
[30]	validation-logloss:0.28697	validation-auc:0.97104	validation-aucpr:0.97550
[31]	validation-logloss:0.28216	validation-auc:0.97124	validation-aucpr:0.97566
[32]	validation-logloss:0.27752	validation-auc:0.97130	validation-aucpr:0.97572
[33]	validation-logloss:0.27324	validation-auc:0.97133	validation-aucpr:0.97576
[34]	validation-logloss:0.26915	validation-auc:0.97133	validation-aucpr:0.97576
[35]	validation-logloss:0.26526	validation-auc:0.97132	validation-aucpr:0.97576
[36]	validation-logloss:0.26158	validation-auc:0.97134	validation-aucpr:0.97577
[37]	validation-logloss:0.25805	validation-auc:0.97143	validation-aucpr:0.97584
[38]	validation-logloss:0.25464	validation-auc:0.97155	validation-aucpr:0.97590
[39]	validation-logloss:0.25141	validation-auc:0.97162	validation-aucpr:0.97596
[40]	validation-logloss:0.24850	validation-auc:0.97165	validation-aucpr:0.97597
[41]	validation-logloss:0.24565	validation-auc:0.97172	validation-aucpr:0.97601
[42]	validation-logloss:0.24301	validation-auc:0.97172	validation-aucpr:0.97602
{'best_iteration': '42', 'best_score': '0.9760215168892972'}
Trial 31, Fold 4: Log loss = 0.24300942956000274, Average precision = 0.9760034541082719, ROC-AUC = 0.9717249237584973, Elapsed Time = 1.2454550999991625 seconds
Trial 31, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 31, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[0]	validation-logloss:0.66161	validation-auc:0.96173	validation-aucpr:0.96283
[1]	validation-logloss:0.63570	validation-auc:0.96160	validation-aucpr:0.96336
[2]	validation-logloss:0.61121	validation-auc:0.96395	validation-aucpr:0.96851
[3]	validation-logloss:0.58782	validation-auc:0.96463	validation-aucpr:0.96896
[4]	validation-logloss:0.56466	validation-auc:0.96560	validation-aucpr:0.96974
[5]	validation-logloss:0.54307	validation-auc:0.96626	validation-aucpr:0.97044
[6]	validation-logloss:0.52326	validation-auc:0.96670	validation-aucpr:0.97072
[7]	validation-logloss:0.50682	validation-auc:0.96592	validation-aucpr:0.96995
[8]	validation-logloss:0.49165	validation-auc:0.96597	validation-aucpr:0.97014
[9]	validation-logloss:0.47517	validation-auc:0.96650	validation-aucpr:0.97123
[10]	validation-logloss:0.46250	validation-auc:0.96624	validation-aucpr:0.97094
[11]	validation-logloss:0.45012	validation-auc:0.96612	validation-aucpr:0.97084
[12]	validation-logloss:0.43659	validation-auc:0.96648	validation-aucpr:0.97103
[13]	validation-logloss:0.42372	validation-auc:0.96679	validation-aucpr:0.97130
[14]	validation-logloss:0.41322	validation-auc:0.96685	validation-aucpr:0.97129
[15]	validation-logloss:0.40190	validation-auc:0.96714	validation-aucpr:0.97154
[16]	validation-logloss:0.39123	validation-auc:0.96727	validation-aucpr:0.97167
[17]	validation-logloss:0.38092	validation-auc:0.96778	validation-aucpr:0.97206
[18]	validation-logloss:0.37132	validation-auc:0.96803	validation-aucpr:0.97229
[19]	validation-logloss:0.36227	validation-auc:0.96819	validation-aucpr:0.97242
[20]	validation-logloss:0.35382	validation-auc:0.96848	validation-aucpr:0.97266
[21]	validation-logloss:0.34570	validation-auc:0.96872	validation-aucpr:0.97283
[22]	validation-logloss:0.33924	validation-auc:0.96875	validation-aucpr:0.97283
[23]	validation-logloss:0.33197	validation-auc:0.96898	validation-aucpr:0.97300
[24]	validation-logloss:0.32501	validation-auc:0.96919	validation-aucpr:0.97316
[25]	validation-logloss:0.31836	validation-auc:0.96936	validation-aucpr:0.97328
[26]	validation-logloss:0.31239	validation-auc:0.96947	validation-aucpr:0.97336
[27]	validation-logloss:0.30660	validation-auc:0.96958	validation-aucpr:0.97344
[28]	validation-logloss:0.30114	validation-auc:0.96962	validation-aucpr:0.97348
[29]	validation-logloss:0.29596	validation-auc:0.96968	validation-aucpr:0.97353
[30]	validation-logloss:0.29106	validation-auc:0.96978	validation-aucpr:0.97358
[31]	validation-logloss:0.28623	validation-auc:0.96996	validation-aucpr:0.97372
[32]	validation-logloss:0.28176	validation-auc:0.97005	validation-aucpr:0.97379
[33]	validation-logloss:0.27762	validation-auc:0.97009	validation-aucpr:0.97382
[34]	validation-logloss:0.27364	validation-auc:0.97009	validation-aucpr:0.97385
[35]	validation-logloss:0.26978	validation-auc:0.97014	validation-aucpr:0.97387
[36]	validation-logloss:0.26623	validation-auc:0.97027	validation-aucpr:0.97395
[37]	validation-logloss:0.26279	validation-auc:0.97047	validation-aucpr:0.97443
[38]	validation-logloss:0.25942	validation-auc:0.97066	validation-aucpr:0.97455
[39]	validation-logloss:0.25635	validation-auc:0.97080	validation-aucpr:0.97463
[40]	validation-logloss:0.25332	validation-auc:0.97090	validation-aucpr:0.97470
[41]	validation-logloss:0.25038	validation-auc:0.97100	validation-aucpr:0.97479
[42]	validation-logloss:0.24800	validation-auc:0.97108	validation-aucpr:0.97485
{'best_iteration': '42', 'best_score': '0.974853958215763'}
Trial 31, Fold 5: Log loss = 0.24799681727472822, Average precision = 0.9748078353358505, ROC-AUC = 0.9710814335878715, Elapsed Time = 1.209885000000213 seconds
Optimization Progress:  32%|###2      | 32/100 [50:30<1:01:27, 54.22s/it]
Trial 32, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 32, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[0]	validation-logloss:0.67263	validation-auc:0.94340	validation-aucpr:0.95202
[1]	validation-logloss:0.65321	validation-auc:0.95034	validation-aucpr:0.95581
[2]	validation-logloss:0.63598	validation-auc:0.95080	validation-aucpr:0.95973
[3]	validation-logloss:0.61883	validation-auc:0.95193	validation-aucpr:0.96076
[4]	validation-logloss:0.60229	validation-auc:0.95492	validation-aucpr:0.96341
[5]	validation-logloss:0.58667	validation-auc:0.95776	validation-aucpr:0.96592
[6]	validation-logloss:0.57125	validation-auc:0.95920	validation-aucpr:0.96671
[7]	validation-logloss:0.55687	validation-auc:0.96026	validation-aucpr:0.96781
[8]	validation-logloss:0.54337	validation-auc:0.96070	validation-aucpr:0.96799
[9]	validation-logloss:0.53106	validation-auc:0.96176	validation-aucpr:0.96795
[10]	validation-logloss:0.51879	validation-auc:0.96239	validation-aucpr:0.96917
[11]	validation-logloss:0.50777	validation-auc:0.96299	validation-aucpr:0.96996
[12]	validation-logloss:0.49780	validation-auc:0.96312	validation-aucpr:0.97013
[13]	validation-logloss:0.48833	validation-auc:0.96363	validation-aucpr:0.97045
[14]	validation-logloss:0.47754	validation-auc:0.96423	validation-aucpr:0.97088
[15]	validation-logloss:0.46756	validation-auc:0.96454	validation-aucpr:0.97113
[16]	validation-logloss:0.45813	validation-auc:0.96443	validation-aucpr:0.97110
[17]	validation-logloss:0.44947	validation-auc:0.96494	validation-aucpr:0.97145
[18]	validation-logloss:0.44132	validation-auc:0.96553	validation-aucpr:0.97181
[19]	validation-logloss:0.43318	validation-auc:0.96540	validation-aucpr:0.97173
[20]	validation-logloss:0.42591	validation-auc:0.96520	validation-aucpr:0.97150
[21]	validation-logloss:0.41936	validation-auc:0.96491	validation-aucpr:0.97136
[22]	validation-logloss:0.41177	validation-auc:0.96509	validation-aucpr:0.97151
[23]	validation-logloss:0.40542	validation-auc:0.96507	validation-aucpr:0.97148
[24]	validation-logloss:0.39810	validation-auc:0.96544	validation-aucpr:0.97167
[25]	validation-logloss:0.39183	validation-auc:0.96581	validation-aucpr:0.97190
[26]	validation-logloss:0.38546	validation-auc:0.96588	validation-aucpr:0.97188
[27]	validation-logloss:0.37882	validation-auc:0.96594	validation-aucpr:0.97199
[28]	validation-logloss:0.37264	validation-auc:0.96584	validation-aucpr:0.97195
[29]	validation-logloss:0.36749	validation-auc:0.96590	validation-aucpr:0.97194
[30]	validation-logloss:0.36203	validation-auc:0.96596	validation-aucpr:0.97202
[31]	validation-logloss:0.35632	validation-auc:0.96619	validation-aucpr:0.97229
[32]	validation-logloss:0.35195	validation-auc:0.96610	validation-aucpr:0.97221
[33]	validation-logloss:0.34733	validation-auc:0.96600	validation-aucpr:0.97213
[34]	validation-logloss:0.34201	validation-auc:0.96642	validation-aucpr:0.97235
[35]	validation-logloss:0.33734	validation-auc:0.96656	validation-aucpr:0.97242
[36]	validation-logloss:0.33317	validation-auc:0.96646	validation-aucpr:0.97237
[37]	validation-logloss:0.32841	validation-auc:0.96671	validation-aucpr:0.97261
[38]	validation-logloss:0.32478	validation-auc:0.96656	validation-aucpr:0.97252
[39]	validation-logloss:0.32075	validation-auc:0.96667	validation-aucpr:0.97261
[40]	validation-logloss:0.31663	validation-auc:0.96684	validation-aucpr:0.97271
[41]	validation-logloss:0.31292	validation-auc:0.96703	validation-aucpr:0.97279
[42]	validation-logloss:0.30980	validation-auc:0.96715	validation-aucpr:0.97284
[43]	validation-logloss:0.30637	validation-auc:0.96700	validation-aucpr:0.97273
[44]	validation-logloss:0.30303	validation-auc:0.96718	validation-aucpr:0.97283
[45]	validation-logloss:0.30017	validation-auc:0.96713	validation-aucpr:0.97278
[46]	validation-logloss:0.29722	validation-auc:0.96719	validation-aucpr:0.97284
[47]	validation-logloss:0.29425	validation-auc:0.96728	validation-aucpr:0.97288
[48]	validation-logloss:0.29113	validation-auc:0.96732	validation-aucpr:0.97294
[49]	validation-logloss:0.28814	validation-auc:0.96732	validation-aucpr:0.97293
[50]	validation-logloss:0.28582	validation-auc:0.96730	validation-aucpr:0.97290
[51]	validation-logloss:0.28280	validation-auc:0.96748	validation-aucpr:0.97303
[52]	validation-logloss:0.28036	validation-auc:0.96770	validation-aucpr:0.97316
[53]	validation-logloss:0.27775	validation-auc:0.96772	validation-aucpr:0.97320
[54]	validation-logloss:0.27522	validation-auc:0.96774	validation-aucpr:0.97323
[55]	validation-logloss:0.27307	validation-auc:0.96779	validation-aucpr:0.97327
[56]	validation-logloss:0.27052	validation-auc:0.96775	validation-aucpr:0.97324
[57]	validation-logloss:0.26850	validation-auc:0.96760	validation-aucpr:0.97314
[58]	validation-logloss:0.26641	validation-auc:0.96754	validation-aucpr:0.97311
[59]	validation-logloss:0.26416	validation-auc:0.96761	validation-aucpr:0.97317
[60]	validation-logloss:0.26259	validation-auc:0.96757	validation-aucpr:0.97315
[61]	validation-logloss:0.26027	validation-auc:0.96777	validation-aucpr:0.97329
[62]	validation-logloss:0.25813	validation-auc:0.96778	validation-aucpr:0.97330
[63]	validation-logloss:0.25674	validation-auc:0.96759	validation-aucpr:0.97312
[64]	validation-logloss:0.25505	validation-auc:0.96765	validation-aucpr:0.97318
[65]	validation-logloss:0.25362	validation-auc:0.96775	validation-aucpr:0.97322
[66]	validation-logloss:0.25198	validation-auc:0.96783	validation-aucpr:0.97327
[67]	validation-logloss:0.25036	validation-auc:0.96784	validation-aucpr:0.97327
[68]	validation-logloss:0.24911	validation-auc:0.96772	validation-aucpr:0.97319
[69]	validation-logloss:0.24788	validation-auc:0.96770	validation-aucpr:0.97314
[70]	validation-logloss:0.24638	validation-auc:0.96768	validation-aucpr:0.97311
[71]	validation-logloss:0.24509	validation-auc:0.96758	validation-aucpr:0.97310
[72]	validation-logloss:0.24377	validation-auc:0.96755	validation-aucpr:0.97309
[73]	validation-logloss:0.24245	validation-auc:0.96759	validation-aucpr:0.97313
[74]	validation-logloss:0.24113	validation-auc:0.96753	validation-aucpr:0.97308
[75]	validation-logloss:0.24001	validation-auc:0.96757	validation-aucpr:0.97309
[76]	validation-logloss:0.23915	validation-auc:0.96739	validation-aucpr:0.97295
[77]	validation-logloss:0.23776	validation-auc:0.96748	validation-aucpr:0.97303
[78]	validation-logloss:0.23676	validation-auc:0.96758	validation-aucpr:0.97311
[79]	validation-logloss:0.23585	validation-auc:0.96760	validation-aucpr:0.97311
[80]	validation-logloss:0.23479	validation-auc:0.96769	validation-aucpr:0.97319
[81]	validation-logloss:0.23382	validation-auc:0.96765	validation-aucpr:0.97316
[82]	validation-logloss:0.23265	validation-auc:0.96776	validation-aucpr:0.97326
[83]	validation-logloss:0.23180	validation-auc:0.96756	validation-aucpr:0.97312
[84]	validation-logloss:0.23077	validation-auc:0.96755	validation-aucpr:0.97309
[85]	validation-logloss:0.23012	validation-auc:0.96753	validation-aucpr:0.97305
[86]	validation-logloss:0.22915	validation-auc:0.96757	validation-aucpr:0.97302
[87]	validation-logloss:0.22848	validation-auc:0.96755	validation-aucpr:0.97301
[88]	validation-logloss:0.22776	validation-auc:0.96749	validation-aucpr:0.97296
[89]	validation-logloss:0.22667	validation-auc:0.96768	validation-aucpr:0.97313
[90]	validation-logloss:0.22566	validation-auc:0.96784	validation-aucpr:0.97322
[91]	validation-logloss:0.22487	validation-auc:0.96790	validation-aucpr:0.97326
[92]	validation-logloss:0.22411	validation-auc:0.96792	validation-aucpr:0.97329
[93]	validation-logloss:0.22307	validation-auc:0.96807	validation-aucpr:0.97337
[94]	validation-logloss:0.22263	validation-auc:0.96794	validation-aucpr:0.97341
[95]	validation-logloss:0.22192	validation-auc:0.96796	validation-aucpr:0.97341
[96]	validation-logloss:0.22136	validation-auc:0.96796	validation-aucpr:0.97340
[97]	validation-logloss:0.22077	validation-auc:0.96801	validation-aucpr:0.97346
{'best_iteration': '97', 'best_score': '0.9734587937630889'}
Trial 32, Fold 1: Log loss = 0.22077211741617622, Average precision = 0.973463242617084, ROC-AUC = 0.9680081181323278, Elapsed Time = 1103.3959042000006 seconds
Trial 32, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 32, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[0]	validation-logloss:0.67236	validation-auc:0.94406	validation-aucpr:0.94290
[1]	validation-logloss:0.65459	validation-auc:0.94981	validation-aucpr:0.94874
[2]	validation-logloss:0.63797	validation-auc:0.95234	validation-aucpr:0.95579
[3]	validation-logloss:0.62092	validation-auc:0.95669	validation-aucpr:0.96050
[4]	validation-logloss:0.60429	validation-auc:0.95935	validation-aucpr:0.96403
[5]	validation-logloss:0.58879	validation-auc:0.96113	validation-aucpr:0.96654
[6]	validation-logloss:0.57537	validation-auc:0.96182	validation-aucpr:0.96684
[7]	validation-logloss:0.56189	validation-auc:0.96251	validation-aucpr:0.96726
[8]	validation-logloss:0.54936	validation-auc:0.96338	validation-aucpr:0.96795
[9]	validation-logloss:0.53656	validation-auc:0.96385	validation-aucpr:0.96843
[10]	validation-logloss:0.52506	validation-auc:0.96410	validation-aucpr:0.96852
[11]	validation-logloss:0.51309	validation-auc:0.96508	validation-aucpr:0.96943
[12]	validation-logloss:0.50140	validation-auc:0.96565	validation-aucpr:0.96975
[13]	validation-logloss:0.49165	validation-auc:0.96541	validation-aucpr:0.96956
[14]	validation-logloss:0.48070	validation-auc:0.96607	validation-aucpr:0.97016
[15]	validation-logloss:0.47181	validation-auc:0.96606	validation-aucpr:0.97006
[16]	validation-logloss:0.46168	validation-auc:0.96680	validation-aucpr:0.97063
[17]	validation-logloss:0.45189	validation-auc:0.96697	validation-aucpr:0.97077
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{'best_iteration': '92', 'best_score': '0.9748518836757191'}
Trial 32, Fold 2: Log loss = 0.21159174969704342, Average precision = 0.9746231769873027, ROC-AUC = 0.9707887030450622, Elapsed Time = 1091.1235703999992 seconds
Trial 32, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 32, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
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{'best_iteration': '94', 'best_score': '0.9754120472963778'}
Trial 32, Fold 3: Log loss = 0.21247699408509904, Average precision = 0.9752734811271323, ROC-AUC = 0.9714012433666246, Elapsed Time = 1086.049289999999 seconds
Trial 32, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 32, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
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{'best_iteration': '97', 'best_score': '0.9738957097915883'}
Trial 32, Fold 4: Log loss = 0.21687468327294243, Average precision = 0.9738999480902333, ROC-AUC = 0.9687965705034205, Elapsed Time = 1075.9968526999983 seconds
Trial 32, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 32, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
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[95]	validation-logloss:0.22074	validation-auc:0.96865	validation-aucpr:0.97287
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[97]	validation-logloss:0.21967	validation-auc:0.96859	validation-aucpr:0.97277
{'best_iteration': '89', 'best_score': '0.972873718449354'}
Trial 32, Fold 5: Log loss = 0.21967443811519236, Average precision = 0.9727732177067483, ROC-AUC = 0.9685915686602381, Elapsed Time = 1074.4078993000003 seconds
Optimization Progress:  33%|###3      | 33/100 [2:21:10<31:04:44, 1669.92s/it]
Trial 33, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 33, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[0]	validation-logloss:0.68334	validation-auc:0.96185	validation-aucpr:0.96505
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[94]	validation-logloss:0.29630	validation-auc:0.97121	validation-aucpr:0.97559
{'best_iteration': '94', 'best_score': '0.9755942644594567'}
Trial 33, Fold 1: Log loss = 0.2962993850750307, Average precision = 0.9755984835078575, ROC-AUC = 0.9712136542110571, Elapsed Time = 2.482240000001184 seconds
Trial 33, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 33, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
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[78]	validation-logloss:0.32348	validation-auc:0.97181	validation-aucpr:0.97448
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[80]	validation-logloss:0.31950	validation-auc:0.97183	validation-aucpr:0.97449
[81]	validation-logloss:0.31742	validation-auc:0.97189	validation-aucpr:0.97453
[82]	validation-logloss:0.31564	validation-auc:0.97189	validation-aucpr:0.97453
[83]	validation-logloss:0.31362	validation-auc:0.97191	validation-aucpr:0.97455
[84]	validation-logloss:0.31167	validation-auc:0.97193	validation-aucpr:0.97456
[85]	validation-logloss:0.30973	validation-auc:0.97193	validation-aucpr:0.97456
[86]	validation-logloss:0.30783	validation-auc:0.97194	validation-aucpr:0.97458
[87]	validation-logloss:0.30591	validation-auc:0.97200	validation-aucpr:0.97462
[88]	validation-logloss:0.30411	validation-auc:0.97200	validation-aucpr:0.97463
[89]	validation-logloss:0.30228	validation-auc:0.97205	validation-aucpr:0.97467
[90]	validation-logloss:0.30049	validation-auc:0.97212	validation-aucpr:0.97472
[91]	validation-logloss:0.29872	validation-auc:0.97216	validation-aucpr:0.97474
[92]	validation-logloss:0.29700	validation-auc:0.97214	validation-aucpr:0.97473
[93]	validation-logloss:0.29530	validation-auc:0.97216	validation-aucpr:0.97474
[94]	validation-logloss:0.29366	validation-auc:0.97215	validation-aucpr:0.97473
{'best_iteration': '93', 'best_score': '0.9747449932848273'}
Trial 33, Fold 2: Log loss = 0.2936584776398004, Average precision = 0.9746742058070549, ROC-AUC = 0.9721450247515813, Elapsed Time = 2.6043852000002516 seconds
Trial 33, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 33, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[0]	validation-logloss:0.68338	validation-auc:0.96407	validation-aucpr:0.96861
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[3]	validation-logloss:0.65551	validation-auc:0.96954	validation-aucpr:0.97351
[4]	validation-logloss:0.64669	validation-auc:0.97044	validation-aucpr:0.97410
[5]	validation-logloss:0.63803	validation-auc:0.97051	validation-aucpr:0.97419
[6]	validation-logloss:0.62958	validation-auc:0.97094	validation-aucpr:0.97443
[7]	validation-logloss:0.62138	validation-auc:0.97061	validation-aucpr:0.97417
[8]	validation-logloss:0.61445	validation-auc:0.97086	validation-aucpr:0.97523
[9]	validation-logloss:0.60675	validation-auc:0.97069	validation-aucpr:0.97510
[10]	validation-logloss:0.59922	validation-auc:0.97059	validation-aucpr:0.97500
[11]	validation-logloss:0.59168	validation-auc:0.97083	validation-aucpr:0.97517
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[78]	validation-logloss:0.32257	validation-auc:0.97202	validation-aucpr:0.97600
[79]	validation-logloss:0.32047	validation-auc:0.97206	validation-aucpr:0.97602
[80]	validation-logloss:0.31844	validation-auc:0.97207	validation-aucpr:0.97606
[81]	validation-logloss:0.31642	validation-auc:0.97207	validation-aucpr:0.97605
[82]	validation-logloss:0.31437	validation-auc:0.97212	validation-aucpr:0.97609
[83]	validation-logloss:0.31240	validation-auc:0.97212	validation-aucpr:0.97608
[84]	validation-logloss:0.31048	validation-auc:0.97213	validation-aucpr:0.97607
[85]	validation-logloss:0.30870	validation-auc:0.97213	validation-aucpr:0.97607
[86]	validation-logloss:0.30680	validation-auc:0.97210	validation-aucpr:0.97604
[87]	validation-logloss:0.30495	validation-auc:0.97211	validation-aucpr:0.97605
[88]	validation-logloss:0.30311	validation-auc:0.97213	validation-aucpr:0.97607
[89]	validation-logloss:0.30130	validation-auc:0.97214	validation-aucpr:0.97608
[90]	validation-logloss:0.29953	validation-auc:0.97214	validation-aucpr:0.97608
[91]	validation-logloss:0.29802	validation-auc:0.97213	validation-aucpr:0.97607
[92]	validation-logloss:0.29652	validation-auc:0.97217	validation-aucpr:0.97611
[93]	validation-logloss:0.29481	validation-auc:0.97217	validation-aucpr:0.97611
[94]	validation-logloss:0.29313	validation-auc:0.97219	validation-aucpr:0.97613
{'best_iteration': '94', 'best_score': '0.9761276928325958'}
Trial 33, Fold 3: Log loss = 0.29313219894809645, Average precision = 0.9761308604244551, ROC-AUC = 0.9721853450559081, Elapsed Time = 2.70938169999863 seconds
Trial 33, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 33, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[0]	validation-logloss:0.68351	validation-auc:0.96007	validation-aucpr:0.96605
[1]	validation-logloss:0.67398	validation-auc:0.96567	validation-aucpr:0.97100
[2]	validation-logloss:0.66490	validation-auc:0.96713	validation-aucpr:0.97232
[3]	validation-logloss:0.65596	validation-auc:0.96765	validation-aucpr:0.97245
[4]	validation-logloss:0.64728	validation-auc:0.96747	validation-aucpr:0.97243
[5]	validation-logloss:0.63882	validation-auc:0.96748	validation-aucpr:0.97239
[6]	validation-logloss:0.63045	validation-auc:0.96818	validation-aucpr:0.97301
[7]	validation-logloss:0.62224	validation-auc:0.96833	validation-aucpr:0.97314
[8]	validation-logloss:0.61430	validation-auc:0.96872	validation-aucpr:0.97340
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[68]	validation-logloss:0.34645	validation-auc:0.97156	validation-aucpr:0.97596
[69]	validation-logloss:0.34401	validation-auc:0.97159	validation-aucpr:0.97598
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[71]	validation-logloss:0.33909	validation-auc:0.97166	validation-aucpr:0.97603
[72]	validation-logloss:0.33703	validation-auc:0.97165	validation-aucpr:0.97603
[73]	validation-logloss:0.33470	validation-auc:0.97168	validation-aucpr:0.97605
[74]	validation-logloss:0.33239	validation-auc:0.97174	validation-aucpr:0.97609
[75]	validation-logloss:0.33015	validation-auc:0.97177	validation-aucpr:0.97612
[76]	validation-logloss:0.32794	validation-auc:0.97185	validation-aucpr:0.97617
[77]	validation-logloss:0.32574	validation-auc:0.97190	validation-aucpr:0.97621
[78]	validation-logloss:0.32361	validation-auc:0.97192	validation-aucpr:0.97623
[79]	validation-logloss:0.32151	validation-auc:0.97195	validation-aucpr:0.97625
[80]	validation-logloss:0.31968	validation-auc:0.97195	validation-aucpr:0.97625
[81]	validation-logloss:0.31758	validation-auc:0.97203	validation-aucpr:0.97631
[82]	validation-logloss:0.31559	validation-auc:0.97205	validation-aucpr:0.97631
[83]	validation-logloss:0.31363	validation-auc:0.97204	validation-aucpr:0.97632
[84]	validation-logloss:0.31168	validation-auc:0.97209	validation-aucpr:0.97635
[85]	validation-logloss:0.30977	validation-auc:0.97213	validation-aucpr:0.97638
[86]	validation-logloss:0.30793	validation-auc:0.97211	validation-aucpr:0.97636
[87]	validation-logloss:0.30606	validation-auc:0.97212	validation-aucpr:0.97637
[88]	validation-logloss:0.30447	validation-auc:0.97209	validation-aucpr:0.97635
[89]	validation-logloss:0.30288	validation-auc:0.97207	validation-aucpr:0.97634
[90]	validation-logloss:0.30108	validation-auc:0.97212	validation-aucpr:0.97638
[91]	validation-logloss:0.29930	validation-auc:0.97220	validation-aucpr:0.97643
[92]	validation-logloss:0.29757	validation-auc:0.97223	validation-aucpr:0.97645
[93]	validation-logloss:0.29584	validation-auc:0.97226	validation-aucpr:0.97648
[94]	validation-logloss:0.29421	validation-auc:0.97225	validation-aucpr:0.97646
{'best_iteration': '93', 'best_score': '0.9764766823302508'}
Trial 33, Fold 4: Log loss = 0.2942099781841265, Average precision = 0.97645302945903, ROC-AUC = 0.9722454702175296, Elapsed Time = 2.9326142000027176 seconds
Trial 33, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 33, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[0]	validation-logloss:0.68343	validation-auc:0.95976	validation-aucpr:0.96214
[1]	validation-logloss:0.67403	validation-auc:0.96579	validation-aucpr:0.96994
[2]	validation-logloss:0.66571	validation-auc:0.96529	validation-aucpr:0.96930
[3]	validation-logloss:0.65698	validation-auc:0.96665	validation-aucpr:0.97030
[4]	validation-logloss:0.64817	validation-auc:0.96694	validation-aucpr:0.97075
[5]	validation-logloss:0.63960	validation-auc:0.96740	validation-aucpr:0.97115
[6]	validation-logloss:0.63132	validation-auc:0.96779	validation-aucpr:0.97176
[7]	validation-logloss:0.62312	validation-auc:0.96820	validation-aucpr:0.97206
[8]	validation-logloss:0.61508	validation-auc:0.96860	validation-aucpr:0.97224
[9]	validation-logloss:0.60731	validation-auc:0.96879	validation-aucpr:0.97238
[10]	validation-logloss:0.59967	validation-auc:0.96916	validation-aucpr:0.97264
[11]	validation-logloss:0.59227	validation-auc:0.96939	validation-aucpr:0.97279
[12]	validation-logloss:0.58516	validation-auc:0.96957	validation-aucpr:0.97289
[13]	validation-logloss:0.57806	validation-auc:0.96978	validation-aucpr:0.97305
[14]	validation-logloss:0.57131	validation-auc:0.96951	validation-aucpr:0.97299
[15]	validation-logloss:0.56515	validation-auc:0.96946	validation-aucpr:0.97304
[16]	validation-logloss:0.55846	validation-auc:0.96954	validation-aucpr:0.97311
[17]	validation-logloss:0.55205	validation-auc:0.96956	validation-aucpr:0.97314
[18]	validation-logloss:0.54573	validation-auc:0.96958	validation-aucpr:0.97317
[19]	validation-logloss:0.53949	validation-auc:0.96961	validation-aucpr:0.97319
[20]	validation-logloss:0.53346	validation-auc:0.96964	validation-aucpr:0.97315
[21]	validation-logloss:0.52755	validation-auc:0.96976	validation-aucpr:0.97324
[22]	validation-logloss:0.52168	validation-auc:0.96990	validation-aucpr:0.97334
[23]	validation-logloss:0.51598	validation-auc:0.96985	validation-aucpr:0.97331
[24]	validation-logloss:0.51038	validation-auc:0.96996	validation-aucpr:0.97337
[25]	validation-logloss:0.50547	validation-auc:0.96981	validation-aucpr:0.97334
[26]	validation-logloss:0.50011	validation-auc:0.96976	validation-aucpr:0.97330
[27]	validation-logloss:0.49500	validation-auc:0.96982	validation-aucpr:0.97335
[28]	validation-logloss:0.49040	validation-auc:0.96966	validation-aucpr:0.97327
[29]	validation-logloss:0.48545	validation-auc:0.96964	validation-aucpr:0.97347
[30]	validation-logloss:0.48056	validation-auc:0.96970	validation-aucpr:0.97350
[31]	validation-logloss:0.47573	validation-auc:0.96967	validation-aucpr:0.97350
[32]	validation-logloss:0.47092	validation-auc:0.96975	validation-aucpr:0.97356
[33]	validation-logloss:0.46622	validation-auc:0.96984	validation-aucpr:0.97363
[34]	validation-logloss:0.46165	validation-auc:0.96980	validation-aucpr:0.97360
[35]	validation-logloss:0.45722	validation-auc:0.96986	validation-aucpr:0.97364
[36]	validation-logloss:0.45288	validation-auc:0.96994	validation-aucpr:0.97404
[37]	validation-logloss:0.44858	validation-auc:0.96994	validation-aucpr:0.97405
[38]	validation-logloss:0.44438	validation-auc:0.97004	validation-aucpr:0.97411
[39]	validation-logloss:0.44020	validation-auc:0.97015	validation-aucpr:0.97420
[40]	validation-logloss:0.43615	validation-auc:0.97019	validation-aucpr:0.97423
[41]	validation-logloss:0.43217	validation-auc:0.97031	validation-aucpr:0.97432
[42]	validation-logloss:0.42826	validation-auc:0.97034	validation-aucpr:0.97433
[43]	validation-logloss:0.42438	validation-auc:0.97039	validation-aucpr:0.97437
[44]	validation-logloss:0.42058	validation-auc:0.97039	validation-aucpr:0.97438
[45]	validation-logloss:0.41696	validation-auc:0.97039	validation-aucpr:0.97437
[46]	validation-logloss:0.41333	validation-auc:0.97049	validation-aucpr:0.97450
[47]	validation-logloss:0.40980	validation-auc:0.97052	validation-aucpr:0.97450
[48]	validation-logloss:0.40665	validation-auc:0.97047	validation-aucpr:0.97449
[49]	validation-logloss:0.40317	validation-auc:0.97058	validation-aucpr:0.97456
[50]	validation-logloss:0.39981	validation-auc:0.97057	validation-aucpr:0.97454
[51]	validation-logloss:0.39653	validation-auc:0.97058	validation-aucpr:0.97455
[52]	validation-logloss:0.39333	validation-auc:0.97063	validation-aucpr:0.97456
[53]	validation-logloss:0.39049	validation-auc:0.97055	validation-aucpr:0.97451
[54]	validation-logloss:0.38737	validation-auc:0.97049	validation-aucpr:0.97447
[55]	validation-logloss:0.38436	validation-auc:0.97046	validation-aucpr:0.97443
[56]	validation-logloss:0.38130	validation-auc:0.97044	validation-aucpr:0.97443
[57]	validation-logloss:0.37828	validation-auc:0.97051	validation-aucpr:0.97449
[58]	validation-logloss:0.37539	validation-auc:0.97052	validation-aucpr:0.97453
[59]	validation-logloss:0.37250	validation-auc:0.97060	validation-aucpr:0.97458
[60]	validation-logloss:0.36969	validation-auc:0.97064	validation-aucpr:0.97459
[61]	validation-logloss:0.36683	validation-auc:0.97072	validation-aucpr:0.97467
[62]	validation-logloss:0.36410	validation-auc:0.97079	validation-aucpr:0.97471
[63]	validation-logloss:0.36170	validation-auc:0.97084	validation-aucpr:0.97476
[64]	validation-logloss:0.35902	validation-auc:0.97089	validation-aucpr:0.97480
[65]	validation-logloss:0.35636	validation-auc:0.97100	validation-aucpr:0.97488
[66]	validation-logloss:0.35377	validation-auc:0.97108	validation-aucpr:0.97493
[67]	validation-logloss:0.35123	validation-auc:0.97112	validation-aucpr:0.97496
[68]	validation-logloss:0.34877	validation-auc:0.97109	validation-aucpr:0.97493
[69]	validation-logloss:0.34639	validation-auc:0.97109	validation-aucpr:0.97492
[70]	validation-logloss:0.34398	validation-auc:0.97115	validation-aucpr:0.97496
[71]	validation-logloss:0.34166	validation-auc:0.97116	validation-aucpr:0.97496
[72]	validation-logloss:0.33933	validation-auc:0.97122	validation-aucpr:0.97501
[73]	validation-logloss:0.33706	validation-auc:0.97125	validation-aucpr:0.97501
[74]	validation-logloss:0.33487	validation-auc:0.97129	validation-aucpr:0.97505
[75]	validation-logloss:0.33272	validation-auc:0.97133	validation-aucpr:0.97506
[76]	validation-logloss:0.33052	validation-auc:0.97137	validation-aucpr:0.97508
[77]	validation-logloss:0.32841	validation-auc:0.97137	validation-aucpr:0.97509
[78]	validation-logloss:0.32638	validation-auc:0.97133	validation-aucpr:0.97506
[79]	validation-logloss:0.32430	validation-auc:0.97135	validation-aucpr:0.97507
[80]	validation-logloss:0.32242	validation-auc:0.97141	validation-aucpr:0.97513
[81]	validation-logloss:0.32044	validation-auc:0.97141	validation-aucpr:0.97514
[82]	validation-logloss:0.31847	validation-auc:0.97143	validation-aucpr:0.97516
[83]	validation-logloss:0.31667	validation-auc:0.97142	validation-aucpr:0.97515
[84]	validation-logloss:0.31470	validation-auc:0.97148	validation-aucpr:0.97518
[85]	validation-logloss:0.31280	validation-auc:0.97155	validation-aucpr:0.97524
[86]	validation-logloss:0.31092	validation-auc:0.97160	validation-aucpr:0.97527
[87]	validation-logloss:0.30912	validation-auc:0.97162	validation-aucpr:0.97528
[88]	validation-logloss:0.30735	validation-auc:0.97163	validation-aucpr:0.97528
[89]	validation-logloss:0.30556	validation-auc:0.97166	validation-aucpr:0.97530
[90]	validation-logloss:0.30381	validation-auc:0.97169	validation-aucpr:0.97531
[91]	validation-logloss:0.30209	validation-auc:0.97173	validation-aucpr:0.97536
[92]	validation-logloss:0.30039	validation-auc:0.97175	validation-aucpr:0.97537
[93]	validation-logloss:0.29871	validation-auc:0.97177	validation-aucpr:0.97538
[94]	validation-logloss:0.29705	validation-auc:0.97176	validation-aucpr:0.97537
{'best_iteration': '93', 'best_score': '0.9753777106220222'}
Trial 33, Fold 5: Log loss = 0.29705146661846155, Average precision = 0.9753714880967246, ROC-AUC = 0.9717643386656263, Elapsed Time = 2.967479999999341 seconds
Optimization Progress:  34%|###4      | 34/100 [2:21:33<21:33:26, 1175.85s/it]
Trial 34, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 34, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[0]	validation-logloss:0.68198	validation-auc:0.95130	validation-aucpr:0.95540
[1]	validation-logloss:0.67112	validation-auc:0.95892	validation-aucpr:0.96265
[2]	validation-logloss:0.66069	validation-auc:0.96145	validation-aucpr:0.96424
[3]	validation-logloss:0.65041	validation-auc:0.96213	validation-aucpr:0.96619
[4]	validation-logloss:0.64063	validation-auc:0.96271	validation-aucpr:0.96646
[5]	validation-logloss:0.63112	validation-auc:0.96362	validation-aucpr:0.96797
{'best_iteration': '5', 'best_score': '0.967973486067347'}
Trial 34, Fold 1: Log loss = 0.6311222487753312, Average precision = 0.9682110235622168, ROC-AUC = 0.9636233677702738, Elapsed Time = 0.641079200002423 seconds
Trial 34, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 34, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[0]	validation-logloss:0.68210	validation-auc:0.95181	validation-aucpr:0.95322
[1]	validation-logloss:0.67115	validation-auc:0.95940	validation-aucpr:0.96118
[2]	validation-logloss:0.66057	validation-auc:0.96273	validation-aucpr:0.96656
[3]	validation-logloss:0.65033	validation-auc:0.96360	validation-aucpr:0.96698
[4]	validation-logloss:0.64081	validation-auc:0.96418	validation-aucpr:0.96799
[5]	validation-logloss:0.63121	validation-auc:0.96547	validation-aucpr:0.96980
{'best_iteration': '5', 'best_score': '0.9698008921278516'}
Trial 34, Fold 2: Log loss = 0.6312073776093977, Average precision = 0.969641383985065, ROC-AUC = 0.9654703984077407, Elapsed Time = 0.6488423999981023 seconds
Trial 34, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 34, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[0]	validation-logloss:0.68197	validation-auc:0.95564	validation-aucpr:0.95792
[1]	validation-logloss:0.67107	validation-auc:0.96225	validation-aucpr:0.96621
[2]	validation-logloss:0.66058	validation-auc:0.96390	validation-aucpr:0.96893
[3]	validation-logloss:0.65033	validation-auc:0.96350	validation-aucpr:0.96671
[4]	validation-logloss:0.64048	validation-auc:0.96404	validation-aucpr:0.96703
[5]	validation-logloss:0.63090	validation-auc:0.96491	validation-aucpr:0.96755
{'best_iteration': '2', 'best_score': '0.968934987538646'}
Trial 34, Fold 3: Log loss = 0.6309028230443496, Average precision = 0.9679194572067682, ROC-AUC = 0.9649145155348595, Elapsed Time = 0.7778741000001901 seconds
Trial 34, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 34, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[0]	validation-logloss:0.68201	validation-auc:0.95272	validation-aucpr:0.95267
[1]	validation-logloss:0.67121	validation-auc:0.95835	validation-aucpr:0.95833
[2]	validation-logloss:0.66074	validation-auc:0.96139	validation-aucpr:0.96191
[3]	validation-logloss:0.65168	validation-auc:0.96197	validation-aucpr:0.96553
[4]	validation-logloss:0.64167	validation-auc:0.96350	validation-aucpr:0.96626
[5]	validation-logloss:0.63191	validation-auc:0.96580	validation-aucpr:0.97126
{'best_iteration': '5', 'best_score': '0.9712628373370135'}
Trial 34, Fold 4: Log loss = 0.6319119314165961, Average precision = 0.971030496877912, ROC-AUC = 0.9657958653142293, Elapsed Time = 0.7649254999996629 seconds
Trial 34, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 34, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[0]	validation-logloss:0.68213	validation-auc:0.94976	validation-aucpr:0.95254
[1]	validation-logloss:0.67129	validation-auc:0.95654	validation-aucpr:0.95866
[2]	validation-logloss:0.66076	validation-auc:0.95844	validation-aucpr:0.95956
[3]	validation-logloss:0.65057	validation-auc:0.95909	validation-aucpr:0.96011
[4]	validation-logloss:0.64076	validation-auc:0.96032	validation-aucpr:0.96077
[5]	validation-logloss:0.63112	validation-auc:0.96092	validation-aucpr:0.96102
{'best_iteration': '5', 'best_score': '0.9610187676182963'}
Trial 34, Fold 5: Log loss = 0.6311226248417754, Average precision = 0.9608989400500028, ROC-AUC = 0.9609236200137488, Elapsed Time = 0.7530755000007048 seconds
Optimization Progress:  35%|###5      | 35/100 [2:21:45<14:55:20, 826.47s/it] 
Trial 35, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 35, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[0]	validation-logloss:0.66299	validation-auc:0.94914	validation-aucpr:0.93790
[1]	validation-logloss:0.63898	validation-auc:0.95939	validation-aucpr:0.95686
[2]	validation-logloss:0.61353	validation-auc:0.96323	validation-aucpr:0.96870
[3]	validation-logloss:0.59262	validation-auc:0.96437	validation-aucpr:0.96937
[4]	validation-logloss:0.57084	validation-auc:0.96506	validation-aucpr:0.97017
[5]	validation-logloss:0.55311	validation-auc:0.96458	validation-aucpr:0.96955
[6]	validation-logloss:0.53632	validation-auc:0.96413	validation-aucpr:0.96899
[7]	validation-logloss:0.52077	validation-auc:0.96405	validation-aucpr:0.96897
[8]	validation-logloss:0.50527	validation-auc:0.96454	validation-aucpr:0.96942
[9]	validation-logloss:0.49136	validation-auc:0.96459	validation-aucpr:0.96880
[10]	validation-logloss:0.47835	validation-auc:0.96473	validation-aucpr:0.96880
[11]	validation-logloss:0.46590	validation-auc:0.96515	validation-aucpr:0.97025
[12]	validation-logloss:0.45412	validation-auc:0.96518	validation-aucpr:0.97015
[13]	validation-logloss:0.44027	validation-auc:0.96578	validation-aucpr:0.97078
[14]	validation-logloss:0.42795	validation-auc:0.96597	validation-aucpr:0.97101
[15]	validation-logloss:0.41839	validation-auc:0.96600	validation-aucpr:0.97100
[16]	validation-logloss:0.40945	validation-auc:0.96598	validation-aucpr:0.97097
[17]	validation-logloss:0.40100	validation-auc:0.96604	validation-aucpr:0.97100
[18]	validation-logloss:0.39091	validation-auc:0.96619	validation-aucpr:0.97119
[19]	validation-logloss:0.38088	validation-auc:0.96644	validation-aucpr:0.97143
[20]	validation-logloss:0.37389	validation-auc:0.96625	validation-aucpr:0.97126
[21]	validation-logloss:0.36672	validation-auc:0.96642	validation-aucpr:0.97136
[22]	validation-logloss:0.36027	validation-auc:0.96638	validation-aucpr:0.97131
[23]	validation-logloss:0.35225	validation-auc:0.96640	validation-aucpr:0.97010
[24]	validation-logloss:0.34465	validation-auc:0.96655	validation-aucpr:0.96938
[25]	validation-logloss:0.33930	validation-auc:0.96643	validation-aucpr:0.96926
[26]	validation-logloss:0.33384	validation-auc:0.96637	validation-aucpr:0.96920
[27]	validation-logloss:0.32846	validation-auc:0.96654	validation-aucpr:0.97149
[28]	validation-logloss:0.32191	validation-auc:0.96675	validation-aucpr:0.97172
[29]	validation-logloss:0.31730	validation-auc:0.96677	validation-aucpr:0.97172
[30]	validation-logloss:0.31262	validation-auc:0.96684	validation-aucpr:0.97181
[31]	validation-logloss:0.30650	validation-auc:0.96725	validation-aucpr:0.97215
[32]	validation-logloss:0.30122	validation-auc:0.96743	validation-aucpr:0.97222
[33]	validation-logloss:0.29752	validation-auc:0.96742	validation-aucpr:0.97228
[34]	validation-logloss:0.29412	validation-auc:0.96743	validation-aucpr:0.97225
[35]	validation-logloss:0.28944	validation-auc:0.96760	validation-aucpr:0.97240
[36]	validation-logloss:0.28606	validation-auc:0.96768	validation-aucpr:0.97252
[37]	validation-logloss:0.28312	validation-auc:0.96760	validation-aucpr:0.97244
[38]	validation-logloss:0.27888	validation-auc:0.96773	validation-aucpr:0.97229
[39]	validation-logloss:0.27477	validation-auc:0.96780	validation-aucpr:0.97157
[40]	validation-logloss:0.27189	validation-auc:0.96785	validation-aucpr:0.97158
[41]	validation-logloss:0.26814	validation-auc:0.96821	validation-aucpr:0.97297
[42]	validation-logloss:0.26437	validation-auc:0.96848	validation-aucpr:0.97320
[43]	validation-logloss:0.26082	validation-auc:0.96871	validation-aucpr:0.97338
[44]	validation-logloss:0.25867	validation-auc:0.96862	validation-aucpr:0.97327
[45]	validation-logloss:0.25570	validation-auc:0.96879	validation-aucpr:0.97338
[46]	validation-logloss:0.25341	validation-auc:0.96880	validation-aucpr:0.97338
[47]	validation-logloss:0.25128	validation-auc:0.96887	validation-aucpr:0.97342
[48]	validation-logloss:0.24854	validation-auc:0.96898	validation-aucpr:0.97351
[49]	validation-logloss:0.24581	validation-auc:0.96909	validation-aucpr:0.97359
[50]	validation-logloss:0.24395	validation-auc:0.96915	validation-aucpr:0.97361
[51]	validation-logloss:0.24234	validation-auc:0.96914	validation-aucpr:0.97356
[52]	validation-logloss:0.23986	validation-auc:0.96931	validation-aucpr:0.97374
[53]	validation-logloss:0.23849	validation-auc:0.96930	validation-aucpr:0.97369
[54]	validation-logloss:0.23644	validation-auc:0.96936	validation-aucpr:0.97373
[55]	validation-logloss:0.23424	validation-auc:0.96956	validation-aucpr:0.97390
[56]	validation-logloss:0.23228	validation-auc:0.96962	validation-aucpr:0.97378
[57]	validation-logloss:0.23057	validation-auc:0.96972	validation-aucpr:0.97397
[58]	validation-logloss:0.22912	validation-auc:0.96987	validation-aucpr:0.97407
[59]	validation-logloss:0.22752	validation-auc:0.96996	validation-aucpr:0.97414
[60]	validation-logloss:0.22577	validation-auc:0.97004	validation-aucpr:0.97396
{'best_iteration': '59', 'best_score': '0.9741431676411648'}
Trial 35, Fold 1: Log loss = 0.22577467774862892, Average precision = 0.9739730615777419, ROC-AUC = 0.9700445218737902, Elapsed Time = 1.6055976000025112 seconds
Trial 35, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 35, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[0]	validation-logloss:0.66261	validation-auc:0.94859	validation-aucpr:0.93978
[1]	validation-logloss:0.63879	validation-auc:0.95982	validation-aucpr:0.95983
[2]	validation-logloss:0.61305	validation-auc:0.96397	validation-aucpr:0.96862
[3]	validation-logloss:0.58926	validation-auc:0.96611	validation-aucpr:0.97050
[4]	validation-logloss:0.56954	validation-auc:0.96643	validation-aucpr:0.97023
[5]	validation-logloss:0.55182	validation-auc:0.96710	validation-aucpr:0.97047
[6]	validation-logloss:0.53210	validation-auc:0.96826	validation-aucpr:0.97156
[7]	validation-logloss:0.51619	validation-auc:0.96785	validation-aucpr:0.97099
[8]	validation-logloss:0.49923	validation-auc:0.96793	validation-aucpr:0.97126
[9]	validation-logloss:0.48561	validation-auc:0.96722	validation-aucpr:0.97071
[10]	validation-logloss:0.47272	validation-auc:0.96712	validation-aucpr:0.97061
[11]	validation-logloss:0.46048	validation-auc:0.96695	validation-aucpr:0.97050
[12]	validation-logloss:0.44716	validation-auc:0.96704	validation-aucpr:0.97059
[13]	validation-logloss:0.43394	validation-auc:0.96738	validation-aucpr:0.97105
[14]	validation-logloss:0.42353	validation-auc:0.96727	validation-aucpr:0.97090
[15]	validation-logloss:0.41392	validation-auc:0.96723	validation-aucpr:0.97081
[16]	validation-logloss:0.40440	validation-auc:0.96741	validation-aucpr:0.97100
[17]	validation-logloss:0.39350	validation-auc:0.96788	validation-aucpr:0.97141
[18]	validation-logloss:0.38477	validation-auc:0.96810	validation-aucpr:0.97158
[19]	validation-logloss:0.37523	validation-auc:0.96844	validation-aucpr:0.97186
[20]	validation-logloss:0.36598	validation-auc:0.96897	validation-aucpr:0.97231
[21]	validation-logloss:0.35845	validation-auc:0.96919	validation-aucpr:0.97298
[22]	validation-logloss:0.35027	validation-auc:0.96937	validation-aucpr:0.97314
[23]	validation-logloss:0.34423	validation-auc:0.96939	validation-aucpr:0.97310
[24]	validation-logloss:0.33807	validation-auc:0.96938	validation-aucpr:0.97308
[25]	validation-logloss:0.33220	validation-auc:0.96923	validation-aucpr:0.97295
[26]	validation-logloss:0.32661	validation-auc:0.96923	validation-aucpr:0.97292
[27]	validation-logloss:0.32000	validation-auc:0.96944	validation-aucpr:0.97310
[28]	validation-logloss:0.31496	validation-auc:0.96952	validation-aucpr:0.97315
[29]	validation-logloss:0.31060	validation-auc:0.96941	validation-aucpr:0.97300
[30]	validation-logloss:0.30565	validation-auc:0.96953	validation-aucpr:0.97309
[31]	validation-logloss:0.30133	validation-auc:0.96959	validation-aucpr:0.97314
[32]	validation-logloss:0.29609	validation-auc:0.96981	validation-aucpr:0.97333
[33]	validation-logloss:0.29227	validation-auc:0.96976	validation-aucpr:0.97329
[34]	validation-logloss:0.28763	validation-auc:0.96982	validation-aucpr:0.97333
[35]	validation-logloss:0.28274	validation-auc:0.97004	validation-aucpr:0.97351
[36]	validation-logloss:0.27824	validation-auc:0.97021	validation-aucpr:0.97363
[37]	validation-logloss:0.27404	validation-auc:0.97030	validation-aucpr:0.97370
[38]	validation-logloss:0.27089	validation-auc:0.97038	validation-aucpr:0.97376
[39]	validation-logloss:0.26802	validation-auc:0.97033	validation-aucpr:0.97371
[40]	validation-logloss:0.26510	validation-auc:0.97035	validation-aucpr:0.97369
[41]	validation-logloss:0.26152	validation-auc:0.97045	validation-aucpr:0.97379
[42]	validation-logloss:0.25807	validation-auc:0.97049	validation-aucpr:0.97385
[43]	validation-logloss:0.25475	validation-auc:0.97067	validation-aucpr:0.97398
[44]	validation-logloss:0.25226	validation-auc:0.97078	validation-aucpr:0.97409
[45]	validation-logloss:0.25012	validation-auc:0.97069	validation-aucpr:0.97402
[46]	validation-logloss:0.24785	validation-auc:0.97072	validation-aucpr:0.97404
[47]	validation-logloss:0.24556	validation-auc:0.97085	validation-aucpr:0.97411
[48]	validation-logloss:0.24263	validation-auc:0.97096	validation-aucpr:0.97419
[49]	validation-logloss:0.24080	validation-auc:0.97083	validation-aucpr:0.97409
[50]	validation-logloss:0.23812	validation-auc:0.97099	validation-aucpr:0.97421
[51]	validation-logloss:0.23563	validation-auc:0.97109	validation-aucpr:0.97427
[52]	validation-logloss:0.23325	validation-auc:0.97119	validation-aucpr:0.97437
[53]	validation-logloss:0.23165	validation-auc:0.97121	validation-aucpr:0.97438
[54]	validation-logloss:0.22932	validation-auc:0.97135	validation-aucpr:0.97449
[55]	validation-logloss:0.22703	validation-auc:0.97149	validation-aucpr:0.97461
[56]	validation-logloss:0.22506	validation-auc:0.97147	validation-aucpr:0.97452
[57]	validation-logloss:0.22372	validation-auc:0.97154	validation-aucpr:0.97458
[58]	validation-logloss:0.22178	validation-auc:0.97164	validation-aucpr:0.97463
[59]	validation-logloss:0.22058	validation-auc:0.97161	validation-aucpr:0.97462
[60]	validation-logloss:0.21927	validation-auc:0.97172	validation-aucpr:0.97503
{'best_iteration': '60', 'best_score': '0.9750271561781708'}
Trial 35, Fold 2: Log loss = 0.21927145707866819, Average precision = 0.9750233171169024, ROC-AUC = 0.9717249110075112, Elapsed Time = 1.8591325000015786 seconds
Trial 35, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 35, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[0]	validation-logloss:0.66260	validation-auc:0.95283	validation-aucpr:0.93270
[1]	validation-logloss:0.63823	validation-auc:0.96152	validation-aucpr:0.96266
[2]	validation-logloss:0.61253	validation-auc:0.96408	validation-aucpr:0.96896
[3]	validation-logloss:0.59181	validation-auc:0.96459	validation-aucpr:0.96976
[4]	validation-logloss:0.56957	validation-auc:0.96579	validation-aucpr:0.97085
[5]	validation-logloss:0.54908	validation-auc:0.96653	validation-aucpr:0.97138
[6]	validation-logloss:0.52994	validation-auc:0.96730	validation-aucpr:0.97207
[7]	validation-logloss:0.51413	validation-auc:0.96684	validation-aucpr:0.97162
[8]	validation-logloss:0.49927	validation-auc:0.96672	validation-aucpr:0.97147
[9]	validation-logloss:0.48541	validation-auc:0.96688	validation-aucpr:0.97153
[10]	validation-logloss:0.47233	validation-auc:0.96691	validation-aucpr:0.97154
[11]	validation-logloss:0.45849	validation-auc:0.96728	validation-aucpr:0.97189
[12]	validation-logloss:0.44407	validation-auc:0.96802	validation-aucpr:0.97252
[13]	validation-logloss:0.43130	validation-auc:0.96804	validation-aucpr:0.97261
[14]	validation-logloss:0.42121	validation-auc:0.96815	validation-aucpr:0.97274
[15]	validation-logloss:0.40972	validation-auc:0.96853	validation-aucpr:0.97303
[16]	validation-logloss:0.39902	validation-auc:0.96898	validation-aucpr:0.97343
[17]	validation-logloss:0.39055	validation-auc:0.96862	validation-aucpr:0.97313
[18]	validation-logloss:0.38225	validation-auc:0.96873	validation-aucpr:0.97313
[19]	validation-logloss:0.37452	validation-auc:0.96863	validation-aucpr:0.97300
[20]	validation-logloss:0.36668	validation-auc:0.96891	validation-aucpr:0.97326
[21]	validation-logloss:0.35786	validation-auc:0.96914	validation-aucpr:0.97347
[22]	validation-logloss:0.35073	validation-auc:0.96913	validation-aucpr:0.97346
[23]	validation-logloss:0.34449	validation-auc:0.96912	validation-aucpr:0.97347
[24]	validation-logloss:0.33722	validation-auc:0.96923	validation-aucpr:0.97356
[25]	validation-logloss:0.33148	validation-auc:0.96930	validation-aucpr:0.97361
[26]	validation-logloss:0.32607	validation-auc:0.96938	validation-aucpr:0.97365
[27]	validation-logloss:0.32062	validation-auc:0.96956	validation-aucpr:0.97379
[28]	validation-logloss:0.31548	validation-auc:0.96969	validation-aucpr:0.97389
[29]	validation-logloss:0.31120	validation-auc:0.96952	validation-aucpr:0.97376
[30]	validation-logloss:0.30652	validation-auc:0.96960	validation-aucpr:0.97379
[31]	validation-logloss:0.30069	validation-auc:0.96985	validation-aucpr:0.97400
[32]	validation-logloss:0.29542	validation-auc:0.96996	validation-aucpr:0.97413
[33]	validation-logloss:0.28986	validation-auc:0.97030	validation-aucpr:0.97438
[34]	validation-logloss:0.28615	validation-auc:0.97037	validation-aucpr:0.97443
[35]	validation-logloss:0.28120	validation-auc:0.97064	validation-aucpr:0.97467
[36]	validation-logloss:0.27658	validation-auc:0.97075	validation-aucpr:0.97478
[37]	validation-logloss:0.27228	validation-auc:0.97084	validation-aucpr:0.97487
[38]	validation-logloss:0.26845	validation-auc:0.97087	validation-aucpr:0.97492
[39]	validation-logloss:0.26457	validation-auc:0.97100	validation-aucpr:0.97504
[40]	validation-logloss:0.26170	validation-auc:0.97095	validation-aucpr:0.97501
[41]	validation-logloss:0.25895	validation-auc:0.97102	validation-aucpr:0.97507
[42]	validation-logloss:0.25632	validation-auc:0.97106	validation-aucpr:0.97509
[43]	validation-logloss:0.25296	validation-auc:0.97115	validation-aucpr:0.97518
[44]	validation-logloss:0.25074	validation-auc:0.97116	validation-aucpr:0.97523
[45]	validation-logloss:0.24847	validation-auc:0.97115	validation-aucpr:0.97520
[46]	validation-logloss:0.24541	validation-auc:0.97131	validation-aucpr:0.97534
[47]	validation-logloss:0.24273	validation-auc:0.97134	validation-aucpr:0.97539
[48]	validation-logloss:0.23982	validation-auc:0.97152	validation-aucpr:0.97553
[49]	validation-logloss:0.23798	validation-auc:0.97152	validation-aucpr:0.97547
[50]	validation-logloss:0.23599	validation-auc:0.97143	validation-aucpr:0.97539
[51]	validation-logloss:0.23368	validation-auc:0.97152	validation-aucpr:0.97546
[52]	validation-logloss:0.23136	validation-auc:0.97160	validation-aucpr:0.97553
[53]	validation-logloss:0.22918	validation-auc:0.97174	validation-aucpr:0.97565
[54]	validation-logloss:0.22728	validation-auc:0.97179	validation-aucpr:0.97573
[55]	validation-logloss:0.22531	validation-auc:0.97181	validation-aucpr:0.97576
[56]	validation-logloss:0.22389	validation-auc:0.97192	validation-aucpr:0.97587
[57]	validation-logloss:0.22264	validation-auc:0.97193	validation-aucpr:0.97588
[58]	validation-logloss:0.22086	validation-auc:0.97206	validation-aucpr:0.97599
[59]	validation-logloss:0.21965	validation-auc:0.97204	validation-aucpr:0.97598
[60]	validation-logloss:0.21796	validation-auc:0.97219	validation-aucpr:0.97610
{'best_iteration': '60', 'best_score': '0.9760955890250614'}
Trial 35, Fold 3: Log loss = 0.21795609703426969, Average precision = 0.976099486575763, ROC-AUC = 0.9721864736928277, Elapsed Time = 1.8033400999993319 seconds
Trial 35, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 35, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[0]	validation-logloss:0.66301	validation-auc:0.94282	validation-aucpr:0.92186
[1]	validation-logloss:0.63903	validation-auc:0.95582	validation-aucpr:0.95447
[2]	validation-logloss:0.61316	validation-auc:0.96062	validation-aucpr:0.96570
[3]	validation-logloss:0.59228	validation-auc:0.96298	validation-aucpr:0.96858
[4]	validation-logloss:0.57368	validation-auc:0.96267	validation-aucpr:0.96830
[5]	validation-logloss:0.55249	validation-auc:0.96377	validation-aucpr:0.96944
[6]	validation-logloss:0.53476	validation-auc:0.96462	validation-aucpr:0.97020
[7]	validation-logloss:0.51660	validation-auc:0.96565	validation-aucpr:0.97095
[8]	validation-logloss:0.50056	validation-auc:0.96608	validation-aucpr:0.97129
[9]	validation-logloss:0.48715	validation-auc:0.96573	validation-aucpr:0.97089
[10]	validation-logloss:0.47515	validation-auc:0.96500	validation-aucpr:0.97036
[11]	validation-logloss:0.46019	validation-auc:0.96547	validation-aucpr:0.97085
[12]	validation-logloss:0.44636	validation-auc:0.96597	validation-aucpr:0.97138
[13]	validation-logloss:0.43538	validation-auc:0.96586	validation-aucpr:0.97128
[14]	validation-logloss:0.42275	validation-auc:0.96628	validation-aucpr:0.97165
[15]	validation-logloss:0.41109	validation-auc:0.96656	validation-aucpr:0.97191
[16]	validation-logloss:0.40068	validation-auc:0.96705	validation-aucpr:0.97228
[17]	validation-logloss:0.39193	validation-auc:0.96724	validation-aucpr:0.97245
[18]	validation-logloss:0.38182	validation-auc:0.96751	validation-aucpr:0.97271
[19]	validation-logloss:0.37227	validation-auc:0.96781	validation-aucpr:0.97297
[20]	validation-logloss:0.36498	validation-auc:0.96778	validation-aucpr:0.97296
[21]	validation-logloss:0.35633	validation-auc:0.96824	validation-aucpr:0.97328
[22]	validation-logloss:0.34981	validation-auc:0.96829	validation-aucpr:0.97325
[23]	validation-logloss:0.34193	validation-auc:0.96864	validation-aucpr:0.97354
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[43]	validation-logloss:0.25512	validation-auc:0.96937	validation-aucpr:0.97413
[44]	validation-logloss:0.25287	validation-auc:0.96937	validation-aucpr:0.97412
[45]	validation-logloss:0.25029	validation-auc:0.96945	validation-aucpr:0.97421
[46]	validation-logloss:0.24812	validation-auc:0.96950	validation-aucpr:0.97425
[47]	validation-logloss:0.24534	validation-auc:0.96955	validation-aucpr:0.97429
[48]	validation-logloss:0.24248	validation-auc:0.96972	validation-aucpr:0.97445
[49]	validation-logloss:0.24045	validation-auc:0.96981	validation-aucpr:0.97453
[50]	validation-logloss:0.23790	validation-auc:0.97003	validation-aucpr:0.97468
[51]	validation-logloss:0.23557	validation-auc:0.97013	validation-aucpr:0.97479
[52]	validation-logloss:0.23350	validation-auc:0.97004	validation-aucpr:0.97473
[53]	validation-logloss:0.23181	validation-auc:0.97014	validation-aucpr:0.97478
[54]	validation-logloss:0.23027	validation-auc:0.97018	validation-aucpr:0.97480
[55]	validation-logloss:0.22864	validation-auc:0.97021	validation-aucpr:0.97482
[56]	validation-logloss:0.22677	validation-auc:0.97021	validation-aucpr:0.97483
[57]	validation-logloss:0.22553	validation-auc:0.97026	validation-aucpr:0.97485
[58]	validation-logloss:0.22373	validation-auc:0.97043	validation-aucpr:0.97497
[59]	validation-logloss:0.22182	validation-auc:0.97058	validation-aucpr:0.97510
[60]	validation-logloss:0.22012	validation-auc:0.97073	validation-aucpr:0.97523
{'best_iteration': '60', 'best_score': '0.9752255955912826'}
Trial 35, Fold 4: Log loss = 0.22011990067822448, Average precision = 0.9752247920061735, ROC-AUC = 0.9707328602664338, Elapsed Time = 1.9176872999996704 seconds
Trial 35, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 35, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[0]	validation-logloss:0.66319	validation-auc:0.94499	validation-aucpr:0.93567
[1]	validation-logloss:0.63932	validation-auc:0.95795	validation-aucpr:0.96326
[2]	validation-logloss:0.61763	validation-auc:0.95886	validation-aucpr:0.96384
[3]	validation-logloss:0.59363	validation-auc:0.96106	validation-aucpr:0.96636
[4]	validation-logloss:0.57361	validation-auc:0.96176	validation-aucpr:0.96686
[5]	validation-logloss:0.55533	validation-auc:0.96193	validation-aucpr:0.96692
[6]	validation-logloss:0.53569	validation-auc:0.96279	validation-aucpr:0.96770
[7]	validation-logloss:0.52028	validation-auc:0.96292	validation-aucpr:0.96761
[8]	validation-logloss:0.50606	validation-auc:0.96280	validation-aucpr:0.96730
[9]	validation-logloss:0.49035	validation-auc:0.96285	validation-aucpr:0.96737
[10]	validation-logloss:0.47752	validation-auc:0.96294	validation-aucpr:0.96783
[11]	validation-logloss:0.46509	validation-auc:0.96295	validation-aucpr:0.96784
[12]	validation-logloss:0.45079	validation-auc:0.96386	validation-aucpr:0.96867
[13]	validation-logloss:0.43756	validation-auc:0.96434	validation-aucpr:0.96906
[14]	validation-logloss:0.42528	validation-auc:0.96485	validation-aucpr:0.96943
[15]	validation-logloss:0.41590	validation-auc:0.96468	validation-aucpr:0.96926
[16]	validation-logloss:0.40674	validation-auc:0.96483	validation-aucpr:0.96931
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[21]	validation-logloss:0.36377	validation-auc:0.96566	validation-aucpr:0.96991
[22]	validation-logloss:0.35592	validation-auc:0.96576	validation-aucpr:0.97002
[23]	validation-logloss:0.34805	validation-auc:0.96609	validation-aucpr:0.97036
[24]	validation-logloss:0.34070	validation-auc:0.96642	validation-aucpr:0.97070
[25]	validation-logloss:0.33506	validation-auc:0.96651	validation-aucpr:0.97075
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[27]	validation-logloss:0.32313	validation-auc:0.96670	validation-aucpr:0.97078
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[31]	validation-logloss:0.30260	validation-auc:0.96702	validation-aucpr:0.97096
[32]	validation-logloss:0.29824	validation-auc:0.96730	validation-aucpr:0.97115
[33]	validation-logloss:0.29336	validation-auc:0.96755	validation-aucpr:0.97134
[34]	validation-logloss:0.28961	validation-auc:0.96759	validation-aucpr:0.97137
[35]	validation-logloss:0.28632	validation-auc:0.96756	validation-aucpr:0.97129
[36]	validation-logloss:0.28333	validation-auc:0.96745	validation-aucpr:0.97118
[37]	validation-logloss:0.27999	validation-auc:0.96759	validation-aucpr:0.97128
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[41]	validation-logloss:0.26554	validation-auc:0.96833	validation-aucpr:0.97200
[42]	validation-logloss:0.26292	validation-auc:0.96836	validation-aucpr:0.97204
[43]	validation-logloss:0.26004	validation-auc:0.96842	validation-aucpr:0.97207
[44]	validation-logloss:0.25794	validation-auc:0.96831	validation-aucpr:0.97202
[45]	validation-logloss:0.25498	validation-auc:0.96855	validation-aucpr:0.97224
[46]	validation-logloss:0.25197	validation-auc:0.96882	validation-aucpr:0.97245
[47]	validation-logloss:0.24991	validation-auc:0.96893	validation-aucpr:0.97248
[48]	validation-logloss:0.24722	validation-auc:0.96916	validation-aucpr:0.97303
[49]	validation-logloss:0.24475	validation-auc:0.96933	validation-aucpr:0.97310
[50]	validation-logloss:0.24300	validation-auc:0.96933	validation-aucpr:0.97310
[51]	validation-logloss:0.24116	validation-auc:0.96942	validation-aucpr:0.97316
[52]	validation-logloss:0.23944	validation-auc:0.96944	validation-aucpr:0.97322
[53]	validation-logloss:0.23726	validation-auc:0.96960	validation-aucpr:0.97338
[54]	validation-logloss:0.23515	validation-auc:0.96985	validation-aucpr:0.97373
[55]	validation-logloss:0.23370	validation-auc:0.96995	validation-aucpr:0.97380
[56]	validation-logloss:0.23228	validation-auc:0.96998	validation-aucpr:0.97381
[57]	validation-logloss:0.23042	validation-auc:0.97012	validation-aucpr:0.97393
[58]	validation-logloss:0.22864	validation-auc:0.97019	validation-aucpr:0.97394
[59]	validation-logloss:0.22669	validation-auc:0.97041	validation-aucpr:0.97414
[60]	validation-logloss:0.22562	validation-auc:0.97026	validation-aucpr:0.97401
{'best_iteration': '59', 'best_score': '0.9741436480585423'}
Trial 35, Fold 5: Log loss = 0.22562292321361685, Average precision = 0.9740183756693583, ROC-AUC = 0.9702630473960947, Elapsed Time = 1.9858647999972163 seconds
Optimization Progress:  36%|###6      | 36/100 [2:22:02<10:22:32, 583.63s/it]
Trial 36, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 36, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[0]	validation-logloss:0.62882	validation-auc:0.93107	validation-aucpr:0.88995
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[2]	validation-logloss:0.53013	validation-auc:0.96362	validation-aucpr:0.96144
[3]	validation-logloss:0.49113	validation-auc:0.96601	validation-aucpr:0.96749
[4]	validation-logloss:0.45755	validation-auc:0.96711	validation-aucpr:0.97002
[5]	validation-logloss:0.42871	validation-auc:0.96667	validation-aucpr:0.96973
[6]	validation-logloss:0.40344	validation-auc:0.96731	validation-aucpr:0.97032
[7]	validation-logloss:0.38041	validation-auc:0.96826	validation-aucpr:0.97101
[8]	validation-logloss:0.36071	validation-auc:0.96869	validation-aucpr:0.97158
[9]	validation-logloss:0.34296	validation-auc:0.96910	validation-aucpr:0.97160
[10]	validation-logloss:0.32781	validation-auc:0.96963	validation-aucpr:0.97423
[11]	validation-logloss:0.31392	validation-auc:0.96985	validation-aucpr:0.97439
[12]	validation-logloss:0.30155	validation-auc:0.96985	validation-aucpr:0.97434
[13]	validation-logloss:0.29024	validation-auc:0.97019	validation-aucpr:0.97461
[14]	validation-logloss:0.28052	validation-auc:0.97022	validation-aucpr:0.97461
[15]	validation-logloss:0.27164	validation-auc:0.97014	validation-aucpr:0.97456
[16]	validation-logloss:0.26314	validation-auc:0.97074	validation-aucpr:0.97493
[17]	validation-logloss:0.25685	validation-auc:0.97086	validation-aucpr:0.97499
[18]	validation-logloss:0.24995	validation-auc:0.97108	validation-aucpr:0.97519
[19]	validation-logloss:0.24424	validation-auc:0.97108	validation-aucpr:0.97519
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[21]	validation-logloss:0.23465	validation-auc:0.97108	validation-aucpr:0.97517
[22]	validation-logloss:0.23038	validation-auc:0.97117	validation-aucpr:0.97521
[23]	validation-logloss:0.22614	validation-auc:0.97137	validation-aucpr:0.97528
[24]	validation-logloss:0.22267	validation-auc:0.97151	validation-aucpr:0.97535
[25]	validation-logloss:0.21939	validation-auc:0.97155	validation-aucpr:0.97536
[26]	validation-logloss:0.21622	validation-auc:0.97172	validation-aucpr:0.97543
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[30]	validation-logloss:0.20771	validation-auc:0.97182	validation-aucpr:0.97548
[31]	validation-logloss:0.20587	validation-auc:0.97189	validation-aucpr:0.97554
[32]	validation-logloss:0.20471	validation-auc:0.97175	validation-aucpr:0.97536
[33]	validation-logloss:0.20335	validation-auc:0.97179	validation-aucpr:0.97541
[34]	validation-logloss:0.20202	validation-auc:0.97200	validation-aucpr:0.97578
[35]	validation-logloss:0.20077	validation-auc:0.97205	validation-aucpr:0.97586
[36]	validation-logloss:0.19983	validation-auc:0.97201	validation-aucpr:0.97582
[37]	validation-logloss:0.19879	validation-auc:0.97223	validation-aucpr:0.97625
[38]	validation-logloss:0.19818	validation-auc:0.97228	validation-aucpr:0.97627
[39]	validation-logloss:0.19741	validation-auc:0.97232	validation-aucpr:0.97628
[40]	validation-logloss:0.19680	validation-auc:0.97239	validation-aucpr:0.97638
[41]	validation-logloss:0.19655	validation-auc:0.97233	validation-aucpr:0.97631
[42]	validation-logloss:0.19579	validation-auc:0.97242	validation-aucpr:0.97640
[43]	validation-logloss:0.19570	validation-auc:0.97227	validation-aucpr:0.97630
[44]	validation-logloss:0.19512	validation-auc:0.97241	validation-aucpr:0.97640
[45]	validation-logloss:0.19445	validation-auc:0.97254	validation-aucpr:0.97652
[46]	validation-logloss:0.19409	validation-auc:0.97253	validation-aucpr:0.97652
[47]	validation-logloss:0.19421	validation-auc:0.97248	validation-aucpr:0.97653
[48]	validation-logloss:0.19414	validation-auc:0.97242	validation-aucpr:0.97646
[49]	validation-logloss:0.19399	validation-auc:0.97239	validation-aucpr:0.97640
[50]	validation-logloss:0.19410	validation-auc:0.97238	validation-aucpr:0.97638
[51]	validation-logloss:0.19412	validation-auc:0.97235	validation-aucpr:0.97633
[52]	validation-logloss:0.19433	validation-auc:0.97224	validation-aucpr:0.97624
[53]	validation-logloss:0.19445	validation-auc:0.97225	validation-aucpr:0.97622
[54]	validation-logloss:0.19440	validation-auc:0.97225	validation-aucpr:0.97621
[55]	validation-logloss:0.19401	validation-auc:0.97239	validation-aucpr:0.97628
[56]	validation-logloss:0.19375	validation-auc:0.97249	validation-aucpr:0.97638
[57]	validation-logloss:0.19420	validation-auc:0.97239	validation-aucpr:0.97624
[58]	validation-logloss:0.19450	validation-auc:0.97230	validation-aucpr:0.97621
[59]	validation-logloss:0.19450	validation-auc:0.97239	validation-aucpr:0.97631
[60]	validation-logloss:0.19462	validation-auc:0.97241	validation-aucpr:0.97632
{'best_iteration': '47', 'best_score': '0.9765331381787777'}
Trial 36, Fold 1: Log loss = 0.19461938434271853, Average precision = 0.9763247913170843, ROC-AUC = 0.9724131070675786, Elapsed Time = 18.183157800001936 seconds
Trial 36, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 36, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[0]	validation-logloss:0.62898	validation-auc:0.93228	validation-aucpr:0.89005
[1]	validation-logloss:0.57487	validation-auc:0.96346	validation-aucpr:0.95779
[2]	validation-logloss:0.52876	validation-auc:0.96766	validation-aucpr:0.96886
[3]	validation-logloss:0.48957	validation-auc:0.96903	validation-aucpr:0.97222
[4]	validation-logloss:0.45558	validation-auc:0.96988	validation-aucpr:0.97319
[5]	validation-logloss:0.42594	validation-auc:0.97049	validation-aucpr:0.97372
[6]	validation-logloss:0.40042	validation-auc:0.97067	validation-aucpr:0.97366
[7]	validation-logloss:0.37774	validation-auc:0.97056	validation-aucpr:0.97366
[8]	validation-logloss:0.35764	validation-auc:0.97058	validation-aucpr:0.97373
[9]	validation-logloss:0.33995	validation-auc:0.97072	validation-aucpr:0.97388
[10]	validation-logloss:0.32435	validation-auc:0.97072	validation-aucpr:0.97397
[11]	validation-logloss:0.31019	validation-auc:0.97094	validation-aucpr:0.97417
[12]	validation-logloss:0.29750	validation-auc:0.97092	validation-aucpr:0.97415
[13]	validation-logloss:0.28614	validation-auc:0.97111	validation-aucpr:0.97431
[14]	validation-logloss:0.27602	validation-auc:0.97132	validation-aucpr:0.97448
[15]	validation-logloss:0.26686	validation-auc:0.97143	validation-aucpr:0.97457
[16]	validation-logloss:0.25854	validation-auc:0.97170	validation-aucpr:0.97473
[17]	validation-logloss:0.25114	validation-auc:0.97165	validation-aucpr:0.97469
[18]	validation-logloss:0.24463	validation-auc:0.97166	validation-aucpr:0.97462
[19]	validation-logloss:0.23913	validation-auc:0.97150	validation-aucpr:0.97448
[20]	validation-logloss:0.23364	validation-auc:0.97162	validation-aucpr:0.97449
[21]	validation-logloss:0.22870	validation-auc:0.97175	validation-aucpr:0.97463
[22]	validation-logloss:0.22466	validation-auc:0.97171	validation-aucpr:0.97453
[23]	validation-logloss:0.22048	validation-auc:0.97193	validation-aucpr:0.97480
[24]	validation-logloss:0.21650	validation-auc:0.97216	validation-aucpr:0.97498
[25]	validation-logloss:0.21295	validation-auc:0.97230	validation-aucpr:0.97510
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[35]	validation-logloss:0.19209	validation-auc:0.97288	validation-aucpr:0.97517
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[47]	validation-logloss:0.18316	validation-auc:0.97310	validation-aucpr:0.97513
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[50]	validation-logloss:0.18156	validation-auc:0.97351	validation-aucpr:0.97596
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[57]	validation-logloss:0.17958	validation-auc:0.97388	validation-aucpr:0.97668
[58]	validation-logloss:0.18019	validation-auc:0.97355	validation-aucpr:0.97621
[59]	validation-logloss:0.18002	validation-auc:0.97355	validation-aucpr:0.97620
[60]	validation-logloss:0.17990	validation-auc:0.97354	validation-aucpr:0.97615
{'best_iteration': '57', 'best_score': '0.9766846401509044'}
Trial 36, Fold 2: Log loss = 0.17990457088399403, Average precision = 0.9761546471904696, ROC-AUC = 0.9735385246655404, Elapsed Time = 18.41648890000215 seconds
Trial 36, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 36, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[0]	validation-logloss:0.62844	validation-auc:0.93154	validation-aucpr:0.89432
[1]	validation-logloss:0.57382	validation-auc:0.96068	validation-aucpr:0.95541
[2]	validation-logloss:0.52780	validation-auc:0.96813	validation-aucpr:0.97256
[3]	validation-logloss:0.48854	validation-auc:0.96884	validation-aucpr:0.97302
[4]	validation-logloss:0.45469	validation-auc:0.96933	validation-aucpr:0.97345
[5]	validation-logloss:0.42741	validation-auc:0.97040	validation-aucpr:0.97430
[6]	validation-logloss:0.40123	validation-auc:0.97079	validation-aucpr:0.97480
[7]	validation-logloss:0.37826	validation-auc:0.97124	validation-aucpr:0.97489
[8]	validation-logloss:0.35859	validation-auc:0.97111	validation-aucpr:0.97500
[9]	validation-logloss:0.34059	validation-auc:0.97112	validation-aucpr:0.97494
[10]	validation-logloss:0.32423	validation-auc:0.97145	validation-aucpr:0.97517
[11]	validation-logloss:0.30996	validation-auc:0.97150	validation-aucpr:0.97527
[12]	validation-logloss:0.29720	validation-auc:0.97180	validation-aucpr:0.97552
[13]	validation-logloss:0.28679	validation-auc:0.97161	validation-aucpr:0.97450
[14]	validation-logloss:0.27664	validation-auc:0.97167	validation-aucpr:0.97471
[15]	validation-logloss:0.26764	validation-auc:0.97175	validation-aucpr:0.97496
[16]	validation-logloss:0.25914	validation-auc:0.97208	validation-aucpr:0.97522
[17]	validation-logloss:0.25169	validation-auc:0.97210	validation-aucpr:0.97526
[18]	validation-logloss:0.24488	validation-auc:0.97216	validation-aucpr:0.97512
[19]	validation-logloss:0.23906	validation-auc:0.97217	validation-aucpr:0.97505
[20]	validation-logloss:0.23373	validation-auc:0.97189	validation-aucpr:0.97315
[21]	validation-logloss:0.22861	validation-auc:0.97207	validation-aucpr:0.97326
[22]	validation-logloss:0.22423	validation-auc:0.97196	validation-aucpr:0.97310
[23]	validation-logloss:0.22045	validation-auc:0.97197	validation-aucpr:0.97317
[24]	validation-logloss:0.21691	validation-auc:0.97202	validation-aucpr:0.97319
[25]	validation-logloss:0.21342	validation-auc:0.97227	validation-aucpr:0.97396
[26]	validation-logloss:0.21050	validation-auc:0.97236	validation-aucpr:0.97406
[27]	validation-logloss:0.20817	validation-auc:0.97228	validation-aucpr:0.97395
[28]	validation-logloss:0.20590	validation-auc:0.97235	validation-aucpr:0.97431
[29]	validation-logloss:0.20358	validation-auc:0.97249	validation-aucpr:0.97441
[30]	validation-logloss:0.20130	validation-auc:0.97263	validation-aucpr:0.97454
[31]	validation-logloss:0.19972	validation-auc:0.97261	validation-aucpr:0.97452
[32]	validation-logloss:0.19800	validation-auc:0.97279	validation-aucpr:0.97463
[33]	validation-logloss:0.19725	validation-auc:0.97258	validation-aucpr:0.97449
[34]	validation-logloss:0.19583	validation-auc:0.97276	validation-aucpr:0.97495
[35]	validation-logloss:0.19457	validation-auc:0.97283	validation-aucpr:0.97506
[36]	validation-logloss:0.19368	validation-auc:0.97285	validation-aucpr:0.97503
[37]	validation-logloss:0.19274	validation-auc:0.97293	validation-aucpr:0.97504
[38]	validation-logloss:0.19166	validation-auc:0.97299	validation-aucpr:0.97507
[39]	validation-logloss:0.19095	validation-auc:0.97301	validation-aucpr:0.97509
[40]	validation-logloss:0.18987	validation-auc:0.97312	validation-aucpr:0.97531
[41]	validation-logloss:0.18945	validation-auc:0.97303	validation-aucpr:0.97512
[42]	validation-logloss:0.18894	validation-auc:0.97305	validation-aucpr:0.97495
[43]	validation-logloss:0.18837	validation-auc:0.97302	validation-aucpr:0.97489
[44]	validation-logloss:0.18801	validation-auc:0.97299	validation-aucpr:0.97490
[45]	validation-logloss:0.18761	validation-auc:0.97310	validation-aucpr:0.97511
[46]	validation-logloss:0.18711	validation-auc:0.97321	validation-aucpr:0.97525
[47]	validation-logloss:0.18673	validation-auc:0.97330	validation-aucpr:0.97523
[48]	validation-logloss:0.18641	validation-auc:0.97339	validation-aucpr:0.97572
[49]	validation-logloss:0.18576	validation-auc:0.97354	validation-aucpr:0.97591
[50]	validation-logloss:0.18567	validation-auc:0.97354	validation-aucpr:0.97591
[51]	validation-logloss:0.18538	validation-auc:0.97364	validation-aucpr:0.97572
[52]	validation-logloss:0.18547	validation-auc:0.97360	validation-aucpr:0.97567
[53]	validation-logloss:0.18523	validation-auc:0.97369	validation-aucpr:0.97577
[54]	validation-logloss:0.18514	validation-auc:0.97369	validation-aucpr:0.97578
[55]	validation-logloss:0.18505	validation-auc:0.97380	validation-aucpr:0.97587
[56]	validation-logloss:0.18471	validation-auc:0.97397	validation-aucpr:0.97609
[57]	validation-logloss:0.18473	validation-auc:0.97393	validation-aucpr:0.97602
[58]	validation-logloss:0.18511	validation-auc:0.97378	validation-aucpr:0.97558
[59]	validation-logloss:0.18507	validation-auc:0.97396	validation-aucpr:0.97735
[60]	validation-logloss:0.18504	validation-auc:0.97401	validation-aucpr:0.97734
{'best_iteration': '59', 'best_score': '0.9773455256355663'}
Trial 36, Fold 3: Log loss = 0.1850426326602382, Average precision = 0.9773415631367709, ROC-AUC = 0.9740058364072388, Elapsed Time = 17.800311499999225 seconds
Trial 36, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 36, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[0]	validation-logloss:0.62889	validation-auc:0.92818	validation-aucpr:0.88664
[1]	validation-logloss:0.57557	validation-auc:0.95670	validation-aucpr:0.94196
[2]	validation-logloss:0.52984	validation-auc:0.96495	validation-aucpr:0.96483
[3]	validation-logloss:0.49059	validation-auc:0.96722	validation-aucpr:0.96900
[4]	validation-logloss:0.45661	validation-auc:0.96855	validation-aucpr:0.97389
[5]	validation-logloss:0.42682	validation-auc:0.96938	validation-aucpr:0.97416
[6]	validation-logloss:0.40070	validation-auc:0.97000	validation-aucpr:0.97468
[7]	validation-logloss:0.38005	validation-auc:0.97005	validation-aucpr:0.97475
[8]	validation-logloss:0.35980	validation-auc:0.97060	validation-aucpr:0.97517
[9]	validation-logloss:0.34155	validation-auc:0.97098	validation-aucpr:0.97542
[10]	validation-logloss:0.32525	validation-auc:0.97119	validation-aucpr:0.97562
[11]	validation-logloss:0.31127	validation-auc:0.97120	validation-aucpr:0.97557
[12]	validation-logloss:0.29845	validation-auc:0.97163	validation-aucpr:0.97585
[13]	validation-logloss:0.28705	validation-auc:0.97167	validation-aucpr:0.97435
[14]	validation-logloss:0.27739	validation-auc:0.97151	validation-aucpr:0.97415
[15]	validation-logloss:0.26812	validation-auc:0.97156	validation-aucpr:0.97417
[16]	validation-logloss:0.25988	validation-auc:0.97156	validation-aucpr:0.97405
[17]	validation-logloss:0.25240	validation-auc:0.97166	validation-aucpr:0.97375
[18]	validation-logloss:0.24577	validation-auc:0.97173	validation-aucpr:0.97380
[19]	validation-logloss:0.23970	validation-auc:0.97188	validation-aucpr:0.97390
[20]	validation-logloss:0.23487	validation-auc:0.97200	validation-aucpr:0.97398
[21]	validation-logloss:0.23005	validation-auc:0.97186	validation-aucpr:0.97388
[22]	validation-logloss:0.22576	validation-auc:0.97176	validation-aucpr:0.97353
[23]	validation-logloss:0.22147	validation-auc:0.97199	validation-aucpr:0.97370
[24]	validation-logloss:0.21736	validation-auc:0.97224	validation-aucpr:0.97387
[25]	validation-logloss:0.21437	validation-auc:0.97204	validation-aucpr:0.97371
[26]	validation-logloss:0.21144	validation-auc:0.97198	validation-aucpr:0.97367
[27]	validation-logloss:0.20868	validation-auc:0.97205	validation-aucpr:0.97368
[28]	validation-logloss:0.20614	validation-auc:0.97210	validation-aucpr:0.97372
[29]	validation-logloss:0.20383	validation-auc:0.97222	validation-aucpr:0.97380
[30]	validation-logloss:0.20181	validation-auc:0.97223	validation-aucpr:0.97380
[31]	validation-logloss:0.19971	validation-auc:0.97237	validation-aucpr:0.97392
[32]	validation-logloss:0.19797	validation-auc:0.97244	validation-aucpr:0.97425
[33]	validation-logloss:0.19667	validation-auc:0.97242	validation-aucpr:0.97430
[34]	validation-logloss:0.19558	validation-auc:0.97236	validation-aucpr:0.97434
[35]	validation-logloss:0.19445	validation-auc:0.97242	validation-aucpr:0.97640
[36]	validation-logloss:0.19343	validation-auc:0.97248	validation-aucpr:0.97652
[37]	validation-logloss:0.19229	validation-auc:0.97253	validation-aucpr:0.97656
[38]	validation-logloss:0.19126	validation-auc:0.97263	validation-aucpr:0.97665
[39]	validation-logloss:0.19080	validation-auc:0.97242	validation-aucpr:0.97648
[40]	validation-logloss:0.19028	validation-auc:0.97235	validation-aucpr:0.97640
[41]	validation-logloss:0.18969	validation-auc:0.97239	validation-aucpr:0.97640
[42]	validation-logloss:0.18913	validation-auc:0.97244	validation-aucpr:0.97642
[43]	validation-logloss:0.18836	validation-auc:0.97256	validation-aucpr:0.97652
[44]	validation-logloss:0.18776	validation-auc:0.97269	validation-aucpr:0.97659
[45]	validation-logloss:0.18727	validation-auc:0.97274	validation-aucpr:0.97663
[46]	validation-logloss:0.18678	validation-auc:0.97283	validation-aucpr:0.97670
[47]	validation-logloss:0.18671	validation-auc:0.97279	validation-aucpr:0.97666
[48]	validation-logloss:0.18654	validation-auc:0.97284	validation-aucpr:0.97667
[49]	validation-logloss:0.18618	validation-auc:0.97294	validation-aucpr:0.97671
[50]	validation-logloss:0.18629	validation-auc:0.97288	validation-aucpr:0.97665
[51]	validation-logloss:0.18620	validation-auc:0.97290	validation-aucpr:0.97666
[52]	validation-logloss:0.18590	validation-auc:0.97292	validation-aucpr:0.97668
[53]	validation-logloss:0.18561	validation-auc:0.97298	validation-aucpr:0.97678
[54]	validation-logloss:0.18563	validation-auc:0.97295	validation-aucpr:0.97675
[55]	validation-logloss:0.18602	validation-auc:0.97286	validation-aucpr:0.97670
[56]	validation-logloss:0.18585	validation-auc:0.97298	validation-aucpr:0.97678
[57]	validation-logloss:0.18564	validation-auc:0.97307	validation-aucpr:0.97687
[58]	validation-logloss:0.18598	validation-auc:0.97298	validation-aucpr:0.97682
[59]	validation-logloss:0.18606	validation-auc:0.97296	validation-aucpr:0.97680
[60]	validation-logloss:0.18627	validation-auc:0.97295	validation-aucpr:0.97680
{'best_iteration': '57', 'best_score': '0.9768669727658384'}
Trial 36, Fold 4: Log loss = 0.1862653102870968, Average precision = 0.976799427274251, ROC-AUC = 0.9729520751518272, Elapsed Time = 18.60203040000124 seconds
Trial 36, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 36, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[0]	validation-logloss:0.62917	validation-auc:0.92813	validation-aucpr:0.88436
[1]	validation-logloss:0.57570	validation-auc:0.95772	validation-aucpr:0.94855
[2]	validation-logloss:0.53065	validation-auc:0.96252	validation-aucpr:0.96071
[3]	validation-logloss:0.49165	validation-auc:0.96473	validation-aucpr:0.96366
[4]	validation-logloss:0.45789	validation-auc:0.96504	validation-aucpr:0.96325
[5]	validation-logloss:0.42881	validation-auc:0.96618	validation-aucpr:0.96591
[6]	validation-logloss:0.40314	validation-auc:0.96712	validation-aucpr:0.96937
[7]	validation-logloss:0.38033	validation-auc:0.96801	validation-aucpr:0.96974
[8]	validation-logloss:0.36129	validation-auc:0.96796	validation-aucpr:0.96962
[9]	validation-logloss:0.34395	validation-auc:0.96835	validation-aucpr:0.97219
[10]	validation-logloss:0.32870	validation-auc:0.96836	validation-aucpr:0.97209
[11]	validation-logloss:0.31462	validation-auc:0.96888	validation-aucpr:0.97246
[12]	validation-logloss:0.30195	validation-auc:0.96961	validation-aucpr:0.97300
[13]	validation-logloss:0.29093	validation-auc:0.97000	validation-aucpr:0.97331
[14]	validation-logloss:0.28113	validation-auc:0.97029	validation-aucpr:0.97357
[15]	validation-logloss:0.27252	validation-auc:0.97024	validation-aucpr:0.97352
[16]	validation-logloss:0.26465	validation-auc:0.97028	validation-aucpr:0.97357
[17]	validation-logloss:0.25799	validation-auc:0.97019	validation-aucpr:0.97349
[18]	validation-logloss:0.25179	validation-auc:0.97000	validation-aucpr:0.97252
[19]	validation-logloss:0.24679	validation-auc:0.97000	validation-aucpr:0.97260
[20]	validation-logloss:0.24130	validation-auc:0.97029	validation-aucpr:0.97258
[21]	validation-logloss:0.23693	validation-auc:0.97021	validation-aucpr:0.97252
[22]	validation-logloss:0.23325	validation-auc:0.97013	validation-aucpr:0.97247
[23]	validation-logloss:0.22951	validation-auc:0.97020	validation-aucpr:0.97251
[24]	validation-logloss:0.22572	validation-auc:0.97045	validation-aucpr:0.97275
[25]	validation-logloss:0.22259	validation-auc:0.97041	validation-aucpr:0.97277
[26]	validation-logloss:0.21962	validation-auc:0.97073	validation-aucpr:0.97409
[27]	validation-logloss:0.21706	validation-auc:0.97084	validation-aucpr:0.97419
[28]	validation-logloss:0.21488	validation-auc:0.97077	validation-aucpr:0.97412
[29]	validation-logloss:0.21306	validation-auc:0.97066	validation-aucpr:0.97405
[30]	validation-logloss:0.21141	validation-auc:0.97053	validation-aucpr:0.97344
[31]	validation-logloss:0.20985	validation-auc:0.97051	validation-aucpr:0.97341
[32]	validation-logloss:0.20822	validation-auc:0.97067	validation-aucpr:0.97353
[33]	validation-logloss:0.20678	validation-auc:0.97080	validation-aucpr:0.97370
[34]	validation-logloss:0.20558	validation-auc:0.97089	validation-aucpr:0.97379
[35]	validation-logloss:0.20438	validation-auc:0.97098	validation-aucpr:0.97347
[36]	validation-logloss:0.20380	validation-auc:0.97084	validation-aucpr:0.97332
[37]	validation-logloss:0.20259	validation-auc:0.97101	validation-aucpr:0.97333
[38]	validation-logloss:0.20168	validation-auc:0.97101	validation-aucpr:0.97337
[39]	validation-logloss:0.20099	validation-auc:0.97101	validation-aucpr:0.97325
[40]	validation-logloss:0.20020	validation-auc:0.97102	validation-aucpr:0.97325
[41]	validation-logloss:0.19923	validation-auc:0.97115	validation-aucpr:0.97317
[42]	validation-logloss:0.19866	validation-auc:0.97124	validation-aucpr:0.97408
[43]	validation-logloss:0.19784	validation-auc:0.97137	validation-aucpr:0.97409
[44]	validation-logloss:0.19775	validation-auc:0.97125	validation-aucpr:0.97398
[45]	validation-logloss:0.19728	validation-auc:0.97129	validation-aucpr:0.97399
[46]	validation-logloss:0.19669	validation-auc:0.97150	validation-aucpr:0.97428
[47]	validation-logloss:0.19636	validation-auc:0.97148	validation-aucpr:0.97428
[48]	validation-logloss:0.19622	validation-auc:0.97153	validation-aucpr:0.97507
[49]	validation-logloss:0.19650	validation-auc:0.97137	validation-aucpr:0.97492
[50]	validation-logloss:0.19648	validation-auc:0.97135	validation-aucpr:0.97486
[51]	validation-logloss:0.19648	validation-auc:0.97133	validation-aucpr:0.97480
[52]	validation-logloss:0.19646	validation-auc:0.97132	validation-aucpr:0.97469
[53]	validation-logloss:0.19640	validation-auc:0.97121	validation-aucpr:0.97454
[54]	validation-logloss:0.19612	validation-auc:0.97132	validation-aucpr:0.97448
[55]	validation-logloss:0.19625	validation-auc:0.97128	validation-aucpr:0.97444
[56]	validation-logloss:0.19652	validation-auc:0.97122	validation-aucpr:0.97430
[57]	validation-logloss:0.19664	validation-auc:0.97123	validation-aucpr:0.97405
[58]	validation-logloss:0.19649	validation-auc:0.97129	validation-aucpr:0.97404
[59]	validation-logloss:0.19664	validation-auc:0.97132	validation-aucpr:0.97415
[60]	validation-logloss:0.19683	validation-auc:0.97129	validation-aucpr:0.97414
{'best_iteration': '48', 'best_score': '0.975065095160132'}
Trial 36, Fold 5: Log loss = 0.1968293727947802, Average precision = 0.9741463844987054, ROC-AUC = 0.9712943537149547, Elapsed Time = 19.465487499997835 seconds
Optimization Progress:  37%|###7      | 37/100 [2:23:42<7:40:34, 438.64s/it] 
Trial 37, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 37, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[0]	validation-logloss:0.65316	validation-auc:0.94886	validation-aucpr:0.95153
[1]	validation-logloss:0.61796	validation-auc:0.95469	validation-aucpr:0.95909
[2]	validation-logloss:0.58590	validation-auc:0.95620	validation-aucpr:0.96087
[3]	validation-logloss:0.55682	validation-auc:0.95743	validation-aucpr:0.96198
[4]	validation-logloss:0.53131	validation-auc:0.95800	validation-aucpr:0.96257
[5]	validation-logloss:0.50323	validation-auc:0.96314	validation-aucpr:0.96681
[6]	validation-logloss:0.48294	validation-auc:0.96276	validation-aucpr:0.96647
[7]	validation-logloss:0.46407	validation-auc:0.96293	validation-aucpr:0.96638
[8]	validation-logloss:0.44304	validation-auc:0.96503	validation-aucpr:0.96855
[9]	validation-logloss:0.42751	validation-auc:0.96508	validation-aucpr:0.96846
[10]	validation-logloss:0.40959	validation-auc:0.96634	validation-aucpr:0.96977
[11]	validation-logloss:0.39313	validation-auc:0.96718	validation-aucpr:0.97054
[12]	validation-logloss:0.37775	validation-auc:0.96762	validation-aucpr:0.97098
[13]	validation-logloss:0.36383	validation-auc:0.96812	validation-aucpr:0.97195
[14]	validation-logloss:0.35081	validation-auc:0.96847	validation-aucpr:0.97236
[15]	validation-logloss:0.34183	validation-auc:0.96840	validation-aucpr:0.97217
[16]	validation-logloss:0.33367	validation-auc:0.96803	validation-aucpr:0.97206
[17]	validation-logloss:0.32332	validation-auc:0.96854	validation-aucpr:0.97373
[18]	validation-logloss:0.31365	validation-auc:0.96890	validation-aucpr:0.97402
[19]	validation-logloss:0.30657	validation-auc:0.96890	validation-aucpr:0.97403
[20]	validation-logloss:0.30042	validation-auc:0.96882	validation-aucpr:0.97400
[21]	validation-logloss:0.29247	validation-auc:0.96911	validation-aucpr:0.97426
[22]	validation-logloss:0.28734	validation-auc:0.96910	validation-aucpr:0.97421
[23]	validation-logloss:0.28114	validation-auc:0.96921	validation-aucpr:0.97428
[24]	validation-logloss:0.27623	validation-auc:0.96925	validation-aucpr:0.97427
[25]	validation-logloss:0.27208	validation-auc:0.96917	validation-aucpr:0.97424
[26]	validation-logloss:0.26751	validation-auc:0.96931	validation-aucpr:0.97434
[27]	validation-logloss:0.26231	validation-auc:0.96947	validation-aucpr:0.97449
[28]	validation-logloss:0.25727	validation-auc:0.96966	validation-aucpr:0.97469
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[73]	validation-logloss:0.19613	validation-auc:0.97204	validation-aucpr:0.97636
[74]	validation-logloss:0.19555	validation-auc:0.97212	validation-aucpr:0.97644
[75]	validation-logloss:0.19499	validation-auc:0.97220	validation-aucpr:0.97650
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[78]	validation-logloss:0.19393	validation-auc:0.97230	validation-aucpr:0.97662
[79]	validation-logloss:0.19369	validation-auc:0.97232	validation-aucpr:0.97662
{'best_iteration': '79', 'best_score': '0.976617268810572'}
Trial 37, Fold 1: Log loss = 0.19369070806982336, Average precision = 0.9766212346890932, ROC-AUC = 0.9723205025260129, Elapsed Time = 2.6088954000006197 seconds
Trial 37, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 37, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[0]	validation-logloss:0.65269	validation-auc:0.94884	validation-aucpr:0.95081
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[2]	validation-logloss:0.58483	validation-auc:0.95774	validation-aucpr:0.96032
[3]	validation-logloss:0.55442	validation-auc:0.96198	validation-aucpr:0.96484
[4]	validation-logloss:0.52515	validation-auc:0.96510	validation-aucpr:0.96840
[5]	validation-logloss:0.50258	validation-auc:0.96459	validation-aucpr:0.96786
[6]	validation-logloss:0.48280	validation-auc:0.96415	validation-aucpr:0.96738
[7]	validation-logloss:0.46364	validation-auc:0.96450	validation-aucpr:0.96800
[8]	validation-logloss:0.44581	validation-auc:0.96446	validation-aucpr:0.96775
[9]	validation-logloss:0.42836	validation-auc:0.96536	validation-aucpr:0.96883
[10]	validation-logloss:0.41042	validation-auc:0.96686	validation-aucpr:0.97040
[11]	validation-logloss:0.39408	validation-auc:0.96753	validation-aucpr:0.97108
[12]	validation-logloss:0.37884	validation-auc:0.96815	validation-aucpr:0.97168
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[61]	validation-logloss:0.19479	validation-auc:0.97276	validation-aucpr:0.97507
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[64]	validation-logloss:0.19326	validation-auc:0.97293	validation-aucpr:0.97559
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[66]	validation-logloss:0.19180	validation-auc:0.97311	validation-aucpr:0.97591
[67]	validation-logloss:0.19156	validation-auc:0.97310	validation-aucpr:0.97587
[68]	validation-logloss:0.19102	validation-auc:0.97319	validation-aucpr:0.97600
[69]	validation-logloss:0.19071	validation-auc:0.97322	validation-aucpr:0.97600
[70]	validation-logloss:0.19019	validation-auc:0.97327	validation-aucpr:0.97608
[71]	validation-logloss:0.18960	validation-auc:0.97334	validation-aucpr:0.97611
[72]	validation-logloss:0.18935	validation-auc:0.97337	validation-aucpr:0.97610
[73]	validation-logloss:0.18892	validation-auc:0.97339	validation-aucpr:0.97608
[74]	validation-logloss:0.18867	validation-auc:0.97343	validation-aucpr:0.97610
[75]	validation-logloss:0.18821	validation-auc:0.97347	validation-aucpr:0.97612
[76]	validation-logloss:0.18780	validation-auc:0.97349	validation-aucpr:0.97612
[77]	validation-logloss:0.18750	validation-auc:0.97356	validation-aucpr:0.97613
[78]	validation-logloss:0.18719	validation-auc:0.97359	validation-aucpr:0.97613
[79]	validation-logloss:0.18681	validation-auc:0.97360	validation-aucpr:0.97621
{'best_iteration': '79', 'best_score': '0.9762111427657041'}
Trial 37, Fold 2: Log loss = 0.1868082831728122, Average precision = 0.9762157308120819, ROC-AUC = 0.9736024863571153, Elapsed Time = 2.777645799997117 seconds
Trial 37, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 37, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[0]	validation-logloss:0.65310	validation-auc:0.94907	validation-aucpr:0.95224
[1]	validation-logloss:0.61741	validation-auc:0.95518	validation-aucpr:0.95762
[2]	validation-logloss:0.58641	validation-auc:0.95650	validation-aucpr:0.95934
[3]	validation-logloss:0.55738	validation-auc:0.95841	validation-aucpr:0.96151
[4]	validation-logloss:0.52935	validation-auc:0.96307	validation-aucpr:0.96726
[5]	validation-logloss:0.50634	validation-auc:0.96323	validation-aucpr:0.96723
[6]	validation-logloss:0.48031	validation-auc:0.96591	validation-aucpr:0.97008
[7]	validation-logloss:0.45933	validation-auc:0.96649	validation-aucpr:0.97114
[8]	validation-logloss:0.44152	validation-auc:0.96671	validation-aucpr:0.97127
[9]	validation-logloss:0.42556	validation-auc:0.96673	validation-aucpr:0.97124
[10]	validation-logloss:0.41120	validation-auc:0.96658	validation-aucpr:0.97108
[11]	validation-logloss:0.39792	validation-auc:0.96661	validation-aucpr:0.97107
[12]	validation-logloss:0.38564	validation-auc:0.96698	validation-aucpr:0.97118
[13]	validation-logloss:0.37386	validation-auc:0.96698	validation-aucpr:0.97115
[14]	validation-logloss:0.36398	validation-auc:0.96696	validation-aucpr:0.97101
[15]	validation-logloss:0.35103	validation-auc:0.96784	validation-aucpr:0.97206
[16]	validation-logloss:0.34179	validation-auc:0.96789	validation-aucpr:0.97208
[17]	validation-logloss:0.33339	validation-auc:0.96794	validation-aucpr:0.97230
[18]	validation-logloss:0.32395	validation-auc:0.96831	validation-aucpr:0.97264
[19]	validation-logloss:0.31691	validation-auc:0.96827	validation-aucpr:0.97261
[20]	validation-logloss:0.30721	validation-auc:0.96881	validation-aucpr:0.97315
[21]	validation-logloss:0.29818	validation-auc:0.96919	validation-aucpr:0.97356
[22]	validation-logloss:0.28990	validation-auc:0.96938	validation-aucpr:0.97377
[23]	validation-logloss:0.28265	validation-auc:0.96962	validation-aucpr:0.97407
[24]	validation-logloss:0.27700	validation-auc:0.96972	validation-aucpr:0.97414
[25]	validation-logloss:0.27280	validation-auc:0.96976	validation-aucpr:0.97420
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[27]	validation-logloss:0.26116	validation-auc:0.97011	validation-aucpr:0.97456
[28]	validation-logloss:0.25748	validation-auc:0.97022	validation-aucpr:0.97460
[29]	validation-logloss:0.25306	validation-auc:0.97031	validation-aucpr:0.97468
[30]	validation-logloss:0.24859	validation-auc:0.97041	validation-aucpr:0.97474
[31]	validation-logloss:0.24414	validation-auc:0.97056	validation-aucpr:0.97488
[32]	validation-logloss:0.24142	validation-auc:0.97061	validation-aucpr:0.97490
[33]	validation-logloss:0.23759	validation-auc:0.97077	validation-aucpr:0.97504
[34]	validation-logloss:0.23566	validation-auc:0.97071	validation-aucpr:0.97500
[35]	validation-logloss:0.23357	validation-auc:0.97065	validation-aucpr:0.97494
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[37]	validation-logloss:0.22909	validation-auc:0.97089	validation-aucpr:0.97511
[38]	validation-logloss:0.22632	validation-auc:0.97100	validation-aucpr:0.97519
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[60]	validation-logloss:0.19695	validation-auc:0.97246	validation-aucpr:0.97624
[61]	validation-logloss:0.19656	validation-auc:0.97251	validation-aucpr:0.97633
[62]	validation-logloss:0.19602	validation-auc:0.97258	validation-aucpr:0.97638
[63]	validation-logloss:0.19503	validation-auc:0.97272	validation-aucpr:0.97649
[64]	validation-logloss:0.19453	validation-auc:0.97276	validation-aucpr:0.97653
[65]	validation-logloss:0.19410	validation-auc:0.97280	validation-aucpr:0.97653
[66]	validation-logloss:0.19338	validation-auc:0.97285	validation-aucpr:0.97658
[67]	validation-logloss:0.19247	validation-auc:0.97297	validation-aucpr:0.97665
[68]	validation-logloss:0.19222	validation-auc:0.97295	validation-aucpr:0.97663
[69]	validation-logloss:0.19179	validation-auc:0.97296	validation-aucpr:0.97666
[70]	validation-logloss:0.19130	validation-auc:0.97300	validation-aucpr:0.97669
[71]	validation-logloss:0.19095	validation-auc:0.97306	validation-aucpr:0.97672
[72]	validation-logloss:0.19068	validation-auc:0.97306	validation-aucpr:0.97670
[73]	validation-logloss:0.19030	validation-auc:0.97312	validation-aucpr:0.97678
[74]	validation-logloss:0.18990	validation-auc:0.97313	validation-aucpr:0.97677
[75]	validation-logloss:0.18957	validation-auc:0.97321	validation-aucpr:0.97686
[76]	validation-logloss:0.18928	validation-auc:0.97324	validation-aucpr:0.97688
[77]	validation-logloss:0.18914	validation-auc:0.97326	validation-aucpr:0.97687
[78]	validation-logloss:0.18896	validation-auc:0.97328	validation-aucpr:0.97687
[79]	validation-logloss:0.18883	validation-auc:0.97329	validation-aucpr:0.97684
{'best_iteration': '76', 'best_score': '0.976880019972459'}
Trial 37, Fold 3: Log loss = 0.1888314692820942, Average precision = 0.9768399914184289, ROC-AUC = 0.9732865184771408, Elapsed Time = 2.7473267999994277 seconds
Trial 37, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 37, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[0]	validation-logloss:0.65303	validation-auc:0.94703	validation-aucpr:0.95109
[1]	validation-logloss:0.61721	validation-auc:0.95355	validation-aucpr:0.95846
[2]	validation-logloss:0.58656	validation-auc:0.95563	validation-aucpr:0.96027
[3]	validation-logloss:0.55582	validation-auc:0.96015	validation-aucpr:0.96540
[4]	validation-logloss:0.53055	validation-auc:0.96035	validation-aucpr:0.96553
[5]	validation-logloss:0.50782	validation-auc:0.96031	validation-aucpr:0.96557
[6]	validation-logloss:0.48737	validation-auc:0.96048	validation-aucpr:0.96587
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[8]	validation-logloss:0.44741	validation-auc:0.96318	validation-aucpr:0.96888
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[68]	validation-logloss:0.19594	validation-auc:0.97129	validation-aucpr:0.97585
[69]	validation-logloss:0.19516	validation-auc:0.97140	validation-aucpr:0.97594
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[71]	validation-logloss:0.19402	validation-auc:0.97154	validation-aucpr:0.97603
[72]	validation-logloss:0.19345	validation-auc:0.97162	validation-aucpr:0.97608
[73]	validation-logloss:0.19302	validation-auc:0.97163	validation-aucpr:0.97609
[74]	validation-logloss:0.19276	validation-auc:0.97167	validation-aucpr:0.97611
[75]	validation-logloss:0.19229	validation-auc:0.97172	validation-aucpr:0.97614
[76]	validation-logloss:0.19203	validation-auc:0.97175	validation-aucpr:0.97616
[77]	validation-logloss:0.19176	validation-auc:0.97177	validation-aucpr:0.97617
[78]	validation-logloss:0.19149	validation-auc:0.97181	validation-aucpr:0.97619
[79]	validation-logloss:0.19112	validation-auc:0.97191	validation-aucpr:0.97626
{'best_iteration': '79', 'best_score': '0.9762577490225597'}
Trial 37, Fold 4: Log loss = 0.19112030204925323, Average precision = 0.9762616418832818, ROC-AUC = 0.9719088213624041, Elapsed Time = 2.7423531000022194 seconds
Trial 37, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 37, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[0]	validation-logloss:0.65387	validation-auc:0.94223	validation-aucpr:0.94643
[1]	validation-logloss:0.61840	validation-auc:0.95093	validation-aucpr:0.95391
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[4]	validation-logloss:0.53436	validation-auc:0.95566	validation-aucpr:0.95856
[5]	validation-logloss:0.51293	validation-auc:0.95564	validation-aucpr:0.95874
[6]	validation-logloss:0.48753	validation-auc:0.96107	validation-aucpr:0.96501
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[8]	validation-logloss:0.44757	validation-auc:0.96321	validation-aucpr:0.96772
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[10]	validation-logloss:0.41510	validation-auc:0.96374	validation-aucpr:0.96847
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[68]	validation-logloss:0.20304	validation-auc:0.97051	validation-aucpr:0.97397
[69]	validation-logloss:0.20220	validation-auc:0.97065	validation-aucpr:0.97400
[70]	validation-logloss:0.20151	validation-auc:0.97075	validation-aucpr:0.97399
[71]	validation-logloss:0.20081	validation-auc:0.97083	validation-aucpr:0.97406
[72]	validation-logloss:0.20058	validation-auc:0.97085	validation-aucpr:0.97407
[73]	validation-logloss:0.19996	validation-auc:0.97099	validation-aucpr:0.97414
[74]	validation-logloss:0.19932	validation-auc:0.97110	validation-aucpr:0.97420
[75]	validation-logloss:0.19900	validation-auc:0.97114	validation-aucpr:0.97423
[76]	validation-logloss:0.19883	validation-auc:0.97116	validation-aucpr:0.97423
[77]	validation-logloss:0.19831	validation-auc:0.97123	validation-aucpr:0.97416
[78]	validation-logloss:0.19779	validation-auc:0.97133	validation-aucpr:0.97421
[79]	validation-logloss:0.19763	validation-auc:0.97134	validation-aucpr:0.97420
{'best_iteration': '75', 'best_score': '0.9742342907888454'}
Trial 37, Fold 5: Log loss = 0.19762597901567752, Average precision = 0.9742038468289268, ROC-AUC = 0.9713423629303457, Elapsed Time = 3.3143954999977723 seconds
Optimization Progress:  38%|###8      | 38/100 [2:24:04<5:24:16, 313.82s/it]
Trial 38, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 38, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[20:22:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.68047	validation-auc:0.91070	validation-aucpr:0.89779
[20:22:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.66627	validation-auc:0.93986	validation-aucpr:0.93921
[20:22:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.65357	validation-auc:0.94255	validation-aucpr:0.94553
[20:22:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.63943	validation-auc:0.95464	validation-aucpr:0.95920
[20:22:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.62532	validation-auc:0.95771	validation-aucpr:0.96321
[20:22:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5]	validation-logloss:0.61454	validation-auc:0.95667	validation-aucpr:0.96179
[20:22:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6]	validation-logloss:0.60442	validation-auc:0.95639	validation-aucpr:0.96159
[20:22:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7]	validation-logloss:0.59377	validation-auc:0.95875	validation-aucpr:0.96465
[20:22:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8]	validation-logloss:0.58136	validation-auc:0.96004	validation-aucpr:0.96635
[20:22:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9]	validation-logloss:0.57201	validation-auc:0.96003	validation-aucpr:0.96629
[20:22:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10]	validation-logloss:0.56250	validation-auc:0.95979	validation-aucpr:0.96599
[20:22:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11]	validation-logloss:0.55420	validation-auc:0.95908	validation-aucpr:0.96513
[20:22:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12]	validation-logloss:0.54528	validation-auc:0.95904	validation-aucpr:0.96511
[20:22:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13]	validation-logloss:0.53712	validation-auc:0.95877	validation-aucpr:0.96484
[20:22:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14]	validation-logloss:0.52924	validation-auc:0.95881	validation-aucpr:0.96484
[20:22:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15]	validation-logloss:0.52129	validation-auc:0.95893	validation-aucpr:0.96493
[20:22:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16]	validation-logloss:0.51374	validation-auc:0.95879	validation-aucpr:0.96480
[20:22:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17]	validation-logloss:0.50583	validation-auc:0.95890	validation-aucpr:0.96506
[20:22:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18]	validation-logloss:0.49861	validation-auc:0.95907	validation-aucpr:0.96517
[20:22:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19]	validation-logloss:0.49061	validation-auc:0.95970	validation-aucpr:0.96592
[20:22:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20]	validation-logloss:0.48259	validation-auc:0.95984	validation-aucpr:0.96610
[20:22:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21]	validation-logloss:0.47688	validation-auc:0.95943	validation-aucpr:0.96573
[20:22:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22]	validation-logloss:0.47153	validation-auc:0.95913	validation-aucpr:0.96540
[20:22:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23]	validation-logloss:0.46471	validation-auc:0.95910	validation-aucpr:0.96547
[20:23:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24]	validation-logloss:0.45931	validation-auc:0.95885	validation-aucpr:0.96517
[20:23:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25]	validation-logloss:0.45356	validation-auc:0.95911	validation-aucpr:0.96538
[20:23:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26]	validation-logloss:0.44761	validation-auc:0.95927	validation-aucpr:0.96555
[20:23:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27]	validation-logloss:0.44187	validation-auc:0.95935	validation-aucpr:0.96556
[20:23:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28]	validation-logloss:0.43537	validation-auc:0.95955	validation-aucpr:0.96578
[20:23:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29]	validation-logloss:0.43072	validation-auc:0.95964	validation-aucpr:0.96587
[20:23:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30]	validation-logloss:0.42670	validation-auc:0.95926	validation-aucpr:0.96549
[20:23:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31]	validation-logloss:0.42200	validation-auc:0.95945	validation-aucpr:0.96561
[20:23:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32]	validation-logloss:0.41743	validation-auc:0.95952	validation-aucpr:0.96564
[20:23:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33]	validation-logloss:0.41275	validation-auc:0.95984	validation-aucpr:0.96595
[20:23:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34]	validation-logloss:0.40867	validation-auc:0.95973	validation-aucpr:0.96582
[20:23:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35]	validation-logloss:0.40456	validation-auc:0.95979	validation-aucpr:0.96588
[20:23:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36]	validation-logloss:0.40057	validation-auc:0.95981	validation-aucpr:0.96594
[20:23:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37]	validation-logloss:0.39640	validation-auc:0.95994	validation-aucpr:0.96604
[20:23:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38]	validation-logloss:0.39223	validation-auc:0.96027	validation-aucpr:0.96632
[20:23:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39]	validation-logloss:0.38729	validation-auc:0.96050	validation-aucpr:0.96656
[20:23:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40]	validation-logloss:0.38388	validation-auc:0.96046	validation-aucpr:0.96653
[20:23:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41]	validation-logloss:0.37950	validation-auc:0.96058	validation-aucpr:0.96670
[20:23:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42]	validation-logloss:0.37575	validation-auc:0.96085	validation-aucpr:0.96692
[20:23:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43]	validation-logloss:0.37287	validation-auc:0.96080	validation-aucpr:0.96685
[20:23:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44]	validation-logloss:0.36943	validation-auc:0.96092	validation-aucpr:0.96695
[20:23:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45]	validation-logloss:0.36624	validation-auc:0.96100	validation-aucpr:0.96696
[20:23:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46]	validation-logloss:0.36348	validation-auc:0.96098	validation-aucpr:0.96692
{'best_iteration': '45', 'best_score': '0.96695969606383'}
Trial 38, Fold 1: Log loss = 0.36347848486850604, Average precision = 0.9669244598053782, ROC-AUC = 0.9609798815139827, Elapsed Time = 3.9911858000014035 seconds
Trial 38, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 38, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[20:23:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.67991	validation-auc:0.91700	validation-aucpr:0.90474
[20:23:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.66559	validation-auc:0.94392	validation-aucpr:0.94367
[20:23:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.65247	validation-auc:0.94750	validation-aucpr:0.94769
[20:23:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.64102	validation-auc:0.94661	validation-aucpr:0.94698
[20:23:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.62646	validation-auc:0.95777	validation-aucpr:0.96131
[20:23:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5]	validation-logloss:0.61526	validation-auc:0.95792	validation-aucpr:0.96174
[20:23:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6]	validation-logloss:0.60483	validation-auc:0.95767	validation-aucpr:0.96140
[20:23:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7]	validation-logloss:0.59424	validation-auc:0.95757	validation-aucpr:0.96126
[20:23:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8]	validation-logloss:0.58437	validation-auc:0.95771	validation-aucpr:0.96131
[20:23:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9]	validation-logloss:0.57301	validation-auc:0.95937	validation-aucpr:0.96349
[20:23:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10]	validation-logloss:0.56199	validation-auc:0.95999	validation-aucpr:0.96423
[20:23:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11]	validation-logloss:0.55212	validation-auc:0.96082	validation-aucpr:0.96498
[20:23:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12]	validation-logloss:0.54278	validation-auc:0.96080	validation-aucpr:0.96502
[20:23:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13]	validation-logloss:0.53517	validation-auc:0.96042	validation-aucpr:0.96461
[20:23:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14]	validation-logloss:0.52775	validation-auc:0.96045	validation-aucpr:0.96472
[20:23:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15]	validation-logloss:0.52030	validation-auc:0.96059	validation-aucpr:0.96479
[20:23:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16]	validation-logloss:0.51314	validation-auc:0.96115	validation-aucpr:0.96548
[20:23:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17]	validation-logloss:0.50611	validation-auc:0.96084	validation-aucpr:0.96507
[20:23:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18]	validation-logloss:0.49955	validation-auc:0.96053	validation-aucpr:0.96480
[20:23:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19]	validation-logloss:0.49278	validation-auc:0.96044	validation-aucpr:0.96468
[20:23:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20]	validation-logloss:0.48583	validation-auc:0.96048	validation-aucpr:0.96474
[20:23:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21]	validation-logloss:0.47870	validation-auc:0.96057	validation-aucpr:0.96491
[20:23:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22]	validation-logloss:0.47333	validation-auc:0.96036	validation-aucpr:0.96471
[20:23:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23]	validation-logloss:0.46818	validation-auc:0.96025	validation-aucpr:0.96466
[20:23:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24]	validation-logloss:0.46213	validation-auc:0.96033	validation-aucpr:0.96471
[20:23:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25]	validation-logloss:0.45619	validation-auc:0.96062	validation-aucpr:0.96497
[20:23:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26]	validation-logloss:0.45052	validation-auc:0.96064	validation-aucpr:0.96498
[20:23:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27]	validation-logloss:0.44513	validation-auc:0.96064	validation-aucpr:0.96491
[20:23:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28]	validation-logloss:0.43872	validation-auc:0.96113	validation-aucpr:0.96544
[20:23:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29]	validation-logloss:0.43388	validation-auc:0.96123	validation-aucpr:0.96551
[20:23:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30]	validation-logloss:0.42963	validation-auc:0.96118	validation-aucpr:0.96541
[20:23:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31]	validation-logloss:0.42376	validation-auc:0.96136	validation-aucpr:0.96565
[20:23:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32]	validation-logloss:0.41946	validation-auc:0.96145	validation-aucpr:0.96568
[20:23:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33]	validation-logloss:0.41429	validation-auc:0.96176	validation-aucpr:0.96599
[20:23:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34]	validation-logloss:0.40889	validation-auc:0.96198	validation-aucpr:0.96621
[20:23:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35]	validation-logloss:0.40497	validation-auc:0.96186	validation-aucpr:0.96609
[20:23:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36]	validation-logloss:0.39914	validation-auc:0.96212	validation-aucpr:0.96636
[20:23:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37]	validation-logloss:0.39472	validation-auc:0.96224	validation-aucpr:0.96646
[20:23:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38]	validation-logloss:0.39100	validation-auc:0.96226	validation-aucpr:0.96644
[20:23:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39]	validation-logloss:0.38873	validation-auc:0.96218	validation-aucpr:0.96624
[20:23:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40]	validation-logloss:0.38544	validation-auc:0.96214	validation-aucpr:0.96613
[20:23:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41]	validation-logloss:0.38067	validation-auc:0.96236	validation-aucpr:0.96638
[20:23:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42]	validation-logloss:0.37808	validation-auc:0.96238	validation-aucpr:0.96629
[20:23:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43]	validation-logloss:0.37478	validation-auc:0.96243	validation-aucpr:0.96632
[20:23:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44]	validation-logloss:0.37209	validation-auc:0.96237	validation-aucpr:0.96625
[20:23:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45]	validation-logloss:0.36884	validation-auc:0.96237	validation-aucpr:0.96626
[20:23:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46]	validation-logloss:0.36678	validation-auc:0.96238	validation-aucpr:0.96620
{'best_iteration': '37', 'best_score': '0.9664614916691174'}
Trial 38, Fold 2: Log loss = 0.36678439359062504, Average precision = 0.9661942089559218, ROC-AUC = 0.9623813446128473, Elapsed Time = 4.435023800000636 seconds
Trial 38, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 38, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[20:23:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.67990	validation-auc:0.91670	validation-aucpr:0.90637
[20:23:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.66606	validation-auc:0.93974	validation-aucpr:0.94017
[20:23:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.65833	validation-auc:0.93809	validation-aucpr:0.94062
[20:23:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.64593	validation-auc:0.94326	validation-aucpr:0.94557
[20:23:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.63434	validation-auc:0.94596	validation-aucpr:0.94895
[20:23:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5]	validation-logloss:0.62376	validation-auc:0.94660	validation-aucpr:0.94965
[20:23:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6]	validation-logloss:0.61339	validation-auc:0.94600	validation-aucpr:0.94934
[20:23:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7]	validation-logloss:0.60377	validation-auc:0.94632	validation-aucpr:0.94958
[20:23:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8]	validation-logloss:0.59333	validation-auc:0.94843	validation-aucpr:0.95202
[20:23:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9]	validation-logloss:0.58330	validation-auc:0.94953	validation-aucpr:0.95288
[20:23:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10]	validation-logloss:0.57362	validation-auc:0.95136	validation-aucpr:0.95496
[20:23:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11]	validation-logloss:0.56403	validation-auc:0.95211	validation-aucpr:0.95568
[20:23:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12]	validation-logloss:0.55528	validation-auc:0.95223	validation-aucpr:0.95587
[20:23:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13]	validation-logloss:0.54731	validation-auc:0.95235	validation-aucpr:0.95601
[20:23:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14]	validation-logloss:0.53963	validation-auc:0.95233	validation-aucpr:0.95595
[20:23:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15]	validation-logloss:0.53109	validation-auc:0.95374	validation-aucpr:0.95744
[20:23:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16]	validation-logloss:0.52385	validation-auc:0.95377	validation-aucpr:0.95745
[20:23:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17]	validation-logloss:0.51606	validation-auc:0.95432	validation-aucpr:0.95798
[20:23:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18]	validation-logloss:0.50888	validation-auc:0.95406	validation-aucpr:0.95774
[20:23:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19]	validation-logloss:0.50197	validation-auc:0.95451	validation-aucpr:0.95816
[20:23:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20]	validation-logloss:0.49534	validation-auc:0.95449	validation-aucpr:0.95812
[20:23:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21]	validation-logloss:0.48733	validation-auc:0.95861	validation-aucpr:0.96279
[20:23:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22]	validation-logloss:0.48157	validation-auc:0.95843	validation-aucpr:0.96263
[20:23:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23]	validation-logloss:0.47534	validation-auc:0.95935	validation-aucpr:0.96362
[20:23:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24]	validation-logloss:0.46954	validation-auc:0.95919	validation-aucpr:0.96341
[20:23:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25]	validation-logloss:0.46341	validation-auc:0.95972	validation-aucpr:0.96403
[20:23:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26]	validation-logloss:0.45837	validation-auc:0.95946	validation-aucpr:0.96376
[20:23:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27]	validation-logloss:0.45358	validation-auc:0.95920	validation-aucpr:0.96349
[20:23:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28]	validation-logloss:0.44845	validation-auc:0.95923	validation-aucpr:0.96352
[20:23:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29]	validation-logloss:0.44362	validation-auc:0.95931	validation-aucpr:0.96352
[20:23:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30]	validation-logloss:0.43884	validation-auc:0.95931	validation-aucpr:0.96360
[20:23:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31]	validation-logloss:0.43456	validation-auc:0.95958	validation-aucpr:0.96389
[20:23:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32]	validation-logloss:0.42962	validation-auc:0.95970	validation-aucpr:0.96394
[20:23:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33]	validation-logloss:0.42423	validation-auc:0.96023	validation-aucpr:0.96449
[20:23:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34]	validation-logloss:0.41740	validation-auc:0.96159	validation-aucpr:0.96600
[20:23:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35]	validation-logloss:0.41321	validation-auc:0.96152	validation-aucpr:0.96590
[20:23:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36]	validation-logloss:0.40969	validation-auc:0.96137	validation-aucpr:0.96574
[20:23:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37]	validation-logloss:0.40516	validation-auc:0.96153	validation-aucpr:0.96582
[20:23:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38]	validation-logloss:0.40094	validation-auc:0.96164	validation-aucpr:0.96593
[20:23:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39]	validation-logloss:0.39723	validation-auc:0.96180	validation-aucpr:0.96607
[20:23:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40]	validation-logloss:0.39317	validation-auc:0.96192	validation-aucpr:0.96615
[20:23:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41]	validation-logloss:0.38947	validation-auc:0.96197	validation-aucpr:0.96616
[20:23:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42]	validation-logloss:0.38641	validation-auc:0.96184	validation-aucpr:0.96606
[20:23:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43]	validation-logloss:0.38126	validation-auc:0.96249	validation-aucpr:0.96686
[20:23:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44]	validation-logloss:0.37767	validation-auc:0.96267	validation-aucpr:0.96697
[20:23:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45]	validation-logloss:0.37454	validation-auc:0.96261	validation-aucpr:0.96687
[20:23:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46]	validation-logloss:0.37180	validation-auc:0.96255	validation-aucpr:0.96681
{'best_iteration': '44', 'best_score': '0.9669745897838138'}
Trial 38, Fold 3: Log loss = 0.37179536140253167, Average precision = 0.966817583403075, ROC-AUC = 0.9625506231279676, Elapsed Time = 4.31358569999793 seconds
Trial 38, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 38, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[20:23:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.67987	validation-auc:0.91555	validation-aucpr:0.90252
[20:23:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.66681	validation-auc:0.93186	validation-aucpr:0.93114
[20:23:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.65318	validation-auc:0.94676	validation-aucpr:0.94888
[20:23:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.64077	validation-auc:0.94892	validation-aucpr:0.95104
[20:23:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.62862	validation-auc:0.95050	validation-aucpr:0.95489
[20:23:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5]	validation-logloss:0.61797	validation-auc:0.94978	validation-aucpr:0.95426
[20:23:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6]	validation-logloss:0.60792	validation-auc:0.94967	validation-aucpr:0.95429
[20:23:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7]	validation-logloss:0.59825	validation-auc:0.94948	validation-aucpr:0.95417
[20:23:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8]	validation-logloss:0.58748	validation-auc:0.95159	validation-aucpr:0.95614
[20:23:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9]	validation-logloss:0.57750	validation-auc:0.95210	validation-aucpr:0.95651
[20:23:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10]	validation-logloss:0.56846	validation-auc:0.95197	validation-aucpr:0.95638
[20:23:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11]	validation-logloss:0.55924	validation-auc:0.95239	validation-aucpr:0.95673
[20:23:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12]	validation-logloss:0.55056	validation-auc:0.95225	validation-aucpr:0.95667
[20:23:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13]	validation-logloss:0.54232	validation-auc:0.95224	validation-aucpr:0.95675
[20:23:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14]	validation-logloss:0.53515	validation-auc:0.95197	validation-aucpr:0.95643
[20:23:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15]	validation-logloss:0.52784	validation-auc:0.95205	validation-aucpr:0.95653
[20:23:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16]	validation-logloss:0.52119	validation-auc:0.95183	validation-aucpr:0.95636
[20:23:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17]	validation-logloss:0.51427	validation-auc:0.95169	validation-aucpr:0.95629
[20:23:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18]	validation-logloss:0.50712	validation-auc:0.95176	validation-aucpr:0.95628
[20:23:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19]	validation-logloss:0.50106	validation-auc:0.95172	validation-aucpr:0.95635
[20:23:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20]	validation-logloss:0.49408	validation-auc:0.95235	validation-aucpr:0.95702
[20:23:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21]	validation-logloss:0.48501	validation-auc:0.95667	validation-aucpr:0.96192
[20:23:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22]	validation-logloss:0.47918	validation-auc:0.95680	validation-aucpr:0.96203
[20:23:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23]	validation-logloss:0.47372	validation-auc:0.95661	validation-aucpr:0.96181
[20:23:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24]	validation-logloss:0.46891	validation-auc:0.95635	validation-aucpr:0.96158
[20:23:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25]	validation-logloss:0.46284	validation-auc:0.95633	validation-aucpr:0.96156
[20:23:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26]	validation-logloss:0.45758	validation-auc:0.95631	validation-aucpr:0.96148
[20:23:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27]	validation-logloss:0.45264	validation-auc:0.95617	validation-aucpr:0.96137
[20:23:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28]	validation-logloss:0.44717	validation-auc:0.95624	validation-aucpr:0.96152
[20:23:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29]	validation-logloss:0.44178	validation-auc:0.95638	validation-aucpr:0.96160
[20:23:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30]	validation-logloss:0.43737	validation-auc:0.95608	validation-aucpr:0.96128
[20:23:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31]	validation-logloss:0.43282	validation-auc:0.95604	validation-aucpr:0.96131
[20:23:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32]	validation-logloss:0.42890	validation-auc:0.95573	validation-aucpr:0.96100
[20:23:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33]	validation-logloss:0.42447	validation-auc:0.95582	validation-aucpr:0.96106
[20:23:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34]	validation-logloss:0.42014	validation-auc:0.95600	validation-aucpr:0.96122
[20:23:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35]	validation-logloss:0.41676	validation-auc:0.95572	validation-aucpr:0.96091
[20:23:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36]	validation-logloss:0.41308	validation-auc:0.95575	validation-aucpr:0.96095
[20:23:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37]	validation-logloss:0.40913	validation-auc:0.95580	validation-aucpr:0.96100
[20:23:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38]	validation-logloss:0.40364	validation-auc:0.95729	validation-aucpr:0.96271
[20:23:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39]	validation-logloss:0.40021	validation-auc:0.95724	validation-aucpr:0.96266
[20:23:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40]	validation-logloss:0.39668	validation-auc:0.95722	validation-aucpr:0.96261
[20:23:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41]	validation-logloss:0.39355	validation-auc:0.95725	validation-aucpr:0.96265
[20:23:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42]	validation-logloss:0.39022	validation-auc:0.95736	validation-aucpr:0.96273
[20:23:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43]	validation-logloss:0.38482	validation-auc:0.95855	validation-aucpr:0.96416
[20:23:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44]	validation-logloss:0.37962	validation-auc:0.95947	validation-aucpr:0.96523
[20:23:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45]	validation-logloss:0.37661	validation-auc:0.95948	validation-aucpr:0.96524
[20:23:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46]	validation-logloss:0.37360	validation-auc:0.95957	validation-aucpr:0.96532
{'best_iteration': '46', 'best_score': '0.965317789327765'}
Trial 38, Fold 4: Log loss = 0.3736001626733337, Average precision = 0.9653230628179911, ROC-AUC = 0.9595715126218731, Elapsed Time = 4.244987300000503 seconds
Trial 38, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 38, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[20:23:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.68022	validation-auc:0.91274	validation-aucpr:0.90548
[20:23:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.66699	validation-auc:0.93385	validation-aucpr:0.93541
[20:23:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.65498	validation-auc:0.93962	validation-aucpr:0.94399
[20:23:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.64212	validation-auc:0.94736	validation-aucpr:0.95141
[20:23:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.63031	validation-auc:0.94783	validation-aucpr:0.95200
[20:23:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5]	validation-logloss:0.61926	validation-auc:0.94828	validation-aucpr:0.95263
[20:23:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6]	validation-logloss:0.60923	validation-auc:0.94794	validation-aucpr:0.95237
[20:23:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7]	validation-logloss:0.59829	validation-auc:0.94880	validation-aucpr:0.95319
[20:23:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8]	validation-logloss:0.58820	validation-auc:0.94991	validation-aucpr:0.95413
[20:23:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9]	validation-logloss:0.57889	validation-auc:0.95053	validation-aucpr:0.95482
[20:23:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10]	validation-logloss:0.56908	validation-auc:0.95076	validation-aucpr:0.95501
[20:23:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11]	validation-logloss:0.56091	validation-auc:0.95049	validation-aucpr:0.95488
[20:23:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12]	validation-logloss:0.55240	validation-auc:0.95070	validation-aucpr:0.95499
[20:23:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13]	validation-logloss:0.54541	validation-auc:0.95058	validation-aucpr:0.95487
[20:23:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14]	validation-logloss:0.53729	validation-auc:0.95096	validation-aucpr:0.95544
[20:23:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15]	validation-logloss:0.52967	validation-auc:0.95131	validation-aucpr:0.95573
[20:23:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16]	validation-logloss:0.52200	validation-auc:0.95163	validation-aucpr:0.95597
[20:23:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17]	validation-logloss:0.51475	validation-auc:0.95194	validation-aucpr:0.95640
[20:23:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18]	validation-logloss:0.50637	validation-auc:0.95498	validation-aucpr:0.96015
[20:23:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19]	validation-logloss:0.50118	validation-auc:0.95488	validation-aucpr:0.96009
[20:23:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20]	validation-logloss:0.49526	validation-auc:0.95476	validation-aucpr:0.95982
[20:23:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21]	validation-logloss:0.48938	validation-auc:0.95430	validation-aucpr:0.95942
[20:23:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22]	validation-logloss:0.48135	validation-auc:0.95583	validation-aucpr:0.96132
[20:23:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23]	validation-logloss:0.47587	validation-auc:0.95569	validation-aucpr:0.96112
[20:23:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24]	validation-logloss:0.47063	validation-auc:0.95557	validation-aucpr:0.96103
[20:23:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25]	validation-logloss:0.46535	validation-auc:0.95558	validation-aucpr:0.96102
[20:23:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26]	validation-logloss:0.45953	validation-auc:0.95600	validation-aucpr:0.96133
[20:23:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27]	validation-logloss:0.45418	validation-auc:0.95630	validation-aucpr:0.96150
[20:23:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28]	validation-logloss:0.44674	validation-auc:0.95741	validation-aucpr:0.96280
[20:23:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29]	validation-logloss:0.44186	validation-auc:0.95735	validation-aucpr:0.96273
[20:23:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30]	validation-logloss:0.43789	validation-auc:0.95724	validation-aucpr:0.96269
[20:23:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31]	validation-logloss:0.43354	validation-auc:0.95731	validation-aucpr:0.96271
[20:23:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32]	validation-logloss:0.42876	validation-auc:0.95736	validation-aucpr:0.96274
[20:23:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33]	validation-logloss:0.42533	validation-auc:0.95717	validation-aucpr:0.96254
[20:23:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34]	validation-logloss:0.41907	validation-auc:0.95782	validation-aucpr:0.96329
[20:23:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35]	validation-logloss:0.41473	validation-auc:0.95794	validation-aucpr:0.96337
[20:23:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36]	validation-logloss:0.41125	validation-auc:0.95783	validation-aucpr:0.96324
[20:23:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37]	validation-logloss:0.40745	validation-auc:0.95794	validation-aucpr:0.96332
[20:23:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38]	validation-logloss:0.40192	validation-auc:0.95830	validation-aucpr:0.96374
[20:23:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39]	validation-logloss:0.39675	validation-auc:0.95840	validation-aucpr:0.96388
[20:23:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40]	validation-logloss:0.39333	validation-auc:0.95845	validation-aucpr:0.96391
[20:23:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41]	validation-logloss:0.38985	validation-auc:0.95859	validation-aucpr:0.96402
[20:23:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42]	validation-logloss:0.38569	validation-auc:0.95890	validation-aucpr:0.96431
[20:23:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43]	validation-logloss:0.38316	validation-auc:0.95882	validation-aucpr:0.96424
[20:23:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44]	validation-logloss:0.38020	validation-auc:0.95886	validation-aucpr:0.96424
[20:23:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45]	validation-logloss:0.37699	validation-auc:0.95910	validation-aucpr:0.96443
[20:23:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46]	validation-logloss:0.37411	validation-auc:0.95900	validation-aucpr:0.96434
{'best_iteration': '45', 'best_score': '0.9644308472576195'}
Trial 38, Fold 5: Log loss = 0.37410844472793614, Average precision = 0.9643466108012383, ROC-AUC = 0.9589996841498989, Elapsed Time = 4.2861682000002475 seconds
Optimization Progress:  39%|###9      | 39/100 [2:24:34<3:52:14, 228.43s/it]
Trial 39, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 39, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[0]	validation-logloss:0.69226	validation-auc:0.63014	validation-aucpr:0.67987
[1]	validation-logloss:0.68619	validation-auc:0.89844	validation-aucpr:0.87978
[2]	validation-logloss:0.68015	validation-auc:0.92588	validation-aucpr:0.92816
[3]	validation-logloss:0.67398	validation-auc:0.93739	validation-aucpr:0.94171
[4]	validation-logloss:0.66769	validation-auc:0.94633	validation-aucpr:0.95256
[5]	validation-logloss:0.66270	validation-auc:0.94563	validation-aucpr:0.95211
[6]	validation-logloss:0.65710	validation-auc:0.94542	validation-aucpr:0.95179
[7]	validation-logloss:0.65143	validation-auc:0.94734	validation-aucpr:0.95300
[8]	validation-logloss:0.64719	validation-auc:0.94662	validation-aucpr:0.95258
[9]	validation-logloss:0.64204	validation-auc:0.94688	validation-aucpr:0.95271
[10]	validation-logloss:0.63700	validation-auc:0.94868	validation-aucpr:0.95488
[11]	validation-logloss:0.63243	validation-auc:0.94884	validation-aucpr:0.95534
[12]	validation-logloss:0.62740	validation-auc:0.94945	validation-aucpr:0.95623
[13]	validation-logloss:0.62268	validation-auc:0.94926	validation-aucpr:0.95599
[14]	validation-logloss:0.61683	validation-auc:0.95290	validation-aucpr:0.95988
[15]	validation-logloss:0.61203	validation-auc:0.95280	validation-aucpr:0.95976
[16]	validation-logloss:0.60998	validation-auc:0.95253	validation-aucpr:0.95944
[17]	validation-logloss:0.60539	validation-auc:0.95241	validation-aucpr:0.95924
[18]	validation-logloss:0.60083	validation-auc:0.95325	validation-aucpr:0.96025
[19]	validation-logloss:0.59550	validation-auc:0.95373	validation-aucpr:0.96071
[20]	validation-logloss:0.59187	validation-auc:0.95387	validation-aucpr:0.96089
[21]	validation-logloss:0.58728	validation-auc:0.95381	validation-aucpr:0.96070
[22]	validation-logloss:0.58309	validation-auc:0.95427	validation-aucpr:0.96106
[23]	validation-logloss:0.57867	validation-auc:0.95426	validation-aucpr:0.96112
[24]	validation-logloss:0.57463	validation-auc:0.95403	validation-aucpr:0.96094
[25]	validation-logloss:0.57059	validation-auc:0.95388	validation-aucpr:0.96077
[26]	validation-logloss:0.56706	validation-auc:0.95368	validation-aucpr:0.96059
[27]	validation-logloss:0.56328	validation-auc:0.95333	validation-aucpr:0.96026
[28]	validation-logloss:0.55978	validation-auc:0.95341	validation-aucpr:0.96029
[29]	validation-logloss:0.55624	validation-auc:0.95323	validation-aucpr:0.96015
[30]	validation-logloss:0.55136	validation-auc:0.95472	validation-aucpr:0.96176
[31]	validation-logloss:0.54812	validation-auc:0.95459	validation-aucpr:0.96163
[32]	validation-logloss:0.54459	validation-auc:0.95463	validation-aucpr:0.96168
[33]	validation-logloss:0.53999	validation-auc:0.95551	validation-aucpr:0.96262
[34]	validation-logloss:0.53558	validation-auc:0.95607	validation-aucpr:0.96315
{'best_iteration': '34', 'best_score': '0.9631540251948735'}
Trial 39, Fold 1: Log loss = 0.5355809136638554, Average precision = 0.9631595856202696, ROC-AUC = 0.9560693016542158, Elapsed Time = 2.151727899999969 seconds
Trial 39, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 39, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[0]	validation-logloss:0.69183	validation-auc:0.62838	validation-aucpr:0.65085
[1]	validation-logloss:0.68522	validation-auc:0.91819	validation-aucpr:0.88707
[2]	validation-logloss:0.67930	validation-auc:0.93380	validation-aucpr:0.92814
[3]	validation-logloss:0.67332	validation-auc:0.93813	validation-aucpr:0.93811
[4]	validation-logloss:0.66756	validation-auc:0.94129	validation-aucpr:0.94215
[5]	validation-logloss:0.66199	validation-auc:0.94103	validation-aucpr:0.94281
[6]	validation-logloss:0.65615	validation-auc:0.94408	validation-aucpr:0.94565
[7]	validation-logloss:0.65107	validation-auc:0.94330	validation-aucpr:0.94496
[8]	validation-logloss:0.64544	validation-auc:0.94814	validation-aucpr:0.95118
[9]	validation-logloss:0.64058	validation-auc:0.94744	validation-aucpr:0.95039
[10]	validation-logloss:0.63569	validation-auc:0.94655	validation-aucpr:0.94948
[11]	validation-logloss:0.63155	validation-auc:0.94802	validation-aucpr:0.95053
[12]	validation-logloss:0.62661	validation-auc:0.94878	validation-aucpr:0.95132
[13]	validation-logloss:0.62266	validation-auc:0.94955	validation-aucpr:0.95189
[14]	validation-logloss:0.61744	validation-auc:0.95020	validation-aucpr:0.95256
[15]	validation-logloss:0.61261	validation-auc:0.95021	validation-aucpr:0.95271
[16]	validation-logloss:0.60739	validation-auc:0.95347	validation-aucpr:0.95646
[17]	validation-logloss:0.60288	validation-auc:0.95307	validation-aucpr:0.95631
[18]	validation-logloss:0.59799	validation-auc:0.95324	validation-aucpr:0.95624
[19]	validation-logloss:0.59362	validation-auc:0.95318	validation-aucpr:0.95621
[20]	validation-logloss:0.58867	validation-auc:0.95502	validation-aucpr:0.95846
[21]	validation-logloss:0.58432	validation-auc:0.95498	validation-aucpr:0.95835
[22]	validation-logloss:0.58034	validation-auc:0.95496	validation-aucpr:0.95829
[23]	validation-logloss:0.57656	validation-auc:0.95457	validation-aucpr:0.95789
[24]	validation-logloss:0.57325	validation-auc:0.95445	validation-aucpr:0.95795
[25]	validation-logloss:0.56989	validation-auc:0.95414	validation-aucpr:0.95760
[26]	validation-logloss:0.56605	validation-auc:0.95402	validation-aucpr:0.95744
[27]	validation-logloss:0.56203	validation-auc:0.95412	validation-aucpr:0.95745
[28]	validation-logloss:0.55888	validation-auc:0.95443	validation-aucpr:0.95764
[29]	validation-logloss:0.55520	validation-auc:0.95454	validation-aucpr:0.95777
[30]	validation-logloss:0.55145	validation-auc:0.95470	validation-aucpr:0.95789
[31]	validation-logloss:0.54783	validation-auc:0.95467	validation-aucpr:0.95783
[32]	validation-logloss:0.54450	validation-auc:0.95466	validation-aucpr:0.95759
[33]	validation-logloss:0.54120	validation-auc:0.95454	validation-aucpr:0.95752
[34]	validation-logloss:0.53651	validation-auc:0.95581	validation-aucpr:0.95902
{'best_iteration': '34', 'best_score': '0.9590245603636176'}
Trial 39, Fold 2: Log loss = 0.5365051738060637, Average precision = 0.9590721938204927, ROC-AUC = 0.9558121071060149, Elapsed Time = 2.1664930999977514 seconds
Trial 39, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 39, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[0]	validation-logloss:0.69216	validation-auc:0.61740	validation-aucpr:0.65215
[1]	validation-logloss:0.68566	validation-auc:0.86971	validation-aucpr:0.81245
[2]	validation-logloss:0.68091	validation-auc:0.91964	validation-aucpr:0.91568
[3]	validation-logloss:0.67510	validation-auc:0.92775	validation-aucpr:0.92560
[4]	validation-logloss:0.66989	validation-auc:0.93403	validation-aucpr:0.93147
[5]	validation-logloss:0.66449	validation-auc:0.93752	validation-aucpr:0.93762
[6]	validation-logloss:0.65879	validation-auc:0.94044	validation-aucpr:0.94151
[7]	validation-logloss:0.65311	validation-auc:0.94236	validation-aucpr:0.94400
[8]	validation-logloss:0.64733	validation-auc:0.94527	validation-aucpr:0.94700
[9]	validation-logloss:0.64229	validation-auc:0.94581	validation-aucpr:0.94753
[10]	validation-logloss:0.63729	validation-auc:0.94573	validation-aucpr:0.94738
[11]	validation-logloss:0.63265	validation-auc:0.94497	validation-aucpr:0.94660
[12]	validation-logloss:0.62648	validation-auc:0.95397	validation-aucpr:0.95737
[13]	validation-logloss:0.62120	validation-auc:0.95544	validation-aucpr:0.95867
[14]	validation-logloss:0.61602	validation-auc:0.95613	validation-aucpr:0.95968
[15]	validation-logloss:0.61144	validation-auc:0.95604	validation-aucpr:0.95994
[16]	validation-logloss:0.60563	validation-auc:0.95853	validation-aucpr:0.96229
[17]	validation-logloss:0.60105	validation-auc:0.95813	validation-aucpr:0.96182
[18]	validation-logloss:0.59685	validation-auc:0.95805	validation-aucpr:0.96171
[19]	validation-logloss:0.59245	validation-auc:0.95819	validation-aucpr:0.96223
[20]	validation-logloss:0.58849	validation-auc:0.95778	validation-aucpr:0.96192
[21]	validation-logloss:0.58433	validation-auc:0.95738	validation-aucpr:0.96139
[22]	validation-logloss:0.58010	validation-auc:0.95719	validation-aucpr:0.96118
[23]	validation-logloss:0.57543	validation-auc:0.95789	validation-aucpr:0.96197
[24]	validation-logloss:0.57184	validation-auc:0.95796	validation-aucpr:0.96218
[25]	validation-logloss:0.56730	validation-auc:0.95900	validation-aucpr:0.96386
[26]	validation-logloss:0.56301	validation-auc:0.95894	validation-aucpr:0.96379
[27]	validation-logloss:0.55920	validation-auc:0.95895	validation-aucpr:0.96379
[28]	validation-logloss:0.55554	validation-auc:0.95877	validation-aucpr:0.96364
[29]	validation-logloss:0.55210	validation-auc:0.95848	validation-aucpr:0.96330
[30]	validation-logloss:0.54750	validation-auc:0.95914	validation-aucpr:0.96416
[31]	validation-logloss:0.54393	validation-auc:0.95902	validation-aucpr:0.96397
[32]	validation-logloss:0.54075	validation-auc:0.95900	validation-aucpr:0.96390
[33]	validation-logloss:0.53771	validation-auc:0.95873	validation-aucpr:0.96357
[34]	validation-logloss:0.53390	validation-auc:0.95867	validation-aucpr:0.96350
{'best_iteration': '30', 'best_score': '0.9641562408617427'}
Trial 39, Fold 3: Log loss = 0.5338974948694302, Average precision = 0.9635049136418115, ROC-AUC = 0.9586714227928076, Elapsed Time = 1.9661199000001943 seconds
Trial 39, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 39, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[0]	validation-logloss:0.69196	validation-auc:0.62449	validation-aucpr:0.66023
[1]	validation-logloss:0.68575	validation-auc:0.90475	validation-aucpr:0.88475
[2]	validation-logloss:0.68009	validation-auc:0.92448	validation-aucpr:0.91614
[3]	validation-logloss:0.67503	validation-auc:0.92851	validation-aucpr:0.92872
[4]	validation-logloss:0.66948	validation-auc:0.93151	validation-aucpr:0.93417
[5]	validation-logloss:0.66433	validation-auc:0.93189	validation-aucpr:0.93452
[6]	validation-logloss:0.65829	validation-auc:0.93704	validation-aucpr:0.94134
[7]	validation-logloss:0.65246	validation-auc:0.94025	validation-aucpr:0.94543
[8]	validation-logloss:0.64834	validation-auc:0.94096	validation-aucpr:0.94616
[9]	validation-logloss:0.64347	validation-auc:0.94169	validation-aucpr:0.94701
[10]	validation-logloss:0.63824	validation-auc:0.94215	validation-aucpr:0.94760
[11]	validation-logloss:0.63301	validation-auc:0.94292	validation-aucpr:0.94808
[12]	validation-logloss:0.62816	validation-auc:0.94324	validation-aucpr:0.94850
[13]	validation-logloss:0.62360	validation-auc:0.94278	validation-aucpr:0.94809
[14]	validation-logloss:0.61860	validation-auc:0.94390	validation-aucpr:0.94929
[15]	validation-logloss:0.61283	validation-auc:0.94953	validation-aucpr:0.95586
[16]	validation-logloss:0.60798	validation-auc:0.95062	validation-aucpr:0.95677
[17]	validation-logloss:0.60376	validation-auc:0.95029	validation-aucpr:0.95645
[18]	validation-logloss:0.59899	validation-auc:0.95100	validation-aucpr:0.95702
[19]	validation-logloss:0.59477	validation-auc:0.95127	validation-aucpr:0.95716
[20]	validation-logloss:0.59084	validation-auc:0.95114	validation-aucpr:0.95692
[21]	validation-logloss:0.58635	validation-auc:0.95153	validation-aucpr:0.95726
[22]	validation-logloss:0.58247	validation-auc:0.95180	validation-aucpr:0.95739
[23]	validation-logloss:0.57815	validation-auc:0.95215	validation-aucpr:0.95748
[24]	validation-logloss:0.57444	validation-auc:0.95205	validation-aucpr:0.95738
[25]	validation-logloss:0.57106	validation-auc:0.95199	validation-aucpr:0.95744
[26]	validation-logloss:0.56674	validation-auc:0.95170	validation-aucpr:0.95698
[27]	validation-logloss:0.56320	validation-auc:0.95158	validation-aucpr:0.95697
[28]	validation-logloss:0.55932	validation-auc:0.95203	validation-aucpr:0.95747
[29]	validation-logloss:0.55530	validation-auc:0.95201	validation-aucpr:0.95732
[30]	validation-logloss:0.55240	validation-auc:0.95193	validation-aucpr:0.95721
[31]	validation-logloss:0.54903	validation-auc:0.95176	validation-aucpr:0.95697
[32]	validation-logloss:0.54522	validation-auc:0.95214	validation-aucpr:0.95734
[33]	validation-logloss:0.54188	validation-auc:0.95239	validation-aucpr:0.95763
[34]	validation-logloss:0.53804	validation-auc:0.95277	validation-aucpr:0.95800
{'best_iteration': '34', 'best_score': '0.9580042989195655'}
Trial 39, Fold 4: Log loss = 0.5380433135204978, Average precision = 0.9580086411722494, ROC-AUC = 0.9527683444566049, Elapsed Time = 1.9444853999993938 seconds
Trial 39, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 39, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[0]	validation-logloss:0.68732	validation-auc:0.89226	validation-aucpr:0.85523
[1]	validation-logloss:0.68097	validation-auc:0.93162	validation-aucpr:0.93449
[2]	validation-logloss:0.67451	validation-auc:0.94454	validation-aucpr:0.94874
[3]	validation-logloss:0.66929	validation-auc:0.94362	validation-aucpr:0.94765
[4]	validation-logloss:0.66235	validation-auc:0.95061	validation-aucpr:0.95586
[5]	validation-logloss:0.65705	validation-auc:0.94966	validation-aucpr:0.95443
[6]	validation-logloss:0.65287	validation-auc:0.94918	validation-aucpr:0.95490
[7]	validation-logloss:0.64715	validation-auc:0.95098	validation-aucpr:0.95671
[8]	validation-logloss:0.64217	validation-auc:0.95160	validation-aucpr:0.95717
[9]	validation-logloss:0.63721	validation-auc:0.95198	validation-aucpr:0.95740
[10]	validation-logloss:0.63220	validation-auc:0.95255	validation-aucpr:0.95795
[11]	validation-logloss:0.62710	validation-auc:0.95267	validation-aucpr:0.95814
[12]	validation-logloss:0.62116	validation-auc:0.95375	validation-aucpr:0.95939
[13]	validation-logloss:0.61702	validation-auc:0.95357	validation-aucpr:0.95912
[14]	validation-logloss:0.61229	validation-auc:0.95371	validation-aucpr:0.95920
[15]	validation-logloss:0.60752	validation-auc:0.95408	validation-aucpr:0.95947
[16]	validation-logloss:0.60282	validation-auc:0.95411	validation-aucpr:0.95929
[17]	validation-logloss:0.59803	validation-auc:0.95475	validation-aucpr:0.95973
[18]	validation-logloss:0.59392	validation-auc:0.95436	validation-aucpr:0.95932
[19]	validation-logloss:0.58845	validation-auc:0.95475	validation-aucpr:0.95990
[20]	validation-logloss:0.58439	validation-auc:0.95441	validation-aucpr:0.95961
[21]	validation-logloss:0.57988	validation-auc:0.95471	validation-aucpr:0.95984
[22]	validation-logloss:0.57582	validation-auc:0.95438	validation-aucpr:0.95944
[23]	validation-logloss:0.57088	validation-auc:0.95479	validation-aucpr:0.95987
[24]	validation-logloss:0.56769	validation-auc:0.95462	validation-aucpr:0.96014
[25]	validation-logloss:0.56391	validation-auc:0.95457	validation-aucpr:0.96006
[26]	validation-logloss:0.56038	validation-auc:0.95463	validation-aucpr:0.95986
[27]	validation-logloss:0.55691	validation-auc:0.95456	validation-aucpr:0.95976
[28]	validation-logloss:0.55317	validation-auc:0.95451	validation-aucpr:0.96033
[29]	validation-logloss:0.54919	validation-auc:0.95472	validation-aucpr:0.96047
[30]	validation-logloss:0.54521	validation-auc:0.95509	validation-aucpr:0.96065
[31]	validation-logloss:0.54210	validation-auc:0.95479	validation-aucpr:0.96037
[32]	validation-logloss:0.53876	validation-auc:0.95480	validation-aucpr:0.96033
[33]	validation-logloss:0.53487	validation-auc:0.95501	validation-aucpr:0.96040
[34]	validation-logloss:0.53123	validation-auc:0.95518	validation-aucpr:0.96049
{'best_iteration': '30', 'best_score': '0.9606467506066156'}
Trial 39, Fold 5: Log loss = 0.53122527454656, Average precision = 0.9604999865216366, ROC-AUC = 0.9551781208776917, Elapsed Time = 1.9197061000013491 seconds
Optimization Progress:  40%|####      | 40/100 [2:24:52<2:45:16, 165.27s/it]
Trial 40, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 40, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[20:23:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.63086	validation-auc:0.92500	validation-aucpr:0.89044
[20:23:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.58262	validation-auc:0.95148	validation-aucpr:0.93704
[20:23:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.53810	validation-auc:0.95822	validation-aucpr:0.95171
[20:23:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.49924	validation-auc:0.96113	validation-aucpr:0.95731
[20:23:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.46479	validation-auc:0.96298	validation-aucpr:0.95741
[20:23:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5]	validation-logloss:0.43537	validation-auc:0.96380	validation-aucpr:0.96024
[20:23:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6]	validation-logloss:0.40962	validation-auc:0.96541	validation-aucpr:0.96554
[20:23:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7]	validation-logloss:0.38675	validation-auc:0.96655	validation-aucpr:0.97040
[20:23:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8]	validation-logloss:0.36661	validation-auc:0.96709	validation-aucpr:0.97055
[20:23:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9]	validation-logloss:0.34893	validation-auc:0.96723	validation-aucpr:0.97031
[20:23:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10]	validation-logloss:0.33281	validation-auc:0.96752	validation-aucpr:0.97008
[20:23:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11]	validation-logloss:0.31876	validation-auc:0.96801	validation-aucpr:0.97031
[20:23:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12]	validation-logloss:0.30626	validation-auc:0.96853	validation-aucpr:0.97081
[20:23:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13]	validation-logloss:0.29482	validation-auc:0.96873	validation-aucpr:0.97098
[20:23:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14]	validation-logloss:0.28462	validation-auc:0.96877	validation-aucpr:0.97108
[20:23:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15]	validation-logloss:0.27554	validation-auc:0.96882	validation-aucpr:0.97108
[20:23:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16]	validation-logloss:0.26827	validation-auc:0.96887	validation-aucpr:0.97061
[20:23:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17]	validation-logloss:0.26081	validation-auc:0.96929	validation-aucpr:0.97242
[20:23:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18]	validation-logloss:0.25427	validation-auc:0.96958	validation-aucpr:0.97268
[20:23:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19]	validation-logloss:0.24872	validation-auc:0.96971	validation-aucpr:0.97294
[20:23:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20]	validation-logloss:0.24336	validation-auc:0.96967	validation-aucpr:0.97285
[20:23:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21]	validation-logloss:0.23875	validation-auc:0.96958	validation-aucpr:0.97293
[20:23:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22]	validation-logloss:0.23440	validation-auc:0.96957	validation-aucpr:0.97291
[20:23:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23]	validation-logloss:0.23050	validation-auc:0.96972	validation-aucpr:0.97297
[20:23:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24]	validation-logloss:0.22743	validation-auc:0.96960	validation-aucpr:0.97235
[20:23:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25]	validation-logloss:0.22443	validation-auc:0.96979	validation-aucpr:0.97322
[20:23:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26]	validation-logloss:0.22213	validation-auc:0.96969	validation-aucpr:0.97311
[20:23:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27]	validation-logloss:0.21927	validation-auc:0.97009	validation-aucpr:0.97401
[20:23:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28]	validation-logloss:0.21714	validation-auc:0.97031	validation-aucpr:0.97417
[20:23:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29]	validation-logloss:0.21412	validation-auc:0.97078	validation-aucpr:0.97455
[20:23:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30]	validation-logloss:0.21255	validation-auc:0.97087	validation-aucpr:0.97457
[20:23:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31]	validation-logloss:0.21084	validation-auc:0.97087	validation-aucpr:0.97457
[20:23:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32]	validation-logloss:0.20880	validation-auc:0.97104	validation-aucpr:0.97470
[20:23:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33]	validation-logloss:0.20764	validation-auc:0.97097	validation-aucpr:0.97464
[20:23:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34]	validation-logloss:0.20657	validation-auc:0.97095	validation-aucpr:0.97480
[20:23:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35]	validation-logloss:0.20569	validation-auc:0.97096	validation-aucpr:0.97471
[20:23:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36]	validation-logloss:0.20429	validation-auc:0.97109	validation-aucpr:0.97478
[20:23:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37]	validation-logloss:0.20324	validation-auc:0.97115	validation-aucpr:0.97476
[20:23:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38]	validation-logloss:0.20260	validation-auc:0.97114	validation-aucpr:0.97459
[20:23:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39]	validation-logloss:0.20206	validation-auc:0.97119	validation-aucpr:0.97455
[20:23:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40]	validation-logloss:0.20156	validation-auc:0.97118	validation-aucpr:0.97451
[20:23:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41]	validation-logloss:0.20090	validation-auc:0.97117	validation-aucpr:0.97434
[20:23:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42]	validation-logloss:0.20026	validation-auc:0.97142	validation-aucpr:0.97565
[20:23:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43]	validation-logloss:0.19967	validation-auc:0.97146	validation-aucpr:0.97565
[20:23:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44]	validation-logloss:0.19959	validation-auc:0.97144	validation-aucpr:0.97563
[20:23:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45]	validation-logloss:0.19928	validation-auc:0.97149	validation-aucpr:0.97568
[20:23:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46]	validation-logloss:0.19988	validation-auc:0.97127	validation-aucpr:0.97548
[20:23:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[47]	validation-logloss:0.19996	validation-auc:0.97125	validation-aucpr:0.97542
[20:23:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[48]	validation-logloss:0.19972	validation-auc:0.97137	validation-aucpr:0.97557
[20:23:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[49]	validation-logloss:0.20021	validation-auc:0.97123	validation-aucpr:0.97545
[20:23:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[50]	validation-logloss:0.20051	validation-auc:0.97113	validation-aucpr:0.97529
[20:23:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[51]	validation-logloss:0.20041	validation-auc:0.97122	validation-aucpr:0.97535
[20:23:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[52]	validation-logloss:0.20049	validation-auc:0.97120	validation-aucpr:0.97530
[20:23:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[53]	validation-logloss:0.20077	validation-auc:0.97124	validation-aucpr:0.97537
[20:23:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[54]	validation-logloss:0.20081	validation-auc:0.97125	validation-aucpr:0.97533
[20:23:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[55]	validation-logloss:0.20059	validation-auc:0.97143	validation-aucpr:0.97552
[20:23:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[56]	validation-logloss:0.20097	validation-auc:0.97139	validation-aucpr:0.97547
[20:23:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[57]	validation-logloss:0.20147	validation-auc:0.97130	validation-aucpr:0.97533
[20:23:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[58]	validation-logloss:0.20180	validation-auc:0.97125	validation-aucpr:0.97525
[20:23:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[59]	validation-logloss:0.20167	validation-auc:0.97149	validation-aucpr:0.97556
[20:23:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[60]	validation-logloss:0.20178	validation-auc:0.97149	validation-aucpr:0.97552
[20:23:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[61]	validation-logloss:0.20223	validation-auc:0.97151	validation-aucpr:0.97549
[20:23:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[62]	validation-logloss:0.20266	validation-auc:0.97155	validation-aucpr:0.97556
[20:23:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[63]	validation-logloss:0.20272	validation-auc:0.97159	validation-aucpr:0.97550
[20:23:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[64]	validation-logloss:0.20292	validation-auc:0.97164	validation-aucpr:0.97561
[20:23:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[65]	validation-logloss:0.20348	validation-auc:0.97160	validation-aucpr:0.97567
{'best_iteration': '45', 'best_score': '0.9756752977752717'}
Trial 40, Fold 1: Log loss = 0.20347556267412154, Average precision = 0.9756785766613285, ROC-AUC = 0.9715972162477355, Elapsed Time = 10.003865800001222 seconds
Trial 40, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 40, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[20:23:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.62933	validation-auc:0.93299	validation-aucpr:0.89951
[20:23:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.57635	validation-auc:0.95740	validation-aucpr:0.95010
[20:23:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.53071	validation-auc:0.96401	validation-aucpr:0.95772
[20:23:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.49117	validation-auc:0.96651	validation-aucpr:0.96493
[20:23:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.45771	validation-auc:0.96900	validation-aucpr:0.96967
[20:23:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5]	validation-logloss:0.42846	validation-auc:0.96988	validation-aucpr:0.97048
[20:23:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6]	validation-logloss:0.40255	validation-auc:0.97074	validation-aucpr:0.97400
[20:23:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7]	validation-logloss:0.37990	validation-auc:0.97084	validation-aucpr:0.97404
[20:23:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8]	validation-logloss:0.35973	validation-auc:0.97108	validation-aucpr:0.97430
[20:23:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9]	validation-logloss:0.34202	validation-auc:0.97155	validation-aucpr:0.97474
[20:23:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10]	validation-logloss:0.32611	validation-auc:0.97216	validation-aucpr:0.97520
[20:23:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11]	validation-logloss:0.31223	validation-auc:0.97221	validation-aucpr:0.97525
[20:23:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12]	validation-logloss:0.29923	validation-auc:0.97264	validation-aucpr:0.97556
[20:23:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13]	validation-logloss:0.28799	validation-auc:0.97276	validation-aucpr:0.97569
[20:23:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14]	validation-logloss:0.27745	validation-auc:0.97295	validation-aucpr:0.97581
[20:23:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15]	validation-logloss:0.26811	validation-auc:0.97308	validation-aucpr:0.97597
[20:23:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16]	validation-logloss:0.25979	validation-auc:0.97321	validation-aucpr:0.97608
[20:23:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17]	validation-logloss:0.25219	validation-auc:0.97341	validation-aucpr:0.97624
[20:23:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18]	validation-logloss:0.24536	validation-auc:0.97338	validation-aucpr:0.97620
[20:23:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19]	validation-logloss:0.23901	validation-auc:0.97341	validation-aucpr:0.97624
[20:23:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20]	validation-logloss:0.23343	validation-auc:0.97348	validation-aucpr:0.97628
[20:23:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21]	validation-logloss:0.22848	validation-auc:0.97342	validation-aucpr:0.97592
[20:23:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22]	validation-logloss:0.22433	validation-auc:0.97349	validation-aucpr:0.97595
[20:23:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23]	validation-logloss:0.22092	validation-auc:0.97339	validation-aucpr:0.97589
[20:23:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24]	validation-logloss:0.21711	validation-auc:0.97353	validation-aucpr:0.97597
[20:23:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25]	validation-logloss:0.21399	validation-auc:0.97327	validation-aucpr:0.97578
[20:23:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26]	validation-logloss:0.21080	validation-auc:0.97343	validation-aucpr:0.97592
[20:23:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27]	validation-logloss:0.20806	validation-auc:0.97339	validation-aucpr:0.97592
[20:23:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28]	validation-logloss:0.20592	validation-auc:0.97331	validation-aucpr:0.97582
[20:23:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29]	validation-logloss:0.20447	validation-auc:0.97303	validation-aucpr:0.97564
[20:23:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30]	validation-logloss:0.20214	validation-auc:0.97325	validation-aucpr:0.97565
[20:23:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31]	validation-logloss:0.19967	validation-auc:0.97343	validation-aucpr:0.97581
[20:23:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32]	validation-logloss:0.19832	validation-auc:0.97339	validation-aucpr:0.97576
[20:23:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33]	validation-logloss:0.19675	validation-auc:0.97343	validation-aucpr:0.97577
[20:24:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34]	validation-logloss:0.19511	validation-auc:0.97362	validation-aucpr:0.97602
[20:24:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35]	validation-logloss:0.19399	validation-auc:0.97358	validation-aucpr:0.97600
[20:24:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36]	validation-logloss:0.19249	validation-auc:0.97365	validation-aucpr:0.97607
[20:24:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37]	validation-logloss:0.19150	validation-auc:0.97367	validation-aucpr:0.97608
[20:24:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38]	validation-logloss:0.18998	validation-auc:0.97389	validation-aucpr:0.97626
[20:24:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39]	validation-logloss:0.18927	validation-auc:0.97370	validation-aucpr:0.97602
[20:24:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40]	validation-logloss:0.18866	validation-auc:0.97367	validation-aucpr:0.97592
[20:24:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41]	validation-logloss:0.18808	validation-auc:0.97368	validation-aucpr:0.97597
[20:24:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42]	validation-logloss:0.18730	validation-auc:0.97377	validation-aucpr:0.97603
[20:24:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43]	validation-logloss:0.18624	validation-auc:0.97389	validation-aucpr:0.97612
[20:24:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44]	validation-logloss:0.18553	validation-auc:0.97384	validation-aucpr:0.97593
[20:24:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45]	validation-logloss:0.18510	validation-auc:0.97384	validation-aucpr:0.97585
[20:24:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46]	validation-logloss:0.18454	validation-auc:0.97395	validation-aucpr:0.97593
[20:24:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[47]	validation-logloss:0.18430	validation-auc:0.97390	validation-aucpr:0.97586
[20:24:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[48]	validation-logloss:0.18375	validation-auc:0.97393	validation-aucpr:0.97592
[20:24:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[49]	validation-logloss:0.18336	validation-auc:0.97401	validation-aucpr:0.97589
[20:24:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[50]	validation-logloss:0.18318	validation-auc:0.97401	validation-aucpr:0.97580
[20:24:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[51]	validation-logloss:0.18312	validation-auc:0.97416	validation-aucpr:0.97649
[20:24:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[52]	validation-logloss:0.18314	validation-auc:0.97414	validation-aucpr:0.97645
[20:24:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[53]	validation-logloss:0.18305	validation-auc:0.97405	validation-aucpr:0.97637
[20:24:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[54]	validation-logloss:0.18301	validation-auc:0.97394	validation-aucpr:0.97641
[20:24:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[55]	validation-logloss:0.18250	validation-auc:0.97420	validation-aucpr:0.97640
[20:24:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[56]	validation-logloss:0.18288	validation-auc:0.97422	validation-aucpr:0.97701
[20:24:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[57]	validation-logloss:0.18335	validation-auc:0.97407	validation-aucpr:0.97681
[20:24:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[58]	validation-logloss:0.18342	validation-auc:0.97409	validation-aucpr:0.97677
[20:24:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[59]	validation-logloss:0.18362	validation-auc:0.97402	validation-aucpr:0.97661
[20:24:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[60]	validation-logloss:0.18394	validation-auc:0.97388	validation-aucpr:0.97657
[20:24:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[61]	validation-logloss:0.18416	validation-auc:0.97381	validation-aucpr:0.97653
[20:24:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[62]	validation-logloss:0.18443	validation-auc:0.97375	validation-aucpr:0.97648
[20:24:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[63]	validation-logloss:0.18457	validation-auc:0.97370	validation-aucpr:0.97646
[20:24:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[64]	validation-logloss:0.18491	validation-auc:0.97366	validation-aucpr:0.97626
[20:24:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[65]	validation-logloss:0.18488	validation-auc:0.97371	validation-aucpr:0.97642
{'best_iteration': '56', 'best_score': '0.9770064115831377'}
Trial 40, Fold 2: Log loss = 0.18488184562218168, Average precision = 0.9764291748176318, ROC-AUC = 0.9737075716985524, Elapsed Time = 10.05679930000042 seconds
Trial 40, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 40, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[20:24:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.62968	validation-auc:0.92847	validation-aucpr:0.88851
[20:24:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.57559	validation-auc:0.95534	validation-aucpr:0.92830
[20:24:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.53082	validation-auc:0.96108	validation-aucpr:0.94587
[20:24:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.49208	validation-auc:0.96333	validation-aucpr:0.95202
[20:24:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.45802	validation-auc:0.96630	validation-aucpr:0.96477
[20:24:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5]	validation-logloss:0.42909	validation-auc:0.96659	validation-aucpr:0.96669
[20:24:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6]	validation-logloss:0.40297	validation-auc:0.96826	validation-aucpr:0.97081
[20:24:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7]	validation-logloss:0.38010	validation-auc:0.96913	validation-aucpr:0.97153
[20:24:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8]	validation-logloss:0.36031	validation-auc:0.96964	validation-aucpr:0.97258
[20:24:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9]	validation-logloss:0.34277	validation-auc:0.96987	validation-aucpr:0.97347
[20:24:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10]	validation-logloss:0.32704	validation-auc:0.96995	validation-aucpr:0.97257
[20:24:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11]	validation-logloss:0.31287	validation-auc:0.97034	validation-aucpr:0.97299
[20:24:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12]	validation-logloss:0.30041	validation-auc:0.97057	validation-aucpr:0.97432
[20:24:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13]	validation-logloss:0.28884	validation-auc:0.97076	validation-aucpr:0.97447
[20:24:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14]	validation-logloss:0.27891	validation-auc:0.97067	validation-aucpr:0.97403
[20:24:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15]	validation-logloss:0.27020	validation-auc:0.97053	validation-aucpr:0.97397
[20:24:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16]	validation-logloss:0.26189	validation-auc:0.97048	validation-aucpr:0.97396
[20:24:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17]	validation-logloss:0.25574	validation-auc:0.97030	validation-aucpr:0.97375
[20:24:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18]	validation-logloss:0.24918	validation-auc:0.97039	validation-aucpr:0.97379
[20:24:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19]	validation-logloss:0.24247	validation-auc:0.97063	validation-aucpr:0.97363
[20:24:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20]	validation-logloss:0.23732	validation-auc:0.97066	validation-aucpr:0.97362
[20:24:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21]	validation-logloss:0.23235	validation-auc:0.97082	validation-aucpr:0.97385
[20:24:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22]	validation-logloss:0.22836	validation-auc:0.97068	validation-aucpr:0.97372
[20:24:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23]	validation-logloss:0.22460	validation-auc:0.97064	validation-aucpr:0.97356
[20:24:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24]	validation-logloss:0.22082	validation-auc:0.97085	validation-aucpr:0.97371
[20:24:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25]	validation-logloss:0.21764	validation-auc:0.97089	validation-aucpr:0.97371
[20:24:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26]	validation-logloss:0.21469	validation-auc:0.97097	validation-aucpr:0.97379
[20:24:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27]	validation-logloss:0.21218	validation-auc:0.97114	validation-aucpr:0.97386
[20:24:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28]	validation-logloss:0.20991	validation-auc:0.97135	validation-aucpr:0.97407
[20:24:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29]	validation-logloss:0.20801	validation-auc:0.97128	validation-aucpr:0.97400
[20:24:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30]	validation-logloss:0.20642	validation-auc:0.97116	validation-aucpr:0.97394
[20:24:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31]	validation-logloss:0.20502	validation-auc:0.97119	validation-aucpr:0.97411
[20:24:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32]	validation-logloss:0.20418	validation-auc:0.97090	validation-aucpr:0.97403
[20:24:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33]	validation-logloss:0.20262	validation-auc:0.97097	validation-aucpr:0.97407
[20:24:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34]	validation-logloss:0.20138	validation-auc:0.97107	validation-aucpr:0.97441
[20:24:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35]	validation-logloss:0.20005	validation-auc:0.97127	validation-aucpr:0.97472
[20:24:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36]	validation-logloss:0.19936	validation-auc:0.97120	validation-aucpr:0.97458
[20:24:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37]	validation-logloss:0.19834	validation-auc:0.97131	validation-aucpr:0.97458
[20:24:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38]	validation-logloss:0.19748	validation-auc:0.97142	validation-aucpr:0.97461
[20:24:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39]	validation-logloss:0.19682	validation-auc:0.97142	validation-aucpr:0.97437
[20:24:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40]	validation-logloss:0.19620	validation-auc:0.97147	validation-aucpr:0.97452
[20:24:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41]	validation-logloss:0.19542	validation-auc:0.97158	validation-aucpr:0.97465
[20:24:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42]	validation-logloss:0.19514	validation-auc:0.97156	validation-aucpr:0.97469
[20:24:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43]	validation-logloss:0.19438	validation-auc:0.97169	validation-aucpr:0.97482
[20:24:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44]	validation-logloss:0.19381	validation-auc:0.97180	validation-aucpr:0.97483
[20:24:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45]	validation-logloss:0.19365	validation-auc:0.97192	validation-aucpr:0.97514
[20:24:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46]	validation-logloss:0.19326	validation-auc:0.97204	validation-aucpr:0.97521
[20:24:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[47]	validation-logloss:0.19287	validation-auc:0.97212	validation-aucpr:0.97524
[20:24:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[48]	validation-logloss:0.19252	validation-auc:0.97218	validation-aucpr:0.97544
[20:24:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[49]	validation-logloss:0.19272	validation-auc:0.97224	validation-aucpr:0.97587
[20:24:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[50]	validation-logloss:0.19284	validation-auc:0.97218	validation-aucpr:0.97577
[20:24:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[51]	validation-logloss:0.19275	validation-auc:0.97219	validation-aucpr:0.97570
[20:24:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[52]	validation-logloss:0.19298	validation-auc:0.97213	validation-aucpr:0.97566
[20:24:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[53]	validation-logloss:0.19312	validation-auc:0.97212	validation-aucpr:0.97560
[20:24:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[54]	validation-logloss:0.19317	validation-auc:0.97208	validation-aucpr:0.97554
[20:24:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[55]	validation-logloss:0.19329	validation-auc:0.97209	validation-aucpr:0.97555
[20:24:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[56]	validation-logloss:0.19391	validation-auc:0.97190	validation-aucpr:0.97515
[20:24:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[57]	validation-logloss:0.19401	validation-auc:0.97205	validation-aucpr:0.97550
[20:24:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[58]	validation-logloss:0.19429	validation-auc:0.97203	validation-aucpr:0.97545
[20:24:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[59]	validation-logloss:0.19472	validation-auc:0.97192	validation-aucpr:0.97526
[20:24:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[60]	validation-logloss:0.19485	validation-auc:0.97195	validation-aucpr:0.97534
[20:24:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[61]	validation-logloss:0.19518	validation-auc:0.97198	validation-aucpr:0.97548
[20:24:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[62]	validation-logloss:0.19588	validation-auc:0.97180	validation-aucpr:0.97537
[20:24:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[63]	validation-logloss:0.19550	validation-auc:0.97199	validation-aucpr:0.97541
[20:24:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[64]	validation-logloss:0.19632	validation-auc:0.97184	validation-aucpr:0.97521
[20:24:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[65]	validation-logloss:0.19636	validation-auc:0.97199	validation-aucpr:0.97540
{'best_iteration': '49', 'best_score': '0.9758689164852605'}
Trial 40, Fold 3: Log loss = 0.1963552571288972, Average precision = 0.9754003522438767, ROC-AUC = 0.9719943796891102, Elapsed Time = 10.401604899998347 seconds
Trial 40, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 40, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[20:24:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.62979	validation-auc:0.92368	validation-aucpr:0.88066
[20:24:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.57616	validation-auc:0.95172	validation-aucpr:0.93060
[20:24:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.53068	validation-auc:0.96166	validation-aucpr:0.94972
[20:24:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.49535	validation-auc:0.96485	validation-aucpr:0.96724
[20:24:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.46143	validation-auc:0.96570	validation-aucpr:0.96976
[20:24:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5]	validation-logloss:0.43121	validation-auc:0.96641	validation-aucpr:0.96948
[20:24:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6]	validation-logloss:0.40529	validation-auc:0.96674	validation-aucpr:0.97005
[20:24:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7]	validation-logloss:0.38467	validation-auc:0.96644	validation-aucpr:0.97005
[20:24:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8]	validation-logloss:0.36431	validation-auc:0.96686	validation-aucpr:0.97039
[20:24:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9]	validation-logloss:0.34664	validation-auc:0.96729	validation-aucpr:0.97045
[20:24:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10]	validation-logloss:0.33041	validation-auc:0.96759	validation-aucpr:0.97054
[20:24:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11]	validation-logloss:0.31703	validation-auc:0.96865	validation-aucpr:0.97089
[20:24:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12]	validation-logloss:0.30367	validation-auc:0.96933	validation-aucpr:0.97101
[20:24:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13]	validation-logloss:0.29249	validation-auc:0.96944	validation-aucpr:0.97073
[20:24:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14]	validation-logloss:0.28241	validation-auc:0.96939	validation-aucpr:0.96966
[20:24:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15]	validation-logloss:0.27276	validation-auc:0.96989	validation-aucpr:0.97023
[20:24:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16]	validation-logloss:0.26428	validation-auc:0.96987	validation-aucpr:0.97029
[20:24:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17]	validation-logloss:0.25633	validation-auc:0.97044	validation-aucpr:0.97060
[20:24:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18]	validation-logloss:0.24953	validation-auc:0.97050	validation-aucpr:0.97066
[20:24:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19]	validation-logloss:0.24389	validation-auc:0.97061	validation-aucpr:0.97073
[20:24:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20]	validation-logloss:0.23843	validation-auc:0.97060	validation-aucpr:0.97075
[20:24:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21]	validation-logloss:0.23385	validation-auc:0.97044	validation-aucpr:0.97065
[20:24:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22]	validation-logloss:0.22903	validation-auc:0.97076	validation-aucpr:0.97089
[20:24:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23]	validation-logloss:0.22443	validation-auc:0.97106	validation-aucpr:0.97111
[20:24:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24]	validation-logloss:0.22085	validation-auc:0.97117	validation-aucpr:0.97109
[20:24:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25]	validation-logloss:0.21764	validation-auc:0.97137	validation-aucpr:0.97167
[20:24:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26]	validation-logloss:0.21432	validation-auc:0.97143	validation-aucpr:0.97169
[20:24:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27]	validation-logloss:0.21143	validation-auc:0.97150	validation-aucpr:0.97194
[20:24:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28]	validation-logloss:0.20944	validation-auc:0.97131	validation-aucpr:0.97181
[20:24:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29]	validation-logloss:0.20740	validation-auc:0.97129	validation-aucpr:0.97179
[20:24:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30]	validation-logloss:0.20496	validation-auc:0.97151	validation-aucpr:0.97179
[20:24:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31]	validation-logloss:0.20366	validation-auc:0.97132	validation-aucpr:0.97188
[20:24:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32]	validation-logloss:0.20189	validation-auc:0.97167	validation-aucpr:0.97355
[20:24:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33]	validation-logloss:0.20018	validation-auc:0.97186	validation-aucpr:0.97366
[20:24:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34]	validation-logloss:0.19918	validation-auc:0.97181	validation-aucpr:0.97393
[20:24:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35]	validation-logloss:0.19726	validation-auc:0.97214	validation-aucpr:0.97419
[20:24:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36]	validation-logloss:0.19609	validation-auc:0.97215	validation-aucpr:0.97392
[20:24:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37]	validation-logloss:0.19531	validation-auc:0.97216	validation-aucpr:0.97410
[20:24:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38]	validation-logloss:0.19406	validation-auc:0.97237	validation-aucpr:0.97408
[20:24:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39]	validation-logloss:0.19343	validation-auc:0.97241	validation-aucpr:0.97410
[20:24:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40]	validation-logloss:0.19290	validation-auc:0.97238	validation-aucpr:0.97456
[20:24:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41]	validation-logloss:0.19252	validation-auc:0.97233	validation-aucpr:0.97439
[20:24:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42]	validation-logloss:0.19170	validation-auc:0.97267	validation-aucpr:0.97656
[20:24:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43]	validation-logloss:0.19197	validation-auc:0.97251	validation-aucpr:0.97649
[20:24:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44]	validation-logloss:0.19176	validation-auc:0.97251	validation-aucpr:0.97645
[20:24:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45]	validation-logloss:0.19156	validation-auc:0.97247	validation-aucpr:0.97638
[20:24:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46]	validation-logloss:0.19144	validation-auc:0.97253	validation-aucpr:0.97641
[20:24:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[47]	validation-logloss:0.19106	validation-auc:0.97253	validation-aucpr:0.97641
[20:24:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[48]	validation-logloss:0.19144	validation-auc:0.97244	validation-aucpr:0.97643
[20:24:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[49]	validation-logloss:0.19094	validation-auc:0.97255	validation-aucpr:0.97652
[20:24:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[50]	validation-logloss:0.19094	validation-auc:0.97258	validation-aucpr:0.97664
[20:24:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[51]	validation-logloss:0.19141	validation-auc:0.97246	validation-aucpr:0.97653
[20:24:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[52]	validation-logloss:0.19122	validation-auc:0.97247	validation-aucpr:0.97656
[20:24:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[53]	validation-logloss:0.19111	validation-auc:0.97246	validation-aucpr:0.97657
[20:24:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[54]	validation-logloss:0.19121	validation-auc:0.97246	validation-aucpr:0.97657
[20:24:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[55]	validation-logloss:0.19154	validation-auc:0.97240	validation-aucpr:0.97652
[20:24:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[56]	validation-logloss:0.19184	validation-auc:0.97234	validation-aucpr:0.97647
[20:24:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[57]	validation-logloss:0.19200	validation-auc:0.97231	validation-aucpr:0.97646
[20:24:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[58]	validation-logloss:0.19232	validation-auc:0.97227	validation-aucpr:0.97644
[20:24:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[59]	validation-logloss:0.19242	validation-auc:0.97230	validation-aucpr:0.97644
[20:24:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[60]	validation-logloss:0.19260	validation-auc:0.97233	validation-aucpr:0.97645
[20:24:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[61]	validation-logloss:0.19250	validation-auc:0.97232	validation-aucpr:0.97645
[20:24:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[62]	validation-logloss:0.19268	validation-auc:0.97230	validation-aucpr:0.97645
[20:24:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[63]	validation-logloss:0.19290	validation-auc:0.97236	validation-aucpr:0.97653
[20:24:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[64]	validation-logloss:0.19347	validation-auc:0.97233	validation-aucpr:0.97649
[20:24:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[65]	validation-logloss:0.19370	validation-auc:0.97231	validation-aucpr:0.97643
{'best_iteration': '50', 'best_score': '0.9766366978311805'}
Trial 40, Fold 4: Log loss = 0.19370183926702098, Average precision = 0.9764367725265992, ROC-AUC = 0.9723107434351237, Elapsed Time = 10.073183600001357 seconds
Trial 40, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 40, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[20:24:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.63070	validation-auc:0.91775	validation-aucpr:0.88462
[20:24:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.57687	validation-auc:0.95126	validation-aucpr:0.92715
[20:24:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.53179	validation-auc:0.96085	validation-aucpr:0.95436
[20:24:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.49368	validation-auc:0.96162	validation-aucpr:0.95762
[20:24:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.45990	validation-auc:0.96465	validation-aucpr:0.96513
[20:24:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5]	validation-logloss:0.43151	validation-auc:0.96584	validation-aucpr:0.96723
[20:24:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6]	validation-logloss:0.40641	validation-auc:0.96743	validation-aucpr:0.97168
[20:24:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7]	validation-logloss:0.38427	validation-auc:0.96770	validation-aucpr:0.97184
[20:24:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8]	validation-logloss:0.36531	validation-auc:0.96746	validation-aucpr:0.97163
[20:24:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9]	validation-logloss:0.34792	validation-auc:0.96732	validation-aucpr:0.97018
[20:24:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10]	validation-logloss:0.33225	validation-auc:0.96775	validation-aucpr:0.97056
[20:24:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11]	validation-logloss:0.31820	validation-auc:0.96843	validation-aucpr:0.97095
[20:24:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12]	validation-logloss:0.30570	validation-auc:0.96908	validation-aucpr:0.97274
[20:24:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13]	validation-logloss:0.29523	validation-auc:0.96941	validation-aucpr:0.97297
[20:24:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14]	validation-logloss:0.28562	validation-auc:0.96956	validation-aucpr:0.97314
[20:24:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15]	validation-logloss:0.27628	validation-auc:0.96959	validation-aucpr:0.97322
[20:24:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16]	validation-logloss:0.26786	validation-auc:0.97016	validation-aucpr:0.97375
[20:24:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17]	validation-logloss:0.26049	validation-auc:0.97005	validation-aucpr:0.97331
[20:24:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18]	validation-logloss:0.25419	validation-auc:0.96992	validation-aucpr:0.97318
[20:24:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19]	validation-logloss:0.24805	validation-auc:0.97006	validation-aucpr:0.97332
[20:24:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20]	validation-logloss:0.24329	validation-auc:0.97002	validation-aucpr:0.97332
[20:24:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21]	validation-logloss:0.23852	validation-auc:0.97006	validation-aucpr:0.97339
[20:24:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22]	validation-logloss:0.23398	validation-auc:0.97003	validation-aucpr:0.97341
[20:24:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23]	validation-logloss:0.23034	validation-auc:0.97003	validation-aucpr:0.97337
[20:24:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24]	validation-logloss:0.22673	validation-auc:0.97034	validation-aucpr:0.97352
[20:24:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25]	validation-logloss:0.22371	validation-auc:0.97046	validation-aucpr:0.97350
[20:24:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26]	validation-logloss:0.22108	validation-auc:0.97055	validation-aucpr:0.97346
[20:24:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27]	validation-logloss:0.21870	validation-auc:0.97071	validation-aucpr:0.97361
[20:24:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28]	validation-logloss:0.21629	validation-auc:0.97073	validation-aucpr:0.97366
[20:24:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29]	validation-logloss:0.21376	validation-auc:0.97093	validation-aucpr:0.97380
[20:24:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30]	validation-logloss:0.21172	validation-auc:0.97110	validation-aucpr:0.97394
[20:24:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31]	validation-logloss:0.21004	validation-auc:0.97112	validation-aucpr:0.97398
[20:24:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32]	validation-logloss:0.20861	validation-auc:0.97105	validation-aucpr:0.97385
[20:24:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33]	validation-logloss:0.20687	validation-auc:0.97120	validation-aucpr:0.97401
[20:24:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34]	validation-logloss:0.20634	validation-auc:0.97091	validation-aucpr:0.97377
[20:24:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35]	validation-logloss:0.20532	validation-auc:0.97099	validation-aucpr:0.97393
[20:24:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36]	validation-logloss:0.20447	validation-auc:0.97098	validation-aucpr:0.97431
[20:24:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37]	validation-logloss:0.20382	validation-auc:0.97093	validation-aucpr:0.97429
[20:24:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38]	validation-logloss:0.20278	validation-auc:0.97099	validation-aucpr:0.97436
[20:24:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39]	validation-logloss:0.20214	validation-auc:0.97095	validation-aucpr:0.97441
[20:24:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40]	validation-logloss:0.20184	validation-auc:0.97096	validation-aucpr:0.97464
[20:24:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41]	validation-logloss:0.20114	validation-auc:0.97102	validation-aucpr:0.97404
[20:24:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42]	validation-logloss:0.20094	validation-auc:0.97086	validation-aucpr:0.97391
[20:24:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43]	validation-logloss:0.20071	validation-auc:0.97078	validation-aucpr:0.97380
[20:24:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44]	validation-logloss:0.20060	validation-auc:0.97073	validation-aucpr:0.97376
[20:24:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45]	validation-logloss:0.20071	validation-auc:0.97066	validation-aucpr:0.97386
[20:24:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46]	validation-logloss:0.20065	validation-auc:0.97057	validation-aucpr:0.97381
[20:24:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[47]	validation-logloss:0.20006	validation-auc:0.97071	validation-aucpr:0.97388
[20:24:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[48]	validation-logloss:0.19982	validation-auc:0.97083	validation-aucpr:0.97462
[20:24:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[49]	validation-logloss:0.19978	validation-auc:0.97082	validation-aucpr:0.97459
[20:24:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[50]	validation-logloss:0.20035	validation-auc:0.97060	validation-aucpr:0.97443
[20:24:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[51]	validation-logloss:0.19996	validation-auc:0.97072	validation-aucpr:0.97452
[20:24:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[52]	validation-logloss:0.20049	validation-auc:0.97051	validation-aucpr:0.97431
[20:24:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[53]	validation-logloss:0.20053	validation-auc:0.97068	validation-aucpr:0.97460
[20:24:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[54]	validation-logloss:0.20073	validation-auc:0.97060	validation-aucpr:0.97446
[20:24:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[55]	validation-logloss:0.20136	validation-auc:0.97047	validation-aucpr:0.97442
[20:24:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[56]	validation-logloss:0.20190	validation-auc:0.97035	validation-aucpr:0.97430
[20:24:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[57]	validation-logloss:0.20211	validation-auc:0.97035	validation-aucpr:0.97424
[20:24:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[58]	validation-logloss:0.20246	validation-auc:0.97038	validation-aucpr:0.97427
[20:24:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[59]	validation-logloss:0.20281	validation-auc:0.97041	validation-aucpr:0.97428
[20:24:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[60]	validation-logloss:0.20269	validation-auc:0.97049	validation-aucpr:0.97428
[20:24:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[61]	validation-logloss:0.20249	validation-auc:0.97060	validation-aucpr:0.97435
[20:24:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[62]	validation-logloss:0.20295	validation-auc:0.97063	validation-aucpr:0.97442
[20:24:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[63]	validation-logloss:0.20341	validation-auc:0.97058	validation-aucpr:0.97431
[20:24:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[64]	validation-logloss:0.20377	validation-auc:0.97061	validation-aucpr:0.97424
[20:24:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[65]	validation-logloss:0.20425	validation-auc:0.97057	validation-aucpr:0.97414
{'best_iteration': '40', 'best_score': '0.9746363737266491'}
Trial 40, Fold 5: Log loss = 0.20424930220962945, Average precision = 0.9741473211463513, ROC-AUC = 0.9705699050591755, Elapsed Time = 13.895724099998915 seconds
Optimization Progress:  41%|####1     | 41/100 [2:25:54<2:12:09, 134.40s/it]
Trial 41, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 41, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[0]	validation-logloss:0.68331	validation-auc:0.95047	validation-aucpr:0.95393
[1]	validation-logloss:0.67501	validation-auc:0.95415	validation-aucpr:0.95984
[2]	validation-logloss:0.66635	validation-auc:0.95465	validation-aucpr:0.96120
[3]	validation-logloss:0.65800	validation-auc:0.95560	validation-aucpr:0.96189
[4]	validation-logloss:0.65015	validation-auc:0.95545	validation-aucpr:0.96146
[5]	validation-logloss:0.64237	validation-auc:0.95490	validation-aucpr:0.96079
[6]	validation-logloss:0.63338	validation-auc:0.95775	validation-aucpr:0.96387
[7]	validation-logloss:0.62638	validation-auc:0.95698	validation-aucpr:0.96304
[8]	validation-logloss:0.61959	validation-auc:0.95631	validation-aucpr:0.96217
[9]	validation-logloss:0.61143	validation-auc:0.95731	validation-aucpr:0.96141
[10]	validation-logloss:0.60467	validation-auc:0.95773	validation-aucpr:0.96159
[11]	validation-logloss:0.59794	validation-auc:0.95751	validation-aucpr:0.96142
[12]	validation-logloss:0.59150	validation-auc:0.95714	validation-aucpr:0.96107
[13]	validation-logloss:0.58518	validation-auc:0.95685	validation-aucpr:0.96086
[14]	validation-logloss:0.57757	validation-auc:0.95794	validation-aucpr:0.96208
[15]	validation-logloss:0.57045	validation-auc:0.95840	validation-aucpr:0.96264
[16]	validation-logloss:0.56431	validation-auc:0.95861	validation-aucpr:0.96285
[17]	validation-logloss:0.55851	validation-auc:0.95866	validation-aucpr:0.96288
[18]	validation-logloss:0.55287	validation-auc:0.95854	validation-aucpr:0.96279
[19]	validation-logloss:0.54760	validation-auc:0.95810	validation-aucpr:0.96242
[20]	validation-logloss:0.54115	validation-auc:0.95854	validation-aucpr:0.96284
[21]	validation-logloss:0.53599	validation-auc:0.95821	validation-aucpr:0.96253
[22]	validation-logloss:0.52952	validation-auc:0.95895	validation-aucpr:0.96315
[23]	validation-logloss:0.52320	validation-auc:0.95932	validation-aucpr:0.96358
[24]	validation-logloss:0.51706	validation-auc:0.95968	validation-aucpr:0.96391
[25]	validation-logloss:0.51138	validation-auc:0.95966	validation-aucpr:0.96395
[26]	validation-logloss:0.50640	validation-auc:0.95978	validation-aucpr:0.96405
[27]	validation-logloss:0.50172	validation-auc:0.95990	validation-aucpr:0.96411
[28]	validation-logloss:0.49741	validation-auc:0.95991	validation-aucpr:0.96404
[29]	validation-logloss:0.49232	validation-auc:0.96003	validation-aucpr:0.96425
[30]	validation-logloss:0.48809	validation-auc:0.96007	validation-aucpr:0.96424
[31]	validation-logloss:0.48318	validation-auc:0.96020	validation-aucpr:0.96490
[32]	validation-logloss:0.47804	validation-auc:0.96044	validation-aucpr:0.96511
[33]	validation-logloss:0.47418	validation-auc:0.96033	validation-aucpr:0.96501
[34]	validation-logloss:0.46926	validation-auc:0.96059	validation-aucpr:0.96528
[35]	validation-logloss:0.46435	validation-auc:0.96088	validation-aucpr:0.96559
[36]	validation-logloss:0.45970	validation-auc:0.96086	validation-aucpr:0.96559
[37]	validation-logloss:0.45593	validation-auc:0.96089	validation-aucpr:0.96559
[38]	validation-logloss:0.45131	validation-auc:0.96104	validation-aucpr:0.96574
[39]	validation-logloss:0.44726	validation-auc:0.96107	validation-aucpr:0.96579
[40]	validation-logloss:0.44277	validation-auc:0.96125	validation-aucpr:0.96602
[41]	validation-logloss:0.43859	validation-auc:0.96130	validation-aucpr:0.96609
[42]	validation-logloss:0.43545	validation-auc:0.96132	validation-aucpr:0.96611
[43]	validation-logloss:0.43150	validation-auc:0.96136	validation-aucpr:0.96617
[44]	validation-logloss:0.42759	validation-auc:0.96152	validation-aucpr:0.96822
[45]	validation-logloss:0.42366	validation-auc:0.96164	validation-aucpr:0.96832
[46]	validation-logloss:0.41974	validation-auc:0.96166	validation-aucpr:0.96838
[47]	validation-logloss:0.41686	validation-auc:0.96159	validation-aucpr:0.96831
[48]	validation-logloss:0.41391	validation-auc:0.96163	validation-aucpr:0.96832
{'best_iteration': '46', 'best_score': '0.9683846721459073'}
Trial 41, Fold 1: Log loss = 0.4139050734398529, Average precision = 0.9682114877618576, ROC-AUC = 0.9616322954455294, Elapsed Time = 4.119934400001512 seconds
Trial 41, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 41, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[0]	validation-logloss:0.68371	validation-auc:0.94526	validation-aucpr:0.94741
[1]	validation-logloss:0.67530	validation-auc:0.95126	validation-aucpr:0.95521
[2]	validation-logloss:0.66527	validation-auc:0.95994	validation-aucpr:0.96478
[3]	validation-logloss:0.65699	validation-auc:0.95987	validation-aucpr:0.96462
[4]	validation-logloss:0.64865	validation-auc:0.96036	validation-aucpr:0.96485
[5]	validation-logloss:0.63981	validation-auc:0.96165	validation-aucpr:0.96635
[6]	validation-logloss:0.63116	validation-auc:0.96223	validation-aucpr:0.96684
[7]	validation-logloss:0.62251	validation-auc:0.96268	validation-aucpr:0.96721
[8]	validation-logloss:0.61413	validation-auc:0.96318	validation-aucpr:0.96766
[9]	validation-logloss:0.60700	validation-auc:0.96329	validation-aucpr:0.96768
[10]	validation-logloss:0.59867	validation-auc:0.96395	validation-aucpr:0.96826
[11]	validation-logloss:0.59178	validation-auc:0.96351	validation-aucpr:0.96787
[12]	validation-logloss:0.58515	validation-auc:0.96352	validation-aucpr:0.96780
[13]	validation-logloss:0.57791	validation-auc:0.96353	validation-aucpr:0.96789
[14]	validation-logloss:0.57170	validation-auc:0.96322	validation-aucpr:0.96765
[15]	validation-logloss:0.56460	validation-auc:0.96329	validation-aucpr:0.96768
[16]	validation-logloss:0.55746	validation-auc:0.96352	validation-aucpr:0.96788
[17]	validation-logloss:0.55055	validation-auc:0.96372	validation-aucpr:0.96801
[18]	validation-logloss:0.54412	validation-auc:0.96359	validation-aucpr:0.96793
[19]	validation-logloss:0.53864	validation-auc:0.96363	validation-aucpr:0.96786
[20]	validation-logloss:0.53364	validation-auc:0.96322	validation-aucpr:0.96752
[21]	validation-logloss:0.52860	validation-auc:0.96300	validation-aucpr:0.96725
[22]	validation-logloss:0.52228	validation-auc:0.96335	validation-aucpr:0.96755
[23]	validation-logloss:0.51648	validation-auc:0.96336	validation-aucpr:0.96756
[24]	validation-logloss:0.51122	validation-auc:0.96327	validation-aucpr:0.96748
[25]	validation-logloss:0.50565	validation-auc:0.96325	validation-aucpr:0.96750
[26]	validation-logloss:0.50024	validation-auc:0.96327	validation-aucpr:0.96755
[27]	validation-logloss:0.49564	validation-auc:0.96315	validation-aucpr:0.96753
[28]	validation-logloss:0.49108	validation-auc:0.96323	validation-aucpr:0.96754
[29]	validation-logloss:0.48579	validation-auc:0.96339	validation-aucpr:0.96763
[30]	validation-logloss:0.48156	validation-auc:0.96343	validation-aucpr:0.96763
[31]	validation-logloss:0.47673	validation-auc:0.96349	validation-aucpr:0.96764
[32]	validation-logloss:0.47228	validation-auc:0.96346	validation-aucpr:0.96761
[33]	validation-logloss:0.46826	validation-auc:0.96346	validation-aucpr:0.96755
[34]	validation-logloss:0.46334	validation-auc:0.96364	validation-aucpr:0.96769
[35]	validation-logloss:0.45901	validation-auc:0.96358	validation-aucpr:0.96769
[36]	validation-logloss:0.45518	validation-auc:0.96368	validation-aucpr:0.96803
[37]	validation-logloss:0.45090	validation-auc:0.96357	validation-aucpr:0.96793
[38]	validation-logloss:0.44655	validation-auc:0.96361	validation-aucpr:0.96791
[39]	validation-logloss:0.44311	validation-auc:0.96358	validation-aucpr:0.96792
[40]	validation-logloss:0.43960	validation-auc:0.96348	validation-aucpr:0.96779
[41]	validation-logloss:0.43520	validation-auc:0.96355	validation-aucpr:0.96786
[42]	validation-logloss:0.43172	validation-auc:0.96367	validation-aucpr:0.96791
[43]	validation-logloss:0.42754	validation-auc:0.96370	validation-aucpr:0.96797
[44]	validation-logloss:0.42439	validation-auc:0.96379	validation-aucpr:0.96802
[45]	validation-logloss:0.42052	validation-auc:0.96388	validation-aucpr:0.96813
[46]	validation-logloss:0.41743	validation-auc:0.96393	validation-aucpr:0.96815
[47]	validation-logloss:0.41383	validation-auc:0.96398	validation-aucpr:0.96821
[48]	validation-logloss:0.41082	validation-auc:0.96399	validation-aucpr:0.96820
{'best_iteration': '10', 'best_score': '0.9682580044293139'}
Trial 41, Fold 2: Log loss = 0.41081987104648127, Average precision = 0.9681047990157229, ROC-AUC = 0.9639932095900958, Elapsed Time = 4.259771599998203 seconds
Trial 41, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 41, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[0]	validation-logloss:0.68306	validation-auc:0.95170	validation-aucpr:0.95260
[1]	validation-logloss:0.67316	validation-auc:0.95748	validation-aucpr:0.96148
[2]	validation-logloss:0.66321	validation-auc:0.96296	validation-aucpr:0.96563
[3]	validation-logloss:0.65478	validation-auc:0.96364	validation-aucpr:0.96816
[4]	validation-logloss:0.64658	validation-auc:0.96294	validation-aucpr:0.96766
[5]	validation-logloss:0.63740	validation-auc:0.96314	validation-aucpr:0.96794
[6]	validation-logloss:0.62997	validation-auc:0.96312	validation-aucpr:0.96784
[7]	validation-logloss:0.62234	validation-auc:0.96269	validation-aucpr:0.96606
[8]	validation-logloss:0.61551	validation-auc:0.96253	validation-aucpr:0.96568
[9]	validation-logloss:0.60844	validation-auc:0.96245	validation-aucpr:0.96742
[10]	validation-logloss:0.60185	validation-auc:0.96209	validation-aucpr:0.96701
[11]	validation-logloss:0.59528	validation-auc:0.96211	validation-aucpr:0.96693
[12]	validation-logloss:0.58888	validation-auc:0.96195	validation-aucpr:0.96674
[13]	validation-logloss:0.58141	validation-auc:0.96205	validation-aucpr:0.96691
[14]	validation-logloss:0.57413	validation-auc:0.96239	validation-aucpr:0.96716
[15]	validation-logloss:0.56678	validation-auc:0.96287	validation-aucpr:0.96762
[16]	validation-logloss:0.55953	validation-auc:0.96336	validation-aucpr:0.96810
[17]	validation-logloss:0.55258	validation-auc:0.96357	validation-aucpr:0.96830
[18]	validation-logloss:0.54665	validation-auc:0.96387	validation-aucpr:0.96862
[19]	validation-logloss:0.54129	validation-auc:0.96371	validation-aucpr:0.96843
[20]	validation-logloss:0.53478	validation-auc:0.96400	validation-aucpr:0.96873
[21]	validation-logloss:0.52968	validation-auc:0.96379	validation-aucpr:0.96853
[22]	validation-logloss:0.52431	validation-auc:0.96372	validation-aucpr:0.96848
[23]	validation-logloss:0.51844	validation-auc:0.96382	validation-aucpr:0.96844
[24]	validation-logloss:0.51257	validation-auc:0.96384	validation-aucpr:0.96854
[25]	validation-logloss:0.50783	validation-auc:0.96364	validation-aucpr:0.96836
[26]	validation-logloss:0.50311	validation-auc:0.96366	validation-aucpr:0.96832
[27]	validation-logloss:0.49863	validation-auc:0.96349	validation-aucpr:0.96813
[28]	validation-logloss:0.49382	validation-auc:0.96356	validation-aucpr:0.96817
[29]	validation-logloss:0.48855	validation-auc:0.96346	validation-aucpr:0.96812
[30]	validation-logloss:0.48418	validation-auc:0.96361	validation-aucpr:0.96889
[31]	validation-logloss:0.47986	validation-auc:0.96379	validation-aucpr:0.96901
[32]	validation-logloss:0.47486	validation-auc:0.96398	validation-aucpr:0.96916
[33]	validation-logloss:0.46987	validation-auc:0.96406	validation-aucpr:0.96931
[34]	validation-logloss:0.46588	validation-auc:0.96409	validation-aucpr:0.96928
[35]	validation-logloss:0.46177	validation-auc:0.96410	validation-aucpr:0.96928
[36]	validation-logloss:0.45746	validation-auc:0.96414	validation-aucpr:0.96933
[37]	validation-logloss:0.45290	validation-auc:0.96426	validation-aucpr:0.96946
[38]	validation-logloss:0.44836	validation-auc:0.96439	validation-aucpr:0.96960
[39]	validation-logloss:0.44411	validation-auc:0.96436	validation-aucpr:0.96956
[40]	validation-logloss:0.44051	validation-auc:0.96441	validation-aucpr:0.96958
[41]	validation-logloss:0.43630	validation-auc:0.96454	validation-aucpr:0.96966
[42]	validation-logloss:0.43266	validation-auc:0.96454	validation-aucpr:0.96967
[43]	validation-logloss:0.42844	validation-auc:0.96456	validation-aucpr:0.96972
[44]	validation-logloss:0.42503	validation-auc:0.96459	validation-aucpr:0.96974
[45]	validation-logloss:0.42173	validation-auc:0.96464	validation-aucpr:0.96977
[46]	validation-logloss:0.41833	validation-auc:0.96480	validation-aucpr:0.96989
[47]	validation-logloss:0.41458	validation-auc:0.96484	validation-aucpr:0.96992
[48]	validation-logloss:0.41149	validation-auc:0.96487	validation-aucpr:0.96990
{'best_iteration': '47', 'best_score': '0.9699236434103997'}
Trial 41, Fold 3: Log loss = 0.4114919188101245, Average precision = 0.9698635641105825, ROC-AUC = 0.9648704986949949, Elapsed Time = 4.189911100002064 seconds
Trial 41, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 41, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[0]	validation-logloss:0.68323	validation-auc:0.94846	validation-aucpr:0.95009
[1]	validation-logloss:0.67334	validation-auc:0.95575	validation-aucpr:0.95982
[2]	validation-logloss:0.66363	validation-auc:0.95780	validation-aucpr:0.96448
[3]	validation-logloss:0.65565	validation-auc:0.95568	validation-aucpr:0.96329
[4]	validation-logloss:0.64788	validation-auc:0.95672	validation-aucpr:0.96365
[5]	validation-logloss:0.63867	validation-auc:0.95851	validation-aucpr:0.96501
[6]	validation-logloss:0.63133	validation-auc:0.95796	validation-aucpr:0.96455
[7]	validation-logloss:0.62366	validation-auc:0.95799	validation-aucpr:0.96498
[8]	validation-logloss:0.61696	validation-auc:0.95686	validation-aucpr:0.96420
[9]	validation-logloss:0.60978	validation-auc:0.95775	validation-aucpr:0.96483
[10]	validation-logloss:0.60245	validation-auc:0.95793	validation-aucpr:0.96499
[11]	validation-logloss:0.59574	validation-auc:0.95807	validation-aucpr:0.96497
[12]	validation-logloss:0.58888	validation-auc:0.95834	validation-aucpr:0.96513
[13]	validation-logloss:0.58268	validation-auc:0.95800	validation-aucpr:0.96483
[14]	validation-logloss:0.57520	validation-auc:0.95887	validation-aucpr:0.96564
[15]	validation-logloss:0.56899	validation-auc:0.95944	validation-aucpr:0.96592
[16]	validation-logloss:0.56254	validation-auc:0.95948	validation-aucpr:0.96600
[17]	validation-logloss:0.55692	validation-auc:0.95948	validation-aucpr:0.96591
[18]	validation-logloss:0.55025	validation-auc:0.96031	validation-aucpr:0.96666
[19]	validation-logloss:0.54397	validation-auc:0.96035	validation-aucpr:0.96678
[20]	validation-logloss:0.53832	validation-auc:0.96047	validation-aucpr:0.96683
[21]	validation-logloss:0.53304	validation-auc:0.96039	validation-aucpr:0.96678
[22]	validation-logloss:0.52691	validation-auc:0.96083	validation-aucpr:0.96712
[23]	validation-logloss:0.52184	validation-auc:0.96053	validation-aucpr:0.96687
[24]	validation-logloss:0.51689	validation-auc:0.96055	validation-aucpr:0.96685
[25]	validation-logloss:0.51114	validation-auc:0.96082	validation-aucpr:0.96711
[26]	validation-logloss:0.50614	validation-auc:0.96095	validation-aucpr:0.96721
[27]	validation-logloss:0.50164	validation-auc:0.96074	validation-aucpr:0.96701
[28]	validation-logloss:0.49612	validation-auc:0.96094	validation-aucpr:0.96722
[29]	validation-logloss:0.49051	validation-auc:0.96138	validation-aucpr:0.96765
[30]	validation-logloss:0.48618	validation-auc:0.96132	validation-aucpr:0.96758
[31]	validation-logloss:0.48213	validation-auc:0.96127	validation-aucpr:0.96754
[32]	validation-logloss:0.47715	validation-auc:0.96140	validation-aucpr:0.96767
[33]	validation-logloss:0.47226	validation-auc:0.96156	validation-aucpr:0.96782
[34]	validation-logloss:0.46760	validation-auc:0.96157	validation-aucpr:0.96785
[35]	validation-logloss:0.46381	validation-auc:0.96155	validation-aucpr:0.96781
[36]	validation-logloss:0.45912	validation-auc:0.96158	validation-aucpr:0.96791
[37]	validation-logloss:0.45454	validation-auc:0.96169	validation-aucpr:0.96804
[38]	validation-logloss:0.44998	validation-auc:0.96181	validation-aucpr:0.96818
[39]	validation-logloss:0.44650	validation-auc:0.96180	validation-aucpr:0.96815
[40]	validation-logloss:0.44300	validation-auc:0.96188	validation-aucpr:0.96818
[41]	validation-logloss:0.43950	validation-auc:0.96185	validation-aucpr:0.96815
[42]	validation-logloss:0.43635	validation-auc:0.96180	validation-aucpr:0.96809
[43]	validation-logloss:0.43234	validation-auc:0.96184	validation-aucpr:0.96818
[44]	validation-logloss:0.42835	validation-auc:0.96196	validation-aucpr:0.96827
[45]	validation-logloss:0.42533	validation-auc:0.96192	validation-aucpr:0.96823
[46]	validation-logloss:0.42230	validation-auc:0.96195	validation-aucpr:0.96822
[47]	validation-logloss:0.41868	validation-auc:0.96199	validation-aucpr:0.96829
[48]	validation-logloss:0.41574	validation-auc:0.96199	validation-aucpr:0.96827
{'best_iteration': '47', 'best_score': '0.968289622878098'}
Trial 41, Fold 4: Log loss = 0.4157433809493005, Average precision = 0.9680932874566976, ROC-AUC = 0.9619874413137145, Elapsed Time = 4.13615320000099 seconds
Trial 41, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 41, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[0]	validation-logloss:0.68440	validation-auc:0.92976	validation-aucpr:0.92645
[1]	validation-logloss:0.67556	validation-auc:0.94104	validation-aucpr:0.94659
[2]	validation-logloss:0.66566	validation-auc:0.95372	validation-aucpr:0.96037
[3]	validation-logloss:0.65798	validation-auc:0.95399	validation-aucpr:0.96125
[4]	validation-logloss:0.64974	validation-auc:0.95305	validation-aucpr:0.96017
[5]	validation-logloss:0.64186	validation-auc:0.95301	validation-aucpr:0.96000
[6]	validation-logloss:0.63323	validation-auc:0.95498	validation-aucpr:0.96207
[7]	validation-logloss:0.62626	validation-auc:0.95426	validation-aucpr:0.96035
[8]	validation-logloss:0.61917	validation-auc:0.95547	validation-aucpr:0.96114
[9]	validation-logloss:0.61248	validation-auc:0.95466	validation-aucpr:0.96039
[10]	validation-logloss:0.60430	validation-auc:0.95563	validation-aucpr:0.96153
[11]	validation-logloss:0.59715	validation-auc:0.95595	validation-aucpr:0.96166
[12]	validation-logloss:0.59094	validation-auc:0.95508	validation-aucpr:0.96094
[13]	validation-logloss:0.58471	validation-auc:0.95447	validation-aucpr:0.96041
[14]	validation-logloss:0.57758	validation-auc:0.95541	validation-aucpr:0.96167
[15]	validation-logloss:0.57130	validation-auc:0.95643	validation-aucpr:0.96241
[16]	validation-logloss:0.56515	validation-auc:0.95653	validation-aucpr:0.96256
[17]	validation-logloss:0.55844	validation-auc:0.95650	validation-aucpr:0.96262
[18]	validation-logloss:0.55182	validation-auc:0.95666	validation-aucpr:0.96287
[19]	validation-logloss:0.54651	validation-auc:0.95675	validation-aucpr:0.96280
[20]	validation-logloss:0.54133	validation-auc:0.95653	validation-aucpr:0.96228
[21]	validation-logloss:0.53643	validation-auc:0.95630	validation-aucpr:0.96195
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[39]	validation-logloss:0.44959	validation-auc:0.95985	validation-aucpr:0.96581
[40]	validation-logloss:0.44624	validation-auc:0.95998	validation-aucpr:0.96590
[41]	validation-logloss:0.44178	validation-auc:0.96003	validation-aucpr:0.96599
[42]	validation-logloss:0.43828	validation-auc:0.96014	validation-aucpr:0.96605
[43]	validation-logloss:0.43408	validation-auc:0.96034	validation-aucpr:0.96624
[44]	validation-logloss:0.43106	validation-auc:0.96029	validation-aucpr:0.96618
[45]	validation-logloss:0.42792	validation-auc:0.96031	validation-aucpr:0.96615
[46]	validation-logloss:0.42403	validation-auc:0.96038	validation-aucpr:0.96624
[47]	validation-logloss:0.42112	validation-auc:0.96046	validation-aucpr:0.96629
[48]	validation-logloss:0.41818	validation-auc:0.96047	validation-aucpr:0.96630
{'best_iteration': '48', 'best_score': '0.9662968778042819'}
Trial 41, Fold 5: Log loss = 0.4181768956842521, Average precision = 0.9661507923621591, ROC-AUC = 0.9604730319751779, Elapsed Time = 4.168298500000674 seconds
Optimization Progress:  42%|####2     | 42/100 [2:26:24<1:39:34, 103.01s/it]
Trial 42, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 42, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[0]	validation-logloss:0.68113	validation-auc:0.94891	validation-aucpr:0.92942
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[3]	validation-logloss:0.64900	validation-auc:0.96546	validation-aucpr:0.96958
[4]	validation-logloss:0.63959	validation-auc:0.96565	validation-aucpr:0.96935
[5]	validation-logloss:0.63061	validation-auc:0.96558	validation-aucpr:0.96940
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[7]	validation-logloss:0.61220	validation-auc:0.96615	validation-aucpr:0.97013
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[9]	validation-logloss:0.59342	validation-auc:0.96671	validation-aucpr:0.97070
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[55]	validation-logloss:0.35615	validation-auc:0.96965	validation-aucpr:0.97426
[56]	validation-logloss:0.35294	validation-auc:0.96969	validation-aucpr:0.97431
[57]	validation-logloss:0.35037	validation-auc:0.96964	validation-aucpr:0.97425
[58]	validation-logloss:0.34730	validation-auc:0.96974	validation-aucpr:0.97432
[59]	validation-logloss:0.34485	validation-auc:0.96969	validation-aucpr:0.97428
[60]	validation-logloss:0.34238	validation-auc:0.96965	validation-aucpr:0.97423
[61]	validation-logloss:0.34002	validation-auc:0.96960	validation-aucpr:0.97416
[62]	validation-logloss:0.33716	validation-auc:0.96964	validation-aucpr:0.97421
[63]	validation-logloss:0.33428	validation-auc:0.96972	validation-aucpr:0.97428
[64]	validation-logloss:0.33204	validation-auc:0.96970	validation-aucpr:0.97426
[65]	validation-logloss:0.32990	validation-auc:0.96962	validation-aucpr:0.97418
[66]	validation-logloss:0.32727	validation-auc:0.96965	validation-aucpr:0.97421
[67]	validation-logloss:0.32518	validation-auc:0.96954	validation-aucpr:0.97411
[68]	validation-logloss:0.32298	validation-auc:0.96957	validation-aucpr:0.97413
[69]	validation-logloss:0.32039	validation-auc:0.96961	validation-aucpr:0.97416
[70]	validation-logloss:0.31792	validation-auc:0.96969	validation-aucpr:0.97421
[71]	validation-logloss:0.31601	validation-auc:0.96967	validation-aucpr:0.97417
[72]	validation-logloss:0.31367	validation-auc:0.96972	validation-aucpr:0.97422
{'best_iteration': '58', 'best_score': '0.9743230383233791'}
Trial 42, Fold 1: Log loss = 0.3136689057542144, Average precision = 0.9742187899161252, ROC-AUC = 0.969719808529655, Elapsed Time = 1.9366607000010845 seconds
Trial 42, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 42, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[0]	validation-logloss:0.68136	validation-auc:0.94753	validation-aucpr:0.90845
[1]	validation-logloss:0.67107	validation-auc:0.96181	validation-aucpr:0.95788
[2]	validation-logloss:0.66099	validation-auc:0.96363	validation-aucpr:0.96454
[3]	validation-logloss:0.65146	validation-auc:0.96404	validation-aucpr:0.96720
[4]	validation-logloss:0.64192	validation-auc:0.96476	validation-aucpr:0.96778
[5]	validation-logloss:0.63260	validation-auc:0.96443	validation-aucpr:0.96743
[6]	validation-logloss:0.62251	validation-auc:0.96641	validation-aucpr:0.96968
[7]	validation-logloss:0.61280	validation-auc:0.96754	validation-aucpr:0.97090
[8]	validation-logloss:0.60463	validation-auc:0.96730	validation-aucpr:0.97064
[9]	validation-logloss:0.59676	validation-auc:0.96671	validation-aucpr:0.97034
[10]	validation-logloss:0.58901	validation-auc:0.96640	validation-aucpr:0.97000
[11]	validation-logloss:0.58023	validation-auc:0.96723	validation-aucpr:0.97075
[12]	validation-logloss:0.57163	validation-auc:0.96762	validation-aucpr:0.97113
[13]	validation-logloss:0.56320	validation-auc:0.96809	validation-aucpr:0.97159
[14]	validation-logloss:0.55529	validation-auc:0.96818	validation-aucpr:0.97170
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[59]	validation-logloss:0.34291	validation-auc:0.97035	validation-aucpr:0.97335
[60]	validation-logloss:0.33996	validation-auc:0.97034	validation-aucpr:0.97336
[61]	validation-logloss:0.33699	validation-auc:0.97047	validation-aucpr:0.97345
[62]	validation-logloss:0.33452	validation-auc:0.97047	validation-aucpr:0.97342
[63]	validation-logloss:0.33216	validation-auc:0.97046	validation-aucpr:0.97342
[64]	validation-logloss:0.32945	validation-auc:0.97053	validation-aucpr:0.97346
[65]	validation-logloss:0.32718	validation-auc:0.97050	validation-aucpr:0.97344
[66]	validation-logloss:0.32442	validation-auc:0.97060	validation-aucpr:0.97348
[67]	validation-logloss:0.32186	validation-auc:0.97063	validation-aucpr:0.97351
[68]	validation-logloss:0.31925	validation-auc:0.97071	validation-aucpr:0.97356
[69]	validation-logloss:0.31706	validation-auc:0.97078	validation-aucpr:0.97360
[70]	validation-logloss:0.31451	validation-auc:0.97084	validation-aucpr:0.97367
[71]	validation-logloss:0.31244	validation-auc:0.97085	validation-aucpr:0.97367
[72]	validation-logloss:0.31005	validation-auc:0.97089	validation-aucpr:0.97369
{'best_iteration': '72', 'best_score': '0.973692450730977'}
Trial 42, Fold 2: Log loss = 0.3100492448380225, Average precision = 0.9736645921803798, ROC-AUC = 0.9708852146366203, Elapsed Time = 2.2306970999998157 seconds
Trial 42, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 42, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[0]	validation-logloss:0.68110	validation-auc:0.94677	validation-aucpr:0.91224
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[2]	validation-logloss:0.65956	validation-auc:0.96645	validation-aucpr:0.96741
[3]	validation-logloss:0.64856	validation-auc:0.96805	validation-aucpr:0.97135
[4]	validation-logloss:0.63795	validation-auc:0.96863	validation-aucpr:0.97192
[5]	validation-logloss:0.62753	validation-auc:0.96904	validation-aucpr:0.97229
[6]	validation-logloss:0.61771	validation-auc:0.96928	validation-aucpr:0.97283
[7]	validation-logloss:0.60954	validation-auc:0.96891	validation-aucpr:0.97234
[8]	validation-logloss:0.59999	validation-auc:0.96962	validation-aucpr:0.97292
[9]	validation-logloss:0.59160	validation-auc:0.96949	validation-aucpr:0.97285
[10]	validation-logloss:0.58350	validation-auc:0.96911	validation-aucpr:0.97249
[11]	validation-logloss:0.57585	validation-auc:0.96850	validation-aucpr:0.97197
[12]	validation-logloss:0.56734	validation-auc:0.96860	validation-aucpr:0.97213
[13]	validation-logloss:0.56000	validation-auc:0.96837	validation-aucpr:0.97192
[14]	validation-logloss:0.55316	validation-auc:0.96833	validation-aucpr:0.97225
[15]	validation-logloss:0.54635	validation-auc:0.96834	validation-aucpr:0.97226
[16]	validation-logloss:0.53954	validation-auc:0.96854	validation-aucpr:0.97247
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[45]	validation-logloss:0.38699	validation-auc:0.97054	validation-aucpr:0.97475
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[55]	validation-logloss:0.35399	validation-auc:0.97101	validation-aucpr:0.97512
[56]	validation-logloss:0.35126	validation-auc:0.97093	validation-aucpr:0.97507
[57]	validation-logloss:0.34813	validation-auc:0.97101	validation-aucpr:0.97514
[58]	validation-logloss:0.34512	validation-auc:0.97106	validation-aucpr:0.97521
[59]	validation-logloss:0.34200	validation-auc:0.97115	validation-aucpr:0.97528
[60]	validation-logloss:0.33905	validation-auc:0.97111	validation-aucpr:0.97527
[61]	validation-logloss:0.33614	validation-auc:0.97111	validation-aucpr:0.97526
[62]	validation-logloss:0.33335	validation-auc:0.97113	validation-aucpr:0.97528
[63]	validation-logloss:0.33105	validation-auc:0.97111	validation-aucpr:0.97528
[64]	validation-logloss:0.32873	validation-auc:0.97108	validation-aucpr:0.97530
[65]	validation-logloss:0.32640	validation-auc:0.97112	validation-aucpr:0.97533
[66]	validation-logloss:0.32371	validation-auc:0.97115	validation-aucpr:0.97535
[67]	validation-logloss:0.32151	validation-auc:0.97107	validation-aucpr:0.97528
[68]	validation-logloss:0.31938	validation-auc:0.97110	validation-aucpr:0.97541
[69]	validation-logloss:0.31726	validation-auc:0.97116	validation-aucpr:0.97545
[70]	validation-logloss:0.31472	validation-auc:0.97119	validation-aucpr:0.97548
[71]	validation-logloss:0.31275	validation-auc:0.97114	validation-aucpr:0.97543
[72]	validation-logloss:0.31030	validation-auc:0.97119	validation-aucpr:0.97549
{'best_iteration': '72', 'best_score': '0.9754871959768607'}
Trial 42, Fold 3: Log loss = 0.3102979970624399, Average precision = 0.9754888562852468, ROC-AUC = 0.9711920693241902, Elapsed Time = 2.2501545999984955 seconds
Trial 42, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 42, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[0]	validation-logloss:0.68115	validation-auc:0.94672	validation-aucpr:0.93040
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[2]	validation-logloss:0.66061	validation-auc:0.96409	validation-aucpr:0.96962
[3]	validation-logloss:0.64959	validation-auc:0.96598	validation-aucpr:0.97121
[4]	validation-logloss:0.64015	validation-auc:0.96565	validation-aucpr:0.97111
[5]	validation-logloss:0.63110	validation-auc:0.96524	validation-aucpr:0.97060
[6]	validation-logloss:0.62102	validation-auc:0.96618	validation-aucpr:0.97146
[7]	validation-logloss:0.61125	validation-auc:0.96686	validation-aucpr:0.97194
[8]	validation-logloss:0.60289	validation-auc:0.96669	validation-aucpr:0.97173
[9]	validation-logloss:0.59483	validation-auc:0.96658	validation-aucpr:0.97153
[10]	validation-logloss:0.58681	validation-auc:0.96644	validation-aucpr:0.97140
[11]	validation-logloss:0.57930	validation-auc:0.96629	validation-aucpr:0.97130
[12]	validation-logloss:0.57186	validation-auc:0.96621	validation-aucpr:0.97128
[13]	validation-logloss:0.56450	validation-auc:0.96634	validation-aucpr:0.97133
[14]	validation-logloss:0.55647	validation-auc:0.96664	validation-aucpr:0.97168
[15]	validation-logloss:0.54983	validation-auc:0.96637	validation-aucpr:0.97146
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[24]	validation-logloss:0.49019	validation-auc:0.96723	validation-aucpr:0.97227
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[35]	validation-logloss:0.43263	validation-auc:0.96785	validation-aucpr:0.97286
[36]	validation-logloss:0.42772	validation-auc:0.96805	validation-aucpr:0.97301
[37]	validation-logloss:0.42365	validation-auc:0.96798	validation-aucpr:0.97294
[38]	validation-logloss:0.41966	validation-auc:0.96788	validation-aucpr:0.97285
[39]	validation-logloss:0.41582	validation-auc:0.96789	validation-aucpr:0.97285
[40]	validation-logloss:0.41125	validation-auc:0.96802	validation-aucpr:0.97295
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[43]	validation-logloss:0.39895	validation-auc:0.96815	validation-aucpr:0.97308
[44]	validation-logloss:0.39477	validation-auc:0.96828	validation-aucpr:0.97321
[45]	validation-logloss:0.39065	validation-auc:0.96840	validation-aucpr:0.97332
[46]	validation-logloss:0.38721	validation-auc:0.96845	validation-aucpr:0.97334
[47]	validation-logloss:0.38329	validation-auc:0.96853	validation-aucpr:0.97342
[48]	validation-logloss:0.37944	validation-auc:0.96866	validation-aucpr:0.97354
[49]	validation-logloss:0.37563	validation-auc:0.96875	validation-aucpr:0.97362
[50]	validation-logloss:0.37190	validation-auc:0.96887	validation-aucpr:0.97372
[51]	validation-logloss:0.36835	validation-auc:0.96894	validation-aucpr:0.97378
[52]	validation-logloss:0.36537	validation-auc:0.96901	validation-aucpr:0.97381
[53]	validation-logloss:0.36251	validation-auc:0.96897	validation-aucpr:0.97376
[54]	validation-logloss:0.35966	validation-auc:0.96894	validation-aucpr:0.97375
[55]	validation-logloss:0.35628	validation-auc:0.96905	validation-aucpr:0.97384
[56]	validation-logloss:0.35350	validation-auc:0.96904	validation-aucpr:0.97382
[57]	validation-logloss:0.35029	validation-auc:0.96905	validation-aucpr:0.97386
[58]	validation-logloss:0.34781	validation-auc:0.96895	validation-aucpr:0.97379
[59]	validation-logloss:0.34464	validation-auc:0.96911	validation-aucpr:0.97394
[60]	validation-logloss:0.34155	validation-auc:0.96925	validation-aucpr:0.97405
[61]	validation-logloss:0.33858	validation-auc:0.96929	validation-aucpr:0.97410
[62]	validation-logloss:0.33617	validation-auc:0.96932	validation-aucpr:0.97411
[63]	validation-logloss:0.33338	validation-auc:0.96936	validation-aucpr:0.97415
[64]	validation-logloss:0.33052	validation-auc:0.96945	validation-aucpr:0.97422
[65]	validation-logloss:0.32845	validation-auc:0.96936	validation-aucpr:0.97416
[66]	validation-logloss:0.32628	validation-auc:0.96931	validation-aucpr:0.97410
[67]	validation-logloss:0.32409	validation-auc:0.96932	validation-aucpr:0.97410
[68]	validation-logloss:0.32146	validation-auc:0.96936	validation-aucpr:0.97415
[69]	validation-logloss:0.31887	validation-auc:0.96948	validation-aucpr:0.97425
[70]	validation-logloss:0.31683	validation-auc:0.96947	validation-aucpr:0.97423
[71]	validation-logloss:0.31490	validation-auc:0.96943	validation-aucpr:0.97420
[72]	validation-logloss:0.31291	validation-auc:0.96942	validation-aucpr:0.97418
{'best_iteration': '69', 'best_score': '0.9742472920910086'}
Trial 42, Fold 4: Log loss = 0.3129062231328123, Average precision = 0.9741810472294776, ROC-AUC = 0.9694151013016492, Elapsed Time = 2.174041499998566 seconds
Trial 42, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 42, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[0]	validation-logloss:0.68107	validation-auc:0.95236	validation-aucpr:0.94188
[1]	validation-logloss:0.66936	validation-auc:0.96419	validation-aucpr:0.96656
[2]	validation-logloss:0.65987	validation-auc:0.96348	validation-aucpr:0.96789
[3]	validation-logloss:0.64997	validation-auc:0.96523	validation-aucpr:0.96913
[4]	validation-logloss:0.64050	validation-auc:0.96514	validation-aucpr:0.96886
[5]	validation-logloss:0.63016	validation-auc:0.96583	validation-aucpr:0.96954
[6]	validation-logloss:0.62177	validation-auc:0.96545	validation-aucpr:0.96914
[7]	validation-logloss:0.61182	validation-auc:0.96661	validation-aucpr:0.97020
[8]	validation-logloss:0.60338	validation-auc:0.96661	validation-aucpr:0.97008
[9]	validation-logloss:0.59418	validation-auc:0.96700	validation-aucpr:0.97075
[10]	validation-logloss:0.58521	validation-auc:0.96728	validation-aucpr:0.97095
[11]	validation-logloss:0.57658	validation-auc:0.96764	validation-aucpr:0.97121
[12]	validation-logloss:0.56951	validation-auc:0.96722	validation-aucpr:0.97089
[13]	validation-logloss:0.56125	validation-auc:0.96745	validation-aucpr:0.97106
[14]	validation-logloss:0.55343	validation-auc:0.96740	validation-aucpr:0.97112
[15]	validation-logloss:0.54564	validation-auc:0.96751	validation-aucpr:0.97119
[16]	validation-logloss:0.53801	validation-auc:0.96765	validation-aucpr:0.97132
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[23]	validation-logloss:0.49223	validation-auc:0.96803	validation-aucpr:0.97172
[24]	validation-logloss:0.48686	validation-auc:0.96796	validation-aucpr:0.97161
[25]	validation-logloss:0.48154	validation-auc:0.96785	validation-aucpr:0.97151
[26]	validation-logloss:0.47635	validation-auc:0.96768	validation-aucpr:0.97134
[27]	validation-logloss:0.47032	validation-auc:0.96790	validation-aucpr:0.97154
[28]	validation-logloss:0.46449	validation-auc:0.96821	validation-aucpr:0.97177
[29]	validation-logloss:0.45888	validation-auc:0.96828	validation-aucpr:0.97186
[30]	validation-logloss:0.45402	validation-auc:0.96822	validation-aucpr:0.97180
[31]	validation-logloss:0.44865	validation-auc:0.96825	validation-aucpr:0.97185
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[34]	validation-logloss:0.43520	validation-auc:0.96834	validation-aucpr:0.97235
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[36]	validation-logloss:0.42621	validation-auc:0.96841	validation-aucpr:0.97241
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[38]	validation-logloss:0.41764	validation-auc:0.96840	validation-aucpr:0.97241
[39]	validation-logloss:0.41305	validation-auc:0.96862	validation-aucpr:0.97260
[40]	validation-logloss:0.40867	validation-auc:0.96865	validation-aucpr:0.97262
[41]	validation-logloss:0.40445	validation-auc:0.96874	validation-aucpr:0.97271
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[45]	validation-logloss:0.38929	validation-auc:0.96874	validation-aucpr:0.97273
[46]	validation-logloss:0.38553	validation-auc:0.96885	validation-aucpr:0.97282
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[49]	validation-logloss:0.37506	validation-auc:0.96891	validation-aucpr:0.97286
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[51]	validation-logloss:0.36922	validation-auc:0.96878	validation-aucpr:0.97271
[52]	validation-logloss:0.36576	validation-auc:0.96892	validation-aucpr:0.97284
[53]	validation-logloss:0.36240	validation-auc:0.96896	validation-aucpr:0.97288
[54]	validation-logloss:0.35906	validation-auc:0.96909	validation-aucpr:0.97297
[55]	validation-logloss:0.35579	validation-auc:0.96923	validation-aucpr:0.97309
[56]	validation-logloss:0.35271	validation-auc:0.96928	validation-aucpr:0.97315
[57]	validation-logloss:0.35008	validation-auc:0.96921	validation-aucpr:0.97307
[58]	validation-logloss:0.34699	validation-auc:0.96931	validation-aucpr:0.97315
[59]	validation-logloss:0.34447	validation-auc:0.96934	validation-aucpr:0.97317
[60]	validation-logloss:0.34200	validation-auc:0.96934	validation-aucpr:0.97315
[61]	validation-logloss:0.33909	validation-auc:0.96945	validation-aucpr:0.97324
[62]	validation-logloss:0.33630	validation-auc:0.96952	validation-aucpr:0.97330
[63]	validation-logloss:0.33347	validation-auc:0.96967	validation-aucpr:0.97341
[64]	validation-logloss:0.33082	validation-auc:0.96975	validation-aucpr:0.97348
[65]	validation-logloss:0.32813	validation-auc:0.96983	validation-aucpr:0.97353
[66]	validation-logloss:0.32592	validation-auc:0.96981	validation-aucpr:0.97355
[67]	validation-logloss:0.32335	validation-auc:0.96990	validation-aucpr:0.97363
[68]	validation-logloss:0.32088	validation-auc:0.96992	validation-aucpr:0.97366
[69]	validation-logloss:0.31838	validation-auc:0.96998	validation-aucpr:0.97372
[70]	validation-logloss:0.31637	validation-auc:0.96990	validation-aucpr:0.97364
[71]	validation-logloss:0.31395	validation-auc:0.96999	validation-aucpr:0.97372
[72]	validation-logloss:0.31165	validation-auc:0.97001	validation-aucpr:0.97374
{'best_iteration': '72', 'best_score': '0.9737411445974385'}
Trial 42, Fold 5: Log loss = 0.3116536302078631, Average precision = 0.9737242713120442, ROC-AUC = 0.9700108132211136, Elapsed Time = 2.2429255999995803 seconds
Optimization Progress:  43%|####3     | 43/100 [2:26:42<1:13:49, 77.72s/it] 
Trial 43, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 43, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[20:25:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.67385	validation-auc:0.94143	validation-aucpr:0.94742
[20:25:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.65498	validation-auc:0.95671	validation-aucpr:0.96049
[20:25:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.63800	validation-auc:0.95647	validation-aucpr:0.95995
[20:25:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.62230	validation-auc:0.95708	validation-aucpr:0.96084
[20:25:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.60781	validation-auc:0.95682	validation-aucpr:0.96138
[20:25:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5]	validation-logloss:0.59385	validation-auc:0.95659	validation-aucpr:0.96136
[20:25:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6]	validation-logloss:0.57868	validation-auc:0.95931	validation-aucpr:0.96438
[20:25:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7]	validation-logloss:0.56312	validation-auc:0.96151	validation-aucpr:0.96677
[20:25:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8]	validation-logloss:0.55050	validation-auc:0.96175	validation-aucpr:0.96706
[20:25:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9]	validation-logloss:0.53826	validation-auc:0.96186	validation-aucpr:0.96713
[20:25:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10]	validation-logloss:0.52539	validation-auc:0.96263	validation-aucpr:0.96795
[20:25:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11]	validation-logloss:0.51261	validation-auc:0.96324	validation-aucpr:0.96890
[20:25:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12]	validation-logloss:0.50210	validation-auc:0.96361	validation-aucpr:0.96916
[20:25:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13]	validation-logloss:0.49218	validation-auc:0.96384	validation-aucpr:0.96935
[20:25:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14]	validation-logloss:0.48074	validation-auc:0.96445	validation-aucpr:0.96984
[20:25:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15]	validation-logloss:0.47129	validation-auc:0.96483	validation-aucpr:0.97009
[20:25:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16]	validation-logloss:0.46290	validation-auc:0.96465	validation-aucpr:0.96996
[20:25:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17]	validation-logloss:0.45273	validation-auc:0.96500	validation-aucpr:0.97034
[20:25:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18]	validation-logloss:0.44265	validation-auc:0.96554	validation-aucpr:0.97084
[20:25:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19]	validation-logloss:0.43481	validation-auc:0.96553	validation-aucpr:0.97078
[20:25:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20]	validation-logloss:0.42595	validation-auc:0.96580	validation-aucpr:0.97105
[20:25:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21]	validation-logloss:0.41885	validation-auc:0.96592	validation-aucpr:0.97106
[20:25:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22]	validation-logloss:0.41081	validation-auc:0.96601	validation-aucpr:0.97118
[20:25:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23]	validation-logloss:0.40276	validation-auc:0.96633	validation-aucpr:0.97152
[20:25:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24]	validation-logloss:0.39522	validation-auc:0.96666	validation-aucpr:0.97186
[20:25:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25]	validation-logloss:0.38782	validation-auc:0.96693	validation-aucpr:0.97209
[20:25:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26]	validation-logloss:0.38213	validation-auc:0.96705	validation-aucpr:0.97216
[20:25:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27]	validation-logloss:0.37615	validation-auc:0.96706	validation-aucpr:0.97216
[20:25:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28]	validation-logloss:0.36947	validation-auc:0.96729	validation-aucpr:0.97241
[20:25:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29]	validation-logloss:0.36354	validation-auc:0.96742	validation-aucpr:0.97253
[20:25:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30]	validation-logloss:0.35877	validation-auc:0.96738	validation-aucpr:0.97246
[20:25:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31]	validation-logloss:0.35410	validation-auc:0.96729	validation-aucpr:0.97239
[20:25:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32]	validation-logloss:0.34979	validation-auc:0.96724	validation-aucpr:0.97232
{'best_iteration': '29', 'best_score': '0.9725345395665433'}
Trial 43, Fold 1: Log loss = 0.3497921846878542, Average precision = 0.972323647074988, ROC-AUC = 0.9672356170210733, Elapsed Time = 2.4518600999981572 seconds
Trial 43, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 43, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[20:25:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.67176	validation-auc:0.95300	validation-aucpr:0.95682
[20:25:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.65324	validation-auc:0.96051	validation-aucpr:0.96394
[20:25:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.63595	validation-auc:0.96163	validation-aucpr:0.96546
[20:25:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.61979	validation-auc:0.96285	validation-aucpr:0.96620
[20:25:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.60494	validation-auc:0.96213	validation-aucpr:0.96530
[20:25:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5]	validation-logloss:0.59031	validation-auc:0.96224	validation-aucpr:0.96529
[20:25:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6]	validation-logloss:0.57627	validation-auc:0.96246	validation-aucpr:0.96533
[20:25:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7]	validation-logloss:0.56302	validation-auc:0.96253	validation-aucpr:0.96525
[20:25:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8]	validation-logloss:0.55059	validation-auc:0.96256	validation-aucpr:0.96517
[20:25:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9]	validation-logloss:0.53914	validation-auc:0.96235	validation-aucpr:0.96488
[20:25:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10]	validation-logloss:0.52789	validation-auc:0.96254	validation-aucpr:0.96513
[20:25:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11]	validation-logloss:0.51472	validation-auc:0.96476	validation-aucpr:0.96765
[20:25:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12]	validation-logloss:0.50443	validation-auc:0.96453	validation-aucpr:0.96733
[20:25:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13]	validation-logloss:0.49465	validation-auc:0.96431	validation-aucpr:0.96703
[20:25:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14]	validation-logloss:0.48296	validation-auc:0.96552	validation-aucpr:0.96843
[20:25:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15]	validation-logloss:0.47463	validation-auc:0.96530	validation-aucpr:0.96819
[20:25:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16]	validation-logloss:0.46602	validation-auc:0.96511	validation-aucpr:0.96782
[20:25:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17]	validation-logloss:0.45579	validation-auc:0.96585	validation-aucpr:0.96909
[20:25:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18]	validation-logloss:0.44767	validation-auc:0.96589	validation-aucpr:0.96910
[20:25:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19]	validation-logloss:0.43946	validation-auc:0.96600	validation-aucpr:0.96919
[20:25:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20]	validation-logloss:0.43205	validation-auc:0.96600	validation-aucpr:0.96913
[20:25:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21]	validation-logloss:0.42504	validation-auc:0.96606	validation-aucpr:0.96914
[20:25:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22]	validation-logloss:0.41813	validation-auc:0.96600	validation-aucpr:0.96905
[20:25:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23]	validation-logloss:0.41036	validation-auc:0.96645	validation-aucpr:0.96956
[20:25:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24]	validation-logloss:0.40417	validation-auc:0.96641	validation-aucpr:0.96954
[20:25:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25]	validation-logloss:0.39650	validation-auc:0.96692	validation-aucpr:0.97011
[20:25:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26]	validation-logloss:0.38941	validation-auc:0.96724	validation-aucpr:0.97045
[20:25:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27]	validation-logloss:0.38340	validation-auc:0.96734	validation-aucpr:0.97057
[20:25:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28]	validation-logloss:0.37810	validation-auc:0.96732	validation-aucpr:0.97067
[20:25:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29]	validation-logloss:0.37114	validation-auc:0.96769	validation-aucpr:0.97104
[20:25:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30]	validation-logloss:0.36624	validation-auc:0.96782	validation-aucpr:0.97121
[20:25:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31]	validation-logloss:0.36149	validation-auc:0.96773	validation-aucpr:0.97116
[20:25:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32]	validation-logloss:0.35701	validation-auc:0.96767	validation-aucpr:0.97112
{'best_iteration': '30', 'best_score': '0.9712114953884028'}
Trial 43, Fold 2: Log loss = 0.35701467220388006, Average precision = 0.9710963596730398, ROC-AUC = 0.9676705895492514, Elapsed Time = 2.6748434000001 seconds
Trial 43, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 43, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[20:25:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.67382	validation-auc:0.94070	validation-aucpr:0.94001
[20:25:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.65604	validation-auc:0.95100	validation-aucpr:0.95679
[20:25:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.63855	validation-auc:0.95879	validation-aucpr:0.96296
[20:25:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.62278	validation-auc:0.95898	validation-aucpr:0.96299
[20:25:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.60629	validation-auc:0.96177	validation-aucpr:0.96583
[20:25:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5]	validation-logloss:0.59131	validation-auc:0.96180	validation-aucpr:0.96575
[20:25:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6]	validation-logloss:0.57491	validation-auc:0.96501	validation-aucpr:0.96936
[20:25:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7]	validation-logloss:0.56225	validation-auc:0.96471	validation-aucpr:0.96908
[20:25:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8]	validation-logloss:0.54733	validation-auc:0.96631	validation-aucpr:0.97065
[20:25:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9]	validation-logloss:0.53580	validation-auc:0.96619	validation-aucpr:0.97036
[20:25:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10]	validation-logloss:0.52216	validation-auc:0.96698	validation-aucpr:0.97118
[20:25:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11]	validation-logloss:0.50873	validation-auc:0.96790	validation-aucpr:0.97198
[20:25:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12]	validation-logloss:0.49808	validation-auc:0.96792	validation-aucpr:0.97201
[20:25:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13]	validation-logloss:0.48663	validation-auc:0.96808	validation-aucpr:0.97226
[20:25:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14]	validation-logloss:0.47519	validation-auc:0.96843	validation-aucpr:0.97256
[20:25:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15]	validation-logloss:0.46613	validation-auc:0.96842	validation-aucpr:0.97248
[20:25:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16]	validation-logloss:0.45656	validation-auc:0.96843	validation-aucpr:0.97237
[20:25:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17]	validation-logloss:0.44822	validation-auc:0.96833	validation-aucpr:0.97225
[20:25:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18]	validation-logloss:0.43848	validation-auc:0.96848	validation-aucpr:0.97267
[20:25:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19]	validation-logloss:0.42922	validation-auc:0.96867	validation-aucpr:0.97286
[20:25:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20]	validation-logloss:0.42026	validation-auc:0.96900	validation-aucpr:0.97317
[20:25:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21]	validation-logloss:0.41331	validation-auc:0.96903	validation-aucpr:0.97313
[20:25:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22]	validation-logloss:0.40523	validation-auc:0.96916	validation-aucpr:0.97326
[20:25:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23]	validation-logloss:0.39886	validation-auc:0.96910	validation-aucpr:0.97324
[20:25:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24]	validation-logloss:0.39269	validation-auc:0.96909	validation-aucpr:0.97323
[20:25:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25]	validation-logloss:0.38665	validation-auc:0.96909	validation-aucpr:0.97321
[20:25:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26]	validation-logloss:0.38110	validation-auc:0.96892	validation-aucpr:0.97302
[20:25:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27]	validation-logloss:0.37410	validation-auc:0.96911	validation-aucpr:0.97323
[20:25:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28]	validation-logloss:0.36727	validation-auc:0.96925	validation-aucpr:0.97340
[20:25:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29]	validation-logloss:0.36214	validation-auc:0.96916	validation-aucpr:0.97331
[20:25:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30]	validation-logloss:0.35716	validation-auc:0.96916	validation-aucpr:0.97329
[20:25:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31]	validation-logloss:0.35248	validation-auc:0.96919	validation-aucpr:0.97330
[20:25:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32]	validation-logloss:0.34807	validation-auc:0.96917	validation-aucpr:0.97326
{'best_iteration': '28', 'best_score': '0.9734004595640693'}
Trial 43, Fold 3: Log loss = 0.3480675914193448, Average precision = 0.973268520424059, ROC-AUC = 0.9691660908110293, Elapsed Time = 2.6797023999970406 seconds
Trial 43, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 43, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[20:25:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.67080	validation-auc:0.95951	validation-aucpr:0.95794
[20:25:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.65173	validation-auc:0.96170	validation-aucpr:0.96288
[20:25:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.63229	validation-auc:0.96368	validation-aucpr:0.96512
[20:25:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.61794	validation-auc:0.96164	validation-aucpr:0.96793
[20:25:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.60059	validation-auc:0.96336	validation-aucpr:0.96925
[20:25:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5]	validation-logloss:0.58404	validation-auc:0.96371	validation-aucpr:0.96966
[20:25:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6]	validation-logloss:0.57067	validation-auc:0.96348	validation-aucpr:0.96956
[20:25:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7]	validation-logloss:0.55840	validation-auc:0.96286	validation-aucpr:0.96895
[20:25:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8]	validation-logloss:0.54428	validation-auc:0.96392	validation-aucpr:0.96957
[20:25:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9]	validation-logloss:0.53052	validation-auc:0.96444	validation-aucpr:0.97017
[20:25:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10]	validation-logloss:0.51729	validation-auc:0.96500	validation-aucpr:0.97072
[20:25:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11]	validation-logloss:0.50510	validation-auc:0.96511	validation-aucpr:0.97089
[20:25:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12]	validation-logloss:0.49417	validation-auc:0.96554	validation-aucpr:0.97127
[20:25:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13]	validation-logloss:0.48409	validation-auc:0.96554	validation-aucpr:0.97131
[20:25:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14]	validation-logloss:0.47354	validation-auc:0.96575	validation-aucpr:0.97153
[20:25:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15]	validation-logloss:0.46279	validation-auc:0.96605	validation-aucpr:0.97182
[20:25:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16]	validation-logloss:0.45428	validation-auc:0.96597	validation-aucpr:0.97174
[20:25:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17]	validation-logloss:0.44603	validation-auc:0.96586	validation-aucpr:0.97161
[20:25:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18]	validation-logloss:0.43800	validation-auc:0.96604	validation-aucpr:0.97173
[20:25:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19]	validation-logloss:0.42877	validation-auc:0.96639	validation-aucpr:0.97199
[20:25:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20]	validation-logloss:0.42200	validation-auc:0.96606	validation-aucpr:0.97173
[20:25:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21]	validation-logloss:0.41340	validation-auc:0.96633	validation-aucpr:0.97199
[20:25:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22]	validation-logloss:0.40636	validation-auc:0.96637	validation-aucpr:0.97200
[20:25:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23]	validation-logloss:0.39860	validation-auc:0.96666	validation-aucpr:0.97226
[20:25:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24]	validation-logloss:0.39217	validation-auc:0.96662	validation-aucpr:0.97226
[20:25:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25]	validation-logloss:0.38671	validation-auc:0.96636	validation-aucpr:0.97206
[20:25:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26]	validation-logloss:0.38118	validation-auc:0.96629	validation-aucpr:0.97196
[20:25:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27]	validation-logloss:0.37453	validation-auc:0.96637	validation-aucpr:0.97203
[20:25:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28]	validation-logloss:0.36924	validation-auc:0.96648	validation-aucpr:0.97207
[20:25:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29]	validation-logloss:0.36295	validation-auc:0.96673	validation-aucpr:0.97228
[20:25:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30]	validation-logloss:0.35834	validation-auc:0.96665	validation-aucpr:0.97221
[20:25:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31]	validation-logloss:0.35346	validation-auc:0.96671	validation-aucpr:0.97226
[20:25:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32]	validation-logloss:0.34885	validation-auc:0.96674	validation-aucpr:0.97228
{'best_iteration': '32', 'best_score': '0.972280019775681'}
Trial 43, Fold 4: Log loss = 0.3488531874288383, Average precision = 0.9722768432375872, ROC-AUC = 0.966742848558982, Elapsed Time = 2.7160441000014544 seconds
Trial 43, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 43, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[20:25:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.67104	validation-auc:0.95828	validation-aucpr:0.95734
[20:25:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.65267	validation-auc:0.95999	validation-aucpr:0.96523
[20:25:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.63352	validation-auc:0.96188	validation-aucpr:0.96721
[20:25:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.61552	validation-auc:0.96312	validation-aucpr:0.96567
[20:25:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.60066	validation-auc:0.96277	validation-aucpr:0.96801
[20:25:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5]	validation-logloss:0.58378	validation-auc:0.96366	validation-aucpr:0.96886
[20:25:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6]	validation-logloss:0.56842	validation-auc:0.96455	validation-aucpr:0.96952
[20:25:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7]	validation-logloss:0.55610	validation-auc:0.96377	validation-aucpr:0.96883
[20:25:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8]	validation-logloss:0.54162	validation-auc:0.96411	validation-aucpr:0.96938
[20:25:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9]	validation-logloss:0.52970	validation-auc:0.96415	validation-aucpr:0.96936
[20:25:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10]	validation-logloss:0.51665	validation-auc:0.96470	validation-aucpr:0.96970
[20:25:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11]	validation-logloss:0.50393	validation-auc:0.96545	validation-aucpr:0.97034
[20:25:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12]	validation-logloss:0.49413	validation-auc:0.96491	validation-aucpr:0.97004
[20:25:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13]	validation-logloss:0.48452	validation-auc:0.96458	validation-aucpr:0.96967
[20:25:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14]	validation-logloss:0.47564	validation-auc:0.96439	validation-aucpr:0.96937
[20:25:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15]	validation-logloss:0.46488	validation-auc:0.96487	validation-aucpr:0.96966
[20:25:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16]	validation-logloss:0.45544	validation-auc:0.96509	validation-aucpr:0.96984
[20:25:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17]	validation-logloss:0.44590	validation-auc:0.96516	validation-aucpr:0.96980
[20:25:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18]	validation-logloss:0.43792	validation-auc:0.96524	validation-aucpr:0.96981
[20:25:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19]	validation-logloss:0.43070	validation-auc:0.96497	validation-aucpr:0.96958
[20:25:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20]	validation-logloss:0.42208	validation-auc:0.96522	validation-aucpr:0.96982
[20:25:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21]	validation-logloss:0.41548	validation-auc:0.96506	validation-aucpr:0.96963
[20:25:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22]	validation-logloss:0.40868	validation-auc:0.96514	validation-aucpr:0.96965
[20:25:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23]	validation-logloss:0.40121	validation-auc:0.96535	validation-aucpr:0.96988
[20:25:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24]	validation-logloss:0.39362	validation-auc:0.96578	validation-aucpr:0.97024
[20:25:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25]	validation-logloss:0.38656	validation-auc:0.96600	validation-aucpr:0.97043
[20:25:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26]	validation-logloss:0.38093	validation-auc:0.96602	validation-aucpr:0.97044
[20:25:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27]	validation-logloss:0.37468	validation-auc:0.96602	validation-aucpr:0.97042
[20:25:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28]	validation-logloss:0.36800	validation-auc:0.96647	validation-aucpr:0.97079
[20:25:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29]	validation-logloss:0.36191	validation-auc:0.96671	validation-aucpr:0.97097
[20:25:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30]	validation-logloss:0.35665	validation-auc:0.96673	validation-aucpr:0.97097
[20:25:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31]	validation-logloss:0.35208	validation-auc:0.96670	validation-aucpr:0.97089
[20:25:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32]	validation-logloss:0.34650	validation-auc:0.96692	validation-aucpr:0.97107
{'best_iteration': '32', 'best_score': '0.9710684845858037'}
Trial 43, Fold 5: Log loss = 0.34649803740362317, Average precision = 0.9710592798843336, ROC-AUC = 0.9669182319826096, Elapsed Time = 2.757409000001644 seconds
Optimization Progress:  44%|####4     | 44/100 [2:27:03<56:40, 60.72s/it]  
Trial 44, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 44, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[0]	validation-logloss:0.65514	validation-auc:0.87809	validation-aucpr:0.88222
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[5]	validation-logloss:0.48533	validation-auc:0.95375	validation-aucpr:0.95984
[6]	validation-logloss:0.46379	validation-auc:0.95533	validation-aucpr:0.96080
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[10]	validation-logloss:0.40000	validation-auc:0.95614	validation-aucpr:0.96111
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[86]	validation-logloss:0.21492	validation-auc:0.96806	validation-aucpr:0.97264
[87]	validation-logloss:0.21471	validation-auc:0.96813	validation-aucpr:0.97266
[88]	validation-logloss:0.21443	validation-auc:0.96816	validation-aucpr:0.97267
[89]	validation-logloss:0.21427	validation-auc:0.96817	validation-aucpr:0.97270
[90]	validation-logloss:0.21416	validation-auc:0.96820	validation-aucpr:0.97274
[91]	validation-logloss:0.21407	validation-auc:0.96823	validation-aucpr:0.97272
[92]	validation-logloss:0.21435	validation-auc:0.96810	validation-aucpr:0.97264
[93]	validation-logloss:0.21269	validation-auc:0.96827	validation-aucpr:0.97283
[94]	validation-logloss:0.21240	validation-auc:0.96834	validation-aucpr:0.97288
[95]	validation-logloss:0.21201	validation-auc:0.96842	validation-aucpr:0.97292
[96]	validation-logloss:0.21179	validation-auc:0.96849	validation-aucpr:0.97299
{'best_iteration': '96', 'best_score': '0.9729902603003616'}
Trial 44, Fold 1: Log loss = 0.2117866213049766, Average precision = 0.9729949916357183, ROC-AUC = 0.9684905579214522, Elapsed Time = 68.23660120000204 seconds
Trial 44, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 44, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[0]	validation-logloss:0.65052	validation-auc:0.89657	validation-aucpr:0.89769
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{'best_iteration': '95', 'best_score': '0.9741451280839953'}
Trial 44, Fold 2: Log loss = 0.20095771260405446, Average precision = 0.9741141990732255, ROC-AUC = 0.9712527477730253, Elapsed Time = 68.58425809999972 seconds
Trial 44, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 44, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
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[91]	validation-logloss:0.20158	validation-auc:0.97075	validation-aucpr:0.97468
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[96]	validation-logloss:0.19974	validation-auc:0.97089	validation-aucpr:0.97486
{'best_iteration': '96', 'best_score': '0.9748559120999989'}
Trial 44, Fold 3: Log loss = 0.19974281941264244, Average precision = 0.974860322917372, ROC-AUC = 0.9708878640531249, Elapsed Time = 66.81937679999828 seconds
Trial 44, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 44, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
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[84]	validation-logloss:0.21045	validation-auc:0.96743	validation-aucpr:0.97272
[85]	validation-logloss:0.20909	validation-auc:0.96761	validation-aucpr:0.97288
[86]	validation-logloss:0.20886	validation-auc:0.96759	validation-aucpr:0.97287
[87]	validation-logloss:0.20862	validation-auc:0.96768	validation-aucpr:0.97293
[88]	validation-logloss:0.20844	validation-auc:0.96767	validation-aucpr:0.97295
[89]	validation-logloss:0.20683	validation-auc:0.96783	validation-aucpr:0.97311
[90]	validation-logloss:0.20697	validation-auc:0.96778	validation-aucpr:0.97308
[91]	validation-logloss:0.20565	validation-auc:0.96791	validation-aucpr:0.97322
[92]	validation-logloss:0.20560	validation-auc:0.96791	validation-aucpr:0.97321
[93]	validation-logloss:0.20453	validation-auc:0.96807	validation-aucpr:0.97336
[94]	validation-logloss:0.20419	validation-auc:0.96817	validation-aucpr:0.97341
[95]	validation-logloss:0.20392	validation-auc:0.96819	validation-aucpr:0.97344
[96]	validation-logloss:0.20383	validation-auc:0.96818	validation-aucpr:0.97345
{'best_iteration': '96', 'best_score': '0.9734476786511739'}
Trial 44, Fold 4: Log loss = 0.20383334708858364, Average precision = 0.9734519949775687, ROC-AUC = 0.9681788593460516, Elapsed Time = 64.23538910000207 seconds
Trial 44, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 44, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[0]	validation-logloss:0.65330	validation-auc:0.88770	validation-aucpr:0.88733
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[2]	validation-logloss:0.58016	validation-auc:0.93163	validation-aucpr:0.93327
[3]	validation-logloss:0.53890	validation-auc:0.95149	validation-aucpr:0.95585
[4]	validation-logloss:0.51155	validation-auc:0.95213	validation-aucpr:0.95581
[5]	validation-logloss:0.48701	validation-auc:0.95344	validation-aucpr:0.95857
[6]	validation-logloss:0.46591	validation-auc:0.95347	validation-aucpr:0.95918
[7]	validation-logloss:0.43846	validation-auc:0.95647	validation-aucpr:0.96209
[8]	validation-logloss:0.42255	validation-auc:0.95610	validation-aucpr:0.96161
[9]	validation-logloss:0.40789	validation-auc:0.95691	validation-aucpr:0.96231
[10]	validation-logloss:0.39005	validation-auc:0.95718	validation-aucpr:0.96266
[11]	validation-logloss:0.37306	validation-auc:0.95856	validation-aucpr:0.96401
[12]	validation-logloss:0.36427	validation-auc:0.95808	validation-aucpr:0.96355
[13]	validation-logloss:0.35356	validation-auc:0.95885	validation-aucpr:0.96414
[14]	validation-logloss:0.34307	validation-auc:0.95939	validation-aucpr:0.96452
[15]	validation-logloss:0.33481	validation-auc:0.95946	validation-aucpr:0.96457
[16]	validation-logloss:0.32709	validation-auc:0.95962	validation-aucpr:0.96473
[17]	validation-logloss:0.32001	validation-auc:0.96010	validation-aucpr:0.96512
[18]	validation-logloss:0.31459	validation-auc:0.96013	validation-aucpr:0.96533
[19]	validation-logloss:0.30917	validation-auc:0.96026	validation-aucpr:0.96539
[20]	validation-logloss:0.30309	validation-auc:0.96039	validation-aucpr:0.96549
[21]	validation-logloss:0.29766	validation-auc:0.96083	validation-aucpr:0.96585
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[23]	validation-logloss:0.29177	validation-auc:0.96075	validation-aucpr:0.96566
[24]	validation-logloss:0.28757	validation-auc:0.96113	validation-aucpr:0.96595
[25]	validation-logloss:0.28038	validation-auc:0.96151	validation-aucpr:0.96637
[26]	validation-logloss:0.27774	validation-auc:0.96149	validation-aucpr:0.96635
[27]	validation-logloss:0.27535	validation-auc:0.96161	validation-aucpr:0.96653
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[30]	validation-logloss:0.26823	validation-auc:0.96196	validation-aucpr:0.96685
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[43]	validation-logloss:0.24459	validation-auc:0.96416	validation-aucpr:0.96859
[44]	validation-logloss:0.24323	validation-auc:0.96432	validation-aucpr:0.96877
[45]	validation-logloss:0.24233	validation-auc:0.96441	validation-aucpr:0.96884
[46]	validation-logloss:0.24102	validation-auc:0.96446	validation-aucpr:0.96888
[47]	validation-logloss:0.24022	validation-auc:0.96462	validation-aucpr:0.96905
[48]	validation-logloss:0.23971	validation-auc:0.96459	validation-aucpr:0.96907
[49]	validation-logloss:0.23883	validation-auc:0.96468	validation-aucpr:0.96915
[50]	validation-logloss:0.23744	validation-auc:0.96491	validation-aucpr:0.96932
[51]	validation-logloss:0.23457	validation-auc:0.96513	validation-aucpr:0.96959
[52]	validation-logloss:0.23355	validation-auc:0.96539	validation-aucpr:0.96977
[53]	validation-logloss:0.23278	validation-auc:0.96544	validation-aucpr:0.96982
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[57]	validation-logloss:0.22906	validation-auc:0.96596	validation-aucpr:0.97019
[58]	validation-logloss:0.22865	validation-auc:0.96597	validation-aucpr:0.97020
[59]	validation-logloss:0.22783	validation-auc:0.96617	validation-aucpr:0.97035
[60]	validation-logloss:0.22423	validation-auc:0.96663	validation-aucpr:0.97078
[61]	validation-logloss:0.22373	validation-auc:0.96675	validation-aucpr:0.97084
[62]	validation-logloss:0.22317	validation-auc:0.96683	validation-aucpr:0.97089
[63]	validation-logloss:0.22276	validation-auc:0.96696	validation-aucpr:0.97101
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[65]	validation-logloss:0.22116	validation-auc:0.96721	validation-aucpr:0.97119
[66]	validation-logloss:0.22107	validation-auc:0.96713	validation-aucpr:0.97116
[67]	validation-logloss:0.22082	validation-auc:0.96714	validation-aucpr:0.97119
[68]	validation-logloss:0.22089	validation-auc:0.96711	validation-aucpr:0.97115
[69]	validation-logloss:0.21832	validation-auc:0.96739	validation-aucpr:0.97142
[70]	validation-logloss:0.21820	validation-auc:0.96737	validation-aucpr:0.97142
[71]	validation-logloss:0.21806	validation-auc:0.96736	validation-aucpr:0.97143
[72]	validation-logloss:0.21758	validation-auc:0.96738	validation-aucpr:0.97145
[73]	validation-logloss:0.21670	validation-auc:0.96764	validation-aucpr:0.97163
[74]	validation-logloss:0.21660	validation-auc:0.96767	validation-aucpr:0.97167
[75]	validation-logloss:0.21614	validation-auc:0.96773	validation-aucpr:0.97172
[76]	validation-logloss:0.21377	validation-auc:0.96808	validation-aucpr:0.97206
[77]	validation-logloss:0.21362	validation-auc:0.96805	validation-aucpr:0.97201
[78]	validation-logloss:0.21350	validation-auc:0.96810	validation-aucpr:0.97202
[79]	validation-logloss:0.21325	validation-auc:0.96816	validation-aucpr:0.97206
[80]	validation-logloss:0.21283	validation-auc:0.96826	validation-aucpr:0.97214
[81]	validation-logloss:0.21241	validation-auc:0.96834	validation-aucpr:0.97227
[82]	validation-logloss:0.21197	validation-auc:0.96848	validation-aucpr:0.97241
[83]	validation-logloss:0.21166	validation-auc:0.96852	validation-aucpr:0.97245
[84]	validation-logloss:0.21136	validation-auc:0.96862	validation-aucpr:0.97251
[85]	validation-logloss:0.21109	validation-auc:0.96872	validation-aucpr:0.97259
[86]	validation-logloss:0.21100	validation-auc:0.96871	validation-aucpr:0.97260
[87]	validation-logloss:0.21092	validation-auc:0.96871	validation-aucpr:0.97261
[88]	validation-logloss:0.20893	validation-auc:0.96904	validation-aucpr:0.97288
[89]	validation-logloss:0.20884	validation-auc:0.96899	validation-aucpr:0.97283
[90]	validation-logloss:0.20869	validation-auc:0.96905	validation-aucpr:0.97287
[91]	validation-logloss:0.20880	validation-auc:0.96900	validation-aucpr:0.97285
[92]	validation-logloss:0.20741	validation-auc:0.96917	validation-aucpr:0.97303
[93]	validation-logloss:0.20573	validation-auc:0.96951	validation-aucpr:0.97333
[94]	validation-logloss:0.20570	validation-auc:0.96948	validation-aucpr:0.97332
[95]	validation-logloss:0.20552	validation-auc:0.96948	validation-aucpr:0.97332
[96]	validation-logloss:0.20552	validation-auc:0.96945	validation-aucpr:0.97329
{'best_iteration': '93', 'best_score': '0.9733290849490748'}
Trial 44, Fold 5: Log loss = 0.20551938913953613, Average precision = 0.9732923313730766, ROC-AUC = 0.969452910465786, Elapsed Time = 65.30914010000197 seconds
Optimization Progress:  45%|####5     | 45/100 [2:32:45<2:12:49, 144.91s/it]
Trial 45, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 45, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[0]	validation-logloss:0.67020	validation-auc:0.93525	validation-aucpr:0.92509
[1]	validation-logloss:0.64890	validation-auc:0.94891	validation-aucpr:0.95024
[2]	validation-logloss:0.62574	validation-auc:0.96047	validation-aucpr:0.96201
[3]	validation-logloss:0.60522	validation-auc:0.96166	validation-aucpr:0.96670
[4]	validation-logloss:0.58834	validation-auc:0.96130	validation-aucpr:0.96647
[5]	validation-logloss:0.57257	validation-auc:0.96252	validation-aucpr:0.96778
[6]	validation-logloss:0.55801	validation-auc:0.96231	validation-aucpr:0.96771
[7]	validation-logloss:0.54446	validation-auc:0.96181	validation-aucpr:0.96734
[8]	validation-logloss:0.53092	validation-auc:0.96239	validation-aucpr:0.96754
[9]	validation-logloss:0.51747	validation-auc:0.96278	validation-aucpr:0.96804
[10]	validation-logloss:0.50541	validation-auc:0.96268	validation-aucpr:0.96677
[11]	validation-logloss:0.49394	validation-auc:0.96286	validation-aucpr:0.96687
[12]	validation-logloss:0.48301	validation-auc:0.96275	validation-aucpr:0.96681
[13]	validation-logloss:0.46951	validation-auc:0.96341	validation-aucpr:0.96766
[14]	validation-logloss:0.45861	validation-auc:0.96354	validation-aucpr:0.96788
[15]	validation-logloss:0.44905	validation-auc:0.96362	validation-aucpr:0.96787
[16]	validation-logloss:0.44024	validation-auc:0.96366	validation-aucpr:0.96771
[17]	validation-logloss:0.43190	validation-auc:0.96368	validation-aucpr:0.96899
[18]	validation-logloss:0.42381	validation-auc:0.96371	validation-aucpr:0.96903
[19]	validation-logloss:0.41327	validation-auc:0.96422	validation-aucpr:0.96947
[20]	validation-logloss:0.40348	validation-auc:0.96464	validation-aucpr:0.96986
[21]	validation-logloss:0.39450	validation-auc:0.96480	validation-aucpr:0.97009
[22]	validation-logloss:0.38759	validation-auc:0.96487	validation-aucpr:0.97012
[23]	validation-logloss:0.37931	validation-auc:0.96516	validation-aucpr:0.97045
[24]	validation-logloss:0.37362	validation-auc:0.96516	validation-aucpr:0.97042
[25]	validation-logloss:0.36794	validation-auc:0.96515	validation-aucpr:0.97042
[26]	validation-logloss:0.36058	validation-auc:0.96552	validation-aucpr:0.97078
[27]	validation-logloss:0.35558	validation-auc:0.96537	validation-aucpr:0.97062
[28]	validation-logloss:0.34877	validation-auc:0.96564	validation-aucpr:0.97095
[29]	validation-logloss:0.34394	validation-auc:0.96560	validation-aucpr:0.97090
[30]	validation-logloss:0.33938	validation-auc:0.96543	validation-aucpr:0.97071
[31]	validation-logloss:0.33510	validation-auc:0.96545	validation-aucpr:0.97072
[32]	validation-logloss:0.33073	validation-auc:0.96548	validation-aucpr:0.97074
[33]	validation-logloss:0.32505	validation-auc:0.96566	validation-aucpr:0.97090
[34]	validation-logloss:0.31949	validation-auc:0.96586	validation-aucpr:0.97106
[35]	validation-logloss:0.31457	validation-auc:0.96591	validation-aucpr:0.97123
[36]	validation-logloss:0.30962	validation-auc:0.96606	validation-aucpr:0.97142
[37]	validation-logloss:0.30635	validation-auc:0.96609	validation-aucpr:0.97142
[38]	validation-logloss:0.30181	validation-auc:0.96616	validation-aucpr:0.97150
[39]	validation-logloss:0.29718	validation-auc:0.96631	validation-aucpr:0.97165
[40]	validation-logloss:0.29408	validation-auc:0.96633	validation-aucpr:0.97165
[41]	validation-logloss:0.29102	validation-auc:0.96640	validation-aucpr:0.97167
[42]	validation-logloss:0.28729	validation-auc:0.96656	validation-aucpr:0.97183
[43]	validation-logloss:0.28366	validation-auc:0.96661	validation-aucpr:0.97188
[44]	validation-logloss:0.28029	validation-auc:0.96670	validation-aucpr:0.97201
{'best_iteration': '44', 'best_score': '0.9720059714082437'}
Trial 45, Fold 1: Log loss = 0.2802868031056375, Average precision = 0.972009096896103, ROC-AUC = 0.966703439631398, Elapsed Time = 0.9623223999988113 seconds
Trial 45, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 45, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[0]	validation-logloss:0.67120	validation-auc:0.92551	validation-aucpr:0.90220
[1]	validation-logloss:0.65013	validation-auc:0.94577	validation-aucpr:0.93924
[2]	validation-logloss:0.62772	validation-auc:0.95785	validation-aucpr:0.95836
[3]	validation-logloss:0.60896	validation-auc:0.95986	validation-aucpr:0.96349
[4]	validation-logloss:0.58978	validation-auc:0.96129	validation-aucpr:0.96573
[5]	validation-logloss:0.57354	validation-auc:0.96166	validation-aucpr:0.96584
[6]	validation-logloss:0.55841	validation-auc:0.96182	validation-aucpr:0.96615
[7]	validation-logloss:0.54384	validation-auc:0.96260	validation-aucpr:0.96672
[8]	validation-logloss:0.53068	validation-auc:0.96251	validation-aucpr:0.96635
[9]	validation-logloss:0.51821	validation-auc:0.96181	validation-aucpr:0.96542
[10]	validation-logloss:0.50572	validation-auc:0.96173	validation-aucpr:0.96542
[11]	validation-logloss:0.49384	validation-auc:0.96165	validation-aucpr:0.96526
[12]	validation-logloss:0.48007	validation-auc:0.96377	validation-aucpr:0.96774
[13]	validation-logloss:0.46869	validation-auc:0.96384	validation-aucpr:0.96790
[14]	validation-logloss:0.45694	validation-auc:0.96446	validation-aucpr:0.96851
[15]	validation-logloss:0.44759	validation-auc:0.96452	validation-aucpr:0.96851
[16]	validation-logloss:0.43819	validation-auc:0.96469	validation-aucpr:0.96864
[17]	validation-logloss:0.42772	validation-auc:0.96503	validation-aucpr:0.96880
[18]	validation-logloss:0.41973	validation-auc:0.96499	validation-aucpr:0.96877
[19]	validation-logloss:0.41073	validation-auc:0.96548	validation-aucpr:0.96922
[20]	validation-logloss:0.40113	validation-auc:0.96572	validation-aucpr:0.96943
[21]	validation-logloss:0.39249	validation-auc:0.96593	validation-aucpr:0.96965
[22]	validation-logloss:0.38644	validation-auc:0.96569	validation-aucpr:0.96941
[23]	validation-logloss:0.37810	validation-auc:0.96584	validation-aucpr:0.96961
[24]	validation-logloss:0.37187	validation-auc:0.96605	validation-aucpr:0.96978
[25]	validation-logloss:0.36653	validation-auc:0.96593	validation-aucpr:0.96995
[26]	validation-logloss:0.35877	validation-auc:0.96624	validation-aucpr:0.97023
[27]	validation-logloss:0.35337	validation-auc:0.96644	validation-aucpr:0.97041
[28]	validation-logloss:0.34821	validation-auc:0.96639	validation-aucpr:0.97032
[29]	validation-logloss:0.34229	validation-auc:0.96645	validation-aucpr:0.97056
[30]	validation-logloss:0.33685	validation-auc:0.96649	validation-aucpr:0.97059
[31]	validation-logloss:0.33111	validation-auc:0.96664	validation-aucpr:0.97073
[32]	validation-logloss:0.32660	validation-auc:0.96678	validation-aucpr:0.97087
[33]	validation-logloss:0.32262	validation-auc:0.96681	validation-aucpr:0.97087
[34]	validation-logloss:0.31837	validation-auc:0.96692	validation-aucpr:0.97089
[35]	validation-logloss:0.31465	validation-auc:0.96697	validation-aucpr:0.97091
[36]	validation-logloss:0.31079	validation-auc:0.96716	validation-aucpr:0.97102
[37]	validation-logloss:0.30724	validation-auc:0.96706	validation-aucpr:0.97092
[38]	validation-logloss:0.30404	validation-auc:0.96713	validation-aucpr:0.97097
[39]	validation-logloss:0.29927	validation-auc:0.96735	validation-aucpr:0.97109
[40]	validation-logloss:0.29478	validation-auc:0.96752	validation-aucpr:0.97123
[41]	validation-logloss:0.29058	validation-auc:0.96759	validation-aucpr:0.97132
[42]	validation-logloss:0.28669	validation-auc:0.96765	validation-aucpr:0.97135
[43]	validation-logloss:0.28378	validation-auc:0.96779	validation-aucpr:0.97141
[44]	validation-logloss:0.28087	validation-auc:0.96790	validation-aucpr:0.97148
{'best_iteration': '44', 'best_score': '0.9714752468667572'}
Trial 45, Fold 2: Log loss = 0.2808740290736027, Average precision = 0.9714337620041505, ROC-AUC = 0.9678955556412089, Elapsed Time = 1.1687717999993765 seconds
Trial 45, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 45, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[0]	validation-logloss:0.67066	validation-auc:0.93383	validation-aucpr:0.93355
[1]	validation-logloss:0.64961	validation-auc:0.94795	validation-aucpr:0.94906
[2]	validation-logloss:0.63024	validation-auc:0.95066	validation-aucpr:0.95428
[3]	validation-logloss:0.61199	validation-auc:0.95437	validation-aucpr:0.95822
[4]	validation-logloss:0.59271	validation-auc:0.96009	validation-aucpr:0.96488
[5]	validation-logloss:0.57738	validation-auc:0.96006	validation-aucpr:0.96502
[6]	validation-logloss:0.56230	validation-auc:0.96039	validation-aucpr:0.96532
[7]	validation-logloss:0.54728	validation-auc:0.96137	validation-aucpr:0.96604
[8]	validation-logloss:0.53354	validation-auc:0.96152	validation-aucpr:0.96613
[9]	validation-logloss:0.52071	validation-auc:0.96188	validation-aucpr:0.96639
[10]	validation-logloss:0.50877	validation-auc:0.96184	validation-aucpr:0.96685
[11]	validation-logloss:0.49462	validation-auc:0.96298	validation-aucpr:0.96799
[12]	validation-logloss:0.48383	validation-auc:0.96290	validation-aucpr:0.96783
[13]	validation-logloss:0.47382	validation-auc:0.96301	validation-aucpr:0.96807
[14]	validation-logloss:0.46076	validation-auc:0.96418	validation-aucpr:0.96921
[15]	validation-logloss:0.44905	validation-auc:0.96465	validation-aucpr:0.96967
[16]	validation-logloss:0.43784	validation-auc:0.96502	validation-aucpr:0.97002
[17]	validation-logloss:0.42992	validation-auc:0.96474	validation-aucpr:0.96988
[18]	validation-logloss:0.42194	validation-auc:0.96493	validation-aucpr:0.96999
[19]	validation-logloss:0.41404	validation-auc:0.96517	validation-aucpr:0.97013
[20]	validation-logloss:0.40541	validation-auc:0.96555	validation-aucpr:0.97042
[21]	validation-logloss:0.39608	validation-auc:0.96585	validation-aucpr:0.97076
[22]	validation-logloss:0.38740	validation-auc:0.96601	validation-aucpr:0.97090
[23]	validation-logloss:0.38071	validation-auc:0.96612	validation-aucpr:0.97102
[24]	validation-logloss:0.37481	validation-auc:0.96585	validation-aucpr:0.97079
[25]	validation-logloss:0.36868	validation-auc:0.96594	validation-aucpr:0.97086
[26]	validation-logloss:0.36332	validation-auc:0.96602	validation-aucpr:0.97093
[27]	validation-logloss:0.35840	validation-auc:0.96593	validation-aucpr:0.97082
[28]	validation-logloss:0.35333	validation-auc:0.96595	validation-aucpr:0.97082
[29]	validation-logloss:0.34877	validation-auc:0.96586	validation-aucpr:0.97072
[30]	validation-logloss:0.34366	validation-auc:0.96592	validation-aucpr:0.97076
[31]	validation-logloss:0.33903	validation-auc:0.96602	validation-aucpr:0.97082
[32]	validation-logloss:0.33293	validation-auc:0.96616	validation-aucpr:0.97099
[33]	validation-logloss:0.32746	validation-auc:0.96625	validation-aucpr:0.97117
[34]	validation-logloss:0.32185	validation-auc:0.96631	validation-aucpr:0.97126
[35]	validation-logloss:0.31656	validation-auc:0.96643	validation-aucpr:0.97135
[36]	validation-logloss:0.31280	validation-auc:0.96645	validation-aucpr:0.97135
[37]	validation-logloss:0.30812	validation-auc:0.96665	validation-aucpr:0.97152
[38]	validation-logloss:0.30497	validation-auc:0.96667	validation-aucpr:0.97154
[39]	validation-logloss:0.30146	validation-auc:0.96678	validation-aucpr:0.97164
[40]	validation-logloss:0.29820	validation-auc:0.96683	validation-aucpr:0.97169
[41]	validation-logloss:0.29515	validation-auc:0.96694	validation-aucpr:0.97175
[42]	validation-logloss:0.29065	validation-auc:0.96715	validation-aucpr:0.97192
[43]	validation-logloss:0.28642	validation-auc:0.96734	validation-aucpr:0.97209
[44]	validation-logloss:0.28359	validation-auc:0.96745	validation-aucpr:0.97217
{'best_iteration': '44', 'best_score': '0.972170186636452'}
Trial 45, Fold 3: Log loss = 0.28359285391712913, Average precision = 0.9721740803263408, ROC-AUC = 0.9674528199670618, Elapsed Time = 1.1913502999996126 seconds
Trial 45, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 45, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[0]	validation-logloss:0.67095	validation-auc:0.92539	validation-aucpr:0.92800
[1]	validation-logloss:0.64635	validation-auc:0.95647	validation-aucpr:0.96293
[2]	validation-logloss:0.62755	validation-auc:0.95729	validation-aucpr:0.96357
[3]	validation-logloss:0.61132	validation-auc:0.95610	validation-aucpr:0.96227
[4]	validation-logloss:0.59410	validation-auc:0.95678	validation-aucpr:0.96299
[5]	validation-logloss:0.57847	validation-auc:0.95727	validation-aucpr:0.96324
[6]	validation-logloss:0.56368	validation-auc:0.95713	validation-aucpr:0.96299
[7]	validation-logloss:0.54591	validation-auc:0.96012	validation-aucpr:0.96585
[8]	validation-logloss:0.53017	validation-auc:0.96098	validation-aucpr:0.96673
[9]	validation-logloss:0.51480	validation-auc:0.96181	validation-aucpr:0.96761
[10]	validation-logloss:0.50292	validation-auc:0.96198	validation-aucpr:0.96776
[11]	validation-logloss:0.49151	validation-auc:0.96186	validation-aucpr:0.96757
[12]	validation-logloss:0.47862	validation-auc:0.96248	validation-aucpr:0.96818
[13]	validation-logloss:0.46816	validation-auc:0.96272	validation-aucpr:0.96838
[14]	validation-logloss:0.45841	validation-auc:0.96269	validation-aucpr:0.96834
[15]	validation-logloss:0.44648	validation-auc:0.96319	validation-aucpr:0.96888
[16]	validation-logloss:0.43797	validation-auc:0.96296	validation-aucpr:0.96868
[17]	validation-logloss:0.42744	validation-auc:0.96355	validation-aucpr:0.96927
[18]	validation-logloss:0.41951	validation-auc:0.96369	validation-aucpr:0.96928
[19]	validation-logloss:0.40988	validation-auc:0.96406	validation-aucpr:0.96972
[20]	validation-logloss:0.40058	validation-auc:0.96437	validation-aucpr:0.97006
[21]	validation-logloss:0.39199	validation-auc:0.96456	validation-aucpr:0.97033
[22]	validation-logloss:0.38535	validation-auc:0.96445	validation-aucpr:0.97026
[23]	validation-logloss:0.37912	validation-auc:0.96447	validation-aucpr:0.97030
[24]	validation-logloss:0.37130	validation-auc:0.96468	validation-aucpr:0.97046
[25]	validation-logloss:0.36608	validation-auc:0.96452	validation-aucpr:0.97040
[26]	validation-logloss:0.36027	validation-auc:0.96464	validation-aucpr:0.97043
[27]	validation-logloss:0.35325	validation-auc:0.96480	validation-aucpr:0.97063
[28]	validation-logloss:0.34832	validation-auc:0.96467	validation-aucpr:0.97049
[29]	validation-logloss:0.34190	validation-auc:0.96479	validation-aucpr:0.97066
[30]	validation-logloss:0.33736	validation-auc:0.96480	validation-aucpr:0.97063
[31]	validation-logloss:0.33247	validation-auc:0.96498	validation-aucpr:0.97072
[32]	validation-logloss:0.32649	validation-auc:0.96544	validation-aucpr:0.97106
[33]	validation-logloss:0.32104	validation-auc:0.96576	validation-aucpr:0.97129
[34]	validation-logloss:0.31617	validation-auc:0.96588	validation-aucpr:0.97142
[35]	validation-logloss:0.31271	validation-auc:0.96578	validation-aucpr:0.97134
[36]	validation-logloss:0.30906	validation-auc:0.96584	validation-aucpr:0.97137
[37]	validation-logloss:0.30541	validation-auc:0.96599	validation-aucpr:0.97146
[38]	validation-logloss:0.30187	validation-auc:0.96613	validation-aucpr:0.97161
[39]	validation-logloss:0.29898	validation-auc:0.96615	validation-aucpr:0.97159
[40]	validation-logloss:0.29469	validation-auc:0.96630	validation-aucpr:0.97177
[41]	validation-logloss:0.29206	validation-auc:0.96616	validation-aucpr:0.97167
[42]	validation-logloss:0.28781	validation-auc:0.96636	validation-aucpr:0.97186
[43]	validation-logloss:0.28481	validation-auc:0.96649	validation-aucpr:0.97195
[44]	validation-logloss:0.28226	validation-auc:0.96658	validation-aucpr:0.97202
{'best_iteration': '44', 'best_score': '0.9720178657714623'}
Trial 45, Fold 4: Log loss = 0.28226231217644576, Average precision = 0.9720161104064858, ROC-AUC = 0.9665810812583008, Elapsed Time = 1.1915703999984544 seconds
Trial 45, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 45, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[0]	validation-logloss:0.67074	validation-auc:0.93172	validation-aucpr:0.93217
[1]	validation-logloss:0.64738	validation-auc:0.95505	validation-aucpr:0.96156
[2]	validation-logloss:0.62848	validation-auc:0.95691	validation-aucpr:0.96271
[3]	validation-logloss:0.61110	validation-auc:0.95738	validation-aucpr:0.96289
[4]	validation-logloss:0.59377	validation-auc:0.95839	validation-aucpr:0.96339
[5]	validation-logloss:0.57738	validation-auc:0.95814	validation-aucpr:0.96248
[6]	validation-logloss:0.55930	validation-auc:0.96079	validation-aucpr:0.96557
[7]	validation-logloss:0.54477	validation-auc:0.96096	validation-aucpr:0.96582
[8]	validation-logloss:0.52923	validation-auc:0.96120	validation-aucpr:0.96612
[9]	validation-logloss:0.51663	validation-auc:0.96147	validation-aucpr:0.96653
[10]	validation-logloss:0.50514	validation-auc:0.96139	validation-aucpr:0.96640
[11]	validation-logloss:0.49352	validation-auc:0.96158	validation-aucpr:0.96658
[12]	validation-logloss:0.48050	validation-auc:0.96185	validation-aucpr:0.96682
[13]	validation-logloss:0.47081	validation-auc:0.96159	validation-aucpr:0.96659
[14]	validation-logloss:0.45957	validation-auc:0.96133	validation-aucpr:0.96646
[15]	validation-logloss:0.44817	validation-auc:0.96174	validation-aucpr:0.96712
[16]	validation-logloss:0.43897	validation-auc:0.96192	validation-aucpr:0.96726
[17]	validation-logloss:0.42869	validation-auc:0.96204	validation-aucpr:0.96742
[18]	validation-logloss:0.42061	validation-auc:0.96223	validation-aucpr:0.96751
[19]	validation-logloss:0.41209	validation-auc:0.96231	validation-aucpr:0.96756
[20]	validation-logloss:0.40513	validation-auc:0.96254	validation-aucpr:0.96770
[21]	validation-logloss:0.39835	validation-auc:0.96270	validation-aucpr:0.96781
[22]	validation-logloss:0.39211	validation-auc:0.96270	validation-aucpr:0.96775
[23]	validation-logloss:0.38608	validation-auc:0.96288	validation-aucpr:0.96793
[24]	validation-logloss:0.38035	validation-auc:0.96286	validation-aucpr:0.96793
[25]	validation-logloss:0.37272	validation-auc:0.96335	validation-aucpr:0.96845
[26]	validation-logloss:0.36503	validation-auc:0.96364	validation-aucpr:0.96871
[27]	validation-logloss:0.35780	validation-auc:0.96378	validation-aucpr:0.96885
[28]	validation-logloss:0.35277	validation-auc:0.96379	validation-aucpr:0.96883
[29]	validation-logloss:0.34634	validation-auc:0.96394	validation-aucpr:0.96896
[30]	validation-logloss:0.34001	validation-auc:0.96417	validation-aucpr:0.96921
[31]	validation-logloss:0.33597	validation-auc:0.96415	validation-aucpr:0.96925
[32]	validation-logloss:0.32987	validation-auc:0.96441	validation-aucpr:0.96951
[33]	validation-logloss:0.32435	validation-auc:0.96462	validation-aucpr:0.96970
[34]	validation-logloss:0.31901	validation-auc:0.96482	validation-aucpr:0.96989
[35]	validation-logloss:0.31552	validation-auc:0.96469	validation-aucpr:0.96975
[36]	validation-logloss:0.31089	validation-auc:0.96485	validation-aucpr:0.96985
[37]	validation-logloss:0.30631	validation-auc:0.96493	validation-aucpr:0.96997
[38]	validation-logloss:0.30311	validation-auc:0.96494	validation-aucpr:0.96993
[39]	validation-logloss:0.29898	validation-auc:0.96502	validation-aucpr:0.96999
[40]	validation-logloss:0.29579	validation-auc:0.96499	validation-aucpr:0.96995
[41]	validation-logloss:0.29176	validation-auc:0.96518	validation-aucpr:0.97011
[42]	validation-logloss:0.28916	validation-auc:0.96517	validation-aucpr:0.97008
[43]	validation-logloss:0.28549	validation-auc:0.96526	validation-aucpr:0.97021
[44]	validation-logloss:0.28291	validation-auc:0.96528	validation-aucpr:0.97024
{'best_iteration': '44', 'best_score': '0.9702411674009223'}
Trial 45, Fold 5: Log loss = 0.2829139369086157, Average precision = 0.9702407906841284, ROC-AUC = 0.9652755141853854, Elapsed Time = 1.2029946000002383 seconds
Optimization Progress:  46%|####6     | 46/100 [2:32:58<1:34:55, 105.48s/it]
Trial 46, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 46, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[20:31:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.64076	validation-auc:0.93869	validation-aucpr:0.93708
[20:32:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.60029	validation-auc:0.95067	validation-aucpr:0.94280
[20:32:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.56030	validation-auc:0.95506	validation-aucpr:0.95160
[20:32:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.53027	validation-auc:0.95681	validation-aucpr:0.95480
[20:32:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.49789	validation-auc:0.96018	validation-aucpr:0.96071
[20:32:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5]	validation-logloss:0.47328	validation-auc:0.96102	validation-aucpr:0.96032
[20:32:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6]	validation-logloss:0.44763	validation-auc:0.96347	validation-aucpr:0.96383
[20:32:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7]	validation-logloss:0.42532	validation-auc:0.96390	validation-aucpr:0.96473
[20:32:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8]	validation-logloss:0.40458	validation-auc:0.96433	validation-aucpr:0.96536
[20:32:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9]	validation-logloss:0.38660	validation-auc:0.96466	validation-aucpr:0.96732
[20:32:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10]	validation-logloss:0.37016	validation-auc:0.96508	validation-aucpr:0.96784
[20:32:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11]	validation-logloss:0.35489	validation-auc:0.96557	validation-aucpr:0.96902
[20:32:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12]	validation-logloss:0.34342	validation-auc:0.96582	validation-aucpr:0.96914
[20:32:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13]	validation-logloss:0.33001	validation-auc:0.96666	validation-aucpr:0.96975
[20:32:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14]	validation-logloss:0.31871	validation-auc:0.96695	validation-aucpr:0.96999
[20:32:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15]	validation-logloss:0.30794	validation-auc:0.96728	validation-aucpr:0.97033
[20:32:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16]	validation-logloss:0.29834	validation-auc:0.96733	validation-aucpr:0.97032
[20:32:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17]	validation-logloss:0.28978	validation-auc:0.96753	validation-aucpr:0.97046
[20:33:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18]	validation-logloss:0.28196	validation-auc:0.96785	validation-aucpr:0.97066
[20:33:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19]	validation-logloss:0.27476	validation-auc:0.96808	validation-aucpr:0.96989
[20:33:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20]	validation-logloss:0.26881	validation-auc:0.96804	validation-aucpr:0.97005
[20:33:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21]	validation-logloss:0.26402	validation-auc:0.96782	validation-aucpr:0.96950
[20:33:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22]	validation-logloss:0.25898	validation-auc:0.96819	validation-aucpr:0.97107
[20:33:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23]	validation-logloss:0.25395	validation-auc:0.96831	validation-aucpr:0.97135
[20:33:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24]	validation-logloss:0.24992	validation-auc:0.96829	validation-aucpr:0.97190
[20:33:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25]	validation-logloss:0.24638	validation-auc:0.96804	validation-aucpr:0.97170
[20:33:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26]	validation-logloss:0.24278	validation-auc:0.96821	validation-aucpr:0.97175
[20:33:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27]	validation-logloss:0.23971	validation-auc:0.96849	validation-aucpr:0.97269
[20:33:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28]	validation-logloss:0.23655	validation-auc:0.96847	validation-aucpr:0.97272
[20:33:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29]	validation-logloss:0.23392	validation-auc:0.96854	validation-aucpr:0.97277
[20:33:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30]	validation-logloss:0.23152	validation-auc:0.96869	validation-aucpr:0.97284
[20:33:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31]	validation-logloss:0.22930	validation-auc:0.96867	validation-aucpr:0.97268
[20:33:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32]	validation-logloss:0.22672	validation-auc:0.96867	validation-aucpr:0.97288
[20:33:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33]	validation-logloss:0.22411	validation-auc:0.96881	validation-aucpr:0.97280
[20:33:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34]	validation-logloss:0.22201	validation-auc:0.96879	validation-aucpr:0.97284
[20:33:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35]	validation-logloss:0.22027	validation-auc:0.96875	validation-aucpr:0.97290
[20:33:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36]	validation-logloss:0.21837	validation-auc:0.96890	validation-aucpr:0.97326
[20:33:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37]	validation-logloss:0.21717	validation-auc:0.96899	validation-aucpr:0.97409
[20:34:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38]	validation-logloss:0.21562	validation-auc:0.96918	validation-aucpr:0.97427
[20:34:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39]	validation-logloss:0.21412	validation-auc:0.96935	validation-aucpr:0.97437
[20:34:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40]	validation-logloss:0.21328	validation-auc:0.96935	validation-aucpr:0.97441
[20:34:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41]	validation-logloss:0.21210	validation-auc:0.96949	validation-aucpr:0.97450
[20:34:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42]	validation-logloss:0.21082	validation-auc:0.96957	validation-aucpr:0.97456
[20:34:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43]	validation-logloss:0.20935	validation-auc:0.96982	validation-aucpr:0.97474
[20:34:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44]	validation-logloss:0.20814	validation-auc:0.97003	validation-aucpr:0.97491
[20:34:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45]	validation-logloss:0.20765	validation-auc:0.96996	validation-aucpr:0.97483
[20:34:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46]	validation-logloss:0.20746	validation-auc:0.96991	validation-aucpr:0.97476
[20:34:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[47]	validation-logloss:0.20698	validation-auc:0.96998	validation-aucpr:0.97478
[20:34:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[48]	validation-logloss:0.20669	validation-auc:0.96991	validation-aucpr:0.97471
[20:34:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[49]	validation-logloss:0.20629	validation-auc:0.96990	validation-aucpr:0.97475
[20:34:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[50]	validation-logloss:0.20609	validation-auc:0.96986	validation-aucpr:0.97469
[20:34:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[51]	validation-logloss:0.20626	validation-auc:0.96972	validation-aucpr:0.97459
[20:34:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[52]	validation-logloss:0.20545	validation-auc:0.96984	validation-aucpr:0.97462
[20:34:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[53]	validation-logloss:0.20534	validation-auc:0.96973	validation-aucpr:0.97461
[20:34:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[54]	validation-logloss:0.20525	validation-auc:0.96974	validation-aucpr:0.97457
[20:34:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[55]	validation-logloss:0.20504	validation-auc:0.96975	validation-aucpr:0.97454
[20:34:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[56]	validation-logloss:0.20480	validation-auc:0.96963	validation-aucpr:0.97446
[20:34:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[57]	validation-logloss:0.20434	validation-auc:0.96968	validation-aucpr:0.97449
[20:34:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[58]	validation-logloss:0.20389	validation-auc:0.96981	validation-aucpr:0.97455
[20:34:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[59]	validation-logloss:0.20355	validation-auc:0.96992	validation-aucpr:0.97461
[20:34:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[60]	validation-logloss:0.20304	validation-auc:0.97009	validation-aucpr:0.97491
[20:34:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[61]	validation-logloss:0.20314	validation-auc:0.97001	validation-aucpr:0.97488
[20:34:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[62]	validation-logloss:0.20279	validation-auc:0.97005	validation-aucpr:0.97492
[20:34:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[63]	validation-logloss:0.20264	validation-auc:0.97006	validation-aucpr:0.97495
[20:34:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[64]	validation-logloss:0.20293	validation-auc:0.96995	validation-aucpr:0.97489
[20:34:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[65]	validation-logloss:0.20292	validation-auc:0.96999	validation-aucpr:0.97489
[20:34:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[66]	validation-logloss:0.20284	validation-auc:0.97001	validation-aucpr:0.97490
[20:34:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[67]	validation-logloss:0.20285	validation-auc:0.97003	validation-aucpr:0.97489
[20:34:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[68]	validation-logloss:0.20308	validation-auc:0.96998	validation-aucpr:0.97488
[20:34:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[69]	validation-logloss:0.20314	validation-auc:0.96999	validation-aucpr:0.97484
[20:34:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[70]	validation-logloss:0.20319	validation-auc:0.97004	validation-aucpr:0.97492
[20:34:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[71]	validation-logloss:0.20310	validation-auc:0.97012	validation-aucpr:0.97498
[20:34:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[72]	validation-logloss:0.20294	validation-auc:0.97020	validation-aucpr:0.97501
[20:34:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[73]	validation-logloss:0.20297	validation-auc:0.97025	validation-aucpr:0.97501
{'best_iteration': '72', 'best_score': '0.9750078737434187'}
Trial 46, Fold 1: Log loss = 0.20296792392887414, Average precision = 0.9750121228461307, ROC-AUC = 0.9702506416598559, Elapsed Time = 173.2442964999973 seconds
Trial 46, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 46, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[20:34:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.64530	validation-auc:0.92763	validation-aucpr:0.91331
[20:34:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.59987	validation-auc:0.95017	validation-aucpr:0.94426
[20:34:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.56506	validation-auc:0.95467	validation-aucpr:0.95053
[20:35:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.53379	validation-auc:0.95752	validation-aucpr:0.96177
[20:35:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.50317	validation-auc:0.96085	validation-aucpr:0.96533
[20:35:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5]	validation-logloss:0.47577	validation-auc:0.96249	validation-aucpr:0.96685
[20:35:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6]	validation-logloss:0.45064	validation-auc:0.96373	validation-aucpr:0.96797
[20:35:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7]	validation-logloss:0.43124	validation-auc:0.96391	validation-aucpr:0.96800
[20:35:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8]	validation-logloss:0.41027	validation-auc:0.96509	validation-aucpr:0.96904
[20:35:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9]	validation-logloss:0.39076	validation-auc:0.96650	validation-aucpr:0.97012
[20:35:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10]	validation-logloss:0.37329	validation-auc:0.96726	validation-aucpr:0.97089
[20:35:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11]	validation-logloss:0.35690	validation-auc:0.96831	validation-aucpr:0.97173
[20:35:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12]	validation-logloss:0.34278	validation-auc:0.96884	validation-aucpr:0.97255
[20:35:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13]	validation-logloss:0.32986	validation-auc:0.96911	validation-aucpr:0.97285
[20:35:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14]	validation-logloss:0.32078	validation-auc:0.96899	validation-aucpr:0.97258
[20:35:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15]	validation-logloss:0.31055	validation-auc:0.96885	validation-aucpr:0.97248
[20:35:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16]	validation-logloss:0.30204	validation-auc:0.96888	validation-aucpr:0.97237
[20:35:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17]	validation-logloss:0.29528	validation-auc:0.96852	validation-aucpr:0.97090
[20:35:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18]	validation-logloss:0.28725	validation-auc:0.96844	validation-aucpr:0.97097
[20:35:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19]	validation-logloss:0.27942	validation-auc:0.96904	validation-aucpr:0.97276
[20:36:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20]	validation-logloss:0.27311	validation-auc:0.96918	validation-aucpr:0.97279
[20:36:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21]	validation-logloss:0.26682	validation-auc:0.96922	validation-aucpr:0.97281
[20:36:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22]	validation-logloss:0.26013	validation-auc:0.96966	validation-aucpr:0.97233
[20:36:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23]	validation-logloss:0.25512	validation-auc:0.97008	validation-aucpr:0.97375
[20:36:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24]	validation-logloss:0.24913	validation-auc:0.97060	validation-aucpr:0.97416
[20:36:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25]	validation-logloss:0.24414	validation-auc:0.97055	validation-aucpr:0.97419
[20:36:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26]	validation-logloss:0.23976	validation-auc:0.97085	validation-aucpr:0.97441
[20:36:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27]	validation-logloss:0.23633	validation-auc:0.97094	validation-aucpr:0.97448
[20:36:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28]	validation-logloss:0.23270	validation-auc:0.97092	validation-aucpr:0.97445
[20:36:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29]	validation-logloss:0.22912	validation-auc:0.97128	validation-aucpr:0.97472
[20:36:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30]	validation-logloss:0.22581	validation-auc:0.97129	validation-aucpr:0.97473
[20:36:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31]	validation-logloss:0.22264	validation-auc:0.97138	validation-aucpr:0.97480
[20:36:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32]	validation-logloss:0.21926	validation-auc:0.97160	validation-aucpr:0.97507
[20:36:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33]	validation-logloss:0.21690	validation-auc:0.97169	validation-aucpr:0.97508
[20:36:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34]	validation-logloss:0.21436	validation-auc:0.97200	validation-aucpr:0.97532
[20:36:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35]	validation-logloss:0.21231	validation-auc:0.97184	validation-aucpr:0.97525
[20:36:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36]	validation-logloss:0.21087	validation-auc:0.97170	validation-aucpr:0.97519
[20:36:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37]	validation-logloss:0.20879	validation-auc:0.97181	validation-aucpr:0.97527
[20:36:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38]	validation-logloss:0.20737	validation-auc:0.97193	validation-aucpr:0.97533
[20:36:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39]	validation-logloss:0.20593	validation-auc:0.97188	validation-aucpr:0.97529
[20:36:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40]	validation-logloss:0.20459	validation-auc:0.97188	validation-aucpr:0.97529
[20:36:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41]	validation-logloss:0.20355	validation-auc:0.97188	validation-aucpr:0.97523
[20:36:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42]	validation-logloss:0.20274	validation-auc:0.97178	validation-aucpr:0.97511
[20:37:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43]	validation-logloss:0.20221	validation-auc:0.97158	validation-aucpr:0.97477
[20:37:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44]	validation-logloss:0.20085	validation-auc:0.97174	validation-aucpr:0.97497
[20:37:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45]	validation-logloss:0.19980	validation-auc:0.97177	validation-aucpr:0.97501
[20:37:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46]	validation-logloss:0.19873	validation-auc:0.97188	validation-aucpr:0.97520
[20:37:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[47]	validation-logloss:0.19776	validation-auc:0.97204	validation-aucpr:0.97529
[20:37:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[48]	validation-logloss:0.19678	validation-auc:0.97221	validation-aucpr:0.97534
[20:37:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[49]	validation-logloss:0.19596	validation-auc:0.97231	validation-aucpr:0.97541
[20:37:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[50]	validation-logloss:0.19549	validation-auc:0.97226	validation-aucpr:0.97520
[20:37:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[51]	validation-logloss:0.19499	validation-auc:0.97227	validation-aucpr:0.97502
[20:37:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[52]	validation-logloss:0.19379	validation-auc:0.97246	validation-aucpr:0.97513
[20:37:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[53]	validation-logloss:0.19348	validation-auc:0.97242	validation-aucpr:0.97502
[20:37:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[54]	validation-logloss:0.19293	validation-auc:0.97246	validation-aucpr:0.97508
[20:37:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[55]	validation-logloss:0.19233	validation-auc:0.97253	validation-aucpr:0.97504
[20:37:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[56]	validation-logloss:0.19182	validation-auc:0.97260	validation-aucpr:0.97508
[20:37:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[57]	validation-logloss:0.19149	validation-auc:0.97259	validation-aucpr:0.97490
[20:37:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[58]	validation-logloss:0.19056	validation-auc:0.97277	validation-aucpr:0.97499
[20:37:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[59]	validation-logloss:0.19033	validation-auc:0.97272	validation-aucpr:0.97473
[20:37:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[60]	validation-logloss:0.18994	validation-auc:0.97281	validation-aucpr:0.97445
[20:37:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[61]	validation-logloss:0.18946	validation-auc:0.97291	validation-aucpr:0.97488
[20:37:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[62]	validation-logloss:0.18955	validation-auc:0.97286	validation-aucpr:0.97481
[20:37:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[63]	validation-logloss:0.18912	validation-auc:0.97297	validation-aucpr:0.97430
[20:37:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[64]	validation-logloss:0.18896	validation-auc:0.97299	validation-aucpr:0.97452
[20:37:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[65]	validation-logloss:0.18846	validation-auc:0.97315	validation-aucpr:0.97447
[20:37:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[66]	validation-logloss:0.18846	validation-auc:0.97314	validation-aucpr:0.97469
[20:37:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[67]	validation-logloss:0.18827	validation-auc:0.97317	validation-aucpr:0.97435
[20:37:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[68]	validation-logloss:0.18866	validation-auc:0.97304	validation-aucpr:0.97393
[20:37:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[69]	validation-logloss:0.18839	validation-auc:0.97311	validation-aucpr:0.97416
[20:37:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[70]	validation-logloss:0.18845	validation-auc:0.97306	validation-aucpr:0.97396
[20:37:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[71]	validation-logloss:0.18825	validation-auc:0.97312	validation-aucpr:0.97427
[20:37:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[72]	validation-logloss:0.18809	validation-auc:0.97320	validation-aucpr:0.97449
[20:37:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[73]	validation-logloss:0.18801	validation-auc:0.97327	validation-aucpr:0.97446
{'best_iteration': '49', 'best_score': '0.9754094178962217'}
Trial 46, Fold 2: Log loss = 0.1880081891241934, Average precision = 0.9745046069561031, ROC-AUC = 0.9732694758419043, Elapsed Time = 168.89245759999903 seconds
Trial 46, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 46, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[20:37:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.64015	validation-auc:0.94881	validation-aucpr:0.95418
[20:37:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.59977	validation-auc:0.95293	validation-aucpr:0.94017
[20:37:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.56469	validation-auc:0.95817	validation-aucpr:0.95175
[20:37:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.53286	validation-auc:0.96073	validation-aucpr:0.96001
[20:37:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.50034	validation-auc:0.96359	validation-aucpr:0.96391
[20:37:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5]	validation-logloss:0.47538	validation-auc:0.96345	validation-aucpr:0.96154
[20:38:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6]	validation-logloss:0.45279	validation-auc:0.96437	validation-aucpr:0.96486
[20:38:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7]	validation-logloss:0.42941	validation-auc:0.96566	validation-aucpr:0.96793
[20:38:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8]	validation-logloss:0.40882	validation-auc:0.96647	validation-aucpr:0.96871
[20:38:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9]	validation-logloss:0.39185	validation-auc:0.96710	validation-aucpr:0.96990
[20:38:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10]	validation-logloss:0.37413	validation-auc:0.96786	validation-aucpr:0.97070
[20:38:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11]	validation-logloss:0.36034	validation-auc:0.96843	validation-aucpr:0.97064
[20:38:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12]	validation-logloss:0.34531	validation-auc:0.96876	validation-aucpr:0.97029
[20:38:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13]	validation-logloss:0.33452	validation-auc:0.96855	validation-aucpr:0.97022
[20:38:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14]	validation-logloss:0.32420	validation-auc:0.96910	validation-aucpr:0.97121
[20:38:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15]	validation-logloss:0.31556	validation-auc:0.96891	validation-aucpr:0.97098
[20:38:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16]	validation-logloss:0.30545	validation-auc:0.96907	validation-aucpr:0.97119
[20:38:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17]	validation-logloss:0.29540	validation-auc:0.96967	validation-aucpr:0.97154
[20:38:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18]	validation-logloss:0.28594	validation-auc:0.97012	validation-aucpr:0.97209
[20:38:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19]	validation-logloss:0.27938	validation-auc:0.97005	validation-aucpr:0.97208
[20:38:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20]	validation-logloss:0.27369	validation-auc:0.97003	validation-aucpr:0.97217
[20:38:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21]	validation-logloss:0.26886	validation-auc:0.96957	validation-aucpr:0.97014
[20:38:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22]	validation-logloss:0.26349	validation-auc:0.96971	validation-aucpr:0.97073
[20:39:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23]	validation-logloss:0.25954	validation-auc:0.96968	validation-aucpr:0.97049
[20:39:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24]	validation-logloss:0.25570	validation-auc:0.96954	validation-aucpr:0.97038
[20:39:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25]	validation-logloss:0.25105	validation-auc:0.96924	validation-aucpr:0.97015
[20:39:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26]	validation-logloss:0.24661	validation-auc:0.96921	validation-aucpr:0.97030
[20:39:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27]	validation-logloss:0.24222	validation-auc:0.96924	validation-aucpr:0.97005
[20:39:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28]	validation-logloss:0.23821	validation-auc:0.96936	validation-aucpr:0.97119
[20:39:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29]	validation-logloss:0.23455	validation-auc:0.96946	validation-aucpr:0.97130
[20:39:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30]	validation-logloss:0.23209	validation-auc:0.96950	validation-aucpr:0.97099
[20:39:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31]	validation-logloss:0.22917	validation-auc:0.96952	validation-aucpr:0.97093
[20:39:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32]	validation-logloss:0.22587	validation-auc:0.96968	validation-aucpr:0.97098
[20:39:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33]	validation-logloss:0.22377	validation-auc:0.96961	validation-aucpr:0.97030
[20:39:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34]	validation-logloss:0.22120	validation-auc:0.96963	validation-aucpr:0.97057
[20:39:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35]	validation-logloss:0.21861	validation-auc:0.96989	validation-aucpr:0.97091
[20:39:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36]	validation-logloss:0.21659	validation-auc:0.97000	validation-aucpr:0.97153
[20:39:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37]	validation-logloss:0.21427	validation-auc:0.97031	validation-aucpr:0.97178
[20:39:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38]	validation-logloss:0.21225	validation-auc:0.97058	validation-aucpr:0.97201
[20:39:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39]	validation-logloss:0.21127	validation-auc:0.97052	validation-aucpr:0.97195
[20:39:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40]	validation-logloss:0.20980	validation-auc:0.97073	validation-aucpr:0.97271
[20:39:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41]	validation-logloss:0.20801	validation-auc:0.97094	validation-aucpr:0.97286
[20:39:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42]	validation-logloss:0.20659	validation-auc:0.97101	validation-aucpr:0.97449
[20:39:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43]	validation-logloss:0.20589	validation-auc:0.97113	validation-aucpr:0.97506
[20:39:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44]	validation-logloss:0.20486	validation-auc:0.97125	validation-aucpr:0.97524
[20:39:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45]	validation-logloss:0.20417	validation-auc:0.97121	validation-aucpr:0.97525
[20:39:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46]	validation-logloss:0.20347	validation-auc:0.97124	validation-aucpr:0.97527
[20:40:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[47]	validation-logloss:0.20292	validation-auc:0.97125	validation-aucpr:0.97523
[20:40:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[48]	validation-logloss:0.20223	validation-auc:0.97127	validation-aucpr:0.97523
[20:40:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[49]	validation-logloss:0.20142	validation-auc:0.97132	validation-aucpr:0.97528
[20:40:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[50]	validation-logloss:0.20047	validation-auc:0.97147	validation-aucpr:0.97539
[20:40:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[51]	validation-logloss:0.19951	validation-auc:0.97170	validation-aucpr:0.97563
[20:40:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[52]	validation-logloss:0.19904	validation-auc:0.97170	validation-aucpr:0.97557
[20:40:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[53]	validation-logloss:0.19862	validation-auc:0.97192	validation-aucpr:0.97572
[20:40:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[54]	validation-logloss:0.19795	validation-auc:0.97204	validation-aucpr:0.97564
[20:40:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[55]	validation-logloss:0.19772	validation-auc:0.97210	validation-aucpr:0.97574
[20:40:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[56]	validation-logloss:0.19823	validation-auc:0.97185	validation-aucpr:0.97563
[20:40:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[57]	validation-logloss:0.19753	validation-auc:0.97205	validation-aucpr:0.97573
[20:40:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[58]	validation-logloss:0.19714	validation-auc:0.97210	validation-aucpr:0.97574
[20:40:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[59]	validation-logloss:0.19676	validation-auc:0.97217	validation-aucpr:0.97582
[20:40:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[60]	validation-logloss:0.19674	validation-auc:0.97208	validation-aucpr:0.97547
[20:40:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[61]	validation-logloss:0.19676	validation-auc:0.97211	validation-aucpr:0.97558
[20:40:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[62]	validation-logloss:0.19740	validation-auc:0.97180	validation-aucpr:0.97515
[20:40:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[63]	validation-logloss:0.19699	validation-auc:0.97192	validation-aucpr:0.97497
[20:40:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[64]	validation-logloss:0.19663	validation-auc:0.97202	validation-aucpr:0.97504
[20:40:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[65]	validation-logloss:0.19680	validation-auc:0.97200	validation-aucpr:0.97542
[20:40:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[66]	validation-logloss:0.19671	validation-auc:0.97207	validation-aucpr:0.97565
[20:40:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[67]	validation-logloss:0.19708	validation-auc:0.97192	validation-aucpr:0.97547
[20:40:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[68]	validation-logloss:0.19685	validation-auc:0.97197	validation-aucpr:0.97546
[20:40:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[69]	validation-logloss:0.19651	validation-auc:0.97209	validation-aucpr:0.97548
[20:40:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[70]	validation-logloss:0.19653	validation-auc:0.97211	validation-aucpr:0.97533
[20:40:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[71]	validation-logloss:0.19678	validation-auc:0.97207	validation-aucpr:0.97539
[20:40:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[72]	validation-logloss:0.19691	validation-auc:0.97210	validation-aucpr:0.97562
[20:40:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[73]	validation-logloss:0.19689	validation-auc:0.97210	validation-aucpr:0.97545
{'best_iteration': '59', 'best_score': '0.9758164074598226'}
Trial 46, Fold 3: Log loss = 0.19688780501448533, Average precision = 0.9754565903667709, ROC-AUC = 0.9721035565004672, Elapsed Time = 172.1257302999984 seconds
Trial 46, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 46, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[20:40:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.64068	validation-auc:0.94491	validation-aucpr:0.95001
[20:40:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.59933	validation-auc:0.95102	validation-aucpr:0.94377
[20:40:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.55913	validation-auc:0.95819	validation-aucpr:0.95572
[20:40:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.52414	validation-auc:0.96134	validation-aucpr:0.96267
[20:40:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.49257	validation-auc:0.96277	validation-aucpr:0.96354
[20:40:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5]	validation-logloss:0.46471	validation-auc:0.96381	validation-aucpr:0.96777
[20:40:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6]	validation-logloss:0.43999	validation-auc:0.96407	validation-aucpr:0.97089
[20:40:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7]	validation-logloss:0.41826	validation-auc:0.96471	validation-aucpr:0.97126
[20:40:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8]	validation-logloss:0.40089	validation-auc:0.96550	validation-aucpr:0.97169
[20:41:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9]	validation-logloss:0.38277	validation-auc:0.96595	validation-aucpr:0.97206
[20:41:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10]	validation-logloss:0.36836	validation-auc:0.96560	validation-aucpr:0.97193
[20:41:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11]	validation-logloss:0.35533	validation-auc:0.96565	validation-aucpr:0.97187
[20:41:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12]	validation-logloss:0.34177	validation-auc:0.96552	validation-aucpr:0.97187
[20:41:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13]	validation-logloss:0.33001	validation-auc:0.96565	validation-aucpr:0.97186
[20:41:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14]	validation-logloss:0.32085	validation-auc:0.96577	validation-aucpr:0.97197
[20:41:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15]	validation-logloss:0.31007	validation-auc:0.96623	validation-aucpr:0.97228
[20:41:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16]	validation-logloss:0.30043	validation-auc:0.96667	validation-aucpr:0.97255
[20:41:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17]	validation-logloss:0.29244	validation-auc:0.96723	validation-aucpr:0.97278
[20:41:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18]	validation-logloss:0.28510	validation-auc:0.96693	validation-aucpr:0.97254
[20:41:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19]	validation-logloss:0.27865	validation-auc:0.96748	validation-aucpr:0.97297
[20:41:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20]	validation-logloss:0.27151	validation-auc:0.96796	validation-aucpr:0.97326
[20:41:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21]	validation-logloss:0.26551	validation-auc:0.96801	validation-aucpr:0.97332
[20:41:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22]	validation-logloss:0.26032	validation-auc:0.96824	validation-aucpr:0.97347
[20:41:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23]	validation-logloss:0.25524	validation-auc:0.96813	validation-aucpr:0.97343
[20:41:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24]	validation-logloss:0.25017	validation-auc:0.96847	validation-aucpr:0.97366
[20:41:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25]	validation-logloss:0.24620	validation-auc:0.96823	validation-aucpr:0.97347
[20:42:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26]	validation-logloss:0.24141	validation-auc:0.96856	validation-aucpr:0.97376
[20:42:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27]	validation-logloss:0.23745	validation-auc:0.96881	validation-aucpr:0.97392
[20:42:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28]	validation-logloss:0.23466	validation-auc:0.96866	validation-aucpr:0.97378
[20:42:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29]	validation-logloss:0.23143	validation-auc:0.96882	validation-aucpr:0.97388
[20:42:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30]	validation-logloss:0.22848	validation-auc:0.96898	validation-aucpr:0.97397
[20:42:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31]	validation-logloss:0.22675	validation-auc:0.96881	validation-aucpr:0.97386
[20:42:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32]	validation-logloss:0.22512	validation-auc:0.96855	validation-aucpr:0.97362
[20:42:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33]	validation-logloss:0.22291	validation-auc:0.96850	validation-aucpr:0.97358
[20:42:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34]	validation-logloss:0.22065	validation-auc:0.96868	validation-aucpr:0.97372
[20:42:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35]	validation-logloss:0.21871	validation-auc:0.96874	validation-aucpr:0.97379
[20:42:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36]	validation-logloss:0.21751	validation-auc:0.96865	validation-aucpr:0.97372
[20:42:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37]	validation-logloss:0.21534	validation-auc:0.96895	validation-aucpr:0.97391
[20:42:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38]	validation-logloss:0.21412	validation-auc:0.96899	validation-aucpr:0.97392
[20:42:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39]	validation-logloss:0.21309	validation-auc:0.96903	validation-aucpr:0.97394
[20:42:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40]	validation-logloss:0.21181	validation-auc:0.96897	validation-aucpr:0.97391
[20:42:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41]	validation-logloss:0.21112	validation-auc:0.96891	validation-aucpr:0.97395
[20:42:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42]	validation-logloss:0.21006	validation-auc:0.96900	validation-aucpr:0.97401
[20:42:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43]	validation-logloss:0.20858	validation-auc:0.96924	validation-aucpr:0.97417
[20:42:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44]	validation-logloss:0.20823	validation-auc:0.96918	validation-aucpr:0.97410
[20:42:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45]	validation-logloss:0.20789	validation-auc:0.96916	validation-aucpr:0.97412
[20:42:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46]	validation-logloss:0.20689	validation-auc:0.96925	validation-aucpr:0.97420
[20:42:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[47]	validation-logloss:0.20608	validation-auc:0.96930	validation-aucpr:0.97422
[20:42:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[48]	validation-logloss:0.20534	validation-auc:0.96934	validation-aucpr:0.97429
[20:42:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[49]	validation-logloss:0.20515	validation-auc:0.96924	validation-aucpr:0.97422
[20:42:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[50]	validation-logloss:0.20486	validation-auc:0.96919	validation-aucpr:0.97419
[20:42:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[51]	validation-logloss:0.20453	validation-auc:0.96917	validation-aucpr:0.97416
[20:42:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[52]	validation-logloss:0.20432	validation-auc:0.96919	validation-aucpr:0.97417
[20:42:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[53]	validation-logloss:0.20364	validation-auc:0.96936	validation-aucpr:0.97428
[20:42:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[54]	validation-logloss:0.20361	validation-auc:0.96930	validation-aucpr:0.97425
[20:43:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[55]	validation-logloss:0.20292	validation-auc:0.96949	validation-aucpr:0.97436
[20:43:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[56]	validation-logloss:0.20261	validation-auc:0.96944	validation-aucpr:0.97435
[20:43:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[57]	validation-logloss:0.20233	validation-auc:0.96950	validation-aucpr:0.97441
[20:43:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[58]	validation-logloss:0.20216	validation-auc:0.96960	validation-aucpr:0.97448
[20:43:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[59]	validation-logloss:0.20197	validation-auc:0.96958	validation-aucpr:0.97447
[20:43:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[60]	validation-logloss:0.20186	validation-auc:0.96955	validation-aucpr:0.97447
[20:43:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[61]	validation-logloss:0.20170	validation-auc:0.96953	validation-aucpr:0.97445
[20:43:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[62]	validation-logloss:0.20162	validation-auc:0.96957	validation-aucpr:0.97445
[20:43:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[63]	validation-logloss:0.20171	validation-auc:0.96954	validation-aucpr:0.97443
[20:43:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[64]	validation-logloss:0.20123	validation-auc:0.96966	validation-aucpr:0.97453
[20:43:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[65]	validation-logloss:0.20144	validation-auc:0.96963	validation-aucpr:0.97449
[20:43:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[66]	validation-logloss:0.20147	validation-auc:0.96962	validation-aucpr:0.97448
[20:43:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[67]	validation-logloss:0.20166	validation-auc:0.96959	validation-aucpr:0.97446
[20:43:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[68]	validation-logloss:0.20177	validation-auc:0.96958	validation-aucpr:0.97441
[20:43:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[69]	validation-logloss:0.20188	validation-auc:0.96963	validation-aucpr:0.97443
[20:43:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[70]	validation-logloss:0.20178	validation-auc:0.96965	validation-aucpr:0.97445
[20:43:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[71]	validation-logloss:0.20162	validation-auc:0.96971	validation-aucpr:0.97450
[20:43:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[72]	validation-logloss:0.20162	validation-auc:0.96977	validation-aucpr:0.97454
[20:43:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[73]	validation-logloss:0.20176	validation-auc:0.96973	validation-aucpr:0.97450
{'best_iteration': '72', 'best_score': '0.974535678305386'}
Trial 46, Fold 4: Log loss = 0.20176173603682535, Average precision = 0.9745087340972265, ROC-AUC = 0.9697287256998838, Elapsed Time = 167.63923059999797 seconds
Trial 46, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 46, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[20:43:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.64095	validation-auc:0.94365	validation-aucpr:0.95270
[20:43:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.60106	validation-auc:0.94824	validation-aucpr:0.93268
[20:43:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.56080	validation-auc:0.95714	validation-aucpr:0.95764
[20:43:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.53025	validation-auc:0.95956	validation-aucpr:0.96236
[20:43:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.50057	validation-auc:0.96036	validation-aucpr:0.96652
[20:43:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5]	validation-logloss:0.47220	validation-auc:0.96245	validation-aucpr:0.96836
[20:43:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6]	validation-logloss:0.44705	validation-auc:0.96355	validation-aucpr:0.96933
[20:43:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7]	validation-logloss:0.42671	validation-auc:0.96425	validation-aucpr:0.96971
[20:43:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8]	validation-logloss:0.40976	validation-auc:0.96418	validation-aucpr:0.96938
[20:43:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9]	validation-logloss:0.39223	validation-auc:0.96447	validation-aucpr:0.96969
[20:43:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10]	validation-logloss:0.37842	validation-auc:0.96468	validation-aucpr:0.96979
[20:43:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11]	validation-logloss:0.36427	validation-auc:0.96431	validation-aucpr:0.96953
[20:44:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12]	validation-logloss:0.35133	validation-auc:0.96507	validation-aucpr:0.97002
[20:44:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13]	validation-logloss:0.34098	validation-auc:0.96486	validation-aucpr:0.96966
[20:44:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14]	validation-logloss:0.33112	validation-auc:0.96476	validation-aucpr:0.96954
[20:44:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15]	validation-logloss:0.32005	validation-auc:0.96486	validation-aucpr:0.96962
[20:44:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16]	validation-logloss:0.31020	validation-auc:0.96497	validation-aucpr:0.96989
[20:44:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17]	validation-logloss:0.30115	validation-auc:0.96523	validation-aucpr:0.97010
[20:44:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18]	validation-logloss:0.29316	validation-auc:0.96527	validation-aucpr:0.97018
[20:44:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19]	validation-logloss:0.28683	validation-auc:0.96546	validation-aucpr:0.97038
[20:44:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20]	validation-logloss:0.27941	validation-auc:0.96576	validation-aucpr:0.97062
[20:44:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21]	validation-logloss:0.27290	validation-auc:0.96592	validation-aucpr:0.97082
[20:44:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22]	validation-logloss:0.26819	validation-auc:0.96583	validation-aucpr:0.97061
[20:44:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23]	validation-logloss:0.26251	validation-auc:0.96603	validation-aucpr:0.97082
[20:44:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24]	validation-logloss:0.25721	validation-auc:0.96638	validation-aucpr:0.97110
[20:44:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25]	validation-logloss:0.25214	validation-auc:0.96667	validation-aucpr:0.97130
[20:44:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26]	validation-logloss:0.24734	validation-auc:0.96707	validation-aucpr:0.97181
[20:44:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27]	validation-logloss:0.24338	validation-auc:0.96733	validation-aucpr:0.97203
[20:44:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28]	validation-logloss:0.24089	validation-auc:0.96726	validation-aucpr:0.97196
[20:45:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29]	validation-logloss:0.23739	validation-auc:0.96751	validation-aucpr:0.97246
[20:45:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30]	validation-logloss:0.23433	validation-auc:0.96770	validation-aucpr:0.97272
[20:45:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31]	validation-logloss:0.23142	validation-auc:0.96788	validation-aucpr:0.97285
[20:45:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32]	validation-logloss:0.22975	validation-auc:0.96775	validation-aucpr:0.97269
[20:45:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33]	validation-logloss:0.22759	validation-auc:0.96774	validation-aucpr:0.97269
[20:45:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34]	validation-logloss:0.22535	validation-auc:0.96789	validation-aucpr:0.97277
[20:45:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35]	validation-logloss:0.22429	validation-auc:0.96757	validation-aucpr:0.97254
[20:45:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36]	validation-logloss:0.22317	validation-auc:0.96743	validation-aucpr:0.97238
[20:45:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37]	validation-logloss:0.22150	validation-auc:0.96740	validation-aucpr:0.97240
[20:45:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38]	validation-logloss:0.22027	validation-auc:0.96744	validation-aucpr:0.97241
[20:45:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39]	validation-logloss:0.21889	validation-auc:0.96751	validation-aucpr:0.97247
[20:45:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40]	validation-logloss:0.21713	validation-auc:0.96773	validation-aucpr:0.97252
[20:45:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41]	validation-logloss:0.21589	validation-auc:0.96786	validation-aucpr:0.97250
[20:45:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42]	validation-logloss:0.21502	validation-auc:0.96787	validation-aucpr:0.97250
[20:45:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43]	validation-logloss:0.21363	validation-auc:0.96800	validation-aucpr:0.97260
[20:45:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44]	validation-logloss:0.21269	validation-auc:0.96818	validation-aucpr:0.97270
[20:45:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45]	validation-logloss:0.21192	validation-auc:0.96821	validation-aucpr:0.97268
[20:45:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46]	validation-logloss:0.21177	validation-auc:0.96800	validation-aucpr:0.97250
[20:45:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[47]	validation-logloss:0.21135	validation-auc:0.96796	validation-aucpr:0.97244
[20:45:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[48]	validation-logloss:0.21064	validation-auc:0.96802	validation-aucpr:0.97254
[20:45:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[49]	validation-logloss:0.21044	validation-auc:0.96793	validation-aucpr:0.97246
[20:45:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[50]	validation-logloss:0.21026	validation-auc:0.96789	validation-aucpr:0.97237
[20:45:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[51]	validation-logloss:0.20991	validation-auc:0.96790	validation-aucpr:0.97240
[20:45:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[52]	validation-logloss:0.20947	validation-auc:0.96794	validation-aucpr:0.97232
[20:45:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[53]	validation-logloss:0.20873	validation-auc:0.96811	validation-aucpr:0.97236
[20:45:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[54]	validation-logloss:0.20815	validation-auc:0.96826	validation-aucpr:0.97252
[20:45:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[55]	validation-logloss:0.20810	validation-auc:0.96830	validation-aucpr:0.97274
[20:45:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[56]	validation-logloss:0.20774	validation-auc:0.96835	validation-aucpr:0.97275
[20:45:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[57]	validation-logloss:0.20711	validation-auc:0.96848	validation-aucpr:0.97279
[20:45:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[58]	validation-logloss:0.20706	validation-auc:0.96840	validation-aucpr:0.97270
[20:45:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[59]	validation-logloss:0.20705	validation-auc:0.96846	validation-aucpr:0.97294
[20:46:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[60]	validation-logloss:0.20714	validation-auc:0.96844	validation-aucpr:0.97287
[20:46:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[61]	validation-logloss:0.20705	validation-auc:0.96854	validation-aucpr:0.97293
[20:46:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[62]	validation-logloss:0.20691	validation-auc:0.96854	validation-aucpr:0.97288
[20:46:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[63]	validation-logloss:0.20695	validation-auc:0.96852	validation-aucpr:0.97278
[20:46:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[64]	validation-logloss:0.20666	validation-auc:0.96865	validation-aucpr:0.97295
[20:46:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[65]	validation-logloss:0.20673	validation-auc:0.96860	validation-aucpr:0.97283
[20:46:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[66]	validation-logloss:0.20700	validation-auc:0.96851	validation-aucpr:0.97272
[20:46:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[67]	validation-logloss:0.20678	validation-auc:0.96859	validation-aucpr:0.97262
[20:46:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[68]	validation-logloss:0.20698	validation-auc:0.96853	validation-aucpr:0.97255
[20:46:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[69]	validation-logloss:0.20728	validation-auc:0.96848	validation-aucpr:0.97250
[20:46:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[70]	validation-logloss:0.20717	validation-auc:0.96851	validation-aucpr:0.97241
[20:46:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[71]	validation-logloss:0.20694	validation-auc:0.96862	validation-aucpr:0.97248
[20:46:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[72]	validation-logloss:0.20677	validation-auc:0.96869	validation-aucpr:0.97249
[20:46:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[73]	validation-logloss:0.20641	validation-auc:0.96886	validation-aucpr:0.97271
{'best_iteration': '64', 'best_score': '0.9729466973423092'}
Trial 46, Fold 5: Log loss = 0.20640855234259178, Average precision = 0.9727156435063508, ROC-AUC = 0.9688557679802315, Elapsed Time = 176.15985360000195 seconds
Optimization Progress:  47%|####6     | 47/100 [2:47:24<4:54:45, 333.68s/it]
Trial 47, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 47, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[20:46:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.68214	validation-auc:0.93801	validation-aucpr:0.91337
[20:46:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.67009	validation-auc:0.95823	validation-aucpr:0.95300
[20:46:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.65805	validation-auc:0.96329	validation-aucpr:0.96345
[20:46:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.64657	validation-auc:0.96524	validation-aucpr:0.96771
[20:46:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.63605	validation-auc:0.96552	validation-aucpr:0.97081
[20:46:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5]	validation-logloss:0.62582	validation-auc:0.96558	validation-aucpr:0.97099
{'best_iteration': '5', 'best_score': '0.9709855396319533'}
Trial 47, Fold 1: Log loss = 0.6258185969607232, Average precision = 0.9708432228852684, ROC-AUC = 0.965581356375733, Elapsed Time = 0.40443649999724585 seconds
Trial 47, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 47, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[20:46:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.68157	validation-auc:0.94237	validation-aucpr:0.93199
[20:46:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.67085	validation-auc:0.95490	validation-aucpr:0.95445
[20:46:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.66064	validation-auc:0.95773	validation-aucpr:0.95933
[20:46:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.64926	validation-auc:0.96375	validation-aucpr:0.96673
[20:46:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.63908	validation-auc:0.96489	validation-aucpr:0.96765
[20:46:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5]	validation-logloss:0.62971	validation-auc:0.96467	validation-aucpr:0.96709
{'best_iteration': '4', 'best_score': '0.967654452445068'}
Trial 47, Fold 2: Log loss = 0.6297064625081882, Average precision = 0.9674295405735265, ROC-AUC = 0.9646678802442896, Elapsed Time = 0.47734399999899324 seconds
Trial 47, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 47, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[20:46:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.68186	validation-auc:0.94238	validation-aucpr:0.94465
[20:46:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.67043	validation-auc:0.95718	validation-aucpr:0.95483
[20:46:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.65883	validation-auc:0.96507	validation-aucpr:0.96715
[20:46:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.64858	validation-auc:0.96542	validation-aucpr:0.96956
[20:46:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.63734	validation-auc:0.96742	validation-aucpr:0.97159
[20:46:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5]	validation-logloss:0.62655	validation-auc:0.96825	validation-aucpr:0.97230
{'best_iteration': '5', 'best_score': '0.9722959147201717'}
Trial 47, Fold 3: Log loss = 0.6265516179895246, Average precision = 0.9721495798330522, ROC-AUC = 0.9682459507517023, Elapsed Time = 0.4703719999997702 seconds
Trial 47, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 47, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[20:46:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.68187	validation-auc:0.93803	validation-aucpr:0.92342
[20:46:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.67100	validation-auc:0.95101	validation-aucpr:0.94634
[20:46:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.65905	validation-auc:0.96171	validation-aucpr:0.96040
[20:46:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.64763	validation-auc:0.96442	validation-aucpr:0.96716
[20:46:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.63786	validation-auc:0.96423	validation-aucpr:0.96973
[20:46:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5]	validation-logloss:0.62843	validation-auc:0.96482	validation-aucpr:0.96998
{'best_iteration': '5', 'best_score': '0.9699807104101339'}
Trial 47, Fold 4: Log loss = 0.6284295527765685, Average precision = 0.9698986077541827, ROC-AUC = 0.9648172141271512, Elapsed Time = 0.46956090000094264 seconds
Trial 47, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 47, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[20:46:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.68206	validation-auc:0.93532	validation-aucpr:0.91851
[20:46:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.67146	validation-auc:0.94912	validation-aucpr:0.94584
[20:46:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.66166	validation-auc:0.95150	validation-aucpr:0.95554
[20:46:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.65157	validation-auc:0.95365	validation-aucpr:0.95748
[20:46:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.64238	validation-auc:0.95407	validation-aucpr:0.95810
[20:46:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5]	validation-logloss:0.63275	validation-auc:0.95544	validation-aucpr:0.95924
{'best_iteration': '5', 'best_score': '0.959242797316479'}
Trial 47, Fold 5: Log loss = 0.6327488828734219, Average precision = 0.9587947031588553, ROC-AUC = 0.9554357059249763, Elapsed Time = 0.46724950000134413 seconds
Optimization Progress:  48%|####8     | 48/100 [2:47:34<3:25:00, 236.55s/it]
Trial 48, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 48, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[0]	validation-logloss:0.65265	validation-auc:0.92815	validation-aucpr:0.91366
[1]	validation-logloss:0.61911	validation-auc:0.95030	validation-aucpr:0.94360
[2]	validation-logloss:0.58864	validation-auc:0.95679	validation-aucpr:0.95677
[3]	validation-logloss:0.56412	validation-auc:0.95688	validation-aucpr:0.95950
[4]	validation-logloss:0.54044	validation-auc:0.95838	validation-aucpr:0.96405
[5]	validation-logloss:0.52041	validation-auc:0.95850	validation-aucpr:0.96382
[6]	validation-logloss:0.50156	validation-auc:0.95872	validation-aucpr:0.96450
[7]	validation-logloss:0.48352	validation-auc:0.95926	validation-aucpr:0.96478
[8]	validation-logloss:0.46713	validation-auc:0.95954	validation-aucpr:0.96497
{'best_iteration': '8', 'best_score': '0.9649673171907855'}
Trial 48, Fold 1: Log loss = 0.4671281350911924, Average precision = 0.9649679365351161, ROC-AUC = 0.9595362215196226, Elapsed Time = 5.979565199999342 seconds
Trial 48, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 48, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[0]	validation-logloss:0.65169	validation-auc:0.93400	validation-aucpr:0.89597
[1]	validation-logloss:0.61991	validation-auc:0.95580	validation-aucpr:0.94681
[2]	validation-logloss:0.58676	validation-auc:0.96162	validation-aucpr:0.96618
[3]	validation-logloss:0.55719	validation-auc:0.96371	validation-aucpr:0.96789
[4]	validation-logloss:0.53294	validation-auc:0.96504	validation-aucpr:0.96922
[5]	validation-logloss:0.51166	validation-auc:0.96557	validation-aucpr:0.96951
[6]	validation-logloss:0.49281	validation-auc:0.96552	validation-aucpr:0.96931
[7]	validation-logloss:0.47469	validation-auc:0.96570	validation-aucpr:0.96899
[8]	validation-logloss:0.45966	validation-auc:0.96478	validation-aucpr:0.96808
{'best_iteration': '5', 'best_score': '0.9695143141055345'}
Trial 48, Fold 2: Log loss = 0.4596563851648362, Average precision = 0.9680996079646447, ROC-AUC = 0.9647844984174224, Elapsed Time = 6.274692999999388 seconds
Trial 48, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 48, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[0]	validation-logloss:0.65214	validation-auc:0.93166	validation-aucpr:0.91066
[1]	validation-logloss:0.62242	validation-auc:0.94954	validation-aucpr:0.94639
[2]	validation-logloss:0.59343	validation-auc:0.95684	validation-aucpr:0.95985
[3]	validation-logloss:0.56865	validation-auc:0.95702	validation-aucpr:0.96193
[4]	validation-logloss:0.54572	validation-auc:0.95796	validation-aucpr:0.96284
[5]	validation-logloss:0.52381	validation-auc:0.95933	validation-aucpr:0.96349
[6]	validation-logloss:0.49920	validation-auc:0.96212	validation-aucpr:0.96605
[7]	validation-logloss:0.48134	validation-auc:0.96264	validation-aucpr:0.96614
[8]	validation-logloss:0.46541	validation-auc:0.96263	validation-aucpr:0.96602
{'best_iteration': '7', 'best_score': '0.9661439859958221'}
Trial 48, Fold 3: Log loss = 0.4654080460313595, Average precision = 0.9660221162005403, ROC-AUC = 0.9626300039246468, Elapsed Time = 6.240940799998498 seconds
Trial 48, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 48, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[0]	validation-logloss:0.65142	validation-auc:0.93416	validation-aucpr:0.90515
[1]	validation-logloss:0.62067	validation-auc:0.95532	validation-aucpr:0.95617
[2]	validation-logloss:0.58966	validation-auc:0.95845	validation-aucpr:0.96334
[3]	validation-logloss:0.56525	validation-auc:0.95871	validation-aucpr:0.96323
[4]	validation-logloss:0.54214	validation-auc:0.95918	validation-aucpr:0.96428
[5]	validation-logloss:0.52043	validation-auc:0.95991	validation-aucpr:0.96552
[6]	validation-logloss:0.50021	validation-auc:0.96001	validation-aucpr:0.96560
[7]	validation-logloss:0.48187	validation-auc:0.96050	validation-aucpr:0.96581
[8]	validation-logloss:0.46433	validation-auc:0.96139	validation-aucpr:0.96660
{'best_iteration': '8', 'best_score': '0.9666011794865117'}
Trial 48, Fold 4: Log loss = 0.4643289259501296, Average precision = 0.9665960273611527, ROC-AUC = 0.9613865700503825, Elapsed Time = 5.843712499998219 seconds
Trial 48, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 48, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[0]	validation-logloss:0.65256	validation-auc:0.92726	validation-aucpr:0.90348
[1]	validation-logloss:0.62107	validation-auc:0.95214	validation-aucpr:0.95102
[2]	validation-logloss:0.59356	validation-auc:0.95333	validation-aucpr:0.95624
[3]	validation-logloss:0.56813	validation-auc:0.95497	validation-aucpr:0.95773
[4]	validation-logloss:0.54440	validation-auc:0.95656	validation-aucpr:0.96067
[5]	validation-logloss:0.52202	validation-auc:0.95745	validation-aucpr:0.96157
[6]	validation-logloss:0.50376	validation-auc:0.95800	validation-aucpr:0.96312
[7]	validation-logloss:0.48722	validation-auc:0.95832	validation-aucpr:0.96341
[8]	validation-logloss:0.47188	validation-auc:0.95810	validation-aucpr:0.96326
{'best_iteration': '7', 'best_score': '0.9634119132513211'}
Trial 48, Fold 5: Log loss = 0.4718818396789914, Average precision = 0.9632099260839972, ROC-AUC = 0.9580976905783771, Elapsed Time = 6.20024960000228 seconds
Optimization Progress:  49%|####9     | 49/100 [2:48:13<2:30:30, 177.07s/it]
Trial 49, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 49, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[0]	validation-logloss:0.66453	validation-auc:0.94548	validation-aucpr:0.90619
[1]	validation-logloss:0.63846	validation-auc:0.96347	validation-aucpr:0.96426
[2]	validation-logloss:0.61384	validation-auc:0.96495	validation-aucpr:0.96393
[3]	validation-logloss:0.59333	validation-auc:0.96549	validation-aucpr:0.96700
[4]	validation-logloss:0.57190	validation-auc:0.96666	validation-aucpr:0.96992
[5]	validation-logloss:0.55194	validation-auc:0.96736	validation-aucpr:0.97049
[6]	validation-logloss:0.53343	validation-auc:0.96833	validation-aucpr:0.97261
[7]	validation-logloss:0.51641	validation-auc:0.96822	validation-aucpr:0.97019
[8]	validation-logloss:0.50181	validation-auc:0.96823	validation-aucpr:0.97071
[9]	validation-logloss:0.48792	validation-auc:0.96822	validation-aucpr:0.97064
[10]	validation-logloss:0.47476	validation-auc:0.96831	validation-aucpr:0.97069
[11]	validation-logloss:0.46081	validation-auc:0.96830	validation-aucpr:0.96846
[12]	validation-logloss:0.44897	validation-auc:0.96835	validation-aucpr:0.96840
[13]	validation-logloss:0.43635	validation-auc:0.96838	validation-aucpr:0.96849
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{'best_iteration': '74', 'best_score': '0.9761984583995469'}
Trial 49, Fold 1: Log loss = 0.20149546090569612, Average precision = 0.9759896129523458, ROC-AUC = 0.972685767697624, Elapsed Time = 7.393411099998048 seconds
Trial 49, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 49, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
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[80]	validation-logloss:0.19641	validation-auc:0.97325	validation-aucpr:0.97588
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[83]	validation-logloss:0.19465	validation-auc:0.97325	validation-aucpr:0.97475
{'best_iteration': '80', 'best_score': '0.9758752127330358'}
Trial 49, Fold 2: Log loss = 0.19464862815883344, Average precision = 0.9751688581640191, ROC-AUC = 0.9732532388288591, Elapsed Time = 7.5820448999984364 seconds
Trial 49, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 49, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
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[69]	validation-logloss:0.20722	validation-auc:0.97228	validation-aucpr:0.97391
[70]	validation-logloss:0.20659	validation-auc:0.97220	validation-aucpr:0.97385
[71]	validation-logloss:0.20563	validation-auc:0.97224	validation-aucpr:0.97387
[72]	validation-logloss:0.20485	validation-auc:0.97217	validation-aucpr:0.97384
[73]	validation-logloss:0.20396	validation-auc:0.97224	validation-aucpr:0.97378
[74]	validation-logloss:0.20321	validation-auc:0.97225	validation-aucpr:0.97376
[75]	validation-logloss:0.20227	validation-auc:0.97231	validation-aucpr:0.97356
[76]	validation-logloss:0.20151	validation-auc:0.97232	validation-aucpr:0.97353
[77]	validation-logloss:0.20097	validation-auc:0.97228	validation-aucpr:0.97344
[78]	validation-logloss:0.20040	validation-auc:0.97230	validation-aucpr:0.97335
[79]	validation-logloss:0.19978	validation-auc:0.97238	validation-aucpr:0.97335
[80]	validation-logloss:0.19924	validation-auc:0.97240	validation-aucpr:0.97319
[81]	validation-logloss:0.19861	validation-auc:0.97247	validation-aucpr:0.97325
[82]	validation-logloss:0.19802	validation-auc:0.97250	validation-aucpr:0.97313
[83]	validation-logloss:0.19761	validation-auc:0.97245	validation-aucpr:0.97292
{'best_iteration': '68', 'best_score': '0.9762039347909636'}
Trial 49, Fold 3: Log loss = 0.1976074858911532, Average precision = 0.9739148147201059, ROC-AUC = 0.9724530577332395, Elapsed Time = 8.235687200001848 seconds
Trial 49, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 49, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[0]	validation-logloss:0.66450	validation-auc:0.94787	validation-aucpr:0.92504
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[2]	validation-logloss:0.61847	validation-auc:0.96429	validation-aucpr:0.96687
[3]	validation-logloss:0.59758	validation-auc:0.96413	validation-aucpr:0.96693
[4]	validation-logloss:0.57829	validation-auc:0.96494	validation-aucpr:0.97019
[5]	validation-logloss:0.55767	validation-auc:0.96672	validation-aucpr:0.97209
[6]	validation-logloss:0.53851	validation-auc:0.96766	validation-aucpr:0.97298
[7]	validation-logloss:0.52259	validation-auc:0.96743	validation-aucpr:0.97273
[8]	validation-logloss:0.50543	validation-auc:0.96843	validation-aucpr:0.97349
[9]	validation-logloss:0.49155	validation-auc:0.96834	validation-aucpr:0.97336
[10]	validation-logloss:0.47649	validation-auc:0.96867	validation-aucpr:0.97366
[11]	validation-logloss:0.46245	validation-auc:0.96901	validation-aucpr:0.97399
[12]	validation-logloss:0.44894	validation-auc:0.96920	validation-aucpr:0.97419
[13]	validation-logloss:0.43639	validation-auc:0.96964	validation-aucpr:0.97433
[14]	validation-logloss:0.42439	validation-auc:0.96974	validation-aucpr:0.97441
[15]	validation-logloss:0.41287	validation-auc:0.97028	validation-aucpr:0.97481
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[45]	validation-logloss:0.24708	validation-auc:0.97206	validation-aucpr:0.97626
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[57]	validation-logloss:0.22346	validation-auc:0.97208	validation-aucpr:0.97634
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[59]	validation-logloss:0.22063	validation-auc:0.97216	validation-aucpr:0.97639
[60]	validation-logloss:0.21925	validation-auc:0.97209	validation-aucpr:0.97635
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[62]	validation-logloss:0.21623	validation-auc:0.97217	validation-aucpr:0.97642
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[65]	validation-logloss:0.21206	validation-auc:0.97236	validation-aucpr:0.97654
[66]	validation-logloss:0.21094	validation-auc:0.97230	validation-aucpr:0.97652
[67]	validation-logloss:0.21018	validation-auc:0.97223	validation-aucpr:0.97645
[68]	validation-logloss:0.20932	validation-auc:0.97222	validation-aucpr:0.97644
[69]	validation-logloss:0.20809	validation-auc:0.97231	validation-aucpr:0.97652
[70]	validation-logloss:0.20704	validation-auc:0.97233	validation-aucpr:0.97655
[71]	validation-logloss:0.20588	validation-auc:0.97242	validation-aucpr:0.97663
[72]	validation-logloss:0.20499	validation-auc:0.97242	validation-aucpr:0.97663
[73]	validation-logloss:0.20408	validation-auc:0.97249	validation-aucpr:0.97668
[74]	validation-logloss:0.20309	validation-auc:0.97256	validation-aucpr:0.97673
[75]	validation-logloss:0.20230	validation-auc:0.97255	validation-aucpr:0.97672
[76]	validation-logloss:0.20167	validation-auc:0.97255	validation-aucpr:0.97671
[77]	validation-logloss:0.20097	validation-auc:0.97255	validation-aucpr:0.97671
[78]	validation-logloss:0.20038	validation-auc:0.97247	validation-aucpr:0.97667
[79]	validation-logloss:0.19976	validation-auc:0.97254	validation-aucpr:0.97671
[80]	validation-logloss:0.19908	validation-auc:0.97254	validation-aucpr:0.97672
[81]	validation-logloss:0.19843	validation-auc:0.97257	validation-aucpr:0.97673
[82]	validation-logloss:0.19805	validation-auc:0.97244	validation-aucpr:0.97667
[83]	validation-logloss:0.19745	validation-auc:0.97252	validation-aucpr:0.97671
{'best_iteration': '81', 'best_score': '0.9767312128066848'}
Trial 49, Fold 4: Log loss = 0.19745498544430348, Average precision = 0.9767147911153203, ROC-AUC = 0.9725193792904473, Elapsed Time = 8.099118099999032 seconds
Trial 49, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 49, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[0]	validation-logloss:0.66484	validation-auc:0.94332	validation-aucpr:0.91325
[1]	validation-logloss:0.63930	validation-auc:0.95927	validation-aucpr:0.96318
[2]	validation-logloss:0.61462	validation-auc:0.96318	validation-aucpr:0.96803
[3]	validation-logloss:0.59367	validation-auc:0.96464	validation-aucpr:0.96923
[4]	validation-logloss:0.57256	validation-auc:0.96532	validation-aucpr:0.96981
[5]	validation-logloss:0.55438	validation-auc:0.96604	validation-aucpr:0.97025
[6]	validation-logloss:0.53571	validation-auc:0.96682	validation-aucpr:0.97086
[7]	validation-logloss:0.51811	validation-auc:0.96739	validation-aucpr:0.97119
[8]	validation-logloss:0.50145	validation-auc:0.96787	validation-aucpr:0.97186
[9]	validation-logloss:0.48783	validation-auc:0.96762	validation-aucpr:0.97145
[10]	validation-logloss:0.47329	validation-auc:0.96773	validation-aucpr:0.97156
[11]	validation-logloss:0.45964	validation-auc:0.96757	validation-aucpr:0.97145
[12]	validation-logloss:0.44821	validation-auc:0.96761	validation-aucpr:0.97135
[13]	validation-logloss:0.43732	validation-auc:0.96783	validation-aucpr:0.97144
[14]	validation-logloss:0.42563	validation-auc:0.96808	validation-aucpr:0.97167
[15]	validation-logloss:0.41466	validation-auc:0.96823	validation-aucpr:0.97181
[16]	validation-logloss:0.40545	validation-auc:0.96825	validation-aucpr:0.97180
[17]	validation-logloss:0.39554	validation-auc:0.96871	validation-aucpr:0.97213
[18]	validation-logloss:0.38599	validation-auc:0.96895	validation-aucpr:0.97231
[19]	validation-logloss:0.37701	validation-auc:0.96897	validation-aucpr:0.97248
[20]	validation-logloss:0.36948	validation-auc:0.96908	validation-aucpr:0.97252
[21]	validation-logloss:0.36152	validation-auc:0.96906	validation-aucpr:0.97249
[22]	validation-logloss:0.35370	validation-auc:0.96919	validation-aucpr:0.97262
[23]	validation-logloss:0.34637	validation-auc:0.96925	validation-aucpr:0.97303
[24]	validation-logloss:0.33926	validation-auc:0.96946	validation-aucpr:0.97326
[25]	validation-logloss:0.33366	validation-auc:0.96929	validation-aucpr:0.97284
[26]	validation-logloss:0.32724	validation-auc:0.96945	validation-aucpr:0.97297
[27]	validation-logloss:0.32095	validation-auc:0.96956	validation-aucpr:0.97307
[28]	validation-logloss:0.31532	validation-auc:0.96953	validation-aucpr:0.97324
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[30]	validation-logloss:0.30617	validation-auc:0.96924	validation-aucpr:0.97293
[31]	validation-logloss:0.30100	validation-auc:0.96937	validation-aucpr:0.97300
[32]	validation-logloss:0.29605	validation-auc:0.96958	validation-aucpr:0.97320
[33]	validation-logloss:0.29211	validation-auc:0.96940	validation-aucpr:0.97284
[34]	validation-logloss:0.28756	validation-auc:0.96952	validation-aucpr:0.97295
[35]	validation-logloss:0.28323	validation-auc:0.96963	validation-aucpr:0.97304
[36]	validation-logloss:0.27916	validation-auc:0.96970	validation-aucpr:0.97307
[37]	validation-logloss:0.27527	validation-auc:0.96973	validation-aucpr:0.97305
[38]	validation-logloss:0.27152	validation-auc:0.96991	validation-aucpr:0.97318
[39]	validation-logloss:0.26807	validation-auc:0.96988	validation-aucpr:0.97321
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[42]	validation-logloss:0.25931	validation-auc:0.97018	validation-aucpr:0.97408
[43]	validation-logloss:0.25634	validation-auc:0.97023	validation-aucpr:0.97418
[44]	validation-logloss:0.25392	validation-auc:0.97021	validation-aucpr:0.97414
[45]	validation-logloss:0.25112	validation-auc:0.97029	validation-aucpr:0.97421
[46]	validation-logloss:0.24827	validation-auc:0.97065	validation-aucpr:0.97442
[47]	validation-logloss:0.24585	validation-auc:0.97070	validation-aucpr:0.97444
[48]	validation-logloss:0.24341	validation-auc:0.97076	validation-aucpr:0.97446
[49]	validation-logloss:0.24108	validation-auc:0.97083	validation-aucpr:0.97453
[50]	validation-logloss:0.23895	validation-auc:0.97087	validation-aucpr:0.97452
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[58]	validation-logloss:0.22527	validation-auc:0.97126	validation-aucpr:0.97479
[59]	validation-logloss:0.22382	validation-auc:0.97128	validation-aucpr:0.97484
[60]	validation-logloss:0.22257	validation-auc:0.97128	validation-aucpr:0.97481
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[62]	validation-logloss:0.22029	validation-auc:0.97123	validation-aucpr:0.97477
[63]	validation-logloss:0.21911	validation-auc:0.97118	validation-aucpr:0.97474
[64]	validation-logloss:0.21802	validation-auc:0.97116	validation-aucpr:0.97472
[65]	validation-logloss:0.21715	validation-auc:0.97105	validation-aucpr:0.97464
[66]	validation-logloss:0.21604	validation-auc:0.97114	validation-aucpr:0.97471
[67]	validation-logloss:0.21519	validation-auc:0.97109	validation-aucpr:0.97465
[68]	validation-logloss:0.21439	validation-auc:0.97102	validation-aucpr:0.97461
[69]	validation-logloss:0.21353	validation-auc:0.97097	validation-aucpr:0.97458
[70]	validation-logloss:0.21268	validation-auc:0.97091	validation-aucpr:0.97453
[71]	validation-logloss:0.21194	validation-auc:0.97086	validation-aucpr:0.97449
[72]	validation-logloss:0.21111	validation-auc:0.97088	validation-aucpr:0.97452
[73]	validation-logloss:0.21016	validation-auc:0.97097	validation-aucpr:0.97460
[74]	validation-logloss:0.20952	validation-auc:0.97096	validation-aucpr:0.97457
[75]	validation-logloss:0.20900	validation-auc:0.97092	validation-aucpr:0.97453
[76]	validation-logloss:0.20829	validation-auc:0.97089	validation-aucpr:0.97451
[77]	validation-logloss:0.20762	validation-auc:0.97092	validation-aucpr:0.97452
[78]	validation-logloss:0.20690	validation-auc:0.97091	validation-aucpr:0.97449
[79]	validation-logloss:0.20638	validation-auc:0.97091	validation-aucpr:0.97446
[80]	validation-logloss:0.20580	validation-auc:0.97099	validation-aucpr:0.97450
[81]	validation-logloss:0.20520	validation-auc:0.97107	validation-aucpr:0.97455
[82]	validation-logloss:0.20476	validation-auc:0.97105	validation-aucpr:0.97456
[83]	validation-logloss:0.20413	validation-auc:0.97117	validation-aucpr:0.97475
{'best_iteration': '61', 'best_score': '0.9748406069880208'}
Trial 49, Fold 5: Log loss = 0.20412901026625851, Average precision = 0.9746657449146889, ROC-AUC = 0.9711680136744514, Elapsed Time = 7.984125700000732 seconds
Optimization Progress:  50%|#####     | 50/100 [2:49:00<1:55:06, 138.13s/it]
Trial 50, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 50, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[0]	validation-logloss:0.68362	validation-auc:0.93883	validation-aucpr:0.94319
[1]	validation-logloss:0.67417	validation-auc:0.94860	validation-aucpr:0.95309
[2]	validation-logloss:0.66495	validation-auc:0.95193	validation-aucpr:0.95658
[3]	validation-logloss:0.65565	validation-auc:0.95530	validation-aucpr:0.95966
[4]	validation-logloss:0.64702	validation-auc:0.95608	validation-aucpr:0.96053
[5]	validation-logloss:0.63888	validation-auc:0.95603	validation-aucpr:0.96063
[6]	validation-logloss:0.62932	validation-auc:0.96132	validation-aucpr:0.96664
[7]	validation-logloss:0.62109	validation-auc:0.96145	validation-aucpr:0.96677
[8]	validation-logloss:0.61321	validation-auc:0.96188	validation-aucpr:0.96707
[9]	validation-logloss:0.60432	validation-auc:0.96388	validation-aucpr:0.96926
[10]	validation-logloss:0.59572	validation-auc:0.96492	validation-aucpr:0.97040
[11]	validation-logloss:0.58729	validation-auc:0.96544	validation-aucpr:0.97095
[12]	validation-logloss:0.58058	validation-auc:0.96508	validation-aucpr:0.97066
[13]	validation-logloss:0.57338	validation-auc:0.96524	validation-aucpr:0.97076
[14]	validation-logloss:0.56675	validation-auc:0.96502	validation-aucpr:0.97055
[15]	validation-logloss:0.56027	validation-auc:0.96487	validation-aucpr:0.97036
[16]	validation-logloss:0.55254	validation-auc:0.96550	validation-aucpr:0.97099
[17]	validation-logloss:0.54638	validation-auc:0.96553	validation-aucpr:0.97097
[18]	validation-logloss:0.54026	validation-auc:0.96552	validation-aucpr:0.97092
[19]	validation-logloss:0.53432	validation-auc:0.96545	validation-aucpr:0.97080
[20]	validation-logloss:0.52749	validation-auc:0.96584	validation-aucpr:0.97120
[21]	validation-logloss:0.52085	validation-auc:0.96597	validation-aucpr:0.97139
[22]	validation-logloss:0.51411	validation-auc:0.96626	validation-aucpr:0.97166
[23]	validation-logloss:0.50896	validation-auc:0.96620	validation-aucpr:0.97165
[24]	validation-logloss:0.50357	validation-auc:0.96622	validation-aucpr:0.97163
[25]	validation-logloss:0.49860	validation-auc:0.96617	validation-aucpr:0.97159
[26]	validation-logloss:0.49245	validation-auc:0.96651	validation-aucpr:0.97189
[27]	validation-logloss:0.48636	validation-auc:0.96688	validation-aucpr:0.97221
[28]	validation-logloss:0.48143	validation-auc:0.96701	validation-aucpr:0.97229
[29]	validation-logloss:0.47686	validation-auc:0.96695	validation-aucpr:0.97219
[30]	validation-logloss:0.47238	validation-auc:0.96684	validation-aucpr:0.97211
[31]	validation-logloss:0.46785	validation-auc:0.96676	validation-aucpr:0.97202
[32]	validation-logloss:0.46337	validation-auc:0.96672	validation-aucpr:0.97198
[33]	validation-logloss:0.45831	validation-auc:0.96681	validation-aucpr:0.97213
[34]	validation-logloss:0.45392	validation-auc:0.96685	validation-aucpr:0.97214
[35]	validation-logloss:0.44982	validation-auc:0.96680	validation-aucpr:0.97208
[36]	validation-logloss:0.44570	validation-auc:0.96680	validation-aucpr:0.97207
[37]	validation-logloss:0.44085	validation-auc:0.96691	validation-aucpr:0.97218
[38]	validation-logloss:0.43690	validation-auc:0.96695	validation-aucpr:0.97218
[39]	validation-logloss:0.43313	validation-auc:0.96692	validation-aucpr:0.97214
[40]	validation-logloss:0.42954	validation-auc:0.96692	validation-aucpr:0.97211
[41]	validation-logloss:0.42593	validation-auc:0.96691	validation-aucpr:0.97208
[42]	validation-logloss:0.42142	validation-auc:0.96706	validation-aucpr:0.97224
[43]	validation-logloss:0.41807	validation-auc:0.96707	validation-aucpr:0.97226
[44]	validation-logloss:0.41456	validation-auc:0.96712	validation-aucpr:0.97228
[45]	validation-logloss:0.41026	validation-auc:0.96734	validation-aucpr:0.97248
[46]	validation-logloss:0.40607	validation-auc:0.96749	validation-aucpr:0.97262
{'best_iteration': '46', 'best_score': '0.9726204836170593'}
Trial 50, Fold 1: Log loss = 0.40606661302723657, Average precision = 0.9726251530010052, ROC-AUC = 0.9674923705806724, Elapsed Time = 1.0990195000013046 seconds
Trial 50, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 50, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[0]	validation-logloss:0.68375	validation-auc:0.94128	validation-aucpr:0.94405
[1]	validation-logloss:0.67298	validation-auc:0.96227	validation-aucpr:0.96690
[2]	validation-logloss:0.66346	validation-auc:0.96311	validation-aucpr:0.96744
[3]	validation-logloss:0.65439	validation-auc:0.96359	validation-aucpr:0.96757
[4]	validation-logloss:0.64435	validation-auc:0.96496	validation-aucpr:0.96920
[5]	validation-logloss:0.63600	validation-auc:0.96431	validation-aucpr:0.96830
[6]	validation-logloss:0.62626	validation-auc:0.96564	validation-aucpr:0.96950
[7]	validation-logloss:0.61849	validation-auc:0.96518	validation-aucpr:0.96898
[8]	validation-logloss:0.60944	validation-auc:0.96597	validation-aucpr:0.96974
[9]	validation-logloss:0.60186	validation-auc:0.96600	validation-aucpr:0.96967
[10]	validation-logloss:0.59421	validation-auc:0.96610	validation-aucpr:0.96971
[11]	validation-logloss:0.58574	validation-auc:0.96672	validation-aucpr:0.97059
[12]	validation-logloss:0.57885	validation-auc:0.96666	validation-aucpr:0.97068
[13]	validation-logloss:0.57186	validation-auc:0.96654	validation-aucpr:0.97047
[14]	validation-logloss:0.56505	validation-auc:0.96636	validation-aucpr:0.97024
[15]	validation-logloss:0.55775	validation-auc:0.96646	validation-aucpr:0.97038
[16]	validation-logloss:0.55150	validation-auc:0.96624	validation-aucpr:0.97013
[17]	validation-logloss:0.54409	validation-auc:0.96670	validation-aucpr:0.97060
[18]	validation-logloss:0.53691	validation-auc:0.96691	validation-aucpr:0.97087
[19]	validation-logloss:0.53141	validation-auc:0.96665	validation-aucpr:0.97057
[20]	validation-logloss:0.52586	validation-auc:0.96657	validation-aucpr:0.97045
[21]	validation-logloss:0.51949	validation-auc:0.96680	validation-aucpr:0.97061
[22]	validation-logloss:0.51407	validation-auc:0.96682	validation-aucpr:0.97062
[23]	validation-logloss:0.50860	validation-auc:0.96686	validation-aucpr:0.97066
[24]	validation-logloss:0.50329	validation-auc:0.96672	validation-aucpr:0.97051
[25]	validation-logloss:0.49796	validation-auc:0.96679	validation-aucpr:0.97053
[26]	validation-logloss:0.49300	validation-auc:0.96673	validation-aucpr:0.97041
[27]	validation-logloss:0.48795	validation-auc:0.96676	validation-aucpr:0.97041
[28]	validation-logloss:0.48193	validation-auc:0.96711	validation-aucpr:0.97075
[29]	validation-logloss:0.47721	validation-auc:0.96714	validation-aucpr:0.97078
[30]	validation-logloss:0.47292	validation-auc:0.96706	validation-aucpr:0.97067
[31]	validation-logloss:0.46739	validation-auc:0.96732	validation-aucpr:0.97091
[32]	validation-logloss:0.46292	validation-auc:0.96740	validation-aucpr:0.97094
[33]	validation-logloss:0.45841	validation-auc:0.96740	validation-aucpr:0.97094
[34]	validation-logloss:0.45420	validation-auc:0.96744	validation-aucpr:0.97094
[35]	validation-logloss:0.44991	validation-auc:0.96746	validation-aucpr:0.97095
[36]	validation-logloss:0.44494	validation-auc:0.96769	validation-aucpr:0.97118
[37]	validation-logloss:0.44081	validation-auc:0.96772	validation-aucpr:0.97117
[38]	validation-logloss:0.43691	validation-auc:0.96770	validation-aucpr:0.97104
[39]	validation-logloss:0.43224	validation-auc:0.96781	validation-aucpr:0.97119
[40]	validation-logloss:0.42867	validation-auc:0.96763	validation-aucpr:0.97103
[41]	validation-logloss:0.42505	validation-auc:0.96758	validation-aucpr:0.97095
[42]	validation-logloss:0.42140	validation-auc:0.96760	validation-aucpr:0.97094
[43]	validation-logloss:0.41730	validation-auc:0.96764	validation-aucpr:0.97094
[44]	validation-logloss:0.41384	validation-auc:0.96764	validation-aucpr:0.97089
[45]	validation-logloss:0.40966	validation-auc:0.96779	validation-aucpr:0.97104
[46]	validation-logloss:0.40630	validation-auc:0.96780	validation-aucpr:0.97104
{'best_iteration': '39', 'best_score': '0.9711898911116464'}
Trial 50, Fold 2: Log loss = 0.406295763397056, Average precision = 0.9710449059893823, ROC-AUC = 0.9677950986072286, Elapsed Time = 1.3552650999990874 seconds
Trial 50, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 50, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[0]	validation-logloss:0.68338	validation-auc:0.94309	validation-aucpr:0.94693
[1]	validation-logloss:0.67353	validation-auc:0.95311	validation-aucpr:0.95467
[2]	validation-logloss:0.66305	validation-auc:0.96262	validation-aucpr:0.96503
[3]	validation-logloss:0.65411	validation-auc:0.96252	validation-aucpr:0.96496
[4]	validation-logloss:0.64533	validation-auc:0.96305	validation-aucpr:0.96633
[5]	validation-logloss:0.63660	validation-auc:0.96315	validation-aucpr:0.96738
[6]	validation-logloss:0.62847	validation-auc:0.96301	validation-aucpr:0.96636
[7]	validation-logloss:0.62023	validation-auc:0.96287	validation-aucpr:0.96619
[8]	validation-logloss:0.61093	validation-auc:0.96504	validation-aucpr:0.96851
[9]	validation-logloss:0.60211	validation-auc:0.96602	validation-aucpr:0.96951
[10]	validation-logloss:0.59335	validation-auc:0.96682	validation-aucpr:0.97029
[11]	validation-logloss:0.58544	validation-auc:0.96738	validation-aucpr:0.97222
[12]	validation-logloss:0.57889	validation-auc:0.96685	validation-aucpr:0.97185
[13]	validation-logloss:0.57071	validation-auc:0.96745	validation-aucpr:0.97238
[14]	validation-logloss:0.56278	validation-auc:0.96783	validation-aucpr:0.97274
[15]	validation-logloss:0.55631	validation-auc:0.96772	validation-aucpr:0.97257
[16]	validation-logloss:0.55018	validation-auc:0.96750	validation-aucpr:0.97238
[17]	validation-logloss:0.54415	validation-auc:0.96723	validation-aucpr:0.97220
[18]	validation-logloss:0.53795	validation-auc:0.96714	validation-aucpr:0.97210
[19]	validation-logloss:0.53206	validation-auc:0.96702	validation-aucpr:0.97203
[20]	validation-logloss:0.52632	validation-auc:0.96695	validation-aucpr:0.97197
[21]	validation-logloss:0.52062	validation-auc:0.96692	validation-aucpr:0.97189
[22]	validation-logloss:0.51494	validation-auc:0.96685	validation-aucpr:0.97179
[23]	validation-logloss:0.50943	validation-auc:0.96691	validation-aucpr:0.97182
[24]	validation-logloss:0.50296	validation-auc:0.96728	validation-aucpr:0.97217
[25]	validation-logloss:0.49677	validation-auc:0.96751	validation-aucpr:0.97241
[26]	validation-logloss:0.49086	validation-auc:0.96764	validation-aucpr:0.97253
[27]	validation-logloss:0.48601	validation-auc:0.96761	validation-aucpr:0.97253
[28]	validation-logloss:0.48126	validation-auc:0.96759	validation-aucpr:0.97250
[29]	validation-logloss:0.47562	validation-auc:0.96779	validation-aucpr:0.97267
[30]	validation-logloss:0.47005	validation-auc:0.96800	validation-aucpr:0.97281
[31]	validation-logloss:0.46550	validation-auc:0.96798	validation-aucpr:0.97279
[32]	validation-logloss:0.46007	validation-auc:0.96819	validation-aucpr:0.97298
[33]	validation-logloss:0.45563	validation-auc:0.96824	validation-aucpr:0.97299
[34]	validation-logloss:0.45150	validation-auc:0.96825	validation-aucpr:0.97298
[35]	validation-logloss:0.44719	validation-auc:0.96832	validation-aucpr:0.97304
[36]	validation-logloss:0.44297	validation-auc:0.96832	validation-aucpr:0.97304
[37]	validation-logloss:0.43794	validation-auc:0.96859	validation-aucpr:0.97327
[38]	validation-logloss:0.43416	validation-auc:0.96852	validation-aucpr:0.97319
[39]	validation-logloss:0.43048	validation-auc:0.96852	validation-aucpr:0.97318
[40]	validation-logloss:0.42678	validation-auc:0.96847	validation-aucpr:0.97317
[41]	validation-logloss:0.42216	validation-auc:0.96866	validation-aucpr:0.97334
[42]	validation-logloss:0.41780	validation-auc:0.96875	validation-aucpr:0.97343
[43]	validation-logloss:0.41427	validation-auc:0.96871	validation-aucpr:0.97341
[44]	validation-logloss:0.41021	validation-auc:0.96879	validation-aucpr:0.97348
[45]	validation-logloss:0.40656	validation-auc:0.96889	validation-aucpr:0.97354
[46]	validation-logloss:0.40306	validation-auc:0.96890	validation-aucpr:0.97355
{'best_iteration': '46', 'best_score': '0.97354798914545'}
Trial 50, Fold 3: Log loss = 0.4030647629357849, Average precision = 0.9735525545776742, ROC-AUC = 0.9688970237693946, Elapsed Time = 1.3142243000002054 seconds
Trial 50, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 50, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[0]	validation-logloss:0.68349	validation-auc:0.94204	validation-aucpr:0.94628
[1]	validation-logloss:0.67442	validation-auc:0.94667	validation-aucpr:0.95214
[2]	validation-logloss:0.66581	validation-auc:0.94897	validation-aucpr:0.95443
[3]	validation-logloss:0.65630	validation-auc:0.95477	validation-aucpr:0.95994
[4]	validation-logloss:0.64760	validation-auc:0.95499	validation-aucpr:0.96006
[5]	validation-logloss:0.63920	validation-auc:0.95576	validation-aucpr:0.96085
[6]	validation-logloss:0.63087	validation-auc:0.95639	validation-aucpr:0.96143
[7]	validation-logloss:0.62133	validation-auc:0.96113	validation-aucpr:0.96650
[8]	validation-logloss:0.61210	validation-auc:0.96282	validation-aucpr:0.96853
[9]	validation-logloss:0.60443	validation-auc:0.96271	validation-aucpr:0.96834
[10]	validation-logloss:0.59695	validation-auc:0.96290	validation-aucpr:0.96838
[11]	validation-logloss:0.58957	validation-auc:0.96281	validation-aucpr:0.96822
[12]	validation-logloss:0.58228	validation-auc:0.96297	validation-aucpr:0.96828
[13]	validation-logloss:0.57531	validation-auc:0.96273	validation-aucpr:0.96805
[14]	validation-logloss:0.56864	validation-auc:0.96263	validation-aucpr:0.96788
[15]	validation-logloss:0.56211	validation-auc:0.96253	validation-aucpr:0.96772
[16]	validation-logloss:0.55470	validation-auc:0.96339	validation-aucpr:0.96874
[17]	validation-logloss:0.54744	validation-auc:0.96393	validation-aucpr:0.96936
[18]	validation-logloss:0.54134	validation-auc:0.96368	validation-aucpr:0.96908
[19]	validation-logloss:0.53522	validation-auc:0.96368	validation-aucpr:0.96904
[20]	validation-logloss:0.52952	validation-auc:0.96350	validation-aucpr:0.96889
[21]	validation-logloss:0.52278	validation-auc:0.96394	validation-aucpr:0.96944
[22]	validation-logloss:0.51618	validation-auc:0.96442	validation-aucpr:0.96994
[23]	validation-logloss:0.50987	validation-auc:0.96469	validation-aucpr:0.97023
[24]	validation-logloss:0.50464	validation-auc:0.96461	validation-aucpr:0.97012
[25]	validation-logloss:0.49952	validation-auc:0.96451	validation-aucpr:0.97003
[26]	validation-logloss:0.49459	validation-auc:0.96447	validation-aucpr:0.97000
[27]	validation-logloss:0.48864	validation-auc:0.96485	validation-aucpr:0.97037
[28]	validation-logloss:0.48291	validation-auc:0.96507	validation-aucpr:0.97060
[29]	validation-logloss:0.47819	validation-auc:0.96504	validation-aucpr:0.97057
[30]	validation-logloss:0.47277	validation-auc:0.96517	validation-aucpr:0.97073
[31]	validation-logloss:0.46834	validation-auc:0.96514	validation-aucpr:0.97071
[32]	validation-logloss:0.46308	validation-auc:0.96535	validation-aucpr:0.97091
[33]	validation-logloss:0.45862	validation-auc:0.96543	validation-aucpr:0.97097
[34]	validation-logloss:0.45433	validation-auc:0.96547	validation-aucpr:0.97097
[35]	validation-logloss:0.45025	validation-auc:0.96535	validation-aucpr:0.97087
[36]	validation-logloss:0.44597	validation-auc:0.96545	validation-aucpr:0.97096
[37]	validation-logloss:0.44207	validation-auc:0.96550	validation-aucpr:0.97099
[38]	validation-logloss:0.43728	validation-auc:0.96570	validation-aucpr:0.97118
[39]	validation-logloss:0.43352	validation-auc:0.96569	validation-aucpr:0.97115
[40]	validation-logloss:0.42890	validation-auc:0.96584	validation-aucpr:0.97132
[41]	validation-logloss:0.42518	validation-auc:0.96589	validation-aucpr:0.97135
[42]	validation-logloss:0.42163	validation-auc:0.96593	validation-aucpr:0.97133
[43]	validation-logloss:0.41817	validation-auc:0.96585	validation-aucpr:0.97127
[44]	validation-logloss:0.41389	validation-auc:0.96603	validation-aucpr:0.97144
[45]	validation-logloss:0.41068	validation-auc:0.96599	validation-aucpr:0.97141
[46]	validation-logloss:0.40753	validation-auc:0.96593	validation-aucpr:0.97135
{'best_iteration': '44', 'best_score': '0.9714400273714907'}
Trial 50, Fold 4: Log loss = 0.4075332225921297, Average precision = 0.9713529212958817, ROC-AUC = 0.9659268588262505, Elapsed Time = 1.3637277999987418 seconds
Trial 50, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 50, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[0]	validation-logloss:0.68359	validation-auc:0.93834	validation-aucpr:0.93939
[1]	validation-logloss:0.67398	validation-auc:0.95067	validation-aucpr:0.95333
[2]	validation-logloss:0.66444	validation-auc:0.95413	validation-aucpr:0.95852
[3]	validation-logloss:0.65589	validation-auc:0.95368	validation-aucpr:0.95783
[4]	validation-logloss:0.64699	validation-auc:0.95520	validation-aucpr:0.95874
[5]	validation-logloss:0.63728	validation-auc:0.96073	validation-aucpr:0.96504
[6]	validation-logloss:0.62902	validation-auc:0.96101	validation-aucpr:0.96520
[7]	validation-logloss:0.62113	validation-auc:0.96095	validation-aucpr:0.96444
[8]	validation-logloss:0.61208	validation-auc:0.96247	validation-aucpr:0.96613
[9]	validation-logloss:0.60432	validation-auc:0.96253	validation-aucpr:0.96615
[10]	validation-logloss:0.59560	validation-auc:0.96339	validation-aucpr:0.96714
[11]	validation-logloss:0.58859	validation-auc:0.96317	validation-aucpr:0.96684
[12]	validation-logloss:0.58046	validation-auc:0.96357	validation-aucpr:0.96729
[13]	validation-logloss:0.57249	validation-auc:0.96398	validation-aucpr:0.96733
[14]	validation-logloss:0.56565	validation-auc:0.96402	validation-aucpr:0.96732
[15]	validation-logloss:0.55915	validation-auc:0.96402	validation-aucpr:0.96690
[16]	validation-logloss:0.55277	validation-auc:0.96398	validation-aucpr:0.96717
[17]	validation-logloss:0.54631	validation-auc:0.96412	validation-aucpr:0.96793
[18]	validation-logloss:0.54023	validation-auc:0.96403	validation-aucpr:0.96858
[19]	validation-logloss:0.53332	validation-auc:0.96420	validation-aucpr:0.96882
[20]	validation-logloss:0.52745	validation-auc:0.96416	validation-aucpr:0.96879
[21]	validation-logloss:0.52059	validation-auc:0.96457	validation-aucpr:0.96896
[22]	validation-logloss:0.51528	validation-auc:0.96441	validation-aucpr:0.96837
[23]	validation-logloss:0.51020	validation-auc:0.96422	validation-aucpr:0.96811
[24]	validation-logloss:0.50399	validation-auc:0.96439	validation-aucpr:0.96827
[25]	validation-logloss:0.49908	validation-auc:0.96432	validation-aucpr:0.96823
[26]	validation-logloss:0.49399	validation-auc:0.96433	validation-aucpr:0.96829
[27]	validation-logloss:0.48912	validation-auc:0.96428	validation-aucpr:0.96819
[28]	validation-logloss:0.48433	validation-auc:0.96433	validation-aucpr:0.96815
[29]	validation-logloss:0.47871	validation-auc:0.96444	validation-aucpr:0.96830
[30]	validation-logloss:0.47438	validation-auc:0.96426	validation-aucpr:0.96814
[31]	validation-logloss:0.46987	validation-auc:0.96431	validation-aucpr:0.96809
[32]	validation-logloss:0.46460	validation-auc:0.96447	validation-aucpr:0.96824
[33]	validation-logloss:0.46030	validation-auc:0.96446	validation-aucpr:0.96817
[34]	validation-logloss:0.45616	validation-auc:0.96437	validation-aucpr:0.96810
[35]	validation-logloss:0.45114	validation-auc:0.96450	validation-aucpr:0.96821
[36]	validation-logloss:0.44640	validation-auc:0.96463	validation-aucpr:0.96812
[37]	validation-logloss:0.44244	validation-auc:0.96471	validation-aucpr:0.96938
[38]	validation-logloss:0.43856	validation-auc:0.96466	validation-aucpr:0.96935
[39]	validation-logloss:0.43402	validation-auc:0.96478	validation-aucpr:0.96942
[40]	validation-logloss:0.42934	validation-auc:0.96501	validation-aucpr:0.96962
[41]	validation-logloss:0.42498	validation-auc:0.96525	validation-aucpr:0.96996
[42]	validation-logloss:0.42063	validation-auc:0.96545	validation-aucpr:0.97023
[43]	validation-logloss:0.41623	validation-auc:0.96564	validation-aucpr:0.97038
[44]	validation-logloss:0.41290	validation-auc:0.96559	validation-aucpr:0.97034
[45]	validation-logloss:0.40871	validation-auc:0.96574	validation-aucpr:0.97047
[46]	validation-logloss:0.40533	validation-auc:0.96573	validation-aucpr:0.97042
{'best_iteration': '45', 'best_score': '0.9704737172269167'}
Trial 50, Fold 5: Log loss = 0.405331832251526, Average precision = 0.9704301972342224, ROC-AUC = 0.9657291492484625, Elapsed Time = 1.3081268000023556 seconds
Optimization Progress:  51%|#####1    | 51/100 [2:49:14<1:22:31, 101.05s/it]
Trial 51, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 51, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[0]	validation-logloss:0.68291	validation-auc:0.95205	validation-aucpr:0.95616
[1]	validation-logloss:0.67265	validation-auc:0.95976	validation-aucpr:0.96540
[2]	validation-logloss:0.66447	validation-auc:0.95993	validation-aucpr:0.96584
[3]	validation-logloss:0.65478	validation-auc:0.96073	validation-aucpr:0.96659
[4]	validation-logloss:0.64542	validation-auc:0.96077	validation-aucpr:0.96667
[5]	validation-logloss:0.63731	validation-auc:0.96148	validation-aucpr:0.96697
[6]	validation-logloss:0.62850	validation-auc:0.96148	validation-aucpr:0.96707
[7]	validation-logloss:0.62023	validation-auc:0.96169	validation-aucpr:0.96733
[8]	validation-logloss:0.61176	validation-auc:0.96215	validation-aucpr:0.96777
[9]	validation-logloss:0.60441	validation-auc:0.96229	validation-aucpr:0.96770
[10]	validation-logloss:0.59730	validation-auc:0.96171	validation-aucpr:0.96716
[11]	validation-logloss:0.59040	validation-auc:0.96166	validation-aucpr:0.96700
[12]	validation-logloss:0.58286	validation-auc:0.96205	validation-aucpr:0.96727
[13]	validation-logloss:0.57540	validation-auc:0.96195	validation-aucpr:0.96468
[14]	validation-logloss:0.56796	validation-auc:0.96199	validation-aucpr:0.96482
[15]	validation-logloss:0.56073	validation-auc:0.96219	validation-aucpr:0.96497
[16]	validation-logloss:0.55482	validation-auc:0.96246	validation-aucpr:0.96850
[17]	validation-logloss:0.54798	validation-auc:0.96249	validation-aucpr:0.96856
[18]	validation-logloss:0.54217	validation-auc:0.96246	validation-aucpr:0.96847
[19]	validation-logloss:0.53597	validation-auc:0.96236	validation-aucpr:0.96843
[20]	validation-logloss:0.52981	validation-auc:0.96217	validation-aucpr:0.96834
[21]	validation-logloss:0.52451	validation-auc:0.96204	validation-aucpr:0.96820
[22]	validation-logloss:0.51833	validation-auc:0.96214	validation-aucpr:0.96829
[23]	validation-logloss:0.51302	validation-auc:0.96236	validation-aucpr:0.96843
[24]	validation-logloss:0.50816	validation-auc:0.96214	validation-aucpr:0.96821
[25]	validation-logloss:0.50237	validation-auc:0.96227	validation-aucpr:0.96830
[26]	validation-logloss:0.49685	validation-auc:0.96239	validation-aucpr:0.96841
[27]	validation-logloss:0.49238	validation-auc:0.96240	validation-aucpr:0.96847
[28]	validation-logloss:0.48712	validation-auc:0.96238	validation-aucpr:0.96850
[29]	validation-logloss:0.48192	validation-auc:0.96254	validation-aucpr:0.96865
[30]	validation-logloss:0.47689	validation-auc:0.96261	validation-aucpr:0.96874
[31]	validation-logloss:0.47189	validation-auc:0.96265	validation-aucpr:0.96880
[32]	validation-logloss:0.46754	validation-auc:0.96266	validation-aucpr:0.96880
[33]	validation-logloss:0.46335	validation-auc:0.96261	validation-aucpr:0.96874
[34]	validation-logloss:0.45853	validation-auc:0.96268	validation-aucpr:0.96881
[35]	validation-logloss:0.45444	validation-auc:0.96273	validation-aucpr:0.96882
[36]	validation-logloss:0.44987	validation-auc:0.96281	validation-aucpr:0.96892
[37]	validation-logloss:0.44598	validation-auc:0.96273	validation-aucpr:0.96882
[38]	validation-logloss:0.44168	validation-auc:0.96280	validation-aucpr:0.96890
[39]	validation-logloss:0.43754	validation-auc:0.96311	validation-aucpr:0.96955
[40]	validation-logloss:0.43336	validation-auc:0.96317	validation-aucpr:0.96960
[41]	validation-logloss:0.42915	validation-auc:0.96335	validation-aucpr:0.96973
[42]	validation-logloss:0.42502	validation-auc:0.96348	validation-aucpr:0.96986
[43]	validation-logloss:0.42167	validation-auc:0.96351	validation-aucpr:0.96986
[44]	validation-logloss:0.41769	validation-auc:0.96366	validation-aucpr:0.96997
[45]	validation-logloss:0.41441	validation-auc:0.96381	validation-aucpr:0.97007
[46]	validation-logloss:0.41057	validation-auc:0.96390	validation-aucpr:0.97017
[47]	validation-logloss:0.40694	validation-auc:0.96395	validation-aucpr:0.97025
[48]	validation-logloss:0.40335	validation-auc:0.96406	validation-aucpr:0.97035
[49]	validation-logloss:0.40034	validation-auc:0.96402	validation-aucpr:0.97030
[50]	validation-logloss:0.39691	validation-auc:0.96415	validation-aucpr:0.97040
[51]	validation-logloss:0.39351	validation-auc:0.96430	validation-aucpr:0.97055
[52]	validation-logloss:0.39025	validation-auc:0.96427	validation-aucpr:0.97053
[53]	validation-logloss:0.38691	validation-auc:0.96435	validation-aucpr:0.97061
[54]	validation-logloss:0.38378	validation-auc:0.96436	validation-aucpr:0.97069
[55]	validation-logloss:0.38122	validation-auc:0.96428	validation-aucpr:0.97060
{'best_iteration': '54', 'best_score': '0.97069156848798'}
Trial 51, Fold 1: Log loss = 0.3812171714360218, Average precision = 0.9703871434420608, ROC-AUC = 0.9642796651180798, Elapsed Time = 0.7973453000013251 seconds
Trial 51, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 51, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[0]	validation-logloss:0.68236	validation-auc:0.95602	validation-aucpr:0.95911
[1]	validation-logloss:0.67211	validation-auc:0.96113	validation-aucpr:0.96672
[2]	validation-logloss:0.66210	validation-auc:0.96291	validation-aucpr:0.96763
[3]	validation-logloss:0.65293	validation-auc:0.96248	validation-aucpr:0.96502
[4]	validation-logloss:0.64365	validation-auc:0.96223	validation-aucpr:0.96440
[5]	validation-logloss:0.63554	validation-auc:0.96194	validation-aucpr:0.96443
[6]	validation-logloss:0.62720	validation-auc:0.96301	validation-aucpr:0.96739
[7]	validation-logloss:0.61892	validation-auc:0.96373	validation-aucpr:0.96800
[8]	validation-logloss:0.61041	validation-auc:0.96439	validation-aucpr:0.96851
[9]	validation-logloss:0.60304	validation-auc:0.96383	validation-aucpr:0.96794
[10]	validation-logloss:0.59576	validation-auc:0.96307	validation-aucpr:0.96719
[11]	validation-logloss:0.58901	validation-auc:0.96296	validation-aucpr:0.96705
[12]	validation-logloss:0.58139	validation-auc:0.96323	validation-aucpr:0.96729
[13]	validation-logloss:0.57480	validation-auc:0.96339	validation-aucpr:0.96749
[14]	validation-logloss:0.56744	validation-auc:0.96348	validation-aucpr:0.96758
[15]	validation-logloss:0.56120	validation-auc:0.96355	validation-aucpr:0.96760
[16]	validation-logloss:0.55417	validation-auc:0.96355	validation-aucpr:0.96761
[17]	validation-logloss:0.54723	validation-auc:0.96359	validation-aucpr:0.96768
[18]	validation-logloss:0.54042	validation-auc:0.96374	validation-aucpr:0.96781
[19]	validation-logloss:0.53391	validation-auc:0.96378	validation-aucpr:0.96790
[20]	validation-logloss:0.52767	validation-auc:0.96392	validation-aucpr:0.96793
[21]	validation-logloss:0.52233	validation-auc:0.96374	validation-aucpr:0.96768
[22]	validation-logloss:0.51623	validation-auc:0.96373	validation-aucpr:0.96774
[23]	validation-logloss:0.51016	validation-auc:0.96397	validation-aucpr:0.96794
[24]	validation-logloss:0.50455	validation-auc:0.96423	validation-aucpr:0.96835
[25]	validation-logloss:0.49884	validation-auc:0.96430	validation-aucpr:0.96842
[26]	validation-logloss:0.49312	validation-auc:0.96447	validation-aucpr:0.96859
[27]	validation-logloss:0.48843	validation-auc:0.96418	validation-aucpr:0.96829
[28]	validation-logloss:0.48317	validation-auc:0.96429	validation-aucpr:0.96836
[29]	validation-logloss:0.47862	validation-auc:0.96412	validation-aucpr:0.96814
[30]	validation-logloss:0.47347	validation-auc:0.96409	validation-aucpr:0.96812
[31]	validation-logloss:0.46914	validation-auc:0.96403	validation-aucpr:0.96803
[32]	validation-logloss:0.46449	validation-auc:0.96407	validation-aucpr:0.96810
[33]	validation-logloss:0.46030	validation-auc:0.96410	validation-aucpr:0.96811
[34]	validation-logloss:0.45558	validation-auc:0.96409	validation-aucpr:0.96800
[35]	validation-logloss:0.45151	validation-auc:0.96422	validation-aucpr:0.96810
[36]	validation-logloss:0.44692	validation-auc:0.96426	validation-aucpr:0.96816
[37]	validation-logloss:0.44305	validation-auc:0.96428	validation-aucpr:0.96813
[38]	validation-logloss:0.43880	validation-auc:0.96441	validation-aucpr:0.96820
[39]	validation-logloss:0.43448	validation-auc:0.96444	validation-aucpr:0.96824
[40]	validation-logloss:0.43039	validation-auc:0.96455	validation-aucpr:0.96835
[41]	validation-logloss:0.42628	validation-auc:0.96470	validation-aucpr:0.96847
[42]	validation-logloss:0.42219	validation-auc:0.96486	validation-aucpr:0.96869
[43]	validation-logloss:0.41821	validation-auc:0.96496	validation-aucpr:0.96879
[44]	validation-logloss:0.41431	validation-auc:0.96508	validation-aucpr:0.96891
[45]	validation-logloss:0.41078	validation-auc:0.96518	validation-aucpr:0.96900
[46]	validation-logloss:0.40702	validation-auc:0.96526	validation-aucpr:0.96907
[47]	validation-logloss:0.40409	validation-auc:0.96517	validation-aucpr:0.96898
[48]	validation-logloss:0.40063	validation-auc:0.96524	validation-aucpr:0.96905
[49]	validation-logloss:0.39711	validation-auc:0.96535	validation-aucpr:0.96914
[50]	validation-logloss:0.39358	validation-auc:0.96558	validation-aucpr:0.96935
[51]	validation-logloss:0.39066	validation-auc:0.96546	validation-aucpr:0.96922
[52]	validation-logloss:0.38725	validation-auc:0.96566	validation-aucpr:0.96938
[53]	validation-logloss:0.38446	validation-auc:0.96561	validation-aucpr:0.96935
[54]	validation-logloss:0.38115	validation-auc:0.96573	validation-aucpr:0.96945
[55]	validation-logloss:0.37860	validation-auc:0.96575	validation-aucpr:0.96945
{'best_iteration': '54', 'best_score': '0.9694506681393924'}
Trial 51, Fold 2: Log loss = 0.37859739271133297, Average precision = 0.9692820372580925, ROC-AUC = 0.9657487797201831, Elapsed Time = 1.055651200000284 seconds
Trial 51, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 51, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[0]	validation-logloss:0.68267	validation-auc:0.95690	validation-aucpr:0.96154
[1]	validation-logloss:0.67228	validation-auc:0.96240	validation-aucpr:0.96740
[2]	validation-logloss:0.66229	validation-auc:0.96191	validation-aucpr:0.96718
[3]	validation-logloss:0.65256	validation-auc:0.96203	validation-aucpr:0.96732
[4]	validation-logloss:0.64333	validation-auc:0.96423	validation-aucpr:0.96932
[5]	validation-logloss:0.63419	validation-auc:0.96451	validation-aucpr:0.96946
[6]	validation-logloss:0.62519	validation-auc:0.96492	validation-aucpr:0.96969
[7]	validation-logloss:0.61746	validation-auc:0.96523	validation-aucpr:0.96998
[8]	validation-logloss:0.60900	validation-auc:0.96533	validation-aucpr:0.97014
[9]	validation-logloss:0.60075	validation-auc:0.96528	validation-aucpr:0.97072
[10]	validation-logloss:0.59267	validation-auc:0.96608	validation-aucpr:0.97123
[11]	validation-logloss:0.58475	validation-auc:0.96581	validation-aucpr:0.97107
[12]	validation-logloss:0.57724	validation-auc:0.96559	validation-aucpr:0.97085
[13]	validation-logloss:0.57071	validation-auc:0.96565	validation-aucpr:0.97093
[14]	validation-logloss:0.56427	validation-auc:0.96531	validation-aucpr:0.97059
[15]	validation-logloss:0.55749	validation-auc:0.96514	validation-aucpr:0.97048
[16]	validation-logloss:0.55162	validation-auc:0.96466	validation-aucpr:0.96997
[17]	validation-logloss:0.54552	validation-auc:0.96477	validation-aucpr:0.97001
[18]	validation-logloss:0.53961	validation-auc:0.96468	validation-aucpr:0.96984
[19]	validation-logloss:0.53316	validation-auc:0.96482	validation-aucpr:0.96995
[20]	validation-logloss:0.52671	validation-auc:0.96489	validation-aucpr:0.97002
[21]	validation-logloss:0.52069	validation-auc:0.96481	validation-aucpr:0.96998
[22]	validation-logloss:0.51523	validation-auc:0.96477	validation-aucpr:0.96992
[23]	validation-logloss:0.50917	validation-auc:0.96486	validation-aucpr:0.97001
[24]	validation-logloss:0.50329	validation-auc:0.96469	validation-aucpr:0.96987
[25]	validation-logloss:0.49831	validation-auc:0.96480	validation-aucpr:0.97004
[26]	validation-logloss:0.49268	validation-auc:0.96485	validation-aucpr:0.97005
[27]	validation-logloss:0.48801	validation-auc:0.96496	validation-aucpr:0.97016
[28]	validation-logloss:0.48255	validation-auc:0.96501	validation-aucpr:0.97020
[29]	validation-logloss:0.47805	validation-auc:0.96495	validation-aucpr:0.97014
[30]	validation-logloss:0.47341	validation-auc:0.96515	validation-aucpr:0.97024
[31]	validation-logloss:0.46852	validation-auc:0.96527	validation-aucpr:0.97038
[32]	validation-logloss:0.46348	validation-auc:0.96542	validation-aucpr:0.97048
[33]	validation-logloss:0.45852	validation-auc:0.96565	validation-aucpr:0.97062
[34]	validation-logloss:0.45391	validation-auc:0.96567	validation-aucpr:0.97063
[35]	validation-logloss:0.44926	validation-auc:0.96584	validation-aucpr:0.97080
[36]	validation-logloss:0.44528	validation-auc:0.96576	validation-aucpr:0.97067
[37]	validation-logloss:0.44103	validation-auc:0.96583	validation-aucpr:0.97075
[38]	validation-logloss:0.43685	validation-auc:0.96585	validation-aucpr:0.97077
[39]	validation-logloss:0.43245	validation-auc:0.96594	validation-aucpr:0.97085
[40]	validation-logloss:0.42887	validation-auc:0.96590	validation-aucpr:0.97076
[41]	validation-logloss:0.42482	validation-auc:0.96600	validation-aucpr:0.97084
[42]	validation-logloss:0.42162	validation-auc:0.96591	validation-aucpr:0.97074
[43]	validation-logloss:0.41758	validation-auc:0.96594	validation-aucpr:0.97076
[44]	validation-logloss:0.41360	validation-auc:0.96599	validation-aucpr:0.97082
[45]	validation-logloss:0.40968	validation-auc:0.96609	validation-aucpr:0.97090
[46]	validation-logloss:0.40640	validation-auc:0.96603	validation-aucpr:0.97085
[47]	validation-logloss:0.40267	validation-auc:0.96616	validation-aucpr:0.97095
[48]	validation-logloss:0.39905	validation-auc:0.96617	validation-aucpr:0.97098
[49]	validation-logloss:0.39541	validation-auc:0.96620	validation-aucpr:0.97102
[50]	validation-logloss:0.39227	validation-auc:0.96629	validation-aucpr:0.97106
[51]	validation-logloss:0.38923	validation-auc:0.96637	validation-aucpr:0.97110
[52]	validation-logloss:0.38594	validation-auc:0.96636	validation-aucpr:0.97112
[53]	validation-logloss:0.38261	validation-auc:0.96644	validation-aucpr:0.97119
[54]	validation-logloss:0.37937	validation-auc:0.96650	validation-aucpr:0.97120
[55]	validation-logloss:0.37612	validation-auc:0.96660	validation-aucpr:0.97130
{'best_iteration': '55', 'best_score': '0.9712978997806914'}
Trial 51, Fold 3: Log loss = 0.37611588215876013, Average precision = 0.9711011629204486, ROC-AUC = 0.9665986675463012, Elapsed Time = 1.0364141000027303 seconds
Trial 51, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 51, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[0]	validation-logloss:0.68242	validation-auc:0.95750	validation-aucpr:0.96134
[1]	validation-logloss:0.67212	validation-auc:0.96272	validation-aucpr:0.96928
[2]	validation-logloss:0.66378	validation-auc:0.96068	validation-aucpr:0.96725
[3]	validation-logloss:0.65554	validation-auc:0.96049	validation-aucpr:0.96693
[4]	validation-logloss:0.64616	validation-auc:0.95988	validation-aucpr:0.96668
[5]	validation-logloss:0.63799	validation-auc:0.96061	validation-aucpr:0.96709
[6]	validation-logloss:0.62904	validation-auc:0.96097	validation-aucpr:0.96739
[7]	validation-logloss:0.62072	validation-auc:0.96165	validation-aucpr:0.96801
[8]	validation-logloss:0.61223	validation-auc:0.96286	validation-aucpr:0.96881
[9]	validation-logloss:0.60501	validation-auc:0.96256	validation-aucpr:0.96841
[10]	validation-logloss:0.59690	validation-auc:0.96274	validation-aucpr:0.96859
[11]	validation-logloss:0.59020	validation-auc:0.96272	validation-aucpr:0.96847
[12]	validation-logloss:0.58233	validation-auc:0.96297	validation-aucpr:0.96880
[13]	validation-logloss:0.57495	validation-auc:0.96308	validation-aucpr:0.96898
[14]	validation-logloss:0.56853	validation-auc:0.96319	validation-aucpr:0.96894
[15]	validation-logloss:0.56241	validation-auc:0.96297	validation-aucpr:0.96865
[16]	validation-logloss:0.55551	validation-auc:0.96300	validation-aucpr:0.96867
[17]	validation-logloss:0.54863	validation-auc:0.96290	validation-aucpr:0.96866
[18]	validation-logloss:0.54290	validation-auc:0.96291	validation-aucpr:0.96867
[19]	validation-logloss:0.53635	validation-auc:0.96288	validation-aucpr:0.96867
[20]	validation-logloss:0.53085	validation-auc:0.96263	validation-aucpr:0.96844
[21]	validation-logloss:0.52526	validation-auc:0.96281	validation-aucpr:0.96857
[22]	validation-logloss:0.51906	validation-auc:0.96322	validation-aucpr:0.96891
[23]	validation-logloss:0.51378	validation-auc:0.96307	validation-aucpr:0.96880
[24]	validation-logloss:0.50888	validation-auc:0.96311	validation-aucpr:0.96885
[25]	validation-logloss:0.50293	validation-auc:0.96340	validation-aucpr:0.96906
[26]	validation-logloss:0.49720	validation-auc:0.96340	validation-aucpr:0.96909
[27]	validation-logloss:0.49165	validation-auc:0.96361	validation-aucpr:0.96928
[28]	validation-logloss:0.48646	validation-auc:0.96357	validation-aucpr:0.96932
[29]	validation-logloss:0.48104	validation-auc:0.96369	validation-aucpr:0.96943
[30]	validation-logloss:0.47652	validation-auc:0.96371	validation-aucpr:0.96941
[31]	validation-logloss:0.47240	validation-auc:0.96366	validation-aucpr:0.96932
[32]	validation-logloss:0.46735	validation-auc:0.96379	validation-aucpr:0.96948
[33]	validation-logloss:0.46241	validation-auc:0.96388	validation-aucpr:0.96961
[34]	validation-logloss:0.45836	validation-auc:0.96370	validation-aucpr:0.96947
[35]	validation-logloss:0.45359	validation-auc:0.96383	validation-aucpr:0.96960
[36]	validation-logloss:0.44967	validation-auc:0.96381	validation-aucpr:0.96954
[37]	validation-logloss:0.44507	validation-auc:0.96404	validation-aucpr:0.96973
[38]	validation-logloss:0.44146	validation-auc:0.96396	validation-aucpr:0.96968
[39]	validation-logloss:0.43728	validation-auc:0.96402	validation-aucpr:0.96979
[40]	validation-logloss:0.43312	validation-auc:0.96408	validation-aucpr:0.96984
[41]	validation-logloss:0.42903	validation-auc:0.96415	validation-aucpr:0.96992
[42]	validation-logloss:0.42565	validation-auc:0.96407	validation-aucpr:0.96984
[43]	validation-logloss:0.42224	validation-auc:0.96398	validation-aucpr:0.96979
[44]	validation-logloss:0.41820	validation-auc:0.96410	validation-aucpr:0.96987
[45]	validation-logloss:0.41428	validation-auc:0.96415	validation-aucpr:0.96993
[46]	validation-logloss:0.41119	validation-auc:0.96409	validation-aucpr:0.96986
[47]	validation-logloss:0.40746	validation-auc:0.96413	validation-aucpr:0.96991
[48]	validation-logloss:0.40442	validation-auc:0.96402	validation-aucpr:0.96984
[49]	validation-logloss:0.40079	validation-auc:0.96421	validation-aucpr:0.96998
[50]	validation-logloss:0.39723	validation-auc:0.96430	validation-aucpr:0.97005
[51]	validation-logloss:0.39386	validation-auc:0.96430	validation-aucpr:0.97007
[52]	validation-logloss:0.39118	validation-auc:0.96415	validation-aucpr:0.96996
[53]	validation-logloss:0.38790	validation-auc:0.96424	validation-aucpr:0.97004
[54]	validation-logloss:0.38449	validation-auc:0.96435	validation-aucpr:0.97015
[55]	validation-logloss:0.38126	validation-auc:0.96433	validation-aucpr:0.97017
{'best_iteration': '55', 'best_score': '0.970168208440948'}
Trial 51, Fold 4: Log loss = 0.3812629807783591, Average precision = 0.9699755871299118, ROC-AUC = 0.9643334024812169, Elapsed Time = 1.0350413000014669 seconds
Trial 51, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 51, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[0]	validation-logloss:0.68251	validation-auc:0.95788	validation-aucpr:0.96177
[1]	validation-logloss:0.67234	validation-auc:0.96178	validation-aucpr:0.96709
[2]	validation-logloss:0.66242	validation-auc:0.96310	validation-aucpr:0.96796
[3]	validation-logloss:0.65281	validation-auc:0.96369	validation-aucpr:0.96845
[4]	validation-logloss:0.64371	validation-auc:0.96500	validation-aucpr:0.96907
[5]	validation-logloss:0.63572	validation-auc:0.96209	validation-aucpr:0.96708
[6]	validation-logloss:0.62687	validation-auc:0.96195	validation-aucpr:0.96705
[7]	validation-logloss:0.61931	validation-auc:0.96172	validation-aucpr:0.96669
[8]	validation-logloss:0.61088	validation-auc:0.96260	validation-aucpr:0.96728
[9]	validation-logloss:0.60271	validation-auc:0.96276	validation-aucpr:0.96771
[10]	validation-logloss:0.59472	validation-auc:0.96272	validation-aucpr:0.96756
[11]	validation-logloss:0.58807	validation-auc:0.96221	validation-aucpr:0.96717
[12]	validation-logloss:0.58043	validation-auc:0.96238	validation-aucpr:0.96735
[13]	validation-logloss:0.57322	validation-auc:0.96219	validation-aucpr:0.96718
[14]	validation-logloss:0.56688	validation-auc:0.96230	validation-aucpr:0.96720
[15]	validation-logloss:0.56067	validation-auc:0.96199	validation-aucpr:0.96684
[16]	validation-logloss:0.55497	validation-auc:0.96153	validation-aucpr:0.96673
[17]	validation-logloss:0.54901	validation-auc:0.96144	validation-aucpr:0.96670
[18]	validation-logloss:0.54327	validation-auc:0.96128	validation-aucpr:0.96658
[19]	validation-logloss:0.53793	validation-auc:0.96111	validation-aucpr:0.96643
[20]	validation-logloss:0.53258	validation-auc:0.96094	validation-aucpr:0.96630
[21]	validation-logloss:0.52627	validation-auc:0.96101	validation-aucpr:0.96640
[22]	validation-logloss:0.52022	validation-auc:0.96110	validation-aucpr:0.96646
[23]	validation-logloss:0.51437	validation-auc:0.96113	validation-aucpr:0.96651
[24]	validation-logloss:0.50879	validation-auc:0.96123	validation-aucpr:0.96657
[25]	validation-logloss:0.50309	validation-auc:0.96124	validation-aucpr:0.96661
[26]	validation-logloss:0.49759	validation-auc:0.96134	validation-aucpr:0.96668
[27]	validation-logloss:0.49289	validation-auc:0.96146	validation-aucpr:0.96676
[28]	validation-logloss:0.48741	validation-auc:0.96165	validation-aucpr:0.96695
[29]	validation-logloss:0.48290	validation-auc:0.96159	validation-aucpr:0.96686
[30]	validation-logloss:0.47775	validation-auc:0.96164	validation-aucpr:0.96690
[31]	validation-logloss:0.47264	validation-auc:0.96177	validation-aucpr:0.96701
[32]	validation-logloss:0.46776	validation-auc:0.96185	validation-aucpr:0.96710
[33]	validation-logloss:0.46295	validation-auc:0.96176	validation-aucpr:0.96687
[34]	validation-logloss:0.45841	validation-auc:0.96175	validation-aucpr:0.96687
[35]	validation-logloss:0.45460	validation-auc:0.96168	validation-aucpr:0.96716
[36]	validation-logloss:0.45027	validation-auc:0.96170	validation-aucpr:0.96723
[37]	validation-logloss:0.44655	validation-auc:0.96169	validation-aucpr:0.96718
[38]	validation-logloss:0.44286	validation-auc:0.96183	validation-aucpr:0.96764
[39]	validation-logloss:0.43850	validation-auc:0.96206	validation-aucpr:0.96783
[40]	validation-logloss:0.43495	validation-auc:0.96194	validation-aucpr:0.96773
[41]	validation-logloss:0.43155	validation-auc:0.96189	validation-aucpr:0.96772
[42]	validation-logloss:0.42830	validation-auc:0.96183	validation-aucpr:0.96766
[43]	validation-logloss:0.42497	validation-auc:0.96186	validation-aucpr:0.96768
[44]	validation-logloss:0.42099	validation-auc:0.96196	validation-aucpr:0.96778
[45]	validation-logloss:0.41785	validation-auc:0.96195	validation-aucpr:0.96777
[46]	validation-logloss:0.41407	validation-auc:0.96195	validation-aucpr:0.96777
[47]	validation-logloss:0.41027	validation-auc:0.96208	validation-aucpr:0.96791
[48]	validation-logloss:0.40660	validation-auc:0.96218	validation-aucpr:0.96799
[49]	validation-logloss:0.40378	validation-auc:0.96215	validation-aucpr:0.96797
[50]	validation-logloss:0.40025	validation-auc:0.96218	validation-aucpr:0.96802
[51]	validation-logloss:0.39694	validation-auc:0.96223	validation-aucpr:0.96808
[52]	validation-logloss:0.39348	validation-auc:0.96226	validation-aucpr:0.96811
[53]	validation-logloss:0.39074	validation-auc:0.96231	validation-aucpr:0.96812
{'best_iteration': '4', 'best_score': '0.9690651836602294'}
Trial 51, Fold 5: Log loss = 0.38745012429890546, Average precision = 0.9680018802991691, ROC-AUC = 0.9624831763372536, Elapsed Time = 1.0867137000022922 seconds
Optimization Progress:  52%|#####2    | 52/100 [2:49:27<59:41, 74.61s/it]   
Trial 52, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 52, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[20:48:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.66455	validation-auc:0.95712	validation-aucpr:0.96324
[20:48:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.63859	validation-auc:0.96221	validation-aucpr:0.96793
[20:48:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.61703	validation-auc:0.96202	validation-aucpr:0.96628
[20:48:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.59423	validation-auc:0.96319	validation-aucpr:0.96731
[20:48:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.57256	validation-auc:0.96511	validation-aucpr:0.97001
[20:48:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5]	validation-logloss:0.55286	validation-auc:0.96614	validation-aucpr:0.97147
[20:48:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6]	validation-logloss:0.53601	validation-auc:0.96659	validation-aucpr:0.97172
[20:48:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7]	validation-logloss:0.52017	validation-auc:0.96653	validation-aucpr:0.97175
[20:48:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8]	validation-logloss:0.50599	validation-auc:0.96628	validation-aucpr:0.97166
[20:48:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9]	validation-logloss:0.49097	validation-auc:0.96631	validation-aucpr:0.97181
[20:48:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10]	validation-logloss:0.47623	validation-auc:0.96653	validation-aucpr:0.97199
[20:48:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11]	validation-logloss:0.46420	validation-auc:0.96635	validation-aucpr:0.97175
[20:48:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12]	validation-logloss:0.45319	validation-auc:0.96608	validation-aucpr:0.97150
[20:48:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13]	validation-logloss:0.44194	validation-auc:0.96659	validation-aucpr:0.97185
[20:48:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14]	validation-logloss:0.42956	validation-auc:0.96706	validation-aucpr:0.97223
[20:48:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15]	validation-logloss:0.41983	validation-auc:0.96708	validation-aucpr:0.97230
[20:48:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16]	validation-logloss:0.41093	validation-auc:0.96676	validation-aucpr:0.97202
[20:48:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17]	validation-logloss:0.40043	validation-auc:0.96689	validation-aucpr:0.97215
[20:48:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18]	validation-logloss:0.39095	validation-auc:0.96714	validation-aucpr:0.97234
[20:48:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19]	validation-logloss:0.38330	validation-auc:0.96717	validation-aucpr:0.97242
[20:48:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20]	validation-logloss:0.37393	validation-auc:0.96760	validation-aucpr:0.97272
[20:48:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21]	validation-logloss:0.36515	validation-auc:0.96792	validation-aucpr:0.97303
[20:48:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22]	validation-logloss:0.35697	validation-auc:0.96828	validation-aucpr:0.97333
[20:48:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23]	validation-logloss:0.34930	validation-auc:0.96854	validation-aucpr:0.97354
[20:48:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24]	validation-logloss:0.34198	validation-auc:0.96874	validation-aucpr:0.97372
[20:48:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25]	validation-logloss:0.33513	validation-auc:0.96906	validation-aucpr:0.97398
[20:48:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26]	validation-logloss:0.32848	validation-auc:0.96919	validation-aucpr:0.97405
[20:48:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27]	validation-logloss:0.32323	validation-auc:0.96923	validation-aucpr:0.97406
[20:48:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28]	validation-logloss:0.31842	validation-auc:0.96919	validation-aucpr:0.97404
[20:48:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29]	validation-logloss:0.31239	validation-auc:0.96954	validation-aucpr:0.97431
[20:48:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30]	validation-logloss:0.30660	validation-auc:0.96976	validation-aucpr:0.97452
[20:48:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31]	validation-logloss:0.30184	validation-auc:0.96971	validation-aucpr:0.97451
[20:48:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32]	validation-logloss:0.29659	validation-auc:0.96978	validation-aucpr:0.97460
[20:48:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33]	validation-logloss:0.29264	validation-auc:0.96975	validation-aucpr:0.97457
[20:48:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34]	validation-logloss:0.28819	validation-auc:0.96983	validation-aucpr:0.97464
[20:48:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35]	validation-logloss:0.28483	validation-auc:0.96974	validation-aucpr:0.97451
[20:48:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36]	validation-logloss:0.28054	validation-auc:0.96987	validation-aucpr:0.97462
[20:48:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37]	validation-logloss:0.27721	validation-auc:0.96980	validation-aucpr:0.97460
[20:48:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38]	validation-logloss:0.27423	validation-auc:0.96974	validation-aucpr:0.97455
[20:48:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39]	validation-logloss:0.27034	validation-auc:0.96996	validation-aucpr:0.97473
[20:48:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40]	validation-logloss:0.26749	validation-auc:0.96986	validation-aucpr:0.97467
[20:48:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41]	validation-logloss:0.26407	validation-auc:0.96987	validation-aucpr:0.97469
[20:48:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42]	validation-logloss:0.26086	validation-auc:0.96992	validation-aucpr:0.97475
[20:48:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43]	validation-logloss:0.25847	validation-auc:0.96989	validation-aucpr:0.97467
[20:48:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44]	validation-logloss:0.25559	validation-auc:0.96991	validation-aucpr:0.97470
[20:48:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45]	validation-logloss:0.25274	validation-auc:0.97005	validation-aucpr:0.97481
[20:48:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46]	validation-logloss:0.24985	validation-auc:0.97018	validation-aucpr:0.97490
[20:48:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[47]	validation-logloss:0.24777	validation-auc:0.97025	validation-aucpr:0.97494
[20:48:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[48]	validation-logloss:0.24531	validation-auc:0.97038	validation-aucpr:0.97502
[20:48:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[49]	validation-logloss:0.24299	validation-auc:0.97038	validation-aucpr:0.97501
[20:48:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[50]	validation-logloss:0.24067	validation-auc:0.97042	validation-aucpr:0.97505
[20:48:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[51]	validation-logloss:0.23903	validation-auc:0.97029	validation-aucpr:0.97495
[20:48:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[52]	validation-logloss:0.23716	validation-auc:0.97038	validation-aucpr:0.97501
[20:48:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[53]	validation-logloss:0.23498	validation-auc:0.97043	validation-aucpr:0.97505
[20:48:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[54]	validation-logloss:0.23335	validation-auc:0.97046	validation-aucpr:0.97507
[20:48:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[55]	validation-logloss:0.23191	validation-auc:0.97042	validation-aucpr:0.97504
[20:48:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[56]	validation-logloss:0.23048	validation-auc:0.97050	validation-aucpr:0.97509
{'best_iteration': '56', 'best_score': '0.9750851646174706'}
Trial 52, Fold 1: Log loss = 0.23047562636104363, Average precision = 0.9750894141029817, ROC-AUC = 0.9705026156302128, Elapsed Time = 22.141144799999893 seconds
Trial 52, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 52, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[20:48:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.66495	validation-auc:0.95334	validation-aucpr:0.95421
[20:48:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.63872	validation-auc:0.96041	validation-aucpr:0.96190
[20:48:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.61403	validation-auc:0.96421	validation-aucpr:0.96762
[20:48:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.59267	validation-auc:0.96583	validation-aucpr:0.96927
[20:48:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.57130	validation-auc:0.96674	validation-aucpr:0.97051
[20:48:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5]	validation-logloss:0.55173	validation-auc:0.96794	validation-aucpr:0.97170
[20:48:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6]	validation-logloss:0.53308	validation-auc:0.96861	validation-aucpr:0.97249
[20:48:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7]	validation-logloss:0.51687	validation-auc:0.96902	validation-aucpr:0.97287
[20:48:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8]	validation-logloss:0.50033	validation-auc:0.96907	validation-aucpr:0.97291
[20:48:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9]	validation-logloss:0.48507	validation-auc:0.96925	validation-aucpr:0.97301
[20:48:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10]	validation-logloss:0.47067	validation-auc:0.96949	validation-aucpr:0.97315
[20:48:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11]	validation-logloss:0.45832	validation-auc:0.96960	validation-aucpr:0.97314
[20:48:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12]	validation-logloss:0.44493	validation-auc:0.96997	validation-aucpr:0.97344
[20:48:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13]	validation-logloss:0.43264	validation-auc:0.97045	validation-aucpr:0.97369
[20:48:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14]	validation-logloss:0.42088	validation-auc:0.97058	validation-aucpr:0.97382
[20:48:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15]	validation-logloss:0.40986	validation-auc:0.97059	validation-aucpr:0.97385
[20:48:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16]	validation-logloss:0.39929	validation-auc:0.97063	validation-aucpr:0.97388
[20:48:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17]	validation-logloss:0.38942	validation-auc:0.97057	validation-aucpr:0.97389
[20:48:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18]	validation-logloss:0.38084	validation-auc:0.97070	validation-aucpr:0.97391
[20:48:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19]	validation-logloss:0.37184	validation-auc:0.97089	validation-aucpr:0.97408
[20:48:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20]	validation-logloss:0.36319	validation-auc:0.97099	validation-aucpr:0.97404
[20:48:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21]	validation-logloss:0.35589	validation-auc:0.97108	validation-aucpr:0.97404
[20:48:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22]	validation-logloss:0.34785	validation-auc:0.97143	validation-aucpr:0.97417
[20:48:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23]	validation-logloss:0.34076	validation-auc:0.97129	validation-aucpr:0.97404
[20:48:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24]	validation-logloss:0.33389	validation-auc:0.97129	validation-aucpr:0.97396
[20:48:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25]	validation-logloss:0.32823	validation-auc:0.97113	validation-aucpr:0.97357
[20:48:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26]	validation-logloss:0.32180	validation-auc:0.97116	validation-aucpr:0.97349
[20:48:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27]	validation-logloss:0.31625	validation-auc:0.97115	validation-aucpr:0.97345
[20:48:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28]	validation-logloss:0.31111	validation-auc:0.97129	validation-aucpr:0.97357
[20:48:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29]	validation-logloss:0.30552	validation-auc:0.97133	validation-aucpr:0.97362
[20:48:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30]	validation-logloss:0.30122	validation-auc:0.97114	validation-aucpr:0.97338
[20:48:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31]	validation-logloss:0.29613	validation-auc:0.97109	validation-aucpr:0.97334
[20:48:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32]	validation-logloss:0.29189	validation-auc:0.97115	validation-aucpr:0.97340
[20:48:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33]	validation-logloss:0.28696	validation-auc:0.97134	validation-aucpr:0.97351
[20:48:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34]	validation-logloss:0.28344	validation-auc:0.97124	validation-aucpr:0.97350
[20:48:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35]	validation-logloss:0.27970	validation-auc:0.97139	validation-aucpr:0.97410
[20:48:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36]	validation-logloss:0.27533	validation-auc:0.97154	validation-aucpr:0.97424
[20:48:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37]	validation-logloss:0.27211	validation-auc:0.97149	validation-aucpr:0.97421
[20:48:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38]	validation-logloss:0.26846	validation-auc:0.97149	validation-aucpr:0.97421
[20:48:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39]	validation-logloss:0.26524	validation-auc:0.97154	validation-aucpr:0.97424
[20:48:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40]	validation-logloss:0.26163	validation-auc:0.97166	validation-aucpr:0.97432
[20:48:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41]	validation-logloss:0.25830	validation-auc:0.97167	validation-aucpr:0.97431
[20:48:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42]	validation-logloss:0.25541	validation-auc:0.97164	validation-aucpr:0.97426
[20:48:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43]	validation-logloss:0.25280	validation-auc:0.97163	validation-aucpr:0.97424
[20:49:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44]	validation-logloss:0.24991	validation-auc:0.97166	validation-aucpr:0.97422
[20:49:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45]	validation-logloss:0.24747	validation-auc:0.97178	validation-aucpr:0.97427
[20:49:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46]	validation-logloss:0.24542	validation-auc:0.97175	validation-aucpr:0.97427
[20:49:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[47]	validation-logloss:0.24270	validation-auc:0.97181	validation-aucpr:0.97431
[20:49:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[48]	validation-logloss:0.24040	validation-auc:0.97193	validation-aucpr:0.97439
[20:49:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[49]	validation-logloss:0.23850	validation-auc:0.97199	validation-aucpr:0.97449
[20:49:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[50]	validation-logloss:0.23660	validation-auc:0.97195	validation-aucpr:0.97445
[20:49:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[51]	validation-logloss:0.23424	validation-auc:0.97200	validation-aucpr:0.97452
[20:49:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[52]	validation-logloss:0.23257	validation-auc:0.97205	validation-aucpr:0.97477
[20:49:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[53]	validation-logloss:0.23095	validation-auc:0.97210	validation-aucpr:0.97478
[20:49:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[54]	validation-logloss:0.22957	validation-auc:0.97198	validation-aucpr:0.97467
[20:49:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[55]	validation-logloss:0.22759	validation-auc:0.97206	validation-aucpr:0.97473
[20:49:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[56]	validation-logloss:0.22601	validation-auc:0.97213	validation-aucpr:0.97478
{'best_iteration': '56', 'best_score': '0.9747847674977682'}
Trial 52, Fold 2: Log loss = 0.22601226216187362, Average precision = 0.9747905876487796, ROC-AUC = 0.9721342506588131, Elapsed Time = 21.478866199999175 seconds
Trial 52, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 52, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[20:49:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.66451	validation-auc:0.95725	validation-aucpr:0.96033
[20:49:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.63800	validation-auc:0.96229	validation-aucpr:0.96447
[20:49:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.61414	validation-auc:0.96422	validation-aucpr:0.96874
[20:49:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.59107	validation-auc:0.96563	validation-aucpr:0.97007
[20:49:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.57216	validation-auc:0.96613	validation-aucpr:0.97121
[20:49:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5]	validation-logloss:0.55180	validation-auc:0.96675	validation-aucpr:0.97166
[20:49:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6]	validation-logloss:0.53332	validation-auc:0.96747	validation-aucpr:0.97233
[20:49:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7]	validation-logloss:0.51555	validation-auc:0.96822	validation-aucpr:0.97278
[20:49:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8]	validation-logloss:0.50039	validation-auc:0.96901	validation-aucpr:0.97352
[20:49:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9]	validation-logloss:0.48610	validation-auc:0.96937	validation-aucpr:0.97385
[20:49:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10]	validation-logloss:0.47158	validation-auc:0.96910	validation-aucpr:0.97361
[20:49:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11]	validation-logloss:0.45941	validation-auc:0.96941	validation-aucpr:0.97403
[20:49:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12]	validation-logloss:0.44798	validation-auc:0.96949	validation-aucpr:0.97402
[20:49:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13]	validation-logloss:0.43518	validation-auc:0.96980	validation-aucpr:0.97434
[20:49:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14]	validation-logloss:0.42490	validation-auc:0.96974	validation-aucpr:0.97433
[20:49:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15]	validation-logloss:0.41358	validation-auc:0.96965	validation-aucpr:0.97424
[20:49:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16]	validation-logloss:0.40277	validation-auc:0.96977	validation-aucpr:0.97440
[20:49:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17]	validation-logloss:0.39425	validation-auc:0.96982	validation-aucpr:0.97433
[20:49:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18]	validation-logloss:0.38432	validation-auc:0.97021	validation-aucpr:0.97441
[20:49:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19]	validation-logloss:0.37662	validation-auc:0.97012	validation-aucpr:0.97434
[20:49:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20]	validation-logloss:0.36744	validation-auc:0.97047	validation-aucpr:0.97351
[20:49:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21]	validation-logloss:0.35875	validation-auc:0.97064	validation-aucpr:0.97323
[20:49:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22]	validation-logloss:0.35176	validation-auc:0.97069	validation-aucpr:0.97328
[20:49:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23]	validation-logloss:0.34417	validation-auc:0.97075	validation-aucpr:0.97339
[20:49:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24]	validation-logloss:0.33817	validation-auc:0.97067	validation-aucpr:0.97408
[20:49:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25]	validation-logloss:0.33229	validation-auc:0.97065	validation-aucpr:0.97401
[20:49:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26]	validation-logloss:0.32554	validation-auc:0.97083	validation-aucpr:0.97409
[20:49:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27]	validation-logloss:0.31902	validation-auc:0.97109	validation-aucpr:0.97429
[20:49:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28]	validation-logloss:0.31331	validation-auc:0.97104	validation-aucpr:0.97425
[20:49:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29]	validation-logloss:0.30833	validation-auc:0.97098	validation-aucpr:0.97418
[20:49:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30]	validation-logloss:0.30246	validation-auc:0.97118	validation-aucpr:0.97431
[20:49:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31]	validation-logloss:0.29719	validation-auc:0.97119	validation-aucpr:0.97431
[20:49:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32]	validation-logloss:0.29300	validation-auc:0.97108	validation-aucpr:0.97421
[20:49:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33]	validation-logloss:0.28918	validation-auc:0.97104	validation-aucpr:0.97529
[20:49:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34]	validation-logloss:0.28431	validation-auc:0.97132	validation-aucpr:0.97550
[20:49:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35]	validation-logloss:0.27978	validation-auc:0.97152	validation-aucpr:0.97562
[20:49:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36]	validation-logloss:0.27599	validation-auc:0.97155	validation-aucpr:0.97560
[20:49:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37]	validation-logloss:0.27289	validation-auc:0.97150	validation-aucpr:0.97551
[20:49:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38]	validation-logloss:0.26900	validation-auc:0.97157	validation-aucpr:0.97559
[20:49:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39]	validation-logloss:0.26519	validation-auc:0.97166	validation-aucpr:0.97566
[20:49:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40]	validation-logloss:0.26233	validation-auc:0.97164	validation-aucpr:0.97571
[20:49:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41]	validation-logloss:0.25952	validation-auc:0.97163	validation-aucpr:0.97569
[20:49:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42]	validation-logloss:0.25632	validation-auc:0.97160	validation-aucpr:0.97568
[20:49:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43]	validation-logloss:0.25367	validation-auc:0.97156	validation-aucpr:0.97565
[20:49:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44]	validation-logloss:0.25138	validation-auc:0.97146	validation-aucpr:0.97557
[20:49:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45]	validation-logloss:0.24817	validation-auc:0.97170	validation-aucpr:0.97576
[20:49:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46]	validation-logloss:0.24577	validation-auc:0.97185	validation-aucpr:0.97585
[20:49:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[47]	validation-logloss:0.24313	validation-auc:0.97173	validation-aucpr:0.97576
[20:49:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[48]	validation-logloss:0.24058	validation-auc:0.97174	validation-aucpr:0.97577
[20:49:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[49]	validation-logloss:0.23829	validation-auc:0.97173	validation-aucpr:0.97575
[20:49:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[50]	validation-logloss:0.23573	validation-auc:0.97191	validation-aucpr:0.97586
[20:49:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[51]	validation-logloss:0.23354	validation-auc:0.97192	validation-aucpr:0.97586
[20:49:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[52]	validation-logloss:0.23201	validation-auc:0.97182	validation-aucpr:0.97576
[20:49:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[53]	validation-logloss:0.23051	validation-auc:0.97183	validation-aucpr:0.97581
[20:49:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[54]	validation-logloss:0.22883	validation-auc:0.97183	validation-aucpr:0.97583
[20:49:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[55]	validation-logloss:0.22679	validation-auc:0.97198	validation-aucpr:0.97596
[20:49:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[56]	validation-logloss:0.22491	validation-auc:0.97200	validation-aucpr:0.97595
{'best_iteration': '55', 'best_score': '0.975958283888624'}
Trial 52, Fold 3: Log loss = 0.22490842763663127, Average precision = 0.9759580309108026, ROC-AUC = 0.972004386936464, Elapsed Time = 22.18626099999892 seconds
Trial 52, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 52, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[20:49:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.66434	validation-auc:0.95254	validation-aucpr:0.95559
[20:49:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.63778	validation-auc:0.96034	validation-aucpr:0.96585
[20:49:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.61346	validation-auc:0.96308	validation-aucpr:0.96743
[20:49:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.59333	validation-auc:0.96395	validation-aucpr:0.96976
[20:49:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.57452	validation-auc:0.96464	validation-aucpr:0.97037
[20:49:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5]	validation-logloss:0.55681	validation-auc:0.96457	validation-aucpr:0.97030
[20:49:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6]	validation-logloss:0.54007	validation-auc:0.96426	validation-aucpr:0.97006
[20:49:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7]	validation-logloss:0.52215	validation-auc:0.96498	validation-aucpr:0.97080
[20:49:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8]	validation-logloss:0.50782	validation-auc:0.96467	validation-aucpr:0.97045
[20:49:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9]	validation-logloss:0.49152	validation-auc:0.96591	validation-aucpr:0.97136
[20:49:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10]	validation-logloss:0.47869	validation-auc:0.96568	validation-aucpr:0.97119
[20:49:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11]	validation-logloss:0.46647	validation-auc:0.96533	validation-aucpr:0.97093
[20:49:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12]	validation-logloss:0.45316	validation-auc:0.96580	validation-aucpr:0.97131
[20:49:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13]	validation-logloss:0.44224	validation-auc:0.96565	validation-aucpr:0.97119
[20:49:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14]	validation-logloss:0.43202	validation-auc:0.96578	validation-aucpr:0.97123
[20:49:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15]	validation-logloss:0.42012	validation-auc:0.96630	validation-aucpr:0.97174
[20:49:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16]	validation-logloss:0.41092	validation-auc:0.96645	validation-aucpr:0.97184
[20:49:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17]	validation-logloss:0.40176	validation-auc:0.96687	validation-aucpr:0.97215
[20:49:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18]	validation-logloss:0.39333	validation-auc:0.96701	validation-aucpr:0.97221
[20:49:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19]	validation-logloss:0.38392	validation-auc:0.96733	validation-aucpr:0.97246
[20:49:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20]	validation-logloss:0.37640	validation-auc:0.96711	validation-aucpr:0.97227
[20:49:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21]	validation-logloss:0.36941	validation-auc:0.96707	validation-aucpr:0.97227
[20:49:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22]	validation-logloss:0.36244	validation-auc:0.96725	validation-aucpr:0.97239
[20:49:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23]	validation-logloss:0.35444	validation-auc:0.96740	validation-aucpr:0.97258
[20:49:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24]	validation-logloss:0.34802	validation-auc:0.96748	validation-aucpr:0.97261
[20:49:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25]	validation-logloss:0.34056	validation-auc:0.96777	validation-aucpr:0.97284
[20:49:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26]	validation-logloss:0.33341	validation-auc:0.96801	validation-aucpr:0.97304
[20:49:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27]	validation-logloss:0.32681	validation-auc:0.96820	validation-aucpr:0.97319
[20:49:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28]	validation-logloss:0.32083	validation-auc:0.96824	validation-aucpr:0.97326
[20:49:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29]	validation-logloss:0.31482	validation-auc:0.96831	validation-aucpr:0.97336
[20:49:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30]	validation-logloss:0.31021	validation-auc:0.96825	validation-aucpr:0.97331
[20:49:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31]	validation-logloss:0.30433	validation-auc:0.96852	validation-aucpr:0.97356
[20:49:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32]	validation-logloss:0.29905	validation-auc:0.96876	validation-aucpr:0.97377
[20:49:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33]	validation-logloss:0.29422	validation-auc:0.96890	validation-aucpr:0.97387
[20:49:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34]	validation-logloss:0.29028	validation-auc:0.96893	validation-aucpr:0.97387
[20:49:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35]	validation-logloss:0.28677	validation-auc:0.96889	validation-aucpr:0.97384
[20:49:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36]	validation-logloss:0.28257	validation-auc:0.96871	validation-aucpr:0.97376
[20:49:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37]	validation-logloss:0.27835	validation-auc:0.96890	validation-aucpr:0.97390
[20:49:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38]	validation-logloss:0.27537	validation-auc:0.96880	validation-aucpr:0.97384
[20:49:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39]	validation-logloss:0.27150	validation-auc:0.96897	validation-aucpr:0.97395
[20:49:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40]	validation-logloss:0.26857	validation-auc:0.96906	validation-aucpr:0.97398
[20:49:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41]	validation-logloss:0.26477	validation-auc:0.96918	validation-aucpr:0.97409
[20:49:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42]	validation-logloss:0.26116	validation-auc:0.96949	validation-aucpr:0.97432
[20:49:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43]	validation-logloss:0.25856	validation-auc:0.96953	validation-aucpr:0.97434
[20:49:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44]	validation-logloss:0.25539	validation-auc:0.96966	validation-aucpr:0.97445
[20:49:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45]	validation-logloss:0.25305	validation-auc:0.96973	validation-aucpr:0.97451
[20:49:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46]	validation-logloss:0.25004	validation-auc:0.96990	validation-aucpr:0.97462
[20:49:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[47]	validation-logloss:0.24715	validation-auc:0.97012	validation-aucpr:0.97476
[20:49:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[48]	validation-logloss:0.24458	validation-auc:0.97015	validation-aucpr:0.97480
[20:49:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[49]	validation-logloss:0.24250	validation-auc:0.97010	validation-aucpr:0.97478
[20:49:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[50]	validation-logloss:0.24074	validation-auc:0.97002	validation-aucpr:0.97469
[20:49:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[51]	validation-logloss:0.23841	validation-auc:0.97007	validation-aucpr:0.97472
[20:49:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[52]	validation-logloss:0.23591	validation-auc:0.97021	validation-aucpr:0.97483
[20:49:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[53]	validation-logloss:0.23431	validation-auc:0.97027	validation-aucpr:0.97488
[20:49:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[54]	validation-logloss:0.23217	validation-auc:0.97039	validation-aucpr:0.97498
[20:49:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[55]	validation-logloss:0.23003	validation-auc:0.97055	validation-aucpr:0.97509
[20:49:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[56]	validation-logloss:0.22803	validation-auc:0.97062	validation-aucpr:0.97514
{'best_iteration': '56', 'best_score': '0.9751414398190122'}
Trial 52, Fold 4: Log loss = 0.22802817105373885, Average precision = 0.9751453337884511, ROC-AUC = 0.9706172163923403, Elapsed Time = 26.99545769999895 seconds
Trial 52, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 52, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[20:49:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.66470	validation-auc:0.95401	validation-aucpr:0.95795
[20:49:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.64216	validation-auc:0.95653	validation-aucpr:0.96240
[20:49:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.61735	validation-auc:0.96173	validation-aucpr:0.96699
[20:49:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.59767	validation-auc:0.96169	validation-aucpr:0.96656
[20:49:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.57658	validation-auc:0.96331	validation-aucpr:0.96798
[20:49:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5]	validation-logloss:0.55899	validation-auc:0.96339	validation-aucpr:0.96800
[20:49:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6]	validation-logloss:0.53999	validation-auc:0.96417	validation-aucpr:0.96870
[20:49:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7]	validation-logloss:0.52215	validation-auc:0.96490	validation-aucpr:0.96959
[20:49:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8]	validation-logloss:0.50791	validation-auc:0.96415	validation-aucpr:0.96899
[20:49:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9]	validation-logloss:0.49176	validation-auc:0.96486	validation-aucpr:0.96963
[20:50:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10]	validation-logloss:0.47686	validation-auc:0.96543	validation-aucpr:0.97003
[20:50:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11]	validation-logloss:0.46490	validation-auc:0.96474	validation-aucpr:0.96944
[20:50:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12]	validation-logloss:0.45146	validation-auc:0.96512	validation-aucpr:0.96976
[20:50:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13]	validation-logloss:0.43874	validation-auc:0.96543	validation-aucpr:0.97001
[20:50:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14]	validation-logloss:0.42667	validation-auc:0.96567	validation-aucpr:0.97030
[20:50:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15]	validation-logloss:0.41711	validation-auc:0.96563	validation-aucpr:0.97005
[20:50:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16]	validation-logloss:0.40640	validation-auc:0.96605	validation-aucpr:0.97044
[20:50:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17]	validation-logloss:0.39647	validation-auc:0.96621	validation-aucpr:0.97053
[20:50:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18]	validation-logloss:0.38828	validation-auc:0.96607	validation-aucpr:0.97040
[20:50:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19]	validation-logloss:0.37899	validation-auc:0.96642	validation-aucpr:0.97066
[20:50:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20]	validation-logloss:0.37165	validation-auc:0.96624	validation-aucpr:0.97054
[20:50:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21]	validation-logloss:0.36370	validation-auc:0.96633	validation-aucpr:0.97063
[20:50:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22]	validation-logloss:0.35726	validation-auc:0.96638	validation-aucpr:0.97059
[20:50:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23]	validation-logloss:0.35071	validation-auc:0.96662	validation-aucpr:0.97068
[20:50:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24]	validation-logloss:0.34446	validation-auc:0.96705	validation-aucpr:0.97098
[20:50:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25]	validation-logloss:0.33908	validation-auc:0.96713	validation-aucpr:0.97165
[20:50:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26]	validation-logloss:0.33343	validation-auc:0.96719	validation-aucpr:0.97167
[20:50:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27]	validation-logloss:0.32804	validation-auc:0.96735	validation-aucpr:0.97180
[20:50:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28]	validation-logloss:0.32201	validation-auc:0.96756	validation-aucpr:0.97206
[20:50:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29]	validation-logloss:0.31724	validation-auc:0.96751	validation-aucpr:0.97206
[20:50:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30]	validation-logloss:0.31139	validation-auc:0.96777	validation-aucpr:0.97230
[20:50:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31]	validation-logloss:0.30604	validation-auc:0.96790	validation-aucpr:0.97239
[20:50:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32]	validation-logloss:0.30090	validation-auc:0.96817	validation-aucpr:0.97256
[20:50:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33]	validation-logloss:0.29658	validation-auc:0.96820	validation-aucpr:0.97259
[20:50:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34]	validation-logloss:0.29278	validation-auc:0.96830	validation-aucpr:0.97267
[20:50:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35]	validation-logloss:0.28913	validation-auc:0.96824	validation-aucpr:0.97263
[20:50:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36]	validation-logloss:0.28489	validation-auc:0.96828	validation-aucpr:0.97269
[20:50:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37]	validation-logloss:0.28071	validation-auc:0.96844	validation-aucpr:0.97281
[20:50:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38]	validation-logloss:0.27669	validation-auc:0.96870	validation-aucpr:0.97299
[20:50:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39]	validation-logloss:0.27340	validation-auc:0.96879	validation-aucpr:0.97303
[20:50:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40]	validation-logloss:0.27059	validation-auc:0.96874	validation-aucpr:0.97298
[20:50:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41]	validation-logloss:0.26703	validation-auc:0.96889	validation-aucpr:0.97308
[20:50:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42]	validation-logloss:0.26359	validation-auc:0.96904	validation-aucpr:0.97318
[20:50:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43]	validation-logloss:0.26025	validation-auc:0.96928	validation-aucpr:0.97338
[20:50:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44]	validation-logloss:0.25720	validation-auc:0.96940	validation-aucpr:0.97345
[20:50:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45]	validation-logloss:0.25407	validation-auc:0.96970	validation-aucpr:0.97368
[20:50:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46]	validation-logloss:0.25126	validation-auc:0.96990	validation-aucpr:0.97384
[20:50:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[47]	validation-logloss:0.24862	validation-auc:0.97006	validation-aucpr:0.97393
[20:50:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[48]	validation-logloss:0.24605	validation-auc:0.97017	validation-aucpr:0.97399
[20:50:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[49]	validation-logloss:0.24363	validation-auc:0.97026	validation-aucpr:0.97403
[20:50:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[50]	validation-logloss:0.24133	validation-auc:0.97037	validation-aucpr:0.97409
[20:50:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[51]	validation-logloss:0.23914	validation-auc:0.97045	validation-aucpr:0.97413
[20:50:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[52]	validation-logloss:0.23750	validation-auc:0.97051	validation-aucpr:0.97417
[20:50:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[53]	validation-logloss:0.23558	validation-auc:0.97052	validation-aucpr:0.97418
[20:50:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[54]	validation-logloss:0.23412	validation-auc:0.97055	validation-aucpr:0.97421
[20:50:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[55]	validation-logloss:0.23225	validation-auc:0.97068	validation-aucpr:0.97429
[20:50:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[56]	validation-logloss:0.23030	validation-auc:0.97080	validation-aucpr:0.97440
{'best_iteration': '56', 'best_score': '0.9744025313585415'}
Trial 52, Fold 5: Log loss = 0.23029926692140149, Average precision = 0.9744071415557151, ROC-AUC = 0.9707958307786633, Elapsed Time = 21.70401050000146 seconds
Optimization Progress:  53%|#####3    | 53/100 [2:51:30<1:09:43, 89.01s/it]
Trial 53, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 53, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[20:50:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.67632	validation-auc:0.94589	validation-aucpr:0.94841
[20:50:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.66148	validation-auc:0.95293	validation-aucpr:0.95900
[20:50:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.64589	validation-auc:0.95853	validation-aucpr:0.96463
[20:50:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.63284	validation-auc:0.95921	validation-aucpr:0.96494
[20:50:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.62094	validation-auc:0.95847	validation-aucpr:0.96314
[20:50:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5]	validation-logloss:0.60894	validation-auc:0.95930	validation-aucpr:0.96523
[20:50:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6]	validation-logloss:0.59628	validation-auc:0.96093	validation-aucpr:0.96676
[20:50:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7]	validation-logloss:0.58542	validation-auc:0.96114	validation-aucpr:0.96710
[20:50:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8]	validation-logloss:0.57393	validation-auc:0.96208	validation-aucpr:0.96788
[20:50:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9]	validation-logloss:0.56345	validation-auc:0.96229	validation-aucpr:0.96812
[20:50:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10]	validation-logloss:0.55222	validation-auc:0.96262	validation-aucpr:0.96862
[20:50:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11]	validation-logloss:0.54278	validation-auc:0.96239	validation-aucpr:0.96846
[20:50:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12]	validation-logloss:0.53371	validation-auc:0.96228	validation-aucpr:0.96838
[20:50:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13]	validation-logloss:0.52324	validation-auc:0.96283	validation-aucpr:0.96882
[20:50:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14]	validation-logloss:0.51355	validation-auc:0.96319	validation-aucpr:0.96906
[20:50:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15]	validation-logloss:0.50558	validation-auc:0.96325	validation-aucpr:0.96922
[20:50:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16]	validation-logloss:0.49681	validation-auc:0.96341	validation-aucpr:0.96940
[20:50:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17]	validation-logloss:0.48831	validation-auc:0.96350	validation-aucpr:0.96942
[20:50:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18]	validation-logloss:0.48068	validation-auc:0.96373	validation-aucpr:0.96955
[20:50:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19]	validation-logloss:0.47318	validation-auc:0.96378	validation-aucpr:0.96958
[20:50:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20]	validation-logloss:0.46668	validation-auc:0.96368	validation-aucpr:0.96947
[20:50:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21]	validation-logloss:0.46003	validation-auc:0.96364	validation-aucpr:0.96941
[20:50:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22]	validation-logloss:0.45362	validation-auc:0.96357	validation-aucpr:0.96944
[20:50:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23]	validation-logloss:0.44623	validation-auc:0.96385	validation-aucpr:0.96961
[20:50:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24]	validation-logloss:0.43976	validation-auc:0.96388	validation-aucpr:0.96971
[20:50:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25]	validation-logloss:0.43409	validation-auc:0.96389	validation-aucpr:0.96967
[20:50:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26]	validation-logloss:0.42716	validation-auc:0.96424	validation-aucpr:0.97000
[20:50:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27]	validation-logloss:0.42124	validation-auc:0.96457	validation-aucpr:0.97005
[20:50:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28]	validation-logloss:0.41596	validation-auc:0.96464	validation-aucpr:0.96996
[20:50:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29]	validation-logloss:0.41002	validation-auc:0.96479	validation-aucpr:0.97008
[20:50:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30]	validation-logloss:0.40511	validation-auc:0.96464	validation-aucpr:0.96997
[20:50:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31]	validation-logloss:0.40070	validation-auc:0.96446	validation-aucpr:0.96982
[20:50:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32]	validation-logloss:0.39550	validation-auc:0.96444	validation-aucpr:0.96980
[20:50:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33]	validation-logloss:0.39124	validation-auc:0.96441	validation-aucpr:0.96975
[20:50:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34]	validation-logloss:0.38569	validation-auc:0.96456	validation-aucpr:0.96992
[20:50:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35]	validation-logloss:0.38114	validation-auc:0.96451	validation-aucpr:0.96829
[20:50:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36]	validation-logloss:0.37687	validation-auc:0.96459	validation-aucpr:0.96835
[20:50:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37]	validation-logloss:0.37190	validation-auc:0.96471	validation-aucpr:0.96848
[20:50:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38]	validation-logloss:0.36813	validation-auc:0.96474	validation-aucpr:0.96848
[20:50:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39]	validation-logloss:0.36308	validation-auc:0.96494	validation-aucpr:0.96866
[20:50:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40]	validation-logloss:0.35834	validation-auc:0.96514	validation-aucpr:0.96897
[20:50:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41]	validation-logloss:0.35461	validation-auc:0.96518	validation-aucpr:0.96892
[20:50:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42]	validation-logloss:0.35113	validation-auc:0.96517	validation-aucpr:0.96909
[20:50:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43]	validation-logloss:0.34779	validation-auc:0.96522	validation-aucpr:0.96908
[20:50:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44]	validation-logloss:0.34344	validation-auc:0.96537	validation-aucpr:0.96930
[20:50:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45]	validation-logloss:0.34024	validation-auc:0.96540	validation-aucpr:0.96922
[20:50:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46]	validation-logloss:0.33733	validation-auc:0.96554	validation-aucpr:0.96927
{'best_iteration': '29', 'best_score': '0.9700769090840297'}
Trial 53, Fold 1: Log loss = 0.33733213328401895, Average precision = 0.9693284708448289, ROC-AUC = 0.9655407298671752, Elapsed Time = 14.781939100001182 seconds
Trial 53, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 53, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[20:50:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.67573	validation-auc:0.94920	validation-aucpr:0.95315
[20:50:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.65968	validation-auc:0.95868	validation-aucpr:0.96208
[20:50:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.64422	validation-auc:0.96191	validation-aucpr:0.96634
[20:50:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.63089	validation-auc:0.96299	validation-aucpr:0.96643
[20:50:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.61864	validation-auc:0.96324	validation-aucpr:0.96574
[20:50:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5]	validation-logloss:0.60691	validation-auc:0.96295	validation-aucpr:0.96554
[20:50:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6]	validation-logloss:0.59383	validation-auc:0.96408	validation-aucpr:0.96649
[20:50:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7]	validation-logloss:0.58077	validation-auc:0.96513	validation-aucpr:0.96760
[20:50:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8]	validation-logloss:0.57019	validation-auc:0.96493	validation-aucpr:0.96914
[20:50:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9]	validation-logloss:0.55994	validation-auc:0.96541	validation-aucpr:0.96941
[20:50:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10]	validation-logloss:0.54842	validation-auc:0.96596	validation-aucpr:0.96991
[20:50:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11]	validation-logloss:0.53914	validation-auc:0.96559	validation-aucpr:0.96961
[20:50:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12]	validation-logloss:0.53012	validation-auc:0.96579	validation-aucpr:0.96958
[20:50:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13]	validation-logloss:0.51986	validation-auc:0.96606	validation-aucpr:0.96976
[20:50:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14]	validation-logloss:0.50998	validation-auc:0.96660	validation-aucpr:0.97032
[20:50:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15]	validation-logloss:0.50138	validation-auc:0.96677	validation-aucpr:0.97036
[20:50:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16]	validation-logloss:0.49209	validation-auc:0.96752	validation-aucpr:0.97099
[20:50:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17]	validation-logloss:0.48462	validation-auc:0.96762	validation-aucpr:0.97092
[20:50:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18]	validation-logloss:0.47744	validation-auc:0.96737	validation-aucpr:0.97081
[20:50:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19]	validation-logloss:0.47018	validation-auc:0.96744	validation-aucpr:0.97081
[20:50:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20]	validation-logloss:0.46361	validation-auc:0.96717	validation-aucpr:0.97047
[20:50:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21]	validation-logloss:0.45684	validation-auc:0.96715	validation-aucpr:0.97041
[20:50:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22]	validation-logloss:0.45019	validation-auc:0.96725	validation-aucpr:0.97042
[20:50:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23]	validation-logloss:0.44259	validation-auc:0.96748	validation-aucpr:0.97081
[20:50:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24]	validation-logloss:0.43631	validation-auc:0.96754	validation-aucpr:0.97087
[20:50:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25]	validation-logloss:0.43034	validation-auc:0.96749	validation-aucpr:0.97082
[20:50:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26]	validation-logloss:0.42453	validation-auc:0.96764	validation-aucpr:0.97090
[20:50:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27]	validation-logloss:0.41815	validation-auc:0.96765	validation-aucpr:0.97099
[20:50:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28]	validation-logloss:0.41267	validation-auc:0.96777	validation-aucpr:0.97104
[20:50:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29]	validation-logloss:0.40704	validation-auc:0.96770	validation-aucpr:0.97096
[20:50:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30]	validation-logloss:0.40149	validation-auc:0.96772	validation-aucpr:0.97107
[20:50:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31]	validation-logloss:0.39700	validation-auc:0.96769	validation-aucpr:0.97112
[20:50:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32]	validation-logloss:0.39173	validation-auc:0.96780	validation-aucpr:0.97134
[20:50:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33]	validation-logloss:0.38704	validation-auc:0.96772	validation-aucpr:0.97127
[20:50:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34]	validation-logloss:0.38163	validation-auc:0.96789	validation-aucpr:0.97139
[20:50:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35]	validation-logloss:0.37734	validation-auc:0.96808	validation-aucpr:0.97150
[20:50:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36]	validation-logloss:0.37358	validation-auc:0.96788	validation-aucpr:0.97137
[20:50:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37]	validation-logloss:0.36829	validation-auc:0.96821	validation-aucpr:0.97170
[20:50:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38]	validation-logloss:0.36403	validation-auc:0.96832	validation-aucpr:0.97182
[20:50:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39]	validation-logloss:0.35979	validation-auc:0.96836	validation-aucpr:0.97184
[20:50:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40]	validation-logloss:0.35628	validation-auc:0.96826	validation-aucpr:0.97174
[20:50:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41]	validation-logloss:0.35248	validation-auc:0.96833	validation-aucpr:0.97175
[20:50:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42]	validation-logloss:0.34900	validation-auc:0.96837	validation-aucpr:0.97177
[20:50:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43]	validation-logloss:0.34474	validation-auc:0.96842	validation-aucpr:0.97186
[20:50:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44]	validation-logloss:0.34168	validation-auc:0.96832	validation-aucpr:0.97177
[20:50:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45]	validation-logloss:0.33750	validation-auc:0.96846	validation-aucpr:0.97187
[20:50:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46]	validation-logloss:0.33439	validation-auc:0.96850	validation-aucpr:0.97188
{'best_iteration': '46', 'best_score': '0.9718843925984001'}
Trial 53, Fold 2: Log loss = 0.3343866096678563, Average precision = 0.971889660200405, ROC-AUC = 0.9685029261525473, Elapsed Time = 14.036364999999932 seconds
Trial 53, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 53, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[20:51:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.67775	validation-auc:0.92623	validation-aucpr:0.93190
[20:51:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.66221	validation-auc:0.94945	validation-aucpr:0.95479
[20:51:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.64725	validation-auc:0.95836	validation-aucpr:0.96343
[20:51:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.63406	validation-auc:0.96058	validation-aucpr:0.96602
[20:51:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.62068	validation-auc:0.96274	validation-aucpr:0.96742
[20:51:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5]	validation-logloss:0.60889	validation-auc:0.96230	validation-aucpr:0.96737
[20:51:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6]	validation-logloss:0.59709	validation-auc:0.96258	validation-aucpr:0.96765
[20:51:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7]	validation-logloss:0.58609	validation-auc:0.96274	validation-aucpr:0.96778
[20:51:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8]	validation-logloss:0.57535	validation-auc:0.96206	validation-aucpr:0.96721
[20:51:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9]	validation-logloss:0.56527	validation-auc:0.96183	validation-aucpr:0.96717
[20:51:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10]	validation-logloss:0.55486	validation-auc:0.96244	validation-aucpr:0.96783
[20:51:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11]	validation-logloss:0.54568	validation-auc:0.96230	validation-aucpr:0.96775
[20:51:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12]	validation-logloss:0.53458	validation-auc:0.96357	validation-aucpr:0.96891
[20:51:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13]	validation-logloss:0.52590	validation-auc:0.96373	validation-aucpr:0.96895
[20:51:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14]	validation-logloss:0.51759	validation-auc:0.96366	validation-aucpr:0.96882
[20:51:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15]	validation-logloss:0.50950	validation-auc:0.96373	validation-aucpr:0.96875
[20:51:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16]	validation-logloss:0.50155	validation-auc:0.96383	validation-aucpr:0.96887
[20:51:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17]	validation-logloss:0.49388	validation-auc:0.96388	validation-aucpr:0.96882
[20:51:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18]	validation-logloss:0.48680	validation-auc:0.96368	validation-aucpr:0.96865
[20:51:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19]	validation-logloss:0.47834	validation-auc:0.96425	validation-aucpr:0.96919
[20:51:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20]	validation-logloss:0.47048	validation-auc:0.96460	validation-aucpr:0.96943
[20:51:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21]	validation-logloss:0.46376	validation-auc:0.96452	validation-aucpr:0.96929
[20:51:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22]	validation-logloss:0.45724	validation-auc:0.96463	validation-aucpr:0.96936
[20:51:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23]	validation-logloss:0.44999	validation-auc:0.96484	validation-aucpr:0.96961
[20:51:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24]	validation-logloss:0.44236	validation-auc:0.96514	validation-aucpr:0.96994
[20:51:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25]	validation-logloss:0.43507	validation-auc:0.96544	validation-aucpr:0.97022
[20:51:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26]	validation-logloss:0.42793	validation-auc:0.96577	validation-aucpr:0.97052
[20:51:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27]	validation-logloss:0.42094	validation-auc:0.96605	validation-aucpr:0.97075
[20:51:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28]	validation-logloss:0.41535	validation-auc:0.96615	validation-aucpr:0.97086
[20:51:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29]	validation-logloss:0.40925	validation-auc:0.96639	validation-aucpr:0.97108
[20:51:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30]	validation-logloss:0.40426	validation-auc:0.96649	validation-aucpr:0.97109
[20:51:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31]	validation-logloss:0.39912	validation-auc:0.96651	validation-aucpr:0.97110
[20:51:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32]	validation-logloss:0.39422	validation-auc:0.96650	validation-aucpr:0.97107
[20:51:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33]	validation-logloss:0.38946	validation-auc:0.96661	validation-aucpr:0.97114
[20:51:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34]	validation-logloss:0.38462	validation-auc:0.96681	validation-aucpr:0.97127
[20:51:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35]	validation-logloss:0.38028	validation-auc:0.96675	validation-aucpr:0.97125
[20:51:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36]	validation-logloss:0.37623	validation-auc:0.96664	validation-aucpr:0.97110
[20:51:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37]	validation-logloss:0.37126	validation-auc:0.96679	validation-aucpr:0.97125
[20:51:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38]	validation-logloss:0.36700	validation-auc:0.96691	validation-aucpr:0.97134
[20:51:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39]	validation-logloss:0.36305	validation-auc:0.96708	validation-aucpr:0.97148
[20:51:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40]	validation-logloss:0.35924	validation-auc:0.96716	validation-aucpr:0.97152
[20:51:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41]	validation-logloss:0.35566	validation-auc:0.96724	validation-aucpr:0.97160
[20:51:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42]	validation-logloss:0.35080	validation-auc:0.96744	validation-aucpr:0.97175
[20:51:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43]	validation-logloss:0.34738	validation-auc:0.96753	validation-aucpr:0.97191
[20:51:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44]	validation-logloss:0.34311	validation-auc:0.96775	validation-aucpr:0.97211
[20:51:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45]	validation-logloss:0.33961	validation-auc:0.96791	validation-aucpr:0.97223
[20:51:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46]	validation-logloss:0.33617	validation-auc:0.96799	validation-aucpr:0.97231
{'best_iteration': '46', 'best_score': '0.9723141068097029'}
Trial 53, Fold 3: Log loss = 0.3361675740453154, Average precision = 0.9723190719744764, ROC-AUC = 0.9679867404724987, Elapsed Time = 14.098342500001309 seconds
Trial 53, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 53, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[20:51:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.67702	validation-auc:0.93491	validation-aucpr:0.93034
[20:51:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.66280	validation-auc:0.94355	validation-aucpr:0.95187
[20:51:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.64932	validation-auc:0.95139	validation-aucpr:0.95804
[20:51:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.63610	validation-auc:0.95323	validation-aucpr:0.95974
[20:51:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.62162	validation-auc:0.95866	validation-aucpr:0.96565
[20:51:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5]	validation-logloss:0.60771	validation-auc:0.96075	validation-aucpr:0.96751
[20:51:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6]	validation-logloss:0.59461	validation-auc:0.96233	validation-aucpr:0.96874
[20:51:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7]	validation-logloss:0.58410	validation-auc:0.96215	validation-aucpr:0.96858
[20:51:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8]	validation-logloss:0.57353	validation-auc:0.96217	validation-aucpr:0.96850
[20:51:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9]	validation-logloss:0.56189	validation-auc:0.96257	validation-aucpr:0.96891
[20:51:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10]	validation-logloss:0.55199	validation-auc:0.96250	validation-aucpr:0.96883
[20:51:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11]	validation-logloss:0.54260	validation-auc:0.96269	validation-aucpr:0.96889
[20:51:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12]	validation-logloss:0.53381	validation-auc:0.96283	validation-aucpr:0.96897
[20:51:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13]	validation-logloss:0.52492	validation-auc:0.96280	validation-aucpr:0.96891
[20:51:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14]	validation-logloss:0.51647	validation-auc:0.96279	validation-aucpr:0.96880
[20:51:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15]	validation-logloss:0.50796	validation-auc:0.96282	validation-aucpr:0.96876
[20:51:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16]	validation-logloss:0.49884	validation-auc:0.96301	validation-aucpr:0.96916
[20:51:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17]	validation-logloss:0.49076	validation-auc:0.96281	validation-aucpr:0.96905
[20:51:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18]	validation-logloss:0.48326	validation-auc:0.96272	validation-aucpr:0.96896
[20:51:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19]	validation-logloss:0.47504	validation-auc:0.96276	validation-aucpr:0.96906
[20:51:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20]	validation-logloss:0.46706	validation-auc:0.96269	validation-aucpr:0.96905
[20:51:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21]	validation-logloss:0.45952	validation-auc:0.96323	validation-aucpr:0.96948
[20:51:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22]	validation-logloss:0.45246	validation-auc:0.96361	validation-aucpr:0.96978
[20:51:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23]	validation-logloss:0.44570	validation-auc:0.96380	validation-aucpr:0.96992
[20:51:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24]	validation-logloss:0.43919	validation-auc:0.96385	validation-aucpr:0.96995
[20:51:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25]	validation-logloss:0.43360	validation-auc:0.96398	validation-aucpr:0.97000
[20:51:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26]	validation-logloss:0.42658	validation-auc:0.96427	validation-aucpr:0.97027
[20:51:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27]	validation-logloss:0.42000	validation-auc:0.96432	validation-aucpr:0.97036
[20:51:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28]	validation-logloss:0.41452	validation-auc:0.96465	validation-aucpr:0.97057
[20:51:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29]	validation-logloss:0.40892	validation-auc:0.96460	validation-aucpr:0.97052
[20:51:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30]	validation-logloss:0.40397	validation-auc:0.96454	validation-aucpr:0.97046
[20:51:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31]	validation-logloss:0.39767	validation-auc:0.96497	validation-aucpr:0.97086
[20:51:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32]	validation-logloss:0.39332	validation-auc:0.96462	validation-aucpr:0.97059
[20:51:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33]	validation-logloss:0.38872	validation-auc:0.96480	validation-aucpr:0.97067
[20:51:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34]	validation-logloss:0.38339	validation-auc:0.96496	validation-aucpr:0.97081
[20:51:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35]	validation-logloss:0.37866	validation-auc:0.96503	validation-aucpr:0.97092
[20:51:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36]	validation-logloss:0.37347	validation-auc:0.96516	validation-aucpr:0.97106
[20:51:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37]	validation-logloss:0.36946	validation-auc:0.96513	validation-aucpr:0.97101
[20:51:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38]	validation-logloss:0.36529	validation-auc:0.96510	validation-aucpr:0.97099
[20:51:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39]	validation-logloss:0.36120	validation-auc:0.96525	validation-aucpr:0.97108
[20:51:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40]	validation-logloss:0.35756	validation-auc:0.96525	validation-aucpr:0.97106
[20:51:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41]	validation-logloss:0.35406	validation-auc:0.96516	validation-aucpr:0.97098
[20:51:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42]	validation-logloss:0.35051	validation-auc:0.96523	validation-aucpr:0.97104
[20:51:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43]	validation-logloss:0.34581	validation-auc:0.96560	validation-aucpr:0.97133
[20:51:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44]	validation-logloss:0.34210	validation-auc:0.96570	validation-aucpr:0.97138
[20:51:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45]	validation-logloss:0.33803	validation-auc:0.96585	validation-aucpr:0.97154
[20:51:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46]	validation-logloss:0.33491	validation-auc:0.96588	validation-aucpr:0.97157
{'best_iteration': '46', 'best_score': '0.9715733178238387'}
Trial 53, Fold 4: Log loss = 0.3349134832867913, Average precision = 0.9715754250844988, ROC-AUC = 0.9658764136569453, Elapsed Time = 14.17093129999921 seconds
Trial 53, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 53, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[20:51:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.67761	validation-auc:0.92559	validation-aucpr:0.93521
[20:51:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.66160	validation-auc:0.94911	validation-aucpr:0.95521
[20:51:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.64580	validation-auc:0.95579	validation-aucpr:0.96039
[20:51:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.63119	validation-auc:0.95911	validation-aucpr:0.96446
[20:51:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.61837	validation-auc:0.96096	validation-aucpr:0.96666
[20:51:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5]	validation-logloss:0.60470	validation-auc:0.96196	validation-aucpr:0.96778
[20:51:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6]	validation-logloss:0.59281	validation-auc:0.96181	validation-aucpr:0.96750
[20:51:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7]	validation-logloss:0.58173	validation-auc:0.96163	validation-aucpr:0.96734
[20:51:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8]	validation-logloss:0.57113	validation-auc:0.96146	validation-aucpr:0.96730
[20:51:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9]	validation-logloss:0.56089	validation-auc:0.96106	validation-aucpr:0.96693
[20:51:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10]	validation-logloss:0.55140	validation-auc:0.96111	validation-aucpr:0.96690
[20:51:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11]	validation-logloss:0.54228	validation-auc:0.96119	validation-aucpr:0.96697
[20:51:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12]	validation-logloss:0.53211	validation-auc:0.96151	validation-aucpr:0.96735
[20:51:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13]	validation-logloss:0.52333	validation-auc:0.96161	validation-aucpr:0.96735
[20:51:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14]	validation-logloss:0.51356	validation-auc:0.96157	validation-aucpr:0.96731
[20:51:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15]	validation-logloss:0.50541	validation-auc:0.96162	validation-aucpr:0.96732
[20:51:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16]	validation-logloss:0.49766	validation-auc:0.96148	validation-aucpr:0.96727
[20:51:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17]	validation-logloss:0.48856	validation-auc:0.96187	validation-aucpr:0.96758
[20:51:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18]	validation-logloss:0.48063	validation-auc:0.96190	validation-aucpr:0.96770
[20:51:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19]	validation-logloss:0.47375	validation-auc:0.96196	validation-aucpr:0.96765
[20:51:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20]	validation-logloss:0.46592	validation-auc:0.96221	validation-aucpr:0.96809
[20:51:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21]	validation-logloss:0.45840	validation-auc:0.96218	validation-aucpr:0.96814
[20:51:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22]	validation-logloss:0.45195	validation-auc:0.96233	validation-aucpr:0.96821
[20:51:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23]	validation-logloss:0.44622	validation-auc:0.96239	validation-aucpr:0.96822
[20:51:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24]	validation-logloss:0.44048	validation-auc:0.96234	validation-aucpr:0.96815
[20:51:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25]	validation-logloss:0.43470	validation-auc:0.96242	validation-aucpr:0.96817
[20:51:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26]	validation-logloss:0.42863	validation-auc:0.96268	validation-aucpr:0.96836
[20:51:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27]	validation-logloss:0.42240	validation-auc:0.96286	validation-aucpr:0.96852
[20:51:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28]	validation-logloss:0.41708	validation-auc:0.96300	validation-aucpr:0.96869
[20:51:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29]	validation-logloss:0.41209	validation-auc:0.96303	validation-aucpr:0.96867
[20:51:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30]	validation-logloss:0.40701	validation-auc:0.96324	validation-aucpr:0.96880
[20:51:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31]	validation-logloss:0.40188	validation-auc:0.96314	validation-aucpr:0.96867
[20:51:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32]	validation-logloss:0.39720	validation-auc:0.96308	validation-aucpr:0.96862
[20:51:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33]	validation-logloss:0.39304	validation-auc:0.96298	validation-aucpr:0.96856
[20:51:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34]	validation-logloss:0.38861	validation-auc:0.96301	validation-aucpr:0.96856
[20:51:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35]	validation-logloss:0.38322	validation-auc:0.96321	validation-aucpr:0.96870
[20:51:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36]	validation-logloss:0.37904	validation-auc:0.96332	validation-aucpr:0.96877
[20:51:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37]	validation-logloss:0.37502	validation-auc:0.96350	validation-aucpr:0.96890
[20:51:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38]	validation-logloss:0.37055	validation-auc:0.96355	validation-aucpr:0.96892
[20:51:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39]	validation-logloss:0.36638	validation-auc:0.96373	validation-aucpr:0.96907
[20:51:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40]	validation-logloss:0.36252	validation-auc:0.96369	validation-aucpr:0.96903
[20:51:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41]	validation-logloss:0.35847	validation-auc:0.96383	validation-aucpr:0.96910
[20:51:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42]	validation-logloss:0.35410	validation-auc:0.96389	validation-aucpr:0.96917
[20:51:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43]	validation-logloss:0.35066	validation-auc:0.96391	validation-aucpr:0.96918
[20:51:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44]	validation-logloss:0.34746	validation-auc:0.96392	validation-aucpr:0.96917
[20:51:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45]	validation-logloss:0.34420	validation-auc:0.96404	validation-aucpr:0.96918
[20:51:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46]	validation-logloss:0.34149	validation-auc:0.96385	validation-aucpr:0.96885
{'best_iteration': '43', 'best_score': '0.9691820538568151'}
Trial 53, Fold 5: Log loss = 0.34148940877067663, Average precision = 0.9688621797793956, ROC-AUC = 0.9638521821526114, Elapsed Time = 16.684623099998134 seconds
Optimization Progress:  54%|#####4    | 54/100 [2:52:52<1:06:36, 86.88s/it]
Trial 54, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 54, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
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[85]	validation-logloss:0.23629	validation-auc:0.96856	validation-aucpr:0.97352
[86]	validation-logloss:0.23534	validation-auc:0.96866	validation-aucpr:0.97359
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[88]	validation-logloss:0.23289	validation-auc:0.96879	validation-aucpr:0.97375
[89]	validation-logloss:0.23202	validation-auc:0.96887	validation-aucpr:0.97380
[90]	validation-logloss:0.23120	validation-auc:0.96888	validation-aucpr:0.97379
[91]	validation-logloss:0.23058	validation-auc:0.96886	validation-aucpr:0.97378
[92]	validation-logloss:0.22989	validation-auc:0.96886	validation-aucpr:0.97376
[93]	validation-logloss:0.22904	validation-auc:0.96883	validation-aucpr:0.97373
[94]	validation-logloss:0.22831	validation-auc:0.96885	validation-aucpr:0.97375
[95]	validation-logloss:0.22726	validation-auc:0.96888	validation-aucpr:0.97380
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[97]	validation-logloss:0.22585	validation-auc:0.96889	validation-aucpr:0.97380
{'best_iteration': '89', 'best_score': '0.9738037843926355'}
Trial 54, Fold 1: Log loss = 0.22584576867637168, Average precision = 0.973802441655735, ROC-AUC = 0.9688949559799831, Elapsed Time = 16.8418285000007 seconds
Trial 54, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 54, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[0]	validation-logloss:0.67522	validation-auc:0.92656	validation-aucpr:0.89492
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[84]	validation-logloss:0.23235	validation-auc:0.97050	validation-aucpr:0.97391
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[86]	validation-logloss:0.22999	validation-auc:0.97057	validation-aucpr:0.97394
[87]	validation-logloss:0.22901	validation-auc:0.97061	validation-aucpr:0.97397
[88]	validation-logloss:0.22774	validation-auc:0.97067	validation-aucpr:0.97401
[89]	validation-logloss:0.22632	validation-auc:0.97074	validation-aucpr:0.97407
[90]	validation-logloss:0.22496	validation-auc:0.97078	validation-aucpr:0.97411
[91]	validation-logloss:0.22376	validation-auc:0.97082	validation-aucpr:0.97413
[92]	validation-logloss:0.22266	validation-auc:0.97078	validation-aucpr:0.97411
[93]	validation-logloss:0.22168	validation-auc:0.97082	validation-aucpr:0.97415
[94]	validation-logloss:0.22109	validation-auc:0.97083	validation-aucpr:0.97416
[95]	validation-logloss:0.22027	validation-auc:0.97093	validation-aucpr:0.97423
[96]	validation-logloss:0.21954	validation-auc:0.97097	validation-aucpr:0.97425
[97]	validation-logloss:0.21868	validation-auc:0.97099	validation-aucpr:0.97426
{'best_iteration': '97', 'best_score': '0.9742619592192957'}
Trial 54, Fold 2: Log loss = 0.21867937219408498, Average precision = 0.9742667819552169, ROC-AUC = 0.9709876443918116, Elapsed Time = 16.60435570000118 seconds
Trial 54, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 54, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[0]	validation-logloss:0.67524	validation-auc:0.92396	validation-aucpr:0.89424
[1]	validation-logloss:0.65846	validation-auc:0.94801	validation-aucpr:0.95016
[2]	validation-logloss:0.64193	validation-auc:0.95385	validation-aucpr:0.95537
[3]	validation-logloss:0.62614	validation-auc:0.95819	validation-aucpr:0.96162
[4]	validation-logloss:0.61126	validation-auc:0.96074	validation-aucpr:0.96425
[5]	validation-logloss:0.59561	validation-auc:0.96404	validation-aucpr:0.96802
[6]	validation-logloss:0.58261	validation-auc:0.96449	validation-aucpr:0.96775
[7]	validation-logloss:0.56775	validation-auc:0.96627	validation-aucpr:0.96947
[8]	validation-logloss:0.55604	validation-auc:0.96620	validation-aucpr:0.96949
[9]	validation-logloss:0.54348	validation-auc:0.96647	validation-aucpr:0.96967
[10]	validation-logloss:0.53034	validation-auc:0.96721	validation-aucpr:0.97044
[11]	validation-logloss:0.51804	validation-auc:0.96784	validation-aucpr:0.97103
[12]	validation-logloss:0.50665	validation-auc:0.96822	validation-aucpr:0.97146
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{'best_iteration': '93', 'best_score': '0.9753493181523499'}
Trial 54, Fold 3: Log loss = 0.216855898710009, Average precision = 0.9752910810816002, ROC-AUC = 0.97141651758627, Elapsed Time = 16.7057109999987 seconds
Trial 54, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 54, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
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[97]	validation-logloss:0.22246	validation-auc:0.96912	validation-aucpr:0.97414
{'best_iteration': '97', 'best_score': '0.9741356325292141'}
Trial 54, Fold 4: Log loss = 0.22246427317922213, Average precision = 0.9741398406367417, ROC-AUC = 0.9691153362852326, Elapsed Time = 16.831737099997554 seconds
Trial 54, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 54, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
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[85]	validation-logloss:0.23637	validation-auc:0.96774	validation-aucpr:0.97184
[86]	validation-logloss:0.23550	validation-auc:0.96773	validation-aucpr:0.97183
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[89]	validation-logloss:0.23269	validation-auc:0.96784	validation-aucpr:0.97194
[90]	validation-logloss:0.23188	validation-auc:0.96786	validation-aucpr:0.97180
[91]	validation-logloss:0.23085	validation-auc:0.96788	validation-aucpr:0.97184
[92]	validation-logloss:0.23022	validation-auc:0.96786	validation-aucpr:0.97181
[93]	validation-logloss:0.22955	validation-auc:0.96788	validation-aucpr:0.97162
[94]	validation-logloss:0.22889	validation-auc:0.96786	validation-aucpr:0.97177
[95]	validation-logloss:0.22824	validation-auc:0.96787	validation-aucpr:0.97188
[96]	validation-logloss:0.22720	validation-auc:0.96794	validation-aucpr:0.97195
[97]	validation-logloss:0.22612	validation-auc:0.96799	validation-aucpr:0.97200
{'best_iteration': '97', 'best_score': '0.9719967325717643'}
Trial 54, Fold 5: Log loss = 0.2261180343303365, Average precision = 0.9720025766905903, ROC-AUC = 0.9679936086803038, Elapsed Time = 16.96568880000268 seconds
Optimization Progress:  55%|#####5    | 55/100 [2:54:24<1:06:21, 88.47s/it]
Trial 55, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 55, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[0]	validation-logloss:0.68388	validation-auc:0.94851	validation-aucpr:0.94833
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[2]	validation-logloss:0.66598	validation-auc:0.96393	validation-aucpr:0.96686
[3]	validation-logloss:0.65723	validation-auc:0.96541	validation-aucpr:0.96763
[4]	validation-logloss:0.64867	validation-auc:0.96675	validation-aucpr:0.97182
[5]	validation-logloss:0.64021	validation-auc:0.96787	validation-aucpr:0.97176
[6]	validation-logloss:0.63302	validation-auc:0.96805	validation-aucpr:0.97183
[7]	validation-logloss:0.62502	validation-auc:0.96887	validation-aucpr:0.97372
[8]	validation-logloss:0.61730	validation-auc:0.96873	validation-aucpr:0.97362
[9]	validation-logloss:0.60971	validation-auc:0.96859	validation-aucpr:0.97364
[10]	validation-logloss:0.60221	validation-auc:0.96868	validation-aucpr:0.97368
[11]	validation-logloss:0.59507	validation-auc:0.96903	validation-aucpr:0.97391
[12]	validation-logloss:0.58874	validation-auc:0.96889	validation-aucpr:0.97378
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[51]	validation-logloss:0.40445	validation-auc:0.97001	validation-aucpr:0.97470
[52]	validation-logloss:0.40113	validation-auc:0.96999	validation-aucpr:0.97468
[53]	validation-logloss:0.39831	validation-auc:0.96992	validation-aucpr:0.97461
[54]	validation-logloss:0.39510	validation-auc:0.96995	validation-aucpr:0.97464
[55]	validation-logloss:0.39237	validation-auc:0.96992	validation-aucpr:0.97467
[56]	validation-logloss:0.38942	validation-auc:0.96995	validation-aucpr:0.97470
[57]	validation-logloss:0.38665	validation-auc:0.96997	validation-aucpr:0.97470
[58]	validation-logloss:0.38358	validation-auc:0.97001	validation-aucpr:0.97473
[59]	validation-logloss:0.38068	validation-auc:0.97002	validation-aucpr:0.97474
[60]	validation-logloss:0.37812	validation-auc:0.97000	validation-aucpr:0.97470
[61]	validation-logloss:0.37521	validation-auc:0.97010	validation-aucpr:0.97478
[62]	validation-logloss:0.37232	validation-auc:0.97015	validation-aucpr:0.97482
[63]	validation-logloss:0.36953	validation-auc:0.97014	validation-aucpr:0.97481
[64]	validation-logloss:0.36677	validation-auc:0.97019	validation-aucpr:0.97486
[65]	validation-logloss:0.36418	validation-auc:0.97020	validation-aucpr:0.97490
{'best_iteration': '65', 'best_score': '0.9748960585897559'}
Trial 55, Fold 1: Log loss = 0.3641809402106429, Average precision = 0.9748999805952879, ROC-AUC = 0.9701964231943906, Elapsed Time = 2.353945900002145 seconds
Trial 55, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 55, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[0]	validation-logloss:0.68338	validation-auc:0.95430	validation-aucpr:0.95662
[1]	validation-logloss:0.67423	validation-auc:0.96548	validation-aucpr:0.96508
[2]	validation-logloss:0.66602	validation-auc:0.96689	validation-aucpr:0.97055
[3]	validation-logloss:0.65723	validation-auc:0.96778	validation-aucpr:0.97152
[4]	validation-logloss:0.64881	validation-auc:0.96885	validation-aucpr:0.97255
[5]	validation-logloss:0.64055	validation-auc:0.96889	validation-aucpr:0.97252
[6]	validation-logloss:0.63234	validation-auc:0.96931	validation-aucpr:0.97313
[7]	validation-logloss:0.62435	validation-auc:0.96930	validation-aucpr:0.97319
[8]	validation-logloss:0.61767	validation-auc:0.96872	validation-aucpr:0.97269
[9]	validation-logloss:0.61097	validation-auc:0.96871	validation-aucpr:0.97268
[10]	validation-logloss:0.60354	validation-auc:0.96905	validation-aucpr:0.97292
[11]	validation-logloss:0.59705	validation-auc:0.96901	validation-aucpr:0.97291
[12]	validation-logloss:0.58995	validation-auc:0.96915	validation-aucpr:0.97301
[13]	validation-logloss:0.58301	validation-auc:0.96936	validation-aucpr:0.97314
[14]	validation-logloss:0.57659	validation-auc:0.96979	validation-aucpr:0.97337
[15]	validation-logloss:0.57050	validation-auc:0.96952	validation-aucpr:0.97313
[16]	validation-logloss:0.56466	validation-auc:0.96953	validation-aucpr:0.97333
[17]	validation-logloss:0.55819	validation-auc:0.96956	validation-aucpr:0.97336
[18]	validation-logloss:0.55274	validation-auc:0.96947	validation-aucpr:0.97323
[19]	validation-logloss:0.54686	validation-auc:0.96967	validation-aucpr:0.97338
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[30]	validation-logloss:0.48791	validation-auc:0.97045	validation-aucpr:0.97399
[31]	validation-logloss:0.48300	validation-auc:0.97069	validation-aucpr:0.97418
[32]	validation-logloss:0.47877	validation-auc:0.97068	validation-aucpr:0.97415
[33]	validation-logloss:0.47451	validation-auc:0.97063	validation-aucpr:0.97411
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[46]	validation-logloss:0.42148	validation-auc:0.97144	validation-aucpr:0.97453
[47]	validation-logloss:0.41774	validation-auc:0.97153	validation-aucpr:0.97461
[48]	validation-logloss:0.41411	validation-auc:0.97168	validation-aucpr:0.97471
[49]	validation-logloss:0.41058	validation-auc:0.97163	validation-aucpr:0.97465
[50]	validation-logloss:0.40752	validation-auc:0.97148	validation-aucpr:0.97454
[51]	validation-logloss:0.40430	validation-auc:0.97150	validation-aucpr:0.97454
[52]	validation-logloss:0.40124	validation-auc:0.97153	validation-aucpr:0.97456
[53]	validation-logloss:0.39829	validation-auc:0.97147	validation-aucpr:0.97450
[54]	validation-logloss:0.39508	validation-auc:0.97146	validation-aucpr:0.97450
[55]	validation-logloss:0.39189	validation-auc:0.97157	validation-aucpr:0.97459
[56]	validation-logloss:0.38870	validation-auc:0.97158	validation-aucpr:0.97459
[57]	validation-logloss:0.38557	validation-auc:0.97160	validation-aucpr:0.97462
[58]	validation-logloss:0.38257	validation-auc:0.97156	validation-aucpr:0.97459
[59]	validation-logloss:0.37948	validation-auc:0.97155	validation-aucpr:0.97459
[60]	validation-logloss:0.37650	validation-auc:0.97161	validation-aucpr:0.97461
[61]	validation-logloss:0.37362	validation-auc:0.97164	validation-aucpr:0.97464
[62]	validation-logloss:0.37076	validation-auc:0.97167	validation-aucpr:0.97466
[63]	validation-logloss:0.36786	validation-auc:0.97176	validation-aucpr:0.97474
[64]	validation-logloss:0.36537	validation-auc:0.97176	validation-aucpr:0.97442
[65]	validation-logloss:0.36256	validation-auc:0.97181	validation-aucpr:0.97447
{'best_iteration': '63', 'best_score': '0.9747379643077329'}
Trial 55, Fold 2: Log loss = 0.36256205802843033, Average precision = 0.9745009545862788, ROC-AUC = 0.9718087516589826, Elapsed Time = 2.5463577000009536 seconds
Trial 55, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 55, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[0]	validation-logloss:0.68367	validation-auc:0.94844	validation-aucpr:0.95205
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[2]	validation-logloss:0.66633	validation-auc:0.96459	validation-aucpr:0.96725
[3]	validation-logloss:0.65755	validation-auc:0.96635	validation-aucpr:0.96968
[4]	validation-logloss:0.64890	validation-auc:0.96834	validation-aucpr:0.97264
[5]	validation-logloss:0.64060	validation-auc:0.96913	validation-aucpr:0.97333
[6]	validation-logloss:0.63325	validation-auc:0.96940	validation-aucpr:0.97358
[7]	validation-logloss:0.62529	validation-auc:0.96949	validation-aucpr:0.97362
[8]	validation-logloss:0.61752	validation-auc:0.96956	validation-aucpr:0.97367
[9]	validation-logloss:0.61002	validation-auc:0.96977	validation-aucpr:0.97384
[10]	validation-logloss:0.60243	validation-auc:0.96986	validation-aucpr:0.97397
[11]	validation-logloss:0.59581	validation-auc:0.96985	validation-aucpr:0.97399
[12]	validation-logloss:0.58874	validation-auc:0.97002	validation-aucpr:0.97412
[13]	validation-logloss:0.58175	validation-auc:0.97018	validation-aucpr:0.97423
[14]	validation-logloss:0.57510	validation-auc:0.97011	validation-aucpr:0.97416
[15]	validation-logloss:0.56841	validation-auc:0.97001	validation-aucpr:0.97413
[16]	validation-logloss:0.56233	validation-auc:0.97001	validation-aucpr:0.97412
[17]	validation-logloss:0.55655	validation-auc:0.96984	validation-aucpr:0.97389
[18]	validation-logloss:0.55107	validation-auc:0.96974	validation-aucpr:0.97377
[19]	validation-logloss:0.54477	validation-auc:0.96993	validation-aucpr:0.97397
[20]	validation-logloss:0.53940	validation-auc:0.96983	validation-aucpr:0.97387
[21]	validation-logloss:0.53340	validation-auc:0.96998	validation-aucpr:0.97407
[22]	validation-logloss:0.52746	validation-auc:0.97005	validation-aucpr:0.97412
[23]	validation-logloss:0.52231	validation-auc:0.97007	validation-aucpr:0.97428
[24]	validation-logloss:0.51673	validation-auc:0.97007	validation-aucpr:0.97427
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[26]	validation-logloss:0.50594	validation-auc:0.97026	validation-aucpr:0.97443
[27]	validation-logloss:0.50056	validation-auc:0.97037	validation-aucpr:0.97454
[28]	validation-logloss:0.49532	validation-auc:0.97051	validation-aucpr:0.97467
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[30]	validation-logloss:0.48527	validation-auc:0.97066	validation-aucpr:0.97478
[31]	validation-logloss:0.48084	validation-auc:0.97066	validation-aucpr:0.97478
[32]	validation-logloss:0.47600	validation-auc:0.97076	validation-aucpr:0.97486
[33]	validation-logloss:0.47167	validation-auc:0.97089	validation-aucpr:0.97495
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[47]	validation-logloss:0.41567	validation-auc:0.97179	validation-aucpr:0.97560
[48]	validation-logloss:0.41202	validation-auc:0.97182	validation-aucpr:0.97562
[49]	validation-logloss:0.40902	validation-auc:0.97172	validation-aucpr:0.97553
[50]	validation-logloss:0.40569	validation-auc:0.97179	validation-aucpr:0.97559
[51]	validation-logloss:0.40276	validation-auc:0.97168	validation-aucpr:0.97548
[52]	validation-logloss:0.39936	validation-auc:0.97166	validation-aucpr:0.97548
[53]	validation-logloss:0.39603	validation-auc:0.97169	validation-aucpr:0.97546
[54]	validation-logloss:0.39292	validation-auc:0.97166	validation-aucpr:0.97543
[55]	validation-logloss:0.38959	validation-auc:0.97179	validation-aucpr:0.97554
[56]	validation-logloss:0.38648	validation-auc:0.97186	validation-aucpr:0.97562
[57]	validation-logloss:0.38334	validation-auc:0.97190	validation-aucpr:0.97564
[58]	validation-logloss:0.38030	validation-auc:0.97194	validation-aucpr:0.97566
[59]	validation-logloss:0.37720	validation-auc:0.97202	validation-aucpr:0.97572
[60]	validation-logloss:0.37423	validation-auc:0.97207	validation-aucpr:0.97576
[61]	validation-logloss:0.37127	validation-auc:0.97211	validation-aucpr:0.97577
[62]	validation-logloss:0.36839	validation-auc:0.97211	validation-aucpr:0.97578
[63]	validation-logloss:0.36550	validation-auc:0.97218	validation-aucpr:0.97583
[64]	validation-logloss:0.36271	validation-auc:0.97222	validation-aucpr:0.97586
[65]	validation-logloss:0.36009	validation-auc:0.97215	validation-aucpr:0.97580
{'best_iteration': '64', 'best_score': '0.9758630569815583'}
Trial 55, Fold 3: Log loss = 0.3600912741941957, Average precision = 0.9758044092545942, ROC-AUC = 0.9721529155550848, Elapsed Time = 2.635833000000275 seconds
Trial 55, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 55, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[0]	validation-logloss:0.68357	validation-auc:0.94645	validation-aucpr:0.93736
[1]	validation-logloss:0.67441	validation-auc:0.95971	validation-aucpr:0.95893
[2]	validation-logloss:0.66542	validation-auc:0.96524	validation-aucpr:0.96967
[3]	validation-logloss:0.65689	validation-auc:0.96651	validation-aucpr:0.97207
[4]	validation-logloss:0.64943	validation-auc:0.96551	validation-aucpr:0.97122
[5]	validation-logloss:0.64171	validation-auc:0.96616	validation-aucpr:0.97169
[6]	validation-logloss:0.63356	validation-auc:0.96677	validation-aucpr:0.97227
[7]	validation-logloss:0.62636	validation-auc:0.96672	validation-aucpr:0.97220
[8]	validation-logloss:0.61846	validation-auc:0.96715	validation-aucpr:0.97258
[9]	validation-logloss:0.61082	validation-auc:0.96777	validation-aucpr:0.97307
[10]	validation-logloss:0.60342	validation-auc:0.96808	validation-aucpr:0.97342
[11]	validation-logloss:0.59610	validation-auc:0.96819	validation-aucpr:0.97354
[12]	validation-logloss:0.58938	validation-auc:0.96830	validation-aucpr:0.97369
[13]	validation-logloss:0.58222	validation-auc:0.96892	validation-aucpr:0.97413
[14]	validation-logloss:0.57540	validation-auc:0.96901	validation-aucpr:0.97423
[15]	validation-logloss:0.56874	validation-auc:0.96927	validation-aucpr:0.97442
[16]	validation-logloss:0.56217	validation-auc:0.96940	validation-aucpr:0.97451
[17]	validation-logloss:0.55570	validation-auc:0.96963	validation-aucpr:0.97471
[18]	validation-logloss:0.55019	validation-auc:0.96935	validation-aucpr:0.97448
[19]	validation-logloss:0.54421	validation-auc:0.96941	validation-aucpr:0.97450
[20]	validation-logloss:0.53826	validation-auc:0.96943	validation-aucpr:0.97450
[21]	validation-logloss:0.53233	validation-auc:0.96963	validation-aucpr:0.97465
[22]	validation-logloss:0.52676	validation-auc:0.96955	validation-aucpr:0.97460
[23]	validation-logloss:0.52171	validation-auc:0.96943	validation-aucpr:0.97450
[24]	validation-logloss:0.51609	validation-auc:0.96958	validation-aucpr:0.97460
[25]	validation-logloss:0.51064	validation-auc:0.96965	validation-aucpr:0.97468
[26]	validation-logloss:0.50524	validation-auc:0.96973	validation-aucpr:0.97476
[27]	validation-logloss:0.50066	validation-auc:0.96964	validation-aucpr:0.97468
[28]	validation-logloss:0.49553	validation-auc:0.96980	validation-aucpr:0.97485
[29]	validation-logloss:0.49046	validation-auc:0.96993	validation-aucpr:0.97492
[30]	validation-logloss:0.48614	validation-auc:0.96981	validation-aucpr:0.97482
[31]	validation-logloss:0.48137	validation-auc:0.96985	validation-aucpr:0.97485
[32]	validation-logloss:0.47655	validation-auc:0.96989	validation-aucpr:0.97489
[33]	validation-logloss:0.47188	validation-auc:0.97001	validation-aucpr:0.97496
[34]	validation-logloss:0.46783	validation-auc:0.96995	validation-aucpr:0.97490
[35]	validation-logloss:0.46399	validation-auc:0.96977	validation-aucpr:0.97477
[36]	validation-logloss:0.45971	validation-auc:0.96975	validation-aucpr:0.97475
[37]	validation-logloss:0.45530	validation-auc:0.96986	validation-aucpr:0.97483
[38]	validation-logloss:0.45109	validation-auc:0.96987	validation-aucpr:0.97485
[39]	validation-logloss:0.44715	validation-auc:0.96989	validation-aucpr:0.97487
[40]	validation-logloss:0.44363	validation-auc:0.96981	validation-aucpr:0.97479
[41]	validation-logloss:0.43978	validation-auc:0.96976	validation-aucpr:0.97476
[42]	validation-logloss:0.43572	validation-auc:0.96991	validation-aucpr:0.97487
[43]	validation-logloss:0.43178	validation-auc:0.96985	validation-aucpr:0.97482
[44]	validation-logloss:0.42800	validation-auc:0.96988	validation-aucpr:0.97484
[45]	validation-logloss:0.42423	validation-auc:0.96984	validation-aucpr:0.97480
[46]	validation-logloss:0.42099	validation-auc:0.96976	validation-aucpr:0.97475
[47]	validation-logloss:0.41723	validation-auc:0.96997	validation-aucpr:0.97490
[48]	validation-logloss:0.41378	validation-auc:0.96999	validation-aucpr:0.97493
[49]	validation-logloss:0.41063	validation-auc:0.96984	validation-aucpr:0.97480
[50]	validation-logloss:0.40721	validation-auc:0.96990	validation-aucpr:0.97485
[51]	validation-logloss:0.40412	validation-auc:0.96993	validation-aucpr:0.97487
[52]	validation-logloss:0.40121	validation-auc:0.96989	validation-aucpr:0.97483
[53]	validation-logloss:0.39783	validation-auc:0.97004	validation-aucpr:0.97496
[54]	validation-logloss:0.39462	validation-auc:0.97001	validation-aucpr:0.97496
[55]	validation-logloss:0.39139	validation-auc:0.97003	validation-aucpr:0.97499
[56]	validation-logloss:0.38867	validation-auc:0.97003	validation-aucpr:0.97497
[57]	validation-logloss:0.38549	validation-auc:0.97019	validation-aucpr:0.97510
[58]	validation-logloss:0.38243	validation-auc:0.97025	validation-aucpr:0.97513
[59]	validation-logloss:0.37947	validation-auc:0.97025	validation-aucpr:0.97514
[60]	validation-logloss:0.37661	validation-auc:0.97039	validation-aucpr:0.97525
[61]	validation-logloss:0.37386	validation-auc:0.97041	validation-aucpr:0.97527
[62]	validation-logloss:0.37095	validation-auc:0.97051	validation-aucpr:0.97535
[63]	validation-logloss:0.36810	validation-auc:0.97054	validation-aucpr:0.97537
[64]	validation-logloss:0.36534	validation-auc:0.97065	validation-aucpr:0.97545
[65]	validation-logloss:0.36298	validation-auc:0.97060	validation-aucpr:0.97541
{'best_iteration': '64', 'best_score': '0.9754461952539737'}
Trial 55, Fold 4: Log loss = 0.3629805179052722, Average precision = 0.9754157021253169, ROC-AUC = 0.9705993333190268, Elapsed Time = 2.696198199999344 seconds
Trial 55, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 55, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[0]	validation-logloss:0.68385	validation-auc:0.93955	validation-aucpr:0.93653
[1]	validation-logloss:0.67499	validation-auc:0.95723	validation-aucpr:0.95397
[2]	validation-logloss:0.66600	validation-auc:0.96296	validation-aucpr:0.96347
[3]	validation-logloss:0.65731	validation-auc:0.96436	validation-aucpr:0.96781
[4]	validation-logloss:0.64894	validation-auc:0.96514	validation-aucpr:0.97009
[5]	validation-logloss:0.64068	validation-auc:0.96584	validation-aucpr:0.97050
[6]	validation-logloss:0.63275	validation-auc:0.96600	validation-aucpr:0.97010
[7]	validation-logloss:0.62498	validation-auc:0.96649	validation-aucpr:0.97041
[8]	validation-logloss:0.61727	validation-auc:0.96682	validation-aucpr:0.97070
[9]	validation-logloss:0.60973	validation-auc:0.96702	validation-aucpr:0.97098
[10]	validation-logloss:0.60228	validation-auc:0.96751	validation-aucpr:0.97138
[11]	validation-logloss:0.59581	validation-auc:0.96772	validation-aucpr:0.97167
[12]	validation-logloss:0.58935	validation-auc:0.96767	validation-aucpr:0.97154
[13]	validation-logloss:0.58243	validation-auc:0.96786	validation-aucpr:0.97167
[14]	validation-logloss:0.57567	validation-auc:0.96763	validation-aucpr:0.97151
[15]	validation-logloss:0.56914	validation-auc:0.96764	validation-aucpr:0.97150
[16]	validation-logloss:0.56281	validation-auc:0.96753	validation-aucpr:0.97150
[17]	validation-logloss:0.55710	validation-auc:0.96746	validation-aucpr:0.97147
[18]	validation-logloss:0.55134	validation-auc:0.96749	validation-aucpr:0.97147
[19]	validation-logloss:0.54528	validation-auc:0.96743	validation-aucpr:0.97145
[20]	validation-logloss:0.53932	validation-auc:0.96776	validation-aucpr:0.97162
[21]	validation-logloss:0.53347	validation-auc:0.96793	validation-aucpr:0.97171
[22]	validation-logloss:0.52840	validation-auc:0.96769	validation-aucpr:0.97103
[23]	validation-logloss:0.52279	validation-auc:0.96795	validation-aucpr:0.97121
[24]	validation-logloss:0.51784	validation-auc:0.96799	validation-aucpr:0.97123
[25]	validation-logloss:0.51243	validation-auc:0.96798	validation-aucpr:0.97122
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[27]	validation-logloss:0.50186	validation-auc:0.96823	validation-aucpr:0.97140
[28]	validation-logloss:0.49674	validation-auc:0.96847	validation-aucpr:0.97160
[29]	validation-logloss:0.49173	validation-auc:0.96860	validation-aucpr:0.97176
[30]	validation-logloss:0.48749	validation-auc:0.96857	validation-aucpr:0.97308
[31]	validation-logloss:0.48261	validation-auc:0.96876	validation-aucpr:0.97323
[32]	validation-logloss:0.47780	validation-auc:0.96896	validation-aucpr:0.97340
[33]	validation-logloss:0.47318	validation-auc:0.96895	validation-aucpr:0.97340
[34]	validation-logloss:0.46867	validation-auc:0.96912	validation-aucpr:0.97353
[35]	validation-logloss:0.46418	validation-auc:0.96928	validation-aucpr:0.97366
[36]	validation-logloss:0.45982	validation-auc:0.96938	validation-aucpr:0.97373
[37]	validation-logloss:0.45602	validation-auc:0.96920	validation-aucpr:0.97359
[38]	validation-logloss:0.45204	validation-auc:0.96934	validation-aucpr:0.97371
[39]	validation-logloss:0.44809	validation-auc:0.96921	validation-aucpr:0.97358
[40]	validation-logloss:0.44406	validation-auc:0.96921	validation-aucpr:0.97358
[41]	validation-logloss:0.44004	validation-auc:0.96933	validation-aucpr:0.97367
[42]	validation-logloss:0.43604	validation-auc:0.96953	validation-aucpr:0.97381
[43]	validation-logloss:0.43216	validation-auc:0.96955	validation-aucpr:0.97383
[44]	validation-logloss:0.42831	validation-auc:0.96962	validation-aucpr:0.97389
[45]	validation-logloss:0.42495	validation-auc:0.96968	validation-aucpr:0.97390
[46]	validation-logloss:0.42125	validation-auc:0.96969	validation-aucpr:0.97391
[47]	validation-logloss:0.41763	validation-auc:0.96984	validation-aucpr:0.97402
[48]	validation-logloss:0.41402	validation-auc:0.96994	validation-aucpr:0.97411
[49]	validation-logloss:0.41055	validation-auc:0.97003	validation-aucpr:0.97417
[50]	validation-logloss:0.40756	validation-auc:0.97000	validation-aucpr:0.97411
[51]	validation-logloss:0.40432	validation-auc:0.96996	validation-aucpr:0.97410
[52]	validation-logloss:0.40095	validation-auc:0.97006	validation-aucpr:0.97417
[53]	validation-logloss:0.39771	validation-auc:0.97018	validation-aucpr:0.97425
[54]	validation-logloss:0.39447	validation-auc:0.97026	validation-aucpr:0.97430
[55]	validation-logloss:0.39135	validation-auc:0.97032	validation-aucpr:0.97434
[56]	validation-logloss:0.38826	validation-auc:0.97033	validation-aucpr:0.97436
[57]	validation-logloss:0.38565	validation-auc:0.97029	validation-aucpr:0.97432
[58]	validation-logloss:0.38303	validation-auc:0.97026	validation-aucpr:0.97429
[59]	validation-logloss:0.38014	validation-auc:0.97032	validation-aucpr:0.97431
[60]	validation-logloss:0.37728	validation-auc:0.97039	validation-aucpr:0.97440
[61]	validation-logloss:0.37442	validation-auc:0.97043	validation-aucpr:0.97444
[62]	validation-logloss:0.37163	validation-auc:0.97043	validation-aucpr:0.97444
[63]	validation-logloss:0.36893	validation-auc:0.97043	validation-aucpr:0.97443
[64]	validation-logloss:0.36624	validation-auc:0.97043	validation-aucpr:0.97444
[65]	validation-logloss:0.36350	validation-auc:0.97052	validation-aucpr:0.97451
{'best_iteration': '65', 'best_score': '0.9745086390557987'}
Trial 55, Fold 5: Log loss = 0.3635039653222269, Average precision = 0.974512820454051, ROC-AUC = 0.9705224903851514, Elapsed Time = 2.5660344999996596 seconds
Optimization Progress:  56%|#####6    | 56/100 [2:54:45<49:55, 68.08s/it]  
Trial 56, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 56, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[20:53:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.66058	validation-auc:0.94692	validation-aucpr:0.93916
[20:53:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.63109	validation-auc:0.96084	validation-aucpr:0.96099
[20:53:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.60382	validation-auc:0.96396	validation-aucpr:0.96927
[20:53:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.57872	validation-auc:0.96459	validation-aucpr:0.97005
[20:53:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.55551	validation-auc:0.96481	validation-aucpr:0.96872
[20:53:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5]	validation-logloss:0.53392	validation-auc:0.96602	validation-aucpr:0.96837
[20:53:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6]	validation-logloss:0.51583	validation-auc:0.96631	validation-aucpr:0.96879
[20:53:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7]	validation-logloss:0.49709	validation-auc:0.96688	validation-aucpr:0.96920
[20:53:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8]	validation-logloss:0.47959	validation-auc:0.96666	validation-aucpr:0.96512
[20:53:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9]	validation-logloss:0.46324	validation-auc:0.96772	validation-aucpr:0.96759
[20:53:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10]	validation-logloss:0.44945	validation-auc:0.96784	validation-aucpr:0.96776
[20:53:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11]	validation-logloss:0.43532	validation-auc:0.96785	validation-aucpr:0.96779
[20:53:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12]	validation-logloss:0.42201	validation-auc:0.96803	validation-aucpr:0.96801
[20:53:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13]	validation-logloss:0.41083	validation-auc:0.96764	validation-aucpr:0.96620
[20:53:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14]	validation-logloss:0.39912	validation-auc:0.96780	validation-aucpr:0.96632
[20:53:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15]	validation-logloss:0.38980	validation-auc:0.96804	validation-aucpr:0.96812
[20:53:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16]	validation-logloss:0.37941	validation-auc:0.96844	validation-aucpr:0.97063
[20:53:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17]	validation-logloss:0.36946	validation-auc:0.96862	validation-aucpr:0.97080
[20:53:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18]	validation-logloss:0.36019	validation-auc:0.96869	validation-aucpr:0.97084
[20:53:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19]	validation-logloss:0.35132	validation-auc:0.96868	validation-aucpr:0.97084
[20:53:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20]	validation-logloss:0.34285	validation-auc:0.96876	validation-aucpr:0.97089
[20:53:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21]	validation-logloss:0.33569	validation-auc:0.96882	validation-aucpr:0.97087
[20:53:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22]	validation-logloss:0.32816	validation-auc:0.96931	validation-aucpr:0.97392
[20:53:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23]	validation-logloss:0.32117	validation-auc:0.96949	validation-aucpr:0.97403
[20:53:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24]	validation-logloss:0.31469	validation-auc:0.96947	validation-aucpr:0.97402
[20:53:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25]	validation-logloss:0.30918	validation-auc:0.96954	validation-aucpr:0.97402
[20:53:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26]	validation-logloss:0.30378	validation-auc:0.96968	validation-aucpr:0.97405
[20:53:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27]	validation-logloss:0.29798	validation-auc:0.96978	validation-aucpr:0.97410
[20:53:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28]	validation-logloss:0.29254	validation-auc:0.96998	validation-aucpr:0.97427
[20:53:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29]	validation-logloss:0.28744	validation-auc:0.96995	validation-aucpr:0.97427
[20:53:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30]	validation-logloss:0.28246	validation-auc:0.97011	validation-aucpr:0.97435
[20:53:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31]	validation-logloss:0.27798	validation-auc:0.97016	validation-aucpr:0.97441
[20:53:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32]	validation-logloss:0.27340	validation-auc:0.97035	validation-aucpr:0.97454
[20:53:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33]	validation-logloss:0.26919	validation-auc:0.97052	validation-aucpr:0.97466
[20:53:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34]	validation-logloss:0.26531	validation-auc:0.97049	validation-aucpr:0.97463
[20:53:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35]	validation-logloss:0.26168	validation-auc:0.97049	validation-aucpr:0.97360
[20:53:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36]	validation-logloss:0.25811	validation-auc:0.97055	validation-aucpr:0.97366
[20:53:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37]	validation-logloss:0.25475	validation-auc:0.97063	validation-aucpr:0.97368
[20:53:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38]	validation-logloss:0.25150	validation-auc:0.97071	validation-aucpr:0.97374
[20:53:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39]	validation-logloss:0.24857	validation-auc:0.97082	validation-aucpr:0.97388
[20:53:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40]	validation-logloss:0.24594	validation-auc:0.97087	validation-aucpr:0.97532
[20:53:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41]	validation-logloss:0.24311	validation-auc:0.97096	validation-aucpr:0.97539
[20:53:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42]	validation-logloss:0.24062	validation-auc:0.97093	validation-aucpr:0.97537
{'best_iteration': '41', 'best_score': '0.975389821361592'}
Trial 56, Fold 1: Log loss = 0.24061675051511633, Average precision = 0.9753669075707132, ROC-AUC = 0.9709288952457424, Elapsed Time = 6.691964800000278 seconds
Trial 56, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 56, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[20:53:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.66045	validation-auc:0.95472	validation-aucpr:0.94123
[20:53:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.63073	validation-auc:0.96408	validation-aucpr:0.95982
[20:53:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.60338	validation-auc:0.96654	validation-aucpr:0.96890
[20:53:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.57840	validation-auc:0.96627	validation-aucpr:0.96846
[20:53:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.55703	validation-auc:0.96781	validation-aucpr:0.97159
[20:53:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5]	validation-logloss:0.53731	validation-auc:0.96831	validation-aucpr:0.97175
[20:53:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6]	validation-logloss:0.51663	validation-auc:0.96931	validation-aucpr:0.97248
[20:53:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7]	validation-logloss:0.49760	validation-auc:0.96978	validation-aucpr:0.97282
[20:53:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8]	validation-logloss:0.48159	validation-auc:0.97049	validation-aucpr:0.97375
[20:53:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9]	validation-logloss:0.46460	validation-auc:0.97134	validation-aucpr:0.97434
[20:53:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10]	validation-logloss:0.45080	validation-auc:0.97079	validation-aucpr:0.97396
[20:53:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11]	validation-logloss:0.43769	validation-auc:0.97068	validation-aucpr:0.97377
[20:53:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12]	validation-logloss:0.42383	validation-auc:0.97101	validation-aucpr:0.97409
[20:53:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13]	validation-logloss:0.41090	validation-auc:0.97131	validation-aucpr:0.97438
[20:53:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14]	validation-logloss:0.39882	validation-auc:0.97155	validation-aucpr:0.97458
[20:53:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15]	validation-logloss:0.38737	validation-auc:0.97165	validation-aucpr:0.97465
[20:53:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16]	validation-logloss:0.37791	validation-auc:0.97158	validation-aucpr:0.97457
[20:53:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17]	validation-logloss:0.36773	validation-auc:0.97185	validation-aucpr:0.97478
[20:53:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18]	validation-logloss:0.35819	validation-auc:0.97171	validation-aucpr:0.97476
[20:53:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19]	validation-logloss:0.34923	validation-auc:0.97189	validation-aucpr:0.97497
[20:53:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20]	validation-logloss:0.34054	validation-auc:0.97201	validation-aucpr:0.97507
[20:53:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21]	validation-logloss:0.33265	validation-auc:0.97192	validation-aucpr:0.97500
[20:53:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22]	validation-logloss:0.32505	validation-auc:0.97201	validation-aucpr:0.97505
[20:53:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23]	validation-logloss:0.31776	validation-auc:0.97204	validation-aucpr:0.97508
[20:53:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24]	validation-logloss:0.31084	validation-auc:0.97223	validation-aucpr:0.97528
[20:53:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25]	validation-logloss:0.30420	validation-auc:0.97230	validation-aucpr:0.97530
[20:53:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26]	validation-logloss:0.29811	validation-auc:0.97243	validation-aucpr:0.97550
[20:53:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27]	validation-logloss:0.29235	validation-auc:0.97246	validation-aucpr:0.97542
[20:53:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28]	validation-logloss:0.28689	validation-auc:0.97242	validation-aucpr:0.97538
[20:53:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29]	validation-logloss:0.28180	validation-auc:0.97235	validation-aucpr:0.97531
[20:53:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30]	validation-logloss:0.27681	validation-auc:0.97237	validation-aucpr:0.97545
[20:53:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31]	validation-logloss:0.27230	validation-auc:0.97229	validation-aucpr:0.97537
[20:53:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32]	validation-logloss:0.26786	validation-auc:0.97232	validation-aucpr:0.97539
[20:53:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33]	validation-logloss:0.26351	validation-auc:0.97247	validation-aucpr:0.97551
[20:53:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34]	validation-logloss:0.25952	validation-auc:0.97252	validation-aucpr:0.97553
[20:53:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35]	validation-logloss:0.25552	validation-auc:0.97273	validation-aucpr:0.97568
[20:53:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36]	validation-logloss:0.25187	validation-auc:0.97280	validation-aucpr:0.97575
[20:53:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37]	validation-logloss:0.24838	validation-auc:0.97275	validation-aucpr:0.97574
[20:53:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38]	validation-logloss:0.24493	validation-auc:0.97283	validation-aucpr:0.97580
[20:53:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39]	validation-logloss:0.24196	validation-auc:0.97270	validation-aucpr:0.97571
[20:53:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40]	validation-logloss:0.23894	validation-auc:0.97281	validation-aucpr:0.97577
[20:53:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41]	validation-logloss:0.23616	validation-auc:0.97280	validation-aucpr:0.97573
[20:53:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42]	validation-logloss:0.23346	validation-auc:0.97279	validation-aucpr:0.97571
{'best_iteration': '38', 'best_score': '0.9757950282631109'}
Trial 56, Fold 2: Log loss = 0.23345912524895623, Average precision = 0.9755510875905027, ROC-AUC = 0.9727877524969339, Elapsed Time = 5.648318900002778 seconds
Trial 56, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 56, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[20:53:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.66060	validation-auc:0.95031	validation-aucpr:0.94385
[20:53:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.63067	validation-auc:0.95917	validation-aucpr:0.94856
[20:53:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.60336	validation-auc:0.96377	validation-aucpr:0.95967
[20:53:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.57806	validation-auc:0.96458	validation-aucpr:0.96060
[20:53:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.55470	validation-auc:0.96615	validation-aucpr:0.96471
[20:53:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5]	validation-logloss:0.53304	validation-auc:0.96699	validation-aucpr:0.96866
[20:53:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6]	validation-logloss:0.51313	validation-auc:0.96758	validation-aucpr:0.97208
[20:53:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7]	validation-logloss:0.49459	validation-auc:0.96756	validation-aucpr:0.96980
[20:53:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8]	validation-logloss:0.47726	validation-auc:0.96761	validation-aucpr:0.96986
[20:53:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9]	validation-logloss:0.46110	validation-auc:0.96798	validation-aucpr:0.97001
[20:53:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10]	validation-logloss:0.44581	validation-auc:0.96831	validation-aucpr:0.97039
[20:53:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11]	validation-logloss:0.43170	validation-auc:0.96861	validation-aucpr:0.97061
[20:53:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12]	validation-logloss:0.41833	validation-auc:0.96877	validation-aucpr:0.97076
[20:53:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13]	validation-logloss:0.40571	validation-auc:0.96916	validation-aucpr:0.96863
[20:53:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14]	validation-logloss:0.39386	validation-auc:0.96949	validation-aucpr:0.97143
[20:53:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15]	validation-logloss:0.38283	validation-auc:0.96967	validation-aucpr:0.97122
[20:53:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16]	validation-logloss:0.37247	validation-auc:0.96963	validation-aucpr:0.97122
[20:53:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17]	validation-logloss:0.36278	validation-auc:0.96957	validation-aucpr:0.97117
[20:53:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18]	validation-logloss:0.35351	validation-auc:0.97014	validation-aucpr:0.97394
[20:53:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19]	validation-logloss:0.34494	validation-auc:0.97015	validation-aucpr:0.97395
[20:53:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20]	validation-logloss:0.33653	validation-auc:0.97045	validation-aucpr:0.97409
[20:53:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21]	validation-logloss:0.32933	validation-auc:0.97083	validation-aucpr:0.97432
[20:53:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22]	validation-logloss:0.32196	validation-auc:0.97096	validation-aucpr:0.97441
[20:53:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23]	validation-logloss:0.31517	validation-auc:0.97086	validation-aucpr:0.97435
[20:53:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24]	validation-logloss:0.30871	validation-auc:0.97096	validation-aucpr:0.97442
[20:53:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25]	validation-logloss:0.30265	validation-auc:0.97099	validation-aucpr:0.97447
[20:53:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26]	validation-logloss:0.29672	validation-auc:0.97122	validation-aucpr:0.97462
[20:53:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27]	validation-logloss:0.29108	validation-auc:0.97139	validation-aucpr:0.97475
[20:53:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28]	validation-logloss:0.28572	validation-auc:0.97152	validation-aucpr:0.97488
[20:53:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29]	validation-logloss:0.28100	validation-auc:0.97170	validation-aucpr:0.97498
[20:53:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30]	validation-logloss:0.27631	validation-auc:0.97161	validation-aucpr:0.97495
[20:53:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31]	validation-logloss:0.27196	validation-auc:0.97159	validation-aucpr:0.97493
[20:53:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32]	validation-logloss:0.26802	validation-auc:0.97176	validation-aucpr:0.97503
[20:53:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33]	validation-logloss:0.26408	validation-auc:0.97169	validation-aucpr:0.97496
[20:53:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34]	validation-logloss:0.26034	validation-auc:0.97168	validation-aucpr:0.97487
[20:53:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35]	validation-logloss:0.25657	validation-auc:0.97176	validation-aucpr:0.97500
[20:53:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36]	validation-logloss:0.25313	validation-auc:0.97175	validation-aucpr:0.97495
[20:53:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37]	validation-logloss:0.24996	validation-auc:0.97173	validation-aucpr:0.97492
[20:53:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38]	validation-logloss:0.24674	validation-auc:0.97183	validation-aucpr:0.97499
[20:53:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39]	validation-logloss:0.24363	validation-auc:0.97189	validation-aucpr:0.97503
[20:53:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40]	validation-logloss:0.24092	validation-auc:0.97195	validation-aucpr:0.97507
[20:53:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41]	validation-logloss:0.23835	validation-auc:0.97200	validation-aucpr:0.97509
[20:53:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42]	validation-logloss:0.23589	validation-auc:0.97197	validation-aucpr:0.97508
{'best_iteration': '41', 'best_score': '0.9750947247890471'}
Trial 56, Fold 3: Log loss = 0.2358894526760947, Average precision = 0.9749897931718626, ROC-AUC = 0.9719713554959502, Elapsed Time = 5.436695800002781 seconds
Trial 56, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 56, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[20:53:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.66077	validation-auc:0.94342	validation-aucpr:0.94184
[20:53:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.63116	validation-auc:0.95862	validation-aucpr:0.95123
[20:53:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.60602	validation-auc:0.96504	validation-aucpr:0.96958
[20:53:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.58052	validation-auc:0.96631	validation-aucpr:0.97024
[20:53:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.55740	validation-auc:0.96677	validation-aucpr:0.97222
[20:53:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5]	validation-logloss:0.53578	validation-auc:0.96742	validation-aucpr:0.97254
[20:53:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6]	validation-logloss:0.51761	validation-auc:0.96740	validation-aucpr:0.97239
[20:53:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7]	validation-logloss:0.49867	validation-auc:0.96791	validation-aucpr:0.97161
[20:53:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8]	validation-logloss:0.48102	validation-auc:0.96826	validation-aucpr:0.97158
[20:53:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9]	validation-logloss:0.46433	validation-auc:0.96889	validation-aucpr:0.97365
[20:53:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10]	validation-logloss:0.44888	validation-auc:0.96888	validation-aucpr:0.97377
[20:53:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11]	validation-logloss:0.43418	validation-auc:0.96935	validation-aucpr:0.97324
[20:54:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12]	validation-logloss:0.42066	validation-auc:0.96965	validation-aucpr:0.97324
[20:54:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13]	validation-logloss:0.40923	validation-auc:0.96938	validation-aucpr:0.97291
[20:54:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14]	validation-logloss:0.39731	validation-auc:0.96956	validation-aucpr:0.97429
[20:54:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15]	validation-logloss:0.38595	validation-auc:0.96974	validation-aucpr:0.97443
[20:54:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16]	validation-logloss:0.37533	validation-auc:0.97000	validation-aucpr:0.97460
[20:54:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17]	validation-logloss:0.36524	validation-auc:0.97021	validation-aucpr:0.97394
[20:54:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18]	validation-logloss:0.35583	validation-auc:0.97022	validation-aucpr:0.97395
[20:54:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19]	validation-logloss:0.34687	validation-auc:0.97044	validation-aucpr:0.97295
[20:54:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20]	validation-logloss:0.33857	validation-auc:0.97033	validation-aucpr:0.97275
[20:54:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21]	validation-logloss:0.33072	validation-auc:0.97044	validation-aucpr:0.97263
[20:54:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22]	validation-logloss:0.32319	validation-auc:0.97050	validation-aucpr:0.97290
[20:54:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23]	validation-logloss:0.31605	validation-auc:0.97069	validation-aucpr:0.97304
[20:54:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24]	validation-logloss:0.30930	validation-auc:0.97106	validation-aucpr:0.97471
[20:54:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25]	validation-logloss:0.30307	validation-auc:0.97121	validation-aucpr:0.97471
[20:54:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26]	validation-logloss:0.29700	validation-auc:0.97123	validation-aucpr:0.97472
[20:54:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27]	validation-logloss:0.29136	validation-auc:0.97113	validation-aucpr:0.97464
[20:54:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28]	validation-logloss:0.28610	validation-auc:0.97117	validation-aucpr:0.97456
[20:54:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29]	validation-logloss:0.28164	validation-auc:0.97111	validation-aucpr:0.97414
[20:54:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30]	validation-logloss:0.27684	validation-auc:0.97106	validation-aucpr:0.97412
[20:54:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31]	validation-logloss:0.27215	validation-auc:0.97132	validation-aucpr:0.97480
[20:54:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32]	validation-logloss:0.26796	validation-auc:0.97117	validation-aucpr:0.97463
[20:54:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33]	validation-logloss:0.26380	validation-auc:0.97129	validation-aucpr:0.97440
[20:54:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34]	validation-logloss:0.26036	validation-auc:0.97126	validation-aucpr:0.97406
[20:54:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35]	validation-logloss:0.25640	validation-auc:0.97169	validation-aucpr:0.97601
[20:54:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36]	validation-logloss:0.25290	validation-auc:0.97174	validation-aucpr:0.97606
[20:54:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37]	validation-logloss:0.25002	validation-auc:0.97170	validation-aucpr:0.97604
[20:54:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38]	validation-logloss:0.24672	validation-auc:0.97181	validation-aucpr:0.97614
[20:54:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39]	validation-logloss:0.24370	validation-auc:0.97194	validation-aucpr:0.97623
[20:54:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40]	validation-logloss:0.24093	validation-auc:0.97204	validation-aucpr:0.97631
[20:54:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41]	validation-logloss:0.23842	validation-auc:0.97192	validation-aucpr:0.97622
[20:54:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42]	validation-logloss:0.23582	validation-auc:0.97196	validation-aucpr:0.97626
{'best_iteration': '40', 'best_score': '0.976311607155693'}
Trial 56, Fold 4: Log loss = 0.23582202103210947, Average precision = 0.9762461607065359, ROC-AUC = 0.9719560624810738, Elapsed Time = 5.414685199997621 seconds
Trial 56, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 56, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[20:54:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.66050	validation-auc:0.94956	validation-aucpr:0.95245
[20:54:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.63061	validation-auc:0.96168	validation-aucpr:0.95996
[20:54:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.60337	validation-auc:0.96329	validation-aucpr:0.96246
[20:54:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.57825	validation-auc:0.96536	validation-aucpr:0.96247
[20:54:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.55729	validation-auc:0.96621	validation-aucpr:0.97090
[20:54:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5]	validation-logloss:0.53588	validation-auc:0.96742	validation-aucpr:0.97180
[20:54:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6]	validation-logloss:0.51580	validation-auc:0.96789	validation-aucpr:0.97089
[20:54:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7]	validation-logloss:0.49737	validation-auc:0.96792	validation-aucpr:0.97075
[20:54:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8]	validation-logloss:0.47988	validation-auc:0.96871	validation-aucpr:0.97288
[20:54:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9]	validation-logloss:0.46348	validation-auc:0.96919	validation-aucpr:0.97326
[20:54:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10]	validation-logloss:0.44839	validation-auc:0.96926	validation-aucpr:0.97332
[20:54:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11]	validation-logloss:0.43432	validation-auc:0.96961	validation-aucpr:0.97355
[20:54:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12]	validation-logloss:0.42096	validation-auc:0.96998	validation-aucpr:0.97396
[20:54:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13]	validation-logloss:0.40859	validation-auc:0.96989	validation-aucpr:0.97392
[20:54:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14]	validation-logloss:0.39686	validation-auc:0.96989	validation-aucpr:0.97394
[20:54:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15]	validation-logloss:0.38587	validation-auc:0.97000	validation-aucpr:0.97402
[20:54:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16]	validation-logloss:0.37564	validation-auc:0.97010	validation-aucpr:0.97415
[20:54:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17]	validation-logloss:0.36579	validation-auc:0.97035	validation-aucpr:0.97421
[20:54:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18]	validation-logloss:0.35666	validation-auc:0.97053	validation-aucpr:0.97435
[20:54:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19]	validation-logloss:0.34802	validation-auc:0.97072	validation-aucpr:0.97437
[20:54:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20]	validation-logloss:0.33994	validation-auc:0.97070	validation-aucpr:0.97434
[20:54:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21]	validation-logloss:0.33198	validation-auc:0.97092	validation-aucpr:0.97448
[20:54:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22]	validation-logloss:0.32470	validation-auc:0.97096	validation-aucpr:0.97461
[20:54:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23]	validation-logloss:0.31881	validation-auc:0.97090	validation-aucpr:0.97437
[20:54:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24]	validation-logloss:0.31242	validation-auc:0.97088	validation-aucpr:0.97436
[20:54:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25]	validation-logloss:0.30611	validation-auc:0.97102	validation-aucpr:0.97448
[20:54:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26]	validation-logloss:0.30035	validation-auc:0.97099	validation-aucpr:0.97446
[20:54:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27]	validation-logloss:0.29486	validation-auc:0.97104	validation-aucpr:0.97450
[20:54:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28]	validation-logloss:0.28960	validation-auc:0.97102	validation-aucpr:0.97447
[20:54:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29]	validation-logloss:0.28492	validation-auc:0.97100	validation-aucpr:0.97447
[20:54:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30]	validation-logloss:0.28035	validation-auc:0.97099	validation-aucpr:0.97447
[20:54:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31]	validation-logloss:0.27613	validation-auc:0.97095	validation-aucpr:0.97438
[20:54:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32]	validation-logloss:0.27192	validation-auc:0.97097	validation-aucpr:0.97441
[20:54:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33]	validation-logloss:0.26798	validation-auc:0.97100	validation-aucpr:0.97444
[20:54:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34]	validation-logloss:0.26446	validation-auc:0.97106	validation-aucpr:0.97446
[20:54:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35]	validation-logloss:0.26090	validation-auc:0.97103	validation-aucpr:0.97443
[20:54:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36]	validation-logloss:0.25730	validation-auc:0.97112	validation-aucpr:0.97453
[20:54:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37]	validation-logloss:0.25402	validation-auc:0.97119	validation-aucpr:0.97457
[20:54:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38]	validation-logloss:0.25090	validation-auc:0.97118	validation-aucpr:0.97456
[20:54:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39]	validation-logloss:0.24801	validation-auc:0.97112	validation-aucpr:0.97451
[20:54:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40]	validation-logloss:0.24531	validation-auc:0.97115	validation-aucpr:0.97454
[20:54:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41]	validation-logloss:0.24258	validation-auc:0.97119	validation-aucpr:0.97457
[20:54:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42]	validation-logloss:0.24017	validation-auc:0.97121	validation-aucpr:0.97457
{'best_iteration': '22', 'best_score': '0.9746072960651041'}
Trial 56, Fold 5: Log loss = 0.24016825303238032, Average precision = 0.9744614167127172, ROC-AUC = 0.9712105233821972, Elapsed Time = 5.48138789999939 seconds
Optimization Progress:  57%|#####6    | 57/100 [2:55:22<42:08, 58.81s/it]
Trial 57, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 57, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[0]	validation-logloss:0.67453	validation-auc:0.92966	validation-aucpr:0.92412
[1]	validation-logloss:0.65734	validation-auc:0.94220	validation-aucpr:0.94079
[2]	validation-logloss:0.64190	validation-auc:0.94381	validation-aucpr:0.94793
[3]	validation-logloss:0.62513	validation-auc:0.95034	validation-aucpr:0.95588
[4]	validation-logloss:0.61047	validation-auc:0.95093	validation-aucpr:0.95709
[5]	validation-logloss:0.59542	validation-auc:0.95285	validation-aucpr:0.95884
[6]	validation-logloss:0.58235	validation-auc:0.95329	validation-aucpr:0.95917
[7]	validation-logloss:0.56761	validation-auc:0.95724	validation-aucpr:0.96378
[8]	validation-logloss:0.55330	validation-auc:0.95864	validation-aucpr:0.96548
[9]	validation-logloss:0.54248	validation-auc:0.95811	validation-aucpr:0.96489
[10]	validation-logloss:0.53158	validation-auc:0.95876	validation-aucpr:0.96530
[11]	validation-logloss:0.51977	validation-auc:0.95895	validation-aucpr:0.96559
[12]	validation-logloss:0.50953	validation-auc:0.95900	validation-aucpr:0.96570
[13]	validation-logloss:0.50093	validation-auc:0.95857	validation-aucpr:0.96533
[14]	validation-logloss:0.49037	validation-auc:0.95885	validation-aucpr:0.96570
[15]	validation-logloss:0.48179	validation-auc:0.95892	validation-aucpr:0.96568
[16]	validation-logloss:0.47358	validation-auc:0.95888	validation-aucpr:0.96558
[17]	validation-logloss:0.46601	validation-auc:0.95864	validation-aucpr:0.96541
[18]	validation-logloss:0.45761	validation-auc:0.95848	validation-aucpr:0.96536
[19]	validation-logloss:0.44815	validation-auc:0.95907	validation-aucpr:0.96591
[20]	validation-logloss:0.43952	validation-auc:0.95952	validation-aucpr:0.96641
[21]	validation-logloss:0.43300	validation-auc:0.95957	validation-aucpr:0.96643
[22]	validation-logloss:0.42651	validation-auc:0.95975	validation-aucpr:0.96658
[23]	validation-logloss:0.41861	validation-auc:0.95982	validation-aucpr:0.96674
[24]	validation-logloss:0.41035	validation-auc:0.96038	validation-aucpr:0.96730
{'best_iteration': '24', 'best_score': '0.967297137732974'}
Trial 57, Fold 1: Log loss = 0.41034771616691573, Average precision = 0.9673015836050309, ROC-AUC = 0.9603838517992763, Elapsed Time = 1.044937900001969 seconds
Trial 57, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 57, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[0]	validation-logloss:0.67493	validation-auc:0.92659	validation-aucpr:0.91603
[1]	validation-logloss:0.65440	validation-auc:0.95558	validation-aucpr:0.96034
[2]	validation-logloss:0.63804	validation-auc:0.95563	validation-aucpr:0.96028
[3]	validation-logloss:0.62193	validation-auc:0.95796	validation-aucpr:0.96183
[4]	validation-logloss:0.60719	validation-auc:0.95712	validation-aucpr:0.96109
[5]	validation-logloss:0.59324	validation-auc:0.95693	validation-aucpr:0.96071
[6]	validation-logloss:0.57882	validation-auc:0.95797	validation-aucpr:0.96157
[7]	validation-logloss:0.56650	validation-auc:0.95736	validation-aucpr:0.96121
[8]	validation-logloss:0.55490	validation-auc:0.95750	validation-aucpr:0.96111
[9]	validation-logloss:0.54254	validation-auc:0.95863	validation-aucpr:0.96241
[10]	validation-logloss:0.53208	validation-auc:0.95793	validation-aucpr:0.96168
[11]	validation-logloss:0.51964	validation-auc:0.95966	validation-aucpr:0.96375
[12]	validation-logloss:0.50705	validation-auc:0.96118	validation-aucpr:0.96578
[13]	validation-logloss:0.49555	validation-auc:0.96162	validation-aucpr:0.96616
[14]	validation-logloss:0.48666	validation-auc:0.96141	validation-aucpr:0.96585
[15]	validation-logloss:0.47513	validation-auc:0.96223	validation-aucpr:0.96669
[16]	validation-logloss:0.46474	validation-auc:0.96290	validation-aucpr:0.96736
[17]	validation-logloss:0.45657	validation-auc:0.96301	validation-aucpr:0.96737
[18]	validation-logloss:0.44932	validation-auc:0.96257	validation-aucpr:0.96691
[19]	validation-logloss:0.44048	validation-auc:0.96280	validation-aucpr:0.96714
[20]	validation-logloss:0.43353	validation-auc:0.96272	validation-aucpr:0.96704
[21]	validation-logloss:0.42738	validation-auc:0.96251	validation-aucpr:0.96683
[22]	validation-logloss:0.42151	validation-auc:0.96242	validation-aucpr:0.96672
[23]	validation-logloss:0.41597	validation-auc:0.96235	validation-aucpr:0.96664
[24]	validation-logloss:0.41013	validation-auc:0.96245	validation-aucpr:0.96669
{'best_iteration': '17', 'best_score': '0.9673667641165824'}
Trial 57, Fold 2: Log loss = 0.4101304550323175, Average precision = 0.9666330371412952, ROC-AUC = 0.9624493276207374, Elapsed Time = 1.3475201000001107 seconds
Trial 57, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 57, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[0]	validation-logloss:0.67449	validation-auc:0.93199	validation-aucpr:0.92687
[1]	validation-logloss:0.65477	validation-auc:0.95745	validation-aucpr:0.96278
[2]	validation-logloss:0.63839	validation-auc:0.95725	validation-aucpr:0.96317
[3]	validation-logloss:0.62300	validation-auc:0.95666	validation-aucpr:0.96261
[4]	validation-logloss:0.60752	validation-auc:0.95706	validation-aucpr:0.96320
[5]	validation-logloss:0.59387	validation-auc:0.95680	validation-aucpr:0.96336
[6]	validation-logloss:0.58044	validation-auc:0.95810	validation-aucpr:0.96379
[7]	validation-logloss:0.56757	validation-auc:0.95870	validation-aucpr:0.96417
[8]	validation-logloss:0.55673	validation-auc:0.95799	validation-aucpr:0.96354
[9]	validation-logloss:0.54440	validation-auc:0.95855	validation-aucpr:0.96395
[10]	validation-logloss:0.53330	validation-auc:0.95814	validation-aucpr:0.96352
[11]	validation-logloss:0.52290	validation-auc:0.95761	validation-aucpr:0.96295
[12]	validation-logloss:0.51239	validation-auc:0.95829	validation-aucpr:0.96379
[13]	validation-logloss:0.50305	validation-auc:0.95823	validation-aucpr:0.96375
[14]	validation-logloss:0.49428	validation-auc:0.95778	validation-aucpr:0.96334
[15]	validation-logloss:0.48541	validation-auc:0.95800	validation-aucpr:0.96341
[16]	validation-logloss:0.47637	validation-auc:0.95854	validation-aucpr:0.96369
[17]	validation-logloss:0.46880	validation-auc:0.95807	validation-aucpr:0.96322
[18]	validation-logloss:0.46171	validation-auc:0.95788	validation-aucpr:0.96306
[19]	validation-logloss:0.45486	validation-auc:0.95767	validation-aucpr:0.96290
[20]	validation-logloss:0.44828	validation-auc:0.95755	validation-aucpr:0.96278
[21]	validation-logloss:0.43986	validation-auc:0.95857	validation-aucpr:0.96410
[22]	validation-logloss:0.43406	validation-auc:0.95835	validation-aucpr:0.96395
[23]	validation-logloss:0.42816	validation-auc:0.95807	validation-aucpr:0.96371
[24]	validation-logloss:0.42208	validation-auc:0.95836	validation-aucpr:0.96387
{'best_iteration': '7', 'best_score': '0.9641735778234024'}
Trial 57, Fold 3: Log loss = 0.42207755835198346, Average precision = 0.9638774698876118, ROC-AUC = 0.9583618753069894, Elapsed Time = 1.2980142000014894 seconds
Trial 57, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 57, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[0]	validation-logloss:0.67487	validation-auc:0.92848	validation-aucpr:0.92519
[1]	validation-logloss:0.65758	validation-auc:0.93797	validation-aucpr:0.93847
[2]	validation-logloss:0.64007	validation-auc:0.95223	validation-aucpr:0.95651
[3]	validation-logloss:0.62384	validation-auc:0.95442	validation-aucpr:0.96065
[4]	validation-logloss:0.60735	validation-auc:0.95637	validation-aucpr:0.96316
[5]	validation-logloss:0.59402	validation-auc:0.95596	validation-aucpr:0.96283
[6]	validation-logloss:0.58079	validation-auc:0.95533	validation-aucpr:0.96204
[7]	validation-logloss:0.56572	validation-auc:0.95708	validation-aucpr:0.96393
[8]	validation-logloss:0.55312	validation-auc:0.95708	validation-aucpr:0.96397
[9]	validation-logloss:0.53975	validation-auc:0.95803	validation-aucpr:0.96481
[10]	validation-logloss:0.52881	validation-auc:0.95869	validation-aucpr:0.96522
[11]	validation-logloss:0.51717	validation-auc:0.95914	validation-aucpr:0.96566
[12]	validation-logloss:0.50687	validation-auc:0.95932	validation-aucpr:0.96581
[13]	validation-logloss:0.49786	validation-auc:0.95874	validation-aucpr:0.96533
[14]	validation-logloss:0.48824	validation-auc:0.95920	validation-aucpr:0.96564
[15]	validation-logloss:0.47747	validation-auc:0.96011	validation-aucpr:0.96646
[16]	validation-logloss:0.46911	validation-auc:0.96021	validation-aucpr:0.96650
[17]	validation-logloss:0.46103	validation-auc:0.96070	validation-aucpr:0.96690
[18]	validation-logloss:0.45382	validation-auc:0.96053	validation-aucpr:0.96667
[19]	validation-logloss:0.44491	validation-auc:0.96111	validation-aucpr:0.96724
[20]	validation-logloss:0.43808	validation-auc:0.96107	validation-aucpr:0.96717
[21]	validation-logloss:0.42941	validation-auc:0.96140	validation-aucpr:0.96752
[22]	validation-logloss:0.42274	validation-auc:0.96152	validation-aucpr:0.96760
[23]	validation-logloss:0.41489	validation-auc:0.96189	validation-aucpr:0.96801
[24]	validation-logloss:0.40935	validation-auc:0.96188	validation-aucpr:0.96799
{'best_iteration': '23', 'best_score': '0.9680064618656231'}
Trial 57, Fold 4: Log loss = 0.409350621791954, Average precision = 0.9679871158277571, ROC-AUC = 0.9618803664122502, Elapsed Time = 1.265817600000446 seconds
Trial 57, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 57, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[0]	validation-logloss:0.67495	validation-auc:0.91969	validation-aucpr:0.90610
[1]	validation-logloss:0.65813	validation-auc:0.93588	validation-aucpr:0.93828
[2]	validation-logloss:0.64248	validation-auc:0.93797	validation-aucpr:0.94127
[3]	validation-logloss:0.62754	validation-auc:0.93957	validation-aucpr:0.94455
[4]	validation-logloss:0.61008	validation-auc:0.94994	validation-aucpr:0.95687
[5]	validation-logloss:0.59344	validation-auc:0.95363	validation-aucpr:0.96018
[6]	validation-logloss:0.58120	validation-auc:0.95313	validation-aucpr:0.96021
[7]	validation-logloss:0.56863	validation-auc:0.95286	validation-aucpr:0.96003
[8]	validation-logloss:0.55453	validation-auc:0.95443	validation-aucpr:0.96131
[9]	validation-logloss:0.54319	validation-auc:0.95455	validation-aucpr:0.96132
[10]	validation-logloss:0.53230	validation-auc:0.95490	validation-aucpr:0.96160
[11]	validation-logloss:0.52226	validation-auc:0.95486	validation-aucpr:0.96151
[12]	validation-logloss:0.51321	validation-auc:0.95474	validation-aucpr:0.96134
[13]	validation-logloss:0.50445	validation-auc:0.95453	validation-aucpr:0.96117
[14]	validation-logloss:0.49649	validation-auc:0.95444	validation-aucpr:0.96145
[15]	validation-logloss:0.48890	validation-auc:0.95432	validation-aucpr:0.96129
[16]	validation-logloss:0.48053	validation-auc:0.95437	validation-aucpr:0.96124
[17]	validation-logloss:0.47301	validation-auc:0.95430	validation-aucpr:0.96114
[18]	validation-logloss:0.46379	validation-auc:0.95513	validation-aucpr:0.96189
[19]	validation-logloss:0.45706	validation-auc:0.95482	validation-aucpr:0.96159
[20]	validation-logloss:0.44790	validation-auc:0.95561	validation-aucpr:0.96244
[21]	validation-logloss:0.44038	validation-auc:0.95607	validation-aucpr:0.96272
[22]	validation-logloss:0.43436	validation-auc:0.95596	validation-aucpr:0.96254
[23]	validation-logloss:0.42855	validation-auc:0.95596	validation-aucpr:0.96258
[24]	validation-logloss:0.42321	validation-auc:0.95581	validation-aucpr:0.96247
{'best_iteration': '21', 'best_score': '0.9627214154090976'}
Trial 57, Fold 5: Log loss = 0.42320609848369695, Average precision = 0.9624687066772707, ROC-AUC = 0.9558063281496758, Elapsed Time = 1.2785884000004444 seconds
Optimization Progress:  58%|#####8    | 58/100 [2:55:36<31:44, 45.34s/it]
Trial 58, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 58, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[0]	validation-logloss:0.68570	validation-auc:0.91254	validation-aucpr:0.88376
[1]	validation-logloss:0.67832	validation-auc:0.94100	validation-aucpr:0.94213
[2]	validation-logloss:0.67010	validation-auc:0.95528	validation-aucpr:0.95899
[3]	validation-logloss:0.66274	validation-auc:0.95750	validation-aucpr:0.96288
[4]	validation-logloss:0.65580	validation-auc:0.95827	validation-aucpr:0.96318
[5]	validation-logloss:0.64802	validation-auc:0.96097	validation-aucpr:0.96472
[6]	validation-logloss:0.64046	validation-auc:0.96242	validation-aucpr:0.96642
[7]	validation-logloss:0.63304	validation-auc:0.96390	validation-aucpr:0.96787
[8]	validation-logloss:0.62653	validation-auc:0.96393	validation-aucpr:0.96765
[9]	validation-logloss:0.61939	validation-auc:0.96481	validation-aucpr:0.96922
[10]	validation-logloss:0.61221	validation-auc:0.96560	validation-aucpr:0.96988
[11]	validation-logloss:0.60527	validation-auc:0.96607	validation-aucpr:0.97025
[12]	validation-logloss:0.59849	validation-auc:0.96652	validation-aucpr:0.97046
[13]	validation-logloss:0.59273	validation-auc:0.96653	validation-aucpr:0.97034
[14]	validation-logloss:0.58714	validation-auc:0.96627	validation-aucpr:0.97131
[15]	validation-logloss:0.58070	validation-auc:0.96658	validation-aucpr:0.97166
[16]	validation-logloss:0.57518	validation-auc:0.96624	validation-aucpr:0.97137
[17]	validation-logloss:0.56915	validation-auc:0.96639	validation-aucpr:0.97148
[18]	validation-logloss:0.56409	validation-auc:0.96615	validation-aucpr:0.97130
[19]	validation-logloss:0.55798	validation-auc:0.96654	validation-aucpr:0.97166
[20]	validation-logloss:0.55210	validation-auc:0.96683	validation-aucpr:0.97193
[21]	validation-logloss:0.54720	validation-auc:0.96682	validation-aucpr:0.97183
[22]	validation-logloss:0.54187	validation-auc:0.96674	validation-aucpr:0.97200
[23]	validation-logloss:0.53698	validation-auc:0.96655	validation-aucpr:0.97188
[24]	validation-logloss:0.53140	validation-auc:0.96689	validation-aucpr:0.97218
[25]	validation-logloss:0.52682	validation-auc:0.96677	validation-aucpr:0.97205
[26]	validation-logloss:0.52235	validation-auc:0.96681	validation-aucpr:0.97206
[27]	validation-logloss:0.51763	validation-auc:0.96699	validation-aucpr:0.97226
[28]	validation-logloss:0.51317	validation-auc:0.96691	validation-aucpr:0.97222
[29]	validation-logloss:0.50894	validation-auc:0.96687	validation-aucpr:0.97215
[30]	validation-logloss:0.50456	validation-auc:0.96687	validation-aucpr:0.97213
[31]	validation-logloss:0.50034	validation-auc:0.96709	validation-aucpr:0.97229
[32]	validation-logloss:0.49615	validation-auc:0.96708	validation-aucpr:0.97226
[33]	validation-logloss:0.49220	validation-auc:0.96704	validation-aucpr:0.97221
[34]	validation-logloss:0.48831	validation-auc:0.96701	validation-aucpr:0.97217
[35]	validation-logloss:0.48379	validation-auc:0.96724	validation-aucpr:0.97236
[36]	validation-logloss:0.47931	validation-auc:0.96735	validation-aucpr:0.97245
[37]	validation-logloss:0.47550	validation-auc:0.96747	validation-aucpr:0.97251
[38]	validation-logloss:0.47186	validation-auc:0.96742	validation-aucpr:0.97245
[39]	validation-logloss:0.46818	validation-auc:0.96740	validation-aucpr:0.97243
[40]	validation-logloss:0.46391	validation-auc:0.96767	validation-aucpr:0.97264
[41]	validation-logloss:0.45976	validation-auc:0.96776	validation-aucpr:0.97275
[42]	validation-logloss:0.45624	validation-auc:0.96792	validation-aucpr:0.97283
[43]	validation-logloss:0.45283	validation-auc:0.96791	validation-aucpr:0.97281
[44]	validation-logloss:0.44912	validation-auc:0.96793	validation-aucpr:0.97287
[45]	validation-logloss:0.44534	validation-auc:0.96811	validation-aucpr:0.97302
[46]	validation-logloss:0.44157	validation-auc:0.96822	validation-aucpr:0.97313
[47]	validation-logloss:0.43845	validation-auc:0.96814	validation-aucpr:0.97304
[48]	validation-logloss:0.43543	validation-auc:0.96803	validation-aucpr:0.97294
[49]	validation-logloss:0.43252	validation-auc:0.96797	validation-aucpr:0.97286
[50]	validation-logloss:0.42945	validation-auc:0.96801	validation-aucpr:0.97288
[51]	validation-logloss:0.42646	validation-auc:0.96806	validation-aucpr:0.97293
{'best_iteration': '46', 'best_score': '0.9731310062419574'}
Trial 58, Fold 1: Log loss = 0.4264604090034749, Average precision = 0.9729299750562228, ROC-AUC = 0.9680568848788135, Elapsed Time = 9.525947300000553 seconds
Trial 58, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 58, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[0]	validation-logloss:0.68512	validation-auc:0.92130	validation-aucpr:0.88875
[1]	validation-logloss:0.67730	validation-auc:0.95032	validation-aucpr:0.94661
[2]	validation-logloss:0.66981	validation-auc:0.95501	validation-aucpr:0.95575
[3]	validation-logloss:0.66258	validation-auc:0.95662	validation-aucpr:0.95999
[4]	validation-logloss:0.65461	validation-auc:0.96271	validation-aucpr:0.96636
[5]	validation-logloss:0.64768	validation-auc:0.96284	validation-aucpr:0.96632
[6]	validation-logloss:0.64000	validation-auc:0.96516	validation-aucpr:0.96887
[7]	validation-logloss:0.63255	validation-auc:0.96619	validation-aucpr:0.96992
[8]	validation-logloss:0.62583	validation-auc:0.96630	validation-aucpr:0.96990
[9]	validation-logloss:0.61871	validation-auc:0.96692	validation-aucpr:0.97051
[10]	validation-logloss:0.61167	validation-auc:0.96710	validation-aucpr:0.97068
[11]	validation-logloss:0.60537	validation-auc:0.96752	validation-aucpr:0.97093
[12]	validation-logloss:0.59870	validation-auc:0.96786	validation-aucpr:0.97133
[13]	validation-logloss:0.59291	validation-auc:0.96797	validation-aucpr:0.97130
[14]	validation-logloss:0.58710	validation-auc:0.96825	validation-aucpr:0.97152
[15]	validation-logloss:0.58073	validation-auc:0.96827	validation-aucpr:0.97163
[16]	validation-logloss:0.57506	validation-auc:0.96863	validation-aucpr:0.97190
[17]	validation-logloss:0.56945	validation-auc:0.96874	validation-aucpr:0.97196
[18]	validation-logloss:0.56390	validation-auc:0.96882	validation-aucpr:0.97196
[19]	validation-logloss:0.55861	validation-auc:0.96877	validation-aucpr:0.97190
[20]	validation-logloss:0.55339	validation-auc:0.96879	validation-aucpr:0.97189
[21]	validation-logloss:0.54818	validation-auc:0.96895	validation-aucpr:0.97195
[22]	validation-logloss:0.54327	validation-auc:0.96897	validation-aucpr:0.97191
[23]	validation-logloss:0.53834	validation-auc:0.96912	validation-aucpr:0.97197
[24]	validation-logloss:0.53287	validation-auc:0.96938	validation-aucpr:0.97223
[25]	validation-logloss:0.52752	validation-auc:0.96982	validation-aucpr:0.97265
[26]	validation-logloss:0.52288	validation-auc:0.96971	validation-aucpr:0.97254
[27]	validation-logloss:0.51762	validation-auc:0.96990	validation-aucpr:0.97270
[28]	validation-logloss:0.51240	validation-auc:0.97010	validation-aucpr:0.97293
[29]	validation-logloss:0.50791	validation-auc:0.97006	validation-aucpr:0.97290
[30]	validation-logloss:0.50298	validation-auc:0.97012	validation-aucpr:0.97299
[31]	validation-logloss:0.49875	validation-auc:0.97004	validation-aucpr:0.97289
[32]	validation-logloss:0.49444	validation-auc:0.97015	validation-aucpr:0.97295
[33]	validation-logloss:0.49041	validation-auc:0.97011	validation-aucpr:0.97292
[34]	validation-logloss:0.48641	validation-auc:0.97007	validation-aucpr:0.97287
[35]	validation-logloss:0.48178	validation-auc:0.97025	validation-aucpr:0.97306
[36]	validation-logloss:0.47736	validation-auc:0.97034	validation-aucpr:0.97317
[37]	validation-logloss:0.47303	validation-auc:0.97040	validation-aucpr:0.97325
[38]	validation-logloss:0.46936	validation-auc:0.97053	validation-aucpr:0.97332
[39]	validation-logloss:0.46493	validation-auc:0.97069	validation-aucpr:0.97348
[40]	validation-logloss:0.46083	validation-auc:0.97075	validation-aucpr:0.97341
[41]	validation-logloss:0.45732	validation-auc:0.97073	validation-aucpr:0.97339
[42]	validation-logloss:0.45385	validation-auc:0.97080	validation-aucpr:0.97343
[43]	validation-logloss:0.44977	validation-auc:0.97095	validation-aucpr:0.97355
[44]	validation-logloss:0.44637	validation-auc:0.97099	validation-aucpr:0.97360
[45]	validation-logloss:0.44316	validation-auc:0.97096	validation-aucpr:0.97357
[46]	validation-logloss:0.43938	validation-auc:0.97102	validation-aucpr:0.97363
[47]	validation-logloss:0.43614	validation-auc:0.97108	validation-aucpr:0.97366
[48]	validation-logloss:0.43322	validation-auc:0.97106	validation-aucpr:0.97364
[49]	validation-logloss:0.43010	validation-auc:0.97108	validation-aucpr:0.97367
[50]	validation-logloss:0.42656	validation-auc:0.97108	validation-aucpr:0.97377
[51]	validation-logloss:0.42303	validation-auc:0.97118	validation-aucpr:0.97386
{'best_iteration': '51', 'best_score': '0.9738633077380496'}
Trial 58, Fold 2: Log loss = 0.4230341057484318, Average precision = 0.9737971510136811, ROC-AUC = 0.9711782396103601, Elapsed Time = 9.858536900002946 seconds
Trial 58, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 58, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[0]	validation-logloss:0.68538	validation-auc:0.91583	validation-aucpr:0.87047
[1]	validation-logloss:0.67757	validation-auc:0.94953	validation-aucpr:0.94006
[2]	validation-logloss:0.67011	validation-auc:0.95516	validation-aucpr:0.95680
[3]	validation-logloss:0.66266	validation-auc:0.95872	validation-aucpr:0.96160
[4]	validation-logloss:0.65549	validation-auc:0.96005	validation-aucpr:0.96284
[5]	validation-logloss:0.64748	validation-auc:0.96583	validation-aucpr:0.96858
[6]	validation-logloss:0.63974	validation-auc:0.96826	validation-aucpr:0.97116
[7]	validation-logloss:0.63209	validation-auc:0.96895	validation-aucpr:0.97183
[8]	validation-logloss:0.62452	validation-auc:0.96928	validation-aucpr:0.97219
[9]	validation-logloss:0.61846	validation-auc:0.96915	validation-aucpr:0.97346
[10]	validation-logloss:0.61129	validation-auc:0.96941	validation-aucpr:0.97366
[11]	validation-logloss:0.60511	validation-auc:0.96930	validation-aucpr:0.97353
[12]	validation-logloss:0.59910	validation-auc:0.96904	validation-aucpr:0.97321
[13]	validation-logloss:0.59231	validation-auc:0.96930	validation-aucpr:0.97346
[14]	validation-logloss:0.58639	validation-auc:0.96944	validation-aucpr:0.97356
[15]	validation-logloss:0.57991	validation-auc:0.96976	validation-aucpr:0.97383
[16]	validation-logloss:0.57415	validation-auc:0.96973	validation-aucpr:0.97378
[17]	validation-logloss:0.56786	validation-auc:0.96999	validation-aucpr:0.97409
[18]	validation-logloss:0.56194	validation-auc:0.97000	validation-aucpr:0.97415
[19]	validation-logloss:0.55605	validation-auc:0.97000	validation-aucpr:0.97419
[20]	validation-logloss:0.55078	validation-auc:0.97018	validation-aucpr:0.97428
[21]	validation-logloss:0.54561	validation-auc:0.97017	validation-aucpr:0.97424
[22]	validation-logloss:0.54077	validation-auc:0.97004	validation-aucpr:0.97412
[23]	validation-logloss:0.53579	validation-auc:0.97005	validation-aucpr:0.97409
[24]	validation-logloss:0.53103	validation-auc:0.97013	validation-aucpr:0.97415
[25]	validation-logloss:0.52574	validation-auc:0.97013	validation-aucpr:0.97417
[26]	validation-logloss:0.52037	validation-auc:0.97022	validation-aucpr:0.97427
[27]	validation-logloss:0.51515	validation-auc:0.97041	validation-aucpr:0.97444
[28]	validation-logloss:0.51076	validation-auc:0.97038	validation-aucpr:0.97440
[29]	validation-logloss:0.50583	validation-auc:0.97034	validation-aucpr:0.97438
[30]	validation-logloss:0.50151	validation-auc:0.97045	validation-aucpr:0.97444
[31]	validation-logloss:0.49740	validation-auc:0.97049	validation-aucpr:0.97445
[32]	validation-logloss:0.49316	validation-auc:0.97048	validation-aucpr:0.97443
[33]	validation-logloss:0.48905	validation-auc:0.97042	validation-aucpr:0.97438
[34]	validation-logloss:0.48519	validation-auc:0.97035	validation-aucpr:0.97432
[35]	validation-logloss:0.48063	validation-auc:0.97034	validation-aucpr:0.97431
[36]	validation-logloss:0.47618	validation-auc:0.97038	validation-aucpr:0.97439
[37]	validation-logloss:0.47175	validation-auc:0.97047	validation-aucpr:0.97446
[38]	validation-logloss:0.46818	validation-auc:0.97036	validation-aucpr:0.97435
[39]	validation-logloss:0.46417	validation-auc:0.97042	validation-aucpr:0.97441
[40]	validation-logloss:0.45997	validation-auc:0.97047	validation-aucpr:0.97446
[41]	validation-logloss:0.45604	validation-auc:0.97044	validation-aucpr:0.97448
[42]	validation-logloss:0.45257	validation-auc:0.97037	validation-aucpr:0.97442
[43]	validation-logloss:0.44853	validation-auc:0.97052	validation-aucpr:0.97455
[44]	validation-logloss:0.44523	validation-auc:0.97049	validation-aucpr:0.97452
[45]	validation-logloss:0.44188	validation-auc:0.97054	validation-aucpr:0.97461
[46]	validation-logloss:0.43879	validation-auc:0.97045	validation-aucpr:0.97452
[47]	validation-logloss:0.43504	validation-auc:0.97047	validation-aucpr:0.97455
[48]	validation-logloss:0.43136	validation-auc:0.97057	validation-aucpr:0.97464
[49]	validation-logloss:0.42786	validation-auc:0.97061	validation-aucpr:0.97467
[50]	validation-logloss:0.42488	validation-auc:0.97049	validation-aucpr:0.97463
[51]	validation-logloss:0.42190	validation-auc:0.97047	validation-aucpr:0.97458
{'best_iteration': '49', 'best_score': '0.9746702082923872'}
Trial 58, Fold 3: Log loss = 0.4218982546432991, Average precision = 0.9745859170622088, ROC-AUC = 0.9704738047885506, Elapsed Time = 9.829640899999504 seconds
Trial 58, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 58, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[0]	validation-logloss:0.68537	validation-auc:0.90713	validation-aucpr:0.86614
[1]	validation-logloss:0.67684	validation-auc:0.94933	validation-aucpr:0.94181
[2]	validation-logloss:0.66957	validation-auc:0.95704	validation-aucpr:0.95876
[3]	validation-logloss:0.66130	validation-auc:0.96109	validation-aucpr:0.96457
[4]	validation-logloss:0.65334	validation-auc:0.96373	validation-aucpr:0.96961
[5]	validation-logloss:0.64557	validation-auc:0.96458	validation-aucpr:0.97034
[6]	validation-logloss:0.63877	validation-auc:0.96470	validation-aucpr:0.97057
[7]	validation-logloss:0.63130	validation-auc:0.96560	validation-aucpr:0.97136
[8]	validation-logloss:0.62498	validation-auc:0.96554	validation-aucpr:0.97123
[9]	validation-logloss:0.61895	validation-auc:0.96544	validation-aucpr:0.97113
[10]	validation-logloss:0.61251	validation-auc:0.96604	validation-aucpr:0.97154
[11]	validation-logloss:0.60555	validation-auc:0.96629	validation-aucpr:0.97177
[12]	validation-logloss:0.59958	validation-auc:0.96604	validation-aucpr:0.97152
[13]	validation-logloss:0.59275	validation-auc:0.96677	validation-aucpr:0.97212
[14]	validation-logloss:0.58626	validation-auc:0.96713	validation-aucpr:0.97245
[15]	validation-logloss:0.57995	validation-auc:0.96738	validation-aucpr:0.97267
[16]	validation-logloss:0.57356	validation-auc:0.96791	validation-aucpr:0.97308
[17]	validation-logloss:0.56808	validation-auc:0.96792	validation-aucpr:0.97308
[18]	validation-logloss:0.56204	validation-auc:0.96814	validation-aucpr:0.97327
[19]	validation-logloss:0.55603	validation-auc:0.96833	validation-aucpr:0.97345
[20]	validation-logloss:0.55038	validation-auc:0.96842	validation-aucpr:0.97350
[21]	validation-logloss:0.54470	validation-auc:0.96841	validation-aucpr:0.97353
[22]	validation-logloss:0.53960	validation-auc:0.96851	validation-aucpr:0.97363
[23]	validation-logloss:0.53410	validation-auc:0.96854	validation-aucpr:0.97370
[24]	validation-logloss:0.52937	validation-auc:0.96854	validation-aucpr:0.97366
[25]	validation-logloss:0.52409	validation-auc:0.96849	validation-aucpr:0.97365
[26]	validation-logloss:0.51938	validation-auc:0.96853	validation-aucpr:0.97369
[27]	validation-logloss:0.51480	validation-auc:0.96865	validation-aucpr:0.97377
[28]	validation-logloss:0.51045	validation-auc:0.96860	validation-aucpr:0.97370
[29]	validation-logloss:0.50556	validation-auc:0.96872	validation-aucpr:0.97380
[30]	validation-logloss:0.50079	validation-auc:0.96884	validation-aucpr:0.97391
[31]	validation-logloss:0.49589	validation-auc:0.96911	validation-aucpr:0.97413
[32]	validation-logloss:0.49123	validation-auc:0.96919	validation-aucpr:0.97420
[33]	validation-logloss:0.48665	validation-auc:0.96928	validation-aucpr:0.97429
[34]	validation-logloss:0.48258	validation-auc:0.96937	validation-aucpr:0.97435
[35]	validation-logloss:0.47814	validation-auc:0.96952	validation-aucpr:0.97444
[36]	validation-logloss:0.47449	validation-auc:0.96946	validation-aucpr:0.97440
[37]	validation-logloss:0.47055	validation-auc:0.96950	validation-aucpr:0.97441
[38]	validation-logloss:0.46635	validation-auc:0.96958	validation-aucpr:0.97447
[39]	validation-logloss:0.46288	validation-auc:0.96941	validation-aucpr:0.97435
[40]	validation-logloss:0.45874	validation-auc:0.96945	validation-aucpr:0.97439
[41]	validation-logloss:0.45467	validation-auc:0.96958	validation-aucpr:0.97452
[42]	validation-logloss:0.45072	validation-auc:0.96965	validation-aucpr:0.97457
[43]	validation-logloss:0.44691	validation-auc:0.96975	validation-aucpr:0.97463
[44]	validation-logloss:0.44304	validation-auc:0.96987	validation-aucpr:0.97471
[45]	validation-logloss:0.43926	validation-auc:0.96996	validation-aucpr:0.97477
[46]	validation-logloss:0.43587	validation-auc:0.97009	validation-aucpr:0.97485
[47]	validation-logloss:0.43221	validation-auc:0.97011	validation-aucpr:0.97486
[48]	validation-logloss:0.42905	validation-auc:0.97013	validation-aucpr:0.97487
[49]	validation-logloss:0.42549	validation-auc:0.97017	validation-aucpr:0.97490
[50]	validation-logloss:0.42197	validation-auc:0.97032	validation-aucpr:0.97502
[51]	validation-logloss:0.41858	validation-auc:0.97035	validation-aucpr:0.97505
{'best_iteration': '51', 'best_score': '0.9750538421258711'}
Trial 58, Fold 4: Log loss = 0.41858497123120914, Average precision = 0.9750528920264302, ROC-AUC = 0.9703511311639974, Elapsed Time = 10.098115500000858 seconds
Trial 58, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 58, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[0]	validation-logloss:0.68543	validation-auc:0.91494	validation-aucpr:0.88175
[1]	validation-logloss:0.67780	validation-auc:0.94480	validation-aucpr:0.93673
[2]	validation-logloss:0.66948	validation-auc:0.95846	validation-aucpr:0.95619
[3]	validation-logloss:0.66231	validation-auc:0.95895	validation-aucpr:0.96348
[4]	validation-logloss:0.65428	validation-auc:0.96190	validation-aucpr:0.96671
[5]	validation-logloss:0.64748	validation-auc:0.96177	validation-aucpr:0.96650
[6]	validation-logloss:0.63987	validation-auc:0.96323	validation-aucpr:0.96802
[7]	validation-logloss:0.63319	validation-auc:0.96324	validation-aucpr:0.96798
[8]	validation-logloss:0.62674	validation-auc:0.96331	validation-aucpr:0.96791
[9]	validation-logloss:0.61949	validation-auc:0.96423	validation-aucpr:0.96881
[10]	validation-logloss:0.61354	validation-auc:0.96399	validation-aucpr:0.96836
[11]	validation-logloss:0.60681	validation-auc:0.96454	validation-aucpr:0.96887
[12]	validation-logloss:0.60094	validation-auc:0.96416	validation-aucpr:0.96850
[13]	validation-logloss:0.59417	validation-auc:0.96471	validation-aucpr:0.96909
[14]	validation-logloss:0.58840	validation-auc:0.96490	validation-aucpr:0.96916
[15]	validation-logloss:0.58220	validation-auc:0.96537	validation-aucpr:0.96948
[16]	validation-logloss:0.57659	validation-auc:0.96532	validation-aucpr:0.96945
[17]	validation-logloss:0.57114	validation-auc:0.96523	validation-aucpr:0.96981
[18]	validation-logloss:0.56575	validation-auc:0.96513	validation-aucpr:0.96972
[19]	validation-logloss:0.55976	validation-auc:0.96524	validation-aucpr:0.96988
[20]	validation-logloss:0.55410	validation-auc:0.96527	validation-aucpr:0.96999
[21]	validation-logloss:0.54920	validation-auc:0.96526	validation-aucpr:0.96989
[22]	validation-logloss:0.54348	validation-auc:0.96570	validation-aucpr:0.97025
[23]	validation-logloss:0.53790	validation-auc:0.96594	validation-aucpr:0.97046
[24]	validation-logloss:0.53318	validation-auc:0.96590	validation-aucpr:0.97041
[25]	validation-logloss:0.52790	validation-auc:0.96602	validation-aucpr:0.97055
[26]	validation-logloss:0.52287	validation-auc:0.96604	validation-aucpr:0.97061
[27]	validation-logloss:0.51843	validation-auc:0.96604	validation-aucpr:0.97059
[28]	validation-logloss:0.51331	validation-auc:0.96634	validation-aucpr:0.97083
[29]	validation-logloss:0.50931	validation-auc:0.96614	validation-aucpr:0.97066
[30]	validation-logloss:0.50516	validation-auc:0.96605	validation-aucpr:0.97058
[31]	validation-logloss:0.50083	validation-auc:0.96625	validation-aucpr:0.97074
[32]	validation-logloss:0.49602	validation-auc:0.96653	validation-aucpr:0.97101
[33]	validation-logloss:0.49224	validation-auc:0.96645	validation-aucpr:0.97093
[34]	validation-logloss:0.48788	validation-auc:0.96638	validation-aucpr:0.97090
[35]	validation-logloss:0.48425	validation-auc:0.96624	validation-aucpr:0.97077
[36]	validation-logloss:0.47971	validation-auc:0.96647	validation-aucpr:0.97096
[37]	validation-logloss:0.47543	validation-auc:0.96667	validation-aucpr:0.97138
[38]	validation-logloss:0.47179	validation-auc:0.96665	validation-aucpr:0.97135
[39]	validation-logloss:0.46751	validation-auc:0.96678	validation-aucpr:0.97147
[40]	validation-logloss:0.46353	validation-auc:0.96667	validation-aucpr:0.97139
[41]	validation-logloss:0.46004	validation-auc:0.96668	validation-aucpr:0.97141
[42]	validation-logloss:0.45657	validation-auc:0.96677	validation-aucpr:0.97146
[43]	validation-logloss:0.45256	validation-auc:0.96690	validation-aucpr:0.97159
[44]	validation-logloss:0.44918	validation-auc:0.96696	validation-aucpr:0.97162
[45]	validation-logloss:0.44598	validation-auc:0.96686	validation-aucpr:0.97155
[46]	validation-logloss:0.44267	validation-auc:0.96679	validation-aucpr:0.97151
[47]	validation-logloss:0.43958	validation-auc:0.96675	validation-aucpr:0.97147
[48]	validation-logloss:0.43632	validation-auc:0.96680	validation-aucpr:0.97150
[49]	validation-logloss:0.43273	validation-auc:0.96683	validation-aucpr:0.97155
[50]	validation-logloss:0.42965	validation-auc:0.96686	validation-aucpr:0.97154
[51]	validation-logloss:0.42667	validation-auc:0.96689	validation-aucpr:0.97162
{'best_iteration': '51', 'best_score': '0.9716200229220133'}
Trial 58, Fold 5: Log loss = 0.42666805150415726, Average precision = 0.9716291604384265, ROC-AUC = 0.9668851606190663, Elapsed Time = 9.674562900003366 seconds
Optimization Progress:  59%|#####8    | 59/100 [2:56:32<33:16, 48.70s/it]
Trial 59, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 59, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[20:55:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.67899	validation-auc:0.92790	validation-aucpr:0.92841
[20:55:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.66477	validation-auc:0.94561	validation-aucpr:0.94981
[20:55:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.64949	validation-auc:0.95777	validation-aucpr:0.96367
[20:55:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.63710	validation-auc:0.95702	validation-aucpr:0.96296
[20:55:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.62566	validation-auc:0.95598	validation-aucpr:0.96186
[20:55:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5]	validation-logloss:0.61183	validation-auc:0.95884	validation-aucpr:0.96522
[20:55:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6]	validation-logloss:0.60139	validation-auc:0.95814	validation-aucpr:0.96473
[20:55:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7]	validation-logloss:0.59172	validation-auc:0.95718	validation-aucpr:0.96394
[20:55:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8]	validation-logloss:0.58145	validation-auc:0.95756	validation-aucpr:0.96419
[20:55:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9]	validation-logloss:0.57007	validation-auc:0.95857	validation-aucpr:0.96552
[20:55:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10]	validation-logloss:0.56014	validation-auc:0.95915	validation-aucpr:0.96575
[20:55:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11]	validation-logloss:0.55085	validation-auc:0.95933	validation-aucpr:0.96571
[20:55:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12]	validation-logloss:0.54272	validation-auc:0.95896	validation-aucpr:0.96524
[20:55:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13]	validation-logloss:0.53227	validation-auc:0.95929	validation-aucpr:0.96572
[20:55:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14]	validation-logloss:0.52406	validation-auc:0.95928	validation-aucpr:0.96576
[20:55:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15]	validation-logloss:0.51473	validation-auc:0.95969	validation-aucpr:0.96618
[20:55:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16]	validation-logloss:0.50589	validation-auc:0.95988	validation-aucpr:0.96641
[20:55:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17]	validation-logloss:0.49852	validation-auc:0.95985	validation-aucpr:0.96647
[20:55:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18]	validation-logloss:0.49187	validation-auc:0.95967	validation-aucpr:0.96628
[20:55:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19]	validation-logloss:0.48482	validation-auc:0.95964	validation-aucpr:0.96614
[20:55:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20]	validation-logloss:0.47792	validation-auc:0.96003	validation-aucpr:0.96645
[20:55:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21]	validation-logloss:0.47136	validation-auc:0.96054	validation-aucpr:0.96680
[20:55:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22]	validation-logloss:0.46562	validation-auc:0.96050	validation-aucpr:0.96668
[20:55:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23]	validation-logloss:0.46021	validation-auc:0.96018	validation-aucpr:0.96640
[20:55:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24]	validation-logloss:0.45432	validation-auc:0.96020	validation-aucpr:0.96644
[20:55:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25]	validation-logloss:0.44804	validation-auc:0.96079	validation-aucpr:0.96692
[20:55:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26]	validation-logloss:0.44086	validation-auc:0.96144	validation-aucpr:0.96750
[20:55:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27]	validation-logloss:0.43622	validation-auc:0.96134	validation-aucpr:0.96740
[20:55:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28]	validation-logloss:0.43010	validation-auc:0.96148	validation-aucpr:0.96760
[20:55:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29]	validation-logloss:0.42475	validation-auc:0.96186	validation-aucpr:0.96790
[20:55:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30]	validation-logloss:0.41999	validation-auc:0.96184	validation-aucpr:0.96786
[20:55:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31]	validation-logloss:0.41374	validation-auc:0.96235	validation-aucpr:0.96831
[20:55:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32]	validation-logloss:0.40748	validation-auc:0.96266	validation-aucpr:0.96861
[20:55:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33]	validation-logloss:0.40208	validation-auc:0.96262	validation-aucpr:0.96865
[20:55:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34]	validation-logloss:0.39824	validation-auc:0.96249	validation-aucpr:0.96855
[20:55:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35]	validation-logloss:0.39417	validation-auc:0.96236	validation-aucpr:0.96841
[20:55:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36]	validation-logloss:0.38962	validation-auc:0.96262	validation-aucpr:0.96860
[20:55:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37]	validation-logloss:0.38586	validation-auc:0.96253	validation-aucpr:0.96854
[20:55:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38]	validation-logloss:0.38239	validation-auc:0.96243	validation-aucpr:0.96848
[20:55:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39]	validation-logloss:0.37886	validation-auc:0.96239	validation-aucpr:0.96840
[20:55:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40]	validation-logloss:0.37507	validation-auc:0.96239	validation-aucpr:0.96841
[20:55:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41]	validation-logloss:0.37205	validation-auc:0.96245	validation-aucpr:0.96844
[20:55:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42]	validation-logloss:0.36889	validation-auc:0.96238	validation-aucpr:0.96837
[20:55:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43]	validation-logloss:0.36558	validation-auc:0.96240	validation-aucpr:0.96838
[20:55:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44]	validation-logloss:0.36150	validation-auc:0.96254	validation-aucpr:0.96853
[20:55:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45]	validation-logloss:0.35818	validation-auc:0.96263	validation-aucpr:0.96862
[20:55:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46]	validation-logloss:0.35467	validation-auc:0.96289	validation-aucpr:0.96882
[20:55:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[47]	validation-logloss:0.35203	validation-auc:0.96292	validation-aucpr:0.96883
[20:55:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[48]	validation-logloss:0.34941	validation-auc:0.96294	validation-aucpr:0.96885
[20:55:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[49]	validation-logloss:0.34667	validation-auc:0.96296	validation-aucpr:0.96888
[20:55:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[50]	validation-logloss:0.34343	validation-auc:0.96307	validation-aucpr:0.96893
[20:55:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[51]	validation-logloss:0.34070	validation-auc:0.96304	validation-aucpr:0.96890
[20:55:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[52]	validation-logloss:0.33798	validation-auc:0.96309	validation-aucpr:0.96895
[20:55:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[53]	validation-logloss:0.33579	validation-auc:0.96307	validation-aucpr:0.96891
[20:55:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[54]	validation-logloss:0.33343	validation-auc:0.96305	validation-aucpr:0.96888
[20:55:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[55]	validation-logloss:0.32938	validation-auc:0.96347	validation-aucpr:0.96926
[20:55:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[56]	validation-logloss:0.32698	validation-auc:0.96355	validation-aucpr:0.96932
[20:55:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[57]	validation-logloss:0.32443	validation-auc:0.96369	validation-aucpr:0.96942
[20:55:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[58]	validation-logloss:0.32194	validation-auc:0.96379	validation-aucpr:0.96952
[20:55:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[59]	validation-logloss:0.31897	validation-auc:0.96389	validation-aucpr:0.96965
[20:55:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[60]	validation-logloss:0.31694	validation-auc:0.96396	validation-aucpr:0.96972
[20:55:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[61]	validation-logloss:0.31474	validation-auc:0.96397	validation-aucpr:0.96972
[20:55:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[62]	validation-logloss:0.31206	validation-auc:0.96409	validation-aucpr:0.96984
[20:55:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[63]	validation-logloss:0.31013	validation-auc:0.96416	validation-aucpr:0.96991
[20:55:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[64]	validation-logloss:0.30882	validation-auc:0.96401	validation-aucpr:0.96980
[20:55:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[65]	validation-logloss:0.30704	validation-auc:0.96401	validation-aucpr:0.96980
[20:55:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[66]	validation-logloss:0.30527	validation-auc:0.96401	validation-aucpr:0.96977
[20:55:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[67]	validation-logloss:0.30285	validation-auc:0.96419	validation-aucpr:0.96991
[20:55:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[68]	validation-logloss:0.30001	validation-auc:0.96439	validation-aucpr:0.97011
[20:55:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[69]	validation-logloss:0.29832	validation-auc:0.96448	validation-aucpr:0.97019
[20:55:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[70]	validation-logloss:0.29685	validation-auc:0.96443	validation-aucpr:0.97015
[20:55:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[71]	validation-logloss:0.29457	validation-auc:0.96454	validation-aucpr:0.97028
[20:55:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[72]	validation-logloss:0.29278	validation-auc:0.96456	validation-aucpr:0.97032
[20:55:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[73]	validation-logloss:0.29124	validation-auc:0.96459	validation-aucpr:0.97034
[20:55:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[74]	validation-logloss:0.28958	validation-auc:0.96475	validation-aucpr:0.97046
[20:55:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[75]	validation-logloss:0.28846	validation-auc:0.96472	validation-aucpr:0.97043
[20:55:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[76]	validation-logloss:0.28718	validation-auc:0.96471	validation-aucpr:0.97041
[20:55:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[77]	validation-logloss:0.28585	validation-auc:0.96479	validation-aucpr:0.97046
[20:55:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[78]	validation-logloss:0.28486	validation-auc:0.96475	validation-aucpr:0.97041
[20:55:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[79]	validation-logloss:0.28372	validation-auc:0.96478	validation-aucpr:0.97041
[20:55:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[80]	validation-logloss:0.28263	validation-auc:0.96470	validation-aucpr:0.97034
[20:55:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[81]	validation-logloss:0.28055	validation-auc:0.96482	validation-aucpr:0.97044
[20:55:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[82]	validation-logloss:0.27859	validation-auc:0.96495	validation-aucpr:0.97057
[20:55:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[83]	validation-logloss:0.27728	validation-auc:0.96506	validation-aucpr:0.97063
[20:55:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[84]	validation-logloss:0.27609	validation-auc:0.96512	validation-aucpr:0.97068
[20:55:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[85]	validation-logloss:0.27490	validation-auc:0.96514	validation-aucpr:0.97068
[20:55:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[86]	validation-logloss:0.27299	validation-auc:0.96524	validation-aucpr:0.97079
[20:55:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[87]	validation-logloss:0.27184	validation-auc:0.96529	validation-aucpr:0.97082
[20:55:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[88]	validation-logloss:0.27078	validation-auc:0.96536	validation-aucpr:0.97087
[20:55:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[89]	validation-logloss:0.26895	validation-auc:0.96549	validation-aucpr:0.97098
[20:55:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[90]	validation-logloss:0.26708	validation-auc:0.96563	validation-aucpr:0.97111
[20:55:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[91]	validation-logloss:0.26582	validation-auc:0.96572	validation-aucpr:0.97116
[20:55:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[92]	validation-logloss:0.26486	validation-auc:0.96570	validation-aucpr:0.97114
[20:55:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[93]	validation-logloss:0.26314	validation-auc:0.96587	validation-aucpr:0.97129
[20:55:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[94]	validation-logloss:0.26224	validation-auc:0.96586	validation-aucpr:0.97129
{'best_iteration': '93', 'best_score': '0.9712917743104803'}
Trial 59, Fold 1: Log loss = 0.2622414109940505, Average precision = 0.9712959878669294, ROC-AUC = 0.9658647710815733, Elapsed Time = 19.233018799997808 seconds
Trial 59, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 59, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[20:55:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.67932	validation-auc:0.92449	validation-aucpr:0.92114
[20:55:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.66522	validation-auc:0.94170	validation-aucpr:0.94047
[20:55:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.65008	validation-auc:0.95676	validation-aucpr:0.95986
[20:55:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.63706	validation-auc:0.95940	validation-aucpr:0.96276
[20:55:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.62527	validation-auc:0.95850	validation-aucpr:0.96176
[20:55:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5]	validation-logloss:0.61394	validation-auc:0.95821	validation-aucpr:0.96169
[20:55:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6]	validation-logloss:0.60367	validation-auc:0.95682	validation-aucpr:0.96054
[20:55:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7]	validation-logloss:0.59286	validation-auc:0.95709	validation-aucpr:0.96050
[20:55:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8]	validation-logloss:0.58275	validation-auc:0.95661	validation-aucpr:0.96011
[20:55:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9]	validation-logloss:0.57086	validation-auc:0.95914	validation-aucpr:0.96318
[20:55:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10]	validation-logloss:0.56181	validation-auc:0.95871	validation-aucpr:0.96264
[20:55:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11]	validation-logloss:0.55230	validation-auc:0.95952	validation-aucpr:0.96338
[20:55:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12]	validation-logloss:0.54340	validation-auc:0.95989	validation-aucpr:0.96389
[20:55:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13]	validation-logloss:0.53516	validation-auc:0.96001	validation-aucpr:0.96412
[20:55:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14]	validation-logloss:0.52523	validation-auc:0.96102	validation-aucpr:0.96543
[20:55:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15]	validation-logloss:0.51763	validation-auc:0.96096	validation-aucpr:0.96530
[20:55:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16]	validation-logloss:0.50847	validation-auc:0.96172	validation-aucpr:0.96629
[20:55:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17]	validation-logloss:0.50124	validation-auc:0.96147	validation-aucpr:0.96608
[20:55:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18]	validation-logloss:0.49359	validation-auc:0.96160	validation-aucpr:0.96622
[20:55:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19]	validation-logloss:0.48659	validation-auc:0.96165	validation-aucpr:0.96622
[20:55:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20]	validation-logloss:0.47800	validation-auc:0.96218	validation-aucpr:0.96680
[20:55:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21]	validation-logloss:0.47133	validation-auc:0.96232	validation-aucpr:0.96684
[20:55:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22]	validation-logloss:0.46581	validation-auc:0.96208	validation-aucpr:0.96657
[20:55:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23]	validation-logloss:0.45967	validation-auc:0.96220	validation-aucpr:0.96666
[20:55:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24]	validation-logloss:0.45270	validation-auc:0.96242	validation-aucpr:0.96684
[20:55:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25]	validation-logloss:0.44727	validation-auc:0.96243	validation-aucpr:0.96687
[20:55:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26]	validation-logloss:0.44135	validation-auc:0.96264	validation-aucpr:0.96700
[20:55:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27]	validation-logloss:0.43663	validation-auc:0.96243	validation-aucpr:0.96683
[20:55:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28]	validation-logloss:0.43170	validation-auc:0.96261	validation-aucpr:0.96694
[20:55:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29]	validation-logloss:0.42722	validation-auc:0.96256	validation-aucpr:0.96684
[20:55:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30]	validation-logloss:0.42141	validation-auc:0.96265	validation-aucpr:0.96697
[20:55:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31]	validation-logloss:0.41661	validation-auc:0.96273	validation-aucpr:0.96701
[20:55:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32]	validation-logloss:0.41232	validation-auc:0.96262	validation-aucpr:0.96688
[20:55:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33]	validation-logloss:0.40617	validation-auc:0.96291	validation-aucpr:0.96717
[20:55:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34]	validation-logloss:0.40151	validation-auc:0.96312	validation-aucpr:0.96730
[20:55:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35]	validation-logloss:0.39738	validation-auc:0.96318	validation-aucpr:0.96737
[20:55:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36]	validation-logloss:0.39314	validation-auc:0.96315	validation-aucpr:0.96730
[20:55:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37]	validation-logloss:0.38893	validation-auc:0.96322	validation-aucpr:0.96736
[20:55:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38]	validation-logloss:0.38568	validation-auc:0.96342	validation-aucpr:0.96753
[20:55:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39]	validation-logloss:0.38199	validation-auc:0.96338	validation-aucpr:0.96748
[20:55:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40]	validation-logloss:0.37691	validation-auc:0.96354	validation-aucpr:0.96767
[20:55:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41]	validation-logloss:0.37309	validation-auc:0.96370	validation-aucpr:0.96777
[20:55:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42]	validation-logloss:0.36978	validation-auc:0.96367	validation-aucpr:0.96772
[20:55:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43]	validation-logloss:0.36582	validation-auc:0.96394	validation-aucpr:0.96795
[20:55:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44]	validation-logloss:0.36234	validation-auc:0.96402	validation-aucpr:0.96803
[20:55:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45]	validation-logloss:0.35938	validation-auc:0.96406	validation-aucpr:0.96794
[20:55:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46]	validation-logloss:0.35598	validation-auc:0.96424	validation-aucpr:0.96808
[20:55:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[47]	validation-logloss:0.35155	validation-auc:0.96450	validation-aucpr:0.96835
[20:55:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[48]	validation-logloss:0.34850	validation-auc:0.96460	validation-aucpr:0.96848
[20:55:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[49]	validation-logloss:0.34544	validation-auc:0.96470	validation-aucpr:0.96855
[20:55:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[50]	validation-logloss:0.34288	validation-auc:0.96459	validation-aucpr:0.96846
[20:55:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[51]	validation-logloss:0.34033	validation-auc:0.96458	validation-aucpr:0.96844
[20:55:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[52]	validation-logloss:0.33802	validation-auc:0.96446	validation-aucpr:0.96832
[20:55:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[53]	validation-logloss:0.33430	validation-auc:0.96454	validation-aucpr:0.96842
[20:55:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[54]	validation-logloss:0.33208	validation-auc:0.96444	validation-aucpr:0.96837
[20:55:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[55]	validation-logloss:0.32915	validation-auc:0.96460	validation-aucpr:0.96848
[20:55:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[56]	validation-logloss:0.32547	validation-auc:0.96487	validation-aucpr:0.96871
[20:55:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[57]	validation-logloss:0.32312	validation-auc:0.96497	validation-aucpr:0.96877
[20:55:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[58]	validation-logloss:0.32086	validation-auc:0.96502	validation-aucpr:0.96879
[20:55:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[59]	validation-logloss:0.31836	validation-auc:0.96520	validation-aucpr:0.96891
[20:55:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[60]	validation-logloss:0.31642	validation-auc:0.96522	validation-aucpr:0.96888
[20:55:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[61]	validation-logloss:0.31444	validation-auc:0.96523	validation-aucpr:0.96886
[20:55:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[62]	validation-logloss:0.31259	validation-auc:0.96524	validation-aucpr:0.96890
[20:55:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[63]	validation-logloss:0.31054	validation-auc:0.96534	validation-aucpr:0.96898
[20:55:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[64]	validation-logloss:0.30887	validation-auc:0.96530	validation-aucpr:0.96893
[20:55:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[65]	validation-logloss:0.30671	validation-auc:0.96528	validation-aucpr:0.96892
[20:55:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[66]	validation-logloss:0.30490	validation-auc:0.96529	validation-aucpr:0.96897
[20:55:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[67]	validation-logloss:0.30290	validation-auc:0.96540	validation-aucpr:0.96904
[20:55:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[68]	validation-logloss:0.30109	validation-auc:0.96537	validation-aucpr:0.96900
[20:55:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[69]	validation-logloss:0.29946	validation-auc:0.96534	validation-aucpr:0.96895
[20:55:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[70]	validation-logloss:0.29767	validation-auc:0.96541	validation-aucpr:0.96899
[20:55:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[71]	validation-logloss:0.29620	validation-auc:0.96544	validation-aucpr:0.96899
[20:55:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[72]	validation-logloss:0.29476	validation-auc:0.96547	validation-aucpr:0.96899
[20:55:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[73]	validation-logloss:0.29331	validation-auc:0.96550	validation-aucpr:0.96904
[20:55:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[74]	validation-logloss:0.29188	validation-auc:0.96548	validation-aucpr:0.96902
[20:55:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[75]	validation-logloss:0.29057	validation-auc:0.96547	validation-aucpr:0.96904
[20:55:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[76]	validation-logloss:0.28927	validation-auc:0.96538	validation-aucpr:0.96895
[20:55:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[77]	validation-logloss:0.28675	validation-auc:0.96557	validation-aucpr:0.96916
[20:55:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[78]	validation-logloss:0.28547	validation-auc:0.96559	validation-aucpr:0.96933
[20:55:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[79]	validation-logloss:0.28414	validation-auc:0.96561	validation-aucpr:0.96934
[20:55:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[80]	validation-logloss:0.28170	validation-auc:0.96580	validation-aucpr:0.96952
[20:56:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[81]	validation-logloss:0.27919	validation-auc:0.96604	validation-aucpr:0.96975
[20:56:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[82]	validation-logloss:0.27801	validation-auc:0.96608	validation-aucpr:0.96978
[20:56:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[83]	validation-logloss:0.27719	validation-auc:0.96607	validation-aucpr:0.96979
[20:56:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[84]	validation-logloss:0.27578	validation-auc:0.96623	validation-aucpr:0.96990
[20:56:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[85]	validation-logloss:0.27470	validation-auc:0.96630	validation-aucpr:0.96994
[20:56:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[86]	validation-logloss:0.27376	validation-auc:0.96632	validation-aucpr:0.96995
[20:56:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[87]	validation-logloss:0.27258	validation-auc:0.96638	validation-aucpr:0.96999
[20:56:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[88]	validation-logloss:0.27137	validation-auc:0.96647	validation-aucpr:0.97006
[20:56:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[89]	validation-logloss:0.27048	validation-auc:0.96643	validation-aucpr:0.97002
[20:56:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[90]	validation-logloss:0.26950	validation-auc:0.96649	validation-aucpr:0.97016
[20:56:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[91]	validation-logloss:0.26862	validation-auc:0.96646	validation-aucpr:0.97008
[20:56:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[92]	validation-logloss:0.26752	validation-auc:0.96652	validation-aucpr:0.97012
[20:56:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[93]	validation-logloss:0.26556	validation-auc:0.96672	validation-aucpr:0.97032
[20:56:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[94]	validation-logloss:0.26463	validation-auc:0.96670	validation-aucpr:0.97027
{'best_iteration': '93', 'best_score': '0.9703206595234433'}
Trial 59, Fold 2: Log loss = 0.2646334476355985, Average precision = 0.9702728597285563, ROC-AUC = 0.9666965205143279, Elapsed Time = 17.663478499998746 seconds
Trial 59, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 59, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[20:56:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.67862	validation-auc:0.93487	validation-aucpr:0.93542
[20:56:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.66407	validation-auc:0.95108	validation-aucpr:0.94911
[20:56:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.64926	validation-auc:0.95705	validation-aucpr:0.96172
[20:56:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.63720	validation-auc:0.95805	validation-aucpr:0.96305
[20:56:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.62432	validation-auc:0.96126	validation-aucpr:0.96610
[20:56:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5]	validation-logloss:0.61216	validation-auc:0.96220	validation-aucpr:0.96693
[20:56:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6]	validation-logloss:0.60128	validation-auc:0.96218	validation-aucpr:0.96715
[20:56:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7]	validation-logloss:0.59030	validation-auc:0.96237	validation-aucpr:0.96722
[20:56:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8]	validation-logloss:0.57927	validation-auc:0.96268	validation-aucpr:0.96760
[20:56:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9]	validation-logloss:0.57012	validation-auc:0.96212	validation-aucpr:0.96706
[20:56:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10]	validation-logloss:0.56130	validation-auc:0.96168	validation-aucpr:0.96674
[20:56:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11]	validation-logloss:0.55239	validation-auc:0.96124	validation-aucpr:0.96628
[20:56:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12]	validation-logloss:0.54219	validation-auc:0.96202	validation-aucpr:0.96717
[20:56:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13]	validation-logloss:0.53377	validation-auc:0.96204	validation-aucpr:0.96719
[20:56:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14]	validation-logloss:0.52553	validation-auc:0.96209	validation-aucpr:0.96720
[20:56:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15]	validation-logloss:0.51737	validation-auc:0.96200	validation-aucpr:0.96710
[20:56:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16]	validation-logloss:0.50973	validation-auc:0.96191	validation-aucpr:0.96699
[20:56:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17]	validation-logloss:0.50272	validation-auc:0.96164	validation-aucpr:0.96673
[20:56:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18]	validation-logloss:0.49402	validation-auc:0.96223	validation-aucpr:0.96737
[20:56:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19]	validation-logloss:0.48741	validation-auc:0.96212	validation-aucpr:0.96720
[20:56:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20]	validation-logloss:0.47941	validation-auc:0.96253	validation-aucpr:0.96766
[20:56:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21]	validation-logloss:0.47182	validation-auc:0.96264	validation-aucpr:0.96778
[20:56:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22]	validation-logloss:0.46651	validation-auc:0.96245	validation-aucpr:0.96758
[20:56:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23]	validation-logloss:0.45853	validation-auc:0.96293	validation-aucpr:0.96807
[20:56:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24]	validation-logloss:0.45272	validation-auc:0.96297	validation-aucpr:0.96807
[20:56:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25]	validation-logloss:0.44688	validation-auc:0.96296	validation-aucpr:0.96804
[20:56:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26]	validation-logloss:0.44094	validation-auc:0.96309	validation-aucpr:0.96810
[20:56:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27]	validation-logloss:0.43581	validation-auc:0.96296	validation-aucpr:0.96796
[20:56:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28]	validation-logloss:0.43060	validation-auc:0.96314	validation-aucpr:0.96807
[20:56:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29]	validation-logloss:0.42366	validation-auc:0.96353	validation-aucpr:0.96846
[20:56:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30]	validation-logloss:0.41726	validation-auc:0.96368	validation-aucpr:0.96857
[20:56:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31]	validation-logloss:0.41077	validation-auc:0.96383	validation-aucpr:0.96880
[20:56:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32]	validation-logloss:0.40630	validation-auc:0.96389	validation-aucpr:0.96879
[20:56:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33]	validation-logloss:0.40211	validation-auc:0.96382	validation-aucpr:0.96869
[20:56:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34]	validation-logloss:0.39791	validation-auc:0.96377	validation-aucpr:0.96866
[20:56:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35]	validation-logloss:0.39225	validation-auc:0.96394	validation-aucpr:0.96888
[20:56:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36]	validation-logloss:0.38717	validation-auc:0.96405	validation-aucpr:0.96900
[20:56:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37]	validation-logloss:0.38332	validation-auc:0.96400	validation-aucpr:0.96896
[20:56:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38]	validation-logloss:0.37964	validation-auc:0.96412	validation-aucpr:0.96905
[20:56:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39]	validation-logloss:0.37552	validation-auc:0.96430	validation-aucpr:0.96921
[20:56:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40]	validation-logloss:0.37056	validation-auc:0.96439	validation-aucpr:0.96937
[20:56:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41]	validation-logloss:0.36677	validation-auc:0.96442	validation-aucpr:0.96937
[20:56:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42]	validation-logloss:0.36212	validation-auc:0.96467	validation-aucpr:0.96960
[20:56:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43]	validation-logloss:0.35920	validation-auc:0.96462	validation-aucpr:0.96954
[20:56:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44]	validation-logloss:0.35440	validation-auc:0.96495	validation-aucpr:0.96987
[20:56:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45]	validation-logloss:0.35154	validation-auc:0.96490	validation-aucpr:0.96984
[20:56:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46]	validation-logloss:0.34820	validation-auc:0.96506	validation-aucpr:0.96998
[20:56:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[47]	validation-logloss:0.34410	validation-auc:0.96514	validation-aucpr:0.97007
[20:56:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[48]	validation-logloss:0.34063	validation-auc:0.96516	validation-aucpr:0.97019
[20:56:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[49]	validation-logloss:0.33745	validation-auc:0.96534	validation-aucpr:0.97033
[20:56:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[50]	validation-logloss:0.33475	validation-auc:0.96537	validation-aucpr:0.97035
[20:56:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[51]	validation-logloss:0.33219	validation-auc:0.96537	validation-aucpr:0.97035
[20:56:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[52]	validation-logloss:0.32958	validation-auc:0.96540	validation-aucpr:0.97035
[20:56:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[53]	validation-logloss:0.32602	validation-auc:0.96549	validation-aucpr:0.97046
[20:56:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[54]	validation-logloss:0.32261	validation-auc:0.96569	validation-aucpr:0.97065
[20:56:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[55]	validation-logloss:0.31992	validation-auc:0.96583	validation-aucpr:0.97075
[20:56:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[56]	validation-logloss:0.31732	validation-auc:0.96597	validation-aucpr:0.97093
[20:56:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[57]	validation-logloss:0.31406	validation-auc:0.96607	validation-aucpr:0.97104
[20:56:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[58]	validation-logloss:0.31213	validation-auc:0.96608	validation-aucpr:0.97102
[20:56:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[59]	validation-logloss:0.31006	validation-auc:0.96608	validation-aucpr:0.97100
[20:56:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[60]	validation-logloss:0.30788	validation-auc:0.96611	validation-aucpr:0.97101
[20:56:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[61]	validation-logloss:0.30603	validation-auc:0.96623	validation-aucpr:0.97111
[20:56:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[62]	validation-logloss:0.30441	validation-auc:0.96616	validation-aucpr:0.97107
[20:56:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[63]	validation-logloss:0.30259	validation-auc:0.96618	validation-aucpr:0.97107
[20:56:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[64]	validation-logloss:0.30075	validation-auc:0.96622	validation-aucpr:0.97108
[20:56:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[65]	validation-logloss:0.29859	validation-auc:0.96626	validation-aucpr:0.97112
[20:56:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[66]	validation-logloss:0.29647	validation-auc:0.96632	validation-aucpr:0.97116
[20:56:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[67]	validation-logloss:0.29390	validation-auc:0.96634	validation-aucpr:0.97121
[20:56:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[68]	validation-logloss:0.29227	validation-auc:0.96633	validation-aucpr:0.97119
[20:56:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[69]	validation-logloss:0.28978	validation-auc:0.96641	validation-aucpr:0.97129
[20:56:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[70]	validation-logloss:0.28740	validation-auc:0.96646	validation-aucpr:0.97135
[20:56:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[71]	validation-logloss:0.28576	validation-auc:0.96656	validation-aucpr:0.97144
[20:56:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[72]	validation-logloss:0.28397	validation-auc:0.96667	validation-aucpr:0.97151
[20:56:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[73]	validation-logloss:0.28254	validation-auc:0.96662	validation-aucpr:0.97144
[20:56:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[74]	validation-logloss:0.28100	validation-auc:0.96669	validation-aucpr:0.97149
[20:56:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[75]	validation-logloss:0.27898	validation-auc:0.96685	validation-aucpr:0.97163
[20:56:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[76]	validation-logloss:0.27757	validation-auc:0.96687	validation-aucpr:0.97163
[20:56:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[77]	validation-logloss:0.27589	validation-auc:0.96696	validation-aucpr:0.97172
[20:56:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[78]	validation-logloss:0.27461	validation-auc:0.96698	validation-aucpr:0.97175
[20:56:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[79]	validation-logloss:0.27334	validation-auc:0.96696	validation-aucpr:0.97171
[20:56:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[80]	validation-logloss:0.27219	validation-auc:0.96697	validation-aucpr:0.97171
[20:56:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[81]	validation-logloss:0.27092	validation-auc:0.96702	validation-aucpr:0.97174
[20:56:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[82]	validation-logloss:0.26970	validation-auc:0.96707	validation-aucpr:0.97181
[20:56:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[83]	validation-logloss:0.26818	validation-auc:0.96718	validation-aucpr:0.97191
[20:56:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[84]	validation-logloss:0.26696	validation-auc:0.96723	validation-aucpr:0.97194
[20:56:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[85]	validation-logloss:0.26611	validation-auc:0.96720	validation-aucpr:0.97193
[20:56:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[86]	validation-logloss:0.26510	validation-auc:0.96720	validation-aucpr:0.97193
[20:56:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[87]	validation-logloss:0.26425	validation-auc:0.96718	validation-aucpr:0.97189
[20:56:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[88]	validation-logloss:0.26333	validation-auc:0.96711	validation-aucpr:0.97183
[20:56:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[89]	validation-logloss:0.26228	validation-auc:0.96708	validation-aucpr:0.97182
[20:56:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[90]	validation-logloss:0.26099	validation-auc:0.96719	validation-aucpr:0.97190
[20:56:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[91]	validation-logloss:0.25908	validation-auc:0.96733	validation-aucpr:0.97203
[20:56:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[92]	validation-logloss:0.25716	validation-auc:0.96750	validation-aucpr:0.97219
[20:56:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[93]	validation-logloss:0.25599	validation-auc:0.96754	validation-aucpr:0.97222
[20:56:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[94]	validation-logloss:0.25507	validation-auc:0.96758	validation-aucpr:0.97225
{'best_iteration': '94', 'best_score': '0.9722450704143667'}
Trial 59, Fold 3: Log loss = 0.25506957399390734, Average precision = 0.9722498977626997, ROC-AUC = 0.9675826884552783, Elapsed Time = 16.725995500000863 seconds
Trial 59, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 59, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[20:56:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.67895	validation-auc:0.92444	validation-aucpr:0.92797
[20:56:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.66482	validation-auc:0.94131	validation-aucpr:0.94623
[20:56:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.65203	validation-auc:0.94545	validation-aucpr:0.94943
[20:56:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.63918	validation-auc:0.94843	validation-aucpr:0.95268
[20:56:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.62454	validation-auc:0.95810	validation-aucpr:0.96384
[20:56:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5]	validation-logloss:0.61036	validation-auc:0.96045	validation-aucpr:0.96660
[20:56:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6]	validation-logloss:0.59884	validation-auc:0.96002	validation-aucpr:0.96626
[20:56:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7]	validation-logloss:0.58798	validation-auc:0.96030	validation-aucpr:0.96647
[20:56:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8]	validation-logloss:0.57610	validation-auc:0.96120	validation-aucpr:0.96750
[20:56:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9]	validation-logloss:0.56643	validation-auc:0.96088	validation-aucpr:0.96719
[20:56:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10]	validation-logloss:0.55546	validation-auc:0.96079	validation-aucpr:0.96725
[20:56:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11]	validation-logloss:0.54647	validation-auc:0.96054	validation-aucpr:0.96710
[20:56:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12]	validation-logloss:0.53760	validation-auc:0.96064	validation-aucpr:0.96725
[20:56:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13]	validation-logloss:0.52897	validation-auc:0.96067	validation-aucpr:0.96717
[20:56:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14]	validation-logloss:0.52174	validation-auc:0.96034	validation-aucpr:0.96686
[20:56:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15]	validation-logloss:0.51412	validation-auc:0.96005	validation-aucpr:0.96662
[20:56:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16]	validation-logloss:0.50671	validation-auc:0.96033	validation-aucpr:0.96674
[20:56:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17]	validation-logloss:0.49844	validation-auc:0.96057	validation-aucpr:0.96705
[20:56:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18]	validation-logloss:0.49138	validation-auc:0.96070	validation-aucpr:0.96704
[20:56:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19]	validation-logloss:0.48431	validation-auc:0.96106	validation-aucpr:0.96725
[20:56:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20]	validation-logloss:0.47811	validation-auc:0.96069	validation-aucpr:0.96700
[20:56:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21]	validation-logloss:0.47224	validation-auc:0.96033	validation-aucpr:0.96675
[20:56:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22]	validation-logloss:0.46632	validation-auc:0.96024	validation-aucpr:0.96668
[20:56:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23]	validation-logloss:0.46063	validation-auc:0.96032	validation-aucpr:0.96668
[20:56:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24]	validation-logloss:0.45328	validation-auc:0.96084	validation-aucpr:0.96718
[20:56:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25]	validation-logloss:0.44810	validation-auc:0.96062	validation-aucpr:0.96702
[20:56:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26]	validation-logloss:0.44087	validation-auc:0.96097	validation-aucpr:0.96742
[20:56:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27]	validation-logloss:0.43547	validation-auc:0.96103	validation-aucpr:0.96741
[20:56:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28]	validation-logloss:0.43078	validation-auc:0.96087	validation-aucpr:0.96728
[20:56:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29]	validation-logloss:0.42404	validation-auc:0.96133	validation-aucpr:0.96767
[20:56:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30]	validation-logloss:0.41927	validation-auc:0.96150	validation-aucpr:0.96782
[20:56:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31]	validation-logloss:0.41470	validation-auc:0.96153	validation-aucpr:0.96781
[20:56:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32]	validation-logloss:0.40989	validation-auc:0.96171	validation-aucpr:0.96791
[20:56:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33]	validation-logloss:0.40544	validation-auc:0.96169	validation-aucpr:0.96787
[20:56:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34]	validation-logloss:0.40141	validation-auc:0.96158	validation-aucpr:0.96777
[20:56:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35]	validation-logloss:0.39764	validation-auc:0.96148	validation-aucpr:0.96769
[20:56:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36]	validation-logloss:0.39189	validation-auc:0.96176	validation-aucpr:0.96795
[20:56:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37]	validation-logloss:0.38812	validation-auc:0.96181	validation-aucpr:0.96797
[20:56:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38]	validation-logloss:0.38455	validation-auc:0.96174	validation-aucpr:0.96790
[20:56:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39]	validation-logloss:0.38086	validation-auc:0.96170	validation-aucpr:0.96786
[20:56:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40]	validation-logloss:0.37743	validation-auc:0.96166	validation-aucpr:0.96782
[20:56:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41]	validation-logloss:0.37383	validation-auc:0.96174	validation-aucpr:0.96787
[20:56:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42]	validation-logloss:0.37055	validation-auc:0.96174	validation-aucpr:0.96786
[20:56:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43]	validation-logloss:0.36743	validation-auc:0.96167	validation-aucpr:0.96782
[20:56:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44]	validation-logloss:0.36407	validation-auc:0.96175	validation-aucpr:0.96785
[20:56:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45]	validation-logloss:0.36093	validation-auc:0.96186	validation-aucpr:0.96790
[20:56:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46]	validation-logloss:0.35842	validation-auc:0.96173	validation-aucpr:0.96776
[20:56:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[47]	validation-logloss:0.35574	validation-auc:0.96163	validation-aucpr:0.96766
[20:56:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[48]	validation-logloss:0.35288	validation-auc:0.96163	validation-aucpr:0.96765
[20:56:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[49]	validation-logloss:0.35021	validation-auc:0.96170	validation-aucpr:0.96771
[20:56:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[50]	validation-logloss:0.34608	validation-auc:0.96183	validation-aucpr:0.96790
[20:56:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[51]	validation-logloss:0.34291	validation-auc:0.96188	validation-aucpr:0.96797
[20:56:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[52]	validation-logloss:0.34016	validation-auc:0.96198	validation-aucpr:0.96804
[20:56:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[53]	validation-logloss:0.33791	validation-auc:0.96206	validation-aucpr:0.96808
[20:56:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[54]	validation-logloss:0.33543	validation-auc:0.96217	validation-aucpr:0.96818
[20:56:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[55]	validation-logloss:0.33130	validation-auc:0.96251	validation-aucpr:0.96851
[20:56:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[56]	validation-logloss:0.32929	validation-auc:0.96256	validation-aucpr:0.96852
[20:56:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[57]	validation-logloss:0.32704	validation-auc:0.96262	validation-aucpr:0.96857
[20:56:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[58]	validation-logloss:0.32461	validation-auc:0.96263	validation-aucpr:0.96858
[20:56:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[59]	validation-logloss:0.32226	validation-auc:0.96269	validation-aucpr:0.96862
[20:56:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[60]	validation-logloss:0.32038	validation-auc:0.96265	validation-aucpr:0.96857
[20:56:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[61]	validation-logloss:0.31861	validation-auc:0.96261	validation-aucpr:0.96853
[20:56:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[62]	validation-logloss:0.31536	validation-auc:0.96292	validation-aucpr:0.96879
[20:56:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[63]	validation-logloss:0.31322	validation-auc:0.96298	validation-aucpr:0.96882
[20:56:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[64]	validation-logloss:0.30992	validation-auc:0.96318	validation-aucpr:0.96904
[20:56:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[65]	validation-logloss:0.30813	validation-auc:0.96318	validation-aucpr:0.96903
[20:56:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[66]	validation-logloss:0.30602	validation-auc:0.96327	validation-aucpr:0.96910
[20:56:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[67]	validation-logloss:0.30442	validation-auc:0.96332	validation-aucpr:0.96913
[20:56:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[68]	validation-logloss:0.30307	validation-auc:0.96327	validation-aucpr:0.96907
[20:56:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[69]	validation-logloss:0.30008	validation-auc:0.96346	validation-aucpr:0.96927
[20:56:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[70]	validation-logloss:0.29847	validation-auc:0.96356	validation-aucpr:0.96933
[20:56:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[71]	validation-logloss:0.29691	validation-auc:0.96354	validation-aucpr:0.96932
[20:56:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[72]	validation-logloss:0.29521	validation-auc:0.96357	validation-aucpr:0.96935
[20:56:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[73]	validation-logloss:0.29410	validation-auc:0.96353	validation-aucpr:0.96933
[20:56:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[74]	validation-logloss:0.29161	validation-auc:0.96361	validation-aucpr:0.96944
[20:56:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[75]	validation-logloss:0.28932	validation-auc:0.96361	validation-aucpr:0.96949
[20:56:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[76]	validation-logloss:0.28756	validation-auc:0.96375	validation-aucpr:0.96962
[20:56:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[77]	validation-logloss:0.28611	validation-auc:0.96387	validation-aucpr:0.96970
[20:56:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[78]	validation-logloss:0.28499	validation-auc:0.96376	validation-aucpr:0.96962
[20:56:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[79]	validation-logloss:0.28358	validation-auc:0.96381	validation-aucpr:0.96964
[20:56:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[80]	validation-logloss:0.28229	validation-auc:0.96380	validation-aucpr:0.96965
[20:56:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[81]	validation-logloss:0.28117	validation-auc:0.96382	validation-aucpr:0.96966
[20:56:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[82]	validation-logloss:0.27893	validation-auc:0.96396	validation-aucpr:0.96982
[20:56:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[83]	validation-logloss:0.27706	validation-auc:0.96406	validation-aucpr:0.96992
[20:56:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[84]	validation-logloss:0.27480	validation-auc:0.96423	validation-aucpr:0.97007
[20:56:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[85]	validation-logloss:0.27302	validation-auc:0.96435	validation-aucpr:0.97018
[20:56:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[86]	validation-logloss:0.27175	validation-auc:0.96445	validation-aucpr:0.97028
[20:56:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[87]	validation-logloss:0.27043	validation-auc:0.96463	validation-aucpr:0.97040
[20:56:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[88]	validation-logloss:0.26939	validation-auc:0.96461	validation-aucpr:0.97038
[20:56:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[89]	validation-logloss:0.26818	validation-auc:0.96467	validation-aucpr:0.97044
[20:56:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[90]	validation-logloss:0.26674	validation-auc:0.96474	validation-aucpr:0.97052
[20:56:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[91]	validation-logloss:0.26581	validation-auc:0.96481	validation-aucpr:0.97057
[20:56:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[92]	validation-logloss:0.26400	validation-auc:0.96486	validation-aucpr:0.97063
[20:56:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[93]	validation-logloss:0.26308	validation-auc:0.96486	validation-aucpr:0.97064
[20:56:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[94]	validation-logloss:0.26146	validation-auc:0.96492	validation-aucpr:0.97070
{'best_iteration': '94', 'best_score': '0.9707029113327627'}
Trial 59, Fold 4: Log loss = 0.2614591833102355, Average precision = 0.9707076760835737, ROC-AUC = 0.9649216065676183, Elapsed Time = 16.850258499998745 seconds
Trial 59, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 59, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[20:56:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.67920	validation-auc:0.92272	validation-aucpr:0.92042
[20:56:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.66623	validation-auc:0.93540	validation-aucpr:0.93593
[20:56:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.65081	validation-auc:0.95197	validation-aucpr:0.95806
[20:56:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.63840	validation-auc:0.95330	validation-aucpr:0.95919
[20:56:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.62729	validation-auc:0.95354	validation-aucpr:0.95930
[20:56:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5]	validation-logloss:0.61550	validation-auc:0.95555	validation-aucpr:0.96082
[20:56:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6]	validation-logloss:0.60527	validation-auc:0.95521	validation-aucpr:0.96018
[20:56:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7]	validation-logloss:0.59564	validation-auc:0.95459	validation-aucpr:0.95941
[20:56:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8]	validation-logloss:0.58274	validation-auc:0.95754	validation-aucpr:0.96242
[20:56:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9]	validation-logloss:0.57273	validation-auc:0.95779	validation-aucpr:0.96249
[20:56:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10]	validation-logloss:0.56377	validation-auc:0.95793	validation-aucpr:0.96262
[20:56:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11]	validation-logloss:0.55540	validation-auc:0.95802	validation-aucpr:0.96279
[20:56:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12]	validation-logloss:0.54626	validation-auc:0.95827	validation-aucpr:0.96295
[20:56:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13]	validation-logloss:0.53700	validation-auc:0.95871	validation-aucpr:0.96340
[20:56:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14]	validation-logloss:0.52872	validation-auc:0.95870	validation-aucpr:0.96345
[20:56:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15]	validation-logloss:0.52170	validation-auc:0.95860	validation-aucpr:0.96339
[20:56:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16]	validation-logloss:0.51253	validation-auc:0.95885	validation-aucpr:0.96378
[20:56:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17]	validation-logloss:0.50548	validation-auc:0.95868	validation-aucpr:0.96377
[20:56:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18]	validation-logloss:0.49808	validation-auc:0.95918	validation-aucpr:0.96411
[20:56:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19]	validation-logloss:0.49152	validation-auc:0.95945	validation-aucpr:0.96434
[20:56:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20]	validation-logloss:0.48438	validation-auc:0.95969	validation-aucpr:0.96456
[20:56:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21]	validation-logloss:0.47654	validation-auc:0.95990	validation-aucpr:0.96490
[20:56:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22]	validation-logloss:0.47021	validation-auc:0.95999	validation-aucpr:0.96501
[20:56:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23]	validation-logloss:0.46361	validation-auc:0.96033	validation-aucpr:0.96527
[20:56:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24]	validation-logloss:0.45619	validation-auc:0.96050	validation-aucpr:0.96567
[20:56:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25]	validation-logloss:0.45080	validation-auc:0.96038	validation-aucpr:0.96554
[20:56:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26]	validation-logloss:0.44537	validation-auc:0.96043	validation-aucpr:0.96557
[20:56:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27]	validation-logloss:0.43849	validation-auc:0.96087	validation-aucpr:0.96594
[20:56:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28]	validation-logloss:0.43303	validation-auc:0.96089	validation-aucpr:0.96599
[20:56:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29]	validation-logloss:0.42862	validation-auc:0.96081	validation-aucpr:0.96592
[20:56:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30]	validation-logloss:0.42411	validation-auc:0.96068	validation-aucpr:0.96584
[20:56:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31]	validation-logloss:0.41980	validation-auc:0.96054	validation-aucpr:0.96567
[20:56:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32]	validation-logloss:0.41370	validation-auc:0.96066	validation-aucpr:0.96585
[20:56:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33]	validation-logloss:0.40974	validation-auc:0.96062	validation-aucpr:0.96581
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[34]	validation-logloss:0.40565	validation-auc:0.96061	validation-aucpr:0.96589
[20:56:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35]	validation-logloss:0.40119	validation-auc:0.96065	validation-aucpr:0.96593
[20:56:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36]	validation-logloss:0.39766	validation-auc:0.96040	validation-aucpr:0.96572
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[37]	validation-logloss:0.39401	validation-auc:0.96045	validation-aucpr:0.96578
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[38]	validation-logloss:0.39070	validation-auc:0.96040	validation-aucpr:0.96570
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[39]	validation-logloss:0.38692	validation-auc:0.96045	validation-aucpr:0.96573
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[40]	validation-logloss:0.38161	validation-auc:0.96070	validation-aucpr:0.96599
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[41]	validation-logloss:0.37827	validation-auc:0.96078	validation-aucpr:0.96604
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[42]	validation-logloss:0.37398	validation-auc:0.96092	validation-aucpr:0.96616
[20:56:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43]	validation-logloss:0.37112	validation-auc:0.96068	validation-aucpr:0.96596
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[44]	validation-logloss:0.36786	validation-auc:0.96080	validation-aucpr:0.96605
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[45]	validation-logloss:0.36469	validation-auc:0.96098	validation-aucpr:0.96619
[20:56:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46]	validation-logloss:0.36149	validation-auc:0.96097	validation-aucpr:0.96619
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[47]	validation-logloss:0.35907	validation-auc:0.96079	validation-aucpr:0.96607
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[48]	validation-logloss:0.35617	validation-auc:0.96087	validation-aucpr:0.96611
[20:56:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[49]	validation-logloss:0.35340	validation-auc:0.96092	validation-aucpr:0.96617
[20:56:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[50]	validation-logloss:0.35059	validation-auc:0.96095	validation-aucpr:0.96622
[20:56:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[51]	validation-logloss:0.34763	validation-auc:0.96104	validation-aucpr:0.96641
[20:56:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[52]	validation-logloss:0.34500	validation-auc:0.96108	validation-aucpr:0.96643
[20:56:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[53]	validation-logloss:0.34175	validation-auc:0.96118	validation-aucpr:0.96658
[20:56:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[54]	validation-logloss:0.33803	validation-auc:0.96130	validation-aucpr:0.96673
[20:56:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[55]	validation-logloss:0.33534	validation-auc:0.96151	validation-aucpr:0.96686
[20:56:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[56]	validation-logloss:0.33164	validation-auc:0.96166	validation-aucpr:0.96701
[20:56:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[57]	validation-logloss:0.32941	validation-auc:0.96168	validation-aucpr:0.96702
[20:56:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[58]	validation-logloss:0.32649	validation-auc:0.96170	validation-aucpr:0.96703
[20:56:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[59]	validation-logloss:0.32462	validation-auc:0.96166	validation-aucpr:0.96698
[20:56:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[60]	validation-logloss:0.32209	validation-auc:0.96181	validation-aucpr:0.96712
[20:56:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[61]	validation-logloss:0.31982	validation-auc:0.96193	validation-aucpr:0.96721
[20:56:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[62]	validation-logloss:0.31801	validation-auc:0.96185	validation-aucpr:0.96714
[20:56:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[63]	validation-logloss:0.31593	validation-auc:0.96196	validation-aucpr:0.96716
[20:56:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[64]	validation-logloss:0.31410	validation-auc:0.96204	validation-aucpr:0.96722
[20:56:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[65]	validation-logloss:0.31233	validation-auc:0.96199	validation-aucpr:0.96717
[20:56:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[66]	validation-logloss:0.31051	validation-auc:0.96223	validation-aucpr:0.96728
[20:56:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[67]	validation-logloss:0.30865	validation-auc:0.96229	validation-aucpr:0.96730
[20:56:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[68]	validation-logloss:0.30694	validation-auc:0.96232	validation-aucpr:0.96726
[20:56:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[69]	validation-logloss:0.30400	validation-auc:0.96243	validation-aucpr:0.96737
[20:56:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[70]	validation-logloss:0.30262	validation-auc:0.96242	validation-aucpr:0.96738
[20:56:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[71]	validation-logloss:0.30106	validation-auc:0.96245	validation-aucpr:0.96739
[20:56:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[72]	validation-logloss:0.29962	validation-auc:0.96248	validation-aucpr:0.96739
[20:56:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[73]	validation-logloss:0.29716	validation-auc:0.96261	validation-aucpr:0.96757
[20:56:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[74]	validation-logloss:0.29508	validation-auc:0.96268	validation-aucpr:0.96767
[20:56:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[75]	validation-logloss:0.29362	validation-auc:0.96276	validation-aucpr:0.96781
[20:56:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[76]	validation-logloss:0.29144	validation-auc:0.96285	validation-aucpr:0.96787
[20:56:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[77]	validation-logloss:0.28988	validation-auc:0.96290	validation-aucpr:0.96792
[20:56:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[78]	validation-logloss:0.28845	validation-auc:0.96295	validation-aucpr:0.96792
[20:56:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[79]	validation-logloss:0.28673	validation-auc:0.96313	validation-aucpr:0.96806
[20:56:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[80]	validation-logloss:0.28573	validation-auc:0.96316	validation-aucpr:0.96805
[20:56:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[81]	validation-logloss:0.28438	validation-auc:0.96326	validation-aucpr:0.96813
[20:56:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[82]	validation-logloss:0.28345	validation-auc:0.96326	validation-aucpr:0.96816
[20:56:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[83]	validation-logloss:0.28233	validation-auc:0.96326	validation-aucpr:0.96816
[20:56:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[84]	validation-logloss:0.28020	validation-auc:0.96339	validation-aucpr:0.96830
[20:56:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[85]	validation-logloss:0.27894	validation-auc:0.96344	validation-aucpr:0.96832
[20:56:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[86]	validation-logloss:0.27785	validation-auc:0.96349	validation-aucpr:0.96835
[20:56:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[87]	validation-logloss:0.27575	validation-auc:0.96361	validation-aucpr:0.96853
[20:56:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[88]	validation-logloss:0.27360	validation-auc:0.96380	validation-aucpr:0.96869
[20:56:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[89]	validation-logloss:0.27167	validation-auc:0.96388	validation-aucpr:0.96878
[20:56:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[90]	validation-logloss:0.27079	validation-auc:0.96384	validation-aucpr:0.96871
[20:56:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[91]	validation-logloss:0.26862	validation-auc:0.96401	validation-aucpr:0.96888
[20:56:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[92]	validation-logloss:0.26775	validation-auc:0.96405	validation-aucpr:0.96891
[20:56:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[93]	validation-logloss:0.26668	validation-auc:0.96410	validation-aucpr:0.96894
[20:56:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[94]	validation-logloss:0.26497	validation-auc:0.96420	validation-aucpr:0.96900
{'best_iteration': '94', 'best_score': '0.9689992016802114'}
Trial 59, Fold 5: Log loss = 0.2649650333683201, Average precision = 0.9690049278285596, ROC-AUC = 0.9642040020065772, Elapsed Time = 16.963513800001238 seconds
Optimization Progress:  60%|######    | 60/100 [2:58:08<41:47, 62.69s/it]
Trial 60, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 60, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[0]	validation-logloss:0.66602	validation-auc:0.94155	validation-aucpr:0.94613
[1]	validation-logloss:0.64043	validation-auc:0.94913	validation-aucpr:0.94290
[2]	validation-logloss:0.61669	validation-auc:0.95412	validation-aucpr:0.94777
[3]	validation-logloss:0.59698	validation-auc:0.95686	validation-aucpr:0.95489
[4]	validation-logloss:0.57847	validation-auc:0.95856	validation-aucpr:0.95584
[5]	validation-logloss:0.55960	validation-auc:0.95962	validation-aucpr:0.95884
[6]	validation-logloss:0.54102	validation-auc:0.96212	validation-aucpr:0.96556
[7]	validation-logloss:0.52451	validation-auc:0.96234	validation-aucpr:0.96771
[8]	validation-logloss:0.50834	validation-auc:0.96280	validation-aucpr:0.96736
[9]	validation-logloss:0.49254	validation-auc:0.96374	validation-aucpr:0.96812
[10]	validation-logloss:0.47963	validation-auc:0.96449	validation-aucpr:0.96957
[11]	validation-logloss:0.46616	validation-auc:0.96517	validation-aucpr:0.97014
[12]	validation-logloss:0.45302	validation-auc:0.96530	validation-aucpr:0.97030
[13]	validation-logloss:0.44041	validation-auc:0.96576	validation-aucpr:0.97060
[14]	validation-logloss:0.42894	validation-auc:0.96632	validation-aucpr:0.97117
[15]	validation-logloss:0.41764	validation-auc:0.96641	validation-aucpr:0.97132
[16]	validation-logloss:0.40736	validation-auc:0.96672	validation-aucpr:0.97152
[17]	validation-logloss:0.39725	validation-auc:0.96707	validation-aucpr:0.97178
[18]	validation-logloss:0.38758	validation-auc:0.96736	validation-aucpr:0.97199
[19]	validation-logloss:0.37838	validation-auc:0.96769	validation-aucpr:0.97221
[20]	validation-logloss:0.37076	validation-auc:0.96777	validation-aucpr:0.97163
[21]	validation-logloss:0.36304	validation-auc:0.96763	validation-aucpr:0.97154
[22]	validation-logloss:0.35561	validation-auc:0.96803	validation-aucpr:0.97183
[23]	validation-logloss:0.34850	validation-auc:0.96802	validation-aucpr:0.97184
[24]	validation-logloss:0.34157	validation-auc:0.96816	validation-aucpr:0.97192
[25]	validation-logloss:0.33467	validation-auc:0.96829	validation-aucpr:0.97193
[26]	validation-logloss:0.32837	validation-auc:0.96821	validation-aucpr:0.97189
[27]	validation-logloss:0.32228	validation-auc:0.96836	validation-aucpr:0.97206
[28]	validation-logloss:0.31662	validation-auc:0.96847	validation-aucpr:0.97213
[29]	validation-logloss:0.31191	validation-auc:0.96850	validation-aucpr:0.97208
[30]	validation-logloss:0.30716	validation-auc:0.96865	validation-aucpr:0.97215
[31]	validation-logloss:0.30284	validation-auc:0.96878	validation-aucpr:0.97218
[32]	validation-logloss:0.29827	validation-auc:0.96878	validation-aucpr:0.97183
[33]	validation-logloss:0.29351	validation-auc:0.96894	validation-aucpr:0.97196
[34]	validation-logloss:0.28971	validation-auc:0.96899	validation-aucpr:0.97173
[35]	validation-logloss:0.28620	validation-auc:0.96904	validation-aucpr:0.97193
[36]	validation-logloss:0.28261	validation-auc:0.96915	validation-aucpr:0.97210
[37]	validation-logloss:0.27921	validation-auc:0.96930	validation-aucpr:0.97288
[38]	validation-logloss:0.27563	validation-auc:0.96930	validation-aucpr:0.97323
[39]	validation-logloss:0.27205	validation-auc:0.96935	validation-aucpr:0.97301
[40]	validation-logloss:0.26890	validation-auc:0.96935	validation-aucpr:0.97301
[41]	validation-logloss:0.26544	validation-auc:0.96958	validation-aucpr:0.97324
[42]	validation-logloss:0.26219	validation-auc:0.96977	validation-aucpr:0.97321
[43]	validation-logloss:0.25921	validation-auc:0.96988	validation-aucpr:0.97328
[44]	validation-logloss:0.25691	validation-auc:0.96988	validation-aucpr:0.97326
[45]	validation-logloss:0.25387	validation-auc:0.97009	validation-aucpr:0.97339
[46]	validation-logloss:0.25169	validation-auc:0.97010	validation-aucpr:0.97351
[47]	validation-logloss:0.24962	validation-auc:0.96992	validation-aucpr:0.97340
[48]	validation-logloss:0.24742	validation-auc:0.96999	validation-aucpr:0.97342
[49]	validation-logloss:0.24520	validation-auc:0.96991	validation-aucpr:0.97340
[50]	validation-logloss:0.24336	validation-auc:0.96989	validation-aucpr:0.97345
[51]	validation-logloss:0.24134	validation-auc:0.96989	validation-aucpr:0.97349
[52]	validation-logloss:0.23971	validation-auc:0.96985	validation-aucpr:0.97343
[53]	validation-logloss:0.23794	validation-auc:0.96987	validation-aucpr:0.97347
[54]	validation-logloss:0.23627	validation-auc:0.96988	validation-aucpr:0.97353
[55]	validation-logloss:0.23466	validation-auc:0.97000	validation-aucpr:0.97365
{'best_iteration': '55', 'best_score': '0.9736481198598432'}
Trial 60, Fold 1: Log loss = 0.23465558197815578, Average precision = 0.9737553055902464, ROC-AUC = 0.9700006093976283, Elapsed Time = 5.350716500000999 seconds
Trial 60, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 60, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[0]	validation-logloss:0.66561	validation-auc:0.93663	validation-aucpr:0.92142
[1]	validation-logloss:0.64060	validation-auc:0.94660	validation-aucpr:0.92801
[2]	validation-logloss:0.61747	validation-auc:0.95261	validation-aucpr:0.95056
[3]	validation-logloss:0.59669	validation-auc:0.95770	validation-aucpr:0.95105
[4]	validation-logloss:0.57806	validation-auc:0.96111	validation-aucpr:0.95966
[5]	validation-logloss:0.55781	validation-auc:0.96360	validation-aucpr:0.96312
[6]	validation-logloss:0.53901	validation-auc:0.96522	validation-aucpr:0.96826
[7]	validation-logloss:0.52318	validation-auc:0.96641	validation-aucpr:0.97076
[8]	validation-logloss:0.50918	validation-auc:0.96654	validation-aucpr:0.97068
[9]	validation-logloss:0.49540	validation-auc:0.96657	validation-aucpr:0.97070
[10]	validation-logloss:0.48099	validation-auc:0.96642	validation-aucpr:0.97062
[11]	validation-logloss:0.46780	validation-auc:0.96652	validation-aucpr:0.97085
[12]	validation-logloss:0.45468	validation-auc:0.96678	validation-aucpr:0.97108
[13]	validation-logloss:0.44218	validation-auc:0.96716	validation-aucpr:0.97136
[14]	validation-logloss:0.43172	validation-auc:0.96740	validation-aucpr:0.97122
[15]	validation-logloss:0.42178	validation-auc:0.96769	validation-aucpr:0.97146
[16]	validation-logloss:0.41139	validation-auc:0.96790	validation-aucpr:0.97165
[17]	validation-logloss:0.40116	validation-auc:0.96811	validation-aucpr:0.97195
[18]	validation-logloss:0.39158	validation-auc:0.96860	validation-aucpr:0.97234
[19]	validation-logloss:0.38248	validation-auc:0.96880	validation-aucpr:0.97259
[20]	validation-logloss:0.37450	validation-auc:0.96856	validation-aucpr:0.97233
[21]	validation-logloss:0.36672	validation-auc:0.96828	validation-aucpr:0.97210
[22]	validation-logloss:0.35888	validation-auc:0.96826	validation-aucpr:0.97216
[23]	validation-logloss:0.35146	validation-auc:0.96826	validation-aucpr:0.97236
[24]	validation-logloss:0.34425	validation-auc:0.96847	validation-aucpr:0.97258
[25]	validation-logloss:0.33732	validation-auc:0.96882	validation-aucpr:0.97286
[26]	validation-logloss:0.33080	validation-auc:0.96890	validation-aucpr:0.97297
[27]	validation-logloss:0.32438	validation-auc:0.96913	validation-aucpr:0.97317
[28]	validation-logloss:0.31857	validation-auc:0.96903	validation-aucpr:0.97312
[29]	validation-logloss:0.31384	validation-auc:0.96905	validation-aucpr:0.97304
[30]	validation-logloss:0.30892	validation-auc:0.96912	validation-aucpr:0.97303
[31]	validation-logloss:0.30405	validation-auc:0.96894	validation-aucpr:0.97292
[32]	validation-logloss:0.29970	validation-auc:0.96905	validation-aucpr:0.97296
[33]	validation-logloss:0.29464	validation-auc:0.96927	validation-aucpr:0.97310
[34]	validation-logloss:0.29041	validation-auc:0.96925	validation-aucpr:0.97309
[35]	validation-logloss:0.28668	validation-auc:0.96903	validation-aucpr:0.97297
[36]	validation-logloss:0.28262	validation-auc:0.96890	validation-aucpr:0.97296
[37]	validation-logloss:0.27844	validation-auc:0.96903	validation-aucpr:0.97306
[38]	validation-logloss:0.27492	validation-auc:0.96904	validation-aucpr:0.97290
[39]	validation-logloss:0.27126	validation-auc:0.96902	validation-aucpr:0.97295
[40]	validation-logloss:0.26835	validation-auc:0.96893	validation-aucpr:0.97290
[41]	validation-logloss:0.26546	validation-auc:0.96898	validation-aucpr:0.97287
[42]	validation-logloss:0.26294	validation-auc:0.96898	validation-aucpr:0.97285
[43]	validation-logloss:0.25972	validation-auc:0.96911	validation-aucpr:0.97288
[44]	validation-logloss:0.25719	validation-auc:0.96924	validation-aucpr:0.97297
[45]	validation-logloss:0.25417	validation-auc:0.96933	validation-aucpr:0.97304
[46]	validation-logloss:0.25177	validation-auc:0.96940	validation-aucpr:0.97303
[47]	validation-logloss:0.24914	validation-auc:0.96948	validation-aucpr:0.97300
[48]	validation-logloss:0.24661	validation-auc:0.96961	validation-aucpr:0.97299
[49]	validation-logloss:0.24405	validation-auc:0.96974	validation-aucpr:0.97306
[50]	validation-logloss:0.24190	validation-auc:0.96994	validation-aucpr:0.97412
[51]	validation-logloss:0.23982	validation-auc:0.96976	validation-aucpr:0.97363
[52]	validation-logloss:0.23787	validation-auc:0.96978	validation-aucpr:0.97361
[53]	validation-logloss:0.23585	validation-auc:0.96969	validation-aucpr:0.97357
[54]	validation-logloss:0.23375	validation-auc:0.96979	validation-aucpr:0.97366
[55]	validation-logloss:0.23195	validation-auc:0.96973	validation-aucpr:0.97365
{'best_iteration': '50', 'best_score': '0.9741150097823695'}
Trial 60, Fold 2: Log loss = 0.23194843281151947, Average precision = 0.9736809321240751, ROC-AUC = 0.9697323867104145, Elapsed Time = 5.66577529999995 seconds
Trial 60, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 60, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[0]	validation-logloss:0.66522	validation-auc:0.94317	validation-aucpr:0.94647
[1]	validation-logloss:0.64209	validation-auc:0.94919	validation-aucpr:0.93701
[2]	validation-logloss:0.61928	validation-auc:0.95353	validation-aucpr:0.94277
[3]	validation-logloss:0.59677	validation-auc:0.95891	validation-aucpr:0.95364
[4]	validation-logloss:0.57658	validation-auc:0.96108	validation-aucpr:0.96035
[5]	validation-logloss:0.55671	validation-auc:0.96269	validation-aucpr:0.96311
[6]	validation-logloss:0.53791	validation-auc:0.96383	validation-aucpr:0.96428
[7]	validation-logloss:0.52076	validation-auc:0.96461	validation-aucpr:0.96546
[8]	validation-logloss:0.50462	validation-auc:0.96527	validation-aucpr:0.96433
[9]	validation-logloss:0.49015	validation-auc:0.96675	validation-aucpr:0.96605
[10]	validation-logloss:0.47716	validation-auc:0.96678	validation-aucpr:0.96615
[11]	validation-logloss:0.46338	validation-auc:0.96728	validation-aucpr:0.96657
[12]	validation-logloss:0.45089	validation-auc:0.96739	validation-aucpr:0.96646
[13]	validation-logloss:0.43987	validation-auc:0.96735	validation-aucpr:0.96628
[14]	validation-logloss:0.42804	validation-auc:0.96745	validation-aucpr:0.96624
[15]	validation-logloss:0.41738	validation-auc:0.96739	validation-aucpr:0.96618
[16]	validation-logloss:0.40822	validation-auc:0.96717	validation-aucpr:0.96592
[17]	validation-logloss:0.39864	validation-auc:0.96718	validation-aucpr:0.96795
[18]	validation-logloss:0.38888	validation-auc:0.96747	validation-aucpr:0.96802
[19]	validation-logloss:0.37975	validation-auc:0.96756	validation-aucpr:0.96811
[20]	validation-logloss:0.37138	validation-auc:0.96784	validation-aucpr:0.96829
[21]	validation-logloss:0.36320	validation-auc:0.96775	validation-aucpr:0.96818
[22]	validation-logloss:0.35540	validation-auc:0.96773	validation-aucpr:0.96803
[23]	validation-logloss:0.34861	validation-auc:0.96787	validation-aucpr:0.96811
[24]	validation-logloss:0.34227	validation-auc:0.96780	validation-aucpr:0.96819
[25]	validation-logloss:0.33566	validation-auc:0.96777	validation-aucpr:0.96818
[26]	validation-logloss:0.32929	validation-auc:0.96780	validation-aucpr:0.96847
[27]	validation-logloss:0.32323	validation-auc:0.96849	validation-aucpr:0.97160
[28]	validation-logloss:0.31742	validation-auc:0.96847	validation-aucpr:0.97161
[29]	validation-logloss:0.31177	validation-auc:0.96866	validation-aucpr:0.97178
[30]	validation-logloss:0.30632	validation-auc:0.96871	validation-aucpr:0.97191
[31]	validation-logloss:0.30106	validation-auc:0.96897	validation-aucpr:0.97208
[32]	validation-logloss:0.29623	validation-auc:0.96907	validation-aucpr:0.97211
[33]	validation-logloss:0.29170	validation-auc:0.96918	validation-aucpr:0.97228
[34]	validation-logloss:0.28712	validation-auc:0.96917	validation-aucpr:0.97228
[35]	validation-logloss:0.28276	validation-auc:0.96925	validation-aucpr:0.97238
[36]	validation-logloss:0.27926	validation-auc:0.96940	validation-aucpr:0.97262
[37]	validation-logloss:0.27583	validation-auc:0.96927	validation-aucpr:0.97266
[38]	validation-logloss:0.27220	validation-auc:0.96932	validation-aucpr:0.97271
[39]	validation-logloss:0.26866	validation-auc:0.96953	validation-aucpr:0.97298
[40]	validation-logloss:0.26518	validation-auc:0.96974	validation-aucpr:0.97316
[41]	validation-logloss:0.26213	validation-auc:0.96985	validation-aucpr:0.97324
[42]	validation-logloss:0.25921	validation-auc:0.96988	validation-aucpr:0.97323
[43]	validation-logloss:0.25680	validation-auc:0.96988	validation-aucpr:0.97323
[44]	validation-logloss:0.25459	validation-auc:0.96982	validation-aucpr:0.97309
[45]	validation-logloss:0.25223	validation-auc:0.96981	validation-aucpr:0.97309
[46]	validation-logloss:0.24942	validation-auc:0.97003	validation-aucpr:0.97315
[47]	validation-logloss:0.24701	validation-auc:0.97010	validation-aucpr:0.97319
[48]	validation-logloss:0.24445	validation-auc:0.97015	validation-aucpr:0.97331
[49]	validation-logloss:0.24209	validation-auc:0.97033	validation-aucpr:0.97343
[50]	validation-logloss:0.23993	validation-auc:0.97025	validation-aucpr:0.97319
[51]	validation-logloss:0.23779	validation-auc:0.97033	validation-aucpr:0.97324
[52]	validation-logloss:0.23548	validation-auc:0.97049	validation-aucpr:0.97339
[53]	validation-logloss:0.23350	validation-auc:0.97061	validation-aucpr:0.97352
[54]	validation-logloss:0.23147	validation-auc:0.97071	validation-aucpr:0.97383
[55]	validation-logloss:0.22979	validation-auc:0.97069	validation-aucpr:0.97381
{'best_iteration': '54', 'best_score': '0.9738305377640103'}
Trial 60, Fold 3: Log loss = 0.22978920308210152, Average precision = 0.9740861129702912, ROC-AUC = 0.9706929860783389, Elapsed Time = 5.672290900001826 seconds
Trial 60, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 60, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[0]	validation-logloss:0.66510	validation-auc:0.94943	validation-aucpr:0.94812
[1]	validation-logloss:0.63930	validation-auc:0.95330	validation-aucpr:0.94564
[2]	validation-logloss:0.61620	validation-auc:0.95717	validation-aucpr:0.96030
[3]	validation-logloss:0.59405	validation-auc:0.95934	validation-aucpr:0.96372
[4]	validation-logloss:0.57315	validation-auc:0.96181	validation-aucpr:0.96657
[5]	validation-logloss:0.55415	validation-auc:0.96287	validation-aucpr:0.96595
[6]	validation-logloss:0.53756	validation-auc:0.96301	validation-aucpr:0.96733
[7]	validation-logloss:0.52092	validation-auc:0.96342	validation-aucpr:0.96702
[8]	validation-logloss:0.50642	validation-auc:0.96408	validation-aucpr:0.96827
[9]	validation-logloss:0.49113	validation-auc:0.96434	validation-aucpr:0.96850
[10]	validation-logloss:0.47701	validation-auc:0.96447	validation-aucpr:0.96860
[11]	validation-logloss:0.46296	validation-auc:0.96511	validation-aucpr:0.96905
[12]	validation-logloss:0.45030	validation-auc:0.96521	validation-aucpr:0.96906
[13]	validation-logloss:0.43967	validation-auc:0.96509	validation-aucpr:0.96922
[14]	validation-logloss:0.42836	validation-auc:0.96514	validation-aucpr:0.96914
[15]	validation-logloss:0.41764	validation-auc:0.96556	validation-aucpr:0.96976
[16]	validation-logloss:0.40724	validation-auc:0.96593	validation-aucpr:0.96970
[17]	validation-logloss:0.39746	validation-auc:0.96622	validation-aucpr:0.97002
[18]	validation-logloss:0.38772	validation-auc:0.96652	validation-aucpr:0.97002
[19]	validation-logloss:0.37854	validation-auc:0.96721	validation-aucpr:0.97040
[20]	validation-logloss:0.37137	validation-auc:0.96743	validation-aucpr:0.97211
[21]	validation-logloss:0.36399	validation-auc:0.96763	validation-aucpr:0.97230
[22]	validation-logloss:0.35625	validation-auc:0.96788	validation-aucpr:0.97319
[23]	validation-logloss:0.34877	validation-auc:0.96815	validation-aucpr:0.97342
[24]	validation-logloss:0.34236	validation-auc:0.96846	validation-aucpr:0.97364
[25]	validation-logloss:0.33695	validation-auc:0.96832	validation-aucpr:0.97352
[26]	validation-logloss:0.33104	validation-auc:0.96814	validation-aucpr:0.97338
[27]	validation-logloss:0.32491	validation-auc:0.96824	validation-aucpr:0.97345
[28]	validation-logloss:0.31903	validation-auc:0.96827	validation-aucpr:0.97346
[29]	validation-logloss:0.31432	validation-auc:0.96816	validation-aucpr:0.97340
[30]	validation-logloss:0.30910	validation-auc:0.96805	validation-aucpr:0.97339
[31]	validation-logloss:0.30434	validation-auc:0.96821	validation-aucpr:0.97342
[32]	validation-logloss:0.29931	validation-auc:0.96843	validation-aucpr:0.97359
[33]	validation-logloss:0.29476	validation-auc:0.96838	validation-aucpr:0.97359
[34]	validation-logloss:0.29066	validation-auc:0.96830	validation-aucpr:0.97355
[35]	validation-logloss:0.28643	validation-auc:0.96830	validation-aucpr:0.97351
[36]	validation-logloss:0.28240	validation-auc:0.96833	validation-aucpr:0.97353
[37]	validation-logloss:0.27847	validation-auc:0.96831	validation-aucpr:0.97353
[38]	validation-logloss:0.27511	validation-auc:0.96827	validation-aucpr:0.97350
[39]	validation-logloss:0.27146	validation-auc:0.96851	validation-aucpr:0.97360
[40]	validation-logloss:0.26809	validation-auc:0.96857	validation-aucpr:0.97362
[41]	validation-logloss:0.26531	validation-auc:0.96828	validation-aucpr:0.97341
[42]	validation-logloss:0.26203	validation-auc:0.96850	validation-aucpr:0.97351
[43]	validation-logloss:0.25869	validation-auc:0.96872	validation-aucpr:0.97367
[44]	validation-logloss:0.25629	validation-auc:0.96871	validation-aucpr:0.97366
[45]	validation-logloss:0.25356	validation-auc:0.96881	validation-aucpr:0.97373
[46]	validation-logloss:0.25121	validation-auc:0.96871	validation-aucpr:0.97366
[47]	validation-logloss:0.24909	validation-auc:0.96875	validation-aucpr:0.97368
[48]	validation-logloss:0.24698	validation-auc:0.96891	validation-aucpr:0.97379
[49]	validation-logloss:0.24454	validation-auc:0.96897	validation-aucpr:0.97383
[50]	validation-logloss:0.24210	validation-auc:0.96903	validation-aucpr:0.97391
[51]	validation-logloss:0.24009	validation-auc:0.96908	validation-aucpr:0.97395
[52]	validation-logloss:0.23808	validation-auc:0.96913	validation-aucpr:0.97400
[53]	validation-logloss:0.23595	validation-auc:0.96924	validation-aucpr:0.97407
[54]	validation-logloss:0.23471	validation-auc:0.96908	validation-aucpr:0.97394
[55]	validation-logloss:0.23294	validation-auc:0.96906	validation-aucpr:0.97395
{'best_iteration': '53', 'best_score': '0.9740728410816708'}
Trial 60, Fold 4: Log loss = 0.23294060156659158, Average precision = 0.9739539172089092, ROC-AUC = 0.9690645185519003, Elapsed Time = 5.714658599998074 seconds
Trial 60, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 60, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[0]	validation-logloss:0.66639	validation-auc:0.92341	validation-aucpr:0.93476
[1]	validation-logloss:0.64094	validation-auc:0.94721	validation-aucpr:0.94052
[2]	validation-logloss:0.61763	validation-auc:0.95311	validation-aucpr:0.95444
[3]	validation-logloss:0.59823	validation-auc:0.95373	validation-aucpr:0.95447
[4]	validation-logloss:0.58049	validation-auc:0.95562	validation-aucpr:0.95451
[5]	validation-logloss:0.56068	validation-auc:0.95938	validation-aucpr:0.96368
[6]	validation-logloss:0.54314	validation-auc:0.95980	validation-aucpr:0.96286
[7]	validation-logloss:0.52596	validation-auc:0.96113	validation-aucpr:0.96660
[8]	validation-logloss:0.50941	validation-auc:0.96209	validation-aucpr:0.96764
[9]	validation-logloss:0.49560	validation-auc:0.96316	validation-aucpr:0.96867
[10]	validation-logloss:0.48136	validation-auc:0.96400	validation-aucpr:0.96968
[11]	validation-logloss:0.46763	validation-auc:0.96486	validation-aucpr:0.97038
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[55]	validation-logloss:0.24105	validation-auc:0.96801	validation-aucpr:0.97223
{'best_iteration': '52', 'best_score': '0.97225150400306'}
Trial 60, Fold 5: Log loss = 0.24104923104785034, Average precision = 0.9722273856003052, ROC-AUC = 0.9680141203574679, Elapsed Time = 5.812461699999403 seconds
Optimization Progress:  61%|######1   | 61/100 [2:58:43<35:29, 54.61s/it]
Trial 61, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 61, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
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[78]	validation-logloss:0.19689	validation-auc:0.97177	validation-aucpr:0.97515
[79]	validation-logloss:0.19663	validation-auc:0.97178	validation-aucpr:0.97527
[80]	validation-logloss:0.19658	validation-auc:0.97177	validation-aucpr:0.97524
[81]	validation-logloss:0.19633	validation-auc:0.97182	validation-aucpr:0.97503
[82]	validation-logloss:0.19641	validation-auc:0.97176	validation-aucpr:0.97485
[83]	validation-logloss:0.19635	validation-auc:0.97179	validation-aucpr:0.97484
[84]	validation-logloss:0.19622	validation-auc:0.97185	validation-aucpr:0.97482
[85]	validation-logloss:0.19602	validation-auc:0.97198	validation-aucpr:0.97538
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[88]	validation-logloss:0.19598	validation-auc:0.97201	validation-aucpr:0.97556
{'best_iteration': '86', 'best_score': '0.9756681810404803'}
Trial 61, Fold 1: Log loss = 0.19597705106820937, Average precision = 0.975568210512143, ROC-AUC = 0.9720051989981981, Elapsed Time = 2.902240300001722 seconds
Trial 61, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 61, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
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[88]	validation-logloss:0.18354	validation-auc:0.97343	validation-aucpr:0.97625
{'best_iteration': '68', 'best_score': '0.9764972411007973'}
Trial 61, Fold 2: Log loss = 0.1835398357728264, Average precision = 0.9762499405488697, ROC-AUC = 0.973432908206854, Elapsed Time = 3.3019077999997535 seconds
Trial 61, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 61, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
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[3]	validation-logloss:0.55286	validation-auc:0.96633	validation-aucpr:0.96346
[4]	validation-logloss:0.52647	validation-auc:0.96743	validation-aucpr:0.97130
[5]	validation-logloss:0.50173	validation-auc:0.96789	validation-aucpr:0.97175
[6]	validation-logloss:0.47979	validation-auc:0.96831	validation-aucpr:0.97334
[7]	validation-logloss:0.45929	validation-auc:0.96828	validation-aucpr:0.97278
[8]	validation-logloss:0.44136	validation-auc:0.96839	validation-aucpr:0.97268
[9]	validation-logloss:0.42410	validation-auc:0.96848	validation-aucpr:0.97269
[10]	validation-logloss:0.40778	validation-auc:0.96858	validation-aucpr:0.97279
[11]	validation-logloss:0.39318	validation-auc:0.96899	validation-aucpr:0.97324
[12]	validation-logloss:0.37912	validation-auc:0.96938	validation-aucpr:0.97365
[13]	validation-logloss:0.36658	validation-auc:0.96961	validation-aucpr:0.97377
[14]	validation-logloss:0.35465	validation-auc:0.96963	validation-aucpr:0.97381
[15]	validation-logloss:0.34340	validation-auc:0.96986	validation-aucpr:0.97370
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[72]	validation-logloss:0.18821	validation-auc:0.97347	validation-aucpr:0.97687
[73]	validation-logloss:0.18787	validation-auc:0.97355	validation-aucpr:0.97693
[74]	validation-logloss:0.18738	validation-auc:0.97365	validation-aucpr:0.97709
[75]	validation-logloss:0.18723	validation-auc:0.97366	validation-aucpr:0.97708
[76]	validation-logloss:0.18715	validation-auc:0.97367	validation-aucpr:0.97710
[77]	validation-logloss:0.18675	validation-auc:0.97380	validation-aucpr:0.97721
[78]	validation-logloss:0.18647	validation-auc:0.97394	validation-aucpr:0.97739
[79]	validation-logloss:0.18630	validation-auc:0.97394	validation-aucpr:0.97736
[80]	validation-logloss:0.18624	validation-auc:0.97387	validation-aucpr:0.97728
[81]	validation-logloss:0.18613	validation-auc:0.97388	validation-aucpr:0.97726
[82]	validation-logloss:0.18605	validation-auc:0.97395	validation-aucpr:0.97741
[83]	validation-logloss:0.18584	validation-auc:0.97398	validation-aucpr:0.97743
[84]	validation-logloss:0.18593	validation-auc:0.97394	validation-aucpr:0.97739
[85]	validation-logloss:0.18595	validation-auc:0.97392	validation-aucpr:0.97730
[86]	validation-logloss:0.18594	validation-auc:0.97392	validation-aucpr:0.97726
[87]	validation-logloss:0.18599	validation-auc:0.97390	validation-aucpr:0.97735
[88]	validation-logloss:0.18598	validation-auc:0.97386	validation-aucpr:0.97730
{'best_iteration': '83', 'best_score': '0.9774279649471089'}
Trial 61, Fold 3: Log loss = 0.1859758454152286, Average precision = 0.9773062201854303, ROC-AUC = 0.9738628004882935, Elapsed Time = 3.588831900000514 seconds
Trial 61, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 61, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
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[2]	validation-logloss:0.58266	validation-auc:0.96118	validation-aucpr:0.95811
[3]	validation-logloss:0.55270	validation-auc:0.96466	validation-aucpr:0.96351
[4]	validation-logloss:0.52872	validation-auc:0.96615	validation-aucpr:0.96626
[5]	validation-logloss:0.50357	validation-auc:0.96793	validation-aucpr:0.96916
[6]	validation-logloss:0.48082	validation-auc:0.96891	validation-aucpr:0.97161
[7]	validation-logloss:0.46213	validation-auc:0.96899	validation-aucpr:0.97168
[8]	validation-logloss:0.44325	validation-auc:0.96925	validation-aucpr:0.97187
[9]	validation-logloss:0.42587	validation-auc:0.96905	validation-aucpr:0.97179
[10]	validation-logloss:0.40992	validation-auc:0.96888	validation-aucpr:0.97183
[11]	validation-logloss:0.39536	validation-auc:0.96865	validation-aucpr:0.97183
[12]	validation-logloss:0.38131	validation-auc:0.96906	validation-aucpr:0.97213
[13]	validation-logloss:0.36810	validation-auc:0.96936	validation-aucpr:0.97232
[14]	validation-logloss:0.35637	validation-auc:0.96954	validation-aucpr:0.97250
[15]	validation-logloss:0.34524	validation-auc:0.96998	validation-aucpr:0.97454
[16]	validation-logloss:0.33494	validation-auc:0.97021	validation-aucpr:0.97474
[17]	validation-logloss:0.32643	validation-auc:0.97042	validation-aucpr:0.97491
[18]	validation-logloss:0.31792	validation-auc:0.97034	validation-aucpr:0.97492
[19]	validation-logloss:0.30972	validation-auc:0.97027	validation-aucpr:0.97484
[20]	validation-logloss:0.30190	validation-auc:0.97041	validation-aucpr:0.97494
[21]	validation-logloss:0.29451	validation-auc:0.97059	validation-aucpr:0.97510
[22]	validation-logloss:0.28800	validation-auc:0.97055	validation-aucpr:0.97509
[23]	validation-logloss:0.28163	validation-auc:0.97063	validation-aucpr:0.97515
[24]	validation-logloss:0.27599	validation-auc:0.97046	validation-aucpr:0.97507
[25]	validation-logloss:0.27012	validation-auc:0.97077	validation-aucpr:0.97533
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[30]	validation-logloss:0.24721	validation-auc:0.97096	validation-aucpr:0.97554
[31]	validation-logloss:0.24384	validation-auc:0.97087	validation-aucpr:0.97548
[32]	validation-logloss:0.24032	validation-auc:0.97097	validation-aucpr:0.97559
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[36]	validation-logloss:0.22826	validation-auc:0.97110	validation-aucpr:0.97569
[37]	validation-logloss:0.22600	validation-auc:0.97096	validation-aucpr:0.97557
[38]	validation-logloss:0.22390	validation-auc:0.97091	validation-aucpr:0.97550
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[66]	validation-logloss:0.19544	validation-auc:0.97089	validation-aucpr:0.97552
[67]	validation-logloss:0.19501	validation-auc:0.97087	validation-aucpr:0.97550
[68]	validation-logloss:0.19443	validation-auc:0.97096	validation-aucpr:0.97556
[69]	validation-logloss:0.19417	validation-auc:0.97104	validation-aucpr:0.97559
[70]	validation-logloss:0.19402	validation-auc:0.97105	validation-aucpr:0.97561
[71]	validation-logloss:0.19404	validation-auc:0.97091	validation-aucpr:0.97552
[72]	validation-logloss:0.19376	validation-auc:0.97092	validation-aucpr:0.97555
[73]	validation-logloss:0.19366	validation-auc:0.97094	validation-aucpr:0.97557
[74]	validation-logloss:0.19360	validation-auc:0.97085	validation-aucpr:0.97550
[75]	validation-logloss:0.19356	validation-auc:0.97084	validation-aucpr:0.97548
[76]	validation-logloss:0.19329	validation-auc:0.97081	validation-aucpr:0.97547
[77]	validation-logloss:0.19324	validation-auc:0.97080	validation-aucpr:0.97545
[78]	validation-logloss:0.19342	validation-auc:0.97080	validation-aucpr:0.97542
[79]	validation-logloss:0.19323	validation-auc:0.97085	validation-aucpr:0.97549
[80]	validation-logloss:0.19299	validation-auc:0.97098	validation-aucpr:0.97555
[81]	validation-logloss:0.19290	validation-auc:0.97099	validation-aucpr:0.97554
[82]	validation-logloss:0.19286	validation-auc:0.97101	validation-aucpr:0.97557
[83]	validation-logloss:0.19315	validation-auc:0.97088	validation-aucpr:0.97549
[84]	validation-logloss:0.19323	validation-auc:0.97091	validation-aucpr:0.97550
{'best_iteration': '34', 'best_score': '0.9757358168229795'}
Trial 61, Fold 4: Log loss = 0.19322964262513223, Average precision = 0.9755070062750073, ROC-AUC = 0.9709110203843192, Elapsed Time = 3.3171443000028376 seconds
Trial 61, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 61, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[0]	validation-logloss:0.65263	validation-auc:0.94417	validation-aucpr:0.94844
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[2]	validation-logloss:0.58831	validation-auc:0.95691	validation-aucpr:0.95541
[3]	validation-logloss:0.55887	validation-auc:0.96060	validation-aucpr:0.96631
[4]	validation-logloss:0.53202	validation-auc:0.96238	validation-aucpr:0.96769
[5]	validation-logloss:0.50769	validation-auc:0.96314	validation-aucpr:0.96876
[6]	validation-logloss:0.48687	validation-auc:0.96369	validation-aucpr:0.96956
[7]	validation-logloss:0.46624	validation-auc:0.96542	validation-aucpr:0.97056
[8]	validation-logloss:0.44754	validation-auc:0.96591	validation-aucpr:0.97090
[9]	validation-logloss:0.42980	validation-auc:0.96646	validation-aucpr:0.97129
[10]	validation-logloss:0.41362	validation-auc:0.96748	validation-aucpr:0.97209
[11]	validation-logloss:0.39842	validation-auc:0.96793	validation-aucpr:0.97237
[12]	validation-logloss:0.38450	validation-auc:0.96806	validation-aucpr:0.97253
[13]	validation-logloss:0.37198	validation-auc:0.96805	validation-aucpr:0.97248
[14]	validation-logloss:0.36010	validation-auc:0.96863	validation-aucpr:0.97285
[15]	validation-logloss:0.34911	validation-auc:0.96892	validation-aucpr:0.97286
[16]	validation-logloss:0.33871	validation-auc:0.96892	validation-aucpr:0.97287
[17]	validation-logloss:0.32960	validation-auc:0.96872	validation-aucpr:0.97278
[18]	validation-logloss:0.32082	validation-auc:0.96869	validation-aucpr:0.97280
[19]	validation-logloss:0.31292	validation-auc:0.96874	validation-aucpr:0.97274
[20]	validation-logloss:0.30565	validation-auc:0.96880	validation-aucpr:0.97292
[21]	validation-logloss:0.29888	validation-auc:0.96875	validation-aucpr:0.97271
[22]	validation-logloss:0.29226	validation-auc:0.96880	validation-aucpr:0.97264
[23]	validation-logloss:0.28574	validation-auc:0.96914	validation-aucpr:0.97293
[24]	validation-logloss:0.28019	validation-auc:0.96905	validation-aucpr:0.97287
[25]	validation-logloss:0.27501	validation-auc:0.96914	validation-aucpr:0.97298
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[31]	validation-logloss:0.24923	validation-auc:0.96979	validation-aucpr:0.97342
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[33]	validation-logloss:0.24240	validation-auc:0.97004	validation-aucpr:0.97381
[34]	validation-logloss:0.23950	validation-auc:0.97030	validation-aucpr:0.97400
[35]	validation-logloss:0.23673	validation-auc:0.97043	validation-aucpr:0.97428
[36]	validation-logloss:0.23386	validation-auc:0.97054	validation-aucpr:0.97441
[37]	validation-logloss:0.23126	validation-auc:0.97067	validation-aucpr:0.97448
[38]	validation-logloss:0.22877	validation-auc:0.97074	validation-aucpr:0.97451
[39]	validation-logloss:0.22667	validation-auc:0.97080	validation-aucpr:0.97440
[40]	validation-logloss:0.22431	validation-auc:0.97099	validation-aucpr:0.97453
[41]	validation-logloss:0.22214	validation-auc:0.97114	validation-aucpr:0.97466
[42]	validation-logloss:0.22032	validation-auc:0.97141	validation-aucpr:0.97532
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[44]	validation-logloss:0.21724	validation-auc:0.97133	validation-aucpr:0.97527
[45]	validation-logloss:0.21574	validation-auc:0.97134	validation-aucpr:0.97527
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[79]	validation-logloss:0.19446	validation-auc:0.97254	validation-aucpr:0.97557
[80]	validation-logloss:0.19431	validation-auc:0.97258	validation-aucpr:0.97573
[81]	validation-logloss:0.19428	validation-auc:0.97258	validation-aucpr:0.97569
[82]	validation-logloss:0.19407	validation-auc:0.97267	validation-aucpr:0.97596
[83]	validation-logloss:0.19422	validation-auc:0.97259	validation-aucpr:0.97578
[84]	validation-logloss:0.19458	validation-auc:0.97247	validation-aucpr:0.97572
[85]	validation-logloss:0.19459	validation-auc:0.97254	validation-aucpr:0.97588
[86]	validation-logloss:0.19454	validation-auc:0.97258	validation-aucpr:0.97586
[87]	validation-logloss:0.19444	validation-auc:0.97260	validation-aucpr:0.97598
[88]	validation-logloss:0.19437	validation-auc:0.97266	validation-aucpr:0.97610
{'best_iteration': '88', 'best_score': '0.9761023838603088'}
Trial 61, Fold 5: Log loss = 0.1943696178039605, Average precision = 0.9761065560095816, ROC-AUC = 0.9726645486130465, Elapsed Time = 3.414511799997854 seconds
Optimization Progress:  62%|######2   | 62/100 [2:59:08<28:50, 45.54s/it]
Trial 62, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 62, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[20:58:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.68248	validation-auc:0.93953	validation-aucpr:0.94317
[20:58:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.67097	validation-auc:0.95882	validation-aucpr:0.96404
[20:58:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.66079	validation-auc:0.96151	validation-aucpr:0.96615
[20:58:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.65115	validation-auc:0.96150	validation-aucpr:0.96613
[20:58:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.64136	validation-auc:0.96256	validation-aucpr:0.96751
[20:58:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5]	validation-logloss:0.63242	validation-auc:0.96148	validation-aucpr:0.96649
[20:58:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6]	validation-logloss:0.62264	validation-auc:0.96329	validation-aucpr:0.96833
[20:58:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7]	validation-logloss:0.61280	validation-auc:0.96421	validation-aucpr:0.96947
[20:58:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8]	validation-logloss:0.60480	validation-auc:0.96407	validation-aucpr:0.96972
[20:58:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9]	validation-logloss:0.59684	validation-auc:0.96406	validation-aucpr:0.96953
[20:58:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10]	validation-logloss:0.58889	validation-auc:0.96398	validation-aucpr:0.96947
[20:58:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11]	validation-logloss:0.57973	validation-auc:0.96465	validation-aucpr:0.97011
[20:58:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12]	validation-logloss:0.57087	validation-auc:0.96537	validation-aucpr:0.97079
[20:58:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13]	validation-logloss:0.56328	validation-auc:0.96577	validation-aucpr:0.97109
[20:58:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14]	validation-logloss:0.55630	validation-auc:0.96551	validation-aucpr:0.97082
[20:58:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15]	validation-logloss:0.54950	validation-auc:0.96561	validation-aucpr:0.97080
[20:58:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16]	validation-logloss:0.54178	validation-auc:0.96585	validation-aucpr:0.97105
[20:58:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17]	validation-logloss:0.53503	validation-auc:0.96595	validation-aucpr:0.97107
[20:58:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18]	validation-logloss:0.52864	validation-auc:0.96609	validation-aucpr:0.97112
[20:58:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19]	validation-logloss:0.52218	validation-auc:0.96617	validation-aucpr:0.97112
[20:58:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20]	validation-logloss:0.51597	validation-auc:0.96612	validation-aucpr:0.97111
[20:58:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21]	validation-logloss:0.51002	validation-auc:0.96608	validation-aucpr:0.97106
[20:58:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22]	validation-logloss:0.50317	validation-auc:0.96633	validation-aucpr:0.97135
[20:58:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23]	validation-logloss:0.49738	validation-auc:0.96652	validation-aucpr:0.97145
[20:58:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24]	validation-logloss:0.49094	validation-auc:0.96674	validation-aucpr:0.97167
[20:58:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25]	validation-logloss:0.48535	validation-auc:0.96687	validation-aucpr:0.97172
[20:58:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26]	validation-logloss:0.47946	validation-auc:0.96701	validation-aucpr:0.97186
[20:58:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27]	validation-logloss:0.47364	validation-auc:0.96714	validation-aucpr:0.97202
[20:58:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28]	validation-logloss:0.46876	validation-auc:0.96702	validation-aucpr:0.97195
[20:58:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29]	validation-logloss:0.46309	validation-auc:0.96711	validation-aucpr:0.97208
[20:58:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30]	validation-logloss:0.45733	validation-auc:0.96723	validation-aucpr:0.97222
[20:58:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31]	validation-logloss:0.45260	validation-auc:0.96733	validation-aucpr:0.97227
[20:58:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32]	validation-logloss:0.44813	validation-auc:0.96724	validation-aucpr:0.97221
[20:58:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33]	validation-logloss:0.44359	validation-auc:0.96726	validation-aucpr:0.97221
[20:58:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34]	validation-logloss:0.43844	validation-auc:0.96733	validation-aucpr:0.97232
[20:58:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35]	validation-logloss:0.43332	validation-auc:0.96747	validation-aucpr:0.97247
[20:58:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36]	validation-logloss:0.42844	validation-auc:0.96765	validation-aucpr:0.97268
[20:58:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37]	validation-logloss:0.42352	validation-auc:0.96782	validation-aucpr:0.97281
[20:58:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38]	validation-logloss:0.41962	validation-auc:0.96784	validation-aucpr:0.97281
[20:58:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39]	validation-logloss:0.41585	validation-auc:0.96773	validation-aucpr:0.97271
[20:58:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40]	validation-logloss:0.41224	validation-auc:0.96769	validation-aucpr:0.97268
[20:58:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41]	validation-logloss:0.40781	validation-auc:0.96787	validation-aucpr:0.97281
{'best_iteration': '37', 'best_score': '0.972812172962304'}
Trial 62, Fold 1: Log loss = 0.40781070738928166, Average precision = 0.9728138503826426, ROC-AUC = 0.9678679417415867, Elapsed Time = 7.990083500000765 seconds
Trial 62, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 62, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[20:58:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.68270	validation-auc:0.94135	validation-aucpr:0.94169
[20:58:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.67085	validation-auc:0.96259	validation-aucpr:0.96625
[20:58:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.66083	validation-auc:0.96391	validation-aucpr:0.96757
[20:58:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.65096	validation-auc:0.96396	validation-aucpr:0.96731
[20:58:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.64171	validation-auc:0.96327	validation-aucpr:0.96660
[20:58:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5]	validation-logloss:0.63239	validation-auc:0.96365	validation-aucpr:0.96694
[20:58:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6]	validation-logloss:0.62233	validation-auc:0.96613	validation-aucpr:0.96964
[20:58:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7]	validation-logloss:0.61335	validation-auc:0.96625	validation-aucpr:0.96969
[20:58:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8]	validation-logloss:0.60353	validation-auc:0.96734	validation-aucpr:0.97093
[20:58:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9]	validation-logloss:0.59392	validation-auc:0.96815	validation-aucpr:0.97172
[20:58:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10]	validation-logloss:0.58475	validation-auc:0.96896	validation-aucpr:0.97239
[20:58:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11]	validation-logloss:0.57716	validation-auc:0.96863	validation-aucpr:0.97207
[20:58:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12]	validation-logloss:0.56929	validation-auc:0.96871	validation-aucpr:0.97206
[20:58:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13]	validation-logloss:0.56201	validation-auc:0.96871	validation-aucpr:0.97201
[20:58:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14]	validation-logloss:0.55467	validation-auc:0.96861	validation-aucpr:0.97188
[20:58:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15]	validation-logloss:0.54778	validation-auc:0.96849	validation-aucpr:0.97208
[20:58:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16]	validation-logloss:0.54077	validation-auc:0.96853	validation-aucpr:0.97213
[20:58:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17]	validation-logloss:0.53408	validation-auc:0.96833	validation-aucpr:0.97187
[20:58:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18]	validation-logloss:0.52759	validation-auc:0.96835	validation-aucpr:0.97185
[20:58:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19]	validation-logloss:0.52120	validation-auc:0.96850	validation-aucpr:0.97191
[20:58:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20]	validation-logloss:0.51501	validation-auc:0.96850	validation-aucpr:0.97183
[20:58:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21]	validation-logloss:0.50793	validation-auc:0.96881	validation-aucpr:0.97218
[20:58:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22]	validation-logloss:0.50102	validation-auc:0.96931	validation-aucpr:0.97262
[20:58:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23]	validation-logloss:0.49549	validation-auc:0.96919	validation-aucpr:0.97246
[20:58:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24]	validation-logloss:0.48889	validation-auc:0.96945	validation-aucpr:0.97314
[20:58:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25]	validation-logloss:0.48321	validation-auc:0.96956	validation-aucpr:0.97321
[20:58:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26]	validation-logloss:0.47814	validation-auc:0.96943	validation-aucpr:0.97308
[20:58:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27]	validation-logloss:0.47221	validation-auc:0.96960	validation-aucpr:0.97321
[20:58:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28]	validation-logloss:0.46615	validation-auc:0.96989	validation-aucpr:0.97346
[20:58:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29]	validation-logloss:0.46125	validation-auc:0.96987	validation-aucpr:0.97340
[20:58:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30]	validation-logloss:0.45654	validation-auc:0.96982	validation-aucpr:0.97337
[20:58:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31]	validation-logloss:0.45094	validation-auc:0.96992	validation-aucpr:0.97351
[20:58:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32]	validation-logloss:0.44649	validation-auc:0.96983	validation-aucpr:0.97345
[20:58:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33]	validation-logloss:0.44216	validation-auc:0.96969	validation-aucpr:0.97333
[20:58:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34]	validation-logloss:0.43782	validation-auc:0.96959	validation-aucpr:0.97322
[20:58:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35]	validation-logloss:0.43360	validation-auc:0.96959	validation-aucpr:0.97323
[20:58:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36]	validation-logloss:0.42858	validation-auc:0.96986	validation-aucpr:0.97343
[20:58:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37]	validation-logloss:0.42388	validation-auc:0.96998	validation-aucpr:0.97354
[20:58:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38]	validation-logloss:0.41982	validation-auc:0.96993	validation-aucpr:0.97348
[20:58:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39]	validation-logloss:0.41507	validation-auc:0.97026	validation-aucpr:0.97374
[20:58:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40]	validation-logloss:0.41032	validation-auc:0.97051	validation-aucpr:0.97393
[20:58:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41]	validation-logloss:0.40678	validation-auc:0.97042	validation-aucpr:0.97379
{'best_iteration': '40', 'best_score': '0.9739341330303594'}
Trial 62, Fold 2: Log loss = 0.40677707386682616, Average precision = 0.9737911873049275, ROC-AUC = 0.9704198041785878, Elapsed Time = 7.774721900001168 seconds
Trial 62, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 62, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[20:58:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.68239	validation-auc:0.94132	validation-aucpr:0.94100
[20:58:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.67076	validation-auc:0.96059	validation-aucpr:0.96419
[20:58:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.66041	validation-auc:0.96454	validation-aucpr:0.96768
[20:58:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.65058	validation-auc:0.96531	validation-aucpr:0.96720
[20:58:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.64083	validation-auc:0.96618	validation-aucpr:0.96969
[20:58:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5]	validation-logloss:0.63020	validation-auc:0.96822	validation-aucpr:0.97200
[20:58:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6]	validation-logloss:0.62098	validation-auc:0.96790	validation-aucpr:0.97178
[20:58:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7]	validation-logloss:0.61215	validation-auc:0.96784	validation-aucpr:0.97170
[20:58:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8]	validation-logloss:0.60426	validation-auc:0.96772	validation-aucpr:0.97171
[20:58:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9]	validation-logloss:0.59491	validation-auc:0.96818	validation-aucpr:0.97235
[20:58:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10]	validation-logloss:0.58589	validation-auc:0.96839	validation-aucpr:0.97262
[20:58:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11]	validation-logloss:0.57680	validation-auc:0.96896	validation-aucpr:0.97313
[20:58:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12]	validation-logloss:0.56939	validation-auc:0.96891	validation-aucpr:0.97309
[20:58:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13]	validation-logloss:0.56173	validation-auc:0.96904	validation-aucpr:0.97321
[20:58:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14]	validation-logloss:0.55469	validation-auc:0.96877	validation-aucpr:0.97299
[20:58:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15]	validation-logloss:0.54760	validation-auc:0.96892	validation-aucpr:0.97306
[20:58:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16]	validation-logloss:0.53952	validation-auc:0.96927	validation-aucpr:0.97346
[20:58:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17]	validation-logloss:0.53311	validation-auc:0.96917	validation-aucpr:0.97334
[20:58:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18]	validation-logloss:0.52664	validation-auc:0.96916	validation-aucpr:0.97330
[20:58:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19]	validation-logloss:0.51923	validation-auc:0.96941	validation-aucpr:0.97355
[20:58:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20]	validation-logloss:0.51289	validation-auc:0.96945	validation-aucpr:0.97355
[20:58:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21]	validation-logloss:0.50691	validation-auc:0.96940	validation-aucpr:0.97348
[20:58:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22]	validation-logloss:0.50005	validation-auc:0.96965	validation-aucpr:0.97375
[20:58:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23]	validation-logloss:0.49444	validation-auc:0.96958	validation-aucpr:0.97369
[20:58:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24]	validation-logloss:0.48802	validation-auc:0.96963	validation-aucpr:0.97388
[20:58:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25]	validation-logloss:0.48160	validation-auc:0.96981	validation-aucpr:0.97404
[20:58:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26]	validation-logloss:0.47546	validation-auc:0.96993	validation-aucpr:0.97418
[20:58:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27]	validation-logloss:0.47016	validation-auc:0.97000	validation-aucpr:0.97423
[20:58:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28]	validation-logloss:0.46418	validation-auc:0.97016	validation-aucpr:0.97440
[20:58:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29]	validation-logloss:0.45944	validation-auc:0.97003	validation-aucpr:0.97431
[20:58:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30]	validation-logloss:0.45470	validation-auc:0.97000	validation-aucpr:0.97426
[20:58:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31]	validation-logloss:0.44993	validation-auc:0.97003	validation-aucpr:0.97424
[20:58:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32]	validation-logloss:0.44527	validation-auc:0.96999	validation-aucpr:0.97420
[20:58:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33]	validation-logloss:0.44061	validation-auc:0.97005	validation-aucpr:0.97423
[20:58:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34]	validation-logloss:0.43540	validation-auc:0.97031	validation-aucpr:0.97444
[20:58:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35]	validation-logloss:0.43107	validation-auc:0.97027	validation-aucpr:0.97440
[20:58:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36]	validation-logloss:0.42620	validation-auc:0.97027	validation-aucpr:0.97444
[20:58:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37]	validation-logloss:0.42224	validation-auc:0.97028	validation-aucpr:0.97444
[20:58:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38]	validation-logloss:0.41740	validation-auc:0.97041	validation-aucpr:0.97455
[20:58:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39]	validation-logloss:0.41257	validation-auc:0.97057	validation-aucpr:0.97472
[20:58:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40]	validation-logloss:0.40791	validation-auc:0.97076	validation-aucpr:0.97489
[20:58:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41]	validation-logloss:0.40408	validation-auc:0.97074	validation-aucpr:0.97486
{'best_iteration': '40', 'best_score': '0.9748941392910312'}
Trial 62, Fold 3: Log loss = 0.40408457793104074, Average precision = 0.974869227836466, ROC-AUC = 0.9707354980689774, Elapsed Time = 7.972263800002111 seconds
Trial 62, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 62, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[20:58:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.68261	validation-auc:0.93563	validation-aucpr:0.93838
[20:58:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.67076	validation-auc:0.96144	validation-aucpr:0.96397
[20:58:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.66066	validation-auc:0.96174	validation-aucpr:0.96735
[20:58:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.65087	validation-auc:0.96233	validation-aucpr:0.96758
[20:58:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.64137	validation-auc:0.96294	validation-aucpr:0.96809
[20:58:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5]	validation-logloss:0.63251	validation-auc:0.96198	validation-aucpr:0.96723
[20:58:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6]	validation-logloss:0.62268	validation-auc:0.96329	validation-aucpr:0.96891
[20:58:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7]	validation-logloss:0.61389	validation-auc:0.96352	validation-aucpr:0.96902
[20:58:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8]	validation-logloss:0.60416	validation-auc:0.96509	validation-aucpr:0.97058
[20:58:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9]	validation-logloss:0.59576	validation-auc:0.96532	validation-aucpr:0.97073
[20:58:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10]	validation-logloss:0.58771	validation-auc:0.96526	validation-aucpr:0.97066
[20:58:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11]	validation-logloss:0.58023	validation-auc:0.96482	validation-aucpr:0.97030
[20:58:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12]	validation-logloss:0.57225	validation-auc:0.96510	validation-aucpr:0.97040
[20:58:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13]	validation-logloss:0.56483	validation-auc:0.96499	validation-aucpr:0.97027
[20:58:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14]	validation-logloss:0.55772	validation-auc:0.96503	validation-aucpr:0.97025
[20:58:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15]	validation-logloss:0.55052	validation-auc:0.96504	validation-aucpr:0.97026
[20:58:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16]	validation-logloss:0.54364	validation-auc:0.96500	validation-aucpr:0.97019
[20:58:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17]	validation-logloss:0.53687	validation-auc:0.96475	validation-aucpr:0.97001
[20:58:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18]	validation-logloss:0.52930	validation-auc:0.96538	validation-aucpr:0.97067
[20:58:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19]	validation-logloss:0.52310	validation-auc:0.96530	validation-aucpr:0.97059
[20:58:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20]	validation-logloss:0.51587	validation-auc:0.96566	validation-aucpr:0.97100
[20:58:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21]	validation-logloss:0.50982	validation-auc:0.96559	validation-aucpr:0.97094
[20:58:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22]	validation-logloss:0.50433	validation-auc:0.96556	validation-aucpr:0.97089
[20:58:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23]	validation-logloss:0.49876	validation-auc:0.96537	validation-aucpr:0.97068
[20:58:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24]	validation-logloss:0.49223	validation-auc:0.96571	validation-aucpr:0.97104
[20:58:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25]	validation-logloss:0.48563	validation-auc:0.96609	validation-aucpr:0.97143
[20:58:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26]	validation-logloss:0.48064	validation-auc:0.96604	validation-aucpr:0.97136
[20:58:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27]	validation-logloss:0.47573	validation-auc:0.96599	validation-aucpr:0.97131
[20:58:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28]	validation-logloss:0.46976	validation-auc:0.96613	validation-aucpr:0.97145
[20:58:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29]	validation-logloss:0.46513	validation-auc:0.96607	validation-aucpr:0.97140
[20:58:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30]	validation-logloss:0.45941	validation-auc:0.96629	validation-aucpr:0.97161
[20:58:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31]	validation-logloss:0.45462	validation-auc:0.96634	validation-aucpr:0.97163
[20:58:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32]	validation-logloss:0.45013	validation-auc:0.96629	validation-aucpr:0.97158
[20:58:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33]	validation-logloss:0.44565	validation-auc:0.96622	validation-aucpr:0.97151
[20:58:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34]	validation-logloss:0.44137	validation-auc:0.96624	validation-aucpr:0.97151
[20:58:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35]	validation-logloss:0.43612	validation-auc:0.96653	validation-aucpr:0.97179
[20:58:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36]	validation-logloss:0.43112	validation-auc:0.96671	validation-aucpr:0.97196
[20:58:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37]	validation-logloss:0.42700	validation-auc:0.96666	validation-aucpr:0.97189
[20:58:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38]	validation-logloss:0.42302	validation-auc:0.96668	validation-aucpr:0.97189
[20:58:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39]	validation-logloss:0.41922	validation-auc:0.96656	validation-aucpr:0.97182
[20:58:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40]	validation-logloss:0.41542	validation-auc:0.96653	validation-aucpr:0.97179
[20:58:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41]	validation-logloss:0.41174	validation-auc:0.96649	validation-aucpr:0.97177
{'best_iteration': '36', 'best_score': '0.9719608080914132'}
Trial 62, Fold 4: Log loss = 0.4117373799923235, Average precision = 0.9717651888435617, ROC-AUC = 0.9664940503015086, Elapsed Time = 7.8552474999996775 seconds
Trial 62, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 62, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[20:58:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.68271	validation-auc:0.93592	validation-aucpr:0.93858
[20:58:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.67102	validation-auc:0.95941	validation-aucpr:0.96306
[20:58:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.65949	validation-auc:0.96464	validation-aucpr:0.96849
[20:58:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.64987	validation-auc:0.96459	validation-aucpr:0.96829
[20:58:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.64025	validation-auc:0.96497	validation-aucpr:0.96857
[20:58:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5]	validation-logloss:0.63030	validation-auc:0.96546	validation-aucpr:0.96957
[20:58:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6]	validation-logloss:0.62159	validation-auc:0.96472	validation-aucpr:0.96857
[20:58:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7]	validation-logloss:0.61179	validation-auc:0.96538	validation-aucpr:0.96880
[20:58:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8]	validation-logloss:0.60329	validation-auc:0.96554	validation-aucpr:0.96941
[20:58:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9]	validation-logloss:0.59502	validation-auc:0.96564	validation-aucpr:0.96938
[20:58:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10]	validation-logloss:0.58633	validation-auc:0.96551	validation-aucpr:0.96936
[20:58:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11]	validation-logloss:0.57923	validation-auc:0.96502	validation-aucpr:0.96880
[20:58:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12]	validation-logloss:0.57144	validation-auc:0.96503	validation-aucpr:0.96884
[20:58:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13]	validation-logloss:0.56416	validation-auc:0.96516	validation-aucpr:0.96963
[20:58:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14]	validation-logloss:0.55714	validation-auc:0.96498	validation-aucpr:0.96942
[20:58:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15]	validation-logloss:0.54911	validation-auc:0.96528	validation-aucpr:0.96974
[20:58:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16]	validation-logloss:0.54262	validation-auc:0.96527	validation-aucpr:0.96988
[20:58:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17]	validation-logloss:0.53480	validation-auc:0.96582	validation-aucpr:0.97038
[20:58:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18]	validation-logloss:0.52843	validation-auc:0.96585	validation-aucpr:0.97044
[20:58:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19]	validation-logloss:0.52225	validation-auc:0.96588	validation-aucpr:0.97043
[20:58:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20]	validation-logloss:0.51624	validation-auc:0.96565	validation-aucpr:0.97026
[20:58:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21]	validation-logloss:0.51029	validation-auc:0.96561	validation-aucpr:0.97018
[20:58:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22]	validation-logloss:0.50349	validation-auc:0.96593	validation-aucpr:0.97045
[20:58:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23]	validation-logloss:0.49768	validation-auc:0.96596	validation-aucpr:0.97046
[20:58:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24]	validation-logloss:0.49146	validation-auc:0.96606	validation-aucpr:0.97057
[20:58:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25]	validation-logloss:0.48512	validation-auc:0.96637	validation-aucpr:0.97084
[20:58:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26]	validation-logloss:0.47986	validation-auc:0.96632	validation-aucpr:0.97082
[20:58:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27]	validation-logloss:0.47374	validation-auc:0.96656	validation-aucpr:0.97107
[20:58:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28]	validation-logloss:0.46888	validation-auc:0.96650	validation-aucpr:0.97099
[20:58:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29]	validation-logloss:0.46289	validation-auc:0.96690	validation-aucpr:0.97130
[20:58:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30]	validation-logloss:0.45827	validation-auc:0.96671	validation-aucpr:0.97117
[20:58:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31]	validation-logloss:0.45364	validation-auc:0.96674	validation-aucpr:0.97123
[20:58:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32]	validation-logloss:0.44826	validation-auc:0.96683	validation-aucpr:0.97135
[20:58:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33]	validation-logloss:0.44305	validation-auc:0.96696	validation-aucpr:0.97148
[20:58:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34]	validation-logloss:0.43877	validation-auc:0.96691	validation-aucpr:0.97141
[20:58:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35]	validation-logloss:0.43360	validation-auc:0.96731	validation-aucpr:0.97175
[20:58:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36]	validation-logloss:0.42864	validation-auc:0.96752	validation-aucpr:0.97193
[20:58:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37]	validation-logloss:0.42471	validation-auc:0.96738	validation-aucpr:0.97181
[20:58:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38]	validation-logloss:0.42069	validation-auc:0.96752	validation-aucpr:0.97187
[20:58:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39]	validation-logloss:0.41602	validation-auc:0.96763	validation-aucpr:0.97201
[20:58:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40]	validation-logloss:0.41150	validation-auc:0.96780	validation-aucpr:0.97217
[20:58:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41]	validation-logloss:0.40700	validation-auc:0.96799	validation-aucpr:0.97233
{'best_iteration': '41', 'best_score': '0.9723345784298949'}
Trial 62, Fold 5: Log loss = 0.40699792927955714, Average precision = 0.9723394565659912, ROC-AUC = 0.9679921966445572, Elapsed Time = 8.108416099999886 seconds
Optimization Progress:  63%|######3   | 63/100 [2:59:56<28:40, 46.50s/it]
Trial 63, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 63, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[0]	validation-logloss:0.67832	validation-auc:0.92353	validation-aucpr:0.92326
[1]	validation-logloss:0.66323	validation-auc:0.94121	validation-aucpr:0.94346
[2]	validation-logloss:0.65079	validation-auc:0.93858	validation-aucpr:0.94112
[3]	validation-logloss:0.63928	validation-auc:0.93992	validation-aucpr:0.94359
[4]	validation-logloss:0.62799	validation-auc:0.93817	validation-aucpr:0.94204
[5]	validation-logloss:0.61553	validation-auc:0.94174	validation-aucpr:0.94691
[6]	validation-logloss:0.60391	validation-auc:0.94419	validation-aucpr:0.95006
[7]	validation-logloss:0.59353	validation-auc:0.94477	validation-aucpr:0.95064
[8]	validation-logloss:0.58297	validation-auc:0.94535	validation-aucpr:0.95151
[9]	validation-logloss:0.57233	validation-auc:0.94666	validation-aucpr:0.95298
[10]	validation-logloss:0.56316	validation-auc:0.94682	validation-aucpr:0.95318
[11]	validation-logloss:0.55484	validation-auc:0.94779	validation-aucpr:0.95413
[12]	validation-logloss:0.54587	validation-auc:0.94813	validation-aucpr:0.95433
[13]	validation-logloss:0.53970	validation-auc:0.94854	validation-aucpr:0.95472
[14]	validation-logloss:0.53207	validation-auc:0.94887	validation-aucpr:0.95516
[15]	validation-logloss:0.52443	validation-auc:0.94913	validation-aucpr:0.95527
[16]	validation-logloss:0.51673	validation-auc:0.94898	validation-aucpr:0.95504
[17]	validation-logloss:0.50996	validation-auc:0.94873	validation-aucpr:0.95471
[18]	validation-logloss:0.50331	validation-auc:0.94898	validation-aucpr:0.95487
[19]	validation-logloss:0.49572	validation-auc:0.94982	validation-aucpr:0.95593
[20]	validation-logloss:0.48951	validation-auc:0.95006	validation-aucpr:0.95639
[21]	validation-logloss:0.48352	validation-auc:0.94987	validation-aucpr:0.95621
[22]	validation-logloss:0.47802	validation-auc:0.94975	validation-aucpr:0.95600
[23]	validation-logloss:0.47252	validation-auc:0.94986	validation-aucpr:0.95602
[24]	validation-logloss:0.46703	validation-auc:0.94978	validation-aucpr:0.95587
[25]	validation-logloss:0.46219	validation-auc:0.94964	validation-aucpr:0.95571
[26]	validation-logloss:0.45673	validation-auc:0.95018	validation-aucpr:0.95624
{'best_iteration': '20', 'best_score': '0.9563913163849957'}
Trial 63, Fold 1: Log loss = 0.45672835759562763, Average precision = 0.9562278721538539, ROC-AUC = 0.9501815198376852, Elapsed Time = 0.45719150000149966 seconds
Trial 63, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 63, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[0]	validation-logloss:0.67858	validation-auc:0.91963	validation-aucpr:0.91597
[1]	validation-logloss:0.66428	validation-auc:0.93085	validation-aucpr:0.93238
[2]	validation-logloss:0.65073	validation-auc:0.93382	validation-aucpr:0.93639
[3]	validation-logloss:0.63874	validation-auc:0.93356	validation-aucpr:0.93664
[4]	validation-logloss:0.62726	validation-auc:0.93563	validation-aucpr:0.93603
[5]	validation-logloss:0.61539	validation-auc:0.93695	validation-aucpr:0.93969
[6]	validation-logloss:0.60487	validation-auc:0.93791	validation-aucpr:0.94148
[7]	validation-logloss:0.59497	validation-auc:0.93961	validation-aucpr:0.94268
[8]	validation-logloss:0.58472	validation-auc:0.94065	validation-aucpr:0.94421
[9]	validation-logloss:0.57620	validation-auc:0.94037	validation-aucpr:0.94378
[10]	validation-logloss:0.56508	validation-auc:0.95059	validation-aucpr:0.95576
[11]	validation-logloss:0.55497	validation-auc:0.95074	validation-aucpr:0.95535
[12]	validation-logloss:0.54663	validation-auc:0.95085	validation-aucpr:0.95556
[13]	validation-logloss:0.53993	validation-auc:0.95195	validation-aucpr:0.95678
[14]	validation-logloss:0.53338	validation-auc:0.95228	validation-aucpr:0.95693
[15]	validation-logloss:0.52579	validation-auc:0.95219	validation-aucpr:0.95670
[16]	validation-logloss:0.51818	validation-auc:0.95189	validation-aucpr:0.95659
[17]	validation-logloss:0.51148	validation-auc:0.95148	validation-aucpr:0.95608
[18]	validation-logloss:0.50464	validation-auc:0.95173	validation-aucpr:0.95635
[19]	validation-logloss:0.49716	validation-auc:0.95292	validation-aucpr:0.95752
[20]	validation-logloss:0.48818	validation-auc:0.95498	validation-aucpr:0.96019
[21]	validation-logloss:0.48203	validation-auc:0.95477	validation-aucpr:0.95997
[22]	validation-logloss:0.47622	validation-auc:0.95474	validation-aucpr:0.95981
[23]	validation-logloss:0.47082	validation-auc:0.95453	validation-aucpr:0.95949
[24]	validation-logloss:0.46308	validation-auc:0.95553	validation-aucpr:0.96049
[25]	validation-logloss:0.45774	validation-auc:0.95569	validation-aucpr:0.96049
[26]	validation-logloss:0.45242	validation-auc:0.95583	validation-aucpr:0.96054
{'best_iteration': '26', 'best_score': '0.9605407520010589'}
Trial 63, Fold 2: Log loss = 0.4524220961549884, Average precision = 0.9604365271065654, ROC-AUC = 0.9558308479575204, Elapsed Time = 0.4858105000021169 seconds
Trial 63, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 63, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[0]	validation-logloss:0.67808	validation-auc:0.92802	validation-aucpr:0.91740
[1]	validation-logloss:0.66489	validation-auc:0.93125	validation-aucpr:0.92869
[2]	validation-logloss:0.65182	validation-auc:0.93639	validation-aucpr:0.93817
[3]	validation-logloss:0.63689	validation-auc:0.94992	validation-aucpr:0.95469
[4]	validation-logloss:0.62565	validation-auc:0.95382	validation-aucpr:0.95862
[5]	validation-logloss:0.61371	validation-auc:0.95547	validation-aucpr:0.96133
[6]	validation-logloss:0.60241	validation-auc:0.95594	validation-aucpr:0.96229
[7]	validation-logloss:0.59327	validation-auc:0.95601	validation-aucpr:0.96199
[8]	validation-logloss:0.58300	validation-auc:0.95635	validation-aucpr:0.96216
[9]	validation-logloss:0.57206	validation-auc:0.95709	validation-aucpr:0.96290
[10]	validation-logloss:0.56253	validation-auc:0.95677	validation-aucpr:0.96287
[11]	validation-logloss:0.55348	validation-auc:0.95712	validation-aucpr:0.96283
[12]	validation-logloss:0.54514	validation-auc:0.95743	validation-aucpr:0.96304
[13]	validation-logloss:0.53661	validation-auc:0.95839	validation-aucpr:0.96421
[14]	validation-logloss:0.52893	validation-auc:0.95898	validation-aucpr:0.96462
[15]	validation-logloss:0.52120	validation-auc:0.95897	validation-aucpr:0.96466
[16]	validation-logloss:0.51400	validation-auc:0.95858	validation-aucpr:0.96437
[17]	validation-logloss:0.50722	validation-auc:0.95835	validation-aucpr:0.96412
[18]	validation-logloss:0.50061	validation-auc:0.95839	validation-aucpr:0.96416
[19]	validation-logloss:0.49371	validation-auc:0.95826	validation-aucpr:0.96400
[20]	validation-logloss:0.48723	validation-auc:0.95826	validation-aucpr:0.96399
[21]	validation-logloss:0.48111	validation-auc:0.95806	validation-aucpr:0.96378
[22]	validation-logloss:0.47444	validation-auc:0.95856	validation-aucpr:0.96416
[23]	validation-logloss:0.46832	validation-auc:0.95830	validation-aucpr:0.96392
[24]	validation-logloss:0.46284	validation-auc:0.95812	validation-aucpr:0.96368
[25]	validation-logloss:0.45699	validation-auc:0.95841	validation-aucpr:0.96389
[26]	validation-logloss:0.45128	validation-auc:0.95816	validation-aucpr:0.96361
{'best_iteration': '15', 'best_score': '0.9646621250956404'}
Trial 63, Fold 3: Log loss = 0.4512801008490302, Average precision = 0.9636091254630796, ROC-AUC = 0.9581633856940606, Elapsed Time = 0.5337794000006397 seconds
Trial 63, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 63, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[0]	validation-logloss:0.67815	validation-auc:0.92384	validation-aucpr:0.91387
[1]	validation-logloss:0.66368	validation-auc:0.93671	validation-aucpr:0.93977
[2]	validation-logloss:0.64849	validation-auc:0.95031	validation-aucpr:0.95568
[3]	validation-logloss:0.63388	validation-auc:0.95210	validation-aucpr:0.95842
[4]	validation-logloss:0.62180	validation-auc:0.95293	validation-aucpr:0.95851
[5]	validation-logloss:0.61078	validation-auc:0.95317	validation-aucpr:0.95918
[6]	validation-logloss:0.59838	validation-auc:0.95474	validation-aucpr:0.96043
[7]	validation-logloss:0.58663	validation-auc:0.95574	validation-aucpr:0.96225
[8]	validation-logloss:0.57625	validation-auc:0.95639	validation-aucpr:0.96303
[9]	validation-logloss:0.56822	validation-auc:0.95654	validation-aucpr:0.96318
[10]	validation-logloss:0.55823	validation-auc:0.95722	validation-aucpr:0.96393
[11]	validation-logloss:0.54818	validation-auc:0.95711	validation-aucpr:0.96403
[12]	validation-logloss:0.53973	validation-auc:0.95669	validation-aucpr:0.96361
[13]	validation-logloss:0.53232	validation-auc:0.95731	validation-aucpr:0.96433
[14]	validation-logloss:0.52495	validation-auc:0.95758	validation-aucpr:0.96463
[15]	validation-logloss:0.51639	validation-auc:0.95789	validation-aucpr:0.96497
[16]	validation-logloss:0.50923	validation-auc:0.95758	validation-aucpr:0.96453
[17]	validation-logloss:0.50267	validation-auc:0.95704	validation-aucpr:0.96406
[18]	validation-logloss:0.49579	validation-auc:0.95712	validation-aucpr:0.96419
[19]	validation-logloss:0.48830	validation-auc:0.95758	validation-aucpr:0.96460
[20]	validation-logloss:0.48195	validation-auc:0.95778	validation-aucpr:0.96478
[21]	validation-logloss:0.47566	validation-auc:0.95755	validation-aucpr:0.96452
[22]	validation-logloss:0.46990	validation-auc:0.95742	validation-aucpr:0.96435
[23]	validation-logloss:0.46449	validation-auc:0.95745	validation-aucpr:0.96428
[24]	validation-logloss:0.45901	validation-auc:0.95758	validation-aucpr:0.96439
[25]	validation-logloss:0.45467	validation-auc:0.95769	validation-aucpr:0.96442
[26]	validation-logloss:0.44921	validation-auc:0.95776	validation-aucpr:0.96452
{'best_iteration': '15', 'best_score': '0.9649736225770621'}
Trial 63, Fold 4: Log loss = 0.4492070739008998, Average precision = 0.9645035016118194, ROC-AUC = 0.9577632358586617, Elapsed Time = 0.5850017999982811 seconds
Trial 63, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 63, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[0]	validation-logloss:0.67909	validation-auc:0.90844	validation-aucpr:0.89348
[1]	validation-logloss:0.66187	validation-auc:0.94828	validation-aucpr:0.95390
[2]	validation-logloss:0.64937	validation-auc:0.94753	validation-aucpr:0.95366
[3]	validation-logloss:0.63876	validation-auc:0.94667	validation-aucpr:0.95276
[4]	validation-logloss:0.62707	validation-auc:0.94690	validation-aucpr:0.95194
[5]	validation-logloss:0.61460	validation-auc:0.95048	validation-aucpr:0.95669
[6]	validation-logloss:0.60402	validation-auc:0.95076	validation-aucpr:0.95645
[7]	validation-logloss:0.59320	validation-auc:0.95066	validation-aucpr:0.95615
[8]	validation-logloss:0.58295	validation-auc:0.94991	validation-aucpr:0.95531
[9]	validation-logloss:0.57265	validation-auc:0.94996	validation-aucpr:0.95521
[10]	validation-logloss:0.56375	validation-auc:0.94980	validation-aucpr:0.95456
[11]	validation-logloss:0.55517	validation-auc:0.95042	validation-aucpr:0.95580
[12]	validation-logloss:0.54707	validation-auc:0.95069	validation-aucpr:0.95599
[13]	validation-logloss:0.53702	validation-auc:0.95196	validation-aucpr:0.95761
[14]	validation-logloss:0.53108	validation-auc:0.95211	validation-aucpr:0.95812
[15]	validation-logloss:0.52338	validation-auc:0.95234	validation-aucpr:0.95847
[16]	validation-logloss:0.51671	validation-auc:0.95211	validation-aucpr:0.95836
[17]	validation-logloss:0.51018	validation-auc:0.95165	validation-aucpr:0.95798
[18]	validation-logloss:0.50264	validation-auc:0.95232	validation-aucpr:0.95865
[19]	validation-logloss:0.49641	validation-auc:0.95235	validation-aucpr:0.95866
[20]	validation-logloss:0.49069	validation-auc:0.95236	validation-aucpr:0.95843
[21]	validation-logloss:0.48386	validation-auc:0.95275	validation-aucpr:0.95877
[22]	validation-logloss:0.47912	validation-auc:0.95247	validation-aucpr:0.95858
[23]	validation-logloss:0.47315	validation-auc:0.95256	validation-aucpr:0.95856
[24]	validation-logloss:0.46778	validation-auc:0.95288	validation-aucpr:0.95877
[25]	validation-logloss:0.46302	validation-auc:0.95264	validation-aucpr:0.95852
[26]	validation-logloss:0.45551	validation-auc:0.95342	validation-aucpr:0.95948
{'best_iteration': '26', 'best_score': '0.9594836374150355'}
Trial 63, Fold 5: Log loss = 0.4555060647716498, Average precision = 0.9594378534684262, ROC-AUC = 0.9534234806681159, Elapsed Time = 0.5480397999999695 seconds
Optimization Progress:  64%|######4   | 64/100 [3:00:09<21:43, 36.20s/it]
Trial 64, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 64, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
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[74]	validation-logloss:0.20326	validation-auc:0.97039	validation-aucpr:0.97505
[75]	validation-logloss:0.20289	validation-auc:0.97056	validation-aucpr:0.97514
[76]	validation-logloss:0.20265	validation-auc:0.97066	validation-aucpr:0.97523
[77]	validation-logloss:0.20267	validation-auc:0.97067	validation-aucpr:0.97522
[78]	validation-logloss:0.20250	validation-auc:0.97076	validation-aucpr:0.97532
[79]	validation-logloss:0.20239	validation-auc:0.97089	validation-aucpr:0.97536
[80]	validation-logloss:0.20232	validation-auc:0.97093	validation-aucpr:0.97539
[81]	validation-logloss:0.20230	validation-auc:0.97093	validation-aucpr:0.97540
[82]	validation-logloss:0.20227	validation-auc:0.97087	validation-aucpr:0.97533
[83]	validation-logloss:0.20229	validation-auc:0.97087	validation-aucpr:0.97532
[84]	validation-logloss:0.20244	validation-auc:0.97082	validation-aucpr:0.97522
[85]	validation-logloss:0.20264	validation-auc:0.97082	validation-aucpr:0.97517
[86]	validation-logloss:0.20264	validation-auc:0.97089	validation-aucpr:0.97529
[87]	validation-logloss:0.20267	validation-auc:0.97093	validation-aucpr:0.97538
[88]	validation-logloss:0.20275	validation-auc:0.97098	validation-aucpr:0.97539
{'best_iteration': '81', 'best_score': '0.9754009204043076'}
Trial 64, Fold 1: Log loss = 0.20275462881741438, Average precision = 0.9753897889959595, ROC-AUC = 0.9709829643490437, Elapsed Time = 16.874604999997246 seconds
Trial 64, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 64, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
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[4]	validation-logloss:0.53460	validation-auc:0.96408	validation-aucpr:0.96791
[5]	validation-logloss:0.51022	validation-auc:0.96425	validation-aucpr:0.96822
[6]	validation-logloss:0.48796	validation-auc:0.96531	validation-aucpr:0.96985
[7]	validation-logloss:0.46778	validation-auc:0.96583	validation-aucpr:0.97035
[8]	validation-logloss:0.44942	validation-auc:0.96616	validation-aucpr:0.97072
[9]	validation-logloss:0.43197	validation-auc:0.96656	validation-aucpr:0.97106
[10]	validation-logloss:0.41559	validation-auc:0.96698	validation-aucpr:0.97153
[11]	validation-logloss:0.40121	validation-auc:0.96758	validation-aucpr:0.97188
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[75]	validation-logloss:0.19260	validation-auc:0.97171	validation-aucpr:0.97441
[76]	validation-logloss:0.19244	validation-auc:0.97170	validation-aucpr:0.97439
[77]	validation-logloss:0.19257	validation-auc:0.97160	validation-aucpr:0.97416
[78]	validation-logloss:0.19226	validation-auc:0.97178	validation-aucpr:0.97422
[79]	validation-logloss:0.19223	validation-auc:0.97177	validation-aucpr:0.97400
[80]	validation-logloss:0.19229	validation-auc:0.97172	validation-aucpr:0.97396
[81]	validation-logloss:0.19192	validation-auc:0.97180	validation-aucpr:0.97392
[82]	validation-logloss:0.19184	validation-auc:0.97190	validation-aucpr:0.97439
[83]	validation-logloss:0.19139	validation-auc:0.97203	validation-aucpr:0.97468
[84]	validation-logloss:0.19147	validation-auc:0.97204	validation-aucpr:0.97467
[85]	validation-logloss:0.19145	validation-auc:0.97207	validation-aucpr:0.97454
[86]	validation-logloss:0.19148	validation-auc:0.97199	validation-aucpr:0.97464
[87]	validation-logloss:0.19151	validation-auc:0.97207	validation-aucpr:0.97509
[88]	validation-logloss:0.19154	validation-auc:0.97201	validation-aucpr:0.97501
{'best_iteration': '87', 'best_score': '0.9750940179143601'}
Trial 64, Fold 2: Log loss = 0.19154041852961148, Average precision = 0.9750110784779089, ROC-AUC = 0.9720083758707668, Elapsed Time = 16.697156499998528 seconds
Trial 64, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 64, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[0]	validation-logloss:0.65331	validation-auc:0.89847	validation-aucpr:0.84646
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[4]	validation-logloss:0.52938	validation-auc:0.96526	validation-aucpr:0.96460
[5]	validation-logloss:0.50616	validation-auc:0.96546	validation-aucpr:0.96502
[6]	validation-logloss:0.48629	validation-auc:0.96584	validation-aucpr:0.96477
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[8]	validation-logloss:0.44571	validation-auc:0.96761	validation-aucpr:0.96779
[9]	validation-logloss:0.42918	validation-auc:0.96838	validation-aucpr:0.97060
[10]	validation-logloss:0.41306	validation-auc:0.96820	validation-aucpr:0.97067
[11]	validation-logloss:0.39846	validation-auc:0.96850	validation-aucpr:0.97091
[12]	validation-logloss:0.38571	validation-auc:0.96871	validation-aucpr:0.97102
[13]	validation-logloss:0.37270	validation-auc:0.96888	validation-aucpr:0.97128
[14]	validation-logloss:0.36208	validation-auc:0.96864	validation-aucpr:0.97114
[15]	validation-logloss:0.35034	validation-auc:0.96882	validation-aucpr:0.97131
[16]	validation-logloss:0.33996	validation-auc:0.96969	validation-aucpr:0.97320
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{'best_iteration': '38', 'best_score': '0.9756599265861333'}
Trial 64, Fold 3: Log loss = 0.19373711385512057, Average precision = 0.9743362243542404, ROC-AUC = 0.972316567908428, Elapsed Time = 17.79897510000228 seconds
Trial 64, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 64, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
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{'best_iteration': '82', 'best_score': '0.975269879696509'}
Trial 64, Fold 4: Log loss = 0.1980478209605085, Average precision = 0.9752385460516755, ROC-AUC = 0.9707982825096391, Elapsed Time = 18.344916800000647 seconds
Trial 64, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 64, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
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[81]	validation-logloss:0.20238	validation-auc:0.97066	validation-aucpr:0.97434
[82]	validation-logloss:0.20237	validation-auc:0.97075	validation-aucpr:0.97463
[83]	validation-logloss:0.20246	validation-auc:0.97072	validation-aucpr:0.97459
[84]	validation-logloss:0.20292	validation-auc:0.97056	validation-aucpr:0.97447
[85]	validation-logloss:0.20309	validation-auc:0.97053	validation-aucpr:0.97440
[86]	validation-logloss:0.20329	validation-auc:0.97045	validation-aucpr:0.97433
[87]	validation-logloss:0.20347	validation-auc:0.97043	validation-aucpr:0.97434
[88]	validation-logloss:0.20347	validation-auc:0.97041	validation-aucpr:0.97431
{'best_iteration': '82', 'best_score': '0.9746318812315987'}
Trial 64, Fold 5: Log loss = 0.20347111347276622, Average precision = 0.9743139083594021, ROC-AUC = 0.9704094532077365, Elapsed Time = 17.027871199999936 seconds
Optimization Progress:  65%|######5   | 65/100 [3:01:43<31:19, 53.69s/it]
Trial 65, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 65, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
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[4]	validation-logloss:0.47213	validation-auc:0.96449	validation-aucpr:0.96940
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[78]	validation-logloss:0.19042	validation-auc:0.97327	validation-aucpr:0.97710
[79]	validation-logloss:0.19051	validation-auc:0.97320	validation-aucpr:0.97705
[80]	validation-logloss:0.19051	validation-auc:0.97321	validation-aucpr:0.97708
[81]	validation-logloss:0.19039	validation-auc:0.97326	validation-aucpr:0.97714
[82]	validation-logloss:0.19030	validation-auc:0.97325	validation-aucpr:0.97714
[83]	validation-logloss:0.19029	validation-auc:0.97328	validation-aucpr:0.97718
[84]	validation-logloss:0.19020	validation-auc:0.97332	validation-aucpr:0.97722
[85]	validation-logloss:0.19029	validation-auc:0.97329	validation-aucpr:0.97718
[86]	validation-logloss:0.19044	validation-auc:0.97319	validation-aucpr:0.97710
[87]	validation-logloss:0.19080	validation-auc:0.97304	validation-aucpr:0.97696
[88]	validation-logloss:0.19075	validation-auc:0.97309	validation-aucpr:0.97700
[89]	validation-logloss:0.19067	validation-auc:0.97317	validation-aucpr:0.97709
[90]	validation-logloss:0.19056	validation-auc:0.97319	validation-aucpr:0.97709
[91]	validation-logloss:0.19071	validation-auc:0.97310	validation-aucpr:0.97699
[92]	validation-logloss:0.19077	validation-auc:0.97303	validation-aucpr:0.97695
[93]	validation-logloss:0.19089	validation-auc:0.97298	validation-aucpr:0.97689
[94]	validation-logloss:0.19094	validation-auc:0.97295	validation-aucpr:0.97685
[95]	validation-logloss:0.19072	validation-auc:0.97301	validation-aucpr:0.97690
[96]	validation-logloss:0.19096	validation-auc:0.97291	validation-aucpr:0.97684
{'best_iteration': '84', 'best_score': '0.9772210952959658'}
Trial 65, Fold 1: Log loss = 0.19095632367941393, Average precision = 0.9768403685158552, ROC-AUC = 0.9729122007379687, Elapsed Time = 2.4595071999974607 seconds
Trial 65, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 65, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[0]	validation-logloss:0.63236	validation-auc:0.95699	validation-aucpr:0.95729
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[2]	validation-logloss:0.53829	validation-auc:0.96638	validation-aucpr:0.97073
[3]	validation-logloss:0.50432	validation-auc:0.96749	validation-aucpr:0.97148
[4]	validation-logloss:0.47047	validation-auc:0.96843	validation-aucpr:0.97218
[5]	validation-logloss:0.44143	validation-auc:0.96867	validation-aucpr:0.97227
[6]	validation-logloss:0.41628	validation-auc:0.96894	validation-aucpr:0.97239
[7]	validation-logloss:0.39390	validation-auc:0.96907	validation-aucpr:0.97237
[8]	validation-logloss:0.37404	validation-auc:0.96921	validation-aucpr:0.97261
[9]	validation-logloss:0.35595	validation-auc:0.96964	validation-aucpr:0.97345
[10]	validation-logloss:0.33948	validation-auc:0.97002	validation-aucpr:0.97378
[11]	validation-logloss:0.32467	validation-auc:0.97021	validation-aucpr:0.97399
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[79]	validation-logloss:0.18194	validation-auc:0.97397	validation-aucpr:0.97681
[80]	validation-logloss:0.18182	validation-auc:0.97395	validation-aucpr:0.97678
[81]	validation-logloss:0.18180	validation-auc:0.97390	validation-aucpr:0.97672
[82]	validation-logloss:0.18152	validation-auc:0.97403	validation-aucpr:0.97680
[83]	validation-logloss:0.18130	validation-auc:0.97405	validation-aucpr:0.97684
[84]	validation-logloss:0.18128	validation-auc:0.97410	validation-aucpr:0.97690
[85]	validation-logloss:0.18140	validation-auc:0.97407	validation-aucpr:0.97686
[86]	validation-logloss:0.18099	validation-auc:0.97421	validation-aucpr:0.97699
[87]	validation-logloss:0.18107	validation-auc:0.97415	validation-aucpr:0.97689
[88]	validation-logloss:0.18101	validation-auc:0.97417	validation-aucpr:0.97692
[89]	validation-logloss:0.18099	validation-auc:0.97416	validation-aucpr:0.97694
[90]	validation-logloss:0.18096	validation-auc:0.97419	validation-aucpr:0.97700
[91]	validation-logloss:0.18111	validation-auc:0.97414	validation-aucpr:0.97695
[92]	validation-logloss:0.18114	validation-auc:0.97406	validation-aucpr:0.97688
[93]	validation-logloss:0.18082	validation-auc:0.97414	validation-aucpr:0.97691
[94]	validation-logloss:0.18068	validation-auc:0.97422	validation-aucpr:0.97695
[95]	validation-logloss:0.18072	validation-auc:0.97417	validation-aucpr:0.97697
[96]	validation-logloss:0.18067	validation-auc:0.97416	validation-aucpr:0.97698
{'best_iteration': '90', 'best_score': '0.9769978487826361'}
Trial 65, Fold 2: Log loss = 0.1806736366945864, Average precision = 0.9769836979905047, ROC-AUC = 0.9741555311612561, Elapsed Time = 2.701753800000006 seconds
Trial 65, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 65, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[0]	validation-logloss:0.63211	validation-auc:0.96084	validation-aucpr:0.96028
[1]	validation-logloss:0.58061	validation-auc:0.96425	validation-aucpr:0.96580
[2]	validation-logloss:0.53715	validation-auc:0.96485	validation-aucpr:0.96824
[3]	validation-logloss:0.49834	validation-auc:0.96757	validation-aucpr:0.97166
[4]	validation-logloss:0.46540	validation-auc:0.96855	validation-aucpr:0.97223
[5]	validation-logloss:0.44027	validation-auc:0.96884	validation-aucpr:0.97263
[6]	validation-logloss:0.41484	validation-auc:0.96813	validation-aucpr:0.97219
[7]	validation-logloss:0.39235	validation-auc:0.96884	validation-aucpr:0.97298
[8]	validation-logloss:0.37443	validation-auc:0.96879	validation-aucpr:0.97298
[9]	validation-logloss:0.35600	validation-auc:0.96889	validation-aucpr:0.97311
[10]	validation-logloss:0.33951	validation-auc:0.96917	validation-aucpr:0.97336
[11]	validation-logloss:0.32506	validation-auc:0.96931	validation-aucpr:0.97343
[12]	validation-logloss:0.31221	validation-auc:0.96947	validation-aucpr:0.97350
[13]	validation-logloss:0.30117	validation-auc:0.96930	validation-aucpr:0.97327
[14]	validation-logloss:0.29119	validation-auc:0.96934	validation-aucpr:0.97330
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{'best_iteration': '93', 'best_score': '0.9766434736304546'}
Trial 65, Fold 3: Log loss = 0.18468022857613342, Average precision = 0.9764594632367195, ROC-AUC = 0.9739638511138292, Elapsed Time = 2.7284695000016654 seconds
Trial 65, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 65, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
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{'best_iteration': '57', 'best_score': '0.976076300519751'}
Trial 65, Fold 4: Log loss = 0.18949136138858005, Average precision = 0.9757470371557592, ROC-AUC = 0.9716955657131412, Elapsed Time = 2.6781521999982942 seconds
Trial 65, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 65, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
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[85]	validation-logloss:0.19052	validation-auc:0.97343	validation-aucpr:0.97634
[86]	validation-logloss:0.19054	validation-auc:0.97335	validation-aucpr:0.97616
[87]	validation-logloss:0.19040	validation-auc:0.97335	validation-aucpr:0.97615
[88]	validation-logloss:0.19033	validation-auc:0.97335	validation-aucpr:0.97614
[89]	validation-logloss:0.19054	validation-auc:0.97324	validation-aucpr:0.97599
[90]	validation-logloss:0.19057	validation-auc:0.97322	validation-aucpr:0.97596
[91]	validation-logloss:0.19064	validation-auc:0.97317	validation-aucpr:0.97588
[92]	validation-logloss:0.19067	validation-auc:0.97313	validation-aucpr:0.97576
[93]	validation-logloss:0.19051	validation-auc:0.97318	validation-aucpr:0.97580
[94]	validation-logloss:0.19071	validation-auc:0.97311	validation-aucpr:0.97585
[95]	validation-logloss:0.19087	validation-auc:0.97303	validation-aucpr:0.97584
[96]	validation-logloss:0.19068	validation-auc:0.97310	validation-aucpr:0.97600
{'best_iteration': '84', 'best_score': '0.9764759330080807'}
Trial 65, Fold 5: Log loss = 0.19067964677015295, Average precision = 0.9760026014555567, ROC-AUC = 0.9731033944596176, Elapsed Time = 2.712488900000608 seconds
Optimization Progress:  66%|######6   | 66/100 [3:02:04<24:52, 43.90s/it]
Trial 66, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 66, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[0]	validation-logloss:0.66326	validation-auc:0.94744	validation-aucpr:0.92501
[1]	validation-logloss:0.63903	validation-auc:0.95849	validation-aucpr:0.95428
[2]	validation-logloss:0.61594	validation-auc:0.96031	validation-aucpr:0.95947
[3]	validation-logloss:0.59520	validation-auc:0.96201	validation-aucpr:0.96459
[4]	validation-logloss:0.57298	validation-auc:0.96370	validation-aucpr:0.96639
[5]	validation-logloss:0.55201	validation-auc:0.96421	validation-aucpr:0.96559
[6]	validation-logloss:0.53231	validation-auc:0.96527	validation-aucpr:0.96632
[7]	validation-logloss:0.51598	validation-auc:0.96536	validation-aucpr:0.96836
[8]	validation-logloss:0.50123	validation-auc:0.96552	validation-aucpr:0.96853
[9]	validation-logloss:0.48724	validation-auc:0.96581	validation-aucpr:0.96871
[10]	validation-logloss:0.47435	validation-auc:0.96579	validation-aucpr:0.96892
[11]	validation-logloss:0.46179	validation-auc:0.96582	validation-aucpr:0.96894
[12]	validation-logloss:0.45031	validation-auc:0.96557	validation-aucpr:0.96872
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[44]	validation-logloss:0.25285	validation-auc:0.97024	validation-aucpr:0.97253
[45]	validation-logloss:0.25040	validation-auc:0.97037	validation-aucpr:0.97265
[46]	validation-logloss:0.24749	validation-auc:0.97048	validation-aucpr:0.97289
[47]	validation-logloss:0.24492	validation-auc:0.97054	validation-aucpr:0.97291
[48]	validation-logloss:0.24312	validation-auc:0.97052	validation-aucpr:0.97290
[49]	validation-logloss:0.24046	validation-auc:0.97065	validation-aucpr:0.97287
[50]	validation-logloss:0.23870	validation-auc:0.97068	validation-aucpr:0.97287
[51]	validation-logloss:0.23643	validation-auc:0.97084	validation-aucpr:0.97301
[52]	validation-logloss:0.23492	validation-auc:0.97082	validation-aucpr:0.97299
[53]	validation-logloss:0.23276	validation-auc:0.97089	validation-aucpr:0.97305
[54]	validation-logloss:0.23135	validation-auc:0.97081	validation-aucpr:0.97296
[55]	validation-logloss:0.22992	validation-auc:0.97086	validation-aucpr:0.97299
[56]	validation-logloss:0.22782	validation-auc:0.97103	validation-aucpr:0.97330
[57]	validation-logloss:0.22604	validation-auc:0.97112	validation-aucpr:0.97341
[58]	validation-logloss:0.22478	validation-auc:0.97118	validation-aucpr:0.97344
[59]	validation-logloss:0.22296	validation-auc:0.97134	validation-aucpr:0.97341
[60]	validation-logloss:0.22122	validation-auc:0.97151	validation-aucpr:0.97356
{'best_iteration': '60', 'best_score': '0.973559109648379'}
Trial 66, Fold 1: Log loss = 0.22121907431323887, Average precision = 0.9740674502888876, ROC-AUC = 0.9715058066034805, Elapsed Time = 1.7955819999988307 seconds
Trial 66, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 66, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[0]	validation-logloss:0.66278	validation-auc:0.94974	validation-aucpr:0.94237
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[2]	validation-logloss:0.61009	validation-auc:0.96484	validation-aucpr:0.96897
[3]	validation-logloss:0.58614	validation-auc:0.96721	validation-aucpr:0.97118
[4]	validation-logloss:0.56672	validation-auc:0.96730	validation-aucpr:0.97108
[5]	validation-logloss:0.54815	validation-auc:0.96765	validation-aucpr:0.97136
[6]	validation-logloss:0.52861	validation-auc:0.96826	validation-aucpr:0.97188
[7]	validation-logloss:0.51075	validation-auc:0.96822	validation-aucpr:0.97189
[8]	validation-logloss:0.49538	validation-auc:0.96805	validation-aucpr:0.97171
[9]	validation-logloss:0.47921	validation-auc:0.96850	validation-aucpr:0.97208
[10]	validation-logloss:0.46583	validation-auc:0.96853	validation-aucpr:0.97203
[11]	validation-logloss:0.45127	validation-auc:0.96906	validation-aucpr:0.97244
[12]	validation-logloss:0.43776	validation-auc:0.96940	validation-aucpr:0.97270
[13]	validation-logloss:0.42483	validation-auc:0.96991	validation-aucpr:0.97309
[14]	validation-logloss:0.41278	validation-auc:0.97008	validation-aucpr:0.97314
[15]	validation-logloss:0.40147	validation-auc:0.97034	validation-aucpr:0.97336
[16]	validation-logloss:0.39220	validation-auc:0.97033	validation-aucpr:0.97336
[17]	validation-logloss:0.38199	validation-auc:0.97054	validation-aucpr:0.97360
[18]	validation-logloss:0.37274	validation-auc:0.97063	validation-aucpr:0.97367
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[38]	validation-logloss:0.25972	validation-auc:0.97203	validation-aucpr:0.97501
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[41]	validation-logloss:0.24992	validation-auc:0.97213	validation-aucpr:0.97499
[42]	validation-logloss:0.24684	validation-auc:0.97225	validation-aucpr:0.97510
[43]	validation-logloss:0.24433	validation-auc:0.97226	validation-aucpr:0.97510
[44]	validation-logloss:0.24144	validation-auc:0.97235	validation-aucpr:0.97516
[45]	validation-logloss:0.23895	validation-auc:0.97228	validation-aucpr:0.97512
[46]	validation-logloss:0.23632	validation-auc:0.97227	validation-aucpr:0.97507
[47]	validation-logloss:0.23421	validation-auc:0.97242	validation-aucpr:0.97516
[48]	validation-logloss:0.23203	validation-auc:0.97252	validation-aucpr:0.97524
[49]	validation-logloss:0.22985	validation-auc:0.97245	validation-aucpr:0.97522
[50]	validation-logloss:0.22794	validation-auc:0.97255	validation-aucpr:0.97530
[51]	validation-logloss:0.22592	validation-auc:0.97251	validation-aucpr:0.97528
[52]	validation-logloss:0.22395	validation-auc:0.97255	validation-aucpr:0.97528
[53]	validation-logloss:0.22205	validation-auc:0.97264	validation-aucpr:0.97528
[54]	validation-logloss:0.22018	validation-auc:0.97270	validation-aucpr:0.97533
[55]	validation-logloss:0.21841	validation-auc:0.97280	validation-aucpr:0.97540
[56]	validation-logloss:0.21675	validation-auc:0.97279	validation-aucpr:0.97539
[57]	validation-logloss:0.21491	validation-auc:0.97291	validation-aucpr:0.97546
[58]	validation-logloss:0.21357	validation-auc:0.97295	validation-aucpr:0.97548
[59]	validation-logloss:0.21187	validation-auc:0.97304	validation-aucpr:0.97556
[60]	validation-logloss:0.21044	validation-auc:0.97316	validation-aucpr:0.97561
{'best_iteration': '60', 'best_score': '0.9756137684891409'}
Trial 66, Fold 2: Log loss = 0.2104350181056542, Average precision = 0.9755512232417399, ROC-AUC = 0.9731561961200518, Elapsed Time = 1.952999400000408 seconds
Trial 66, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 66, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[0]	validation-logloss:0.66268	validation-auc:0.95210	validation-aucpr:0.95295
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[2]	validation-logloss:0.61340	validation-auc:0.96345	validation-aucpr:0.96666
[3]	validation-logloss:0.59172	validation-auc:0.96439	validation-aucpr:0.96763
[4]	validation-logloss:0.56913	validation-auc:0.96544	validation-aucpr:0.96915
[5]	validation-logloss:0.54809	validation-auc:0.96597	validation-aucpr:0.96983
[6]	validation-logloss:0.53156	validation-auc:0.96662	validation-aucpr:0.97098
[7]	validation-logloss:0.51295	validation-auc:0.96777	validation-aucpr:0.97216
[8]	validation-logloss:0.49552	validation-auc:0.96809	validation-aucpr:0.97253
[9]	validation-logloss:0.48132	validation-auc:0.96821	validation-aucpr:0.97234
[10]	validation-logloss:0.46554	validation-auc:0.96850	validation-aucpr:0.97257
[11]	validation-logloss:0.45330	validation-auc:0.96839	validation-aucpr:0.97312
[12]	validation-logloss:0.44156	validation-auc:0.96875	validation-aucpr:0.97340
[13]	validation-logloss:0.42889	validation-auc:0.96894	validation-aucpr:0.97358
[14]	validation-logloss:0.41655	validation-auc:0.96931	validation-aucpr:0.97388
[15]	validation-logloss:0.40508	validation-auc:0.96949	validation-aucpr:0.97407
[16]	validation-logloss:0.39463	validation-auc:0.96950	validation-aucpr:0.97409
[17]	validation-logloss:0.38596	validation-auc:0.96937	validation-aucpr:0.97402
[18]	validation-logloss:0.37743	validation-auc:0.96943	validation-aucpr:0.97402
[19]	validation-logloss:0.36811	validation-auc:0.96964	validation-aucpr:0.97416
[20]	validation-logloss:0.35891	validation-auc:0.96999	validation-aucpr:0.97444
[21]	validation-logloss:0.35066	validation-auc:0.97034	validation-aucpr:0.97473
[22]	validation-logloss:0.34269	validation-auc:0.97038	validation-aucpr:0.97480
[23]	validation-logloss:0.33504	validation-auc:0.97053	validation-aucpr:0.97492
[24]	validation-logloss:0.32876	validation-auc:0.97056	validation-aucpr:0.97495
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[37]	validation-logloss:0.26565	validation-auc:0.97120	validation-aucpr:0.97522
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[44]	validation-logloss:0.24410	validation-auc:0.97194	validation-aucpr:0.97594
[45]	validation-logloss:0.24124	validation-auc:0.97197	validation-aucpr:0.97598
[46]	validation-logloss:0.23874	validation-auc:0.97197	validation-aucpr:0.97599
[47]	validation-logloss:0.23613	validation-auc:0.97209	validation-aucpr:0.97608
[48]	validation-logloss:0.23380	validation-auc:0.97214	validation-aucpr:0.97612
[49]	validation-logloss:0.23159	validation-auc:0.97214	validation-aucpr:0.97615
[50]	validation-logloss:0.22934	validation-auc:0.97221	validation-aucpr:0.97621
[51]	validation-logloss:0.22729	validation-auc:0.97224	validation-aucpr:0.97624
[52]	validation-logloss:0.22544	validation-auc:0.97220	validation-aucpr:0.97621
[53]	validation-logloss:0.22340	validation-auc:0.97220	validation-aucpr:0.97620
[54]	validation-logloss:0.22168	validation-auc:0.97216	validation-aucpr:0.97617
[55]	validation-logloss:0.22050	validation-auc:0.97210	validation-aucpr:0.97609
[56]	validation-logloss:0.21922	validation-auc:0.97210	validation-aucpr:0.97606
[57]	validation-logloss:0.21764	validation-auc:0.97215	validation-aucpr:0.97610
[58]	validation-logloss:0.21587	validation-auc:0.97229	validation-aucpr:0.97622
[59]	validation-logloss:0.21453	validation-auc:0.97230	validation-aucpr:0.97622
[60]	validation-logloss:0.21306	validation-auc:0.97238	validation-aucpr:0.97629
{'best_iteration': '60', 'best_score': '0.9762925367757626'}
Trial 66, Fold 3: Log loss = 0.2130604057966504, Average precision = 0.9762961959857607, ROC-AUC = 0.9723778905143936, Elapsed Time = 2.1198683000002347 seconds
Trial 66, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 66, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[0]	validation-logloss:0.66279	validation-auc:0.94747	validation-aucpr:0.92289
[1]	validation-logloss:0.63758	validation-auc:0.96398	validation-aucpr:0.96642
[2]	validation-logloss:0.61157	validation-auc:0.96684	validation-aucpr:0.97024
[3]	validation-logloss:0.58741	validation-auc:0.96806	validation-aucpr:0.97142
[4]	validation-logloss:0.56524	validation-auc:0.96855	validation-aucpr:0.97155
[5]	validation-logloss:0.54712	validation-auc:0.96722	validation-aucpr:0.97034
[6]	validation-logloss:0.52758	validation-auc:0.96724	validation-aucpr:0.97028
[7]	validation-logloss:0.51174	validation-auc:0.96695	validation-aucpr:0.97024
[8]	validation-logloss:0.49663	validation-auc:0.96697	validation-aucpr:0.97022
[9]	validation-logloss:0.48034	validation-auc:0.96761	validation-aucpr:0.97080
[10]	validation-logloss:0.46539	validation-auc:0.96827	validation-aucpr:0.97328
[11]	validation-logloss:0.45201	validation-auc:0.96816	validation-aucpr:0.97317
[12]	validation-logloss:0.44130	validation-auc:0.96763	validation-aucpr:0.97282
[13]	validation-logloss:0.43029	validation-auc:0.96772	validation-aucpr:0.97293
[14]	validation-logloss:0.41830	validation-auc:0.96798	validation-aucpr:0.97316
[15]	validation-logloss:0.40696	validation-auc:0.96795	validation-aucpr:0.97319
[16]	validation-logloss:0.39594	validation-auc:0.96827	validation-aucpr:0.97349
[17]	validation-logloss:0.38568	validation-auc:0.96855	validation-aucpr:0.97369
[18]	validation-logloss:0.37765	validation-auc:0.96838	validation-aucpr:0.97354
[19]	validation-logloss:0.36979	validation-auc:0.96831	validation-aucpr:0.97342
[20]	validation-logloss:0.36112	validation-auc:0.96860	validation-aucpr:0.97371
[21]	validation-logloss:0.35269	validation-auc:0.96885	validation-aucpr:0.97391
[22]	validation-logloss:0.34596	validation-auc:0.96916	validation-aucpr:0.97405
[23]	validation-logloss:0.33948	validation-auc:0.96905	validation-aucpr:0.97395
[24]	validation-logloss:0.33326	validation-auc:0.96915	validation-aucpr:0.97399
[25]	validation-logloss:0.32740	validation-auc:0.96925	validation-aucpr:0.97408
[26]	validation-logloss:0.32057	validation-auc:0.96944	validation-aucpr:0.97424
[27]	validation-logloss:0.31421	validation-auc:0.96974	validation-aucpr:0.97449
[28]	validation-logloss:0.30820	validation-auc:0.96998	validation-aucpr:0.97469
[29]	validation-logloss:0.30345	validation-auc:0.96997	validation-aucpr:0.97467
[30]	validation-logloss:0.29787	validation-auc:0.97016	validation-aucpr:0.97483
[31]	validation-logloss:0.29386	validation-auc:0.97006	validation-aucpr:0.97475
[32]	validation-logloss:0.28948	validation-auc:0.97013	validation-aucpr:0.97477
[33]	validation-logloss:0.28566	validation-auc:0.97003	validation-aucpr:0.97472
[34]	validation-logloss:0.28105	validation-auc:0.97017	validation-aucpr:0.97487
[35]	validation-logloss:0.27677	validation-auc:0.97017	validation-aucpr:0.97490
[36]	validation-logloss:0.27360	validation-auc:0.97021	validation-aucpr:0.97491
[37]	validation-logloss:0.26931	validation-auc:0.97039	validation-aucpr:0.97507
[38]	validation-logloss:0.26628	validation-auc:0.97035	validation-aucpr:0.97503
[39]	validation-logloss:0.26254	validation-auc:0.97047	validation-aucpr:0.97514
[40]	validation-logloss:0.25897	validation-auc:0.97060	validation-aucpr:0.97527
[41]	validation-logloss:0.25562	validation-auc:0.97071	validation-aucpr:0.97535
[42]	validation-logloss:0.25217	validation-auc:0.97101	validation-aucpr:0.97560
[43]	validation-logloss:0.24992	validation-auc:0.97092	validation-aucpr:0.97553
[44]	validation-logloss:0.24738	validation-auc:0.97106	validation-aucpr:0.97563
[45]	validation-logloss:0.24446	validation-auc:0.97116	validation-aucpr:0.97570
[46]	validation-logloss:0.24213	validation-auc:0.97136	validation-aucpr:0.97582
[47]	validation-logloss:0.24013	validation-auc:0.97134	validation-aucpr:0.97580
[48]	validation-logloss:0.23765	validation-auc:0.97145	validation-aucpr:0.97591
[49]	validation-logloss:0.23501	validation-auc:0.97159	validation-aucpr:0.97603
[50]	validation-logloss:0.23267	validation-auc:0.97172	validation-aucpr:0.97612
[51]	validation-logloss:0.23050	validation-auc:0.97179	validation-aucpr:0.97620
[52]	validation-logloss:0.22872	validation-auc:0.97171	validation-aucpr:0.97613
[53]	validation-logloss:0.22685	validation-auc:0.97177	validation-aucpr:0.97617
[54]	validation-logloss:0.22477	validation-auc:0.97198	validation-aucpr:0.97634
[55]	validation-logloss:0.22336	validation-auc:0.97198	validation-aucpr:0.97634
[56]	validation-logloss:0.22201	validation-auc:0.97196	validation-aucpr:0.97630
[57]	validation-logloss:0.22000	validation-auc:0.97212	validation-aucpr:0.97643
[58]	validation-logloss:0.21838	validation-auc:0.97215	validation-aucpr:0.97647
[59]	validation-logloss:0.21718	validation-auc:0.97207	validation-aucpr:0.97642
[60]	validation-logloss:0.21548	validation-auc:0.97218	validation-aucpr:0.97651
{'best_iteration': '60', 'best_score': '0.9765077996628351'}
Trial 66, Fold 4: Log loss = 0.2154846902786677, Average precision = 0.976506590273033, ROC-AUC = 0.9721760242828291, Elapsed Time = 2.0137092000004486 seconds
Trial 66, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 66, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[0]	validation-logloss:0.66316	validation-auc:0.95118	validation-aucpr:0.94310
[1]	validation-logloss:0.63899	validation-auc:0.95792	validation-aucpr:0.96211
[2]	validation-logloss:0.61668	validation-auc:0.95951	validation-aucpr:0.96374
[3]	validation-logloss:0.59250	validation-auc:0.96209	validation-aucpr:0.96623
[4]	validation-logloss:0.57003	validation-auc:0.96449	validation-aucpr:0.96846
[5]	validation-logloss:0.54925	validation-auc:0.96476	validation-aucpr:0.96865
[6]	validation-logloss:0.53042	validation-auc:0.96586	validation-aucpr:0.97049
[7]	validation-logloss:0.51210	validation-auc:0.96628	validation-aucpr:0.97083
[8]	validation-logloss:0.49565	validation-auc:0.96602	validation-aucpr:0.97063
[9]	validation-logloss:0.47992	validation-auc:0.96680	validation-aucpr:0.97132
[10]	validation-logloss:0.46640	validation-auc:0.96639	validation-aucpr:0.97103
[11]	validation-logloss:0.45441	validation-auc:0.96608	validation-aucpr:0.97075
[12]	validation-logloss:0.44309	validation-auc:0.96611	validation-aucpr:0.97069
[13]	validation-logloss:0.43034	validation-auc:0.96671	validation-aucpr:0.97112
[14]	validation-logloss:0.41829	validation-auc:0.96729	validation-aucpr:0.97191
[15]	validation-logloss:0.40694	validation-auc:0.96763	validation-aucpr:0.97218
[16]	validation-logloss:0.39647	validation-auc:0.96780	validation-aucpr:0.97229
[17]	validation-logloss:0.38670	validation-auc:0.96783	validation-aucpr:0.97229
[18]	validation-logloss:0.37724	validation-auc:0.96811	validation-aucpr:0.97254
[19]	validation-logloss:0.36811	validation-auc:0.96864	validation-aucpr:0.97291
[20]	validation-logloss:0.36023	validation-auc:0.96869	validation-aucpr:0.97294
[21]	validation-logloss:0.35236	validation-auc:0.96889	validation-aucpr:0.97310
[22]	validation-logloss:0.34451	validation-auc:0.96918	validation-aucpr:0.97336
[23]	validation-logloss:0.33746	validation-auc:0.96914	validation-aucpr:0.97339
[24]	validation-logloss:0.33179	validation-auc:0.96897	validation-aucpr:0.97323
[25]	validation-logloss:0.32547	validation-auc:0.96898	validation-aucpr:0.97326
[26]	validation-logloss:0.31902	validation-auc:0.96932	validation-aucpr:0.97353
[27]	validation-logloss:0.31315	validation-auc:0.96945	validation-aucpr:0.97364
[28]	validation-logloss:0.30807	validation-auc:0.96951	validation-aucpr:0.97370
[29]	validation-logloss:0.30356	validation-auc:0.96954	validation-aucpr:0.97370
[30]	validation-logloss:0.29927	validation-auc:0.96954	validation-aucpr:0.97364
[31]	validation-logloss:0.29424	validation-auc:0.96972	validation-aucpr:0.97378
[32]	validation-logloss:0.28943	validation-auc:0.96983	validation-aucpr:0.97386
[33]	validation-logloss:0.28500	validation-auc:0.96985	validation-aucpr:0.97386
[34]	validation-logloss:0.28153	validation-auc:0.96985	validation-aucpr:0.97390
[35]	validation-logloss:0.27722	validation-auc:0.97009	validation-aucpr:0.97409
[36]	validation-logloss:0.27395	validation-auc:0.97011	validation-aucpr:0.97409
[37]	validation-logloss:0.27092	validation-auc:0.96996	validation-aucpr:0.97398
[38]	validation-logloss:0.26802	validation-auc:0.96985	validation-aucpr:0.97389
[39]	validation-logloss:0.26507	validation-auc:0.96998	validation-aucpr:0.97401
[40]	validation-logloss:0.26236	validation-auc:0.96993	validation-aucpr:0.97395
[41]	validation-logloss:0.25912	validation-auc:0.97007	validation-aucpr:0.97408
[42]	validation-logloss:0.25610	validation-auc:0.97014	validation-aucpr:0.97417
[43]	validation-logloss:0.25363	validation-auc:0.97022	validation-aucpr:0.97424
[44]	validation-logloss:0.25085	validation-auc:0.97026	validation-aucpr:0.97428
[45]	validation-logloss:0.24862	validation-auc:0.97033	validation-aucpr:0.97432
[46]	validation-logloss:0.24588	validation-auc:0.97041	validation-aucpr:0.97437
[47]	validation-logloss:0.24375	validation-auc:0.97040	validation-aucpr:0.97437
[48]	validation-logloss:0.24167	validation-auc:0.97055	validation-aucpr:0.97445
[49]	validation-logloss:0.23955	validation-auc:0.97064	validation-aucpr:0.97450
[50]	validation-logloss:0.23784	validation-auc:0.97065	validation-aucpr:0.97449
[51]	validation-logloss:0.23618	validation-auc:0.97057	validation-aucpr:0.97442
[52]	validation-logloss:0.23442	validation-auc:0.97059	validation-aucpr:0.97444
[53]	validation-logloss:0.23224	validation-auc:0.97081	validation-aucpr:0.97461
[54]	validation-logloss:0.23025	validation-auc:0.97094	validation-aucpr:0.97474
[55]	validation-logloss:0.22894	validation-auc:0.97090	validation-aucpr:0.97469
[56]	validation-logloss:0.22710	validation-auc:0.97105	validation-aucpr:0.97482
[57]	validation-logloss:0.22557	validation-auc:0.97108	validation-aucpr:0.97484
[58]	validation-logloss:0.22391	validation-auc:0.97117	validation-aucpr:0.97493
[59]	validation-logloss:0.22217	validation-auc:0.97139	validation-aucpr:0.97511
[60]	validation-logloss:0.22073	validation-auc:0.97145	validation-aucpr:0.97512
{'best_iteration': '60', 'best_score': '0.975122968769573'}
Trial 66, Fold 5: Log loss = 0.2207293657428227, Average precision = 0.975127238399295, ROC-AUC = 0.9714478197053305, Elapsed Time = 2.1293003000027966 seconds
Optimization Progress:  67%|######7   | 67/100 [3:02:22<19:51, 36.11s/it]
Trial 67, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 67, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[0]	validation-logloss:0.67790	validation-auc:0.95553	validation-aucpr:0.96092
[1]	validation-logloss:0.66503	validation-auc:0.95859	validation-aucpr:0.96439
[2]	validation-logloss:0.65245	validation-auc:0.95961	validation-aucpr:0.96443
[3]	validation-logloss:0.64023	validation-auc:0.96095	validation-aucpr:0.96598
[4]	validation-logloss:0.62862	validation-auc:0.96154	validation-aucpr:0.96621
[5]	validation-logloss:0.61621	validation-auc:0.96280	validation-aucpr:0.96709
[6]	validation-logloss:0.60377	validation-auc:0.96375	validation-aucpr:0.96800
[7]	validation-logloss:0.59216	validation-auc:0.96451	validation-aucpr:0.96998
[8]	validation-logloss:0.58076	validation-auc:0.96534	validation-aucpr:0.97092
[9]	validation-logloss:0.57128	validation-auc:0.96557	validation-aucpr:0.97103
[10]	validation-logloss:0.56224	validation-auc:0.96568	validation-aucpr:0.97098
[11]	validation-logloss:0.55305	validation-auc:0.96598	validation-aucpr:0.97114
[12]	validation-logloss:0.54469	validation-auc:0.96576	validation-aucpr:0.97092
[13]	validation-logloss:0.53612	validation-auc:0.96562	validation-aucpr:0.97079
[14]	validation-logloss:0.52686	validation-auc:0.96552	validation-aucpr:0.97076
[15]	validation-logloss:0.51927	validation-auc:0.96533	validation-aucpr:0.97058
[16]	validation-logloss:0.51172	validation-auc:0.96523	validation-aucpr:0.97046
[17]	validation-logloss:0.50450	validation-auc:0.96513	validation-aucpr:0.97036
[18]	validation-logloss:0.49774	validation-auc:0.96502	validation-aucpr:0.97035
[19]	validation-logloss:0.49092	validation-auc:0.96510	validation-aucpr:0.97040
[20]	validation-logloss:0.48288	validation-auc:0.96534	validation-aucpr:0.97064
[21]	validation-logloss:0.47516	validation-auc:0.96564	validation-aucpr:0.97093
[22]	validation-logloss:0.46888	validation-auc:0.96566	validation-aucpr:0.97089
[23]	validation-logloss:0.46154	validation-auc:0.96584	validation-aucpr:0.97108
[24]	validation-logloss:0.45452	validation-auc:0.96614	validation-aucpr:0.97139
[25]	validation-logloss:0.44906	validation-auc:0.96585	validation-aucpr:0.97110
[26]	validation-logloss:0.44358	validation-auc:0.96596	validation-aucpr:0.97122
[27]	validation-logloss:0.43696	validation-auc:0.96620	validation-aucpr:0.97145
[28]	validation-logloss:0.43182	validation-auc:0.96607	validation-aucpr:0.97139
[29]	validation-logloss:0.42615	validation-auc:0.96607	validation-aucpr:0.97140
[30]	validation-logloss:0.42044	validation-auc:0.96625	validation-aucpr:0.97159
[31]	validation-logloss:0.41479	validation-auc:0.96627	validation-aucpr:0.97166
[32]	validation-logloss:0.41016	validation-auc:0.96623	validation-aucpr:0.97160
[33]	validation-logloss:0.40555	validation-auc:0.96626	validation-aucpr:0.97163
[34]	validation-logloss:0.40121	validation-auc:0.96630	validation-aucpr:0.97160
[35]	validation-logloss:0.39598	validation-auc:0.96644	validation-aucpr:0.97172
[36]	validation-logloss:0.39184	validation-auc:0.96645	validation-aucpr:0.97170
[37]	validation-logloss:0.38787	validation-auc:0.96649	validation-aucpr:0.97173
[38]	validation-logloss:0.38321	validation-auc:0.96645	validation-aucpr:0.97172
[39]	validation-logloss:0.37852	validation-auc:0.96658	validation-aucpr:0.97188
[40]	validation-logloss:0.37513	validation-auc:0.96652	validation-aucpr:0.97182
[41]	validation-logloss:0.37082	validation-auc:0.96662	validation-aucpr:0.97194
[42]	validation-logloss:0.36719	validation-auc:0.96670	validation-aucpr:0.97196
[43]	validation-logloss:0.36289	validation-auc:0.96675	validation-aucpr:0.97203
[44]	validation-logloss:0.35947	validation-auc:0.96676	validation-aucpr:0.97204
[45]	validation-logloss:0.35625	validation-auc:0.96676	validation-aucpr:0.97201
[46]	validation-logloss:0.35315	validation-auc:0.96670	validation-aucpr:0.97194
[47]	validation-logloss:0.35009	validation-auc:0.96671	validation-aucpr:0.97193
[48]	validation-logloss:0.34688	validation-auc:0.96674	validation-aucpr:0.97193
[49]	validation-logloss:0.34341	validation-auc:0.96678	validation-aucpr:0.97195
[50]	validation-logloss:0.33964	validation-auc:0.96697	validation-aucpr:0.97210
[51]	validation-logloss:0.33605	validation-auc:0.96706	validation-aucpr:0.97217
[52]	validation-logloss:0.33335	validation-auc:0.96703	validation-aucpr:0.97212
[53]	validation-logloss:0.33064	validation-auc:0.96701	validation-aucpr:0.97208
[54]	validation-logloss:0.32790	validation-auc:0.96704	validation-aucpr:0.97209
[55]	validation-logloss:0.32474	validation-auc:0.96719	validation-aucpr:0.97222
[56]	validation-logloss:0.32225	validation-auc:0.96722	validation-aucpr:0.97225
[57]	validation-logloss:0.31938	validation-auc:0.96723	validation-aucpr:0.97230
[58]	validation-logloss:0.31725	validation-auc:0.96717	validation-aucpr:0.97224
[59]	validation-logloss:0.31414	validation-auc:0.96730	validation-aucpr:0.97237
[60]	validation-logloss:0.31202	validation-auc:0.96727	validation-aucpr:0.97234
{'best_iteration': '59', 'best_score': '0.9723680795391926'}
Trial 67, Fold 1: Log loss = 0.31202037811636174, Average precision = 0.9723428888836552, ROC-AUC = 0.9672663856268192, Elapsed Time = 1.4499145000008866 seconds
Trial 67, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 67, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[0]	validation-logloss:0.67734	validation-auc:0.95774	validation-aucpr:0.96414
[1]	validation-logloss:0.66279	validation-auc:0.96283	validation-aucpr:0.96703
[2]	validation-logloss:0.64856	validation-auc:0.96545	validation-aucpr:0.96898
[3]	validation-logloss:0.63506	validation-auc:0.96584	validation-aucpr:0.96995
[4]	validation-logloss:0.62224	validation-auc:0.96586	validation-aucpr:0.97016
[5]	validation-logloss:0.60985	validation-auc:0.96734	validation-aucpr:0.97134
[6]	validation-logloss:0.59958	validation-auc:0.96733	validation-aucpr:0.97056
[7]	validation-logloss:0.58905	validation-auc:0.96671	validation-aucpr:0.97011
[8]	validation-logloss:0.57791	validation-auc:0.96692	validation-aucpr:0.97025
[9]	validation-logloss:0.56677	validation-auc:0.96753	validation-aucpr:0.97081
[10]	validation-logloss:0.55610	validation-auc:0.96803	validation-aucpr:0.97107
[11]	validation-logloss:0.54698	validation-auc:0.96814	validation-aucpr:0.97108
[12]	validation-logloss:0.53699	validation-auc:0.96855	validation-aucpr:0.97136
[13]	validation-logloss:0.52857	validation-auc:0.96860	validation-aucpr:0.97135
[14]	validation-logloss:0.51941	validation-auc:0.96860	validation-aucpr:0.97126
[15]	validation-logloss:0.51163	validation-auc:0.96850	validation-aucpr:0.97117
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[20]	validation-logloss:0.47509	validation-auc:0.96866	validation-aucpr:0.97259
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[22]	validation-logloss:0.46113	validation-auc:0.96894	validation-aucpr:0.97275
[23]	validation-logloss:0.45398	validation-auc:0.96917	validation-aucpr:0.97297
[24]	validation-logloss:0.44831	validation-auc:0.96901	validation-aucpr:0.97280
[25]	validation-logloss:0.44163	validation-auc:0.96901	validation-aucpr:0.97279
[26]	validation-logloss:0.43622	validation-auc:0.96897	validation-aucpr:0.97271
[27]	validation-logloss:0.42998	validation-auc:0.96917	validation-aucpr:0.97290
[28]	validation-logloss:0.42470	validation-auc:0.96900	validation-aucpr:0.97276
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[30]	validation-logloss:0.41470	validation-auc:0.96896	validation-aucpr:0.97273
[31]	validation-logloss:0.41013	validation-auc:0.96889	validation-aucpr:0.97263
[32]	validation-logloss:0.40457	validation-auc:0.96902	validation-aucpr:0.97273
[33]	validation-logloss:0.40003	validation-auc:0.96898	validation-aucpr:0.97269
[34]	validation-logloss:0.39589	validation-auc:0.96886	validation-aucpr:0.97260
[35]	validation-logloss:0.39071	validation-auc:0.96891	validation-aucpr:0.97268
[36]	validation-logloss:0.38664	validation-auc:0.96900	validation-aucpr:0.97274
[37]	validation-logloss:0.38159	validation-auc:0.96917	validation-aucpr:0.97290
[38]	validation-logloss:0.37790	validation-auc:0.96917	validation-aucpr:0.97289
[39]	validation-logloss:0.37312	validation-auc:0.96923	validation-aucpr:0.97295
[40]	validation-logloss:0.36934	validation-auc:0.96918	validation-aucpr:0.97290
[41]	validation-logloss:0.36472	validation-auc:0.96938	validation-aucpr:0.97303
[42]	validation-logloss:0.36042	validation-auc:0.96945	validation-aucpr:0.97310
[43]	validation-logloss:0.35615	validation-auc:0.96955	validation-aucpr:0.97320
[44]	validation-logloss:0.35274	validation-auc:0.96951	validation-aucpr:0.97317
[45]	validation-logloss:0.34947	validation-auc:0.96949	validation-aucpr:0.97314
[46]	validation-logloss:0.34557	validation-auc:0.96947	validation-aucpr:0.97312
[47]	validation-logloss:0.34196	validation-auc:0.96943	validation-aucpr:0.97310
[48]	validation-logloss:0.33907	validation-auc:0.96936	validation-aucpr:0.97304
[49]	validation-logloss:0.33610	validation-auc:0.96940	validation-aucpr:0.97306
[50]	validation-logloss:0.33311	validation-auc:0.96936	validation-aucpr:0.97293
[51]	validation-logloss:0.32951	validation-auc:0.96943	validation-aucpr:0.97298
[52]	validation-logloss:0.32664	validation-auc:0.96945	validation-aucpr:0.97299
[53]	validation-logloss:0.32330	validation-auc:0.96953	validation-aucpr:0.97304
[54]	validation-logloss:0.32064	validation-auc:0.96953	validation-aucpr:0.97303
[55]	validation-logloss:0.31733	validation-auc:0.96968	validation-aucpr:0.97317
[56]	validation-logloss:0.31485	validation-auc:0.96972	validation-aucpr:0.97319
[57]	validation-logloss:0.31239	validation-auc:0.96975	validation-aucpr:0.97319
[58]	validation-logloss:0.30924	validation-auc:0.96986	validation-aucpr:0.97328
[59]	validation-logloss:0.30623	validation-auc:0.97004	validation-aucpr:0.97344
[60]	validation-logloss:0.30382	validation-auc:0.97008	validation-aucpr:0.97338
{'best_iteration': '59', 'best_score': '0.9734417834944555'}
Trial 67, Fold 2: Log loss = 0.3038166756126164, Average precision = 0.9733877376478585, ROC-AUC = 0.9700820136081343, Elapsed Time = 1.8838468999965698 seconds
Trial 67, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 67, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[0]	validation-logloss:0.67764	validation-auc:0.95744	validation-aucpr:0.96229
[1]	validation-logloss:0.66499	validation-auc:0.95839	validation-aucpr:0.96064
[2]	validation-logloss:0.65268	validation-auc:0.96027	validation-aucpr:0.96414
[3]	validation-logloss:0.64100	validation-auc:0.96113	validation-aucpr:0.96504
[4]	validation-logloss:0.62797	validation-auc:0.96349	validation-aucpr:0.96657
[5]	validation-logloss:0.61668	validation-auc:0.96525	validation-aucpr:0.97016
[6]	validation-logloss:0.60609	validation-auc:0.96553	validation-aucpr:0.97001
[7]	validation-logloss:0.59395	validation-auc:0.96700	validation-aucpr:0.97146
[8]	validation-logloss:0.58272	validation-auc:0.96729	validation-aucpr:0.97175
[9]	validation-logloss:0.57319	validation-auc:0.96732	validation-aucpr:0.97200
[10]	validation-logloss:0.56416	validation-auc:0.96685	validation-aucpr:0.97015
[11]	validation-logloss:0.55511	validation-auc:0.96694	validation-aucpr:0.97167
[12]	validation-logloss:0.54659	validation-auc:0.96710	validation-aucpr:0.97166
[13]	validation-logloss:0.53805	validation-auc:0.96719	validation-aucpr:0.97168
[14]	validation-logloss:0.53014	validation-auc:0.96702	validation-aucpr:0.97152
[15]	validation-logloss:0.52067	validation-auc:0.96741	validation-aucpr:0.97184
[16]	validation-logloss:0.51162	validation-auc:0.96777	validation-aucpr:0.97215
[17]	validation-logloss:0.50294	validation-auc:0.96806	validation-aucpr:0.97251
[18]	validation-logloss:0.49605	validation-auc:0.96803	validation-aucpr:0.97246
[19]	validation-logloss:0.48766	validation-auc:0.96815	validation-aucpr:0.97258
[20]	validation-logloss:0.48120	validation-auc:0.96814	validation-aucpr:0.97256
[21]	validation-logloss:0.47343	validation-auc:0.96849	validation-aucpr:0.97290
[22]	validation-logloss:0.46718	validation-auc:0.96837	validation-aucpr:0.97280
[23]	validation-logloss:0.45972	validation-auc:0.96865	validation-aucpr:0.97306
[24]	validation-logloss:0.45383	validation-auc:0.96867	validation-aucpr:0.97306
[25]	validation-logloss:0.44694	validation-auc:0.96883	validation-aucpr:0.97320
[26]	validation-logloss:0.44121	validation-auc:0.96888	validation-aucpr:0.97323
[27]	validation-logloss:0.43553	validation-auc:0.96900	validation-aucpr:0.97331
[28]	validation-logloss:0.43031	validation-auc:0.96897	validation-aucpr:0.97327
[29]	validation-logloss:0.42440	validation-auc:0.96906	validation-aucpr:0.97334
[30]	validation-logloss:0.41843	validation-auc:0.96913	validation-aucpr:0.97348
[31]	validation-logloss:0.41363	validation-auc:0.96922	validation-aucpr:0.97354
[32]	validation-logloss:0.40916	validation-auc:0.96911	validation-aucpr:0.97344
[33]	validation-logloss:0.40425	validation-auc:0.96913	validation-aucpr:0.97342
[34]	validation-logloss:0.39970	validation-auc:0.96922	validation-aucpr:0.97349
[35]	validation-logloss:0.39436	validation-auc:0.96935	validation-aucpr:0.97361
[36]	validation-logloss:0.38913	validation-auc:0.96945	validation-aucpr:0.97370
[37]	validation-logloss:0.38415	validation-auc:0.96948	validation-aucpr:0.97374
[38]	validation-logloss:0.37917	validation-auc:0.96959	validation-aucpr:0.97384
[39]	validation-logloss:0.37446	validation-auc:0.96965	validation-aucpr:0.97388
[40]	validation-logloss:0.37067	validation-auc:0.96967	validation-aucpr:0.97389
[41]	validation-logloss:0.36608	validation-auc:0.96976	validation-aucpr:0.97396
[42]	validation-logloss:0.36261	validation-auc:0.96981	validation-aucpr:0.97399
[43]	validation-logloss:0.35890	validation-auc:0.96989	validation-aucpr:0.97404
[44]	validation-logloss:0.35529	validation-auc:0.96999	validation-aucpr:0.97416
[45]	validation-logloss:0.35196	validation-auc:0.97001	validation-aucpr:0.97416
[46]	validation-logloss:0.34789	validation-auc:0.97008	validation-aucpr:0.97422
[47]	validation-logloss:0.34463	validation-auc:0.97016	validation-aucpr:0.97429
[48]	validation-logloss:0.34173	validation-auc:0.97008	validation-aucpr:0.97422
[49]	validation-logloss:0.33805	validation-auc:0.97012	validation-aucpr:0.97431
[50]	validation-logloss:0.33487	validation-auc:0.97015	validation-aucpr:0.97433
[51]	validation-logloss:0.33196	validation-auc:0.97011	validation-aucpr:0.97429
[52]	validation-logloss:0.32836	validation-auc:0.97016	validation-aucpr:0.97433
[53]	validation-logloss:0.32548	validation-auc:0.97023	validation-aucpr:0.97436
[54]	validation-logloss:0.32207	validation-auc:0.97026	validation-aucpr:0.97442
[55]	validation-logloss:0.31958	validation-auc:0.97023	validation-aucpr:0.97439
[56]	validation-logloss:0.31726	validation-auc:0.97017	validation-aucpr:0.97432
[57]	validation-logloss:0.31405	validation-auc:0.97024	validation-aucpr:0.97438
[58]	validation-logloss:0.31093	validation-auc:0.97031	validation-aucpr:0.97444
[59]	validation-logloss:0.30784	validation-auc:0.97040	validation-aucpr:0.97453
[60]	validation-logloss:0.30496	validation-auc:0.97042	validation-aucpr:0.97456
{'best_iteration': '60', 'best_score': '0.9745641931920642'}
Trial 67, Fold 3: Log loss = 0.30495799683777036, Average precision = 0.9745684705321301, ROC-AUC = 0.9704159433358053, Elapsed Time = 1.829988699999376 seconds
Trial 67, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 67, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[0]	validation-logloss:0.67757	validation-auc:0.95496	validation-aucpr:0.95939
[1]	validation-logloss:0.66306	validation-auc:0.96062	validation-aucpr:0.96533
[2]	validation-logloss:0.64938	validation-auc:0.96258	validation-aucpr:0.96685
[3]	validation-logloss:0.63606	validation-auc:0.96429	validation-aucpr:0.97027
[4]	validation-logloss:0.62445	validation-auc:0.96421	validation-aucpr:0.97008
[5]	validation-logloss:0.61199	validation-auc:0.96530	validation-aucpr:0.97089
[6]	validation-logloss:0.59982	validation-auc:0.96530	validation-aucpr:0.97093
[7]	validation-logloss:0.58815	validation-auc:0.96611	validation-aucpr:0.97158
[8]	validation-logloss:0.57673	validation-auc:0.96724	validation-aucpr:0.97248
[9]	validation-logloss:0.56693	validation-auc:0.96727	validation-aucpr:0.97250
[10]	validation-logloss:0.55785	validation-auc:0.96723	validation-aucpr:0.97251
[11]	validation-logloss:0.54906	validation-auc:0.96683	validation-aucpr:0.97215
[12]	validation-logloss:0.54060	validation-auc:0.96679	validation-aucpr:0.97209
[13]	validation-logloss:0.53118	validation-auc:0.96685	validation-aucpr:0.97219
[14]	validation-logloss:0.52181	validation-auc:0.96703	validation-aucpr:0.97238
[15]	validation-logloss:0.51307	validation-auc:0.96710	validation-aucpr:0.97247
[16]	validation-logloss:0.50531	validation-auc:0.96741	validation-aucpr:0.97275
[17]	validation-logloss:0.49807	validation-auc:0.96748	validation-aucpr:0.97276
[18]	validation-logloss:0.49097	validation-auc:0.96742	validation-aucpr:0.97270
[19]	validation-logloss:0.48416	validation-auc:0.96731	validation-aucpr:0.97258
[20]	validation-logloss:0.47726	validation-auc:0.96727	validation-aucpr:0.97251
[21]	validation-logloss:0.46976	validation-auc:0.96736	validation-aucpr:0.97263
[22]	validation-logloss:0.46357	validation-auc:0.96742	validation-aucpr:0.97261
[23]	validation-logloss:0.45741	validation-auc:0.96732	validation-aucpr:0.97255
[24]	validation-logloss:0.45181	validation-auc:0.96703	validation-aucpr:0.97232
[25]	validation-logloss:0.44489	validation-auc:0.96717	validation-aucpr:0.97246
[26]	validation-logloss:0.43840	validation-auc:0.96719	validation-aucpr:0.97250
[27]	validation-logloss:0.43204	validation-auc:0.96736	validation-aucpr:0.97262
[28]	validation-logloss:0.42684	validation-auc:0.96730	validation-aucpr:0.97254
[29]	validation-logloss:0.42179	validation-auc:0.96730	validation-aucpr:0.97252
[30]	validation-logloss:0.41675	validation-auc:0.96745	validation-aucpr:0.97261
[31]	validation-logloss:0.41211	validation-auc:0.96735	validation-aucpr:0.97249
[32]	validation-logloss:0.40740	validation-auc:0.96732	validation-aucpr:0.97244
[33]	validation-logloss:0.40193	validation-auc:0.96746	validation-aucpr:0.97258
[34]	validation-logloss:0.39748	validation-auc:0.96753	validation-aucpr:0.97266
[35]	validation-logloss:0.39316	validation-auc:0.96753	validation-aucpr:0.97263
[36]	validation-logloss:0.38915	validation-auc:0.96753	validation-aucpr:0.97259
[37]	validation-logloss:0.38435	validation-auc:0.96756	validation-aucpr:0.97267
[38]	validation-logloss:0.38037	validation-auc:0.96761	validation-aucpr:0.97273
[39]	validation-logloss:0.37568	validation-auc:0.96766	validation-aucpr:0.97280
[40]	validation-logloss:0.37201	validation-auc:0.96757	validation-aucpr:0.97274
[41]	validation-logloss:0.36832	validation-auc:0.96758	validation-aucpr:0.97271
[42]	validation-logloss:0.36451	validation-auc:0.96752	validation-aucpr:0.97271
[43]	validation-logloss:0.36109	validation-auc:0.96754	validation-aucpr:0.97274
[44]	validation-logloss:0.35773	validation-auc:0.96754	validation-aucpr:0.97272
[45]	validation-logloss:0.35445	validation-auc:0.96762	validation-aucpr:0.97276
[46]	validation-logloss:0.35144	validation-auc:0.96757	validation-aucpr:0.97271
[47]	validation-logloss:0.34826	validation-auc:0.96758	validation-aucpr:0.97270
[48]	validation-logloss:0.34499	validation-auc:0.96767	validation-aucpr:0.97276
[49]	validation-logloss:0.34107	validation-auc:0.96777	validation-aucpr:0.97288
[50]	validation-logloss:0.33840	validation-auc:0.96768	validation-aucpr:0.97281
[51]	validation-logloss:0.33481	validation-auc:0.96775	validation-aucpr:0.97289
[52]	validation-logloss:0.33145	validation-auc:0.96776	validation-aucpr:0.97293
[53]	validation-logloss:0.32786	validation-auc:0.96790	validation-aucpr:0.97306
[54]	validation-logloss:0.32531	validation-auc:0.96790	validation-aucpr:0.97305
[55]	validation-logloss:0.32291	validation-auc:0.96787	validation-aucpr:0.97303
[56]	validation-logloss:0.31963	validation-auc:0.96790	validation-aucpr:0.97309
[57]	validation-logloss:0.31729	validation-auc:0.96789	validation-aucpr:0.97306
[58]	validation-logloss:0.31484	validation-auc:0.96793	validation-aucpr:0.97308
[59]	validation-logloss:0.31261	validation-auc:0.96791	validation-aucpr:0.97305
[60]	validation-logloss:0.30951	validation-auc:0.96800	validation-aucpr:0.97316
{'best_iteration': '60', 'best_score': '0.973162413525967'}
Trial 67, Fold 4: Log loss = 0.3095125853597949, Average precision = 0.9731652736050046, ROC-AUC = 0.9680012953306103, Elapsed Time = 1.7693674999973155 seconds
Trial 67, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 67, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[0]	validation-logloss:0.67778	validation-auc:0.95233	validation-aucpr:0.94662
[1]	validation-logloss:0.66519	validation-auc:0.95641	validation-aucpr:0.95980
[2]	validation-logloss:0.65176	validation-auc:0.95921	validation-aucpr:0.96469
[3]	validation-logloss:0.63841	validation-auc:0.96148	validation-aucpr:0.96672
[4]	validation-logloss:0.62749	validation-auc:0.96130	validation-aucpr:0.96454
[5]	validation-logloss:0.61654	validation-auc:0.96128	validation-aucpr:0.96399
[6]	validation-logloss:0.60449	validation-auc:0.96262	validation-aucpr:0.96727
[7]	validation-logloss:0.59433	validation-auc:0.96241	validation-aucpr:0.96695
[8]	validation-logloss:0.58470	validation-auc:0.96183	validation-aucpr:0.96658
[9]	validation-logloss:0.57403	validation-auc:0.96261	validation-aucpr:0.96779
[10]	validation-logloss:0.56505	validation-auc:0.96257	validation-aucpr:0.96766
[11]	validation-logloss:0.55493	validation-auc:0.96299	validation-aucpr:0.96804
[12]	validation-logloss:0.54536	validation-auc:0.96287	validation-aucpr:0.96811
[13]	validation-logloss:0.53602	validation-auc:0.96317	validation-aucpr:0.96838
[14]	validation-logloss:0.52718	validation-auc:0.96346	validation-aucpr:0.96848
[15]	validation-logloss:0.51805	validation-auc:0.96377	validation-aucpr:0.96878
[16]	validation-logloss:0.50944	validation-auc:0.96411	validation-aucpr:0.96910
[17]	validation-logloss:0.50215	validation-auc:0.96407	validation-aucpr:0.96905
[18]	validation-logloss:0.49389	validation-auc:0.96439	validation-aucpr:0.96929
[19]	validation-logloss:0.48625	validation-auc:0.96439	validation-aucpr:0.96932
[20]	validation-logloss:0.47948	validation-auc:0.96439	validation-aucpr:0.96934
[21]	validation-logloss:0.47332	validation-auc:0.96432	validation-aucpr:0.96932
[22]	validation-logloss:0.46714	validation-auc:0.96431	validation-aucpr:0.96932
[23]	validation-logloss:0.46117	validation-auc:0.96429	validation-aucpr:0.96926
[24]	validation-logloss:0.45547	validation-auc:0.96415	validation-aucpr:0.96903
[25]	validation-logloss:0.44895	validation-auc:0.96429	validation-aucpr:0.96922
[26]	validation-logloss:0.44355	validation-auc:0.96422	validation-aucpr:0.96909
[27]	validation-logloss:0.43807	validation-auc:0.96430	validation-aucpr:0.96919
[28]	validation-logloss:0.43289	validation-auc:0.96442	validation-aucpr:0.96933
[29]	validation-logloss:0.42702	validation-auc:0.96449	validation-aucpr:0.96955
[30]	validation-logloss:0.42219	validation-auc:0.96460	validation-aucpr:0.96958
[31]	validation-logloss:0.41743	validation-auc:0.96464	validation-aucpr:0.96958
[32]	validation-logloss:0.41158	validation-auc:0.96483	validation-aucpr:0.96975
[33]	validation-logloss:0.40693	validation-auc:0.96488	validation-aucpr:0.96979
[34]	validation-logloss:0.40250	validation-auc:0.96490	validation-aucpr:0.96977
[35]	validation-logloss:0.39846	validation-auc:0.96490	validation-aucpr:0.96976
[36]	validation-logloss:0.39429	validation-auc:0.96495	validation-aucpr:0.96975
[37]	validation-logloss:0.39031	validation-auc:0.96499	validation-aucpr:0.96978
[38]	validation-logloss:0.38533	validation-auc:0.96517	validation-aucpr:0.96995
[39]	validation-logloss:0.38162	validation-auc:0.96511	validation-aucpr:0.96990
[40]	validation-logloss:0.37792	validation-auc:0.96504	validation-aucpr:0.96985
[41]	validation-logloss:0.37434	validation-auc:0.96505	validation-aucpr:0.96983
[42]	validation-logloss:0.36979	validation-auc:0.96519	validation-aucpr:0.96996
[43]	validation-logloss:0.36572	validation-auc:0.96532	validation-aucpr:0.97006
[44]	validation-logloss:0.36266	validation-auc:0.96526	validation-aucpr:0.97001
[45]	validation-logloss:0.35940	validation-auc:0.96528	validation-aucpr:0.97003
[46]	validation-logloss:0.35533	validation-auc:0.96547	validation-aucpr:0.97019
[47]	validation-logloss:0.35212	validation-auc:0.96552	validation-aucpr:0.97020
[48]	validation-logloss:0.34907	validation-auc:0.96555	validation-aucpr:0.97021
[49]	validation-logloss:0.34531	validation-auc:0.96568	validation-aucpr:0.97031
[50]	validation-logloss:0.34247	validation-auc:0.96561	validation-aucpr:0.97027
[51]	validation-logloss:0.33961	validation-auc:0.96564	validation-aucpr:0.97027
[52]	validation-logloss:0.33617	validation-auc:0.96572	validation-aucpr:0.97039
[53]	validation-logloss:0.33334	validation-auc:0.96578	validation-aucpr:0.97044
[54]	validation-logloss:0.33000	validation-auc:0.96594	validation-aucpr:0.97056
[55]	validation-logloss:0.32669	validation-auc:0.96616	validation-aucpr:0.97075
[56]	validation-logloss:0.32369	validation-auc:0.96624	validation-aucpr:0.97087
[57]	validation-logloss:0.32123	validation-auc:0.96626	validation-aucpr:0.97088
[58]	validation-logloss:0.31879	validation-auc:0.96631	validation-aucpr:0.97091
[59]	validation-logloss:0.31655	validation-auc:0.96631	validation-aucpr:0.97093
[60]	validation-logloss:0.31453	validation-auc:0.96627	validation-aucpr:0.97087
{'best_iteration': '59', 'best_score': '0.9709289988232364'}
Trial 67, Fold 5: Log loss = 0.31453136502821993, Average precision = 0.9708783903919904, ROC-AUC = 0.9662654998792338, Elapsed Time = 1.8825285999992047 seconds
Optimization Progress:  68%|######8   | 68/100 [3:02:39<16:08, 30.27s/it]
Trial 68, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 68, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[21:01:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.68643	validation-auc:0.93634	validation-aucpr:0.92382
[21:01:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.67955	validation-auc:0.95075	validation-aucpr:0.95354
[21:01:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.67197	validation-auc:0.96136	validation-aucpr:0.96432
[21:01:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.66458	validation-auc:0.96420	validation-aucpr:0.96792
[21:01:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.65817	validation-auc:0.96400	validation-aucpr:0.96774
[21:01:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5]	validation-logloss:0.65122	validation-auc:0.96518	validation-aucpr:0.96927
[21:01:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6]	validation-logloss:0.64440	validation-auc:0.96529	validation-aucpr:0.96981
[21:01:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7]	validation-logloss:0.63837	validation-auc:0.96549	validation-aucpr:0.97009
[21:01:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8]	validation-logloss:0.63246	validation-auc:0.96565	validation-aucpr:0.97025
[21:01:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9]	validation-logloss:0.62587	validation-auc:0.96619	validation-aucpr:0.97074
[21:01:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10]	validation-logloss:0.62027	validation-auc:0.96605	validation-aucpr:0.97063
[21:01:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11]	validation-logloss:0.61491	validation-auc:0.96615	validation-aucpr:0.97098
[21:01:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12]	validation-logloss:0.60966	validation-auc:0.96584	validation-aucpr:0.97063
[21:01:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13]	validation-logloss:0.60360	validation-auc:0.96613	validation-aucpr:0.97083
[21:01:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14]	validation-logloss:0.59817	validation-auc:0.96625	validation-aucpr:0.97099
[21:01:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15]	validation-logloss:0.59299	validation-auc:0.96619	validation-aucpr:0.97126
[21:01:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16]	validation-logloss:0.58802	validation-auc:0.96595	validation-aucpr:0.97105
[21:01:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17]	validation-logloss:0.58288	validation-auc:0.96607	validation-aucpr:0.97115
[21:01:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18]	validation-logloss:0.57784	validation-auc:0.96617	validation-aucpr:0.97119
[21:01:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19]	validation-logloss:0.57238	validation-auc:0.96641	validation-aucpr:0.97141
[21:01:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20]	validation-logloss:0.56763	validation-auc:0.96634	validation-aucpr:0.97130
[21:01:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21]	validation-logloss:0.56304	validation-auc:0.96638	validation-aucpr:0.97128
[21:01:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22]	validation-logloss:0.55770	validation-auc:0.96661	validation-aucpr:0.97156
[21:01:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23]	validation-logloss:0.55317	validation-auc:0.96658	validation-aucpr:0.97154
[21:01:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24]	validation-logloss:0.54886	validation-auc:0.96654	validation-aucpr:0.97156
[21:01:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25]	validation-logloss:0.54391	validation-auc:0.96670	validation-aucpr:0.97173
[21:01:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26]	validation-logloss:0.53888	validation-auc:0.96695	validation-aucpr:0.97197
[21:01:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27]	validation-logloss:0.53436	validation-auc:0.96701	validation-aucpr:0.97205
[21:01:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28]	validation-logloss:0.52955	validation-auc:0.96725	validation-aucpr:0.97229
[21:01:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29]	validation-logloss:0.52496	validation-auc:0.96734	validation-aucpr:0.97234
[21:01:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30]	validation-logloss:0.52040	validation-auc:0.96734	validation-aucpr:0.97237
[21:01:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31]	validation-logloss:0.51643	validation-auc:0.96727	validation-aucpr:0.97229
[21:01:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32]	validation-logloss:0.51259	validation-auc:0.96725	validation-aucpr:0.97225
[21:01:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33]	validation-logloss:0.50820	validation-auc:0.96743	validation-aucpr:0.97239
[21:01:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34]	validation-logloss:0.50399	validation-auc:0.96754	validation-aucpr:0.97247
[21:01:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35]	validation-logloss:0.49977	validation-auc:0.96767	validation-aucpr:0.97259
[21:01:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36]	validation-logloss:0.49556	validation-auc:0.96775	validation-aucpr:0.97268
[21:01:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37]	validation-logloss:0.49180	validation-auc:0.96770	validation-aucpr:0.97265
[21:01:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38]	validation-logloss:0.48782	validation-auc:0.96772	validation-aucpr:0.97265
[21:01:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39]	validation-logloss:0.48388	validation-auc:0.96779	validation-aucpr:0.97271
[21:01:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40]	validation-logloss:0.48042	validation-auc:0.96783	validation-aucpr:0.97271
[21:01:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41]	validation-logloss:0.47707	validation-auc:0.96787	validation-aucpr:0.97272
[21:01:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42]	validation-logloss:0.47352	validation-auc:0.96792	validation-aucpr:0.97275
[21:01:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43]	validation-logloss:0.46981	validation-auc:0.96787	validation-aucpr:0.97274
[21:01:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44]	validation-logloss:0.46617	validation-auc:0.96789	validation-aucpr:0.97276
[21:01:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45]	validation-logloss:0.46295	validation-auc:0.96792	validation-aucpr:0.97277
[21:01:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46]	validation-logloss:0.45980	validation-auc:0.96796	validation-aucpr:0.97278
[21:01:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[47]	validation-logloss:0.45621	validation-auc:0.96800	validation-aucpr:0.97283
[21:01:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[48]	validation-logloss:0.45315	validation-auc:0.96802	validation-aucpr:0.97284
[21:01:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[49]	validation-logloss:0.45015	validation-auc:0.96798	validation-aucpr:0.97279
[21:01:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[50]	validation-logloss:0.44729	validation-auc:0.96787	validation-aucpr:0.97269
[21:01:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[51]	validation-logloss:0.44443	validation-auc:0.96776	validation-aucpr:0.97260
[21:01:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[52]	validation-logloss:0.44106	validation-auc:0.96792	validation-aucpr:0.97274
[21:01:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[53]	validation-logloss:0.43840	validation-auc:0.96791	validation-aucpr:0.97279
[21:01:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[54]	validation-logloss:0.43565	validation-auc:0.96788	validation-aucpr:0.97274
[21:01:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[55]	validation-logloss:0.43241	validation-auc:0.96791	validation-aucpr:0.97278
[21:01:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[56]	validation-logloss:0.42971	validation-auc:0.96788	validation-aucpr:0.97275
[21:01:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[57]	validation-logloss:0.42714	validation-auc:0.96782	validation-aucpr:0.97268
[21:01:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[58]	validation-logloss:0.42414	validation-auc:0.96789	validation-aucpr:0.97274
[21:01:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[59]	validation-logloss:0.42154	validation-auc:0.96791	validation-aucpr:0.97277
[21:01:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[60]	validation-logloss:0.41885	validation-auc:0.96793	validation-aucpr:0.97280
[21:01:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[61]	validation-logloss:0.41589	validation-auc:0.96797	validation-aucpr:0.97284
[21:01:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[62]	validation-logloss:0.41363	validation-auc:0.96792	validation-aucpr:0.97279
[21:01:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[63]	validation-logloss:0.41117	validation-auc:0.96795	validation-aucpr:0.97280
[21:01:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[64]	validation-logloss:0.40885	validation-auc:0.96794	validation-aucpr:0.97276
[21:01:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[65]	validation-logloss:0.40655	validation-auc:0.96788	validation-aucpr:0.97271
[21:01:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[66]	validation-logloss:0.40418	validation-auc:0.96789	validation-aucpr:0.97272
[21:01:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[67]	validation-logloss:0.40192	validation-auc:0.96789	validation-aucpr:0.97271
[21:01:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[68]	validation-logloss:0.39976	validation-auc:0.96786	validation-aucpr:0.97267
[21:01:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[69]	validation-logloss:0.39712	validation-auc:0.96792	validation-aucpr:0.97274
[21:01:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[70]	validation-logloss:0.39457	validation-auc:0.96789	validation-aucpr:0.97274
[21:01:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[71]	validation-logloss:0.39198	validation-auc:0.96798	validation-aucpr:0.97281
[21:01:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[72]	validation-logloss:0.38990	validation-auc:0.96793	validation-aucpr:0.97275
[21:01:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[73]	validation-logloss:0.38772	validation-auc:0.96794	validation-aucpr:0.97276
[21:01:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[74]	validation-logloss:0.38536	validation-auc:0.96799	validation-aucpr:0.97281
[21:01:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[75]	validation-logloss:0.38328	validation-auc:0.96796	validation-aucpr:0.97278
[21:01:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[76]	validation-logloss:0.38117	validation-auc:0.96800	validation-aucpr:0.97281
[21:01:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[77]	validation-logloss:0.37916	validation-auc:0.96797	validation-aucpr:0.97278
[21:01:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[78]	validation-logloss:0.37723	validation-auc:0.96795	validation-aucpr:0.97276
[21:01:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[79]	validation-logloss:0.37531	validation-auc:0.96796	validation-aucpr:0.97276
{'best_iteration': '61', 'best_score': '0.9728415399042182'}
Trial 68, Fold 1: Log loss = 0.375314446571651, Average precision = 0.9727612448617955, ROC-AUC = 0.96795927670476, Elapsed Time = 21.340347599998495 seconds
Trial 68, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 68, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[21:02:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.68585	validation-auc:0.94018	validation-aucpr:0.92886
[21:02:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.67924	validation-auc:0.95496	validation-aucpr:0.95415
[21:02:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.67287	validation-auc:0.95648	validation-aucpr:0.95858
[21:02:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.66572	validation-auc:0.96356	validation-aucpr:0.96661
[21:02:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.65871	validation-auc:0.96475	validation-aucpr:0.96835
[21:02:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5]	validation-logloss:0.65247	validation-auc:0.96501	validation-aucpr:0.96876
[21:02:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6]	validation-logloss:0.64641	validation-auc:0.96517	validation-aucpr:0.96893
[21:02:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7]	validation-logloss:0.64049	validation-auc:0.96497	validation-aucpr:0.96863
[21:02:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8]	validation-logloss:0.63470	validation-auc:0.96491	validation-aucpr:0.96839
[21:02:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9]	validation-logloss:0.62821	validation-auc:0.96618	validation-aucpr:0.96964
[21:02:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10]	validation-logloss:0.62270	validation-auc:0.96555	validation-aucpr:0.96901
[21:02:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11]	validation-logloss:0.61712	validation-auc:0.96548	validation-aucpr:0.96892
[21:02:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12]	validation-logloss:0.61173	validation-auc:0.96526	validation-aucpr:0.96870
[21:02:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13]	validation-logloss:0.60566	validation-auc:0.96592	validation-aucpr:0.96904
[21:02:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14]	validation-logloss:0.60029	validation-auc:0.96619	validation-aucpr:0.96922
[21:02:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15]	validation-logloss:0.59502	validation-auc:0.96631	validation-aucpr:0.96930
[21:02:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16]	validation-logloss:0.58994	validation-auc:0.96609	validation-aucpr:0.96899
[21:02:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17]	validation-logloss:0.58488	validation-auc:0.96626	validation-aucpr:0.96906
[21:02:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18]	validation-logloss:0.58003	validation-auc:0.96635	validation-aucpr:0.96903
[21:02:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19]	validation-logloss:0.57499	validation-auc:0.96649	validation-aucpr:0.96910
[21:02:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20]	validation-logloss:0.56932	validation-auc:0.96739	validation-aucpr:0.97000
[21:02:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21]	validation-logloss:0.56390	validation-auc:0.96790	validation-aucpr:0.97093
[21:02:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22]	validation-logloss:0.55941	validation-auc:0.96782	validation-aucpr:0.97080
[21:02:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23]	validation-logloss:0.55421	validation-auc:0.96820	validation-aucpr:0.97119
[21:02:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24]	validation-logloss:0.54981	validation-auc:0.96805	validation-aucpr:0.97106
[21:02:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25]	validation-logloss:0.54544	validation-auc:0.96805	validation-aucpr:0.97101
[21:02:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26]	validation-logloss:0.54035	validation-auc:0.96839	validation-aucpr:0.97166
[21:02:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27]	validation-logloss:0.53552	validation-auc:0.96857	validation-aucpr:0.97187
[21:02:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28]	validation-logloss:0.53068	validation-auc:0.96883	validation-aucpr:0.97212
[21:02:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29]	validation-logloss:0.52663	validation-auc:0.96890	validation-aucpr:0.97239
[21:02:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30]	validation-logloss:0.52248	validation-auc:0.96893	validation-aucpr:0.97240
[21:02:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31]	validation-logloss:0.51801	validation-auc:0.96903	validation-aucpr:0.97250
[21:02:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32]	validation-logloss:0.51421	validation-auc:0.96897	validation-aucpr:0.97243
[21:02:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33]	validation-logloss:0.51029	validation-auc:0.96893	validation-aucpr:0.97238
[21:02:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34]	validation-logloss:0.50595	validation-auc:0.96907	validation-aucpr:0.97252
[21:02:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35]	validation-logloss:0.50151	validation-auc:0.96922	validation-aucpr:0.97268
[21:02:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36]	validation-logloss:0.49719	validation-auc:0.96937	validation-aucpr:0.97283
[21:02:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37]	validation-logloss:0.49299	validation-auc:0.96953	validation-aucpr:0.97299
[21:02:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38]	validation-logloss:0.48876	validation-auc:0.96973	validation-aucpr:0.97317
[21:02:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39]	validation-logloss:0.48465	validation-auc:0.96984	validation-aucpr:0.97328
[21:02:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40]	validation-logloss:0.48079	validation-auc:0.96987	validation-aucpr:0.97330
[21:02:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41]	validation-logloss:0.47745	validation-auc:0.96973	validation-aucpr:0.97318
[21:02:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42]	validation-logloss:0.47368	validation-auc:0.96976	validation-aucpr:0.97320
[21:02:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43]	validation-logloss:0.47010	validation-auc:0.96984	validation-aucpr:0.97325
[21:02:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44]	validation-logloss:0.46629	validation-auc:0.96992	validation-aucpr:0.97335
[21:02:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45]	validation-logloss:0.46309	validation-auc:0.96999	validation-aucpr:0.97341
[21:02:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46]	validation-logloss:0.45994	validation-auc:0.97000	validation-aucpr:0.97346
[21:02:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[47]	validation-logloss:0.45643	validation-auc:0.97010	validation-aucpr:0.97354
[21:02:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[48]	validation-logloss:0.45339	validation-auc:0.97010	validation-aucpr:0.97353
[21:02:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[49]	validation-logloss:0.45029	validation-auc:0.97017	validation-aucpr:0.97356
[21:02:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[50]	validation-logloss:0.44666	validation-auc:0.97034	validation-aucpr:0.97374
[21:02:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[51]	validation-logloss:0.44320	validation-auc:0.97039	validation-aucpr:0.97378
[21:02:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[52]	validation-logloss:0.44045	validation-auc:0.97037	validation-aucpr:0.97374
[21:02:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[53]	validation-logloss:0.43728	validation-auc:0.97040	validation-aucpr:0.97376
[21:02:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[54]	validation-logloss:0.43448	validation-auc:0.97038	validation-aucpr:0.97375
[21:02:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[55]	validation-logloss:0.43176	validation-auc:0.97037	validation-aucpr:0.97372
[21:02:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[56]	validation-logloss:0.42857	validation-auc:0.97046	validation-aucpr:0.97381
[21:02:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[57]	validation-logloss:0.42593	validation-auc:0.97038	validation-aucpr:0.97374
[21:02:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[58]	validation-logloss:0.42281	validation-auc:0.97051	validation-aucpr:0.97386
[21:02:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[59]	validation-logloss:0.41975	validation-auc:0.97058	validation-aucpr:0.97392
[21:02:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[60]	validation-logloss:0.41688	validation-auc:0.97064	validation-aucpr:0.97393
[21:02:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[61]	validation-logloss:0.41397	validation-auc:0.97061	validation-aucpr:0.97393
[21:02:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[62]	validation-logloss:0.41103	validation-auc:0.97066	validation-aucpr:0.97398
[21:02:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[63]	validation-logloss:0.40804	validation-auc:0.97082	validation-aucpr:0.97412
[21:02:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[64]	validation-logloss:0.40532	validation-auc:0.97085	validation-aucpr:0.97410
[21:02:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[65]	validation-logloss:0.40301	validation-auc:0.97084	validation-aucpr:0.97408
[21:02:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[66]	validation-logloss:0.40069	validation-auc:0.97088	validation-aucpr:0.97410
[21:02:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[67]	validation-logloss:0.39790	validation-auc:0.97092	validation-aucpr:0.97416
[21:02:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[68]	validation-logloss:0.39520	validation-auc:0.97095	validation-aucpr:0.97419
[21:02:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[69]	validation-logloss:0.39255	validation-auc:0.97099	validation-aucpr:0.97422
[21:02:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[70]	validation-logloss:0.39039	validation-auc:0.97096	validation-aucpr:0.97413
[21:02:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[71]	validation-logloss:0.38783	validation-auc:0.97102	validation-aucpr:0.97419
[21:02:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[72]	validation-logloss:0.38525	validation-auc:0.97109	validation-aucpr:0.97427
[21:02:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[73]	validation-logloss:0.38296	validation-auc:0.97111	validation-aucpr:0.97428
[21:02:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[74]	validation-logloss:0.38046	validation-auc:0.97114	validation-aucpr:0.97431
[21:02:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[75]	validation-logloss:0.37843	validation-auc:0.97114	validation-aucpr:0.97429
[21:02:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[76]	validation-logloss:0.37634	validation-auc:0.97119	validation-aucpr:0.97433
[21:02:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[77]	validation-logloss:0.37389	validation-auc:0.97122	validation-aucpr:0.97437
[21:02:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[78]	validation-logloss:0.37190	validation-auc:0.97120	validation-aucpr:0.97435
[21:02:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[79]	validation-logloss:0.36992	validation-auc:0.97119	validation-aucpr:0.97435
{'best_iteration': '77', 'best_score': '0.9743684100818301'}
Trial 68, Fold 2: Log loss = 0.36992255065135127, Average precision = 0.9743500422679949, ROC-AUC = 0.9711869651080246, Elapsed Time = 21.50118659999862 seconds
Trial 68, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 68, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[21:02:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.68606	validation-auc:0.94324	validation-aucpr:0.92817
[21:02:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.67925	validation-auc:0.95504	validation-aucpr:0.95683
[21:02:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.67178	validation-auc:0.96425	validation-aucpr:0.96810
[21:02:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.66504	validation-auc:0.96577	validation-aucpr:0.96976
[21:02:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.65868	validation-auc:0.96595	validation-aucpr:0.96956
[21:02:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5]	validation-logloss:0.65238	validation-auc:0.96531	validation-aucpr:0.96883
[21:02:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6]	validation-logloss:0.64546	validation-auc:0.96727	validation-aucpr:0.97124
[21:02:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7]	validation-logloss:0.63946	validation-auc:0.96694	validation-aucpr:0.97104
[21:02:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8]	validation-logloss:0.63349	validation-auc:0.96724	validation-aucpr:0.97106
[21:02:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9]	validation-logloss:0.62763	validation-auc:0.96742	validation-aucpr:0.97118
[21:02:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10]	validation-logloss:0.62185	validation-auc:0.96741	validation-aucpr:0.97108
[21:02:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11]	validation-logloss:0.61548	validation-auc:0.96845	validation-aucpr:0.97222
[21:02:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12]	validation-logloss:0.60921	validation-auc:0.96895	validation-aucpr:0.97276
[21:02:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13]	validation-logloss:0.60389	validation-auc:0.96877	validation-aucpr:0.97253
[21:02:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14]	validation-logloss:0.59806	validation-auc:0.96916	validation-aucpr:0.97302
[21:02:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15]	validation-logloss:0.59283	validation-auc:0.96894	validation-aucpr:0.97283
[21:02:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16]	validation-logloss:0.58692	validation-auc:0.96936	validation-aucpr:0.97329
[21:02:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17]	validation-logloss:0.58185	validation-auc:0.96927	validation-aucpr:0.97321
[21:02:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18]	validation-logloss:0.57707	validation-auc:0.96911	validation-aucpr:0.97308
[21:02:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19]	validation-logloss:0.57243	validation-auc:0.96884	validation-aucpr:0.97282
[21:02:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20]	validation-logloss:0.56709	validation-auc:0.96908	validation-aucpr:0.97306
[21:02:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21]	validation-logloss:0.56247	validation-auc:0.96896	validation-aucpr:0.97292
[21:02:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22]	validation-logloss:0.55714	validation-auc:0.96923	validation-aucpr:0.97326
[21:02:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23]	validation-logloss:0.55254	validation-auc:0.96921	validation-aucpr:0.97328
[21:02:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24]	validation-logloss:0.54819	validation-auc:0.96904	validation-aucpr:0.97311
[21:02:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25]	validation-logloss:0.54316	validation-auc:0.96924	validation-aucpr:0.97333
[21:02:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26]	validation-logloss:0.53889	validation-auc:0.96925	validation-aucpr:0.97338
[21:02:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27]	validation-logloss:0.53392	validation-auc:0.96948	validation-aucpr:0.97358
[21:02:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28]	validation-logloss:0.52975	validation-auc:0.96950	validation-aucpr:0.97356
[21:02:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29]	validation-logloss:0.52501	validation-auc:0.96968	validation-aucpr:0.97375
[21:02:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30]	validation-logloss:0.52102	validation-auc:0.96967	validation-aucpr:0.97373
[21:02:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31]	validation-logloss:0.51647	validation-auc:0.96979	validation-aucpr:0.97386
[21:02:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32]	validation-logloss:0.51190	validation-auc:0.96992	validation-aucpr:0.97398
[21:02:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33]	validation-logloss:0.50796	validation-auc:0.96993	validation-aucpr:0.97399
[21:02:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34]	validation-logloss:0.50418	validation-auc:0.96986	validation-aucpr:0.97394
[21:02:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35]	validation-logloss:0.49990	validation-auc:0.96987	validation-aucpr:0.97397
[21:02:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36]	validation-logloss:0.49569	validation-auc:0.96992	validation-aucpr:0.97403
[21:02:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37]	validation-logloss:0.49145	validation-auc:0.97010	validation-aucpr:0.97418
[21:02:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38]	validation-logloss:0.48782	validation-auc:0.97001	validation-aucpr:0.97411
[21:02:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39]	validation-logloss:0.48372	validation-auc:0.97009	validation-aucpr:0.97420
[21:02:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40]	validation-logloss:0.47983	validation-auc:0.97016	validation-aucpr:0.97425
[21:02:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41]	validation-logloss:0.47647	validation-auc:0.97009	validation-aucpr:0.97419
[21:02:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42]	validation-logloss:0.47263	validation-auc:0.97014	validation-aucpr:0.97425
[21:02:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43]	validation-logloss:0.46882	validation-auc:0.97023	validation-aucpr:0.97435
[21:02:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44]	validation-logloss:0.46560	validation-auc:0.97023	validation-aucpr:0.97433
[21:02:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45]	validation-logloss:0.46241	validation-auc:0.97025	validation-aucpr:0.97434
[21:02:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46]	validation-logloss:0.45860	validation-auc:0.97042	validation-aucpr:0.97449
[21:02:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[47]	validation-logloss:0.45528	validation-auc:0.97044	validation-aucpr:0.97450
[21:02:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[48]	validation-logloss:0.45233	validation-auc:0.97035	validation-aucpr:0.97444
[21:02:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[49]	validation-logloss:0.44875	validation-auc:0.97048	validation-aucpr:0.97453
[21:02:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[50]	validation-logloss:0.44568	validation-auc:0.97050	validation-aucpr:0.97454
[21:02:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[51]	validation-logloss:0.44223	validation-auc:0.97059	validation-aucpr:0.97463
[21:02:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[52]	validation-logloss:0.43890	validation-auc:0.97059	validation-aucpr:0.97463
[21:02:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[53]	validation-logloss:0.43605	validation-auc:0.97060	validation-aucpr:0.97464
[21:02:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[54]	validation-logloss:0.43300	validation-auc:0.97060	validation-aucpr:0.97465
[21:02:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[55]	validation-logloss:0.43024	validation-auc:0.97063	validation-aucpr:0.97467
[21:02:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[56]	validation-logloss:0.42694	validation-auc:0.97070	validation-aucpr:0.97475
[21:02:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[57]	validation-logloss:0.42425	validation-auc:0.97068	validation-aucpr:0.97472
[21:02:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[58]	validation-logloss:0.42155	validation-auc:0.97070	validation-aucpr:0.97474
[21:02:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[59]	validation-logloss:0.41898	validation-auc:0.97067	validation-aucpr:0.97471
[21:02:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[60]	validation-logloss:0.41594	validation-auc:0.97070	validation-aucpr:0.97473
[21:02:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[61]	validation-logloss:0.41292	validation-auc:0.97075	validation-aucpr:0.97479
[21:02:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[62]	validation-logloss:0.41018	validation-auc:0.97075	validation-aucpr:0.97480
[21:02:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[63]	validation-logloss:0.40774	validation-auc:0.97075	validation-aucpr:0.97478
[21:02:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[64]	validation-logloss:0.40523	validation-auc:0.97079	validation-aucpr:0.97482
[21:02:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[65]	validation-logloss:0.40234	validation-auc:0.97084	validation-aucpr:0.97487
[21:02:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[66]	validation-logloss:0.40002	validation-auc:0.97082	validation-aucpr:0.97484
[21:02:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[67]	validation-logloss:0.39774	validation-auc:0.97080	validation-aucpr:0.97485
[21:02:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[68]	validation-logloss:0.39507	validation-auc:0.97083	validation-aucpr:0.97488
[21:02:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[69]	validation-logloss:0.39241	validation-auc:0.97086	validation-aucpr:0.97491
[21:02:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[70]	validation-logloss:0.38984	validation-auc:0.97091	validation-aucpr:0.97496
[21:02:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[71]	validation-logloss:0.38778	validation-auc:0.97090	validation-aucpr:0.97495
[21:02:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[72]	validation-logloss:0.38522	validation-auc:0.97094	validation-aucpr:0.97498
[21:02:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[73]	validation-logloss:0.38323	validation-auc:0.97089	validation-aucpr:0.97493
[21:02:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[74]	validation-logloss:0.38125	validation-auc:0.97086	validation-aucpr:0.97489
[21:02:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[75]	validation-logloss:0.37876	validation-auc:0.97088	validation-aucpr:0.97492
[21:02:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[76]	validation-logloss:0.37672	validation-auc:0.97091	validation-aucpr:0.97492
[21:02:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[77]	validation-logloss:0.37427	validation-auc:0.97095	validation-aucpr:0.97496
[21:02:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[78]	validation-logloss:0.37222	validation-auc:0.97096	validation-aucpr:0.97496
[21:02:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[79]	validation-logloss:0.37027	validation-auc:0.97096	validation-aucpr:0.97494
{'best_iteration': '72', 'best_score': '0.9749780107157653'}
Trial 68, Fold 3: Log loss = 0.37027450129392103, Average precision = 0.9749421781509551, ROC-AUC = 0.970958667209215, Elapsed Time = 20.445873399999982 seconds
Trial 68, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 68, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[21:02:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.68598	validation-auc:0.93902	validation-aucpr:0.91408
[21:02:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.67910	validation-auc:0.95301	validation-aucpr:0.95750
[21:02:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.67157	validation-auc:0.96192	validation-aucpr:0.96755
[21:02:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.66491	validation-auc:0.96249	validation-aucpr:0.96810
[21:02:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.65867	validation-auc:0.96173	validation-aucpr:0.96762
[21:02:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5]	validation-logloss:0.65177	validation-auc:0.96410	validation-aucpr:0.96977
[21:02:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6]	validation-logloss:0.64494	validation-auc:0.96510	validation-aucpr:0.97077
[21:02:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7]	validation-logloss:0.63875	validation-auc:0.96570	validation-aucpr:0.97109
[21:02:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8]	validation-logloss:0.63288	validation-auc:0.96528	validation-aucpr:0.97072
[21:02:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9]	validation-logloss:0.62712	validation-auc:0.96564	validation-aucpr:0.97100
[21:02:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10]	validation-logloss:0.62140	validation-auc:0.96561	validation-aucpr:0.97091
[21:02:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11]	validation-logloss:0.61507	validation-auc:0.96625	validation-aucpr:0.97152
[21:02:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12]	validation-logloss:0.60954	validation-auc:0.96652	validation-aucpr:0.97164
[21:02:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13]	validation-logloss:0.60349	validation-auc:0.96683	validation-aucpr:0.97209
[21:02:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14]	validation-logloss:0.59758	validation-auc:0.96695	validation-aucpr:0.97225
[21:02:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15]	validation-logloss:0.59155	validation-auc:0.96741	validation-aucpr:0.97271
[21:02:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16]	validation-logloss:0.58654	validation-auc:0.96728	validation-aucpr:0.97257
[21:02:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17]	validation-logloss:0.58163	validation-auc:0.96728	validation-aucpr:0.97254
[21:02:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18]	validation-logloss:0.57594	validation-auc:0.96753	validation-aucpr:0.97281
[21:02:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19]	validation-logloss:0.57048	validation-auc:0.96773	validation-aucpr:0.97300
[21:02:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20]	validation-logloss:0.56550	validation-auc:0.96766	validation-aucpr:0.97295
[21:02:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21]	validation-logloss:0.56075	validation-auc:0.96751	validation-aucpr:0.97285
[21:02:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22]	validation-logloss:0.55609	validation-auc:0.96760	validation-aucpr:0.97287
[21:02:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23]	validation-logloss:0.55155	validation-auc:0.96742	validation-aucpr:0.97271
[21:02:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24]	validation-logloss:0.54650	validation-auc:0.96759	validation-aucpr:0.97285
[21:02:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25]	validation-logloss:0.54242	validation-auc:0.96760	validation-aucpr:0.97284
[21:02:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26]	validation-logloss:0.53743	validation-auc:0.96784	validation-aucpr:0.97306
[21:02:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27]	validation-logloss:0.53316	validation-auc:0.96775	validation-aucpr:0.97299
[21:02:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28]	validation-logloss:0.52915	validation-auc:0.96756	validation-aucpr:0.97281
[21:02:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29]	validation-logloss:0.52512	validation-auc:0.96759	validation-aucpr:0.97279
[21:02:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30]	validation-logloss:0.52111	validation-auc:0.96761	validation-aucpr:0.97278
[21:02:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31]	validation-logloss:0.51714	validation-auc:0.96756	validation-aucpr:0.97272
[21:02:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32]	validation-logloss:0.51312	validation-auc:0.96765	validation-aucpr:0.97278
[21:02:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33]	validation-logloss:0.50942	validation-auc:0.96756	validation-aucpr:0.97268
[21:02:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34]	validation-logloss:0.50498	validation-auc:0.96763	validation-aucpr:0.97277
[21:02:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35]	validation-logloss:0.50129	validation-auc:0.96766	validation-aucpr:0.97276
[21:02:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36]	validation-logloss:0.49754	validation-auc:0.96765	validation-aucpr:0.97273
[21:02:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37]	validation-logloss:0.49350	validation-auc:0.96780	validation-aucpr:0.97284
[21:02:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38]	validation-logloss:0.48929	validation-auc:0.96796	validation-aucpr:0.97298
[21:02:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39]	validation-logloss:0.48546	validation-auc:0.96800	validation-aucpr:0.97303
[21:02:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40]	validation-logloss:0.48198	validation-auc:0.96794	validation-aucpr:0.97299
[21:02:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41]	validation-logloss:0.47875	validation-auc:0.96790	validation-aucpr:0.97295
[21:02:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42]	validation-logloss:0.47550	validation-auc:0.96779	validation-aucpr:0.97285
[21:02:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43]	validation-logloss:0.47220	validation-auc:0.96777	validation-aucpr:0.97283
[21:02:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44]	validation-logloss:0.46900	validation-auc:0.96785	validation-aucpr:0.97289
[21:02:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45]	validation-logloss:0.46597	validation-auc:0.96785	validation-aucpr:0.97288
[21:02:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46]	validation-logloss:0.46268	validation-auc:0.96795	validation-aucpr:0.97294
[21:02:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[47]	validation-logloss:0.45913	validation-auc:0.96793	validation-aucpr:0.97296
[21:02:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[48]	validation-logloss:0.45562	validation-auc:0.96801	validation-aucpr:0.97303
[21:02:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[49]	validation-logloss:0.45206	validation-auc:0.96809	validation-aucpr:0.97311
[21:02:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[50]	validation-logloss:0.44903	validation-auc:0.96808	validation-aucpr:0.97310
[21:02:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[51]	validation-logloss:0.44612	validation-auc:0.96799	validation-aucpr:0.97303
[21:02:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[52]	validation-logloss:0.44316	validation-auc:0.96798	validation-aucpr:0.97302
[21:02:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[53]	validation-logloss:0.43975	validation-auc:0.96805	validation-aucpr:0.97310
[21:02:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[54]	validation-logloss:0.43654	validation-auc:0.96810	validation-aucpr:0.97315
[21:02:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[55]	validation-logloss:0.43386	validation-auc:0.96805	validation-aucpr:0.97312
[21:02:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[56]	validation-logloss:0.43119	validation-auc:0.96806	validation-aucpr:0.97311
[21:02:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[57]	validation-logloss:0.42861	validation-auc:0.96801	validation-aucpr:0.97307
[21:02:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[58]	validation-logloss:0.42541	validation-auc:0.96810	validation-aucpr:0.97316
[21:02:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[59]	validation-logloss:0.42280	validation-auc:0.96808	validation-aucpr:0.97315
[21:02:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[60]	validation-logloss:0.42036	validation-auc:0.96799	validation-aucpr:0.97308
[21:02:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[61]	validation-logloss:0.41730	validation-auc:0.96805	validation-aucpr:0.97316
[21:02:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[62]	validation-logloss:0.41497	validation-auc:0.96798	validation-aucpr:0.97311
[21:02:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[63]	validation-logloss:0.41206	validation-auc:0.96805	validation-aucpr:0.97319
[21:02:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[64]	validation-logloss:0.40975	validation-auc:0.96800	validation-aucpr:0.97314
[21:02:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[65]	validation-logloss:0.40689	validation-auc:0.96809	validation-aucpr:0.97323
[21:02:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[66]	validation-logloss:0.40405	validation-auc:0.96819	validation-aucpr:0.97332
[21:02:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[67]	validation-logloss:0.40177	validation-auc:0.96814	validation-aucpr:0.97328
[21:02:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[68]	validation-logloss:0.39900	validation-auc:0.96821	validation-aucpr:0.97335
[21:02:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[69]	validation-logloss:0.39695	validation-auc:0.96816	validation-aucpr:0.97329
[21:02:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[70]	validation-logloss:0.39472	validation-auc:0.96813	validation-aucpr:0.97327
[21:02:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[71]	validation-logloss:0.39212	validation-auc:0.96818	validation-aucpr:0.97331
[21:02:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[72]	validation-logloss:0.38994	validation-auc:0.96823	validation-aucpr:0.97336
[21:02:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[73]	validation-logloss:0.38789	validation-auc:0.96823	validation-aucpr:0.97335
[21:02:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[74]	validation-logloss:0.38574	validation-auc:0.96827	validation-aucpr:0.97336
[21:02:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[75]	validation-logloss:0.38380	validation-auc:0.96826	validation-aucpr:0.97336
[21:02:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[76]	validation-logloss:0.38132	validation-auc:0.96834	validation-aucpr:0.97343
[21:02:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[77]	validation-logloss:0.37931	validation-auc:0.96833	validation-aucpr:0.97341
[21:02:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[78]	validation-logloss:0.37731	validation-auc:0.96831	validation-aucpr:0.97339
[21:02:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[79]	validation-logloss:0.37494	validation-auc:0.96840	validation-aucpr:0.97347
{'best_iteration': '79', 'best_score': '0.9734720863134078'}
Trial 68, Fold 4: Log loss = 0.37494391354788814, Average precision = 0.9734713875983636, ROC-AUC = 0.9684023977624698, Elapsed Time = 20.46157889999813 seconds
Trial 68, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 68, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[21:03:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.68626	validation-auc:0.93212	validation-aucpr:0.90712
[21:03:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.67984	validation-auc:0.94790	validation-aucpr:0.94679
[21:03:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.67253	validation-auc:0.95790	validation-aucpr:0.95955
[21:03:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.66593	validation-auc:0.95991	validation-aucpr:0.96408
[21:03:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.65908	validation-auc:0.96234	validation-aucpr:0.96654
[21:03:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5]	validation-logloss:0.65302	validation-auc:0.96251	validation-aucpr:0.96687
[21:03:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6]	validation-logloss:0.64701	validation-auc:0.96232	validation-aucpr:0.96676
[21:03:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7]	validation-logloss:0.64105	validation-auc:0.96234	validation-aucpr:0.96659
[21:03:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8]	validation-logloss:0.63436	validation-auc:0.96408	validation-aucpr:0.96848
[21:03:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9]	validation-logloss:0.62802	validation-auc:0.96443	validation-aucpr:0.96890
[21:03:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10]	validation-logloss:0.62226	validation-auc:0.96468	validation-aucpr:0.96895
[21:03:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11]	validation-logloss:0.61601	validation-auc:0.96523	validation-aucpr:0.96953
[21:03:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12]	validation-logloss:0.60993	validation-auc:0.96542	validation-aucpr:0.96909
[21:03:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13]	validation-logloss:0.60460	validation-auc:0.96549	validation-aucpr:0.96918
[21:03:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14]	validation-logloss:0.59876	validation-auc:0.96558	validation-aucpr:0.96929
[21:03:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15]	validation-logloss:0.59288	validation-auc:0.96574	validation-aucpr:0.96944
[21:03:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16]	validation-logloss:0.58732	validation-auc:0.96565	validation-aucpr:0.96923
[21:03:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17]	validation-logloss:0.58236	validation-auc:0.96559	validation-aucpr:0.96916
[21:03:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18]	validation-logloss:0.57752	validation-auc:0.96571	validation-aucpr:0.96911
[21:03:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19]	validation-logloss:0.57202	validation-auc:0.96594	validation-aucpr:0.96934
[21:03:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20]	validation-logloss:0.56720	validation-auc:0.96599	validation-aucpr:0.96933
[21:03:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21]	validation-logloss:0.56273	validation-auc:0.96605	validation-aucpr:0.97055
[21:03:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22]	validation-logloss:0.55763	validation-auc:0.96608	validation-aucpr:0.97061
[21:03:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23]	validation-logloss:0.55254	validation-auc:0.96627	validation-aucpr:0.97083
[21:03:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24]	validation-logloss:0.54770	validation-auc:0.96634	validation-aucpr:0.97090
[21:03:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25]	validation-logloss:0.54271	validation-auc:0.96662	validation-aucpr:0.97113
[21:03:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26]	validation-logloss:0.53779	validation-auc:0.96695	validation-aucpr:0.97139
[21:03:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27]	validation-logloss:0.53358	validation-auc:0.96693	validation-aucpr:0.97133
[21:03:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28]	validation-logloss:0.52943	validation-auc:0.96698	validation-aucpr:0.97134
[21:03:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29]	validation-logloss:0.52477	validation-auc:0.96706	validation-aucpr:0.97144
[21:03:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30]	validation-logloss:0.52087	validation-auc:0.96713	validation-aucpr:0.97146
[21:03:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31]	validation-logloss:0.51688	validation-auc:0.96710	validation-aucpr:0.97142
[21:03:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32]	validation-logloss:0.51252	validation-auc:0.96714	validation-aucpr:0.97146
[21:03:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33]	validation-logloss:0.50807	validation-auc:0.96724	validation-aucpr:0.97147
[21:03:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34]	validation-logloss:0.50383	validation-auc:0.96736	validation-aucpr:0.97158
[21:03:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35]	validation-logloss:0.49960	validation-auc:0.96747	validation-aucpr:0.97176
[21:03:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36]	validation-logloss:0.49597	validation-auc:0.96741	validation-aucpr:0.97171
[21:03:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37]	validation-logloss:0.49244	validation-auc:0.96733	validation-aucpr:0.97162
[21:03:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38]	validation-logloss:0.48907	validation-auc:0.96722	validation-aucpr:0.97153
[21:03:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39]	validation-logloss:0.48555	validation-auc:0.96721	validation-aucpr:0.97152
[21:03:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40]	validation-logloss:0.48221	validation-auc:0.96719	validation-aucpr:0.97149
[21:03:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41]	validation-logloss:0.47892	validation-auc:0.96705	validation-aucpr:0.97034
[21:03:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42]	validation-logloss:0.47504	validation-auc:0.96710	validation-aucpr:0.97037
[21:03:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43]	validation-logloss:0.47134	validation-auc:0.96718	validation-aucpr:0.97047
[21:03:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44]	validation-logloss:0.46757	validation-auc:0.96724	validation-aucpr:0.97053
[21:03:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45]	validation-logloss:0.46451	validation-auc:0.96726	validation-aucpr:0.97053
[21:03:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46]	validation-logloss:0.46120	validation-auc:0.96721	validation-aucpr:0.97052
[21:03:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[47]	validation-logloss:0.45826	validation-auc:0.96716	validation-aucpr:0.97046
[21:03:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[48]	validation-logloss:0.45537	validation-auc:0.96708	validation-aucpr:0.97038
[21:03:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[49]	validation-logloss:0.45190	validation-auc:0.96718	validation-aucpr:0.97046
[21:03:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[50]	validation-logloss:0.44860	validation-auc:0.96730	validation-aucpr:0.97188
[21:03:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[51]	validation-logloss:0.44541	validation-auc:0.96736	validation-aucpr:0.97195
[21:03:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[52]	validation-logloss:0.44204	validation-auc:0.96749	validation-aucpr:0.97206
[21:03:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[53]	validation-logloss:0.43928	validation-auc:0.96747	validation-aucpr:0.97204
[21:03:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[54]	validation-logloss:0.43600	validation-auc:0.96756	validation-aucpr:0.97212
[21:03:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[55]	validation-logloss:0.43282	validation-auc:0.96759	validation-aucpr:0.97217
[21:03:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[56]	validation-logloss:0.43003	validation-auc:0.96762	validation-aucpr:0.97219
[21:03:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[57]	validation-logloss:0.42742	validation-auc:0.96760	validation-aucpr:0.97216
[21:03:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[58]	validation-logloss:0.42445	validation-auc:0.96764	validation-aucpr:0.97220
[21:03:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[59]	validation-logloss:0.42182	validation-auc:0.96761	validation-aucpr:0.97216
[21:03:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[60]	validation-logloss:0.41935	validation-auc:0.96756	validation-aucpr:0.97211
[21:03:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[61]	validation-logloss:0.41680	validation-auc:0.96758	validation-aucpr:0.97212
[21:03:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[62]	validation-logloss:0.41390	validation-auc:0.96764	validation-aucpr:0.97216
[21:03:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[63]	validation-logloss:0.41158	validation-auc:0.96757	validation-aucpr:0.97211
[21:03:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[64]	validation-logloss:0.40918	validation-auc:0.96756	validation-aucpr:0.97208
[21:03:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[65]	validation-logloss:0.40676	validation-auc:0.96759	validation-aucpr:0.97211
[21:03:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[66]	validation-logloss:0.40403	validation-auc:0.96765	validation-aucpr:0.97216
[21:03:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[67]	validation-logloss:0.40197	validation-auc:0.96755	validation-aucpr:0.97204
[21:03:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[68]	validation-logloss:0.39975	validation-auc:0.96756	validation-aucpr:0.97203
[21:03:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[69]	validation-logloss:0.39749	validation-auc:0.96760	validation-aucpr:0.97207
[21:03:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[70]	validation-logloss:0.39523	validation-auc:0.96766	validation-aucpr:0.97210
[21:03:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[71]	validation-logloss:0.39312	validation-auc:0.96770	validation-aucpr:0.97212
[21:03:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[72]	validation-logloss:0.39102	validation-auc:0.96766	validation-aucpr:0.97209
[21:03:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[73]	validation-logloss:0.38856	validation-auc:0.96772	validation-aucpr:0.97215
[21:03:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[74]	validation-logloss:0.38605	validation-auc:0.96777	validation-aucpr:0.97221
[21:03:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[75]	validation-logloss:0.38359	validation-auc:0.96786	validation-aucpr:0.97228
[21:03:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[76]	validation-logloss:0.38163	validation-auc:0.96783	validation-aucpr:0.97225
[21:03:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[77]	validation-logloss:0.37916	validation-auc:0.96798	validation-aucpr:0.97236
[21:03:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[78]	validation-logloss:0.37683	validation-auc:0.96807	validation-aucpr:0.97244
[21:03:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[79]	validation-logloss:0.37495	validation-auc:0.96801	validation-aucpr:0.97239
{'best_iteration': '78', 'best_score': '0.9724375478366029'}
Trial 68, Fold 5: Log loss = 0.3749512595837695, Average precision = 0.9723950680655613, ROC-AUC = 0.968007729037772, Elapsed Time = 20.992388499998924 seconds
Optimization Progress:  69%|######9   | 69/100 [3:04:32<28:27, 55.10s/it]
Trial 69, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 69, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
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[81]	validation-logloss:0.34987	validation-auc:0.96716	validation-aucpr:0.97280
[82]	validation-logloss:0.34788	validation-auc:0.96716	validation-aucpr:0.97280
[83]	validation-logloss:0.34615	validation-auc:0.96720	validation-aucpr:0.97282
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[86]	validation-logloss:0.34046	validation-auc:0.96725	validation-aucpr:0.97286
[87]	validation-logloss:0.33885	validation-auc:0.96721	validation-aucpr:0.97283
[88]	validation-logloss:0.33715	validation-auc:0.96720	validation-aucpr:0.97282
[89]	validation-logloss:0.33550	validation-auc:0.96725	validation-aucpr:0.97285
[90]	validation-logloss:0.33369	validation-auc:0.96728	validation-aucpr:0.97289
[91]	validation-logloss:0.33189	validation-auc:0.96731	validation-aucpr:0.97292
[92]	validation-logloss:0.33006	validation-auc:0.96733	validation-aucpr:0.97294
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[94]	validation-logloss:0.32682	validation-auc:0.96733	validation-aucpr:0.97293
{'best_iteration': '92', 'best_score': '0.9729422850626687'}
Trial 69, Fold 1: Log loss = 0.3268189989634485, Average precision = 0.9729359479843955, ROC-AUC = 0.9673291177356218, Elapsed Time = 225.05377579999913 seconds
Trial 69, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 69, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
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[4]	validation-logloss:0.65561	validation-auc:0.96301	validation-aucpr:0.96716
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[94]	validation-logloss:0.32659	validation-auc:0.96920	validation-aucpr:0.97282
{'best_iteration': '92', 'best_score': '0.9728419835631521'}
Trial 69, Fold 2: Log loss = 0.32658608862626937, Average precision = 0.9728280145197388, ROC-AUC = 0.9691953512976407, Elapsed Time = 241.70224149999922 seconds
Trial 69, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 69, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[0]	validation-logloss:0.68478	validation-auc:0.95556	validation-aucpr:0.95743
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{'best_iteration': '94', 'best_score': '0.9733583680595139'}
Trial 69, Fold 3: Log loss = 0.3263034784487951, Average precision = 0.9733629369980912, ROC-AUC = 0.9684713019233178, Elapsed Time = 236.4137340999987 seconds
Trial 69, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 69, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
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{'best_iteration': '93', 'best_score': '0.9730163723177887'}
Trial 69, Fold 4: Log loss = 0.3274444627638355, Average precision = 0.9730170377555121, ROC-AUC = 0.9677517519450822, Elapsed Time = 237.86633739999888 seconds
Trial 69, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 69, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
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[78]	validation-logloss:0.36040	validation-auc:0.96547	validation-aucpr:0.97064
[79]	validation-logloss:0.35812	validation-auc:0.96554	validation-aucpr:0.97069
[80]	validation-logloss:0.35614	validation-auc:0.96560	validation-aucpr:0.97075
[81]	validation-logloss:0.35434	validation-auc:0.96563	validation-aucpr:0.97078
[82]	validation-logloss:0.35230	validation-auc:0.96567	validation-aucpr:0.97081
[83]	validation-logloss:0.35058	validation-auc:0.96565	validation-aucpr:0.97079
[84]	validation-logloss:0.34858	validation-auc:0.96562	validation-aucpr:0.97078
[85]	validation-logloss:0.34677	validation-auc:0.96568	validation-aucpr:0.97081
[86]	validation-logloss:0.34478	validation-auc:0.96569	validation-aucpr:0.97082
[87]	validation-logloss:0.34317	validation-auc:0.96567	validation-aucpr:0.97080
[88]	validation-logloss:0.34135	validation-auc:0.96572	validation-aucpr:0.97084
[89]	validation-logloss:0.33957	validation-auc:0.96574	validation-aucpr:0.97085
[90]	validation-logloss:0.33773	validation-auc:0.96576	validation-aucpr:0.97085
[91]	validation-logloss:0.33624	validation-auc:0.96572	validation-aucpr:0.97080
[92]	validation-logloss:0.33443	validation-auc:0.96579	validation-aucpr:0.97086
[93]	validation-logloss:0.33281	validation-auc:0.96582	validation-aucpr:0.97087
[94]	validation-logloss:0.33098	validation-auc:0.96591	validation-aucpr:0.97094
{'best_iteration': '94', 'best_score': '0.9709447835729923'}
Trial 69, Fold 5: Log loss = 0.3309771129872184, Average precision = 0.9709498977967147, ROC-AUC = 0.9659115248128123, Elapsed Time = 239.0036919000013 seconds
Optimization Progress:  70%|#######   | 70/100 [3:24:19<3:17:27, 394.91s/it]
Trial 70, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 70, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
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[3]	validation-logloss:0.61769	validation-auc:0.96293	validation-aucpr:0.96850
[4]	validation-logloss:0.60076	validation-auc:0.96394	validation-aucpr:0.96921
[5]	validation-logloss:0.58723	validation-auc:0.96332	validation-aucpr:0.96823
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[79]	validation-logloss:0.23367	validation-auc:0.96951	validation-aucpr:0.97427
[80]	validation-logloss:0.23218	validation-auc:0.96956	validation-aucpr:0.97431
[81]	validation-logloss:0.23149	validation-auc:0.96950	validation-aucpr:0.97427
[82]	validation-logloss:0.23019	validation-auc:0.96960	validation-aucpr:0.97437
[83]	validation-logloss:0.22928	validation-auc:0.96964	validation-aucpr:0.97443
[84]	validation-logloss:0.22815	validation-auc:0.96968	validation-aucpr:0.97447
[85]	validation-logloss:0.22729	validation-auc:0.96974	validation-aucpr:0.97453
[86]	validation-logloss:0.22620	validation-auc:0.96981	validation-aucpr:0.97459
[87]	validation-logloss:0.22560	validation-auc:0.96972	validation-aucpr:0.97452
[88]	validation-logloss:0.22441	validation-auc:0.96982	validation-aucpr:0.97459
[89]	validation-logloss:0.22382	validation-auc:0.96980	validation-aucpr:0.97458
[90]	validation-logloss:0.22299	validation-auc:0.96987	validation-aucpr:0.97462
[91]	validation-logloss:0.22204	validation-auc:0.96993	validation-aucpr:0.97465
[92]	validation-logloss:0.22136	validation-auc:0.96997	validation-aucpr:0.97468
[93]	validation-logloss:0.22070	validation-auc:0.96996	validation-aucpr:0.97467
[94]	validation-logloss:0.21973	validation-auc:0.97002	validation-aucpr:0.97473
[95]	validation-logloss:0.21885	validation-auc:0.97008	validation-aucpr:0.97478
[96]	validation-logloss:0.21840	validation-auc:0.97003	validation-aucpr:0.97475
[97]	validation-logloss:0.21745	validation-auc:0.97011	validation-aucpr:0.97480
{'best_iteration': '97', 'best_score': '0.9747997415825582'}
Trial 70, Fold 1: Log loss = 0.21744990998351488, Average precision = 0.9748039295492716, ROC-AUC = 0.970106283128528, Elapsed Time = 17.291013699999894 seconds
Trial 70, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 70, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[0]	validation-logloss:0.67321	validation-auc:0.94151	validation-aucpr:0.94429
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[2]	validation-logloss:0.63629	validation-auc:0.96110	validation-aucpr:0.96444
[3]	validation-logloss:0.61961	validation-auc:0.96219	validation-aucpr:0.96562
[4]	validation-logloss:0.60220	validation-auc:0.96457	validation-aucpr:0.96845
[5]	validation-logloss:0.58604	validation-auc:0.96492	validation-aucpr:0.96916
[6]	validation-logloss:0.57080	validation-auc:0.96607	validation-aucpr:0.96979
[7]	validation-logloss:0.55641	validation-auc:0.96640	validation-aucpr:0.97011
[8]	validation-logloss:0.54462	validation-auc:0.96626	validation-aucpr:0.96975
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[82]	validation-logloss:0.22206	validation-auc:0.97181	validation-aucpr:0.97483
[83]	validation-logloss:0.22077	validation-auc:0.97183	validation-aucpr:0.97484
[84]	validation-logloss:0.21990	validation-auc:0.97187	validation-aucpr:0.97487
[85]	validation-logloss:0.21904	validation-auc:0.97186	validation-aucpr:0.97491
[86]	validation-logloss:0.21798	validation-auc:0.97192	validation-aucpr:0.97495
[87]	validation-logloss:0.21688	validation-auc:0.97187	validation-aucpr:0.97494
[88]	validation-logloss:0.21557	validation-auc:0.97199	validation-aucpr:0.97504
[89]	validation-logloss:0.21445	validation-auc:0.97201	validation-aucpr:0.97509
[90]	validation-logloss:0.21369	validation-auc:0.97201	validation-aucpr:0.97508
[91]	validation-logloss:0.21259	validation-auc:0.97204	validation-aucpr:0.97509
[92]	validation-logloss:0.21165	validation-auc:0.97204	validation-aucpr:0.97526
[93]	validation-logloss:0.21064	validation-auc:0.97207	validation-aucpr:0.97529
[94]	validation-logloss:0.20978	validation-auc:0.97205	validation-aucpr:0.97528
[95]	validation-logloss:0.20922	validation-auc:0.97205	validation-aucpr:0.97523
[96]	validation-logloss:0.20853	validation-auc:0.97208	validation-aucpr:0.97524
[97]	validation-logloss:0.20795	validation-auc:0.97204	validation-aucpr:0.97515
{'best_iteration': '93', 'best_score': '0.9752944596780706'}
Trial 70, Fold 2: Log loss = 0.2079469238659796, Average precision = 0.9751581080665546, ROC-AUC = 0.972035993967722, Elapsed Time = 17.38810250000097 seconds
Trial 70, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 70, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
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[2]	validation-logloss:0.63460	validation-auc:0.96466	validation-aucpr:0.96501
[3]	validation-logloss:0.61683	validation-auc:0.96580	validation-aucpr:0.96614
[4]	validation-logloss:0.60173	validation-auc:0.96600	validation-aucpr:0.96583
[5]	validation-logloss:0.58757	validation-auc:0.96655	validation-aucpr:0.96787
[6]	validation-logloss:0.57243	validation-auc:0.96720	validation-aucpr:0.97052
[7]	validation-logloss:0.55745	validation-auc:0.96826	validation-aucpr:0.97138
[8]	validation-logloss:0.54348	validation-auc:0.96842	validation-aucpr:0.97208
[9]	validation-logloss:0.53019	validation-auc:0.96889	validation-aucpr:0.97284
[10]	validation-logloss:0.51868	validation-auc:0.96910	validation-aucpr:0.97300
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[78]	validation-logloss:0.22975	validation-auc:0.97111	validation-aucpr:0.97514
[79]	validation-logloss:0.22869	validation-auc:0.97112	validation-aucpr:0.97516
[80]	validation-logloss:0.22746	validation-auc:0.97122	validation-aucpr:0.97526
[81]	validation-logloss:0.22615	validation-auc:0.97120	validation-aucpr:0.97525
[82]	validation-logloss:0.22528	validation-auc:0.97119	validation-aucpr:0.97524
[83]	validation-logloss:0.22413	validation-auc:0.97125	validation-aucpr:0.97528
[84]	validation-logloss:0.22300	validation-auc:0.97131	validation-aucpr:0.97533
[85]	validation-logloss:0.22202	validation-auc:0.97139	validation-aucpr:0.97540
[86]	validation-logloss:0.22089	validation-auc:0.97144	validation-aucpr:0.97544
[87]	validation-logloss:0.22005	validation-auc:0.97146	validation-aucpr:0.97545
[88]	validation-logloss:0.21903	validation-auc:0.97143	validation-aucpr:0.97542
[89]	validation-logloss:0.21780	validation-auc:0.97151	validation-aucpr:0.97550
[90]	validation-logloss:0.21732	validation-auc:0.97144	validation-aucpr:0.97544
[91]	validation-logloss:0.21679	validation-auc:0.97138	validation-aucpr:0.97539
[92]	validation-logloss:0.21582	validation-auc:0.97144	validation-aucpr:0.97545
[93]	validation-logloss:0.21502	validation-auc:0.97146	validation-aucpr:0.97547
[94]	validation-logloss:0.21411	validation-auc:0.97153	validation-aucpr:0.97553
[95]	validation-logloss:0.21321	validation-auc:0.97156	validation-aucpr:0.97555
[96]	validation-logloss:0.21245	validation-auc:0.97160	validation-aucpr:0.97559
[97]	validation-logloss:0.21155	validation-auc:0.97165	validation-aucpr:0.97564
{'best_iteration': '97', 'best_score': '0.9756388331758201'}
Trial 70, Fold 3: Log loss = 0.21154853985304786, Average precision = 0.9756430835055213, ROC-AUC = 0.9716489415492483, Elapsed Time = 17.922511900000245 seconds
Trial 70, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 70, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[0]	validation-logloss:0.67400	validation-auc:0.93355	validation-aucpr:0.93296
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[2]	validation-logloss:0.63952	validation-auc:0.95213	validation-aucpr:0.95769
[3]	validation-logloss:0.62193	validation-auc:0.95913	validation-aucpr:0.96460
[4]	validation-logloss:0.60666	validation-auc:0.95981	validation-aucpr:0.96526
[5]	validation-logloss:0.59201	validation-auc:0.96034	validation-aucpr:0.96573
[6]	validation-logloss:0.57629	validation-auc:0.96247	validation-aucpr:0.96798
[7]	validation-logloss:0.56330	validation-auc:0.96270	validation-aucpr:0.96824
[8]	validation-logloss:0.55092	validation-auc:0.96321	validation-aucpr:0.96868
[9]	validation-logloss:0.53897	validation-auc:0.96340	validation-aucpr:0.96876
[10]	validation-logloss:0.52795	validation-auc:0.96376	validation-aucpr:0.96889
[11]	validation-logloss:0.51504	validation-auc:0.96494	validation-aucpr:0.96998
[12]	validation-logloss:0.50458	validation-auc:0.96510	validation-aucpr:0.97007
[13]	validation-logloss:0.49466	validation-auc:0.96526	validation-aucpr:0.97031
[14]	validation-logloss:0.48392	validation-auc:0.96563	validation-aucpr:0.97076
[15]	validation-logloss:0.47482	validation-auc:0.96545	validation-aucpr:0.97054
[16]	validation-logloss:0.46462	validation-auc:0.96575	validation-aucpr:0.97094
[17]	validation-logloss:0.45514	validation-auc:0.96605	validation-aucpr:0.97118
[18]	validation-logloss:0.44597	validation-auc:0.96644	validation-aucpr:0.97156
[19]	validation-logloss:0.43850	validation-auc:0.96633	validation-aucpr:0.97145
[20]	validation-logloss:0.43108	validation-auc:0.96631	validation-aucpr:0.97141
[21]	validation-logloss:0.42439	validation-auc:0.96614	validation-aucpr:0.97124
[22]	validation-logloss:0.41650	validation-auc:0.96639	validation-aucpr:0.97157
[23]	validation-logloss:0.40839	validation-auc:0.96673	validation-aucpr:0.97192
[24]	validation-logloss:0.40230	validation-auc:0.96665	validation-aucpr:0.97185
[25]	validation-logloss:0.39507	validation-auc:0.96671	validation-aucpr:0.97196
[26]	validation-logloss:0.38779	validation-auc:0.96692	validation-aucpr:0.97217
[27]	validation-logloss:0.38113	validation-auc:0.96715	validation-aucpr:0.97236
[28]	validation-logloss:0.37580	validation-auc:0.96701	validation-aucpr:0.97225
[29]	validation-logloss:0.36945	validation-auc:0.96713	validation-aucpr:0.97236
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[46]	validation-logloss:0.29599	validation-auc:0.96865	validation-aucpr:0.97369
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[48]	validation-logloss:0.28982	validation-auc:0.96900	validation-aucpr:0.97394
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[79]	validation-logloss:0.22873	validation-auc:0.97017	validation-aucpr:0.97492
[80]	validation-logloss:0.22767	validation-auc:0.97016	validation-aucpr:0.97490
[81]	validation-logloss:0.22667	validation-auc:0.97017	validation-aucpr:0.97491
[82]	validation-logloss:0.22515	validation-auc:0.97031	validation-aucpr:0.97502
[83]	validation-logloss:0.22420	validation-auc:0.97037	validation-aucpr:0.97507
[84]	validation-logloss:0.22318	validation-auc:0.97033	validation-aucpr:0.97504
[85]	validation-logloss:0.22244	validation-auc:0.97030	validation-aucpr:0.97501
[86]	validation-logloss:0.22136	validation-auc:0.97035	validation-aucpr:0.97506
[87]	validation-logloss:0.22048	validation-auc:0.97038	validation-aucpr:0.97509
[88]	validation-logloss:0.21959	validation-auc:0.97041	validation-aucpr:0.97510
[89]	validation-logloss:0.21855	validation-auc:0.97048	validation-aucpr:0.97515
[90]	validation-logloss:0.21755	validation-auc:0.97050	validation-aucpr:0.97519
[91]	validation-logloss:0.21687	validation-auc:0.97050	validation-aucpr:0.97520
[92]	validation-logloss:0.21580	validation-auc:0.97057	validation-aucpr:0.97525
[93]	validation-logloss:0.21515	validation-auc:0.97055	validation-aucpr:0.97523
[94]	validation-logloss:0.21446	validation-auc:0.97049	validation-aucpr:0.97519
[95]	validation-logloss:0.21348	validation-auc:0.97055	validation-aucpr:0.97522
[96]	validation-logloss:0.21263	validation-auc:0.97058	validation-aucpr:0.97524
[97]	validation-logloss:0.21186	validation-auc:0.97055	validation-aucpr:0.97522
{'best_iteration': '92', 'best_score': '0.9752471491651576'}
Trial 70, Fold 4: Log loss = 0.2118629863121309, Average precision = 0.9752193204989157, ROC-AUC = 0.9705480685088616, Elapsed Time = 17.842629599999782 seconds
Trial 70, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 70, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[0]	validation-logloss:0.67302	validation-auc:0.93436	validation-aucpr:0.93150
[1]	validation-logloss:0.65307	validation-auc:0.95469	validation-aucpr:0.95539
[2]	validation-logloss:0.63662	validation-auc:0.95763	validation-aucpr:0.96329
[3]	validation-logloss:0.61845	validation-auc:0.96103	validation-aucpr:0.96637
[4]	validation-logloss:0.60163	validation-auc:0.96285	validation-aucpr:0.96784
[5]	validation-logloss:0.58611	validation-auc:0.96243	validation-aucpr:0.96737
[6]	validation-logloss:0.57224	validation-auc:0.96350	validation-aucpr:0.96805
[7]	validation-logloss:0.55781	validation-auc:0.96369	validation-aucpr:0.96827
[8]	validation-logloss:0.54535	validation-auc:0.96445	validation-aucpr:0.96883
[9]	validation-logloss:0.53413	validation-auc:0.96404	validation-aucpr:0.96848
[10]	validation-logloss:0.52284	validation-auc:0.96410	validation-aucpr:0.96879
[11]	validation-logloss:0.51080	validation-auc:0.96477	validation-aucpr:0.96934
[12]	validation-logloss:0.50012	validation-auc:0.96525	validation-aucpr:0.96969
[13]	validation-logloss:0.49004	validation-auc:0.96519	validation-aucpr:0.96968
[14]	validation-logloss:0.48079	validation-auc:0.96525	validation-aucpr:0.96957
[15]	validation-logloss:0.47022	validation-auc:0.96564	validation-aucpr:0.96993
[16]	validation-logloss:0.46173	validation-auc:0.96552	validation-aucpr:0.96971
[17]	validation-logloss:0.45217	validation-auc:0.96586	validation-aucpr:0.97009
[18]	validation-logloss:0.44306	validation-auc:0.96619	validation-aucpr:0.97035
[19]	validation-logloss:0.43522	validation-auc:0.96630	validation-aucpr:0.97041
[20]	validation-logloss:0.42786	validation-auc:0.96629	validation-aucpr:0.97038
[21]	validation-logloss:0.41973	validation-auc:0.96658	validation-aucpr:0.97065
[22]	validation-logloss:0.41310	validation-auc:0.96643	validation-aucpr:0.97058
[23]	validation-logloss:0.40655	validation-auc:0.96627	validation-aucpr:0.97048
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{'best_iteration': '96', 'best_score': '0.9732505139835228'}
Trial 70, Fold 5: Log loss = 0.21767668827980782, Average precision = 0.973240621004372, ROC-AUC = 0.969341879865485, Elapsed Time = 19.734101900001406 seconds
Optimization Progress:  71%|#######1  | 71/100 [3:25:57<2:27:49, 305.83s/it]
Trial 71, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 71, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
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{'best_iteration': '75', 'best_score': '0.9727365897316989'}
Trial 71, Fold 1: Log loss = 0.37296171100424547, Average precision = 0.9726960282798622, ROC-AUC = 0.9677037180424714, Elapsed Time = 6.413239400000748 seconds
Trial 71, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 71, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
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[73]	validation-logloss:0.38228	validation-auc:0.97137	validation-aucpr:0.97460
[74]	validation-logloss:0.37997	validation-auc:0.97147	validation-aucpr:0.97469
[75]	validation-logloss:0.37784	validation-auc:0.97140	validation-aucpr:0.97463
[76]	validation-logloss:0.37548	validation-auc:0.97145	validation-aucpr:0.97468
[77]	validation-logloss:0.37320	validation-auc:0.97148	validation-aucpr:0.97471
[78]	validation-logloss:0.37102	validation-auc:0.97144	validation-aucpr:0.97464
[79]	validation-logloss:0.36909	validation-auc:0.97148	validation-aucpr:0.97464
{'best_iteration': '68', 'best_score': '0.9747844255814891'}
Trial 71, Fold 2: Log loss = 0.36908960303185984, Average precision = 0.9747278218123956, ROC-AUC = 0.9714808246945849, Elapsed Time = 5.026995500000339 seconds
Trial 71, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 71, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[0]	validation-logloss:0.68556	validation-auc:0.87778	validation-aucpr:0.79278
[1]	validation-logloss:0.67836	validation-auc:0.93716	validation-aucpr:0.90135
[2]	validation-logloss:0.67119	validation-auc:0.95347	validation-aucpr:0.94140
[3]	validation-logloss:0.66417	validation-auc:0.95902	validation-aucpr:0.95109
[4]	validation-logloss:0.65731	validation-auc:0.96171	validation-aucpr:0.95796
[5]	validation-logloss:0.65079	validation-auc:0.96148	validation-aucpr:0.95872
[6]	validation-logloss:0.64443	validation-auc:0.96200	validation-aucpr:0.96199
[7]	validation-logloss:0.63853	validation-auc:0.96237	validation-aucpr:0.96683
[8]	validation-logloss:0.63295	validation-auc:0.96219	validation-aucpr:0.96576
[9]	validation-logloss:0.62704	validation-auc:0.96273	validation-aucpr:0.96657
[10]	validation-logloss:0.62084	validation-auc:0.96302	validation-aucpr:0.96595
[11]	validation-logloss:0.61471	validation-auc:0.96386	validation-aucpr:0.96732
[12]	validation-logloss:0.60910	validation-auc:0.96416	validation-aucpr:0.96836
[13]	validation-logloss:0.60332	validation-auc:0.96452	validation-aucpr:0.96791
[14]	validation-logloss:0.59766	validation-auc:0.96489	validation-aucpr:0.96828
[15]	validation-logloss:0.59218	validation-auc:0.96502	validation-aucpr:0.96819
[16]	validation-logloss:0.58659	validation-auc:0.96549	validation-aucpr:0.96885
[17]	validation-logloss:0.58180	validation-auc:0.96549	validation-aucpr:0.96906
[18]	validation-logloss:0.57644	validation-auc:0.96597	validation-aucpr:0.96915
[19]	validation-logloss:0.57128	validation-auc:0.96607	validation-aucpr:0.96922
[20]	validation-logloss:0.56639	validation-auc:0.96612	validation-aucpr:0.96912
[21]	validation-logloss:0.56133	validation-auc:0.96649	validation-aucpr:0.96935
[22]	validation-logloss:0.55645	validation-auc:0.96643	validation-aucpr:0.96937
[23]	validation-logloss:0.55196	validation-auc:0.96644	validation-aucpr:0.96907
[24]	validation-logloss:0.54704	validation-auc:0.96652	validation-aucpr:0.96910
[25]	validation-logloss:0.54231	validation-auc:0.96705	validation-aucpr:0.97070
[26]	validation-logloss:0.53769	validation-auc:0.96693	validation-aucpr:0.97069
[27]	validation-logloss:0.53300	validation-auc:0.96724	validation-aucpr:0.97101
[28]	validation-logloss:0.52846	validation-auc:0.96722	validation-aucpr:0.97048
[29]	validation-logloss:0.52399	validation-auc:0.96729	validation-aucpr:0.97122
[30]	validation-logloss:0.51947	validation-auc:0.96768	validation-aucpr:0.97158
[31]	validation-logloss:0.51538	validation-auc:0.96752	validation-aucpr:0.97150
[32]	validation-logloss:0.51113	validation-auc:0.96764	validation-aucpr:0.97151
[33]	validation-logloss:0.50726	validation-auc:0.96785	validation-aucpr:0.97163
[34]	validation-logloss:0.50302	validation-auc:0.96792	validation-aucpr:0.97177
[35]	validation-logloss:0.49890	validation-auc:0.96812	validation-aucpr:0.97192
[36]	validation-logloss:0.49492	validation-auc:0.96796	validation-aucpr:0.97183
[37]	validation-logloss:0.49106	validation-auc:0.96825	validation-aucpr:0.97214
[38]	validation-logloss:0.48704	validation-auc:0.96827	validation-aucpr:0.97237
[39]	validation-logloss:0.48317	validation-auc:0.96850	validation-aucpr:0.97258
[40]	validation-logloss:0.47938	validation-auc:0.96862	validation-aucpr:0.97276
[41]	validation-logloss:0.47565	validation-auc:0.96868	validation-aucpr:0.97274
[42]	validation-logloss:0.47212	validation-auc:0.96883	validation-aucpr:0.97283
[43]	validation-logloss:0.46842	validation-auc:0.96893	validation-aucpr:0.97282
[44]	validation-logloss:0.46486	validation-auc:0.96888	validation-aucpr:0.97286
[45]	validation-logloss:0.46143	validation-auc:0.96878	validation-aucpr:0.97280
[46]	validation-logloss:0.45815	validation-auc:0.96865	validation-aucpr:0.97257
[47]	validation-logloss:0.45476	validation-auc:0.96868	validation-aucpr:0.97275
[48]	validation-logloss:0.45137	validation-auc:0.96864	validation-aucpr:0.97225
[49]	validation-logloss:0.44808	validation-auc:0.96872	validation-aucpr:0.97225
[50]	validation-logloss:0.44465	validation-auc:0.96889	validation-aucpr:0.97253
[51]	validation-logloss:0.44148	validation-auc:0.96884	validation-aucpr:0.97250
[52]	validation-logloss:0.43837	validation-auc:0.96879	validation-aucpr:0.97256
[53]	validation-logloss:0.43519	validation-auc:0.96893	validation-aucpr:0.97270
[54]	validation-logloss:0.43206	validation-auc:0.96893	validation-aucpr:0.97264
[55]	validation-logloss:0.42896	validation-auc:0.96896	validation-aucpr:0.97266
[56]	validation-logloss:0.42600	validation-auc:0.96889	validation-aucpr:0.97236
[57]	validation-logloss:0.42335	validation-auc:0.96913	validation-aucpr:0.97298
[58]	validation-logloss:0.42036	validation-auc:0.96920	validation-aucpr:0.97305
[59]	validation-logloss:0.41758	validation-auc:0.96921	validation-aucpr:0.97283
[60]	validation-logloss:0.41475	validation-auc:0.96920	validation-aucpr:0.97290
[61]	validation-logloss:0.41200	validation-auc:0.96913	validation-aucpr:0.97286
[62]	validation-logloss:0.40918	validation-auc:0.96926	validation-aucpr:0.97289
[63]	validation-logloss:0.40658	validation-auc:0.96918	validation-aucpr:0.97282
[64]	validation-logloss:0.40410	validation-auc:0.96914	validation-aucpr:0.97277
[65]	validation-logloss:0.40171	validation-auc:0.96910	validation-aucpr:0.97242
[66]	validation-logloss:0.39914	validation-auc:0.96920	validation-aucpr:0.97242
[67]	validation-logloss:0.39644	validation-auc:0.96922	validation-aucpr:0.97225
[68]	validation-logloss:0.39410	validation-auc:0.96917	validation-aucpr:0.97213
[69]	validation-logloss:0.39156	validation-auc:0.96922	validation-aucpr:0.97223
[70]	validation-logloss:0.38908	validation-auc:0.96925	validation-aucpr:0.97215
[71]	validation-logloss:0.38659	validation-auc:0.96932	validation-aucpr:0.97221
[72]	validation-logloss:0.38424	validation-auc:0.96940	validation-aucpr:0.97273
[73]	validation-logloss:0.38187	validation-auc:0.96940	validation-aucpr:0.97274
[74]	validation-logloss:0.37965	validation-auc:0.96926	validation-aucpr:0.97261
[75]	validation-logloss:0.37728	validation-auc:0.96928	validation-aucpr:0.97262
[76]	validation-logloss:0.37522	validation-auc:0.96933	validation-aucpr:0.97264
[77]	validation-logloss:0.37310	validation-auc:0.96931	validation-aucpr:0.97264
[78]	validation-logloss:0.37081	validation-auc:0.96934	validation-aucpr:0.97266
[79]	validation-logloss:0.36866	validation-auc:0.96927	validation-aucpr:0.97259
{'best_iteration': '58', 'best_score': '0.9730494269389566'}
Trial 71, Fold 3: Log loss = 0.3686551244484038, Average precision = 0.9727055199215334, ROC-AUC = 0.9692703768624011, Elapsed Time = 5.743944699999702 seconds
Trial 71, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 71, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[0]	validation-logloss:0.68543	validation-auc:0.91799	validation-aucpr:0.87581
[1]	validation-logloss:0.67808	validation-auc:0.93524	validation-aucpr:0.89612
[2]	validation-logloss:0.67092	validation-auc:0.95223	validation-aucpr:0.93929
[3]	validation-logloss:0.66413	validation-auc:0.95879	validation-aucpr:0.95791
[4]	validation-logloss:0.65794	validation-auc:0.96139	validation-aucpr:0.96407
[5]	validation-logloss:0.65120	validation-auc:0.96168	validation-aucpr:0.96285
[6]	validation-logloss:0.64454	validation-auc:0.96318	validation-aucpr:0.96619
[7]	validation-logloss:0.63851	validation-auc:0.96315	validation-aucpr:0.96764
[8]	validation-logloss:0.63226	validation-auc:0.96392	validation-aucpr:0.96885
[9]	validation-logloss:0.62593	validation-auc:0.96525	validation-aucpr:0.97016
[10]	validation-logloss:0.61999	validation-auc:0.96574	validation-aucpr:0.97148
[11]	validation-logloss:0.61458	validation-auc:0.96537	validation-aucpr:0.97125
[12]	validation-logloss:0.60875	validation-auc:0.96565	validation-aucpr:0.97144
[13]	validation-logloss:0.60311	validation-auc:0.96562	validation-aucpr:0.97148
[14]	validation-logloss:0.59751	validation-auc:0.96552	validation-aucpr:0.97158
[15]	validation-logloss:0.59188	validation-auc:0.96606	validation-aucpr:0.97203
[16]	validation-logloss:0.58629	validation-auc:0.96639	validation-aucpr:0.97224
[17]	validation-logloss:0.58111	validation-auc:0.96633	validation-aucpr:0.97219
[18]	validation-logloss:0.57583	validation-auc:0.96648	validation-aucpr:0.97227
[19]	validation-logloss:0.57060	validation-auc:0.96639	validation-aucpr:0.97221
[20]	validation-logloss:0.56571	validation-auc:0.96662	validation-aucpr:0.97242
[21]	validation-logloss:0.56062	validation-auc:0.96679	validation-aucpr:0.97252
[22]	validation-logloss:0.55575	validation-auc:0.96684	validation-aucpr:0.97256
[23]	validation-logloss:0.55079	validation-auc:0.96715	validation-aucpr:0.97275
[24]	validation-logloss:0.54590	validation-auc:0.96753	validation-aucpr:0.97304
[25]	validation-logloss:0.54109	validation-auc:0.96747	validation-aucpr:0.97300
[26]	validation-logloss:0.53653	validation-auc:0.96720	validation-aucpr:0.97281
[27]	validation-logloss:0.53205	validation-auc:0.96697	validation-aucpr:0.97265
[28]	validation-logloss:0.52772	validation-auc:0.96684	validation-aucpr:0.97256
[29]	validation-logloss:0.52349	validation-auc:0.96651	validation-aucpr:0.97233
[30]	validation-logloss:0.51911	validation-auc:0.96680	validation-aucpr:0.97256
[31]	validation-logloss:0.51497	validation-auc:0.96670	validation-aucpr:0.97250
[32]	validation-logloss:0.51080	validation-auc:0.96672	validation-aucpr:0.97254
[33]	validation-logloss:0.50662	validation-auc:0.96686	validation-aucpr:0.97266
[34]	validation-logloss:0.50284	validation-auc:0.96692	validation-aucpr:0.97268
[35]	validation-logloss:0.49870	validation-auc:0.96718	validation-aucpr:0.97286
[36]	validation-logloss:0.49466	validation-auc:0.96745	validation-aucpr:0.97308
[37]	validation-logloss:0.49128	validation-auc:0.96745	validation-aucpr:0.97307
[38]	validation-logloss:0.48737	validation-auc:0.96753	validation-aucpr:0.97312
[39]	validation-logloss:0.48347	validation-auc:0.96777	validation-aucpr:0.97326
[40]	validation-logloss:0.47977	validation-auc:0.96784	validation-aucpr:0.97328
[41]	validation-logloss:0.47619	validation-auc:0.96769	validation-aucpr:0.97318
[42]	validation-logloss:0.47253	validation-auc:0.96771	validation-aucpr:0.97322
[43]	validation-logloss:0.46882	validation-auc:0.96793	validation-aucpr:0.97339
[44]	validation-logloss:0.46516	validation-auc:0.96806	validation-aucpr:0.97351
[45]	validation-logloss:0.46180	validation-auc:0.96796	validation-aucpr:0.97339
[46]	validation-logloss:0.45818	validation-auc:0.96805	validation-aucpr:0.97346
[47]	validation-logloss:0.45524	validation-auc:0.96792	validation-aucpr:0.97336
[48]	validation-logloss:0.45232	validation-auc:0.96787	validation-aucpr:0.97330
[49]	validation-logloss:0.44918	validation-auc:0.96812	validation-aucpr:0.97345
[50]	validation-logloss:0.44606	validation-auc:0.96824	validation-aucpr:0.97356
[51]	validation-logloss:0.44317	validation-auc:0.96827	validation-aucpr:0.97356
[52]	validation-logloss:0.44002	validation-auc:0.96829	validation-aucpr:0.97356
[53]	validation-logloss:0.43684	validation-auc:0.96845	validation-aucpr:0.97371
[54]	validation-logloss:0.43370	validation-auc:0.96840	validation-aucpr:0.97368
[55]	validation-logloss:0.43063	validation-auc:0.96837	validation-aucpr:0.97370
[56]	validation-logloss:0.42770	validation-auc:0.96828	validation-aucpr:0.97363
[57]	validation-logloss:0.42485	validation-auc:0.96841	validation-aucpr:0.97371
[58]	validation-logloss:0.42194	validation-auc:0.96843	validation-aucpr:0.97374
[59]	validation-logloss:0.41906	validation-auc:0.96839	validation-aucpr:0.97372
[60]	validation-logloss:0.41634	validation-auc:0.96830	validation-aucpr:0.97366
[61]	validation-logloss:0.41357	validation-auc:0.96832	validation-aucpr:0.97367
[62]	validation-logloss:0.41079	validation-auc:0.96842	validation-aucpr:0.97374
[63]	validation-logloss:0.40819	validation-auc:0.96840	validation-aucpr:0.97372
[64]	validation-logloss:0.40548	validation-auc:0.96848	validation-aucpr:0.97376
[65]	validation-logloss:0.40274	validation-auc:0.96849	validation-aucpr:0.97377
[66]	validation-logloss:0.40012	validation-auc:0.96861	validation-aucpr:0.97385
[67]	validation-logloss:0.39751	validation-auc:0.96871	validation-aucpr:0.97392
[68]	validation-logloss:0.39495	validation-auc:0.96871	validation-aucpr:0.97394
[69]	validation-logloss:0.39245	validation-auc:0.96874	validation-aucpr:0.97395
[70]	validation-logloss:0.39013	validation-auc:0.96868	validation-aucpr:0.97389
[71]	validation-logloss:0.38768	validation-auc:0.96875	validation-aucpr:0.97395
[72]	validation-logloss:0.38565	validation-auc:0.96875	validation-aucpr:0.97393
[73]	validation-logloss:0.38331	validation-auc:0.96873	validation-aucpr:0.97391
[74]	validation-logloss:0.38094	validation-auc:0.96880	validation-aucpr:0.97393
[75]	validation-logloss:0.37869	validation-auc:0.96886	validation-aucpr:0.97397
[76]	validation-logloss:0.37642	validation-auc:0.96885	validation-aucpr:0.97398
[77]	validation-logloss:0.37424	validation-auc:0.96889	validation-aucpr:0.97399
[78]	validation-logloss:0.37207	validation-auc:0.96889	validation-aucpr:0.97399
[79]	validation-logloss:0.37004	validation-auc:0.96888	validation-aucpr:0.97398
{'best_iteration': '78', 'best_score': '0.9739904032743923'}
Trial 71, Fold 4: Log loss = 0.3700416251857441, Average precision = 0.9739796125881007, ROC-AUC = 0.9688816641272703, Elapsed Time = 5.088956399999006 seconds
Trial 71, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 71, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[0]	validation-logloss:0.68568	validation-auc:0.90673	validation-aucpr:0.84899
[1]	validation-logloss:0.67851	validation-auc:0.93090	validation-aucpr:0.90040
[2]	validation-logloss:0.67134	validation-auc:0.94797	validation-aucpr:0.93347
[3]	validation-logloss:0.66457	validation-auc:0.95152	validation-aucpr:0.94272
[4]	validation-logloss:0.65825	validation-auc:0.95428	validation-aucpr:0.95744
[5]	validation-logloss:0.65153	validation-auc:0.95624	validation-aucpr:0.96116
[6]	validation-logloss:0.64499	validation-auc:0.95835	validation-aucpr:0.96292
[7]	validation-logloss:0.63851	validation-auc:0.95894	validation-aucpr:0.96571
[8]	validation-logloss:0.63227	validation-auc:0.95984	validation-aucpr:0.96626
[9]	validation-logloss:0.62610	validation-auc:0.96109	validation-aucpr:0.96722
[10]	validation-logloss:0.62014	validation-auc:0.96131	validation-aucpr:0.96745
[11]	validation-logloss:0.61419	validation-auc:0.96147	validation-aucpr:0.96708
[12]	validation-logloss:0.60834	validation-auc:0.96163	validation-aucpr:0.96739
[13]	validation-logloss:0.60259	validation-auc:0.96209	validation-aucpr:0.96780
[14]	validation-logloss:0.59701	validation-auc:0.96237	validation-aucpr:0.96765
[15]	validation-logloss:0.59147	validation-auc:0.96290	validation-aucpr:0.96790
[16]	validation-logloss:0.58621	validation-auc:0.96301	validation-aucpr:0.96763
[17]	validation-logloss:0.58067	validation-auc:0.96357	validation-aucpr:0.96808
[18]	validation-logloss:0.57531	validation-auc:0.96403	validation-aucpr:0.96842
[19]	validation-logloss:0.57016	validation-auc:0.96433	validation-aucpr:0.96866
[20]	validation-logloss:0.56512	validation-auc:0.96407	validation-aucpr:0.96861
[21]	validation-logloss:0.56015	validation-auc:0.96430	validation-aucpr:0.96879
[22]	validation-logloss:0.55527	validation-auc:0.96412	validation-aucpr:0.96867
[23]	validation-logloss:0.55058	validation-auc:0.96430	validation-aucpr:0.96885
[24]	validation-logloss:0.54642	validation-auc:0.96425	validation-aucpr:0.96883
[25]	validation-logloss:0.54170	validation-auc:0.96463	validation-aucpr:0.96901
[26]	validation-logloss:0.53706	validation-auc:0.96485	validation-aucpr:0.96928
[27]	validation-logloss:0.53247	validation-auc:0.96480	validation-aucpr:0.96923
[28]	validation-logloss:0.52807	validation-auc:0.96513	validation-aucpr:0.97001
[29]	validation-logloss:0.52385	validation-auc:0.96515	validation-aucpr:0.96998
[30]	validation-logloss:0.51998	validation-auc:0.96512	validation-aucpr:0.96975
[31]	validation-logloss:0.51552	validation-auc:0.96547	validation-aucpr:0.96986
[32]	validation-logloss:0.51130	validation-auc:0.96551	validation-aucpr:0.96990
[33]	validation-logloss:0.50717	validation-auc:0.96575	validation-aucpr:0.97003
[34]	validation-logloss:0.50301	validation-auc:0.96596	validation-aucpr:0.97019
[35]	validation-logloss:0.49922	validation-auc:0.96598	validation-aucpr:0.97017
[36]	validation-logloss:0.49526	validation-auc:0.96613	validation-aucpr:0.97028
[37]	validation-logloss:0.49131	validation-auc:0.96626	validation-aucpr:0.97043
[38]	validation-logloss:0.48757	validation-auc:0.96627	validation-aucpr:0.97041
[39]	validation-logloss:0.48376	validation-auc:0.96635	validation-aucpr:0.97034
[40]	validation-logloss:0.48006	validation-auc:0.96635	validation-aucpr:0.97032
[41]	validation-logloss:0.47653	validation-auc:0.96624	validation-aucpr:0.97023
[42]	validation-logloss:0.47294	validation-auc:0.96640	validation-aucpr:0.97045
[43]	validation-logloss:0.46977	validation-auc:0.96643	validation-aucpr:0.97053
[44]	validation-logloss:0.46635	validation-auc:0.96666	validation-aucpr:0.97107
[45]	validation-logloss:0.46314	validation-auc:0.96668	validation-aucpr:0.97117
[46]	validation-logloss:0.45957	validation-auc:0.96687	validation-aucpr:0.97135
[47]	validation-logloss:0.45643	validation-auc:0.96674	validation-aucpr:0.97071
[48]	validation-logloss:0.45305	validation-auc:0.96686	validation-aucpr:0.97075
[49]	validation-logloss:0.44976	validation-auc:0.96692	validation-aucpr:0.97082
[50]	validation-logloss:0.44649	validation-auc:0.96708	validation-aucpr:0.97092
[51]	validation-logloss:0.44328	validation-auc:0.96704	validation-aucpr:0.97099
[52]	validation-logloss:0.44019	validation-auc:0.96706	validation-aucpr:0.97104
[53]	validation-logloss:0.43749	validation-auc:0.96706	validation-aucpr:0.97101
[54]	validation-logloss:0.43445	validation-auc:0.96709	validation-aucpr:0.97076
[55]	validation-logloss:0.43169	validation-auc:0.96697	validation-aucpr:0.97064
[56]	validation-logloss:0.42873	validation-auc:0.96710	validation-aucpr:0.97073
[57]	validation-logloss:0.42576	validation-auc:0.96723	validation-aucpr:0.97083
[58]	validation-logloss:0.42287	validation-auc:0.96729	validation-aucpr:0.97082
[59]	validation-logloss:0.42014	validation-auc:0.96737	validation-aucpr:0.97087
[60]	validation-logloss:0.41733	validation-auc:0.96746	validation-aucpr:0.97095
[61]	validation-logloss:0.41460	validation-auc:0.96755	validation-aucpr:0.97099
[62]	validation-logloss:0.41189	validation-auc:0.96754	validation-aucpr:0.97099
[63]	validation-logloss:0.40923	validation-auc:0.96754	validation-aucpr:0.97098
[64]	validation-logloss:0.40654	validation-auc:0.96758	validation-aucpr:0.97100
[65]	validation-logloss:0.40382	validation-auc:0.96776	validation-aucpr:0.97108
[66]	validation-logloss:0.40127	validation-auc:0.96771	validation-aucpr:0.97110
[67]	validation-logloss:0.39909	validation-auc:0.96751	validation-aucpr:0.97096
[68]	validation-logloss:0.39665	validation-auc:0.96750	validation-aucpr:0.97094
[69]	validation-logloss:0.39411	validation-auc:0.96754	validation-aucpr:0.97084
[70]	validation-logloss:0.39181	validation-auc:0.96768	validation-aucpr:0.97102
[71]	validation-logloss:0.38938	validation-auc:0.96780	validation-aucpr:0.97112
[72]	validation-logloss:0.38701	validation-auc:0.96779	validation-aucpr:0.97111
[73]	validation-logloss:0.38481	validation-auc:0.96771	validation-aucpr:0.97105
[74]	validation-logloss:0.38263	validation-auc:0.96781	validation-aucpr:0.97095
[75]	validation-logloss:0.38030	validation-auc:0.96784	validation-aucpr:0.97097
[76]	validation-logloss:0.37836	validation-auc:0.96788	validation-aucpr:0.97091
[77]	validation-logloss:0.37626	validation-auc:0.96793	validation-aucpr:0.97094
[78]	validation-logloss:0.37414	validation-auc:0.96793	validation-aucpr:0.97103
[79]	validation-logloss:0.37183	validation-auc:0.96796	validation-aucpr:0.97104
{'best_iteration': '46', 'best_score': '0.9713535849713747'}
Trial 71, Fold 5: Log loss = 0.3718332745016931, Average precision = 0.9711940729228865, ROC-AUC = 0.9679583077866339, Elapsed Time = 5.344965599997522 seconds
Optimization Progress:  72%|#######2  | 72/100 [3:26:35<1:45:09, 225.35s/it]
Trial 72, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 72, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[21:25:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.63479	validation-auc:0.95862	validation-aucpr:0.96396
[21:25:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.58509	validation-auc:0.96387	validation-aucpr:0.96927
[21:25:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.54597	validation-auc:0.96471	validation-aucpr:0.96937
[21:25:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.51280	validation-auc:0.96481	validation-aucpr:0.96913
[21:25:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.48051	validation-auc:0.96534	validation-aucpr:0.96970
[21:25:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5]	validation-logloss:0.45177	validation-auc:0.96644	validation-aucpr:0.97122
[21:25:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6]	validation-logloss:0.42585	validation-auc:0.96732	validation-aucpr:0.97216
[21:25:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7]	validation-logloss:0.40293	validation-auc:0.96790	validation-aucpr:0.97295
[21:25:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8]	validation-logloss:0.38464	validation-auc:0.96848	validation-aucpr:0.97343
[21:25:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9]	validation-logloss:0.36603	validation-auc:0.96873	validation-aucpr:0.97162
[21:25:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10]	validation-logloss:0.34968	validation-auc:0.96902	validation-aucpr:0.97160
[21:25:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11]	validation-logloss:0.33498	validation-auc:0.96952	validation-aucpr:0.97203
[21:25:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12]	validation-logloss:0.32329	validation-auc:0.96981	validation-aucpr:0.97218
[21:25:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13]	validation-logloss:0.31113	validation-auc:0.96993	validation-aucpr:0.97227
[21:25:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14]	validation-logloss:0.30081	validation-auc:0.97003	validation-aucpr:0.97234
[21:25:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15]	validation-logloss:0.29289	validation-auc:0.96968	validation-aucpr:0.97212
[21:25:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16]	validation-logloss:0.28387	validation-auc:0.96992	validation-aucpr:0.97225
[21:25:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17]	validation-logloss:0.27559	validation-auc:0.97030	validation-aucpr:0.97337
[21:25:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18]	validation-logloss:0.26793	validation-auc:0.97054	validation-aucpr:0.97357
[21:25:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19]	validation-logloss:0.26252	validation-auc:0.97042	validation-aucpr:0.97332
[21:25:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20]	validation-logloss:0.25663	validation-auc:0.97087	validation-aucpr:0.97361
[21:25:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21]	validation-logloss:0.25189	validation-auc:0.97096	validation-aucpr:0.97534
[21:25:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22]	validation-logloss:0.24657	validation-auc:0.97100	validation-aucpr:0.97546
[21:25:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23]	validation-logloss:0.24305	validation-auc:0.97094	validation-aucpr:0.97538
[21:25:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24]	validation-logloss:0.23976	validation-auc:0.97078	validation-aucpr:0.97523
[21:25:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25]	validation-logloss:0.23582	validation-auc:0.97094	validation-aucpr:0.97531
[21:25:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26]	validation-logloss:0.23299	validation-auc:0.97091	validation-aucpr:0.97540
[21:25:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27]	validation-logloss:0.22990	validation-auc:0.97096	validation-aucpr:0.97539
[21:25:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28]	validation-logloss:0.22666	validation-auc:0.97112	validation-aucpr:0.97550
[21:25:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29]	validation-logloss:0.22455	validation-auc:0.97102	validation-aucpr:0.97539
[21:25:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30]	validation-logloss:0.22152	validation-auc:0.97107	validation-aucpr:0.97542
[21:25:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31]	validation-logloss:0.21898	validation-auc:0.97098	validation-aucpr:0.97540
[21:25:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32]	validation-logloss:0.21673	validation-auc:0.97116	validation-aucpr:0.97561
[21:25:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33]	validation-logloss:0.21486	validation-auc:0.97120	validation-aucpr:0.97564
[21:25:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34]	validation-logloss:0.21233	validation-auc:0.97142	validation-aucpr:0.97582
[21:25:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35]	validation-logloss:0.21038	validation-auc:0.97157	validation-aucpr:0.97596
[21:25:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36]	validation-logloss:0.20846	validation-auc:0.97172	validation-aucpr:0.97608
[21:25:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37]	validation-logloss:0.20671	validation-auc:0.97190	validation-aucpr:0.97618
[21:25:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38]	validation-logloss:0.20543	validation-auc:0.97184	validation-aucpr:0.97612
[21:25:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39]	validation-logloss:0.20442	validation-auc:0.97188	validation-aucpr:0.97614
[21:25:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40]	validation-logloss:0.20296	validation-auc:0.97203	validation-aucpr:0.97638
[21:25:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41]	validation-logloss:0.20167	validation-auc:0.97220	validation-aucpr:0.97650
[21:25:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42]	validation-logloss:0.20079	validation-auc:0.97223	validation-aucpr:0.97654
[21:25:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43]	validation-logloss:0.19997	validation-auc:0.97220	validation-aucpr:0.97652
[21:25:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44]	validation-logloss:0.19926	validation-auc:0.97224	validation-aucpr:0.97655
[21:25:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45]	validation-logloss:0.19850	validation-auc:0.97228	validation-aucpr:0.97652
[21:25:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46]	validation-logloss:0.19752	validation-auc:0.97240	validation-aucpr:0.97663
[21:25:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[47]	validation-logloss:0.19686	validation-auc:0.97242	validation-aucpr:0.97664
[21:25:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[48]	validation-logloss:0.19625	validation-auc:0.97250	validation-aucpr:0.97673
[21:25:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[49]	validation-logloss:0.19582	validation-auc:0.97253	validation-aucpr:0.97676
[21:25:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[50]	validation-logloss:0.19535	validation-auc:0.97259	validation-aucpr:0.97677
[21:25:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[51]	validation-logloss:0.19509	validation-auc:0.97254	validation-aucpr:0.97673
[21:25:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[52]	validation-logloss:0.19491	validation-auc:0.97253	validation-aucpr:0.97668
[21:25:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[53]	validation-logloss:0.19474	validation-auc:0.97249	validation-aucpr:0.97666
{'best_iteration': '50', 'best_score': '0.9767685335164963'}
Trial 72, Fold 1: Log loss = 0.19473958140355582, Average precision = 0.9766591920176859, ROC-AUC = 0.9724918209279094, Elapsed Time = 6.8159644000006665 seconds
Trial 72, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 72, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[21:25:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.63593	validation-auc:0.95733	validation-aucpr:0.96176
[21:25:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.58589	validation-auc:0.96568	validation-aucpr:0.96911
[21:25:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.54672	validation-auc:0.96585	validation-aucpr:0.96950
[21:25:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.51010	validation-auc:0.96673	validation-aucpr:0.97029
[21:25:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.47851	validation-auc:0.96782	validation-aucpr:0.97105
[21:25:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5]	validation-logloss:0.44841	validation-auc:0.96928	validation-aucpr:0.97230
[21:25:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6]	validation-logloss:0.42594	validation-auc:0.96903	validation-aucpr:0.97225
[21:25:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7]	validation-logloss:0.40177	validation-auc:0.96956	validation-aucpr:0.97282
[21:25:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8]	validation-logloss:0.38107	validation-auc:0.96962	validation-aucpr:0.97294
[21:25:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9]	validation-logloss:0.36276	validation-auc:0.96967	validation-aucpr:0.97292
[21:25:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10]	validation-logloss:0.34636	validation-auc:0.97010	validation-aucpr:0.97326
[21:25:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11]	validation-logloss:0.33115	validation-auc:0.97042	validation-aucpr:0.97348
[21:25:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12]	validation-logloss:0.31946	validation-auc:0.97029	validation-aucpr:0.97335
[21:25:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13]	validation-logloss:0.30711	validation-auc:0.97063	validation-aucpr:0.97359
[21:25:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14]	validation-logloss:0.29606	validation-auc:0.97065	validation-aucpr:0.97363
[21:25:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15]	validation-logloss:0.28644	validation-auc:0.97086	validation-aucpr:0.97384
[21:25:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16]	validation-logloss:0.27874	validation-auc:0.97075	validation-aucpr:0.97379
[21:25:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17]	validation-logloss:0.27079	validation-auc:0.97097	validation-aucpr:0.97392
[21:25:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18]	validation-logloss:0.26468	validation-auc:0.97095	validation-aucpr:0.97386
[21:25:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19]	validation-logloss:0.25857	validation-auc:0.97102	validation-aucpr:0.97387
[21:25:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20]	validation-logloss:0.25311	validation-auc:0.97115	validation-aucpr:0.97400
[21:25:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21]	validation-logloss:0.24857	validation-auc:0.97111	validation-aucpr:0.97394
[21:25:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22]	validation-logloss:0.24284	validation-auc:0.97130	validation-aucpr:0.97401
[21:25:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23]	validation-logloss:0.23756	validation-auc:0.97153	validation-aucpr:0.97423
[21:25:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24]	validation-logloss:0.23293	validation-auc:0.97175	validation-aucpr:0.97438
[21:25:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25]	validation-logloss:0.22852	validation-auc:0.97176	validation-aucpr:0.97367
[21:25:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26]	validation-logloss:0.22477	validation-auc:0.97195	validation-aucpr:0.97488
[21:25:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27]	validation-logloss:0.22184	validation-auc:0.97204	validation-aucpr:0.97486
[21:25:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28]	validation-logloss:0.21841	validation-auc:0.97235	validation-aucpr:0.97510
[21:25:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29]	validation-logloss:0.21566	validation-auc:0.97224	validation-aucpr:0.97506
[21:25:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30]	validation-logloss:0.21296	validation-auc:0.97209	validation-aucpr:0.97493
[21:25:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31]	validation-logloss:0.21077	validation-auc:0.97212	validation-aucpr:0.97496
[21:25:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32]	validation-logloss:0.20933	validation-auc:0.97190	validation-aucpr:0.97476
[21:25:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33]	validation-logloss:0.20709	validation-auc:0.97187	validation-aucpr:0.97478
[21:25:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34]	validation-logloss:0.20542	validation-auc:0.97196	validation-aucpr:0.97481
[21:25:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35]	validation-logloss:0.20374	validation-auc:0.97217	validation-aucpr:0.97503
[21:25:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36]	validation-logloss:0.20176	validation-auc:0.97225	validation-aucpr:0.97467
[21:25:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37]	validation-logloss:0.20049	validation-auc:0.97217	validation-aucpr:0.97460
[21:25:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38]	validation-logloss:0.19912	validation-auc:0.97228	validation-aucpr:0.97468
[21:25:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39]	validation-logloss:0.19782	validation-auc:0.97235	validation-aucpr:0.97472
[21:25:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40]	validation-logloss:0.19660	validation-auc:0.97227	validation-aucpr:0.97460
[21:25:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41]	validation-logloss:0.19510	validation-auc:0.97249	validation-aucpr:0.97478
[21:25:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42]	validation-logloss:0.19476	validation-auc:0.97236	validation-aucpr:0.97465
[21:25:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43]	validation-logloss:0.19377	validation-auc:0.97232	validation-aucpr:0.97459
[21:25:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44]	validation-logloss:0.19245	validation-auc:0.97248	validation-aucpr:0.97473
[21:25:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45]	validation-logloss:0.19133	validation-auc:0.97263	validation-aucpr:0.97485
[21:25:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46]	validation-logloss:0.19048	validation-auc:0.97281	validation-aucpr:0.97575
[21:25:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[47]	validation-logloss:0.18984	validation-auc:0.97290	validation-aucpr:0.97578
[21:25:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[48]	validation-logloss:0.18904	validation-auc:0.97293	validation-aucpr:0.97580
[21:25:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[49]	validation-logloss:0.18872	validation-auc:0.97293	validation-aucpr:0.97582
[21:25:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[50]	validation-logloss:0.18799	validation-auc:0.97307	validation-aucpr:0.97587
[21:25:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[51]	validation-logloss:0.18766	validation-auc:0.97295	validation-aucpr:0.97571
[21:25:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[52]	validation-logloss:0.18715	validation-auc:0.97306	validation-aucpr:0.97591
[21:25:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[53]	validation-logloss:0.18652	validation-auc:0.97301	validation-aucpr:0.97578
{'best_iteration': '52', 'best_score': '0.9759079413380883'}
Trial 72, Fold 2: Log loss = 0.18651565331945985, Average precision = 0.9757842427832537, ROC-AUC = 0.9730090766420401, Elapsed Time = 7.354374000002281 seconds
Trial 72, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 72, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[21:25:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.63908	validation-auc:0.94558	validation-aucpr:0.95267
[21:25:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.58812	validation-auc:0.96595	validation-aucpr:0.96939
[21:25:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.54491	validation-auc:0.96801	validation-aucpr:0.97151
[21:25:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.51018	validation-auc:0.96880	validation-aucpr:0.97192
[21:25:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.47795	validation-auc:0.96847	validation-aucpr:0.97190
[21:25:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5]	validation-logloss:0.44799	validation-auc:0.96963	validation-aucpr:0.97300
[21:25:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6]	validation-logloss:0.42190	validation-auc:0.96994	validation-aucpr:0.97329
[21:25:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7]	validation-logloss:0.39899	validation-auc:0.97041	validation-aucpr:0.97357
[21:25:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8]	validation-logloss:0.38088	validation-auc:0.97020	validation-aucpr:0.97320
[21:25:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9]	validation-logloss:0.36289	validation-auc:0.97018	validation-aucpr:0.97293
[21:25:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10]	validation-logloss:0.34803	validation-auc:0.97027	validation-aucpr:0.97300
[21:25:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11]	validation-logloss:0.33386	validation-auc:0.96978	validation-aucpr:0.97263
[21:25:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12]	validation-logloss:0.31990	validation-auc:0.97017	validation-aucpr:0.97294
[21:25:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13]	validation-logloss:0.30734	validation-auc:0.97054	validation-aucpr:0.97360
[21:25:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14]	validation-logloss:0.29624	validation-auc:0.97062	validation-aucpr:0.97389
[21:25:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15]	validation-logloss:0.28787	validation-auc:0.97062	validation-aucpr:0.97384
[21:25:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16]	validation-logloss:0.27853	validation-auc:0.97114	validation-aucpr:0.97420
[21:25:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17]	validation-logloss:0.26982	validation-auc:0.97168	validation-aucpr:0.97560
[21:25:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18]	validation-logloss:0.26188	validation-auc:0.97177	validation-aucpr:0.97568
[21:25:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19]	validation-logloss:0.25544	validation-auc:0.97156	validation-aucpr:0.97546
[21:25:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20]	validation-logloss:0.24884	validation-auc:0.97182	validation-aucpr:0.97572
[21:25:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21]	validation-logloss:0.24330	validation-auc:0.97174	validation-aucpr:0.97568
[21:25:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22]	validation-logloss:0.23817	validation-auc:0.97181	validation-aucpr:0.97574
[21:25:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23]	validation-logloss:0.23423	validation-auc:0.97183	validation-aucpr:0.97576
[21:25:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24]	validation-logloss:0.22991	validation-auc:0.97193	validation-aucpr:0.97588
[21:25:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25]	validation-logloss:0.22601	validation-auc:0.97189	validation-aucpr:0.97583
[21:25:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26]	validation-logloss:0.22288	validation-auc:0.97187	validation-aucpr:0.97485
[21:25:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27]	validation-logloss:0.22021	validation-auc:0.97179	validation-aucpr:0.97472
[21:25:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28]	validation-logloss:0.21686	validation-auc:0.97195	validation-aucpr:0.97486
[21:25:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29]	validation-logloss:0.21445	validation-auc:0.97192	validation-aucpr:0.97495
[21:25:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30]	validation-logloss:0.21232	validation-auc:0.97211	validation-aucpr:0.97629
[21:25:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31]	validation-logloss:0.21030	validation-auc:0.97195	validation-aucpr:0.97614
[21:25:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32]	validation-logloss:0.20858	validation-auc:0.97183	validation-aucpr:0.97604
[21:25:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33]	validation-logloss:0.20687	validation-auc:0.97183	validation-aucpr:0.97600
[21:25:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34]	validation-logloss:0.20543	validation-auc:0.97173	validation-aucpr:0.97594
[21:25:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35]	validation-logloss:0.20346	validation-auc:0.97191	validation-aucpr:0.97606
[21:25:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36]	validation-logloss:0.20212	validation-auc:0.97192	validation-aucpr:0.97606
[21:25:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37]	validation-logloss:0.20056	validation-auc:0.97203	validation-aucpr:0.97622
[21:25:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38]	validation-logloss:0.19926	validation-auc:0.97200	validation-aucpr:0.97617
[21:25:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39]	validation-logloss:0.19855	validation-auc:0.97191	validation-aucpr:0.97608
[21:25:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40]	validation-logloss:0.19808	validation-auc:0.97186	validation-aucpr:0.97596
[21:25:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41]	validation-logloss:0.19748	validation-auc:0.97173	validation-aucpr:0.97586
[21:25:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42]	validation-logloss:0.19673	validation-auc:0.97167	validation-aucpr:0.97579
[21:25:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43]	validation-logloss:0.19598	validation-auc:0.97167	validation-aucpr:0.97583
[21:25:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44]	validation-logloss:0.19544	validation-auc:0.97171	validation-aucpr:0.97593
[21:25:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45]	validation-logloss:0.19466	validation-auc:0.97180	validation-aucpr:0.97606
[21:25:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46]	validation-logloss:0.19373	validation-auc:0.97197	validation-aucpr:0.97620
[21:25:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[47]	validation-logloss:0.19315	validation-auc:0.97201	validation-aucpr:0.97620
[21:25:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[48]	validation-logloss:0.19244	validation-auc:0.97202	validation-aucpr:0.97619
[21:25:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[49]	validation-logloss:0.19208	validation-auc:0.97200	validation-aucpr:0.97618
[21:25:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[50]	validation-logloss:0.19152	validation-auc:0.97208	validation-aucpr:0.97621
[21:25:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[51]	validation-logloss:0.19106	validation-auc:0.97219	validation-aucpr:0.97640
[21:25:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[52]	validation-logloss:0.19105	validation-auc:0.97216	validation-aucpr:0.97635
[21:25:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[53]	validation-logloss:0.19085	validation-auc:0.97213	validation-aucpr:0.97630
{'best_iteration': '51', 'best_score': '0.9764002137695778'}
Trial 72, Fold 3: Log loss = 0.19085098155620686, Average precision = 0.9763054316523052, ROC-AUC = 0.97213192290838, Elapsed Time = 6.96847870000056 seconds
Trial 72, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 72, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[21:25:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.63561	validation-auc:0.95069	validation-aucpr:0.95005
[21:25:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.58593	validation-auc:0.96165	validation-aucpr:0.96418
[21:25:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.54323	validation-auc:0.96460	validation-aucpr:0.97045
[21:25:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.50675	validation-auc:0.96527	validation-aucpr:0.97088
[21:25:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.47843	validation-auc:0.96388	validation-aucpr:0.96988
[21:25:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5]	validation-logloss:0.44957	validation-auc:0.96529	validation-aucpr:0.97101
[21:25:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6]	validation-logloss:0.42455	validation-auc:0.96597	validation-aucpr:0.97152
[21:25:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7]	validation-logloss:0.40281	validation-auc:0.96649	validation-aucpr:0.97180
[21:25:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8]	validation-logloss:0.38329	validation-auc:0.96671	validation-aucpr:0.97206
[21:25:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9]	validation-logloss:0.36544	validation-auc:0.96715	validation-aucpr:0.97231
[21:25:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10]	validation-logloss:0.34904	validation-auc:0.96763	validation-aucpr:0.97277
[21:25:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11]	validation-logloss:0.33525	validation-auc:0.96778	validation-aucpr:0.97278
[21:25:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12]	validation-logloss:0.32436	validation-auc:0.96765	validation-aucpr:0.97286
[21:25:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13]	validation-logloss:0.31452	validation-auc:0.96707	validation-aucpr:0.97249
[21:25:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14]	validation-logloss:0.30327	validation-auc:0.96725	validation-aucpr:0.97266
[21:25:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15]	validation-logloss:0.29394	validation-auc:0.96786	validation-aucpr:0.97306
[21:25:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16]	validation-logloss:0.28626	validation-auc:0.96785	validation-aucpr:0.97308
[21:25:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17]	validation-logloss:0.27920	validation-auc:0.96788	validation-aucpr:0.97313
[21:25:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18]	validation-logloss:0.27295	validation-auc:0.96782	validation-aucpr:0.97313
[21:25:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19]	validation-logloss:0.26733	validation-auc:0.96778	validation-aucpr:0.97311
[21:25:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20]	validation-logloss:0.26043	validation-auc:0.96818	validation-aucpr:0.97343
[21:25:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21]	validation-logloss:0.25523	validation-auc:0.96840	validation-aucpr:0.97360
[21:25:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22]	validation-logloss:0.25057	validation-auc:0.96855	validation-aucpr:0.97368
[21:25:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23]	validation-logloss:0.24550	validation-auc:0.96880	validation-aucpr:0.97388
[21:25:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24]	validation-logloss:0.24164	validation-auc:0.96888	validation-aucpr:0.97396
[21:25:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25]	validation-logloss:0.23699	validation-auc:0.96919	validation-aucpr:0.97420
[21:25:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26]	validation-logloss:0.23358	validation-auc:0.96911	validation-aucpr:0.97413
[21:25:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27]	validation-logloss:0.22971	validation-auc:0.96938	validation-aucpr:0.97434
[21:25:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28]	validation-logloss:0.22671	validation-auc:0.96965	validation-aucpr:0.97454
[21:25:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29]	validation-logloss:0.22409	validation-auc:0.96963	validation-aucpr:0.97458
[21:25:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30]	validation-logloss:0.22053	validation-auc:0.97000	validation-aucpr:0.97485
[21:25:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31]	validation-logloss:0.21709	validation-auc:0.97037	validation-aucpr:0.97511
[21:25:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32]	validation-logloss:0.21527	validation-auc:0.97041	validation-aucpr:0.97514
[21:25:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33]	validation-logloss:0.21306	validation-auc:0.97060	validation-aucpr:0.97527
[21:25:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34]	validation-logloss:0.21116	validation-auc:0.97075	validation-aucpr:0.97539
[21:25:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35]	validation-logloss:0.20924	validation-auc:0.97087	validation-aucpr:0.97548
[21:25:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36]	validation-logloss:0.20719	validation-auc:0.97112	validation-aucpr:0.97569
[21:25:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37]	validation-logloss:0.20612	validation-auc:0.97105	validation-aucpr:0.97563
[21:25:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38]	validation-logloss:0.20411	validation-auc:0.97136	validation-aucpr:0.97587
[21:25:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39]	validation-logloss:0.20268	validation-auc:0.97139	validation-aucpr:0.97588
[21:25:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40]	validation-logloss:0.20090	validation-auc:0.97168	validation-aucpr:0.97609
[21:25:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41]	validation-logloss:0.19927	validation-auc:0.97192	validation-aucpr:0.97628
[21:25:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42]	validation-logloss:0.19854	validation-auc:0.97184	validation-aucpr:0.97620
[21:25:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43]	validation-logloss:0.19782	validation-auc:0.97172	validation-aucpr:0.97612
[21:25:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44]	validation-logloss:0.19699	validation-auc:0.97185	validation-aucpr:0.97622
[21:25:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45]	validation-logloss:0.19609	validation-auc:0.97191	validation-aucpr:0.97625
[21:25:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46]	validation-logloss:0.19544	validation-auc:0.97200	validation-aucpr:0.97629
[21:25:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[47]	validation-logloss:0.19455	validation-auc:0.97209	validation-aucpr:0.97635
[21:25:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[48]	validation-logloss:0.19404	validation-auc:0.97216	validation-aucpr:0.97641
[21:25:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[49]	validation-logloss:0.19402	validation-auc:0.97199	validation-aucpr:0.97630
[21:25:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[50]	validation-logloss:0.19337	validation-auc:0.97204	validation-aucpr:0.97634
[21:25:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[51]	validation-logloss:0.19285	validation-auc:0.97203	validation-aucpr:0.97634
[21:25:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[52]	validation-logloss:0.19231	validation-auc:0.97218	validation-aucpr:0.97643
[21:25:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[53]	validation-logloss:0.19179	validation-auc:0.97232	validation-aucpr:0.97653
{'best_iteration': '53', 'best_score': '0.9765328333816946'}
Trial 72, Fold 4: Log loss = 0.1917944273199209, Average precision = 0.9765367007556147, ROC-AUC = 0.9723240067144979, Elapsed Time = 7.33326169999782 seconds
Trial 72, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 72, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[21:26:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.63486	validation-auc:0.95834	validation-aucpr:0.96338
[21:26:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.58509	validation-auc:0.96294	validation-aucpr:0.96551
[21:26:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.54780	validation-auc:0.96268	validation-aucpr:0.96743
[21:26:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.51017	validation-auc:0.96475	validation-aucpr:0.96938
[21:26:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.48221	validation-auc:0.96398	validation-aucpr:0.96878
[21:26:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5]	validation-logloss:0.45338	validation-auc:0.96498	validation-aucpr:0.96938
[21:26:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6]	validation-logloss:0.42716	validation-auc:0.96556	validation-aucpr:0.96986
[21:26:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7]	validation-logloss:0.40416	validation-auc:0.96620	validation-aucpr:0.97036
[21:26:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8]	validation-logloss:0.38420	validation-auc:0.96707	validation-aucpr:0.97107
[21:26:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9]	validation-logloss:0.36584	validation-auc:0.96771	validation-aucpr:0.97156
[21:26:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10]	validation-logloss:0.34969	validation-auc:0.96790	validation-aucpr:0.97172
[21:26:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11]	validation-logloss:0.33478	validation-auc:0.96846	validation-aucpr:0.97211
[21:26:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12]	validation-logloss:0.32356	validation-auc:0.96850	validation-aucpr:0.97202
[21:26:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13]	validation-logloss:0.31169	validation-auc:0.96855	validation-aucpr:0.97208
[21:26:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14]	validation-logloss:0.30058	validation-auc:0.96894	validation-aucpr:0.97226
[21:26:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15]	validation-logloss:0.29067	validation-auc:0.96948	validation-aucpr:0.97377
[21:26:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16]	validation-logloss:0.28270	validation-auc:0.96956	validation-aucpr:0.97385
[21:26:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17]	validation-logloss:0.27603	validation-auc:0.96973	validation-aucpr:0.97381
[21:26:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18]	validation-logloss:0.26845	validation-auc:0.96985	validation-aucpr:0.97391
[21:26:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19]	validation-logloss:0.26232	validation-auc:0.96991	validation-aucpr:0.97387
[21:26:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20]	validation-logloss:0.25577	validation-auc:0.97001	validation-aucpr:0.97398
[21:26:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21]	validation-logloss:0.25121	validation-auc:0.96978	validation-aucpr:0.97236
[21:26:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22]	validation-logloss:0.24593	validation-auc:0.97007	validation-aucpr:0.97257
[21:26:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23]	validation-logloss:0.24169	validation-auc:0.97005	validation-aucpr:0.97258
[21:26:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24]	validation-logloss:0.23741	validation-auc:0.97020	validation-aucpr:0.97269
[21:26:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25]	validation-logloss:0.23375	validation-auc:0.97025	validation-aucpr:0.97275
[21:26:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26]	validation-logloss:0.23013	validation-auc:0.97046	validation-aucpr:0.97287
[21:26:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27]	validation-logloss:0.22742	validation-auc:0.97048	validation-aucpr:0.97303
[21:26:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28]	validation-logloss:0.22396	validation-auc:0.97086	validation-aucpr:0.97326
[21:26:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29]	validation-logloss:0.22091	validation-auc:0.97115	validation-aucpr:0.97339
[21:26:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30]	validation-logloss:0.21820	validation-auc:0.97140	validation-aucpr:0.97357
[21:26:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31]	validation-logloss:0.21586	validation-auc:0.97135	validation-aucpr:0.97351
[21:26:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32]	validation-logloss:0.21414	validation-auc:0.97127	validation-aucpr:0.97348
[21:26:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33]	validation-logloss:0.21251	validation-auc:0.97135	validation-aucpr:0.97377
[21:26:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34]	validation-logloss:0.21091	validation-auc:0.97125	validation-aucpr:0.97342
[21:26:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35]	validation-logloss:0.20952	validation-auc:0.97126	validation-aucpr:0.97338
[21:26:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36]	validation-logloss:0.20803	validation-auc:0.97137	validation-aucpr:0.97350
[21:26:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37]	validation-logloss:0.20695	validation-auc:0.97140	validation-aucpr:0.97347
[21:26:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38]	validation-logloss:0.20588	validation-auc:0.97137	validation-aucpr:0.97344
[21:26:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39]	validation-logloss:0.20455	validation-auc:0.97153	validation-aucpr:0.97355
[21:26:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40]	validation-logloss:0.20322	validation-auc:0.97188	validation-aucpr:0.97498
[21:26:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41]	validation-logloss:0.20194	validation-auc:0.97207	validation-aucpr:0.97512
[21:26:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42]	validation-logloss:0.20116	validation-auc:0.97209	validation-aucpr:0.97501
[21:26:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43]	validation-logloss:0.20019	validation-auc:0.97212	validation-aucpr:0.97495
[21:26:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44]	validation-logloss:0.19953	validation-auc:0.97216	validation-aucpr:0.97554
[21:26:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45]	validation-logloss:0.19895	validation-auc:0.97216	validation-aucpr:0.97544
[21:26:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46]	validation-logloss:0.19823	validation-auc:0.97221	validation-aucpr:0.97545
[21:26:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[47]	validation-logloss:0.19771	validation-auc:0.97212	validation-aucpr:0.97538
[21:26:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[48]	validation-logloss:0.19706	validation-auc:0.97212	validation-aucpr:0.97536
[21:26:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[49]	validation-logloss:0.19638	validation-auc:0.97222	validation-aucpr:0.97542
[21:26:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[50]	validation-logloss:0.19578	validation-auc:0.97223	validation-aucpr:0.97537
[21:26:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[51]	validation-logloss:0.19542	validation-auc:0.97223	validation-aucpr:0.97537
[21:26:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[52]	validation-logloss:0.19499	validation-auc:0.97235	validation-aucpr:0.97552
[21:26:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[53]	validation-logloss:0.19465	validation-auc:0.97235	validation-aucpr:0.97560
{'best_iteration': '53', 'best_score': '0.9756027609368605'}
Trial 72, Fold 5: Log loss = 0.19464730760572593, Average precision = 0.9756072587949387, ROC-AUC = 0.9723509280419153, Elapsed Time = 7.446603000000323 seconds
Optimization Progress:  73%|#######3  | 73/100 [3:27:19<1:16:53, 170.88s/it]
Trial 73, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 73, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[0]	validation-logloss:0.66221	validation-auc:0.95893	validation-aucpr:0.96232
[1]	validation-logloss:0.63373	validation-auc:0.96436	validation-aucpr:0.96920
[2]	validation-logloss:0.60796	validation-auc:0.96393	validation-aucpr:0.96892
[3]	validation-logloss:0.58642	validation-auc:0.96412	validation-aucpr:0.96906
[4]	validation-logloss:0.56384	validation-auc:0.96473	validation-aucpr:0.96955
[5]	validation-logloss:0.54293	validation-auc:0.96522	validation-aucpr:0.97028
[6]	validation-logloss:0.52508	validation-auc:0.96544	validation-aucpr:0.97039
[7]	validation-logloss:0.50736	validation-auc:0.96576	validation-aucpr:0.97049
[8]	validation-logloss:0.49035	validation-auc:0.96622	validation-aucpr:0.97123
[9]	validation-logloss:0.47459	validation-auc:0.96620	validation-aucpr:0.97130
[10]	validation-logloss:0.45961	validation-auc:0.96631	validation-aucpr:0.97137
[11]	validation-logloss:0.44543	validation-auc:0.96639	validation-aucpr:0.97145
[12]	validation-logloss:0.43265	validation-auc:0.96645	validation-aucpr:0.97152
[13]	validation-logloss:0.42071	validation-auc:0.96654	validation-aucpr:0.97190
[14]	validation-logloss:0.40943	validation-auc:0.96661	validation-aucpr:0.97197
[15]	validation-logloss:0.39851	validation-auc:0.96699	validation-aucpr:0.97223
[16]	validation-logloss:0.38834	validation-auc:0.96691	validation-aucpr:0.97217
[17]	validation-logloss:0.37839	validation-auc:0.96715	validation-aucpr:0.97232
[18]	validation-logloss:0.36913	validation-auc:0.96727	validation-aucpr:0.97239
[19]	validation-logloss:0.36175	validation-auc:0.96725	validation-aucpr:0.97234
[20]	validation-logloss:0.35374	validation-auc:0.96734	validation-aucpr:0.97237
[21]	validation-logloss:0.34657	validation-auc:0.96720	validation-aucpr:0.97230
[22]	validation-logloss:0.33906	validation-auc:0.96747	validation-aucpr:0.97248
[23]	validation-logloss:0.33236	validation-auc:0.96742	validation-aucpr:0.97247
[24]	validation-logloss:0.32558	validation-auc:0.96758	validation-aucpr:0.97255
[25]	validation-logloss:0.31926	validation-auc:0.96771	validation-aucpr:0.97264
[26]	validation-logloss:0.31390	validation-auc:0.96783	validation-aucpr:0.97274
[27]	validation-logloss:0.30809	validation-auc:0.96792	validation-aucpr:0.97281
[28]	validation-logloss:0.30267	validation-auc:0.96801	validation-aucpr:0.97285
[29]	validation-logloss:0.29765	validation-auc:0.96828	validation-aucpr:0.97363
[30]	validation-logloss:0.29344	validation-auc:0.96830	validation-aucpr:0.97362
[31]	validation-logloss:0.28881	validation-auc:0.96856	validation-aucpr:0.97380
[32]	validation-logloss:0.28421	validation-auc:0.96874	validation-aucpr:0.97393
[33]	validation-logloss:0.28055	validation-auc:0.96886	validation-aucpr:0.97401
[34]	validation-logloss:0.27648	validation-auc:0.96907	validation-aucpr:0.97411
[35]	validation-logloss:0.27261	validation-auc:0.96921	validation-aucpr:0.97422
[36]	validation-logloss:0.26899	validation-auc:0.96927	validation-aucpr:0.97425
[37]	validation-logloss:0.26567	validation-auc:0.96930	validation-aucpr:0.97426
[38]	validation-logloss:0.26241	validation-auc:0.96932	validation-aucpr:0.97428
[39]	validation-logloss:0.25972	validation-auc:0.96938	validation-aucpr:0.97430
[40]	validation-logloss:0.25659	validation-auc:0.96949	validation-aucpr:0.97436
[41]	validation-logloss:0.25374	validation-auc:0.96959	validation-aucpr:0.97445
[42]	validation-logloss:0.25129	validation-auc:0.96970	validation-aucpr:0.97457
{'best_iteration': '42', 'best_score': '0.9745716988150757'}
Trial 73, Fold 1: Log loss = 0.25128757216862274, Average precision = 0.9745746531559298, ROC-AUC = 0.9696964333510179, Elapsed Time = 1.0446025999990525 seconds
Trial 73, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 73, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[0]	validation-logloss:0.66232	validation-auc:0.96079	validation-aucpr:0.96036
[1]	validation-logloss:0.63411	validation-auc:0.96436	validation-aucpr:0.96732
[2]	validation-logloss:0.60766	validation-auc:0.96736	validation-aucpr:0.97094
[3]	validation-logloss:0.58347	validation-auc:0.96748	validation-aucpr:0.97103
[4]	validation-logloss:0.56134	validation-auc:0.96845	validation-aucpr:0.97189
[5]	validation-logloss:0.54033	validation-auc:0.96884	validation-aucpr:0.97221
[6]	validation-logloss:0.52085	validation-auc:0.96888	validation-aucpr:0.97220
[7]	validation-logloss:0.50500	validation-auc:0.96859	validation-aucpr:0.97199
[8]	validation-logloss:0.48768	validation-auc:0.96905	validation-aucpr:0.97233
[9]	validation-logloss:0.47156	validation-auc:0.96946	validation-aucpr:0.97264
[10]	validation-logloss:0.45651	validation-auc:0.96976	validation-aucpr:0.97284
[11]	validation-logloss:0.44261	validation-auc:0.96967	validation-aucpr:0.97278
[12]	validation-logloss:0.43086	validation-auc:0.96969	validation-aucpr:0.97283
[13]	validation-logloss:0.41852	validation-auc:0.97000	validation-aucpr:0.97301
[14]	validation-logloss:0.40708	validation-auc:0.97007	validation-aucpr:0.97302
[15]	validation-logloss:0.39585	validation-auc:0.97020	validation-aucpr:0.97312
[16]	validation-logloss:0.38553	validation-auc:0.97022	validation-aucpr:0.97314
[17]	validation-logloss:0.37584	validation-auc:0.97032	validation-aucpr:0.97322
[18]	validation-logloss:0.36656	validation-auc:0.97034	validation-aucpr:0.97323
[19]	validation-logloss:0.35874	validation-auc:0.97055	validation-aucpr:0.97338
[20]	validation-logloss:0.35063	validation-auc:0.97049	validation-aucpr:0.97336
[21]	validation-logloss:0.34274	validation-auc:0.97056	validation-aucpr:0.97342
[22]	validation-logloss:0.33619	validation-auc:0.97062	validation-aucpr:0.97348
[23]	validation-logloss:0.32934	validation-auc:0.97070	validation-aucpr:0.97356
[24]	validation-logloss:0.32258	validation-auc:0.97070	validation-aucpr:0.97358
[25]	validation-logloss:0.31621	validation-auc:0.97081	validation-aucpr:0.97366
[26]	validation-logloss:0.31095	validation-auc:0.97078	validation-aucpr:0.97364
[27]	validation-logloss:0.30513	validation-auc:0.97087	validation-aucpr:0.97372
[28]	validation-logloss:0.30036	validation-auc:0.97077	validation-aucpr:0.97365
[29]	validation-logloss:0.29538	validation-auc:0.97076	validation-aucpr:0.97365
[30]	validation-logloss:0.29032	validation-auc:0.97088	validation-aucpr:0.97374
[31]	validation-logloss:0.28535	validation-auc:0.97102	validation-aucpr:0.97386
[32]	validation-logloss:0.28084	validation-auc:0.97104	validation-aucpr:0.97388
[33]	validation-logloss:0.27670	validation-auc:0.97105	validation-aucpr:0.97389
[34]	validation-logloss:0.27271	validation-auc:0.97118	validation-aucpr:0.97396
[35]	validation-logloss:0.26930	validation-auc:0.97129	validation-aucpr:0.97405
[36]	validation-logloss:0.26552	validation-auc:0.97131	validation-aucpr:0.97408
[37]	validation-logloss:0.26201	validation-auc:0.97139	validation-aucpr:0.97407
[38]	validation-logloss:0.25857	validation-auc:0.97142	validation-aucpr:0.97409
[39]	validation-logloss:0.25520	validation-auc:0.97151	validation-aucpr:0.97417
[40]	validation-logloss:0.25217	validation-auc:0.97151	validation-aucpr:0.97418
[41]	validation-logloss:0.24933	validation-auc:0.97154	validation-aucpr:0.97418
[42]	validation-logloss:0.24689	validation-auc:0.97166	validation-aucpr:0.97428
{'best_iteration': '42', 'best_score': '0.9742818391162781'}
Trial 73, Fold 2: Log loss = 0.24689143229259836, Average precision = 0.9742754776508732, ROC-AUC = 0.9716611769376143, Elapsed Time = 1.2765495000021474 seconds
Trial 73, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 73, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[0]	validation-logloss:0.66213	validation-auc:0.96246	validation-aucpr:0.96394
[1]	validation-logloss:0.63405	validation-auc:0.96415	validation-aucpr:0.96683
[2]	validation-logloss:0.60788	validation-auc:0.96467	validation-aucpr:0.96728
[3]	validation-logloss:0.58403	validation-auc:0.96499	validation-aucpr:0.96704
[4]	validation-logloss:0.56355	validation-auc:0.96602	validation-aucpr:0.96843
[5]	validation-logloss:0.54273	validation-auc:0.96700	validation-aucpr:0.97014
[6]	validation-logloss:0.52286	validation-auc:0.96751	validation-aucpr:0.97058
[7]	validation-logloss:0.50495	validation-auc:0.96759	validation-aucpr:0.97057
[8]	validation-logloss:0.48795	validation-auc:0.96782	validation-aucpr:0.97069
[9]	validation-logloss:0.47198	validation-auc:0.96846	validation-aucpr:0.97249
[10]	validation-logloss:0.45684	validation-auc:0.96869	validation-aucpr:0.97273
[11]	validation-logloss:0.44263	validation-auc:0.96889	validation-aucpr:0.97289
[12]	validation-logloss:0.42975	validation-auc:0.96923	validation-aucpr:0.97347
[13]	validation-logloss:0.41851	validation-auc:0.96942	validation-aucpr:0.97364
[14]	validation-logloss:0.40790	validation-auc:0.96956	validation-aucpr:0.97377
[15]	validation-logloss:0.39684	validation-auc:0.96962	validation-aucpr:0.97380
[16]	validation-logloss:0.38670	validation-auc:0.96960	validation-aucpr:0.97383
[17]	validation-logloss:0.37721	validation-auc:0.96959	validation-aucpr:0.97380
[18]	validation-logloss:0.36798	validation-auc:0.96964	validation-aucpr:0.97385
[19]	validation-logloss:0.35934	validation-auc:0.96989	validation-aucpr:0.97404
[20]	validation-logloss:0.35106	validation-auc:0.96995	validation-aucpr:0.97412
[21]	validation-logloss:0.34318	validation-auc:0.97001	validation-aucpr:0.97415
[22]	validation-logloss:0.33563	validation-auc:0.97003	validation-aucpr:0.97415
[23]	validation-logloss:0.32875	validation-auc:0.96995	validation-aucpr:0.97407
[24]	validation-logloss:0.32259	validation-auc:0.97005	validation-aucpr:0.97415
[25]	validation-logloss:0.31612	validation-auc:0.97013	validation-aucpr:0.97421
[26]	validation-logloss:0.31093	validation-auc:0.97012	validation-aucpr:0.97419
[27]	validation-logloss:0.30530	validation-auc:0.97012	validation-aucpr:0.97419
[28]	validation-logloss:0.29981	validation-auc:0.97017	validation-aucpr:0.97432
[29]	validation-logloss:0.29480	validation-auc:0.97022	validation-aucpr:0.97435
[30]	validation-logloss:0.28996	validation-auc:0.97030	validation-aucpr:0.97440
[31]	validation-logloss:0.28523	validation-auc:0.97043	validation-aucpr:0.97449
[32]	validation-logloss:0.28082	validation-auc:0.97047	validation-aucpr:0.97452
[33]	validation-logloss:0.27672	validation-auc:0.97045	validation-aucpr:0.97450
[34]	validation-logloss:0.27259	validation-auc:0.97059	validation-aucpr:0.97462
[35]	validation-logloss:0.26864	validation-auc:0.97063	validation-aucpr:0.97466
[36]	validation-logloss:0.26501	validation-auc:0.97065	validation-aucpr:0.97467
[37]	validation-logloss:0.26154	validation-auc:0.97071	validation-aucpr:0.97473
[38]	validation-logloss:0.25815	validation-auc:0.97079	validation-aucpr:0.97479
[39]	validation-logloss:0.25506	validation-auc:0.97086	validation-aucpr:0.97481
[40]	validation-logloss:0.25242	validation-auc:0.97096	validation-aucpr:0.97489
[41]	validation-logloss:0.24960	validation-auc:0.97098	validation-aucpr:0.97490
[42]	validation-logloss:0.24717	validation-auc:0.97102	validation-aucpr:0.97493
{'best_iteration': '42', 'best_score': '0.974931153155075'}
Trial 73, Fold 3: Log loss = 0.24717121871577077, Average precision = 0.9749117773427436, ROC-AUC = 0.9710236766957845, Elapsed Time = 1.2841706999970484 seconds
Trial 73, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 73, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[0]	validation-logloss:0.66257	validation-auc:0.95890	validation-aucpr:0.96040
[1]	validation-logloss:0.63457	validation-auc:0.96546	validation-aucpr:0.97052
[2]	validation-logloss:0.61137	validation-auc:0.96493	validation-aucpr:0.96950
[3]	validation-logloss:0.58698	validation-auc:0.96604	validation-aucpr:0.97025
[4]	validation-logloss:0.56421	validation-auc:0.96624	validation-aucpr:0.97053
[5]	validation-logloss:0.54369	validation-auc:0.96600	validation-aucpr:0.97028
[6]	validation-logloss:0.52434	validation-auc:0.96650	validation-aucpr:0.97091
[7]	validation-logloss:0.50810	validation-auc:0.96650	validation-aucpr:0.97129
[8]	validation-logloss:0.49099	validation-auc:0.96656	validation-aucpr:0.97141
[9]	validation-logloss:0.47485	validation-auc:0.96692	validation-aucpr:0.97174
[10]	validation-logloss:0.45963	validation-auc:0.96734	validation-aucpr:0.97209
[11]	validation-logloss:0.44534	validation-auc:0.96782	validation-aucpr:0.97259
[12]	validation-logloss:0.43252	validation-auc:0.96829	validation-aucpr:0.97304
[13]	validation-logloss:0.41980	validation-auc:0.96853	validation-aucpr:0.97322
[14]	validation-logloss:0.40819	validation-auc:0.96883	validation-aucpr:0.97345
[15]	validation-logloss:0.39732	validation-auc:0.96898	validation-aucpr:0.97356
[16]	validation-logloss:0.38820	validation-auc:0.96881	validation-aucpr:0.97349
[17]	validation-logloss:0.37830	validation-auc:0.96895	validation-aucpr:0.97365
[18]	validation-logloss:0.36889	validation-auc:0.96922	validation-aucpr:0.97384
[19]	validation-logloss:0.35996	validation-auc:0.96937	validation-aucpr:0.97395
[20]	validation-logloss:0.35176	validation-auc:0.96947	validation-aucpr:0.97400
[21]	validation-logloss:0.34387	validation-auc:0.96957	validation-aucpr:0.97410
[22]	validation-logloss:0.33636	validation-auc:0.96968	validation-aucpr:0.97423
[23]	validation-logloss:0.33032	validation-auc:0.96953	validation-aucpr:0.97410
[24]	validation-logloss:0.32371	validation-auc:0.96965	validation-aucpr:0.97419
[25]	validation-logloss:0.31749	validation-auc:0.96959	validation-aucpr:0.97416
[26]	validation-logloss:0.31213	validation-auc:0.96955	validation-aucpr:0.97414
[27]	validation-logloss:0.30647	validation-auc:0.96970	validation-aucpr:0.97425
[28]	validation-logloss:0.30094	validation-auc:0.96982	validation-aucpr:0.97434
[29]	validation-logloss:0.29647	validation-auc:0.96972	validation-aucpr:0.97428
[30]	validation-logloss:0.29150	validation-auc:0.96974	validation-aucpr:0.97431
[31]	validation-logloss:0.28749	validation-auc:0.96973	validation-aucpr:0.97428
[32]	validation-logloss:0.28299	validation-auc:0.96979	validation-aucpr:0.97435
[33]	validation-logloss:0.27873	validation-auc:0.96998	validation-aucpr:0.97449
[34]	validation-logloss:0.27479	validation-auc:0.96994	validation-aucpr:0.97446
[35]	validation-logloss:0.27084	validation-auc:0.97003	validation-aucpr:0.97453
[36]	validation-logloss:0.26714	validation-auc:0.97005	validation-aucpr:0.97456
[37]	validation-logloss:0.26348	validation-auc:0.97020	validation-aucpr:0.97466
[38]	validation-logloss:0.26006	validation-auc:0.97026	validation-aucpr:0.97472
[39]	validation-logloss:0.25698	validation-auc:0.97032	validation-aucpr:0.97476
[40]	validation-logloss:0.25433	validation-auc:0.97038	validation-aucpr:0.97481
[41]	validation-logloss:0.25143	validation-auc:0.97040	validation-aucpr:0.97483
[42]	validation-logloss:0.24939	validation-auc:0.97027	validation-aucpr:0.97472
{'best_iteration': '41', 'best_score': '0.9748262195518107'}
Trial 73, Fold 4: Log loss = 0.2493894087452096, Average precision = 0.9747037275156256, ROC-AUC = 0.9702705828212813, Elapsed Time = 1.254519900001469 seconds
Trial 73, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 73, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[0]	validation-logloss:0.66242	validation-auc:0.95556	validation-aucpr:0.95546
[1]	validation-logloss:0.63502	validation-auc:0.96010	validation-aucpr:0.96559
[2]	validation-logloss:0.60888	validation-auc:0.96173	validation-aucpr:0.96679
[3]	validation-logloss:0.58724	validation-auc:0.96131	validation-aucpr:0.96589
[4]	validation-logloss:0.56487	validation-auc:0.96170	validation-aucpr:0.96641
[5]	validation-logloss:0.54386	validation-auc:0.96290	validation-aucpr:0.96790
[6]	validation-logloss:0.52449	validation-auc:0.96336	validation-aucpr:0.96819
[7]	validation-logloss:0.50653	validation-auc:0.96335	validation-aucpr:0.96823
[8]	validation-logloss:0.48948	validation-auc:0.96382	validation-aucpr:0.96888
[9]	validation-logloss:0.47386	validation-auc:0.96408	validation-aucpr:0.96901
[10]	validation-logloss:0.45934	validation-auc:0.96484	validation-aucpr:0.96940
[11]	validation-logloss:0.44557	validation-auc:0.96517	validation-aucpr:0.96966
[12]	validation-logloss:0.43241	validation-auc:0.96547	validation-aucpr:0.96988
[13]	validation-logloss:0.42017	validation-auc:0.96572	validation-aucpr:0.97002
[14]	validation-logloss:0.40878	validation-auc:0.96587	validation-aucpr:0.97011
[15]	validation-logloss:0.39920	validation-auc:0.96635	validation-aucpr:0.97125
[16]	validation-logloss:0.38891	validation-auc:0.96658	validation-aucpr:0.97139
[17]	validation-logloss:0.37948	validation-auc:0.96659	validation-aucpr:0.97141
[18]	validation-logloss:0.37016	validation-auc:0.96668	validation-aucpr:0.97148
[19]	validation-logloss:0.36176	validation-auc:0.96682	validation-aucpr:0.97153
[20]	validation-logloss:0.35374	validation-auc:0.96694	validation-aucpr:0.97161
[21]	validation-logloss:0.34619	validation-auc:0.96718	validation-aucpr:0.97177
[22]	validation-logloss:0.33880	validation-auc:0.96730	validation-aucpr:0.97181
[23]	validation-logloss:0.33212	validation-auc:0.96728	validation-aucpr:0.97182
[24]	validation-logloss:0.32574	validation-auc:0.96739	validation-aucpr:0.97187
[25]	validation-logloss:0.31957	validation-auc:0.96749	validation-aucpr:0.97197
[26]	validation-logloss:0.31394	validation-auc:0.96761	validation-aucpr:0.97200
[27]	validation-logloss:0.30834	validation-auc:0.96768	validation-aucpr:0.97202
[28]	validation-logloss:0.30353	validation-auc:0.96781	validation-aucpr:0.97214
[29]	validation-logloss:0.29902	validation-auc:0.96801	validation-aucpr:0.97225
[30]	validation-logloss:0.29430	validation-auc:0.96807	validation-aucpr:0.97231
[31]	validation-logloss:0.28972	validation-auc:0.96814	validation-aucpr:0.97233
[32]	validation-logloss:0.28566	validation-auc:0.96823	validation-aucpr:0.97244
[33]	validation-logloss:0.28187	validation-auc:0.96811	validation-aucpr:0.97235
[34]	validation-logloss:0.27788	validation-auc:0.96821	validation-aucpr:0.97243
[35]	validation-logloss:0.27406	validation-auc:0.96836	validation-aucpr:0.97255
[36]	validation-logloss:0.27055	validation-auc:0.96846	validation-aucpr:0.97262
[37]	validation-logloss:0.26719	validation-auc:0.96855	validation-aucpr:0.97267
[38]	validation-logloss:0.26443	validation-auc:0.96872	validation-aucpr:0.97284
[39]	validation-logloss:0.26149	validation-auc:0.96881	validation-aucpr:0.97291
[40]	validation-logloss:0.25859	validation-auc:0.96882	validation-aucpr:0.97292
[41]	validation-logloss:0.25619	validation-auc:0.96884	validation-aucpr:0.97291
[42]	validation-logloss:0.25354	validation-auc:0.96887	validation-aucpr:0.97293
{'best_iteration': '42', 'best_score': '0.9729319944965127'}
Trial 73, Fold 5: Log loss = 0.25353626477607055, Average precision = 0.9729489695128064, ROC-AUC = 0.968868922207978, Elapsed Time = 1.2993155000003753 seconds
Optimization Progress:  74%|#######4  | 74/100 [3:27:33<53:39, 123.83s/it]  
Trial 74, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 74, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[0]	validation-logloss:0.68198	validation-auc:0.94945	validation-aucpr:0.95422
[1]	validation-logloss:0.67126	validation-auc:0.95865	validation-aucpr:0.96317
[2]	validation-logloss:0.66095	validation-auc:0.96173	validation-aucpr:0.96655
[3]	validation-logloss:0.65066	validation-auc:0.96379	validation-aucpr:0.96784
[4]	validation-logloss:0.64073	validation-auc:0.96415	validation-aucpr:0.96847
[5]	validation-logloss:0.63119	validation-auc:0.96441	validation-aucpr:0.96861
{'best_iteration': '5', 'best_score': '0.968609721886661'}
Trial 74, Fold 1: Log loss = 0.6311894079791622, Average precision = 0.9684504452958994, ROC-AUC = 0.9644080418978793, Elapsed Time = 0.4654045999996015 seconds
Trial 74, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 74, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[0]	validation-logloss:0.68211	validation-auc:0.95194	validation-aucpr:0.95339
[1]	validation-logloss:0.67099	validation-auc:0.96093	validation-aucpr:0.96313
[2]	validation-logloss:0.66030	validation-auc:0.96405	validation-aucpr:0.96679
[3]	validation-logloss:0.65158	validation-auc:0.96409	validation-aucpr:0.96527
[4]	validation-logloss:0.64153	validation-auc:0.96526	validation-aucpr:0.96644
[5]	validation-logloss:0.63184	validation-auc:0.96703	validation-aucpr:0.96916
{'best_iteration': '5', 'best_score': '0.9691621140187124'}
Trial 74, Fold 2: Log loss = 0.6318357320456525, Average precision = 0.9696004383607427, ROC-AUC = 0.9670322624896773, Elapsed Time = 0.4971901999997499 seconds
Trial 74, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 74, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[0]	validation-logloss:0.68193	validation-auc:0.95612	validation-aucpr:0.95651
[1]	validation-logloss:0.67224	validation-auc:0.95906	validation-aucpr:0.96173
[2]	validation-logloss:0.66207	validation-auc:0.96266	validation-aucpr:0.96536
[3]	validation-logloss:0.65210	validation-auc:0.96273	validation-aucpr:0.96726
[4]	validation-logloss:0.64232	validation-auc:0.96351	validation-aucpr:0.96798
[5]	validation-logloss:0.63264	validation-auc:0.96433	validation-aucpr:0.96881
{'best_iteration': '5', 'best_score': '0.9688053773871279'}
Trial 74, Fold 3: Log loss = 0.6326356588194274, Average precision = 0.9686266263110179, ROC-AUC = 0.9643291291858885, Elapsed Time = 0.5874459000006027 seconds
Trial 74, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 74, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[0]	validation-logloss:0.68194	validation-auc:0.95429	validation-aucpr:0.96112
[1]	validation-logloss:0.67105	validation-auc:0.95921	validation-aucpr:0.96506
[2]	validation-logloss:0.66069	validation-auc:0.96126	validation-aucpr:0.96711
[3]	validation-logloss:0.65036	validation-auc:0.96230	validation-aucpr:0.96624
[4]	validation-logloss:0.64061	validation-auc:0.96461	validation-aucpr:0.96963
[5]	validation-logloss:0.63092	validation-auc:0.96428	validation-aucpr:0.96778
{'best_iteration': '4', 'best_score': '0.969634442922803'}
Trial 74, Fold 4: Log loss = 0.6309225193685589, Average precision = 0.9680199805645648, ROC-AUC = 0.9642778904411397, Elapsed Time = 0.5735706000014034 seconds
Trial 74, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 74, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[0]	validation-logloss:0.68212	validation-auc:0.94916	validation-aucpr:0.94737
[1]	validation-logloss:0.67140	validation-auc:0.95587	validation-aucpr:0.95626
[2]	validation-logloss:0.66091	validation-auc:0.95810	validation-aucpr:0.95942
[3]	validation-logloss:0.65063	validation-auc:0.95948	validation-aucpr:0.96048
[4]	validation-logloss:0.64079	validation-auc:0.96033	validation-aucpr:0.96113
[5]	validation-logloss:0.63112	validation-auc:0.96114	validation-aucpr:0.96241
{'best_iteration': '5', 'best_score': '0.9624078909143001'}
Trial 74, Fold 5: Log loss = 0.6311159088796015, Average precision = 0.9619067479372349, ROC-AUC = 0.9611428571428572, Elapsed Time = 0.578624599998875 seconds
Optimization Progress:  75%|#######5  | 75/100 [3:27:43<37:24, 89.78s/it] 
Trial 75, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 75, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[0]	validation-logloss:0.68520	validation-auc:0.96108	validation-aucpr:0.96645
[1]	validation-logloss:0.67745	validation-auc:0.96484	validation-aucpr:0.96898
[2]	validation-logloss:0.67011	validation-auc:0.96581	validation-aucpr:0.97084
[3]	validation-logloss:0.66370	validation-auc:0.96473	validation-aucpr:0.97008
[4]	validation-logloss:0.65640	validation-auc:0.96545	validation-aucpr:0.97065
[5]	validation-logloss:0.64993	validation-auc:0.96599	validation-aucpr:0.97107
[6]	validation-logloss:0.64306	validation-auc:0.96662	validation-aucpr:0.97164
[7]	validation-logloss:0.63706	validation-auc:0.96605	validation-aucpr:0.97109
[8]	validation-logloss:0.63108	validation-auc:0.96553	validation-aucpr:0.97105
[9]	validation-logloss:0.62520	validation-auc:0.96538	validation-aucpr:0.97085
[10]	validation-logloss:0.61936	validation-auc:0.96544	validation-aucpr:0.97082
[11]	validation-logloss:0.61288	validation-auc:0.96615	validation-aucpr:0.97143
[12]	validation-logloss:0.60647	validation-auc:0.96655	validation-aucpr:0.97180
[13]	validation-logloss:0.60124	validation-auc:0.96619	validation-aucpr:0.97155
[14]	validation-logloss:0.59540	validation-auc:0.96616	validation-aucpr:0.97171
[15]	validation-logloss:0.58946	validation-auc:0.96650	validation-aucpr:0.97201
[16]	validation-logloss:0.58392	validation-auc:0.96635	validation-aucpr:0.97186
[17]	validation-logloss:0.57882	validation-auc:0.96627	validation-aucpr:0.97173
[18]	validation-logloss:0.57378	validation-auc:0.96627	validation-aucpr:0.97164
[19]	validation-logloss:0.56900	validation-auc:0.96604	validation-aucpr:0.97146
[20]	validation-logloss:0.56450	validation-auc:0.96591	validation-aucpr:0.97131
[21]	validation-logloss:0.55906	validation-auc:0.96615	validation-aucpr:0.97154
[22]	validation-logloss:0.55365	validation-auc:0.96640	validation-aucpr:0.97173
[23]	validation-logloss:0.54929	validation-auc:0.96628	validation-aucpr:0.97165
[24]	validation-logloss:0.54412	validation-auc:0.96654	validation-aucpr:0.97186
[25]	validation-logloss:0.53992	validation-auc:0.96637	validation-aucpr:0.97168
[26]	validation-logloss:0.53571	validation-auc:0.96629	validation-aucpr:0.97159
[27]	validation-logloss:0.53092	validation-auc:0.96637	validation-aucpr:0.97166
[28]	validation-logloss:0.52619	validation-auc:0.96648	validation-aucpr:0.97176
[29]	validation-logloss:0.52200	validation-auc:0.96650	validation-aucpr:0.97175
[30]	validation-logloss:0.51787	validation-auc:0.96643	validation-aucpr:0.97169
[31]	validation-logloss:0.51321	validation-auc:0.96664	validation-aucpr:0.97184
[32]	validation-logloss:0.50940	validation-auc:0.96661	validation-aucpr:0.97178
[33]	validation-logloss:0.50574	validation-auc:0.96653	validation-aucpr:0.97172
[34]	validation-logloss:0.50147	validation-auc:0.96674	validation-aucpr:0.97189
[35]	validation-logloss:0.49728	validation-auc:0.96680	validation-aucpr:0.97196
[36]	validation-logloss:0.49297	validation-auc:0.96695	validation-aucpr:0.97210
[37]	validation-logloss:0.48882	validation-auc:0.96709	validation-aucpr:0.97226
[38]	validation-logloss:0.48489	validation-auc:0.96720	validation-aucpr:0.97230
{'best_iteration': '38', 'best_score': '0.9723014364966841'}
Trial 75, Fold 1: Log loss = 0.48489445876785703, Average precision = 0.9722991842862466, ROC-AUC = 0.9671955132800888, Elapsed Time = 0.7628678000000946 seconds
Trial 75, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 75, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[0]	validation-logloss:0.68538	validation-auc:0.96151	validation-aucpr:0.96532
[1]	validation-logloss:0.67845	validation-auc:0.96332	validation-aucpr:0.96709
[2]	validation-logloss:0.67073	validation-auc:0.96615	validation-aucpr:0.97000
[3]	validation-logloss:0.66326	validation-auc:0.96708	validation-aucpr:0.97098
[4]	validation-logloss:0.65601	validation-auc:0.96793	validation-aucpr:0.97179
[5]	validation-logloss:0.64977	validation-auc:0.96821	validation-aucpr:0.97174
[6]	validation-logloss:0.64270	validation-auc:0.96862	validation-aucpr:0.97212
[7]	validation-logloss:0.63596	validation-auc:0.96870	validation-aucpr:0.97229
[8]	validation-logloss:0.62931	validation-auc:0.96865	validation-aucpr:0.97229
[9]	validation-logloss:0.62352	validation-auc:0.96906	validation-aucpr:0.97239
[10]	validation-logloss:0.61717	validation-auc:0.96915	validation-aucpr:0.97245
[11]	validation-logloss:0.61139	validation-auc:0.96916	validation-aucpr:0.97241
[12]	validation-logloss:0.60508	validation-auc:0.96939	validation-aucpr:0.97263
[13]	validation-logloss:0.59969	validation-auc:0.96937	validation-aucpr:0.97257
[14]	validation-logloss:0.59419	validation-auc:0.96928	validation-aucpr:0.97249
[15]	validation-logloss:0.58887	validation-auc:0.96934	validation-aucpr:0.97253
[16]	validation-logloss:0.58375	validation-auc:0.96933	validation-aucpr:0.97252
[17]	validation-logloss:0.57853	validation-auc:0.96920	validation-aucpr:0.97237
[18]	validation-logloss:0.57300	validation-auc:0.96915	validation-aucpr:0.97231
[19]	validation-logloss:0.56746	validation-auc:0.96921	validation-aucpr:0.97235
[20]	validation-logloss:0.56253	validation-auc:0.96917	validation-aucpr:0.97231
[21]	validation-logloss:0.55782	validation-auc:0.96909	validation-aucpr:0.97222
[22]	validation-logloss:0.55249	validation-auc:0.96914	validation-aucpr:0.97229
[23]	validation-logloss:0.54732	validation-auc:0.96930	validation-aucpr:0.97243
[24]	validation-logloss:0.54210	validation-auc:0.96950	validation-aucpr:0.97260
[25]	validation-logloss:0.53759	validation-auc:0.96943	validation-aucpr:0.97254
[26]	validation-logloss:0.53268	validation-auc:0.96970	validation-aucpr:0.97278
[27]	validation-logloss:0.52785	validation-auc:0.96976	validation-aucpr:0.97285
[28]	validation-logloss:0.52319	validation-auc:0.96973	validation-aucpr:0.97284
[29]	validation-logloss:0.51905	validation-auc:0.96957	validation-aucpr:0.97267
[30]	validation-logloss:0.51499	validation-auc:0.96955	validation-aucpr:0.97265
[31]	validation-logloss:0.51099	validation-auc:0.96950	validation-aucpr:0.97259
[32]	validation-logloss:0.50692	validation-auc:0.96960	validation-aucpr:0.97267
[33]	validation-logloss:0.50297	validation-auc:0.96962	validation-aucpr:0.97266
[34]	validation-logloss:0.49860	validation-auc:0.96977	validation-aucpr:0.97280
[35]	validation-logloss:0.49434	validation-auc:0.96983	validation-aucpr:0.97286
[36]	validation-logloss:0.49055	validation-auc:0.96971	validation-aucpr:0.97275
[37]	validation-logloss:0.48643	validation-auc:0.96980	validation-aucpr:0.97283
[38]	validation-logloss:0.48235	validation-auc:0.96987	validation-aucpr:0.97289
{'best_iteration': '38', 'best_score': '0.9728927317821991'}
Trial 75, Fold 2: Log loss = 0.4823548176019976, Average precision = 0.9728926908705433, ROC-AUC = 0.969873663898686, Elapsed Time = 0.9614445000006526 seconds
Trial 75, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 75, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[0]	validation-logloss:0.68520	validation-auc:0.96196	validation-aucpr:0.96490
[1]	validation-logloss:0.67738	validation-auc:0.96760	validation-aucpr:0.97189
[2]	validation-logloss:0.67076	validation-auc:0.96759	validation-aucpr:0.97146
[3]	validation-logloss:0.66369	validation-auc:0.96795	validation-aucpr:0.97194
[4]	validation-logloss:0.65705	validation-auc:0.96823	validation-aucpr:0.97223
[5]	validation-logloss:0.64979	validation-auc:0.96885	validation-aucpr:0.97271
[6]	validation-logloss:0.64351	validation-auc:0.96870	validation-aucpr:0.97261
[7]	validation-logloss:0.63662	validation-auc:0.96945	validation-aucpr:0.97353
[8]	validation-logloss:0.62978	validation-auc:0.96968	validation-aucpr:0.97380
[9]	validation-logloss:0.62306	validation-auc:0.96995	validation-aucpr:0.97404
[10]	validation-logloss:0.61646	validation-auc:0.97016	validation-aucpr:0.97423
[11]	validation-logloss:0.61004	validation-auc:0.97055	validation-aucpr:0.97490
[12]	validation-logloss:0.60383	validation-auc:0.97055	validation-aucpr:0.97492
[13]	validation-logloss:0.59832	validation-auc:0.97024	validation-aucpr:0.97463
[14]	validation-logloss:0.59295	validation-auc:0.97010	validation-aucpr:0.97450
[15]	validation-logloss:0.58770	validation-auc:0.96991	validation-aucpr:0.97429
[16]	validation-logloss:0.58254	validation-auc:0.96987	validation-aucpr:0.97420
[17]	validation-logloss:0.57739	validation-auc:0.96978	validation-aucpr:0.97417
[18]	validation-logloss:0.57163	validation-auc:0.97000	validation-aucpr:0.97437
[19]	validation-logloss:0.56656	validation-auc:0.96997	validation-aucpr:0.97429
[20]	validation-logloss:0.56101	validation-auc:0.97011	validation-aucpr:0.97444
[21]	validation-logloss:0.55568	validation-auc:0.97028	validation-aucpr:0.97452
[22]	validation-logloss:0.55091	validation-auc:0.97017	validation-aucpr:0.97439
[23]	validation-logloss:0.54635	validation-auc:0.97017	validation-aucpr:0.97437
[24]	validation-logloss:0.54135	validation-auc:0.97024	validation-aucpr:0.97447
[25]	validation-logloss:0.53701	validation-auc:0.97022	validation-aucpr:0.97446
[26]	validation-logloss:0.53260	validation-auc:0.97022	validation-aucpr:0.97444
[27]	validation-logloss:0.52838	validation-auc:0.97018	validation-aucpr:0.97437
[28]	validation-logloss:0.52434	validation-auc:0.97013	validation-aucpr:0.97433
[29]	validation-logloss:0.51953	validation-auc:0.97017	validation-aucpr:0.97438
[30]	validation-logloss:0.51557	validation-auc:0.97010	validation-aucpr:0.97433
[31]	validation-logloss:0.51100	validation-auc:0.97017	validation-aucpr:0.97440
[32]	validation-logloss:0.50649	validation-auc:0.97016	validation-aucpr:0.97442
[33]	validation-logloss:0.50268	validation-auc:0.97012	validation-aucpr:0.97436
[34]	validation-logloss:0.49827	validation-auc:0.97025	validation-aucpr:0.97447
[35]	validation-logloss:0.49390	validation-auc:0.97036	validation-aucpr:0.97458
[36]	validation-logloss:0.49026	validation-auc:0.97030	validation-aucpr:0.97451
[37]	validation-logloss:0.48610	validation-auc:0.97043	validation-aucpr:0.97462
[38]	validation-logloss:0.48194	validation-auc:0.97053	validation-aucpr:0.97472
{'best_iteration': '12', 'best_score': '0.9749222820063228'}
Trial 75, Fold 3: Log loss = 0.4819393169675427, Average precision = 0.9747282369942932, ROC-AUC = 0.970529709937302, Elapsed Time = 1.0155684999990626 seconds
Trial 75, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 75, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[0]	validation-logloss:0.68524	validation-auc:0.96118	validation-aucpr:0.96774
[1]	validation-logloss:0.67771	validation-auc:0.96477	validation-aucpr:0.97058
[2]	validation-logloss:0.67012	validation-auc:0.96584	validation-aucpr:0.97144
[3]	validation-logloss:0.66333	validation-auc:0.96563	validation-aucpr:0.97149
[4]	validation-logloss:0.65606	validation-auc:0.96598	validation-aucpr:0.97182
[5]	validation-logloss:0.64892	validation-auc:0.96707	validation-aucpr:0.97260
[6]	validation-logloss:0.64224	validation-auc:0.96750	validation-aucpr:0.97303
[7]	validation-logloss:0.63609	validation-auc:0.96701	validation-aucpr:0.97258
[8]	validation-logloss:0.63014	validation-auc:0.96695	validation-aucpr:0.97246
[9]	validation-logloss:0.62353	validation-auc:0.96755	validation-aucpr:0.97287
[10]	validation-logloss:0.61771	validation-auc:0.96768	validation-aucpr:0.97289
[11]	validation-logloss:0.61214	validation-auc:0.96745	validation-aucpr:0.97265
[12]	validation-logloss:0.60590	validation-auc:0.96768	validation-aucpr:0.97291
[13]	validation-logloss:0.59968	validation-auc:0.96809	validation-aucpr:0.97325
[14]	validation-logloss:0.59357	validation-auc:0.96821	validation-aucpr:0.97337
[15]	validation-logloss:0.58771	validation-auc:0.96852	validation-aucpr:0.97367
[16]	validation-logloss:0.58210	validation-auc:0.96875	validation-aucpr:0.97383
[17]	validation-logloss:0.57638	validation-auc:0.96893	validation-aucpr:0.97396
[18]	validation-logloss:0.57147	validation-auc:0.96859	validation-aucpr:0.97367
[19]	validation-logloss:0.56593	validation-auc:0.96873	validation-aucpr:0.97380
[20]	validation-logloss:0.56130	validation-auc:0.96857	validation-aucpr:0.97365
[21]	validation-logloss:0.55594	validation-auc:0.96875	validation-aucpr:0.97382
[22]	validation-logloss:0.55143	validation-auc:0.96863	validation-aucpr:0.97371
[23]	validation-logloss:0.54674	validation-auc:0.96870	validation-aucpr:0.97374
[24]	validation-logloss:0.54222	validation-auc:0.96875	validation-aucpr:0.97377
[25]	validation-logloss:0.53794	validation-auc:0.96867	validation-aucpr:0.97372
[26]	validation-logloss:0.53304	validation-auc:0.96872	validation-aucpr:0.97378
[27]	validation-logloss:0.52814	validation-auc:0.96885	validation-aucpr:0.97390
[28]	validation-logloss:0.52396	validation-auc:0.96884	validation-aucpr:0.97388
[29]	validation-logloss:0.51916	validation-auc:0.96895	validation-aucpr:0.97396
[30]	validation-logloss:0.51457	validation-auc:0.96906	validation-aucpr:0.97407
[31]	validation-logloss:0.51000	validation-auc:0.96926	validation-aucpr:0.97423
[32]	validation-logloss:0.50563	validation-auc:0.96934	validation-aucpr:0.97432
[33]	validation-logloss:0.50120	validation-auc:0.96942	validation-aucpr:0.97438
[34]	validation-logloss:0.49739	validation-auc:0.96937	validation-aucpr:0.97433
[35]	validation-logloss:0.49321	validation-auc:0.96932	validation-aucpr:0.97430
[36]	validation-logloss:0.48949	validation-auc:0.96923	validation-aucpr:0.97421
[37]	validation-logloss:0.48535	validation-auc:0.96925	validation-aucpr:0.97423
[38]	validation-logloss:0.48120	validation-auc:0.96934	validation-aucpr:0.97431
{'best_iteration': '33', 'best_score': '0.974379595658719'}
Trial 75, Fold 4: Log loss = 0.48119895275105207, Average precision = 0.9743045863136259, ROC-AUC = 0.9693426748547299, Elapsed Time = 0.9456118000016431 seconds
Trial 75, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 75, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[0]	validation-logloss:0.68529	validation-auc:0.95807	validation-aucpr:0.96371
[1]	validation-logloss:0.67757	validation-auc:0.96367	validation-aucpr:0.96895
[2]	validation-logloss:0.67090	validation-auc:0.96391	validation-aucpr:0.96884
[3]	validation-logloss:0.66434	validation-auc:0.96452	validation-aucpr:0.96890
[4]	validation-logloss:0.65789	validation-auc:0.96409	validation-aucpr:0.96841
[5]	validation-logloss:0.65181	validation-auc:0.96425	validation-aucpr:0.96843
[6]	validation-logloss:0.64589	validation-auc:0.96385	validation-aucpr:0.96789
[7]	validation-logloss:0.63896	validation-auc:0.96482	validation-aucpr:0.96886
[8]	validation-logloss:0.63318	validation-auc:0.96461	validation-aucpr:0.96856
[9]	validation-logloss:0.62740	validation-auc:0.96456	validation-aucpr:0.96842
[10]	validation-logloss:0.62077	validation-auc:0.96512	validation-aucpr:0.96908
[11]	validation-logloss:0.61483	validation-auc:0.96502	validation-aucpr:0.96897
[12]	validation-logloss:0.60916	validation-auc:0.96548	validation-aucpr:0.96932
[13]	validation-logloss:0.60311	validation-auc:0.96566	validation-aucpr:0.96953
[14]	validation-logloss:0.59711	validation-auc:0.96600	validation-aucpr:0.96975
[15]	validation-logloss:0.59199	validation-auc:0.96596	validation-aucpr:0.96970
[16]	validation-logloss:0.58622	validation-auc:0.96617	validation-aucpr:0.96995
[17]	validation-logloss:0.58052	validation-auc:0.96626	validation-aucpr:0.97007
[18]	validation-logloss:0.57547	validation-auc:0.96631	validation-aucpr:0.97015
[19]	validation-logloss:0.57066	validation-auc:0.96624	validation-aucpr:0.97005
[20]	validation-logloss:0.56587	validation-auc:0.96624	validation-aucpr:0.97042
[21]	validation-logloss:0.56041	validation-auc:0.96650	validation-aucpr:0.97064
[22]	validation-logloss:0.55513	validation-auc:0.96657	validation-aucpr:0.97071
[23]	validation-logloss:0.55006	validation-auc:0.96663	validation-aucpr:0.97075
[24]	validation-logloss:0.54507	validation-auc:0.96667	validation-aucpr:0.97081
[25]	validation-logloss:0.54009	validation-auc:0.96688	validation-aucpr:0.97100
[26]	validation-logloss:0.53589	validation-auc:0.96673	validation-aucpr:0.97082
[27]	validation-logloss:0.53166	validation-auc:0.96667	validation-aucpr:0.97077
[28]	validation-logloss:0.52735	validation-auc:0.96674	validation-aucpr:0.97081
[29]	validation-logloss:0.52271	validation-auc:0.96686	validation-aucpr:0.97092
[30]	validation-logloss:0.51806	validation-auc:0.96694	validation-aucpr:0.97099
[31]	validation-logloss:0.51408	validation-auc:0.96690	validation-aucpr:0.97094
[32]	validation-logloss:0.50964	validation-auc:0.96694	validation-aucpr:0.97099
[33]	validation-logloss:0.50519	validation-auc:0.96710	validation-aucpr:0.97112
[34]	validation-logloss:0.50157	validation-auc:0.96701	validation-aucpr:0.97104
[35]	validation-logloss:0.49784	validation-auc:0.96700	validation-aucpr:0.97101
[36]	validation-logloss:0.49433	validation-auc:0.96699	validation-aucpr:0.97096
[37]	validation-logloss:0.49012	validation-auc:0.96718	validation-aucpr:0.97113
[38]	validation-logloss:0.48670	validation-auc:0.96704	validation-aucpr:0.97102
{'best_iteration': '37', 'best_score': '0.9711287945679274'}
Trial 75, Fold 5: Log loss = 0.4866973349365086, Average precision = 0.9710253972086283, ROC-AUC = 0.9670447949761255, Elapsed Time = 0.9918016000010539 seconds
Optimization Progress:  76%|#######6  | 76/100 [3:27:56<26:38, 66.61s/it]
Trial 76, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 76, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[0]	validation-logloss:0.64608	validation-auc:0.94500	validation-aucpr:0.93574
[1]	validation-logloss:0.60419	validation-auc:0.96165	validation-aucpr:0.95555
[2]	validation-logloss:0.57083	validation-auc:0.96431	validation-aucpr:0.96414
[3]	validation-logloss:0.53722	validation-auc:0.96636	validation-aucpr:0.96455
[4]	validation-logloss:0.50807	validation-auc:0.96670	validation-aucpr:0.96465
[5]	validation-logloss:0.48209	validation-auc:0.96723	validation-aucpr:0.96472
[6]	validation-logloss:0.45801	validation-auc:0.96734	validation-aucpr:0.96508
[7]	validation-logloss:0.43931	validation-auc:0.96743	validation-aucpr:0.96713
[8]	validation-logloss:0.41907	validation-auc:0.96811	validation-aucpr:0.96767
[9]	validation-logloss:0.40061	validation-auc:0.96850	validation-aucpr:0.96786
[10]	validation-logloss:0.38401	validation-auc:0.96878	validation-aucpr:0.96794
[11]	validation-logloss:0.37031	validation-auc:0.96934	validation-aucpr:0.97037
[12]	validation-logloss:0.35621	validation-auc:0.96947	validation-aucpr:0.97043
[13]	validation-logloss:0.34498	validation-auc:0.96927	validation-aucpr:0.96968
[14]	validation-logloss:0.33586	validation-auc:0.96946	validation-aucpr:0.97358
[15]	validation-logloss:0.32629	validation-auc:0.96934	validation-aucpr:0.97342
[16]	validation-logloss:0.31768	validation-auc:0.96928	validation-aucpr:0.97342
[17]	validation-logloss:0.30808	validation-auc:0.96968	validation-aucpr:0.97400
[18]	validation-logloss:0.30050	validation-auc:0.96970	validation-aucpr:0.97400
[19]	validation-logloss:0.29237	validation-auc:0.96995	validation-aucpr:0.97420
[20]	validation-logloss:0.28472	validation-auc:0.97006	validation-aucpr:0.97427
[21]	validation-logloss:0.27861	validation-auc:0.97014	validation-aucpr:0.97434
[22]	validation-logloss:0.27216	validation-auc:0.97022	validation-aucpr:0.97441
[23]	validation-logloss:0.26724	validation-auc:0.97028	validation-aucpr:0.97440
[24]	validation-logloss:0.26157	validation-auc:0.97037	validation-aucpr:0.97456
[25]	validation-logloss:0.25602	validation-auc:0.97080	validation-aucpr:0.97492
[26]	validation-logloss:0.25123	validation-auc:0.97089	validation-aucpr:0.97498
[27]	validation-logloss:0.24680	validation-auc:0.97091	validation-aucpr:0.97432
[28]	validation-logloss:0.24280	validation-auc:0.97109	validation-aucpr:0.97516
[29]	validation-logloss:0.23996	validation-auc:0.97102	validation-aucpr:0.97549
[30]	validation-logloss:0.23712	validation-auc:0.97100	validation-aucpr:0.97544
[31]	validation-logloss:0.23370	validation-auc:0.97107	validation-aucpr:0.97549
[32]	validation-logloss:0.23060	validation-auc:0.97115	validation-aucpr:0.97555
[33]	validation-logloss:0.22813	validation-auc:0.97123	validation-aucpr:0.97557
[34]	validation-logloss:0.22565	validation-auc:0.97129	validation-aucpr:0.97564
[35]	validation-logloss:0.22329	validation-auc:0.97126	validation-aucpr:0.97562
[36]	validation-logloss:0.22090	validation-auc:0.97135	validation-aucpr:0.97574
[37]	validation-logloss:0.21873	validation-auc:0.97132	validation-aucpr:0.97572
[38]	validation-logloss:0.21661	validation-auc:0.97138	validation-aucpr:0.97574
[39]	validation-logloss:0.21496	validation-auc:0.97133	validation-aucpr:0.97571
[40]	validation-logloss:0.21345	validation-auc:0.97143	validation-aucpr:0.97580
[41]	validation-logloss:0.21238	validation-auc:0.97142	validation-aucpr:0.97577
[42]	validation-logloss:0.21103	validation-auc:0.97146	validation-aucpr:0.97579
[43]	validation-logloss:0.20994	validation-auc:0.97140	validation-aucpr:0.97574
[44]	validation-logloss:0.20875	validation-auc:0.97145	validation-aucpr:0.97579
[45]	validation-logloss:0.20809	validation-auc:0.97132	validation-aucpr:0.97567
[46]	validation-logloss:0.20702	validation-auc:0.97147	validation-aucpr:0.97575
[47]	validation-logloss:0.20582	validation-auc:0.97161	validation-aucpr:0.97588
[48]	validation-logloss:0.20496	validation-auc:0.97156	validation-aucpr:0.97579
[49]	validation-logloss:0.20457	validation-auc:0.97150	validation-aucpr:0.97572
[50]	validation-logloss:0.20404	validation-auc:0.97145	validation-aucpr:0.97567
[51]	validation-logloss:0.20346	validation-auc:0.97140	validation-aucpr:0.97561
[52]	validation-logloss:0.20295	validation-auc:0.97141	validation-aucpr:0.97563
[53]	validation-logloss:0.20212	validation-auc:0.97149	validation-aucpr:0.97569
[54]	validation-logloss:0.20126	validation-auc:0.97159	validation-aucpr:0.97578
[55]	validation-logloss:0.20067	validation-auc:0.97161	validation-aucpr:0.97580
[56]	validation-logloss:0.20049	validation-auc:0.97162	validation-aucpr:0.97596
[57]	validation-logloss:0.19994	validation-auc:0.97166	validation-aucpr:0.97600
[58]	validation-logloss:0.19936	validation-auc:0.97167	validation-aucpr:0.97601
[59]	validation-logloss:0.19872	validation-auc:0.97173	validation-aucpr:0.97606
[60]	validation-logloss:0.19836	validation-auc:0.97175	validation-aucpr:0.97612
[61]	validation-logloss:0.19770	validation-auc:0.97190	validation-aucpr:0.97628
[62]	validation-logloss:0.19746	validation-auc:0.97187	validation-aucpr:0.97627
[63]	validation-logloss:0.19706	validation-auc:0.97193	validation-aucpr:0.97635
[64]	validation-logloss:0.19684	validation-auc:0.97189	validation-aucpr:0.97634
[65]	validation-logloss:0.19653	validation-auc:0.97196	validation-aucpr:0.97643
[66]	validation-logloss:0.19623	validation-auc:0.97199	validation-aucpr:0.97647
[67]	validation-logloss:0.19586	validation-auc:0.97205	validation-aucpr:0.97651
[68]	validation-logloss:0.19568	validation-auc:0.97205	validation-aucpr:0.97651
[69]	validation-logloss:0.19548	validation-auc:0.97208	validation-aucpr:0.97653
[70]	validation-logloss:0.19539	validation-auc:0.97206	validation-aucpr:0.97650
[71]	validation-logloss:0.19514	validation-auc:0.97210	validation-aucpr:0.97653
[72]	validation-logloss:0.19503	validation-auc:0.97209	validation-aucpr:0.97651
{'best_iteration': '69', 'best_score': '0.9765318621486272'}
Trial 76, Fold 1: Log loss = 0.19503386461973604, Average precision = 0.9765164255619482, ROC-AUC = 0.9720876469126243, Elapsed Time = 3.084799500000372 seconds
Trial 76, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 76, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[0]	validation-logloss:0.64557	validation-auc:0.95077	validation-aucpr:0.92546
[1]	validation-logloss:0.60372	validation-auc:0.96275	validation-aucpr:0.95631
[2]	validation-logloss:0.57040	validation-auc:0.96634	validation-aucpr:0.97026
[3]	validation-logloss:0.54066	validation-auc:0.96691	validation-aucpr:0.96904
[4]	validation-logloss:0.51390	validation-auc:0.96783	validation-aucpr:0.97153
[5]	validation-logloss:0.48639	validation-auc:0.96948	validation-aucpr:0.97304
[6]	validation-logloss:0.46163	validation-auc:0.97035	validation-aucpr:0.97373
[7]	validation-logloss:0.43963	validation-auc:0.97058	validation-aucpr:0.97403
[8]	validation-logloss:0.41950	validation-auc:0.97099	validation-aucpr:0.97475
[9]	validation-logloss:0.40118	validation-auc:0.97112	validation-aucpr:0.97479
[10]	validation-logloss:0.38650	validation-auc:0.97084	validation-aucpr:0.97436
[11]	validation-logloss:0.37254	validation-auc:0.97097	validation-aucpr:0.97435
[12]	validation-logloss:0.35792	validation-auc:0.97144	validation-aucpr:0.97482
[13]	validation-logloss:0.34459	validation-auc:0.97170	validation-aucpr:0.97503
[14]	validation-logloss:0.33389	validation-auc:0.97156	validation-aucpr:0.97489
[15]	validation-logloss:0.32223	validation-auc:0.97189	validation-aucpr:0.97520
[16]	validation-logloss:0.31181	validation-auc:0.97223	validation-aucpr:0.97517
[17]	validation-logloss:0.30233	validation-auc:0.97223	validation-aucpr:0.97517
[18]	validation-logloss:0.29361	validation-auc:0.97226	validation-aucpr:0.97521
[19]	validation-logloss:0.28562	validation-auc:0.97217	validation-aucpr:0.97516
[20]	validation-logloss:0.27810	validation-auc:0.97242	validation-aucpr:0.97536
[21]	validation-logloss:0.27126	validation-auc:0.97241	validation-aucpr:0.97532
[22]	validation-logloss:0.26546	validation-auc:0.97249	validation-aucpr:0.97535
[23]	validation-logloss:0.26019	validation-auc:0.97277	validation-aucpr:0.97551
[24]	validation-logloss:0.25444	validation-auc:0.97282	validation-aucpr:0.97552
[25]	validation-logloss:0.25027	validation-auc:0.97260	validation-aucpr:0.97533
[26]	validation-logloss:0.24577	validation-auc:0.97270	validation-aucpr:0.97539
[27]	validation-logloss:0.24190	validation-auc:0.97264	validation-aucpr:0.97534
[28]	validation-logloss:0.23761	validation-auc:0.97258	validation-aucpr:0.97543
[29]	validation-logloss:0.23434	validation-auc:0.97240	validation-aucpr:0.97531
[30]	validation-logloss:0.23048	validation-auc:0.97250	validation-aucpr:0.97561
[31]	validation-logloss:0.22696	validation-auc:0.97247	validation-aucpr:0.97559
[32]	validation-logloss:0.22367	validation-auc:0.97251	validation-aucpr:0.97562
[33]	validation-logloss:0.22111	validation-auc:0.97261	validation-aucpr:0.97567
[34]	validation-logloss:0.21842	validation-auc:0.97280	validation-aucpr:0.97580
[35]	validation-logloss:0.21641	validation-auc:0.97269	validation-aucpr:0.97571
[36]	validation-logloss:0.21438	validation-auc:0.97265	validation-aucpr:0.97570
[37]	validation-logloss:0.21240	validation-auc:0.97270	validation-aucpr:0.97575
[38]	validation-logloss:0.21037	validation-auc:0.97278	validation-aucpr:0.97583
[39]	validation-logloss:0.20847	validation-auc:0.97294	validation-aucpr:0.97595
[40]	validation-logloss:0.20676	validation-auc:0.97300	validation-aucpr:0.97601
[41]	validation-logloss:0.20475	validation-auc:0.97308	validation-aucpr:0.97608
[42]	validation-logloss:0.20273	validation-auc:0.97312	validation-aucpr:0.97613
[43]	validation-logloss:0.20166	validation-auc:0.97301	validation-aucpr:0.97605
[44]	validation-logloss:0.19983	validation-auc:0.97318	validation-aucpr:0.97619
[45]	validation-logloss:0.19863	validation-auc:0.97321	validation-aucpr:0.97622
[46]	validation-logloss:0.19721	validation-auc:0.97331	validation-aucpr:0.97628
[47]	validation-logloss:0.19570	validation-auc:0.97342	validation-aucpr:0.97635
[48]	validation-logloss:0.19442	validation-auc:0.97350	validation-aucpr:0.97645
[49]	validation-logloss:0.19342	validation-auc:0.97356	validation-aucpr:0.97650
[50]	validation-logloss:0.19208	validation-auc:0.97368	validation-aucpr:0.97669
[51]	validation-logloss:0.19119	validation-auc:0.97372	validation-aucpr:0.97671
[52]	validation-logloss:0.19047	validation-auc:0.97377	validation-aucpr:0.97674
[53]	validation-logloss:0.18961	validation-auc:0.97380	validation-aucpr:0.97678
[54]	validation-logloss:0.18924	validation-auc:0.97378	validation-aucpr:0.97677
[55]	validation-logloss:0.18846	validation-auc:0.97384	validation-aucpr:0.97683
[56]	validation-logloss:0.18736	validation-auc:0.97403	validation-aucpr:0.97698
[57]	validation-logloss:0.18648	validation-auc:0.97412	validation-aucpr:0.97703
[58]	validation-logloss:0.18609	validation-auc:0.97403	validation-aucpr:0.97690
[59]	validation-logloss:0.18595	validation-auc:0.97391	validation-aucpr:0.97683
[60]	validation-logloss:0.18555	validation-auc:0.97395	validation-aucpr:0.97693
[61]	validation-logloss:0.18504	validation-auc:0.97390	validation-aucpr:0.97629
[62]	validation-logloss:0.18472	validation-auc:0.97388	validation-aucpr:0.97627
[63]	validation-logloss:0.18416	validation-auc:0.97390	validation-aucpr:0.97619
[64]	validation-logloss:0.18404	validation-auc:0.97383	validation-aucpr:0.97600
[65]	validation-logloss:0.18367	validation-auc:0.97382	validation-aucpr:0.97597
[66]	validation-logloss:0.18338	validation-auc:0.97383	validation-aucpr:0.97602
[67]	validation-logloss:0.18301	validation-auc:0.97382	validation-aucpr:0.97591
[68]	validation-logloss:0.18276	validation-auc:0.97383	validation-aucpr:0.97589
[69]	validation-logloss:0.18241	validation-auc:0.97393	validation-aucpr:0.97594
[70]	validation-logloss:0.18215	validation-auc:0.97391	validation-aucpr:0.97590
[71]	validation-logloss:0.18196	validation-auc:0.97397	validation-aucpr:0.97656
[72]	validation-logloss:0.18172	validation-auc:0.97394	validation-aucpr:0.97627
{'best_iteration': '57', 'best_score': '0.9770323608248587'}
Trial 76, Fold 2: Log loss = 0.18172125853299292, Average precision = 0.9762585221188115, ROC-AUC = 0.9739378489630013, Elapsed Time = 3.2547775999992155 seconds
Trial 76, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 76, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[0]	validation-logloss:0.64618	validation-auc:0.94209	validation-aucpr:0.91996
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[2]	validation-logloss:0.57157	validation-auc:0.96142	validation-aucpr:0.96026
[3]	validation-logloss:0.54184	validation-auc:0.96483	validation-aucpr:0.96469
[4]	validation-logloss:0.51496	validation-auc:0.96569	validation-aucpr:0.96904
[5]	validation-logloss:0.48797	validation-auc:0.96747	validation-aucpr:0.97111
[6]	validation-logloss:0.46349	validation-auc:0.96825	validation-aucpr:0.96973
[7]	validation-logloss:0.44460	validation-auc:0.96793	validation-aucpr:0.97005
[8]	validation-logloss:0.42620	validation-auc:0.96820	validation-aucpr:0.97088
[9]	validation-logloss:0.40728	validation-auc:0.96878	validation-aucpr:0.97074
[10]	validation-logloss:0.39298	validation-auc:0.96857	validation-aucpr:0.96968
[11]	validation-logloss:0.37665	validation-auc:0.96890	validation-aucpr:0.97021
[12]	validation-logloss:0.36352	validation-auc:0.96897	validation-aucpr:0.97048
[13]	validation-logloss:0.35209	validation-auc:0.96927	validation-aucpr:0.97079
[14]	validation-logloss:0.33936	validation-auc:0.96943	validation-aucpr:0.97088
[15]	validation-logloss:0.32925	validation-auc:0.96986	validation-aucpr:0.97121
[16]	validation-logloss:0.31842	validation-auc:0.97015	validation-aucpr:0.97058
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[20]	validation-logloss:0.28526	validation-auc:0.97080	validation-aucpr:0.97218
[21]	validation-logloss:0.27761	validation-auc:0.97107	validation-aucpr:0.97264
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[24]	validation-logloss:0.25865	validation-auc:0.97135	validation-aucpr:0.97273
[25]	validation-logloss:0.25437	validation-auc:0.97123	validation-aucpr:0.97269
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[36]	validation-logloss:0.21710	validation-auc:0.97230	validation-aucpr:0.97614
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[45]	validation-logloss:0.20141	validation-auc:0.97298	validation-aucpr:0.97674
[46]	validation-logloss:0.20023	validation-auc:0.97290	validation-aucpr:0.97667
[47]	validation-logloss:0.19882	validation-auc:0.97301	validation-aucpr:0.97677
[48]	validation-logloss:0.19801	validation-auc:0.97303	validation-aucpr:0.97676
[49]	validation-logloss:0.19686	validation-auc:0.97310	validation-aucpr:0.97682
[50]	validation-logloss:0.19611	validation-auc:0.97319	validation-aucpr:0.97688
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[53]	validation-logloss:0.19429	validation-auc:0.97324	validation-aucpr:0.97692
[54]	validation-logloss:0.19368	validation-auc:0.97328	validation-aucpr:0.97694
[55]	validation-logloss:0.19292	validation-auc:0.97329	validation-aucpr:0.97692
[56]	validation-logloss:0.19235	validation-auc:0.97324	validation-aucpr:0.97688
[57]	validation-logloss:0.19209	validation-auc:0.97321	validation-aucpr:0.97685
[58]	validation-logloss:0.19150	validation-auc:0.97318	validation-aucpr:0.97676
[59]	validation-logloss:0.19086	validation-auc:0.97325	validation-aucpr:0.97683
[60]	validation-logloss:0.19028	validation-auc:0.97327	validation-aucpr:0.97685
[61]	validation-logloss:0.18947	validation-auc:0.97347	validation-aucpr:0.97700
[62]	validation-logloss:0.18888	validation-auc:0.97358	validation-aucpr:0.97709
[63]	validation-logloss:0.18880	validation-auc:0.97356	validation-aucpr:0.97707
[64]	validation-logloss:0.18843	validation-auc:0.97360	validation-aucpr:0.97708
[65]	validation-logloss:0.18815	validation-auc:0.97360	validation-aucpr:0.97706
[66]	validation-logloss:0.18784	validation-auc:0.97362	validation-aucpr:0.97702
[67]	validation-logloss:0.18781	validation-auc:0.97360	validation-aucpr:0.97701
[68]	validation-logloss:0.18744	validation-auc:0.97360	validation-aucpr:0.97694
[69]	validation-logloss:0.18704	validation-auc:0.97363	validation-aucpr:0.97684
[70]	validation-logloss:0.18681	validation-auc:0.97365	validation-aucpr:0.97685
[71]	validation-logloss:0.18665	validation-auc:0.97371	validation-aucpr:0.97689
[72]	validation-logloss:0.18657	validation-auc:0.97372	validation-aucpr:0.97690
{'best_iteration': '62', 'best_score': '0.977090692573389'}
Trial 76, Fold 3: Log loss = 0.18657246436692548, Average precision = 0.9769053106660622, ROC-AUC = 0.9737197645693483, Elapsed Time = 3.1280955000001995 seconds
Trial 76, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 76, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[0]	validation-logloss:0.64659	validation-auc:0.93764	validation-aucpr:0.92147
[1]	validation-logloss:0.60502	validation-auc:0.95930	validation-aucpr:0.95793
[2]	validation-logloss:0.57194	validation-auc:0.96263	validation-aucpr:0.96390
[3]	validation-logloss:0.53894	validation-auc:0.96500	validation-aucpr:0.96913
[4]	validation-logloss:0.50959	validation-auc:0.96607	validation-aucpr:0.96995
[5]	validation-logloss:0.48599	validation-auc:0.96667	validation-aucpr:0.97177
[6]	validation-logloss:0.46111	validation-auc:0.96792	validation-aucpr:0.97280
[7]	validation-logloss:0.44152	validation-auc:0.96790	validation-aucpr:0.97294
[8]	validation-logloss:0.42174	validation-auc:0.96815	validation-aucpr:0.97308
[9]	validation-logloss:0.40373	validation-auc:0.96839	validation-aucpr:0.97337
[10]	validation-logloss:0.38711	validation-auc:0.96869	validation-aucpr:0.97357
[11]	validation-logloss:0.37177	validation-auc:0.96899	validation-aucpr:0.97384
[12]	validation-logloss:0.35759	validation-auc:0.96949	validation-aucpr:0.97417
[13]	validation-logloss:0.34603	validation-auc:0.96966	validation-aucpr:0.97439
[14]	validation-logloss:0.33556	validation-auc:0.96959	validation-aucpr:0.97433
[15]	validation-logloss:0.32472	validation-auc:0.96965	validation-aucpr:0.97439
[16]	validation-logloss:0.31410	validation-auc:0.97007	validation-aucpr:0.97476
[17]	validation-logloss:0.30594	validation-auc:0.97003	validation-aucpr:0.97471
[18]	validation-logloss:0.29787	validation-auc:0.97028	validation-aucpr:0.97485
[19]	validation-logloss:0.29105	validation-auc:0.97005	validation-aucpr:0.97471
[20]	validation-logloss:0.28309	validation-auc:0.97055	validation-aucpr:0.97511
[21]	validation-logloss:0.27581	validation-auc:0.97072	validation-aucpr:0.97526
[22]	validation-logloss:0.27026	validation-auc:0.97068	validation-aucpr:0.97524
[23]	validation-logloss:0.26511	validation-auc:0.97075	validation-aucpr:0.97528
[24]	validation-logloss:0.26040	validation-auc:0.97078	validation-aucpr:0.97528
[25]	validation-logloss:0.25613	validation-auc:0.97061	validation-aucpr:0.97514
[26]	validation-logloss:0.25064	validation-auc:0.97101	validation-aucpr:0.97545
[27]	validation-logloss:0.24679	validation-auc:0.97109	validation-aucpr:0.97550
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[29]	validation-logloss:0.23924	validation-auc:0.97112	validation-aucpr:0.97556
[30]	validation-logloss:0.23540	validation-auc:0.97110	validation-aucpr:0.97556
[31]	validation-logloss:0.23229	validation-auc:0.97122	validation-aucpr:0.97562
[32]	validation-logloss:0.22890	validation-auc:0.97123	validation-aucpr:0.97562
[33]	validation-logloss:0.22648	validation-auc:0.97112	validation-aucpr:0.97558
[34]	validation-logloss:0.22369	validation-auc:0.97105	validation-aucpr:0.97553
[35]	validation-logloss:0.22082	validation-auc:0.97119	validation-aucpr:0.97565
[36]	validation-logloss:0.21816	validation-auc:0.97136	validation-aucpr:0.97577
[37]	validation-logloss:0.21624	validation-auc:0.97142	validation-aucpr:0.97582
[38]	validation-logloss:0.21421	validation-auc:0.97138	validation-aucpr:0.97580
[39]	validation-logloss:0.21197	validation-auc:0.97148	validation-aucpr:0.97590
[40]	validation-logloss:0.21039	validation-auc:0.97161	validation-aucpr:0.97597
[41]	validation-logloss:0.20841	validation-auc:0.97182	validation-aucpr:0.97613
[42]	validation-logloss:0.20650	validation-auc:0.97197	validation-aucpr:0.97628
[43]	validation-logloss:0.20530	validation-auc:0.97199	validation-aucpr:0.97631
[44]	validation-logloss:0.20385	validation-auc:0.97198	validation-aucpr:0.97632
[45]	validation-logloss:0.20265	validation-auc:0.97202	validation-aucpr:0.97634
[46]	validation-logloss:0.20146	validation-auc:0.97200	validation-aucpr:0.97630
[47]	validation-logloss:0.20000	validation-auc:0.97225	validation-aucpr:0.97650
[48]	validation-logloss:0.19882	validation-auc:0.97229	validation-aucpr:0.97654
[49]	validation-logloss:0.19821	validation-auc:0.97220	validation-aucpr:0.97646
[50]	validation-logloss:0.19717	validation-auc:0.97227	validation-aucpr:0.97651
[51]	validation-logloss:0.19675	validation-auc:0.97223	validation-aucpr:0.97648
[52]	validation-logloss:0.19603	validation-auc:0.97231	validation-aucpr:0.97653
[53]	validation-logloss:0.19545	validation-auc:0.97238	validation-aucpr:0.97656
[54]	validation-logloss:0.19468	validation-auc:0.97244	validation-aucpr:0.97660
[55]	validation-logloss:0.19380	validation-auc:0.97252	validation-aucpr:0.97666
[56]	validation-logloss:0.19348	validation-auc:0.97249	validation-aucpr:0.97662
[57]	validation-logloss:0.19285	validation-auc:0.97254	validation-aucpr:0.97664
[58]	validation-logloss:0.19217	validation-auc:0.97260	validation-aucpr:0.97668
[59]	validation-logloss:0.19195	validation-auc:0.97258	validation-aucpr:0.97667
[60]	validation-logloss:0.19129	validation-auc:0.97267	validation-aucpr:0.97673
[61]	validation-logloss:0.19107	validation-auc:0.97262	validation-aucpr:0.97670
[62]	validation-logloss:0.19046	validation-auc:0.97270	validation-aucpr:0.97676
[63]	validation-logloss:0.19013	validation-auc:0.97268	validation-aucpr:0.97675
[64]	validation-logloss:0.18967	validation-auc:0.97274	validation-aucpr:0.97679
[65]	validation-logloss:0.18939	validation-auc:0.97272	validation-aucpr:0.97679
[66]	validation-logloss:0.18891	validation-auc:0.97281	validation-aucpr:0.97687
[67]	validation-logloss:0.18854	validation-auc:0.97287	validation-aucpr:0.97690
[68]	validation-logloss:0.18828	validation-auc:0.97288	validation-aucpr:0.97690
[69]	validation-logloss:0.18805	validation-auc:0.97292	validation-aucpr:0.97692
[70]	validation-logloss:0.18774	validation-auc:0.97300	validation-aucpr:0.97697
[71]	validation-logloss:0.18749	validation-auc:0.97302	validation-aucpr:0.97698
[72]	validation-logloss:0.18729	validation-auc:0.97305	validation-aucpr:0.97700
{'best_iteration': '72', 'best_score': '0.9770020497729272'}
Trial 76, Fold 4: Log loss = 0.18728633940029324, Average precision = 0.9770056518527942, ROC-AUC = 0.9730516242599385, Elapsed Time = 2.883299500001158 seconds
Trial 76, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 76, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[0]	validation-logloss:0.64594	validation-auc:0.94709	validation-aucpr:0.92992
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[2]	validation-logloss:0.57336	validation-auc:0.96311	validation-aucpr:0.96487
[3]	validation-logloss:0.54018	validation-auc:0.96505	validation-aucpr:0.96831
[4]	validation-logloss:0.51340	validation-auc:0.96578	validation-aucpr:0.96861
[5]	validation-logloss:0.48612	validation-auc:0.96710	validation-aucpr:0.96967
[6]	validation-logloss:0.46197	validation-auc:0.96746	validation-aucpr:0.96999
[7]	validation-logloss:0.44051	validation-auc:0.96773	validation-aucpr:0.97035
[8]	validation-logloss:0.41998	validation-auc:0.96834	validation-aucpr:0.97097
[9]	validation-logloss:0.40408	validation-auc:0.96789	validation-aucpr:0.97060
[10]	validation-logloss:0.38930	validation-auc:0.96816	validation-aucpr:0.97255
[11]	validation-logloss:0.37574	validation-auc:0.96845	validation-aucpr:0.97268
[12]	validation-logloss:0.36118	validation-auc:0.96933	validation-aucpr:0.97334
[13]	validation-logloss:0.34984	validation-auc:0.96969	validation-aucpr:0.97361
[14]	validation-logloss:0.33777	validation-auc:0.97013	validation-aucpr:0.97401
[15]	validation-logloss:0.32650	validation-auc:0.97030	validation-aucpr:0.97411
[16]	validation-logloss:0.31739	validation-auc:0.97023	validation-aucpr:0.97403
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[21]	validation-logloss:0.28001	validation-auc:0.97012	validation-aucpr:0.97411
[22]	validation-logloss:0.27424	validation-auc:0.97037	validation-aucpr:0.97430
[23]	validation-logloss:0.26793	validation-auc:0.97078	validation-aucpr:0.97452
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[25]	validation-logloss:0.25717	validation-auc:0.97115	validation-aucpr:0.97469
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[62]	validation-logloss:0.19668	validation-auc:0.97205	validation-aucpr:0.97530
[63]	validation-logloss:0.19610	validation-auc:0.97211	validation-aucpr:0.97533
[64]	validation-logloss:0.19541	validation-auc:0.97221	validation-aucpr:0.97537
[65]	validation-logloss:0.19489	validation-auc:0.97238	validation-aucpr:0.97573
[66]	validation-logloss:0.19482	validation-auc:0.97234	validation-aucpr:0.97567
[67]	validation-logloss:0.19435	validation-auc:0.97239	validation-aucpr:0.97570
[68]	validation-logloss:0.19395	validation-auc:0.97242	validation-aucpr:0.97569
[69]	validation-logloss:0.19384	validation-auc:0.97243	validation-aucpr:0.97567
[70]	validation-logloss:0.19325	validation-auc:0.97259	validation-aucpr:0.97575
[71]	validation-logloss:0.19291	validation-auc:0.97266	validation-aucpr:0.97588
[72]	validation-logloss:0.19265	validation-auc:0.97267	validation-aucpr:0.97589
{'best_iteration': '72', 'best_score': '0.9758881601556328'}
Trial 76, Fold 5: Log loss = 0.19264611417775424, Average precision = 0.9758934275824167, ROC-AUC = 0.9726742099102614, Elapsed Time = 2.890385700000479 seconds
Optimization Progress:  77%|#######7  | 77/100 [3:28:19<20:32, 53.57s/it]
Trial 77, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 77, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[21:27:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.64124	validation-auc:0.94910	validation-aucpr:0.95290
[21:27:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.59675	validation-auc:0.95926	validation-aucpr:0.95782
[21:27:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.55690	validation-auc:0.96340	validation-aucpr:0.96624
[21:27:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.52630	validation-auc:0.96339	validation-aucpr:0.96582
[21:27:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.49429	validation-auc:0.96532	validation-aucpr:0.97072
[21:27:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5]	validation-logloss:0.46657	validation-auc:0.96587	validation-aucpr:0.97128
[21:27:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6]	validation-logloss:0.44524	validation-auc:0.96575	validation-aucpr:0.97127
[21:27:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7]	validation-logloss:0.42281	validation-auc:0.96566	validation-aucpr:0.97133
[21:27:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8]	validation-logloss:0.40169	validation-auc:0.96646	validation-aucpr:0.97201
[21:27:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9]	validation-logloss:0.38353	validation-auc:0.96657	validation-aucpr:0.97228
[21:27:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10]	validation-logloss:0.36888	validation-auc:0.96677	validation-aucpr:0.97232
[21:27:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11]	validation-logloss:0.35346	validation-auc:0.96710	validation-aucpr:0.97270
[21:27:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12]	validation-logloss:0.33982	validation-auc:0.96740	validation-aucpr:0.97293
[21:27:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13]	validation-logloss:0.32805	validation-auc:0.96748	validation-aucpr:0.97297
[21:27:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14]	validation-logloss:0.31919	validation-auc:0.96714	validation-aucpr:0.97255
[21:27:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15]	validation-logloss:0.31018	validation-auc:0.96737	validation-aucpr:0.97268
[21:27:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16]	validation-logloss:0.30094	validation-auc:0.96755	validation-aucpr:0.97280
[21:27:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17]	validation-logloss:0.29209	validation-auc:0.96757	validation-aucpr:0.97283
[21:27:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18]	validation-logloss:0.28461	validation-auc:0.96795	validation-aucpr:0.97308
[21:27:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19]	validation-logloss:0.27727	validation-auc:0.96816	validation-aucpr:0.97319
[21:27:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20]	validation-logloss:0.27078	validation-auc:0.96829	validation-aucpr:0.97325
[21:27:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21]	validation-logloss:0.26427	validation-auc:0.96878	validation-aucpr:0.97368
[21:27:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22]	validation-logloss:0.25896	validation-auc:0.96872	validation-aucpr:0.97359
[21:27:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23]	validation-logloss:0.25461	validation-auc:0.96873	validation-aucpr:0.97364
[21:27:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24]	validation-logloss:0.25072	validation-auc:0.96865	validation-aucpr:0.97356
[21:27:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25]	validation-logloss:0.24637	validation-auc:0.96885	validation-aucpr:0.97376
[21:27:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26]	validation-logloss:0.24208	validation-auc:0.96912	validation-aucpr:0.97392
[21:27:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27]	validation-logloss:0.23809	validation-auc:0.96920	validation-aucpr:0.97398
[21:27:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28]	validation-logloss:0.23496	validation-auc:0.96919	validation-aucpr:0.97395
[21:27:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29]	validation-logloss:0.23216	validation-auc:0.96917	validation-aucpr:0.97398
[21:27:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30]	validation-logloss:0.22992	validation-auc:0.96904	validation-aucpr:0.97387
[21:27:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31]	validation-logloss:0.22818	validation-auc:0.96874	validation-aucpr:0.97278
[21:27:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32]	validation-logloss:0.22556	validation-auc:0.96893	validation-aucpr:0.97293
[21:27:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33]	validation-logloss:0.22341	validation-auc:0.96901	validation-aucpr:0.97297
[21:27:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34]	validation-logloss:0.22187	validation-auc:0.96892	validation-aucpr:0.97290
[21:27:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35]	validation-logloss:0.22019	validation-auc:0.96889	validation-aucpr:0.97289
[21:27:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36]	validation-logloss:0.21836	validation-auc:0.96900	validation-aucpr:0.97297
[21:27:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37]	validation-logloss:0.21702	validation-auc:0.96911	validation-aucpr:0.97398
[21:27:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38]	validation-logloss:0.21616	validation-auc:0.96908	validation-aucpr:0.97396
[21:27:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39]	validation-logloss:0.21498	validation-auc:0.96915	validation-aucpr:0.97400
[21:27:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40]	validation-logloss:0.21394	validation-auc:0.96921	validation-aucpr:0.97400
[21:27:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41]	validation-logloss:0.21258	validation-auc:0.96940	validation-aucpr:0.97419
[21:27:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42]	validation-logloss:0.21167	validation-auc:0.96935	validation-aucpr:0.97416
[21:27:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43]	validation-logloss:0.21047	validation-auc:0.96956	validation-aucpr:0.97432
[21:27:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44]	validation-logloss:0.20967	validation-auc:0.96958	validation-aucpr:0.97431
[21:27:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45]	validation-logloss:0.20897	validation-auc:0.96965	validation-aucpr:0.97435
[21:27:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46]	validation-logloss:0.20833	validation-auc:0.96972	validation-aucpr:0.97439
[21:27:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[47]	validation-logloss:0.20764	validation-auc:0.96980	validation-aucpr:0.97450
[21:27:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[48]	validation-logloss:0.20710	validation-auc:0.96982	validation-aucpr:0.97459
[21:27:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[49]	validation-logloss:0.20713	validation-auc:0.96972	validation-aucpr:0.97450
[21:27:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[50]	validation-logloss:0.20682	validation-auc:0.96979	validation-aucpr:0.97454
[21:27:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[51]	validation-logloss:0.20640	validation-auc:0.96980	validation-aucpr:0.97460
[21:27:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[52]	validation-logloss:0.20576	validation-auc:0.96987	validation-aucpr:0.97469
[21:27:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[53]	validation-logloss:0.20552	validation-auc:0.96992	validation-aucpr:0.97471
[21:27:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[54]	validation-logloss:0.20507	validation-auc:0.97011	validation-aucpr:0.97483
[21:27:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[55]	validation-logloss:0.20495	validation-auc:0.97007	validation-aucpr:0.97480
[21:27:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[56]	validation-logloss:0.20468	validation-auc:0.97001	validation-aucpr:0.97478
[21:27:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[57]	validation-logloss:0.20445	validation-auc:0.97007	validation-aucpr:0.97480
[21:27:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[58]	validation-logloss:0.20439	validation-auc:0.97007	validation-aucpr:0.97479
[21:27:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[59]	validation-logloss:0.20412	validation-auc:0.97010	validation-aucpr:0.97479
[21:27:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[60]	validation-logloss:0.20385	validation-auc:0.97023	validation-aucpr:0.97489
[21:27:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[61]	validation-logloss:0.20420	validation-auc:0.97013	validation-aucpr:0.97483
[21:27:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[62]	validation-logloss:0.20448	validation-auc:0.97007	validation-aucpr:0.97476
[21:27:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[63]	validation-logloss:0.20443	validation-auc:0.97011	validation-aucpr:0.97480
[21:27:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[64]	validation-logloss:0.20463	validation-auc:0.97012	validation-aucpr:0.97480
[21:27:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[65]	validation-logloss:0.20464	validation-auc:0.97011	validation-aucpr:0.97481
[21:27:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[66]	validation-logloss:0.20489	validation-auc:0.97001	validation-aucpr:0.97472
[21:27:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[67]	validation-logloss:0.20531	validation-auc:0.96993	validation-aucpr:0.97466
[21:27:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[68]	validation-logloss:0.20533	validation-auc:0.97004	validation-aucpr:0.97474
[21:27:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[69]	validation-logloss:0.20556	validation-auc:0.97008	validation-aucpr:0.97478
[21:27:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[70]	validation-logloss:0.20598	validation-auc:0.96997	validation-aucpr:0.97471
[21:27:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[71]	validation-logloss:0.20631	validation-auc:0.96991	validation-aucpr:0.97469
[21:27:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[72]	validation-logloss:0.20650	validation-auc:0.96992	validation-aucpr:0.97470
[21:27:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[73]	validation-logloss:0.20667	validation-auc:0.96998	validation-aucpr:0.97474
{'best_iteration': '60', 'best_score': '0.9748918052690786'}
Trial 77, Fold 1: Log loss = 0.20666904008919706, Average precision = 0.9747405903084637, ROC-AUC = 0.9699798480568579, Elapsed Time = 17.862047299997357 seconds
Trial 77, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 77, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[21:27:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.63982	validation-auc:0.95206	validation-aucpr:0.94168
[21:27:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.59510	validation-auc:0.96174	validation-aucpr:0.96048
[21:27:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.55920	validation-auc:0.96427	validation-aucpr:0.96810
[21:27:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.52369	validation-auc:0.96565	validation-aucpr:0.96799
[21:27:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.49574	validation-auc:0.96634	validation-aucpr:0.97002
[21:27:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5]	validation-logloss:0.46684	validation-auc:0.96787	validation-aucpr:0.97141
[21:27:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6]	validation-logloss:0.44162	validation-auc:0.96820	validation-aucpr:0.97189
[21:27:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7]	validation-logloss:0.42232	validation-auc:0.96779	validation-aucpr:0.97148
[21:27:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8]	validation-logloss:0.40197	validation-auc:0.96833	validation-aucpr:0.97181
[21:27:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9]	validation-logloss:0.38408	validation-auc:0.96835	validation-aucpr:0.97189
[21:27:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10]	validation-logloss:0.36711	validation-auc:0.96926	validation-aucpr:0.97262
[21:27:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11]	validation-logloss:0.35153	validation-auc:0.96962	validation-aucpr:0.97289
[21:27:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12]	validation-logloss:0.33790	validation-auc:0.96951	validation-aucpr:0.97287
[21:27:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13]	validation-logloss:0.32695	validation-auc:0.96956	validation-aucpr:0.97287
[21:27:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14]	validation-logloss:0.31540	validation-auc:0.96969	validation-aucpr:0.97313
[21:27:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15]	validation-logloss:0.30624	validation-auc:0.96981	validation-aucpr:0.97323
[21:27:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16]	validation-logloss:0.29676	validation-auc:0.96988	validation-aucpr:0.97329
[21:27:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17]	validation-logloss:0.28729	validation-auc:0.97045	validation-aucpr:0.97365
[21:27:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18]	validation-logloss:0.27911	validation-auc:0.97067	validation-aucpr:0.97382
[21:27:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19]	validation-logloss:0.27139	validation-auc:0.97089	validation-aucpr:0.97400
[21:27:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20]	validation-logloss:0.26394	validation-auc:0.97127	validation-aucpr:0.97431
[21:27:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21]	validation-logloss:0.25736	validation-auc:0.97158	validation-aucpr:0.97449
[21:27:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22]	validation-logloss:0.25109	validation-auc:0.97186	validation-aucpr:0.97469
[21:27:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23]	validation-logloss:0.24653	validation-auc:0.97176	validation-aucpr:0.97456
[21:27:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24]	validation-logloss:0.24181	validation-auc:0.97197	validation-aucpr:0.97475
[21:27:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25]	validation-logloss:0.23683	validation-auc:0.97206	validation-aucpr:0.97486
[21:27:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26]	validation-logloss:0.23278	validation-auc:0.97201	validation-aucpr:0.97505
[21:27:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27]	validation-logloss:0.22876	validation-auc:0.97204	validation-aucpr:0.97507
[21:27:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28]	validation-logloss:0.22594	validation-auc:0.97197	validation-aucpr:0.97499
[21:27:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29]	validation-logloss:0.22266	validation-auc:0.97187	validation-aucpr:0.97491
[21:27:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30]	validation-logloss:0.21937	validation-auc:0.97199	validation-aucpr:0.97504
[21:27:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31]	validation-logloss:0.21704	validation-auc:0.97178	validation-aucpr:0.97450
[21:27:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32]	validation-logloss:0.21484	validation-auc:0.97169	validation-aucpr:0.97442
[21:27:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33]	validation-logloss:0.21261	validation-auc:0.97189	validation-aucpr:0.97460
[21:27:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34]	validation-logloss:0.21034	validation-auc:0.97192	validation-aucpr:0.97461
[21:27:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35]	validation-logloss:0.20812	validation-auc:0.97202	validation-aucpr:0.97465
[21:27:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36]	validation-logloss:0.20627	validation-auc:0.97203	validation-aucpr:0.97475
[21:27:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37]	validation-logloss:0.20410	validation-auc:0.97220	validation-aucpr:0.97485
[21:27:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38]	validation-logloss:0.20256	validation-auc:0.97226	validation-aucpr:0.97485
[21:27:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39]	validation-logloss:0.20162	validation-auc:0.97220	validation-aucpr:0.97476
[21:27:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40]	validation-logloss:0.20080	validation-auc:0.97212	validation-aucpr:0.97473
[21:27:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41]	validation-logloss:0.19963	validation-auc:0.97205	validation-aucpr:0.97470
[21:27:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42]	validation-logloss:0.19851	validation-auc:0.97219	validation-aucpr:0.97470
[21:27:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43]	validation-logloss:0.19726	validation-auc:0.97221	validation-aucpr:0.97464
[21:27:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44]	validation-logloss:0.19623	validation-auc:0.97226	validation-aucpr:0.97467
[21:27:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45]	validation-logloss:0.19520	validation-auc:0.97231	validation-aucpr:0.97449
[21:27:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46]	validation-logloss:0.19427	validation-auc:0.97242	validation-aucpr:0.97498
[21:27:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[47]	validation-logloss:0.19366	validation-auc:0.97235	validation-aucpr:0.97490
[21:27:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[48]	validation-logloss:0.19305	validation-auc:0.97229	validation-aucpr:0.97507
[21:27:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[49]	validation-logloss:0.19242	validation-auc:0.97234	validation-aucpr:0.97512
[21:27:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[50]	validation-logloss:0.19197	validation-auc:0.97234	validation-aucpr:0.97497
[21:27:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[51]	validation-logloss:0.19120	validation-auc:0.97256	validation-aucpr:0.97510
[21:27:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[52]	validation-logloss:0.19063	validation-auc:0.97257	validation-aucpr:0.97511
[21:27:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[53]	validation-logloss:0.19044	validation-auc:0.97257	validation-aucpr:0.97508
[21:27:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[54]	validation-logloss:0.18966	validation-auc:0.97262	validation-aucpr:0.97514
[21:27:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[55]	validation-logloss:0.18946	validation-auc:0.97255	validation-aucpr:0.97510
[21:27:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[56]	validation-logloss:0.18899	validation-auc:0.97261	validation-aucpr:0.97515
[21:27:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[57]	validation-logloss:0.18876	validation-auc:0.97262	validation-aucpr:0.97453
[21:27:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[58]	validation-logloss:0.18818	validation-auc:0.97279	validation-aucpr:0.97452
[21:27:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[59]	validation-logloss:0.18800	validation-auc:0.97268	validation-aucpr:0.97436
[21:27:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[60]	validation-logloss:0.18768	validation-auc:0.97278	validation-aucpr:0.97457
[21:27:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[61]	validation-logloss:0.18733	validation-auc:0.97280	validation-aucpr:0.97455
[21:27:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[62]	validation-logloss:0.18702	validation-auc:0.97300	validation-aucpr:0.97566
[21:27:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[63]	validation-logloss:0.18684	validation-auc:0.97299	validation-aucpr:0.97563
[21:27:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[64]	validation-logloss:0.18671	validation-auc:0.97296	validation-aucpr:0.97573
[21:27:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[65]	validation-logloss:0.18660	validation-auc:0.97299	validation-aucpr:0.97587
[21:27:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[66]	validation-logloss:0.18659	validation-auc:0.97301	validation-aucpr:0.97581
[21:27:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[67]	validation-logloss:0.18641	validation-auc:0.97300	validation-aucpr:0.97577
[21:27:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[68]	validation-logloss:0.18650	validation-auc:0.97295	validation-aucpr:0.97565
[21:27:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[69]	validation-logloss:0.18637	validation-auc:0.97299	validation-aucpr:0.97565
[21:27:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[70]	validation-logloss:0.18649	validation-auc:0.97301	validation-aucpr:0.97569
[21:27:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[71]	validation-logloss:0.18668	validation-auc:0.97299	validation-aucpr:0.97563
[21:27:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[72]	validation-logloss:0.18699	validation-auc:0.97290	validation-aucpr:0.97542
[21:27:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[73]	validation-logloss:0.18704	validation-auc:0.97290	validation-aucpr:0.97538
{'best_iteration': '65', 'best_score': '0.9758713375134983'}
Trial 77, Fold 2: Log loss = 0.18703814435806546, Average precision = 0.9753815790402707, ROC-AUC = 0.9728992112453606, Elapsed Time = 18.450700099998357 seconds
Trial 77, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 77, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[21:27:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.64064	validation-auc:0.95030	validation-aucpr:0.94347
[21:27:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.59775	validation-auc:0.95936	validation-aucpr:0.95585
[21:27:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.56142	validation-auc:0.96249	validation-aucpr:0.96331
[21:27:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.52527	validation-auc:0.96578	validation-aucpr:0.96963
[21:27:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.49362	validation-auc:0.96684	validation-aucpr:0.96861
[21:27:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5]	validation-logloss:0.46545	validation-auc:0.96709	validation-aucpr:0.96766
[21:27:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6]	validation-logloss:0.44088	validation-auc:0.96728	validation-aucpr:0.96980
[21:27:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7]	validation-logloss:0.42082	validation-auc:0.96754	validation-aucpr:0.96976
[21:27:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8]	validation-logloss:0.40211	validation-auc:0.96831	validation-aucpr:0.97035
[21:27:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9]	validation-logloss:0.38525	validation-auc:0.96887	validation-aucpr:0.97070
[21:27:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10]	validation-logloss:0.37034	validation-auc:0.96902	validation-aucpr:0.97083
[21:27:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11]	validation-logloss:0.35419	validation-auc:0.96927	validation-aucpr:0.97099
[21:27:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12]	validation-logloss:0.33951	validation-auc:0.96979	validation-aucpr:0.97125
[21:27:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13]	validation-logloss:0.32854	validation-auc:0.96988	validation-aucpr:0.97128
[21:27:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14]	validation-logloss:0.31829	validation-auc:0.97010	validation-aucpr:0.97147
[21:27:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15]	validation-logloss:0.30711	validation-auc:0.97035	validation-aucpr:0.97172
[21:27:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16]	validation-logloss:0.29755	validation-auc:0.97019	validation-aucpr:0.97164
[21:27:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17]	validation-logloss:0.28795	validation-auc:0.97057	validation-aucpr:0.97186
[21:27:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18]	validation-logloss:0.27982	validation-auc:0.97077	validation-aucpr:0.97199
[21:27:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19]	validation-logloss:0.27195	validation-auc:0.97071	validation-aucpr:0.97213
[21:27:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20]	validation-logloss:0.26461	validation-auc:0.97088	validation-aucpr:0.97224
[21:27:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21]	validation-logloss:0.25850	validation-auc:0.97078	validation-aucpr:0.97215
[21:27:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22]	validation-logloss:0.25362	validation-auc:0.97092	validation-aucpr:0.97221
[21:27:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23]	validation-logloss:0.24877	validation-auc:0.97084	validation-aucpr:0.97199
[21:27:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24]	validation-logloss:0.24395	validation-auc:0.97121	validation-aucpr:0.97519
[21:27:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25]	validation-logloss:0.23963	validation-auc:0.97115	validation-aucpr:0.97515
[21:27:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26]	validation-logloss:0.23578	validation-auc:0.97095	validation-aucpr:0.97498
[21:27:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27]	validation-logloss:0.23217	validation-auc:0.97090	validation-aucpr:0.97494
[21:27:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28]	validation-logloss:0.22843	validation-auc:0.97122	validation-aucpr:0.97520
[21:27:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29]	validation-logloss:0.22485	validation-auc:0.97142	validation-aucpr:0.97532
[21:27:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30]	validation-logloss:0.22164	validation-auc:0.97161	validation-aucpr:0.97558
[21:27:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31]	validation-logloss:0.21937	validation-auc:0.97172	validation-aucpr:0.97564
[21:28:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32]	validation-logloss:0.21728	validation-auc:0.97175	validation-aucpr:0.97564
[21:28:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33]	validation-logloss:0.21538	validation-auc:0.97160	validation-aucpr:0.97554
[21:28:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34]	validation-logloss:0.21333	validation-auc:0.97164	validation-aucpr:0.97559
[21:28:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35]	validation-logloss:0.21184	validation-auc:0.97162	validation-aucpr:0.97556
[21:28:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36]	validation-logloss:0.20987	validation-auc:0.97176	validation-aucpr:0.97568
[21:28:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37]	validation-logloss:0.20825	validation-auc:0.97177	validation-aucpr:0.97571
[21:28:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38]	validation-logloss:0.20673	validation-auc:0.97190	validation-aucpr:0.97577
[21:28:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39]	validation-logloss:0.20513	validation-auc:0.97195	validation-aucpr:0.97581
[21:28:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40]	validation-logloss:0.20368	validation-auc:0.97196	validation-aucpr:0.97578
[21:28:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41]	validation-logloss:0.20266	validation-auc:0.97185	validation-aucpr:0.97571
[21:28:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42]	validation-logloss:0.20183	validation-auc:0.97185	validation-aucpr:0.97571
[21:28:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43]	validation-logloss:0.20085	validation-auc:0.97184	validation-aucpr:0.97572
[21:28:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44]	validation-logloss:0.19977	validation-auc:0.97193	validation-aucpr:0.97588
[21:28:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45]	validation-logloss:0.19894	validation-auc:0.97192	validation-aucpr:0.97587
[21:28:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46]	validation-logloss:0.19785	validation-auc:0.97199	validation-aucpr:0.97594
[21:28:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[47]	validation-logloss:0.19699	validation-auc:0.97212	validation-aucpr:0.97592
[21:28:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[48]	validation-logloss:0.19584	validation-auc:0.97226	validation-aucpr:0.97605
[21:28:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[49]	validation-logloss:0.19494	validation-auc:0.97240	validation-aucpr:0.97611
[21:28:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[50]	validation-logloss:0.19429	validation-auc:0.97243	validation-aucpr:0.97613
[21:28:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[51]	validation-logloss:0.19376	validation-auc:0.97251	validation-aucpr:0.97620
[21:28:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[52]	validation-logloss:0.19347	validation-auc:0.97253	validation-aucpr:0.97619
[21:28:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[53]	validation-logloss:0.19315	validation-auc:0.97257	validation-aucpr:0.97619
[21:28:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[54]	validation-logloss:0.19251	validation-auc:0.97267	validation-aucpr:0.97620
[21:28:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[55]	validation-logloss:0.19262	validation-auc:0.97260	validation-aucpr:0.97612
[21:28:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[56]	validation-logloss:0.19228	validation-auc:0.97267	validation-aucpr:0.97609
[21:28:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[57]	validation-logloss:0.19174	validation-auc:0.97279	validation-aucpr:0.97618
[21:28:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[58]	validation-logloss:0.19175	validation-auc:0.97272	validation-aucpr:0.97604
[21:28:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[59]	validation-logloss:0.19162	validation-auc:0.97274	validation-aucpr:0.97603
[21:28:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[60]	validation-logloss:0.19156	validation-auc:0.97272	validation-aucpr:0.97597
[21:28:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[61]	validation-logloss:0.19153	validation-auc:0.97271	validation-aucpr:0.97596
[21:28:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[62]	validation-logloss:0.19164	validation-auc:0.97263	validation-aucpr:0.97584
[21:28:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[63]	validation-logloss:0.19160	validation-auc:0.97265	validation-aucpr:0.97576
[21:28:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[64]	validation-logloss:0.19170	validation-auc:0.97265	validation-aucpr:0.97567
[21:28:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[65]	validation-logloss:0.19170	validation-auc:0.97267	validation-aucpr:0.97556
[21:28:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[66]	validation-logloss:0.19160	validation-auc:0.97266	validation-aucpr:0.97547
[21:28:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[67]	validation-logloss:0.19156	validation-auc:0.97271	validation-aucpr:0.97554
[21:28:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[68]	validation-logloss:0.19179	validation-auc:0.97268	validation-aucpr:0.97552
[21:28:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[69]	validation-logloss:0.19168	validation-auc:0.97279	validation-aucpr:0.97556
[21:28:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[70]	validation-logloss:0.19168	validation-auc:0.97282	validation-aucpr:0.97575
[21:28:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[71]	validation-logloss:0.19206	validation-auc:0.97274	validation-aucpr:0.97561
[21:28:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[72]	validation-logloss:0.19217	validation-auc:0.97279	validation-aucpr:0.97581
[21:28:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[73]	validation-logloss:0.19239	validation-auc:0.97276	validation-aucpr:0.97567
{'best_iteration': '51', 'best_score': '0.9761959525993174'}
Trial 77, Fold 3: Log loss = 0.1923887270627478, Average precision = 0.975678562191017, ROC-AUC = 0.9727598212479895, Elapsed Time = 18.685897399998794 seconds
Trial 77, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 77, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[21:28:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.64013	validation-auc:0.95125	validation-aucpr:0.95040
[21:28:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.59626	validation-auc:0.95914	validation-aucpr:0.96319
[21:28:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.55707	validation-auc:0.96243	validation-aucpr:0.96865
[21:28:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.52566	validation-auc:0.96284	validation-aucpr:0.96903
[21:28:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.49407	validation-auc:0.96478	validation-aucpr:0.97051
[21:28:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5]	validation-logloss:0.46892	validation-auc:0.96527	validation-aucpr:0.97100
[21:28:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6]	validation-logloss:0.44417	validation-auc:0.96540	validation-aucpr:0.97121
[21:28:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7]	validation-logloss:0.42383	validation-auc:0.96579	validation-aucpr:0.97156
[21:28:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8]	validation-logloss:0.40444	validation-auc:0.96615	validation-aucpr:0.97194
[21:28:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9]	validation-logloss:0.38775	validation-auc:0.96647	validation-aucpr:0.97221
[21:28:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10]	validation-logloss:0.37094	validation-auc:0.96684	validation-aucpr:0.97257
[21:28:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11]	validation-logloss:0.35772	validation-auc:0.96693	validation-aucpr:0.97252
[21:28:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12]	validation-logloss:0.34462	validation-auc:0.96700	validation-aucpr:0.97261
[21:28:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13]	validation-logloss:0.33165	validation-auc:0.96732	validation-aucpr:0.97289
[21:28:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14]	validation-logloss:0.31952	validation-auc:0.96784	validation-aucpr:0.97330
[21:28:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15]	validation-logloss:0.30864	validation-auc:0.96796	validation-aucpr:0.97340
[21:28:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16]	validation-logloss:0.29956	validation-auc:0.96796	validation-aucpr:0.97342
[21:28:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17]	validation-logloss:0.29048	validation-auc:0.96808	validation-aucpr:0.97352
[21:28:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18]	validation-logloss:0.28335	validation-auc:0.96802	validation-aucpr:0.97349
[21:28:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19]	validation-logloss:0.27546	validation-auc:0.96847	validation-aucpr:0.97385
[21:28:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20]	validation-logloss:0.26838	validation-auc:0.96876	validation-aucpr:0.97406
[21:28:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21]	validation-logloss:0.26266	validation-auc:0.96886	validation-aucpr:0.97414
[21:28:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22]	validation-logloss:0.25681	validation-auc:0.96910	validation-aucpr:0.97433
[21:28:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23]	validation-logloss:0.25150	validation-auc:0.96906	validation-aucpr:0.97430
[21:28:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24]	validation-logloss:0.24609	validation-auc:0.96944	validation-aucpr:0.97453
[21:28:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25]	validation-logloss:0.24177	validation-auc:0.96931	validation-aucpr:0.97447
[21:28:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26]	validation-logloss:0.23742	validation-auc:0.96954	validation-aucpr:0.97459
[21:28:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27]	validation-logloss:0.23340	validation-auc:0.96970	validation-aucpr:0.97472
[21:28:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28]	validation-logloss:0.23042	validation-auc:0.96939	validation-aucpr:0.97454
[21:28:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29]	validation-logloss:0.22666	validation-auc:0.96981	validation-aucpr:0.97482
[21:28:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30]	validation-logloss:0.22340	validation-auc:0.97017	validation-aucpr:0.97505
[21:28:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31]	validation-logloss:0.22031	validation-auc:0.97045	validation-aucpr:0.97527
[21:28:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32]	validation-logloss:0.21774	validation-auc:0.97064	validation-aucpr:0.97539
[21:28:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33]	validation-logloss:0.21522	validation-auc:0.97099	validation-aucpr:0.97562
[21:28:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34]	validation-logloss:0.21304	validation-auc:0.97106	validation-aucpr:0.97566
[21:28:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35]	validation-logloss:0.21174	validation-auc:0.97094	validation-aucpr:0.97559
[21:28:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36]	validation-logloss:0.20963	validation-auc:0.97109	validation-aucpr:0.97572
[21:28:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37]	validation-logloss:0.20797	validation-auc:0.97115	validation-aucpr:0.97578
[21:28:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38]	validation-logloss:0.20643	validation-auc:0.97114	validation-aucpr:0.97575
[21:28:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39]	validation-logloss:0.20468	validation-auc:0.97137	validation-aucpr:0.97591
[21:28:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40]	validation-logloss:0.20319	validation-auc:0.97156	validation-aucpr:0.97604
[21:28:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41]	validation-logloss:0.20203	validation-auc:0.97158	validation-aucpr:0.97605
[21:28:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42]	validation-logloss:0.20098	validation-auc:0.97157	validation-aucpr:0.97605
[21:28:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43]	validation-logloss:0.19991	validation-auc:0.97167	validation-aucpr:0.97612
[21:28:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44]	validation-logloss:0.19921	validation-auc:0.97167	validation-aucpr:0.97612
[21:28:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45]	validation-logloss:0.19885	validation-auc:0.97148	validation-aucpr:0.97597
[21:28:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46]	validation-logloss:0.19807	validation-auc:0.97158	validation-aucpr:0.97604
[21:28:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[47]	validation-logloss:0.19750	validation-auc:0.97163	validation-aucpr:0.97609
[21:28:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[48]	validation-logloss:0.19693	validation-auc:0.97164	validation-aucpr:0.97608
[21:28:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[49]	validation-logloss:0.19615	validation-auc:0.97174	validation-aucpr:0.97616
[21:28:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[50]	validation-logloss:0.19548	validation-auc:0.97183	validation-aucpr:0.97622
[21:28:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[51]	validation-logloss:0.19544	validation-auc:0.97169	validation-aucpr:0.97611
[21:28:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[52]	validation-logloss:0.19485	validation-auc:0.97177	validation-aucpr:0.97617
[21:28:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[53]	validation-logloss:0.19432	validation-auc:0.97186	validation-aucpr:0.97624
[21:28:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[54]	validation-logloss:0.19432	validation-auc:0.97180	validation-aucpr:0.97621
[21:28:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[55]	validation-logloss:0.19378	validation-auc:0.97185	validation-aucpr:0.97622
[21:28:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[56]	validation-logloss:0.19398	validation-auc:0.97174	validation-aucpr:0.97613
[21:28:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[57]	validation-logloss:0.19402	validation-auc:0.97167	validation-aucpr:0.97608
[21:28:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[58]	validation-logloss:0.19372	validation-auc:0.97175	validation-aucpr:0.97612
[21:28:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[59]	validation-logloss:0.19356	validation-auc:0.97171	validation-aucpr:0.97609
[21:28:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[60]	validation-logloss:0.19359	validation-auc:0.97169	validation-aucpr:0.97608
[21:28:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[61]	validation-logloss:0.19308	validation-auc:0.97187	validation-aucpr:0.97622
[21:28:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[62]	validation-logloss:0.19326	validation-auc:0.97186	validation-aucpr:0.97621
[21:28:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[63]	validation-logloss:0.19318	validation-auc:0.97187	validation-aucpr:0.97624
[21:28:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[64]	validation-logloss:0.19310	validation-auc:0.97192	validation-aucpr:0.97627
[21:28:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[65]	validation-logloss:0.19315	validation-auc:0.97195	validation-aucpr:0.97627
[21:28:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[66]	validation-logloss:0.19281	validation-auc:0.97206	validation-aucpr:0.97638
[21:28:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[67]	validation-logloss:0.19275	validation-auc:0.97203	validation-aucpr:0.97639
[21:28:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[68]	validation-logloss:0.19298	validation-auc:0.97202	validation-aucpr:0.97637
[21:28:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[69]	validation-logloss:0.19341	validation-auc:0.97193	validation-aucpr:0.97630
[21:28:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[70]	validation-logloss:0.19336	validation-auc:0.97199	validation-aucpr:0.97635
[21:28:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[71]	validation-logloss:0.19369	validation-auc:0.97194	validation-aucpr:0.97631
[21:28:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[72]	validation-logloss:0.19353	validation-auc:0.97201	validation-aucpr:0.97637
[21:28:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[73]	validation-logloss:0.19371	validation-auc:0.97196	validation-aucpr:0.97631
{'best_iteration': '67', 'best_score': '0.9763874139328066'}
Trial 77, Fold 4: Log loss = 0.19370908844916987, Average precision = 0.976315444049942, ROC-AUC = 0.9719556154042409, Elapsed Time = 18.724122399999032 seconds
Trial 77, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 77, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[21:28:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.64019	validation-auc:0.94752	validation-aucpr:0.94616
[21:28:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.59981	validation-auc:0.95419	validation-aucpr:0.95434
[21:28:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.56013	validation-auc:0.95930	validation-aucpr:0.96385
[21:28:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.52420	validation-auc:0.96318	validation-aucpr:0.96572
[21:28:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.49357	validation-auc:0.96375	validation-aucpr:0.96887
[21:28:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5]	validation-logloss:0.46594	validation-auc:0.96571	validation-aucpr:0.97069
[21:28:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6]	validation-logloss:0.44097	validation-auc:0.96600	validation-aucpr:0.97123
[21:28:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7]	validation-logloss:0.42117	validation-auc:0.96620	validation-aucpr:0.97124
[21:28:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8]	validation-logloss:0.40138	validation-auc:0.96645	validation-aucpr:0.97147
[21:28:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9]	validation-logloss:0.38282	validation-auc:0.96729	validation-aucpr:0.97222
[21:28:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10]	validation-logloss:0.36594	validation-auc:0.96781	validation-aucpr:0.97261
[21:28:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11]	validation-logloss:0.35293	validation-auc:0.96784	validation-aucpr:0.97255
[21:28:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12]	validation-logloss:0.33939	validation-auc:0.96806	validation-aucpr:0.97272
[21:28:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13]	validation-logloss:0.32797	validation-auc:0.96836	validation-aucpr:0.97275
[21:28:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14]	validation-logloss:0.31683	validation-auc:0.96829	validation-aucpr:0.97277
[21:28:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15]	validation-logloss:0.30635	validation-auc:0.96878	validation-aucpr:0.97312
[21:28:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16]	validation-logloss:0.29713	validation-auc:0.96886	validation-aucpr:0.97315
[21:28:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17]	validation-logloss:0.28814	validation-auc:0.96929	validation-aucpr:0.97350
[21:28:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18]	validation-logloss:0.28009	validation-auc:0.96975	validation-aucpr:0.97384
[21:28:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19]	validation-logloss:0.27320	validation-auc:0.96969	validation-aucpr:0.97380
[21:28:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20]	validation-logloss:0.26630	validation-auc:0.97002	validation-aucpr:0.97410
[21:28:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21]	validation-logloss:0.26135	validation-auc:0.96985	validation-aucpr:0.97394
[21:28:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22]	validation-logloss:0.25568	validation-auc:0.97008	validation-aucpr:0.97411
[21:28:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23]	validation-logloss:0.25113	validation-auc:0.96997	validation-aucpr:0.97407
[21:28:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24]	validation-logloss:0.24645	validation-auc:0.96997	validation-aucpr:0.97406
[21:28:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25]	validation-logloss:0.24306	validation-auc:0.96998	validation-aucpr:0.97401
[21:28:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26]	validation-logloss:0.23883	validation-auc:0.97024	validation-aucpr:0.97414
[21:28:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27]	validation-logloss:0.23611	validation-auc:0.97018	validation-aucpr:0.97407
[21:28:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28]	validation-logloss:0.23283	validation-auc:0.97023	validation-aucpr:0.97415
[21:28:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29]	validation-logloss:0.22952	validation-auc:0.97044	validation-aucpr:0.97429
[21:28:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30]	validation-logloss:0.22636	validation-auc:0.97066	validation-aucpr:0.97446
[21:28:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31]	validation-logloss:0.22401	validation-auc:0.97074	validation-aucpr:0.97471
[21:28:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32]	validation-logloss:0.22167	validation-auc:0.97065	validation-aucpr:0.97465
[21:28:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33]	validation-logloss:0.22001	validation-auc:0.97057	validation-aucpr:0.97455
[21:28:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34]	validation-logloss:0.21787	validation-auc:0.97074	validation-aucpr:0.97468
[21:28:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35]	validation-logloss:0.21646	validation-auc:0.97068	validation-aucpr:0.97442
[21:28:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36]	validation-logloss:0.21499	validation-auc:0.97073	validation-aucpr:0.97444
[21:28:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37]	validation-logloss:0.21325	validation-auc:0.97077	validation-aucpr:0.97449
[21:28:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38]	validation-logloss:0.21214	validation-auc:0.97066	validation-aucpr:0.97440
[21:28:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39]	validation-logloss:0.21022	validation-auc:0.97095	validation-aucpr:0.97468
[21:28:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40]	validation-logloss:0.20870	validation-auc:0.97112	validation-aucpr:0.97480
[21:28:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41]	validation-logloss:0.20742	validation-auc:0.97112	validation-aucpr:0.97481
[21:28:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42]	validation-logloss:0.20656	validation-auc:0.97106	validation-aucpr:0.97477
[21:28:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43]	validation-logloss:0.20555	validation-auc:0.97117	validation-aucpr:0.97480
[21:28:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44]	validation-logloss:0.20484	validation-auc:0.97114	validation-aucpr:0.97480
[21:28:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45]	validation-logloss:0.20435	validation-auc:0.97110	validation-aucpr:0.97478
[21:28:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46]	validation-logloss:0.20374	validation-auc:0.97108	validation-aucpr:0.97467
[21:28:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[47]	validation-logloss:0.20353	validation-auc:0.97100	validation-aucpr:0.97458
[21:28:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[48]	validation-logloss:0.20274	validation-auc:0.97108	validation-aucpr:0.97461
[21:28:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[49]	validation-logloss:0.20208	validation-auc:0.97122	validation-aucpr:0.97468
[21:28:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[50]	validation-logloss:0.20176	validation-auc:0.97120	validation-aucpr:0.97460
[21:28:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[51]	validation-logloss:0.20113	validation-auc:0.97131	validation-aucpr:0.97498
[21:28:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[52]	validation-logloss:0.20034	validation-auc:0.97142	validation-aucpr:0.97497
[21:28:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[53]	validation-logloss:0.19986	validation-auc:0.97154	validation-aucpr:0.97512
[21:28:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[54]	validation-logloss:0.19947	validation-auc:0.97166	validation-aucpr:0.97526
[21:28:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[55]	validation-logloss:0.19964	validation-auc:0.97153	validation-aucpr:0.97522
[21:28:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[56]	validation-logloss:0.20003	validation-auc:0.97132	validation-aucpr:0.97505
[21:28:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[57]	validation-logloss:0.20002	validation-auc:0.97126	validation-aucpr:0.97494
[21:28:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[58]	validation-logloss:0.19993	validation-auc:0.97132	validation-aucpr:0.97493
[21:28:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[59]	validation-logloss:0.19983	validation-auc:0.97133	validation-aucpr:0.97490
[21:28:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[60]	validation-logloss:0.19956	validation-auc:0.97140	validation-aucpr:0.97494
[21:28:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[61]	validation-logloss:0.19935	validation-auc:0.97148	validation-aucpr:0.97501
[21:28:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[62]	validation-logloss:0.19941	validation-auc:0.97144	validation-aucpr:0.97493
[21:28:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[63]	validation-logloss:0.19950	validation-auc:0.97144	validation-aucpr:0.97482
[21:28:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[64]	validation-logloss:0.19973	validation-auc:0.97137	validation-aucpr:0.97477
[21:28:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[65]	validation-logloss:0.19986	validation-auc:0.97132	validation-aucpr:0.97469
[21:28:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[66]	validation-logloss:0.19992	validation-auc:0.97139	validation-aucpr:0.97468
[21:28:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[67]	validation-logloss:0.20011	validation-auc:0.97133	validation-aucpr:0.97456
[21:28:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[68]	validation-logloss:0.20035	validation-auc:0.97128	validation-aucpr:0.97449
[21:28:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[69]	validation-logloss:0.20002	validation-auc:0.97145	validation-aucpr:0.97502
[21:28:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[70]	validation-logloss:0.19996	validation-auc:0.97146	validation-aucpr:0.97501
[21:28:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[71]	validation-logloss:0.19986	validation-auc:0.97154	validation-aucpr:0.97513
[21:28:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[72]	validation-logloss:0.20026	validation-auc:0.97149	validation-aucpr:0.97507
[21:28:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[73]	validation-logloss:0.20060	validation-auc:0.97139	validation-aucpr:0.97493
{'best_iteration': '54', 'best_score': '0.9752626205930159'}
Trial 77, Fold 5: Log loss = 0.20059891678402936, Average precision = 0.9749373043194629, ROC-AUC = 0.9713875480742434, Elapsed Time = 18.439465700001165 seconds
Optimization Progress:  78%|#######8  | 78/100 [3:29:59<24:45, 67.54s/it]
Trial 78, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 78, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[21:28:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.66663	validation-auc:0.94171	validation-aucpr:0.94109
[21:28:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.64201	validation-auc:0.96091	validation-aucpr:0.95450
[21:28:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.61922	validation-auc:0.96447	validation-aucpr:0.96116
[21:28:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.59807	validation-auc:0.96590	validation-aucpr:0.97163
[21:28:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.57823	validation-auc:0.96652	validation-aucpr:0.97227
[21:28:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5]	validation-logloss:0.56166	validation-auc:0.96675	validation-aucpr:0.97258
[21:28:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6]	validation-logloss:0.54621	validation-auc:0.96617	validation-aucpr:0.97196
[21:28:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7]	validation-logloss:0.52950	validation-auc:0.96628	validation-aucpr:0.97213
[21:28:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8]	validation-logloss:0.51374	validation-auc:0.96620	validation-aucpr:0.97204
[21:28:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9]	validation-logloss:0.50042	validation-auc:0.96596	validation-aucpr:0.97177
[21:28:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10]	validation-logloss:0.48820	validation-auc:0.96597	validation-aucpr:0.97164
[21:28:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11]	validation-logloss:0.47439	validation-auc:0.96634	validation-aucpr:0.97190
[21:28:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12]	validation-logloss:0.46125	validation-auc:0.96692	validation-aucpr:0.97242
[21:28:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13]	validation-logloss:0.44984	validation-auc:0.96731	validation-aucpr:0.97269
[21:28:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14]	validation-logloss:0.43864	validation-auc:0.96730	validation-aucpr:0.97268
[21:28:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15]	validation-logloss:0.42793	validation-auc:0.96719	validation-aucpr:0.97260
[21:28:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16]	validation-logloss:0.41757	validation-auc:0.96747	validation-aucpr:0.97287
[21:28:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17]	validation-logloss:0.40755	validation-auc:0.96773	validation-aucpr:0.97304
[21:28:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18]	validation-logloss:0.39867	validation-auc:0.96805	validation-aucpr:0.97329
[21:28:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19]	validation-logloss:0.39055	validation-auc:0.96818	validation-aucpr:0.97334
[21:28:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20]	validation-logloss:0.38315	validation-auc:0.96796	validation-aucpr:0.97317
[21:28:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21]	validation-logloss:0.37470	validation-auc:0.96817	validation-aucpr:0.97334
[21:28:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22]	validation-logloss:0.36665	validation-auc:0.96836	validation-aucpr:0.97346
[21:28:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23]	validation-logloss:0.36061	validation-auc:0.96817	validation-aucpr:0.97328
[21:28:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24]	validation-logloss:0.35471	validation-auc:0.96813	validation-aucpr:0.97319
[21:28:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25]	validation-logloss:0.34775	validation-auc:0.96832	validation-aucpr:0.97332
[21:28:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26]	validation-logloss:0.34221	validation-auc:0.96836	validation-aucpr:0.97333
[21:28:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27]	validation-logloss:0.33556	validation-auc:0.96869	validation-aucpr:0.97356
[21:28:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28]	validation-logloss:0.32957	validation-auc:0.96863	validation-aucpr:0.97353
[21:28:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29]	validation-logloss:0.32399	validation-auc:0.96854	validation-aucpr:0.97344
[21:28:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30]	validation-logloss:0.31850	validation-auc:0.96857	validation-aucpr:0.97348
[21:28:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31]	validation-logloss:0.31311	validation-auc:0.96863	validation-aucpr:0.97355
[21:28:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32]	validation-logloss:0.30810	validation-auc:0.96874	validation-aucpr:0.97366
[21:28:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33]	validation-logloss:0.30305	validation-auc:0.96903	validation-aucpr:0.97386
[21:28:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34]	validation-logloss:0.29832	validation-auc:0.96916	validation-aucpr:0.97396
[21:28:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35]	validation-logloss:0.29376	validation-auc:0.96944	validation-aucpr:0.97421
[21:28:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36]	validation-logloss:0.28972	validation-auc:0.96944	validation-aucpr:0.97422
[21:28:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37]	validation-logloss:0.28561	validation-auc:0.96955	validation-aucpr:0.97429
[21:28:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38]	validation-logloss:0.28227	validation-auc:0.96952	validation-aucpr:0.97426
[21:28:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39]	validation-logloss:0.27906	validation-auc:0.96955	validation-aucpr:0.97424
[21:28:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40]	validation-logloss:0.27555	validation-auc:0.96953	validation-aucpr:0.97422
[21:28:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41]	validation-logloss:0.27196	validation-auc:0.96967	validation-aucpr:0.97432
[21:29:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42]	validation-logloss:0.26921	validation-auc:0.96966	validation-aucpr:0.97430
[21:29:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43]	validation-logloss:0.26660	validation-auc:0.96971	validation-aucpr:0.97429
[21:29:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44]	validation-logloss:0.26347	validation-auc:0.96975	validation-aucpr:0.97434
{'best_iteration': '44', 'best_score': '0.9743414384395959'}
Trial 78, Fold 1: Log loss = 0.2634666136903201, Average precision = 0.9743449721498787, ROC-AUC = 0.9697506518164828, Elapsed Time = 7.150237099998776 seconds
Trial 78, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 78, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[21:29:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.66660	validation-auc:0.94258	validation-aucpr:0.91905
[21:29:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.64419	validation-auc:0.96093	validation-aucpr:0.95317
[21:29:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.62103	validation-auc:0.96378	validation-aucpr:0.96142
[21:29:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.59913	validation-auc:0.96715	validation-aucpr:0.96839
[21:29:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.57891	validation-auc:0.96845	validation-aucpr:0.97224
[21:29:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5]	validation-logloss:0.55967	validation-auc:0.96904	validation-aucpr:0.97274
[21:29:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6]	validation-logloss:0.54164	validation-auc:0.96914	validation-aucpr:0.97281
[21:29:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7]	validation-logloss:0.52494	validation-auc:0.96906	validation-aucpr:0.97278
[21:29:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8]	validation-logloss:0.50911	validation-auc:0.96911	validation-aucpr:0.97282
[21:29:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9]	validation-logloss:0.49435	validation-auc:0.96934	validation-aucpr:0.97302
[21:29:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10]	validation-logloss:0.48176	validation-auc:0.96954	validation-aucpr:0.97311
[21:29:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11]	validation-logloss:0.46845	validation-auc:0.96965	validation-aucpr:0.97315
[21:29:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12]	validation-logloss:0.45737	validation-auc:0.96975	validation-aucpr:0.97320
[21:29:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13]	validation-logloss:0.44521	validation-auc:0.96982	validation-aucpr:0.97322
[21:29:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14]	validation-logloss:0.43365	validation-auc:0.96982	validation-aucpr:0.97322
[21:29:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15]	validation-logloss:0.42311	validation-auc:0.96970	validation-aucpr:0.97314
[21:29:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16]	validation-logloss:0.41271	validation-auc:0.96989	validation-aucpr:0.97325
[21:29:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17]	validation-logloss:0.40279	validation-auc:0.97014	validation-aucpr:0.97347
[21:29:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18]	validation-logloss:0.39337	validation-auc:0.97046	validation-aucpr:0.97363
[21:29:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19]	validation-logloss:0.38431	validation-auc:0.97055	validation-aucpr:0.97366
[21:29:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20]	validation-logloss:0.37660	validation-auc:0.97064	validation-aucpr:0.97372
[21:29:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21]	validation-logloss:0.36910	validation-auc:0.97082	validation-aucpr:0.97395
[21:29:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22]	validation-logloss:0.36109	validation-auc:0.97101	validation-aucpr:0.97413
[21:29:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23]	validation-logloss:0.35379	validation-auc:0.97089	validation-aucpr:0.97404
[21:29:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24]	validation-logloss:0.34745	validation-auc:0.97110	validation-aucpr:0.97416
[21:29:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25]	validation-logloss:0.34142	validation-auc:0.97123	validation-aucpr:0.97425
[21:29:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26]	validation-logloss:0.33480	validation-auc:0.97137	validation-aucpr:0.97467
[21:29:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27]	validation-logloss:0.32924	validation-auc:0.97135	validation-aucpr:0.97470
[21:29:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28]	validation-logloss:0.32314	validation-auc:0.97155	validation-aucpr:0.97484
[21:29:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29]	validation-logloss:0.31739	validation-auc:0.97164	validation-aucpr:0.97491
[21:29:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30]	validation-logloss:0.31247	validation-auc:0.97165	validation-aucpr:0.97489
[21:29:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31]	validation-logloss:0.30778	validation-auc:0.97182	validation-aucpr:0.97503
[21:29:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32]	validation-logloss:0.30239	validation-auc:0.97213	validation-aucpr:0.97525
[21:29:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33]	validation-logloss:0.29785	validation-auc:0.97231	validation-aucpr:0.97538
[21:29:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34]	validation-logloss:0.29402	validation-auc:0.97216	validation-aucpr:0.97525
[21:29:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35]	validation-logloss:0.28944	validation-auc:0.97215	validation-aucpr:0.97526
[21:29:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36]	validation-logloss:0.28513	validation-auc:0.97215	validation-aucpr:0.97515
[21:29:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37]	validation-logloss:0.28078	validation-auc:0.97236	validation-aucpr:0.97533
[21:29:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38]	validation-logloss:0.27663	validation-auc:0.97248	validation-aucpr:0.97542
[21:29:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39]	validation-logloss:0.27339	validation-auc:0.97245	validation-aucpr:0.97537
[21:29:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40]	validation-logloss:0.26972	validation-auc:0.97247	validation-aucpr:0.97538
[21:29:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41]	validation-logloss:0.26687	validation-auc:0.97240	validation-aucpr:0.97532
[21:29:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42]	validation-logloss:0.26392	validation-auc:0.97255	validation-aucpr:0.97540
[21:29:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43]	validation-logloss:0.26067	validation-auc:0.97263	validation-aucpr:0.97548
[21:29:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44]	validation-logloss:0.25753	validation-auc:0.97261	validation-aucpr:0.97546
{'best_iteration': '43', 'best_score': '0.9754849993403878'}
Trial 78, Fold 2: Log loss = 0.25753349892157557, Average precision = 0.9754106024749805, ROC-AUC = 0.9726118768135756, Elapsed Time = 5.847930500000075 seconds
Trial 78, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 78, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[21:29:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.66640	validation-auc:0.93718	validation-aucpr:0.89307
[21:29:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.64415	validation-auc:0.95825	validation-aucpr:0.94779
[21:29:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.62339	validation-auc:0.96143	validation-aucpr:0.95842
[21:29:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.60217	validation-auc:0.96362	validation-aucpr:0.96591
[21:29:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.58403	validation-auc:0.96465	validation-aucpr:0.96847
[21:29:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5]	validation-logloss:0.56457	validation-auc:0.96590	validation-aucpr:0.96969
[21:29:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6]	validation-logloss:0.54855	validation-auc:0.96645	validation-aucpr:0.97121
[21:29:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7]	validation-logloss:0.53085	validation-auc:0.96767	validation-aucpr:0.97206
[21:29:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8]	validation-logloss:0.51647	validation-auc:0.96795	validation-aucpr:0.97229
[21:29:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9]	validation-logloss:0.50280	validation-auc:0.96803	validation-aucpr:0.97265
[21:29:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10]	validation-logloss:0.48803	validation-auc:0.96832	validation-aucpr:0.97293
[21:29:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11]	validation-logloss:0.47399	validation-auc:0.96876	validation-aucpr:0.97335
[21:29:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12]	validation-logloss:0.46217	validation-auc:0.96925	validation-aucpr:0.97364
[21:29:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13]	validation-logloss:0.45143	validation-auc:0.96925	validation-aucpr:0.97369
[21:29:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14]	validation-logloss:0.43936	validation-auc:0.96967	validation-aucpr:0.97404
[21:29:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15]	validation-logloss:0.42941	validation-auc:0.96959	validation-aucpr:0.97400
[21:29:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16]	validation-logloss:0.41834	validation-auc:0.96986	validation-aucpr:0.97426
[21:29:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17]	validation-logloss:0.40824	validation-auc:0.97011	validation-aucpr:0.97446
[21:29:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18]	validation-logloss:0.39822	validation-auc:0.97030	validation-aucpr:0.97462
[21:29:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19]	validation-logloss:0.38872	validation-auc:0.97065	validation-aucpr:0.97496
[21:29:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20]	validation-logloss:0.37978	validation-auc:0.97085	validation-aucpr:0.97514
[21:29:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21]	validation-logloss:0.37121	validation-auc:0.97102	validation-aucpr:0.97527
[21:29:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22]	validation-logloss:0.36463	validation-auc:0.97073	validation-aucpr:0.97500
[21:29:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23]	validation-logloss:0.35712	validation-auc:0.97072	validation-aucpr:0.97499
[21:29:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24]	validation-logloss:0.35068	validation-auc:0.97078	validation-aucpr:0.97499
[21:29:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25]	validation-logloss:0.34402	validation-auc:0.97070	validation-aucpr:0.97495
[21:29:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26]	validation-logloss:0.33741	validation-auc:0.97082	validation-aucpr:0.97504
[21:29:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27]	validation-logloss:0.33091	validation-auc:0.97100	validation-aucpr:0.97518
[21:29:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28]	validation-logloss:0.32460	validation-auc:0.97108	validation-aucpr:0.97528
[21:29:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29]	validation-logloss:0.31868	validation-auc:0.97121	validation-aucpr:0.97539
[21:29:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30]	validation-logloss:0.31284	validation-auc:0.97138	validation-aucpr:0.97552
[21:29:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31]	validation-logloss:0.30823	validation-auc:0.97150	validation-aucpr:0.97560
[21:29:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32]	validation-logloss:0.30289	validation-auc:0.97156	validation-aucpr:0.97568
[21:29:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33]	validation-logloss:0.29858	validation-auc:0.97164	validation-aucpr:0.97568
[21:29:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34]	validation-logloss:0.29388	validation-auc:0.97170	validation-aucpr:0.97574
[21:29:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35]	validation-logloss:0.28937	validation-auc:0.97180	validation-aucpr:0.97580
[21:29:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36]	validation-logloss:0.28585	validation-auc:0.97180	validation-aucpr:0.97579
[21:29:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37]	validation-logloss:0.28155	validation-auc:0.97198	validation-aucpr:0.97590
[21:29:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38]	validation-logloss:0.27798	validation-auc:0.97193	validation-aucpr:0.97585
[21:29:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39]	validation-logloss:0.27399	validation-auc:0.97193	validation-aucpr:0.97586
[21:29:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40]	validation-logloss:0.27051	validation-auc:0.97180	validation-aucpr:0.97578
[21:29:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41]	validation-logloss:0.26770	validation-auc:0.97173	validation-aucpr:0.97570
[21:29:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42]	validation-logloss:0.26416	validation-auc:0.97176	validation-aucpr:0.97572
[21:29:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43]	validation-logloss:0.26119	validation-auc:0.97185	validation-aucpr:0.97577
[21:29:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44]	validation-logloss:0.25780	validation-auc:0.97200	validation-aucpr:0.97589
{'best_iteration': '37', 'best_score': '0.9759014238086381'}
Trial 78, Fold 3: Log loss = 0.25779883428118283, Average precision = 0.9758932484338817, ROC-AUC = 0.9719994209340177, Elapsed Time = 5.3621460999966075 seconds
Trial 78, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 78, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[21:29:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.66641	validation-auc:0.93185	validation-aucpr:0.90151
[21:29:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.64424	validation-auc:0.95972	validation-aucpr:0.95595
[21:29:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.62119	validation-auc:0.96387	validation-aucpr:0.96402
[21:29:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.60025	validation-auc:0.96519	validation-aucpr:0.96611
[21:29:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.58232	validation-auc:0.96579	validation-aucpr:0.97122
[21:29:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5]	validation-logloss:0.56548	validation-auc:0.96609	validation-aucpr:0.97147
[21:29:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6]	validation-logloss:0.54932	validation-auc:0.96608	validation-aucpr:0.97150
[21:29:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7]	validation-logloss:0.53427	validation-auc:0.96647	validation-aucpr:0.97159
[21:29:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8]	validation-logloss:0.51783	validation-auc:0.96669	validation-aucpr:0.97192
[21:29:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9]	validation-logloss:0.50426	validation-auc:0.96679	validation-aucpr:0.97197
[21:29:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10]	validation-logloss:0.49001	validation-auc:0.96746	validation-aucpr:0.97252
[21:29:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11]	validation-logloss:0.47603	validation-auc:0.96777	validation-aucpr:0.97283
[21:29:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12]	validation-logloss:0.46507	validation-auc:0.96763	validation-aucpr:0.97266
[21:29:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13]	validation-logloss:0.45254	validation-auc:0.96763	validation-aucpr:0.97269
[21:29:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14]	validation-logloss:0.44031	validation-auc:0.96814	validation-aucpr:0.97312
[21:29:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15]	validation-logloss:0.43050	validation-auc:0.96822	validation-aucpr:0.97316
[21:29:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16]	validation-logloss:0.41979	validation-auc:0.96841	validation-aucpr:0.97337
[21:29:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17]	validation-logloss:0.40966	validation-auc:0.96841	validation-aucpr:0.97337
[21:29:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18]	validation-logloss:0.40134	validation-auc:0.96841	validation-aucpr:0.97334
[21:29:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19]	validation-logloss:0.39195	validation-auc:0.96851	validation-aucpr:0.97344
[21:29:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20]	validation-logloss:0.38308	validation-auc:0.96868	validation-aucpr:0.97362
[21:29:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21]	validation-logloss:0.37462	validation-auc:0.96881	validation-aucpr:0.97373
[21:29:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22]	validation-logloss:0.36652	validation-auc:0.96900	validation-aucpr:0.97390
[21:29:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23]	validation-logloss:0.36000	validation-auc:0.96900	validation-aucpr:0.97387
[21:29:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24]	validation-logloss:0.35396	validation-auc:0.96908	validation-aucpr:0.97392
[21:29:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25]	validation-logloss:0.34677	validation-auc:0.96928	validation-aucpr:0.97407
[21:29:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26]	validation-logloss:0.33972	validation-auc:0.96943	validation-aucpr:0.97419
[21:29:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27]	validation-logloss:0.33434	validation-auc:0.96947	validation-aucpr:0.97422
[21:29:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28]	validation-logloss:0.32940	validation-auc:0.96946	validation-aucpr:0.97422
[21:29:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29]	validation-logloss:0.32462	validation-auc:0.96946	validation-aucpr:0.97421
[21:29:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30]	validation-logloss:0.31964	validation-auc:0.96946	validation-aucpr:0.97420
[21:29:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31]	validation-logloss:0.31510	validation-auc:0.96937	validation-aucpr:0.97412
[21:29:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32]	validation-logloss:0.30987	validation-auc:0.96943	validation-aucpr:0.97420
[21:29:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33]	validation-logloss:0.30572	validation-auc:0.96942	validation-aucpr:0.97420
[21:29:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34]	validation-logloss:0.30157	validation-auc:0.96953	validation-aucpr:0.97426
[21:29:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35]	validation-logloss:0.29670	validation-auc:0.96957	validation-aucpr:0.97430
[21:29:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36]	validation-logloss:0.29218	validation-auc:0.96958	validation-aucpr:0.97433
[21:29:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37]	validation-logloss:0.28767	validation-auc:0.96979	validation-aucpr:0.97448
[21:29:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38]	validation-logloss:0.28379	validation-auc:0.96984	validation-aucpr:0.97451
[21:29:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39]	validation-logloss:0.28068	validation-auc:0.96976	validation-aucpr:0.97444
[21:29:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40]	validation-logloss:0.27762	validation-auc:0.96973	validation-aucpr:0.97440
[21:29:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41]	validation-logloss:0.27476	validation-auc:0.96966	validation-aucpr:0.97436
[21:29:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42]	validation-logloss:0.27107	validation-auc:0.96970	validation-aucpr:0.97441
[21:29:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43]	validation-logloss:0.26832	validation-auc:0.96968	validation-aucpr:0.97441
[21:29:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44]	validation-logloss:0.26568	validation-auc:0.96968	validation-aucpr:0.97444
{'best_iteration': '38', 'best_score': '0.9745071265456206'}
Trial 78, Fold 4: Log loss = 0.26568473621462146, Average precision = 0.9744429625257657, ROC-AUC = 0.9696823787348798, Elapsed Time = 5.064143999999942 seconds
Trial 78, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 78, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[21:29:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.66649	validation-auc:0.94023	validation-aucpr:0.90318
[21:29:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.64191	validation-auc:0.96175	validation-aucpr:0.95572
[21:29:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.62133	validation-auc:0.96121	validation-aucpr:0.95928
[21:29:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.60181	validation-auc:0.96250	validation-aucpr:0.96298
[21:29:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.58141	validation-auc:0.96401	validation-aucpr:0.96614
[21:29:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5]	validation-logloss:0.56207	validation-auc:0.96564	validation-aucpr:0.96820
[21:29:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6]	validation-logloss:0.54427	validation-auc:0.96648	validation-aucpr:0.97033
[21:29:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7]	validation-logloss:0.52953	validation-auc:0.96651	validation-aucpr:0.97007
[21:29:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8]	validation-logloss:0.51536	validation-auc:0.96716	validation-aucpr:0.97036
[21:29:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9]	validation-logloss:0.50229	validation-auc:0.96688	validation-aucpr:0.97000
[21:29:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10]	validation-logloss:0.49014	validation-auc:0.96678	validation-aucpr:0.97114
[21:29:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11]	validation-logloss:0.47858	validation-auc:0.96683	validation-aucpr:0.97107
[21:29:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12]	validation-logloss:0.46550	validation-auc:0.96703	validation-aucpr:0.97125
[21:29:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13]	validation-logloss:0.45298	validation-auc:0.96751	validation-aucpr:0.97163
[21:29:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14]	validation-logloss:0.44273	validation-auc:0.96774	validation-aucpr:0.97177
[21:29:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15]	validation-logloss:0.43314	validation-auc:0.96785	validation-aucpr:0.97181
[21:29:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16]	validation-logloss:0.42218	validation-auc:0.96810	validation-aucpr:0.97204
[21:29:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17]	validation-logloss:0.41345	validation-auc:0.96791	validation-aucpr:0.97184
[21:29:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18]	validation-logloss:0.40395	validation-auc:0.96798	validation-aucpr:0.97193
[21:29:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19]	validation-logloss:0.39503	validation-auc:0.96821	validation-aucpr:0.97217
[21:29:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20]	validation-logloss:0.38682	validation-auc:0.96847	validation-aucpr:0.97277
[21:29:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21]	validation-logloss:0.37973	validation-auc:0.96833	validation-aucpr:0.97262
[21:29:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22]	validation-logloss:0.37172	validation-auc:0.96864	validation-aucpr:0.97282
[21:29:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23]	validation-logloss:0.36388	validation-auc:0.96876	validation-aucpr:0.97294
[21:29:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24]	validation-logloss:0.35756	validation-auc:0.96862	validation-aucpr:0.97283
[21:29:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25]	validation-logloss:0.35167	validation-auc:0.96850	validation-aucpr:0.97271
[21:29:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26]	validation-logloss:0.34505	validation-auc:0.96853	validation-aucpr:0.97274
[21:29:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27]	validation-logloss:0.33843	validation-auc:0.96870	validation-aucpr:0.97293
[21:29:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28]	validation-logloss:0.33330	validation-auc:0.96864	validation-aucpr:0.97285
[21:29:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29]	validation-logloss:0.32694	validation-auc:0.96902	validation-aucpr:0.97314
[21:29:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30]	validation-logloss:0.32232	validation-auc:0.96896	validation-aucpr:0.97308
[21:29:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31]	validation-logloss:0.31683	validation-auc:0.96905	validation-aucpr:0.97314
[21:29:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32]	validation-logloss:0.31144	validation-auc:0.96921	validation-aucpr:0.97326
[21:29:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33]	validation-logloss:0.30640	validation-auc:0.96927	validation-aucpr:0.97333
[21:29:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34]	validation-logloss:0.30173	validation-auc:0.96941	validation-aucpr:0.97347
[21:29:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35]	validation-logloss:0.29740	validation-auc:0.96948	validation-aucpr:0.97352
[21:29:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36]	validation-logloss:0.29391	validation-auc:0.96941	validation-aucpr:0.97344
[21:29:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37]	validation-logloss:0.29059	validation-auc:0.96926	validation-aucpr:0.97330
[21:29:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38]	validation-logloss:0.28721	validation-auc:0.96928	validation-aucpr:0.97329
[21:29:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39]	validation-logloss:0.28318	validation-auc:0.96936	validation-aucpr:0.97336
[21:29:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40]	validation-logloss:0.27995	validation-auc:0.96946	validation-aucpr:0.97343
[21:29:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41]	validation-logloss:0.27615	validation-auc:0.96967	validation-aucpr:0.97362
[21:29:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42]	validation-logloss:0.27337	validation-auc:0.96978	validation-aucpr:0.97369
[21:29:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43]	validation-logloss:0.27063	validation-auc:0.96978	validation-aucpr:0.97367
[21:29:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44]	validation-logloss:0.26746	validation-auc:0.96984	validation-aucpr:0.97371
{'best_iteration': '44', 'best_score': '0.9737137481701315'}
Trial 78, Fold 5: Log loss = 0.26746121894649966, Average precision = 0.9737135246742918, ROC-AUC = 0.9698394366720547, Elapsed Time = 5.0369890999972995 seconds
Optimization Progress:  79%|#######9  | 79/100 [3:30:36<20:23, 58.25s/it]
Trial 79, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 79, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[21:29:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.68590	validation-auc:0.95495	validation-aucpr:0.95753
[21:29:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.67954	validation-auc:0.95942	validation-aucpr:0.96511
[21:29:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.67347	validation-auc:0.95824	validation-aucpr:0.96384
[21:29:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.66658	validation-auc:0.96148	validation-aucpr:0.96716
[21:29:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.65966	validation-auc:0.96332	validation-aucpr:0.96875
[21:29:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5]	validation-logloss:0.65328	validation-auc:0.96403	validation-aucpr:0.96940
[21:29:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6]	validation-logloss:0.64700	validation-auc:0.96436	validation-aucpr:0.97035
[21:29:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7]	validation-logloss:0.64135	validation-auc:0.96499	validation-aucpr:0.97074
[21:29:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8]	validation-logloss:0.63588	validation-auc:0.96486	validation-aucpr:0.97050
[21:29:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9]	validation-logloss:0.63057	validation-auc:0.96474	validation-aucpr:0.97038
[21:29:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10]	validation-logloss:0.62444	validation-auc:0.96540	validation-aucpr:0.97100
[21:29:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11]	validation-logloss:0.61837	validation-auc:0.96565	validation-aucpr:0.97126
[21:29:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12]	validation-logloss:0.61323	validation-auc:0.96516	validation-aucpr:0.97079
[21:29:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13]	validation-logloss:0.60753	validation-auc:0.96558	validation-aucpr:0.97115
[21:29:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14]	validation-logloss:0.60192	validation-auc:0.96600	validation-aucpr:0.97151
[21:29:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15]	validation-logloss:0.59630	validation-auc:0.96621	validation-aucpr:0.97173
[21:29:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16]	validation-logloss:0.59077	validation-auc:0.96616	validation-aucpr:0.97174
[21:29:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17]	validation-logloss:0.58610	validation-auc:0.96589	validation-aucpr:0.97150
[21:29:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18]	validation-logloss:0.58079	validation-auc:0.96603	validation-aucpr:0.97169
[21:29:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19]	validation-logloss:0.57555	validation-auc:0.96616	validation-aucpr:0.97180
[21:29:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20]	validation-logloss:0.57027	validation-auc:0.96643	validation-aucpr:0.97204
[21:29:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21]	validation-logloss:0.56511	validation-auc:0.96675	validation-aucpr:0.97233
[21:30:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22]	validation-logloss:0.56010	validation-auc:0.96681	validation-aucpr:0.97240
[21:30:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23]	validation-logloss:0.55540	validation-auc:0.96665	validation-aucpr:0.97234
[21:30:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24]	validation-logloss:0.55067	validation-auc:0.96663	validation-aucpr:0.97232
[21:30:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25]	validation-logloss:0.54595	validation-auc:0.96677	validation-aucpr:0.97242
[21:30:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26]	validation-logloss:0.54119	validation-auc:0.96690	validation-aucpr:0.97252
[21:30:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27]	validation-logloss:0.53729	validation-auc:0.96685	validation-aucpr:0.97246
[21:30:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28]	validation-logloss:0.53295	validation-auc:0.96689	validation-aucpr:0.97248
[21:30:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29]	validation-logloss:0.52850	validation-auc:0.96706	validation-aucpr:0.97259
[21:30:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30]	validation-logloss:0.52413	validation-auc:0.96740	validation-aucpr:0.97333
[21:30:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31]	validation-logloss:0.51988	validation-auc:0.96731	validation-aucpr:0.97326
[21:30:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32]	validation-logloss:0.51553	validation-auc:0.96726	validation-aucpr:0.97325
[21:30:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33]	validation-logloss:0.51140	validation-auc:0.96712	validation-aucpr:0.97316
[21:30:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34]	validation-logloss:0.50720	validation-auc:0.96729	validation-aucpr:0.97326
[21:30:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35]	validation-logloss:0.50314	validation-auc:0.96731	validation-aucpr:0.97330
[21:30:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36]	validation-logloss:0.49919	validation-auc:0.96744	validation-aucpr:0.97338
[21:30:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37]	validation-logloss:0.49563	validation-auc:0.96752	validation-aucpr:0.97341
[21:30:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38]	validation-logloss:0.49172	validation-auc:0.96757	validation-aucpr:0.97344
[21:30:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39]	validation-logloss:0.48777	validation-auc:0.96767	validation-aucpr:0.97354
[21:30:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40]	validation-logloss:0.48397	validation-auc:0.96771	validation-aucpr:0.97354
[21:30:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41]	validation-logloss:0.48072	validation-auc:0.96778	validation-aucpr:0.97357
[21:30:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42]	validation-logloss:0.47714	validation-auc:0.96778	validation-aucpr:0.97359
[21:30:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43]	validation-logloss:0.47366	validation-auc:0.96782	validation-aucpr:0.97361
[21:30:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44]	validation-logloss:0.47059	validation-auc:0.96796	validation-aucpr:0.97367
[21:30:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45]	validation-logloss:0.46750	validation-auc:0.96797	validation-aucpr:0.97369
[21:30:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46]	validation-logloss:0.46398	validation-auc:0.96806	validation-aucpr:0.97375
[21:30:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[47]	validation-logloss:0.46073	validation-auc:0.96805	validation-aucpr:0.97376
[21:30:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[48]	validation-logloss:0.45743	validation-auc:0.96809	validation-aucpr:0.97378
[21:30:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[49]	validation-logloss:0.45467	validation-auc:0.96810	validation-aucpr:0.97373
[21:30:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[50]	validation-logloss:0.45145	validation-auc:0.96808	validation-aucpr:0.97374
[21:30:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[51]	validation-logloss:0.44830	validation-auc:0.96809	validation-aucpr:0.97373
[21:30:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[52]	validation-logloss:0.44516	validation-auc:0.96812	validation-aucpr:0.97375
[21:30:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[53]	validation-logloss:0.44251	validation-auc:0.96815	validation-aucpr:0.97374
[21:30:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[54]	validation-logloss:0.43985	validation-auc:0.96815	validation-aucpr:0.97372
[21:30:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[55]	validation-logloss:0.43695	validation-auc:0.96811	validation-aucpr:0.97371
[21:30:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[56]	validation-logloss:0.43394	validation-auc:0.96817	validation-aucpr:0.97375
[21:30:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[57]	validation-logloss:0.43096	validation-auc:0.96824	validation-aucpr:0.97382
[21:30:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[58]	validation-logloss:0.42801	validation-auc:0.96829	validation-aucpr:0.97388
[21:30:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[59]	validation-logloss:0.42564	validation-auc:0.96821	validation-aucpr:0.97380
[21:30:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[60]	validation-logloss:0.42322	validation-auc:0.96819	validation-aucpr:0.97376
[21:30:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[61]	validation-logloss:0.42055	validation-auc:0.96821	validation-aucpr:0.97378
[21:31:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[62]	validation-logloss:0.41823	validation-auc:0.96823	validation-aucpr:0.97378
[21:31:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[63]	validation-logloss:0.41586	validation-auc:0.96820	validation-aucpr:0.97373
[21:31:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[64]	validation-logloss:0.41319	validation-auc:0.96822	validation-aucpr:0.97374
[21:31:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[65]	validation-logloss:0.41059	validation-auc:0.96825	validation-aucpr:0.97378
[21:31:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[66]	validation-logloss:0.40791	validation-auc:0.96829	validation-aucpr:0.97381
[21:31:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[67]	validation-logloss:0.40536	validation-auc:0.96827	validation-aucpr:0.97379
[21:31:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[68]	validation-logloss:0.40284	validation-auc:0.96830	validation-aucpr:0.97382
[21:31:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[69]	validation-logloss:0.40034	validation-auc:0.96824	validation-aucpr:0.97377
[21:31:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[70]	validation-logloss:0.39779	validation-auc:0.96831	validation-aucpr:0.97381
[21:31:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[71]	validation-logloss:0.39525	validation-auc:0.96843	validation-aucpr:0.97390
[21:31:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[72]	validation-logloss:0.39323	validation-auc:0.96841	validation-aucpr:0.97385
[21:31:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[73]	validation-logloss:0.39084	validation-auc:0.96838	validation-aucpr:0.97384
[21:31:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[74]	validation-logloss:0.38847	validation-auc:0.96844	validation-aucpr:0.97389
[21:31:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[75]	validation-logloss:0.38612	validation-auc:0.96859	validation-aucpr:0.97400
[21:31:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[76]	validation-logloss:0.38377	validation-auc:0.96862	validation-aucpr:0.97402
[21:31:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[77]	validation-logloss:0.38149	validation-auc:0.96867	validation-aucpr:0.97406
[21:31:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[78]	validation-logloss:0.37957	validation-auc:0.96865	validation-aucpr:0.97403
[21:31:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[79]	validation-logloss:0.37742	validation-auc:0.96866	validation-aucpr:0.97404
[21:31:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[80]	validation-logloss:0.37518	validation-auc:0.96869	validation-aucpr:0.97407
[21:31:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[81]	validation-logloss:0.37317	validation-auc:0.96863	validation-aucpr:0.97404
[21:31:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[82]	validation-logloss:0.37146	validation-auc:0.96857	validation-aucpr:0.97397
[21:31:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[83]	validation-logloss:0.36946	validation-auc:0.96858	validation-aucpr:0.97399
[21:31:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[84]	validation-logloss:0.36747	validation-auc:0.96860	validation-aucpr:0.97403
[21:31:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[85]	validation-logloss:0.36542	validation-auc:0.96862	validation-aucpr:0.97405
[21:31:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[86]	validation-logloss:0.36345	validation-auc:0.96864	validation-aucpr:0.97405
[21:31:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[87]	validation-logloss:0.36151	validation-auc:0.96863	validation-aucpr:0.97406
[21:31:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[88]	validation-logloss:0.35980	validation-auc:0.96863	validation-aucpr:0.97405
{'best_iteration': '80', 'best_score': '0.9740666100234081'}
Trial 79, Fold 1: Log loss = 0.35979713209064984, Average precision = 0.9740550756475213, ROC-AUC = 0.9686318545284218, Elapsed Time = 132.87924360000034 seconds
Trial 79, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 79, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[21:31:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.68605	validation-auc:0.95094	validation-aucpr:0.95519
[21:31:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.67981	validation-auc:0.95808	validation-aucpr:0.96221
[21:31:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.67373	validation-auc:0.95829	validation-aucpr:0.96214
[21:31:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.66684	validation-auc:0.96122	validation-aucpr:0.96543
[21:31:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.66015	validation-auc:0.96275	validation-aucpr:0.96702
[21:31:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5]	validation-logloss:0.65359	validation-auc:0.96371	validation-aucpr:0.96797
[21:31:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6]	validation-logloss:0.64721	validation-auc:0.96480	validation-aucpr:0.96897
[21:31:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7]	validation-logloss:0.64075	validation-auc:0.96549	validation-aucpr:0.96960
[21:31:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8]	validation-logloss:0.63449	validation-auc:0.96575	validation-aucpr:0.96980
[21:31:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9]	validation-logloss:0.62856	validation-auc:0.96641	validation-aucpr:0.97035
[21:31:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10]	validation-logloss:0.62306	validation-auc:0.96669	validation-aucpr:0.97046
[21:31:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11]	validation-logloss:0.61715	validation-auc:0.96680	validation-aucpr:0.97051
[21:32:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12]	validation-logloss:0.61143	validation-auc:0.96684	validation-aucpr:0.97057
[21:32:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13]	validation-logloss:0.60555	validation-auc:0.96732	validation-aucpr:0.97099
[21:32:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14]	validation-logloss:0.59989	validation-auc:0.96783	validation-aucpr:0.97128
[21:32:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15]	validation-logloss:0.59432	validation-auc:0.96801	validation-aucpr:0.97139
[21:32:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16]	validation-logloss:0.58878	validation-auc:0.96843	validation-aucpr:0.97176
[21:32:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17]	validation-logloss:0.58347	validation-auc:0.96885	validation-aucpr:0.97210
[21:32:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18]	validation-logloss:0.57819	validation-auc:0.96900	validation-aucpr:0.97223
[21:32:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19]	validation-logloss:0.57367	validation-auc:0.96897	validation-aucpr:0.97214
[21:32:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20]	validation-logloss:0.56867	validation-auc:0.96889	validation-aucpr:0.97207
[21:32:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21]	validation-logloss:0.56373	validation-auc:0.96890	validation-aucpr:0.97209
[21:32:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22]	validation-logloss:0.55891	validation-auc:0.96895	validation-aucpr:0.97210
[21:32:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23]	validation-logloss:0.55409	validation-auc:0.96911	validation-aucpr:0.97221
[21:32:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24]	validation-logloss:0.54930	validation-auc:0.96925	validation-aucpr:0.97232
[21:32:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25]	validation-logloss:0.54467	validation-auc:0.96936	validation-aucpr:0.97242
[21:32:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26]	validation-logloss:0.53998	validation-auc:0.96943	validation-aucpr:0.97249
[21:32:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27]	validation-logloss:0.53586	validation-auc:0.96916	validation-aucpr:0.97231
[21:32:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28]	validation-logloss:0.53139	validation-auc:0.96925	validation-aucpr:0.97237
[21:32:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29]	validation-logloss:0.52688	validation-auc:0.96944	validation-aucpr:0.97257
[21:32:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30]	validation-logloss:0.52314	validation-auc:0.96932	validation-aucpr:0.97237
[21:32:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31]	validation-logloss:0.51885	validation-auc:0.96949	validation-aucpr:0.97248
[21:32:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32]	validation-logloss:0.51459	validation-auc:0.96947	validation-aucpr:0.97248
[21:32:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33]	validation-logloss:0.51038	validation-auc:0.96966	validation-aucpr:0.97263
[21:32:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34]	validation-logloss:0.50622	validation-auc:0.96974	validation-aucpr:0.97268
[21:32:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35]	validation-logloss:0.50213	validation-auc:0.96981	validation-aucpr:0.97274
[21:32:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36]	validation-logloss:0.49809	validation-auc:0.96989	validation-aucpr:0.97279
[21:32:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37]	validation-logloss:0.49419	validation-auc:0.96992	validation-aucpr:0.97282
[21:32:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38]	validation-logloss:0.49075	validation-auc:0.96993	validation-aucpr:0.97285
[21:32:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39]	validation-logloss:0.48700	validation-auc:0.96985	validation-aucpr:0.97277
[21:32:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40]	validation-logloss:0.48313	validation-auc:0.96992	validation-aucpr:0.97283
[21:32:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41]	validation-logloss:0.47951	validation-auc:0.96986	validation-aucpr:0.97279
[21:32:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42]	validation-logloss:0.47598	validation-auc:0.96985	validation-aucpr:0.97279
[21:32:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43]	validation-logloss:0.47239	validation-auc:0.96982	validation-aucpr:0.97276
[21:32:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44]	validation-logloss:0.46930	validation-auc:0.96977	validation-aucpr:0.97271
[21:32:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45]	validation-logloss:0.46590	validation-auc:0.96988	validation-aucpr:0.97279
[21:32:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46]	validation-logloss:0.46250	validation-auc:0.97005	validation-aucpr:0.97292
[21:32:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[47]	validation-logloss:0.45914	validation-auc:0.97001	validation-aucpr:0.97289
[21:32:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[48]	validation-logloss:0.45581	validation-auc:0.97005	validation-aucpr:0.97291
[21:32:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[49]	validation-logloss:0.45263	validation-auc:0.97008	validation-aucpr:0.97325
[21:32:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[50]	validation-logloss:0.44935	validation-auc:0.97009	validation-aucpr:0.97327
[21:32:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[51]	validation-logloss:0.44658	validation-auc:0.97007	validation-aucpr:0.97327
[21:32:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[52]	validation-logloss:0.44343	validation-auc:0.97004	validation-aucpr:0.97324
[21:32:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[53]	validation-logloss:0.44029	validation-auc:0.97003	validation-aucpr:0.97324
[21:32:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[54]	validation-logloss:0.43721	validation-auc:0.97008	validation-aucpr:0.97327
[21:33:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[55]	validation-logloss:0.43459	validation-auc:0.97000	validation-aucpr:0.97320
[21:33:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[56]	validation-logloss:0.43163	validation-auc:0.97009	validation-aucpr:0.97327
[21:33:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[57]	validation-logloss:0.42905	validation-auc:0.97005	validation-aucpr:0.97323
[21:33:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[58]	validation-logloss:0.42610	validation-auc:0.97009	validation-aucpr:0.97328
[21:33:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[59]	validation-logloss:0.42363	validation-auc:0.97009	validation-aucpr:0.97328
[21:33:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[60]	validation-logloss:0.42082	validation-auc:0.97017	validation-aucpr:0.97332
[21:33:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[61]	validation-logloss:0.41809	validation-auc:0.97015	validation-aucpr:0.97333
[21:33:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[62]	validation-logloss:0.41570	validation-auc:0.97016	validation-aucpr:0.97335
[21:33:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[63]	validation-logloss:0.41301	validation-auc:0.97013	validation-aucpr:0.97333
[21:33:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[64]	validation-logloss:0.41038	validation-auc:0.97013	validation-aucpr:0.97333
[21:33:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[65]	validation-logloss:0.40773	validation-auc:0.97025	validation-aucpr:0.97343
[21:33:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[66]	validation-logloss:0.40517	validation-auc:0.97031	validation-aucpr:0.97349
[21:33:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[67]	validation-logloss:0.40265	validation-auc:0.97031	validation-aucpr:0.97352
[21:33:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[68]	validation-logloss:0.40014	validation-auc:0.97025	validation-aucpr:0.97347
[21:33:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[69]	validation-logloss:0.39762	validation-auc:0.97024	validation-aucpr:0.97348
[21:33:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[70]	validation-logloss:0.39507	validation-auc:0.97028	validation-aucpr:0.97351
[21:33:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[71]	validation-logloss:0.39260	validation-auc:0.97035	validation-aucpr:0.97356
[21:33:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[72]	validation-logloss:0.39022	validation-auc:0.97032	validation-aucpr:0.97355
[21:33:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[73]	validation-logloss:0.38794	validation-auc:0.97038	validation-aucpr:0.97359
[21:33:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[74]	validation-logloss:0.38556	validation-auc:0.97040	validation-aucpr:0.97359
[21:33:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[75]	validation-logloss:0.38324	validation-auc:0.97039	validation-aucpr:0.97359
[21:33:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[76]	validation-logloss:0.38091	validation-auc:0.97045	validation-aucpr:0.97362
[21:33:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[77]	validation-logloss:0.37877	validation-auc:0.97039	validation-aucpr:0.97358
[21:33:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[78]	validation-logloss:0.37686	validation-auc:0.97037	validation-aucpr:0.97357
[21:33:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[79]	validation-logloss:0.37476	validation-auc:0.97032	validation-aucpr:0.97353
[21:33:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[80]	validation-logloss:0.37274	validation-auc:0.97029	validation-aucpr:0.97349
[21:33:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[81]	validation-logloss:0.37052	validation-auc:0.97035	validation-aucpr:0.97361
[21:33:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[82]	validation-logloss:0.36845	validation-auc:0.97037	validation-aucpr:0.97362
[21:33:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[83]	validation-logloss:0.36666	validation-auc:0.97035	validation-aucpr:0.97346
[21:33:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[84]	validation-logloss:0.36451	validation-auc:0.97045	validation-aucpr:0.97355
[21:33:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[85]	validation-logloss:0.36252	validation-auc:0.97046	validation-aucpr:0.97357
[21:33:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[86]	validation-logloss:0.36047	validation-auc:0.97053	validation-aucpr:0.97363
[21:33:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[87]	validation-logloss:0.35844	validation-auc:0.97050	validation-aucpr:0.97360
[21:33:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[88]	validation-logloss:0.35648	validation-auc:0.97045	validation-aucpr:0.97357
{'best_iteration': '86', 'best_score': '0.9736287736436597'}
Trial 79, Fold 2: Log loss = 0.35647722594563985, Average precision = 0.9735348578673697, ROC-AUC = 0.9704456013021172, Elapsed Time = 126.08551309999893 seconds
Trial 79, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 79, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[21:33:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.68583	validation-auc:0.95657	validation-aucpr:0.95778
[21:33:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.67871	validation-auc:0.96236	validation-aucpr:0.96444
[21:33:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.67191	validation-auc:0.96376	validation-aucpr:0.96792
[21:33:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.66503	validation-auc:0.96431	validation-aucpr:0.96843
[21:33:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.65824	validation-auc:0.96523	validation-aucpr:0.96927
[21:33:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5]	validation-logloss:0.65242	validation-auc:0.96612	validation-aucpr:0.97044
[21:34:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6]	validation-logloss:0.64601	validation-auc:0.96652	validation-aucpr:0.97130
[21:34:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7]	validation-logloss:0.64026	validation-auc:0.96709	validation-aucpr:0.97163
[21:34:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8]	validation-logloss:0.63468	validation-auc:0.96723	validation-aucpr:0.97166
[21:34:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9]	validation-logloss:0.62852	validation-auc:0.96721	validation-aucpr:0.97173
[21:34:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10]	validation-logloss:0.62249	validation-auc:0.96728	validation-aucpr:0.97179
[21:34:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11]	validation-logloss:0.61643	validation-auc:0.96765	validation-aucpr:0.97207
[21:34:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12]	validation-logloss:0.61119	validation-auc:0.96762	validation-aucpr:0.97194
[21:34:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13]	validation-logloss:0.60593	validation-auc:0.96765	validation-aucpr:0.97194
[21:34:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14]	validation-logloss:0.60109	validation-auc:0.96752	validation-aucpr:0.97182
[21:34:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15]	validation-logloss:0.59564	validation-auc:0.96766	validation-aucpr:0.97191
[21:34:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16]	validation-logloss:0.59012	validation-auc:0.96772	validation-aucpr:0.97194
[21:34:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17]	validation-logloss:0.58486	validation-auc:0.96761	validation-aucpr:0.97175
[21:34:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18]	validation-logloss:0.57959	validation-auc:0.96774	validation-aucpr:0.97185
[21:34:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19]	validation-logloss:0.57496	validation-auc:0.96794	validation-aucpr:0.97201
[21:34:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20]	validation-logloss:0.56983	validation-auc:0.96814	validation-aucpr:0.97218
[21:34:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21]	validation-logloss:0.56496	validation-auc:0.96798	validation-aucpr:0.97206
[21:34:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22]	validation-logloss:0.55993	validation-auc:0.96808	validation-aucpr:0.97214
[21:34:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23]	validation-logloss:0.55505	validation-auc:0.96824	validation-aucpr:0.97227
[21:34:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24]	validation-logloss:0.55016	validation-auc:0.96836	validation-aucpr:0.97235
[21:34:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25]	validation-logloss:0.54544	validation-auc:0.96851	validation-aucpr:0.97246
[21:34:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26]	validation-logloss:0.54075	validation-auc:0.96850	validation-aucpr:0.97250
[21:34:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27]	validation-logloss:0.53626	validation-auc:0.96871	validation-aucpr:0.97296
[21:34:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28]	validation-logloss:0.53166	validation-auc:0.96884	validation-aucpr:0.97306
[21:34:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29]	validation-logloss:0.52730	validation-auc:0.96882	validation-aucpr:0.97302
[21:34:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30]	validation-logloss:0.52292	validation-auc:0.96878	validation-aucpr:0.97298
[21:34:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31]	validation-logloss:0.51867	validation-auc:0.96884	validation-aucpr:0.97301
[21:34:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32]	validation-logloss:0.51500	validation-auc:0.96880	validation-aucpr:0.97294
[21:34:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33]	validation-logloss:0.51143	validation-auc:0.96865	validation-aucpr:0.97283
[21:34:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34]	validation-logloss:0.50766	validation-auc:0.96869	validation-aucpr:0.97287
[21:34:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35]	validation-logloss:0.50364	validation-auc:0.96861	validation-aucpr:0.97281
[21:34:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36]	validation-logloss:0.49952	validation-auc:0.96876	validation-aucpr:0.97291
[21:34:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37]	validation-logloss:0.49552	validation-auc:0.96892	validation-aucpr:0.97303
[21:34:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38]	validation-logloss:0.49215	validation-auc:0.96895	validation-aucpr:0.97307
[21:34:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39]	validation-logloss:0.48833	validation-auc:0.96897	validation-aucpr:0.97310
[21:34:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40]	validation-logloss:0.48504	validation-auc:0.96901	validation-aucpr:0.97316
[21:34:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41]	validation-logloss:0.48146	validation-auc:0.96903	validation-aucpr:0.97317
[21:34:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42]	validation-logloss:0.47835	validation-auc:0.96893	validation-aucpr:0.97310
[21:34:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43]	validation-logloss:0.47475	validation-auc:0.96896	validation-aucpr:0.97312
[21:34:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44]	validation-logloss:0.47116	validation-auc:0.96895	validation-aucpr:0.97312
[21:34:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45]	validation-logloss:0.46805	validation-auc:0.96893	validation-aucpr:0.97311
[21:34:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46]	validation-logloss:0.46506	validation-auc:0.96896	validation-aucpr:0.97313
[21:34:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[47]	validation-logloss:0.46207	validation-auc:0.96887	validation-aucpr:0.97303
[21:35:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[48]	validation-logloss:0.45904	validation-auc:0.96891	validation-aucpr:0.97308
[21:35:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[49]	validation-logloss:0.45585	validation-auc:0.96899	validation-aucpr:0.97314
[21:35:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[50]	validation-logloss:0.45252	validation-auc:0.96915	validation-aucpr:0.97328
[21:35:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[51]	validation-logloss:0.44978	validation-auc:0.96913	validation-aucpr:0.97324
[21:35:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[52]	validation-logloss:0.44652	validation-auc:0.96923	validation-aucpr:0.97333
[21:35:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[53]	validation-logloss:0.44379	validation-auc:0.96917	validation-aucpr:0.97326
[21:35:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[54]	validation-logloss:0.44069	validation-auc:0.96928	validation-aucpr:0.97335
[21:35:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[55]	validation-logloss:0.43803	validation-auc:0.96928	validation-aucpr:0.97341
[21:35:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[56]	validation-logloss:0.43544	validation-auc:0.96919	validation-aucpr:0.97335
[21:35:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[57]	validation-logloss:0.43241	validation-auc:0.96932	validation-aucpr:0.97345
[21:35:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[58]	validation-logloss:0.42999	validation-auc:0.96926	validation-aucpr:0.97340
[21:35:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[59]	validation-logloss:0.42703	validation-auc:0.96935	validation-aucpr:0.97358
[21:35:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[60]	validation-logloss:0.42422	validation-auc:0.96936	validation-aucpr:0.97359
[21:35:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[61]	validation-logloss:0.42131	validation-auc:0.96942	validation-aucpr:0.97366
[21:35:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[62]	validation-logloss:0.41858	validation-auc:0.96942	validation-aucpr:0.97366
[21:35:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[63]	validation-logloss:0.41625	validation-auc:0.96943	validation-aucpr:0.97365
[21:35:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[64]	validation-logloss:0.41353	validation-auc:0.96947	validation-aucpr:0.97368
[21:35:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[65]	validation-logloss:0.41079	validation-auc:0.96950	validation-aucpr:0.97370
[21:35:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[66]	validation-logloss:0.40811	validation-auc:0.96951	validation-aucpr:0.97371
[21:35:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[67]	validation-logloss:0.40588	validation-auc:0.96948	validation-aucpr:0.97367
[21:35:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[68]	validation-logloss:0.40333	validation-auc:0.96954	validation-aucpr:0.97370
[21:35:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[69]	validation-logloss:0.40120	validation-auc:0.96955	validation-aucpr:0.97370
[21:35:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[70]	validation-logloss:0.39900	validation-auc:0.96954	validation-aucpr:0.97367
[21:35:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[71]	validation-logloss:0.39696	validation-auc:0.96950	validation-aucpr:0.97365
[21:35:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[72]	validation-logloss:0.39447	validation-auc:0.96954	validation-aucpr:0.97369
[21:35:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[73]	validation-logloss:0.39209	validation-auc:0.96956	validation-aucpr:0.97371
[21:35:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[74]	validation-logloss:0.38963	validation-auc:0.96956	validation-aucpr:0.97372
[21:35:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[75]	validation-logloss:0.38724	validation-auc:0.96965	validation-aucpr:0.97378
[21:35:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[76]	validation-logloss:0.38526	validation-auc:0.96961	validation-aucpr:0.97375
[21:35:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[77]	validation-logloss:0.38296	validation-auc:0.96967	validation-aucpr:0.97380
[21:35:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[78]	validation-logloss:0.38074	validation-auc:0.96964	validation-aucpr:0.97377
[21:35:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[79]	validation-logloss:0.37877	validation-auc:0.96966	validation-aucpr:0.97378
[21:35:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[80]	validation-logloss:0.37693	validation-auc:0.96961	validation-aucpr:0.97374
[21:35:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[81]	validation-logloss:0.37473	validation-auc:0.96964	validation-aucpr:0.97376
[21:35:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[82]	validation-logloss:0.37257	validation-auc:0.96966	validation-aucpr:0.97378
[21:35:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[83]	validation-logloss:0.37076	validation-auc:0.96958	validation-aucpr:0.97371
[21:35:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[84]	validation-logloss:0.36860	validation-auc:0.96965	validation-aucpr:0.97377
[21:35:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[85]	validation-logloss:0.36651	validation-auc:0.96965	validation-aucpr:0.97377
[21:35:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[86]	validation-logloss:0.36443	validation-auc:0.96969	validation-aucpr:0.97380
[21:36:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[87]	validation-logloss:0.36237	validation-auc:0.96978	validation-aucpr:0.97385
[21:36:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[88]	validation-logloss:0.36042	validation-auc:0.96981	validation-aucpr:0.97388
{'best_iteration': '88', 'best_score': '0.9738848959265011'}
Trial 79, Fold 3: Log loss = 0.3604212559366444, Average precision = 0.9738371377642552, ROC-AUC = 0.9698099405524363, Elapsed Time = 131.3719807000016 seconds
Trial 79, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 79, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[21:36:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.68578	validation-auc:0.95664	validation-aucpr:0.96286
[21:36:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.67868	validation-auc:0.96245	validation-aucpr:0.96876
[21:36:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.67179	validation-auc:0.96277	validation-aucpr:0.96940
[21:36:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.66498	validation-auc:0.96323	validation-aucpr:0.96985
[21:36:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.65822	validation-auc:0.96500	validation-aucpr:0.97089
[21:36:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5]	validation-logloss:0.65229	validation-auc:0.96512	validation-aucpr:0.97133
[21:36:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6]	validation-logloss:0.64572	validation-auc:0.96562	validation-aucpr:0.97165
[21:36:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7]	validation-logloss:0.63929	validation-auc:0.96661	validation-aucpr:0.97224
[21:36:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8]	validation-logloss:0.63388	validation-auc:0.96613	validation-aucpr:0.97183
[21:36:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9]	validation-logloss:0.62763	validation-auc:0.96682	validation-aucpr:0.97235
[21:36:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10]	validation-logloss:0.62161	validation-auc:0.96696	validation-aucpr:0.97247
[21:36:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11]	validation-logloss:0.61637	validation-auc:0.96676	validation-aucpr:0.97230
[21:36:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12]	validation-logloss:0.61072	validation-auc:0.96705	validation-aucpr:0.97248
[21:36:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13]	validation-logloss:0.60495	validation-auc:0.96696	validation-aucpr:0.97242
[21:36:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14]	validation-logloss:0.59995	validation-auc:0.96676	validation-aucpr:0.97223
[21:36:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15]	validation-logloss:0.59497	validation-auc:0.96685	validation-aucpr:0.97223
[21:36:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16]	validation-logloss:0.58957	validation-auc:0.96695	validation-aucpr:0.97230
[21:36:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17]	validation-logloss:0.58428	validation-auc:0.96711	validation-aucpr:0.97244
[21:36:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18]	validation-logloss:0.57904	validation-auc:0.96710	validation-aucpr:0.97245
[21:36:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19]	validation-logloss:0.57441	validation-auc:0.96706	validation-aucpr:0.97237
[21:36:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20]	validation-logloss:0.56929	validation-auc:0.96707	validation-aucpr:0.97240
[21:36:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21]	validation-logloss:0.56489	validation-auc:0.96687	validation-aucpr:0.97221
[21:36:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22]	validation-logloss:0.55997	validation-auc:0.96716	validation-aucpr:0.97242
[21:36:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23]	validation-logloss:0.55511	validation-auc:0.96734	validation-aucpr:0.97254
[21:36:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24]	validation-logloss:0.55020	validation-auc:0.96755	validation-aucpr:0.97269
[21:36:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25]	validation-logloss:0.54542	validation-auc:0.96763	validation-aucpr:0.97274
[21:36:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26]	validation-logloss:0.54071	validation-auc:0.96775	validation-aucpr:0.97283
[21:36:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27]	validation-logloss:0.53625	validation-auc:0.96798	validation-aucpr:0.97305
[21:36:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28]	validation-logloss:0.53193	validation-auc:0.96805	validation-aucpr:0.97309
[21:36:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29]	validation-logloss:0.52795	validation-auc:0.96809	validation-aucpr:0.97307
[21:36:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30]	validation-logloss:0.52363	validation-auc:0.96827	validation-aucpr:0.97320
[21:36:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31]	validation-logloss:0.51932	validation-auc:0.96834	validation-aucpr:0.97325
[21:36:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32]	validation-logloss:0.51512	validation-auc:0.96837	validation-aucpr:0.97328
[21:36:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33]	validation-logloss:0.51145	validation-auc:0.96831	validation-aucpr:0.97324
[21:36:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34]	validation-logloss:0.50748	validation-auc:0.96841	validation-aucpr:0.97332
[21:36:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35]	validation-logloss:0.50348	validation-auc:0.96838	validation-aucpr:0.97330
[21:36:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36]	validation-logloss:0.49946	validation-auc:0.96849	validation-aucpr:0.97340
[21:36:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37]	validation-logloss:0.49601	validation-auc:0.96840	validation-aucpr:0.97334
[21:36:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38]	validation-logloss:0.49213	validation-auc:0.96845	validation-aucpr:0.97338
[21:36:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39]	validation-logloss:0.48880	validation-auc:0.96820	validation-aucpr:0.97320
[21:36:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40]	validation-logloss:0.48505	validation-auc:0.96843	validation-aucpr:0.97337
[21:36:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41]	validation-logloss:0.48124	validation-auc:0.96851	validation-aucpr:0.97343
[21:37:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42]	validation-logloss:0.47749	validation-auc:0.96877	validation-aucpr:0.97362
[21:37:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43]	validation-logloss:0.47407	validation-auc:0.96887	validation-aucpr:0.97371
[21:37:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44]	validation-logloss:0.47047	validation-auc:0.96892	validation-aucpr:0.97374
[21:37:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45]	validation-logloss:0.46695	validation-auc:0.96902	validation-aucpr:0.97382
[21:37:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46]	validation-logloss:0.46362	validation-auc:0.96900	validation-aucpr:0.97380
[21:37:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[47]	validation-logloss:0.46038	validation-auc:0.96900	validation-aucpr:0.97379
[21:37:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[48]	validation-logloss:0.45711	validation-auc:0.96907	validation-aucpr:0.97385
[21:37:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[49]	validation-logloss:0.45429	validation-auc:0.96889	validation-aucpr:0.97372
[21:37:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[50]	validation-logloss:0.45099	validation-auc:0.96894	validation-aucpr:0.97375
[21:37:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[51]	validation-logloss:0.44790	validation-auc:0.96892	validation-aucpr:0.97374
[21:37:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[52]	validation-logloss:0.44479	validation-auc:0.96886	validation-aucpr:0.97370
[21:37:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[53]	validation-logloss:0.44180	validation-auc:0.96880	validation-aucpr:0.97365
[21:37:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[54]	validation-logloss:0.43888	validation-auc:0.96885	validation-aucpr:0.97371
[21:37:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[55]	validation-logloss:0.43583	validation-auc:0.96887	validation-aucpr:0.97373
[21:37:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[56]	validation-logloss:0.43292	validation-auc:0.96877	validation-aucpr:0.97365
[21:37:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[57]	validation-logloss:0.42997	validation-auc:0.96874	validation-aucpr:0.97364
[21:37:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[58]	validation-logloss:0.42704	validation-auc:0.96876	validation-aucpr:0.97365
[21:37:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[59]	validation-logloss:0.42412	validation-auc:0.96886	validation-aucpr:0.97375
[21:37:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[60]	validation-logloss:0.42132	validation-auc:0.96891	validation-aucpr:0.97377
[21:37:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[61]	validation-logloss:0.41853	validation-auc:0.96886	validation-aucpr:0.97374
[21:37:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[62]	validation-logloss:0.41579	validation-auc:0.96898	validation-aucpr:0.97382
[21:37:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[63]	validation-logloss:0.41310	validation-auc:0.96891	validation-aucpr:0.97376
[21:37:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[64]	validation-logloss:0.41074	validation-auc:0.96888	validation-aucpr:0.97374
[21:37:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[65]	validation-logloss:0.40811	validation-auc:0.96889	validation-aucpr:0.97374
[21:37:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[66]	validation-logloss:0.40561	validation-auc:0.96891	validation-aucpr:0.97375
[21:37:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[67]	validation-logloss:0.40350	validation-auc:0.96882	validation-aucpr:0.97370
[21:37:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[68]	validation-logloss:0.40123	validation-auc:0.96880	validation-aucpr:0.97369
[21:37:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[69]	validation-logloss:0.39906	validation-auc:0.96889	validation-aucpr:0.97373
[21:37:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[70]	validation-logloss:0.39657	validation-auc:0.96888	validation-aucpr:0.97373
[21:37:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[71]	validation-logloss:0.39411	validation-auc:0.96892	validation-aucpr:0.97376
[21:37:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[72]	validation-logloss:0.39167	validation-auc:0.96898	validation-aucpr:0.97380
[21:37:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[73]	validation-logloss:0.38967	validation-auc:0.96896	validation-aucpr:0.97377
[21:37:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[74]	validation-logloss:0.38737	validation-auc:0.96896	validation-aucpr:0.97377
[21:37:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[75]	validation-logloss:0.38515	validation-auc:0.96897	validation-aucpr:0.97377
[21:37:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[76]	validation-logloss:0.38326	validation-auc:0.96888	validation-aucpr:0.97370
[21:37:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[77]	validation-logloss:0.38101	validation-auc:0.96886	validation-aucpr:0.97369
[21:37:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[78]	validation-logloss:0.37884	validation-auc:0.96897	validation-aucpr:0.97377
[21:37:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[79]	validation-logloss:0.37696	validation-auc:0.96898	validation-aucpr:0.97377
[21:37:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[80]	validation-logloss:0.37519	validation-auc:0.96894	validation-aucpr:0.97373
[21:37:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[81]	validation-logloss:0.37334	validation-auc:0.96894	validation-aucpr:0.97374
[21:38:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[82]	validation-logloss:0.37123	validation-auc:0.96901	validation-aucpr:0.97379
[21:38:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[83]	validation-logloss:0.36922	validation-auc:0.96899	validation-aucpr:0.97378
[21:38:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[84]	validation-logloss:0.36718	validation-auc:0.96908	validation-aucpr:0.97384
[21:38:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[85]	validation-logloss:0.36514	validation-auc:0.96917	validation-aucpr:0.97391
[21:38:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[86]	validation-logloss:0.36313	validation-auc:0.96915	validation-aucpr:0.97389
[21:38:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[87]	validation-logloss:0.36116	validation-auc:0.96913	validation-aucpr:0.97389
[21:38:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[88]	validation-logloss:0.35930	validation-auc:0.96914	validation-aucpr:0.97389
{'best_iteration': '85', 'best_score': '0.9739107500294601'}
Trial 79, Fold 4: Log loss = 0.35929864099093195, Average precision = 0.9738837765660591, ROC-AUC = 0.9691444707921726, Elapsed Time = 127.51060520000101 seconds
Trial 79, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 79, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[21:38:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.68595	validation-auc:0.95081	validation-aucpr:0.95268
[21:38:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.67976	validation-auc:0.95345	validation-aucpr:0.95767
[21:38:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.67292	validation-auc:0.95778	validation-aucpr:0.96435
[21:38:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.66600	validation-auc:0.96044	validation-aucpr:0.96643
[21:38:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.65921	validation-auc:0.96249	validation-aucpr:0.96808
[21:38:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5]	validation-logloss:0.65286	validation-auc:0.96266	validation-aucpr:0.96809
[21:38:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6]	validation-logloss:0.64709	validation-auc:0.96269	validation-aucpr:0.96786
[21:38:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7]	validation-logloss:0.64074	validation-auc:0.96344	validation-aucpr:0.96843
[21:38:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8]	validation-logloss:0.63440	validation-auc:0.96396	validation-aucpr:0.96873
[21:38:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9]	validation-logloss:0.62835	validation-auc:0.96428	validation-aucpr:0.96904
[21:38:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10]	validation-logloss:0.62242	validation-auc:0.96491	validation-aucpr:0.96944
[21:38:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11]	validation-logloss:0.61653	validation-auc:0.96516	validation-aucpr:0.96974
[21:38:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12]	validation-logloss:0.61076	validation-auc:0.96506	validation-aucpr:0.96978
[21:38:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13]	validation-logloss:0.60514	validation-auc:0.96464	validation-aucpr:0.96948
[21:38:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14]	validation-logloss:0.60015	validation-auc:0.96454	validation-aucpr:0.96945
[21:38:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15]	validation-logloss:0.59520	validation-auc:0.96473	validation-aucpr:0.96952
[21:38:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16]	validation-logloss:0.58992	validation-auc:0.96483	validation-aucpr:0.96962
[21:38:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17]	validation-logloss:0.58459	validation-auc:0.96490	validation-aucpr:0.96966
[21:38:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18]	validation-logloss:0.57966	validation-auc:0.96483	validation-aucpr:0.96961
[21:38:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19]	validation-logloss:0.57448	validation-auc:0.96497	validation-aucpr:0.96968
[21:38:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20]	validation-logloss:0.56950	validation-auc:0.96518	validation-aucpr:0.96981
[21:38:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21]	validation-logloss:0.56452	validation-auc:0.96525	validation-aucpr:0.96986
[21:38:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22]	validation-logloss:0.55970	validation-auc:0.96557	validation-aucpr:0.97007
[21:38:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23]	validation-logloss:0.55503	validation-auc:0.96577	validation-aucpr:0.97021
[21:38:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24]	validation-logloss:0.55085	validation-auc:0.96552	validation-aucpr:0.97001
[21:38:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25]	validation-logloss:0.54687	validation-auc:0.96549	validation-aucpr:0.97034
[21:38:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26]	validation-logloss:0.54272	validation-auc:0.96554	validation-aucpr:0.97034
[21:38:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27]	validation-logloss:0.53815	validation-auc:0.96571	validation-aucpr:0.97050
[21:38:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28]	validation-logloss:0.53376	validation-auc:0.96567	validation-aucpr:0.97048
[21:38:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29]	validation-logloss:0.52943	validation-auc:0.96574	validation-aucpr:0.97054
[21:38:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30]	validation-logloss:0.52514	validation-auc:0.96578	validation-aucpr:0.97055
[21:38:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31]	validation-logloss:0.52142	validation-auc:0.96559	validation-aucpr:0.97041
[21:38:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32]	validation-logloss:0.51732	validation-auc:0.96568	validation-aucpr:0.97047
[21:38:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33]	validation-logloss:0.51320	validation-auc:0.96576	validation-aucpr:0.97054
[21:38:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34]	validation-logloss:0.50975	validation-auc:0.96573	validation-aucpr:0.97085
[21:38:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35]	validation-logloss:0.50574	validation-auc:0.96583	validation-aucpr:0.97092
[21:39:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36]	validation-logloss:0.50179	validation-auc:0.96601	validation-aucpr:0.97105
[21:39:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37]	validation-logloss:0.49833	validation-auc:0.96598	validation-aucpr:0.97100
[21:39:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38]	validation-logloss:0.49449	validation-auc:0.96600	validation-aucpr:0.97101
[21:39:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39]	validation-logloss:0.49065	validation-auc:0.96605	validation-aucpr:0.97103
[21:39:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40]	validation-logloss:0.48692	validation-auc:0.96611	validation-aucpr:0.97107
[21:39:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41]	validation-logloss:0.48321	validation-auc:0.96624	validation-aucpr:0.97119
[21:39:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42]	validation-logloss:0.47965	validation-auc:0.96636	validation-aucpr:0.97126
[21:39:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43]	validation-logloss:0.47622	validation-auc:0.96634	validation-aucpr:0.97123
[21:39:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44]	validation-logloss:0.47315	validation-auc:0.96622	validation-aucpr:0.97114
[21:39:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45]	validation-logloss:0.46964	validation-auc:0.96627	validation-aucpr:0.97117
[21:39:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46]	validation-logloss:0.46622	validation-auc:0.96642	validation-aucpr:0.97127
[21:39:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[47]	validation-logloss:0.46299	validation-auc:0.96640	validation-aucpr:0.97123
[21:39:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[48]	validation-logloss:0.45981	validation-auc:0.96645	validation-aucpr:0.97128
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[49]	validation-logloss:0.45651	validation-auc:0.96643	validation-aucpr:0.97128
[21:39:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[50]	validation-logloss:0.45332	validation-auc:0.96649	validation-aucpr:0.97135
[21:39:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[51]	validation-logloss:0.45007	validation-auc:0.96645	validation-aucpr:0.97133
[21:39:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[52]	validation-logloss:0.44695	validation-auc:0.96652	validation-aucpr:0.97141
[21:39:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[53]	validation-logloss:0.44431	validation-auc:0.96646	validation-aucpr:0.97136
[21:39:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[54]	validation-logloss:0.44171	validation-auc:0.96638	validation-aucpr:0.97128
[21:39:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[55]	validation-logloss:0.43875	validation-auc:0.96644	validation-aucpr:0.97134
[21:39:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[56]	validation-logloss:0.43614	validation-auc:0.96636	validation-aucpr:0.97128
[21:39:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[57]	validation-logloss:0.43330	validation-auc:0.96636	validation-aucpr:0.97130
[21:39:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[58]	validation-logloss:0.43039	validation-auc:0.96644	validation-aucpr:0.97136
[21:39:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[59]	validation-logloss:0.42757	validation-auc:0.96653	validation-aucpr:0.97142
[21:39:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[60]	validation-logloss:0.42480	validation-auc:0.96660	validation-aucpr:0.97149
[21:39:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[61]	validation-logloss:0.42201	validation-auc:0.96667	validation-aucpr:0.97154
[21:39:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[62]	validation-logloss:0.41932	validation-auc:0.96665	validation-aucpr:0.97151
[21:39:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[63]	validation-logloss:0.41651	validation-auc:0.96676	validation-aucpr:0.97161
[21:39:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[64]	validation-logloss:0.41391	validation-auc:0.96678	validation-aucpr:0.97162
[21:39:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[65]	validation-logloss:0.41126	validation-auc:0.96680	validation-aucpr:0.97162
[21:39:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[66]	validation-logloss:0.40862	validation-auc:0.96699	validation-aucpr:0.97176
[21:39:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[67]	validation-logloss:0.40622	validation-auc:0.96703	validation-aucpr:0.97178
[21:39:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[68]	validation-logloss:0.40371	validation-auc:0.96707	validation-aucpr:0.97180
[21:39:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[69]	validation-logloss:0.40130	validation-auc:0.96708	validation-aucpr:0.97180
[21:39:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[70]	validation-logloss:0.39885	validation-auc:0.96710	validation-aucpr:0.97183
[21:39:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[71]	validation-logloss:0.39646	validation-auc:0.96714	validation-aucpr:0.97186
[21:39:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[72]	validation-logloss:0.39413	validation-auc:0.96717	validation-aucpr:0.97188
[21:39:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[73]	validation-logloss:0.39208	validation-auc:0.96726	validation-aucpr:0.97194
[21:40:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[74]	validation-logloss:0.38973	validation-auc:0.96734	validation-aucpr:0.97199
[21:40:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[75]	validation-logloss:0.38750	validation-auc:0.96730	validation-aucpr:0.97195
[21:40:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[76]	validation-logloss:0.38525	validation-auc:0.96736	validation-aucpr:0.97195
[21:40:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[77]	validation-logloss:0.38305	validation-auc:0.96735	validation-aucpr:0.97195
[21:40:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[78]	validation-logloss:0.38083	validation-auc:0.96743	validation-aucpr:0.97201
[21:40:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[79]	validation-logloss:0.37870	validation-auc:0.96746	validation-aucpr:0.97216
[21:40:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[80]	validation-logloss:0.37644	validation-auc:0.96762	validation-aucpr:0.97228
[21:40:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[81]	validation-logloss:0.37436	validation-auc:0.96763	validation-aucpr:0.97231
[21:40:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[82]	validation-logloss:0.37229	validation-auc:0.96769	validation-aucpr:0.97234
[21:40:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[83]	validation-logloss:0.37056	validation-auc:0.96767	validation-aucpr:0.97231
[21:40:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[84]	validation-logloss:0.36853	validation-auc:0.96770	validation-aucpr:0.97234
[21:40:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[85]	validation-logloss:0.36651	validation-auc:0.96769	validation-aucpr:0.97233
[21:40:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[86]	validation-logloss:0.36448	validation-auc:0.96781	validation-aucpr:0.97241
[21:40:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[87]	validation-logloss:0.36248	validation-auc:0.96784	validation-aucpr:0.97243
[21:40:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[88]	validation-logloss:0.36055	validation-auc:0.96792	validation-aucpr:0.97248
{'best_iteration': '88', 'best_score': '0.9724756751127037'}
Trial 79, Fold 5: Log loss = 0.36055336322219295, Average precision = 0.9724776594775508, ROC-AUC = 0.9679193653270907, Elapsed Time = 133.4455952999997 seconds
Optimization Progress:  80%|########  | 80/100 [3:41:35<1:19:33, 238.65s/it]
Trial 80, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 80, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[0]	validation-logloss:0.65533	validation-auc:0.93478	validation-aucpr:0.92750
[1]	validation-logloss:0.61742	validation-auc:0.95600	validation-aucpr:0.96118
[2]	validation-logloss:0.58746	validation-auc:0.95738	validation-aucpr:0.96255
[3]	validation-logloss:0.56010	validation-auc:0.95831	validation-aucpr:0.96297
[4]	validation-logloss:0.53597	validation-auc:0.95916	validation-aucpr:0.96383
[5]	validation-logloss:0.51466	validation-auc:0.95938	validation-aucpr:0.96429
[6]	validation-logloss:0.49433	validation-auc:0.95993	validation-aucpr:0.96474
[7]	validation-logloss:0.47623	validation-auc:0.96009	validation-aucpr:0.96453
[8]	validation-logloss:0.45946	validation-auc:0.96034	validation-aucpr:0.96501
[9]	validation-logloss:0.44537	validation-auc:0.96008	validation-aucpr:0.96468
[10]	validation-logloss:0.43100	validation-auc:0.96034	validation-aucpr:0.96487
[11]	validation-logloss:0.41344	validation-auc:0.96248	validation-aucpr:0.96735
[12]	validation-logloss:0.40175	validation-auc:0.96239	validation-aucpr:0.96726
[13]	validation-logloss:0.38653	validation-auc:0.96354	validation-aucpr:0.96861
[14]	validation-logloss:0.37610	validation-auc:0.96364	validation-aucpr:0.96870
[15]	validation-logloss:0.36606	validation-auc:0.96361	validation-aucpr:0.96874
[16]	validation-logloss:0.35691	validation-auc:0.96360	validation-aucpr:0.96869
[17]	validation-logloss:0.34883	validation-auc:0.96371	validation-aucpr:0.96864
[18]	validation-logloss:0.33813	validation-auc:0.96445	validation-aucpr:0.96954
[19]	validation-logloss:0.33035	validation-auc:0.96463	validation-aucpr:0.96965
[20]	validation-logloss:0.32439	validation-auc:0.96455	validation-aucpr:0.96955
[21]	validation-logloss:0.31525	validation-auc:0.96498	validation-aucpr:0.97005
[22]	validation-logloss:0.30812	validation-auc:0.96506	validation-aucpr:0.97014
[23]	validation-logloss:0.30077	validation-auc:0.96533	validation-aucpr:0.97059
[24]	validation-logloss:0.29346	validation-auc:0.96570	validation-aucpr:0.97091
[25]	validation-logloss:0.28878	validation-auc:0.96569	validation-aucpr:0.97089
[26]	validation-logloss:0.28417	validation-auc:0.96580	validation-aucpr:0.97092
[27]	validation-logloss:0.27844	validation-auc:0.96609	validation-aucpr:0.97137
[28]	validation-logloss:0.27446	validation-auc:0.96616	validation-aucpr:0.97139
[29]	validation-logloss:0.27105	validation-auc:0.96626	validation-aucpr:0.97139
[30]	validation-logloss:0.26756	validation-auc:0.96622	validation-aucpr:0.97138
[31]	validation-logloss:0.26467	validation-auc:0.96624	validation-aucpr:0.97133
[32]	validation-logloss:0.26140	validation-auc:0.96629	validation-aucpr:0.97136
[33]	validation-logloss:0.25781	validation-auc:0.96646	validation-aucpr:0.97151
[34]	validation-logloss:0.25492	validation-auc:0.96676	validation-aucpr:0.97178
[35]	validation-logloss:0.25226	validation-auc:0.96688	validation-aucpr:0.97186
[36]	validation-logloss:0.24984	validation-auc:0.96691	validation-aucpr:0.97188
[37]	validation-logloss:0.24759	validation-auc:0.96695	validation-aucpr:0.97191
[38]	validation-logloss:0.24360	validation-auc:0.96727	validation-aucpr:0.97219
[39]	validation-logloss:0.24166	validation-auc:0.96745	validation-aucpr:0.97228
[40]	validation-logloss:0.23828	validation-auc:0.96773	validation-aucpr:0.97256
[41]	validation-logloss:0.23632	validation-auc:0.96784	validation-aucpr:0.97261
[42]	validation-logloss:0.23461	validation-auc:0.96785	validation-aucpr:0.97262
[43]	validation-logloss:0.23161	validation-auc:0.96809	validation-aucpr:0.97283
[44]	validation-logloss:0.22868	validation-auc:0.96840	validation-aucpr:0.97313
[45]	validation-logloss:0.22706	validation-auc:0.96857	validation-aucpr:0.97324
[46]	validation-logloss:0.22572	validation-auc:0.96860	validation-aucpr:0.97328
[47]	validation-logloss:0.22461	validation-auc:0.96864	validation-aucpr:0.97326
[48]	validation-logloss:0.22363	validation-auc:0.96869	validation-aucpr:0.97329
[49]	validation-logloss:0.22238	validation-auc:0.96880	validation-aucpr:0.97333
{'best_iteration': '49', 'best_score': '0.9733320179106767'}
Trial 80, Fold 1: Log loss = 0.22238400995920363, Average precision = 0.9733366848619621, ROC-AUC = 0.9687998869627144, Elapsed Time = 1.4775561999995261 seconds
Trial 80, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 80, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[0]	validation-logloss:0.65402	validation-auc:0.94272	validation-aucpr:0.94373
[1]	validation-logloss:0.61759	validation-auc:0.95731	validation-aucpr:0.95915
[2]	validation-logloss:0.58746	validation-auc:0.95880	validation-aucpr:0.95995
[3]	validation-logloss:0.56106	validation-auc:0.95912	validation-aucpr:0.96075
[4]	validation-logloss:0.53748	validation-auc:0.95895	validation-aucpr:0.96050
[5]	validation-logloss:0.51407	validation-auc:0.96039	validation-aucpr:0.96260
[6]	validation-logloss:0.49406	validation-auc:0.96073	validation-aucpr:0.96255
[7]	validation-logloss:0.47616	validation-auc:0.96078	validation-aucpr:0.96316
[8]	validation-logloss:0.45925	validation-auc:0.96111	validation-aucpr:0.96338
[9]	validation-logloss:0.43884	validation-auc:0.96421	validation-aucpr:0.96687
[10]	validation-logloss:0.42451	validation-auc:0.96405	validation-aucpr:0.96658
[11]	validation-logloss:0.40673	validation-auc:0.96547	validation-aucpr:0.96827
[12]	validation-logloss:0.39538	validation-auc:0.96546	validation-aucpr:0.96849
[13]	validation-logloss:0.38054	validation-auc:0.96611	validation-aucpr:0.96924
[14]	validation-logloss:0.37019	validation-auc:0.96609	validation-aucpr:0.96912
[15]	validation-logloss:0.36089	validation-auc:0.96604	validation-aucpr:0.96992
[16]	validation-logloss:0.34938	validation-auc:0.96651	validation-aucpr:0.97037
[17]	validation-logloss:0.33788	validation-auc:0.96696	validation-aucpr:0.97084
[18]	validation-logloss:0.33014	validation-auc:0.96715	validation-aucpr:0.97102
[19]	validation-logloss:0.32366	validation-auc:0.96731	validation-aucpr:0.97107
[20]	validation-logloss:0.31612	validation-auc:0.96756	validation-aucpr:0.97126
[21]	validation-logloss:0.30994	validation-auc:0.96763	validation-aucpr:0.97124
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[26]	validation-logloss:0.27651	validation-auc:0.96861	validation-aucpr:0.97196
[27]	validation-logloss:0.27213	validation-auc:0.96866	validation-aucpr:0.97194
[28]	validation-logloss:0.26618	validation-auc:0.96891	validation-aucpr:0.97217
[29]	validation-logloss:0.26230	validation-auc:0.96909	validation-aucpr:0.97225
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[31]	validation-logloss:0.25592	validation-auc:0.96927	validation-aucpr:0.97239
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[35]	validation-logloss:0.24381	validation-auc:0.96941	validation-aucpr:0.97254
[36]	validation-logloss:0.24031	validation-auc:0.96954	validation-aucpr:0.97276
[37]	validation-logloss:0.23785	validation-auc:0.96967	validation-aucpr:0.97287
[38]	validation-logloss:0.23413	validation-auc:0.96995	validation-aucpr:0.97302
[39]	validation-logloss:0.23209	validation-auc:0.97006	validation-aucpr:0.97312
[40]	validation-logloss:0.23031	validation-auc:0.97007	validation-aucpr:0.97315
[41]	validation-logloss:0.22808	validation-auc:0.97027	validation-aucpr:0.97335
[42]	validation-logloss:0.22613	validation-auc:0.97032	validation-aucpr:0.97337
[43]	validation-logloss:0.22338	validation-auc:0.97046	validation-aucpr:0.97359
[44]	validation-logloss:0.22142	validation-auc:0.97062	validation-aucpr:0.97367
[45]	validation-logloss:0.21851	validation-auc:0.97089	validation-aucpr:0.97399
[46]	validation-logloss:0.21730	validation-auc:0.97093	validation-aucpr:0.97430
[47]	validation-logloss:0.21561	validation-auc:0.97103	validation-aucpr:0.97439
[48]	validation-logloss:0.21432	validation-auc:0.97114	validation-aucpr:0.97456
[49]	validation-logloss:0.21187	validation-auc:0.97133	validation-aucpr:0.97472
{'best_iteration': '49', 'best_score': '0.9747160669731868'}
Trial 80, Fold 2: Log loss = 0.21186570833554003, Average precision = 0.9747205743499491, ROC-AUC = 0.9713305185130781, Elapsed Time = 1.8612036000013177 seconds
Trial 80, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 80, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[0]	validation-logloss:0.65376	validation-auc:0.94462	validation-aucpr:0.94750
[1]	validation-logloss:0.62036	validation-auc:0.95173	validation-aucpr:0.95404
[2]	validation-logloss:0.58383	validation-auc:0.96344	validation-aucpr:0.96719
[3]	validation-logloss:0.55726	validation-auc:0.96357	validation-aucpr:0.96767
[4]	validation-logloss:0.53355	validation-auc:0.96278	validation-aucpr:0.96694
[5]	validation-logloss:0.51090	validation-auc:0.96318	validation-aucpr:0.96770
[6]	validation-logloss:0.48865	validation-auc:0.96368	validation-aucpr:0.96833
[7]	validation-logloss:0.46807	validation-auc:0.96439	validation-aucpr:0.96901
[8]	validation-logloss:0.45127	validation-auc:0.96430	validation-aucpr:0.96885
[9]	validation-logloss:0.43555	validation-auc:0.96475	validation-aucpr:0.96903
[10]	validation-logloss:0.42111	validation-auc:0.96505	validation-aucpr:0.96934
[11]	validation-logloss:0.40809	validation-auc:0.96506	validation-aucpr:0.96952
[12]	validation-logloss:0.39647	validation-auc:0.96516	validation-aucpr:0.96950
[13]	validation-logloss:0.38509	validation-auc:0.96521	validation-aucpr:0.96959
[14]	validation-logloss:0.37432	validation-auc:0.96518	validation-aucpr:0.96949
[15]	validation-logloss:0.36371	validation-auc:0.96543	validation-aucpr:0.96976
[16]	validation-logloss:0.35475	validation-auc:0.96563	validation-aucpr:0.96986
[17]	validation-logloss:0.34299	validation-auc:0.96641	validation-aucpr:0.97067
[18]	validation-logloss:0.33576	validation-auc:0.96627	validation-aucpr:0.97060
[19]	validation-logloss:0.32862	validation-auc:0.96637	validation-aucpr:0.97076
[20]	validation-logloss:0.32162	validation-auc:0.96654	validation-aucpr:0.97094
[21]	validation-logloss:0.31567	validation-auc:0.96653	validation-aucpr:0.97092
[22]	validation-logloss:0.31016	validation-auc:0.96632	validation-aucpr:0.97075
[23]	validation-logloss:0.30461	validation-auc:0.96633	validation-aucpr:0.97073
[24]	validation-logloss:0.29695	validation-auc:0.96672	validation-aucpr:0.97113
[25]	validation-logloss:0.29207	validation-auc:0.96689	validation-aucpr:0.97127
[26]	validation-logloss:0.28713	validation-auc:0.96716	validation-aucpr:0.97144
[27]	validation-logloss:0.28267	validation-auc:0.96724	validation-aucpr:0.97147
[28]	validation-logloss:0.27852	validation-auc:0.96740	validation-aucpr:0.97162
[29]	validation-logloss:0.27214	validation-auc:0.96778	validation-aucpr:0.97201
[30]	validation-logloss:0.26870	validation-auc:0.96784	validation-aucpr:0.97212
[31]	validation-logloss:0.26521	validation-auc:0.96788	validation-aucpr:0.97217
[32]	validation-logloss:0.26241	validation-auc:0.96786	validation-aucpr:0.97212
[33]	validation-logloss:0.25897	validation-auc:0.96807	validation-aucpr:0.97230
[34]	validation-logloss:0.25367	validation-auc:0.96856	validation-aucpr:0.97273
[35]	validation-logloss:0.25119	validation-auc:0.96863	validation-aucpr:0.97276
[36]	validation-logloss:0.24718	validation-auc:0.96889	validation-aucpr:0.97298
[37]	validation-logloss:0.24480	validation-auc:0.96903	validation-aucpr:0.97303
[38]	validation-logloss:0.24199	validation-auc:0.96918	validation-aucpr:0.97315
[39]	validation-logloss:0.23784	validation-auc:0.96954	validation-aucpr:0.97347
[40]	validation-logloss:0.23613	validation-auc:0.96955	validation-aucpr:0.97350
[41]	validation-logloss:0.23262	validation-auc:0.96974	validation-aucpr:0.97371
[42]	validation-logloss:0.22988	validation-auc:0.97011	validation-aucpr:0.97406
[43]	validation-logloss:0.22840	validation-auc:0.97010	validation-aucpr:0.97406
[44]	validation-logloss:0.22517	validation-auc:0.97040	validation-aucpr:0.97436
[45]	validation-logloss:0.22359	validation-auc:0.97055	validation-aucpr:0.97446
[46]	validation-logloss:0.22068	validation-auc:0.97082	validation-aucpr:0.97471
[47]	validation-logloss:0.21966	validation-auc:0.97081	validation-aucpr:0.97475
[48]	validation-logloss:0.21864	validation-auc:0.97076	validation-aucpr:0.97470
[49]	validation-logloss:0.21779	validation-auc:0.97073	validation-aucpr:0.97465
{'best_iteration': '47', 'best_score': '0.9747548137468102'}
Trial 80, Fold 3: Log loss = 0.21778798181689787, Average precision = 0.9746578127203783, ROC-AUC = 0.9707257165490075, Elapsed Time = 1.7147274000017205 seconds
Trial 80, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 80, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[0]	validation-logloss:0.65456	validation-auc:0.94003	validation-aucpr:0.94400
[1]	validation-logloss:0.61672	validation-auc:0.95860	validation-aucpr:0.96379
[2]	validation-logloss:0.58654	validation-auc:0.95883	validation-aucpr:0.96450
[3]	validation-logloss:0.55935	validation-auc:0.95933	validation-aucpr:0.96450
[4]	validation-logloss:0.53390	validation-auc:0.96083	validation-aucpr:0.96645
[5]	validation-logloss:0.50728	validation-auc:0.96183	validation-aucpr:0.96783
[6]	validation-logloss:0.48299	validation-auc:0.96273	validation-aucpr:0.96863
[7]	validation-logloss:0.46173	validation-auc:0.96385	validation-aucpr:0.96969
[8]	validation-logloss:0.44543	validation-auc:0.96361	validation-aucpr:0.96963
[9]	validation-logloss:0.42956	validation-auc:0.96405	validation-aucpr:0.96999
[10]	validation-logloss:0.41507	validation-auc:0.96418	validation-aucpr:0.97011
[11]	validation-logloss:0.40258	validation-auc:0.96418	validation-aucpr:0.97002
[12]	validation-logloss:0.39091	validation-auc:0.96441	validation-aucpr:0.97020
[13]	validation-logloss:0.38037	validation-auc:0.96443	validation-aucpr:0.97017
[14]	validation-logloss:0.37022	validation-auc:0.96479	validation-aucpr:0.97037
[15]	validation-logloss:0.35768	validation-auc:0.96526	validation-aucpr:0.97088
[16]	validation-logloss:0.34916	validation-auc:0.96534	validation-aucpr:0.97091
[17]	validation-logloss:0.34153	validation-auc:0.96543	validation-aucpr:0.97093
[18]	validation-logloss:0.33189	validation-auc:0.96590	validation-aucpr:0.97132
[19]	validation-logloss:0.32530	validation-auc:0.96568	validation-aucpr:0.97115
[20]	validation-logloss:0.31846	validation-auc:0.96567	validation-aucpr:0.97115
[21]	validation-logloss:0.31212	validation-auc:0.96596	validation-aucpr:0.97131
[22]	validation-logloss:0.30621	validation-auc:0.96611	validation-aucpr:0.97144
[23]	validation-logloss:0.30028	validation-auc:0.96613	validation-aucpr:0.97147
[24]	validation-logloss:0.29545	validation-auc:0.96614	validation-aucpr:0.97147
[25]	validation-logloss:0.29112	validation-auc:0.96611	validation-aucpr:0.97139
[26]	validation-logloss:0.28413	validation-auc:0.96639	validation-aucpr:0.97167
[27]	validation-logloss:0.27983	validation-auc:0.96639	validation-aucpr:0.97169
[28]	validation-logloss:0.27586	validation-auc:0.96645	validation-aucpr:0.97170
[29]	validation-logloss:0.27226	validation-auc:0.96658	validation-aucpr:0.97183
[30]	validation-logloss:0.26897	validation-auc:0.96641	validation-aucpr:0.97173
[31]	validation-logloss:0.26566	validation-auc:0.96655	validation-aucpr:0.97179
[32]	validation-logloss:0.25989	validation-auc:0.96693	validation-aucpr:0.97214
[33]	validation-logloss:0.25542	validation-auc:0.96684	validation-aucpr:0.97219
[34]	validation-logloss:0.25286	validation-auc:0.96698	validation-aucpr:0.97228
[35]	validation-logloss:0.25021	validation-auc:0.96712	validation-aucpr:0.97237
[36]	validation-logloss:0.24779	validation-auc:0.96714	validation-aucpr:0.97239
[37]	validation-logloss:0.24595	validation-auc:0.96707	validation-aucpr:0.97237
[38]	validation-logloss:0.24282	validation-auc:0.96712	validation-aucpr:0.97243
[39]	validation-logloss:0.24117	validation-auc:0.96703	validation-aucpr:0.97236
[40]	validation-logloss:0.23910	validation-auc:0.96724	validation-aucpr:0.97252
[41]	validation-logloss:0.23552	validation-auc:0.96750	validation-aucpr:0.97277
[42]	validation-logloss:0.23372	validation-auc:0.96762	validation-aucpr:0.97287
[43]	validation-logloss:0.23213	validation-auc:0.96768	validation-aucpr:0.97291
[44]	validation-logloss:0.22871	validation-auc:0.96799	validation-aucpr:0.97317
[45]	validation-logloss:0.22711	validation-auc:0.96810	validation-aucpr:0.97326
[46]	validation-logloss:0.22598	validation-auc:0.96814	validation-aucpr:0.97327
[47]	validation-logloss:0.22322	validation-auc:0.96826	validation-aucpr:0.97341
[48]	validation-logloss:0.22074	validation-auc:0.96835	validation-aucpr:0.97349
[49]	validation-logloss:0.21964	validation-auc:0.96840	validation-aucpr:0.97350
{'best_iteration': '49', 'best_score': '0.9734999163688453'}
Trial 80, Fold 4: Log loss = 0.21964332522966643, Average precision = 0.973504239992713, ROC-AUC = 0.9684028448393027, Elapsed Time = 1.6603410000025178 seconds
Trial 80, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 80, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[0]	validation-logloss:0.64959	validation-auc:0.95433	validation-aucpr:0.95874
[1]	validation-logloss:0.61442	validation-auc:0.95794	validation-aucpr:0.96264
[2]	validation-logloss:0.58089	validation-auc:0.96110	validation-aucpr:0.96519
[3]	validation-logloss:0.55395	validation-auc:0.96038	validation-aucpr:0.96570
[4]	validation-logloss:0.53024	validation-auc:0.96010	validation-aucpr:0.96546
[5]	validation-logloss:0.50856	validation-auc:0.96017	validation-aucpr:0.96536
[6]	validation-logloss:0.48844	validation-auc:0.96061	validation-aucpr:0.96573
[7]	validation-logloss:0.47027	validation-auc:0.96081	validation-aucpr:0.96596
[8]	validation-logloss:0.45368	validation-auc:0.96094	validation-aucpr:0.96600
[9]	validation-logloss:0.43961	validation-auc:0.96071	validation-aucpr:0.96577
[10]	validation-logloss:0.42562	validation-auc:0.96077	validation-aucpr:0.96596
[11]	validation-logloss:0.41295	validation-auc:0.96094	validation-aucpr:0.96608
[12]	validation-logloss:0.40169	validation-auc:0.96074	validation-aucpr:0.96575
[13]	validation-logloss:0.39029	validation-auc:0.96078	validation-aucpr:0.96587
[14]	validation-logloss:0.37974	validation-auc:0.96091	validation-aucpr:0.96597
[15]	validation-logloss:0.36909	validation-auc:0.96136	validation-aucpr:0.96638
[16]	validation-logloss:0.35984	validation-auc:0.96172	validation-aucpr:0.96655
[17]	validation-logloss:0.35116	validation-auc:0.96201	validation-aucpr:0.96675
[18]	validation-logloss:0.34334	validation-auc:0.96227	validation-aucpr:0.96685
[19]	validation-logloss:0.33647	validation-auc:0.96225	validation-aucpr:0.96678
[20]	validation-logloss:0.32650	validation-auc:0.96319	validation-aucpr:0.96769
[21]	validation-logloss:0.31995	validation-auc:0.96326	validation-aucpr:0.96774
[22]	validation-logloss:0.31109	validation-auc:0.96390	validation-aucpr:0.96837
[23]	validation-logloss:0.30295	validation-auc:0.96435	validation-aucpr:0.96889
[24]	validation-logloss:0.29614	validation-auc:0.96462	validation-aucpr:0.96911
[25]	validation-logloss:0.29111	validation-auc:0.96464	validation-aucpr:0.96909
[26]	validation-logloss:0.28589	validation-auc:0.96482	validation-aucpr:0.96919
[27]	validation-logloss:0.28150	validation-auc:0.96501	validation-aucpr:0.96941
[28]	validation-logloss:0.27760	validation-auc:0.96518	validation-aucpr:0.96953
[29]	validation-logloss:0.27148	validation-auc:0.96563	validation-aucpr:0.96996
[30]	validation-logloss:0.26822	validation-auc:0.96572	validation-aucpr:0.97012
[31]	validation-logloss:0.26320	validation-auc:0.96594	validation-aucpr:0.97031
[32]	validation-logloss:0.26022	validation-auc:0.96598	validation-aucpr:0.97030
[33]	validation-logloss:0.25566	validation-auc:0.96634	validation-aucpr:0.97063
[34]	validation-logloss:0.25321	validation-auc:0.96639	validation-aucpr:0.97060
[35]	validation-logloss:0.25107	validation-auc:0.96648	validation-aucpr:0.97062
[36]	validation-logloss:0.24876	validation-auc:0.96649	validation-aucpr:0.97062
[37]	validation-logloss:0.24628	validation-auc:0.96652	validation-aucpr:0.97066
[38]	validation-logloss:0.24442	validation-auc:0.96651	validation-aucpr:0.97067
[39]	validation-logloss:0.24284	validation-auc:0.96649	validation-aucpr:0.97052
[40]	validation-logloss:0.23969	validation-auc:0.96669	validation-aucpr:0.97073
[41]	validation-logloss:0.23814	validation-auc:0.96672	validation-aucpr:0.97080
[42]	validation-logloss:0.23652	validation-auc:0.96671	validation-aucpr:0.97073
[43]	validation-logloss:0.23515	validation-auc:0.96674	validation-aucpr:0.97072
[44]	validation-logloss:0.23216	validation-auc:0.96702	validation-aucpr:0.97097
[45]	validation-logloss:0.23103	validation-auc:0.96708	validation-aucpr:0.97133
[46]	validation-logloss:0.22971	validation-auc:0.96722	validation-aucpr:0.97140
[47]	validation-logloss:0.22879	validation-auc:0.96718	validation-aucpr:0.97132
[48]	validation-logloss:0.22767	validation-auc:0.96728	validation-aucpr:0.97136
[49]	validation-logloss:0.22644	validation-auc:0.96733	validation-aucpr:0.97139
{'best_iteration': '46', 'best_score': '0.9713952085868922'}
Trial 80, Fold 5: Log loss = 0.2264398003286916, Average precision = 0.971399548138062, ROC-AUC = 0.9673263846311058, Elapsed Time = 1.7558240999969712 seconds
Optimization Progress:  81%|########1 | 81/100 [3:41:52<54:27, 171.96s/it]  
Trial 81, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 81, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[21:40:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.66439	validation-auc:0.95973	validation-aucpr:0.96404
[21:40:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.63782	validation-auc:0.96425	validation-aucpr:0.96873
[21:40:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.61348	validation-auc:0.96444	validation-aucpr:0.96878
[21:40:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.59234	validation-auc:0.96531	validation-aucpr:0.96962
[21:40:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.57198	validation-auc:0.96639	validation-aucpr:0.97135
[21:40:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5]	validation-logloss:0.55183	validation-auc:0.96726	validation-aucpr:0.97221
[21:40:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6]	validation-logloss:0.53357	validation-auc:0.96764	validation-aucpr:0.97281
[21:40:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7]	validation-logloss:0.51769	validation-auc:0.96735	validation-aucpr:0.97255
[21:40:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8]	validation-logloss:0.50084	validation-auc:0.96778	validation-aucpr:0.97296
[21:40:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9]	validation-logloss:0.48514	validation-auc:0.96789	validation-aucpr:0.97304
[21:40:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10]	validation-logloss:0.47038	validation-auc:0.96796	validation-aucpr:0.97312
[21:40:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11]	validation-logloss:0.45701	validation-auc:0.96781	validation-aucpr:0.97302
[21:40:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12]	validation-logloss:0.44416	validation-auc:0.96798	validation-aucpr:0.97316
{'best_iteration': '12', 'best_score': '0.9731590378806133'}
Trial 81, Fold 1: Log loss = 0.44415862414143403, Average precision = 0.9731129177684463, ROC-AUC = 0.9679839214617895, Elapsed Time = 0.706329900000128 seconds
Trial 81, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 81, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[21:40:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.66391	validation-auc:0.96426	validation-aucpr:0.96762
[21:40:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.64014	validation-auc:0.96421	validation-aucpr:0.96796
[21:40:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.61528	validation-auc:0.96705	validation-aucpr:0.97057
[21:40:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.59523	validation-auc:0.96707	validation-aucpr:0.97068
[21:40:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.57355	validation-auc:0.96800	validation-aucpr:0.97157
[21:40:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5]	validation-logloss:0.55572	validation-auc:0.96775	validation-aucpr:0.97122
[21:40:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6]	validation-logloss:0.53911	validation-auc:0.96777	validation-aucpr:0.97114
[21:40:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7]	validation-logloss:0.52110	validation-auc:0.96827	validation-aucpr:0.97153
[21:40:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8]	validation-logloss:0.50399	validation-auc:0.96878	validation-aucpr:0.97193
[21:40:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9]	validation-logloss:0.49005	validation-auc:0.96905	validation-aucpr:0.97204
[21:40:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10]	validation-logloss:0.47493	validation-auc:0.96971	validation-aucpr:0.97276
[21:40:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11]	validation-logloss:0.46275	validation-auc:0.96938	validation-aucpr:0.97230
[21:40:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12]	validation-logloss:0.44916	validation-auc:0.96987	validation-aucpr:0.97278
{'best_iteration': '12', 'best_score': '0.972779721455394'}
Trial 81, Fold 2: Log loss = 0.44916450695000154, Average precision = 0.9726980587779851, ROC-AUC = 0.9698728292858658, Elapsed Time = 0.8429739999992307 seconds
Trial 81, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 81, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[21:40:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.66408	validation-auc:0.96278	validation-aucpr:0.96586
[21:40:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.63717	validation-auc:0.96630	validation-aucpr:0.97063
[21:40:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.61231	validation-auc:0.96720	validation-aucpr:0.97136
[21:40:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.58920	validation-auc:0.96770	validation-aucpr:0.97171
[21:40:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.56785	validation-auc:0.96851	validation-aucpr:0.97262
[21:40:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5]	validation-logloss:0.54993	validation-auc:0.96975	validation-aucpr:0.97351
[21:40:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6]	validation-logloss:0.53285	validation-auc:0.96942	validation-aucpr:0.97319
[21:40:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7]	validation-logloss:0.51481	validation-auc:0.96965	validation-aucpr:0.97342
[21:40:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8]	validation-logloss:0.49791	validation-auc:0.97032	validation-aucpr:0.97366
[21:40:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9]	validation-logloss:0.48242	validation-auc:0.97037	validation-aucpr:0.97470
[21:40:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10]	validation-logloss:0.46758	validation-auc:0.97087	validation-aucpr:0.97510
[21:40:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11]	validation-logloss:0.45390	validation-auc:0.97110	validation-aucpr:0.97531
[21:40:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12]	validation-logloss:0.44238	validation-auc:0.97106	validation-aucpr:0.97527
{'best_iteration': '11', 'best_score': '0.975307214390828'}
Trial 81, Fold 3: Log loss = 0.44238187145411595, Average precision = 0.9752327037809126, ROC-AUC = 0.9710605455018251, Elapsed Time = 0.8559263000024657 seconds
Trial 81, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 81, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[21:40:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.66407	validation-auc:0.96168	validation-aucpr:0.96773
[21:40:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.63820	validation-auc:0.96481	validation-aucpr:0.97022
[21:40:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.61422	validation-auc:0.96630	validation-aucpr:0.97147
[21:40:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.59109	validation-auc:0.96894	validation-aucpr:0.97339
[21:40:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.56992	validation-auc:0.96826	validation-aucpr:0.97303
[21:40:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5]	validation-logloss:0.55230	validation-auc:0.96743	validation-aucpr:0.97269
[21:40:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6]	validation-logloss:0.53403	validation-auc:0.96768	validation-aucpr:0.97288
[21:40:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7]	validation-logloss:0.51724	validation-auc:0.96822	validation-aucpr:0.97325
[21:40:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8]	validation-logloss:0.50079	validation-auc:0.96870	validation-aucpr:0.97359
[21:40:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9]	validation-logloss:0.48502	validation-auc:0.96899	validation-aucpr:0.97391
[21:40:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10]	validation-logloss:0.47198	validation-auc:0.96888	validation-aucpr:0.97389
[21:40:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11]	validation-logloss:0.45789	validation-auc:0.96910	validation-aucpr:0.97408
[21:40:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12]	validation-logloss:0.44504	validation-auc:0.96901	validation-aucpr:0.97403
{'best_iteration': '11', 'best_score': '0.9740761196721902'}
Trial 81, Fold 4: Log loss = 0.44503664140822147, Average precision = 0.9739748843423582, ROC-AUC = 0.9690058769739933, Elapsed Time = 0.8716018000013719 seconds
Trial 81, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 81, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[21:40:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.66431	validation-auc:0.96151	validation-aucpr:0.96659
[21:40:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.63806	validation-auc:0.96450	validation-aucpr:0.96951
[21:40:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.61372	validation-auc:0.96548	validation-aucpr:0.97003
[21:40:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.59104	validation-auc:0.96700	validation-aucpr:0.97096
[21:40:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.57164	validation-auc:0.96555	validation-aucpr:0.96999
[21:40:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5]	validation-logloss:0.55163	validation-auc:0.96634	validation-aucpr:0.97067
[21:40:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6]	validation-logloss:0.53362	validation-auc:0.96668	validation-aucpr:0.97090
[21:40:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7]	validation-logloss:0.51637	validation-auc:0.96681	validation-aucpr:0.97106
[21:40:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8]	validation-logloss:0.50208	validation-auc:0.96657	validation-aucpr:0.97070
[21:40:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9]	validation-logloss:0.48906	validation-auc:0.96645	validation-aucpr:0.97083
[21:40:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10]	validation-logloss:0.47680	validation-auc:0.96575	validation-aucpr:0.97021
[21:40:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11]	validation-logloss:0.46304	validation-auc:0.96648	validation-aucpr:0.97075
[21:40:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12]	validation-logloss:0.44987	validation-auc:0.96701	validation-aucpr:0.97118
{'best_iteration': '12', 'best_score': '0.9711795928982662'}
Trial 81, Fold 5: Log loss = 0.44987265941620824, Average precision = 0.9710778666302752, ROC-AUC = 0.9670054066105569, Elapsed Time = 0.9390024000022095 seconds
Optimization Progress:  82%|########2 | 82/100 [3:42:04<37:11, 123.97s/it]
Trial 82, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 82, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[21:41:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.66022	validation-auc:0.95140	validation-aucpr:0.95862
[21:41:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.63158	validation-auc:0.95985	validation-aucpr:0.96379
[21:41:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.60299	validation-auc:0.96466	validation-aucpr:0.97005
[21:41:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.57893	validation-auc:0.96492	validation-aucpr:0.97004
[21:41:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.55352	validation-auc:0.96680	validation-aucpr:0.97186
[21:41:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5]	validation-logloss:0.53363	validation-auc:0.96682	validation-aucpr:0.97172
[21:41:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6]	validation-logloss:0.51515	validation-auc:0.96664	validation-aucpr:0.97154
[21:41:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7]	validation-logloss:0.49533	validation-auc:0.96719	validation-aucpr:0.97214
[21:41:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8]	validation-logloss:0.47949	validation-auc:0.96715	validation-aucpr:0.97228
[21:41:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9]	validation-logloss:0.46524	validation-auc:0.96706	validation-aucpr:0.97213
[21:41:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10]	validation-logloss:0.44909	validation-auc:0.96763	validation-aucpr:0.97263
[21:41:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11]	validation-logloss:0.43392	validation-auc:0.96795	validation-aucpr:0.97296
[21:41:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12]	validation-logloss:0.42032	validation-auc:0.96823	validation-aucpr:0.97318
[21:41:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13]	validation-logloss:0.40691	validation-auc:0.96846	validation-aucpr:0.97343
[21:41:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14]	validation-logloss:0.39433	validation-auc:0.96868	validation-aucpr:0.97365
[21:41:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15]	validation-logloss:0.38313	validation-auc:0.96869	validation-aucpr:0.97370
[21:41:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16]	validation-logloss:0.37264	validation-auc:0.96881	validation-aucpr:0.97378
[21:41:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17]	validation-logloss:0.36408	validation-auc:0.96891	validation-aucpr:0.97381
[21:41:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18]	validation-logloss:0.35645	validation-auc:0.96897	validation-aucpr:0.97384
[21:41:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19]	validation-logloss:0.34878	validation-auc:0.96892	validation-aucpr:0.97379
[21:41:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20]	validation-logloss:0.34190	validation-auc:0.96887	validation-aucpr:0.97367
[21:41:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21]	validation-logloss:0.33506	validation-auc:0.96882	validation-aucpr:0.97370
[21:41:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22]	validation-logloss:0.32710	validation-auc:0.96895	validation-aucpr:0.97384
[21:41:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23]	validation-logloss:0.31955	validation-auc:0.96908	validation-aucpr:0.97398
[21:41:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24]	validation-logloss:0.31255	validation-auc:0.96928	validation-aucpr:0.97413
[21:41:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25]	validation-logloss:0.30734	validation-auc:0.96927	validation-aucpr:0.97414
[21:41:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26]	validation-logloss:0.30157	validation-auc:0.96942	validation-aucpr:0.97431
[21:41:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27]	validation-logloss:0.29611	validation-auc:0.96959	validation-aucpr:0.97460
[21:41:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28]	validation-logloss:0.29173	validation-auc:0.96963	validation-aucpr:0.97462
[21:41:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29]	validation-logloss:0.28730	validation-auc:0.96965	validation-aucpr:0.97463
[21:41:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30]	validation-logloss:0.28223	validation-auc:0.96978	validation-aucpr:0.97480
[21:41:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31]	validation-logloss:0.27879	validation-auc:0.96967	validation-aucpr:0.97471
[21:41:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32]	validation-logloss:0.27538	validation-auc:0.96969	validation-aucpr:0.97473
[21:41:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33]	validation-logloss:0.27105	validation-auc:0.96977	validation-aucpr:0.97482
[21:41:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34]	validation-logloss:0.26781	validation-auc:0.96977	validation-aucpr:0.97482
[21:41:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35]	validation-logloss:0.26466	validation-auc:0.96982	validation-aucpr:0.97486
[21:41:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36]	validation-logloss:0.26094	validation-auc:0.96989	validation-aucpr:0.97496
[21:41:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37]	validation-logloss:0.25720	validation-auc:0.97010	validation-aucpr:0.97511
[21:41:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38]	validation-logloss:0.25449	validation-auc:0.97015	validation-aucpr:0.97513
[21:41:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39]	validation-logloss:0.25112	validation-auc:0.97021	validation-aucpr:0.97520
[21:41:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40]	validation-logloss:0.24876	validation-auc:0.97028	validation-aucpr:0.97524
[21:41:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41]	validation-logloss:0.24637	validation-auc:0.97034	validation-aucpr:0.97526
[21:41:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42]	validation-logloss:0.24430	validation-auc:0.97036	validation-aucpr:0.97525
[21:41:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43]	validation-logloss:0.24175	validation-auc:0.97041	validation-aucpr:0.97530
[21:41:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44]	validation-logloss:0.23970	validation-auc:0.97047	validation-aucpr:0.97533
[21:41:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45]	validation-logloss:0.23714	validation-auc:0.97054	validation-aucpr:0.97540
[21:41:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46]	validation-logloss:0.23480	validation-auc:0.97059	validation-aucpr:0.97546
[21:41:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[47]	validation-logloss:0.23270	validation-auc:0.97067	validation-aucpr:0.97552
[21:41:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[48]	validation-logloss:0.23110	validation-auc:0.97067	validation-aucpr:0.97551
[21:41:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[49]	validation-logloss:0.22951	validation-auc:0.97071	validation-aucpr:0.97556
[21:41:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[50]	validation-logloss:0.22767	validation-auc:0.97073	validation-aucpr:0.97561
[21:41:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[51]	validation-logloss:0.22565	validation-auc:0.97085	validation-aucpr:0.97571
[21:41:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[52]	validation-logloss:0.22424	validation-auc:0.97080	validation-aucpr:0.97567
[21:41:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[53]	validation-logloss:0.22297	validation-auc:0.97083	validation-aucpr:0.97569
[21:41:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[54]	validation-logloss:0.22182	validation-auc:0.97084	validation-aucpr:0.97569
[21:41:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[55]	validation-logloss:0.22034	validation-auc:0.97086	validation-aucpr:0.97569
[21:41:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[56]	validation-logloss:0.21888	validation-auc:0.97087	validation-aucpr:0.97572
[21:41:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[57]	validation-logloss:0.21785	validation-auc:0.97087	validation-aucpr:0.97573
[21:41:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[58]	validation-logloss:0.21675	validation-auc:0.97095	validation-aucpr:0.97579
[21:41:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[59]	validation-logloss:0.21552	validation-auc:0.97103	validation-aucpr:0.97587
[21:41:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[60]	validation-logloss:0.21425	validation-auc:0.97114	validation-aucpr:0.97594
[21:41:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[61]	validation-logloss:0.21330	validation-auc:0.97112	validation-aucpr:0.97593
[21:41:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[62]	validation-logloss:0.21213	validation-auc:0.97117	validation-aucpr:0.97598
[21:41:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[63]	validation-logloss:0.21138	validation-auc:0.97119	validation-aucpr:0.97597
[21:41:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[64]	validation-logloss:0.21012	validation-auc:0.97127	validation-aucpr:0.97603
[21:41:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[65]	validation-logloss:0.20956	validation-auc:0.97127	validation-aucpr:0.97602
[21:41:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[66]	validation-logloss:0.20869	validation-auc:0.97126	validation-aucpr:0.97602
[21:41:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[67]	validation-logloss:0.20776	validation-auc:0.97131	validation-aucpr:0.97605
[21:41:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[68]	validation-logloss:0.20705	validation-auc:0.97137	validation-aucpr:0.97608
[21:41:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[69]	validation-logloss:0.20626	validation-auc:0.97137	validation-aucpr:0.97608
[21:41:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[70]	validation-logloss:0.20541	validation-auc:0.97144	validation-aucpr:0.97613
[21:41:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[71]	validation-logloss:0.20478	validation-auc:0.97140	validation-aucpr:0.97614
[21:41:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[72]	validation-logloss:0.20407	validation-auc:0.97143	validation-aucpr:0.97616
[21:41:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[73]	validation-logloss:0.20360	validation-auc:0.97147	validation-aucpr:0.97617
[21:41:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[74]	validation-logloss:0.20287	validation-auc:0.97156	validation-aucpr:0.97623
[21:41:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[75]	validation-logloss:0.20243	validation-auc:0.97159	validation-aucpr:0.97625
[21:41:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[76]	validation-logloss:0.20191	validation-auc:0.97162	validation-aucpr:0.97627
[21:41:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[77]	validation-logloss:0.20146	validation-auc:0.97161	validation-aucpr:0.97628
[21:41:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[78]	validation-logloss:0.20109	validation-auc:0.97161	validation-aucpr:0.97626
[21:41:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[79]	validation-logloss:0.20056	validation-auc:0.97164	validation-aucpr:0.97629
{'best_iteration': '79', 'best_score': '0.9762865072365965'}
Trial 82, Fold 1: Log loss = 0.20056391807788446, Average precision = 0.9762904774080723, ROC-AUC = 0.9716443400104195, Elapsed Time = 21.972112199997355 seconds
Trial 82, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 82, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[21:41:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.65852	validation-auc:0.96324	validation-aucpr:0.96664
[21:41:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.62994	validation-auc:0.96438	validation-aucpr:0.96797
[21:41:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.60106	validation-auc:0.96664	validation-aucpr:0.97067
[21:41:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.57880	validation-auc:0.96552	validation-aucpr:0.97007
[21:41:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.55430	validation-auc:0.96654	validation-aucpr:0.97091
[21:41:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5]	validation-logloss:0.53413	validation-auc:0.96695	validation-aucpr:0.97102
[21:41:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6]	validation-logloss:0.51642	validation-auc:0.96688	validation-aucpr:0.97069
[21:41:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7]	validation-logloss:0.49698	validation-auc:0.96785	validation-aucpr:0.97155
[21:41:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8]	validation-logloss:0.47898	validation-auc:0.96839	validation-aucpr:0.97205
[21:41:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9]	validation-logloss:0.46398	validation-auc:0.96845	validation-aucpr:0.97208
[21:41:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10]	validation-logloss:0.44807	validation-auc:0.96889	validation-aucpr:0.97240
[21:41:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11]	validation-logloss:0.43286	validation-auc:0.96911	validation-aucpr:0.97265
[21:41:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12]	validation-logloss:0.41842	validation-auc:0.96966	validation-aucpr:0.97312
[21:41:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13]	validation-logloss:0.40715	validation-auc:0.96953	validation-aucpr:0.97297
[21:41:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14]	validation-logloss:0.39453	validation-auc:0.96985	validation-aucpr:0.97326
[21:41:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15]	validation-logloss:0.38331	validation-auc:0.97000	validation-aucpr:0.97333
[21:41:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16]	validation-logloss:0.37254	validation-auc:0.96997	validation-aucpr:0.97326
[21:41:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17]	validation-logloss:0.36256	validation-auc:0.97021	validation-aucpr:0.97335
[21:41:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18]	validation-logloss:0.35322	validation-auc:0.97019	validation-aucpr:0.97336
[21:41:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19]	validation-logloss:0.34460	validation-auc:0.97005	validation-aucpr:0.97329
[21:41:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20]	validation-logloss:0.33771	validation-auc:0.96996	validation-aucpr:0.97319
[21:41:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21]	validation-logloss:0.33074	validation-auc:0.97000	validation-aucpr:0.97321
[21:41:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22]	validation-logloss:0.32393	validation-auc:0.97009	validation-aucpr:0.97327
[21:41:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23]	validation-logloss:0.31633	validation-auc:0.97047	validation-aucpr:0.97356
[21:41:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24]	validation-logloss:0.31062	validation-auc:0.97046	validation-aucpr:0.97354
[21:41:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25]	validation-logloss:0.30435	validation-auc:0.97066	validation-aucpr:0.97370
[21:41:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26]	validation-logloss:0.29952	validation-auc:0.97069	validation-aucpr:0.97370
[21:41:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27]	validation-logloss:0.29377	validation-auc:0.97085	validation-aucpr:0.97441
[21:41:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28]	validation-logloss:0.28952	validation-auc:0.97079	validation-aucpr:0.97434
[21:41:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29]	validation-logloss:0.28420	validation-auc:0.97086	validation-aucpr:0.97438
[21:41:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30]	validation-logloss:0.27905	validation-auc:0.97083	validation-aucpr:0.97438
[21:41:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31]	validation-logloss:0.27502	validation-auc:0.97094	validation-aucpr:0.97448
[21:41:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32]	validation-logloss:0.27154	validation-auc:0.97102	validation-aucpr:0.97450
[21:41:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33]	validation-logloss:0.26720	validation-auc:0.97120	validation-aucpr:0.97465
[21:41:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34]	validation-logloss:0.26309	validation-auc:0.97118	validation-aucpr:0.97466
[21:41:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35]	validation-logloss:0.25912	validation-auc:0.97126	validation-aucpr:0.97478
[21:41:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36]	validation-logloss:0.25509	validation-auc:0.97140	validation-aucpr:0.97489
[21:41:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37]	validation-logloss:0.25160	validation-auc:0.97146	validation-aucpr:0.97493
[21:41:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38]	validation-logloss:0.24891	validation-auc:0.97146	validation-aucpr:0.97494
[21:41:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39]	validation-logloss:0.24574	validation-auc:0.97156	validation-aucpr:0.97498
[21:41:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40]	validation-logloss:0.24268	validation-auc:0.97161	validation-aucpr:0.97502
[21:41:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41]	validation-logloss:0.23995	validation-auc:0.97171	validation-aucpr:0.97508
[21:41:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42]	validation-logloss:0.23790	validation-auc:0.97167	validation-aucpr:0.97504
[21:41:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43]	validation-logloss:0.23516	validation-auc:0.97175	validation-aucpr:0.97511
[21:41:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44]	validation-logloss:0.23270	validation-auc:0.97182	validation-aucpr:0.97522
[21:41:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45]	validation-logloss:0.23085	validation-auc:0.97193	validation-aucpr:0.97528
[21:41:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46]	validation-logloss:0.22865	validation-auc:0.97206	validation-aucpr:0.97538
[21:41:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[47]	validation-logloss:0.22635	validation-auc:0.97215	validation-aucpr:0.97544
[21:41:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[48]	validation-logloss:0.22464	validation-auc:0.97217	validation-aucpr:0.97544
[21:41:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[49]	validation-logloss:0.22286	validation-auc:0.97221	validation-aucpr:0.97541
[21:41:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[50]	validation-logloss:0.22076	validation-auc:0.97232	validation-aucpr:0.97559
[21:41:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[51]	validation-logloss:0.21947	validation-auc:0.97230	validation-aucpr:0.97556
[21:41:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[52]	validation-logloss:0.21821	validation-auc:0.97229	validation-aucpr:0.97555
[21:41:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[53]	validation-logloss:0.21706	validation-auc:0.97231	validation-aucpr:0.97557
[21:41:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[54]	validation-logloss:0.21544	validation-auc:0.97232	validation-aucpr:0.97557
[21:41:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[55]	validation-logloss:0.21390	validation-auc:0.97242	validation-aucpr:0.97564
[21:41:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[56]	validation-logloss:0.21245	validation-auc:0.97244	validation-aucpr:0.97564
[21:41:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[57]	validation-logloss:0.21142	validation-auc:0.97247	validation-aucpr:0.97565
[21:41:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[58]	validation-logloss:0.20965	validation-auc:0.97270	validation-aucpr:0.97582
[21:41:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[59]	validation-logloss:0.20828	validation-auc:0.97275	validation-aucpr:0.97585
[21:41:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[60]	validation-logloss:0.20703	validation-auc:0.97278	validation-aucpr:0.97587
[21:41:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[61]	validation-logloss:0.20604	validation-auc:0.97283	validation-aucpr:0.97591
[21:41:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[62]	validation-logloss:0.20491	validation-auc:0.97287	validation-aucpr:0.97595
[21:41:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[63]	validation-logloss:0.20411	validation-auc:0.97286	validation-aucpr:0.97595
[21:41:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[64]	validation-logloss:0.20313	validation-auc:0.97287	validation-aucpr:0.97594
[21:41:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[65]	validation-logloss:0.20258	validation-auc:0.97285	validation-aucpr:0.97587
[21:41:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[66]	validation-logloss:0.20152	validation-auc:0.97296	validation-aucpr:0.97594
[21:41:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[67]	validation-logloss:0.20068	validation-auc:0.97300	validation-aucpr:0.97597
[21:41:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[68]	validation-logloss:0.20021	validation-auc:0.97292	validation-aucpr:0.97591
[21:41:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[69]	validation-logloss:0.19932	validation-auc:0.97301	validation-aucpr:0.97593
[21:41:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[70]	validation-logloss:0.19841	validation-auc:0.97305	validation-aucpr:0.97596
[21:41:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[71]	validation-logloss:0.19785	validation-auc:0.97310	validation-aucpr:0.97611
[21:41:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[72]	validation-logloss:0.19691	validation-auc:0.97314	validation-aucpr:0.97616
[21:41:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[73]	validation-logloss:0.19613	validation-auc:0.97317	validation-aucpr:0.97617
[21:41:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[74]	validation-logloss:0.19551	validation-auc:0.97314	validation-aucpr:0.97614
[21:41:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[75]	validation-logloss:0.19509	validation-auc:0.97305	validation-aucpr:0.97601
[21:41:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[76]	validation-logloss:0.19467	validation-auc:0.97303	validation-aucpr:0.97594
[21:41:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[77]	validation-logloss:0.19436	validation-auc:0.97300	validation-aucpr:0.97599
[21:41:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[78]	validation-logloss:0.19418	validation-auc:0.97295	validation-aucpr:0.97599
[21:41:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[79]	validation-logloss:0.19370	validation-auc:0.97299	validation-aucpr:0.97603
{'best_iteration': '73', 'best_score': '0.9761700567932763'}
Trial 82, Fold 2: Log loss = 0.193696307689738, Average precision = 0.9760314877461944, ROC-AUC = 0.9729945088546351, Elapsed Time = 27.715898099999322 seconds
Trial 82, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 82, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[21:41:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.65969	validation-auc:0.95616	validation-aucpr:0.96138
[21:41:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.62780	validation-auc:0.96550	validation-aucpr:0.97001
[21:41:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.60241	validation-auc:0.96617	validation-aucpr:0.97053
[21:41:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.57612	validation-auc:0.96671	validation-aucpr:0.97084
[21:41:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.55490	validation-auc:0.96624	validation-aucpr:0.97064
[21:41:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5]	validation-logloss:0.53444	validation-auc:0.96705	validation-aucpr:0.97130
[21:41:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6]	validation-logloss:0.51546	validation-auc:0.96762	validation-aucpr:0.97163
[21:41:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7]	validation-logloss:0.49534	validation-auc:0.96825	validation-aucpr:0.97227
[21:41:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8]	validation-logloss:0.47684	validation-auc:0.96860	validation-aucpr:0.97259
[21:41:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9]	validation-logloss:0.46186	validation-auc:0.96845	validation-aucpr:0.97241
[21:41:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10]	validation-logloss:0.44637	validation-auc:0.96845	validation-aucpr:0.97244
[21:41:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11]	validation-logloss:0.43114	validation-auc:0.96883	validation-aucpr:0.97280
[21:41:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12]	validation-logloss:0.41744	validation-auc:0.96901	validation-aucpr:0.97293
[21:41:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13]	validation-logloss:0.40437	validation-auc:0.96915	validation-aucpr:0.97305
[21:41:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14]	validation-logloss:0.39322	validation-auc:0.96930	validation-aucpr:0.97326
[21:41:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15]	validation-logloss:0.38328	validation-auc:0.96921	validation-aucpr:0.97316
[21:41:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16]	validation-logloss:0.37279	validation-auc:0.96926	validation-aucpr:0.97319
[21:41:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17]	validation-logloss:0.36451	validation-auc:0.96926	validation-aucpr:0.97384
[21:41:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18]	validation-logloss:0.35614	validation-auc:0.96937	validation-aucpr:0.97393
[21:41:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19]	validation-logloss:0.34686	validation-auc:0.96958	validation-aucpr:0.97412
[21:41:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20]	validation-logloss:0.33938	validation-auc:0.96965	validation-aucpr:0.97418
[21:41:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21]	validation-logloss:0.33154	validation-auc:0.96965	validation-aucpr:0.97420
[21:41:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22]	validation-logloss:0.32370	validation-auc:0.96985	validation-aucpr:0.97436
[21:41:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23]	validation-logloss:0.31787	validation-auc:0.96971	validation-aucpr:0.97422
[21:41:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24]	validation-logloss:0.31264	validation-auc:0.96972	validation-aucpr:0.97422
[21:41:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25]	validation-logloss:0.30605	validation-auc:0.96987	validation-aucpr:0.97436
[21:41:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26]	validation-logloss:0.29983	validation-auc:0.96996	validation-aucpr:0.97445
[21:41:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27]	validation-logloss:0.29397	validation-auc:0.97007	validation-aucpr:0.97453
[21:41:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28]	validation-logloss:0.28841	validation-auc:0.97021	validation-aucpr:0.97462
[21:41:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29]	validation-logloss:0.28291	validation-auc:0.97035	validation-aucpr:0.97471
[21:41:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30]	validation-logloss:0.27821	validation-auc:0.97038	validation-aucpr:0.97474
[21:41:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31]	validation-logloss:0.27374	validation-auc:0.97038	validation-aucpr:0.97474
[21:41:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32]	validation-logloss:0.27010	validation-auc:0.97033	validation-aucpr:0.97469
[21:41:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33]	validation-logloss:0.26627	validation-auc:0.97025	validation-aucpr:0.97471
[21:41:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34]	validation-logloss:0.26275	validation-auc:0.97035	validation-aucpr:0.97480
[21:42:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35]	validation-logloss:0.25987	validation-auc:0.97041	validation-aucpr:0.97485
[21:42:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36]	validation-logloss:0.25676	validation-auc:0.97051	validation-aucpr:0.97491
[21:42:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37]	validation-logloss:0.25403	validation-auc:0.97044	validation-aucpr:0.97485
[21:42:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38]	validation-logloss:0.25157	validation-auc:0.97043	validation-aucpr:0.97482
[21:42:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39]	validation-logloss:0.24830	validation-auc:0.97055	validation-aucpr:0.97490
[21:42:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40]	validation-logloss:0.24546	validation-auc:0.97047	validation-aucpr:0.97483
[21:42:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41]	validation-logloss:0.24233	validation-auc:0.97059	validation-aucpr:0.97492
[21:42:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42]	validation-logloss:0.23956	validation-auc:0.97066	validation-aucpr:0.97495
[21:42:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43]	validation-logloss:0.23690	validation-auc:0.97066	validation-aucpr:0.97496
[21:42:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44]	validation-logloss:0.23419	validation-auc:0.97076	validation-aucpr:0.97505
[21:42:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45]	validation-logloss:0.23217	validation-auc:0.97087	validation-aucpr:0.97513
[21:42:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46]	validation-logloss:0.23050	validation-auc:0.97085	validation-aucpr:0.97510
[21:42:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[47]	validation-logloss:0.22833	validation-auc:0.97092	validation-aucpr:0.97514
[21:42:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[48]	validation-logloss:0.22673	validation-auc:0.97096	validation-aucpr:0.97522
[21:42:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[49]	validation-logloss:0.22486	validation-auc:0.97104	validation-aucpr:0.97532
[21:42:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[50]	validation-logloss:0.22282	validation-auc:0.97117	validation-aucpr:0.97541
[21:42:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[51]	validation-logloss:0.22138	validation-auc:0.97122	validation-aucpr:0.97542
[21:42:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[52]	validation-logloss:0.22030	validation-auc:0.97118	validation-aucpr:0.97537
[21:42:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[53]	validation-logloss:0.21916	validation-auc:0.97119	validation-aucpr:0.97536
[21:42:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[54]	validation-logloss:0.21785	validation-auc:0.97112	validation-aucpr:0.97530
[21:42:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[55]	validation-logloss:0.21622	validation-auc:0.97122	validation-aucpr:0.97535
[21:42:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[56]	validation-logloss:0.21481	validation-auc:0.97125	validation-aucpr:0.97537
[21:42:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[57]	validation-logloss:0.21341	validation-auc:0.97131	validation-aucpr:0.97543
[21:42:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[58]	validation-logloss:0.21206	validation-auc:0.97140	validation-aucpr:0.97553
[21:42:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[59]	validation-logloss:0.21106	validation-auc:0.97145	validation-aucpr:0.97556
[21:42:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[60]	validation-logloss:0.20988	validation-auc:0.97147	validation-aucpr:0.97558
[21:42:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[61]	validation-logloss:0.20865	validation-auc:0.97154	validation-aucpr:0.97562
[21:42:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[62]	validation-logloss:0.20731	validation-auc:0.97170	validation-aucpr:0.97572
[21:42:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[63]	validation-logloss:0.20645	validation-auc:0.97176	validation-aucpr:0.97574
[21:42:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[64]	validation-logloss:0.20568	validation-auc:0.97184	validation-aucpr:0.97578
[21:42:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[65]	validation-logloss:0.20460	validation-auc:0.97191	validation-aucpr:0.97584
[21:42:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[66]	validation-logloss:0.20367	validation-auc:0.97192	validation-aucpr:0.97583
[21:42:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[67]	validation-logloss:0.20292	validation-auc:0.97199	validation-aucpr:0.97587
[21:42:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[68]	validation-logloss:0.20232	validation-auc:0.97199	validation-aucpr:0.97586
[21:42:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[69]	validation-logloss:0.20165	validation-auc:0.97197	validation-aucpr:0.97583
[21:42:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[70]	validation-logloss:0.20095	validation-auc:0.97197	validation-aucpr:0.97583
[21:42:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[71]	validation-logloss:0.20025	validation-auc:0.97200	validation-aucpr:0.97586
[21:42:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[72]	validation-logloss:0.19964	validation-auc:0.97200	validation-aucpr:0.97582
[21:42:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[73]	validation-logloss:0.19917	validation-auc:0.97201	validation-aucpr:0.97587
[21:42:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[74]	validation-logloss:0.19870	validation-auc:0.97204	validation-aucpr:0.97589
[21:42:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[75]	validation-logloss:0.19816	validation-auc:0.97208	validation-aucpr:0.97592
[21:42:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[76]	validation-logloss:0.19755	validation-auc:0.97215	validation-aucpr:0.97595
[21:42:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[77]	validation-logloss:0.19710	validation-auc:0.97212	validation-aucpr:0.97592
[21:42:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[78]	validation-logloss:0.19660	validation-auc:0.97219	validation-aucpr:0.97597
[21:42:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[79]	validation-logloss:0.19618	validation-auc:0.97219	validation-aucpr:0.97595
{'best_iteration': '78', 'best_score': '0.975968739866936'}
Trial 82, Fold 3: Log loss = 0.1961760106049815, Average precision = 0.9759582368256433, ROC-AUC = 0.9721851193285241, Elapsed Time = 22.21717020000142 seconds
Trial 82, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 82, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[21:42:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.65844	validation-auc:0.95920	validation-aucpr:0.96307
[21:42:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.63012	validation-auc:0.96310	validation-aucpr:0.96845
[21:42:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.60162	validation-auc:0.96409	validation-aucpr:0.97045
[21:42:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.57455	validation-auc:0.96526	validation-aucpr:0.97157
[21:42:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.55353	validation-auc:0.96459	validation-aucpr:0.97083
[21:42:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5]	validation-logloss:0.53391	validation-auc:0.96454	validation-aucpr:0.97067
[21:42:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6]	validation-logloss:0.51284	validation-auc:0.96493	validation-aucpr:0.97097
[21:42:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7]	validation-logloss:0.49576	validation-auc:0.96486	validation-aucpr:0.97070
[21:42:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8]	validation-logloss:0.47775	validation-auc:0.96532	validation-aucpr:0.97118
[21:42:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9]	validation-logloss:0.46365	validation-auc:0.96514	validation-aucpr:0.97099
[21:42:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10]	validation-logloss:0.44946	validation-auc:0.96532	validation-aucpr:0.97108
[21:42:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11]	validation-logloss:0.43454	validation-auc:0.96570	validation-aucpr:0.97135
[21:42:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12]	validation-logloss:0.42270	validation-auc:0.96575	validation-aucpr:0.97133
[21:42:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13]	validation-logloss:0.40997	validation-auc:0.96610	validation-aucpr:0.97167
[21:42:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14]	validation-logloss:0.39947	validation-auc:0.96607	validation-aucpr:0.97159
[21:42:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15]	validation-logloss:0.38966	validation-auc:0.96626	validation-aucpr:0.97165
[21:42:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16]	validation-logloss:0.38065	validation-auc:0.96626	validation-aucpr:0.97164
[21:42:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17]	validation-logloss:0.37008	validation-auc:0.96656	validation-aucpr:0.97190
[21:42:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18]	validation-logloss:0.35992	validation-auc:0.96693	validation-aucpr:0.97221
[21:42:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19]	validation-logloss:0.35059	validation-auc:0.96711	validation-aucpr:0.97239
[21:42:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20]	validation-logloss:0.34337	validation-auc:0.96713	validation-aucpr:0.97236
[21:42:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21]	validation-logloss:0.33478	validation-auc:0.96747	validation-aucpr:0.97266
[21:42:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22]	validation-logloss:0.32689	validation-auc:0.96788	validation-aucpr:0.97297
[21:42:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23]	validation-logloss:0.31944	validation-auc:0.96838	validation-aucpr:0.97337
[21:42:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24]	validation-logloss:0.31388	validation-auc:0.96822	validation-aucpr:0.97324
[21:42:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25]	validation-logloss:0.30846	validation-auc:0.96827	validation-aucpr:0.97328
[21:42:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26]	validation-logloss:0.30366	validation-auc:0.96811	validation-aucpr:0.97313
[21:42:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27]	validation-logloss:0.29902	validation-auc:0.96803	validation-aucpr:0.97309
[21:42:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28]	validation-logloss:0.29328	validation-auc:0.96811	validation-aucpr:0.97317
[21:42:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29]	validation-logloss:0.28777	validation-auc:0.96836	validation-aucpr:0.97341
[21:42:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30]	validation-logloss:0.28265	validation-auc:0.96856	validation-aucpr:0.97359
[21:42:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31]	validation-logloss:0.27772	validation-auc:0.96878	validation-aucpr:0.97377
[21:42:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32]	validation-logloss:0.27329	validation-auc:0.96877	validation-aucpr:0.97377
[21:42:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33]	validation-logloss:0.26900	validation-auc:0.96895	validation-aucpr:0.97392
[21:42:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34]	validation-logloss:0.26580	validation-auc:0.96890	validation-aucpr:0.97389
[21:42:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35]	validation-logloss:0.26284	validation-auc:0.96900	validation-aucpr:0.97396
[21:42:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36]	validation-logloss:0.25933	validation-auc:0.96913	validation-aucpr:0.97407
[21:42:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37]	validation-logloss:0.25564	validation-auc:0.96937	validation-aucpr:0.97426
[21:42:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38]	validation-logloss:0.25316	validation-auc:0.96944	validation-aucpr:0.97432
[21:42:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39]	validation-logloss:0.25002	validation-auc:0.96945	validation-aucpr:0.97434
[21:42:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40]	validation-logloss:0.24767	validation-auc:0.96954	validation-aucpr:0.97439
[21:42:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41]	validation-logloss:0.24454	validation-auc:0.96974	validation-aucpr:0.97455
[21:42:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42]	validation-logloss:0.24178	validation-auc:0.96984	validation-aucpr:0.97463
[21:42:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43]	validation-logloss:0.23897	validation-auc:0.97000	validation-aucpr:0.97475
[21:42:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44]	validation-logloss:0.23651	validation-auc:0.97014	validation-aucpr:0.97486
[21:42:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45]	validation-logloss:0.23400	validation-auc:0.97025	validation-aucpr:0.97494
[21:42:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46]	validation-logloss:0.23242	validation-auc:0.97021	validation-aucpr:0.97490
[21:42:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[47]	validation-logloss:0.23029	validation-auc:0.97018	validation-aucpr:0.97490
[21:42:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[48]	validation-logloss:0.22819	validation-auc:0.97028	validation-aucpr:0.97498
[21:42:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[49]	validation-logloss:0.22653	validation-auc:0.97039	validation-aucpr:0.97504
[21:42:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[50]	validation-logloss:0.22516	validation-auc:0.97042	validation-aucpr:0.97505
[21:42:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[51]	validation-logloss:0.22384	validation-auc:0.97047	validation-aucpr:0.97509
[21:42:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[52]	validation-logloss:0.22190	validation-auc:0.97060	validation-aucpr:0.97518
[21:42:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[53]	validation-logloss:0.22055	validation-auc:0.97071	validation-aucpr:0.97524
[21:42:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[54]	validation-logloss:0.21937	validation-auc:0.97071	validation-aucpr:0.97522
[21:42:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[55]	validation-logloss:0.21775	validation-auc:0.97078	validation-aucpr:0.97527
[21:42:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[56]	validation-logloss:0.21642	validation-auc:0.97081	validation-aucpr:0.97529
[21:42:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[57]	validation-logloss:0.21533	validation-auc:0.97090	validation-aucpr:0.97535
[21:42:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[58]	validation-logloss:0.21435	validation-auc:0.97091	validation-aucpr:0.97537
[21:42:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[59]	validation-logloss:0.21337	validation-auc:0.97090	validation-aucpr:0.97535
[21:42:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[60]	validation-logloss:0.21248	validation-auc:0.97091	validation-aucpr:0.97536
[21:42:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[61]	validation-logloss:0.21157	validation-auc:0.97094	validation-aucpr:0.97538
[21:42:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[62]	validation-logloss:0.21077	validation-auc:0.97094	validation-aucpr:0.97537
[21:42:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[63]	validation-logloss:0.21026	validation-auc:0.97088	validation-aucpr:0.97533
[21:42:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[64]	validation-logloss:0.20956	validation-auc:0.97087	validation-aucpr:0.97532
[21:42:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[65]	validation-logloss:0.20875	validation-auc:0.97088	validation-aucpr:0.97533
[21:42:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[66]	validation-logloss:0.20800	validation-auc:0.97089	validation-aucpr:0.97535
[21:42:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[67]	validation-logloss:0.20728	validation-auc:0.97096	validation-aucpr:0.97540
[21:42:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[68]	validation-logloss:0.20667	validation-auc:0.97099	validation-aucpr:0.97540
[21:42:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[69]	validation-logloss:0.20610	validation-auc:0.97095	validation-aucpr:0.97539
[21:42:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[70]	validation-logloss:0.20522	validation-auc:0.97102	validation-aucpr:0.97544
[21:42:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[71]	validation-logloss:0.20453	validation-auc:0.97111	validation-aucpr:0.97550
[21:42:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[72]	validation-logloss:0.20354	validation-auc:0.97124	validation-aucpr:0.97560
[21:42:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[73]	validation-logloss:0.20273	validation-auc:0.97131	validation-aucpr:0.97565
[21:42:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[74]	validation-logloss:0.20190	validation-auc:0.97144	validation-aucpr:0.97576
[21:42:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[75]	validation-logloss:0.20140	validation-auc:0.97149	validation-aucpr:0.97578
[21:42:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[76]	validation-logloss:0.20091	validation-auc:0.97150	validation-aucpr:0.97579
[21:42:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[77]	validation-logloss:0.19996	validation-auc:0.97162	validation-aucpr:0.97589
[21:42:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[78]	validation-logloss:0.19941	validation-auc:0.97164	validation-aucpr:0.97591
[21:42:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[79]	validation-logloss:0.19866	validation-auc:0.97174	validation-aucpr:0.97598
{'best_iteration': '79', 'best_score': '0.9759841043421987'}
Trial 82, Fold 4: Log loss = 0.1986568267174289, Average precision = 0.9759880043423315, ROC-AUC = 0.9717427323190055, Elapsed Time = 22.36301789999925 seconds
Trial 82, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 82, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[21:42:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.65841	validation-auc:0.96003	validation-aucpr:0.96572
[21:42:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.63094	validation-auc:0.96140	validation-aucpr:0.96647
[21:42:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.60218	validation-auc:0.96399	validation-aucpr:0.96934
[21:42:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.57621	validation-auc:0.96503	validation-aucpr:0.97020
[21:42:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.55177	validation-auc:0.96614	validation-aucpr:0.97085
[21:42:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5]	validation-logloss:0.52964	validation-auc:0.96619	validation-aucpr:0.97119
[21:42:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6]	validation-logloss:0.50891	validation-auc:0.96641	validation-aucpr:0.97138
[21:42:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7]	validation-logloss:0.49048	validation-auc:0.96635	validation-aucpr:0.97131
[21:42:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8]	validation-logloss:0.47244	validation-auc:0.96709	validation-aucpr:0.97176
[21:42:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9]	validation-logloss:0.45609	validation-auc:0.96711	validation-aucpr:0.97180
[21:42:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10]	validation-logloss:0.44099	validation-auc:0.96714	validation-aucpr:0.97172
[21:42:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11]	validation-logloss:0.42691	validation-auc:0.96722	validation-aucpr:0.97176
[21:42:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12]	validation-logloss:0.41372	validation-auc:0.96777	validation-aucpr:0.97213
[21:42:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13]	validation-logloss:0.40274	validation-auc:0.96760	validation-aucpr:0.97210
[21:42:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14]	validation-logloss:0.39146	validation-auc:0.96752	validation-aucpr:0.97199
[21:42:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15]	validation-logloss:0.38195	validation-auc:0.96743	validation-aucpr:0.97194
[21:42:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16]	validation-logloss:0.37155	validation-auc:0.96770	validation-aucpr:0.97217
[21:42:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17]	validation-logloss:0.36297	validation-auc:0.96764	validation-aucpr:0.97215
[21:42:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18]	validation-logloss:0.35341	validation-auc:0.96786	validation-aucpr:0.97234
[21:42:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19]	validation-logloss:0.34495	validation-auc:0.96781	validation-aucpr:0.97232
[21:42:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20]	validation-logloss:0.33735	validation-auc:0.96792	validation-aucpr:0.97248
[21:42:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21]	validation-logloss:0.33008	validation-auc:0.96799	validation-aucpr:0.97252
[21:42:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22]	validation-logloss:0.32267	validation-auc:0.96813	validation-aucpr:0.97261
[21:42:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23]	validation-logloss:0.31655	validation-auc:0.96823	validation-aucpr:0.97272
[21:42:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24]	validation-logloss:0.31009	validation-auc:0.96820	validation-aucpr:0.97272
[21:42:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25]	validation-logloss:0.30439	validation-auc:0.96826	validation-aucpr:0.97287
[21:42:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26]	validation-logloss:0.29898	validation-auc:0.96817	validation-aucpr:0.97278
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[27]	validation-logloss:0.29418	validation-auc:0.96830	validation-aucpr:0.97284
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[28]	validation-logloss:0.28891	validation-auc:0.96837	validation-aucpr:0.97291
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[29]	validation-logloss:0.28382	validation-auc:0.96852	validation-aucpr:0.97302
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[30]	validation-logloss:0.27985	validation-auc:0.96865	validation-aucpr:0.97314
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[31]	validation-logloss:0.27534	validation-auc:0.96871	validation-aucpr:0.97320
[21:42:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32]	validation-logloss:0.27115	validation-auc:0.96881	validation-aucpr:0.97331
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[33]	validation-logloss:0.26803	validation-auc:0.96885	validation-aucpr:0.97347
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[34]	validation-logloss:0.26511	validation-auc:0.96876	validation-aucpr:0.97338
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[35]	validation-logloss:0.26264	validation-auc:0.96855	validation-aucpr:0.97319
[21:42:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36]	validation-logloss:0.25974	validation-auc:0.96859	validation-aucpr:0.97318
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[37]	validation-logloss:0.25657	validation-auc:0.96864	validation-aucpr:0.97322
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[38]	validation-logloss:0.25417	validation-auc:0.96863	validation-aucpr:0.97321
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[39]	validation-logloss:0.25178	validation-auc:0.96871	validation-aucpr:0.97327
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[40]	validation-logloss:0.24940	validation-auc:0.96871	validation-aucpr:0.97326
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[41]	validation-logloss:0.24668	validation-auc:0.96874	validation-aucpr:0.97327
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[42]	validation-logloss:0.24406	validation-auc:0.96879	validation-aucpr:0.97333
[21:42:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43]	validation-logloss:0.24173	validation-auc:0.96886	validation-aucpr:0.97342
[21:42:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44]	validation-logloss:0.23987	validation-auc:0.96887	validation-aucpr:0.97344
[21:42:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45]	validation-logloss:0.23770	validation-auc:0.96896	validation-aucpr:0.97355
[21:42:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46]	validation-logloss:0.23536	validation-auc:0.96912	validation-aucpr:0.97368
[21:42:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[47]	validation-logloss:0.23324	validation-auc:0.96932	validation-aucpr:0.97387
[21:42:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[48]	validation-logloss:0.23179	validation-auc:0.96935	validation-aucpr:0.97389
[21:42:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[49]	validation-logloss:0.22986	validation-auc:0.96940	validation-aucpr:0.97392
[21:42:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[50]	validation-logloss:0.22806	validation-auc:0.96955	validation-aucpr:0.97404
[21:42:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[51]	validation-logloss:0.22632	validation-auc:0.96962	validation-aucpr:0.97408
[21:42:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[52]	validation-logloss:0.22525	validation-auc:0.96957	validation-aucpr:0.97401
[21:42:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[53]	validation-logloss:0.22381	validation-auc:0.96957	validation-aucpr:0.97398
[21:42:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[54]	validation-logloss:0.22227	validation-auc:0.96972	validation-aucpr:0.97408
[21:42:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[55]	validation-logloss:0.22096	validation-auc:0.96979	validation-aucpr:0.97412
[21:42:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[56]	validation-logloss:0.21974	validation-auc:0.96982	validation-aucpr:0.97415
[21:42:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[57]	validation-logloss:0.21837	validation-auc:0.96995	validation-aucpr:0.97426
[21:42:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[58]	validation-logloss:0.21744	validation-auc:0.97002	validation-aucpr:0.97430
[21:42:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[59]	validation-logloss:0.21631	validation-auc:0.97002	validation-aucpr:0.97429
[21:42:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[60]	validation-logloss:0.21554	validation-auc:0.96997	validation-aucpr:0.97427
[21:42:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[61]	validation-logloss:0.21465	validation-auc:0.96998	validation-aucpr:0.97426
[21:42:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[62]	validation-logloss:0.21394	validation-auc:0.96993	validation-aucpr:0.97419
[21:42:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[63]	validation-logloss:0.21308	validation-auc:0.96992	validation-aucpr:0.97416
[21:42:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[64]	validation-logloss:0.21235	validation-auc:0.96997	validation-aucpr:0.97418
[21:42:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[65]	validation-logloss:0.21180	validation-auc:0.96998	validation-aucpr:0.97418
[21:42:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[66]	validation-logloss:0.21117	validation-auc:0.96998	validation-aucpr:0.97419
[21:42:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[67]	validation-logloss:0.21033	validation-auc:0.97004	validation-aucpr:0.97420
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[68]	validation-logloss:0.20987	validation-auc:0.97001	validation-aucpr:0.97416
[21:42:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[69]	validation-logloss:0.20912	validation-auc:0.97003	validation-aucpr:0.97416
[21:42:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[70]	validation-logloss:0.20855	validation-auc:0.96999	validation-aucpr:0.97411
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[71]	validation-logloss:0.20782	validation-auc:0.97004	validation-aucpr:0.97414
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[72]	validation-logloss:0.20707	validation-auc:0.97015	validation-aucpr:0.97418
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[73]	validation-logloss:0.20640	validation-auc:0.97022	validation-aucpr:0.97420
[21:42:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[74]	validation-logloss:0.20593	validation-auc:0.97024	validation-aucpr:0.97419
[21:42:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[75]	validation-logloss:0.20544	validation-auc:0.97029	validation-aucpr:0.97426
[21:42:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[76]	validation-logloss:0.20484	validation-auc:0.97036	validation-aucpr:0.97429
[21:42:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[77]	validation-logloss:0.20445	validation-auc:0.97037	validation-aucpr:0.97430
[21:42:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[78]	validation-logloss:0.20403	validation-auc:0.97038	validation-aucpr:0.97427
[21:42:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[79]	validation-logloss:0.20365	validation-auc:0.97033	validation-aucpr:0.97423
{'best_iteration': '77', 'best_score': '0.9743001257171137'}
Trial 82, Fold 5: Log loss = 0.2036515239633219, Average precision = 0.9742372043288416, ROC-AUC = 0.9703341694071308, Elapsed Time = 22.53138409999883 seconds
Optimization Progress:  83%|########2 | 83/100 [3:44:08<35:11, 124.22s/it]
Trial 83, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 83, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[0]	validation-logloss:0.67223	validation-auc:0.91925	validation-aucpr:0.92320
[1]	validation-logloss:0.65105	validation-auc:0.94335	validation-aucpr:0.94678
[2]	validation-logloss:0.63256	validation-auc:0.94560	validation-aucpr:0.94919
[3]	validation-logloss:0.61417	validation-auc:0.94817	validation-aucpr:0.95285
[4]	validation-logloss:0.59744	validation-auc:0.95010	validation-aucpr:0.95527
[5]	validation-logloss:0.58148	validation-auc:0.95143	validation-aucpr:0.95652
[6]	validation-logloss:0.56842	validation-auc:0.95139	validation-aucpr:0.95635
[7]	validation-logloss:0.55433	validation-auc:0.95200	validation-aucpr:0.95683
[8]	validation-logloss:0.53832	validation-auc:0.95750	validation-aucpr:0.96241
[9]	validation-logloss:0.52726	validation-auc:0.95686	validation-aucpr:0.96173
[10]	validation-logloss:0.51634	validation-auc:0.95645	validation-aucpr:0.96134
[11]	validation-logloss:0.50580	validation-auc:0.95617	validation-aucpr:0.96100
[12]	validation-logloss:0.49557	validation-auc:0.95592	validation-aucpr:0.96066
[13]	validation-logloss:0.48549	validation-auc:0.95596	validation-aucpr:0.96079
[14]	validation-logloss:0.47593	validation-auc:0.95602	validation-aucpr:0.96065
[15]	validation-logloss:0.46453	validation-auc:0.95798	validation-aucpr:0.96345
[16]	validation-logloss:0.45637	validation-auc:0.95780	validation-aucpr:0.96330
[17]	validation-logloss:0.44784	validation-auc:0.95833	validation-aucpr:0.96377
[18]	validation-logloss:0.43932	validation-auc:0.95862	validation-aucpr:0.96401
[19]	validation-logloss:0.43174	validation-auc:0.95864	validation-aucpr:0.96395
[20]	validation-logloss:0.42202	validation-auc:0.95981	validation-aucpr:0.96522
[21]	validation-logloss:0.41564	validation-auc:0.95973	validation-aucpr:0.96522
[22]	validation-logloss:0.40837	validation-auc:0.96005	validation-aucpr:0.96560
[23]	validation-logloss:0.39975	validation-auc:0.96060	validation-aucpr:0.96617
[24]	validation-logloss:0.39368	validation-auc:0.96074	validation-aucpr:0.96632
[25]	validation-logloss:0.38775	validation-auc:0.96083	validation-aucpr:0.96635
[26]	validation-logloss:0.38179	validation-auc:0.96121	validation-aucpr:0.96679
[27]	validation-logloss:0.37692	validation-auc:0.96119	validation-aucpr:0.96678
[28]	validation-logloss:0.36912	validation-auc:0.96181	validation-aucpr:0.96739
[29]	validation-logloss:0.36392	validation-auc:0.96209	validation-aucpr:0.96758
[30]	validation-logloss:0.35898	validation-auc:0.96224	validation-aucpr:0.96773
[31]	validation-logloss:0.35456	validation-auc:0.96233	validation-aucpr:0.96775
[32]	validation-logloss:0.35042	validation-auc:0.96224	validation-aucpr:0.96765
[33]	validation-logloss:0.34433	validation-auc:0.96256	validation-aucpr:0.96801
[34]	validation-logloss:0.34098	validation-auc:0.96255	validation-aucpr:0.96798
[35]	validation-logloss:0.33695	validation-auc:0.96260	validation-aucpr:0.96802
[36]	validation-logloss:0.33302	validation-auc:0.96273	validation-aucpr:0.96814
[37]	validation-logloss:0.32967	validation-auc:0.96280	validation-aucpr:0.96822
[38]	validation-logloss:0.32629	validation-auc:0.96294	validation-aucpr:0.96830
[39]	validation-logloss:0.32336	validation-auc:0.96295	validation-aucpr:0.96830
[40]	validation-logloss:0.32027	validation-auc:0.96295	validation-aucpr:0.96827
[41]	validation-logloss:0.31732	validation-auc:0.96304	validation-aucpr:0.96844
[42]	validation-logloss:0.31478	validation-auc:0.96304	validation-aucpr:0.96841
[43]	validation-logloss:0.31163	validation-auc:0.96308	validation-aucpr:0.96843
[44]	validation-logloss:0.30930	validation-auc:0.96301	validation-aucpr:0.96833
{'best_iteration': '41', 'best_score': '0.9684363819628538'}
Trial 83, Fold 1: Log loss = 0.30929638720353547, Average precision = 0.9683288201497099, ROC-AUC = 0.963007398206698, Elapsed Time = 0.9747122000007948 seconds
Trial 83, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 83, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[0]	validation-logloss:0.67241	validation-auc:0.92366	validation-aucpr:0.92099
[1]	validation-logloss:0.65275	validation-auc:0.93620	validation-aucpr:0.93624
[2]	validation-logloss:0.63482	validation-auc:0.93809	validation-aucpr:0.93925
[3]	validation-logloss:0.61323	validation-auc:0.95316	validation-aucpr:0.95566
[4]	validation-logloss:0.59301	validation-auc:0.95891	validation-aucpr:0.96264
[5]	validation-logloss:0.57816	validation-auc:0.95847	validation-aucpr:0.96152
[6]	validation-logloss:0.56341	validation-auc:0.95844	validation-aucpr:0.96152
[7]	validation-logloss:0.55052	validation-auc:0.95825	validation-aucpr:0.96060
[8]	validation-logloss:0.53769	validation-auc:0.95796	validation-aucpr:0.96014
[9]	validation-logloss:0.52170	validation-auc:0.96019	validation-aucpr:0.96257
[10]	validation-logloss:0.50951	validation-auc:0.96053	validation-aucpr:0.96288
[11]	validation-logloss:0.49885	validation-auc:0.96036	validation-aucpr:0.96254
[12]	validation-logloss:0.48795	validation-auc:0.96128	validation-aucpr:0.96337
[13]	validation-logloss:0.47844	validation-auc:0.96114	validation-aucpr:0.96333
[14]	validation-logloss:0.46896	validation-auc:0.96126	validation-aucpr:0.96344
[15]	validation-logloss:0.46017	validation-auc:0.96127	validation-aucpr:0.96335
[16]	validation-logloss:0.45220	validation-auc:0.96120	validation-aucpr:0.96330
[17]	validation-logloss:0.44383	validation-auc:0.96158	validation-aucpr:0.96355
[18]	validation-logloss:0.43627	validation-auc:0.96135	validation-aucpr:0.96332
[19]	validation-logloss:0.42876	validation-auc:0.96150	validation-aucpr:0.96338
[20]	validation-logloss:0.42017	validation-auc:0.96173	validation-aucpr:0.96365
[21]	validation-logloss:0.41059	validation-auc:0.96252	validation-aucpr:0.96452
[22]	validation-logloss:0.40446	validation-auc:0.96263	validation-aucpr:0.96535
[23]	validation-logloss:0.39797	validation-auc:0.96276	validation-aucpr:0.96544
[24]	validation-logloss:0.38918	validation-auc:0.96335	validation-aucpr:0.96613
[25]	validation-logloss:0.38329	validation-auc:0.96336	validation-aucpr:0.96612
[26]	validation-logloss:0.37564	validation-auc:0.96368	validation-aucpr:0.96647
[27]	validation-logloss:0.36814	validation-auc:0.96397	validation-aucpr:0.96683
[28]	validation-logloss:0.36340	validation-auc:0.96384	validation-aucpr:0.96673
[29]	validation-logloss:0.35857	validation-auc:0.96379	validation-aucpr:0.96637
[30]	validation-logloss:0.35391	validation-auc:0.96399	validation-aucpr:0.96655
[31]	validation-logloss:0.34897	validation-auc:0.96416	validation-aucpr:0.96670
[32]	validation-logloss:0.34464	validation-auc:0.96416	validation-aucpr:0.96705
[33]	validation-logloss:0.34070	validation-auc:0.96423	validation-aucpr:0.96712
[34]	validation-logloss:0.33642	validation-auc:0.96429	validation-aucpr:0.96718
[35]	validation-logloss:0.33299	validation-auc:0.96426	validation-aucpr:0.96711
[36]	validation-logloss:0.32957	validation-auc:0.96430	validation-aucpr:0.96727
[37]	validation-logloss:0.32367	validation-auc:0.96464	validation-aucpr:0.96761
[38]	validation-logloss:0.32007	validation-auc:0.96480	validation-aucpr:0.96762
[39]	validation-logloss:0.31689	validation-auc:0.96487	validation-aucpr:0.96724
[40]	validation-logloss:0.31195	validation-auc:0.96518	validation-aucpr:0.96794
[41]	validation-logloss:0.30835	validation-auc:0.96528	validation-aucpr:0.96827
[42]	validation-logloss:0.30388	validation-auc:0.96560	validation-aucpr:0.96900
[43]	validation-logloss:0.30113	validation-auc:0.96565	validation-aucpr:0.96900
[44]	validation-logloss:0.29833	validation-auc:0.96569	validation-aucpr:0.96901
{'best_iteration': '44', 'best_score': '0.9690120397681583'}
Trial 83, Fold 2: Log loss = 0.2983271238709228, Average precision = 0.9690123800135595, ROC-AUC = 0.965694833382449, Elapsed Time = 1.1981218000000808 seconds
Trial 83, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 83, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[0]	validation-logloss:0.67127	validation-auc:0.93340	validation-aucpr:0.93520
[1]	validation-logloss:0.64749	validation-auc:0.95962	validation-aucpr:0.96329
[2]	validation-logloss:0.62856	validation-auc:0.96076	validation-aucpr:0.96361
[3]	validation-logloss:0.61204	validation-auc:0.95898	validation-aucpr:0.96137
[4]	validation-logloss:0.59547	validation-auc:0.95885	validation-aucpr:0.96133
[5]	validation-logloss:0.58060	validation-auc:0.95870	validation-aucpr:0.96233
[6]	validation-logloss:0.56688	validation-auc:0.95913	validation-aucpr:0.96348
[7]	validation-logloss:0.55315	validation-auc:0.95845	validation-aucpr:0.96280
[8]	validation-logloss:0.53699	validation-auc:0.95979	validation-aucpr:0.96426
[9]	validation-logloss:0.52384	validation-auc:0.96084	validation-aucpr:0.96381
[10]	validation-logloss:0.51142	validation-auc:0.96083	validation-aucpr:0.96376
[11]	validation-logloss:0.49979	validation-auc:0.96119	validation-aucpr:0.96410
[12]	validation-logloss:0.48579	validation-auc:0.96221	validation-aucpr:0.96519
[13]	validation-logloss:0.47580	validation-auc:0.96215	validation-aucpr:0.96552
[14]	validation-logloss:0.46577	validation-auc:0.96230	validation-aucpr:0.96549
[15]	validation-logloss:0.45417	validation-auc:0.96305	validation-aucpr:0.96737
[16]	validation-logloss:0.44563	validation-auc:0.96319	validation-aucpr:0.96741
[17]	validation-logloss:0.43799	validation-auc:0.96358	validation-aucpr:0.96852
[18]	validation-logloss:0.42974	validation-auc:0.96362	validation-aucpr:0.96849
[19]	validation-logloss:0.41938	validation-auc:0.96396	validation-aucpr:0.96881
[20]	validation-logloss:0.41213	validation-auc:0.96394	validation-aucpr:0.96881
[21]	validation-logloss:0.40511	validation-auc:0.96385	validation-aucpr:0.96873
[22]	validation-logloss:0.39575	validation-auc:0.96436	validation-aucpr:0.96914
[23]	validation-logloss:0.38936	validation-auc:0.96447	validation-aucpr:0.96929
[24]	validation-logloss:0.38385	validation-auc:0.96435	validation-aucpr:0.96912
[25]	validation-logloss:0.37855	validation-auc:0.96429	validation-aucpr:0.96921
[26]	validation-logloss:0.37263	validation-auc:0.96441	validation-aucpr:0.96933
[27]	validation-logloss:0.36594	validation-auc:0.96444	validation-aucpr:0.96938
[28]	validation-logloss:0.36107	validation-auc:0.96457	validation-aucpr:0.96945
[29]	validation-logloss:0.35668	validation-auc:0.96458	validation-aucpr:0.96942
[30]	validation-logloss:0.35219	validation-auc:0.96450	validation-aucpr:0.96934
[31]	validation-logloss:0.34820	validation-auc:0.96446	validation-aucpr:0.96930
[32]	validation-logloss:0.34228	validation-auc:0.96457	validation-aucpr:0.96942
[33]	validation-logloss:0.33874	validation-auc:0.96448	validation-aucpr:0.96932
[34]	validation-logloss:0.33459	validation-auc:0.96446	validation-aucpr:0.96931
[35]	validation-logloss:0.33122	validation-auc:0.96449	validation-aucpr:0.96935
[36]	validation-logloss:0.32579	validation-auc:0.96462	validation-aucpr:0.96947
[37]	validation-logloss:0.32164	validation-auc:0.96497	validation-aucpr:0.96974
[38]	validation-logloss:0.31657	validation-auc:0.96508	validation-aucpr:0.96985
[39]	validation-logloss:0.31356	validation-auc:0.96512	validation-aucpr:0.96987
[40]	validation-logloss:0.31023	validation-auc:0.96521	validation-aucpr:0.96993
[41]	validation-logloss:0.30704	validation-auc:0.96532	validation-aucpr:0.96999
[42]	validation-logloss:0.30420	validation-auc:0.96543	validation-aucpr:0.97000
[43]	validation-logloss:0.29993	validation-auc:0.96550	validation-aucpr:0.97014
[44]	validation-logloss:0.29763	validation-auc:0.96553	validation-aucpr:0.97015
{'best_iteration': '44', 'best_score': '0.9701487873135217'}
Trial 83, Fold 3: Log loss = 0.2976307423822404, Average precision = 0.9701537445481727, ROC-AUC = 0.9655256348055974, Elapsed Time = 1.1555424999969546 seconds
Trial 83, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 83, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[0]	validation-logloss:0.67158	validation-auc:0.92830	validation-aucpr:0.92897
[1]	validation-logloss:0.65023	validation-auc:0.94723	validation-aucpr:0.95055
[2]	validation-logloss:0.63138	validation-auc:0.94807	validation-aucpr:0.95194
[3]	validation-logloss:0.61474	validation-auc:0.94994	validation-aucpr:0.95445
[4]	validation-logloss:0.59778	validation-auc:0.95102	validation-aucpr:0.95606
[5]	validation-logloss:0.58273	validation-auc:0.95161	validation-aucpr:0.95671
[6]	validation-logloss:0.56685	validation-auc:0.95257	validation-aucpr:0.95782
[7]	validation-logloss:0.55357	validation-auc:0.95316	validation-aucpr:0.95841
[8]	validation-logloss:0.53982	validation-auc:0.95453	validation-aucpr:0.95997
[9]	validation-logloss:0.52812	validation-auc:0.95437	validation-aucpr:0.95985
[10]	validation-logloss:0.51658	validation-auc:0.95449	validation-aucpr:0.95991
[11]	validation-logloss:0.50351	validation-auc:0.95782	validation-aucpr:0.96340
[12]	validation-logloss:0.49348	validation-auc:0.95750	validation-aucpr:0.96305
[13]	validation-logloss:0.48375	validation-auc:0.95738	validation-aucpr:0.96297
[14]	validation-logloss:0.47095	validation-auc:0.95983	validation-aucpr:0.96582
[15]	validation-logloss:0.46174	validation-auc:0.95975	validation-aucpr:0.96576
[16]	validation-logloss:0.45340	validation-auc:0.95984	validation-aucpr:0.96578
[17]	validation-logloss:0.44503	validation-auc:0.96011	validation-aucpr:0.96599
[18]	validation-logloss:0.43446	validation-auc:0.96112	validation-aucpr:0.96723
[19]	validation-logloss:0.42644	validation-auc:0.96129	validation-aucpr:0.96736
[20]	validation-logloss:0.41965	validation-auc:0.96123	validation-aucpr:0.96734
[21]	validation-logloss:0.41093	validation-auc:0.96153	validation-aucpr:0.96767
[22]	validation-logloss:0.40173	validation-auc:0.96200	validation-aucpr:0.96817
[23]	validation-logloss:0.39619	validation-auc:0.96179	validation-aucpr:0.96797
[24]	validation-logloss:0.38943	validation-auc:0.96236	validation-aucpr:0.96839
[25]	validation-logloss:0.38387	validation-auc:0.96251	validation-aucpr:0.96852
[26]	validation-logloss:0.37843	validation-auc:0.96255	validation-aucpr:0.96848
[27]	validation-logloss:0.37380	validation-auc:0.96254	validation-aucpr:0.96855
[28]	validation-logloss:0.36657	validation-auc:0.96289	validation-aucpr:0.96895
[29]	validation-logloss:0.36132	validation-auc:0.96301	validation-aucpr:0.96904
[30]	validation-logloss:0.35709	validation-auc:0.96303	validation-aucpr:0.96909
[31]	validation-logloss:0.35061	validation-auc:0.96338	validation-aucpr:0.96945
[32]	validation-logloss:0.34666	validation-auc:0.96331	validation-aucpr:0.96932
[33]	validation-logloss:0.34091	validation-auc:0.96346	validation-aucpr:0.96948
[34]	validation-logloss:0.33717	validation-auc:0.96342	validation-aucpr:0.96940
[35]	validation-logloss:0.33388	validation-auc:0.96338	validation-aucpr:0.96933
[36]	validation-logloss:0.33004	validation-auc:0.96338	validation-aucpr:0.96935
[37]	validation-logloss:0.32666	validation-auc:0.96344	validation-aucpr:0.96939
[38]	validation-logloss:0.32288	validation-auc:0.96356	validation-aucpr:0.96948
[39]	validation-logloss:0.31996	validation-auc:0.96343	validation-aucpr:0.96941
[40]	validation-logloss:0.31480	validation-auc:0.96366	validation-aucpr:0.96963
[41]	validation-logloss:0.31004	validation-auc:0.96377	validation-aucpr:0.96974
[42]	validation-logloss:0.30703	validation-auc:0.96382	validation-aucpr:0.96979
[43]	validation-logloss:0.30224	validation-auc:0.96403	validation-aucpr:0.96997
[44]	validation-logloss:0.29991	validation-auc:0.96413	validation-aucpr:0.97002
{'best_iteration': '44', 'best_score': '0.9700243644905334'}
Trial 83, Fold 4: Log loss = 0.29991398528608265, Average precision = 0.9700287573244741, ROC-AUC = 0.9641328140088843, Elapsed Time = 1.205062100001669 seconds
Trial 83, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 83, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[0]	validation-logloss:0.67158	validation-auc:0.92778	validation-aucpr:0.92848
[1]	validation-logloss:0.64804	validation-auc:0.95263	validation-aucpr:0.95765
[2]	validation-logloss:0.62899	validation-auc:0.95517	validation-aucpr:0.95894
[3]	validation-logloss:0.61059	validation-auc:0.95643	validation-aucpr:0.95747
[4]	validation-logloss:0.59455	validation-auc:0.95621	validation-aucpr:0.96105
[5]	validation-logloss:0.57741	validation-auc:0.95642	validation-aucpr:0.96115
[6]	validation-logloss:0.56226	validation-auc:0.95703	validation-aucpr:0.96162
[7]	validation-logloss:0.54856	validation-auc:0.95710	validation-aucpr:0.96223
[8]	validation-logloss:0.53547	validation-auc:0.95692	validation-aucpr:0.96204
[9]	validation-logloss:0.52349	validation-auc:0.95670	validation-aucpr:0.96159
[10]	validation-logloss:0.50925	validation-auc:0.95856	validation-aucpr:0.96377
[11]	validation-logloss:0.49892	validation-auc:0.95835	validation-aucpr:0.96356
[12]	validation-logloss:0.48538	validation-auc:0.95901	validation-aucpr:0.96436
[13]	validation-logloss:0.47371	validation-auc:0.95916	validation-aucpr:0.96478
[14]	validation-logloss:0.46406	validation-auc:0.95931	validation-aucpr:0.96487
[15]	validation-logloss:0.45263	validation-auc:0.95969	validation-aucpr:0.96523
[16]	validation-logloss:0.44433	validation-auc:0.95963	validation-aucpr:0.96514
[17]	validation-logloss:0.43349	validation-auc:0.96033	validation-aucpr:0.96585
[18]	validation-logloss:0.42564	validation-auc:0.96045	validation-aucpr:0.96593
[19]	validation-logloss:0.41793	validation-auc:0.96055	validation-aucpr:0.96591
[20]	validation-logloss:0.41107	validation-auc:0.96065	validation-aucpr:0.96599
[21]	validation-logloss:0.40408	validation-auc:0.96091	validation-aucpr:0.96614
[22]	validation-logloss:0.39781	validation-auc:0.96080	validation-aucpr:0.96601
[23]	validation-logloss:0.38994	validation-auc:0.96092	validation-aucpr:0.96616
[24]	validation-logloss:0.38462	validation-auc:0.96094	validation-aucpr:0.96596
[25]	validation-logloss:0.37944	validation-auc:0.96089	validation-aucpr:0.96583
[26]	validation-logloss:0.37406	validation-auc:0.96087	validation-aucpr:0.96576
[27]	validation-logloss:0.36873	validation-auc:0.96085	validation-aucpr:0.96587
[28]	validation-logloss:0.36350	validation-auc:0.96113	validation-aucpr:0.96595
[29]	validation-logloss:0.35890	validation-auc:0.96134	validation-aucpr:0.96614
[30]	validation-logloss:0.35414	validation-auc:0.96144	validation-aucpr:0.96616
[31]	validation-logloss:0.35049	validation-auc:0.96135	validation-aucpr:0.96663
[32]	validation-logloss:0.34640	validation-auc:0.96129	validation-aucpr:0.96656
[33]	validation-logloss:0.34249	validation-auc:0.96140	validation-aucpr:0.96671
[34]	validation-logloss:0.33917	validation-auc:0.96133	validation-aucpr:0.96662
[35]	validation-logloss:0.33609	validation-auc:0.96123	validation-aucpr:0.96662
[36]	validation-logloss:0.33254	validation-auc:0.96121	validation-aucpr:0.96660
[37]	validation-logloss:0.32934	validation-auc:0.96115	validation-aucpr:0.96657
[38]	validation-logloss:0.32597	validation-auc:0.96119	validation-aucpr:0.96659
[39]	validation-logloss:0.32079	validation-auc:0.96136	validation-aucpr:0.96680
[40]	validation-logloss:0.31752	validation-auc:0.96146	validation-aucpr:0.96684
[41]	validation-logloss:0.31419	validation-auc:0.96158	validation-aucpr:0.96690
[42]	validation-logloss:0.31127	validation-auc:0.96168	validation-aucpr:0.96694
[43]	validation-logloss:0.30856	validation-auc:0.96176	validation-aucpr:0.96702
[44]	validation-logloss:0.30585	validation-auc:0.96184	validation-aucpr:0.96707
{'best_iteration': '44', 'best_score': '0.9670711622018966'}
Trial 83, Fold 5: Log loss = 0.30585460800323083, Average precision = 0.9670759297537443, ROC-AUC = 0.9618388421306877, Elapsed Time = 1.164583699999639 seconds
Optimization Progress:  84%|########4 | 84/100 [3:44:22<24:18, 91.18s/it] 
Trial 84, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 84, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[0]	validation-logloss:0.68515	validation-auc:0.94861	validation-aucpr:0.92425
[1]	validation-logloss:0.67829	validation-auc:0.96219	validation-aucpr:0.96205
[2]	validation-logloss:0.67094	validation-auc:0.96421	validation-aucpr:0.96619
[3]	validation-logloss:0.66356	validation-auc:0.96568	validation-aucpr:0.96876
[4]	validation-logloss:0.65724	validation-auc:0.96543	validation-aucpr:0.96880
[5]	validation-logloss:0.65032	validation-auc:0.96593	validation-aucpr:0.96942
[6]	validation-logloss:0.64396	validation-auc:0.96623	validation-aucpr:0.96967
[7]	validation-logloss:0.63782	validation-auc:0.96662	validation-aucpr:0.97154
[8]	validation-logloss:0.63165	validation-auc:0.96649	validation-aucpr:0.97135
[9]	validation-logloss:0.62510	validation-auc:0.96694	validation-aucpr:0.97169
[10]	validation-logloss:0.61862	validation-auc:0.96716	validation-aucpr:0.97186
[11]	validation-logloss:0.61284	validation-auc:0.96724	validation-aucpr:0.97182
[12]	validation-logloss:0.60680	validation-auc:0.96734	validation-aucpr:0.97047
[13]	validation-logloss:0.60066	validation-auc:0.96758	validation-aucpr:0.97075
[14]	validation-logloss:0.59459	validation-auc:0.96805	validation-aucpr:0.97137
[15]	validation-logloss:0.58865	validation-auc:0.96830	validation-aucpr:0.97154
[16]	validation-logloss:0.58280	validation-auc:0.96849	validation-aucpr:0.97172
[17]	validation-logloss:0.57709	validation-auc:0.96878	validation-aucpr:0.97159
[18]	validation-logloss:0.57219	validation-auc:0.96850	validation-aucpr:0.97134
[19]	validation-logloss:0.56734	validation-auc:0.96834	validation-aucpr:0.97099
[20]	validation-logloss:0.56273	validation-auc:0.96814	validation-aucpr:0.97078
[21]	validation-logloss:0.55741	validation-auc:0.96842	validation-aucpr:0.97137
[22]	validation-logloss:0.55220	validation-auc:0.96862	validation-aucpr:0.97153
[23]	validation-logloss:0.54771	validation-auc:0.96849	validation-aucpr:0.97336
[24]	validation-logloss:0.54255	validation-auc:0.96866	validation-aucpr:0.97352
[25]	validation-logloss:0.53767	validation-auc:0.96862	validation-aucpr:0.97344
[26]	validation-logloss:0.53288	validation-auc:0.96879	validation-aucpr:0.97217
[27]	validation-logloss:0.52815	validation-auc:0.96887	validation-aucpr:0.97226
[28]	validation-logloss:0.52349	validation-auc:0.96887	validation-aucpr:0.97230
[29]	validation-logloss:0.51878	validation-auc:0.96902	validation-aucpr:0.97246
[30]	validation-logloss:0.51484	validation-auc:0.96886	validation-aucpr:0.97230
[31]	validation-logloss:0.51031	validation-auc:0.96902	validation-aucpr:0.97244
[32]	validation-logloss:0.50645	validation-auc:0.96882	validation-aucpr:0.97227
[33]	validation-logloss:0.50212	validation-auc:0.96891	validation-aucpr:0.97236
[34]	validation-logloss:0.49778	validation-auc:0.96896	validation-aucpr:0.97244
[35]	validation-logloss:0.49420	validation-auc:0.96886	validation-aucpr:0.97361
[36]	validation-logloss:0.49054	validation-auc:0.96884	validation-aucpr:0.97356
[37]	validation-logloss:0.48654	validation-auc:0.96892	validation-aucpr:0.97362
[38]	validation-logloss:0.48250	validation-auc:0.96896	validation-aucpr:0.97363
[39]	validation-logloss:0.47853	validation-auc:0.96897	validation-aucpr:0.97210
[40]	validation-logloss:0.47460	validation-auc:0.96903	validation-aucpr:0.97216
[41]	validation-logloss:0.47073	validation-auc:0.96911	validation-aucpr:0.97222
{'best_iteration': '38', 'best_score': '0.9736331182222254'}
Trial 84, Fold 1: Log loss = 0.47073261328856786, Average precision = 0.9729380187269572, ROC-AUC = 0.9691089172796489, Elapsed Time = 1.6764398999985133 seconds
Trial 84, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 84, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[0]	validation-logloss:0.68546	validation-auc:0.94384	validation-aucpr:0.91530
[1]	validation-logloss:0.67803	validation-auc:0.96147	validation-aucpr:0.95453
[2]	validation-logloss:0.67052	validation-auc:0.96678	validation-aucpr:0.96787
[3]	validation-logloss:0.66394	validation-auc:0.96759	validation-aucpr:0.97095
[4]	validation-logloss:0.65766	validation-auc:0.96731	validation-aucpr:0.97088
[5]	validation-logloss:0.65123	validation-auc:0.96732	validation-aucpr:0.97067
[6]	validation-logloss:0.64434	validation-auc:0.96779	validation-aucpr:0.97125
[7]	validation-logloss:0.63766	validation-auc:0.96773	validation-aucpr:0.97147
[8]	validation-logloss:0.63100	validation-auc:0.96825	validation-aucpr:0.97193
[9]	validation-logloss:0.62516	validation-auc:0.96835	validation-aucpr:0.97198
[10]	validation-logloss:0.61868	validation-auc:0.96875	validation-aucpr:0.97233
[11]	validation-logloss:0.61231	validation-auc:0.96948	validation-aucpr:0.97297
[12]	validation-logloss:0.60673	validation-auc:0.96930	validation-aucpr:0.97277
[13]	validation-logloss:0.60094	validation-auc:0.96897	validation-aucpr:0.97259
[14]	validation-logloss:0.59554	validation-auc:0.96885	validation-aucpr:0.97250
[15]	validation-logloss:0.58966	validation-auc:0.96907	validation-aucpr:0.97272
[16]	validation-logloss:0.58385	validation-auc:0.96925	validation-aucpr:0.97290
[17]	validation-logloss:0.57866	validation-auc:0.96938	validation-aucpr:0.97293
[18]	validation-logloss:0.57387	validation-auc:0.96928	validation-aucpr:0.97281
[19]	validation-logloss:0.56844	validation-auc:0.96920	validation-aucpr:0.97279
[20]	validation-logloss:0.56299	validation-auc:0.96931	validation-aucpr:0.97283
[21]	validation-logloss:0.55838	validation-auc:0.96936	validation-aucpr:0.97282
[22]	validation-logloss:0.55374	validation-auc:0.96923	validation-aucpr:0.97265
[23]	validation-logloss:0.54920	validation-auc:0.96903	validation-aucpr:0.97246
[24]	validation-logloss:0.54483	validation-auc:0.96896	validation-aucpr:0.97238
[25]	validation-logloss:0.54034	validation-auc:0.96906	validation-aucpr:0.97246
[26]	validation-logloss:0.53534	validation-auc:0.96926	validation-aucpr:0.97264
[27]	validation-logloss:0.53116	validation-auc:0.96919	validation-aucpr:0.97257
[28]	validation-logloss:0.52645	validation-auc:0.96942	validation-aucpr:0.97277
[29]	validation-logloss:0.52166	validation-auc:0.96961	validation-aucpr:0.97294
[30]	validation-logloss:0.51705	validation-auc:0.96963	validation-aucpr:0.97296
[31]	validation-logloss:0.51255	validation-auc:0.96963	validation-aucpr:0.97289
[32]	validation-logloss:0.50798	validation-auc:0.96980	validation-aucpr:0.97305
[33]	validation-logloss:0.50415	validation-auc:0.96976	validation-aucpr:0.97304
[34]	validation-logloss:0.49980	validation-auc:0.96981	validation-aucpr:0.97309
[35]	validation-logloss:0.49545	validation-auc:0.96994	validation-aucpr:0.97317
[36]	validation-logloss:0.49119	validation-auc:0.97001	validation-aucpr:0.97324
[37]	validation-logloss:0.48705	validation-auc:0.97016	validation-aucpr:0.97338
[38]	validation-logloss:0.48294	validation-auc:0.97023	validation-aucpr:0.97345
[39]	validation-logloss:0.47889	validation-auc:0.97037	validation-aucpr:0.97357
[40]	validation-logloss:0.47495	validation-auc:0.97046	validation-aucpr:0.97365
[41]	validation-logloss:0.47155	validation-auc:0.97039	validation-aucpr:0.97358
{'best_iteration': '40', 'best_score': '0.9736509735508068'}
Trial 84, Fold 2: Log loss = 0.47154738969745186, Average precision = 0.9735147670790396, ROC-AUC = 0.9703878612697465, Elapsed Time = 1.938225699999748 seconds
Trial 84, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 84, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[0]	validation-logloss:0.68526	validation-auc:0.94306	validation-aucpr:0.92744
[1]	validation-logloss:0.67746	validation-auc:0.96507	validation-aucpr:0.96408
[2]	validation-logloss:0.67032	validation-auc:0.96592	validation-aucpr:0.96783
[3]	validation-logloss:0.66304	validation-auc:0.96704	validation-aucpr:0.96927
[4]	validation-logloss:0.65669	validation-auc:0.96760	validation-aucpr:0.97000
[5]	validation-logloss:0.65030	validation-auc:0.96725	validation-aucpr:0.96975
[6]	validation-logloss:0.64333	validation-auc:0.96799	validation-aucpr:0.97172
[7]	validation-logloss:0.63640	validation-auc:0.96903	validation-aucpr:0.97357
[8]	validation-logloss:0.62983	validation-auc:0.96935	validation-aucpr:0.97393
[9]	validation-logloss:0.62392	validation-auc:0.96938	validation-aucpr:0.97383
[10]	validation-logloss:0.61816	validation-auc:0.96933	validation-aucpr:0.97369
[11]	validation-logloss:0.61186	validation-auc:0.96968	validation-aucpr:0.97395
[12]	validation-logloss:0.60628	validation-auc:0.96951	validation-aucpr:0.97384
[13]	validation-logloss:0.60003	validation-auc:0.96992	validation-aucpr:0.97417
[14]	validation-logloss:0.59396	validation-auc:0.97017	validation-aucpr:0.97439
[15]	validation-logloss:0.58876	validation-auc:0.96993	validation-aucpr:0.97415
[16]	validation-logloss:0.58306	validation-auc:0.97006	validation-aucpr:0.97426
[17]	validation-logloss:0.57805	validation-auc:0.96994	validation-aucpr:0.97405
[18]	validation-logloss:0.57233	validation-auc:0.97016	validation-aucpr:0.97418
[19]	validation-logloss:0.56740	validation-auc:0.97000	validation-aucpr:0.97412
[20]	validation-logloss:0.56258	validation-auc:0.97004	validation-aucpr:0.97413
[21]	validation-logloss:0.55780	validation-auc:0.97016	validation-aucpr:0.97422
[22]	validation-logloss:0.55253	validation-auc:0.97045	validation-aucpr:0.97459
[23]	validation-logloss:0.54728	validation-auc:0.97068	validation-aucpr:0.97475
[24]	validation-logloss:0.54275	validation-auc:0.97060	validation-aucpr:0.97467
[25]	validation-logloss:0.53820	validation-auc:0.97059	validation-aucpr:0.97464
[26]	validation-logloss:0.53327	validation-auc:0.97064	validation-aucpr:0.97469
[27]	validation-logloss:0.52841	validation-auc:0.97072	validation-aucpr:0.97474
[28]	validation-logloss:0.52358	validation-auc:0.97089	validation-aucpr:0.97486
[29]	validation-logloss:0.51909	validation-auc:0.97097	validation-aucpr:0.97490
[30]	validation-logloss:0.51449	validation-auc:0.97097	validation-aucpr:0.97491
[31]	validation-logloss:0.51042	validation-auc:0.97104	validation-aucpr:0.97496
[32]	validation-logloss:0.50648	validation-auc:0.97102	validation-aucpr:0.97495
[33]	validation-logloss:0.50207	validation-auc:0.97108	validation-aucpr:0.97500
[34]	validation-logloss:0.49833	validation-auc:0.97099	validation-aucpr:0.97494
[35]	validation-logloss:0.49445	validation-auc:0.97096	validation-aucpr:0.97490
[36]	validation-logloss:0.49077	validation-auc:0.97092	validation-aucpr:0.97483
[37]	validation-logloss:0.48657	validation-auc:0.97105	validation-aucpr:0.97496
[38]	validation-logloss:0.48247	validation-auc:0.97109	validation-aucpr:0.97500
[39]	validation-logloss:0.47843	validation-auc:0.97110	validation-aucpr:0.97501
[40]	validation-logloss:0.47497	validation-auc:0.97103	validation-aucpr:0.97495
[41]	validation-logloss:0.47155	validation-auc:0.97103	validation-aucpr:0.97493
{'best_iteration': '39', 'best_score': '0.9750053475125442'}
Trial 84, Fold 3: Log loss = 0.4715512103153635, Average precision = 0.9749272533373887, ROC-AUC = 0.9710345116102128, Elapsed Time = 1.825174599998718 seconds
Trial 84, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 84, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[0]	validation-logloss:0.68530	validation-auc:0.93955	validation-aucpr:0.92656
[1]	validation-logloss:0.67866	validation-auc:0.95745	validation-aucpr:0.96017
[2]	validation-logloss:0.67118	validation-auc:0.96223	validation-aucpr:0.96612
[3]	validation-logloss:0.66378	validation-auc:0.96525	validation-aucpr:0.97073
[4]	validation-logloss:0.65664	validation-auc:0.96665	validation-aucpr:0.97220
[5]	validation-logloss:0.64954	validation-auc:0.96688	validation-aucpr:0.97241
[6]	validation-logloss:0.64324	validation-auc:0.96713	validation-aucpr:0.97254
[7]	validation-logloss:0.63645	validation-auc:0.96759	validation-aucpr:0.97297
[8]	validation-logloss:0.63032	validation-auc:0.96801	validation-aucpr:0.97327
[9]	validation-logloss:0.62367	validation-auc:0.96840	validation-aucpr:0.97362
[10]	validation-logloss:0.61727	validation-auc:0.96850	validation-aucpr:0.97374
[11]	validation-logloss:0.61138	validation-auc:0.96822	validation-aucpr:0.97348
[12]	validation-logloss:0.60527	validation-auc:0.96835	validation-aucpr:0.97363
[13]	validation-logloss:0.59980	validation-auc:0.96854	validation-aucpr:0.97374
[14]	validation-logloss:0.59379	validation-auc:0.96895	validation-aucpr:0.97404
[15]	validation-logloss:0.58863	validation-auc:0.96887	validation-aucpr:0.97398
[16]	validation-logloss:0.58282	validation-auc:0.96893	validation-aucpr:0.97402
[17]	validation-logloss:0.57703	validation-auc:0.96922	validation-aucpr:0.97426
[18]	validation-logloss:0.57220	validation-auc:0.96905	validation-aucpr:0.97411
[19]	validation-logloss:0.56671	validation-auc:0.96913	validation-aucpr:0.97418
[20]	validation-logloss:0.56127	validation-auc:0.96921	validation-aucpr:0.97426
[21]	validation-logloss:0.55595	validation-auc:0.96942	validation-aucpr:0.97440
[22]	validation-logloss:0.55073	validation-auc:0.96948	validation-aucpr:0.97446
[23]	validation-logloss:0.54605	validation-auc:0.96952	validation-aucpr:0.97449
[24]	validation-logloss:0.54106	validation-auc:0.96973	validation-aucpr:0.97465
[25]	validation-logloss:0.53634	validation-auc:0.96967	validation-aucpr:0.97461
[26]	validation-logloss:0.53149	validation-auc:0.96973	validation-aucpr:0.97467
[27]	validation-logloss:0.52669	validation-auc:0.96985	validation-aucpr:0.97473
[28]	validation-logloss:0.52191	validation-auc:0.97000	validation-aucpr:0.97486
[29]	validation-logloss:0.51727	validation-auc:0.97014	validation-aucpr:0.97496
[30]	validation-logloss:0.51270	validation-auc:0.97025	validation-aucpr:0.97507
[31]	validation-logloss:0.50834	validation-auc:0.97021	validation-aucpr:0.97505
[32]	validation-logloss:0.50438	validation-auc:0.97006	validation-aucpr:0.97494
[33]	validation-logloss:0.50002	validation-auc:0.97013	validation-aucpr:0.97500
[34]	validation-logloss:0.49580	validation-auc:0.97025	validation-aucpr:0.97509
[35]	validation-logloss:0.49203	validation-auc:0.97005	validation-aucpr:0.97493
[36]	validation-logloss:0.48834	validation-auc:0.96996	validation-aucpr:0.97484
[37]	validation-logloss:0.48443	validation-auc:0.97001	validation-aucpr:0.97488
[38]	validation-logloss:0.48080	validation-auc:0.96985	validation-aucpr:0.97474
[39]	validation-logloss:0.47737	validation-auc:0.96974	validation-aucpr:0.97467
[40]	validation-logloss:0.47389	validation-auc:0.96970	validation-aucpr:0.97462
[41]	validation-logloss:0.47001	validation-auc:0.96977	validation-aucpr:0.97469
{'best_iteration': '34', 'best_score': '0.9750907393814487'}
Trial 84, Fold 4: Log loss = 0.4700130718515467, Average precision = 0.9746817828452765, ROC-AUC = 0.969767695897146, Elapsed Time = 1.8695074999995995 seconds
Trial 84, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 84, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[0]	validation-logloss:0.68533	validation-auc:0.93718	validation-aucpr:0.91104
[1]	validation-logloss:0.67771	validation-auc:0.95918	validation-aucpr:0.95235
[2]	validation-logloss:0.67031	validation-auc:0.96389	validation-aucpr:0.96061
[3]	validation-logloss:0.66381	validation-auc:0.96514	validation-aucpr:0.96797
[4]	validation-logloss:0.65673	validation-auc:0.96609	validation-aucpr:0.96704
[5]	validation-logloss:0.65056	validation-auc:0.96544	validation-aucpr:0.96856
[6]	validation-logloss:0.64433	validation-auc:0.96527	validation-aucpr:0.96825
[7]	validation-logloss:0.63829	validation-auc:0.96553	validation-aucpr:0.96835
[8]	validation-logloss:0.63258	validation-auc:0.96513	validation-aucpr:0.96766
[9]	validation-logloss:0.62671	validation-auc:0.96496	validation-aucpr:0.96745
[10]	validation-logloss:0.62023	validation-auc:0.96532	validation-aucpr:0.96796
[11]	validation-logloss:0.61392	validation-auc:0.96582	validation-aucpr:0.96833
[12]	validation-logloss:0.60777	validation-auc:0.96653	validation-aucpr:0.96896
[13]	validation-logloss:0.60165	validation-auc:0.96680	validation-aucpr:0.96928
[14]	validation-logloss:0.59644	validation-auc:0.96675	validation-aucpr:0.97101
[15]	validation-logloss:0.59052	validation-auc:0.96711	validation-aucpr:0.97139
[16]	validation-logloss:0.58478	validation-auc:0.96741	validation-aucpr:0.97167
[17]	validation-logloss:0.57918	validation-auc:0.96747	validation-aucpr:0.97171
[18]	validation-logloss:0.57353	validation-auc:0.96767	validation-aucpr:0.97192
[19]	validation-logloss:0.56865	validation-auc:0.96762	validation-aucpr:0.97183
[20]	validation-logloss:0.56391	validation-auc:0.96757	validation-aucpr:0.97179
[21]	validation-logloss:0.55857	validation-auc:0.96791	validation-aucpr:0.97205
[22]	validation-logloss:0.55349	validation-auc:0.96801	validation-aucpr:0.97217
[23]	validation-logloss:0.54892	validation-auc:0.96797	validation-aucpr:0.97211
[24]	validation-logloss:0.54392	validation-auc:0.96804	validation-aucpr:0.97222
[25]	validation-logloss:0.53970	validation-auc:0.96780	validation-aucpr:0.97200
[26]	validation-logloss:0.53490	validation-auc:0.96785	validation-aucpr:0.97206
[27]	validation-logloss:0.53051	validation-auc:0.96774	validation-aucpr:0.97196
[28]	validation-logloss:0.52645	validation-auc:0.96759	validation-aucpr:0.97180
[29]	validation-logloss:0.52184	validation-auc:0.96774	validation-aucpr:0.97194
[30]	validation-logloss:0.51782	validation-auc:0.96777	validation-aucpr:0.97197
[31]	validation-logloss:0.51328	validation-auc:0.96788	validation-aucpr:0.97208
[32]	validation-logloss:0.50889	validation-auc:0.96805	validation-aucpr:0.97226
[33]	validation-logloss:0.50515	validation-auc:0.96777	validation-aucpr:0.97203
[34]	validation-logloss:0.50093	validation-auc:0.96786	validation-aucpr:0.97214
[35]	validation-logloss:0.49667	validation-auc:0.96793	validation-aucpr:0.97224
[36]	validation-logloss:0.49252	validation-auc:0.96797	validation-aucpr:0.97230
[37]	validation-logloss:0.48844	validation-auc:0.96799	validation-aucpr:0.97233
[38]	validation-logloss:0.48495	validation-auc:0.96787	validation-aucpr:0.97219
[39]	validation-logloss:0.48104	validation-auc:0.96789	validation-aucpr:0.97225
[40]	validation-logloss:0.47710	validation-auc:0.96797	validation-aucpr:0.97232
[41]	validation-logloss:0.47323	validation-auc:0.96813	validation-aucpr:0.97246
{'best_iteration': '41', 'best_score': '0.9724589335569357'}
Trial 84, Fold 5: Log loss = 0.4732269235506302, Average precision = 0.9724522509729793, ROC-AUC = 0.9681272318525538, Elapsed Time = 1.8348221000014746 seconds
Optimization Progress:  85%|########5 | 85/100 [3:44:40<17:15, 69.03s/it]
Trial 85, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 85, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[0]	validation-logloss:0.66495	validation-auc:0.95626	validation-aucpr:0.96087
[1]	validation-logloss:0.63938	validation-auc:0.96148	validation-aucpr:0.96693
[2]	validation-logloss:0.61518	validation-auc:0.96324	validation-aucpr:0.96821
[3]	validation-logloss:0.59263	validation-auc:0.96340	validation-aucpr:0.96846
[4]	validation-logloss:0.57147	validation-auc:0.96446	validation-aucpr:0.96972
[5]	validation-logloss:0.55163	validation-auc:0.96551	validation-aucpr:0.97048
[6]	validation-logloss:0.53320	validation-auc:0.96628	validation-aucpr:0.97106
[7]	validation-logloss:0.51596	validation-auc:0.96735	validation-aucpr:0.97224
[8]	validation-logloss:0.50137	validation-auc:0.96757	validation-aucpr:0.97228
[9]	validation-logloss:0.48649	validation-auc:0.96820	validation-aucpr:0.97197
[10]	validation-logloss:0.47192	validation-auc:0.96832	validation-aucpr:0.97204
[11]	validation-logloss:0.45854	validation-auc:0.96853	validation-aucpr:0.97214
[12]	validation-logloss:0.44623	validation-auc:0.96822	validation-aucpr:0.97198
[13]	validation-logloss:0.43410	validation-auc:0.96828	validation-aucpr:0.97198
[14]	validation-logloss:0.42250	validation-auc:0.96852	validation-aucpr:0.97217
[15]	validation-logloss:0.41193	validation-auc:0.96863	validation-aucpr:0.97219
[16]	validation-logloss:0.40194	validation-auc:0.96865	validation-aucpr:0.97225
[17]	validation-logloss:0.39339	validation-auc:0.96874	validation-aucpr:0.97228
[18]	validation-logloss:0.38428	validation-auc:0.96885	validation-aucpr:0.97236
[19]	validation-logloss:0.37558	validation-auc:0.96886	validation-aucpr:0.97234
[20]	validation-logloss:0.36790	validation-auc:0.96908	validation-aucpr:0.97248
[21]	validation-logloss:0.36033	validation-auc:0.96892	validation-aucpr:0.97240
[22]	validation-logloss:0.35253	validation-auc:0.96924	validation-aucpr:0.97256
[23]	validation-logloss:0.34501	validation-auc:0.96957	validation-aucpr:0.97281
[24]	validation-logloss:0.33803	validation-auc:0.96964	validation-aucpr:0.97283
[25]	validation-logloss:0.33149	validation-auc:0.96974	validation-aucpr:0.97287
[26]	validation-logloss:0.32532	validation-auc:0.96975	validation-aucpr:0.97288
[27]	validation-logloss:0.32003	validation-auc:0.96979	validation-aucpr:0.97288
[28]	validation-logloss:0.31434	validation-auc:0.96989	validation-aucpr:0.97295
[29]	validation-logloss:0.30885	validation-auc:0.97003	validation-aucpr:0.97306
[30]	validation-logloss:0.30372	validation-auc:0.97005	validation-aucpr:0.97320
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[71]	validation-logloss:0.21285	validation-auc:0.97112	validation-aucpr:0.97527
[72]	validation-logloss:0.21230	validation-auc:0.97103	validation-aucpr:0.97517
[73]	validation-logloss:0.21139	validation-auc:0.97113	validation-aucpr:0.97522
[74]	validation-logloss:0.21082	validation-auc:0.97108	validation-aucpr:0.97519
[75]	validation-logloss:0.21027	validation-auc:0.97112	validation-aucpr:0.97520
[76]	validation-logloss:0.20969	validation-auc:0.97118	validation-aucpr:0.97543
[77]	validation-logloss:0.20897	validation-auc:0.97120	validation-aucpr:0.97542
[78]	validation-logloss:0.20833	validation-auc:0.97119	validation-aucpr:0.97540
[79]	validation-logloss:0.20775	validation-auc:0.97120	validation-aucpr:0.97540
[80]	validation-logloss:0.20720	validation-auc:0.97124	validation-aucpr:0.97542
[81]	validation-logloss:0.20658	validation-auc:0.97128	validation-aucpr:0.97544
[82]	validation-logloss:0.20614	validation-auc:0.97127	validation-aucpr:0.97540
[83]	validation-logloss:0.20568	validation-auc:0.97129	validation-aucpr:0.97540
{'best_iteration': '81', 'best_score': '0.9754383400541304'}
Trial 85, Fold 1: Log loss = 0.2056844300096283, Average precision = 0.9754078842769631, ROC-AUC = 0.9712908744497497, Elapsed Time = 6.905310800000734 seconds
Trial 85, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 85, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
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[2]	validation-logloss:0.62009	validation-auc:0.96397	validation-aucpr:0.96656
[3]	validation-logloss:0.59731	validation-auc:0.96644	validation-aucpr:0.96914
[4]	validation-logloss:0.57560	validation-auc:0.96853	validation-aucpr:0.97184
[5]	validation-logloss:0.55564	validation-auc:0.96901	validation-aucpr:0.97219
[6]	validation-logloss:0.53747	validation-auc:0.96931	validation-aucpr:0.97248
[7]	validation-logloss:0.52029	validation-auc:0.96960	validation-aucpr:0.97292
[8]	validation-logloss:0.50561	validation-auc:0.96987	validation-aucpr:0.97309
[9]	validation-logloss:0.48998	validation-auc:0.97028	validation-aucpr:0.97339
[10]	validation-logloss:0.47713	validation-auc:0.97004	validation-aucpr:0.97282
[11]	validation-logloss:0.46337	validation-auc:0.97027	validation-aucpr:0.97328
[12]	validation-logloss:0.45010	validation-auc:0.97069	validation-aucpr:0.97362
[13]	validation-logloss:0.43780	validation-auc:0.97063	validation-aucpr:0.97382
[14]	validation-logloss:0.42574	validation-auc:0.97101	validation-aucpr:0.97413
[15]	validation-logloss:0.41449	validation-auc:0.97097	validation-aucpr:0.97411
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[58]	validation-logloss:0.22239	validation-auc:0.97241	validation-aucpr:0.97556
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[65]	validation-logloss:0.21264	validation-auc:0.97259	validation-aucpr:0.97564
[66]	validation-logloss:0.21134	validation-auc:0.97271	validation-aucpr:0.97573
[67]	validation-logloss:0.21016	validation-auc:0.97277	validation-aucpr:0.97576
[68]	validation-logloss:0.20918	validation-auc:0.97270	validation-aucpr:0.97573
[69]	validation-logloss:0.20826	validation-auc:0.97278	validation-aucpr:0.97577
[70]	validation-logloss:0.20738	validation-auc:0.97280	validation-aucpr:0.97577
[71]	validation-logloss:0.20604	validation-auc:0.97295	validation-aucpr:0.97588
[72]	validation-logloss:0.20491	validation-auc:0.97301	validation-aucpr:0.97595
[73]	validation-logloss:0.20423	validation-auc:0.97304	validation-aucpr:0.97597
[74]	validation-logloss:0.20355	validation-auc:0.97306	validation-aucpr:0.97594
[75]	validation-logloss:0.20263	validation-auc:0.97311	validation-aucpr:0.97596
[76]	validation-logloss:0.20193	validation-auc:0.97308	validation-aucpr:0.97593
[77]	validation-logloss:0.20097	validation-auc:0.97311	validation-aucpr:0.97586
[78]	validation-logloss:0.20015	validation-auc:0.97319	validation-aucpr:0.97591
[79]	validation-logloss:0.19919	validation-auc:0.97326	validation-aucpr:0.97592
[80]	validation-logloss:0.19848	validation-auc:0.97334	validation-aucpr:0.97601
[81]	validation-logloss:0.19798	validation-auc:0.97333	validation-aucpr:0.97611
[82]	validation-logloss:0.19712	validation-auc:0.97345	validation-aucpr:0.97621
[83]	validation-logloss:0.19638	validation-auc:0.97355	validation-aucpr:0.97628
{'best_iteration': '83', 'best_score': '0.9762840686470307'}
Trial 85, Fold 2: Log loss = 0.19637594776935594, Average precision = 0.9762886215576242, ROC-AUC = 0.9735509679839487, Elapsed Time = 6.715063700001338 seconds
Trial 85, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 85, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[0]	validation-logloss:0.66523	validation-auc:0.95901	validation-aucpr:0.96350
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[2]	validation-logloss:0.61473	validation-auc:0.96702	validation-aucpr:0.97074
[3]	validation-logloss:0.59218	validation-auc:0.96777	validation-aucpr:0.97130
[4]	validation-logloss:0.57326	validation-auc:0.96813	validation-aucpr:0.97176
[5]	validation-logloss:0.55355	validation-auc:0.96841	validation-aucpr:0.97198
[6]	validation-logloss:0.53491	validation-auc:0.96908	validation-aucpr:0.97251
[7]	validation-logloss:0.51802	validation-auc:0.96881	validation-aucpr:0.97268
[8]	validation-logloss:0.50192	validation-auc:0.96876	validation-aucpr:0.97266
[9]	validation-logloss:0.48642	validation-auc:0.96923	validation-aucpr:0.97301
[10]	validation-logloss:0.47154	validation-auc:0.96972	validation-aucpr:0.97336
[11]	validation-logloss:0.45916	validation-auc:0.96982	validation-aucpr:0.97407
[12]	validation-logloss:0.44652	validation-auc:0.96991	validation-aucpr:0.97413
[13]	validation-logloss:0.43456	validation-auc:0.96991	validation-aucpr:0.97409
[14]	validation-logloss:0.42321	validation-auc:0.97017	validation-aucpr:0.97420
[15]	validation-logloss:0.41307	validation-auc:0.97045	validation-aucpr:0.97458
[16]	validation-logloss:0.40250	validation-auc:0.97057	validation-aucpr:0.97468
[17]	validation-logloss:0.39391	validation-auc:0.97055	validation-aucpr:0.97471
[18]	validation-logloss:0.38446	validation-auc:0.97079	validation-aucpr:0.97485
[19]	validation-logloss:0.37529	validation-auc:0.97094	validation-aucpr:0.97500
[20]	validation-logloss:0.36765	validation-auc:0.97105	validation-aucpr:0.97500
[21]	validation-logloss:0.36030	validation-auc:0.97104	validation-aucpr:0.97497
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[23]	validation-logloss:0.34599	validation-auc:0.97141	validation-aucpr:0.97545
[24]	validation-logloss:0.33916	validation-auc:0.97129	validation-aucpr:0.97535
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[31]	validation-logloss:0.30025	validation-auc:0.97093	validation-aucpr:0.97511
[32]	validation-logloss:0.29546	validation-auc:0.97074	validation-aucpr:0.97497
[33]	validation-logloss:0.29164	validation-auc:0.97059	validation-aucpr:0.97486
[34]	validation-logloss:0.28796	validation-auc:0.97042	validation-aucpr:0.97473
[35]	validation-logloss:0.28364	validation-auc:0.97039	validation-aucpr:0.97470
[36]	validation-logloss:0.27940	validation-auc:0.97054	validation-aucpr:0.97485
[37]	validation-logloss:0.27543	validation-auc:0.97069	validation-aucpr:0.97495
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[66]	validation-logloss:0.21404	validation-auc:0.97152	validation-aucpr:0.97547
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[68]	validation-logloss:0.21214	validation-auc:0.97158	validation-aucpr:0.97551
[69]	validation-logloss:0.21110	validation-auc:0.97168	validation-aucpr:0.97558
[70]	validation-logloss:0.21021	validation-auc:0.97167	validation-aucpr:0.97556
[71]	validation-logloss:0.20915	validation-auc:0.97173	validation-aucpr:0.97558
[72]	validation-logloss:0.20830	validation-auc:0.97176	validation-aucpr:0.97560
[73]	validation-logloss:0.20741	validation-auc:0.97179	validation-aucpr:0.97561
[74]	validation-logloss:0.20669	validation-auc:0.97180	validation-aucpr:0.97564
[75]	validation-logloss:0.20578	validation-auc:0.97185	validation-aucpr:0.97568
[76]	validation-logloss:0.20476	validation-auc:0.97194	validation-aucpr:0.97573
[77]	validation-logloss:0.20396	validation-auc:0.97212	validation-aucpr:0.97599
[78]	validation-logloss:0.20312	validation-auc:0.97216	validation-aucpr:0.97605
[79]	validation-logloss:0.20250	validation-auc:0.97221	validation-aucpr:0.97608
[80]	validation-logloss:0.20198	validation-auc:0.97216	validation-aucpr:0.97601
[81]	validation-logloss:0.20152	validation-auc:0.97211	validation-aucpr:0.97595
[82]	validation-logloss:0.20090	validation-auc:0.97213	validation-aucpr:0.97594
[83]	validation-logloss:0.20036	validation-auc:0.97210	validation-aucpr:0.97588
{'best_iteration': '79', 'best_score': '0.976080442116701'}
Trial 85, Fold 3: Log loss = 0.20036022393956576, Average precision = 0.9758860880754837, ROC-AUC = 0.9720970104063333, Elapsed Time = 6.468014099998982 seconds
Trial 85, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 85, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[0]	validation-logloss:0.66541	validation-auc:0.95157	validation-aucpr:0.95421
[1]	validation-logloss:0.63949	validation-auc:0.95963	validation-aucpr:0.96707
[2]	validation-logloss:0.61516	validation-auc:0.96363	validation-aucpr:0.96952
[3]	validation-logloss:0.59546	validation-auc:0.96362	validation-aucpr:0.96963
[4]	validation-logloss:0.57432	validation-auc:0.96463	validation-aucpr:0.97042
[5]	validation-logloss:0.55469	validation-auc:0.96526	validation-aucpr:0.97097
[6]	validation-logloss:0.53592	validation-auc:0.96579	validation-aucpr:0.97145
[7]	validation-logloss:0.51877	validation-auc:0.96653	validation-aucpr:0.97192
[8]	validation-logloss:0.50209	validation-auc:0.96768	validation-aucpr:0.97263
[9]	validation-logloss:0.48678	validation-auc:0.96755	validation-aucpr:0.97256
[10]	validation-logloss:0.47250	validation-auc:0.96763	validation-aucpr:0.97276
[11]	validation-logloss:0.46030	validation-auc:0.96731	validation-aucpr:0.97255
[12]	validation-logloss:0.44766	validation-auc:0.96730	validation-aucpr:0.97251
[13]	validation-logloss:0.43514	validation-auc:0.96773	validation-aucpr:0.97284
[14]	validation-logloss:0.42346	validation-auc:0.96800	validation-aucpr:0.97303
[15]	validation-logloss:0.41215	validation-auc:0.96844	validation-aucpr:0.97340
[16]	validation-logloss:0.40150	validation-auc:0.96880	validation-aucpr:0.97367
[17]	validation-logloss:0.39161	validation-auc:0.96903	validation-aucpr:0.97386
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[19]	validation-logloss:0.37540	validation-auc:0.96910	validation-aucpr:0.97399
[20]	validation-logloss:0.36845	validation-auc:0.96888	validation-aucpr:0.97382
[21]	validation-logloss:0.36019	validation-auc:0.96925	validation-aucpr:0.97409
[22]	validation-logloss:0.35359	validation-auc:0.96910	validation-aucpr:0.97396
[23]	validation-logloss:0.34598	validation-auc:0.96935	validation-aucpr:0.97417
[24]	validation-logloss:0.33907	validation-auc:0.96952	validation-aucpr:0.97438
[25]	validation-logloss:0.33250	validation-auc:0.96963	validation-aucpr:0.97445
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[27]	validation-logloss:0.32108	validation-auc:0.96973	validation-aucpr:0.97452
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[30]	validation-logloss:0.30565	validation-auc:0.96996	validation-aucpr:0.97467
[31]	validation-logloss:0.30067	validation-auc:0.97002	validation-aucpr:0.97474
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[33]	validation-logloss:0.29106	validation-auc:0.97024	validation-aucpr:0.97487
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[35]	validation-logloss:0.28349	validation-auc:0.97003	validation-aucpr:0.97470
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[44]	validation-logloss:0.25359	validation-auc:0.97024	validation-aucpr:0.97498
[45]	validation-logloss:0.25084	validation-auc:0.97029	validation-aucpr:0.97499
[46]	validation-logloss:0.24858	validation-auc:0.97019	validation-aucpr:0.97492
[47]	validation-logloss:0.24659	validation-auc:0.97017	validation-aucpr:0.97491
[48]	validation-logloss:0.24419	validation-auc:0.97017	validation-aucpr:0.97492
[49]	validation-logloss:0.24212	validation-auc:0.97021	validation-aucpr:0.97496
[50]	validation-logloss:0.23987	validation-auc:0.97028	validation-aucpr:0.97501
[51]	validation-logloss:0.23833	validation-auc:0.97017	validation-aucpr:0.97493
[52]	validation-logloss:0.23640	validation-auc:0.97019	validation-aucpr:0.97494
[53]	validation-logloss:0.23447	validation-auc:0.97020	validation-aucpr:0.97495
[54]	validation-logloss:0.23278	validation-auc:0.97015	validation-aucpr:0.97492
[55]	validation-logloss:0.23094	validation-auc:0.97017	validation-aucpr:0.97493
[56]	validation-logloss:0.22944	validation-auc:0.97011	validation-aucpr:0.97489
[57]	validation-logloss:0.22765	validation-auc:0.97012	validation-aucpr:0.97491
[58]	validation-logloss:0.22628	validation-auc:0.97004	validation-aucpr:0.97485
[59]	validation-logloss:0.22503	validation-auc:0.97001	validation-aucpr:0.97483
[60]	validation-logloss:0.22346	validation-auc:0.97004	validation-aucpr:0.97485
[61]	validation-logloss:0.22208	validation-auc:0.97001	validation-aucpr:0.97486
[62]	validation-logloss:0.22068	validation-auc:0.97004	validation-aucpr:0.97488
[63]	validation-logloss:0.21967	validation-auc:0.96992	validation-aucpr:0.97480
[64]	validation-logloss:0.21854	validation-auc:0.96987	validation-aucpr:0.97476
[65]	validation-logloss:0.21752	validation-auc:0.96985	validation-aucpr:0.97473
[66]	validation-logloss:0.21659	validation-auc:0.96988	validation-aucpr:0.97475
[67]	validation-logloss:0.21580	validation-auc:0.96992	validation-aucpr:0.97476
[68]	validation-logloss:0.21492	validation-auc:0.96986	validation-aucpr:0.97472
[69]	validation-logloss:0.21392	validation-auc:0.96990	validation-aucpr:0.97474
[70]	validation-logloss:0.21309	validation-auc:0.96986	validation-aucpr:0.97472
[71]	validation-logloss:0.21224	validation-auc:0.96992	validation-aucpr:0.97477
[72]	validation-logloss:0.21161	validation-auc:0.96980	validation-aucpr:0.97468
[73]	validation-logloss:0.21064	validation-auc:0.96989	validation-aucpr:0.97475
[74]	validation-logloss:0.20979	validation-auc:0.96997	validation-aucpr:0.97479
[75]	validation-logloss:0.20932	validation-auc:0.96992	validation-aucpr:0.97476
[76]	validation-logloss:0.20891	validation-auc:0.96984	validation-aucpr:0.97470
[77]	validation-logloss:0.20819	validation-auc:0.96990	validation-aucpr:0.97474
[78]	validation-logloss:0.20738	validation-auc:0.96997	validation-aucpr:0.97480
[79]	validation-logloss:0.20662	validation-auc:0.97002	validation-aucpr:0.97485
[80]	validation-logloss:0.20605	validation-auc:0.97004	validation-aucpr:0.97484
[81]	validation-logloss:0.20540	validation-auc:0.97007	validation-aucpr:0.97487
[82]	validation-logloss:0.20480	validation-auc:0.97011	validation-aucpr:0.97489
[83]	validation-logloss:0.20433	validation-auc:0.97010	validation-aucpr:0.97488
{'best_iteration': '50', 'best_score': '0.9750077463971537'}
Trial 85, Fold 4: Log loss = 0.20432554100239475, Average precision = 0.9748823764951907, ROC-AUC = 0.9700994269071104, Elapsed Time = 6.552320899998449 seconds
Trial 85, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 85, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[0]	validation-logloss:0.66500	validation-auc:0.95722	validation-aucpr:0.95513
[1]	validation-logloss:0.63950	validation-auc:0.95967	validation-aucpr:0.96068
[2]	validation-logloss:0.61596	validation-auc:0.96211	validation-aucpr:0.96738
[3]	validation-logloss:0.59631	validation-auc:0.96338	validation-aucpr:0.96852
[4]	validation-logloss:0.57777	validation-auc:0.96342	validation-aucpr:0.96844
[5]	validation-logloss:0.55803	validation-auc:0.96414	validation-aucpr:0.96916
[6]	validation-logloss:0.54156	validation-auc:0.96437	validation-aucpr:0.96915
[7]	validation-logloss:0.52352	validation-auc:0.96608	validation-aucpr:0.97071
[8]	validation-logloss:0.50701	validation-auc:0.96609	validation-aucpr:0.97082
[9]	validation-logloss:0.49140	validation-auc:0.96686	validation-aucpr:0.97133
[10]	validation-logloss:0.47655	validation-auc:0.96729	validation-aucpr:0.97172
[11]	validation-logloss:0.46279	validation-auc:0.96773	validation-aucpr:0.97207
[12]	validation-logloss:0.44967	validation-auc:0.96819	validation-aucpr:0.97227
[13]	validation-logloss:0.43699	validation-auc:0.96855	validation-aucpr:0.97264
[14]	validation-logloss:0.42547	validation-auc:0.96880	validation-aucpr:0.97293
[15]	validation-logloss:0.41474	validation-auc:0.96909	validation-aucpr:0.97325
[16]	validation-logloss:0.40442	validation-auc:0.96941	validation-aucpr:0.97345
[17]	validation-logloss:0.39472	validation-auc:0.96950	validation-aucpr:0.97357
[18]	validation-logloss:0.38528	validation-auc:0.96980	validation-aucpr:0.97387
[19]	validation-logloss:0.37643	validation-auc:0.96977	validation-aucpr:0.97384
[20]	validation-logloss:0.36924	validation-auc:0.96958	validation-aucpr:0.97346
[21]	validation-logloss:0.36220	validation-auc:0.96948	validation-aucpr:0.97336
[22]	validation-logloss:0.35551	validation-auc:0.96956	validation-aucpr:0.97347
[23]	validation-logloss:0.34865	validation-auc:0.96950	validation-aucpr:0.97345
[24]	validation-logloss:0.34173	validation-auc:0.96962	validation-aucpr:0.97354
[25]	validation-logloss:0.33500	validation-auc:0.96971	validation-aucpr:0.97362
[26]	validation-logloss:0.32866	validation-auc:0.96982	validation-aucpr:0.97370
[27]	validation-logloss:0.32326	validation-auc:0.96987	validation-aucpr:0.97371
[28]	validation-logloss:0.31759	validation-auc:0.96991	validation-aucpr:0.97378
[29]	validation-logloss:0.31331	validation-auc:0.96965	validation-aucpr:0.97383
[30]	validation-logloss:0.30814	validation-auc:0.96975	validation-aucpr:0.97390
[31]	validation-logloss:0.30307	validation-auc:0.96980	validation-aucpr:0.97394
[32]	validation-logloss:0.29867	validation-auc:0.96975	validation-aucpr:0.97391
[33]	validation-logloss:0.29406	validation-auc:0.96978	validation-aucpr:0.97393
[34]	validation-logloss:0.28974	validation-auc:0.96969	validation-aucpr:0.97391
[35]	validation-logloss:0.28623	validation-auc:0.96958	validation-aucpr:0.97383
[36]	validation-logloss:0.28243	validation-auc:0.96954	validation-aucpr:0.97382
[37]	validation-logloss:0.27896	validation-auc:0.96963	validation-aucpr:0.97387
[38]	validation-logloss:0.27601	validation-auc:0.96959	validation-aucpr:0.97386
[39]	validation-logloss:0.27224	validation-auc:0.96974	validation-aucpr:0.97398
[40]	validation-logloss:0.26951	validation-auc:0.96946	validation-aucpr:0.97378
[41]	validation-logloss:0.26624	validation-auc:0.96957	validation-aucpr:0.97388
[42]	validation-logloss:0.26330	validation-auc:0.96954	validation-aucpr:0.97383
[43]	validation-logloss:0.26032	validation-auc:0.96954	validation-aucpr:0.97383
[44]	validation-logloss:0.25768	validation-auc:0.96952	validation-aucpr:0.97380
[45]	validation-logloss:0.25477	validation-auc:0.96986	validation-aucpr:0.97397
[46]	validation-logloss:0.25230	validation-auc:0.96988	validation-aucpr:0.97399
[47]	validation-logloss:0.24977	validation-auc:0.96996	validation-aucpr:0.97404
[48]	validation-logloss:0.24706	validation-auc:0.97020	validation-aucpr:0.97424
[49]	validation-logloss:0.24498	validation-auc:0.97021	validation-aucpr:0.97423
[50]	validation-logloss:0.24267	validation-auc:0.97024	validation-aucpr:0.97427
[51]	validation-logloss:0.24086	validation-auc:0.97017	validation-aucpr:0.97419
[52]	validation-logloss:0.23876	validation-auc:0.97035	validation-aucpr:0.97434
[53]	validation-logloss:0.23742	validation-auc:0.97024	validation-aucpr:0.97426
[54]	validation-logloss:0.23543	validation-auc:0.97032	validation-aucpr:0.97431
[55]	validation-logloss:0.23389	validation-auc:0.97039	validation-aucpr:0.97437
[56]	validation-logloss:0.23212	validation-auc:0.97045	validation-aucpr:0.97445
[57]	validation-logloss:0.23032	validation-auc:0.97051	validation-aucpr:0.97439
[58]	validation-logloss:0.22839	validation-auc:0.97086	validation-aucpr:0.97465
[59]	validation-logloss:0.22725	validation-auc:0.97071	validation-aucpr:0.97454
[60]	validation-logloss:0.22578	validation-auc:0.97072	validation-aucpr:0.97453
[61]	validation-logloss:0.22435	validation-auc:0.97079	validation-aucpr:0.97460
[62]	validation-logloss:0.22316	validation-auc:0.97083	validation-aucpr:0.97454
[63]	validation-logloss:0.22186	validation-auc:0.97087	validation-aucpr:0.97454
[64]	validation-logloss:0.22047	validation-auc:0.97094	validation-aucpr:0.97460
[65]	validation-logloss:0.21933	validation-auc:0.97091	validation-aucpr:0.97455
[66]	validation-logloss:0.21814	validation-auc:0.97107	validation-aucpr:0.97467
[67]	validation-logloss:0.21723	validation-auc:0.97104	validation-aucpr:0.97465
[68]	validation-logloss:0.21630	validation-auc:0.97106	validation-aucpr:0.97463
[69]	validation-logloss:0.21524	validation-auc:0.97107	validation-aucpr:0.97464
[70]	validation-logloss:0.21436	validation-auc:0.97103	validation-aucpr:0.97454
[71]	validation-logloss:0.21352	validation-auc:0.97095	validation-aucpr:0.97447
[72]	validation-logloss:0.21269	validation-auc:0.97091	validation-aucpr:0.97445
[73]	validation-logloss:0.21162	validation-auc:0.97101	validation-aucpr:0.97454
[74]	validation-logloss:0.21098	validation-auc:0.97099	validation-aucpr:0.97451
[75]	validation-logloss:0.21021	validation-auc:0.97099	validation-aucpr:0.97441
[76]	validation-logloss:0.20960	validation-auc:0.97096	validation-aucpr:0.97448
[77]	validation-logloss:0.20921	validation-auc:0.97090	validation-aucpr:0.97435
[78]	validation-logloss:0.20891	validation-auc:0.97073	validation-aucpr:0.97421
[79]	validation-logloss:0.20821	validation-auc:0.97078	validation-aucpr:0.97422
[80]	validation-logloss:0.20735	validation-auc:0.97097	validation-aucpr:0.97440
[81]	validation-logloss:0.20709	validation-auc:0.97078	validation-aucpr:0.97424
[82]	validation-logloss:0.20644	validation-auc:0.97083	validation-aucpr:0.97434
[83]	validation-logloss:0.20587	validation-auc:0.97086	validation-aucpr:0.97437
{'best_iteration': '66', 'best_score': '0.9746692647212228'}
Trial 85, Fold 5: Log loss = 0.20586828511529295, Average precision = 0.9743731212573705, ROC-AUC = 0.9708630882708136, Elapsed Time = 6.65404910000143 seconds
Optimization Progress:  86%|########6 | 86/100 [3:45:22<14:14, 61.02s/it]
Trial 86, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 86, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[0]	validation-logloss:0.68238	validation-auc:0.95468	validation-aucpr:0.94200
[1]	validation-logloss:0.67214	validation-auc:0.96377	validation-aucpr:0.96689
[2]	validation-logloss:0.66215	validation-auc:0.96635	validation-aucpr:0.97169
[3]	validation-logloss:0.65219	validation-auc:0.96699	validation-aucpr:0.97231
[4]	validation-logloss:0.64346	validation-auc:0.96786	validation-aucpr:0.97295
[5]	validation-logloss:0.63442	validation-auc:0.96695	validation-aucpr:0.97228
[6]	validation-logloss:0.62549	validation-auc:0.96680	validation-aucpr:0.97217
[7]	validation-logloss:0.61751	validation-auc:0.96697	validation-aucpr:0.97218
[8]	validation-logloss:0.60864	validation-auc:0.96780	validation-aucpr:0.97290
[9]	validation-logloss:0.60004	validation-auc:0.96856	validation-aucpr:0.97349
[10]	validation-logloss:0.59168	validation-auc:0.96890	validation-aucpr:0.97374
[11]	validation-logloss:0.58369	validation-auc:0.96919	validation-aucpr:0.97400
[12]	validation-logloss:0.57608	validation-auc:0.96917	validation-aucpr:0.97423
[13]	validation-logloss:0.56913	validation-auc:0.96895	validation-aucpr:0.97402
[14]	validation-logloss:0.56172	validation-auc:0.96891	validation-aucpr:0.97399
[15]	validation-logloss:0.55456	validation-auc:0.96899	validation-aucpr:0.97398
[16]	validation-logloss:0.54756	validation-auc:0.96892	validation-aucpr:0.97398
[17]	validation-logloss:0.54063	validation-auc:0.96912	validation-aucpr:0.97407
[18]	validation-logloss:0.53414	validation-auc:0.96895	validation-aucpr:0.97176
[19]	validation-logloss:0.52825	validation-auc:0.96885	validation-aucpr:0.97163
[20]	validation-logloss:0.52179	validation-auc:0.96891	validation-aucpr:0.97168
[21]	validation-logloss:0.51549	validation-auc:0.96915	validation-aucpr:0.97184
[22]	validation-logloss:0.50928	validation-auc:0.96928	validation-aucpr:0.97193
[23]	validation-logloss:0.50358	validation-auc:0.96926	validation-aucpr:0.97231
[24]	validation-logloss:0.49826	validation-auc:0.96936	validation-aucpr:0.97236
[25]	validation-logloss:0.49247	validation-auc:0.96961	validation-aucpr:0.97448
[26]	validation-logloss:0.48699	validation-auc:0.96962	validation-aucpr:0.97449
[27]	validation-logloss:0.48139	validation-auc:0.96967	validation-aucpr:0.97456
[28]	validation-logloss:0.47650	validation-auc:0.96960	validation-aucpr:0.97447
[29]	validation-logloss:0.47115	validation-auc:0.96964	validation-aucpr:0.97449
[30]	validation-logloss:0.46648	validation-auc:0.96957	validation-aucpr:0.97443
[31]	validation-logloss:0.46157	validation-auc:0.96956	validation-aucpr:0.97443
[32]	validation-logloss:0.45649	validation-auc:0.96960	validation-aucpr:0.97448
[33]	validation-logloss:0.45168	validation-auc:0.96965	validation-aucpr:0.97454
[34]	validation-logloss:0.44754	validation-auc:0.96966	validation-aucpr:0.97450
[35]	validation-logloss:0.44278	validation-auc:0.96976	validation-aucpr:0.97456
[36]	validation-logloss:0.43811	validation-auc:0.96976	validation-aucpr:0.97458
[37]	validation-logloss:0.43363	validation-auc:0.96983	validation-aucpr:0.97462
[38]	validation-logloss:0.42915	validation-auc:0.96996	validation-aucpr:0.97471
[39]	validation-logloss:0.42494	validation-auc:0.96998	validation-aucpr:0.97472
[40]	validation-logloss:0.42107	validation-auc:0.97002	validation-aucpr:0.97475
[41]	validation-logloss:0.41738	validation-auc:0.96998	validation-aucpr:0.97470
[42]	validation-logloss:0.41391	validation-auc:0.96993	validation-aucpr:0.97463
[43]	validation-logloss:0.40992	validation-auc:0.97006	validation-aucpr:0.97473
[44]	validation-logloss:0.40655	validation-auc:0.96998	validation-aucpr:0.97467
[45]	validation-logloss:0.40311	validation-auc:0.96998	validation-aucpr:0.97464
[46]	validation-logloss:0.39980	validation-auc:0.97003	validation-aucpr:0.97464
[47]	validation-logloss:0.39655	validation-auc:0.96998	validation-aucpr:0.97460
[48]	validation-logloss:0.39290	validation-auc:0.96992	validation-aucpr:0.97456
{'best_iteration': '40', 'best_score': '0.9747540236047042'}
Trial 86, Fold 1: Log loss = 0.39290141609750506, Average precision = 0.9745635372166829, ROC-AUC = 0.9699236878832633, Elapsed Time = 56.130409400000644 seconds
Trial 86, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 86, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[0]	validation-logloss:0.68258	validation-auc:0.95691	validation-aucpr:0.95557
[1]	validation-logloss:0.67317	validation-auc:0.96309	validation-aucpr:0.96389
[2]	validation-logloss:0.66402	validation-auc:0.96405	validation-aucpr:0.96728
[3]	validation-logloss:0.65427	validation-auc:0.96643	validation-aucpr:0.96952
[4]	validation-logloss:0.64592	validation-auc:0.96592	validation-aucpr:0.96893
[5]	validation-logloss:0.63759	validation-auc:0.96605	validation-aucpr:0.96898
[6]	validation-logloss:0.62865	validation-auc:0.96662	validation-aucpr:0.96968
[7]	validation-logloss:0.61963	validation-auc:0.96732	validation-aucpr:0.97036
[8]	validation-logloss:0.61080	validation-auc:0.96795	validation-aucpr:0.97096
[9]	validation-logloss:0.60216	validation-auc:0.96867	validation-aucpr:0.97164
[10]	validation-logloss:0.59405	validation-auc:0.96902	validation-aucpr:0.97189
[11]	validation-logloss:0.58687	validation-auc:0.96899	validation-aucpr:0.97202
[12]	validation-logloss:0.57899	validation-auc:0.96915	validation-aucpr:0.97222
[13]	validation-logloss:0.57124	validation-auc:0.96935	validation-aucpr:0.97242
[14]	validation-logloss:0.56392	validation-auc:0.96961	validation-aucpr:0.97262
[15]	validation-logloss:0.55725	validation-auc:0.96971	validation-aucpr:0.97268
[16]	validation-logloss:0.54994	validation-auc:0.97002	validation-aucpr:0.97298
[17]	validation-logloss:0.54368	validation-auc:0.96982	validation-aucpr:0.97271
[18]	validation-logloss:0.53699	validation-auc:0.96978	validation-aucpr:0.97272
[19]	validation-logloss:0.53080	validation-auc:0.96971	validation-aucpr:0.97264
[20]	validation-logloss:0.52509	validation-auc:0.96961	validation-aucpr:0.97240
[21]	validation-logloss:0.51955	validation-auc:0.96922	validation-aucpr:0.97204
[22]	validation-logloss:0.51404	validation-auc:0.96914	validation-aucpr:0.97196
[23]	validation-logloss:0.50846	validation-auc:0.96918	validation-aucpr:0.97199
[24]	validation-logloss:0.50314	validation-auc:0.96914	validation-aucpr:0.97199
[25]	validation-logloss:0.49712	validation-auc:0.96922	validation-aucpr:0.97208
[26]	validation-logloss:0.49138	validation-auc:0.96926	validation-aucpr:0.97209
[27]	validation-logloss:0.48618	validation-auc:0.96932	validation-aucpr:0.97212
[28]	validation-logloss:0.48078	validation-auc:0.96951	validation-aucpr:0.97226
[29]	validation-logloss:0.47600	validation-auc:0.96951	validation-aucpr:0.97225
[30]	validation-logloss:0.47142	validation-auc:0.96946	validation-aucpr:0.97222
[31]	validation-logloss:0.46679	validation-auc:0.96945	validation-aucpr:0.97217
[32]	validation-logloss:0.46153	validation-auc:0.96961	validation-aucpr:0.97233
[33]	validation-logloss:0.45652	validation-auc:0.96964	validation-aucpr:0.97235
[34]	validation-logloss:0.45157	validation-auc:0.96976	validation-aucpr:0.97247
[35]	validation-logloss:0.44679	validation-auc:0.96985	validation-aucpr:0.97254
[36]	validation-logloss:0.44212	validation-auc:0.97000	validation-aucpr:0.97263
[37]	validation-logloss:0.43752	validation-auc:0.97014	validation-aucpr:0.97275
[38]	validation-logloss:0.43302	validation-auc:0.97031	validation-aucpr:0.97286
[39]	validation-logloss:0.42914	validation-auc:0.97028	validation-aucpr:0.97286
[40]	validation-logloss:0.42530	validation-auc:0.97023	validation-aucpr:0.97282
[41]	validation-logloss:0.42159	validation-auc:0.97020	validation-aucpr:0.97280
[42]	validation-logloss:0.41725	validation-auc:0.97033	validation-aucpr:0.97287
[43]	validation-logloss:0.41332	validation-auc:0.97033	validation-aucpr:0.97296
[44]	validation-logloss:0.40983	validation-auc:0.97028	validation-aucpr:0.97292
[45]	validation-logloss:0.40579	validation-auc:0.97053	validation-aucpr:0.97311
[46]	validation-logloss:0.40189	validation-auc:0.97056	validation-aucpr:0.97315
[47]	validation-logloss:0.39825	validation-auc:0.97062	validation-aucpr:0.97320
[48]	validation-logloss:0.39500	validation-auc:0.97064	validation-aucpr:0.97320
{'best_iteration': '48', 'best_score': '0.9731980560776368'}
Trial 86, Fold 2: Log loss = 0.39500035361280744, Average precision = 0.9732003530395197, ROC-AUC = 0.9706408248281229, Elapsed Time = 56.37032370000088 seconds
Trial 86, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 86, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[0]	validation-logloss:0.68239	validation-auc:0.94580	validation-aucpr:0.92497
[1]	validation-logloss:0.67194	validation-auc:0.96398	validation-aucpr:0.95526
[2]	validation-logloss:0.66194	validation-auc:0.96750	validation-aucpr:0.96994
[3]	validation-logloss:0.65197	validation-auc:0.96821	validation-aucpr:0.97268
[4]	validation-logloss:0.64325	validation-auc:0.96979	validation-aucpr:0.97371
[5]	validation-logloss:0.63491	validation-auc:0.96951	validation-aucpr:0.97344
[6]	validation-logloss:0.62639	validation-auc:0.96986	validation-aucpr:0.97396
[7]	validation-logloss:0.61829	validation-auc:0.96972	validation-aucpr:0.97367
[8]	validation-logloss:0.60951	validation-auc:0.96980	validation-aucpr:0.97394
[9]	validation-logloss:0.60181	validation-auc:0.96954	validation-aucpr:0.97371
[10]	validation-logloss:0.59435	validation-auc:0.96917	validation-aucpr:0.97337
[11]	validation-logloss:0.58726	validation-auc:0.96914	validation-aucpr:0.97327
[12]	validation-logloss:0.57920	validation-auc:0.96940	validation-aucpr:0.97358
[13]	validation-logloss:0.57214	validation-auc:0.96961	validation-aucpr:0.97369
[14]	validation-logloss:0.56552	validation-auc:0.96965	validation-aucpr:0.97384
[15]	validation-logloss:0.55889	validation-auc:0.96961	validation-aucpr:0.97383
[16]	validation-logloss:0.55168	validation-auc:0.96976	validation-aucpr:0.97400
[17]	validation-logloss:0.54465	validation-auc:0.96999	validation-aucpr:0.97431
[18]	validation-logloss:0.53780	validation-auc:0.97008	validation-aucpr:0.97439
[19]	validation-logloss:0.53178	validation-auc:0.96996	validation-aucpr:0.97429
[20]	validation-logloss:0.52609	validation-auc:0.96980	validation-aucpr:0.97413
[21]	validation-logloss:0.51956	validation-auc:0.96996	validation-aucpr:0.97429
[22]	validation-logloss:0.51331	validation-auc:0.96994	validation-aucpr:0.97428
[23]	validation-logloss:0.50723	validation-auc:0.97010	validation-aucpr:0.97440
[24]	validation-logloss:0.50165	validation-auc:0.97034	validation-aucpr:0.97457
[25]	validation-logloss:0.49566	validation-auc:0.97049	validation-aucpr:0.97470
[26]	validation-logloss:0.48998	validation-auc:0.97065	validation-aucpr:0.97483
[27]	validation-logloss:0.48432	validation-auc:0.97070	validation-aucpr:0.97487
[28]	validation-logloss:0.47936	validation-auc:0.97068	validation-aucpr:0.97482
[29]	validation-logloss:0.47379	validation-auc:0.97094	validation-aucpr:0.97504
[30]	validation-logloss:0.46916	validation-auc:0.97097	validation-aucpr:0.97502
[31]	validation-logloss:0.46389	validation-auc:0.97105	validation-aucpr:0.97512
[32]	validation-logloss:0.45932	validation-auc:0.97111	validation-aucpr:0.97513
[33]	validation-logloss:0.45428	validation-auc:0.97124	validation-aucpr:0.97525
[34]	validation-logloss:0.45006	validation-auc:0.97123	validation-aucpr:0.97524
[35]	validation-logloss:0.44600	validation-auc:0.97121	validation-aucpr:0.97523
[36]	validation-logloss:0.44156	validation-auc:0.97124	validation-aucpr:0.97524
[37]	validation-logloss:0.43693	validation-auc:0.97129	validation-aucpr:0.97532
[38]	validation-logloss:0.43235	validation-auc:0.97132	validation-aucpr:0.97536
[39]	validation-logloss:0.42781	validation-auc:0.97139	validation-aucpr:0.97544
[40]	validation-logloss:0.42341	validation-auc:0.97145	validation-aucpr:0.97548
[41]	validation-logloss:0.41932	validation-auc:0.97140	validation-aucpr:0.97547
[42]	validation-logloss:0.41559	validation-auc:0.97140	validation-aucpr:0.97546
[43]	validation-logloss:0.41204	validation-auc:0.97130	validation-aucpr:0.97536
[44]	validation-logloss:0.40842	validation-auc:0.97130	validation-aucpr:0.97538
[45]	validation-logloss:0.40508	validation-auc:0.97120	validation-aucpr:0.97528
[46]	validation-logloss:0.40181	validation-auc:0.97112	validation-aucpr:0.97520
[47]	validation-logloss:0.39836	validation-auc:0.97122	validation-aucpr:0.97527
[48]	validation-logloss:0.39475	validation-auc:0.97128	validation-aucpr:0.97533
{'best_iteration': '40', 'best_score': '0.9754833863268657'}
Trial 86, Fold 3: Log loss = 0.3947504659735634, Average precision = 0.9753386299852591, ROC-AUC = 0.971284316581753, Elapsed Time = 52.0949412000009 seconds
Trial 86, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 86, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[0]	validation-logloss:0.68240	validation-auc:0.95348	validation-aucpr:0.94323
[1]	validation-logloss:0.67235	validation-auc:0.96156	validation-aucpr:0.96249
[2]	validation-logloss:0.66228	validation-auc:0.96536	validation-aucpr:0.97044
[3]	validation-logloss:0.65339	validation-auc:0.96512	validation-aucpr:0.97043
[4]	validation-logloss:0.64514	validation-auc:0.96469	validation-aucpr:0.97004
[5]	validation-logloss:0.63685	validation-auc:0.96557	validation-aucpr:0.97062
[6]	validation-logloss:0.62772	validation-auc:0.96670	validation-aucpr:0.97179
[7]	validation-logloss:0.61874	validation-auc:0.96722	validation-aucpr:0.97235
[8]	validation-logloss:0.61037	validation-auc:0.96784	validation-aucpr:0.97279
[9]	validation-logloss:0.60194	validation-auc:0.96816	validation-aucpr:0.97308
[10]	validation-logloss:0.59368	validation-auc:0.96834	validation-aucpr:0.97323
[11]	validation-logloss:0.58573	validation-auc:0.96869	validation-aucpr:0.97350
[12]	validation-logloss:0.57783	validation-auc:0.96891	validation-aucpr:0.97367
[13]	validation-logloss:0.57040	validation-auc:0.96904	validation-aucpr:0.97376
[14]	validation-logloss:0.56286	validation-auc:0.96928	validation-aucpr:0.97400
[15]	validation-logloss:0.55632	validation-auc:0.96924	validation-aucpr:0.97398
[16]	validation-logloss:0.55019	validation-auc:0.96889	validation-aucpr:0.97367
[17]	validation-logloss:0.54361	validation-auc:0.96891	validation-aucpr:0.97369
[18]	validation-logloss:0.53745	validation-auc:0.96902	validation-aucpr:0.97387
[19]	validation-logloss:0.53066	validation-auc:0.96924	validation-aucpr:0.97406
[20]	validation-logloss:0.52420	validation-auc:0.96929	validation-aucpr:0.97413
[21]	validation-logloss:0.51790	validation-auc:0.96928	validation-aucpr:0.97416
[22]	validation-logloss:0.51187	validation-auc:0.96939	validation-aucpr:0.97427
[23]	validation-logloss:0.50574	validation-auc:0.96949	validation-aucpr:0.97442
[24]	validation-logloss:0.50050	validation-auc:0.96932	validation-aucpr:0.97426
[25]	validation-logloss:0.49460	validation-auc:0.96944	validation-aucpr:0.97437
[26]	validation-logloss:0.48879	validation-auc:0.96960	validation-aucpr:0.97449
[27]	validation-logloss:0.48385	validation-auc:0.96961	validation-aucpr:0.97447
[28]	validation-logloss:0.47907	validation-auc:0.96955	validation-aucpr:0.97440
[29]	validation-logloss:0.47425	validation-auc:0.96971	validation-aucpr:0.97451
[30]	validation-logloss:0.46976	validation-auc:0.96958	validation-aucpr:0.97440
[31]	validation-logloss:0.46513	validation-auc:0.96951	validation-aucpr:0.97433
[32]	validation-logloss:0.46006	validation-auc:0.96958	validation-aucpr:0.97439
[33]	validation-logloss:0.45500	validation-auc:0.96971	validation-aucpr:0.97451
[34]	validation-logloss:0.45015	validation-auc:0.96976	validation-aucpr:0.97455
[35]	validation-logloss:0.44534	validation-auc:0.96985	validation-aucpr:0.97460
[36]	validation-logloss:0.44133	validation-auc:0.96966	validation-aucpr:0.97444
[37]	validation-logloss:0.43667	validation-auc:0.96976	validation-aucpr:0.97452
[38]	validation-logloss:0.43223	validation-auc:0.96968	validation-aucpr:0.97448
[39]	validation-logloss:0.42835	validation-auc:0.96964	validation-aucpr:0.97443
[40]	validation-logloss:0.42448	validation-auc:0.96964	validation-aucpr:0.97443
[41]	validation-logloss:0.42085	validation-auc:0.96959	validation-aucpr:0.97438
[42]	validation-logloss:0.41670	validation-auc:0.96966	validation-aucpr:0.97445
[43]	validation-logloss:0.41249	validation-auc:0.96981	validation-aucpr:0.97457
[44]	validation-logloss:0.40898	validation-auc:0.96985	validation-aucpr:0.97459
[45]	validation-logloss:0.40564	validation-auc:0.96973	validation-aucpr:0.97449
[46]	validation-logloss:0.40173	validation-auc:0.96991	validation-aucpr:0.97467
[47]	validation-logloss:0.39821	validation-auc:0.96996	validation-aucpr:0.97471
[48]	validation-logloss:0.39452	validation-auc:0.97001	validation-aucpr:0.97476
{'best_iteration': '48', 'best_score': '0.9747625947591851'}
Trial 86, Fold 4: Log loss = 0.39452383486087683, Average precision = 0.9747594994211715, ROC-AUC = 0.9700139607192333, Elapsed Time = 50.71849939999811 seconds
Trial 86, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 86, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[0]	validation-logloss:0.68288	validation-auc:0.94632	validation-aucpr:0.93265
[1]	validation-logloss:0.67243	validation-auc:0.96482	validation-aucpr:0.96976
[2]	validation-logloss:0.66338	validation-auc:0.96409	validation-aucpr:0.96862
[3]	validation-logloss:0.65344	validation-auc:0.96560	validation-aucpr:0.97002
[4]	validation-logloss:0.64390	validation-auc:0.96642	validation-aucpr:0.97075
[5]	validation-logloss:0.63465	validation-auc:0.96676	validation-aucpr:0.97093
[6]	validation-logloss:0.62646	validation-auc:0.96666	validation-aucpr:0.97083
[7]	validation-logloss:0.61769	validation-auc:0.96701	validation-aucpr:0.97117
[8]	validation-logloss:0.60998	validation-auc:0.96640	validation-aucpr:0.97056
[9]	validation-logloss:0.60208	validation-auc:0.96633	validation-aucpr:0.97050
[10]	validation-logloss:0.59372	validation-auc:0.96698	validation-aucpr:0.97104
[11]	validation-logloss:0.58569	validation-auc:0.96741	validation-aucpr:0.97134
[12]	validation-logloss:0.57868	validation-auc:0.96760	validation-aucpr:0.97159
[13]	validation-logloss:0.57115	validation-auc:0.96781	validation-aucpr:0.97175
[14]	validation-logloss:0.56439	validation-auc:0.96759	validation-aucpr:0.97158
[15]	validation-logloss:0.55718	validation-auc:0.96769	validation-aucpr:0.97170
[16]	validation-logloss:0.55008	validation-auc:0.96778	validation-aucpr:0.97180
[17]	validation-logloss:0.54395	validation-auc:0.96767	validation-aucpr:0.97169
[18]	validation-logloss:0.53751	validation-auc:0.96769	validation-aucpr:0.97175
[19]	validation-logloss:0.53157	validation-auc:0.96755	validation-aucpr:0.97162
[20]	validation-logloss:0.52516	validation-auc:0.96753	validation-aucpr:0.97162
[21]	validation-logloss:0.51906	validation-auc:0.96770	validation-aucpr:0.97177
[22]	validation-logloss:0.51274	validation-auc:0.96781	validation-aucpr:0.97185
[23]	validation-logloss:0.50679	validation-auc:0.96797	validation-aucpr:0.97199
[24]	validation-logloss:0.50167	validation-auc:0.96789	validation-aucpr:0.97192
[25]	validation-logloss:0.49644	validation-auc:0.96792	validation-aucpr:0.97190
[26]	validation-logloss:0.49094	validation-auc:0.96794	validation-aucpr:0.97192
[27]	validation-logloss:0.48530	validation-auc:0.96807	validation-aucpr:0.97205
[28]	validation-logloss:0.48056	validation-auc:0.96800	validation-aucpr:0.97198
[29]	validation-logloss:0.47596	validation-auc:0.96781	validation-aucpr:0.97182
[30]	validation-logloss:0.47056	validation-auc:0.96812	validation-aucpr:0.97207
[31]	validation-logloss:0.46552	validation-auc:0.96821	validation-aucpr:0.97210
[32]	validation-logloss:0.46083	validation-auc:0.96823	validation-aucpr:0.97211
[33]	validation-logloss:0.45666	validation-auc:0.96815	validation-aucpr:0.97203
[34]	validation-logloss:0.45176	validation-auc:0.96836	validation-aucpr:0.97221
[35]	validation-logloss:0.44698	validation-auc:0.96857	validation-aucpr:0.97237
[36]	validation-logloss:0.44255	validation-auc:0.96858	validation-aucpr:0.97236
[37]	validation-logloss:0.43884	validation-auc:0.96836	validation-aucpr:0.97034
[38]	validation-logloss:0.43492	validation-auc:0.96833	validation-aucpr:0.97058
[39]	validation-logloss:0.43117	validation-auc:0.96823	validation-aucpr:0.97024
[40]	validation-logloss:0.42692	validation-auc:0.96834	validation-aucpr:0.97058
[41]	validation-logloss:0.42295	validation-auc:0.96839	validation-aucpr:0.97076
[42]	validation-logloss:0.41936	validation-auc:0.96829	validation-aucpr:0.97066
[43]	validation-logloss:0.41520	validation-auc:0.96848	validation-aucpr:0.97084
[44]	validation-logloss:0.41170	validation-auc:0.96858	validation-aucpr:0.97090
[45]	validation-logloss:0.40771	validation-auc:0.96867	validation-aucpr:0.97098
[46]	validation-logloss:0.40385	validation-auc:0.96866	validation-aucpr:0.97099
[47]	validation-logloss:0.40064	validation-auc:0.96860	validation-aucpr:0.97087
[48]	validation-logloss:0.39730	validation-auc:0.96861	validation-aucpr:0.97091
{'best_iteration': '35', 'best_score': '0.9723703196133963'}
Trial 86, Fold 5: Log loss = 0.39729713898022145, Average precision = 0.9716444141942279, ROC-AUC = 0.9686050201586683, Elapsed Time = 53.13384550000046 seconds
Optimization Progress:  87%|########7 | 87/100 [3:49:59<27:13, 125.67s/it]
Trial 87, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 87, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[0]	validation-logloss:0.66841	validation-auc:0.91973	validation-aucpr:0.87848
[1]	validation-logloss:0.64508	validation-auc:0.95583	validation-aucpr:0.94072
[2]	validation-logloss:0.62419	validation-auc:0.96261	validation-aucpr:0.96576
[3]	validation-logloss:0.60364	validation-auc:0.96458	validation-aucpr:0.96851
[4]	validation-logloss:0.58423	validation-auc:0.96598	validation-aucpr:0.96908
[5]	validation-logloss:0.56593	validation-auc:0.96685	validation-aucpr:0.96984
[6]	validation-logloss:0.54867	validation-auc:0.96743	validation-aucpr:0.96912
[7]	validation-logloss:0.53240	validation-auc:0.96778	validation-aucpr:0.96876
[8]	validation-logloss:0.51698	validation-auc:0.96802	validation-aucpr:0.96898
[9]	validation-logloss:0.50226	validation-auc:0.96867	validation-aucpr:0.97061
[10]	validation-logloss:0.48878	validation-auc:0.96896	validation-aucpr:0.97042
[11]	validation-logloss:0.47621	validation-auc:0.96886	validation-aucpr:0.97023
[12]	validation-logloss:0.46388	validation-auc:0.96936	validation-aucpr:0.97278
[13]	validation-logloss:0.45209	validation-auc:0.96940	validation-aucpr:0.97279
[14]	validation-logloss:0.44108	validation-auc:0.96937	validation-aucpr:0.97398
[15]	validation-logloss:0.43087	validation-auc:0.96972	validation-aucpr:0.97432
[16]	validation-logloss:0.42079	validation-auc:0.96986	validation-aucpr:0.97436
[17]	validation-logloss:0.41123	validation-auc:0.96982	validation-aucpr:0.97432
[18]	validation-logloss:0.40217	validation-auc:0.96979	validation-aucpr:0.97222
[19]	validation-logloss:0.39323	validation-auc:0.97004	validation-aucpr:0.97239
[20]	validation-logloss:0.38504	validation-auc:0.97046	validation-aucpr:0.97496
[21]	validation-logloss:0.37726	validation-auc:0.97047	validation-aucpr:0.97492
[22]	validation-logloss:0.36956	validation-auc:0.97073	validation-aucpr:0.97514
[23]	validation-logloss:0.36231	validation-auc:0.97065	validation-aucpr:0.97506
[24]	validation-logloss:0.35534	validation-auc:0.97064	validation-aucpr:0.97504
[25]	validation-logloss:0.34881	validation-auc:0.97083	validation-aucpr:0.97515
[26]	validation-logloss:0.34228	validation-auc:0.97104	validation-aucpr:0.97534
[27]	validation-logloss:0.33620	validation-auc:0.97112	validation-aucpr:0.97535
[28]	validation-logloss:0.33030	validation-auc:0.97138	validation-aucpr:0.97564
[29]	validation-logloss:0.32487	validation-auc:0.97133	validation-aucpr:0.97562
[30]	validation-logloss:0.31951	validation-auc:0.97137	validation-aucpr:0.97564
[31]	validation-logloss:0.31445	validation-auc:0.97145	validation-aucpr:0.97570
[32]	validation-logloss:0.31010	validation-auc:0.97150	validation-aucpr:0.97576
[33]	validation-logloss:0.30558	validation-auc:0.97137	validation-aucpr:0.97566
[34]	validation-logloss:0.30094	validation-auc:0.97147	validation-aucpr:0.97574
[35]	validation-logloss:0.29664	validation-auc:0.97157	validation-aucpr:0.97575
[36]	validation-logloss:0.29248	validation-auc:0.97165	validation-aucpr:0.97581
[37]	validation-logloss:0.28851	validation-auc:0.97172	validation-aucpr:0.97588
[38]	validation-logloss:0.28479	validation-auc:0.97169	validation-aucpr:0.97586
[39]	validation-logloss:0.28150	validation-auc:0.97174	validation-aucpr:0.97592
[40]	validation-logloss:0.27796	validation-auc:0.97175	validation-aucpr:0.97592
[41]	validation-logloss:0.27447	validation-auc:0.97187	validation-aucpr:0.97601
{'best_iteration': '41', 'best_score': '0.9760065105274797'}
Trial 87, Fold 1: Log loss = 0.2744740229206771, Average precision = 0.9760110559976684, ROC-AUC = 0.9718659934615219, Elapsed Time = 1.948451500000374 seconds
Trial 87, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 87, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[0]	validation-logloss:0.66807	validation-auc:0.92792	validation-aucpr:0.88731
[1]	validation-logloss:0.64453	validation-auc:0.95960	validation-aucpr:0.94918
[2]	validation-logloss:0.62291	validation-auc:0.96457	validation-aucpr:0.96300
[3]	validation-logloss:0.60414	validation-auc:0.96694	validation-aucpr:0.97097
[4]	validation-logloss:0.58510	validation-auc:0.96733	validation-aucpr:0.97137
[5]	validation-logloss:0.56695	validation-auc:0.96736	validation-aucpr:0.97152
[6]	validation-logloss:0.54976	validation-auc:0.96734	validation-aucpr:0.97136
[7]	validation-logloss:0.53352	validation-auc:0.96769	validation-aucpr:0.97165
[8]	validation-logloss:0.51795	validation-auc:0.96808	validation-aucpr:0.97208
[9]	validation-logloss:0.50358	validation-auc:0.96837	validation-aucpr:0.97216
[10]	validation-logloss:0.48987	validation-auc:0.96834	validation-aucpr:0.97210
[11]	validation-logloss:0.47681	validation-auc:0.96817	validation-aucpr:0.97203
[12]	validation-logloss:0.46467	validation-auc:0.96838	validation-aucpr:0.97230
[13]	validation-logloss:0.45303	validation-auc:0.96826	validation-aucpr:0.97222
[14]	validation-logloss:0.44183	validation-auc:0.96840	validation-aucpr:0.97186
[15]	validation-logloss:0.43098	validation-auc:0.96881	validation-aucpr:0.97230
[16]	validation-logloss:0.42095	validation-auc:0.96910	validation-aucpr:0.97251
[17]	validation-logloss:0.41125	validation-auc:0.96930	validation-aucpr:0.97265
[18]	validation-logloss:0.40237	validation-auc:0.96935	validation-aucpr:0.97256
[19]	validation-logloss:0.39360	validation-auc:0.96960	validation-aucpr:0.97276
[20]	validation-logloss:0.38512	validation-auc:0.96982	validation-aucpr:0.97296
[21]	validation-logloss:0.37739	validation-auc:0.96967	validation-aucpr:0.97293
[22]	validation-logloss:0.36977	validation-auc:0.96977	validation-aucpr:0.97294
[23]	validation-logloss:0.36256	validation-auc:0.96999	validation-aucpr:0.97285
[24]	validation-logloss:0.35628	validation-auc:0.97005	validation-aucpr:0.97275
[25]	validation-logloss:0.35010	validation-auc:0.97005	validation-aucpr:0.97293
[26]	validation-logloss:0.34346	validation-auc:0.97029	validation-aucpr:0.97312
[27]	validation-logloss:0.33727	validation-auc:0.97028	validation-aucpr:0.97315
[28]	validation-logloss:0.33114	validation-auc:0.97043	validation-aucpr:0.97330
[29]	validation-logloss:0.32588	validation-auc:0.97032	validation-aucpr:0.97326
[30]	validation-logloss:0.32041	validation-auc:0.97029	validation-aucpr:0.97321
[31]	validation-logloss:0.31485	validation-auc:0.97054	validation-aucpr:0.97347
[32]	validation-logloss:0.30969	validation-auc:0.97084	validation-aucpr:0.97358
[33]	validation-logloss:0.30478	validation-auc:0.97107	validation-aucpr:0.97364
[34]	validation-logloss:0.30002	validation-auc:0.97119	validation-aucpr:0.97368
[35]	validation-logloss:0.29577	validation-auc:0.97122	validation-aucpr:0.97370
[36]	validation-logloss:0.29218	validation-auc:0.97119	validation-aucpr:0.97446
[37]	validation-logloss:0.28816	validation-auc:0.97109	validation-aucpr:0.97437
[38]	validation-logloss:0.28417	validation-auc:0.97119	validation-aucpr:0.97443
[39]	validation-logloss:0.28046	validation-auc:0.97114	validation-aucpr:0.97441
[40]	validation-logloss:0.27673	validation-auc:0.97131	validation-aucpr:0.97459
[41]	validation-logloss:0.27316	validation-auc:0.97142	validation-aucpr:0.97465
{'best_iteration': '41', 'best_score': '0.9746504882779937'}
Trial 87, Fold 2: Log loss = 0.27315787486291515, Average precision = 0.97459003043263, ROC-AUC = 0.9714154972729405, Elapsed Time = 2.1508365999980015 seconds
Trial 87, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 87, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[0]	validation-logloss:0.66822	validation-auc:0.91816	validation-aucpr:0.87591
[1]	validation-logloss:0.64466	validation-auc:0.95738	validation-aucpr:0.94106
[2]	validation-logloss:0.62257	validation-auc:0.96645	validation-aucpr:0.96665
[3]	validation-logloss:0.60396	validation-auc:0.96735	validation-aucpr:0.97051
[4]	validation-logloss:0.58440	validation-auc:0.96790	validation-aucpr:0.97252
[5]	validation-logloss:0.56591	validation-auc:0.96850	validation-aucpr:0.97304
[6]	validation-logloss:0.54841	validation-auc:0.96944	validation-aucpr:0.97378
[7]	validation-logloss:0.53217	validation-auc:0.96951	validation-aucpr:0.97405
[8]	validation-logloss:0.51695	validation-auc:0.96940	validation-aucpr:0.97397
[9]	validation-logloss:0.50214	validation-auc:0.96948	validation-aucpr:0.97315
[10]	validation-logloss:0.48805	validation-auc:0.96932	validation-aucpr:0.97288
[11]	validation-logloss:0.47528	validation-auc:0.96947	validation-aucpr:0.97360
[12]	validation-logloss:0.46293	validation-auc:0.97000	validation-aucpr:0.97374
[13]	validation-logloss:0.45131	validation-auc:0.97006	validation-aucpr:0.97375
[14]	validation-logloss:0.44097	validation-auc:0.97010	validation-aucpr:0.97371
[15]	validation-logloss:0.42999	validation-auc:0.97050	validation-aucpr:0.97402
[16]	validation-logloss:0.42006	validation-auc:0.97081	validation-aucpr:0.97423
[17]	validation-logloss:0.41021	validation-auc:0.97118	validation-aucpr:0.97543
[18]	validation-logloss:0.40159	validation-auc:0.97119	validation-aucpr:0.97540
[19]	validation-logloss:0.39248	validation-auc:0.97125	validation-aucpr:0.97534
[20]	validation-logloss:0.38402	validation-auc:0.97118	validation-aucpr:0.97531
[21]	validation-logloss:0.37603	validation-auc:0.97128	validation-aucpr:0.97534
[22]	validation-logloss:0.36869	validation-auc:0.97109	validation-aucpr:0.97520
[23]	validation-logloss:0.36126	validation-auc:0.97121	validation-aucpr:0.97531
[24]	validation-logloss:0.35453	validation-auc:0.97098	validation-aucpr:0.97512
[25]	validation-logloss:0.34807	validation-auc:0.97083	validation-aucpr:0.97500
[26]	validation-logloss:0.34165	validation-auc:0.97083	validation-aucpr:0.97498
[27]	validation-logloss:0.33542	validation-auc:0.97114	validation-aucpr:0.97533
[28]	validation-logloss:0.32992	validation-auc:0.97130	validation-aucpr:0.97543
[29]	validation-logloss:0.32410	validation-auc:0.97140	validation-aucpr:0.97549
[30]	validation-logloss:0.31842	validation-auc:0.97152	validation-aucpr:0.97555
[31]	validation-logloss:0.31315	validation-auc:0.97155	validation-aucpr:0.97557
[32]	validation-logloss:0.30862	validation-auc:0.97166	validation-aucpr:0.97571
[33]	validation-logloss:0.30371	validation-auc:0.97177	validation-aucpr:0.97584
[34]	validation-logloss:0.29900	validation-auc:0.97195	validation-aucpr:0.97599
[35]	validation-logloss:0.29472	validation-auc:0.97178	validation-aucpr:0.97586
[36]	validation-logloss:0.29034	validation-auc:0.97190	validation-aucpr:0.97610
[37]	validation-logloss:0.28630	validation-auc:0.97191	validation-aucpr:0.97613
[38]	validation-logloss:0.28245	validation-auc:0.97193	validation-aucpr:0.97614
[39]	validation-logloss:0.27878	validation-auc:0.97198	validation-aucpr:0.97617
[40]	validation-logloss:0.27513	validation-auc:0.97194	validation-aucpr:0.97617
[41]	validation-logloss:0.27177	validation-auc:0.97190	validation-aucpr:0.97610
{'best_iteration': '40', 'best_score': '0.9761697992665844'}
Trial 87, Fold 3: Log loss = 0.27177006105110235, Average precision = 0.976105101470461, ROC-AUC = 0.9718986712783272, Elapsed Time = 2.32190780000019 seconds
Trial 87, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 87, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[0]	validation-logloss:0.66814	validation-auc:0.91552	validation-aucpr:0.88203
[1]	validation-logloss:0.64555	validation-auc:0.94976	validation-aucpr:0.94123
[2]	validation-logloss:0.62354	validation-auc:0.96001	validation-aucpr:0.96171
[3]	validation-logloss:0.60277	validation-auc:0.96264	validation-aucpr:0.96326
[4]	validation-logloss:0.58355	validation-auc:0.96383	validation-aucpr:0.96660
[5]	validation-logloss:0.56655	validation-auc:0.96375	validation-aucpr:0.96597
[6]	validation-logloss:0.55093	validation-auc:0.96487	validation-aucpr:0.97111
[7]	validation-logloss:0.53626	validation-auc:0.96556	validation-aucpr:0.97145
[8]	validation-logloss:0.52066	validation-auc:0.96600	validation-aucpr:0.97183
[9]	validation-logloss:0.50599	validation-auc:0.96686	validation-aucpr:0.97244
[10]	validation-logloss:0.49231	validation-auc:0.96712	validation-aucpr:0.97259
[11]	validation-logloss:0.47915	validation-auc:0.96704	validation-aucpr:0.97256
[12]	validation-logloss:0.46657	validation-auc:0.96721	validation-aucpr:0.97274
[13]	validation-logloss:0.45487	validation-auc:0.96747	validation-aucpr:0.97294
[14]	validation-logloss:0.44465	validation-auc:0.96782	validation-aucpr:0.97314
[15]	validation-logloss:0.43398	validation-auc:0.96795	validation-aucpr:0.97330
[16]	validation-logloss:0.42347	validation-auc:0.96829	validation-aucpr:0.97356
[17]	validation-logloss:0.41386	validation-auc:0.96799	validation-aucpr:0.97334
[18]	validation-logloss:0.40456	validation-auc:0.96804	validation-aucpr:0.97341
[19]	validation-logloss:0.39541	validation-auc:0.96832	validation-aucpr:0.97364
[20]	validation-logloss:0.38787	validation-auc:0.96853	validation-aucpr:0.97375
[21]	validation-logloss:0.37969	validation-auc:0.96879	validation-aucpr:0.97394
[22]	validation-logloss:0.37189	validation-auc:0.96881	validation-aucpr:0.97397
[23]	validation-logloss:0.36457	validation-auc:0.96893	validation-aucpr:0.97407
[24]	validation-logloss:0.35758	validation-auc:0.96891	validation-aucpr:0.97405
[25]	validation-logloss:0.35160	validation-auc:0.96884	validation-aucpr:0.97398
[26]	validation-logloss:0.34503	validation-auc:0.96917	validation-aucpr:0.97419
[27]	validation-logloss:0.33866	validation-auc:0.96950	validation-aucpr:0.97442
[28]	validation-logloss:0.33275	validation-auc:0.96964	validation-aucpr:0.97454
[29]	validation-logloss:0.32750	validation-auc:0.96964	validation-aucpr:0.97454
[30]	validation-logloss:0.32197	validation-auc:0.96959	validation-aucpr:0.97452
[31]	validation-logloss:0.31669	validation-auc:0.96968	validation-aucpr:0.97460
[32]	validation-logloss:0.31238	validation-auc:0.96958	validation-aucpr:0.97453
[33]	validation-logloss:0.30742	validation-auc:0.96966	validation-aucpr:0.97458
[34]	validation-logloss:0.30340	validation-auc:0.96969	validation-aucpr:0.97459
[35]	validation-logloss:0.29881	validation-auc:0.96976	validation-aucpr:0.97466
[36]	validation-logloss:0.29451	validation-auc:0.96976	validation-aucpr:0.97468
[37]	validation-logloss:0.29028	validation-auc:0.96984	validation-aucpr:0.97477
[38]	validation-logloss:0.28635	validation-auc:0.96993	validation-aucpr:0.97483
[39]	validation-logloss:0.28254	validation-auc:0.97000	validation-aucpr:0.97491
[40]	validation-logloss:0.27890	validation-auc:0.96989	validation-aucpr:0.97487
[41]	validation-logloss:0.27548	validation-auc:0.96993	validation-aucpr:0.97489
{'best_iteration': '39', 'best_score': '0.974910544350713'}
Trial 87, Fold 4: Log loss = 0.2754796669893886, Average precision = 0.9748833936863853, ROC-AUC = 0.9699267062240249, Elapsed Time = 2.1952992000005906 seconds
Trial 87, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 87, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[0]	validation-logloss:0.66835	validation-auc:0.91932	validation-aucpr:0.88248
[1]	validation-logloss:0.64550	validation-auc:0.95531	validation-aucpr:0.94997
[2]	validation-logloss:0.62387	validation-auc:0.96068	validation-aucpr:0.96237
[3]	validation-logloss:0.60353	validation-auc:0.96315	validation-aucpr:0.96870
[4]	validation-logloss:0.58473	validation-auc:0.96522	validation-aucpr:0.97014
[5]	validation-logloss:0.56741	validation-auc:0.96485	validation-aucpr:0.96944
[6]	validation-logloss:0.55094	validation-auc:0.96529	validation-aucpr:0.96948
[7]	validation-logloss:0.53694	validation-auc:0.96464	validation-aucpr:0.96877
[8]	validation-logloss:0.52136	validation-auc:0.96520	validation-aucpr:0.96929
[9]	validation-logloss:0.50686	validation-auc:0.96604	validation-aucpr:0.96986
[10]	validation-logloss:0.49325	validation-auc:0.96631	validation-aucpr:0.97006
[11]	validation-logloss:0.48042	validation-auc:0.96698	validation-aucpr:0.97007
[12]	validation-logloss:0.46817	validation-auc:0.96733	validation-aucpr:0.97028
[13]	validation-logloss:0.45616	validation-auc:0.96780	validation-aucpr:0.97075
[14]	validation-logloss:0.44472	validation-auc:0.96828	validation-aucpr:0.97113
[15]	validation-logloss:0.43416	validation-auc:0.96869	validation-aucpr:0.97138
[16]	validation-logloss:0.42421	validation-auc:0.96863	validation-aucpr:0.97148
[17]	validation-logloss:0.41465	validation-auc:0.96885	validation-aucpr:0.97167
[18]	validation-logloss:0.40545	validation-auc:0.96921	validation-aucpr:0.97188
[19]	validation-logloss:0.39687	validation-auc:0.96908	validation-aucpr:0.97181
[20]	validation-logloss:0.38885	validation-auc:0.96902	validation-aucpr:0.97189
[21]	validation-logloss:0.38101	validation-auc:0.96899	validation-aucpr:0.97187
[22]	validation-logloss:0.37384	validation-auc:0.96914	validation-aucpr:0.97195
[23]	validation-logloss:0.36661	validation-auc:0.96919	validation-aucpr:0.97201
[24]	validation-logloss:0.35959	validation-auc:0.96957	validation-aucpr:0.97231
[25]	validation-logloss:0.35332	validation-auc:0.96975	validation-aucpr:0.97244
[26]	validation-logloss:0.34690	validation-auc:0.96993	validation-aucpr:0.97229
[27]	validation-logloss:0.34128	validation-auc:0.96999	validation-aucpr:0.97235
[28]	validation-logloss:0.33538	validation-auc:0.97006	validation-aucpr:0.97237
[29]	validation-logloss:0.32988	validation-auc:0.97002	validation-aucpr:0.97240
[30]	validation-logloss:0.32452	validation-auc:0.97013	validation-aucpr:0.97266
[31]	validation-logloss:0.31997	validation-auc:0.96998	validation-aucpr:0.97249
[32]	validation-logloss:0.31541	validation-auc:0.96995	validation-aucpr:0.97246
[33]	validation-logloss:0.31069	validation-auc:0.96985	validation-aucpr:0.97239
[34]	validation-logloss:0.30615	validation-auc:0.96996	validation-aucpr:0.97230
[35]	validation-logloss:0.30192	validation-auc:0.96995	validation-aucpr:0.97230
[36]	validation-logloss:0.29763	validation-auc:0.97012	validation-aucpr:0.97244
[37]	validation-logloss:0.29347	validation-auc:0.97024	validation-aucpr:0.97254
[38]	validation-logloss:0.28982	validation-auc:0.97040	validation-aucpr:0.97265
[39]	validation-logloss:0.28612	validation-auc:0.97038	validation-aucpr:0.97278
[40]	validation-logloss:0.28272	validation-auc:0.97038	validation-aucpr:0.97286
[41]	validation-logloss:0.27934	validation-auc:0.97038	validation-aucpr:0.97296
{'best_iteration': '41', 'best_score': '0.9729616450654627'}
Trial 87, Fold 5: Log loss = 0.2793432269406746, Average precision = 0.9731349045573595, ROC-AUC = 0.9703788343273322, Elapsed Time = 2.298222099998384 seconds
Optimization Progress:  88%|########8 | 88/100 [3:50:18<18:44, 93.73s/it] 
Trial 88, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 88, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[0]	validation-logloss:0.67879	validation-auc:0.94026	validation-aucpr:0.94310
[1]	validation-logloss:0.66553	validation-auc:0.94625	validation-aucpr:0.94913
[2]	validation-logloss:0.65201	validation-auc:0.94978	validation-aucpr:0.95335
[3]	validation-logloss:0.63958	validation-auc:0.95177	validation-aucpr:0.95584
[4]	validation-logloss:0.62735	validation-auc:0.95260	validation-aucpr:0.95705
[5]	validation-logloss:0.61310	validation-auc:0.96017	validation-aucpr:0.96567
[6]	validation-logloss:0.60127	validation-auc:0.96091	validation-aucpr:0.96615
[7]	validation-logloss:0.58892	validation-auc:0.96204	validation-aucpr:0.96761
[8]	validation-logloss:0.57856	validation-auc:0.96218	validation-aucpr:0.96769
[9]	validation-logloss:0.56692	validation-auc:0.96295	validation-aucpr:0.96882
[10]	validation-logloss:0.55737	validation-auc:0.96287	validation-aucpr:0.96867
[11]	validation-logloss:0.54626	validation-auc:0.96343	validation-aucpr:0.96941
[12]	validation-logloss:0.53608	validation-auc:0.96331	validation-aucpr:0.96943
[13]	validation-logloss:0.52713	validation-auc:0.96336	validation-aucpr:0.96943
[14]	validation-logloss:0.51890	validation-auc:0.96317	validation-aucpr:0.96923
[15]	validation-logloss:0.51083	validation-auc:0.96314	validation-aucpr:0.96908
[16]	validation-logloss:0.50245	validation-auc:0.96317	validation-aucpr:0.96911
[17]	validation-logloss:0.49529	validation-auc:0.96306	validation-aucpr:0.96898
[18]	validation-logloss:0.48806	validation-auc:0.96298	validation-aucpr:0.96893
[19]	validation-logloss:0.47969	validation-auc:0.96339	validation-aucpr:0.96935
[20]	validation-logloss:0.47139	validation-auc:0.96365	validation-aucpr:0.96962
[21]	validation-logloss:0.46376	validation-auc:0.96387	validation-aucpr:0.96986
[22]	validation-logloss:0.45609	validation-auc:0.96400	validation-aucpr:0.97005
[23]	validation-logloss:0.45027	validation-auc:0.96386	validation-aucpr:0.96991
[24]	validation-logloss:0.44469	validation-auc:0.96375	validation-aucpr:0.96979
[25]	validation-logloss:0.43922	validation-auc:0.96369	validation-aucpr:0.96970
[26]	validation-logloss:0.43375	validation-auc:0.96371	validation-aucpr:0.96964
[27]	validation-logloss:0.42869	validation-auc:0.96365	validation-aucpr:0.96956
[28]	validation-logloss:0.42344	validation-auc:0.96372	validation-aucpr:0.96962
[29]	validation-logloss:0.41871	validation-auc:0.96366	validation-aucpr:0.96955
[30]	validation-logloss:0.41273	validation-auc:0.96383	validation-aucpr:0.96972
[31]	validation-logloss:0.40783	validation-auc:0.96398	validation-aucpr:0.96983
[32]	validation-logloss:0.40183	validation-auc:0.96426	validation-aucpr:0.97008
[33]	validation-logloss:0.39593	validation-auc:0.96455	validation-aucpr:0.97036
[34]	validation-logloss:0.39195	validation-auc:0.96446	validation-aucpr:0.97029
[35]	validation-logloss:0.38791	validation-auc:0.96443	validation-aucpr:0.97026
[36]	validation-logloss:0.38274	validation-auc:0.96452	validation-aucpr:0.97039
[37]	validation-logloss:0.37730	validation-auc:0.96479	validation-aucpr:0.97063
[38]	validation-logloss:0.37219	validation-auc:0.96499	validation-aucpr:0.97083
[39]	validation-logloss:0.36870	validation-auc:0.96496	validation-aucpr:0.97080
[40]	validation-logloss:0.36393	validation-auc:0.96517	validation-aucpr:0.97096
[41]	validation-logloss:0.36027	validation-auc:0.96522	validation-aucpr:0.97098
[42]	validation-logloss:0.35569	validation-auc:0.96538	validation-aucpr:0.97115
[43]	validation-logloss:0.35282	validation-auc:0.96529	validation-aucpr:0.97107
[44]	validation-logloss:0.34946	validation-auc:0.96536	validation-aucpr:0.97110
[45]	validation-logloss:0.34551	validation-auc:0.96547	validation-aucpr:0.97123
[46]	validation-logloss:0.34134	validation-auc:0.96565	validation-aucpr:0.97138
{'best_iteration': '46', 'best_score': '0.9713768990895306'}
Trial 88, Fold 1: Log loss = 0.3413363609274223, Average precision = 0.9713801334846242, ROC-AUC = 0.9656482706251225, Elapsed Time = 1.1811795000030543 seconds
Trial 88, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 88, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[0]	validation-logloss:0.67841	validation-auc:0.94098	validation-aucpr:0.94155
[1]	validation-logloss:0.66472	validation-auc:0.94942	validation-aucpr:0.95172
[2]	validation-logloss:0.64888	validation-auc:0.96139	validation-aucpr:0.96578
[3]	validation-logloss:0.63668	validation-auc:0.96151	validation-aucpr:0.96570
[4]	validation-logloss:0.62391	validation-auc:0.96223	validation-aucpr:0.96659
[5]	validation-logloss:0.61175	validation-auc:0.96246	validation-aucpr:0.96654
[6]	validation-logloss:0.59849	validation-auc:0.96350	validation-aucpr:0.96765
[7]	validation-logloss:0.58573	validation-auc:0.96443	validation-aucpr:0.96850
[8]	validation-logloss:0.57492	validation-auc:0.96434	validation-aucpr:0.96842
[9]	validation-logloss:0.56313	validation-auc:0.96482	validation-aucpr:0.96891
[10]	validation-logloss:0.55358	validation-auc:0.96462	validation-aucpr:0.96869
[11]	validation-logloss:0.54242	validation-auc:0.96504	validation-aucpr:0.96914
[12]	validation-logloss:0.53391	validation-auc:0.96483	validation-aucpr:0.96930
[13]	validation-logloss:0.52351	validation-auc:0.96505	validation-aucpr:0.96951
[14]	validation-logloss:0.51369	validation-auc:0.96539	validation-aucpr:0.96986
[15]	validation-logloss:0.50569	validation-auc:0.96529	validation-aucpr:0.96975
[16]	validation-logloss:0.49620	validation-auc:0.96572	validation-aucpr:0.97016
[17]	validation-logloss:0.48717	validation-auc:0.96610	validation-aucpr:0.97047
[18]	validation-logloss:0.47857	validation-auc:0.96645	validation-aucpr:0.97080
[19]	validation-logloss:0.47169	validation-auc:0.96632	validation-aucpr:0.97062
[20]	validation-logloss:0.46523	validation-auc:0.96597	validation-aucpr:0.97032
[21]	validation-logloss:0.45770	validation-auc:0.96628	validation-aucpr:0.97061
[22]	validation-logloss:0.45128	validation-auc:0.96622	validation-aucpr:0.97053
[23]	validation-logloss:0.44510	validation-auc:0.96626	validation-aucpr:0.97047
[24]	validation-logloss:0.43910	validation-auc:0.96626	validation-aucpr:0.97045
[25]	validation-logloss:0.43247	validation-auc:0.96640	validation-aucpr:0.97060
[26]	validation-logloss:0.42556	validation-auc:0.96667	validation-aucpr:0.97084
[27]	validation-logloss:0.42026	validation-auc:0.96653	validation-aucpr:0.97066
[28]	validation-logloss:0.41500	validation-auc:0.96640	validation-aucpr:0.97053
[29]	validation-logloss:0.41002	validation-auc:0.96632	validation-aucpr:0.97043
[30]	validation-logloss:0.40533	validation-auc:0.96632	validation-aucpr:0.97034
[31]	validation-logloss:0.40071	validation-auc:0.96629	validation-aucpr:0.97029
[32]	validation-logloss:0.39475	validation-auc:0.96659	validation-aucpr:0.97056
[33]	validation-logloss:0.39062	validation-auc:0.96650	validation-aucpr:0.97048
[34]	validation-logloss:0.38651	validation-auc:0.96645	validation-aucpr:0.97040
[35]	validation-logloss:0.38135	validation-auc:0.96666	validation-aucpr:0.97051
[36]	validation-logloss:0.37598	validation-auc:0.96688	validation-aucpr:0.97072
[37]	validation-logloss:0.37226	validation-auc:0.96677	validation-aucpr:0.97061
[38]	validation-logloss:0.36845	validation-auc:0.96682	validation-aucpr:0.97063
[39]	validation-logloss:0.36363	validation-auc:0.96703	validation-aucpr:0.97084
[40]	validation-logloss:0.35914	validation-auc:0.96715	validation-aucpr:0.97096
[41]	validation-logloss:0.35458	validation-auc:0.96729	validation-aucpr:0.97115
[42]	validation-logloss:0.35017	validation-auc:0.96747	validation-aucpr:0.97132
[43]	validation-logloss:0.34612	validation-auc:0.96760	validation-aucpr:0.97135
[44]	validation-logloss:0.34296	validation-auc:0.96757	validation-aucpr:0.97131
[45]	validation-logloss:0.33931	validation-auc:0.96762	validation-aucpr:0.97136
[46]	validation-logloss:0.33617	validation-auc:0.96765	validation-aucpr:0.97138
{'best_iteration': '46', 'best_score': '0.9713759033775468'}
Trial 88, Fold 2: Log loss = 0.33617017571275093, Average precision = 0.9713869132696409, ROC-AUC = 0.967648434372573, Elapsed Time = 1.597771599997941 seconds
Trial 88, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 88, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[0]	validation-logloss:0.67819	validation-auc:0.94612	validation-aucpr:0.94908
[1]	validation-logloss:0.66173	validation-auc:0.96430	validation-aucpr:0.96780
[2]	validation-logloss:0.64828	validation-auc:0.96401	validation-aucpr:0.96794
[3]	validation-logloss:0.63366	validation-auc:0.96519	validation-aucpr:0.96896
[4]	validation-logloss:0.62120	validation-auc:0.96508	validation-aucpr:0.96889
[5]	validation-logloss:0.60920	validation-auc:0.96536	validation-aucpr:0.96945
[6]	validation-logloss:0.59791	validation-auc:0.96511	validation-aucpr:0.96914
[7]	validation-logloss:0.58509	validation-auc:0.96580	validation-aucpr:0.96985
[8]	validation-logloss:0.57464	validation-auc:0.96604	validation-aucpr:0.96995
[9]	validation-logloss:0.56292	validation-auc:0.96636	validation-aucpr:0.97038
[10]	validation-logloss:0.55346	validation-auc:0.96628	validation-aucpr:0.97060
[11]	validation-logloss:0.54364	validation-auc:0.96639	validation-aucpr:0.97067
[12]	validation-logloss:0.53489	validation-auc:0.96599	validation-aucpr:0.97032
[13]	validation-logloss:0.52466	validation-auc:0.96628	validation-aucpr:0.97061
[14]	validation-logloss:0.51639	validation-auc:0.96599	validation-aucpr:0.97036
[15]	validation-logloss:0.50808	validation-auc:0.96601	validation-aucpr:0.97040
[16]	validation-logloss:0.49875	validation-auc:0.96649	validation-aucpr:0.97119
[17]	validation-logloss:0.48964	validation-auc:0.96666	validation-aucpr:0.97139
[18]	validation-logloss:0.48105	validation-auc:0.96685	validation-aucpr:0.97157
[19]	validation-logloss:0.47438	validation-auc:0.96659	validation-aucpr:0.97135
[20]	validation-logloss:0.46646	validation-auc:0.96683	validation-aucpr:0.97162
[21]	validation-logloss:0.46013	validation-auc:0.96678	validation-aucpr:0.97160
[22]	validation-logloss:0.45380	validation-auc:0.96675	validation-aucpr:0.97152
[23]	validation-logloss:0.44629	validation-auc:0.96693	validation-aucpr:0.97170
[24]	validation-logloss:0.44003	validation-auc:0.96705	validation-aucpr:0.97180
[25]	validation-logloss:0.43426	validation-auc:0.96698	validation-aucpr:0.97175
[26]	validation-logloss:0.42826	validation-auc:0.96706	validation-aucpr:0.97179
[27]	validation-logloss:0.42306	validation-auc:0.96704	validation-aucpr:0.97174
[28]	validation-logloss:0.41803	validation-auc:0.96695	validation-aucpr:0.97164
[29]	validation-logloss:0.41309	validation-auc:0.96689	validation-aucpr:0.97158
[30]	validation-logloss:0.40831	validation-auc:0.96677	validation-aucpr:0.97146
[31]	validation-logloss:0.40237	validation-auc:0.96682	validation-aucpr:0.97156
[32]	validation-logloss:0.39755	validation-auc:0.96698	validation-aucpr:0.97169
[33]	validation-logloss:0.39309	validation-auc:0.96707	validation-aucpr:0.97177
[34]	validation-logloss:0.38855	validation-auc:0.96722	validation-aucpr:0.97191
[35]	validation-logloss:0.38443	validation-auc:0.96721	validation-aucpr:0.97190
[36]	validation-logloss:0.38017	validation-auc:0.96722	validation-aucpr:0.97187
[37]	validation-logloss:0.37500	validation-auc:0.96729	validation-aucpr:0.97195
[38]	validation-logloss:0.36976	validation-auc:0.96752	validation-aucpr:0.97216
[39]	validation-logloss:0.36500	validation-auc:0.96762	validation-aucpr:0.97232
[40]	validation-logloss:0.36145	validation-auc:0.96755	validation-aucpr:0.97225
[41]	validation-logloss:0.35791	validation-auc:0.96758	validation-aucpr:0.97225
[42]	validation-logloss:0.35440	validation-auc:0.96760	validation-aucpr:0.97225
[43]	validation-logloss:0.34966	validation-auc:0.96779	validation-aucpr:0.97244
[44]	validation-logloss:0.34549	validation-auc:0.96786	validation-aucpr:0.97251
[45]	validation-logloss:0.34244	validation-auc:0.96786	validation-aucpr:0.97252
[46]	validation-logloss:0.33934	validation-auc:0.96789	validation-aucpr:0.97254
{'best_iteration': '46', 'best_score': '0.9725354782972834'}
Trial 88, Fold 3: Log loss = 0.33934060727163606, Average precision = 0.9725391929748513, ROC-AUC = 0.9678902796371026, Elapsed Time = 1.4755301000004692 seconds
Trial 88, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 88, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[0]	validation-logloss:0.67847	validation-auc:0.93894	validation-aucpr:0.94389
[1]	validation-logloss:0.66476	validation-auc:0.94607	validation-aucpr:0.95173
[2]	validation-logloss:0.65105	validation-auc:0.95285	validation-aucpr:0.95696
[3]	validation-logloss:0.63844	validation-auc:0.95362	validation-aucpr:0.95798
[4]	validation-logloss:0.62411	validation-auc:0.96066	validation-aucpr:0.96642
[5]	validation-logloss:0.61000	validation-auc:0.96201	validation-aucpr:0.96803
[6]	validation-logloss:0.59770	validation-auc:0.96247	validation-aucpr:0.96858
[7]	validation-logloss:0.58557	validation-auc:0.96268	validation-aucpr:0.96880
[8]	validation-logloss:0.57522	validation-auc:0.96253	validation-aucpr:0.96862
[9]	validation-logloss:0.56321	validation-auc:0.96291	validation-aucpr:0.96912
[10]	validation-logloss:0.55170	validation-auc:0.96324	validation-aucpr:0.96944
[11]	validation-logloss:0.54062	validation-auc:0.96351	validation-aucpr:0.96982
[12]	validation-logloss:0.53121	validation-auc:0.96378	validation-aucpr:0.96994
[13]	validation-logloss:0.52250	validation-auc:0.96363	validation-aucpr:0.96979
[14]	validation-logloss:0.51428	validation-auc:0.96369	validation-aucpr:0.96976
[15]	validation-logloss:0.50623	validation-auc:0.96360	validation-aucpr:0.96964
[16]	validation-logloss:0.49861	validation-auc:0.96358	validation-aucpr:0.96962
[17]	validation-logloss:0.49103	validation-auc:0.96376	validation-aucpr:0.96979
[18]	validation-logloss:0.48222	validation-auc:0.96403	validation-aucpr:0.97005
[19]	validation-logloss:0.47372	validation-auc:0.96430	validation-aucpr:0.97033
[20]	validation-logloss:0.46676	validation-auc:0.96431	validation-aucpr:0.97032
[21]	validation-logloss:0.46007	validation-auc:0.96441	validation-aucpr:0.97043
[22]	validation-logloss:0.45395	validation-auc:0.96434	validation-aucpr:0.97035
[23]	validation-logloss:0.44625	validation-auc:0.96469	validation-aucpr:0.97066
[24]	validation-logloss:0.44035	validation-auc:0.96473	validation-aucpr:0.97064
[25]	validation-logloss:0.43315	validation-auc:0.96489	validation-aucpr:0.97083
[26]	validation-logloss:0.42625	validation-auc:0.96501	validation-aucpr:0.97097
[27]	validation-logloss:0.41948	validation-auc:0.96526	validation-aucpr:0.97118
[28]	validation-logloss:0.41340	validation-auc:0.96533	validation-aucpr:0.97126
[29]	validation-logloss:0.40845	validation-auc:0.96524	validation-aucpr:0.97117
[30]	validation-logloss:0.40244	validation-auc:0.96537	validation-aucpr:0.97131
[31]	validation-logloss:0.39779	validation-auc:0.96542	validation-aucpr:0.97131
[32]	validation-logloss:0.39229	validation-auc:0.96550	validation-aucpr:0.97140
[33]	validation-logloss:0.38790	validation-auc:0.96557	validation-aucpr:0.97145
[34]	validation-logloss:0.38359	validation-auc:0.96559	validation-aucpr:0.97143
[35]	validation-logloss:0.37826	validation-auc:0.96564	validation-aucpr:0.97149
[36]	validation-logloss:0.37438	validation-auc:0.96565	validation-aucpr:0.97149
[37]	validation-logloss:0.37026	validation-auc:0.96576	validation-aucpr:0.97156
[38]	validation-logloss:0.36665	validation-auc:0.96566	validation-aucpr:0.97149
[39]	validation-logloss:0.36220	validation-auc:0.96577	validation-aucpr:0.97160
[40]	validation-logloss:0.35866	validation-auc:0.96574	validation-aucpr:0.97155
[41]	validation-logloss:0.35544	validation-auc:0.96568	validation-aucpr:0.97149
[42]	validation-logloss:0.35186	validation-auc:0.96570	validation-aucpr:0.97149
[43]	validation-logloss:0.34775	validation-auc:0.96579	validation-aucpr:0.97158
[44]	validation-logloss:0.34356	validation-auc:0.96596	validation-aucpr:0.97174
[45]	validation-logloss:0.34071	validation-auc:0.96587	validation-aucpr:0.97166
[46]	validation-logloss:0.33759	validation-auc:0.96586	validation-aucpr:0.97163
{'best_iteration': '44', 'best_score': '0.9717392143244231'}
Trial 88, Fold 4: Log loss = 0.3375868960426251, Average precision = 0.9716230766946939, ROC-AUC = 0.9658556990970241, Elapsed Time = 1.6040769999999611 seconds
Trial 88, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 88, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[0]	validation-logloss:0.67878	validation-auc:0.93635	validation-aucpr:0.93760
[1]	validation-logloss:0.66563	validation-auc:0.94369	validation-aucpr:0.94688
[2]	validation-logloss:0.64986	validation-auc:0.95830	validation-aucpr:0.96298
[3]	validation-logloss:0.63732	validation-auc:0.95859	validation-aucpr:0.96372
[4]	validation-logloss:0.62287	validation-auc:0.95984	validation-aucpr:0.96491
[5]	validation-logloss:0.61064	validation-auc:0.96044	validation-aucpr:0.96541
[6]	validation-logloss:0.59754	validation-auc:0.96110	validation-aucpr:0.96619
[7]	validation-logloss:0.58545	validation-auc:0.96139	validation-aucpr:0.96667
[8]	validation-logloss:0.57514	validation-auc:0.96133	validation-aucpr:0.96681
[9]	validation-logloss:0.56583	validation-auc:0.96118	validation-aucpr:0.96652
[10]	validation-logloss:0.55442	validation-auc:0.96139	validation-aucpr:0.96679
[11]	validation-logloss:0.54397	validation-auc:0.96133	validation-aucpr:0.96670
[12]	validation-logloss:0.53485	validation-auc:0.96127	validation-aucpr:0.96674
[13]	validation-logloss:0.52590	validation-auc:0.96140	validation-aucpr:0.96681
[14]	validation-logloss:0.51768	validation-auc:0.96132	validation-aucpr:0.96673
[15]	validation-logloss:0.50971	validation-auc:0.96144	validation-aucpr:0.96679
[16]	validation-logloss:0.50240	validation-auc:0.96129	validation-aucpr:0.96667
[17]	validation-logloss:0.49305	validation-auc:0.96169	validation-aucpr:0.96702
[18]	validation-logloss:0.48464	validation-auc:0.96182	validation-aucpr:0.96716
[19]	validation-logloss:0.47638	validation-auc:0.96204	validation-aucpr:0.96739
[20]	validation-logloss:0.46811	validation-auc:0.96239	validation-aucpr:0.96771
[21]	validation-logloss:0.46176	validation-auc:0.96234	validation-aucpr:0.96751
[22]	validation-logloss:0.45577	validation-auc:0.96218	validation-aucpr:0.96736
[23]	validation-logloss:0.44881	validation-auc:0.96242	validation-aucpr:0.96757
[24]	validation-logloss:0.44186	validation-auc:0.96251	validation-aucpr:0.96764
[25]	validation-logloss:0.43626	validation-auc:0.96247	validation-aucpr:0.96756
[26]	validation-logloss:0.42946	validation-auc:0.96261	validation-aucpr:0.96771
[27]	validation-logloss:0.42423	validation-auc:0.96260	validation-aucpr:0.96766
[28]	validation-logloss:0.41916	validation-auc:0.96259	validation-aucpr:0.96764
[29]	validation-logloss:0.41298	validation-auc:0.96276	validation-aucpr:0.96791
[30]	validation-logloss:0.40677	validation-auc:0.96300	validation-aucpr:0.96810
[31]	validation-logloss:0.40195	validation-auc:0.96308	validation-aucpr:0.96814
[32]	validation-logloss:0.39767	validation-auc:0.96303	validation-aucpr:0.96804
[33]	validation-logloss:0.39322	validation-auc:0.96306	validation-aucpr:0.96802
[34]	validation-logloss:0.38900	validation-auc:0.96301	validation-aucpr:0.96794
[35]	validation-logloss:0.38513	validation-auc:0.96294	validation-aucpr:0.96788
[36]	validation-logloss:0.37973	validation-auc:0.96321	validation-aucpr:0.96812
[37]	validation-logloss:0.37584	validation-auc:0.96326	validation-aucpr:0.96815
[38]	validation-logloss:0.37208	validation-auc:0.96328	validation-aucpr:0.96812
[39]	validation-logloss:0.36733	validation-auc:0.96343	validation-aucpr:0.96828
[40]	validation-logloss:0.36311	validation-auc:0.96352	validation-aucpr:0.96877
[41]	validation-logloss:0.35959	validation-auc:0.96365	validation-aucpr:0.96885
[42]	validation-logloss:0.35602	validation-auc:0.96377	validation-aucpr:0.96892
[43]	validation-logloss:0.35273	validation-auc:0.96383	validation-aucpr:0.96895
[44]	validation-logloss:0.34956	validation-auc:0.96384	validation-aucpr:0.96893
[45]	validation-logloss:0.34679	validation-auc:0.96376	validation-aucpr:0.96884
[46]	validation-logloss:0.34271	validation-auc:0.96390	validation-aucpr:0.96899
{'best_iteration': '46', 'best_score': '0.968991717837239'}
Trial 88, Fold 5: Log loss = 0.34270535412207304, Average precision = 0.9689859086742532, ROC-AUC = 0.9639023465804581, Elapsed Time = 1.4417032999990624 seconds
Optimization Progress:  89%|########9 | 89/100 [3:50:33<12:51, 70.17s/it]
Trial 89, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 89, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[0]	validation-logloss:0.65288	validation-auc:0.95789	validation-aucpr:0.96334
[1]	validation-logloss:0.61737	validation-auc:0.96161	validation-aucpr:0.96669
[2]	validation-logloss:0.58472	validation-auc:0.96400	validation-aucpr:0.96993
[3]	validation-logloss:0.55552	validation-auc:0.96476	validation-aucpr:0.97077
[4]	validation-logloss:0.52917	validation-auc:0.96521	validation-aucpr:0.97137
[5]	validation-logloss:0.50523	validation-auc:0.96574	validation-aucpr:0.97169
[6]	validation-logloss:0.48312	validation-auc:0.96599	validation-aucpr:0.97131
[7]	validation-logloss:0.46302	validation-auc:0.96659	validation-aucpr:0.97149
[8]	validation-logloss:0.44414	validation-auc:0.96723	validation-aucpr:0.97191
[9]	validation-logloss:0.42642	validation-auc:0.96779	validation-aucpr:0.97241
[10]	validation-logloss:0.41255	validation-auc:0.96788	validation-aucpr:0.97247
[11]	validation-logloss:0.39786	validation-auc:0.96819	validation-aucpr:0.97258
[12]	validation-logloss:0.38413	validation-auc:0.96844	validation-aucpr:0.97283
[13]	validation-logloss:0.37098	validation-auc:0.96881	validation-aucpr:0.97304
[14]	validation-logloss:0.35929	validation-auc:0.96888	validation-aucpr:0.97312
[15]	validation-logloss:0.34832	validation-auc:0.96906	validation-aucpr:0.97326
[16]	validation-logloss:0.33861	validation-auc:0.96906	validation-aucpr:0.97326
[17]	validation-logloss:0.32923	validation-auc:0.96936	validation-aucpr:0.97341
[18]	validation-logloss:0.32011	validation-auc:0.96954	validation-aucpr:0.97390
[19]	validation-logloss:0.31192	validation-auc:0.96963	validation-aucpr:0.97405
[20]	validation-logloss:0.30384	validation-auc:0.97019	validation-aucpr:0.97441
[21]	validation-logloss:0.29739	validation-auc:0.97022	validation-aucpr:0.97440
[22]	validation-logloss:0.29085	validation-auc:0.97030	validation-aucpr:0.97449
[23]	validation-logloss:0.28552	validation-auc:0.97027	validation-aucpr:0.97446
[24]	validation-logloss:0.27984	validation-auc:0.97043	validation-aucpr:0.97524
[25]	validation-logloss:0.27456	validation-auc:0.97047	validation-aucpr:0.97525
[26]	validation-logloss:0.26977	validation-auc:0.97040	validation-aucpr:0.97523
[27]	validation-logloss:0.26472	validation-auc:0.97053	validation-aucpr:0.97531
[28]	validation-logloss:0.26056	validation-auc:0.97032	validation-aucpr:0.97517
[29]	validation-logloss:0.25613	validation-auc:0.97055	validation-aucpr:0.97534
[30]	validation-logloss:0.25199	validation-auc:0.97079	validation-aucpr:0.97548
[31]	validation-logloss:0.24875	validation-auc:0.97052	validation-aucpr:0.97526
[32]	validation-logloss:0.24518	validation-auc:0.97066	validation-aucpr:0.97534
[33]	validation-logloss:0.24210	validation-auc:0.97060	validation-aucpr:0.97530
[34]	validation-logloss:0.23881	validation-auc:0.97084	validation-aucpr:0.97546
[35]	validation-logloss:0.23616	validation-auc:0.97073	validation-aucpr:0.97537
[36]	validation-logloss:0.23347	validation-auc:0.97075	validation-aucpr:0.97543
[37]	validation-logloss:0.23094	validation-auc:0.97092	validation-aucpr:0.97553
[38]	validation-logloss:0.22854	validation-auc:0.97092	validation-aucpr:0.97552
[39]	validation-logloss:0.22634	validation-auc:0.97110	validation-aucpr:0.97571
[40]	validation-logloss:0.22474	validation-auc:0.97100	validation-aucpr:0.97562
[41]	validation-logloss:0.22347	validation-auc:0.97079	validation-aucpr:0.97540
[42]	validation-logloss:0.22140	validation-auc:0.97086	validation-aucpr:0.97543
[43]	validation-logloss:0.21945	validation-auc:0.97101	validation-aucpr:0.97557
[44]	validation-logloss:0.21773	validation-auc:0.97115	validation-aucpr:0.97565
[45]	validation-logloss:0.21650	validation-auc:0.97113	validation-aucpr:0.97563
[46]	validation-logloss:0.21513	validation-auc:0.97116	validation-aucpr:0.97564
[47]	validation-logloss:0.21385	validation-auc:0.97124	validation-aucpr:0.97570
[48]	validation-logloss:0.21293	validation-auc:0.97119	validation-aucpr:0.97574
[49]	validation-logloss:0.21136	validation-auc:0.97131	validation-aucpr:0.97585
[50]	validation-logloss:0.20978	validation-auc:0.97149	validation-aucpr:0.97599
[51]	validation-logloss:0.20852	validation-auc:0.97156	validation-aucpr:0.97606
[52]	validation-logloss:0.20741	validation-auc:0.97172	validation-aucpr:0.97618
[53]	validation-logloss:0.20668	validation-auc:0.97163	validation-aucpr:0.97613
[54]	validation-logloss:0.20607	validation-auc:0.97148	validation-aucpr:0.97598
[55]	validation-logloss:0.20509	validation-auc:0.97156	validation-aucpr:0.97605
[56]	validation-logloss:0.20398	validation-auc:0.97178	validation-aucpr:0.97618
{'best_iteration': '52', 'best_score': '0.976176684221807'}
Trial 89, Fold 1: Log loss = 0.2039760299158349, Average precision = 0.9761799411108688, ROC-AUC = 0.9717819025632937, Elapsed Time = 12.004220999999234 seconds
Trial 89, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 89, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[0]	validation-logloss:0.65220	validation-auc:0.95974	validation-aucpr:0.96371
[1]	validation-logloss:0.61616	validation-auc:0.96514	validation-aucpr:0.96885
[2]	validation-logloss:0.58510	validation-auc:0.96508	validation-aucpr:0.96870
[3]	validation-logloss:0.55633	validation-auc:0.96588	validation-aucpr:0.96964
[4]	validation-logloss:0.52896	validation-auc:0.96729	validation-aucpr:0.97087
[5]	validation-logloss:0.50677	validation-auc:0.96826	validation-aucpr:0.97161
[6]	validation-logloss:0.48432	validation-auc:0.96853	validation-aucpr:0.97176
[7]	validation-logloss:0.46352	validation-auc:0.96885	validation-aucpr:0.97255
[8]	validation-logloss:0.44485	validation-auc:0.96899	validation-aucpr:0.97262
[9]	validation-logloss:0.42724	validation-auc:0.96924	validation-aucpr:0.97290
[10]	validation-logloss:0.41329	validation-auc:0.96942	validation-aucpr:0.97302
[11]	validation-logloss:0.39979	validation-auc:0.96983	validation-aucpr:0.97328
[12]	validation-logloss:0.38577	validation-auc:0.97014	validation-aucpr:0.97357
[13]	validation-logloss:0.37266	validation-auc:0.97043	validation-aucpr:0.97379
[14]	validation-logloss:0.36108	validation-auc:0.97041	validation-aucpr:0.97377
[15]	validation-logloss:0.35131	validation-auc:0.97065	validation-aucpr:0.97392
[16]	validation-logloss:0.34075	validation-auc:0.97058	validation-aucpr:0.97378
[17]	validation-logloss:0.33208	validation-auc:0.97061	validation-aucpr:0.97379
[18]	validation-logloss:0.32282	validation-auc:0.97096	validation-aucpr:0.97406
[19]	validation-logloss:0.31413	validation-auc:0.97111	validation-aucpr:0.97413
[20]	validation-logloss:0.30582	validation-auc:0.97140	validation-aucpr:0.97430
[21]	validation-logloss:0.29973	validation-auc:0.97113	validation-aucpr:0.97411
[22]	validation-logloss:0.29394	validation-auc:0.97124	validation-aucpr:0.97419
[23]	validation-logloss:0.28759	validation-auc:0.97116	validation-aucpr:0.97409
[24]	validation-logloss:0.28124	validation-auc:0.97130	validation-aucpr:0.97420
[25]	validation-logloss:0.27554	validation-auc:0.97120	validation-aucpr:0.97416
[26]	validation-logloss:0.26983	validation-auc:0.97138	validation-aucpr:0.97432
[27]	validation-logloss:0.26468	validation-auc:0.97138	validation-aucpr:0.97432
[28]	validation-logloss:0.25982	validation-auc:0.97129	validation-aucpr:0.97433
[29]	validation-logloss:0.25532	validation-auc:0.97125	validation-aucpr:0.97433
[30]	validation-logloss:0.25155	validation-auc:0.97133	validation-aucpr:0.97441
[31]	validation-logloss:0.24752	validation-auc:0.97137	validation-aucpr:0.97439
[32]	validation-logloss:0.24386	validation-auc:0.97142	validation-aucpr:0.97441
[33]	validation-logloss:0.24081	validation-auc:0.97167	validation-aucpr:0.97459
[34]	validation-logloss:0.23747	validation-auc:0.97160	validation-aucpr:0.97455
[35]	validation-logloss:0.23508	validation-auc:0.97168	validation-aucpr:0.97490
[36]	validation-logloss:0.23257	validation-auc:0.97162	validation-aucpr:0.97486
[37]	validation-logloss:0.22964	validation-auc:0.97166	validation-aucpr:0.97487
[38]	validation-logloss:0.22697	validation-auc:0.97180	validation-aucpr:0.97497
[39]	validation-logloss:0.22466	validation-auc:0.97195	validation-aucpr:0.97506
[40]	validation-logloss:0.22219	validation-auc:0.97202	validation-aucpr:0.97508
[41]	validation-logloss:0.21982	validation-auc:0.97192	validation-aucpr:0.97499
[42]	validation-logloss:0.21823	validation-auc:0.97195	validation-aucpr:0.97497
[43]	validation-logloss:0.21677	validation-auc:0.97195	validation-aucpr:0.97493
[44]	validation-logloss:0.21478	validation-auc:0.97222	validation-aucpr:0.97515
[45]	validation-logloss:0.21297	validation-auc:0.97216	validation-aucpr:0.97501
[46]	validation-logloss:0.21145	validation-auc:0.97223	validation-aucpr:0.97505
[47]	validation-logloss:0.20975	validation-auc:0.97228	validation-aucpr:0.97509
[48]	validation-logloss:0.20793	validation-auc:0.97245	validation-aucpr:0.97521
[49]	validation-logloss:0.20650	validation-auc:0.97238	validation-aucpr:0.97513
[50]	validation-logloss:0.20518	validation-auc:0.97234	validation-aucpr:0.97503
[51]	validation-logloss:0.20395	validation-auc:0.97231	validation-aucpr:0.97499
[52]	validation-logloss:0.20237	validation-auc:0.97254	validation-aucpr:0.97517
[53]	validation-logloss:0.20136	validation-auc:0.97249	validation-aucpr:0.97508
[54]	validation-logloss:0.20000	validation-auc:0.97266	validation-aucpr:0.97520
[55]	validation-logloss:0.19878	validation-auc:0.97278	validation-aucpr:0.97531
[56]	validation-logloss:0.19779	validation-auc:0.97278	validation-aucpr:0.97531
{'best_iteration': '56', 'best_score': '0.9753079018133185'}
Trial 89, Fold 2: Log loss = 0.19778888192740518, Average precision = 0.975314491837502, ROC-AUC = 0.9727828206939062, Elapsed Time = 11.682237000000896 seconds
Trial 89, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 89, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[0]	validation-logloss:0.65183	validation-auc:0.96410	validation-aucpr:0.96861
[1]	validation-logloss:0.61589	validation-auc:0.96421	validation-aucpr:0.96958
[2]	validation-logloss:0.58678	validation-auc:0.96644	validation-aucpr:0.97093
[3]	validation-logloss:0.55724	validation-auc:0.96706	validation-aucpr:0.97161
[4]	validation-logloss:0.53337	validation-auc:0.96768	validation-aucpr:0.97198
[5]	validation-logloss:0.50880	validation-auc:0.96797	validation-aucpr:0.97228
[6]	validation-logloss:0.48597	validation-auc:0.96861	validation-aucpr:0.97261
[7]	validation-logloss:0.46724	validation-auc:0.96905	validation-aucpr:0.97301
[8]	validation-logloss:0.45022	validation-auc:0.96924	validation-aucpr:0.97352
[9]	validation-logloss:0.43204	validation-auc:0.96919	validation-aucpr:0.97345
[10]	validation-logloss:0.41515	validation-auc:0.96941	validation-aucpr:0.97356
[11]	validation-logloss:0.40154	validation-auc:0.96924	validation-aucpr:0.97334
[12]	validation-logloss:0.38915	validation-auc:0.96927	validation-aucpr:0.97335
[13]	validation-logloss:0.37579	validation-auc:0.96958	validation-aucpr:0.97354
[14]	validation-logloss:0.36525	validation-auc:0.96956	validation-aucpr:0.97351
[15]	validation-logloss:0.35341	validation-auc:0.96972	validation-aucpr:0.97362
[16]	validation-logloss:0.34239	validation-auc:0.97035	validation-aucpr:0.97406
[17]	validation-logloss:0.33251	validation-auc:0.97046	validation-aucpr:0.97413
[18]	validation-logloss:0.32270	validation-auc:0.97089	validation-aucpr:0.97449
[19]	validation-logloss:0.31366	validation-auc:0.97116	validation-aucpr:0.97471
[20]	validation-logloss:0.30569	validation-auc:0.97119	validation-aucpr:0.97472
[21]	validation-logloss:0.29800	validation-auc:0.97127	validation-aucpr:0.97491
[22]	validation-logloss:0.29141	validation-auc:0.97158	validation-aucpr:0.97512
[23]	validation-logloss:0.28484	validation-auc:0.97176	validation-aucpr:0.97535
[24]	validation-logloss:0.27937	validation-auc:0.97168	validation-aucpr:0.97526
[25]	validation-logloss:0.27333	validation-auc:0.97175	validation-aucpr:0.97524
[26]	validation-logloss:0.26864	validation-auc:0.97169	validation-aucpr:0.97517
[27]	validation-logloss:0.26322	validation-auc:0.97202	validation-aucpr:0.97537
[28]	validation-logloss:0.25820	validation-auc:0.97207	validation-aucpr:0.97541
[29]	validation-logloss:0.25438	validation-auc:0.97209	validation-aucpr:0.97540
[30]	validation-logloss:0.25076	validation-auc:0.97213	validation-aucpr:0.97469
[31]	validation-logloss:0.24684	validation-auc:0.97203	validation-aucpr:0.97472
[32]	validation-logloss:0.24296	validation-auc:0.97205	validation-aucpr:0.97475
[33]	validation-logloss:0.23960	validation-auc:0.97196	validation-aucpr:0.97465
[34]	validation-logloss:0.23671	validation-auc:0.97196	validation-aucpr:0.97464
[35]	validation-logloss:0.23380	validation-auc:0.97197	validation-aucpr:0.97461
[36]	validation-logloss:0.23067	validation-auc:0.97214	validation-aucpr:0.97480
[37]	validation-logloss:0.22796	validation-auc:0.97219	validation-aucpr:0.97518
[38]	validation-logloss:0.22504	validation-auc:0.97227	validation-aucpr:0.97523
[39]	validation-logloss:0.22276	validation-auc:0.97222	validation-aucpr:0.97516
[40]	validation-logloss:0.22057	validation-auc:0.97235	validation-aucpr:0.97525
[41]	validation-logloss:0.21832	validation-auc:0.97230	validation-aucpr:0.97519
[42]	validation-logloss:0.21607	validation-auc:0.97238	validation-aucpr:0.97524
[43]	validation-logloss:0.21482	validation-auc:0.97227	validation-aucpr:0.97590
[44]	validation-logloss:0.21324	validation-auc:0.97234	validation-aucpr:0.97593
[45]	validation-logloss:0.21161	validation-auc:0.97226	validation-aucpr:0.97587
[46]	validation-logloss:0.21051	validation-auc:0.97225	validation-aucpr:0.97584
[47]	validation-logloss:0.20868	validation-auc:0.97241	validation-aucpr:0.97605
[48]	validation-logloss:0.20670	validation-auc:0.97270	validation-aucpr:0.97628
[49]	validation-logloss:0.20550	validation-auc:0.97269	validation-aucpr:0.97626
[50]	validation-logloss:0.20409	validation-auc:0.97273	validation-aucpr:0.97625
[51]	validation-logloss:0.20264	validation-auc:0.97279	validation-aucpr:0.97634
[52]	validation-logloss:0.20146	validation-auc:0.97287	validation-aucpr:0.97640
[53]	validation-logloss:0.20027	validation-auc:0.97293	validation-aucpr:0.97645
[54]	validation-logloss:0.19940	validation-auc:0.97298	validation-aucpr:0.97646
[55]	validation-logloss:0.19850	validation-auc:0.97290	validation-aucpr:0.97639
[56]	validation-logloss:0.19759	validation-auc:0.97294	validation-aucpr:0.97639
{'best_iteration': '54', 'best_score': '0.9764551443514747'}
Trial 89, Fold 3: Log loss = 0.19758623619424412, Average precision = 0.9763968490881044, ROC-AUC = 0.9729429613988114, Elapsed Time = 10.560710200003086 seconds
Trial 89, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 89, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[0]	validation-logloss:0.65225	validation-auc:0.95540	validation-aucpr:0.95811
[1]	validation-logloss:0.61561	validation-auc:0.96157	validation-aucpr:0.96507
[2]	validation-logloss:0.58339	validation-auc:0.96360	validation-aucpr:0.96762
[3]	validation-logloss:0.55708	validation-auc:0.96406	validation-aucpr:0.96979
[4]	validation-logloss:0.52991	validation-auc:0.96531	validation-aucpr:0.97079
[5]	validation-logloss:0.50509	validation-auc:0.96687	validation-aucpr:0.97220
[6]	validation-logloss:0.48227	validation-auc:0.96739	validation-aucpr:0.97261
[7]	validation-logloss:0.46261	validation-auc:0.96778	validation-aucpr:0.97287
[8]	validation-logloss:0.44365	validation-auc:0.96797	validation-aucpr:0.97301
[9]	validation-logloss:0.42648	validation-auc:0.96851	validation-aucpr:0.97344
[10]	validation-logloss:0.41246	validation-auc:0.96883	validation-aucpr:0.97357
[11]	validation-logloss:0.39939	validation-auc:0.96829	validation-aucpr:0.97318
[12]	validation-logloss:0.38724	validation-auc:0.96827	validation-aucpr:0.97314
[13]	validation-logloss:0.37425	validation-auc:0.96837	validation-aucpr:0.97329
[14]	validation-logloss:0.36229	validation-auc:0.96863	validation-aucpr:0.97351
[15]	validation-logloss:0.35095	validation-auc:0.96890	validation-aucpr:0.97369
[16]	validation-logloss:0.34195	validation-auc:0.96873	validation-aucpr:0.97356
[17]	validation-logloss:0.33226	validation-auc:0.96899	validation-aucpr:0.97384
[18]	validation-logloss:0.32354	validation-auc:0.96899	validation-aucpr:0.97385
[19]	validation-logloss:0.31471	validation-auc:0.96921	validation-aucpr:0.97406
[20]	validation-logloss:0.30803	validation-auc:0.96954	validation-aucpr:0.97436
[21]	validation-logloss:0.30076	validation-auc:0.96948	validation-aucpr:0.97432
[22]	validation-logloss:0.29405	validation-auc:0.96950	validation-aucpr:0.97431
[23]	validation-logloss:0.28809	validation-auc:0.96957	validation-aucpr:0.97436
[24]	validation-logloss:0.28198	validation-auc:0.96971	validation-aucpr:0.97448
[25]	validation-logloss:0.27607	validation-auc:0.96981	validation-aucpr:0.97456
[26]	validation-logloss:0.27111	validation-auc:0.96986	validation-aucpr:0.97461
[27]	validation-logloss:0.26569	validation-auc:0.97013	validation-aucpr:0.97481
[28]	validation-logloss:0.26117	validation-auc:0.96996	validation-aucpr:0.97470
[29]	validation-logloss:0.25733	validation-auc:0.96998	validation-aucpr:0.97467
[30]	validation-logloss:0.25292	validation-auc:0.97025	validation-aucpr:0.97487
[31]	validation-logloss:0.24986	validation-auc:0.97007	validation-aucpr:0.97470
[32]	validation-logloss:0.24660	validation-auc:0.97020	validation-aucpr:0.97479
[33]	validation-logloss:0.24295	validation-auc:0.97051	validation-aucpr:0.97505
[34]	validation-logloss:0.23945	validation-auc:0.97069	validation-aucpr:0.97522
[35]	validation-logloss:0.23636	validation-auc:0.97073	validation-aucpr:0.97528
[36]	validation-logloss:0.23332	validation-auc:0.97094	validation-aucpr:0.97543
[37]	validation-logloss:0.23088	validation-auc:0.97093	validation-aucpr:0.97541
[38]	validation-logloss:0.22898	validation-auc:0.97084	validation-aucpr:0.97536
[39]	validation-logloss:0.22634	validation-auc:0.97107	validation-aucpr:0.97552
[40]	validation-logloss:0.22398	validation-auc:0.97124	validation-aucpr:0.97562
[41]	validation-logloss:0.22257	validation-auc:0.97111	validation-aucpr:0.97554
[42]	validation-logloss:0.22034	validation-auc:0.97129	validation-aucpr:0.97568
[43]	validation-logloss:0.21859	validation-auc:0.97130	validation-aucpr:0.97570
[44]	validation-logloss:0.21661	validation-auc:0.97148	validation-aucpr:0.97583
[45]	validation-logloss:0.21468	validation-auc:0.97156	validation-aucpr:0.97590
[46]	validation-logloss:0.21352	validation-auc:0.97146	validation-aucpr:0.97584
[47]	validation-logloss:0.21203	validation-auc:0.97139	validation-aucpr:0.97583
[48]	validation-logloss:0.21031	validation-auc:0.97157	validation-aucpr:0.97595
[49]	validation-logloss:0.20906	validation-auc:0.97153	validation-aucpr:0.97592
[50]	validation-logloss:0.20791	validation-auc:0.97149	validation-aucpr:0.97589
[51]	validation-logloss:0.20664	validation-auc:0.97174	validation-aucpr:0.97605
[52]	validation-logloss:0.20545	validation-auc:0.97183	validation-aucpr:0.97609
[53]	validation-logloss:0.20491	validation-auc:0.97169	validation-aucpr:0.97599
[54]	validation-logloss:0.20397	validation-auc:0.97160	validation-aucpr:0.97595
[55]	validation-logloss:0.20282	validation-auc:0.97164	validation-aucpr:0.97599
[56]	validation-logloss:0.20183	validation-auc:0.97157	validation-aucpr:0.97595
{'best_iteration': '52', 'best_score': '0.9760938284206304'}
Trial 89, Fold 4: Log loss = 0.20183458227168163, Average precision = 0.9759502113850549, ROC-AUC = 0.9715703096871415, Elapsed Time = 10.825298800002201 seconds
Trial 89, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 89, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[0]	validation-logloss:0.65276	validation-auc:0.95644	validation-aucpr:0.96185
[1]	validation-logloss:0.62152	validation-auc:0.95868	validation-aucpr:0.96409
[2]	validation-logloss:0.59298	validation-auc:0.95954	validation-aucpr:0.96431
[3]	validation-logloss:0.56285	validation-auc:0.96259	validation-aucpr:0.96699
[4]	validation-logloss:0.53503	validation-auc:0.96485	validation-aucpr:0.96886
[5]	validation-logloss:0.51198	validation-auc:0.96489	validation-aucpr:0.96878
[6]	validation-logloss:0.48924	validation-auc:0.96562	validation-aucpr:0.96947
[7]	validation-logloss:0.46824	validation-auc:0.96644	validation-aucpr:0.97020
[8]	validation-logloss:0.45220	validation-auc:0.96629	validation-aucpr:0.96987
[9]	validation-logloss:0.43381	validation-auc:0.96712	validation-aucpr:0.97046
[10]	validation-logloss:0.41953	validation-auc:0.96696	validation-aucpr:0.97030
[11]	validation-logloss:0.40397	validation-auc:0.96768	validation-aucpr:0.97087
[12]	validation-logloss:0.39028	validation-auc:0.96815	validation-aucpr:0.97148
[13]	validation-logloss:0.37725	validation-auc:0.96809	validation-aucpr:0.97029
[14]	validation-logloss:0.36693	validation-auc:0.96791	validation-aucpr:0.97011
[15]	validation-logloss:0.35704	validation-auc:0.96823	validation-aucpr:0.97223
[16]	validation-logloss:0.34606	validation-auc:0.96880	validation-aucpr:0.97277
[17]	validation-logloss:0.33817	validation-auc:0.96842	validation-aucpr:0.97228
[18]	validation-logloss:0.33036	validation-auc:0.96823	validation-aucpr:0.97208
[19]	validation-logloss:0.32150	validation-auc:0.96855	validation-aucpr:0.97230
[20]	validation-logloss:0.31339	validation-auc:0.96881	validation-aucpr:0.97250
[21]	validation-logloss:0.30692	validation-auc:0.96874	validation-aucpr:0.97247
[22]	validation-logloss:0.29955	validation-auc:0.96898	validation-aucpr:0.97264
[23]	validation-logloss:0.29436	validation-auc:0.96890	validation-aucpr:0.97282
[24]	validation-logloss:0.28795	validation-auc:0.96909	validation-aucpr:0.97292
[25]	validation-logloss:0.28333	validation-auc:0.96901	validation-aucpr:0.97283
[26]	validation-logloss:0.27758	validation-auc:0.96919	validation-aucpr:0.97298
[27]	validation-logloss:0.27353	validation-auc:0.96904	validation-aucpr:0.97306
[28]	validation-logloss:0.26843	validation-auc:0.96929	validation-aucpr:0.97320
[29]	validation-logloss:0.26456	validation-auc:0.96932	validation-aucpr:0.97321
[30]	validation-logloss:0.26005	validation-auc:0.96955	validation-aucpr:0.97341
[31]	validation-logloss:0.25677	validation-auc:0.96946	validation-aucpr:0.97331
[32]	validation-logloss:0.25262	validation-auc:0.96982	validation-aucpr:0.97365
[33]	validation-logloss:0.24985	validation-auc:0.96966	validation-aucpr:0.97350
[34]	validation-logloss:0.24712	validation-auc:0.96966	validation-aucpr:0.97347
[35]	validation-logloss:0.24399	validation-auc:0.96955	validation-aucpr:0.97344
[36]	validation-logloss:0.24083	validation-auc:0.96971	validation-aucpr:0.97354
[37]	validation-logloss:0.23755	validation-auc:0.97002	validation-aucpr:0.97382
[38]	validation-logloss:0.23485	validation-auc:0.97013	validation-aucpr:0.97390
[39]	validation-logloss:0.23222	validation-auc:0.97032	validation-aucpr:0.97403
[40]	validation-logloss:0.23036	validation-auc:0.97023	validation-aucpr:0.97393
[41]	validation-logloss:0.22854	validation-auc:0.97036	validation-aucpr:0.97406
[42]	validation-logloss:0.22604	validation-auc:0.97055	validation-aucpr:0.97419
[43]	validation-logloss:0.22468	validation-auc:0.97039	validation-aucpr:0.97404
[44]	validation-logloss:0.22264	validation-auc:0.97054	validation-aucpr:0.97415
[45]	validation-logloss:0.22064	validation-auc:0.97064	validation-aucpr:0.97422
[46]	validation-logloss:0.21937	validation-auc:0.97071	validation-aucpr:0.97425
[47]	validation-logloss:0.21739	validation-auc:0.97095	validation-aucpr:0.97438
[48]	validation-logloss:0.21560	validation-auc:0.97111	validation-aucpr:0.97446
[49]	validation-logloss:0.21391	validation-auc:0.97135	validation-aucpr:0.97465
[50]	validation-logloss:0.21264	validation-auc:0.97131	validation-aucpr:0.97461
[51]	validation-logloss:0.21130	validation-auc:0.97140	validation-aucpr:0.97473
[52]	validation-logloss:0.20998	validation-auc:0.97157	validation-aucpr:0.97492
[53]	validation-logloss:0.20877	validation-auc:0.97167	validation-aucpr:0.97497
[54]	validation-logloss:0.20782	validation-auc:0.97171	validation-aucpr:0.97492
[55]	validation-logloss:0.20683	validation-auc:0.97173	validation-aucpr:0.97493
[56]	validation-logloss:0.20575	validation-auc:0.97182	validation-aucpr:0.97479
{'best_iteration': '53', 'best_score': '0.9749698823576478'}
Trial 89, Fold 5: Log loss = 0.20574513483962278, Average precision = 0.9748008098075681, ROC-AUC = 0.9718218605428905, Elapsed Time = 11.012917900003231 seconds
Optimization Progress:  90%|######### | 90/100 [3:51:38<11:27, 68.71s/it]
Trial 90, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 90, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[21:50:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.68354	validation-auc:0.92709	validation-aucpr:0.92888
[21:50:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.67487	validation-auc:0.95282	validation-aucpr:0.94079
[21:50:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.66557	validation-auc:0.96054	validation-aucpr:0.95758
[21:50:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.65668	validation-auc:0.96288	validation-aucpr:0.96216
[21:50:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.64834	validation-auc:0.96400	validation-aucpr:0.96566
[21:50:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5]	validation-logloss:0.63979	validation-auc:0.96555	validation-aucpr:0.97077
[21:50:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6]	validation-logloss:0.63156	validation-auc:0.96603	validation-aucpr:0.97110
[21:50:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7]	validation-logloss:0.62432	validation-auc:0.96592	validation-aucpr:0.97096
[21:50:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8]	validation-logloss:0.61660	validation-auc:0.96646	validation-aucpr:0.97132
[21:50:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9]	validation-logloss:0.60976	validation-auc:0.96623	validation-aucpr:0.97109
[21:50:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10]	validation-logloss:0.60202	validation-auc:0.96670	validation-aucpr:0.97103
[21:50:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11]	validation-logloss:0.59529	validation-auc:0.96675	validation-aucpr:0.97129
[21:50:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12]	validation-logloss:0.58876	validation-auc:0.96668	validation-aucpr:0.97118
[21:50:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13]	validation-logloss:0.58162	validation-auc:0.96673	validation-aucpr:0.97120
[21:50:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14]	validation-logloss:0.57436	validation-auc:0.96723	validation-aucpr:0.97022
[21:50:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15]	validation-logloss:0.56836	validation-auc:0.96765	validation-aucpr:0.97103
[21:50:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16]	validation-logloss:0.56162	validation-auc:0.96798	validation-aucpr:0.97305
[21:50:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17]	validation-logloss:0.55592	validation-auc:0.96783	validation-aucpr:0.97289
[21:50:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18]	validation-logloss:0.54955	validation-auc:0.96822	validation-aucpr:0.97321
[21:50:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19]	validation-logloss:0.54312	validation-auc:0.96861	validation-aucpr:0.97346
[21:50:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20]	validation-logloss:0.53768	validation-auc:0.96851	validation-aucpr:0.97336
[21:50:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21]	validation-logloss:0.53159	validation-auc:0.96872	validation-aucpr:0.97357
[21:50:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22]	validation-logloss:0.52656	validation-auc:0.96852	validation-aucpr:0.97339
[21:50:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23]	validation-logloss:0.52076	validation-auc:0.96870	validation-aucpr:0.97354
[21:50:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24]	validation-logloss:0.51577	validation-auc:0.96864	validation-aucpr:0.97345
[21:50:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25]	validation-logloss:0.51095	validation-auc:0.96845	validation-aucpr:0.97329
[21:50:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26]	validation-logloss:0.50561	validation-auc:0.96839	validation-aucpr:0.97325
[21:50:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27]	validation-logloss:0.50098	validation-auc:0.96833	validation-aucpr:0.97317
[21:50:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28]	validation-logloss:0.49566	validation-auc:0.96840	validation-aucpr:0.97322
[21:50:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29]	validation-logloss:0.49130	validation-auc:0.96818	validation-aucpr:0.97306
[21:50:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30]	validation-logloss:0.48694	validation-auc:0.96796	validation-aucpr:0.97286
[21:50:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31]	validation-logloss:0.48257	validation-auc:0.96796	validation-aucpr:0.97284
[21:50:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32]	validation-logloss:0.47842	validation-auc:0.96776	validation-aucpr:0.97269
[21:50:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33]	validation-logloss:0.47353	validation-auc:0.96797	validation-aucpr:0.97288
[21:50:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34]	validation-logloss:0.46935	validation-auc:0.96797	validation-aucpr:0.97289
[21:50:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35]	validation-logloss:0.46466	validation-auc:0.96811	validation-aucpr:0.97301
[21:50:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36]	validation-logloss:0.46080	validation-auc:0.96792	validation-aucpr:0.97286
[21:50:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37]	validation-logloss:0.45619	validation-auc:0.96814	validation-aucpr:0.97305
[21:50:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38]	validation-logloss:0.45172	validation-auc:0.96828	validation-aucpr:0.97315
[21:50:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39]	validation-logloss:0.44734	validation-auc:0.96829	validation-aucpr:0.97319
[21:50:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40]	validation-logloss:0.44310	validation-auc:0.96844	validation-aucpr:0.97339
[21:50:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41]	validation-logloss:0.43887	validation-auc:0.96872	validation-aucpr:0.97361
[21:50:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42]	validation-logloss:0.43468	validation-auc:0.96901	validation-aucpr:0.97381
[21:50:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43]	validation-logloss:0.43078	validation-auc:0.96897	validation-aucpr:0.97379
{'best_iteration': '42', 'best_score': '0.9738097543498954'}
Trial 90, Fold 1: Log loss = 0.430776386850286, Average precision = 0.9737941693851264, ROC-AUC = 0.9689733711159862, Elapsed Time = 6.182111800000712 seconds
Trial 90, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 90, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[21:50:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.68314	validation-auc:0.93332	validation-aucpr:0.93694
[21:50:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.67376	validation-auc:0.95557	validation-aucpr:0.94053
[21:50:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.66562	validation-auc:0.96129	validation-aucpr:0.95678
[21:50:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.65646	validation-auc:0.96455	validation-aucpr:0.96381
[21:50:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.64867	validation-auc:0.96572	validation-aucpr:0.96888
[21:50:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5]	validation-logloss:0.63997	validation-auc:0.96678	validation-aucpr:0.97098
[21:50:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6]	validation-logloss:0.63256	validation-auc:0.96687	validation-aucpr:0.97096
[21:50:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7]	validation-logloss:0.62427	validation-auc:0.96759	validation-aucpr:0.97164
[21:50:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8]	validation-logloss:0.61730	validation-auc:0.96759	validation-aucpr:0.97148
[21:50:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9]	validation-logloss:0.61039	validation-auc:0.96742	validation-aucpr:0.97125
[21:50:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10]	validation-logloss:0.60353	validation-auc:0.96722	validation-aucpr:0.97102
[21:50:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11]	validation-logloss:0.59681	validation-auc:0.96745	validation-aucpr:0.97112
[21:50:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12]	validation-logloss:0.58935	validation-auc:0.96774	validation-aucpr:0.97148
[21:50:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13]	validation-logloss:0.58212	validation-auc:0.96801	validation-aucpr:0.97167
[21:50:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14]	validation-logloss:0.57526	validation-auc:0.96837	validation-aucpr:0.97198
[21:50:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15]	validation-logloss:0.56850	validation-auc:0.96853	validation-aucpr:0.97210
[21:50:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16]	validation-logloss:0.56171	validation-auc:0.96867	validation-aucpr:0.97230
[21:50:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17]	validation-logloss:0.55550	validation-auc:0.96859	validation-aucpr:0.97223
[21:50:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18]	validation-logloss:0.54988	validation-auc:0.96842	validation-aucpr:0.97193
[21:50:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19]	validation-logloss:0.54354	validation-auc:0.96854	validation-aucpr:0.97202
[21:50:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20]	validation-logloss:0.53726	validation-auc:0.96894	validation-aucpr:0.97231
[21:50:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21]	validation-logloss:0.53186	validation-auc:0.96876	validation-aucpr:0.97224
[21:50:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22]	validation-logloss:0.52656	validation-auc:0.96868	validation-aucpr:0.97214
[21:50:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23]	validation-logloss:0.52155	validation-auc:0.96863	validation-aucpr:0.97204
[21:50:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24]	validation-logloss:0.51588	validation-auc:0.96859	validation-aucpr:0.97196
[21:50:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25]	validation-logloss:0.51013	validation-auc:0.96890	validation-aucpr:0.97221
[21:50:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26]	validation-logloss:0.50525	validation-auc:0.96880	validation-aucpr:0.97212
[21:50:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27]	validation-logloss:0.50035	validation-auc:0.96898	validation-aucpr:0.97227
[21:50:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28]	validation-logloss:0.49581	validation-auc:0.96917	validation-aucpr:0.97236
[21:50:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29]	validation-logloss:0.49056	validation-auc:0.96934	validation-aucpr:0.97250
[21:50:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30]	validation-logloss:0.48597	validation-auc:0.96937	validation-aucpr:0.97245
[21:50:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31]	validation-logloss:0.48173	validation-auc:0.96945	validation-aucpr:0.97251
[21:50:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32]	validation-logloss:0.47746	validation-auc:0.96930	validation-aucpr:0.97241
[21:50:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33]	validation-logloss:0.47315	validation-auc:0.96950	validation-aucpr:0.97253
[21:50:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34]	validation-logloss:0.46909	validation-auc:0.96949	validation-aucpr:0.97241
[21:50:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35]	validation-logloss:0.46441	validation-auc:0.96963	validation-aucpr:0.97353
[21:50:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36]	validation-logloss:0.45973	validation-auc:0.96976	validation-aucpr:0.97367
[21:50:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37]	validation-logloss:0.45586	validation-auc:0.96978	validation-aucpr:0.97365
[21:50:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38]	validation-logloss:0.45133	validation-auc:0.96992	validation-aucpr:0.97377
[21:50:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39]	validation-logloss:0.44703	validation-auc:0.96998	validation-aucpr:0.97382
[21:50:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40]	validation-logloss:0.44321	validation-auc:0.97006	validation-aucpr:0.97386
[21:50:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41]	validation-logloss:0.43898	validation-auc:0.97013	validation-aucpr:0.97390
[21:50:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42]	validation-logloss:0.43478	validation-auc:0.97035	validation-aucpr:0.97408
[21:50:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43]	validation-logloss:0.43077	validation-auc:0.97038	validation-aucpr:0.97411
{'best_iteration': '43', 'best_score': '0.9741106364338346'}
Trial 90, Fold 2: Log loss = 0.43076507082274706, Average precision = 0.9741145879228299, ROC-AUC = 0.9703752662035527, Elapsed Time = 6.197583100001793 seconds
Trial 90, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 90, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[21:50:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.68327	validation-auc:0.93318	validation-aucpr:0.94252
[21:50:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.67480	validation-auc:0.95431	validation-aucpr:0.93696
[21:50:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.66570	validation-auc:0.96271	validation-aucpr:0.95550
[21:50:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.65667	validation-auc:0.96478	validation-aucpr:0.96069
[21:50:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.64886	validation-auc:0.96514	validation-aucpr:0.96758
[21:50:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5]	validation-logloss:0.64028	validation-auc:0.96608	validation-aucpr:0.96940
[21:50:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6]	validation-logloss:0.63192	validation-auc:0.96657	validation-aucpr:0.97014
[21:50:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7]	validation-logloss:0.62368	validation-auc:0.96688	validation-aucpr:0.97021
[21:50:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8]	validation-logloss:0.61653	validation-auc:0.96736	validation-aucpr:0.96978
[21:50:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9]	validation-logloss:0.60849	validation-auc:0.96858	validation-aucpr:0.97065
[21:50:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10]	validation-logloss:0.60163	validation-auc:0.96898	validation-aucpr:0.97107
[21:50:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11]	validation-logloss:0.59481	validation-auc:0.96932	validation-aucpr:0.97121
[21:50:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12]	validation-logloss:0.58772	validation-auc:0.96964	validation-aucpr:0.97144
[21:50:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13]	validation-logloss:0.58129	validation-auc:0.96967	validation-aucpr:0.97148
[21:50:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14]	validation-logloss:0.57427	validation-auc:0.96969	validation-aucpr:0.97096
[21:50:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15]	validation-logloss:0.56741	validation-auc:0.97002	validation-aucpr:0.97094
[21:50:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16]	validation-logloss:0.56137	validation-auc:0.96998	validation-aucpr:0.97085
[21:50:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17]	validation-logloss:0.55562	validation-auc:0.96998	validation-aucpr:0.97125
[21:50:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18]	validation-logloss:0.54918	validation-auc:0.97008	validation-aucpr:0.97160
[21:50:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19]	validation-logloss:0.54356	validation-auc:0.97043	validation-aucpr:0.97363
[21:50:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20]	validation-logloss:0.53805	validation-auc:0.97042	validation-aucpr:0.97353
[21:50:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21]	validation-logloss:0.53201	validation-auc:0.97039	validation-aucpr:0.97342
[21:50:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22]	validation-logloss:0.52677	validation-auc:0.97045	validation-aucpr:0.97337
[21:50:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23]	validation-logloss:0.52095	validation-auc:0.97062	validation-aucpr:0.97436
[21:50:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24]	validation-logloss:0.51588	validation-auc:0.97047	validation-aucpr:0.97335
[21:50:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25]	validation-logloss:0.51030	validation-auc:0.97053	validation-aucpr:0.97340
[21:50:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26]	validation-logloss:0.50515	validation-auc:0.97051	validation-aucpr:0.97407
[21:50:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27]	validation-logloss:0.49958	validation-auc:0.97073	validation-aucpr:0.97423
[21:50:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28]	validation-logloss:0.49435	validation-auc:0.97062	validation-aucpr:0.97414
[21:50:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29]	validation-logloss:0.48971	validation-auc:0.97055	validation-aucpr:0.97407
[21:50:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30]	validation-logloss:0.48441	validation-auc:0.97059	validation-aucpr:0.97349
[21:50:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31]	validation-logloss:0.47994	validation-auc:0.97067	validation-aucpr:0.97339
[21:50:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32]	validation-logloss:0.47596	validation-auc:0.97047	validation-aucpr:0.97314
[21:50:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33]	validation-logloss:0.47157	validation-auc:0.97044	validation-aucpr:0.97314
[21:50:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34]	validation-logloss:0.46679	validation-auc:0.97068	validation-aucpr:0.97359
[21:50:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35]	validation-logloss:0.46251	validation-auc:0.97086	validation-aucpr:0.97372
[21:50:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36]	validation-logloss:0.45795	validation-auc:0.97082	validation-aucpr:0.97367
[21:50:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37]	validation-logloss:0.45336	validation-auc:0.97099	validation-aucpr:0.97400
[21:50:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38]	validation-logloss:0.44901	validation-auc:0.97113	validation-aucpr:0.97521
[21:50:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39]	validation-logloss:0.44506	validation-auc:0.97108	validation-aucpr:0.97518
[21:50:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40]	validation-logloss:0.44146	validation-auc:0.97110	validation-aucpr:0.97519
[21:50:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41]	validation-logloss:0.43724	validation-auc:0.97116	validation-aucpr:0.97526
[21:50:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42]	validation-logloss:0.43318	validation-auc:0.97120	validation-aucpr:0.97531
[21:50:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43]	validation-logloss:0.42915	validation-auc:0.97126	validation-aucpr:0.97536
{'best_iteration': '43', 'best_score': '0.9753572218732132'}
Trial 90, Fold 3: Log loss = 0.42914536504407685, Average precision = 0.9753615635108435, ROC-AUC = 0.9712575302655276, Elapsed Time = 6.2764660000029835 seconds
Trial 90, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 90, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[21:50:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.68309	validation-auc:0.92522	validation-aucpr:0.91691
[21:50:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.67472	validation-auc:0.95181	validation-aucpr:0.94043
[21:50:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.66553	validation-auc:0.95816	validation-aucpr:0.95189
[21:50:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.65765	validation-auc:0.96004	validation-aucpr:0.95955
[21:50:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.64888	validation-auc:0.96237	validation-aucpr:0.96545
[21:50:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5]	validation-logloss:0.64020	validation-auc:0.96373	validation-aucpr:0.96673
[21:50:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6]	validation-logloss:0.63268	validation-auc:0.96432	validation-aucpr:0.96678
[21:50:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7]	validation-logloss:0.62543	validation-auc:0.96487	validation-aucpr:0.97023
[21:50:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8]	validation-logloss:0.61842	validation-auc:0.96460	validation-aucpr:0.96989
[21:50:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9]	validation-logloss:0.61055	validation-auc:0.96503	validation-aucpr:0.97049
[21:50:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10]	validation-logloss:0.60274	validation-auc:0.96561	validation-aucpr:0.97107
[21:50:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11]	validation-logloss:0.59510	validation-auc:0.96584	validation-aucpr:0.97128
[21:50:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12]	validation-logloss:0.58775	validation-auc:0.96621	validation-aucpr:0.97162
[21:50:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13]	validation-logloss:0.58155	validation-auc:0.96587	validation-aucpr:0.97134
[21:50:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14]	validation-logloss:0.57447	validation-auc:0.96625	validation-aucpr:0.97160
[21:50:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15]	validation-logloss:0.56840	validation-auc:0.96655	validation-aucpr:0.97190
[21:50:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16]	validation-logloss:0.56165	validation-auc:0.96700	validation-aucpr:0.97225
[21:50:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17]	validation-logloss:0.55580	validation-auc:0.96722	validation-aucpr:0.97241
[21:50:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18]	validation-logloss:0.54934	validation-auc:0.96726	validation-aucpr:0.97245
[21:50:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19]	validation-logloss:0.54309	validation-auc:0.96748	validation-aucpr:0.97263
[21:50:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20]	validation-logloss:0.53701	validation-auc:0.96758	validation-aucpr:0.97272
[21:50:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21]	validation-logloss:0.53165	validation-auc:0.96760	validation-aucpr:0.97271
[21:50:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22]	validation-logloss:0.52629	validation-auc:0.96775	validation-aucpr:0.97281
[21:50:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23]	validation-logloss:0.52036	validation-auc:0.96801	validation-aucpr:0.97300
[21:50:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24]	validation-logloss:0.51513	validation-auc:0.96815	validation-aucpr:0.97313
[21:50:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25]	validation-logloss:0.51019	validation-auc:0.96802	validation-aucpr:0.97300
[21:50:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26]	validation-logloss:0.50538	validation-auc:0.96797	validation-aucpr:0.97292
[21:50:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27]	validation-logloss:0.49998	validation-auc:0.96812	validation-aucpr:0.97308
[21:50:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28]	validation-logloss:0.49476	validation-auc:0.96830	validation-aucpr:0.97330
[21:50:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29]	validation-logloss:0.49034	validation-auc:0.96818	validation-aucpr:0.97319
[21:50:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30]	validation-logloss:0.48579	validation-auc:0.96821	validation-aucpr:0.97322
[21:50:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31]	validation-logloss:0.48071	validation-auc:0.96839	validation-aucpr:0.97337
[21:50:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32]	validation-logloss:0.47636	validation-auc:0.96841	validation-aucpr:0.97338
[21:50:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33]	validation-logloss:0.47151	validation-auc:0.96858	validation-aucpr:0.97351
[21:50:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34]	validation-logloss:0.46675	validation-auc:0.96867	validation-aucpr:0.97365
[21:50:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35]	validation-logloss:0.46282	validation-auc:0.96857	validation-aucpr:0.97356
[21:50:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36]	validation-logloss:0.45816	validation-auc:0.96869	validation-aucpr:0.97364
[21:50:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37]	validation-logloss:0.45415	validation-auc:0.96868	validation-aucpr:0.97362
[21:50:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38]	validation-logloss:0.45045	validation-auc:0.96851	validation-aucpr:0.97350
[21:50:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39]	validation-logloss:0.44606	validation-auc:0.96851	validation-aucpr:0.97353
[21:50:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40]	validation-logloss:0.44240	validation-auc:0.96855	validation-aucpr:0.97357
[21:50:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41]	validation-logloss:0.43830	validation-auc:0.96858	validation-aucpr:0.97359
[21:50:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42]	validation-logloss:0.43406	validation-auc:0.96870	validation-aucpr:0.97368
[21:50:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43]	validation-logloss:0.43003	validation-auc:0.96879	validation-aucpr:0.97378
{'best_iteration': '43', 'best_score': '0.9737783677406094'}
Trial 90, Fold 4: Log loss = 0.4300305917885512, Average precision = 0.9737804054317507, ROC-AUC = 0.9687914291198428, Elapsed Time = 6.178327800000261 seconds
Trial 90, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 90, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[21:51:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.68339	validation-auc:0.93084	validation-aucpr:0.92778
[21:51:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.67511	validation-auc:0.95050	validation-aucpr:0.93833
[21:51:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.66581	validation-auc:0.95928	validation-aucpr:0.95885
[21:51:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.65679	validation-auc:0.96248	validation-aucpr:0.96825
[21:51:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.64912	validation-auc:0.96295	validation-aucpr:0.96555
[21:51:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5]	validation-logloss:0.64146	validation-auc:0.96371	validation-aucpr:0.96758
[21:51:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6]	validation-logloss:0.63312	validation-auc:0.96474	validation-aucpr:0.96983
[21:51:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7]	validation-logloss:0.62501	validation-auc:0.96503	validation-aucpr:0.96929
[21:51:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8]	validation-logloss:0.61720	validation-auc:0.96575	validation-aucpr:0.97081
[21:51:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9]	validation-logloss:0.61011	validation-auc:0.96632	validation-aucpr:0.97131
[21:51:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10]	validation-logloss:0.60262	validation-auc:0.96657	validation-aucpr:0.97153
[21:51:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11]	validation-logloss:0.59530	validation-auc:0.96642	validation-aucpr:0.97157
[21:51:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12]	validation-logloss:0.58891	validation-auc:0.96656	validation-aucpr:0.97166
[21:51:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13]	validation-logloss:0.58163	validation-auc:0.96705	validation-aucpr:0.97204
[21:51:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14]	validation-logloss:0.57459	validation-auc:0.96745	validation-aucpr:0.97235
[21:51:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15]	validation-logloss:0.56772	validation-auc:0.96807	validation-aucpr:0.97271
[21:51:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16]	validation-logloss:0.56197	validation-auc:0.96793	validation-aucpr:0.97251
[21:51:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17]	validation-logloss:0.55629	validation-auc:0.96797	validation-aucpr:0.97248
[21:51:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18]	validation-logloss:0.55004	validation-auc:0.96777	validation-aucpr:0.97238
[21:51:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19]	validation-logloss:0.54456	validation-auc:0.96758	validation-aucpr:0.97220
[21:51:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20]	validation-logloss:0.53833	validation-auc:0.96766	validation-aucpr:0.97227
[21:51:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21]	validation-logloss:0.53304	validation-auc:0.96743	validation-aucpr:0.97207
[21:51:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22]	validation-logloss:0.52802	validation-auc:0.96721	validation-aucpr:0.97190
[21:51:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23]	validation-logloss:0.52291	validation-auc:0.96701	validation-aucpr:0.97171
[21:51:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24]	validation-logloss:0.51717	validation-auc:0.96715	validation-aucpr:0.97183
[21:51:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25]	validation-logloss:0.51222	validation-auc:0.96709	validation-aucpr:0.97176
[21:51:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26]	validation-logloss:0.50671	validation-auc:0.96737	validation-aucpr:0.97197
[21:51:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27]	validation-logloss:0.50172	validation-auc:0.96733	validation-aucpr:0.97199
[21:51:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28]	validation-logloss:0.49724	validation-auc:0.96712	validation-aucpr:0.97184
[21:51:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29]	validation-logloss:0.49264	validation-auc:0.96714	validation-aucpr:0.97186
[21:51:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30]	validation-logloss:0.48747	validation-auc:0.96747	validation-aucpr:0.97215
[21:51:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31]	validation-logloss:0.48312	validation-auc:0.96738	validation-aucpr:0.97196
[21:51:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32]	validation-logloss:0.47811	validation-auc:0.96757	validation-aucpr:0.97210
[21:51:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33]	validation-logloss:0.47324	validation-auc:0.96776	validation-aucpr:0.97226
[21:51:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34]	validation-logloss:0.46913	validation-auc:0.96783	validation-aucpr:0.97231
[21:51:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35]	validation-logloss:0.46449	validation-auc:0.96797	validation-aucpr:0.97243
[21:51:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36]	validation-logloss:0.45998	validation-auc:0.96801	validation-aucpr:0.97246
[21:51:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37]	validation-logloss:0.45613	validation-auc:0.96800	validation-aucpr:0.97245
[21:51:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38]	validation-logloss:0.45181	validation-auc:0.96820	validation-aucpr:0.97259
[21:51:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39]	validation-logloss:0.44759	validation-auc:0.96828	validation-aucpr:0.97268
[21:51:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40]	validation-logloss:0.44329	validation-auc:0.96833	validation-aucpr:0.97275
[21:51:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41]	validation-logloss:0.43971	validation-auc:0.96832	validation-aucpr:0.97274
[21:51:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42]	validation-logloss:0.43615	validation-auc:0.96837	validation-aucpr:0.97281
[21:51:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43]	validation-logloss:0.43208	validation-auc:0.96858	validation-aucpr:0.97299
{'best_iteration': '43', 'best_score': '0.9729927994550973'}
Trial 90, Fold 5: Log loss = 0.43207551940990796, Average precision = 0.9729978877294114, ROC-AUC = 0.9685836909871245, Elapsed Time = 6.390422000000399 seconds
Optimization Progress:  91%|#########1| 91/100 [3:52:18<08:59, 59.98s/it]
Trial 91, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 91, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[21:51:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.68534	validation-auc:0.95976	validation-aucpr:0.96326
[21:51:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.67859	validation-auc:0.96192	validation-aucpr:0.96653
[21:51:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.67123	validation-auc:0.96368	validation-aucpr:0.96761
[21:51:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.66410	validation-auc:0.96501	validation-aucpr:0.96989
[21:51:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.65765	validation-auc:0.96517	validation-aucpr:0.96987
[21:51:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5]	validation-logloss:0.65063	validation-auc:0.96550	validation-aucpr:0.97027
[21:51:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6]	validation-logloss:0.64455	validation-auc:0.96508	validation-aucpr:0.96975
[21:51:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7]	validation-logloss:0.63845	validation-auc:0.96501	validation-aucpr:0.96999
[21:51:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8]	validation-logloss:0.63181	validation-auc:0.96566	validation-aucpr:0.97066
[21:51:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9]	validation-logloss:0.62605	validation-auc:0.96561	validation-aucpr:0.97054
[21:51:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10]	validation-logloss:0.62051	validation-auc:0.96524	validation-aucpr:0.97017
[21:51:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11]	validation-logloss:0.61508	validation-auc:0.96516	validation-aucpr:0.97011
[21:51:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12]	validation-logloss:0.60994	validation-auc:0.96489	validation-aucpr:0.96987
[21:51:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13]	validation-logloss:0.60474	validation-auc:0.96469	validation-aucpr:0.96974
[21:51:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14]	validation-logloss:0.59873	validation-auc:0.96485	validation-aucpr:0.96999
[21:51:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15]	validation-logloss:0.59354	validation-auc:0.96493	validation-aucpr:0.97003
[21:51:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16]	validation-logloss:0.58782	validation-auc:0.96522	validation-aucpr:0.97033
[21:51:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17]	validation-logloss:0.58203	validation-auc:0.96567	validation-aucpr:0.97071
[21:51:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18]	validation-logloss:0.57715	validation-auc:0.96577	validation-aucpr:0.97075
[21:51:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19]	validation-logloss:0.57167	validation-auc:0.96612	validation-aucpr:0.97120
[21:51:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20]	validation-logloss:0.56626	validation-auc:0.96626	validation-aucpr:0.97133
[21:51:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21]	validation-logloss:0.56096	validation-auc:0.96639	validation-aucpr:0.97147
[21:51:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22]	validation-logloss:0.55584	validation-auc:0.96644	validation-aucpr:0.97161
[21:51:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23]	validation-logloss:0.55078	validation-auc:0.96661	validation-aucpr:0.97230
[21:51:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24]	validation-logloss:0.54588	validation-auc:0.96670	validation-aucpr:0.97237
[21:51:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25]	validation-logloss:0.54104	validation-auc:0.96688	validation-aucpr:0.97246
[21:51:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26]	validation-logloss:0.53619	validation-auc:0.96709	validation-aucpr:0.97266
[21:51:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27]	validation-logloss:0.53146	validation-auc:0.96728	validation-aucpr:0.97280
[21:51:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28]	validation-logloss:0.52680	validation-auc:0.96731	validation-aucpr:0.97283
[21:51:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29]	validation-logloss:0.52228	validation-auc:0.96742	validation-aucpr:0.97298
[21:51:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30]	validation-logloss:0.51851	validation-auc:0.96735	validation-aucpr:0.97283
[21:51:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31]	validation-logloss:0.51415	validation-auc:0.96738	validation-aucpr:0.97288
[21:51:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32]	validation-logloss:0.51023	validation-auc:0.96736	validation-aucpr:0.97279
[21:51:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33]	validation-logloss:0.50652	validation-auc:0.96727	validation-aucpr:0.97267
[21:51:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34]	validation-logloss:0.50265	validation-auc:0.96736	validation-aucpr:0.97273
[21:51:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35]	validation-logloss:0.49893	validation-auc:0.96740	validation-aucpr:0.97276
[21:51:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36]	validation-logloss:0.49487	validation-auc:0.96748	validation-aucpr:0.97283
[21:51:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37]	validation-logloss:0.49070	validation-auc:0.96773	validation-aucpr:0.97302
[21:51:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38]	validation-logloss:0.48711	validation-auc:0.96784	validation-aucpr:0.97306
[21:51:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39]	validation-logloss:0.48309	validation-auc:0.96787	validation-aucpr:0.97311
[21:52:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40]	validation-logloss:0.47914	validation-auc:0.96801	validation-aucpr:0.97325
[21:52:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41]	validation-logloss:0.47539	validation-auc:0.96810	validation-aucpr:0.97334
[21:52:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42]	validation-logloss:0.47161	validation-auc:0.96810	validation-aucpr:0.97336
[21:52:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43]	validation-logloss:0.46834	validation-auc:0.96810	validation-aucpr:0.97333
{'best_iteration': '42', 'best_score': '0.9733574134621086'}
Trial 91, Fold 1: Log loss = 0.4683359849337515, Average precision = 0.9733385803221706, ROC-AUC = 0.9681011707603847, Elapsed Time = 51.88532340000165 seconds
Trial 91, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 91, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[21:52:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.68550	validation-auc:0.96132	validation-aucpr:0.96534
[21:52:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.67810	validation-auc:0.96501	validation-aucpr:0.96922
[21:52:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.67070	validation-auc:0.96661	validation-aucpr:0.97081
[21:52:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.66416	validation-auc:0.96751	validation-aucpr:0.97149
[21:52:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.65707	validation-auc:0.96772	validation-aucpr:0.97172
[21:52:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5]	validation-logloss:0.65085	validation-auc:0.96768	validation-aucpr:0.97168
[21:52:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6]	validation-logloss:0.64462	validation-auc:0.96776	validation-aucpr:0.97164
[21:52:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7]	validation-logloss:0.63780	validation-auc:0.96829	validation-aucpr:0.97202
[21:52:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8]	validation-logloss:0.63113	validation-auc:0.96837	validation-aucpr:0.97210
[21:52:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9]	validation-logloss:0.62530	validation-auc:0.96858	validation-aucpr:0.97215
[21:52:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10]	validation-logloss:0.61892	validation-auc:0.96934	validation-aucpr:0.97274
[21:52:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11]	validation-logloss:0.61328	validation-auc:0.96963	validation-aucpr:0.97291
[21:52:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12]	validation-logloss:0.60700	validation-auc:0.97004	validation-aucpr:0.97329
[21:52:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13]	validation-logloss:0.60169	validation-auc:0.96959	validation-aucpr:0.97285
[21:52:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14]	validation-logloss:0.59664	validation-auc:0.96923	validation-aucpr:0.97253
[21:52:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15]	validation-logloss:0.59145	validation-auc:0.96913	validation-aucpr:0.97246
[21:52:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16]	validation-logloss:0.58579	validation-auc:0.96941	validation-aucpr:0.97273
[21:52:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17]	validation-logloss:0.58017	validation-auc:0.96944	validation-aucpr:0.97276
[21:52:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18]	validation-logloss:0.57477	validation-auc:0.96953	validation-aucpr:0.97284
[21:52:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19]	validation-logloss:0.57001	validation-auc:0.96932	validation-aucpr:0.97268
[21:52:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20]	validation-logloss:0.56456	validation-auc:0.96963	validation-aucpr:0.97292
[21:52:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21]	validation-logloss:0.55988	validation-auc:0.96969	validation-aucpr:0.97294
[21:52:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22]	validation-logloss:0.55477	validation-auc:0.96967	validation-aucpr:0.97297
[21:52:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23]	validation-logloss:0.55032	validation-auc:0.96962	validation-aucpr:0.97292
[21:52:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24]	validation-logloss:0.54588	validation-auc:0.96958	validation-aucpr:0.97286
[21:52:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25]	validation-logloss:0.54098	validation-auc:0.96966	validation-aucpr:0.97297
[21:52:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26]	validation-logloss:0.53674	validation-auc:0.96949	validation-aucpr:0.97269
[21:52:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27]	validation-logloss:0.53193	validation-auc:0.96952	validation-aucpr:0.97276
[21:52:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28]	validation-logloss:0.52717	validation-auc:0.96967	validation-aucpr:0.97290
[21:52:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29]	validation-logloss:0.52242	validation-auc:0.96980	validation-aucpr:0.97302
[21:52:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30]	validation-logloss:0.51848	validation-auc:0.96976	validation-aucpr:0.97296
[21:52:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31]	validation-logloss:0.51381	validation-auc:0.97003	validation-aucpr:0.97321
[21:52:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32]	validation-logloss:0.51004	validation-auc:0.96987	validation-aucpr:0.97305
[21:52:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33]	validation-logloss:0.50585	validation-auc:0.96986	validation-aucpr:0.97307
[21:52:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34]	validation-logloss:0.50151	validation-auc:0.96995	validation-aucpr:0.97316
[21:52:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35]	validation-logloss:0.49782	validation-auc:0.97001	validation-aucpr:0.97277
[21:52:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36]	validation-logloss:0.49422	validation-auc:0.96996	validation-aucpr:0.97272
[21:52:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37]	validation-logloss:0.49066	validation-auc:0.96987	validation-aucpr:0.97264
[21:52:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38]	validation-logloss:0.48705	validation-auc:0.96992	validation-aucpr:0.97268
[21:52:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39]	validation-logloss:0.48357	validation-auc:0.96991	validation-aucpr:0.97265
[21:52:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40]	validation-logloss:0.47958	validation-auc:0.97004	validation-aucpr:0.97278
[21:52:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41]	validation-logloss:0.47636	validation-auc:0.96999	validation-aucpr:0.97275
[21:52:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42]	validation-logloss:0.47245	validation-auc:0.97010	validation-aucpr:0.97283
[21:52:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43]	validation-logloss:0.46912	validation-auc:0.97005	validation-aucpr:0.97279
{'best_iteration': '12', 'best_score': '0.9732866427723065'}
Trial 91, Fold 2: Log loss = 0.469120277958387, Average precision = 0.9727538310090776, ROC-AUC = 0.9700462011307639, Elapsed Time = 50.572803199997 seconds
Trial 91, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 91, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[21:52:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.68535	validation-auc:0.96075	validation-aucpr:0.96245
[21:52:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.67866	validation-auc:0.96244	validation-aucpr:0.96594
[21:52:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.67133	validation-auc:0.96557	validation-aucpr:0.96968
[21:53:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.66397	validation-auc:0.96694	validation-aucpr:0.97038
[21:53:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.65708	validation-auc:0.96715	validation-aucpr:0.97051
[21:53:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5]	validation-logloss:0.65020	validation-auc:0.96761	validation-aucpr:0.97109
[21:53:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6]	validation-logloss:0.64349	validation-auc:0.96723	validation-aucpr:0.97087
[21:53:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7]	validation-logloss:0.63665	validation-auc:0.96774	validation-aucpr:0.97129
[21:53:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8]	validation-logloss:0.63011	validation-auc:0.96757	validation-aucpr:0.97131
[21:53:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9]	validation-logloss:0.62363	validation-auc:0.96714	validation-aucpr:0.97135
[21:53:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10]	validation-logloss:0.61802	validation-auc:0.96746	validation-aucpr:0.97168
[21:53:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11]	validation-logloss:0.61255	validation-auc:0.96742	validation-aucpr:0.97158
[21:53:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12]	validation-logloss:0.60652	validation-auc:0.96778	validation-aucpr:0.97180
[21:53:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13]	validation-logloss:0.60050	validation-auc:0.96842	validation-aucpr:0.97197
[21:53:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14]	validation-logloss:0.59530	validation-auc:0.96843	validation-aucpr:0.97192
[21:53:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15]	validation-logloss:0.59008	validation-auc:0.96865	validation-aucpr:0.97201
[21:53:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16]	validation-logloss:0.58447	validation-auc:0.96863	validation-aucpr:0.97185
[21:53:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17]	validation-logloss:0.57895	validation-auc:0.96871	validation-aucpr:0.97190
[21:53:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18]	validation-logloss:0.57421	validation-auc:0.96870	validation-aucpr:0.97342
[21:53:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19]	validation-logloss:0.56936	validation-auc:0.96882	validation-aucpr:0.97352
[21:53:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20]	validation-logloss:0.56463	validation-auc:0.96862	validation-aucpr:0.97336
[21:53:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21]	validation-logloss:0.55947	validation-auc:0.96865	validation-aucpr:0.97343
[21:53:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22]	validation-logloss:0.55444	validation-auc:0.96852	validation-aucpr:0.97332
[21:53:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23]	validation-logloss:0.54919	validation-auc:0.96897	validation-aucpr:0.97368
[21:53:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24]	validation-logloss:0.54473	validation-auc:0.96889	validation-aucpr:0.97359
[21:53:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25]	validation-logloss:0.54042	validation-auc:0.96888	validation-aucpr:0.97357
[21:53:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26]	validation-logloss:0.53552	validation-auc:0.96909	validation-aucpr:0.97372
[21:53:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27]	validation-logloss:0.53073	validation-auc:0.96925	validation-aucpr:0.97387
[21:53:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28]	validation-logloss:0.52643	validation-auc:0.96938	validation-aucpr:0.97399
[21:53:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29]	validation-logloss:0.52180	validation-auc:0.96956	validation-aucpr:0.97415
[21:53:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30]	validation-logloss:0.51775	validation-auc:0.96953	validation-aucpr:0.97411
[21:53:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31]	validation-logloss:0.51384	validation-auc:0.96944	validation-aucpr:0.97404
[21:53:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32]	validation-logloss:0.50960	validation-auc:0.96940	validation-aucpr:0.97399
[21:53:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33]	validation-logloss:0.50590	validation-auc:0.96932	validation-aucpr:0.97390
[21:53:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34]	validation-logloss:0.50196	validation-auc:0.96946	validation-aucpr:0.97401
[21:53:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35]	validation-logloss:0.49766	validation-auc:0.96960	validation-aucpr:0.97413
[21:53:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36]	validation-logloss:0.49416	validation-auc:0.96947	validation-aucpr:0.97398
[21:53:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37]	validation-logloss:0.49068	validation-auc:0.96949	validation-aucpr:0.97396
[21:53:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38]	validation-logloss:0.48651	validation-auc:0.96969	validation-aucpr:0.97414
[21:53:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39]	validation-logloss:0.48232	validation-auc:0.96997	validation-aucpr:0.97435
[21:53:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40]	validation-logloss:0.47912	validation-auc:0.96990	validation-aucpr:0.97428
[21:53:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41]	validation-logloss:0.47584	validation-auc:0.96986	validation-aucpr:0.97421
[21:53:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42]	validation-logloss:0.47191	validation-auc:0.97000	validation-aucpr:0.97435
[21:53:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43]	validation-logloss:0.46860	validation-auc:0.97007	validation-aucpr:0.97441
{'best_iteration': '43', 'best_score': '0.9744061651856183'}
Trial 91, Fold 3: Log loss = 0.4686027267876157, Average precision = 0.9744106591318193, ROC-AUC = 0.9700656896784198, Elapsed Time = 50.93511260000014 seconds
Trial 91, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 91, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[21:53:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.68538	validation-auc:0.95451	validation-aucpr:0.95174
[21:53:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.67861	validation-auc:0.96242	validation-aucpr:0.96761
[21:53:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.67120	validation-auc:0.96456	validation-aucpr:0.97007
[21:53:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.66400	validation-auc:0.96577	validation-aucpr:0.97157
[21:53:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.65773	validation-auc:0.96489	validation-aucpr:0.97086
[21:53:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5]	validation-logloss:0.65065	validation-auc:0.96518	validation-aucpr:0.97125
[21:53:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6]	validation-logloss:0.64453	validation-auc:0.96506	validation-aucpr:0.97099
[21:53:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7]	validation-logloss:0.63779	validation-auc:0.96587	validation-aucpr:0.97173
[21:53:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8]	validation-logloss:0.63123	validation-auc:0.96651	validation-aucpr:0.97228
[21:53:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9]	validation-logloss:0.62539	validation-auc:0.96680	validation-aucpr:0.97228
[21:53:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10]	validation-logloss:0.61905	validation-auc:0.96684	validation-aucpr:0.97234
[21:54:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11]	validation-logloss:0.61302	validation-auc:0.96725	validation-aucpr:0.97271
[21:54:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12]	validation-logloss:0.60685	validation-auc:0.96748	validation-aucpr:0.97299
[21:54:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13]	validation-logloss:0.60171	validation-auc:0.96696	validation-aucpr:0.97256
[21:54:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14]	validation-logloss:0.59576	validation-auc:0.96704	validation-aucpr:0.97267
[21:54:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15]	validation-logloss:0.59002	validation-auc:0.96728	validation-aucpr:0.97284
[21:54:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16]	validation-logloss:0.58489	validation-auc:0.96723	validation-aucpr:0.97280
[21:54:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17]	validation-logloss:0.57987	validation-auc:0.96723	validation-aucpr:0.97273
[21:54:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18]	validation-logloss:0.57529	validation-auc:0.96689	validation-aucpr:0.97245
[21:54:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19]	validation-logloss:0.57048	validation-auc:0.96683	validation-aucpr:0.97238
[21:54:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20]	validation-logloss:0.56510	validation-auc:0.96697	validation-aucpr:0.97253
[21:54:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21]	validation-logloss:0.55993	validation-auc:0.96708	validation-aucpr:0.97265
[21:54:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22]	validation-logloss:0.55501	validation-auc:0.96721	validation-aucpr:0.97275
[21:54:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23]	validation-logloss:0.55052	validation-auc:0.96709	validation-aucpr:0.97265
[21:54:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24]	validation-logloss:0.54615	validation-auc:0.96699	validation-aucpr:0.97253
[21:54:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25]	validation-logloss:0.54128	validation-auc:0.96696	validation-aucpr:0.97253
[21:54:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26]	validation-logloss:0.53644	validation-auc:0.96712	validation-aucpr:0.97268
[21:54:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27]	validation-logloss:0.53221	validation-auc:0.96707	validation-aucpr:0.97262
[21:54:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28]	validation-logloss:0.52816	validation-auc:0.96703	validation-aucpr:0.97256
[21:54:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29]	validation-logloss:0.52350	validation-auc:0.96732	validation-aucpr:0.97281
[21:54:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30]	validation-logloss:0.51949	validation-auc:0.96726	validation-aucpr:0.97274
[21:54:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31]	validation-logloss:0.51520	validation-auc:0.96726	validation-aucpr:0.97274
[21:54:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32]	validation-logloss:0.51089	validation-auc:0.96734	validation-aucpr:0.97281
[21:54:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33]	validation-logloss:0.50696	validation-auc:0.96735	validation-aucpr:0.97281
[21:54:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34]	validation-logloss:0.50271	validation-auc:0.96737	validation-aucpr:0.97284
[21:54:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35]	validation-logloss:0.49847	validation-auc:0.96736	validation-aucpr:0.97285
[21:54:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36]	validation-logloss:0.49489	validation-auc:0.96730	validation-aucpr:0.97278
[21:54:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37]	validation-logloss:0.49083	validation-auc:0.96740	validation-aucpr:0.97287
[21:54:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38]	validation-logloss:0.48739	validation-auc:0.96739	validation-aucpr:0.97285
[21:54:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39]	validation-logloss:0.48348	validation-auc:0.96742	validation-aucpr:0.97288
[21:54:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40]	validation-logloss:0.47952	validation-auc:0.96759	validation-aucpr:0.97302
[21:54:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41]	validation-logloss:0.47573	validation-auc:0.96764	validation-aucpr:0.97307
[21:54:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42]	validation-logloss:0.47246	validation-auc:0.96752	validation-aucpr:0.97298
[21:54:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43]	validation-logloss:0.46871	validation-auc:0.96771	validation-aucpr:0.97314
{'best_iteration': '43', 'best_score': '0.9731358137383671'}
Trial 91, Fold 4: Log loss = 0.46871306628357956, Average precision = 0.9731291862601925, ROC-AUC = 0.967713228824653, Elapsed Time = 50.220442699999694 seconds
Trial 91, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 91, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[21:54:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.68537	validation-auc:0.95408	validation-aucpr:0.95892
[21:54:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.67777	validation-auc:0.96220	validation-aucpr:0.96781
[21:54:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.67069	validation-auc:0.96237	validation-aucpr:0.96809
[21:54:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.66347	validation-auc:0.96512	validation-aucpr:0.97000
[21:54:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.65654	validation-auc:0.96510	validation-aucpr:0.96975
[21:54:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5]	validation-logloss:0.64959	validation-auc:0.96544	validation-aucpr:0.97009
[21:54:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6]	validation-logloss:0.64344	validation-auc:0.96634	validation-aucpr:0.97102
[21:54:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7]	validation-logloss:0.63701	validation-auc:0.96590	validation-aucpr:0.97096
[21:54:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8]	validation-logloss:0.63054	validation-auc:0.96633	validation-aucpr:0.97125
[21:54:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9]	validation-logloss:0.62486	validation-auc:0.96644	validation-aucpr:0.97131
[21:54:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10]	validation-logloss:0.61852	validation-auc:0.96666	validation-aucpr:0.97149
[21:54:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11]	validation-logloss:0.61320	validation-auc:0.96647	validation-aucpr:0.97128
[21:54:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12]	validation-logloss:0.60776	validation-auc:0.96679	validation-aucpr:0.97156
[21:54:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13]	validation-logloss:0.60259	validation-auc:0.96615	validation-aucpr:0.97103
[21:54:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14]	validation-logloss:0.59728	validation-auc:0.96645	validation-aucpr:0.97112
[21:54:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15]	validation-logloss:0.59146	validation-auc:0.96702	validation-aucpr:0.97165
[21:54:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16]	validation-logloss:0.58584	validation-auc:0.96717	validation-aucpr:0.97180
[21:54:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17]	validation-logloss:0.58048	validation-auc:0.96706	validation-aucpr:0.97172
[21:54:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18]	validation-logloss:0.57512	validation-auc:0.96709	validation-aucpr:0.97181
[21:54:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19]	validation-logloss:0.56974	validation-auc:0.96736	validation-aucpr:0.97203
[21:54:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20]	validation-logloss:0.56443	validation-auc:0.96746	validation-aucpr:0.97213
[21:55:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21]	validation-logloss:0.55926	validation-auc:0.96761	validation-aucpr:0.97223
[21:55:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22]	validation-logloss:0.55413	validation-auc:0.96774	validation-aucpr:0.97233
[21:55:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23]	validation-logloss:0.54914	validation-auc:0.96782	validation-aucpr:0.97243
[21:55:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24]	validation-logloss:0.54408	validation-auc:0.96801	validation-aucpr:0.97256
[21:55:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25]	validation-logloss:0.53985	validation-auc:0.96774	validation-aucpr:0.97234
[21:55:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26]	validation-logloss:0.53498	validation-auc:0.96795	validation-aucpr:0.97253
[21:55:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27]	validation-logloss:0.53036	validation-auc:0.96817	validation-aucpr:0.97269
[21:55:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28]	validation-logloss:0.52563	validation-auc:0.96840	validation-aucpr:0.97284
[21:55:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29]	validation-logloss:0.52102	validation-auc:0.96853	validation-aucpr:0.97295
[21:55:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30]	validation-logloss:0.51648	validation-auc:0.96873	validation-aucpr:0.97308
[21:55:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31]	validation-logloss:0.51208	validation-auc:0.96869	validation-aucpr:0.97304
[21:55:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32]	validation-logloss:0.50828	validation-auc:0.96871	validation-aucpr:0.97303
[21:55:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33]	validation-logloss:0.50406	validation-auc:0.96875	validation-aucpr:0.97307
[21:55:14] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34]	validation-logloss:0.49979	validation-auc:0.96888	validation-aucpr:0.97319
[21:55:15] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35]	validation-logloss:0.49576	validation-auc:0.96890	validation-aucpr:0.97320
[21:55:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36]	validation-logloss:0.49208	validation-auc:0.96882	validation-aucpr:0.97314
[21:55:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37]	validation-logloss:0.48805	validation-auc:0.96889	validation-aucpr:0.97318
[21:55:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38]	validation-logloss:0.48457	validation-auc:0.96868	validation-aucpr:0.97301
[21:55:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39]	validation-logloss:0.48065	validation-auc:0.96876	validation-aucpr:0.97308
[21:55:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40]	validation-logloss:0.47679	validation-auc:0.96890	validation-aucpr:0.97317
[21:55:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41]	validation-logloss:0.47291	validation-auc:0.96908	validation-aucpr:0.97331
[21:55:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42]	validation-logloss:0.46930	validation-auc:0.96918	validation-aucpr:0.97338
[21:55:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43]	validation-logloss:0.46572	validation-auc:0.96916	validation-aucpr:0.97336
{'best_iteration': '42', 'best_score': '0.9733813409437337'}
Trial 91, Fold 5: Log loss = 0.46572245851236477, Average precision = 0.9733665370275528, ROC-AUC = 0.9691603961131858, Elapsed Time = 46.25501720000102 seconds
Optimization Progress:  92%|#########2| 92/100 [3:56:36<15:55, 119.39s/it]
Trial 92, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 92, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[0]	validation-logloss:0.65897	validation-auc:0.94764	validation-aucpr:0.95389
[1]	validation-logloss:0.63078	validation-auc:0.91919	validation-aucpr:0.88950
[2]	validation-logloss:0.60254	validation-auc:0.94006	validation-aucpr:0.92849
[3]	validation-logloss:0.57999	validation-auc:0.94494	validation-aucpr:0.93579
[4]	validation-logloss:0.55751	validation-auc:0.95116	validation-aucpr:0.94720
[5]	validation-logloss:0.53479	validation-auc:0.95516	validation-aucpr:0.95929
[6]	validation-logloss:0.51695	validation-auc:0.95573	validation-aucpr:0.95946
[7]	validation-logloss:0.50079	validation-auc:0.95573	validation-aucpr:0.95886
[8]	validation-logloss:0.48499	validation-auc:0.95665	validation-aucpr:0.96060
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{'best_iteration': '92', 'best_score': '0.9739895790503507'}
Trial 92, Fold 1: Log loss = 0.20569699593105253, Average precision = 0.9739613171371575, ROC-AUC = 0.9695516267331985, Elapsed Time = 2.5761713000028976 seconds
Trial 92, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 92, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
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{'best_iteration': '84', 'best_score': '0.9737342955246244'}
Trial 92, Fold 2: Log loss = 0.19739752275694591, Average precision = 0.9734464038101037, ROC-AUC = 0.9702782234947456, Elapsed Time = 3.030435299999226 seconds
Trial 92, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 92, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
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{'best_iteration': '62', 'best_score': '0.9743978942246921'}
Trial 92, Fold 3: Log loss = 0.19853437848993327, Average precision = 0.9742558475354604, ROC-AUC = 0.9707452795889475, Elapsed Time = 3.047268400001485 seconds
Trial 92, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 92, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
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{'best_iteration': '93', 'best_score': '0.9740735088385705'}
Trial 92, Fold 4: Log loss = 0.2035398037214788, Average precision = 0.9740776692600727, ROC-AUC = 0.9695270195354693, Elapsed Time = 2.9379043000008096 seconds
Trial 92, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 92, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
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[84]	validation-logloss:0.21046	validation-auc:0.96768	validation-aucpr:0.97222
[85]	validation-logloss:0.21004	validation-auc:0.96784	validation-aucpr:0.97229
[86]	validation-logloss:0.21040	validation-auc:0.96772	validation-aucpr:0.97213
[87]	validation-logloss:0.21030	validation-auc:0.96771	validation-aucpr:0.97214
[88]	validation-logloss:0.20992	validation-auc:0.96781	validation-aucpr:0.97230
[89]	validation-logloss:0.21021	validation-auc:0.96773	validation-aucpr:0.97227
[90]	validation-logloss:0.21028	validation-auc:0.96768	validation-aucpr:0.97225
[91]	validation-logloss:0.21062	validation-auc:0.96760	validation-aucpr:0.97220
[92]	validation-logloss:0.21034	validation-auc:0.96771	validation-aucpr:0.97226
[93]	validation-logloss:0.21025	validation-auc:0.96775	validation-aucpr:0.97225
{'best_iteration': '88', 'best_score': '0.9722974589530706'}
Trial 92, Fold 5: Log loss = 0.21024819867975683, Average precision = 0.9722566510920048, ROC-AUC = 0.9677525221559555, Elapsed Time = 3.7220796999972663 seconds
Optimization Progress:  93%|#########3| 93/100 [3:56:59<10:34, 90.62s/it] 
Trial 93, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 93, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[0]	validation-logloss:0.67575	validation-auc:0.93460	validation-aucpr:0.93800
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[3]	validation-logloss:0.63090	validation-auc:0.94449	validation-aucpr:0.94784
[4]	validation-logloss:0.61637	validation-auc:0.94767	validation-aucpr:0.95120
[5]	validation-logloss:0.59997	validation-auc:0.95603	validation-aucpr:0.96103
[6]	validation-logloss:0.58508	validation-auc:0.95694	validation-aucpr:0.96212
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[8]	validation-logloss:0.55955	validation-auc:0.95805	validation-aucpr:0.96472
[9]	validation-logloss:0.54840	validation-auc:0.95826	validation-aucpr:0.96488
[10]	validation-logloss:0.53508	validation-auc:0.95968	validation-aucpr:0.96614
[11]	validation-logloss:0.52519	validation-auc:0.95954	validation-aucpr:0.96584
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[56]	validation-logloss:0.29742	validation-auc:0.96360	validation-aucpr:0.96908
[57]	validation-logloss:0.29432	validation-auc:0.96375	validation-aucpr:0.96921
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[59]	validation-logloss:0.29060	validation-auc:0.96383	validation-aucpr:0.96923
[60]	validation-logloss:0.28796	validation-auc:0.96394	validation-aucpr:0.96941
[61]	validation-logloss:0.28544	validation-auc:0.96412	validation-aucpr:0.96957
[62]	validation-logloss:0.28292	validation-auc:0.96422	validation-aucpr:0.96971
[63]	validation-logloss:0.28004	validation-auc:0.96440	validation-aucpr:0.96987
[64]	validation-logloss:0.27858	validation-auc:0.96445	validation-aucpr:0.96988
[65]	validation-logloss:0.27607	validation-auc:0.96456	validation-aucpr:0.97002
[66]	validation-logloss:0.27367	validation-auc:0.96469	validation-aucpr:0.97011
[67]	validation-logloss:0.27142	validation-auc:0.96482	validation-aucpr:0.97027
[68]	validation-logloss:0.26915	validation-auc:0.96490	validation-aucpr:0.97034
[69]	validation-logloss:0.26776	validation-auc:0.96494	validation-aucpr:0.97037
[70]	validation-logloss:0.26636	validation-auc:0.96504	validation-aucpr:0.97046
[71]	validation-logloss:0.26506	validation-auc:0.96511	validation-aucpr:0.97054
[72]	validation-logloss:0.26302	validation-auc:0.96518	validation-aucpr:0.97061
[73]	validation-logloss:0.26105	validation-auc:0.96526	validation-aucpr:0.97072
[74]	validation-logloss:0.25909	validation-auc:0.96536	validation-aucpr:0.97081
{'best_iteration': '74', 'best_score': '0.9708118262327414'}
Trial 93, Fold 1: Log loss = 0.2590858465904124, Average precision = 0.9707767569767338, ROC-AUC = 0.9653620927192518, Elapsed Time = 7.22263490000114 seconds
Trial 93, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 93, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[0]	validation-logloss:0.67552	validation-auc:0.93147	validation-aucpr:0.93164
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[2]	validation-logloss:0.64406	validation-auc:0.94359	validation-aucpr:0.94518
[3]	validation-logloss:0.63037	validation-auc:0.94403	validation-aucpr:0.94610
[4]	validation-logloss:0.61580	validation-auc:0.94694	validation-aucpr:0.94908
[5]	validation-logloss:0.59967	validation-auc:0.95573	validation-aucpr:0.95969
[6]	validation-logloss:0.58490	validation-auc:0.95757	validation-aucpr:0.96145
[7]	validation-logloss:0.57307	validation-auc:0.95758	validation-aucpr:0.96143
[8]	validation-logloss:0.55913	validation-auc:0.95846	validation-aucpr:0.96222
[9]	validation-logloss:0.54799	validation-auc:0.95884	validation-aucpr:0.96275
[10]	validation-logloss:0.53493	validation-auc:0.95985	validation-aucpr:0.96389
[11]	validation-logloss:0.52482	validation-auc:0.95980	validation-aucpr:0.96390
[12]	validation-logloss:0.51530	validation-auc:0.96003	validation-aucpr:0.96395
[13]	validation-logloss:0.50636	validation-auc:0.96003	validation-aucpr:0.96436
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[24]	validation-logloss:0.42039	validation-auc:0.96114	validation-aucpr:0.96504
[25]	validation-logloss:0.41446	validation-auc:0.96114	validation-aucpr:0.96500
[26]	validation-logloss:0.40700	validation-auc:0.96146	validation-aucpr:0.96539
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[65]	validation-logloss:0.27792	validation-auc:0.96459	validation-aucpr:0.96849
[66]	validation-logloss:0.27524	validation-auc:0.96474	validation-aucpr:0.96864
[67]	validation-logloss:0.27267	validation-auc:0.96487	validation-aucpr:0.96879
[68]	validation-logloss:0.27138	validation-auc:0.96483	validation-aucpr:0.96875
[69]	validation-logloss:0.27017	validation-auc:0.96483	validation-aucpr:0.96858
[70]	validation-logloss:0.26891	validation-auc:0.96482	validation-aucpr:0.96852
[71]	validation-logloss:0.26643	validation-auc:0.96499	validation-aucpr:0.96869
[72]	validation-logloss:0.26511	validation-auc:0.96502	validation-aucpr:0.96870
[73]	validation-logloss:0.26364	validation-auc:0.96515	validation-aucpr:0.96879
[74]	validation-logloss:0.26242	validation-auc:0.96521	validation-aucpr:0.96882
{'best_iteration': '74', 'best_score': '0.9688176110599449'}
Trial 93, Fold 2: Log loss = 0.26241678949069214, Average precision = 0.9687837016793314, ROC-AUC = 0.9652092404689492, Elapsed Time = 7.36766390000048 seconds
Trial 93, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 93, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[0]	validation-logloss:0.67562	validation-auc:0.93375	validation-aucpr:0.93331
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[2]	validation-logloss:0.64383	validation-auc:0.94486	validation-aucpr:0.94795
[3]	validation-logloss:0.62994	validation-auc:0.94719	validation-aucpr:0.94897
[4]	validation-logloss:0.61579	validation-auc:0.94976	validation-aucpr:0.95056
[5]	validation-logloss:0.60285	validation-auc:0.95017	validation-aucpr:0.95187
[6]	validation-logloss:0.59043	validation-auc:0.94965	validation-aucpr:0.95346
[7]	validation-logloss:0.57838	validation-auc:0.95054	validation-aucpr:0.95439
[8]	validation-logloss:0.56478	validation-auc:0.95896	validation-aucpr:0.96397
[9]	validation-logloss:0.55409	validation-auc:0.95874	validation-aucpr:0.96359
[10]	validation-logloss:0.54148	validation-auc:0.96012	validation-aucpr:0.96522
[11]	validation-logloss:0.53196	validation-auc:0.95982	validation-aucpr:0.96494
[12]	validation-logloss:0.52050	validation-auc:0.96050	validation-aucpr:0.96571
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[56]	validation-logloss:0.29065	validation-auc:0.96607	validation-aucpr:0.97092
[57]	validation-logloss:0.28875	validation-auc:0.96607	validation-aucpr:0.97095
[58]	validation-logloss:0.28695	validation-auc:0.96602	validation-aucpr:0.97093
[59]	validation-logloss:0.28521	validation-auc:0.96603	validation-aucpr:0.97092
[60]	validation-logloss:0.28349	validation-auc:0.96604	validation-aucpr:0.97089
[61]	validation-logloss:0.28066	validation-auc:0.96612	validation-aucpr:0.97098
[62]	validation-logloss:0.27898	validation-auc:0.96614	validation-aucpr:0.97098
[63]	validation-logloss:0.27632	validation-auc:0.96623	validation-aucpr:0.97108
[64]	validation-logloss:0.27445	validation-auc:0.96636	validation-aucpr:0.97119
[65]	validation-logloss:0.27293	validation-auc:0.96641	validation-aucpr:0.97123
[66]	validation-logloss:0.27048	validation-auc:0.96648	validation-aucpr:0.97132
[67]	validation-logloss:0.26800	validation-auc:0.96657	validation-aucpr:0.97143
[68]	validation-logloss:0.26655	validation-auc:0.96667	validation-aucpr:0.97149
[69]	validation-logloss:0.26469	validation-auc:0.96671	validation-aucpr:0.97152
[70]	validation-logloss:0.26335	validation-auc:0.96671	validation-aucpr:0.97151
[71]	validation-logloss:0.26100	validation-auc:0.96683	validation-aucpr:0.97162
[72]	validation-logloss:0.25969	validation-auc:0.96681	validation-aucpr:0.97158
[73]	validation-logloss:0.25820	validation-auc:0.96693	validation-aucpr:0.97167
[74]	validation-logloss:0.25698	validation-auc:0.96698	validation-aucpr:0.97171
{'best_iteration': '74', 'best_score': '0.971711828125484'}
Trial 93, Fold 3: Log loss = 0.256981879734254, Average precision = 0.971697582692562, ROC-AUC = 0.9669770618842157, Elapsed Time = 7.480115800000931 seconds
Trial 93, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 93, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[0]	validation-logloss:0.67552	validation-auc:0.93217	validation-aucpr:0.93573
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[2]	validation-logloss:0.64398	validation-auc:0.94157	validation-aucpr:0.94618
[3]	validation-logloss:0.63029	validation-auc:0.94242	validation-aucpr:0.94709
[4]	validation-logloss:0.61586	validation-auc:0.94668	validation-aucpr:0.95153
[5]	validation-logloss:0.59969	validation-auc:0.95483	validation-aucpr:0.96118
[6]	validation-logloss:0.58475	validation-auc:0.95626	validation-aucpr:0.96281
[7]	validation-logloss:0.57301	validation-auc:0.95628	validation-aucpr:0.96285
[8]	validation-logloss:0.55925	validation-auc:0.95726	validation-aucpr:0.96380
[9]	validation-logloss:0.54806	validation-auc:0.95782	validation-aucpr:0.96440
[10]	validation-logloss:0.53478	validation-auc:0.95881	validation-aucpr:0.96540
[11]	validation-logloss:0.52485	validation-auc:0.95844	validation-aucpr:0.96493
[12]	validation-logloss:0.51541	validation-auc:0.95842	validation-aucpr:0.96488
[13]	validation-logloss:0.50631	validation-auc:0.95866	validation-aucpr:0.96510
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[20]	validation-logloss:0.44483	validation-auc:0.96022	validation-aucpr:0.96657
[21]	validation-logloss:0.43851	validation-auc:0.96035	validation-aucpr:0.96660
[22]	validation-logloss:0.43218	validation-auc:0.96030	validation-aucpr:0.96648
[23]	validation-logloss:0.42367	validation-auc:0.96067	validation-aucpr:0.96687
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[25]	validation-logloss:0.41219	validation-auc:0.96056	validation-aucpr:0.96664
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[38]	validation-logloss:0.34673	validation-auc:0.96212	validation-aucpr:0.96841
[39]	validation-logloss:0.34343	validation-auc:0.96209	validation-aucpr:0.96840
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[41]	validation-logloss:0.33693	validation-auc:0.96217	validation-aucpr:0.96835
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[47]	validation-logloss:0.31735	validation-auc:0.96265	validation-aucpr:0.96875
[48]	validation-logloss:0.31361	validation-auc:0.96281	validation-aucpr:0.96892
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[51]	validation-logloss:0.30478	validation-auc:0.96312	validation-aucpr:0.96917
[52]	validation-logloss:0.30244	validation-auc:0.96310	validation-aucpr:0.96912
[53]	validation-logloss:0.29939	validation-auc:0.96332	validation-aucpr:0.96934
[54]	validation-logloss:0.29730	validation-auc:0.96332	validation-aucpr:0.96929
[55]	validation-logloss:0.29524	validation-auc:0.96337	validation-aucpr:0.96932
[56]	validation-logloss:0.29351	validation-auc:0.96328	validation-aucpr:0.96923
[57]	validation-logloss:0.29166	validation-auc:0.96333	validation-aucpr:0.96924
[58]	validation-logloss:0.28992	validation-auc:0.96344	validation-aucpr:0.96935
[59]	validation-logloss:0.28808	validation-auc:0.96340	validation-aucpr:0.96928
[60]	validation-logloss:0.28639	validation-auc:0.96347	validation-aucpr:0.96930
[61]	validation-logloss:0.28362	validation-auc:0.96362	validation-aucpr:0.96947
[62]	validation-logloss:0.28195	validation-auc:0.96369	validation-aucpr:0.96953
[63]	validation-logloss:0.27926	validation-auc:0.96388	validation-aucpr:0.96971
[64]	validation-logloss:0.27737	validation-auc:0.96400	validation-aucpr:0.96980
[65]	validation-logloss:0.27580	validation-auc:0.96403	validation-aucpr:0.96986
[66]	validation-logloss:0.27329	validation-auc:0.96413	validation-aucpr:0.97000
[67]	validation-logloss:0.27084	validation-auc:0.96425	validation-aucpr:0.97014
[68]	validation-logloss:0.26946	validation-auc:0.96424	validation-aucpr:0.97014
[69]	validation-logloss:0.26750	validation-auc:0.96439	validation-aucpr:0.97031
[70]	validation-logloss:0.26623	validation-auc:0.96435	validation-aucpr:0.97022
[71]	validation-logloss:0.26400	validation-auc:0.96445	validation-aucpr:0.97037
[72]	validation-logloss:0.26267	validation-auc:0.96448	validation-aucpr:0.97037
[73]	validation-logloss:0.26116	validation-auc:0.96463	validation-aucpr:0.97049
[74]	validation-logloss:0.25995	validation-auc:0.96471	validation-aucpr:0.97053
{'best_iteration': '74', 'best_score': '0.970527887052778'}
Trial 93, Fold 4: Log loss = 0.2599548907235923, Average precision = 0.9704986765151592, ROC-AUC = 0.9647127471738784, Elapsed Time = 7.700011400000221 seconds
Trial 93, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 93, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[0]	validation-logloss:0.67591	validation-auc:0.92772	validation-aucpr:0.92825
[1]	validation-logloss:0.65975	validation-auc:0.93253	validation-aucpr:0.93482
[2]	validation-logloss:0.64457	validation-auc:0.94027	validation-aucpr:0.94445
[3]	validation-logloss:0.63095	validation-auc:0.94259	validation-aucpr:0.94718
[4]	validation-logloss:0.61670	validation-auc:0.94487	validation-aucpr:0.94922
[5]	validation-logloss:0.60374	validation-auc:0.94523	validation-aucpr:0.94979
[6]	validation-logloss:0.59138	validation-auc:0.94542	validation-aucpr:0.95025
[7]	validation-logloss:0.57952	validation-auc:0.94576	validation-aucpr:0.95051
[8]	validation-logloss:0.56519	validation-auc:0.95392	validation-aucpr:0.96006
[9]	validation-logloss:0.55464	validation-auc:0.95459	validation-aucpr:0.96061
[10]	validation-logloss:0.54426	validation-auc:0.95467	validation-aucpr:0.96084
[11]	validation-logloss:0.53326	validation-auc:0.95499	validation-aucpr:0.96118
[12]	validation-logloss:0.52150	validation-auc:0.95611	validation-aucpr:0.96241
[13]	validation-logloss:0.51267	validation-auc:0.95605	validation-aucpr:0.96222
[14]	validation-logloss:0.50409	validation-auc:0.95607	validation-aucpr:0.96217
[15]	validation-logloss:0.49301	validation-auc:0.95652	validation-aucpr:0.96259
[16]	validation-logloss:0.48500	validation-auc:0.95653	validation-aucpr:0.96258
[17]	validation-logloss:0.47458	validation-auc:0.95698	validation-aucpr:0.96300
[18]	validation-logloss:0.46469	validation-auc:0.95734	validation-aucpr:0.96341
[19]	validation-logloss:0.45776	validation-auc:0.95720	validation-aucpr:0.96329
[20]	validation-logloss:0.44897	validation-auc:0.95749	validation-aucpr:0.96364
[21]	validation-logloss:0.44265	validation-auc:0.95760	validation-aucpr:0.96373
[22]	validation-logloss:0.43639	validation-auc:0.95772	validation-aucpr:0.96382
[23]	validation-logloss:0.42799	validation-auc:0.95809	validation-aucpr:0.96417
[24]	validation-logloss:0.42227	validation-auc:0.95820	validation-aucpr:0.96420
[25]	validation-logloss:0.41635	validation-auc:0.95841	validation-aucpr:0.96429
[26]	validation-logloss:0.40914	validation-auc:0.95901	validation-aucpr:0.96492
[27]	validation-logloss:0.40393	validation-auc:0.95903	validation-aucpr:0.96488
[28]	validation-logloss:0.39741	validation-auc:0.95928	validation-aucpr:0.96512
[29]	validation-logloss:0.39271	validation-auc:0.95943	validation-aucpr:0.96523
[30]	validation-logloss:0.38858	validation-auc:0.95945	validation-aucpr:0.96519
[31]	validation-logloss:0.38258	validation-auc:0.95992	validation-aucpr:0.96576
[32]	validation-logloss:0.37857	validation-auc:0.95984	validation-aucpr:0.96567
[33]	validation-logloss:0.37420	validation-auc:0.96007	validation-aucpr:0.96584
[34]	validation-logloss:0.36854	validation-auc:0.96030	validation-aucpr:0.96609
[35]	validation-logloss:0.36307	validation-auc:0.96049	validation-aucpr:0.96631
[36]	validation-logloss:0.35815	validation-auc:0.96066	validation-aucpr:0.96644
[37]	validation-logloss:0.35432	validation-auc:0.96070	validation-aucpr:0.96645
[38]	validation-logloss:0.35070	validation-auc:0.96089	validation-aucpr:0.96655
[39]	validation-logloss:0.34745	validation-auc:0.96087	validation-aucpr:0.96651
[40]	validation-logloss:0.34411	validation-auc:0.96095	validation-aucpr:0.96655
[41]	validation-logloss:0.34092	validation-auc:0.96110	validation-aucpr:0.96671
[42]	validation-logloss:0.33780	validation-auc:0.96114	validation-aucpr:0.96677
[43]	validation-logloss:0.33507	validation-auc:0.96113	validation-aucpr:0.96672
[44]	validation-logloss:0.33256	validation-auc:0.96100	validation-aucpr:0.96662
[45]	validation-logloss:0.32860	validation-auc:0.96118	validation-aucpr:0.96679
[46]	validation-logloss:0.32459	validation-auc:0.96133	validation-aucpr:0.96690
[47]	validation-logloss:0.32182	validation-auc:0.96136	validation-aucpr:0.96687
[48]	validation-logloss:0.31802	validation-auc:0.96143	validation-aucpr:0.96697
[49]	validation-logloss:0.31520	validation-auc:0.96157	validation-aucpr:0.96705
[50]	validation-logloss:0.31293	validation-auc:0.96160	validation-aucpr:0.96707
[51]	validation-logloss:0.31069	validation-auc:0.96165	validation-aucpr:0.96710
[52]	validation-logloss:0.30847	validation-auc:0.96164	validation-aucpr:0.96706
[53]	validation-logloss:0.30628	validation-auc:0.96171	validation-aucpr:0.96707
[54]	validation-logloss:0.30286	validation-auc:0.96187	validation-aucpr:0.96727
[55]	validation-logloss:0.30086	validation-auc:0.96190	validation-aucpr:0.96727
[56]	validation-logloss:0.29918	validation-auc:0.96188	validation-aucpr:0.96723
[57]	validation-logloss:0.29727	validation-auc:0.96196	validation-aucpr:0.96729
[58]	validation-logloss:0.29552	validation-auc:0.96199	validation-aucpr:0.96734
[59]	validation-logloss:0.29375	validation-auc:0.96197	validation-aucpr:0.96729
[60]	validation-logloss:0.29217	validation-auc:0.96200	validation-aucpr:0.96731
[61]	validation-logloss:0.28923	validation-auc:0.96204	validation-aucpr:0.96736
[62]	validation-logloss:0.28747	validation-auc:0.96214	validation-aucpr:0.96745
[63]	validation-logloss:0.28480	validation-auc:0.96223	validation-aucpr:0.96756
[64]	validation-logloss:0.28298	validation-auc:0.96231	validation-aucpr:0.96761
[65]	validation-logloss:0.28147	validation-auc:0.96242	validation-aucpr:0.96769
[66]	validation-logloss:0.27893	validation-auc:0.96261	validation-aucpr:0.96787
[67]	validation-logloss:0.27647	validation-auc:0.96271	validation-aucpr:0.96799
[68]	validation-logloss:0.27376	validation-auc:0.96294	validation-aucpr:0.96820
[69]	validation-logloss:0.27137	validation-auc:0.96304	validation-aucpr:0.96829
[70]	validation-logloss:0.26897	validation-auc:0.96326	validation-aucpr:0.96846
[71]	validation-logloss:0.26679	validation-auc:0.96335	validation-aucpr:0.96856
[72]	validation-logloss:0.26525	validation-auc:0.96352	validation-aucpr:0.96870
[73]	validation-logloss:0.26413	validation-auc:0.96361	validation-aucpr:0.96875
[74]	validation-logloss:0.26266	validation-auc:0.96378	validation-aucpr:0.96890
{'best_iteration': '74', 'best_score': '0.9689014911357019'}
Trial 93, Fold 5: Log loss = 0.2626649744827682, Average precision = 0.9689000073615628, ROC-AUC = 0.9637839585307395, Elapsed Time = 8.099983399999473 seconds
Optimization Progress:  94%|#########3| 94/100 [3:57:46<07:43, 77.27s/it]
Trial 94, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 94, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[0]	validation-logloss:0.68325	validation-auc:0.94038	validation-aucpr:0.94328
[1]	validation-logloss:0.67350	validation-auc:0.94740	validation-aucpr:0.95020
[2]	validation-logloss:0.66237	validation-auc:0.95695	validation-aucpr:0.96034
[3]	validation-logloss:0.65320	validation-auc:0.95779	validation-aucpr:0.96079
[4]	validation-logloss:0.64298	validation-auc:0.95912	validation-aucpr:0.96398
[5]	validation-logloss:0.63455	validation-auc:0.95918	validation-aucpr:0.96426
[6]	validation-logloss:0.62506	validation-auc:0.96062	validation-aucpr:0.96583
[7]	validation-logloss:0.61546	validation-auc:0.96104	validation-aucpr:0.96626
[8]	validation-logloss:0.60785	validation-auc:0.96098	validation-aucpr:0.96606
[9]	validation-logloss:0.60007	validation-auc:0.96089	validation-aucpr:0.96589
[10]	validation-logloss:0.59154	validation-auc:0.96141	validation-aucpr:0.96645
[11]	validation-logloss:0.58289	validation-auc:0.96152	validation-aucpr:0.96662
[12]	validation-logloss:0.57562	validation-auc:0.96166	validation-aucpr:0.96667
[13]	validation-logloss:0.56903	validation-auc:0.96141	validation-aucpr:0.96637
[14]	validation-logloss:0.56089	validation-auc:0.96187	validation-aucpr:0.96681
[15]	validation-logloss:0.55308	validation-auc:0.96218	validation-aucpr:0.96694
[16]	validation-logloss:0.54550	validation-auc:0.96239	validation-aucpr:0.96724
[17]	validation-logloss:0.53854	validation-auc:0.96250	validation-aucpr:0.96808
[18]	validation-logloss:0.53288	validation-auc:0.96231	validation-aucpr:0.96792
[19]	validation-logloss:0.52578	validation-auc:0.96246	validation-aucpr:0.96811
[20]	validation-logloss:0.52031	validation-auc:0.96243	validation-aucpr:0.96805
[21]	validation-logloss:0.51489	validation-auc:0.96262	validation-aucpr:0.96842
[22]	validation-logloss:0.50830	validation-auc:0.96269	validation-aucpr:0.96854
[23]	validation-logloss:0.50312	validation-auc:0.96274	validation-aucpr:0.96860
[24]	validation-logloss:0.49695	validation-auc:0.96289	validation-aucpr:0.96876
[25]	validation-logloss:0.49177	validation-auc:0.96298	validation-aucpr:0.96879
[26]	validation-logloss:0.48570	validation-auc:0.96312	validation-aucpr:0.96894
[27]	validation-logloss:0.48023	validation-auc:0.96316	validation-aucpr:0.96901
[28]	validation-logloss:0.47471	validation-auc:0.96321	validation-aucpr:0.96907
[29]	validation-logloss:0.46901	validation-auc:0.96335	validation-aucpr:0.96922
[30]	validation-logloss:0.46391	validation-auc:0.96349	validation-aucpr:0.96934
[31]	validation-logloss:0.45936	validation-auc:0.96358	validation-aucpr:0.96935
[32]	validation-logloss:0.45401	validation-auc:0.96367	validation-aucpr:0.96946
[33]	validation-logloss:0.44971	validation-auc:0.96370	validation-aucpr:0.96945
[34]	validation-logloss:0.44454	validation-auc:0.96386	validation-aucpr:0.96958
[35]	validation-logloss:0.44055	validation-auc:0.96388	validation-aucpr:0.96959
[36]	validation-logloss:0.43579	validation-auc:0.96394	validation-aucpr:0.96966
[37]	validation-logloss:0.43123	validation-auc:0.96401	validation-aucpr:0.96976
[38]	validation-logloss:0.42736	validation-auc:0.96397	validation-aucpr:0.96969
[39]	validation-logloss:0.42292	validation-auc:0.96398	validation-aucpr:0.96971
[40]	validation-logloss:0.41918	validation-auc:0.96413	validation-aucpr:0.96978
[41]	validation-logloss:0.41485	validation-auc:0.96425	validation-aucpr:0.96989
[42]	validation-logloss:0.41109	validation-auc:0.96427	validation-aucpr:0.96991
[43]	validation-logloss:0.40702	validation-auc:0.96431	validation-aucpr:0.96997
[44]	validation-logloss:0.40285	validation-auc:0.96449	validation-aucpr:0.97010
[45]	validation-logloss:0.39945	validation-auc:0.96451	validation-aucpr:0.97009
[46]	validation-logloss:0.39562	validation-auc:0.96457	validation-aucpr:0.97015
[47]	validation-logloss:0.39265	validation-auc:0.96460	validation-aucpr:0.97013
[48]	validation-logloss:0.38894	validation-auc:0.96470	validation-aucpr:0.97020
[49]	validation-logloss:0.38521	validation-auc:0.96488	validation-aucpr:0.97035
[50]	validation-logloss:0.38236	validation-auc:0.96478	validation-aucpr:0.97024
[51]	validation-logloss:0.37939	validation-auc:0.96484	validation-aucpr:0.97024
[52]	validation-logloss:0.37605	validation-auc:0.96486	validation-aucpr:0.97026
[53]	validation-logloss:0.37322	validation-auc:0.96483	validation-aucpr:0.97020
[54]	validation-logloss:0.36994	validation-auc:0.96490	validation-aucpr:0.97027
[55]	validation-logloss:0.36737	validation-auc:0.96490	validation-aucpr:0.97026
{'best_iteration': '49', 'best_score': '0.9703483133017742'}
Trial 94, Fold 1: Log loss = 0.3673695350323967, Average precision = 0.9698315584733633, ROC-AUC = 0.9649038496006654, Elapsed Time = 1.0445719999988796 seconds
Trial 94, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 94, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[0]	validation-logloss:0.68219	validation-auc:0.95006	validation-aucpr:0.94543
[1]	validation-logloss:0.67232	validation-auc:0.95474	validation-aucpr:0.95743
[2]	validation-logloss:0.66131	validation-auc:0.96087	validation-aucpr:0.96474
[3]	validation-logloss:0.65200	validation-auc:0.96064	validation-aucpr:0.96456
[4]	validation-logloss:0.64192	validation-auc:0.96278	validation-aucpr:0.96663
[5]	validation-logloss:0.63331	validation-auc:0.96275	validation-aucpr:0.96643
[6]	validation-logloss:0.62387	validation-auc:0.96392	validation-aucpr:0.96778
[7]	validation-logloss:0.61423	validation-auc:0.96490	validation-aucpr:0.96888
[8]	validation-logloss:0.60653	validation-auc:0.96465	validation-aucpr:0.96844
[9]	validation-logloss:0.59880	validation-auc:0.96410	validation-aucpr:0.96780
[10]	validation-logloss:0.59021	validation-auc:0.96461	validation-aucpr:0.96830
[11]	validation-logloss:0.58151	validation-auc:0.96514	validation-aucpr:0.96886
[12]	validation-logloss:0.57430	validation-auc:0.96500	validation-aucpr:0.96863
[13]	validation-logloss:0.56776	validation-auc:0.96477	validation-aucpr:0.96840
[14]	validation-logloss:0.55966	validation-auc:0.96514	validation-aucpr:0.96880
[15]	validation-logloss:0.55189	validation-auc:0.96534	validation-aucpr:0.96919
[16]	validation-logloss:0.54425	validation-auc:0.96553	validation-aucpr:0.96944
[17]	validation-logloss:0.53733	validation-auc:0.96539	validation-aucpr:0.96938
[18]	validation-logloss:0.53163	validation-auc:0.96521	validation-aucpr:0.96927
[19]	validation-logloss:0.52454	validation-auc:0.96534	validation-aucpr:0.96943
[20]	validation-logloss:0.51810	validation-auc:0.96534	validation-aucpr:0.96931
[21]	validation-logloss:0.51290	validation-auc:0.96508	validation-aucpr:0.96909
[22]	validation-logloss:0.50751	validation-auc:0.96501	validation-aucpr:0.96898
[23]	validation-logloss:0.50199	validation-auc:0.96481	validation-aucpr:0.96881
[24]	validation-logloss:0.49670	validation-auc:0.96476	validation-aucpr:0.96872
[25]	validation-logloss:0.49169	validation-auc:0.96465	validation-aucpr:0.96862
[26]	validation-logloss:0.48680	validation-auc:0.96458	validation-aucpr:0.96855
[27]	validation-logloss:0.48107	validation-auc:0.96469	validation-aucpr:0.96868
[28]	validation-logloss:0.47515	validation-auc:0.96486	validation-aucpr:0.96883
[29]	validation-logloss:0.47047	validation-auc:0.96488	validation-aucpr:0.96880
[30]	validation-logloss:0.46590	validation-auc:0.96491	validation-aucpr:0.96878
[31]	validation-logloss:0.46132	validation-auc:0.96483	validation-aucpr:0.96868
[32]	validation-logloss:0.45608	validation-auc:0.96499	validation-aucpr:0.96882
[33]	validation-logloss:0.45082	validation-auc:0.96506	validation-aucpr:0.96887
[34]	validation-logloss:0.44580	validation-auc:0.96518	validation-aucpr:0.96898
[35]	validation-logloss:0.44096	validation-auc:0.96540	validation-aucpr:0.96922
[36]	validation-logloss:0.43630	validation-auc:0.96553	validation-aucpr:0.96934
[37]	validation-logloss:0.43243	validation-auc:0.96548	validation-aucpr:0.96928
[38]	validation-logloss:0.42879	validation-auc:0.96544	validation-aucpr:0.96911
[39]	validation-logloss:0.42499	validation-auc:0.96546	validation-aucpr:0.96909
[40]	validation-logloss:0.42161	validation-auc:0.96537	validation-aucpr:0.96902
[41]	validation-logloss:0.41722	validation-auc:0.96544	validation-aucpr:0.96911
[42]	validation-logloss:0.41289	validation-auc:0.96545	validation-aucpr:0.96913
[43]	validation-logloss:0.40868	validation-auc:0.96545	validation-aucpr:0.96913
[44]	validation-logloss:0.40455	validation-auc:0.96548	validation-aucpr:0.96918
[45]	validation-logloss:0.40130	validation-auc:0.96549	validation-aucpr:0.96917
[46]	validation-logloss:0.39802	validation-auc:0.96557	validation-aucpr:0.96922
[47]	validation-logloss:0.39421	validation-auc:0.96567	validation-aucpr:0.96932
[48]	validation-logloss:0.39034	validation-auc:0.96574	validation-aucpr:0.96940
[49]	validation-logloss:0.38654	validation-auc:0.96582	validation-aucpr:0.96945
[50]	validation-logloss:0.38368	validation-auc:0.96587	validation-aucpr:0.96950
[51]	validation-logloss:0.38074	validation-auc:0.96580	validation-aucpr:0.96939
[52]	validation-logloss:0.37734	validation-auc:0.96590	validation-aucpr:0.96949
[53]	validation-logloss:0.37451	validation-auc:0.96584	validation-aucpr:0.96941
[54]	validation-logloss:0.37098	validation-auc:0.96595	validation-aucpr:0.96951
[55]	validation-logloss:0.36825	validation-auc:0.96597	validation-aucpr:0.96953
{'best_iteration': '55', 'best_score': '0.9695323887159993'}
Trial 94, Fold 2: Log loss = 0.3682469709595792, Average precision = 0.9692905878067982, ROC-AUC = 0.9659652479361542, Elapsed Time = 1.2049114000001282 seconds
Trial 94, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 94, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[0]	validation-logloss:0.68261	validation-auc:0.94370	validation-aucpr:0.93423
[1]	validation-logloss:0.67270	validation-auc:0.95496	validation-aucpr:0.95408
[2]	validation-logloss:0.66175	validation-auc:0.96100	validation-aucpr:0.96347
[3]	validation-logloss:0.65249	validation-auc:0.96148	validation-aucpr:0.96356
[4]	validation-logloss:0.64201	validation-auc:0.96287	validation-aucpr:0.96673
[5]	validation-logloss:0.63347	validation-auc:0.96293	validation-aucpr:0.96672
[6]	validation-logloss:0.62390	validation-auc:0.96422	validation-aucpr:0.96796
[7]	validation-logloss:0.61428	validation-auc:0.96461	validation-aucpr:0.96834
[8]	validation-logloss:0.60650	validation-auc:0.96487	validation-aucpr:0.96877
[9]	validation-logloss:0.59873	validation-auc:0.96458	validation-aucpr:0.96841
[10]	validation-logloss:0.59016	validation-auc:0.96495	validation-aucpr:0.96889
[11]	validation-logloss:0.58160	validation-auc:0.96495	validation-aucpr:0.96891
[12]	validation-logloss:0.57443	validation-auc:0.96475	validation-aucpr:0.96869
[13]	validation-logloss:0.56776	validation-auc:0.96458	validation-aucpr:0.96852
[14]	validation-logloss:0.55981	validation-auc:0.96463	validation-aucpr:0.96858
[15]	validation-logloss:0.55199	validation-auc:0.96487	validation-aucpr:0.96912
[16]	validation-logloss:0.54431	validation-auc:0.96513	validation-aucpr:0.96939
[17]	validation-logloss:0.53742	validation-auc:0.96520	validation-aucpr:0.96980
[18]	validation-logloss:0.53170	validation-auc:0.96500	validation-aucpr:0.96963
[19]	validation-logloss:0.52455	validation-auc:0.96520	validation-aucpr:0.96978
[20]	validation-logloss:0.51806	validation-auc:0.96543	validation-aucpr:0.96996
[21]	validation-logloss:0.51271	validation-auc:0.96512	validation-aucpr:0.96967
[22]	validation-logloss:0.50720	validation-auc:0.96501	validation-aucpr:0.96955
[23]	validation-logloss:0.50168	validation-auc:0.96490	validation-aucpr:0.96945
[24]	validation-logloss:0.49634	validation-auc:0.96496	validation-aucpr:0.96946
[25]	validation-logloss:0.49109	validation-auc:0.96510	validation-aucpr:0.96962
[26]	validation-logloss:0.48608	validation-auc:0.96517	validation-aucpr:0.96971
[27]	validation-logloss:0.48041	validation-auc:0.96527	validation-aucpr:0.97023
[28]	validation-logloss:0.47460	validation-auc:0.96532	validation-aucpr:0.97025
[29]	validation-logloss:0.46980	validation-auc:0.96544	validation-aucpr:0.97031
[30]	validation-logloss:0.46521	validation-auc:0.96537	validation-aucpr:0.97020
[31]	validation-logloss:0.46058	validation-auc:0.96532	validation-aucpr:0.97016
[32]	validation-logloss:0.45538	validation-auc:0.96534	validation-aucpr:0.97018
[33]	validation-logloss:0.45009	validation-auc:0.96542	validation-aucpr:0.97026
[34]	validation-logloss:0.44511	validation-auc:0.96555	validation-aucpr:0.97041
[35]	validation-logloss:0.44020	validation-auc:0.96563	validation-aucpr:0.97051
[36]	validation-logloss:0.43544	validation-auc:0.96567	validation-aucpr:0.97057
[37]	validation-logloss:0.43154	validation-auc:0.96573	validation-aucpr:0.97062
[38]	validation-logloss:0.42789	validation-auc:0.96571	validation-aucpr:0.97064
[39]	validation-logloss:0.42405	validation-auc:0.96564	validation-aucpr:0.97056
[40]	validation-logloss:0.42061	validation-auc:0.96550	validation-aucpr:0.97046
[41]	validation-logloss:0.41616	validation-auc:0.96565	validation-aucpr:0.97061
[42]	validation-logloss:0.41180	validation-auc:0.96577	validation-aucpr:0.97073
[43]	validation-logloss:0.40752	validation-auc:0.96587	validation-aucpr:0.97080
[44]	validation-logloss:0.40319	validation-auc:0.96601	validation-aucpr:0.97091
[45]	validation-logloss:0.39978	validation-auc:0.96613	validation-aucpr:0.97098
[46]	validation-logloss:0.39575	validation-auc:0.96620	validation-aucpr:0.97105
[47]	validation-logloss:0.39274	validation-auc:0.96621	validation-aucpr:0.97103
[48]	validation-logloss:0.38890	validation-auc:0.96639	validation-aucpr:0.97120
[49]	validation-logloss:0.38515	validation-auc:0.96639	validation-aucpr:0.97122
[50]	validation-logloss:0.38228	validation-auc:0.96640	validation-aucpr:0.97121
[51]	validation-logloss:0.37940	validation-auc:0.96644	validation-aucpr:0.97122
[52]	validation-logloss:0.37584	validation-auc:0.96651	validation-aucpr:0.97130
[53]	validation-logloss:0.37301	validation-auc:0.96660	validation-aucpr:0.97135
[54]	validation-logloss:0.37023	validation-auc:0.96665	validation-aucpr:0.97139
[55]	validation-logloss:0.36678	validation-auc:0.96674	validation-aucpr:0.97149
{'best_iteration': '55', 'best_score': '0.9714851566138424'}
Trial 94, Fold 3: Log loss = 0.36677932299082194, Average precision = 0.971296260368586, ROC-AUC = 0.9667447131636988, Elapsed Time = 1.1741581999995105 seconds
Trial 94, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 94, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[0]	validation-logloss:0.68171	validation-auc:0.95216	validation-aucpr:0.94627
[1]	validation-logloss:0.67187	validation-auc:0.95852	validation-aucpr:0.96196
[2]	validation-logloss:0.66088	validation-auc:0.96030	validation-aucpr:0.96401
[3]	validation-logloss:0.65158	validation-auc:0.96063	validation-aucpr:0.96492
[4]	validation-logloss:0.64159	validation-auc:0.96117	validation-aucpr:0.96443
[5]	validation-logloss:0.63308	validation-auc:0.96155	validation-aucpr:0.96729
[6]	validation-logloss:0.62359	validation-auc:0.96292	validation-aucpr:0.96869
[7]	validation-logloss:0.61399	validation-auc:0.96356	validation-aucpr:0.96931
[8]	validation-logloss:0.60637	validation-auc:0.96324	validation-aucpr:0.96878
[9]	validation-logloss:0.59852	validation-auc:0.96321	validation-aucpr:0.96876
[10]	validation-logloss:0.59094	validation-auc:0.96308	validation-aucpr:0.96858
[11]	validation-logloss:0.58245	validation-auc:0.96351	validation-aucpr:0.96903
[12]	validation-logloss:0.57394	validation-auc:0.96372	validation-aucpr:0.96923
[13]	validation-logloss:0.56578	validation-auc:0.96376	validation-aucpr:0.96941
[14]	validation-logloss:0.55927	validation-auc:0.96367	validation-aucpr:0.96936
[15]	validation-logloss:0.55267	validation-auc:0.96361	validation-aucpr:0.96924
[16]	validation-logloss:0.54623	validation-auc:0.96368	validation-aucpr:0.96934
[17]	validation-logloss:0.53994	validation-auc:0.96367	validation-aucpr:0.96923
[18]	validation-logloss:0.53435	validation-auc:0.96329	validation-aucpr:0.96892
[19]	validation-logloss:0.52839	validation-auc:0.96327	validation-aucpr:0.96881
[20]	validation-logloss:0.52255	validation-auc:0.96318	validation-aucpr:0.96867
[21]	validation-logloss:0.51693	validation-auc:0.96342	validation-aucpr:0.96883
[22]	validation-logloss:0.51024	validation-auc:0.96354	validation-aucpr:0.96899
[23]	validation-logloss:0.50503	validation-auc:0.96349	validation-aucpr:0.96897
[24]	validation-logloss:0.49910	validation-auc:0.96356	validation-aucpr:0.96921
[25]	validation-logloss:0.49387	validation-auc:0.96358	validation-aucpr:0.96915
[26]	validation-logloss:0.48759	validation-auc:0.96391	validation-aucpr:0.96947
[27]	validation-logloss:0.48150	validation-auc:0.96407	validation-aucpr:0.96963
[28]	validation-logloss:0.47705	validation-auc:0.96400	validation-aucpr:0.96956
[29]	validation-logloss:0.47235	validation-auc:0.96399	validation-aucpr:0.96950
[30]	validation-logloss:0.46815	validation-auc:0.96370	validation-aucpr:0.96927
[31]	validation-logloss:0.46275	validation-auc:0.96391	validation-aucpr:0.96951
[32]	validation-logloss:0.45831	validation-auc:0.96394	validation-aucpr:0.96951
[33]	validation-logloss:0.45295	validation-auc:0.96408	validation-aucpr:0.96966
[34]	validation-logloss:0.44881	validation-auc:0.96413	validation-aucpr:0.96967
[35]	validation-logloss:0.44473	validation-auc:0.96417	validation-aucpr:0.96967
[36]	validation-logloss:0.44003	validation-auc:0.96431	validation-aucpr:0.96990
[37]	validation-logloss:0.43514	validation-auc:0.96443	validation-aucpr:0.97005
[38]	validation-logloss:0.43044	validation-auc:0.96455	validation-aucpr:0.97017
[39]	validation-logloss:0.42587	validation-auc:0.96470	validation-aucpr:0.97028
[40]	validation-logloss:0.42159	validation-auc:0.96477	validation-aucpr:0.97036
[41]	validation-logloss:0.41721	validation-auc:0.96486	validation-aucpr:0.97043
[42]	validation-logloss:0.41302	validation-auc:0.96480	validation-aucpr:0.97042
[43]	validation-logloss:0.40869	validation-auc:0.96483	validation-aucpr:0.97047
[44]	validation-logloss:0.40560	validation-auc:0.96486	validation-aucpr:0.97049
[45]	validation-logloss:0.40161	validation-auc:0.96501	validation-aucpr:0.97065
[46]	validation-logloss:0.39794	validation-auc:0.96490	validation-aucpr:0.97062
[47]	validation-logloss:0.39487	validation-auc:0.96482	validation-aucpr:0.97053
[48]	validation-logloss:0.39181	validation-auc:0.96475	validation-aucpr:0.97044
[49]	validation-logloss:0.38896	validation-auc:0.96471	validation-aucpr:0.97041
[50]	validation-logloss:0.38539	validation-auc:0.96470	validation-aucpr:0.97042
[51]	validation-logloss:0.38167	validation-auc:0.96480	validation-aucpr:0.97054
[52]	validation-logloss:0.37867	validation-auc:0.96486	validation-aucpr:0.97055
[53]	validation-logloss:0.37512	validation-auc:0.96503	validation-aucpr:0.97072
[54]	validation-logloss:0.37190	validation-auc:0.96505	validation-aucpr:0.97079
[55]	validation-logloss:0.36850	validation-auc:0.96516	validation-aucpr:0.97089
{'best_iteration': '55', 'best_score': '0.9708877542981628'}
Trial 94, Fold 4: Log loss = 0.3685043068161344, Average precision = 0.9707699630531544, ROC-AUC = 0.9651570670329119, Elapsed Time = 1.1587060000019846 seconds
Trial 94, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 94, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[0]	validation-logloss:0.68254	validation-auc:0.94775	validation-aucpr:0.94739
[1]	validation-logloss:0.67277	validation-auc:0.95156	validation-aucpr:0.95367
[2]	validation-logloss:0.66178	validation-auc:0.95803	validation-aucpr:0.96181
[3]	validation-logloss:0.65278	validation-auc:0.95746	validation-aucpr:0.96125
[4]	validation-logloss:0.64278	validation-auc:0.95835	validation-aucpr:0.96195
[5]	validation-logloss:0.63412	validation-auc:0.95922	validation-aucpr:0.96406
[6]	validation-logloss:0.62476	validation-auc:0.96033	validation-aucpr:0.96525
[7]	validation-logloss:0.61521	validation-auc:0.96062	validation-aucpr:0.96575
[8]	validation-logloss:0.60764	validation-auc:0.96039	validation-aucpr:0.96528
[9]	validation-logloss:0.59995	validation-auc:0.96039	validation-aucpr:0.96506
[10]	validation-logloss:0.59142	validation-auc:0.96095	validation-aucpr:0.96557
[11]	validation-logloss:0.58288	validation-auc:0.96130	validation-aucpr:0.96584
[12]	validation-logloss:0.57568	validation-auc:0.96161	validation-aucpr:0.96605
[13]	validation-logloss:0.56751	validation-auc:0.96177	validation-aucpr:0.96629
[14]	validation-logloss:0.56083	validation-auc:0.96189	validation-aucpr:0.96661
[15]	validation-logloss:0.55341	validation-auc:0.96192	validation-aucpr:0.96697
[16]	validation-logloss:0.54587	validation-auc:0.96192	validation-aucpr:0.96700
[17]	validation-logloss:0.53896	validation-auc:0.96196	validation-aucpr:0.96705
[18]	validation-logloss:0.53303	validation-auc:0.96192	validation-aucpr:0.96701
[19]	validation-logloss:0.52716	validation-auc:0.96195	validation-aucpr:0.96700
[20]	validation-logloss:0.52159	validation-auc:0.96190	validation-aucpr:0.96693
[21]	validation-logloss:0.51483	validation-auc:0.96202	validation-aucpr:0.96704
[22]	validation-logloss:0.50949	validation-auc:0.96192	validation-aucpr:0.96700
[23]	validation-logloss:0.50299	validation-auc:0.96202	validation-aucpr:0.96713
[24]	validation-logloss:0.49681	validation-auc:0.96206	validation-aucpr:0.96720
[25]	validation-logloss:0.49223	validation-auc:0.96178	validation-aucpr:0.96695
[26]	validation-logloss:0.48627	validation-auc:0.96191	validation-aucpr:0.96710
[27]	validation-logloss:0.48164	validation-auc:0.96189	validation-aucpr:0.96707
[28]	validation-logloss:0.47588	validation-auc:0.96191	validation-aucpr:0.96708
[29]	validation-logloss:0.47153	validation-auc:0.96177	validation-aucpr:0.96699
[30]	validation-logloss:0.46627	validation-auc:0.96177	validation-aucpr:0.96697
[31]	validation-logloss:0.46102	validation-auc:0.96194	validation-aucpr:0.96712
[32]	validation-logloss:0.45563	validation-auc:0.96207	validation-aucpr:0.96725
[33]	validation-logloss:0.45147	validation-auc:0.96203	validation-aucpr:0.96717
[34]	validation-logloss:0.44760	validation-auc:0.96206	validation-aucpr:0.96717
[35]	validation-logloss:0.44346	validation-auc:0.96207	validation-aucpr:0.96716
[36]	validation-logloss:0.43976	validation-auc:0.96206	validation-aucpr:0.96718
[37]	validation-logloss:0.43591	validation-auc:0.96210	validation-aucpr:0.96721
[38]	validation-logloss:0.43203	validation-auc:0.96223	validation-aucpr:0.96727
[39]	validation-logloss:0.42753	validation-auc:0.96232	validation-aucpr:0.96735
[40]	validation-logloss:0.42402	validation-auc:0.96234	validation-aucpr:0.96735
[41]	validation-logloss:0.42048	validation-auc:0.96227	validation-aucpr:0.96730
[42]	validation-logloss:0.41703	validation-auc:0.96234	validation-aucpr:0.96733
[43]	validation-logloss:0.41286	validation-auc:0.96244	validation-aucpr:0.96741
[44]	validation-logloss:0.40972	validation-auc:0.96234	validation-aucpr:0.96732
[45]	validation-logloss:0.40572	validation-auc:0.96250	validation-aucpr:0.96752
[46]	validation-logloss:0.40232	validation-auc:0.96249	validation-aucpr:0.96753
[47]	validation-logloss:0.39931	validation-auc:0.96245	validation-aucpr:0.96749
[48]	validation-logloss:0.39548	validation-auc:0.96249	validation-aucpr:0.96755
[49]	validation-logloss:0.39177	validation-auc:0.96256	validation-aucpr:0.96762
[50]	validation-logloss:0.38813	validation-auc:0.96270	validation-aucpr:0.96776
[51]	validation-logloss:0.38447	validation-auc:0.96287	validation-aucpr:0.96790
[52]	validation-logloss:0.38095	validation-auc:0.96296	validation-aucpr:0.96798
[53]	validation-logloss:0.37832	validation-auc:0.96300	validation-aucpr:0.96798
[54]	validation-logloss:0.37496	validation-auc:0.96308	validation-aucpr:0.96807
[55]	validation-logloss:0.37261	validation-auc:0.96304	validation-aucpr:0.96803
{'best_iteration': '54', 'best_score': '0.968069327592028'}
Trial 94, Fold 5: Log loss = 0.3726133849620998, Average precision = 0.967905009267924, ROC-AUC = 0.9630352823142523, Elapsed Time = 1.158533500001795 seconds
Optimization Progress:  95%|#########5| 95/100 [3:57:59<04:51, 58.20s/it]
Trial 95, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 95, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[0]	validation-logloss:0.66971	validation-auc:0.93383	validation-aucpr:0.90166
[1]	validation-logloss:0.64720	validation-auc:0.95771	validation-aucpr:0.95242
[2]	validation-logloss:0.62615	validation-auc:0.96304	validation-aucpr:0.96622
[3]	validation-logloss:0.60645	validation-auc:0.96451	validation-aucpr:0.96875
[4]	validation-logloss:0.58754	validation-auc:0.96530	validation-aucpr:0.96909
[5]	validation-logloss:0.56988	validation-auc:0.96587	validation-aucpr:0.96997
[6]	validation-logloss:0.55326	validation-auc:0.96596	validation-aucpr:0.97020
[7]	validation-logloss:0.53816	validation-auc:0.96587	validation-aucpr:0.96927
[8]	validation-logloss:0.52323	validation-auc:0.96686	validation-aucpr:0.97025
[9]	validation-logloss:0.50994	validation-auc:0.96737	validation-aucpr:0.97291
[10]	validation-logloss:0.49618	validation-auc:0.96782	validation-aucpr:0.97323
[11]	validation-logloss:0.48370	validation-auc:0.96760	validation-aucpr:0.97308
[12]	validation-logloss:0.47155	validation-auc:0.96753	validation-aucpr:0.97303
[13]	validation-logloss:0.46132	validation-auc:0.96767	validation-aucpr:0.97322
[14]	validation-logloss:0.45019	validation-auc:0.96793	validation-aucpr:0.97336
[15]	validation-logloss:0.43988	validation-auc:0.96801	validation-aucpr:0.97345
[16]	validation-logloss:0.42974	validation-auc:0.96806	validation-aucpr:0.97355
[17]	validation-logloss:0.42043	validation-auc:0.96814	validation-aucpr:0.97356
[18]	validation-logloss:0.41199	validation-auc:0.96843	validation-aucpr:0.97388
{'best_iteration': '18', 'best_score': '0.9738836899662131'}
Trial 95, Fold 1: Log loss = 0.41199033206300667, Average precision = 0.9738432506185263, ROC-AUC = 0.968429170072124, Elapsed Time = 1.558035199999722 seconds
Trial 95, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 95, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[0]	validation-logloss:0.67021	validation-auc:0.93318	validation-aucpr:0.90261
[1]	validation-logloss:0.64763	validation-auc:0.95870	validation-aucpr:0.95098
[2]	validation-logloss:0.62867	validation-auc:0.96013	validation-aucpr:0.96485
[3]	validation-logloss:0.60860	validation-auc:0.96347	validation-aucpr:0.96772
[4]	validation-logloss:0.59048	validation-auc:0.96454	validation-aucpr:0.96876
[5]	validation-logloss:0.57283	validation-auc:0.96522	validation-aucpr:0.96957
[6]	validation-logloss:0.55635	validation-auc:0.96588	validation-aucpr:0.96786
[7]	validation-logloss:0.54047	validation-auc:0.96648	validation-aucpr:0.96858
[8]	validation-logloss:0.52704	validation-auc:0.96655	validation-aucpr:0.96837
[9]	validation-logloss:0.51461	validation-auc:0.96694	validation-aucpr:0.96877
[10]	validation-logloss:0.50246	validation-auc:0.96691	validation-aucpr:0.97035
[11]	validation-logloss:0.49007	validation-auc:0.96708	validation-aucpr:0.97046
[12]	validation-logloss:0.47778	validation-auc:0.96763	validation-aucpr:0.97091
[13]	validation-logloss:0.46588	validation-auc:0.96786	validation-aucpr:0.96931
[14]	validation-logloss:0.45537	validation-auc:0.96822	validation-aucpr:0.97142
[15]	validation-logloss:0.44450	validation-auc:0.96842	validation-aucpr:0.97170
[16]	validation-logloss:0.43455	validation-auc:0.96907	validation-aucpr:0.97230
[17]	validation-logloss:0.42465	validation-auc:0.96941	validation-aucpr:0.97255
[18]	validation-logloss:0.41603	validation-auc:0.96964	validation-aucpr:0.97270
{'best_iteration': '18', 'best_score': '0.9727045585634997'}
Trial 95, Fold 2: Log loss = 0.4160298962320222, Average precision = 0.9725908240025432, ROC-AUC = 0.9696389859484585, Elapsed Time = 1.7594687999990128 seconds
Trial 95, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 95, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[0]	validation-logloss:0.67059	validation-auc:0.92465	validation-aucpr:0.87755
[1]	validation-logloss:0.64834	validation-auc:0.95652	validation-aucpr:0.94821
[2]	validation-logloss:0.62705	validation-auc:0.96279	validation-aucpr:0.96143
[3]	validation-logloss:0.60711	validation-auc:0.96491	validation-aucpr:0.96733
[4]	validation-logloss:0.58878	validation-auc:0.96567	validation-aucpr:0.97121
[5]	validation-logloss:0.57091	validation-auc:0.96607	validation-aucpr:0.97160
[6]	validation-logloss:0.55419	validation-auc:0.96724	validation-aucpr:0.97252
[7]	validation-logloss:0.53819	validation-auc:0.96767	validation-aucpr:0.97286
[8]	validation-logloss:0.52335	validation-auc:0.96806	validation-aucpr:0.97308
[9]	validation-logloss:0.50879	validation-auc:0.96848	validation-aucpr:0.97332
[10]	validation-logloss:0.49531	validation-auc:0.96870	validation-aucpr:0.97356
[11]	validation-logloss:0.48245	validation-auc:0.96891	validation-aucpr:0.97376
[12]	validation-logloss:0.47038	validation-auc:0.96865	validation-aucpr:0.97356
[13]	validation-logloss:0.45897	validation-auc:0.96903	validation-aucpr:0.97387
[14]	validation-logloss:0.44787	validation-auc:0.96908	validation-aucpr:0.97391
[15]	validation-logloss:0.43733	validation-auc:0.96935	validation-aucpr:0.97406
[16]	validation-logloss:0.42764	validation-auc:0.96970	validation-aucpr:0.97423
[17]	validation-logloss:0.41846	validation-auc:0.96952	validation-aucpr:0.97413
[18]	validation-logloss:0.40935	validation-auc:0.96984	validation-aucpr:0.97438
{'best_iteration': '18', 'best_score': '0.974380799626537'}
Trial 95, Fold 3: Log loss = 0.40934568100738855, Average precision = 0.9743398942778332, ROC-AUC = 0.9698392098698848, Elapsed Time = 1.8469833000017388 seconds
Trial 95, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 95, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[0]	validation-logloss:0.66999	validation-auc:0.93093	validation-aucpr:0.91191
[1]	validation-logloss:0.64894	validation-auc:0.95747	validation-aucpr:0.95670
[2]	validation-logloss:0.62772	validation-auc:0.96340	validation-aucpr:0.96424
[3]	validation-logloss:0.60792	validation-auc:0.96536	validation-aucpr:0.97088
[4]	validation-logloss:0.58997	validation-auc:0.96549	validation-aucpr:0.97109
[5]	validation-logloss:0.57242	validation-auc:0.96663	validation-aucpr:0.97201
[6]	validation-logloss:0.55560	validation-auc:0.96702	validation-aucpr:0.97243
[7]	validation-logloss:0.53998	validation-auc:0.96754	validation-aucpr:0.97284
[8]	validation-logloss:0.52548	validation-auc:0.96762	validation-aucpr:0.97291
[9]	validation-logloss:0.51100	validation-auc:0.96805	validation-aucpr:0.97322
[10]	validation-logloss:0.49800	validation-auc:0.96792	validation-aucpr:0.97315
[11]	validation-logloss:0.48483	validation-auc:0.96846	validation-aucpr:0.97360
[12]	validation-logloss:0.47264	validation-auc:0.96897	validation-aucpr:0.97391
[13]	validation-logloss:0.46065	validation-auc:0.96928	validation-aucpr:0.97415
[14]	validation-logloss:0.45103	validation-auc:0.96935	validation-aucpr:0.97417
[15]	validation-logloss:0.44033	validation-auc:0.96935	validation-aucpr:0.97423
[16]	validation-logloss:0.43028	validation-auc:0.96944	validation-aucpr:0.97433
[17]	validation-logloss:0.42070	validation-auc:0.96969	validation-aucpr:0.97452
[18]	validation-logloss:0.41148	validation-auc:0.96961	validation-aucpr:0.97450
{'best_iteration': '17', 'best_score': '0.974523158907407'}
Trial 95, Fold 4: Log loss = 0.4114788840127488, Average precision = 0.9744901295180513, ROC-AUC = 0.969611815108097, Elapsed Time = 1.8002224000010756 seconds
Trial 95, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 95, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[0]	validation-logloss:0.66984	validation-auc:0.92772	validation-aucpr:0.90006
[1]	validation-logloss:0.64844	validation-auc:0.95360	validation-aucpr:0.94183
[2]	validation-logloss:0.62801	validation-auc:0.95915	validation-aucpr:0.95796
[3]	validation-logloss:0.61014	validation-auc:0.96151	validation-aucpr:0.96392
[4]	validation-logloss:0.59133	validation-auc:0.96352	validation-aucpr:0.96524
[5]	validation-logloss:0.57388	validation-auc:0.96433	validation-aucpr:0.96541
[6]	validation-logloss:0.55786	validation-auc:0.96487	validation-aucpr:0.96603
[7]	validation-logloss:0.54292	validation-auc:0.96506	validation-aucpr:0.96654
[8]	validation-logloss:0.52797	validation-auc:0.96524	validation-aucpr:0.96763
[9]	validation-logloss:0.51360	validation-auc:0.96594	validation-aucpr:0.96824
[10]	validation-logloss:0.50009	validation-auc:0.96632	validation-aucpr:0.96853
[11]	validation-logloss:0.48888	validation-auc:0.96624	validation-aucpr:0.97111
[12]	validation-logloss:0.47679	validation-auc:0.96649	validation-aucpr:0.97128
[13]	validation-logloss:0.46527	validation-auc:0.96644	validation-aucpr:0.97130
[14]	validation-logloss:0.45418	validation-auc:0.96691	validation-aucpr:0.97171
[15]	validation-logloss:0.44343	validation-auc:0.96740	validation-aucpr:0.97206
[16]	validation-logloss:0.43343	validation-auc:0.96774	validation-aucpr:0.97232
[17]	validation-logloss:0.42382	validation-auc:0.96786	validation-aucpr:0.97262
[18]	validation-logloss:0.41446	validation-auc:0.96809	validation-aucpr:0.97280
{'best_iteration': '18', 'best_score': '0.9727953837135653'}
Trial 95, Fold 5: Log loss = 0.4144598869762319, Average precision = 0.9727891260068996, ROC-AUC = 0.9680885123460231, Elapsed Time = 1.8103469999987283 seconds
Optimization Progress:  96%|#########6| 96/100 [3:58:16<03:03, 45.79s/it]
Trial 96, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 96, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[21:57:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.62898	validation-auc:0.92845	validation-aucpr:0.86784
[21:57:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.57505	validation-auc:0.95687	validation-aucpr:0.93799
[21:57:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.52993	validation-auc:0.96239	validation-aucpr:0.95987
[21:57:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.49115	validation-auc:0.96512	validation-aucpr:0.97003
[21:57:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.45687	validation-auc:0.96635	validation-aucpr:0.97089
[21:57:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5]	validation-logloss:0.42798	validation-auc:0.96647	validation-aucpr:0.96934
[21:57:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6]	validation-logloss:0.40235	validation-auc:0.96705	validation-aucpr:0.97122
[21:57:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7]	validation-logloss:0.37945	validation-auc:0.96804	validation-aucpr:0.97221
[21:57:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8]	validation-logloss:0.36011	validation-auc:0.96831	validation-aucpr:0.97283
[21:57:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9]	validation-logloss:0.34476	validation-auc:0.96885	validation-aucpr:0.97334
[21:57:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10]	validation-logloss:0.32943	validation-auc:0.96865	validation-aucpr:0.97322
[21:57:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11]	validation-logloss:0.31572	validation-auc:0.96909	validation-aucpr:0.97378
[21:57:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12]	validation-logloss:0.30299	validation-auc:0.96932	validation-aucpr:0.97399
[21:57:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13]	validation-logloss:0.29170	validation-auc:0.96966	validation-aucpr:0.97423
[21:57:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14]	validation-logloss:0.28176	validation-auc:0.96966	validation-aucpr:0.97339
[21:57:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15]	validation-logloss:0.27283	validation-auc:0.97016	validation-aucpr:0.97369
[21:57:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16]	validation-logloss:0.26482	validation-auc:0.97009	validation-aucpr:0.97365
[21:57:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17]	validation-logloss:0.25797	validation-auc:0.96999	validation-aucpr:0.97359
[21:57:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18]	validation-logloss:0.25221	validation-auc:0.97009	validation-aucpr:0.97370
[21:57:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19]	validation-logloss:0.24652	validation-auc:0.97007	validation-aucpr:0.97402
[21:57:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20]	validation-logloss:0.24136	validation-auc:0.97014	validation-aucpr:0.97474
[21:57:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21]	validation-logloss:0.23666	validation-auc:0.97009	validation-aucpr:0.97473
[21:57:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22]	validation-logloss:0.23231	validation-auc:0.97018	validation-aucpr:0.97479
[21:57:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23]	validation-logloss:0.22895	validation-auc:0.97015	validation-aucpr:0.97475
[21:57:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24]	validation-logloss:0.22533	validation-auc:0.97037	validation-aucpr:0.97489
[21:57:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25]	validation-logloss:0.22221	validation-auc:0.97050	validation-aucpr:0.97497
[21:57:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26]	validation-logloss:0.21949	validation-auc:0.97068	validation-aucpr:0.97510
[21:57:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27]	validation-logloss:0.21704	validation-auc:0.97080	validation-aucpr:0.97522
[21:57:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28]	validation-logloss:0.21484	validation-auc:0.97089	validation-aucpr:0.97528
[21:57:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29]	validation-logloss:0.21329	validation-auc:0.97078	validation-aucpr:0.97520
[21:57:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30]	validation-logloss:0.21159	validation-auc:0.97083	validation-aucpr:0.97523
[21:57:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31]	validation-logloss:0.21008	validation-auc:0.97092	validation-aucpr:0.97533
[21:57:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32]	validation-logloss:0.20848	validation-auc:0.97097	validation-aucpr:0.97538
[21:57:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33]	validation-logloss:0.20707	validation-auc:0.97112	validation-aucpr:0.97549
[21:57:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34]	validation-logloss:0.20581	validation-auc:0.97122	validation-aucpr:0.97555
[21:57:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35]	validation-logloss:0.20488	validation-auc:0.97135	validation-aucpr:0.97565
[21:57:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36]	validation-logloss:0.20417	validation-auc:0.97134	validation-aucpr:0.97563
[21:57:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37]	validation-logloss:0.20362	validation-auc:0.97126	validation-aucpr:0.97556
[21:57:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38]	validation-logloss:0.20317	validation-auc:0.97126	validation-aucpr:0.97558
[21:57:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39]	validation-logloss:0.20304	validation-auc:0.97128	validation-aucpr:0.97555
[21:57:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40]	validation-logloss:0.20285	validation-auc:0.97125	validation-aucpr:0.97553
[21:57:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41]	validation-logloss:0.20225	validation-auc:0.97144	validation-aucpr:0.97573
[21:57:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42]	validation-logloss:0.20190	validation-auc:0.97153	validation-aucpr:0.97578
[21:57:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43]	validation-logloss:0.20163	validation-auc:0.97153	validation-aucpr:0.97576
[21:57:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44]	validation-logloss:0.20201	validation-auc:0.97151	validation-aucpr:0.97590
[21:57:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45]	validation-logloss:0.20150	validation-auc:0.97169	validation-aucpr:0.97592
[21:57:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46]	validation-logloss:0.20141	validation-auc:0.97170	validation-aucpr:0.97597
[21:57:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[47]	validation-logloss:0.20115	validation-auc:0.97188	validation-aucpr:0.97636
[21:57:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[48]	validation-logloss:0.20116	validation-auc:0.97195	validation-aucpr:0.97644
[21:57:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[49]	validation-logloss:0.20118	validation-auc:0.97204	validation-aucpr:0.97651
[21:57:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[50]	validation-logloss:0.20172	validation-auc:0.97192	validation-aucpr:0.97641
[21:57:34] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[51]	validation-logloss:0.20206	validation-auc:0.97195	validation-aucpr:0.97642
[21:57:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[52]	validation-logloss:0.20201	validation-auc:0.97208	validation-aucpr:0.97651
[21:57:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[53]	validation-logloss:0.20252	validation-auc:0.97204	validation-aucpr:0.97642
[21:57:35] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[54]	validation-logloss:0.20308	validation-auc:0.97207	validation-aucpr:0.97642
[21:57:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[55]	validation-logloss:0.20359	validation-auc:0.97204	validation-aucpr:0.97637
[21:57:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[56]	validation-logloss:0.20420	validation-auc:0.97201	validation-aucpr:0.97639
[21:57:36] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[57]	validation-logloss:0.20423	validation-auc:0.97213	validation-aucpr:0.97646
[21:57:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[58]	validation-logloss:0.20441	validation-auc:0.97222	validation-aucpr:0.97657
[21:57:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[59]	validation-logloss:0.20509	validation-auc:0.97218	validation-aucpr:0.97652
[21:57:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[60]	validation-logloss:0.20573	validation-auc:0.97213	validation-aucpr:0.97648
{'best_iteration': '58', 'best_score': '0.976570721942885'}
Trial 96, Fold 1: Log loss = 0.20572811828880266, Average precision = 0.9764804945313532, ROC-AUC = 0.9721327542860967, Elapsed Time = 27.369219000000157 seconds
Trial 96, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 96, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[21:57:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.62831	validation-auc:0.93587	validation-aucpr:0.89205
[21:57:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.57441	validation-auc:0.95918	validation-aucpr:0.95031
[21:57:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.52866	validation-auc:0.96432	validation-aucpr:0.96373
[21:57:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.49263	validation-auc:0.96662	validation-aucpr:0.97075
[21:57:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.46241	validation-auc:0.96662	validation-aucpr:0.97057
[21:57:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5]	validation-logloss:0.43262	validation-auc:0.96697	validation-aucpr:0.97100
[21:57:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6]	validation-logloss:0.40614	validation-auc:0.96731	validation-aucpr:0.97136
[21:57:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7]	validation-logloss:0.38442	validation-auc:0.96831	validation-aucpr:0.97204
[21:57:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8]	validation-logloss:0.36339	validation-auc:0.96896	validation-aucpr:0.97258
[21:57:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9]	validation-logloss:0.34686	validation-auc:0.96926	validation-aucpr:0.97276
[21:57:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10]	validation-logloss:0.33063	validation-auc:0.96945	validation-aucpr:0.97292
[21:57:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11]	validation-logloss:0.31610	validation-auc:0.96943	validation-aucpr:0.97303
[21:57:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12]	validation-logloss:0.30338	validation-auc:0.96930	validation-aucpr:0.97298
[21:57:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13]	validation-logloss:0.29096	validation-auc:0.97006	validation-aucpr:0.97358
[21:57:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14]	validation-logloss:0.28065	validation-auc:0.97018	validation-aucpr:0.97366
[21:57:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15]	validation-logloss:0.27226	validation-auc:0.97035	validation-aucpr:0.97372
[21:57:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16]	validation-logloss:0.26480	validation-auc:0.97053	validation-aucpr:0.97379
[21:57:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17]	validation-logloss:0.25711	validation-auc:0.97060	validation-aucpr:0.97385
[21:57:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18]	validation-logloss:0.25078	validation-auc:0.97074	validation-aucpr:0.97398
[21:57:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19]	validation-logloss:0.24541	validation-auc:0.97073	validation-aucpr:0.97394
[21:57:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20]	validation-logloss:0.23953	validation-auc:0.97084	validation-aucpr:0.97398
[21:57:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21]	validation-logloss:0.23407	validation-auc:0.97096	validation-aucpr:0.97404
[21:57:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22]	validation-logloss:0.22953	validation-auc:0.97085	validation-aucpr:0.97397
[21:57:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23]	validation-logloss:0.22547	validation-auc:0.97080	validation-aucpr:0.97390
[21:57:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24]	validation-logloss:0.22134	validation-auc:0.97101	validation-aucpr:0.97409
[21:57:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25]	validation-logloss:0.21862	validation-auc:0.97092	validation-aucpr:0.97405
[21:57:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26]	validation-logloss:0.21540	validation-auc:0.97110	validation-aucpr:0.97442
[21:57:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27]	validation-logloss:0.21330	validation-auc:0.97107	validation-aucpr:0.97439
[21:57:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28]	validation-logloss:0.21082	validation-auc:0.97105	validation-aucpr:0.97439
[21:57:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29]	validation-logloss:0.20811	validation-auc:0.97129	validation-aucpr:0.97457
[21:57:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30]	validation-logloss:0.20601	validation-auc:0.97126	validation-aucpr:0.97451
[21:57:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31]	validation-logloss:0.20387	validation-auc:0.97137	validation-aucpr:0.97463
[21:57:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32]	validation-logloss:0.20179	validation-auc:0.97161	validation-aucpr:0.97477
[21:57:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33]	validation-logloss:0.20015	validation-auc:0.97167	validation-aucpr:0.97478
[21:57:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34]	validation-logloss:0.19895	validation-auc:0.97179	validation-aucpr:0.97490
[21:57:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35]	validation-logloss:0.19818	validation-auc:0.97176	validation-aucpr:0.97485
[21:57:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36]	validation-logloss:0.19718	validation-auc:0.97179	validation-aucpr:0.97484
[21:57:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37]	validation-logloss:0.19581	validation-auc:0.97194	validation-aucpr:0.97496
[21:57:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38]	validation-logloss:0.19497	validation-auc:0.97199	validation-aucpr:0.97501
[21:57:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39]	validation-logloss:0.19419	validation-auc:0.97212	validation-aucpr:0.97505
[21:57:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40]	validation-logloss:0.19376	validation-auc:0.97200	validation-aucpr:0.97491
[21:57:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41]	validation-logloss:0.19364	validation-auc:0.97192	validation-aucpr:0.97486
[21:58:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42]	validation-logloss:0.19339	validation-auc:0.97192	validation-aucpr:0.97485
[21:58:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43]	validation-logloss:0.19278	validation-auc:0.97199	validation-aucpr:0.97488
[21:58:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44]	validation-logloss:0.19220	validation-auc:0.97210	validation-aucpr:0.97501
[21:58:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45]	validation-logloss:0.19166	validation-auc:0.97213	validation-aucpr:0.97504
[21:58:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46]	validation-logloss:0.19107	validation-auc:0.97227	validation-aucpr:0.97506
[21:58:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[47]	validation-logloss:0.19066	validation-auc:0.97237	validation-aucpr:0.97511
[21:58:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[48]	validation-logloss:0.19054	validation-auc:0.97244	validation-aucpr:0.97527
[21:58:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[49]	validation-logloss:0.19061	validation-auc:0.97227	validation-aucpr:0.97513
[21:58:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[50]	validation-logloss:0.19042	validation-auc:0.97234	validation-aucpr:0.97516
[21:58:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[51]	validation-logloss:0.19050	validation-auc:0.97231	validation-aucpr:0.97485
[21:58:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[52]	validation-logloss:0.19038	validation-auc:0.97233	validation-aucpr:0.97484
[21:58:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[53]	validation-logloss:0.19033	validation-auc:0.97233	validation-aucpr:0.97482
[21:58:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[54]	validation-logloss:0.19070	validation-auc:0.97230	validation-aucpr:0.97433
[21:58:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[55]	validation-logloss:0.19102	validation-auc:0.97228	validation-aucpr:0.97429
[21:58:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[56]	validation-logloss:0.19081	validation-auc:0.97232	validation-aucpr:0.97433
[21:58:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[57]	validation-logloss:0.19116	validation-auc:0.97240	validation-aucpr:0.97426
[21:58:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[58]	validation-logloss:0.19138	validation-auc:0.97238	validation-aucpr:0.97422
[21:58:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[59]	validation-logloss:0.19171	validation-auc:0.97244	validation-aucpr:0.97412
[21:58:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[60]	validation-logloss:0.19156	validation-auc:0.97249	validation-aucpr:0.97413
{'best_iteration': '48', 'best_score': '0.9752665860623102'}
Trial 96, Fold 2: Log loss = 0.19155986050609108, Average precision = 0.9742061023984951, ROC-AUC = 0.9724941205320521, Elapsed Time = 27.559297799998603 seconds
Trial 96, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 96, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[21:58:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.62854	validation-auc:0.93540	validation-aucpr:0.88474
[21:58:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.57500	validation-auc:0.95578	validation-aucpr:0.93764
[21:58:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.52904	validation-auc:0.96384	validation-aucpr:0.96444
[21:58:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.49038	validation-auc:0.96541	validation-aucpr:0.96817
[21:58:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.45652	validation-auc:0.96621	validation-aucpr:0.96876
[21:58:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5]	validation-logloss:0.42740	validation-auc:0.96648	validation-aucpr:0.96873
[21:58:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6]	validation-logloss:0.40194	validation-auc:0.96676	validation-aucpr:0.96886
[21:58:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7]	validation-logloss:0.38083	validation-auc:0.96736	validation-aucpr:0.96932
[21:58:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8]	validation-logloss:0.36028	validation-auc:0.96849	validation-aucpr:0.97213
[21:58:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9]	validation-logloss:0.34389	validation-auc:0.96909	validation-aucpr:0.97342
[21:58:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10]	validation-logloss:0.32781	validation-auc:0.96912	validation-aucpr:0.97350
[21:58:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11]	validation-logloss:0.31350	validation-auc:0.96944	validation-aucpr:0.97375
[21:58:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12]	validation-logloss:0.30032	validation-auc:0.96981	validation-aucpr:0.97406
[21:58:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13]	validation-logloss:0.29026	validation-auc:0.96994	validation-aucpr:0.97406
[21:58:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14]	validation-logloss:0.28002	validation-auc:0.97009	validation-aucpr:0.97423
[21:58:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15]	validation-logloss:0.27121	validation-auc:0.97002	validation-aucpr:0.97415
[21:58:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16]	validation-logloss:0.26270	validation-auc:0.97040	validation-aucpr:0.97438
[21:58:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17]	validation-logloss:0.25534	validation-auc:0.97050	validation-aucpr:0.97452
[21:58:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18]	validation-logloss:0.24836	validation-auc:0.97073	validation-aucpr:0.97474
[21:58:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19]	validation-logloss:0.24211	validation-auc:0.97080	validation-aucpr:0.97482
[21:58:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20]	validation-logloss:0.23651	validation-auc:0.97122	validation-aucpr:0.97512
[21:58:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21]	validation-logloss:0.23182	validation-auc:0.97106	validation-aucpr:0.97509
[21:58:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22]	validation-logloss:0.22748	validation-auc:0.97122	validation-aucpr:0.97522
[21:58:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23]	validation-logloss:0.22372	validation-auc:0.97140	validation-aucpr:0.97528
[21:58:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24]	validation-logloss:0.22066	validation-auc:0.97142	validation-aucpr:0.97528
[21:58:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25]	validation-logloss:0.21760	validation-auc:0.97133	validation-aucpr:0.97526
[21:58:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26]	validation-logloss:0.21453	validation-auc:0.97147	validation-aucpr:0.97535
[21:58:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27]	validation-logloss:0.21188	validation-auc:0.97150	validation-aucpr:0.97543
[21:58:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28]	validation-logloss:0.20981	validation-auc:0.97151	validation-aucpr:0.97541
[21:58:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29]	validation-logloss:0.20792	validation-auc:0.97158	validation-aucpr:0.97575
[21:58:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30]	validation-logloss:0.20574	validation-auc:0.97180	validation-aucpr:0.97591
[21:58:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31]	validation-logloss:0.20442	validation-auc:0.97167	validation-aucpr:0.97582
[21:58:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32]	validation-logloss:0.20297	validation-auc:0.97176	validation-aucpr:0.97588
[21:58:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33]	validation-logloss:0.20209	validation-auc:0.97167	validation-aucpr:0.97581
[21:58:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34]	validation-logloss:0.20057	validation-auc:0.97190	validation-aucpr:0.97604
[21:58:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35]	validation-logloss:0.19954	validation-auc:0.97202	validation-aucpr:0.97611
[21:58:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36]	validation-logloss:0.19862	validation-auc:0.97211	validation-aucpr:0.97612
[21:58:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37]	validation-logloss:0.19766	validation-auc:0.97230	validation-aucpr:0.97617
[21:58:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38]	validation-logloss:0.19688	validation-auc:0.97236	validation-aucpr:0.97618
[21:58:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39]	validation-logloss:0.19656	validation-auc:0.97238	validation-aucpr:0.97618
[21:58:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40]	validation-logloss:0.19588	validation-auc:0.97251	validation-aucpr:0.97624
[21:58:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41]	validation-logloss:0.19546	validation-auc:0.97249	validation-aucpr:0.97621
[21:58:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42]	validation-logloss:0.19532	validation-auc:0.97250	validation-aucpr:0.97622
[21:58:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43]	validation-logloss:0.19526	validation-auc:0.97249	validation-aucpr:0.97618
[21:58:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44]	validation-logloss:0.19488	validation-auc:0.97251	validation-aucpr:0.97613
[21:58:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45]	validation-logloss:0.19493	validation-auc:0.97243	validation-aucpr:0.97608
[21:58:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46]	validation-logloss:0.19438	validation-auc:0.97255	validation-aucpr:0.97609
[21:58:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[47]	validation-logloss:0.19498	validation-auc:0.97243	validation-aucpr:0.97598
[21:58:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[48]	validation-logloss:0.19492	validation-auc:0.97250	validation-aucpr:0.97599
[21:58:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[49]	validation-logloss:0.19502	validation-auc:0.97247	validation-aucpr:0.97593
[21:58:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[50]	validation-logloss:0.19527	validation-auc:0.97252	validation-aucpr:0.97612
[21:58:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[51]	validation-logloss:0.19560	validation-auc:0.97250	validation-aucpr:0.97613
[21:58:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[52]	validation-logloss:0.19595	validation-auc:0.97254	validation-aucpr:0.97608
[21:58:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[53]	validation-logloss:0.19647	validation-auc:0.97245	validation-aucpr:0.97596
[21:58:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[54]	validation-logloss:0.19678	validation-auc:0.97246	validation-aucpr:0.97599
[21:58:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[55]	validation-logloss:0.19660	validation-auc:0.97256	validation-aucpr:0.97606
[21:58:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[56]	validation-logloss:0.19673	validation-auc:0.97264	validation-aucpr:0.97612
[21:58:32] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[57]	validation-logloss:0.19687	validation-auc:0.97275	validation-aucpr:0.97621
[21:58:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[58]	validation-logloss:0.19717	validation-auc:0.97275	validation-aucpr:0.97613
[21:58:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[59]	validation-logloss:0.19796	validation-auc:0.97272	validation-aucpr:0.97626
[21:58:33] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[60]	validation-logloss:0.19811	validation-auc:0.97276	validation-aucpr:0.97623
{'best_iteration': '59', 'best_score': '0.9762626557997987'}
Trial 96, Fold 3: Log loss = 0.19811367177543784, Average precision = 0.9762333554119768, ROC-AUC = 0.972757263004305, Elapsed Time = 27.47824279999986 seconds
Trial 96, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 96, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[21:58:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.62844	validation-auc:0.93617	validation-aucpr:0.88997
[21:58:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.57530	validation-auc:0.96105	validation-aucpr:0.95438
[21:58:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.52950	validation-auc:0.96479	validation-aucpr:0.96434
[21:58:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.48986	validation-auc:0.96819	validation-aucpr:0.97292
[21:58:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.45614	validation-auc:0.96935	validation-aucpr:0.97402
[21:58:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5]	validation-logloss:0.42734	validation-auc:0.96886	validation-aucpr:0.97372
[21:58:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6]	validation-logloss:0.40166	validation-auc:0.96878	validation-aucpr:0.97374
[21:58:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7]	validation-logloss:0.37911	validation-auc:0.96889	validation-aucpr:0.97389
[21:58:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8]	validation-logloss:0.36122	validation-auc:0.96896	validation-aucpr:0.97394
[21:58:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9]	validation-logloss:0.34385	validation-auc:0.96920	validation-aucpr:0.97411
[21:58:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10]	validation-logloss:0.32770	validation-auc:0.96989	validation-aucpr:0.97454
[21:58:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11]	validation-logloss:0.31396	validation-auc:0.96993	validation-aucpr:0.97454
[21:58:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12]	validation-logloss:0.30194	validation-auc:0.97043	validation-aucpr:0.97492
[21:58:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13]	validation-logloss:0.29033	validation-auc:0.97090	validation-aucpr:0.97527
[21:58:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14]	validation-logloss:0.28015	validation-auc:0.97092	validation-aucpr:0.97528
[21:58:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15]	validation-logloss:0.27242	validation-auc:0.97066	validation-aucpr:0.97516
[21:58:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16]	validation-logloss:0.26367	validation-auc:0.97080	validation-aucpr:0.97531
[21:58:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17]	validation-logloss:0.25596	validation-auc:0.97106	validation-aucpr:0.97549
[21:58:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18]	validation-logloss:0.24916	validation-auc:0.97124	validation-aucpr:0.97560
[21:58:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19]	validation-logloss:0.24322	validation-auc:0.97136	validation-aucpr:0.97570
[21:58:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20]	validation-logloss:0.23784	validation-auc:0.97153	validation-aucpr:0.97582
[21:58:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21]	validation-logloss:0.23272	validation-auc:0.97171	validation-aucpr:0.97601
[21:58:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22]	validation-logloss:0.22845	validation-auc:0.97165	validation-aucpr:0.97600
[21:58:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23]	validation-logloss:0.22476	validation-auc:0.97162	validation-aucpr:0.97599
[21:58:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24]	validation-logloss:0.22102	validation-auc:0.97168	validation-aucpr:0.97606
[21:58:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25]	validation-logloss:0.21834	validation-auc:0.97150	validation-aucpr:0.97594
[21:58:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26]	validation-logloss:0.21518	validation-auc:0.97172	validation-aucpr:0.97607
[21:58:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27]	validation-logloss:0.21267	validation-auc:0.97174	validation-aucpr:0.97607
[21:58:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28]	validation-logloss:0.21011	validation-auc:0.97193	validation-aucpr:0.97618
[21:58:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29]	validation-logloss:0.20854	validation-auc:0.97174	validation-aucpr:0.97607
[21:58:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30]	validation-logloss:0.20717	validation-auc:0.97173	validation-aucpr:0.97605
[21:58:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31]	validation-logloss:0.20533	validation-auc:0.97175	validation-aucpr:0.97608
[21:58:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32]	validation-logloss:0.20430	validation-auc:0.97156	validation-aucpr:0.97595
[21:58:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33]	validation-logloss:0.20294	validation-auc:0.97159	validation-aucpr:0.97597
[21:58:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34]	validation-logloss:0.20175	validation-auc:0.97165	validation-aucpr:0.97602
[21:58:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35]	validation-logloss:0.20075	validation-auc:0.97173	validation-aucpr:0.97606
[21:58:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36]	validation-logloss:0.19985	validation-auc:0.97174	validation-aucpr:0.97606
[21:58:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37]	validation-logloss:0.19920	validation-auc:0.97178	validation-aucpr:0.97607
[21:58:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38]	validation-logloss:0.19827	validation-auc:0.97193	validation-aucpr:0.97618
[21:58:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39]	validation-logloss:0.19832	validation-auc:0.97170	validation-aucpr:0.97601
[21:58:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40]	validation-logloss:0.19779	validation-auc:0.97173	validation-aucpr:0.97603
[21:58:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41]	validation-logloss:0.19740	validation-auc:0.97178	validation-aucpr:0.97610
[21:58:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42]	validation-logloss:0.19753	validation-auc:0.97178	validation-aucpr:0.97606
[21:58:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43]	validation-logloss:0.19697	validation-auc:0.97188	validation-aucpr:0.97614
[21:58:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44]	validation-logloss:0.19634	validation-auc:0.97200	validation-aucpr:0.97623
[21:58:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45]	validation-logloss:0.19627	validation-auc:0.97203	validation-aucpr:0.97624
[21:58:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46]	validation-logloss:0.19603	validation-auc:0.97218	validation-aucpr:0.97634
[21:58:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[47]	validation-logloss:0.19614	validation-auc:0.97222	validation-aucpr:0.97638
[21:58:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[48]	validation-logloss:0.19644	validation-auc:0.97220	validation-aucpr:0.97637
[21:58:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[49]	validation-logloss:0.19654	validation-auc:0.97220	validation-aucpr:0.97635
[21:58:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[50]	validation-logloss:0.19653	validation-auc:0.97217	validation-aucpr:0.97632
[21:58:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[51]	validation-logloss:0.19668	validation-auc:0.97212	validation-aucpr:0.97629
[21:58:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[52]	validation-logloss:0.19677	validation-auc:0.97214	validation-aucpr:0.97631
[21:58:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[53]	validation-logloss:0.19705	validation-auc:0.97209	validation-aucpr:0.97627
[21:59:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[54]	validation-logloss:0.19765	validation-auc:0.97196	validation-aucpr:0.97618
[21:59:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[55]	validation-logloss:0.19801	validation-auc:0.97197	validation-aucpr:0.97619
[21:59:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[56]	validation-logloss:0.19842	validation-auc:0.97192	validation-aucpr:0.97617
[21:59:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[57]	validation-logloss:0.19873	validation-auc:0.97190	validation-aucpr:0.97617
[21:59:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[58]	validation-logloss:0.19920	validation-auc:0.97196	validation-aucpr:0.97618
[21:59:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[59]	validation-logloss:0.19961	validation-auc:0.97193	validation-aucpr:0.97615
[21:59:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[60]	validation-logloss:0.20026	validation-auc:0.97191	validation-aucpr:0.97612
{'best_iteration': '47', 'best_score': '0.9763758129206098'}
Trial 96, Fold 4: Log loss = 0.20025557918199285, Average precision = 0.9761245672747666, ROC-AUC = 0.9719141862843981, Elapsed Time = 27.80174470000202 seconds
Trial 96, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 96, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[21:59:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.62839	validation-auc:0.93519	validation-aucpr:0.89789
[21:59:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.57898	validation-auc:0.95845	validation-aucpr:0.95465
[21:59:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.53731	validation-auc:0.96030	validation-aucpr:0.95839
[21:59:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.50217	validation-auc:0.96126	validation-aucpr:0.96048
[21:59:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.46734	validation-auc:0.96368	validation-aucpr:0.96446
[21:59:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5]	validation-logloss:0.43656	validation-auc:0.96572	validation-aucpr:0.96920
[21:59:16] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6]	validation-logloss:0.40993	validation-auc:0.96666	validation-aucpr:0.97110
[21:59:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7]	validation-logloss:0.38940	validation-auc:0.96721	validation-aucpr:0.97134
[21:59:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8]	validation-logloss:0.36860	validation-auc:0.96778	validation-aucpr:0.97199
[21:59:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9]	validation-logloss:0.35279	validation-auc:0.96756	validation-aucpr:0.97173
[21:59:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10]	validation-logloss:0.33625	validation-auc:0.96782	validation-aucpr:0.97193
[21:59:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11]	validation-logloss:0.32411	validation-auc:0.96761	validation-aucpr:0.97159
[21:59:17] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12]	validation-logloss:0.31280	validation-auc:0.96772	validation-aucpr:0.97156
[21:59:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13]	validation-logloss:0.30078	validation-auc:0.96806	validation-aucpr:0.97185
[21:59:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14]	validation-logloss:0.28999	validation-auc:0.96819	validation-aucpr:0.97223
[21:59:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15]	validation-logloss:0.28010	validation-auc:0.96840	validation-aucpr:0.97247
[21:59:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16]	validation-logloss:0.27136	validation-auc:0.96857	validation-aucpr:0.97265
[21:59:18] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17]	validation-logloss:0.26364	validation-auc:0.96877	validation-aucpr:0.97228
[21:59:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18]	validation-logloss:0.25789	validation-auc:0.96888	validation-aucpr:0.97228
[21:59:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19]	validation-logloss:0.25197	validation-auc:0.96863	validation-aucpr:0.97214
[21:59:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20]	validation-logloss:0.24590	validation-auc:0.96918	validation-aucpr:0.97317
[21:59:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21]	validation-logloss:0.24144	validation-auc:0.96919	validation-aucpr:0.97317
[21:59:19] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22]	validation-logloss:0.23699	validation-auc:0.96914	validation-aucpr:0.97344
[21:59:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23]	validation-logloss:0.23361	validation-auc:0.96923	validation-aucpr:0.97343
[21:59:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24]	validation-logloss:0.22980	validation-auc:0.96946	validation-aucpr:0.97355
[21:59:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25]	validation-logloss:0.22724	validation-auc:0.96939	validation-aucpr:0.97343
[21:59:20] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26]	validation-logloss:0.22416	validation-auc:0.96956	validation-aucpr:0.97354
[21:59:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27]	validation-logloss:0.22090	validation-auc:0.96982	validation-aucpr:0.97376
[21:59:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28]	validation-logloss:0.21810	validation-auc:0.96998	validation-aucpr:0.97389
[21:59:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29]	validation-logloss:0.21607	validation-auc:0.97000	validation-aucpr:0.97390
[21:59:21] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30]	validation-logloss:0.21424	validation-auc:0.97009	validation-aucpr:0.97395
[21:59:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31]	validation-logloss:0.21229	validation-auc:0.97030	validation-aucpr:0.97412
[21:59:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32]	validation-logloss:0.21049	validation-auc:0.97051	validation-aucpr:0.97440
[21:59:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33]	validation-logloss:0.20905	validation-auc:0.97053	validation-aucpr:0.97448
[21:59:22] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34]	validation-logloss:0.20765	validation-auc:0.97069	validation-aucpr:0.97458
[21:59:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35]	validation-logloss:0.20723	validation-auc:0.97056	validation-aucpr:0.97443
[21:59:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36]	validation-logloss:0.20677	validation-auc:0.97055	validation-aucpr:0.97436
[21:59:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37]	validation-logloss:0.20554	validation-auc:0.97072	validation-aucpr:0.97451
[21:59:23] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38]	validation-logloss:0.20505	validation-auc:0.97066	validation-aucpr:0.97444
[21:59:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39]	validation-logloss:0.20449	validation-auc:0.97077	validation-aucpr:0.97470
[21:59:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40]	validation-logloss:0.20403	validation-auc:0.97081	validation-aucpr:0.97471
[21:59:24] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41]	validation-logloss:0.20342	validation-auc:0.97087	validation-aucpr:0.97480
[21:59:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42]	validation-logloss:0.20301	validation-auc:0.97095	validation-aucpr:0.97486
[21:59:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43]	validation-logloss:0.20243	validation-auc:0.97106	validation-aucpr:0.97490
[21:59:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44]	validation-logloss:0.20235	validation-auc:0.97109	validation-aucpr:0.97488
[21:59:25] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45]	validation-logloss:0.20210	validation-auc:0.97118	validation-aucpr:0.97494
[21:59:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46]	validation-logloss:0.20198	validation-auc:0.97124	validation-aucpr:0.97493
[21:59:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[47]	validation-logloss:0.20192	validation-auc:0.97125	validation-aucpr:0.97495
[21:59:26] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[48]	validation-logloss:0.20199	validation-auc:0.97121	validation-aucpr:0.97482
[21:59:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[49]	validation-logloss:0.20212	validation-auc:0.97118	validation-aucpr:0.97478
[21:59:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[50]	validation-logloss:0.20192	validation-auc:0.97131	validation-aucpr:0.97489
[21:59:27] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[51]	validation-logloss:0.20181	validation-auc:0.97134	validation-aucpr:0.97492
[21:59:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[52]	validation-logloss:0.20187	validation-auc:0.97145	validation-aucpr:0.97512
[21:59:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[53]	validation-logloss:0.20251	validation-auc:0.97136	validation-aucpr:0.97501
[21:59:28] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[54]	validation-logloss:0.20274	validation-auc:0.97143	validation-aucpr:0.97504
[21:59:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[55]	validation-logloss:0.20294	validation-auc:0.97145	validation-aucpr:0.97506
[21:59:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[56]	validation-logloss:0.20285	validation-auc:0.97156	validation-aucpr:0.97494
[21:59:29] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[57]	validation-logloss:0.20294	validation-auc:0.97162	validation-aucpr:0.97496
[21:59:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[58]	validation-logloss:0.20318	validation-auc:0.97163	validation-aucpr:0.97491
[21:59:30] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[59]	validation-logloss:0.20374	validation-auc:0.97159	validation-aucpr:0.97474
[21:59:31] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[60]	validation-logloss:0.20419	validation-auc:0.97162	validation-aucpr:0.97477
{'best_iteration': '52', 'best_score': '0.9751215601216484'}
Trial 96, Fold 5: Log loss = 0.2041886193925169, Average precision = 0.9747760514099615, ROC-AUC = 0.9716203853371236, Elapsed Time = 28.233958600001642 seconds
Optimization Progress:  97%|#########7| 97/100 [4:00:43<03:47, 76.00s/it]
Trial 97, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 97, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[21:59:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.64867	validation-auc:0.95670	validation-aucpr:0.96258
[21:59:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.60951	validation-auc:0.96225	validation-aucpr:0.96166
[21:59:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.57441	validation-auc:0.96412	validation-aucpr:0.96074
[21:59:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.54647	validation-auc:0.96526	validation-aucpr:0.96692
[21:59:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.51790	validation-auc:0.96650	validation-aucpr:0.96834
[21:59:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5]	validation-logloss:0.49202	validation-auc:0.96625	validation-aucpr:0.96698
[21:59:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6]	validation-logloss:0.47174	validation-auc:0.96654	validation-aucpr:0.97127
[21:59:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7]	validation-logloss:0.45055	validation-auc:0.96698	validation-aucpr:0.97175
[21:59:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8]	validation-logloss:0.43051	validation-auc:0.96747	validation-aucpr:0.97218
[21:59:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9]	validation-logloss:0.41292	validation-auc:0.96788	validation-aucpr:0.97253
[21:59:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10]	validation-logloss:0.39664	validation-auc:0.96821	validation-aucpr:0.97272
[21:59:37] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11]	validation-logloss:0.38360	validation-auc:0.96824	validation-aucpr:0.97275
[21:59:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12]	validation-logloss:0.36961	validation-auc:0.96868	validation-aucpr:0.97313
[21:59:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13]	validation-logloss:0.35663	validation-auc:0.96895	validation-aucpr:0.97330
[21:59:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14]	validation-logloss:0.34479	validation-auc:0.96915	validation-aucpr:0.97342
[21:59:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15]	validation-logloss:0.33570	validation-auc:0.96905	validation-aucpr:0.97362
[21:59:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16]	validation-logloss:0.32540	validation-auc:0.96930	validation-aucpr:0.97387
[21:59:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17]	validation-logloss:0.31689	validation-auc:0.96926	validation-aucpr:0.97379
[21:59:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18]	validation-logloss:0.30786	validation-auc:0.96959	validation-aucpr:0.97406
[21:59:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19]	validation-logloss:0.30076	validation-auc:0.96983	validation-aucpr:0.97422
[21:59:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20]	validation-logloss:0.29305	validation-auc:0.96999	validation-aucpr:0.97434
[21:59:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21]	validation-logloss:0.28598	validation-auc:0.97010	validation-aucpr:0.97441
[21:59:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22]	validation-logloss:0.28064	validation-auc:0.97001	validation-aucpr:0.97431
[21:59:38] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23]	validation-logloss:0.27448	validation-auc:0.97014	validation-aucpr:0.97441
[21:59:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24]	validation-logloss:0.26983	validation-auc:0.97001	validation-aucpr:0.97424
[21:59:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25]	validation-logloss:0.26474	validation-auc:0.96998	validation-aucpr:0.97419
[21:59:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26]	validation-logloss:0.25992	validation-auc:0.97000	validation-aucpr:0.97424
[21:59:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27]	validation-logloss:0.25517	validation-auc:0.97028	validation-aucpr:0.97445
[21:59:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28]	validation-logloss:0.25077	validation-auc:0.97034	validation-aucpr:0.97451
[21:59:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29]	validation-logloss:0.24746	validation-auc:0.97047	validation-aucpr:0.97460
[21:59:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30]	validation-logloss:0.24383	validation-auc:0.97054	validation-aucpr:0.97458
[21:59:39] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31]	validation-logloss:0.24001	validation-auc:0.97078	validation-aucpr:0.97480
[21:59:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32]	validation-logloss:0.23669	validation-auc:0.97088	validation-aucpr:0.97487
[21:59:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33]	validation-logloss:0.23377	validation-auc:0.97089	validation-aucpr:0.97489
[21:59:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34]	validation-logloss:0.23160	validation-auc:0.97095	validation-aucpr:0.97490
[21:59:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35]	validation-logloss:0.22891	validation-auc:0.97113	validation-aucpr:0.97503
[21:59:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36]	validation-logloss:0.22629	validation-auc:0.97121	validation-aucpr:0.97507
[21:59:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37]	validation-logloss:0.22400	validation-auc:0.97135	validation-aucpr:0.97516
[21:59:40] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38]	validation-logloss:0.22147	validation-auc:0.97161	validation-aucpr:0.97534
[21:59:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39]	validation-logloss:0.21930	validation-auc:0.97184	validation-aucpr:0.97598
[21:59:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40]	validation-logloss:0.21776	validation-auc:0.97188	validation-aucpr:0.97597
[21:59:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41]	validation-logloss:0.21644	validation-auc:0.97178	validation-aucpr:0.97587
[21:59:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42]	validation-logloss:0.21440	validation-auc:0.97195	validation-aucpr:0.97601
[21:59:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43]	validation-logloss:0.21275	validation-auc:0.97206	validation-aucpr:0.97609
[21:59:41] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44]	validation-logloss:0.21126	validation-auc:0.97210	validation-aucpr:0.97616
[21:59:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45]	validation-logloss:0.20968	validation-auc:0.97213	validation-aucpr:0.97617
[21:59:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46]	validation-logloss:0.20811	validation-auc:0.97223	validation-aucpr:0.97625
[21:59:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[47]	validation-logloss:0.20666	validation-auc:0.97239	validation-aucpr:0.97637
[21:59:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[48]	validation-logloss:0.20540	validation-auc:0.97243	validation-aucpr:0.97639
[21:59:42] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[49]	validation-logloss:0.20408	validation-auc:0.97258	validation-aucpr:0.97649
{'best_iteration': '49', 'best_score': '0.9764873601218161'}
Trial 97, Fold 1: Log loss = 0.20407838779868182, Average precision = 0.9764898931492989, ROC-AUC = 0.9725778535342673, Elapsed Time = 5.513618299999507 seconds
Trial 97, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 97, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[21:59:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.64888	validation-auc:0.95914	validation-aucpr:0.95894
[21:59:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.60917	validation-auc:0.96414	validation-aucpr:0.96715
[21:59:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.57409	validation-auc:0.96652	validation-aucpr:0.96728
[21:59:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.54269	validation-auc:0.96667	validation-aucpr:0.97064
[21:59:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.51440	validation-auc:0.96649	validation-aucpr:0.97065
[21:59:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5]	validation-logloss:0.48842	validation-auc:0.96777	validation-aucpr:0.97157
[21:59:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6]	validation-logloss:0.46542	validation-auc:0.96869	validation-aucpr:0.97244
[21:59:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7]	validation-logloss:0.44628	validation-auc:0.96878	validation-aucpr:0.97248
[21:59:43] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8]	validation-logloss:0.42715	validation-auc:0.96921	validation-aucpr:0.97277
[21:59:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9]	validation-logloss:0.40905	validation-auc:0.97001	validation-aucpr:0.97332
[21:59:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10]	validation-logloss:0.39284	validation-auc:0.97031	validation-aucpr:0.97344
[21:59:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11]	validation-logloss:0.37946	validation-auc:0.97019	validation-aucpr:0.97326
[21:59:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12]	validation-logloss:0.36566	validation-auc:0.97009	validation-aucpr:0.97321
[21:59:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13]	validation-logloss:0.35292	validation-auc:0.96986	validation-aucpr:0.97306
[21:59:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14]	validation-logloss:0.34118	validation-auc:0.96988	validation-aucpr:0.97308
[21:59:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15]	validation-logloss:0.33134	validation-auc:0.97014	validation-aucpr:0.97329
[21:59:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16]	validation-logloss:0.32116	validation-auc:0.97019	validation-aucpr:0.97324
[21:59:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17]	validation-logloss:0.31305	validation-auc:0.97037	validation-aucpr:0.97339
[21:59:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18]	validation-logloss:0.30537	validation-auc:0.97060	validation-aucpr:0.97359
[21:59:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19]	validation-logloss:0.29727	validation-auc:0.97052	validation-aucpr:0.97368
[21:59:44] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20]	validation-logloss:0.28970	validation-auc:0.97057	validation-aucpr:0.97368
[21:59:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21]	validation-logloss:0.28236	validation-auc:0.97089	validation-aucpr:0.97393
[21:59:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22]	validation-logloss:0.27561	validation-auc:0.97115	validation-aucpr:0.97411
[21:59:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23]	validation-logloss:0.26958	validation-auc:0.97099	validation-aucpr:0.97400
[21:59:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24]	validation-logloss:0.26461	validation-auc:0.97087	validation-aucpr:0.97393
[21:59:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25]	validation-logloss:0.25924	validation-auc:0.97110	validation-aucpr:0.97409
[21:59:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26]	validation-logloss:0.25420	validation-auc:0.97118	validation-aucpr:0.97415
[21:59:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27]	validation-logloss:0.24962	validation-auc:0.97113	validation-aucpr:0.97414
[21:59:45] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28]	validation-logloss:0.24538	validation-auc:0.97123	validation-aucpr:0.97422
[21:59:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29]	validation-logloss:0.24131	validation-auc:0.97120	validation-aucpr:0.97419
[21:59:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30]	validation-logloss:0.23747	validation-auc:0.97146	validation-aucpr:0.97439
[21:59:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31]	validation-logloss:0.23397	validation-auc:0.97163	validation-aucpr:0.97452
[21:59:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32]	validation-logloss:0.23092	validation-auc:0.97200	validation-aucpr:0.97477
[21:59:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33]	validation-logloss:0.22804	validation-auc:0.97195	validation-aucpr:0.97469
[21:59:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34]	validation-logloss:0.22526	validation-auc:0.97187	validation-aucpr:0.97463
[21:59:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35]	validation-logloss:0.22267	validation-auc:0.97214	validation-aucpr:0.97483
[21:59:46] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36]	validation-logloss:0.22086	validation-auc:0.97213	validation-aucpr:0.97519
[21:59:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37]	validation-logloss:0.21832	validation-auc:0.97220	validation-aucpr:0.97521
[21:59:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38]	validation-logloss:0.21595	validation-auc:0.97222	validation-aucpr:0.97522
[21:59:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39]	validation-logloss:0.21371	validation-auc:0.97232	validation-aucpr:0.97530
[21:59:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40]	validation-logloss:0.21160	validation-auc:0.97248	validation-aucpr:0.97540
[21:59:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41]	validation-logloss:0.20973	validation-auc:0.97247	validation-aucpr:0.97539
[21:59:47] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42]	validation-logloss:0.20802	validation-auc:0.97240	validation-aucpr:0.97533
[21:59:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43]	validation-logloss:0.20638	validation-auc:0.97250	validation-aucpr:0.97540
[21:59:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44]	validation-logloss:0.20478	validation-auc:0.97251	validation-aucpr:0.97538
[21:59:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45]	validation-logloss:0.20331	validation-auc:0.97258	validation-aucpr:0.97545
[21:59:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46]	validation-logloss:0.20178	validation-auc:0.97263	validation-aucpr:0.97548
[21:59:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[47]	validation-logloss:0.20034	validation-auc:0.97273	validation-aucpr:0.97559
[21:59:48] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[48]	validation-logloss:0.19908	validation-auc:0.97287	validation-aucpr:0.97565
[21:59:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[49]	validation-logloss:0.19801	validation-auc:0.97283	validation-aucpr:0.97561
{'best_iteration': '48', 'best_score': '0.975645861673028'}
Trial 97, Fold 2: Log loss = 0.19801219240028642, Average precision = 0.9755632458595235, ROC-AUC = 0.972825461821623, Elapsed Time = 5.8320394000002125 seconds
Trial 97, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 97, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[21:59:49] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.64838	validation-auc:0.96097	validation-aucpr:0.96283
[21:59:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.60883	validation-auc:0.96486	validation-aucpr:0.96844
[21:59:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.57343	validation-auc:0.96625	validation-aucpr:0.96988
[21:59:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.54120	validation-auc:0.96950	validation-aucpr:0.97233
[21:59:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.51260	validation-auc:0.96927	validation-aucpr:0.97209
[21:59:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5]	validation-logloss:0.48677	validation-auc:0.97006	validation-aucpr:0.97259
[21:59:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6]	validation-logloss:0.46385	validation-auc:0.96987	validation-aucpr:0.97202
[21:59:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7]	validation-logloss:0.44239	validation-auc:0.97010	validation-aucpr:0.97299
[21:59:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8]	validation-logloss:0.42575	validation-auc:0.97042	validation-aucpr:0.97321
[21:59:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9]	validation-logloss:0.40816	validation-auc:0.97024	validation-aucpr:0.97312
[21:59:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10]	validation-logloss:0.39209	validation-auc:0.97014	validation-aucpr:0.97305
[21:59:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11]	validation-logloss:0.37705	validation-auc:0.97041	validation-aucpr:0.97322
[21:59:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12]	validation-logloss:0.36479	validation-auc:0.97031	validation-aucpr:0.97326
[21:59:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13]	validation-logloss:0.35187	validation-auc:0.97049	validation-aucpr:0.97340
[21:59:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14]	validation-logloss:0.34143	validation-auc:0.97043	validation-aucpr:0.97342
[21:59:50] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15]	validation-logloss:0.33037	validation-auc:0.97067	validation-aucpr:0.97361
[21:59:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16]	validation-logloss:0.32055	validation-auc:0.97066	validation-aucpr:0.97378
[21:59:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17]	validation-logloss:0.31226	validation-auc:0.97086	validation-aucpr:0.97393
[21:59:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18]	validation-logloss:0.30338	validation-auc:0.97095	validation-aucpr:0.97401
[21:59:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19]	validation-logloss:0.29539	validation-auc:0.97060	validation-aucpr:0.97273
[21:59:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20]	validation-logloss:0.28877	validation-auc:0.97077	validation-aucpr:0.97321
[21:59:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21]	validation-logloss:0.28252	validation-auc:0.97102	validation-aucpr:0.97336
[21:59:51] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22]	validation-logloss:0.27570	validation-auc:0.97129	validation-aucpr:0.97359
[21:59:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23]	validation-logloss:0.26972	validation-auc:0.97145	validation-aucpr:0.97550
[21:59:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24]	validation-logloss:0.26388	validation-auc:0.97141	validation-aucpr:0.97547
[21:59:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25]	validation-logloss:0.25876	validation-auc:0.97140	validation-aucpr:0.97546
[21:59:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26]	validation-logloss:0.25394	validation-auc:0.97148	validation-aucpr:0.97552
[21:59:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27]	validation-logloss:0.25000	validation-auc:0.97155	validation-aucpr:0.97563
[21:59:52] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28]	validation-logloss:0.24648	validation-auc:0.97162	validation-aucpr:0.97566
[21:59:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29]	validation-logloss:0.24217	validation-auc:0.97176	validation-aucpr:0.97573
[21:59:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30]	validation-logloss:0.23862	validation-auc:0.97199	validation-aucpr:0.97588
[21:59:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31]	validation-logloss:0.23512	validation-auc:0.97202	validation-aucpr:0.97590
[21:59:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32]	validation-logloss:0.23244	validation-auc:0.97208	validation-aucpr:0.97585
[21:59:53] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33]	validation-logloss:0.22980	validation-auc:0.97205	validation-aucpr:0.97579
[21:59:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34]	validation-logloss:0.22733	validation-auc:0.97208	validation-aucpr:0.97577
[21:59:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35]	validation-logloss:0.22464	validation-auc:0.97212	validation-aucpr:0.97579
[21:59:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36]	validation-logloss:0.22217	validation-auc:0.97205	validation-aucpr:0.97571
[21:59:54] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37]	validation-logloss:0.21975	validation-auc:0.97214	validation-aucpr:0.97577
[21:59:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38]	validation-logloss:0.21761	validation-auc:0.97212	validation-aucpr:0.97576
[21:59:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39]	validation-logloss:0.21561	validation-auc:0.97215	validation-aucpr:0.97578
[21:59:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40]	validation-logloss:0.21386	validation-auc:0.97230	validation-aucpr:0.97606
[21:59:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41]	validation-logloss:0.21162	validation-auc:0.97247	validation-aucpr:0.97621
[21:59:55] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42]	validation-logloss:0.20973	validation-auc:0.97258	validation-aucpr:0.97636
[21:59:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43]	validation-logloss:0.20819	validation-auc:0.97256	validation-aucpr:0.97632
[21:59:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44]	validation-logloss:0.20656	validation-auc:0.97265	validation-aucpr:0.97640
[21:59:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45]	validation-logloss:0.20502	validation-auc:0.97272	validation-aucpr:0.97646
[21:59:56] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46]	validation-logloss:0.20365	validation-auc:0.97272	validation-aucpr:0.97645
[21:59:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[47]	validation-logloss:0.20246	validation-auc:0.97283	validation-aucpr:0.97653
[21:59:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[48]	validation-logloss:0.20146	validation-auc:0.97279	validation-aucpr:0.97650
[21:59:57] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[49]	validation-logloss:0.20023	validation-auc:0.97291	validation-aucpr:0.97657
{'best_iteration': '49', 'best_score': '0.9765698410238164'}
Trial 97, Fold 3: Log loss = 0.20022721945089825, Average precision = 0.9765723226286877, ROC-AUC = 0.9729139930512083, Elapsed Time = 7.785095100000035 seconds
Trial 97, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 97, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[21:59:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.64852	validation-auc:0.95775	validation-aucpr:0.95917
[21:59:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.60866	validation-auc:0.96676	validation-aucpr:0.96997
[21:59:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.57367	validation-auc:0.96795	validation-aucpr:0.97094
[21:59:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.54182	validation-auc:0.96924	validation-aucpr:0.97401
[21:59:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.51680	validation-auc:0.96821	validation-aucpr:0.97303
[21:59:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5]	validation-logloss:0.49102	validation-auc:0.96826	validation-aucpr:0.97310
[21:59:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6]	validation-logloss:0.46785	validation-auc:0.96834	validation-aucpr:0.97341
[21:59:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7]	validation-logloss:0.44666	validation-auc:0.96850	validation-aucpr:0.97370
[21:59:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8]	validation-logloss:0.42706	validation-auc:0.96885	validation-aucpr:0.97398
[21:59:58] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9]	validation-logloss:0.40939	validation-auc:0.96900	validation-aucpr:0.97412
[21:59:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10]	validation-logloss:0.39326	validation-auc:0.96917	validation-aucpr:0.97425
[21:59:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11]	validation-logloss:0.37855	validation-auc:0.96917	validation-aucpr:0.97427
[21:59:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12]	validation-logloss:0.36467	validation-auc:0.96949	validation-aucpr:0.97453
[21:59:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13]	validation-logloss:0.35205	validation-auc:0.96994	validation-aucpr:0.97487
[21:59:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14]	validation-logloss:0.34049	validation-auc:0.97003	validation-aucpr:0.97490
[21:59:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15]	validation-logloss:0.33047	validation-auc:0.97035	validation-aucpr:0.97515
[21:59:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16]	validation-logloss:0.32075	validation-auc:0.97027	validation-aucpr:0.97514
[21:59:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17]	validation-logloss:0.31266	validation-auc:0.97038	validation-aucpr:0.97526
[21:59:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18]	validation-logloss:0.30524	validation-auc:0.97050	validation-aucpr:0.97536
[21:59:59] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19]	validation-logloss:0.29699	validation-auc:0.97069	validation-aucpr:0.97549
[22:00:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20]	validation-logloss:0.28937	validation-auc:0.97075	validation-aucpr:0.97552
[22:00:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21]	validation-logloss:0.28228	validation-auc:0.97089	validation-aucpr:0.97563
[22:00:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22]	validation-logloss:0.27684	validation-auc:0.97061	validation-aucpr:0.97541
[22:00:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23]	validation-logloss:0.27060	validation-auc:0.97059	validation-aucpr:0.97538
[22:00:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24]	validation-logloss:0.26491	validation-auc:0.97063	validation-aucpr:0.97538
[22:00:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25]	validation-logloss:0.25937	validation-auc:0.97075	validation-aucpr:0.97548
[22:00:00] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26]	validation-logloss:0.25437	validation-auc:0.97090	validation-aucpr:0.97558
[22:00:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27]	validation-logloss:0.24977	validation-auc:0.97098	validation-aucpr:0.97563
[22:00:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28]	validation-logloss:0.24538	validation-auc:0.97092	validation-aucpr:0.97561
[22:00:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29]	validation-logloss:0.24167	validation-auc:0.97087	validation-aucpr:0.97555
[22:00:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30]	validation-logloss:0.23820	validation-auc:0.97075	validation-aucpr:0.97545
[22:00:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31]	validation-logloss:0.23533	validation-auc:0.97054	validation-aucpr:0.97529
[22:00:01] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32]	validation-logloss:0.23236	validation-auc:0.97047	validation-aucpr:0.97526
[22:00:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33]	validation-logloss:0.22965	validation-auc:0.97046	validation-aucpr:0.97525
[22:00:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34]	validation-logloss:0.22683	validation-auc:0.97052	validation-aucpr:0.97528
[22:00:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35]	validation-logloss:0.22451	validation-auc:0.97066	validation-aucpr:0.97537
[22:00:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36]	validation-logloss:0.22199	validation-auc:0.97082	validation-aucpr:0.97547
[22:00:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37]	validation-logloss:0.21962	validation-auc:0.97099	validation-aucpr:0.97560
[22:00:02] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38]	validation-logloss:0.21745	validation-auc:0.97103	validation-aucpr:0.97563
[22:00:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39]	validation-logloss:0.21588	validation-auc:0.97105	validation-aucpr:0.97565
[22:00:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40]	validation-logloss:0.21385	validation-auc:0.97119	validation-aucpr:0.97574
[22:00:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41]	validation-logloss:0.21231	validation-auc:0.97105	validation-aucpr:0.97563
[22:00:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42]	validation-logloss:0.21059	validation-auc:0.97114	validation-aucpr:0.97570
[22:00:03] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43]	validation-logloss:0.20902	validation-auc:0.97126	validation-aucpr:0.97579
[22:00:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44]	validation-logloss:0.20771	validation-auc:0.97114	validation-aucpr:0.97570
[22:00:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45]	validation-logloss:0.20642	validation-auc:0.97124	validation-aucpr:0.97579
[22:00:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46]	validation-logloss:0.20477	validation-auc:0.97150	validation-aucpr:0.97596
[22:00:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[47]	validation-logloss:0.20348	validation-auc:0.97154	validation-aucpr:0.97599
[22:00:04] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[48]	validation-logloss:0.20223	validation-auc:0.97162	validation-aucpr:0.97604
[22:00:05] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[49]	validation-logloss:0.20124	validation-auc:0.97161	validation-aucpr:0.97604
{'best_iteration': '48', 'best_score': '0.9760388955289463'}
Trial 97, Fold 4: Log loss = 0.20123741217815116, Average precision = 0.9760271817156592, ROC-AUC = 0.9716141232167597, Elapsed Time = 7.109914699998626 seconds
Trial 97, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 97, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[22:00:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[0]	validation-logloss:0.64889	validation-auc:0.95644	validation-aucpr:0.96122
[22:00:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[1]	validation-logloss:0.60933	validation-auc:0.96394	validation-aucpr:0.96845
[22:00:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[2]	validation-logloss:0.57441	validation-auc:0.96582	validation-aucpr:0.97007
[22:00:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[3]	validation-logloss:0.54731	validation-auc:0.96355	validation-aucpr:0.96828
[22:00:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[4]	validation-logloss:0.51876	validation-auc:0.96506	validation-aucpr:0.96967
[22:00:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[5]	validation-logloss:0.49593	validation-auc:0.96520	validation-aucpr:0.96963
[22:00:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[6]	validation-logloss:0.47211	validation-auc:0.96584	validation-aucpr:0.97025
[22:00:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[7]	validation-logloss:0.45092	validation-auc:0.96602	validation-aucpr:0.97038
[22:00:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[8]	validation-logloss:0.43155	validation-auc:0.96675	validation-aucpr:0.97088
[22:00:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[9]	validation-logloss:0.41429	validation-auc:0.96716	validation-aucpr:0.97126
[22:00:06] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[10]	validation-logloss:0.39916	validation-auc:0.96746	validation-aucpr:0.97141
[22:00:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[11]	validation-logloss:0.38598	validation-auc:0.96724	validation-aucpr:0.97111
[22:00:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[12]	validation-logloss:0.37172	validation-auc:0.96784	validation-aucpr:0.97166
[22:00:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[13]	validation-logloss:0.35967	validation-auc:0.96785	validation-aucpr:0.97176
[22:00:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[14]	validation-logloss:0.34969	validation-auc:0.96773	validation-aucpr:0.97161
[22:00:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[15]	validation-logloss:0.33863	validation-auc:0.96797	validation-aucpr:0.97181
[22:00:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[16]	validation-logloss:0.32863	validation-auc:0.96818	validation-aucpr:0.97197
[22:00:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[17]	validation-logloss:0.32070	validation-auc:0.96803	validation-aucpr:0.97179
[22:00:07] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[18]	validation-logloss:0.31323	validation-auc:0.96804	validation-aucpr:0.97171
[22:00:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[19]	validation-logloss:0.30485	validation-auc:0.96831	validation-aucpr:0.97193
[22:00:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[20]	validation-logloss:0.29723	validation-auc:0.96871	validation-aucpr:0.97225
[22:00:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[21]	validation-logloss:0.29003	validation-auc:0.96888	validation-aucpr:0.97241
[22:00:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[22]	validation-logloss:0.28468	validation-auc:0.96872	validation-aucpr:0.97229
[22:00:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[23]	validation-logloss:0.27832	validation-auc:0.96904	validation-aucpr:0.97262
[22:00:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[24]	validation-logloss:0.27266	validation-auc:0.96907	validation-aucpr:0.97264
[22:00:08] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[25]	validation-logloss:0.26737	validation-auc:0.96925	validation-aucpr:0.97282
[22:00:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[26]	validation-logloss:0.26238	validation-auc:0.96937	validation-aucpr:0.97291
[22:00:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[27]	validation-logloss:0.25755	validation-auc:0.96961	validation-aucpr:0.97311
[22:00:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[28]	validation-logloss:0.25348	validation-auc:0.96985	validation-aucpr:0.97323
[22:00:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[29]	validation-logloss:0.24905	validation-auc:0.97013	validation-aucpr:0.97349
[22:00:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[30]	validation-logloss:0.24522	validation-auc:0.97025	validation-aucpr:0.97363
[22:00:09] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[31]	validation-logloss:0.24230	validation-auc:0.97016	validation-aucpr:0.97353
[22:00:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[32]	validation-logloss:0.23978	validation-auc:0.97010	validation-aucpr:0.97347
[22:00:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[33]	validation-logloss:0.23681	validation-auc:0.97008	validation-aucpr:0.97343
[22:00:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[34]	validation-logloss:0.23389	validation-auc:0.97015	validation-aucpr:0.97349
[22:00:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[35]	validation-logloss:0.23120	validation-auc:0.97017	validation-aucpr:0.97349
[22:00:10] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[36]	validation-logloss:0.22894	validation-auc:0.97026	validation-aucpr:0.97341
[22:00:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[37]	validation-logloss:0.22674	validation-auc:0.97026	validation-aucpr:0.97341
[22:00:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[38]	validation-logloss:0.22480	validation-auc:0.97037	validation-aucpr:0.97352
[22:00:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[39]	validation-logloss:0.22316	validation-auc:0.97028	validation-aucpr:0.97341
[22:00:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[40]	validation-logloss:0.22100	validation-auc:0.97046	validation-aucpr:0.97361
[22:00:11] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[41]	validation-logloss:0.21895	validation-auc:0.97067	validation-aucpr:0.97409
[22:00:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[42]	validation-logloss:0.21693	validation-auc:0.97090	validation-aucpr:0.97427
[22:00:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[43]	validation-logloss:0.21514	validation-auc:0.97095	validation-aucpr:0.97429
[22:00:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[44]	validation-logloss:0.21363	validation-auc:0.97112	validation-aucpr:0.97445
[22:00:12] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[45]	validation-logloss:0.21242	validation-auc:0.97120	validation-aucpr:0.97451
[22:00:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[46]	validation-logloss:0.21067	validation-auc:0.97142	validation-aucpr:0.97469
[22:00:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[47]	validation-logloss:0.20927	validation-auc:0.97149	validation-aucpr:0.97478
[22:00:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[48]	validation-logloss:0.20784	validation-auc:0.97162	validation-aucpr:0.97485
[22:00:13] INFO: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\gbm\gbtree.cc:887: drop 0 trees, weight = 1
[49]	validation-logloss:0.20652	validation-auc:0.97173	validation-aucpr:0.97498
{'best_iteration': '49', 'best_score': '0.9749812712453916'}
Trial 97, Fold 5: Log loss = 0.20652109625057113, Average precision = 0.9749294362547176, ROC-AUC = 0.9717331995615258, Elapsed Time = 7.842935299999226 seconds
Optimization Progress:  98%|#########8| 98/100 [4:01:25<02:12, 66.03s/it]
Trial 98, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 98, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[0]	validation-logloss:0.66884	validation-auc:0.94051	validation-aucpr:0.94034
[1]	validation-logloss:0.64799	validation-auc:0.95079	validation-aucpr:0.95078
[2]	validation-logloss:0.62482	validation-auc:0.95751	validation-aucpr:0.95860
[3]	validation-logloss:0.60614	validation-auc:0.95857	validation-aucpr:0.96514
[4]	validation-logloss:0.58710	validation-auc:0.95926	validation-aucpr:0.96610
[5]	validation-logloss:0.57165	validation-auc:0.96036	validation-aucpr:0.96675
[6]	validation-logloss:0.55371	validation-auc:0.96162	validation-aucpr:0.96842
[7]	validation-logloss:0.53786	validation-auc:0.96269	validation-aucpr:0.96927
[8]	validation-logloss:0.52480	validation-auc:0.96279	validation-aucpr:0.96925
[9]	validation-logloss:0.50960	validation-auc:0.96317	validation-aucpr:0.96967
[10]	validation-logloss:0.49487	validation-auc:0.96389	validation-aucpr:0.97028
[11]	validation-logloss:0.48348	validation-auc:0.96459	validation-aucpr:0.97087
[12]	validation-logloss:0.47409	validation-auc:0.96409	validation-aucpr:0.97044
[13]	validation-logloss:0.46168	validation-auc:0.96412	validation-aucpr:0.97067
[14]	validation-logloss:0.45211	validation-auc:0.96373	validation-aucpr:0.97028
[15]	validation-logloss:0.44119	validation-auc:0.96388	validation-aucpr:0.97045
[16]	validation-logloss:0.43201	validation-auc:0.96395	validation-aucpr:0.97052
[17]	validation-logloss:0.42372	validation-auc:0.96375	validation-aucpr:0.97036
[18]	validation-logloss:0.41592	validation-auc:0.96355	validation-aucpr:0.97012
[19]	validation-logloss:0.40686	validation-auc:0.96354	validation-aucpr:0.97013
[20]	validation-logloss:0.39940	validation-auc:0.96360	validation-aucpr:0.97009
[21]	validation-logloss:0.39086	validation-auc:0.96375	validation-aucpr:0.97029
[22]	validation-logloss:0.38466	validation-auc:0.96361	validation-aucpr:0.97019
[23]	validation-logloss:0.37694	validation-auc:0.96374	validation-aucpr:0.97036
[24]	validation-logloss:0.37103	validation-auc:0.96374	validation-aucpr:0.97031
[25]	validation-logloss:0.36507	validation-auc:0.96399	validation-aucpr:0.97047
[26]	validation-logloss:0.35950	validation-auc:0.96379	validation-aucpr:0.97037
[27]	validation-logloss:0.35412	validation-auc:0.96366	validation-aucpr:0.97028
[28]	validation-logloss:0.34911	validation-auc:0.96360	validation-aucpr:0.97020
[29]	validation-logloss:0.34321	validation-auc:0.96368	validation-aucpr:0.97027
[30]	validation-logloss:0.33869	validation-auc:0.96365	validation-aucpr:0.97021
[31]	validation-logloss:0.33474	validation-auc:0.96387	validation-aucpr:0.97029
[32]	validation-logloss:0.33052	validation-auc:0.96389	validation-aucpr:0.97029
[33]	validation-logloss:0.32658	validation-auc:0.96375	validation-aucpr:0.97010
[34]	validation-logloss:0.32243	validation-auc:0.96376	validation-aucpr:0.97013
[35]	validation-logloss:0.31939	validation-auc:0.96354	validation-aucpr:0.96992
[36]	validation-logloss:0.31576	validation-auc:0.96353	validation-aucpr:0.96985
[37]	validation-logloss:0.31113	validation-auc:0.96393	validation-aucpr:0.97022
[38]	validation-logloss:0.30638	validation-auc:0.96428	validation-aucpr:0.97051
[39]	validation-logloss:0.30234	validation-auc:0.96429	validation-aucpr:0.97052
[40]	validation-logloss:0.29918	validation-auc:0.96440	validation-aucpr:0.97055
[41]	validation-logloss:0.29528	validation-auc:0.96468	validation-aucpr:0.97081
[42]	validation-logloss:0.29164	validation-auc:0.96483	validation-aucpr:0.97096
[43]	validation-logloss:0.28894	validation-auc:0.96478	validation-aucpr:0.97090
[44]	validation-logloss:0.28677	validation-auc:0.96463	validation-aucpr:0.97075
{'best_iteration': '42', 'best_score': '0.9709633010271024'}
Trial 98, Fold 1: Log loss = 0.2867742175548801, Average precision = 0.9707397207243473, ROC-AUC = 0.9646308902462923, Elapsed Time = 0.839261200002511 seconds
Trial 98, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 98, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
[0]	validation-logloss:0.67039	validation-auc:0.93017	validation-aucpr:0.91660
[1]	validation-logloss:0.64871	validation-auc:0.95007	validation-aucpr:0.95231
[2]	validation-logloss:0.62938	validation-auc:0.95374	validation-aucpr:0.95551
[3]	validation-logloss:0.60925	validation-auc:0.95643	validation-aucpr:0.96082
[4]	validation-logloss:0.59263	validation-auc:0.95691	validation-aucpr:0.96111
[5]	validation-logloss:0.57408	validation-auc:0.95835	validation-aucpr:0.96276
[6]	validation-logloss:0.55616	validation-auc:0.95922	validation-aucpr:0.96345
[7]	validation-logloss:0.54222	validation-auc:0.95911	validation-aucpr:0.96359
[8]	validation-logloss:0.52724	validation-auc:0.95964	validation-aucpr:0.96396
[9]	validation-logloss:0.51176	validation-auc:0.96018	validation-aucpr:0.96461
[10]	validation-logloss:0.49829	validation-auc:0.96090	validation-aucpr:0.96574
[11]	validation-logloss:0.48504	validation-auc:0.96133	validation-aucpr:0.96633
[12]	validation-logloss:0.47425	validation-auc:0.96134	validation-aucpr:0.96626
[13]	validation-logloss:0.46392	validation-auc:0.96193	validation-aucpr:0.96661
[14]	validation-logloss:0.45399	validation-auc:0.96206	validation-aucpr:0.96676
[15]	validation-logloss:0.44308	validation-auc:0.96231	validation-aucpr:0.96696
[16]	validation-logloss:0.43509	validation-auc:0.96218	validation-aucpr:0.96669
[17]	validation-logloss:0.42479	validation-auc:0.96248	validation-aucpr:0.96685
[18]	validation-logloss:0.41703	validation-auc:0.96264	validation-aucpr:0.96688
[19]	validation-logloss:0.40777	validation-auc:0.96331	validation-aucpr:0.96742
[20]	validation-logloss:0.39873	validation-auc:0.96391	validation-aucpr:0.96794
[21]	validation-logloss:0.39183	validation-auc:0.96395	validation-aucpr:0.96772
[22]	validation-logloss:0.38513	validation-auc:0.96396	validation-aucpr:0.96758
[23]	validation-logloss:0.37734	validation-auc:0.96422	validation-aucpr:0.96783
[24]	validation-logloss:0.36972	validation-auc:0.96465	validation-aucpr:0.96829
[25]	validation-logloss:0.36281	validation-auc:0.96491	validation-aucpr:0.96845
[26]	validation-logloss:0.35695	validation-auc:0.96501	validation-aucpr:0.96851
[27]	validation-logloss:0.35008	validation-auc:0.96540	validation-aucpr:0.96893
[28]	validation-logloss:0.34507	validation-auc:0.96559	validation-aucpr:0.96914
[29]	validation-logloss:0.33898	validation-auc:0.96559	validation-aucpr:0.96921
[30]	validation-logloss:0.33309	validation-auc:0.96577	validation-aucpr:0.96936
[31]	validation-logloss:0.32756	validation-auc:0.96596	validation-aucpr:0.96954
[32]	validation-logloss:0.32204	validation-auc:0.96629	validation-aucpr:0.96984
[33]	validation-logloss:0.31816	validation-auc:0.96642	validation-aucpr:0.96991
[34]	validation-logloss:0.31330	validation-auc:0.96659	validation-aucpr:0.97017
[35]	validation-logloss:0.30971	validation-auc:0.96645	validation-aucpr:0.97010
[36]	validation-logloss:0.30558	validation-auc:0.96661	validation-aucpr:0.97032
[37]	validation-logloss:0.30148	validation-auc:0.96665	validation-aucpr:0.97034
[38]	validation-logloss:0.29827	validation-auc:0.96668	validation-aucpr:0.97034
[39]	validation-logloss:0.29526	validation-auc:0.96672	validation-aucpr:0.97040
[40]	validation-logloss:0.29280	validation-auc:0.96673	validation-aucpr:0.97073
[41]	validation-logloss:0.28926	validation-auc:0.96685	validation-aucpr:0.97076
[42]	validation-logloss:0.28663	validation-auc:0.96683	validation-aucpr:0.97074
[43]	validation-logloss:0.28313	validation-auc:0.96696	validation-aucpr:0.97083
[44]	validation-logloss:0.27967	validation-auc:0.96714	validation-aucpr:0.97099
{'best_iteration': '44', 'best_score': '0.9709925090997215'}
Trial 98, Fold 2: Log loss = 0.2796652386858184, Average precision = 0.9709801210620819, ROC-AUC = 0.9671362855966161, Elapsed Time = 1.1067725000029895 seconds
Trial 98, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 98, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
[0]	validation-logloss:0.66926	validation-auc:0.93339	validation-aucpr:0.91513
[1]	validation-logloss:0.64911	validation-auc:0.94741	validation-aucpr:0.94176
[2]	validation-logloss:0.62933	validation-auc:0.95574	validation-aucpr:0.95849
[3]	validation-logloss:0.60876	validation-auc:0.95955	validation-aucpr:0.96526
[4]	validation-logloss:0.59157	validation-auc:0.96022	validation-aucpr:0.96597
[5]	validation-logloss:0.57577	validation-auc:0.95997	validation-aucpr:0.96580
[6]	validation-logloss:0.55910	validation-auc:0.95986	validation-aucpr:0.96602
[7]	validation-logloss:0.54490	validation-auc:0.96022	validation-aucpr:0.96609
[8]	validation-logloss:0.52932	validation-auc:0.96093	validation-aucpr:0.96658
[9]	validation-logloss:0.51402	validation-auc:0.96148	validation-aucpr:0.96713
[10]	validation-logloss:0.50185	validation-auc:0.96141	validation-aucpr:0.96731
[11]	validation-logloss:0.48753	validation-auc:0.96242	validation-aucpr:0.96822
[12]	validation-logloss:0.47425	validation-auc:0.96278	validation-aucpr:0.96854
[13]	validation-logloss:0.46183	validation-auc:0.96284	validation-aucpr:0.96874
[14]	validation-logloss:0.45221	validation-auc:0.96289	validation-aucpr:0.96866
[15]	validation-logloss:0.44315	validation-auc:0.96287	validation-aucpr:0.96860
[16]	validation-logloss:0.43438	validation-auc:0.96334	validation-aucpr:0.96871
[17]	validation-logloss:0.42426	validation-auc:0.96357	validation-aucpr:0.96901
[18]	validation-logloss:0.41664	validation-auc:0.96364	validation-aucpr:0.96900
[19]	validation-logloss:0.40722	validation-auc:0.96369	validation-aucpr:0.96917
[20]	validation-logloss:0.39961	validation-auc:0.96397	validation-aucpr:0.96936
[21]	validation-logloss:0.39028	validation-auc:0.96478	validation-aucpr:0.97005
[22]	validation-logloss:0.38212	validation-auc:0.96487	validation-aucpr:0.97011
[23]	validation-logloss:0.37402	validation-auc:0.96515	validation-aucpr:0.97047
[24]	validation-logloss:0.36725	validation-auc:0.96515	validation-aucpr:0.97060
[25]	validation-logloss:0.36014	validation-auc:0.96541	validation-aucpr:0.97078
[26]	validation-logloss:0.35408	validation-auc:0.96537	validation-aucpr:0.97081
[27]	validation-logloss:0.34867	validation-auc:0.96557	validation-aucpr:0.97092
[28]	validation-logloss:0.34361	validation-auc:0.96555	validation-aucpr:0.97088
[29]	validation-logloss:0.33740	validation-auc:0.96588	validation-aucpr:0.97111
[30]	validation-logloss:0.33286	validation-auc:0.96584	validation-aucpr:0.97101
[31]	validation-logloss:0.32842	validation-auc:0.96589	validation-aucpr:0.97108
[32]	validation-logloss:0.32427	validation-auc:0.96607	validation-aucpr:0.97120
[33]	validation-logloss:0.32029	validation-auc:0.96605	validation-aucpr:0.97115
[34]	validation-logloss:0.31545	validation-auc:0.96607	validation-aucpr:0.97121
[35]	validation-logloss:0.31171	validation-auc:0.96607	validation-aucpr:0.97122
[36]	validation-logloss:0.30840	validation-auc:0.96601	validation-aucpr:0.97114
[37]	validation-logloss:0.30413	validation-auc:0.96618	validation-aucpr:0.97128
[38]	validation-logloss:0.29924	validation-auc:0.96633	validation-aucpr:0.97141
[39]	validation-logloss:0.29613	validation-auc:0.96648	validation-aucpr:0.97149
[40]	validation-logloss:0.29314	validation-auc:0.96653	validation-aucpr:0.97156
[41]	validation-logloss:0.28938	validation-auc:0.96647	validation-aucpr:0.97153
[42]	validation-logloss:0.28565	validation-auc:0.96650	validation-aucpr:0.97153
[43]	validation-logloss:0.28295	validation-auc:0.96672	validation-aucpr:0.97172
[44]	validation-logloss:0.27939	validation-auc:0.96678	validation-aucpr:0.97179
{'best_iteration': '44', 'best_score': '0.9717887583922987'}
Trial 98, Fold 3: Log loss = 0.2793851269635118, Average precision = 0.9717884446967304, ROC-AUC = 0.9667844411832689, Elapsed Time = 1.0817418000006 seconds
Trial 98, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 98, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[0]	validation-logloss:0.66794	validation-auc:0.93771	validation-aucpr:0.92727
[1]	validation-logloss:0.64542	validation-auc:0.95095	validation-aucpr:0.95402
[2]	validation-logloss:0.62701	validation-auc:0.95216	validation-aucpr:0.95924
[3]	validation-logloss:0.60977	validation-auc:0.95322	validation-aucpr:0.96004
[4]	validation-logloss:0.59292	validation-auc:0.95518	validation-aucpr:0.96135
[5]	validation-logloss:0.57694	validation-auc:0.95516	validation-aucpr:0.96170
[6]	validation-logloss:0.55957	validation-auc:0.95681	validation-aucpr:0.96365
[7]	validation-logloss:0.54466	validation-auc:0.95690	validation-aucpr:0.96394
[8]	validation-logloss:0.53135	validation-auc:0.95738	validation-aucpr:0.96429
[9]	validation-logloss:0.51857	validation-auc:0.95879	validation-aucpr:0.96504
[10]	validation-logloss:0.50427	validation-auc:0.95977	validation-aucpr:0.96613
[11]	validation-logloss:0.49322	validation-auc:0.95992	validation-aucpr:0.96620
[12]	validation-logloss:0.48257	validation-auc:0.95986	validation-aucpr:0.96613
[13]	validation-logloss:0.47021	validation-auc:0.96038	validation-aucpr:0.96676
[14]	validation-logloss:0.45778	validation-auc:0.96110	validation-aucpr:0.96735
[15]	validation-logloss:0.44832	validation-auc:0.96162	validation-aucpr:0.96771
[16]	validation-logloss:0.43948	validation-auc:0.96199	validation-aucpr:0.96799
[17]	validation-logloss:0.43147	validation-auc:0.96180	validation-aucpr:0.96788
[18]	validation-logloss:0.42168	validation-auc:0.96230	validation-aucpr:0.96841
[19]	validation-logloss:0.41173	validation-auc:0.96261	validation-aucpr:0.96871
[20]	validation-logloss:0.40307	validation-auc:0.96273	validation-aucpr:0.96892
[21]	validation-logloss:0.39647	validation-auc:0.96250	validation-aucpr:0.96867
[22]	validation-logloss:0.38995	validation-auc:0.96266	validation-aucpr:0.96884
[23]	validation-logloss:0.38394	validation-auc:0.96266	validation-aucpr:0.96883
[24]	validation-logloss:0.37825	validation-auc:0.96270	validation-aucpr:0.96882
[25]	validation-logloss:0.37215	validation-auc:0.96281	validation-aucpr:0.96886
[26]	validation-logloss:0.36671	validation-auc:0.96275	validation-aucpr:0.96879
[27]	validation-logloss:0.35998	validation-auc:0.96295	validation-aucpr:0.96896
[28]	validation-logloss:0.35288	validation-auc:0.96305	validation-aucpr:0.96911
[29]	validation-logloss:0.34753	validation-auc:0.96306	validation-aucpr:0.96914
[30]	validation-logloss:0.34088	validation-auc:0.96362	validation-aucpr:0.96962
[31]	validation-logloss:0.33598	validation-auc:0.96379	validation-aucpr:0.96975
[32]	validation-logloss:0.32998	validation-auc:0.96409	validation-aucpr:0.97001
[33]	validation-logloss:0.32595	validation-auc:0.96421	validation-aucpr:0.97004
[34]	validation-logloss:0.32041	validation-auc:0.96462	validation-aucpr:0.97039
[35]	validation-logloss:0.31555	validation-auc:0.96467	validation-aucpr:0.97046
[36]	validation-logloss:0.31219	validation-auc:0.96469	validation-aucpr:0.97041
[37]	validation-logloss:0.30742	validation-auc:0.96497	validation-aucpr:0.97066
[38]	validation-logloss:0.30273	validation-auc:0.96506	validation-aucpr:0.97074
[39]	validation-logloss:0.29897	validation-auc:0.96497	validation-aucpr:0.97070
[40]	validation-logloss:0.29483	validation-auc:0.96517	validation-aucpr:0.97084
[41]	validation-logloss:0.29102	validation-auc:0.96527	validation-aucpr:0.97093
[42]	validation-logloss:0.28748	validation-auc:0.96540	validation-aucpr:0.97104
[43]	validation-logloss:0.28405	validation-auc:0.96550	validation-aucpr:0.97111
[44]	validation-logloss:0.28180	validation-auc:0.96549	validation-aucpr:0.97117
{'best_iteration': '44', 'best_score': '0.9711655496123021'}
Trial 98, Fold 4: Log loss = 0.2818028283500285, Average precision = 0.9711585620081205, ROC-AUC = 0.965494088452065, Elapsed Time = 1.1227743000017654 seconds
Trial 98, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 98, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[0]	validation-logloss:0.66844	validation-auc:0.93178	validation-aucpr:0.90280
[1]	validation-logloss:0.64632	validation-auc:0.95347	validation-aucpr:0.95278
[2]	validation-logloss:0.62748	validation-auc:0.95508	validation-aucpr:0.95885
[3]	validation-logloss:0.60708	validation-auc:0.95689	validation-aucpr:0.96282
[4]	validation-logloss:0.59037	validation-auc:0.95719	validation-aucpr:0.96288
[5]	validation-logloss:0.57161	validation-auc:0.95712	validation-aucpr:0.96285
[6]	validation-logloss:0.55689	validation-auc:0.95774	validation-aucpr:0.96310
[7]	validation-logloss:0.54047	validation-auc:0.95874	validation-aucpr:0.96421
[8]	validation-logloss:0.52453	validation-auc:0.95916	validation-aucpr:0.96486
[9]	validation-logloss:0.50992	validation-auc:0.95966	validation-aucpr:0.96553
[10]	validation-logloss:0.49817	validation-auc:0.95968	validation-aucpr:0.96561
[11]	validation-logloss:0.48470	validation-auc:0.96022	validation-aucpr:0.96627
[12]	validation-logloss:0.47239	validation-auc:0.96068	validation-aucpr:0.96655
[13]	validation-logloss:0.46029	validation-auc:0.96138	validation-aucpr:0.96708
[14]	validation-logloss:0.44871	validation-auc:0.96167	validation-aucpr:0.96739
[15]	validation-logloss:0.43954	validation-auc:0.96193	validation-aucpr:0.96747
[16]	validation-logloss:0.43048	validation-auc:0.96227	validation-aucpr:0.96770
[17]	validation-logloss:0.42048	validation-auc:0.96265	validation-aucpr:0.96802
[18]	validation-logloss:0.41102	validation-auc:0.96260	validation-aucpr:0.96802
[19]	validation-logloss:0.40273	validation-auc:0.96274	validation-aucpr:0.96820
[20]	validation-logloss:0.39417	validation-auc:0.96324	validation-aucpr:0.96867
[21]	validation-logloss:0.38585	validation-auc:0.96358	validation-aucpr:0.96886
[22]	validation-logloss:0.37959	validation-auc:0.96328	validation-aucpr:0.96858
[23]	validation-logloss:0.37353	validation-auc:0.96308	validation-aucpr:0.96838
[24]	validation-logloss:0.36786	validation-auc:0.96310	validation-aucpr:0.96837
[25]	validation-logloss:0.36124	validation-auc:0.96352	validation-aucpr:0.96737
[26]	validation-logloss:0.35562	validation-auc:0.96350	validation-aucpr:0.96738
[27]	validation-logloss:0.34951	validation-auc:0.96351	validation-aucpr:0.96743
[28]	validation-logloss:0.34351	validation-auc:0.96359	validation-aucpr:0.96754
[29]	validation-logloss:0.33814	validation-auc:0.96373	validation-aucpr:0.96761
[30]	validation-logloss:0.33281	validation-auc:0.96398	validation-aucpr:0.96787
[31]	validation-logloss:0.32892	validation-auc:0.96372	validation-aucpr:0.96787
[32]	validation-logloss:0.32353	validation-auc:0.96406	validation-aucpr:0.96812
[33]	validation-logloss:0.31918	validation-auc:0.96388	validation-aucpr:0.96792
[34]	validation-logloss:0.31568	validation-auc:0.96374	validation-aucpr:0.96781
[35]	validation-logloss:0.31241	validation-auc:0.96370	validation-aucpr:0.96773
[36]	validation-logloss:0.30865	validation-auc:0.96381	validation-aucpr:0.96780
[37]	validation-logloss:0.30537	validation-auc:0.96384	validation-aucpr:0.96775
[38]	validation-logloss:0.30100	validation-auc:0.96407	validation-aucpr:0.96791
[39]	validation-logloss:0.29796	validation-auc:0.96404	validation-aucpr:0.96785
[40]	validation-logloss:0.29454	validation-auc:0.96405	validation-aucpr:0.96922
[41]	validation-logloss:0.29101	validation-auc:0.96405	validation-aucpr:0.96922
[42]	validation-logloss:0.28869	validation-auc:0.96389	validation-aucpr:0.96905
[43]	validation-logloss:0.28629	validation-auc:0.96391	validation-aucpr:0.96906
[44]	validation-logloss:0.28312	validation-auc:0.96394	validation-aucpr:0.96925
{'best_iteration': '44', 'best_score': '0.9692529772877284'}
Trial 98, Fold 5: Log loss = 0.2831176401223858, Average precision = 0.9692541584306964, ROC-AUC = 0.9639441131114951, Elapsed Time = 1.0847797999995237 seconds
Optimization Progress:  99%|#########9| 99/100 [4:01:39<00:50, 50.25s/it]
Trial 99, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 99, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
[0]	validation-logloss:0.68213	validation-auc:0.92188	validation-aucpr:0.91559
[1]	validation-logloss:0.67129	validation-auc:0.94124	validation-aucpr:0.92973
[2]	validation-logloss:0.65879	validation-auc:0.95926	validation-aucpr:0.95791
[3]	validation-logloss:0.64899	validation-auc:0.96048	validation-aucpr:0.96295
[4]	validation-logloss:0.63889	validation-auc:0.96090	validation-aucpr:0.96284
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{'best_iteration': '83', 'best_score': '0.9732988522297122'}
Trial 99, Fold 1: Log loss = 0.28804067052732774, Average precision = 0.9732776156643419, ROC-AUC = 0.9688033222924822, Elapsed Time = 3.208025500000076 seconds
Trial 99, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 99, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
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[80]	validation-logloss:0.28532	validation-auc:0.97171	validation-aucpr:0.97347
{'best_iteration': '31', 'best_score': '0.973848252770876'}
Trial 99, Fold 2: Log loss = 0.28372156805296517, Average precision = 0.9737019709783394, ROC-AUC = 0.9716679297140677, Elapsed Time = 3.3942110999996657 seconds
Trial 99, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 99, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
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[64]	validation-logloss:0.31859	validation-auc:0.97128	validation-aucpr:0.97532
[65]	validation-logloss:0.31590	validation-auc:0.97127	validation-aucpr:0.97532
[66]	validation-logloss:0.31378	validation-auc:0.97125	validation-aucpr:0.97530
[67]	validation-logloss:0.31148	validation-auc:0.97125	validation-aucpr:0.97529
[68]	validation-logloss:0.30932	validation-auc:0.97127	validation-aucpr:0.97530
[69]	validation-logloss:0.30717	validation-auc:0.97126	validation-aucpr:0.97528
[70]	validation-logloss:0.30512	validation-auc:0.97129	validation-aucpr:0.97529
[71]	validation-logloss:0.30318	validation-auc:0.97125	validation-aucpr:0.97526
[72]	validation-logloss:0.30127	validation-auc:0.97121	validation-aucpr:0.97523
[73]	validation-logloss:0.29947	validation-auc:0.97116	validation-aucpr:0.97518
[74]	validation-logloss:0.29697	validation-auc:0.97129	validation-aucpr:0.97530
[75]	validation-logloss:0.29520	validation-auc:0.97135	validation-aucpr:0.97546
[76]	validation-logloss:0.29291	validation-auc:0.97133	validation-aucpr:0.97544
[77]	validation-logloss:0.29069	validation-auc:0.97137	validation-aucpr:0.97547
[78]	validation-logloss:0.28888	validation-auc:0.97147	validation-aucpr:0.97561
[79]	validation-logloss:0.28686	validation-auc:0.97145	validation-aucpr:0.97560
[80]	validation-logloss:0.28476	validation-auc:0.97149	validation-aucpr:0.97561
[81]	validation-logloss:0.28305	validation-auc:0.97151	validation-aucpr:0.97561
[82]	validation-logloss:0.28099	validation-auc:0.97156	validation-aucpr:0.97567
[83]	validation-logloss:0.27901	validation-auc:0.97167	validation-aucpr:0.97576
[84]	validation-logloss:0.27746	validation-auc:0.97167	validation-aucpr:0.97575
{'best_iteration': '83', 'best_score': '0.9757566802452886'}
Trial 99, Fold 3: Log loss = 0.27745504517041203, Average precision = 0.9757531703086391, ROC-AUC = 0.9716715142876405, Elapsed Time = 3.325000699998782 seconds
Trial 99, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 99, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
[0]	validation-logloss:0.68215	validation-auc:0.90771	validation-aucpr:0.88290
[1]	validation-logloss:0.67142	validation-auc:0.93736	validation-aucpr:0.93239
[2]	validation-logloss:0.66080	validation-auc:0.94664	validation-aucpr:0.94827
[3]	validation-logloss:0.64937	validation-auc:0.95628	validation-aucpr:0.95805
[4]	validation-logloss:0.63946	validation-auc:0.95789	validation-aucpr:0.96436
[5]	validation-logloss:0.62814	validation-auc:0.96087	validation-aucpr:0.96708
[6]	validation-logloss:0.61857	validation-auc:0.96151	validation-aucpr:0.96762
[7]	validation-logloss:0.60803	validation-auc:0.96319	validation-aucpr:0.96900
[8]	validation-logloss:0.59798	validation-auc:0.96390	validation-aucpr:0.96968
[9]	validation-logloss:0.58901	validation-auc:0.96453	validation-aucpr:0.97033
[10]	validation-logloss:0.57950	validation-auc:0.96548	validation-aucpr:0.97108
[11]	validation-logloss:0.57053	validation-auc:0.96563	validation-aucpr:0.97132
[12]	validation-logloss:0.56169	validation-auc:0.96587	validation-aucpr:0.97159
[13]	validation-logloss:0.55274	validation-auc:0.96646	validation-aucpr:0.97211
[14]	validation-logloss:0.54420	validation-auc:0.96696	validation-aucpr:0.97245
[15]	validation-logloss:0.53723	validation-auc:0.96663	validation-aucpr:0.97217
[16]	validation-logloss:0.53034	validation-auc:0.96684	validation-aucpr:0.97226
[17]	validation-logloss:0.52352	validation-auc:0.96691	validation-aucpr:0.97226
[18]	validation-logloss:0.51690	validation-auc:0.96695	validation-aucpr:0.97220
[19]	validation-logloss:0.51061	validation-auc:0.96679	validation-aucpr:0.97206
[20]	validation-logloss:0.50417	validation-auc:0.96675	validation-aucpr:0.97197
[21]	validation-logloss:0.49803	validation-auc:0.96675	validation-aucpr:0.97197
[22]	validation-logloss:0.49124	validation-auc:0.96708	validation-aucpr:0.97224
[23]	validation-logloss:0.48538	validation-auc:0.96719	validation-aucpr:0.97232
[24]	validation-logloss:0.47847	validation-auc:0.96768	validation-aucpr:0.97275
[25]	validation-logloss:0.47294	validation-auc:0.96771	validation-aucpr:0.97276
[26]	validation-logloss:0.46657	validation-auc:0.96787	validation-aucpr:0.97292
[27]	validation-logloss:0.46157	validation-auc:0.96780	validation-aucpr:0.97285
[28]	validation-logloss:0.45647	validation-auc:0.96766	validation-aucpr:0.97273
[29]	validation-logloss:0.45040	validation-auc:0.96783	validation-aucpr:0.97293
[30]	validation-logloss:0.44564	validation-auc:0.96788	validation-aucpr:0.97295
[31]	validation-logloss:0.43991	validation-auc:0.96810	validation-aucpr:0.97315
[32]	validation-logloss:0.43541	validation-auc:0.96805	validation-aucpr:0.97308
[33]	validation-logloss:0.43013	validation-auc:0.96810	validation-aucpr:0.97315
[34]	validation-logloss:0.42575	validation-auc:0.96807	validation-aucpr:0.97317
[35]	validation-logloss:0.42058	validation-auc:0.96809	validation-aucpr:0.97320
[36]	validation-logloss:0.41643	validation-auc:0.96804	validation-aucpr:0.97313
[37]	validation-logloss:0.41151	validation-auc:0.96820	validation-aucpr:0.97326
[38]	validation-logloss:0.40764	validation-auc:0.96809	validation-aucpr:0.97316
[39]	validation-logloss:0.40370	validation-auc:0.96807	validation-aucpr:0.97312
[40]	validation-logloss:0.39907	validation-auc:0.96821	validation-aucpr:0.97325
[41]	validation-logloss:0.39457	validation-auc:0.96835	validation-aucpr:0.97338
[42]	validation-logloss:0.39023	validation-auc:0.96847	validation-aucpr:0.97350
[43]	validation-logloss:0.38597	validation-auc:0.96860	validation-aucpr:0.97362
[44]	validation-logloss:0.38253	validation-auc:0.96863	validation-aucpr:0.97363
[45]	validation-logloss:0.37903	validation-auc:0.96865	validation-aucpr:0.97367
[46]	validation-logloss:0.37494	validation-auc:0.96877	validation-aucpr:0.97377
[47]	validation-logloss:0.37154	validation-auc:0.96875	validation-aucpr:0.97375
[48]	validation-logloss:0.36836	validation-auc:0.96873	validation-aucpr:0.97373
[49]	validation-logloss:0.36519	validation-auc:0.96877	validation-aucpr:0.97374
[50]	validation-logloss:0.36225	validation-auc:0.96879	validation-aucpr:0.97373
[51]	validation-logloss:0.35860	validation-auc:0.96881	validation-aucpr:0.97374
[52]	validation-logloss:0.35578	validation-auc:0.96876	validation-aucpr:0.97370
[53]	validation-logloss:0.35239	validation-auc:0.96883	validation-aucpr:0.97376
[54]	validation-logloss:0.34898	validation-auc:0.96899	validation-aucpr:0.97389
[55]	validation-logloss:0.34633	validation-auc:0.96893	validation-aucpr:0.97385
[56]	validation-logloss:0.34295	validation-auc:0.96901	validation-aucpr:0.97394
[57]	validation-logloss:0.34039	validation-auc:0.96895	validation-aucpr:0.97389
[58]	validation-logloss:0.33740	validation-auc:0.96892	validation-aucpr:0.97389
[59]	validation-logloss:0.33504	validation-auc:0.96880	validation-aucpr:0.97381
[60]	validation-logloss:0.33197	validation-auc:0.96889	validation-aucpr:0.97388
[61]	validation-logloss:0.32897	validation-auc:0.96898	validation-aucpr:0.97396
[62]	validation-logloss:0.32671	validation-auc:0.96898	validation-aucpr:0.97395
[63]	validation-logloss:0.32374	validation-auc:0.96909	validation-aucpr:0.97404
[64]	validation-logloss:0.32080	validation-auc:0.96928	validation-aucpr:0.97419
[65]	validation-logloss:0.31849	validation-auc:0.96938	validation-aucpr:0.97427
[66]	validation-logloss:0.31629	validation-auc:0.96941	validation-aucpr:0.97427
[67]	validation-logloss:0.31412	validation-auc:0.96939	validation-aucpr:0.97425
[68]	validation-logloss:0.31202	validation-auc:0.96940	validation-aucpr:0.97424
[69]	validation-logloss:0.30995	validation-auc:0.96943	validation-aucpr:0.97427
[70]	validation-logloss:0.30732	validation-auc:0.96958	validation-aucpr:0.97439
[71]	validation-logloss:0.30548	validation-auc:0.96957	validation-aucpr:0.97439
[72]	validation-logloss:0.30300	validation-auc:0.96965	validation-aucpr:0.97446
[73]	validation-logloss:0.30106	validation-auc:0.96965	validation-aucpr:0.97445
[74]	validation-logloss:0.29920	validation-auc:0.96966	validation-aucpr:0.97446
[75]	validation-logloss:0.29740	validation-auc:0.96965	validation-aucpr:0.97444
[76]	validation-logloss:0.29573	validation-auc:0.96962	validation-aucpr:0.97440
[77]	validation-logloss:0.29341	validation-auc:0.96970	validation-aucpr:0.97449
[78]	validation-logloss:0.29132	validation-auc:0.96974	validation-aucpr:0.97454
[79]	validation-logloss:0.28917	validation-auc:0.96980	validation-aucpr:0.97462
[80]	validation-logloss:0.28763	validation-auc:0.96974	validation-aucpr:0.97458
[81]	validation-logloss:0.28552	validation-auc:0.96984	validation-aucpr:0.97467
[82]	validation-logloss:0.28404	validation-auc:0.96986	validation-aucpr:0.97467
[83]	validation-logloss:0.28201	validation-auc:0.96993	validation-aucpr:0.97474
[84]	validation-logloss:0.28049	validation-auc:0.96990	validation-aucpr:0.97471
{'best_iteration': '83', 'best_score': '0.9747392883128421'}
Trial 99, Fold 4: Log loss = 0.2804945606615627, Average precision = 0.9747112338846573, ROC-AUC = 0.9699017444341914, Elapsed Time = 3.4602881999999227 seconds
Trial 99, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 99, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
[0]	validation-logloss:0.68206	validation-auc:0.91551	validation-aucpr:0.89662
[1]	validation-logloss:0.66970	validation-auc:0.95149	validation-aucpr:0.94137
[2]	validation-logloss:0.65907	validation-auc:0.95651	validation-aucpr:0.95462
[3]	validation-logloss:0.64886	validation-auc:0.95838	validation-aucpr:0.96078
[4]	validation-logloss:0.63917	validation-auc:0.95898	validation-aucpr:0.96250
[5]	validation-logloss:0.62949	validation-auc:0.95902	validation-aucpr:0.96220
[6]	validation-logloss:0.61915	validation-auc:0.96088	validation-aucpr:0.96426
[7]	validation-logloss:0.61081	validation-auc:0.96048	validation-aucpr:0.96355
[8]	validation-logloss:0.60224	validation-auc:0.96072	validation-aucpr:0.96455
[9]	validation-logloss:0.59219	validation-auc:0.96196	validation-aucpr:0.96575
[10]	validation-logloss:0.58382	validation-auc:0.96249	validation-aucpr:0.96615
[11]	validation-logloss:0.57577	validation-auc:0.96232	validation-aucpr:0.96606
[12]	validation-logloss:0.56800	validation-auc:0.96246	validation-aucpr:0.96613
[13]	validation-logloss:0.56054	validation-auc:0.96255	validation-aucpr:0.96627
[14]	validation-logloss:0.55189	validation-auc:0.96330	validation-aucpr:0.96711
[15]	validation-logloss:0.54496	validation-auc:0.96316	validation-aucpr:0.96718
[16]	validation-logloss:0.53800	validation-auc:0.96303	validation-aucpr:0.96704
[17]	validation-logloss:0.53004	validation-auc:0.96373	validation-aucpr:0.96731
[18]	validation-logloss:0.52336	validation-auc:0.96385	validation-aucpr:0.96737
[19]	validation-logloss:0.51584	validation-auc:0.96399	validation-aucpr:0.96752
[20]	validation-logloss:0.50934	validation-auc:0.96426	validation-aucpr:0.96774
[21]	validation-logloss:0.50343	validation-auc:0.96446	validation-aucpr:0.96807
[22]	validation-logloss:0.49636	validation-auc:0.96461	validation-aucpr:0.96824
[23]	validation-logloss:0.49038	validation-auc:0.96467	validation-aucpr:0.96825
[24]	validation-logloss:0.48493	validation-auc:0.96469	validation-aucpr:0.96834
[25]	validation-logloss:0.47837	validation-auc:0.96496	validation-aucpr:0.96877
[26]	validation-logloss:0.47196	validation-auc:0.96552	validation-aucpr:0.96921
[27]	validation-logloss:0.46670	validation-auc:0.96545	validation-aucpr:0.96912
[28]	validation-logloss:0.46044	validation-auc:0.96583	validation-aucpr:0.96963
[29]	validation-logloss:0.45532	validation-auc:0.96595	validation-aucpr:0.96974
[30]	validation-logloss:0.45053	validation-auc:0.96587	validation-aucpr:0.96953
[31]	validation-logloss:0.44472	validation-auc:0.96606	validation-aucpr:0.96970
[32]	validation-logloss:0.44022	validation-auc:0.96602	validation-aucpr:0.96967
[33]	validation-logloss:0.43583	validation-auc:0.96593	validation-aucpr:0.96959
[34]	validation-logloss:0.43155	validation-auc:0.96600	validation-aucpr:0.96966
[35]	validation-logloss:0.42633	validation-auc:0.96622	validation-aucpr:0.96987
[36]	validation-logloss:0.42200	validation-auc:0.96631	validation-aucpr:0.96994
[37]	validation-logloss:0.41805	validation-auc:0.96632	validation-aucpr:0.96993
[38]	validation-logloss:0.41418	validation-auc:0.96630	validation-aucpr:0.96992
[39]	validation-logloss:0.40949	validation-auc:0.96625	validation-aucpr:0.96997
[40]	validation-logloss:0.40601	validation-auc:0.96609	validation-aucpr:0.96981
[41]	validation-logloss:0.40234	validation-auc:0.96609	validation-aucpr:0.96981
[42]	validation-logloss:0.39867	validation-auc:0.96611	validation-aucpr:0.96983
[43]	validation-logloss:0.39499	validation-auc:0.96624	validation-aucpr:0.96992
[44]	validation-logloss:0.39069	validation-auc:0.96636	validation-aucpr:0.97005
[45]	validation-logloss:0.38723	validation-auc:0.96638	validation-aucpr:0.97005
[46]	validation-logloss:0.38304	validation-auc:0.96652	validation-aucpr:0.97018
[47]	validation-logloss:0.37892	validation-auc:0.96661	validation-aucpr:0.97027
[48]	validation-logloss:0.37599	validation-auc:0.96651	validation-aucpr:0.97017
[49]	validation-logloss:0.37203	validation-auc:0.96671	validation-aucpr:0.97034
[50]	validation-logloss:0.36886	validation-auc:0.96676	validation-aucpr:0.97040
[51]	validation-logloss:0.36573	validation-auc:0.96677	validation-aucpr:0.97039
[52]	validation-logloss:0.36281	validation-auc:0.96677	validation-aucpr:0.97035
[53]	validation-logloss:0.35980	validation-auc:0.96683	validation-aucpr:0.97041
[54]	validation-logloss:0.35621	validation-auc:0.96693	validation-aucpr:0.97052
[55]	validation-logloss:0.35350	validation-auc:0.96688	validation-aucpr:0.97049
[56]	validation-logloss:0.35120	validation-auc:0.96681	validation-aucpr:0.97039
[57]	validation-logloss:0.34854	validation-auc:0.96686	validation-aucpr:0.97054
[58]	validation-logloss:0.34523	validation-auc:0.96701	validation-aucpr:0.97067
[59]	validation-logloss:0.34197	validation-auc:0.96711	validation-aucpr:0.97075
[60]	validation-logloss:0.33902	validation-auc:0.96710	validation-aucpr:0.97074
[61]	validation-logloss:0.33671	validation-auc:0.96713	validation-aucpr:0.97069
[62]	validation-logloss:0.33426	validation-auc:0.96721	validation-aucpr:0.97074
[63]	validation-logloss:0.33216	validation-auc:0.96723	validation-aucpr:0.97145
[64]	validation-logloss:0.32993	validation-auc:0.96726	validation-aucpr:0.97147
[65]	validation-logloss:0.32699	validation-auc:0.96733	validation-aucpr:0.97154
[66]	validation-logloss:0.32485	validation-auc:0.96736	validation-aucpr:0.97156
[67]	validation-logloss:0.32262	validation-auc:0.96738	validation-aucpr:0.97158
[68]	validation-logloss:0.32065	validation-auc:0.96732	validation-aucpr:0.97154
[69]	validation-logloss:0.31869	validation-auc:0.96733	validation-aucpr:0.97153
[70]	validation-logloss:0.31662	validation-auc:0.96731	validation-aucpr:0.97151
[71]	validation-logloss:0.31391	validation-auc:0.96743	validation-aucpr:0.97162
[72]	validation-logloss:0.31198	validation-auc:0.96741	validation-aucpr:0.97160
[73]	validation-logloss:0.31024	validation-auc:0.96735	validation-aucpr:0.97155
[74]	validation-logloss:0.30853	validation-auc:0.96733	validation-aucpr:0.97152
[75]	validation-logloss:0.30668	validation-auc:0.96735	validation-aucpr:0.97153
[76]	validation-logloss:0.30431	validation-auc:0.96742	validation-aucpr:0.97160
[77]	validation-logloss:0.30184	validation-auc:0.96755	validation-aucpr:0.97172
[78]	validation-logloss:0.30006	validation-auc:0.96752	validation-aucpr:0.97171
[79]	validation-logloss:0.29835	validation-auc:0.96753	validation-aucpr:0.97170
[80]	validation-logloss:0.29670	validation-auc:0.96756	validation-aucpr:0.97172
[81]	validation-logloss:0.29449	validation-auc:0.96763	validation-aucpr:0.97179
[82]	validation-logloss:0.29248	validation-auc:0.96771	validation-aucpr:0.97190
[83]	validation-logloss:0.29091	validation-auc:0.96777	validation-aucpr:0.97193
[84]	validation-logloss:0.28872	validation-auc:0.96794	validation-aucpr:0.97207
{'best_iteration': '84', 'best_score': '0.9720716435454212'}
Trial 99, Fold 5: Log loss = 0.28871626715298626, Average precision = 0.9720776131216773, ROC-AUC = 0.9679444103821786, Elapsed Time = 3.337888300000486 seconds
Optimization Progress: 100%|##########| 100/100 [4:02:03<00:00, 42.55s/it]
Optuna Optimization Elapsed Time: 14523.8524339 seconds
Optimization Progress: 100%|##########| 100/100 [4:02:03<00:00, 145.24s/it]

Training with Best Trial 65

Full_params: {'objective': 'binary:logistic', 'device': 'cpu', 'verbosity': 1, 'validate_parameters': True, 'eval_metric': ['logloss', 'auc', 'aucpr'], 'seed': 42, 'max_depth': 0, 'sampling_method': 'uniform', 'num_parallel_tree': 1, 'booster': 'gbtree', 'eta': 0.08759379181951674, 'gamma': 4.671021255322478e-06, 'min_child_weight': 2.6427694911817074e-05, 'max_delta_step': 65.5415383777442, 'subsample': 0.5614298448675313, 'colsample_bytree': 0.8129876656630327, 'colsample_bylevel': 0.9184442500782993, 'colsample_bynode': 0.6255082544056032, 'lambda': 7.025644820629422, 'alpha': 0.03727945540630586, 'tree_method': 'auto', 'scale_pos_weight': 1, 'grow_policy': 'depthwise', 'max_leaves': 128, 'max_bin': 47, 'num_boost_round': 97}
Training Elapsed Time: 2.7084125000001222 seconds
Log loss: (Train) 0.18729623942177334 vs (Test) 0.1927517325225934
PR-AUC:   (Train) 0.976406633670879 vs (Test) 0.975962677739389
ROC-AUC:  (Train) 0.9731661086371626 vs (Test) 0.9725283483506415

Training with Best Trial 76

Full_params: {'objective': 'binary:logistic', 'device': 'cpu', 'verbosity': 1, 'validate_parameters': True, 'eval_metric': ['logloss', 'auc', 'aucpr'], 'seed': 42, 'max_depth': 0, 'sampling_method': 'uniform', 'num_parallel_tree': 1, 'booster': 'gbtree', 'eta': 0.06489083823042531, 'gamma': 1.733278388607598, 'min_child_weight': 0.0015768729934482134, 'max_delta_step': 37.67348822307136, 'subsample': 0.8942728317991482, 'colsample_bytree': 0.570634316092556, 'colsample_bylevel': 0.8376499027022023, 'colsample_bynode': 0.9166953679419032, 'lambda': 0.005622055563920644, 'alpha': 4.06229817623886e-08, 'tree_method': 'hist', 'scale_pos_weight': 1, 'grow_policy': 'depthwise', 'max_leaves': 196, 'max_bin': 92, 'num_boost_round': 73}
Training Elapsed Time: 3.324780799997825 seconds
Log loss: (Train) 0.18865200821954037 vs (Test) 0.19412531851812243
PR-AUC:   (Train) 0.9765158675564066 vs (Test) 0.9760041164203919
ROC-AUC:  (Train) 0.9730942189230347 vs (Test) 0.9721837774893096
In [33]:
save_results(clf_name = "XGBClassifier",
             best_trials = best_trials_xgb,
             exec_time = exec_time_xgb,
             lloss_auc_train = lloss_auc_train_xgb,
             lloss_auc_test = lloss_auc_test_xgb,
             df_metrics = df_metrics_xgb,
             cm_final = cm_final_xgb,
             cm_all = cm_xgb_all,
             cm_labels = cm_labels_xgb_all)

Optuna with CatBoostClassifier¶

In [34]:
gc.collect();
In [35]:
# Aggregated loan level data
X_df = clean_df.drop(columns = ["target", "anon_ssn"])

# https://catboost.ai/docs/en/concepts/algorithm-missing-values-processing
# Replace <NA> with NaN
X_df = X_df.fillna(np.nan)

# Identify categorical columns
cat_cols = X_df.select_dtypes(include = "category").columns.tolist()

# Convert missing NaN in Dtype categorical to "nan" as Dtype object
for col in cat_cols:
    X_df[col] = X_df[col].astype(str)
    
# Convert missing <NA> in Dtype nullable boolean and nullable Int to NaN as float
X_df = X_df.apply(lambda x: x.map(lambda z: np.nan if pd.isna(z) else z)) 

y_df = clean_df.target

anon_ssn = clean_df.anon_ssn;
In [36]:
# A single train-test split (80%-20%) using GroupShuffleSplit, ensuring that no anon_ssn (grouped by anon_ssn) appear in both sets
gss = GroupShuffleSplit(n_splits = 1, test_size = 0.2, random_state = 42)
train_idx, test_idx = next(gss.split(X_df, y_df, groups = anon_ssn))

X_train, X_test = X_df.iloc[train_idx], X_df.iloc[test_idx]
y_train, y_test = y_df.iloc[train_idx], y_df.iloc[test_idx]

# Keep track of anon_ssn for cross-validation
anon_ssn_train = anon_ssn[train_idx]  
In [37]:
del X_df, y_df, gss, train_idx, test_idx;
In [38]:
# Define the objective function
def objective(trial):
    
    # Error: bayesian bootstrap doesn't support 'subsample' option
    bootstrap_type = trial.suggest_categorical("bootstrap_type", ["Bayesian", "Bernoulli", 
                                                                  #"Poisson" # CPU doesn't support
                                                                 ] 
                                              )
    if bootstrap_type == "Bayesian":
        # Do not suggest subsample for Bayesian
        subsample = None
    else:
        subsample = trial.suggest_float("subsample", 1e-1, 1e0)
    
    grow_policy = trial.suggest_categorical("grow_policy", ["SymmetricTree", 
                                                            "Lossguide", # Only for 'Ordered' and CPU
                                                            "Depthwise", 
                                                            #"Region" # GrowPolicy Region is unimplemented for CPU
                                                           ] 
                                           )
    
    # Conditionally set sampling_frequency based on grow_policy
    if grow_policy == "Lossguide":
        sampling_frequency = "PerTree"
    else:
        sampling_frequency = trial.suggest_categorical("sampling_frequency", ["PerTree", "PerTreeLevel"])      

    posterior_sampling = trial.suggest_categorical("posterior_sampling", [True, False]) # CPU only
    if posterior_sampling == True:
        model_shrink_mode = "Constant"
    else: 
        model_shrink_mode = trial.suggest_categorical("model_shrink_mode", ["Constant", "Decreasing"]) #CPU only

    # https://catboost.ai/docs/en/references/training-parameters/
    # https://catboost.ai/docs/en/concepts/python-reference_catboostclassifier
    # https://catboost.ai/docs/en/concepts/speed-up-training
    params = {"task_type": "CPU",
              "objective": "Logloss",
              "eval_metric": "Logloss",
              "custom_metric": ["AUC", "PRAUC"],
              "iterations": trial.suggest_int("iterations", 5, 100),
              "learning_rate": trial.suggest_float("learning_rate", 1e-2, 1e-1, log = True), 
              "random_seed": seed,
              "l2_leaf_reg": trial.suggest_float("l2_leaf_reg", 1e-8, 1e1, log = True), 
              "bootstrap_type": bootstrap_type,
              "subsample": subsample,
              "sampling_frequency":  sampling_frequency,
              "random_strength": trial.suggest_float("random_strength", 1e-2, 10.0, log = True),
              "depth": trial.suggest_int("depth", 1, 16),
              "grow_policy": grow_policy, 
              "min_data_in_leaf": trial.suggest_int("min_data_in_leaf", 5, 200), # Minimum data in one leaf
              "has_time": trial.suggest_categorical("has_time", [True, False]),
              "rsm": trial.suggest_float("rsm", 1e-1, 1e0),
              "leaf_estimation_method": trial.suggest_categorical("leaf_estimation_method", ["Newton", "Gradient"]),
              "leaf_estimation_backtracking": trial.suggest_categorical("leaf_estimation_backtracking", ["No", "AnyImprovement"]),
              "fold_len_multiplier": trial.suggest_float("fold_len_multiplier", 1e0, 1e1),
              "approx_on_full_history": False, # Can't use approx-on-full-history with Plain boosting-type 
              "auto_class_weights": trial.suggest_categorical("auto_class_weights", ["None", "Balanced", "SqrtBalanced"]),
              "boosting_type": "Plain", # for CPU 
              "boost_from_average": trial.suggest_categorical("boost_from_average", [True, False]),
              "posterior_sampling": posterior_sampling,
              "allow_const_label": trial.suggest_categorical("allow_const_label", [True, False]),
              "score_function": trial.suggest_categorical("score_function", ["Cosine", "L2", # CPU only
                                                                            ]
                                                         ),
              "model_shrink_mode": model_shrink_mode,
              "thread_count": -1,
              "border_count": trial.suggest_int("border_count", 40, 255),
              "verbose": True, # Keeps live per-iteration training logs visible in the output cell
              "allow_writing_files": False # Avoid CatBoost "Error 32" on Windows (file-lock in catboost_info); disables writing into catboost_info/ (so no file lock issues)
             }

    # Ensure max_leaves (# Number of leaves in one tree) is only used when grow_policy is Lossguide
    if params["grow_policy"] == "Lossguide":
        params["max_leaves"] = trial.suggest_int("max_leaves", 2, 256)
        
    sgkf = StratifiedGroupKFold(n_splits = 5, shuffle = True, random_state = seed)
    lloss_scores, pr_auc_scores, roc_auc_scores = [], [], []
    
    for fold_idx, (train_index, valid_index) in enumerate(sgkf.split(X_train, y_train, groups = anon_ssn_train), start = 1):

        # Split data into training and validation sets
        X_train_fold, X_valid_fold = X_train.iloc[train_index], X_train.iloc[valid_index]
        y_train_fold, y_valid_fold = y_train.iloc[train_index], y_train.iloc[valid_index]   
        
        # Summarize the composition of classes in the train and validation sets
        train_0, train_1 = len(y_train_fold[y_train_fold == 0]), len(y_train_fold[y_train_fold == 1])
        valid_0, valid_1 = len(y_valid_fold[y_valid_fold == 0]), len(y_valid_fold[y_valid_fold == 1])

        print(f'Trial {trial.number}, Fold {fold_idx}: Train size = {len(train_index)} where 0 = {train_0}, 1 = {train_1}, 0/1 = {train_0/train_1}')
        print(f'Trial {trial.number}, Fold {fold_idx}: Validation size = {len(valid_index)} where  0 = {valid_0}, 1 = {valid_1}, 0/1 = {valid_0/valid_1}')

        # Create Pool objects for training and validation
        train_pool_fold = Pool(X_train_fold, y_train_fold, cat_features = cat_cols)
        valid_pool_fold = Pool(X_valid_fold, y_valid_fold, cat_features = cat_cols)
      
        start_fold = time.perf_counter()
        clf = CatBoostClassifier(**params).fit(train_pool_fold, eval_set = valid_pool_fold) 
        end_fold = time.perf_counter()
        
        # Get probabilities and for the positive class
        y_prob_fold = clf.predict_proba(X_valid_fold)[:, 1]         
        y_pred_fold = clf.predict(X_valid_fold) # Class prediction

        print(f'Trial {trial.number}, Fold {fold_idx}: '
              f'Log loss = {log_loss(y_valid_fold, y_prob_fold)}, '
              f'Average precision = {average_precision_score(y_valid_fold, y_prob_fold)}, '
              f'ROC-AUC = {roc_auc_score(y_valid_fold, y_prob_fold)}, '
              f'Elapsed Time = {end_fold - start_fold} seconds')     
        
        lloss_scores.append(log_loss(y_valid_fold, y_prob_fold))
        pr_auc_scores.append(average_precision_score(y_valid_fold, y_prob_fold))
        roc_auc_scores.append(roc_auc_score(y_valid_fold, y_prob_fold))

        del X_train_fold, X_valid_fold, y_train_fold, y_valid_fold, train_pool_fold, valid_pool_fold, clf, start_fold, end_fold
        gc.collect()        

    mean_lloss = np.mean(lloss_scores)
    mean_pr_auc = np.mean(pr_auc_scores)
    mean_roc_auc = np.mean(roc_auc_scores)

    del lloss_scores, pr_auc_scores, roc_auc_scores
    gc.collect()
    
    return mean_lloss, mean_pr_auc, mean_roc_auc
In [39]:
trial_progress = tqdm(total = n_trials, desc = "Optimization Progress", leave = True,
                      ascii = True,  # Plain text mode
                      dynamic_ncols = True   # Auto-fit width
                     )

def update_progress(study_cbc, trial):
    trial_progress.update(1)

# Disable Optuna's stdout handler so notebook isn’t spammed
optuna.logging.disable_default_handler()

# Enable propagation to Python’s logging
optuna.logging.enable_propagation()
optuna.logging.set_verbosity(optuna.logging.DEBUG)

# Configure Python logging
logging.basicConfig(filename = "optuna_debug_CatBoostClassifier.log", filemode = "w", level = logging.DEBUG, format="%(asctime)s %(levelname)s %(message)s")

study_cbc = optuna.create_study(study_name = "Optuna for CatBoostClassifier",
                                directions = ["minimize", "maximize", "maximize"],
                                sampler = module.AutoSampler(seed = seed)
                               )

start_optuna = time.perf_counter()
study_cbc.optimize(objective, n_trials = n_trials, n_jobs = 1, callbacks = [update_progress])
end_optuna = time.perf_counter()
print(f'Optuna Optimization Elapsed Time: {end_optuna - start_optuna} seconds')

gc.collect()

fig = plot_pareto_front(study_cbc, target_names = ["Log loss", "PR-AUC", "ROC-AUC"])
fig.update_layout(width = 900, height = 400)
fig.show()

trial_progress.close()

# Plot optimization history for each objective
metrics = ["Log loss", "PR-AUC", "ROC-AUC"]
for i, obj in enumerate(metrics):
    optuna.visualization.plot_optimization_history(study_cbc,
                                                   target = lambda t: t.values[i],  # Correctly target each objective
                                                   target_name = obj).show()

best_trials = study_cbc.best_trials
best_trials_cbc = {}

exec_time_cbc, lloss_auc_train_cbc, lloss_auc_test_cbc, all_metrics = [],[], [], []
cm_cbc_all, cm_labels_cbc_all = [],[]

# Prepare the data - 80% training set and 20% test set
train_pool_all = Pool(X_train, y_train, cat_features = cat_cols)
test_pool_all = Pool(X_test, y_test, cat_features = cat_cols)

for i, trial in enumerate(best_trials):

    display(Markdown(f"<span style = 'font-size: 18px; font-weight: bold;'> Training with Best Trial {trial.number} </span>"))

    best_params = trial.params

    display(HTML(best_params.__repr__()))

    # Non-optimized and best Optuna optimized parameters
    full_params = {"objective": "Logloss",
                   "eval_metric": "Logloss",
                   "custom_metric": ["AUC", "PRAUC"],                
                   "random_seed": seed,
                   "thread_count": -1,
                   "verbose": True,              
                   "allow_writing_files": False,
                   **best_params
                  }      
    print("Full_params:", full_params)  
    best_trials_cbc[trial.number] = full_params

    # https://catboost.ai/docs/en/references/training-parameters/common#auto_class_weights
    # https://catboost.ai/docs/en/references/training-parameters/performance
    final_cbc = CatBoostClassifier(**full_params)

    start_train = time.perf_counter()
    final_cbc.fit(train_pool_all) 
    end_train = time.perf_counter()
   
    print(f'Training Elapsed Time: {end_train - start_train} seconds') 

    y_prob_all = final_cbc.predict_proba(X_test)[:, 1]      
    y_pred_all = final_cbc.predict(X_test)  

    print(f'Log loss: (Train) {trial.values[0]} vs (Test) {log_loss(y_test, y_prob_all)}')
    print(f'PR-AUC:   (Train) {trial.values[1]} vs (Test) {average_precision_score(y_test, y_prob_all)}')
    print(f'ROC-AUC:  (Train) {trial.values[2]} vs (Test) {roc_auc_score(y_test, y_prob_all)}')

    exec_time_cbc.append({"Classifier": "CatBoostClassifier",
                          "Best Trial": trial.number,
                          "Optimization Elapsed Time (s)": end_optuna - start_optuna,
                          "Training Elapsed Time (s)": end_train - start_train})

    lloss_auc_train_cbc.append({"Classifier": "CatBoostClassifier",
                                "Best Trial": trial.number,
                                "Set": "Training",
                                "Log loss": trial.values[0], 
                                "PR-AUC": trial.values[1],                       
                                "ROC-AUC": trial.values[2]})

    lloss_auc_test_cbc.append({"Classifier": "CatBoostClassifier",
                               "Best Trial": trial.number,
                               "Set": "Test",
                               "Log loss": log_loss(y_test, y_prob_all), 
                               "PR-AUC": average_precision_score(y_test, y_prob_all), 
                               "ROC-AUC": roc_auc_score(y_test, y_prob_all)})
    
    report = classification_report(y_test, y_pred_all, target_names = ["Safe", "Risky"], output_dict = True)
    all_metrics.append({"Classifier": "CatBoostClassifier",
                        "Trial": trial.number,
                        "Accuracy": accuracy_score(y_test, y_pred_all),
                        "Precision (Safe)": report["Safe"]["precision"],
                        "Recall (Safe)": report["Safe"]["recall"],
                        "F1-score (Safe)": report["Safe"]["f1-score"],
                        "Precision (Risky)": report["Risky"]["precision"],
                        "Recall (Risky)": report["Risky"]["recall"],
                        "F1-score (Risky)": report["Risky"]["f1-score"],
                        "Precision (Macro avg)": report["macro avg"]["precision"],
                        "Recall (Macro avg)": report["macro avg"]["recall"],
                        "F1-score (Macro avg)": report["macro avg"]["f1-score"],
                        "Precision (Weighted avg)": report["weighted avg"]["precision"],
                        "Recall (Weighted avg)": report["weighted avg"]["recall"],
                        "F1-score (Weighted avg)": report["weighted avg"]["f1-score"]})
    
    # Store confusion matrix
    cm_final_cbc = confusion_matrix(y_test, y_pred_all)
    cm_cbc_all.append(cm_final_cbc)
    cm_labels_cbc_all.append(f'CatBoostClassifier Confusion Matrix for Best Trial {trial.number}')  # Store label for subplots                                      
            
df_metrics_cbc = pd.DataFrame(all_metrics)    

gc.collect();
Optimization Progress:   0%|          | 0/100 [00:00<?, ?it/s]
Trial 0, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 0, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
0:	learn: 0.6749843	test: 0.6749868	best: 0.6749868 (0)	total: 427ms	remaining: 8.54s
1:	learn: 0.6566303	test: 0.6566865	best: 0.6566865 (1)	total: 639ms	remaining: 6.07s
2:	learn: 0.6411094	test: 0.6411880	best: 0.6411880 (2)	total: 906ms	remaining: 5.43s
3:	learn: 0.6222098	test: 0.6219594	best: 0.6219594 (3)	total: 1.16s	remaining: 4.92s
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16:	learn: 0.4802413	test: 0.4807960	best: 0.4807960 (16)	total: 3.98s	remaining: 937ms
17:	learn: 0.4703788	test: 0.4709560	best: 0.4709560 (17)	total: 4.19s	remaining: 698ms
18:	learn: 0.4640860	test: 0.4645814	best: 0.4645814 (18)	total: 4.39s	remaining: 462ms
19:	learn: 0.4585899	test: 0.4592058	best: 0.4592058 (19)	total: 4.63s	remaining: 231ms
20:	learn: 0.4498745	test: 0.4506160	best: 0.4506160 (20)	total: 4.85s	remaining: 0us

bestTest = 0.4506159933
bestIteration = 20

Trial 0, Fold 1: Log loss = 0.45061599328166135, Average precision = 0.9576893964281532, ROC-AUC = 0.9511977800002867, Elapsed Time = 6.380537100001675 seconds
Trial 0, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 0, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
0:	learn: 0.6800830	test: 0.6798725	best: 0.6798725 (0)	total: 215ms	remaining: 4.29s
1:	learn: 0.6683618	test: 0.6681444	best: 0.6681444 (1)	total: 415ms	remaining: 3.94s
2:	learn: 0.6575797	test: 0.6575538	best: 0.6575538 (2)	total: 667ms	remaining: 4s
3:	learn: 0.6419279	test: 0.6419122	best: 0.6419122 (3)	total: 840ms	remaining: 3.57s
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13:	learn: 0.4975025	test: 0.4987796	best: 0.4987796 (13)	total: 3.08s	remaining: 1.54s
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16:	learn: 0.4702601	test: 0.4712615	best: 0.4712615 (16)	total: 3.69s	remaining: 870ms
17:	learn: 0.4645747	test: 0.4655337	best: 0.4655337 (17)	total: 3.87s	remaining: 644ms
18:	learn: 0.4580701	test: 0.4591078	best: 0.4591078 (18)	total: 4.08s	remaining: 430ms
19:	learn: 0.4515486	test: 0.4524031	best: 0.4524031 (19)	total: 4.31s	remaining: 215ms
20:	learn: 0.4440383	test: 0.4451925	best: 0.4451925 (20)	total: 4.52s	remaining: 0us

bestTest = 0.4451924663
bestIteration = 20

Trial 0, Fold 2: Log loss = 0.44519246627841624, Average precision = 0.9594557994686177, ROC-AUC = 0.9531840630833753, Elapsed Time = 4.633024899998418 seconds
Trial 0, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 0, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
0:	learn: 0.6732959	test: 0.6732722	best: 0.6732722 (0)	total: 206ms	remaining: 4.11s
1:	learn: 0.6557122	test: 0.6558222	best: 0.6558222 (1)	total: 406ms	remaining: 3.85s
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12:	learn: 0.5060153	test: 0.5054326	best: 0.5054326 (12)	total: 2.89s	remaining: 1.78s
13:	learn: 0.4982009	test: 0.4975340	best: 0.4975340 (13)	total: 3.09s	remaining: 1.54s
14:	learn: 0.4910292	test: 0.4903469	best: 0.4903469 (14)	total: 3.27s	remaining: 1.31s
15:	learn: 0.4797889	test: 0.4790524	best: 0.4790524 (15)	total: 3.42s	remaining: 1.07s
16:	learn: 0.4746006	test: 0.4737270	best: 0.4737270 (16)	total: 3.58s	remaining: 843ms
17:	learn: 0.4697465	test: 0.4688606	best: 0.4688606 (17)	total: 3.76s	remaining: 626ms
18:	learn: 0.4631186	test: 0.4618613	best: 0.4618613 (18)	total: 3.97s	remaining: 418ms
19:	learn: 0.4559915	test: 0.4547173	best: 0.4547173 (19)	total: 4.13s	remaining: 206ms
20:	learn: 0.4526034	test: 0.4512378	best: 0.4512378 (20)	total: 4.31s	remaining: 0us

bestTest = 0.4512377735
bestIteration = 20

Trial 0, Fold 3: Log loss = 0.4512377735099868, Average precision = 0.9558717887144259, ROC-AUC = 0.950449904773141, Elapsed Time = 4.419320900000457 seconds
Trial 0, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 0, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
0:	learn: 0.6736458	test: 0.6734979	best: 0.6734979 (0)	total: 243ms	remaining: 4.85s
1:	learn: 0.6607966	test: 0.6606037	best: 0.6606037 (1)	total: 407ms	remaining: 3.87s
2:	learn: 0.6435052	test: 0.6435020	best: 0.6435020 (2)	total: 652ms	remaining: 3.91s
3:	learn: 0.6286524	test: 0.6286423	best: 0.6286423 (3)	total: 824ms	remaining: 3.5s
4:	learn: 0.6180952	test: 0.6181924	best: 0.6181924 (4)	total: 990ms	remaining: 3.17s
5:	learn: 0.6021332	test: 0.6022106	best: 0.6022106 (5)	total: 1.2s	remaining: 3s
6:	learn: 0.5861475	test: 0.5861958	best: 0.5861958 (6)	total: 1.44s	remaining: 2.88s
7:	learn: 0.5719001	test: 0.5720800	best: 0.5720800 (7)	total: 1.7s	remaining: 2.75s
8:	learn: 0.5545450	test: 0.5550844	best: 0.5550844 (8)	total: 1.91s	remaining: 2.55s
9:	learn: 0.5439840	test: 0.5445666	best: 0.5445666 (9)	total: 2.1s	remaining: 2.31s
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15:	learn: 0.4893386	test: 0.4898711	best: 0.4898711 (15)	total: 3.38s	remaining: 1.05s
16:	learn: 0.4801160	test: 0.4805847	best: 0.4805847 (16)	total: 3.57s	remaining: 840ms
17:	learn: 0.4722899	test: 0.4726789	best: 0.4726789 (17)	total: 3.75s	remaining: 624ms
18:	learn: 0.4597518	test: 0.4602520	best: 0.4602520 (18)	total: 3.96s	remaining: 417ms
19:	learn: 0.4527845	test: 0.4533538	best: 0.4533538 (19)	total: 4.12s	remaining: 206ms
20:	learn: 0.4466671	test: 0.4472081	best: 0.4472081 (20)	total: 4.35s	remaining: 0us

bestTest = 0.4472080578
bestIteration = 20

Trial 0, Fold 4: Log loss = 0.4472080578107475, Average precision = 0.9574408749777041, ROC-AUC = 0.952917146529134, Elapsed Time = 4.461729999999079 seconds
Trial 0, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 0, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
0:	learn: 0.6752415	test: 0.6759679	best: 0.6759679 (0)	total: 202ms	remaining: 4.03s
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12:	learn: 0.5071831	test: 0.5101551	best: 0.5101551 (12)	total: 2.88s	remaining: 1.77s
13:	learn: 0.5009780	test: 0.5040582	best: 0.5040582 (13)	total: 3.06s	remaining: 1.53s
14:	learn: 0.4892587	test: 0.4927897	best: 0.4927897 (14)	total: 3.28s	remaining: 1.31s
15:	learn: 0.4781244	test: 0.4818967	best: 0.4818967 (15)	total: 3.52s	remaining: 1.1s
16:	learn: 0.4711806	test: 0.4752788	best: 0.4752788 (16)	total: 3.7s	remaining: 870ms
17:	learn: 0.4623234	test: 0.4667260	best: 0.4667260 (17)	total: 3.87s	remaining: 645ms
18:	learn: 0.4573321	test: 0.4618792	best: 0.4618792 (18)	total: 4s	remaining: 421ms
19:	learn: 0.4503683	test: 0.4551646	best: 0.4551646 (19)	total: 4.23s	remaining: 211ms
20:	learn: 0.4426500	test: 0.4475367	best: 0.4475367 (20)	total: 4.38s	remaining: 0us

bestTest = 0.4475367208
bestIteration = 20

Optimization Progress:   1%|1         | 1/100 [00:33<54:45, 33.18s/it]
Trial 0, Fold 5: Log loss = 0.44753672082994483, Average precision = 0.9531065401172362, ROC-AUC = 0.9474237407799639, Elapsed Time = 4.487887499999488 seconds
Trial 1, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 1, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
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43:	learn: 0.4386625	test: 0.4383334	best: 0.4383334 (43)	total: 1.06s	remaining: 312ms
44:	learn: 0.4352729	test: 0.4350294	best: 0.4350294 (44)	total: 1.08s	remaining: 289ms
45:	learn: 0.4317688	test: 0.4314594	best: 0.4314594 (45)	total: 1.1s	remaining: 264ms
46:	learn: 0.4263420	test: 0.4259080	best: 0.4259080 (46)	total: 1.13s	remaining: 241ms
47:	learn: 0.4226148	test: 0.4221341	best: 0.4221341 (47)	total: 1.16s	remaining: 218ms
48:	learn: 0.4201554	test: 0.4197055	best: 0.4197055 (48)	total: 1.19s	remaining: 194ms
49:	learn: 0.4154722	test: 0.4149478	best: 0.4149478 (49)	total: 1.21s	remaining: 170ms
50:	learn: 0.4133536	test: 0.4128292	best: 0.4128292 (50)	total: 1.24s	remaining: 145ms
51:	learn: 0.4093914	test: 0.4088026	best: 0.4088026 (51)	total: 1.26s	remaining: 121ms
52:	learn: 0.4064468	test: 0.4058603	best: 0.4058603 (52)	total: 1.28s	remaining: 97ms
53:	learn: 0.4044185	test: 0.4038812	best: 0.4038812 (53)	total: 1.31s	remaining: 72.8ms
54:	learn: 0.4006588	test: 0.4000630	best: 0.4000630 (54)	total: 1.33s	remaining: 48.5ms
55:	learn: 0.3983366	test: 0.3976774	best: 0.3976774 (55)	total: 1.36s	remaining: 24.3ms
56:	learn: 0.3964040	test: 0.3957977	best: 0.3957977 (56)	total: 1.38s	remaining: 0us

bestTest = 0.3957976797
bestIteration = 56

Trial 1, Fold 1: Log loss = 0.3959331328819855, Average precision = 0.9563936087724884, ROC-AUC = 0.9505523114093576, Elapsed Time = 1.4943979000017862 seconds
Trial 1, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 1, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
0:	learn: 0.6826367	test: 0.6827674	best: 0.6827674 (0)	total: 19.5ms	remaining: 1.09s
1:	learn: 0.6745653	test: 0.6747239	best: 0.6747239 (1)	total: 40.4ms	remaining: 1.11s
2:	learn: 0.6659111	test: 0.6660634	best: 0.6660634 (2)	total: 59.8ms	remaining: 1.07s
3:	learn: 0.6556101	test: 0.6558801	best: 0.6558801 (3)	total: 76.4ms	remaining: 1.01s
4:	learn: 0.6481322	test: 0.6484127	best: 0.6484127 (4)	total: 96.9ms	remaining: 1.01s
5:	learn: 0.6379685	test: 0.6383400	best: 0.6383400 (5)	total: 118ms	remaining: 1s
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48:	learn: 0.4321877	test: 0.4329456	best: 0.4329456 (48)	total: 1.25s	remaining: 204ms
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50:	learn: 0.4283098	test: 0.4290120	best: 0.4290120 (50)	total: 1.31s	remaining: 154ms
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53:	learn: 0.4218364	test: 0.4225527	best: 0.4225527 (53)	total: 1.39s	remaining: 77.3ms
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55:	learn: 0.4180829	test: 0.4188054	best: 0.4188054 (55)	total: 1.45s	remaining: 25.9ms
56:	learn: 0.4163095	test: 0.4170789	best: 0.4170789 (56)	total: 1.48s	remaining: 0us

bestTest = 0.4170788521
bestIteration = 56

Trial 1, Fold 2: Log loss = 0.4171490212653478, Average precision = 0.9530273823259603, ROC-AUC = 0.9503425099265812, Elapsed Time = 1.588232499998412 seconds
Trial 1, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 1, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
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2:	learn: 0.6671088	test: 0.6663788	best: 0.6663788 (2)	total: 72.5ms	remaining: 1.3s
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48:	learn: 0.4283092	test: 0.4230551	best: 0.4230551 (48)	total: 1.32s	remaining: 216ms
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53:	learn: 0.4151678	test: 0.4096025	best: 0.4096025 (53)	total: 1.47s	remaining: 81.7ms
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56:	learn: 0.4050047	test: 0.3994722	best: 0.3994722 (56)	total: 1.56s	remaining: 0us

bestTest = 0.3994721782
bestIteration = 56

Trial 1, Fold 3: Log loss = 0.39974561984306517, Average precision = 0.9582582800143149, ROC-AUC = 0.9555932537007252, Elapsed Time = 1.690450599999167 seconds
Trial 1, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 1, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
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53:	learn: 0.4312761	test: 0.4312099	best: 0.4312099 (53)	total: 1.66s	remaining: 92.2ms
54:	learn: 0.4297543	test: 0.4296693	best: 0.4296693 (54)	total: 1.69s	remaining: 61.5ms
55:	learn: 0.4251316	test: 0.4250730	best: 0.4250730 (55)	total: 1.72s	remaining: 30.8ms
56:	learn: 0.4227583	test: 0.4226421	best: 0.4226421 (56)	total: 1.75s	remaining: 0us

bestTest = 0.4226421495
bestIteration = 56

Trial 1, Fold 4: Log loss = 0.4226799843478286, Average precision = 0.9541335907356011, ROC-AUC = 0.9498205433592994, Elapsed Time = 1.8859120999986771 seconds
Trial 1, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 1, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
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56:	learn: 0.3967051	test: 0.4035929	best: 0.4035929 (56)	total: 1.52s	remaining: 0us

bestTest = 0.4035928781
bestIteration = 56

Trial 1, Fold 5: Log loss = 0.4036063243832434, Average precision = 0.9527932311333421, ROC-AUC = 0.9487819705330434, Elapsed Time = 1.6435883999984071 seconds
Optimization Progress:   2%|2         | 2/100 [00:49<37:38, 23.05s/it]
Trial 2, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 2, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
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63:	learn: 0.2974361	test: 0.3102289	best: 0.3102289 (63)	total: 10.9s	remaining: 2.21s
64:	learn: 0.2951749	test: 0.3081751	best: 0.3081751 (64)	total: 11.1s	remaining: 2.04s
65:	learn: 0.2928650	test: 0.3061166	best: 0.3061166 (65)	total: 11.3s	remaining: 1.88s
66:	learn: 0.2905230	test: 0.3039803	best: 0.3039803 (66)	total: 11.4s	remaining: 1.71s
67:	learn: 0.2883662	test: 0.3020124	best: 0.3020124 (67)	total: 11.6s	remaining: 1.54s
68:	learn: 0.2863271	test: 0.3001502	best: 0.3001502 (68)	total: 11.8s	remaining: 1.37s
69:	learn: 0.2842113	test: 0.2982080	best: 0.2982080 (69)	total: 11.9s	remaining: 1.19s
70:	learn: 0.2822013	test: 0.2963492	best: 0.2963492 (70)	total: 12.1s	remaining: 1.02s
71:	learn: 0.2802272	test: 0.2946159	best: 0.2946159 (71)	total: 12.3s	remaining: 851ms
72:	learn: 0.2780386	test: 0.2927888	best: 0.2927888 (72)	total: 12.5s	remaining: 683ms
73:	learn: 0.2759209	test: 0.2908612	best: 0.2908612 (73)	total: 12.6s	remaining: 512ms
74:	learn: 0.2740745	test: 0.2892156	best: 0.2892156 (74)	total: 12.8s	remaining: 342ms
75:	learn: 0.2722139	test: 0.2875322	best: 0.2875322 (75)	total: 13s	remaining: 171ms
76:	learn: 0.2704869	test: 0.2859927	best: 0.2859927 (76)	total: 13.1s	remaining: 0us

bestTest = 0.2859926523
bestIteration = 76

Trial 2, Fold 1: Log loss = 0.2859926522625346, Average precision = 0.9734384342518939, ROC-AUC = 0.9685485104410128, Elapsed Time = 13.314232200002152 seconds
Trial 2, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 2, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
0:	learn: 0.6802992	test: 0.6804238	best: 0.6804238 (0)	total: 145ms	remaining: 11s
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63:	learn: 0.2982947	test: 0.3082945	best: 0.3082945 (63)	total: 9.74s	remaining: 1.98s
64:	learn: 0.2961092	test: 0.3061363	best: 0.3061363 (64)	total: 9.86s	remaining: 1.82s
65:	learn: 0.2938981	test: 0.3041990	best: 0.3041990 (65)	total: 10s	remaining: 1.67s
66:	learn: 0.2917545	test: 0.3022080	best: 0.3022080 (66)	total: 10.2s	remaining: 1.52s
67:	learn: 0.2895628	test: 0.3001347	best: 0.3001347 (67)	total: 10.4s	remaining: 1.37s
68:	learn: 0.2872912	test: 0.2980296	best: 0.2980296 (68)	total: 10.5s	remaining: 1.22s
69:	learn: 0.2853264	test: 0.2961620	best: 0.2961620 (69)	total: 10.7s	remaining: 1.06s
70:	learn: 0.2832790	test: 0.2942043	best: 0.2942043 (70)	total: 10.8s	remaining: 914ms
71:	learn: 0.2811378	test: 0.2921425	best: 0.2921425 (71)	total: 11s	remaining: 763ms
72:	learn: 0.2791569	test: 0.2902568	best: 0.2902568 (72)	total: 11.1s	remaining: 609ms
73:	learn: 0.2772488	test: 0.2885083	best: 0.2885083 (73)	total: 11.3s	remaining: 457ms
74:	learn: 0.2752813	test: 0.2866618	best: 0.2866618 (74)	total: 11.4s	remaining: 304ms
75:	learn: 0.2735958	test: 0.2850857	best: 0.2850857 (75)	total: 11.6s	remaining: 152ms
76:	learn: 0.2717767	test: 0.2834597	best: 0.2834597 (76)	total: 11.7s	remaining: 0us

bestTest = 0.2834596634
bestIteration = 76

Trial 2, Fold 2: Log loss = 0.28345966337626055, Average precision = 0.9743472051969249, ROC-AUC = 0.9711451585931281, Elapsed Time = 11.851431200000661 seconds
Trial 2, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 2, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
0:	learn: 0.6802592	test: 0.6803151	best: 0.6803151 (0)	total: 170ms	remaining: 13s
1:	learn: 0.6676447	test: 0.6677462	best: 0.6677462 (1)	total: 316ms	remaining: 11.8s
2:	learn: 0.6552994	test: 0.6555048	best: 0.6555048 (2)	total: 471ms	remaining: 11.6s
3:	learn: 0.6433840	test: 0.6436023	best: 0.6436023 (3)	total: 624ms	remaining: 11.4s
4:	learn: 0.6316158	test: 0.6319003	best: 0.6319003 (4)	total: 785ms	remaining: 11.3s
5:	learn: 0.6203085	test: 0.6206607	best: 0.6206607 (5)	total: 954ms	remaining: 11.3s
6:	learn: 0.6095564	test: 0.6100167	best: 0.6100167 (6)	total: 1.1s	remaining: 11s
7:	learn: 0.5989344	test: 0.5994262	best: 0.5994262 (7)	total: 1.26s	remaining: 10.9s
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11:	learn: 0.5591708	test: 0.5600841	best: 0.5600841 (11)	total: 1.84s	remaining: 10s
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63:	learn: 0.2984109	test: 0.3057562	best: 0.3057562 (63)	total: 9.74s	remaining: 1.98s
64:	learn: 0.2961105	test: 0.3035791	best: 0.3035791 (64)	total: 9.89s	remaining: 1.82s
65:	learn: 0.2938764	test: 0.3015200	best: 0.3015200 (65)	total: 10s	remaining: 1.67s
66:	learn: 0.2914835	test: 0.2993054	best: 0.2993054 (66)	total: 10.2s	remaining: 1.52s
67:	learn: 0.2892736	test: 0.2972764	best: 0.2972764 (67)	total: 10.4s	remaining: 1.37s
68:	learn: 0.2869875	test: 0.2951774	best: 0.2951774 (68)	total: 10.5s	remaining: 1.22s
69:	learn: 0.2848428	test: 0.2931857	best: 0.2931857 (69)	total: 10.7s	remaining: 1.07s
70:	learn: 0.2830059	test: 0.2915703	best: 0.2915703 (70)	total: 10.8s	remaining: 915ms
71:	learn: 0.2809394	test: 0.2896555	best: 0.2896555 (71)	total: 11s	remaining: 763ms
72:	learn: 0.2789101	test: 0.2878104	best: 0.2878104 (72)	total: 11.2s	remaining: 611ms
73:	learn: 0.2768541	test: 0.2858728	best: 0.2858728 (73)	total: 11.3s	remaining: 459ms
74:	learn: 0.2749603	test: 0.2840883	best: 0.2840883 (74)	total: 11.5s	remaining: 307ms
75:	learn: 0.2730775	test: 0.2823078	best: 0.2823078 (75)	total: 11.6s	remaining: 153ms
76:	learn: 0.2712717	test: 0.2806472	best: 0.2806472 (76)	total: 11.8s	remaining: 0us

bestTest = 0.2806471975
bestIteration = 76

Trial 2, Fold 3: Log loss = 0.2806471974698048, Average precision = 0.9745374116291409, ROC-AUC = 0.9724172423216573, Elapsed Time = 11.957781299999624 seconds
Trial 2, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 2, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
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5:	learn: 0.6209630	test: 0.6219154	best: 0.6219154 (5)	total: 922ms	remaining: 10.9s
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63:	learn: 0.2987159	test: 0.3088220	best: 0.3088220 (63)	total: 9.96s	remaining: 2.02s
64:	learn: 0.2963793	test: 0.3065724	best: 0.3065724 (64)	total: 10.1s	remaining: 1.87s
65:	learn: 0.2940492	test: 0.3044140	best: 0.3044140 (65)	total: 10.3s	remaining: 1.71s
66:	learn: 0.2919439	test: 0.3024226	best: 0.3024226 (66)	total: 10.4s	remaining: 1.55s
67:	learn: 0.2897408	test: 0.3003406	best: 0.3003406 (67)	total: 10.6s	remaining: 1.4s
68:	learn: 0.2876030	test: 0.2983477	best: 0.2983477 (68)	total: 10.7s	remaining: 1.24s
69:	learn: 0.2853172	test: 0.2962224	best: 0.2962224 (69)	total: 10.9s	remaining: 1.09s
70:	learn: 0.2833135	test: 0.2943562	best: 0.2943562 (70)	total: 11s	remaining: 932ms
71:	learn: 0.2812629	test: 0.2924578	best: 0.2924578 (71)	total: 11.2s	remaining: 776ms
72:	learn: 0.2792819	test: 0.2906007	best: 0.2906007 (72)	total: 11.3s	remaining: 621ms
73:	learn: 0.2773128	test: 0.2887388	best: 0.2887388 (73)	total: 11.5s	remaining: 466ms
74:	learn: 0.2756504	test: 0.2871270	best: 0.2871270 (74)	total: 11.7s	remaining: 311ms
75:	learn: 0.2738701	test: 0.2854474	best: 0.2854474 (75)	total: 11.8s	remaining: 155ms
76:	learn: 0.2721302	test: 0.2837640	best: 0.2837640 (76)	total: 11.9s	remaining: 0us

bestTest = 0.283763995
bestIteration = 76

Trial 2, Fold 4: Log loss = 0.2837639949721159, Average precision = 0.9759807966923569, ROC-AUC = 0.9718746944974974, Elapsed Time = 12.100321099998837 seconds
Trial 2, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 2, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
0:	learn: 0.6802851	test: 0.6806926	best: 0.6806926 (0)	total: 154ms	remaining: 11.7s
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64:	learn: 0.2951367	test: 0.3107652	best: 0.3107652 (64)	total: 10s	remaining: 1.85s
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66:	learn: 0.2903381	test: 0.3063619	best: 0.3063619 (66)	total: 10.3s	remaining: 1.54s
67:	learn: 0.2880257	test: 0.3042008	best: 0.3042008 (67)	total: 10.5s	remaining: 1.39s
68:	learn: 0.2857154	test: 0.3020620	best: 0.3020620 (68)	total: 10.7s	remaining: 1.24s
69:	learn: 0.2835880	test: 0.3000606	best: 0.3000606 (69)	total: 10.8s	remaining: 1.08s
70:	learn: 0.2814944	test: 0.2981734	best: 0.2981734 (70)	total: 11s	remaining: 926ms
71:	learn: 0.2792715	test: 0.2961647	best: 0.2961647 (71)	total: 11.1s	remaining: 773ms
72:	learn: 0.2772610	test: 0.2943931	best: 0.2943931 (72)	total: 11.3s	remaining: 619ms
73:	learn: 0.2752017	test: 0.2925324	best: 0.2925324 (73)	total: 11.5s	remaining: 466ms
74:	learn: 0.2733737	test: 0.2908720	best: 0.2908720 (74)	total: 11.6s	remaining: 310ms
75:	learn: 0.2714866	test: 0.2891508	best: 0.2891508 (75)	total: 11.8s	remaining: 155ms
76:	learn: 0.2697065	test: 0.2875601	best: 0.2875601 (76)	total: 11.9s	remaining: 0us

bestTest = 0.2875601376
bestIteration = 76

Trial 2, Fold 5: Log loss = 0.28756013755104853, Average precision = 0.9731313191971385, ROC-AUC = 0.9708124779369416, Elapsed Time = 12.088010999999824 seconds
Optimization Progress:   3%|3         | 3/100 [02:00<1:12:45, 45.00s/it]
Trial 3, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 3, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
0:	learn: 0.6210638	test: 0.6256852	best: 0.6256852 (0)	total: 1.97s	remaining: 1m 42s
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2:	learn: 0.5057908	test: 0.5203140	best: 0.5203140 (2)	total: 6.39s	remaining: 1m 46s
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5:	learn: 0.3901047	test: 0.4136660	best: 0.4136660 (5)	total: 12.5s	remaining: 1m 38s
6:	learn: 0.3625389	test: 0.3881014	best: 0.3881014 (6)	total: 14.6s	remaining: 1m 35s
7:	learn: 0.3376649	test: 0.3665084	best: 0.3665084 (7)	total: 16.6s	remaining: 1m 33s
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40:	learn: 0.1284694	test: 0.2179048	best: 0.2179048 (40)	total: 1m 30s	remaining: 26.5s
41:	learn: 0.1257432	test: 0.2173047	best: 0.2173047 (41)	total: 1m 32s	remaining: 24.3s
42:	learn: 0.1242866	test: 0.2170200	best: 0.2170200 (42)	total: 1m 34s	remaining: 22.1s
43:	learn: 0.1220583	test: 0.2166082	best: 0.2166082 (43)	total: 1m 37s	remaining: 19.9s
44:	learn: 0.1206991	test: 0.2161925	best: 0.2161925 (44)	total: 1m 39s	remaining: 17.6s
45:	learn: 0.1181242	test: 0.2160194	best: 0.2160194 (45)	total: 1m 41s	remaining: 15.4s
46:	learn: 0.1155360	test: 0.2159972	best: 0.2159972 (46)	total: 1m 43s	remaining: 13.2s
47:	learn: 0.1143342	test: 0.2154056	best: 0.2154056 (47)	total: 1m 45s	remaining: 11s
48:	learn: 0.1126665	test: 0.2151294	best: 0.2151294 (48)	total: 1m 47s	remaining: 8.81s
49:	learn: 0.1107846	test: 0.2147983	best: 0.2147983 (49)	total: 1m 50s	remaining: 6.6s
50:	learn: 0.1095653	test: 0.2143256	best: 0.2143256 (50)	total: 1m 52s	remaining: 4.4s
51:	learn: 0.1079845	test: 0.2141729	best: 0.2141729 (51)	total: 1m 54s	remaining: 2.2s
52:	learn: 0.1062828	test: 0.2140891	best: 0.2140891 (52)	total: 1m 56s	remaining: 0us

bestTest = 0.2140891159
bestIteration = 52

Trial 3, Fold 1: Log loss = 0.21320658424277797, Average precision = 0.9727751291264074, ROC-AUC = 0.9670637758516034, Elapsed Time = 116.65948170000047 seconds
Trial 3, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 3, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
0:	learn: 0.6210098	test: 0.6260702	best: 0.6260702 (0)	total: 1.89s	remaining: 1m 38s
1:	learn: 0.5589420	test: 0.5694954	best: 0.5694954 (1)	total: 4.01s	remaining: 1m 42s
2:	learn: 0.5062144	test: 0.5203870	best: 0.5203870 (2)	total: 6.03s	remaining: 1m 40s
3:	learn: 0.4609391	test: 0.4794926	best: 0.4794926 (3)	total: 8.09s	remaining: 1m 39s
4:	learn: 0.4218096	test: 0.4443732	best: 0.4443732 (4)	total: 10.1s	remaining: 1m 37s
5:	learn: 0.3890962	test: 0.4142500	best: 0.4142500 (5)	total: 12.1s	remaining: 1m 35s
6:	learn: 0.3600858	test: 0.3887928	best: 0.3887928 (6)	total: 14.2s	remaining: 1m 33s
7:	learn: 0.3349769	test: 0.3667618	best: 0.3667618 (7)	total: 16.2s	remaining: 1m 31s
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44:	learn: 0.1159663	test: 0.2110871	best: 0.2110871 (44)	total: 1m 31s	remaining: 16.2s
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46:	learn: 0.1119715	test: 0.2100675	best: 0.2100675 (46)	total: 1m 35s	remaining: 12.2s
47:	learn: 0.1102047	test: 0.2096369	best: 0.2096369 (47)	total: 1m 37s	remaining: 10.1s
48:	learn: 0.1093285	test: 0.2092641	best: 0.2092641 (48)	total: 1m 39s	remaining: 8.11s
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50:	learn: 0.1064957	test: 0.2084397	best: 0.2084397 (50)	total: 1m 43s	remaining: 4.05s
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52:	learn: 0.1034676	test: 0.2079332	best: 0.2079332 (52)	total: 1m 47s	remaining: 0us

bestTest = 0.2079332213
bestIteration = 52

Trial 3, Fold 2: Log loss = 0.20740025849944024, Average precision = 0.972057189393349, ROC-AUC = 0.9680150570222652, Elapsed Time = 107.9001986000003 seconds
Trial 3, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 3, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
0:	learn: 0.6217426	test: 0.6264151	best: 0.6264151 (0)	total: 1.79s	remaining: 1m 33s
1:	learn: 0.5598746	test: 0.5689335	best: 0.5689335 (1)	total: 3.78s	remaining: 1m 36s
2:	learn: 0.5060771	test: 0.5210192	best: 0.5210192 (2)	total: 5.8s	remaining: 1m 36s
3:	learn: 0.4612454	test: 0.4789047	best: 0.4789047 (3)	total: 7.81s	remaining: 1m 35s
4:	learn: 0.4218588	test: 0.4441229	best: 0.4441229 (4)	total: 9.78s	remaining: 1m 33s
5:	learn: 0.3892971	test: 0.4143956	best: 0.4143956 (5)	total: 11.8s	remaining: 1m 32s
6:	learn: 0.3611752	test: 0.3884644	best: 0.3884644 (6)	total: 13.8s	remaining: 1m 30s
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44:	learn: 0.1183339	test: 0.2124573	best: 0.2124573 (44)	total: 1m 30s	remaining: 16.1s
45:	learn: 0.1150490	test: 0.2121544	best: 0.2121544 (45)	total: 1m 32s	remaining: 14.1s
46:	learn: 0.1133476	test: 0.2117959	best: 0.2117959 (46)	total: 1m 34s	remaining: 12.1s
47:	learn: 0.1119658	test: 0.2111476	best: 0.2111476 (47)	total: 1m 36s	remaining: 10.1s
48:	learn: 0.1102662	test: 0.2107405	best: 0.2107405 (48)	total: 1m 38s	remaining: 8.06s
49:	learn: 0.1085176	test: 0.2107458	best: 0.2107405 (48)	total: 1m 40s	remaining: 6.05s
50:	learn: 0.1075040	test: 0.2105479	best: 0.2105479 (50)	total: 1m 42s	remaining: 4.03s
51:	learn: 0.1064721	test: 0.2101094	best: 0.2101094 (51)	total: 1m 44s	remaining: 2.01s
52:	learn: 0.1051549	test: 0.2099617	best: 0.2099617 (52)	total: 1m 46s	remaining: 0us

bestTest = 0.2099616769
bestIteration = 52

Trial 3, Fold 3: Log loss = 0.20948046517183116, Average precision = 0.9721914561978554, ROC-AUC = 0.9677968285001588, Elapsed Time = 106.99108250000063 seconds
Trial 3, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 3, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
0:	learn: 0.6209726	test: 0.6265059	best: 0.6265059 (0)	total: 1.82s	remaining: 1m 34s
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2:	learn: 0.5068209	test: 0.5210777	best: 0.5210777 (2)	total: 5.9s	remaining: 1m 38s
3:	learn: 0.4615579	test: 0.4799427	best: 0.4799427 (3)	total: 7.95s	remaining: 1m 37s
4:	learn: 0.4231729	test: 0.4449089	best: 0.4449089 (4)	total: 9.96s	remaining: 1m 35s
5:	learn: 0.3909425	test: 0.4145341	best: 0.4145341 (5)	total: 12s	remaining: 1m 33s
6:	learn: 0.3632540	test: 0.3884701	best: 0.3884701 (6)	total: 14s	remaining: 1m 31s
7:	learn: 0.3386476	test: 0.3662129	best: 0.3662129 (7)	total: 16.1s	remaining: 1m 30s
8:	learn: 0.3178142	test: 0.3477776	best: 0.3477776 (8)	total: 18.1s	remaining: 1m 28s
9:	learn: 0.2985394	test: 0.3315617	best: 0.3315617 (9)	total: 20.4s	remaining: 1m 27s
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38:	learn: 0.1305917	test: 0.2170761	best: 0.2170761 (38)	total: 1m 19s	remaining: 28.6s
39:	learn: 0.1279707	test: 0.2166135	best: 0.2166135 (39)	total: 1m 21s	remaining: 26.5s
40:	learn: 0.1258383	test: 0.2161659	best: 0.2161659 (40)	total: 1m 23s	remaining: 24.4s
41:	learn: 0.1238137	test: 0.2155271	best: 0.2155271 (41)	total: 1m 25s	remaining: 22.4s
42:	learn: 0.1224795	test: 0.2149981	best: 0.2149981 (42)	total: 1m 27s	remaining: 20.4s
43:	learn: 0.1205790	test: 0.2143704	best: 0.2143704 (43)	total: 1m 29s	remaining: 18.3s
44:	learn: 0.1178470	test: 0.2140639	best: 0.2140639 (44)	total: 1m 31s	remaining: 16.3s
45:	learn: 0.1170938	test: 0.2135718	best: 0.2135718 (45)	total: 1m 33s	remaining: 14.2s
46:	learn: 0.1150506	test: 0.2130614	best: 0.2130614 (46)	total: 1m 35s	remaining: 12.2s
47:	learn: 0.1128442	test: 0.2127740	best: 0.2127740 (47)	total: 1m 37s	remaining: 10.2s
48:	learn: 0.1113120	test: 0.2124517	best: 0.2124517 (48)	total: 1m 39s	remaining: 8.13s
49:	learn: 0.1102172	test: 0.2120023	best: 0.2120023 (49)	total: 1m 41s	remaining: 6.09s
50:	learn: 0.1081928	test: 0.2116876	best: 0.2116876 (50)	total: 1m 43s	remaining: 4.06s
51:	learn: 0.1069263	test: 0.2112646	best: 0.2112646 (51)	total: 1m 45s	remaining: 2.03s
52:	learn: 0.1050433	test: 0.2110767	best: 0.2110767 (52)	total: 1m 47s	remaining: 0us

bestTest = 0.2110767146
bestIteration = 52

Trial 3, Fold 4: Log loss = 0.21053058723983187, Average precision = 0.973023916473308, ROC-AUC = 0.9685567882954094, Elapsed Time = 107.8088351999977 seconds
Trial 3, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 3, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
0:	learn: 0.6207121	test: 0.6262120	best: 0.6262120 (0)	total: 1.79s	remaining: 1m 33s
1:	learn: 0.5581805	test: 0.5706434	best: 0.5706434 (1)	total: 3.78s	remaining: 1m 36s
2:	learn: 0.5045762	test: 0.5235112	best: 0.5235112 (2)	total: 5.82s	remaining: 1m 36s
3:	learn: 0.4583798	test: 0.4827181	best: 0.4827181 (3)	total: 7.84s	remaining: 1m 36s
4:	learn: 0.4199114	test: 0.4480349	best: 0.4480349 (4)	total: 9.86s	remaining: 1m 34s
5:	learn: 0.3866340	test: 0.4181617	best: 0.4181617 (5)	total: 11.9s	remaining: 1m 33s
6:	learn: 0.3590075	test: 0.3920679	best: 0.3920679 (6)	total: 13.9s	remaining: 1m 31s
7:	learn: 0.3346293	test: 0.3705247	best: 0.3705247 (7)	total: 15.9s	remaining: 1m 29s
8:	learn: 0.3141363	test: 0.3514561	best: 0.3514561 (8)	total: 17.9s	remaining: 1m 27s
9:	learn: 0.2960314	test: 0.3356264	best: 0.3356264 (9)	total: 19.9s	remaining: 1m 25s
10:	learn: 0.2788938	test: 0.3219420	best: 0.3219420 (10)	total: 22s	remaining: 1m 23s
11:	learn: 0.2642754	test: 0.3102246	best: 0.3102246 (11)	total: 24s	remaining: 1m 21s
12:	learn: 0.2510122	test: 0.2999487	best: 0.2999487 (12)	total: 25.9s	remaining: 1m 19s
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40:	learn: 0.1240416	test: 0.2230700	best: 0.2230700 (40)	total: 1m 22s	remaining: 24.2s
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44:	learn: 0.1156621	test: 0.2214997	best: 0.2214997 (44)	total: 1m 30s	remaining: 16.1s
45:	learn: 0.1143192	test: 0.2210419	best: 0.2210419 (45)	total: 1m 32s	remaining: 14.1s
46:	learn: 0.1127190	test: 0.2204376	best: 0.2204376 (46)	total: 1m 34s	remaining: 12.1s
47:	learn: 0.1103712	test: 0.2201195	best: 0.2201195 (47)	total: 1m 36s	remaining: 10.1s
48:	learn: 0.1084872	test: 0.2200150	best: 0.2200150 (48)	total: 1m 38s	remaining: 8.07s
49:	learn: 0.1069574	test: 0.2195649	best: 0.2195649 (49)	total: 1m 40s	remaining: 6.05s
50:	learn: 0.1049957	test: 0.2192373	best: 0.2192373 (50)	total: 1m 42s	remaining: 4.03s
51:	learn: 0.1026196	test: 0.2190961	best: 0.2190961 (51)	total: 1m 44s	remaining: 2.02s
52:	learn: 0.1006730	test: 0.2186315	best: 0.2186315 (52)	total: 1m 46s	remaining: 0us

bestTest = 0.2186315184
bestIteration = 52

Trial 3, Fold 5: Log loss = 0.217743816459266, Average precision = 0.9712940630805605, ROC-AUC = 0.966626758077402, Elapsed Time = 107.1136712999978 seconds
Optimization Progress:   4%|4         | 4/100 [11:14<6:33:50, 246.15s/it]
Trial 4, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 4, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
0:	learn: 0.6751291	test: 0.6756952	best: 0.6756952 (0)	total: 424ms	remaining: 8.48s
1:	learn: 0.6580755	test: 0.6588961	best: 0.6588961 (1)	total: 878ms	remaining: 8.34s
2:	learn: 0.6414485	test: 0.6425834	best: 0.6425834 (2)	total: 1.32s	remaining: 7.91s
3:	learn: 0.6255995	test: 0.6270070	best: 0.6270070 (3)	total: 1.79s	remaining: 7.62s
4:	learn: 0.6102941	test: 0.6119646	best: 0.6119646 (4)	total: 2.18s	remaining: 6.97s
5:	learn: 0.5955309	test: 0.5974463	best: 0.5974463 (5)	total: 2.58s	remaining: 6.44s
6:	learn: 0.5812521	test: 0.5834002	best: 0.5834002 (6)	total: 3.01s	remaining: 6.01s
7:	learn: 0.5677320	test: 0.5701475	best: 0.5701475 (7)	total: 3.37s	remaining: 5.47s
8:	learn: 0.5546584	test: 0.5572836	best: 0.5572836 (8)	total: 3.81s	remaining: 5.08s
9:	learn: 0.5420135	test: 0.5449337	best: 0.5449337 (9)	total: 4.29s	remaining: 4.72s
10:	learn: 0.5297653	test: 0.5329147	best: 0.5329147 (10)	total: 4.74s	remaining: 4.31s
11:	learn: 0.5181672	test: 0.5216831	best: 0.5216831 (11)	total: 5.2s	remaining: 3.9s
12:	learn: 0.5069608	test: 0.5106155	best: 0.5106155 (12)	total: 5.62s	remaining: 3.46s
13:	learn: 0.4961092	test: 0.4999910	best: 0.4999910 (13)	total: 6s	remaining: 3s
14:	learn: 0.4856447	test: 0.4897795	best: 0.4897795 (14)	total: 6.43s	remaining: 2.57s
15:	learn: 0.4758098	test: 0.4802741	best: 0.4802741 (15)	total: 6.86s	remaining: 2.14s
16:	learn: 0.4660743	test: 0.4708078	best: 0.4708078 (16)	total: 7.25s	remaining: 1.71s
17:	learn: 0.4568298	test: 0.4618825	best: 0.4618825 (17)	total: 7.78s	remaining: 1.3s
18:	learn: 0.4479008	test: 0.4531683	best: 0.4531683 (18)	total: 8.14s	remaining: 857ms
19:	learn: 0.4391787	test: 0.4449043	best: 0.4449043 (19)	total: 8.58s	remaining: 429ms
20:	learn: 0.4308150	test: 0.4368316	best: 0.4368316 (20)	total: 9.02s	remaining: 0us

bestTest = 0.4368316113
bestIteration = 20

Trial 4, Fold 1: Log loss = 0.43683161132526216, Average precision = 0.9725119914716599, ROC-AUC = 0.9691511867720087, Elapsed Time = 9.127943200001027 seconds
Trial 4, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 4, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
0:	learn: 0.6751665	test: 0.6752813	best: 0.6752813 (0)	total: 496ms	remaining: 9.93s
1:	learn: 0.6580671	test: 0.6585045	best: 0.6585045 (1)	total: 957ms	remaining: 9.09s
2:	learn: 0.6415744	test: 0.6422953	best: 0.6422953 (2)	total: 1.43s	remaining: 8.59s
3:	learn: 0.6256481	test: 0.6266891	best: 0.6266891 (3)	total: 2s	remaining: 8.5s
4:	learn: 0.6102856	test: 0.6116619	best: 0.6116619 (4)	total: 2.51s	remaining: 8.03s
5:	learn: 0.5956096	test: 0.5972144	best: 0.5972144 (5)	total: 3.01s	remaining: 7.53s
6:	learn: 0.5814869	test: 0.5834651	best: 0.5834651 (6)	total: 3.53s	remaining: 7.06s
7:	learn: 0.5679892	test: 0.5702089	best: 0.5702089 (7)	total: 3.96s	remaining: 6.43s
8:	learn: 0.5548435	test: 0.5574491	best: 0.5574491 (8)	total: 4.49s	remaining: 5.98s
9:	learn: 0.5420610	test: 0.5449568	best: 0.5449568 (9)	total: 5.02s	remaining: 5.52s
10:	learn: 0.5298530	test: 0.5329699	best: 0.5329699 (10)	total: 5.51s	remaining: 5.01s
11:	learn: 0.5181822	test: 0.5215523	best: 0.5215523 (11)	total: 5.92s	remaining: 4.44s
12:	learn: 0.5069713	test: 0.5105400	best: 0.5105400 (12)	total: 6.39s	remaining: 3.93s
13:	learn: 0.4962473	test: 0.5000775	best: 0.5000775 (13)	total: 6.81s	remaining: 3.4s
14:	learn: 0.4858733	test: 0.4900487	best: 0.4900487 (14)	total: 7.28s	remaining: 2.91s
15:	learn: 0.4760014	test: 0.4803204	best: 0.4803204 (15)	total: 7.65s	remaining: 2.39s
16:	learn: 0.4663710	test: 0.4710931	best: 0.4710931 (16)	total: 8.12s	remaining: 1.91s
17:	learn: 0.4569903	test: 0.4620164	best: 0.4620164 (17)	total: 8.56s	remaining: 1.43s
18:	learn: 0.4480771	test: 0.4533657	best: 0.4533657 (18)	total: 8.97s	remaining: 944ms
19:	learn: 0.4395562	test: 0.4450377	best: 0.4450377 (19)	total: 9.33s	remaining: 466ms
20:	learn: 0.4314206	test: 0.4371695	best: 0.4371695 (20)	total: 9.73s	remaining: 0us

bestTest = 0.4371695184
bestIteration = 20

Trial 4, Fold 2: Log loss = 0.4371695183522166, Average precision = 0.9743944292205545, ROC-AUC = 0.9718838668127897, Elapsed Time = 9.848131099999591 seconds
Trial 4, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 4, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
0:	learn: 0.6753183	test: 0.6755595	best: 0.6755595 (0)	total: 396ms	remaining: 7.92s
1:	learn: 0.6580844	test: 0.6586462	best: 0.6586462 (1)	total: 952ms	remaining: 9.05s
2:	learn: 0.6415270	test: 0.6423197	best: 0.6423197 (2)	total: 1.39s	remaining: 8.35s
3:	learn: 0.6256985	test: 0.6266717	best: 0.6266717 (3)	total: 1.8s	remaining: 7.65s
4:	learn: 0.6105781	test: 0.6116335	best: 0.6116335 (4)	total: 2.22s	remaining: 7.11s
5:	learn: 0.5959607	test: 0.5972124	best: 0.5972124 (5)	total: 2.72s	remaining: 6.81s
6:	learn: 0.5816621	test: 0.5831419	best: 0.5831419 (6)	total: 3.25s	remaining: 6.49s
7:	learn: 0.5681023	test: 0.5696667	best: 0.5696667 (7)	total: 3.63s	remaining: 5.9s
8:	learn: 0.5551196	test: 0.5567472	best: 0.5567472 (8)	total: 4s	remaining: 5.33s
9:	learn: 0.5426289	test: 0.5443377	best: 0.5443377 (9)	total: 4.42s	remaining: 4.87s
10:	learn: 0.5304244	test: 0.5322596	best: 0.5322596 (10)	total: 4.92s	remaining: 4.47s
11:	learn: 0.5187494	test: 0.5208432	best: 0.5208432 (11)	total: 5.34s	remaining: 4.01s
12:	learn: 0.5074847	test: 0.5097322	best: 0.5097322 (12)	total: 5.73s	remaining: 3.53s
13:	learn: 0.4966125	test: 0.4988804	best: 0.4988804 (13)	total: 6.13s	remaining: 3.07s
14:	learn: 0.4861146	test: 0.4884831	best: 0.4884831 (14)	total: 6.54s	remaining: 2.62s
15:	learn: 0.4761447	test: 0.4785506	best: 0.4785506 (15)	total: 6.87s	remaining: 2.15s
16:	learn: 0.4664826	test: 0.4690408	best: 0.4690408 (16)	total: 7.28s	remaining: 1.71s
17:	learn: 0.4572168	test: 0.4598743	best: 0.4598743 (17)	total: 7.7s	remaining: 1.28s
18:	learn: 0.4485156	test: 0.4513245	best: 0.4513245 (18)	total: 8.06s	remaining: 848ms
19:	learn: 0.4400284	test: 0.4430049	best: 0.4430049 (19)	total: 8.42s	remaining: 421ms
20:	learn: 0.4317150	test: 0.4348469	best: 0.4348469 (20)	total: 8.82s	remaining: 0us

bestTest = 0.4348469358
bestIteration = 20

Trial 4, Fold 3: Log loss = 0.4348469357579093, Average precision = 0.9742232301269391, ROC-AUC = 0.9718304263659215, Elapsed Time = 8.931193399999756 seconds
Trial 4, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 4, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
0:	learn: 0.6752998	test: 0.6754327	best: 0.6754327 (0)	total: 413ms	remaining: 8.27s
1:	learn: 0.6583339	test: 0.6587253	best: 0.6587253 (1)	total: 798ms	remaining: 7.58s
2:	learn: 0.6418742	test: 0.6424550	best: 0.6424550 (2)	total: 1.21s	remaining: 7.26s
3:	learn: 0.6260680	test: 0.6269050	best: 0.6269050 (3)	total: 1.74s	remaining: 7.4s
4:	learn: 0.6108499	test: 0.6118316	best: 0.6118316 (4)	total: 2.17s	remaining: 6.93s
5:	learn: 0.5962492	test: 0.5974495	best: 0.5974495 (5)	total: 2.56s	remaining: 6.4s
6:	learn: 0.5822532	test: 0.5836620	best: 0.5836620 (6)	total: 2.93s	remaining: 5.87s
7:	learn: 0.5684085	test: 0.5700877	best: 0.5700877 (7)	total: 3.45s	remaining: 5.6s
8:	learn: 0.5552990	test: 0.5571120	best: 0.5571120 (8)	total: 3.79s	remaining: 5.05s
9:	learn: 0.5429237	test: 0.5448646	best: 0.5448646 (9)	total: 4.12s	remaining: 4.54s
10:	learn: 0.5306034	test: 0.5327420	best: 0.5327420 (10)	total: 4.6s	remaining: 4.18s
11:	learn: 0.5188968	test: 0.5213811	best: 0.5213811 (11)	total: 5.08s	remaining: 3.81s
12:	learn: 0.5077550	test: 0.5105051	best: 0.5105051 (12)	total: 5.44s	remaining: 3.35s
13:	learn: 0.4968071	test: 0.4997981	best: 0.4997981 (13)	total: 5.86s	remaining: 2.93s
14:	learn: 0.4863505	test: 0.4896662	best: 0.4896662 (14)	total: 6.32s	remaining: 2.53s
15:	learn: 0.4763280	test: 0.4798449	best: 0.4798449 (15)	total: 6.69s	remaining: 2.09s
16:	learn: 0.4666449	test: 0.4705958	best: 0.4705958 (16)	total: 7.13s	remaining: 1.68s
17:	learn: 0.4572764	test: 0.4615315	best: 0.4615315 (17)	total: 7.63s	remaining: 1.27s
18:	learn: 0.4482824	test: 0.4526776	best: 0.4526776 (18)	total: 8s	remaining: 842ms
19:	learn: 0.4396185	test: 0.4442756	best: 0.4442756 (19)	total: 8.48s	remaining: 424ms
20:	learn: 0.4312288	test: 0.4361223	best: 0.4361223 (20)	total: 8.93s	remaining: 0us

bestTest = 0.4361223162
bestIteration = 20

Trial 4, Fold 4: Log loss = 0.43612231617886416, Average precision = 0.9760371125173493, ROC-AUC = 0.9719734984775543, Elapsed Time = 9.036765299999388 seconds
Trial 4, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 4, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
0:	learn: 0.6750407	test: 0.6755989	best: 0.6755989 (0)	total: 535ms	remaining: 10.7s
1:	learn: 0.6580667	test: 0.6591000	best: 0.6591000 (1)	total: 903ms	remaining: 8.58s
2:	learn: 0.6416029	test: 0.6429922	best: 0.6429922 (2)	total: 1.27s	remaining: 7.61s
3:	learn: 0.6254916	test: 0.6274327	best: 0.6274327 (3)	total: 1.76s	remaining: 7.46s
4:	learn: 0.6103637	test: 0.6126868	best: 0.6126868 (4)	total: 2.11s	remaining: 6.74s
5:	learn: 0.5955315	test: 0.5982971	best: 0.5982971 (5)	total: 2.5s	remaining: 6.25s
6:	learn: 0.5814512	test: 0.5846105	best: 0.5846105 (6)	total: 2.83s	remaining: 5.67s
7:	learn: 0.5679944	test: 0.5714976	best: 0.5714976 (7)	total: 3.19s	remaining: 5.19s
8:	learn: 0.5548615	test: 0.5589816	best: 0.5589816 (8)	total: 3.55s	remaining: 4.73s
9:	learn: 0.5421431	test: 0.5465440	best: 0.5465440 (9)	total: 3.91s	remaining: 4.3s
10:	learn: 0.5299288	test: 0.5348384	best: 0.5348384 (10)	total: 4.36s	remaining: 3.96s
11:	learn: 0.5182163	test: 0.5234442	best: 0.5234442 (11)	total: 4.8s	remaining: 3.6s
12:	learn: 0.5068309	test: 0.5124603	best: 0.5124603 (12)	total: 5.21s	remaining: 3.21s
13:	learn: 0.4959101	test: 0.5019123	best: 0.5019123 (13)	total: 5.58s	remaining: 2.79s
14:	learn: 0.4854658	test: 0.4918334	best: 0.4918334 (14)	total: 5.94s	remaining: 2.38s
15:	learn: 0.4751767	test: 0.4819665	best: 0.4819665 (15)	total: 6.41s	remaining: 2s
16:	learn: 0.4656092	test: 0.4727705	best: 0.4727705 (16)	total: 6.75s	remaining: 1.59s
17:	learn: 0.4561120	test: 0.4637867	best: 0.4637867 (17)	total: 7.26s	remaining: 1.21s
18:	learn: 0.4469322	test: 0.4549357	best: 0.4549357 (18)	total: 7.65s	remaining: 805ms
19:	learn: 0.4382787	test: 0.4466474	best: 0.4466474 (19)	total: 8.11s	remaining: 405ms
20:	learn: 0.4301429	test: 0.4387201	best: 0.4387201 (20)	total: 8.45s	remaining: 0us

bestTest = 0.4387200902
bestIteration = 20

Trial 4, Fold 5: Log loss = 0.4387200902369523, Average precision = 0.9717789847898515, ROC-AUC = 0.9691278449733385, Elapsed Time = 8.559081100000185 seconds
Optimization Progress:   5%|5         | 5/100 [12:07<4:39:15, 176.38s/it]
Trial 5, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 5, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
0:	learn: 0.6773268	test: 0.6774498	best: 0.6774498 (0)	total: 101ms	remaining: 6.8s
1:	learn: 0.6619110	test: 0.6625295	best: 0.6625295 (1)	total: 204ms	remaining: 6.75s
2:	learn: 0.6468069	test: 0.6475521	best: 0.6475521 (2)	total: 313ms	remaining: 6.79s
3:	learn: 0.6321747	test: 0.6329820	best: 0.6329820 (3)	total: 416ms	remaining: 6.66s
4:	learn: 0.6188141	test: 0.6200537	best: 0.6200537 (4)	total: 531ms	remaining: 6.7s
5:	learn: 0.6056048	test: 0.6070380	best: 0.6070380 (5)	total: 649ms	remaining: 6.7s
6:	learn: 0.5929134	test: 0.5947511	best: 0.5947511 (6)	total: 770ms	remaining: 6.71s
7:	learn: 0.5799498	test: 0.5820043	best: 0.5820043 (7)	total: 884ms	remaining: 6.63s
8:	learn: 0.5677034	test: 0.5698802	best: 0.5698802 (8)	total: 999ms	remaining: 6.55s
9:	learn: 0.5559479	test: 0.5581774	best: 0.5581774 (9)	total: 1.1s	remaining: 6.38s
10:	learn: 0.5446578	test: 0.5469422	best: 0.5469422 (10)	total: 1.19s	remaining: 6.16s
11:	learn: 0.5337419	test: 0.5362091	best: 0.5362091 (11)	total: 1.29s	remaining: 6.05s
12:	learn: 0.5228115	test: 0.5254390	best: 0.5254390 (12)	total: 1.4s	remaining: 5.91s
13:	learn: 0.5125758	test: 0.5155250	best: 0.5155250 (13)	total: 1.52s	remaining: 5.85s
14:	learn: 0.5034445	test: 0.5069767	best: 0.5069767 (14)	total: 1.63s	remaining: 5.76s
15:	learn: 0.4938416	test: 0.4976427	best: 0.4976427 (15)	total: 1.74s	remaining: 5.67s
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26:	learn: 0.4075694	test: 0.4138252	best: 0.4138252 (26)	total: 3s	remaining: 4.56s
27:	learn: 0.4011371	test: 0.4074825	best: 0.4074825 (27)	total: 3.1s	remaining: 4.43s
28:	learn: 0.3948034	test: 0.4013427	best: 0.4013427 (28)	total: 3.21s	remaining: 4.31s
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31:	learn: 0.3780011	test: 0.3855947	best: 0.3855947 (31)	total: 3.56s	remaining: 4s
32:	learn: 0.3731962	test: 0.3812924	best: 0.3812924 (32)	total: 3.67s	remaining: 3.89s
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37:	learn: 0.3490938	test: 0.3585225	best: 0.3585225 (37)	total: 4.23s	remaining: 3.34s
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48:	learn: 0.3075631	test: 0.3199507	best: 0.3199507 (48)	total: 5.54s	remaining: 2.15s
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53:	learn: 0.2924210	test: 0.3061113	best: 0.3061113 (53)	total: 6.09s	remaining: 1.58s
54:	learn: 0.2899059	test: 0.3037648	best: 0.3037648 (54)	total: 6.18s	remaining: 1.46s
55:	learn: 0.2870995	test: 0.3011696	best: 0.3011696 (55)	total: 6.29s	remaining: 1.35s
56:	learn: 0.2843963	test: 0.2987296	best: 0.2987296 (56)	total: 6.42s	remaining: 1.24s
57:	learn: 0.2816931	test: 0.2962818	best: 0.2962818 (57)	total: 6.54s	remaining: 1.13s
58:	learn: 0.2793023	test: 0.2941391	best: 0.2941391 (58)	total: 6.65s	remaining: 1.01s
59:	learn: 0.2772561	test: 0.2923968	best: 0.2923968 (59)	total: 6.75s	remaining: 899ms
60:	learn: 0.2749554	test: 0.2904426	best: 0.2904426 (60)	total: 6.85s	remaining: 787ms
61:	learn: 0.2727164	test: 0.2884453	best: 0.2884453 (61)	total: 6.95s	remaining: 673ms
62:	learn: 0.2703777	test: 0.2862871	best: 0.2862871 (62)	total: 7.07s	remaining: 561ms
63:	learn: 0.2681265	test: 0.2842805	best: 0.2842805 (63)	total: 7.17s	remaining: 448ms
64:	learn: 0.2660524	test: 0.2824905	best: 0.2824905 (64)	total: 7.29s	remaining: 337ms
65:	learn: 0.2639212	test: 0.2806233	best: 0.2806233 (65)	total: 7.4s	remaining: 224ms
66:	learn: 0.2618865	test: 0.2788137	best: 0.2788137 (66)	total: 7.51s	remaining: 112ms
67:	learn: 0.2598070	test: 0.2771105	best: 0.2771105 (67)	total: 7.63s	remaining: 0us

bestTest = 0.2771105337
bestIteration = 67

Trial 5, Fold 1: Log loss = 0.2769973588825008, Average precision = 0.9738246884967362, ROC-AUC = 0.9688121346601473, Elapsed Time = 7.768258100000821 seconds
Trial 5, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 5, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
0:	learn: 0.6782274	test: 0.6790685	best: 0.6790685 (0)	total: 112ms	remaining: 7.53s
1:	learn: 0.6627084	test: 0.6638819	best: 0.6638819 (1)	total: 229ms	remaining: 7.55s
2:	learn: 0.6476905	test: 0.6490303	best: 0.6490303 (2)	total: 344ms	remaining: 7.45s
3:	learn: 0.6331870	test: 0.6346911	best: 0.6346911 (3)	total: 439ms	remaining: 7.03s
4:	learn: 0.6191109	test: 0.6208684	best: 0.6208684 (4)	total: 539ms	remaining: 6.79s
5:	learn: 0.6056036	test: 0.6074614	best: 0.6074614 (5)	total: 654ms	remaining: 6.75s
6:	learn: 0.5926216	test: 0.5945403	best: 0.5945403 (6)	total: 761ms	remaining: 6.63s
7:	learn: 0.5797980	test: 0.5819329	best: 0.5819329 (7)	total: 880ms	remaining: 6.6s
8:	learn: 0.5677802	test: 0.5700745	best: 0.5700745 (8)	total: 998ms	remaining: 6.54s
9:	learn: 0.5561671	test: 0.5587100	best: 0.5587100 (9)	total: 1.11s	remaining: 6.45s
10:	learn: 0.5448090	test: 0.5475637	best: 0.5475637 (10)	total: 1.22s	remaining: 6.32s
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60:	learn: 0.2755043	test: 0.2872001	best: 0.2872001 (60)	total: 6.94s	remaining: 796ms
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64:	learn: 0.2666751	test: 0.2789453	best: 0.2789453 (64)	total: 7.39s	remaining: 341ms
65:	learn: 0.2645032	test: 0.2769432	best: 0.2769432 (65)	total: 7.5s	remaining: 227ms
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67:	learn: 0.2607091	test: 0.2733853	best: 0.2733853 (67)	total: 7.73s	remaining: 0us

bestTest = 0.2733852713
bestIteration = 67

Trial 5, Fold 2: Log loss = 0.2733049031910323, Average precision = 0.9753165771130878, ROC-AUC = 0.9720532173413727, Elapsed Time = 7.869337500000256 seconds
Trial 5, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 5, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
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59:	learn: 0.2797722	test: 0.2897492	best: 0.2897492 (59)	total: 6.85s	remaining: 914ms
60:	learn: 0.2776242	test: 0.2877678	best: 0.2877678 (60)	total: 6.96s	remaining: 799ms
61:	learn: 0.2755373	test: 0.2858922	best: 0.2858922 (61)	total: 7.07s	remaining: 684ms
62:	learn: 0.2731528	test: 0.2836383	best: 0.2836383 (62)	total: 7.18s	remaining: 570ms
63:	learn: 0.2707401	test: 0.2813843	best: 0.2813843 (63)	total: 7.3s	remaining: 456ms
64:	learn: 0.2685654	test: 0.2793232	best: 0.2793232 (64)	total: 7.42s	remaining: 342ms
65:	learn: 0.2663744	test: 0.2772995	best: 0.2772995 (65)	total: 7.53s	remaining: 228ms
66:	learn: 0.2643077	test: 0.2754240	best: 0.2754240 (66)	total: 7.67s	remaining: 114ms
67:	learn: 0.2622474	test: 0.2734148	best: 0.2734148 (67)	total: 7.77s	remaining: 0us

bestTest = 0.2734148285
bestIteration = 67

Trial 5, Fold 3: Log loss = 0.2734589256030984, Average precision = 0.973806963901712, ROC-AUC = 0.9717785090676194, Elapsed Time = 7.914452499997424 seconds
Trial 5, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 5, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
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60:	learn: 0.2746903	test: 0.2873258	best: 0.2873258 (60)	total: 6.99s	remaining: 802ms
61:	learn: 0.2727044	test: 0.2855098	best: 0.2855098 (61)	total: 7.11s	remaining: 688ms
62:	learn: 0.2706245	test: 0.2835272	best: 0.2835272 (62)	total: 7.22s	remaining: 573ms
63:	learn: 0.2683730	test: 0.2813912	best: 0.2813912 (63)	total: 7.34s	remaining: 459ms
64:	learn: 0.2662880	test: 0.2793947	best: 0.2793947 (64)	total: 7.46s	remaining: 344ms
65:	learn: 0.2640477	test: 0.2772726	best: 0.2772726 (65)	total: 7.58s	remaining: 230ms
66:	learn: 0.2619216	test: 0.2754482	best: 0.2754482 (66)	total: 7.69s	remaining: 115ms
67:	learn: 0.2602140	test: 0.2739280	best: 0.2739280 (67)	total: 7.8s	remaining: 0us

bestTest = 0.2739279915
bestIteration = 67

Trial 5, Fold 4: Log loss = 0.27384385937702393, Average precision = 0.9763841751003696, ROC-AUC = 0.972039963900036, Elapsed Time = 7.9421445000007225 seconds
Trial 5, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 5, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
0:	learn: 0.6768609	test: 0.6773147	best: 0.6773147 (0)	total: 85.2ms	remaining: 5.71s
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5:	learn: 0.6042531	test: 0.6065956	best: 0.6065956 (5)	total: 654ms	remaining: 6.75s
6:	learn: 0.5913034	test: 0.5939922	best: 0.5939922 (6)	total: 757ms	remaining: 6.59s
7:	learn: 0.5785977	test: 0.5815350	best: 0.5815350 (7)	total: 861ms	remaining: 6.45s
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55:	learn: 0.2886764	test: 0.3056935	best: 0.3056935 (55)	total: 6.3s	remaining: 1.35s
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57:	learn: 0.2833266	test: 0.3008953	best: 0.3008953 (57)	total: 6.52s	remaining: 1.12s
58:	learn: 0.2807482	test: 0.2985523	best: 0.2985523 (58)	total: 6.64s	remaining: 1.01s
59:	learn: 0.2782630	test: 0.2962800	best: 0.2962800 (59)	total: 6.74s	remaining: 899ms
60:	learn: 0.2758378	test: 0.2940852	best: 0.2940852 (60)	total: 6.86s	remaining: 787ms
61:	learn: 0.2732940	test: 0.2918319	best: 0.2918319 (61)	total: 6.97s	remaining: 674ms
62:	learn: 0.2708756	test: 0.2896735	best: 0.2896735 (62)	total: 7.08s	remaining: 562ms
63:	learn: 0.2685466	test: 0.2875952	best: 0.2875952 (63)	total: 7.2s	remaining: 450ms
64:	learn: 0.2663356	test: 0.2856111	best: 0.2856111 (64)	total: 7.32s	remaining: 338ms
65:	learn: 0.2642497	test: 0.2836738	best: 0.2836738 (65)	total: 7.43s	remaining: 225ms
66:	learn: 0.2621468	test: 0.2817574	best: 0.2817574 (66)	total: 7.54s	remaining: 113ms
67:	learn: 0.2602072	test: 0.2800199	best: 0.2800199 (67)	total: 7.66s	remaining: 0us

bestTest = 0.2800199454
bestIteration = 67

Trial 5, Fold 5: Log loss = 0.2798110219588214, Average precision = 0.9725522608394847, ROC-AUC = 0.969878304813927, Elapsed Time = 7.809099500002048 seconds
Optimization Progress:   6%|6         | 6/100 [12:54<3:27:22, 132.37s/it]
Trial 6, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 6, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
0:	learn: 0.6802252	test: 0.6804421	best: 0.6804421 (0)	total: 21.6ms	remaining: 260ms
1:	learn: 0.6682112	test: 0.6683883	best: 0.6683883 (1)	total: 43.2ms	remaining: 237ms
2:	learn: 0.6589036	test: 0.6589366	best: 0.6589366 (2)	total: 64.3ms	remaining: 214ms
3:	learn: 0.6448929	test: 0.6448146	best: 0.6448146 (3)	total: 85ms	remaining: 191ms
4:	learn: 0.6338868	test: 0.6337594	best: 0.6337594 (4)	total: 106ms	remaining: 169ms
5:	learn: 0.6256641	test: 0.6255702	best: 0.6255702 (5)	total: 125ms	remaining: 146ms
6:	learn: 0.6150270	test: 0.6149716	best: 0.6149716 (6)	total: 145ms	remaining: 124ms
7:	learn: 0.6067636	test: 0.6065486	best: 0.6065486 (7)	total: 165ms	remaining: 103ms
8:	learn: 0.5986717	test: 0.5983345	best: 0.5983345 (8)	total: 185ms	remaining: 82.1ms
9:	learn: 0.5880264	test: 0.5876876	best: 0.5876876 (9)	total: 204ms	remaining: 61.3ms
10:	learn: 0.5761084	test: 0.5757119	best: 0.5757119 (10)	total: 225ms	remaining: 40.9ms
11:	learn: 0.5677784	test: 0.5674551	best: 0.5674551 (11)	total: 246ms	remaining: 20.5ms
12:	learn: 0.5564968	test: 0.5561573	best: 0.5561573 (12)	total: 266ms	remaining: 0us

bestTest = 0.5561572861
bestIteration = 12

Trial 6, Fold 1: Log loss = 0.5561536599010769, Average precision = 0.9460395785417441, ROC-AUC = 0.9484670662403273, Elapsed Time = 0.3607932000013534 seconds
Trial 6, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 6, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
0:	learn: 0.6804183	test: 0.6804112	best: 0.6804112 (0)	total: 20.2ms	remaining: 242ms
1:	learn: 0.6681285	test: 0.6682514	best: 0.6682514 (1)	total: 42ms	remaining: 231ms
2:	learn: 0.6561352	test: 0.6563840	best: 0.6563840 (2)	total: 63.1ms	remaining: 210ms
3:	learn: 0.6433992	test: 0.6436953	best: 0.6436953 (3)	total: 84.7ms	remaining: 191ms
4:	learn: 0.6326166	test: 0.6330715	best: 0.6330715 (4)	total: 105ms	remaining: 168ms
5:	learn: 0.6242096	test: 0.6246486	best: 0.6246486 (5)	total: 126ms	remaining: 147ms
6:	learn: 0.6157876	test: 0.6162656	best: 0.6162656 (6)	total: 147ms	remaining: 126ms
7:	learn: 0.6028798	test: 0.6035262	best: 0.6035262 (7)	total: 168ms	remaining: 105ms
8:	learn: 0.5953127	test: 0.5959643	best: 0.5959643 (8)	total: 190ms	remaining: 84.3ms
9:	learn: 0.5856283	test: 0.5864070	best: 0.5864070 (9)	total: 211ms	remaining: 63.2ms
10:	learn: 0.5740877	test: 0.5749715	best: 0.5749715 (10)	total: 232ms	remaining: 42.3ms
11:	learn: 0.5672548	test: 0.5681372	best: 0.5681372 (11)	total: 254ms	remaining: 21.2ms
12:	learn: 0.5558938	test: 0.5569927	best: 0.5569927 (12)	total: 275ms	remaining: 0us

bestTest = 0.556992702
bestIteration = 12

Trial 6, Fold 2: Log loss = 0.5570118298070835, Average precision = 0.9433713404357043, ROC-AUC = 0.9467596688377728, Elapsed Time = 0.3714698000003409 seconds
Trial 6, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 6, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
0:	learn: 0.6806873	test: 0.6805359	best: 0.6805359 (0)	total: 21.8ms	remaining: 261ms
1:	learn: 0.6683826	test: 0.6681212	best: 0.6681212 (1)	total: 43ms	remaining: 236ms
2:	learn: 0.6590432	test: 0.6588794	best: 0.6588794 (2)	total: 64.4ms	remaining: 215ms
3:	learn: 0.6446687	test: 0.6442932	best: 0.6442932 (3)	total: 87ms	remaining: 196ms
4:	learn: 0.6308389	test: 0.6302799	best: 0.6302799 (4)	total: 108ms	remaining: 173ms
5:	learn: 0.6198787	test: 0.6192151	best: 0.6192151 (5)	total: 129ms	remaining: 151ms
6:	learn: 0.6114146	test: 0.6108602	best: 0.6108602 (6)	total: 150ms	remaining: 129ms
7:	learn: 0.5988403	test: 0.5981104	best: 0.5981104 (7)	total: 171ms	remaining: 107ms
8:	learn: 0.5898561	test: 0.5888247	best: 0.5888247 (8)	total: 192ms	remaining: 85.2ms
9:	learn: 0.5823062	test: 0.5814279	best: 0.5814279 (9)	total: 212ms	remaining: 63.7ms
10:	learn: 0.5721491	test: 0.5712091	best: 0.5712091 (10)	total: 234ms	remaining: 42.5ms
11:	learn: 0.5635528	test: 0.5624689	best: 0.5624689 (11)	total: 254ms	remaining: 21.2ms
12:	learn: 0.5557207	test: 0.5543627	best: 0.5543627 (12)	total: 275ms	remaining: 0us

bestTest = 0.5543626585
bestIteration = 12

Trial 6, Fold 3: Log loss = 0.5544209334880373, Average precision = 0.9478648611501131, ROC-AUC = 0.9500570638826555, Elapsed Time = 0.37057730000015 seconds
Trial 6, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 6, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
0:	learn: 0.6804686	test: 0.6802410	best: 0.6802410 (0)	total: 20.3ms	remaining: 244ms
1:	learn: 0.6705542	test: 0.6703085	best: 0.6703085 (1)	total: 41.1ms	remaining: 226ms
2:	learn: 0.6584628	test: 0.6581211	best: 0.6581211 (2)	total: 62.4ms	remaining: 208ms
3:	learn: 0.6442829	test: 0.6439356	best: 0.6439356 (3)	total: 83.9ms	remaining: 189ms
4:	learn: 0.6333709	test: 0.6329557	best: 0.6329557 (4)	total: 105ms	remaining: 168ms
5:	learn: 0.6247979	test: 0.6242577	best: 0.6242577 (5)	total: 126ms	remaining: 146ms
6:	learn: 0.6158961	test: 0.6152921	best: 0.6152921 (6)	total: 145ms	remaining: 125ms
7:	learn: 0.6057282	test: 0.6050771	best: 0.6050771 (7)	total: 165ms	remaining: 103ms
8:	learn: 0.5979378	test: 0.5971554	best: 0.5971554 (8)	total: 186ms	remaining: 82.6ms
9:	learn: 0.5905088	test: 0.5896786	best: 0.5896786 (9)	total: 207ms	remaining: 62ms
10:	learn: 0.5833599	test: 0.5824848	best: 0.5824848 (10)	total: 227ms	remaining: 41.3ms
11:	learn: 0.5749975	test: 0.5741083	best: 0.5741083 (11)	total: 248ms	remaining: 20.7ms
12:	learn: 0.5638812	test: 0.5629968	best: 0.5629968 (12)	total: 270ms	remaining: 0us

bestTest = 0.5629968452
bestIteration = 12

Trial 6, Fold 4: Log loss = 0.5629718942723798, Average precision = 0.9458023229317589, ROC-AUC = 0.9472448592125248, Elapsed Time = 0.36387010000180453 seconds
Trial 6, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 6, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
0:	learn: 0.6803936	test: 0.6807428	best: 0.6807428 (0)	total: 20.2ms	remaining: 243ms
1:	learn: 0.6676559	test: 0.6682857	best: 0.6682857 (1)	total: 40.7ms	remaining: 224ms
2:	learn: 0.6557227	test: 0.6566625	best: 0.6566625 (2)	total: 61.4ms	remaining: 205ms
3:	learn: 0.6463381	test: 0.6474383	best: 0.6474383 (3)	total: 81.9ms	remaining: 184ms
4:	learn: 0.6326081	test: 0.6338504	best: 0.6338504 (4)	total: 103ms	remaining: 165ms
5:	learn: 0.6238334	test: 0.6253332	best: 0.6253332 (5)	total: 124ms	remaining: 144ms
6:	learn: 0.6108541	test: 0.6124725	best: 0.6124725 (6)	total: 144ms	remaining: 124ms
7:	learn: 0.5984452	test: 0.6002132	best: 0.6002132 (7)	total: 165ms	remaining: 103ms
8:	learn: 0.5894687	test: 0.5914183	best: 0.5914183 (8)	total: 186ms	remaining: 82.7ms
9:	learn: 0.5820750	test: 0.5841134	best: 0.5841134 (9)	total: 207ms	remaining: 62ms
10:	learn: 0.5718870	test: 0.5740480	best: 0.5740480 (10)	total: 228ms	remaining: 41.4ms
11:	learn: 0.5650425	test: 0.5673183	best: 0.5673183 (11)	total: 249ms	remaining: 20.7ms
12:	learn: 0.5570764	test: 0.5595637	best: 0.5595637 (12)	total: 270ms	remaining: 0us

bestTest = 0.5595636953
bestIteration = 12

Trial 6, Fold 5: Log loss = 0.5595586354766492, Average precision = 0.9440261715292246, ROC-AUC = 0.9444601007004441, Elapsed Time = 0.3644702999990841 seconds
Optimization Progress:   7%|7         | 7/100 [13:03<2:22:45, 92.10s/it] 
Trial 7, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 7, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
0:	learn: 0.5989916	test: 0.5987729	best: 0.5987729 (0)	total: 48.7ms	remaining: 682ms
1:	learn: 0.5212612	test: 0.5209932	best: 0.5209932 (1)	total: 97ms	remaining: 631ms
2:	learn: 0.4632245	test: 0.4633237	best: 0.4633237 (2)	total: 142ms	remaining: 568ms
3:	learn: 0.4174577	test: 0.4177841	best: 0.4177841 (3)	total: 189ms	remaining: 519ms
4:	learn: 0.3814671	test: 0.3829626	best: 0.3829626 (4)	total: 233ms	remaining: 465ms
5:	learn: 0.3555651	test: 0.3578947	best: 0.3578947 (5)	total: 281ms	remaining: 422ms
6:	learn: 0.3310748	test: 0.3339130	best: 0.3339130 (6)	total: 325ms	remaining: 372ms
7:	learn: 0.3140789	test: 0.3184745	best: 0.3184745 (7)	total: 372ms	remaining: 325ms
8:	learn: 0.2981974	test: 0.3031023	best: 0.3031023 (8)	total: 418ms	remaining: 278ms
9:	learn: 0.2870217	test: 0.2923589	best: 0.2923589 (9)	total: 463ms	remaining: 231ms
10:	learn: 0.2751626	test: 0.2807556	best: 0.2807556 (10)	total: 510ms	remaining: 186ms
11:	learn: 0.2690857	test: 0.2752113	best: 0.2752113 (11)	total: 559ms	remaining: 140ms
12:	learn: 0.2609884	test: 0.2675063	best: 0.2675063 (12)	total: 606ms	remaining: 93.2ms
13:	learn: 0.2550054	test: 0.2618784	best: 0.2618784 (13)	total: 656ms	remaining: 46.8ms
14:	learn: 0.2478576	test: 0.2548913	best: 0.2548913 (14)	total: 702ms	remaining: 0us

bestTest = 0.254891292
bestIteration = 14

Trial 7, Fold 1: Log loss = 0.2547158355731668, Average precision = 0.9696817835744587, ROC-AUC = 0.9642371715824742, Elapsed Time = 0.8005144000017026 seconds
Trial 7, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 7, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
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2:	learn: 0.4770784	test: 0.4856927	best: 0.4856927 (2)	total: 153ms	remaining: 611ms
3:	learn: 0.4344532	test: 0.4450714	best: 0.4450714 (3)	total: 202ms	remaining: 555ms
4:	learn: 0.4058501	test: 0.4173530	best: 0.4173530 (4)	total: 250ms	remaining: 500ms
5:	learn: 0.3777201	test: 0.3892724	best: 0.3892724 (5)	total: 301ms	remaining: 451ms
6:	learn: 0.3514552	test: 0.3616842	best: 0.3616842 (6)	total: 348ms	remaining: 398ms
7:	learn: 0.3347482	test: 0.3463019	best: 0.3463019 (7)	total: 398ms	remaining: 348ms
8:	learn: 0.3156493	test: 0.3259220	best: 0.3259220 (8)	total: 450ms	remaining: 300ms
9:	learn: 0.2978537	test: 0.3071819	best: 0.3071819 (9)	total: 499ms	remaining: 249ms
10:	learn: 0.2839342	test: 0.2924212	best: 0.2924212 (10)	total: 549ms	remaining: 200ms
11:	learn: 0.2730108	test: 0.2810501	best: 0.2810501 (11)	total: 593ms	remaining: 148ms
12:	learn: 0.2657488	test: 0.2737289	best: 0.2737289 (12)	total: 641ms	remaining: 98.6ms
13:	learn: 0.2574214	test: 0.2650518	best: 0.2650518 (13)	total: 690ms	remaining: 49.3ms
14:	learn: 0.2515038	test: 0.2586885	best: 0.2586885 (14)	total: 739ms	remaining: 0us

bestTest = 0.2586884716
bestIteration = 14

Trial 7, Fold 2: Log loss = 0.2585835950241426, Average precision = 0.9692300288483594, ROC-AUC = 0.9664363489361418, Elapsed Time = 0.8348800000021583 seconds
Trial 7, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 7, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
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1:	learn: 0.5225025	test: 0.5213271	best: 0.5213271 (1)	total: 99.3ms	remaining: 646ms
2:	learn: 0.4704166	test: 0.4702962	best: 0.4702962 (2)	total: 147ms	remaining: 590ms
3:	learn: 0.4238724	test: 0.4237657	best: 0.4237657 (3)	total: 198ms	remaining: 545ms
4:	learn: 0.3920162	test: 0.3924085	best: 0.3924085 (4)	total: 248ms	remaining: 495ms
5:	learn: 0.3605599	test: 0.3604553	best: 0.3604553 (5)	total: 296ms	remaining: 444ms
6:	learn: 0.3370087	test: 0.3368984	best: 0.3368984 (6)	total: 345ms	remaining: 394ms
7:	learn: 0.3183141	test: 0.3179494	best: 0.3179494 (7)	total: 393ms	remaining: 344ms
8:	learn: 0.3035164	test: 0.3031106	best: 0.3031106 (8)	total: 442ms	remaining: 295ms
9:	learn: 0.2918984	test: 0.2919343	best: 0.2919343 (9)	total: 498ms	remaining: 249ms
10:	learn: 0.2790050	test: 0.2788281	best: 0.2788281 (10)	total: 556ms	remaining: 202ms
11:	learn: 0.2707991	test: 0.2701632	best: 0.2701632 (11)	total: 605ms	remaining: 151ms
12:	learn: 0.2640301	test: 0.2632638	best: 0.2632638 (12)	total: 653ms	remaining: 100ms
13:	learn: 0.2592847	test: 0.2584041	best: 0.2584041 (13)	total: 705ms	remaining: 50.4ms
14:	learn: 0.2540698	test: 0.2532622	best: 0.2532622 (14)	total: 757ms	remaining: 0us

bestTest = 0.2532622122
bestIteration = 14

Trial 7, Fold 3: Log loss = 0.253209611880942, Average precision = 0.971279655308492, ROC-AUC = 0.9674125652502625, Elapsed Time = 0.8535821000004944 seconds
Trial 7, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 7, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
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1:	learn: 0.5268457	test: 0.5283785	best: 0.5283785 (1)	total: 98.1ms	remaining: 638ms
2:	learn: 0.4640682	test: 0.4657834	best: 0.4657834 (2)	total: 148ms	remaining: 593ms
3:	learn: 0.4229567	test: 0.4269933	best: 0.4269933 (3)	total: 199ms	remaining: 548ms
4:	learn: 0.3854017	test: 0.3896033	best: 0.3896033 (4)	total: 247ms	remaining: 495ms
5:	learn: 0.3578307	test: 0.3618753	best: 0.3618753 (5)	total: 293ms	remaining: 440ms
6:	learn: 0.3345831	test: 0.3387825	best: 0.3387825 (6)	total: 342ms	remaining: 391ms
7:	learn: 0.3160064	test: 0.3206016	best: 0.3206016 (7)	total: 386ms	remaining: 337ms
8:	learn: 0.2990350	test: 0.3031130	best: 0.3031130 (8)	total: 433ms	remaining: 289ms
9:	learn: 0.2872985	test: 0.2915213	best: 0.2915213 (9)	total: 482ms	remaining: 241ms
10:	learn: 0.2756518	test: 0.2798150	best: 0.2798150 (10)	total: 531ms	remaining: 193ms
11:	learn: 0.2670230	test: 0.2712782	best: 0.2712782 (11)	total: 579ms	remaining: 145ms
12:	learn: 0.2591719	test: 0.2634481	best: 0.2634481 (12)	total: 627ms	remaining: 96.4ms
13:	learn: 0.2534180	test: 0.2575998	best: 0.2575998 (13)	total: 677ms	remaining: 48.3ms
14:	learn: 0.2473836	test: 0.2516235	best: 0.2516235 (14)	total: 732ms	remaining: 0us

bestTest = 0.2516234838
bestIteration = 14

Trial 7, Fold 4: Log loss = 0.2515106919620331, Average precision = 0.9708737713312391, ROC-AUC = 0.9652914881339848, Elapsed Time = 0.8270278999989387 seconds
Trial 7, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 7, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
0:	learn: 0.5964152	test: 0.5978923	best: 0.5978923 (0)	total: 48.9ms	remaining: 684ms
1:	learn: 0.5218618	test: 0.5249103	best: 0.5249103 (1)	total: 96.7ms	remaining: 628ms
2:	learn: 0.4624925	test: 0.4663094	best: 0.4663094 (2)	total: 145ms	remaining: 579ms
3:	learn: 0.4187886	test: 0.4236072	best: 0.4236072 (3)	total: 191ms	remaining: 526ms
4:	learn: 0.3819607	test: 0.3878818	best: 0.3878818 (4)	total: 237ms	remaining: 475ms
5:	learn: 0.3544906	test: 0.3608849	best: 0.3608849 (5)	total: 284ms	remaining: 426ms
6:	learn: 0.3331826	test: 0.3416908	best: 0.3416908 (6)	total: 332ms	remaining: 379ms
7:	learn: 0.3115865	test: 0.3207907	best: 0.3207907 (7)	total: 379ms	remaining: 331ms
8:	learn: 0.2949340	test: 0.3046269	best: 0.3046269 (8)	total: 427ms	remaining: 285ms
9:	learn: 0.2836907	test: 0.2935630	best: 0.2935630 (9)	total: 477ms	remaining: 239ms
10:	learn: 0.2732865	test: 0.2835165	best: 0.2835165 (10)	total: 525ms	remaining: 191ms
11:	learn: 0.2648832	test: 0.2759568	best: 0.2759568 (11)	total: 576ms	remaining: 144ms
12:	learn: 0.2580136	test: 0.2691159	best: 0.2691159 (12)	total: 624ms	remaining: 96ms
13:	learn: 0.2543786	test: 0.2658205	best: 0.2658205 (13)	total: 673ms	remaining: 48.1ms
14:	learn: 0.2493514	test: 0.2609888	best: 0.2609888 (14)	total: 721ms	remaining: 0us

bestTest = 0.2609888218
bestIteration = 14

Trial 7, Fold 5: Log loss = 0.26076694101564946, Average precision = 0.9651209070356066, ROC-AUC = 0.9620912992586813, Elapsed Time = 0.81645330000174 seconds
Optimization Progress:   8%|8         | 8/100 [13:15<1:41:53, 66.45s/it]
Trial 8, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 8, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
0:	learn: 0.6235132	test: 0.6233448	best: 0.6233448 (0)	total: 229ms	remaining: 20.1s
1:	learn: 0.5639866	test: 0.5645558	best: 0.5645558 (1)	total: 471ms	remaining: 20.5s
2:	learn: 0.5133377	test: 0.5151054	best: 0.5151054 (2)	total: 720ms	remaining: 20.6s
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80:	learn: 0.1674586	test: 0.2076358	best: 0.2076358 (80)	total: 24.1s	remaining: 2.38s
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82:	learn: 0.1671188	test: 0.2074760	best: 0.2074760 (82)	total: 24.6s	remaining: 1.77s
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85:	learn: 0.1660414	test: 0.2071192	best: 0.2071192 (85)	total: 25.4s	remaining: 885ms
86:	learn: 0.1655952	test: 0.2068205	best: 0.2068205 (86)	total: 25.6s	remaining: 590ms
87:	learn: 0.1655175	test: 0.2068100	best: 0.2068100 (87)	total: 25.9s	remaining: 295ms
88:	learn: 0.1652717	test: 0.2066757	best: 0.2066757 (88)	total: 26.2s	remaining: 0us

bestTest = 0.2066756533
bestIteration = 88

Trial 8, Fold 1: Log loss = 0.20633682757903246, Average precision = 0.9733903949856466, ROC-AUC = 0.9689592563915057, Elapsed Time = 26.378331899999466 seconds
Trial 8, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 8, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
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86:	learn: 0.1676349	test: 0.2004561	best: 0.2004561 (86)	total: 21.9s	remaining: 504ms
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88:	learn: 0.1670596	test: 0.2002621	best: 0.2002621 (88)	total: 22.5s	remaining: 0us

bestTest = 0.2002620715
bestIteration = 88

Trial 8, Fold 2: Log loss = 0.20005981628069316, Average precision = 0.97440544640823, ROC-AUC = 0.9712539617553091, Elapsed Time = 22.641231000001426 seconds
Trial 8, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 8, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
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80:	learn: 0.1716791	test: 0.2002173	best: 0.2002173 (80)	total: 21.1s	remaining: 2.09s
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84:	learn: 0.1706641	test: 0.2000664	best: 0.2000664 (84)	total: 22s	remaining: 1.03s
85:	learn: 0.1700734	test: 0.2000005	best: 0.2000005 (85)	total: 22.3s	remaining: 777ms
86:	learn: 0.1693848	test: 0.1997752	best: 0.1997752 (86)	total: 22.6s	remaining: 519ms
87:	learn: 0.1689204	test: 0.1997132	best: 0.1997132 (87)	total: 22.8s	remaining: 260ms
88:	learn: 0.1683673	test: 0.1993576	best: 0.1993576 (88)	total: 23.1s	remaining: 0us

bestTest = 0.1993575998
bestIteration = 88

Trial 8, Fold 3: Log loss = 0.19917795720290504, Average precision = 0.9747259111593369, ROC-AUC = 0.9710141961456596, Elapsed Time = 23.275047400002222 seconds
Trial 8, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 8, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
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2:	learn: 0.5138476	test: 0.5170890	best: 0.5170890 (2)	total: 731ms	remaining: 20.9s
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87:	learn: 0.1665549	test: 0.2043614	best: 0.2043614 (87)	total: 23.2s	remaining: 263ms
88:	learn: 0.1662487	test: 0.2041590	best: 0.2041590 (88)	total: 23.4s	remaining: 0us

bestTest = 0.2041590354
bestIteration = 88

Trial 8, Fold 4: Log loss = 0.20391093724402545, Average precision = 0.9735056098800242, ROC-AUC = 0.9687904604533717, Elapsed Time = 23.611795300002996 seconds
Trial 8, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 8, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
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84:	learn: 0.1691877	test: 0.2114318	best: 0.2114318 (84)	total: 21.8s	remaining: 1.02s
85:	learn: 0.1686102	test: 0.2113363	best: 0.2113363 (85)	total: 22s	remaining: 769ms
86:	learn: 0.1681279	test: 0.2110884	best: 0.2110884 (86)	total: 22.3s	remaining: 513ms
87:	learn: 0.1676486	test: 0.2110622	best: 0.2110622 (87)	total: 22.6s	remaining: 257ms
88:	learn: 0.1676000	test: 0.2110129	best: 0.2110129 (88)	total: 22.7s	remaining: 0us

bestTest = 0.2110128776
bestIteration = 88

Trial 8, Fold 5: Log loss = 0.21064118275161223, Average precision = 0.9721801808196247, ROC-AUC = 0.9683430503688013, Elapsed Time = 22.82268710000062 seconds
Optimization Progress:   9%|9         | 9/100 [15:21<2:09:17, 85.25s/it]
Trial 9, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 9, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
0:	learn: 0.5936707	test: 0.5933712	best: 0.5933712 (0)	total: 446ms	remaining: 14.3s
1:	learn: 0.5169584	test: 0.5169710	best: 0.5169710 (1)	total: 842ms	remaining: 13.1s
2:	learn: 0.4557980	test: 0.4561473	best: 0.4561473 (2)	total: 1.29s	remaining: 12.9s
3:	learn: 0.4102913	test: 0.4111520	best: 0.4111520 (3)	total: 1.64s	remaining: 11.9s
4:	learn: 0.3729478	test: 0.3742570	best: 0.3742570 (4)	total: 2.02s	remaining: 11.3s
5:	learn: 0.3443626	test: 0.3463007	best: 0.3463007 (5)	total: 2.37s	remaining: 10.6s
6:	learn: 0.3212944	test: 0.3238142	best: 0.3238142 (6)	total: 2.85s	remaining: 10.6s
7:	learn: 0.3018028	test: 0.3048864	best: 0.3048864 (7)	total: 3.23s	remaining: 10.1s
8:	learn: 0.2870346	test: 0.2905007	best: 0.2905007 (8)	total: 3.51s	remaining: 9.35s
9:	learn: 0.2742177	test: 0.2782839	best: 0.2782839 (9)	total: 3.83s	remaining: 8.81s
10:	learn: 0.2643747	test: 0.2688520	best: 0.2688520 (10)	total: 4.21s	remaining: 8.41s
11:	learn: 0.2552880	test: 0.2605364	best: 0.2605364 (11)	total: 4.63s	remaining: 8.11s
12:	learn: 0.2479670	test: 0.2538750	best: 0.2538750 (12)	total: 4.96s	remaining: 7.63s
13:	learn: 0.2423350	test: 0.2489049	best: 0.2489049 (13)	total: 5.32s	remaining: 7.23s
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15:	learn: 0.2329669	test: 0.2408450	best: 0.2408450 (15)	total: 6.19s	remaining: 6.57s
16:	learn: 0.2284540	test: 0.2367572	best: 0.2367572 (16)	total: 6.61s	remaining: 6.22s
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18:	learn: 0.2207452	test: 0.2299804	best: 0.2299804 (18)	total: 7.36s	remaining: 5.42s
19:	learn: 0.2182944	test: 0.2278850	best: 0.2278850 (19)	total: 7.81s	remaining: 5.08s
20:	learn: 0.2160060	test: 0.2260179	best: 0.2260179 (20)	total: 8.14s	remaining: 4.65s
21:	learn: 0.2141739	test: 0.2245275	best: 0.2245275 (21)	total: 8.51s	remaining: 4.26s
22:	learn: 0.2121770	test: 0.2228235	best: 0.2228235 (22)	total: 8.85s	remaining: 3.85s
23:	learn: 0.2101391	test: 0.2211456	best: 0.2211456 (23)	total: 9.21s	remaining: 3.45s
24:	learn: 0.2083146	test: 0.2197165	best: 0.2197165 (24)	total: 9.67s	remaining: 3.09s
25:	learn: 0.2066468	test: 0.2183103	best: 0.2183103 (25)	total: 10s	remaining: 2.7s
26:	learn: 0.2055387	test: 0.2176514	best: 0.2176514 (26)	total: 10.5s	remaining: 2.33s
27:	learn: 0.2045847	test: 0.2170696	best: 0.2170696 (27)	total: 10.9s	remaining: 1.94s
28:	learn: 0.2035615	test: 0.2162629	best: 0.2162629 (28)	total: 11.2s	remaining: 1.54s
29:	learn: 0.2026365	test: 0.2157242	best: 0.2157242 (29)	total: 11.6s	remaining: 1.16s
30:	learn: 0.2010455	test: 0.2144278	best: 0.2144278 (30)	total: 11.9s	remaining: 771ms
31:	learn: 0.2000132	test: 0.2135985	best: 0.2135985 (31)	total: 12.3s	remaining: 383ms
32:	learn: 0.1992913	test: 0.2130620	best: 0.2130620 (32)	total: 12.7s	remaining: 0us

bestTest = 0.213062041
bestIteration = 32

Trial 9, Fold 1: Log loss = 0.21251230814945316, Average precision = 0.9724544033652528, ROC-AUC = 0.9683726364931197, Elapsed Time = 12.858461199997691 seconds
Trial 9, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 9, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
0:	learn: 0.5928393	test: 0.5935655	best: 0.5935655 (0)	total: 469ms	remaining: 15s
1:	learn: 0.5154737	test: 0.5166009	best: 0.5166009 (1)	total: 887ms	remaining: 13.7s
2:	learn: 0.4556854	test: 0.4572782	best: 0.4572782 (2)	total: 1.35s	remaining: 13.5s
3:	learn: 0.4129612	test: 0.4147107	best: 0.4147107 (3)	total: 1.84s	remaining: 13.4s
4:	learn: 0.3752124	test: 0.3773215	best: 0.3773215 (4)	total: 2.25s	remaining: 12.6s
5:	learn: 0.3455114	test: 0.3479752	best: 0.3479752 (5)	total: 2.66s	remaining: 12s
6:	learn: 0.3221872	test: 0.3247950	best: 0.3247950 (6)	total: 3.09s	remaining: 11.5s
7:	learn: 0.3042221	test: 0.3071683	best: 0.3071683 (7)	total: 3.57s	remaining: 11.2s
8:	learn: 0.2890753	test: 0.2925294	best: 0.2925294 (8)	total: 4.06s	remaining: 10.8s
9:	learn: 0.2777986	test: 0.2813481	best: 0.2813481 (9)	total: 4.48s	remaining: 10.3s
10:	learn: 0.2665410	test: 0.2701667	best: 0.2701667 (10)	total: 4.86s	remaining: 9.72s
11:	learn: 0.2576710	test: 0.2612841	best: 0.2612841 (11)	total: 5.18s	remaining: 9.07s
12:	learn: 0.2515531	test: 0.2553608	best: 0.2553608 (12)	total: 5.56s	remaining: 8.56s
13:	learn: 0.2445893	test: 0.2483747	best: 0.2483747 (13)	total: 5.87s	remaining: 7.96s
14:	learn: 0.2386219	test: 0.2423030	best: 0.2423030 (14)	total: 6.41s	remaining: 7.69s
15:	learn: 0.2338035	test: 0.2376632	best: 0.2376632 (15)	total: 6.92s	remaining: 7.35s
16:	learn: 0.2303003	test: 0.2343276	best: 0.2343276 (16)	total: 7.31s	remaining: 6.88s
17:	learn: 0.2275353	test: 0.2313350	best: 0.2313350 (17)	total: 7.68s	remaining: 6.4s
18:	learn: 0.2242389	test: 0.2280582	best: 0.2280582 (18)	total: 8.17s	remaining: 6.02s
19:	learn: 0.2212634	test: 0.2252850	best: 0.2252850 (19)	total: 8.53s	remaining: 5.55s
20:	learn: 0.2184880	test: 0.2229192	best: 0.2229192 (20)	total: 8.98s	remaining: 5.13s
21:	learn: 0.2166862	test: 0.2212277	best: 0.2212277 (21)	total: 9.36s	remaining: 4.68s
22:	learn: 0.2141873	test: 0.2187356	best: 0.2187356 (22)	total: 9.65s	remaining: 4.2s
23:	learn: 0.2127087	test: 0.2174144	best: 0.2174144 (23)	total: 10.1s	remaining: 3.8s
24:	learn: 0.2110162	test: 0.2157953	best: 0.2157953 (24)	total: 10.5s	remaining: 3.35s
25:	learn: 0.2099695	test: 0.2148078	best: 0.2148078 (25)	total: 10.7s	remaining: 2.89s
26:	learn: 0.2091740	test: 0.2139935	best: 0.2139935 (26)	total: 11.1s	remaining: 2.46s
27:	learn: 0.2075386	test: 0.2126314	best: 0.2126314 (27)	total: 11.5s	remaining: 2.04s
28:	learn: 0.2060038	test: 0.2113207	best: 0.2113207 (28)	total: 11.9s	remaining: 1.64s
29:	learn: 0.2049571	test: 0.2104891	best: 0.2104891 (29)	total: 12.2s	remaining: 1.22s
30:	learn: 0.2045220	test: 0.2102507	best: 0.2102507 (30)	total: 12.5s	remaining: 808ms
31:	learn: 0.2034800	test: 0.2092401	best: 0.2092401 (31)	total: 12.9s	remaining: 403ms
32:	learn: 0.2024597	test: 0.2083785	best: 0.2083785 (32)	total: 13.3s	remaining: 0us

bestTest = 0.2083784637
bestIteration = 32

Trial 9, Fold 2: Log loss = 0.2079913903409887, Average precision = 0.9739655657248937, ROC-AUC = 0.9714503992635983, Elapsed Time = 13.429159499999514 seconds
Trial 9, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 9, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
0:	learn: 0.5950911	test: 0.5955450	best: 0.5955450 (0)	total: 423ms	remaining: 13.5s
1:	learn: 0.5173761	test: 0.5176026	best: 0.5176026 (1)	total: 816ms	remaining: 12.7s
2:	learn: 0.4566581	test: 0.4563989	best: 0.4563989 (2)	total: 1.15s	remaining: 11.5s
3:	learn: 0.4092815	test: 0.4087400	best: 0.4087400 (3)	total: 1.54s	remaining: 11.2s
4:	learn: 0.3725337	test: 0.3715264	best: 0.3715264 (4)	total: 1.93s	remaining: 10.8s
5:	learn: 0.3437913	test: 0.3430945	best: 0.3430945 (5)	total: 2.29s	remaining: 10.3s
6:	learn: 0.3217600	test: 0.3208128	best: 0.3208128 (6)	total: 2.64s	remaining: 9.81s
7:	learn: 0.3046259	test: 0.3037606	best: 0.3037606 (7)	total: 3.04s	remaining: 9.49s
8:	learn: 0.2898816	test: 0.2889390	best: 0.2889390 (8)	total: 3.39s	remaining: 9.04s
9:	learn: 0.2784385	test: 0.2777004	best: 0.2777004 (9)	total: 3.75s	remaining: 8.63s
10:	learn: 0.2684511	test: 0.2680242	best: 0.2680242 (10)	total: 4.13s	remaining: 8.26s
11:	learn: 0.2601644	test: 0.2600098	best: 0.2600098 (11)	total: 4.48s	remaining: 7.84s
12:	learn: 0.2514578	test: 0.2513683	best: 0.2513683 (12)	total: 4.85s	remaining: 7.46s
13:	learn: 0.2442318	test: 0.2444738	best: 0.2444738 (13)	total: 5.24s	remaining: 7.12s
14:	learn: 0.2385207	test: 0.2387314	best: 0.2387314 (14)	total: 5.63s	remaining: 6.76s
15:	learn: 0.2343976	test: 0.2347551	best: 0.2347551 (15)	total: 6.04s	remaining: 6.42s
16:	learn: 0.2309014	test: 0.2314345	best: 0.2314345 (16)	total: 6.4s	remaining: 6.03s
17:	learn: 0.2277368	test: 0.2288450	best: 0.2288450 (17)	total: 6.84s	remaining: 5.7s
18:	learn: 0.2250554	test: 0.2264560	best: 0.2264560 (18)	total: 7.24s	remaining: 5.34s
19:	learn: 0.2218533	test: 0.2235187	best: 0.2235187 (19)	total: 7.65s	remaining: 4.97s
20:	learn: 0.2191715	test: 0.2211074	best: 0.2211074 (20)	total: 8.06s	remaining: 4.61s
21:	learn: 0.2165034	test: 0.2189425	best: 0.2189425 (21)	total: 8.47s	remaining: 4.23s
22:	learn: 0.2145110	test: 0.2169396	best: 0.2169396 (22)	total: 8.8s	remaining: 3.83s
23:	learn: 0.2123530	test: 0.2149414	best: 0.2149414 (23)	total: 9.23s	remaining: 3.46s
24:	learn: 0.2113452	test: 0.2138891	best: 0.2138891 (24)	total: 9.48s	remaining: 3.03s
25:	learn: 0.2099055	test: 0.2125900	best: 0.2125900 (25)	total: 9.94s	remaining: 2.67s
26:	learn: 0.2081948	test: 0.2112036	best: 0.2112036 (26)	total: 10.3s	remaining: 2.29s
27:	learn: 0.2066223	test: 0.2096369	best: 0.2096369 (27)	total: 10.6s	remaining: 1.9s
28:	learn: 0.2053198	test: 0.2083839	best: 0.2083839 (28)	total: 11s	remaining: 1.52s
29:	learn: 0.2044157	test: 0.2077249	best: 0.2077249 (29)	total: 11.3s	remaining: 1.13s
30:	learn: 0.2038872	test: 0.2074261	best: 0.2074261 (30)	total: 11.7s	remaining: 755ms
31:	learn: 0.2028381	test: 0.2067167	best: 0.2067167 (31)	total: 12.2s	remaining: 382ms
32:	learn: 0.2017426	test: 0.2057500	best: 0.2057500 (32)	total: 12.6s	remaining: 0us

bestTest = 0.2057499915
bestIteration = 32

Trial 9, Fold 3: Log loss = 0.20547208442232048, Average precision = 0.9730721419563638, ROC-AUC = 0.9710076500515261, Elapsed Time = 12.712617600001977 seconds
Trial 9, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 9, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
0:	learn: 0.5949473	test: 0.5951608	best: 0.5951608 (0)	total: 500ms	remaining: 16s
1:	learn: 0.5185676	test: 0.5185231	best: 0.5185231 (1)	total: 915ms	remaining: 14.2s
2:	learn: 0.4583759	test: 0.4584267	best: 0.4584267 (2)	total: 1.27s	remaining: 12.7s
3:	learn: 0.4125898	test: 0.4133054	best: 0.4133054 (3)	total: 1.76s	remaining: 12.7s
4:	learn: 0.3761021	test: 0.3773940	best: 0.3773940 (4)	total: 2.11s	remaining: 11.8s
5:	learn: 0.3491898	test: 0.3509079	best: 0.3509079 (5)	total: 2.48s	remaining: 11.1s
6:	learn: 0.3266943	test: 0.3285672	best: 0.3285672 (6)	total: 2.85s	remaining: 10.6s
7:	learn: 0.3071887	test: 0.3092082	best: 0.3092082 (7)	total: 3.27s	remaining: 10.2s
8:	learn: 0.2920120	test: 0.2944397	best: 0.2944397 (8)	total: 3.6s	remaining: 9.59s
9:	learn: 0.2785118	test: 0.2810644	best: 0.2810644 (9)	total: 4.03s	remaining: 9.28s
10:	learn: 0.2672285	test: 0.2701709	best: 0.2701709 (10)	total: 4.45s	remaining: 8.9s
11:	learn: 0.2587271	test: 0.2621087	best: 0.2621087 (11)	total: 4.92s	remaining: 8.61s
12:	learn: 0.2520679	test: 0.2559825	best: 0.2559825 (12)	total: 5.42s	remaining: 8.35s
13:	learn: 0.2461723	test: 0.2507004	best: 0.2507004 (13)	total: 5.8s	remaining: 7.87s
14:	learn: 0.2408587	test: 0.2452545	best: 0.2452545 (14)	total: 6.13s	remaining: 7.35s
15:	learn: 0.2349641	test: 0.2391542	best: 0.2391542 (15)	total: 6.43s	remaining: 6.83s
16:	learn: 0.2312228	test: 0.2357763	best: 0.2357763 (16)	total: 6.78s	remaining: 6.38s
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18:	learn: 0.2243084	test: 0.2290304	best: 0.2290304 (18)	total: 7.62s	remaining: 5.61s
19:	learn: 0.2218237	test: 0.2265505	best: 0.2265505 (19)	total: 8s	remaining: 5.2s
20:	learn: 0.2192685	test: 0.2243022	best: 0.2243022 (20)	total: 8.45s	remaining: 4.83s
21:	learn: 0.2169257	test: 0.2223026	best: 0.2223026 (21)	total: 8.91s	remaining: 4.45s
22:	learn: 0.2143877	test: 0.2201183	best: 0.2201183 (22)	total: 9.36s	remaining: 4.07s
23:	learn: 0.2126784	test: 0.2187079	best: 0.2187079 (23)	total: 9.73s	remaining: 3.65s
24:	learn: 0.2111809	test: 0.2173300	best: 0.2173300 (24)	total: 10.1s	remaining: 3.23s
25:	learn: 0.2095688	test: 0.2159504	best: 0.2159504 (25)	total: 10.4s	remaining: 2.8s
26:	learn: 0.2080576	test: 0.2145398	best: 0.2145398 (26)	total: 10.8s	remaining: 2.41s
27:	learn: 0.2067230	test: 0.2134166	best: 0.2134166 (27)	total: 11.3s	remaining: 2.01s
28:	learn: 0.2052727	test: 0.2122740	best: 0.2122740 (28)	total: 11.6s	remaining: 1.6s
29:	learn: 0.2040002	test: 0.2115598	best: 0.2115598 (29)	total: 12.1s	remaining: 1.21s
30:	learn: 0.2029557	test: 0.2107107	best: 0.2107107 (30)	total: 12.5s	remaining: 806ms
31:	learn: 0.2019009	test: 0.2098873	best: 0.2098873 (31)	total: 12.8s	remaining: 401ms
32:	learn: 0.2009859	test: 0.2091389	best: 0.2091389 (32)	total: 13.2s	remaining: 0us

bestTest = 0.2091388992
bestIteration = 32

Trial 9, Fold 4: Log loss = 0.20869698135130227, Average precision = 0.9742626799703638, ROC-AUC = 0.9696986970392782, Elapsed Time = 13.32215240000005 seconds
Trial 9, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 9, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
0:	learn: 0.5927489	test: 0.5943014	best: 0.5943014 (0)	total: 370ms	remaining: 11.8s
1:	learn: 0.5149027	test: 0.5185038	best: 0.5185038 (1)	total: 788ms	remaining: 12.2s
2:	learn: 0.4548540	test: 0.4593431	best: 0.4593431 (2)	total: 1.12s	remaining: 11.2s
3:	learn: 0.4069713	test: 0.4124602	best: 0.4124602 (3)	total: 1.49s	remaining: 10.8s
4:	learn: 0.3710401	test: 0.3772466	best: 0.3772466 (4)	total: 1.88s	remaining: 10.5s
5:	learn: 0.3438637	test: 0.3506857	best: 0.3506857 (5)	total: 2.31s	remaining: 10.4s
6:	learn: 0.3213823	test: 0.3291563	best: 0.3291563 (6)	total: 2.72s	remaining: 10.1s
7:	learn: 0.3029190	test: 0.3111729	best: 0.3111729 (7)	total: 3.05s	remaining: 9.53s
8:	learn: 0.2877737	test: 0.2966636	best: 0.2966636 (8)	total: 3.44s	remaining: 9.18s
9:	learn: 0.2755021	test: 0.2847018	best: 0.2847018 (9)	total: 3.85s	remaining: 8.86s
10:	learn: 0.2642726	test: 0.2740277	best: 0.2740277 (10)	total: 4.2s	remaining: 8.4s
11:	learn: 0.2556348	test: 0.2657291	best: 0.2657291 (11)	total: 4.57s	remaining: 7.99s
12:	learn: 0.2478406	test: 0.2583892	best: 0.2583892 (12)	total: 4.98s	remaining: 7.67s
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16:	learn: 0.2291655	test: 0.2410815	best: 0.2410815 (16)	total: 6.49s	remaining: 6.11s
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18:	learn: 0.2219219	test: 0.2348467	best: 0.2348467 (18)	total: 7.28s	remaining: 5.37s
19:	learn: 0.2189307	test: 0.2321812	best: 0.2321812 (19)	total: 7.68s	remaining: 4.99s
20:	learn: 0.2160560	test: 0.2298557	best: 0.2298557 (20)	total: 8.06s	remaining: 4.6s
21:	learn: 0.2137852	test: 0.2280290	best: 0.2280290 (21)	total: 8.44s	remaining: 4.22s
22:	learn: 0.2122558	test: 0.2268850	best: 0.2268850 (22)	total: 8.8s	remaining: 3.83s
23:	learn: 0.2111656	test: 0.2259762	best: 0.2259762 (23)	total: 9.17s	remaining: 3.44s
24:	learn: 0.2092945	test: 0.2241585	best: 0.2241585 (24)	total: 9.57s	remaining: 3.06s
25:	learn: 0.2083738	test: 0.2233689	best: 0.2233689 (25)	total: 9.87s	remaining: 2.66s
26:	learn: 0.2068820	test: 0.2220799	best: 0.2220799 (26)	total: 10.3s	remaining: 2.28s
27:	learn: 0.2056296	test: 0.2212331	best: 0.2212331 (27)	total: 10.7s	remaining: 1.92s
28:	learn: 0.2042925	test: 0.2202110	best: 0.2202110 (28)	total: 11.1s	remaining: 1.53s
29:	learn: 0.2029629	test: 0.2191241	best: 0.2191241 (29)	total: 11.5s	remaining: 1.15s
30:	learn: 0.2018787	test: 0.2182633	best: 0.2182633 (30)	total: 11.8s	remaining: 760ms
31:	learn: 0.2010627	test: 0.2177216	best: 0.2177216 (31)	total: 12.1s	remaining: 378ms
32:	learn: 0.2004656	test: 0.2173738	best: 0.2173738 (32)	total: 12.4s	remaining: 0us

bestTest = 0.2173738237
bestIteration = 32

Trial 9, Fold 5: Log loss = 0.21671369139124652, Average precision = 0.9710542606095034, ROC-AUC = 0.9675792133474537, Elapsed Time = 12.527992699997412 seconds
Optimization Progress:  10%|#         | 10/100 [16:33<2:01:52, 81.25s/it]
Trial 10, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 10, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
0:	learn: 0.6506128	test: 0.6515501	best: 0.6515501 (0)	total: 248ms	remaining: 3.47s
1:	learn: 0.6110494	test: 0.6131081	best: 0.6131081 (1)	total: 553ms	remaining: 3.6s
2:	learn: 0.5753256	test: 0.5784900	best: 0.5784900 (2)	total: 841ms	remaining: 3.36s
3:	learn: 0.5420402	test: 0.5461008	best: 0.5461008 (3)	total: 1.13s	remaining: 3.1s
4:	learn: 0.5115528	test: 0.5169230	best: 0.5169230 (4)	total: 1.42s	remaining: 2.84s
5:	learn: 0.4844937	test: 0.4911414	best: 0.4911414 (5)	total: 1.71s	remaining: 2.57s
6:	learn: 0.4594776	test: 0.4672055	best: 0.4672055 (6)	total: 1.96s	remaining: 2.23s
7:	learn: 0.4367891	test: 0.4453762	best: 0.4453762 (7)	total: 2.23s	remaining: 1.95s
8:	learn: 0.4159033	test: 0.4258133	best: 0.4258133 (8)	total: 2.55s	remaining: 1.7s
9:	learn: 0.3964793	test: 0.4081034	best: 0.4081034 (9)	total: 2.91s	remaining: 1.45s
10:	learn: 0.3795007	test: 0.3922869	best: 0.3922869 (10)	total: 3.21s	remaining: 1.17s
11:	learn: 0.3638085	test: 0.3777383	best: 0.3777383 (11)	total: 3.53s	remaining: 882ms
12:	learn: 0.3491671	test: 0.3642213	best: 0.3642213 (12)	total: 3.85s	remaining: 592ms
13:	learn: 0.3358037	test: 0.3520950	best: 0.3520950 (13)	total: 4.19s	remaining: 299ms
14:	learn: 0.3233857	test: 0.3410935	best: 0.3410935 (14)	total: 4.52s	remaining: 0us

bestTest = 0.3410934927
bestIteration = 14

Trial 10, Fold 1: Log loss = 0.34114290750540777, Average precision = 0.9737290436163282, ROC-AUC = 0.9693178002657452, Elapsed Time = 4.637054099999659 seconds
Trial 10, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 10, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
0:	learn: 0.6495233	test: 0.6508443	best: 0.6508443 (0)	total: 308ms	remaining: 4.32s
1:	learn: 0.6101453	test: 0.6125148	best: 0.6125148 (1)	total: 603ms	remaining: 3.92s
2:	learn: 0.5741949	test: 0.5776522	best: 0.5776522 (2)	total: 897ms	remaining: 3.59s
3:	learn: 0.5413039	test: 0.5456660	best: 0.5456660 (3)	total: 1.2s	remaining: 3.3s
4:	learn: 0.5116834	test: 0.5169992	best: 0.5169992 (4)	total: 1.48s	remaining: 2.95s
5:	learn: 0.4843079	test: 0.4904038	best: 0.4904038 (5)	total: 1.79s	remaining: 2.68s
6:	learn: 0.4598820	test: 0.4667204	best: 0.4667204 (6)	total: 2.14s	remaining: 2.45s
7:	learn: 0.4374509	test: 0.4453623	best: 0.4453623 (7)	total: 2.42s	remaining: 2.12s
8:	learn: 0.4168376	test: 0.4258038	best: 0.4258038 (8)	total: 2.76s	remaining: 1.84s
9:	learn: 0.3978310	test: 0.4080304	best: 0.4080304 (9)	total: 3.14s	remaining: 1.57s
10:	learn: 0.3806902	test: 0.3916813	best: 0.3916813 (10)	total: 3.39s	remaining: 1.23s
11:	learn: 0.3637069	test: 0.3761094	best: 0.3761094 (11)	total: 3.76s	remaining: 941ms
12:	learn: 0.3483987	test: 0.3622137	best: 0.3622137 (12)	total: 4.12s	remaining: 634ms
13:	learn: 0.3345895	test: 0.3494607	best: 0.3494607 (13)	total: 4.48s	remaining: 320ms
14:	learn: 0.3228142	test: 0.3383884	best: 0.3383884 (14)	total: 4.78s	remaining: 0us

bestTest = 0.3383883706
bestIteration = 14

Trial 10, Fold 2: Log loss = 0.33840529229879684, Average precision = 0.9743811706503799, ROC-AUC = 0.9717702835953663, Elapsed Time = 4.909788400000252 seconds
Trial 10, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 10, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
0:	learn: 0.6493013	test: 0.6503620	best: 0.6503620 (0)	total: 297ms	remaining: 4.15s
1:	learn: 0.6100346	test: 0.6116639	best: 0.6116639 (1)	total: 594ms	remaining: 3.86s
2:	learn: 0.5742804	test: 0.5766965	best: 0.5766965 (2)	total: 894ms	remaining: 3.58s
3:	learn: 0.5412897	test: 0.5450626	best: 0.5450626 (3)	total: 1.2s	remaining: 3.29s
4:	learn: 0.5117832	test: 0.5162098	best: 0.5162098 (4)	total: 1.5s	remaining: 3.01s
5:	learn: 0.4846486	test: 0.4902072	best: 0.4902072 (5)	total: 1.86s	remaining: 2.79s
6:	learn: 0.4591154	test: 0.4658467	best: 0.4658467 (6)	total: 2.2s	remaining: 2.51s
7:	learn: 0.4359811	test: 0.4440841	best: 0.4440841 (7)	total: 2.57s	remaining: 2.25s
8:	learn: 0.4152104	test: 0.4240599	best: 0.4240599 (8)	total: 2.87s	remaining: 1.91s
9:	learn: 0.3957711	test: 0.4053465	best: 0.4053465 (9)	total: 3.18s	remaining: 1.59s
10:	learn: 0.3791731	test: 0.3894543	best: 0.3894543 (10)	total: 3.45s	remaining: 1.25s
11:	learn: 0.3625424	test: 0.3741448	best: 0.3741448 (11)	total: 3.8s	remaining: 950ms
12:	learn: 0.3471749	test: 0.3603103	best: 0.3603103 (12)	total: 4.16s	remaining: 639ms
13:	learn: 0.3343698	test: 0.3482857	best: 0.3482857 (13)	total: 4.47s	remaining: 319ms
14:	learn: 0.3216278	test: 0.3366030	best: 0.3366030 (14)	total: 4.8s	remaining: 0us

bestTest = 0.3366030155
bestIteration = 14

Trial 10, Fold 3: Log loss = 0.3367046290656582, Average precision = 0.9750636133066876, ROC-AUC = 0.9722684127331914, Elapsed Time = 4.930267300002015 seconds
Trial 10, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 10, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
0:	learn: 0.6497768	test: 0.6504533	best: 0.6504533 (0)	total: 284ms	remaining: 3.97s
1:	learn: 0.6101131	test: 0.6118149	best: 0.6118149 (1)	total: 565ms	remaining: 3.67s
2:	learn: 0.5742091	test: 0.5767182	best: 0.5767182 (2)	total: 837ms	remaining: 3.35s
3:	learn: 0.5416570	test: 0.5453346	best: 0.5453346 (3)	total: 1.15s	remaining: 3.15s
4:	learn: 0.5117165	test: 0.5169491	best: 0.5169491 (4)	total: 1.46s	remaining: 2.91s
5:	learn: 0.4848987	test: 0.4910289	best: 0.4910289 (5)	total: 1.72s	remaining: 2.57s
6:	learn: 0.4605750	test: 0.4681571	best: 0.4681571 (6)	total: 2.08s	remaining: 2.38s
7:	learn: 0.4376421	test: 0.4460392	best: 0.4460392 (7)	total: 2.36s	remaining: 2.07s
8:	learn: 0.4165617	test: 0.4259678	best: 0.4259678 (8)	total: 2.69s	remaining: 1.79s
9:	learn: 0.3976068	test: 0.4078765	best: 0.4078765 (9)	total: 3.02s	remaining: 1.51s
10:	learn: 0.3800087	test: 0.3919319	best: 0.3919319 (10)	total: 3.36s	remaining: 1.22s
11:	learn: 0.3639558	test: 0.3765887	best: 0.3765887 (11)	total: 3.62s	remaining: 906ms
12:	learn: 0.3494335	test: 0.3631847	best: 0.3631847 (12)	total: 3.97s	remaining: 611ms
13:	learn: 0.3359265	test: 0.3507470	best: 0.3507470 (13)	total: 4.25s	remaining: 304ms
14:	learn: 0.3240637	test: 0.3401326	best: 0.3401326 (14)	total: 4.56s	remaining: 0us

bestTest = 0.3401326333
bestIteration = 14

Trial 10, Fold 4: Log loss = 0.34016948226569266, Average precision = 0.9761613962132126, ROC-AUC = 0.9718627724486218, Elapsed Time = 4.686669899998378 seconds
Trial 10, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 10, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
0:	learn: 0.6497697	test: 0.6513156	best: 0.6513156 (0)	total: 293ms	remaining: 4.1s
1:	learn: 0.6100821	test: 0.6129789	best: 0.6129789 (1)	total: 551ms	remaining: 3.58s
2:	learn: 0.5739950	test: 0.5783090	best: 0.5783090 (2)	total: 811ms	remaining: 3.24s
3:	learn: 0.5414654	test: 0.5472702	best: 0.5472702 (3)	total: 1.1s	remaining: 3.04s
4:	learn: 0.5115031	test: 0.5185709	best: 0.5185709 (4)	total: 1.36s	remaining: 2.73s
5:	learn: 0.4846643	test: 0.4926821	best: 0.4926821 (5)	total: 1.61s	remaining: 2.42s
6:	learn: 0.4595928	test: 0.4689463	best: 0.4689463 (6)	total: 1.88s	remaining: 2.15s
7:	learn: 0.4369477	test: 0.4474768	best: 0.4474768 (7)	total: 2.13s	remaining: 1.87s
8:	learn: 0.4156743	test: 0.4278795	best: 0.4278795 (8)	total: 2.41s	remaining: 1.6s
9:	learn: 0.3970332	test: 0.4104888	best: 0.4104888 (9)	total: 2.68s	remaining: 1.34s
10:	learn: 0.3790900	test: 0.3940605	best: 0.3940605 (10)	total: 3.04s	remaining: 1.1s
11:	learn: 0.3633643	test: 0.3793960	best: 0.3793960 (11)	total: 3.31s	remaining: 829ms
12:	learn: 0.3481699	test: 0.3657617	best: 0.3657617 (12)	total: 3.65s	remaining: 561ms
13:	learn: 0.3340227	test: 0.3534457	best: 0.3534457 (13)	total: 4s	remaining: 286ms
14:	learn: 0.3222709	test: 0.3430015	best: 0.3430015 (14)	total: 4.26s	remaining: 0us

bestTest = 0.343001456
bestIteration = 14

Trial 10, Fold 5: Log loss = 0.34299012822241326, Average precision = 0.9727276665629832, ROC-AUC = 0.9681648365940212, Elapsed Time = 4.384535199998936 seconds
Optimization Progress:  11%|#1        | 11/100 [17:05<1:37:52, 65.99s/it]
Trial 11, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 11, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
0:	learn: 0.6792982	test: 0.6795236	best: 0.6795236 (0)	total: 211ms	remaining: 13.9s
1:	learn: 0.6613199	test: 0.6614242	best: 0.6614242 (1)	total: 430ms	remaining: 14s
2:	learn: 0.6454800	test: 0.6455000	best: 0.6455000 (2)	total: 672ms	remaining: 14.3s
3:	learn: 0.6316974	test: 0.6319361	best: 0.6319361 (3)	total: 902ms	remaining: 14.2s
4:	learn: 0.6164283	test: 0.6165959	best: 0.6165959 (4)	total: 1.13s	remaining: 14s
5:	learn: 0.6018727	test: 0.6020022	best: 0.6020022 (5)	total: 1.34s	remaining: 13.7s
6:	learn: 0.5880905	test: 0.5883114	best: 0.5883114 (6)	total: 1.56s	remaining: 13.4s
7:	learn: 0.5758505	test: 0.5760261	best: 0.5760261 (7)	total: 1.76s	remaining: 13s
8:	learn: 0.5646255	test: 0.5648380	best: 0.5648380 (8)	total: 1.96s	remaining: 12.6s
9:	learn: 0.5531004	test: 0.5532903	best: 0.5532903 (9)	total: 2.16s	remaining: 12.3s
10:	learn: 0.5418647	test: 0.5421599	best: 0.5421599 (10)	total: 2.36s	remaining: 12s
11:	learn: 0.5296037	test: 0.5297705	best: 0.5297705 (11)	total: 2.56s	remaining: 11.7s
12:	learn: 0.5189116	test: 0.5191465	best: 0.5191465 (12)	total: 2.77s	remaining: 11.5s
13:	learn: 0.5090852	test: 0.5092889	best: 0.5092889 (13)	total: 3.02s	remaining: 11.4s
14:	learn: 0.5002797	test: 0.5005569	best: 0.5005569 (14)	total: 3.25s	remaining: 11.3s
15:	learn: 0.4913775	test: 0.4917325	best: 0.4917325 (15)	total: 3.49s	remaining: 11.1s
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27:	learn: 0.4051751	test: 0.4060397	best: 0.4060397 (27)	total: 6.08s	remaining: 8.46s
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56:	learn: 0.2948005	test: 0.2979023	best: 0.2979023 (56)	total: 12.2s	remaining: 2.13s
57:	learn: 0.2924727	test: 0.2956265	best: 0.2956265 (57)	total: 12.4s	remaining: 1.92s
58:	learn: 0.2905947	test: 0.2937887	best: 0.2937887 (58)	total: 12.6s	remaining: 1.7s
59:	learn: 0.2888714	test: 0.2921896	best: 0.2921896 (59)	total: 12.8s	remaining: 1.49s
60:	learn: 0.2867790	test: 0.2902468	best: 0.2902468 (60)	total: 13s	remaining: 1.28s
61:	learn: 0.2850776	test: 0.2887336	best: 0.2887336 (61)	total: 13.2s	remaining: 1.07s
62:	learn: 0.2833522	test: 0.2870603	best: 0.2870603 (62)	total: 13.4s	remaining: 851ms
63:	learn: 0.2815315	test: 0.2853507	best: 0.2853507 (63)	total: 13.6s	remaining: 638ms
64:	learn: 0.2796751	test: 0.2835420	best: 0.2835420 (64)	total: 13.8s	remaining: 426ms
65:	learn: 0.2774963	test: 0.2814237	best: 0.2814237 (65)	total: 14s	remaining: 213ms
66:	learn: 0.2754841	test: 0.2795543	best: 0.2795543 (66)	total: 14.3s	remaining: 0us

bestTest = 0.2795543458
bestIteration = 66

Trial 11, Fold 1: Log loss = 0.2794368553787128, Average precision = 0.9710326929799092, ROC-AUC = 0.9648835363463862, Elapsed Time = 14.426432200001727 seconds
Trial 11, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 11, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
0:	learn: 0.6768745	test: 0.6769331	best: 0.6769331 (0)	total: 188ms	remaining: 12.4s
1:	learn: 0.6605359	test: 0.6606861	best: 0.6606861 (1)	total: 452ms	remaining: 14.7s
2:	learn: 0.6460412	test: 0.6463768	best: 0.6463768 (2)	total: 680ms	remaining: 14.5s
3:	learn: 0.6318890	test: 0.6321683	best: 0.6321683 (3)	total: 993ms	remaining: 15.6s
4:	learn: 0.6181038	test: 0.6185037	best: 0.6185037 (4)	total: 1.24s	remaining: 15.4s
5:	learn: 0.6071810	test: 0.6075869	best: 0.6075869 (5)	total: 1.48s	remaining: 15.1s
6:	learn: 0.5948793	test: 0.5954034	best: 0.5954034 (6)	total: 1.72s	remaining: 14.8s
7:	learn: 0.5811800	test: 0.5817882	best: 0.5817882 (7)	total: 1.95s	remaining: 14.4s
8:	learn: 0.5675538	test: 0.5683075	best: 0.5683075 (8)	total: 2.16s	remaining: 13.9s
9:	learn: 0.5544078	test: 0.5551949	best: 0.5551949 (9)	total: 2.38s	remaining: 13.6s
10:	learn: 0.5426587	test: 0.5435545	best: 0.5435545 (10)	total: 2.58s	remaining: 13.1s
11:	learn: 0.5307968	test: 0.5317359	best: 0.5317359 (11)	total: 2.81s	remaining: 12.9s
12:	learn: 0.5203673	test: 0.5214711	best: 0.5214711 (12)	total: 3.03s	remaining: 12.6s
13:	learn: 0.5101300	test: 0.5112535	best: 0.5112535 (13)	total: 3.25s	remaining: 12.3s
14:	learn: 0.5023139	test: 0.5035386	best: 0.5035386 (14)	total: 3.46s	remaining: 12s
15:	learn: 0.4931253	test: 0.4944312	best: 0.4944312 (15)	total: 3.68s	remaining: 11.7s
16:	learn: 0.4845059	test: 0.4858758	best: 0.4858758 (16)	total: 3.9s	remaining: 11.5s
17:	learn: 0.4757084	test: 0.4770090	best: 0.4770090 (17)	total: 4.11s	remaining: 11.2s
18:	learn: 0.4668061	test: 0.4681375	best: 0.4681375 (18)	total: 4.33s	remaining: 10.9s
19:	learn: 0.4580350	test: 0.4595314	best: 0.4595314 (19)	total: 4.54s	remaining: 10.7s
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24:	learn: 0.4241404	test: 0.4259386	best: 0.4259386 (24)	total: 5.62s	remaining: 9.44s
25:	learn: 0.4175541	test: 0.4194418	best: 0.4194418 (25)	total: 5.86s	remaining: 9.23s
26:	learn: 0.4105208	test: 0.4124478	best: 0.4124478 (26)	total: 6.08s	remaining: 9s
27:	learn: 0.4036903	test: 0.4056795	best: 0.4056795 (27)	total: 6.28s	remaining: 8.74s
28:	learn: 0.3981490	test: 0.4001420	best: 0.4001420 (28)	total: 6.5s	remaining: 8.52s
29:	learn: 0.3921423	test: 0.3941180	best: 0.3941180 (29)	total: 6.72s	remaining: 8.29s
30:	learn: 0.3861784	test: 0.3881387	best: 0.3881387 (30)	total: 6.95s	remaining: 8.07s
31:	learn: 0.3807530	test: 0.3828415	best: 0.3828415 (31)	total: 7.18s	remaining: 7.86s
32:	learn: 0.3767053	test: 0.3788195	best: 0.3788195 (32)	total: 7.39s	remaining: 7.61s
33:	learn: 0.3716546	test: 0.3737365	best: 0.3737365 (33)	total: 7.62s	remaining: 7.39s
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40:	learn: 0.3418760	test: 0.3441171	best: 0.3441171 (40)	total: 9.09s	remaining: 5.76s
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47:	learn: 0.3194208	test: 0.3217285	best: 0.3217285 (47)	total: 10.6s	remaining: 4.21s
48:	learn: 0.3162123	test: 0.3184769	best: 0.3184769 (48)	total: 10.9s	remaining: 4s
49:	learn: 0.3128895	test: 0.3151844	best: 0.3151844 (49)	total: 11.1s	remaining: 3.76s
50:	learn: 0.3096733	test: 0.3119715	best: 0.3119715 (50)	total: 11.3s	remaining: 3.55s
51:	learn: 0.3069216	test: 0.3091944	best: 0.3091944 (51)	total: 11.6s	remaining: 3.33s
52:	learn: 0.3044808	test: 0.3068331	best: 0.3068331 (52)	total: 11.8s	remaining: 3.11s
53:	learn: 0.3023612	test: 0.3046962	best: 0.3046962 (53)	total: 12s	remaining: 2.88s
54:	learn: 0.2994012	test: 0.3017882	best: 0.3017882 (54)	total: 12.2s	remaining: 2.65s
55:	learn: 0.2971688	test: 0.2995405	best: 0.2995405 (55)	total: 12.4s	remaining: 2.43s
56:	learn: 0.2950217	test: 0.2974087	best: 0.2974087 (56)	total: 12.6s	remaining: 2.21s
57:	learn: 0.2931551	test: 0.2955341	best: 0.2955341 (57)	total: 12.8s	remaining: 1.99s
58:	learn: 0.2909885	test: 0.2934032	best: 0.2934032 (58)	total: 13s	remaining: 1.77s
59:	learn: 0.2883323	test: 0.2907928	best: 0.2907928 (59)	total: 13.3s	remaining: 1.55s
60:	learn: 0.2861939	test: 0.2887035	best: 0.2887035 (60)	total: 13.5s	remaining: 1.33s
61:	learn: 0.2842688	test: 0.2867902	best: 0.2867902 (61)	total: 13.8s	remaining: 1.11s
62:	learn: 0.2822182	test: 0.2847322	best: 0.2847322 (62)	total: 14s	remaining: 888ms
63:	learn: 0.2804294	test: 0.2829481	best: 0.2829481 (63)	total: 14.2s	remaining: 666ms
64:	learn: 0.2787728	test: 0.2813100	best: 0.2813100 (64)	total: 14.4s	remaining: 443ms
65:	learn: 0.2768484	test: 0.2794160	best: 0.2794160 (65)	total: 14.6s	remaining: 221ms
66:	learn: 0.2753172	test: 0.2778421	best: 0.2778421 (66)	total: 14.8s	remaining: 0us

bestTest = 0.2778421475
bestIteration = 66

Trial 11, Fold 2: Log loss = 0.27777743832662527, Average precision = 0.9701344665845565, ROC-AUC = 0.9662593351443926, Elapsed Time = 14.970460399999865 seconds
Trial 11, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 11, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
0:	learn: 0.6745818	test: 0.6745439	best: 0.6745439 (0)	total: 301ms	remaining: 19.8s
1:	learn: 0.6600438	test: 0.6598261	best: 0.6598261 (1)	total: 616ms	remaining: 20s
2:	learn: 0.6436126	test: 0.6432539	best: 0.6432539 (2)	total: 832ms	remaining: 17.8s
3:	learn: 0.6296694	test: 0.6291667	best: 0.6291667 (3)	total: 1.17s	remaining: 18.5s
4:	learn: 0.6140972	test: 0.6135292	best: 0.6135292 (4)	total: 1.44s	remaining: 17.9s
5:	learn: 0.6002503	test: 0.5996492	best: 0.5996492 (5)	total: 1.74s	remaining: 17.7s
6:	learn: 0.5857951	test: 0.5851386	best: 0.5851386 (6)	total: 1.99s	remaining: 17.1s
7:	learn: 0.5721890	test: 0.5714577	best: 0.5714577 (7)	total: 2.32s	remaining: 17.1s
8:	learn: 0.5614667	test: 0.5606751	best: 0.5606751 (8)	total: 2.65s	remaining: 17.1s
9:	learn: 0.5509425	test: 0.5500872	best: 0.5500872 (9)	total: 2.92s	remaining: 16.6s
10:	learn: 0.5388606	test: 0.5379885	best: 0.5379885 (10)	total: 3.22s	remaining: 16.4s
11:	learn: 0.5277555	test: 0.5268546	best: 0.5268546 (11)	total: 3.48s	remaining: 16s
12:	learn: 0.5177943	test: 0.5168698	best: 0.5168698 (12)	total: 3.74s	remaining: 15.5s
13:	learn: 0.5080051	test: 0.5070389	best: 0.5070389 (13)	total: 3.99s	remaining: 15.1s
14:	learn: 0.4993816	test: 0.4983255	best: 0.4983255 (14)	total: 4.22s	remaining: 14.6s
15:	learn: 0.4894705	test: 0.4884585	best: 0.4884585 (15)	total: 4.47s	remaining: 14.3s
16:	learn: 0.4804009	test: 0.4793427	best: 0.4793427 (16)	total: 4.7s	remaining: 13.8s
17:	learn: 0.4726732	test: 0.4715583	best: 0.4715583 (17)	total: 4.94s	remaining: 13.5s
18:	learn: 0.4635668	test: 0.4623785	best: 0.4623785 (18)	total: 5.19s	remaining: 13.1s
19:	learn: 0.4555370	test: 0.4542507	best: 0.4542507 (19)	total: 5.45s	remaining: 12.8s
20:	learn: 0.4472793	test: 0.4460816	best: 0.4460816 (20)	total: 5.7s	remaining: 12.5s
21:	learn: 0.4390947	test: 0.4378865	best: 0.4378865 (21)	total: 5.97s	remaining: 12.2s
22:	learn: 0.4332559	test: 0.4319120	best: 0.4319120 (22)	total: 6.21s	remaining: 11.9s
23:	learn: 0.4263962	test: 0.4250459	best: 0.4250459 (23)	total: 6.43s	remaining: 11.5s
24:	learn: 0.4188998	test: 0.4175155	best: 0.4175155 (24)	total: 6.69s	remaining: 11.2s
25:	learn: 0.4132966	test: 0.4117496	best: 0.4117496 (25)	total: 6.95s	remaining: 11s
26:	learn: 0.4070446	test: 0.4054401	best: 0.4054401 (26)	total: 7.23s	remaining: 10.7s
27:	learn: 0.4004617	test: 0.3988641	best: 0.3988641 (27)	total: 7.52s	remaining: 10.5s
28:	learn: 0.3949450	test: 0.3932897	best: 0.3932897 (28)	total: 7.77s	remaining: 10.2s
29:	learn: 0.3894224	test: 0.3877362	best: 0.3877362 (29)	total: 8.03s	remaining: 9.9s
30:	learn: 0.3841958	test: 0.3824798	best: 0.3824798 (30)	total: 8.24s	remaining: 9.57s
31:	learn: 0.3784917	test: 0.3766846	best: 0.3766846 (31)	total: 8.48s	remaining: 9.28s
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48:	learn: 0.3153573	test: 0.3135196	best: 0.3135196 (48)	total: 12.3s	remaining: 4.54s
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51:	learn: 0.3068746	test: 0.3051386	best: 0.3051386 (51)	total: 13s	remaining: 3.75s
52:	learn: 0.3046266	test: 0.3029323	best: 0.3029323 (52)	total: 13.2s	remaining: 3.48s
53:	learn: 0.3018328	test: 0.3001466	best: 0.3001466 (53)	total: 13.4s	remaining: 3.22s
54:	learn: 0.2997672	test: 0.2981099	best: 0.2981099 (54)	total: 13.6s	remaining: 2.97s
55:	learn: 0.2974619	test: 0.2958449	best: 0.2958449 (55)	total: 13.8s	remaining: 2.71s
56:	learn: 0.2956165	test: 0.2939894	best: 0.2939894 (56)	total: 14s	remaining: 2.45s
57:	learn: 0.2930828	test: 0.2915011	best: 0.2915011 (57)	total: 14.2s	remaining: 2.2s
58:	learn: 0.2907967	test: 0.2891978	best: 0.2891978 (58)	total: 14.4s	remaining: 1.96s
59:	learn: 0.2889759	test: 0.2873388	best: 0.2873388 (59)	total: 14.6s	remaining: 1.7s
60:	learn: 0.2867605	test: 0.2851635	best: 0.2851635 (60)	total: 14.8s	remaining: 1.46s
61:	learn: 0.2845229	test: 0.2829976	best: 0.2829976 (61)	total: 15.1s	remaining: 1.22s
62:	learn: 0.2828839	test: 0.2813537	best: 0.2813537 (62)	total: 15.3s	remaining: 971ms
63:	learn: 0.2809485	test: 0.2794361	best: 0.2794361 (63)	total: 15.5s	remaining: 728ms
64:	learn: 0.2792572	test: 0.2777364	best: 0.2777364 (64)	total: 15.8s	remaining: 485ms
65:	learn: 0.2774225	test: 0.2758960	best: 0.2758960 (65)	total: 16s	remaining: 242ms
66:	learn: 0.2759123	test: 0.2744278	best: 0.2744278 (66)	total: 16.2s	remaining: 0us

bestTest = 0.2744277971
bestIteration = 66

Trial 11, Fold 3: Log loss = 0.27449709591265703, Average precision = 0.9707993364138457, ROC-AUC = 0.9673232524486907, Elapsed Time = 16.35089109999899 seconds
Trial 11, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 11, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
0:	learn: 0.6773956	test: 0.6774482	best: 0.6774482 (0)	total: 229ms	remaining: 15.1s
1:	learn: 0.6596448	test: 0.6596878	best: 0.6596878 (1)	total: 447ms	remaining: 14.5s
2:	learn: 0.6431136	test: 0.6431593	best: 0.6431593 (2)	total: 665ms	remaining: 14.2s
3:	learn: 0.6284000	test: 0.6282933	best: 0.6282933 (3)	total: 907ms	remaining: 14.3s
4:	learn: 0.6123555	test: 0.6123598	best: 0.6123598 (4)	total: 1.13s	remaining: 14s
5:	learn: 0.5974285	test: 0.5974745	best: 0.5974745 (5)	total: 1.36s	remaining: 13.8s
6:	learn: 0.5846733	test: 0.5847007	best: 0.5847007 (6)	total: 1.6s	remaining: 13.7s
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8:	learn: 0.5586835	test: 0.5588197	best: 0.5588197 (8)	total: 2.06s	remaining: 13.3s
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10:	learn: 0.5355511	test: 0.5356398	best: 0.5356398 (10)	total: 2.52s	remaining: 12.8s
11:	learn: 0.5239600	test: 0.5240280	best: 0.5240280 (11)	total: 2.74s	remaining: 12.6s
12:	learn: 0.5132575	test: 0.5133898	best: 0.5133898 (12)	total: 2.97s	remaining: 12.3s
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62:	learn: 0.2823554	test: 0.2842884	best: 0.2842884 (62)	total: 14.2s	remaining: 905ms
63:	learn: 0.2806743	test: 0.2826149	best: 0.2826149 (63)	total: 14.5s	remaining: 678ms
64:	learn: 0.2788672	test: 0.2808704	best: 0.2808704 (64)	total: 14.7s	remaining: 452ms
65:	learn: 0.2772859	test: 0.2793294	best: 0.2793294 (65)	total: 14.9s	remaining: 226ms
66:	learn: 0.2757826	test: 0.2778709	best: 0.2778709 (66)	total: 15.1s	remaining: 0us

bestTest = 0.2778709347
bestIteration = 66

Trial 11, Fold 4: Log loss = 0.2777909289006165, Average precision = 0.9711449705618475, ROC-AUC = 0.9652710716252852, Elapsed Time = 15.248579500002961 seconds
Trial 11, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 11, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
0:	learn: 0.6753409	test: 0.6755724	best: 0.6755724 (0)	total: 224ms	remaining: 14.8s
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2:	learn: 0.6434900	test: 0.6442327	best: 0.6442327 (2)	total: 692ms	remaining: 14.8s
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58:	learn: 0.2877724	test: 0.2955616	best: 0.2955616 (58)	total: 13.4s	remaining: 1.81s
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60:	learn: 0.2839536	test: 0.2918789	best: 0.2918789 (60)	total: 13.8s	remaining: 1.36s
61:	learn: 0.2818728	test: 0.2899406	best: 0.2899406 (61)	total: 14s	remaining: 1.13s
62:	learn: 0.2799409	test: 0.2880880	best: 0.2880880 (62)	total: 14.2s	remaining: 902ms
63:	learn: 0.2780779	test: 0.2862848	best: 0.2862848 (63)	total: 14.4s	remaining: 677ms
64:	learn: 0.2763721	test: 0.2846905	best: 0.2846905 (64)	total: 14.7s	remaining: 452ms
65:	learn: 0.2746583	test: 0.2830798	best: 0.2830798 (65)	total: 14.9s	remaining: 226ms
66:	learn: 0.2729542	test: 0.2814548	best: 0.2814548 (66)	total: 15.1s	remaining: 0us

bestTest = 0.2814547793
bestIteration = 66

Trial 11, Fold 5: Log loss = 0.2812293273669448, Average precision = 0.9692282383735199, ROC-AUC = 0.963279936086803, Elapsed Time = 15.249750499999209 seconds
Optimization Progress:  12%|#2        | 12/100 [18:29<1:44:46, 71.44s/it]
Trial 12, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 12, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
0:	learn: 0.5384561	test: 0.5403447	best: 0.5403447 (0)	total: 491ms	remaining: 48.6s
1:	learn: 0.4310432	test: 0.4363623	best: 0.4363623 (1)	total: 1.12s	remaining: 55s
2:	learn: 0.3619269	test: 0.3696297	best: 0.3696297 (2)	total: 1.76s	remaining: 56.9s
3:	learn: 0.3111326	test: 0.3220037	best: 0.3220037 (3)	total: 2.42s	remaining: 58.1s
4:	learn: 0.2729093	test: 0.2877730	best: 0.2877730 (4)	total: 3.1s	remaining: 58.9s
5:	learn: 0.2457871	test: 0.2640025	best: 0.2640025 (5)	total: 3.77s	remaining: 59.1s
6:	learn: 0.2231111	test: 0.2444385	best: 0.2444385 (6)	total: 4.47s	remaining: 59.4s
7:	learn: 2.4701636	test: 0.2325594	best: 0.2325594 (7)	total: 5.11s	remaining: 58.8s
8:	learn: 10.6390693	test: 12.9046381	best: 0.2325594 (7)	total: 5.78s	remaining: 58.5s
9:	learn: 13.3196306	test: 12.8510000	best: 0.2325594 (7)	total: 6.38s	remaining: 57.4s
10:	learn: 579.0603453	test: 561.8532997	best: 0.2325594 (7)	total: 6.97s	remaining: 56.4s
11:	learn: 2295.0834727	test: 2260.0907995	best: 0.2325594 (7)	total: 7.55s	remaining: 55.4s
12:	learn: 379934.8232905	test: 383171.6665990	best: 0.2325594 (7)	total: 8.02s	remaining: 53.6s
13:	learn: 315249.5239398	test: 347726.9128801	best: 0.2325594 (7)	total: 8.63s	remaining: 53s
Training has stopped (degenerate solution on iteration 14, probably too small l2-regularization, try to increase it)
bestTest = 0.2325594147
bestIteration = 7

Shrink model to first 8 iterations.
Trial 12, Fold 1: Log loss = 0.23255941469882188, Average precision = 0.9719359413186033, ROC-AUC = 0.9677732461297275, Elapsed Time = 9.332107500002166 seconds
Trial 12, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 12, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
0:	learn: 0.5252559	test: 0.5296265	best: 0.5296265 (0)	total: 665ms	remaining: 1m 5s
1:	learn: 0.4190479	test: 0.4251401	best: 0.4251401 (1)	total: 1.4s	remaining: 1m 8s
2:	learn: 0.3497104	test: 0.3597729	best: 0.3597729 (2)	total: 2.08s	remaining: 1m 7s
3:	learn: 0.3042432	test: 0.3148924	best: 0.3148924 (3)	total: 2.8s	remaining: 1m 7s
4:	learn: 0.2699026	test: 0.2838566	best: 0.2838566 (4)	total: 3.57s	remaining: 1m 7s
5:	learn: 0.2428225	test: 0.2598137	best: 0.2598137 (5)	total: 4.29s	remaining: 1m 7s
6:	learn: 0.2255687	test: 0.2455997	best: 0.2455997 (6)	total: 5.01s	remaining: 1m 6s
7:	learn: 0.2102940	test: 0.2322544	best: 0.2322544 (7)	total: 5.66s	remaining: 1m 5s
8:	learn: 0.1985501	test: 0.2221343	best: 0.2221343 (8)	total: 6.38s	remaining: 1m 4s
9:	learn: 0.1890692	test: 0.2149487	best: 0.2149487 (9)	total: 7.1s	remaining: 1m 3s
10:	learn: 3.7943339	test: 0.2106575	best: 0.2106575 (10)	total: 7.6s	remaining: 1m 1s
11:	learn: 3.7868560	test: 0.2055239	best: 0.2055239 (11)	total: 8.21s	remaining: 1m
12:	learn: 3.7811813	test: 0.2018917	best: 0.2018917 (12)	total: 8.87s	remaining: 59.4s
13:	learn: 6.4812439	test: 0.1997345	best: 0.1997345 (13)	total: 9.43s	remaining: 58s
14:	learn: 5.9006283	test: 0.1976059	best: 0.1976059 (14)	total: 9.92s	remaining: 56.2s
15:	learn: 635.8730364	test: 455.3360458	best: 0.1976059 (14)	total: 10.5s	remaining: 55s
16:	learn: 450220.0936542	test: 444025.8463026	best: 0.1976059 (14)	total: 11s	remaining: 53.5s
17:	learn: 73317.3068615	test: 48122.8515844	best: 0.1976059 (14)	total: 11.5s	remaining: 52.2s
18:	learn: 93627.5119791	test: 69235.4602546	best: 0.1976059 (14)	total: 11.8s	remaining: 50.5s
19:	learn: 98438.4261827	test: 72874.4917515	best: 0.1976059 (14)	total: 12.1s	remaining: 48.6s
20:	learn: 108014.5175455	test: 82281.6648393	best: 0.1976059 (14)	total: 12.5s	remaining: 46.9s
Training has stopped (degenerate solution on iteration 21, probably too small l2-regularization, try to increase it)
bestTest = 0.1976059236
bestIteration = 14

Shrink model to first 15 iterations.
Trial 12, Fold 2: Log loss = 0.19760592357393936, Average precision = 0.9738911728355482, ROC-AUC = 0.970783315998678, Elapsed Time = 13.0962698000003 seconds
Trial 12, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 12, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
0:	learn: 0.5405717	test: 0.5405482	best: 0.5405482 (0)	total: 592ms	remaining: 58.6s
1:	learn: 0.4380485	test: 0.4406064	best: 0.4406064 (1)	total: 1.23s	remaining: 1m
2:	learn: 0.3555325	test: 0.3610371	best: 0.3610371 (2)	total: 1.81s	remaining: 58.7s
3:	learn: 0.3060056	test: 0.3158127	best: 0.3158127 (3)	total: 2.53s	remaining: 1m
4:	learn: 0.2712005	test: 0.2833835	best: 0.2833835 (4)	total: 3.16s	remaining: 1m
5:	learn: 0.2432929	test: 0.2578149	best: 0.2578149 (5)	total: 3.88s	remaining: 1m
6:	learn: 2.9382892	test: 0.2442496	best: 0.2442496 (6)	total: 4.54s	remaining: 1m
7:	learn: 2.9224494	test: 0.2308975	best: 0.2308975 (7)	total: 5.17s	remaining: 59.4s
8:	learn: 9.2361234	test: 0.2209059	best: 0.2209059 (8)	total: 5.84s	remaining: 59.1s
9:	learn: 34.0982740	test: 11.2996876	best: 0.2209059 (8)	total: 6.48s	remaining: 58.3s
10:	learn: 34.0905015	test: 11.2945373	best: 0.2209059 (8)	total: 7.01s	remaining: 56.7s
11:	learn: 34.0842501	test: 11.2905138	best: 0.2209059 (8)	total: 7.61s	remaining: 55.8s
12:	learn: 34.0786996	test: 11.2873439	best: 0.2209059 (8)	total: 8.21s	remaining: 54.9s
13:	learn: 74.6171724	test: 60.0718407	best: 0.2209059 (8)	total: 8.77s	remaining: 53.9s
14:	learn: 74.6130974	test: 60.0694122	best: 0.2209059 (8)	total: 9.27s	remaining: 52.5s
Training has stopped (degenerate solution on iteration 15, probably too small l2-regularization, try to increase it)
bestTest = 0.2209059064
bestIteration = 8

Shrink model to first 9 iterations.
Trial 12, Fold 3: Log loss = 0.22090590640476881, Average precision = 0.972677075566525, ROC-AUC = 0.9699477847415512, Elapsed Time = 9.988151199999265 seconds
Trial 12, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 12, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
0:	learn: 0.5337580	test: 0.5358030	best: 0.5358030 (0)	total: 569ms	remaining: 56.4s
1:	learn: 0.4279728	test: 0.4322761	best: 0.4322761 (1)	total: 1.15s	remaining: 56.5s
2:	learn: 0.3636670	test: 0.3725890	best: 0.3725890 (2)	total: 1.82s	remaining: 58.7s
3:	learn: 0.3006959	test: 0.3121006	best: 0.3121006 (3)	total: 2.6s	remaining: 1m 2s
4:	learn: 0.2591788	test: 0.2731217	best: 0.2731217 (4)	total: 3.38s	remaining: 1m 4s
5:	learn: 0.2354564	test: 0.2517987	best: 0.2517987 (5)	total: 4.02s	remaining: 1m 3s
6:	learn: 0.2205635	test: 0.2389304	best: 0.2389304 (6)	total: 4.63s	remaining: 1m 1s
7:	learn: 2.9137287	test: 11.0164602	best: 0.2389304 (6)	total: 5.23s	remaining: 1m
8:	learn: 2.9037325	test: 11.0083992	best: 0.2389304 (6)	total: 5.76s	remaining: 58.3s
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bestTest = 0.1899794542
bestIteration = 25

Shrink model to first 26 iterations.
Trial 12, Fold 4: Log loss = 0.1899794542385773, Average precision = 0.9755532823387798, ROC-AUC = 0.9715319355923231, Elapsed Time = 38.34923730000082 seconds
Trial 12, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 12, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
0:	learn: 0.5334887	test: 0.5377967	best: 0.5377967 (0)	total: 728ms	remaining: 1m 12s
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2:	learn: 0.3648393	test: 0.3753602	best: 0.3753602 (2)	total: 2.1s	remaining: 1m 7s
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15:	learn: 45460.0929514	test: 42949.6156958	best: 0.2189337 (11)	total: 10s	remaining: 52.7s
Training has stopped (degenerate solution on iteration 16, probably too small l2-regularization, try to increase it)
bestTest = 0.2189336578
bestIteration = 11

Shrink model to first 12 iterations.
Trial 12, Fold 5: Log loss = 0.21893365778022436, Average precision = 0.972602290968769, ROC-AUC = 0.9685415528677331, Elapsed Time = 10.713243800000782 seconds
Optimization Progress:  13%|#3        | 13/100 [19:58<1:51:13, 76.70s/it]
Trial 13, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 13, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
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35:	learn: 25.6039619	test: 0.2225528	best: 0.2225528 (35)	total: 6.82s	remaining: 0us

bestTest = 0.2225528386
bestIteration = 35

Trial 13, Fold 1: Log loss = 0.22255283857359395, Average precision = 0.9739927683394082, ROC-AUC = 0.9694795594891575, Elapsed Time = 6.948956299998827 seconds
Trial 13, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 13, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
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23:	learn: 0.2509020	test: 0.2575585	best: 0.2575585 (23)	total: 4.56s	remaining: 2.28s
24:	learn: 0.2468644	test: 0.2537102	best: 0.2537102 (24)	total: 4.74s	remaining: 2.08s
25:	learn: 0.2429230	test: 0.2499420	best: 0.2499420 (25)	total: 4.94s	remaining: 1.9s
26:	learn: 0.2374097	test: 0.2446994	best: 0.2446994 (26)	total: 5.14s	remaining: 1.71s
27:	learn: 115.0324122	test: 0.2413381	best: 0.2413381 (27)	total: 5.35s	remaining: 1.53s
28:	learn: 115.0296409	test: 0.2387022	best: 0.2387022 (28)	total: 5.54s	remaining: 1.34s
29:	learn: 115.0249799	test: 0.2344858	best: 0.2344858 (29)	total: 5.74s	remaining: 1.15s
30:	learn: 115.0222988	test: 0.2321476	best: 0.2321476 (30)	total: 5.93s	remaining: 956ms
31:	learn: 115.0194505	test: 0.2297740	best: 0.2297740 (31)	total: 6.15s	remaining: 769ms
32:	learn: 115.0161886	test: 0.2266689	best: 0.2266689 (32)	total: 6.36s	remaining: 578ms
33:	learn: 115.0132828	test: 0.2243788	best: 0.2243788 (33)	total: 6.57s	remaining: 386ms
34:	learn: 115.0118309	test: 0.2230723	best: 0.2230723 (34)	total: 6.77s	remaining: 193ms
35:	learn: 115.0099578	test: 0.2214299	best: 0.2214299 (35)	total: 6.98s	remaining: 0us

bestTest = 0.2214298734
bestIteration = 35

Trial 13, Fold 2: Log loss = 0.22142987336946682, Average precision = 0.9734090091011551, ROC-AUC = 0.9698588684896027, Elapsed Time = 7.118063699999766 seconds
Trial 13, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 13, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
0:	learn: 0.6466042	test: 0.6468302	best: 0.6468302 (0)	total: 168ms	remaining: 5.87s
1:	learn: 0.5963553	test: 0.5962675	best: 0.5962675 (1)	total: 360ms	remaining: 6.13s
2:	learn: 0.5572160	test: 0.5573220	best: 0.5573220 (2)	total: 520ms	remaining: 5.72s
3:	learn: 0.5172537	test: 0.5171124	best: 0.5171124 (3)	total: 697ms	remaining: 5.58s
4:	learn: 0.4863424	test: 0.4861273	best: 0.4861273 (4)	total: 875ms	remaining: 5.42s
5:	learn: 0.4577305	test: 0.4576526	best: 0.4576526 (5)	total: 1.08s	remaining: 5.38s
6:	learn: 0.4322471	test: 0.4320593	best: 0.4320593 (6)	total: 1.25s	remaining: 5.19s
7:	learn: 0.4132913	test: 0.4130888	best: 0.4130888 (7)	total: 1.43s	remaining: 5s
8:	learn: 0.3902830	test: 0.3900960	best: 0.3900960 (8)	total: 1.6s	remaining: 4.81s
9:	learn: 0.3727125	test: 0.3727354	best: 0.3727354 (9)	total: 1.79s	remaining: 4.66s
10:	learn: 0.3571500	test: 0.3571792	best: 0.3571792 (10)	total: 1.97s	remaining: 4.47s
11:	learn: 0.3424003	test: 0.3425473	best: 0.3425473 (11)	total: 2.15s	remaining: 4.3s
12:	learn: 0.3304501	test: 0.3309722	best: 0.3309722 (12)	total: 2.34s	remaining: 4.13s
13:	learn: 0.3182785	test: 0.3189781	best: 0.3189781 (13)	total: 2.54s	remaining: 3.99s
14:	learn: 0.3080659	test: 0.3088771	best: 0.3088771 (14)	total: 2.72s	remaining: 3.8s
15:	learn: 0.2994057	test: 0.3002695	best: 0.3002695 (15)	total: 2.94s	remaining: 3.67s
16:	learn: 0.2916006	test: 0.2924914	best: 0.2924914 (16)	total: 3.13s	remaining: 3.5s
17:	learn: 0.2846112	test: 0.2859538	best: 0.2859538 (17)	total: 3.34s	remaining: 3.34s
18:	learn: 0.2785793	test: 0.2801477	best: 0.2801477 (18)	total: 3.53s	remaining: 3.16s
19:	learn: 0.2726033	test: 0.2745009	best: 0.2745009 (19)	total: 3.73s	remaining: 2.98s
20:	learn: 0.2645065	test: 0.2666863	best: 0.2666863 (20)	total: 3.93s	remaining: 2.81s
21:	learn: 0.2590203	test: 0.2616787	best: 0.2616787 (21)	total: 4.13s	remaining: 2.63s
22:	learn: 0.2548280	test: 0.2578550	best: 0.2578550 (22)	total: 4.32s	remaining: 2.44s
23:	learn: 0.2502271	test: 0.2534140	best: 0.2534140 (23)	total: 4.5s	remaining: 2.25s
24:	learn: 0.2455800	test: 0.2490834	best: 0.2490834 (24)	total: 4.68s	remaining: 2.06s
25:	learn: 0.2414810	test: 0.2452255	best: 0.2452255 (25)	total: 4.86s	remaining: 1.87s
26:	learn: 0.2375463	test: 0.2418027	best: 0.2418027 (26)	total: 5.06s	remaining: 1.69s
27:	learn: 0.2338022	test: 0.2384819	best: 0.2384819 (27)	total: 5.25s	remaining: 1.5s
28:	learn: 0.2303229	test: 0.2355415	best: 0.2355415 (28)	total: 5.44s	remaining: 1.31s
29:	learn: 0.2261626	test: 0.2320020	best: 0.2320020 (29)	total: 5.66s	remaining: 1.13s
30:	learn: 0.2224263	test: 0.2285268	best: 0.2285268 (30)	total: 5.87s	remaining: 946ms
31:	learn: 0.2198848	test: 0.2263913	best: 0.2263913 (31)	total: 6.05s	remaining: 756ms
32:	learn: 0.2175499	test: 0.2244188	best: 0.2244188 (32)	total: 6.24s	remaining: 567ms
33:	learn: 0.2160925	test: 0.2232195	best: 0.2232195 (33)	total: 6.42s	remaining: 377ms
34:	learn: 0.2138605	test: 0.2213452	best: 0.2213452 (34)	total: 6.63s	remaining: 189ms
35:	learn: 0.2114873	test: 0.2192736	best: 0.2192736 (35)	total: 6.83s	remaining: 0us

bestTest = 0.2192736263
bestIteration = 35

Trial 13, Fold 3: Log loss = 0.21927362628077832, Average precision = 0.9712415754523683, ROC-AUC = 0.9694828615731331, Elapsed Time = 6.961125500001799 seconds
Trial 13, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 13, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
0:	learn: 0.6292556	test: 0.6290567	best: 0.6290567 (0)	total: 182ms	remaining: 6.37s
1:	learn: 0.5865732	test: 0.5866598	best: 0.5866598 (1)	total: 342ms	remaining: 5.82s
2:	learn: 0.5439731	test: 0.5443882	best: 0.5443882 (2)	total: 540ms	remaining: 5.94s
3:	learn: 0.5124213	test: 0.5131079	best: 0.5131079 (3)	total: 708ms	remaining: 5.66s
4:	learn: 0.4847606	test: 0.4853535	best: 0.4853535 (4)	total: 885ms	remaining: 5.49s
5:	learn: 0.4579177	test: 0.4592255	best: 0.4592255 (5)	total: 1.07s	remaining: 5.34s
6:	learn: 0.4320823	test: 0.4332697	best: 0.4332697 (6)	total: 1.26s	remaining: 5.23s
7:	learn: 0.4129328	test: 0.4145319	best: 0.4145319 (7)	total: 1.41s	remaining: 4.93s
8:	learn: 0.3943310	test: 0.3961700	best: 0.3961700 (8)	total: 1.6s	remaining: 4.8s
9:	learn: 0.3772095	test: 0.3792984	best: 0.3792984 (9)	total: 1.8s	remaining: 4.69s
10:	learn: 0.3613679	test: 0.3637563	best: 0.3637563 (10)	total: 1.98s	remaining: 4.5s
11:	learn: 0.3484218	test: 0.3510269	best: 0.3510269 (11)	total: 2.17s	remaining: 4.34s
12:	learn: 0.3375404	test: 0.3403678	best: 0.3403678 (12)	total: 2.34s	remaining: 4.14s
13:	learn: 0.3260996	test: 0.3291135	best: 0.3291135 (13)	total: 2.52s	remaining: 3.97s
14:	learn: 0.3157898	test: 0.3188949	best: 0.3188949 (14)	total: 2.71s	remaining: 3.79s
15:	learn: 0.3049987	test: 0.3086094	best: 0.3086094 (15)	total: 2.9s	remaining: 3.63s
16:	learn: 0.2966381	test: 0.3004102	best: 0.3004102 (16)	total: 3.07s	remaining: 3.43s
17:	learn: 0.2874092	test: 0.2912290	best: 0.2912290 (17)	total: 3.26s	remaining: 3.26s
18:	learn: 0.2809379	test: 0.2851852	best: 0.2851852 (18)	total: 3.42s	remaining: 3.06s
19:	learn: 0.2738296	test: 0.2783650	best: 0.2783650 (19)	total: 3.61s	remaining: 2.88s
20:	learn: 0.2660666	test: 0.2707975	best: 0.2707975 (20)	total: 3.8s	remaining: 2.71s
21:	learn: 0.2600301	test: 0.2649686	best: 0.2649686 (21)	total: 3.98s	remaining: 2.54s
22:	learn: 0.2544076	test: 0.2596404	best: 0.2596404 (22)	total: 4.17s	remaining: 2.36s
23:	learn: 0.2484161	test: 0.2539750	best: 0.2539750 (23)	total: 4.38s	remaining: 2.19s
24:	learn: 0.2433901	test: 0.2498655	best: 0.2498655 (24)	total: 4.58s	remaining: 2.02s
25:	learn: 0.2374331	test: 0.2442497	best: 0.2442497 (25)	total: 4.79s	remaining: 1.84s
26:	learn: 0.2336108	test: 0.2407654	best: 0.2407654 (26)	total: 5s	remaining: 1.67s
27:	learn: 0.2302201	test: 0.2377074	best: 0.2377074 (27)	total: 5.2s	remaining: 1.49s
28:	learn: 0.2273847	test: 0.2355093	best: 0.2355093 (28)	total: 5.4s	remaining: 1.3s
29:	learn: 0.2237075	test: 0.2323485	best: 0.2323485 (29)	total: 5.6s	remaining: 1.12s
30:	learn: 0.2208885	test: 0.2297270	best: 0.2297270 (30)	total: 5.79s	remaining: 934ms
31:	learn: 0.2188529	test: 0.2279287	best: 0.2279287 (31)	total: 5.99s	remaining: 748ms
32:	learn: 128.0484387	test: 1019.3316459	best: 0.2279287 (31)	total: 6.16s	remaining: 560ms
33:	learn: 128.0462237	test: 1019.3299203	best: 0.2279287 (31)	total: 6.34s	remaining: 373ms
34:	learn: 128.0442746	test: 1019.3285110	best: 0.2279287 (31)	total: 6.56s	remaining: 187ms
35:	learn: 128.0416541	test: 1019.3264245	best: 0.2279287 (31)	total: 6.76s	remaining: 0us

bestTest = 0.2279287088
bestIteration = 31

Shrink model to first 32 iterations.
Trial 13, Fold 4: Log loss = 0.2279287088411435, Average precision = 0.9726621824986958, ROC-AUC = 0.9689829270299077, Elapsed Time = 6.896853700000065 seconds
Trial 13, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 13, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
0:	learn: 0.6438806	test: 0.6446860	best: 0.6446860 (0)	total: 173ms	remaining: 6.06s
1:	learn: 0.5959006	test: 0.5976127	best: 0.5976127 (1)	total: 371ms	remaining: 6.31s
2:	learn: 0.5540793	test: 0.5567142	best: 0.5567142 (2)	total: 557ms	remaining: 6.13s
3:	learn: 0.5162174	test: 0.5197316	best: 0.5197316 (3)	total: 735ms	remaining: 5.88s
4:	learn: 0.4821013	test: 0.4863610	best: 0.4863610 (4)	total: 914ms	remaining: 5.67s
5:	learn: 0.4558803	test: 0.4609406	best: 0.4609406 (5)	total: 1.08s	remaining: 5.38s
6:	learn: 0.4301470	test: 0.4355561	best: 0.4355561 (6)	total: 1.24s	remaining: 5.14s
7:	learn: 0.4098010	test: 0.4156831	best: 0.4156831 (7)	total: 1.41s	remaining: 4.94s
8:	learn: 0.3910446	test: 0.3971811	best: 0.3971811 (8)	total: 1.6s	remaining: 4.79s
9:	learn: 0.3754452	test: 0.3819208	best: 0.3819208 (9)	total: 1.76s	remaining: 4.57s
10:	learn: 0.3599687	test: 0.3668101	best: 0.3668101 (10)	total: 1.94s	remaining: 4.4s
11:	learn: 0.3447094	test: 0.3522747	best: 0.3522747 (11)	total: 2.12s	remaining: 4.24s
12:	learn: 0.3344576	test: 0.3422896	best: 0.3422896 (12)	total: 2.28s	remaining: 4.04s
13:	learn: 0.3211561	test: 0.3292972	best: 0.3292972 (13)	total: 2.47s	remaining: 3.88s
14:	learn: 0.3108631	test: 0.3194641	best: 0.3194641 (14)	total: 2.64s	remaining: 3.69s
15:	learn: 0.3008288	test: 0.3099759	best: 0.3099759 (15)	total: 2.84s	remaining: 3.55s
16:	learn: 0.2928958	test: 0.3024424	best: 0.3024424 (16)	total: 3.02s	remaining: 3.38s
17:	learn: 0.2856383	test: 0.2955359	best: 0.2955359 (17)	total: 3.21s	remaining: 3.21s
18:	learn: 0.2784109	test: 0.2886771	best: 0.2886771 (18)	total: 3.38s	remaining: 3.03s
19:	learn: 0.2719536	test: 0.2830028	best: 0.2830028 (19)	total: 3.55s	remaining: 2.84s
20:	learn: 0.2647132	test: 0.2763195	best: 0.2763195 (20)	total: 3.74s	remaining: 2.67s
21:	learn: 0.2588569	test: 0.2707512	best: 0.2707512 (21)	total: 3.93s	remaining: 2.5s
22:	learn: 0.2536089	test: 0.2657579	best: 0.2657579 (22)	total: 4.1s	remaining: 2.32s
23:	learn: 0.2480647	test: 0.2608990	best: 0.2608990 (23)	total: 4.28s	remaining: 2.14s
24:	learn: 0.2443657	test: 0.2577369	best: 0.2577369 (24)	total: 4.45s	remaining: 1.96s
25:	learn: 0.2395493	test: 0.2535182	best: 0.2535182 (25)	total: 4.65s	remaining: 1.79s
26:	learn: 0.2353475	test: 0.2498460	best: 0.2498460 (26)	total: 4.84s	remaining: 1.61s
27:	learn: 0.2326519	test: 0.2475142	best: 0.2475142 (27)	total: 5.01s	remaining: 1.43s
28:	learn: 0.2281577	test: 0.2435103	best: 0.2435103 (28)	total: 5.21s	remaining: 1.26s
29:	learn: 0.2258064	test: 0.2415264	best: 0.2415264 (29)	total: 5.39s	remaining: 1.08s
30:	learn: 0.2224247	test: 0.2385384	best: 0.2385384 (30)	total: 5.58s	remaining: 900ms
31:	learn: 0.2205953	test: 0.2372016	best: 0.2372016 (31)	total: 5.76s	remaining: 721ms
32:	learn: 0.2176616	test: 0.2350868	best: 0.2350868 (32)	total: 5.96s	remaining: 542ms
33:	learn: 0.2148242	test: 0.2330774	best: 0.2330774 (33)	total: 6.18s	remaining: 363ms
34:	learn: 153.6551022	test: 0.2308258	best: 0.2308258 (34)	total: 6.38s	remaining: 182ms
35:	learn: 153.6526900	test: 0.2290186	best: 0.2290186 (35)	total: 6.57s	remaining: 0us

bestTest = 0.2290186192
bestIteration = 35

Trial 13, Fold 5: Log loss = 0.22901861922288552, Average precision = 0.9708279634013677, ROC-AUC = 0.9669413447782546, Elapsed Time = 6.705726799998956 seconds
Optimization Progress:  14%|#4        | 14/100 [20:40<1:35:04, 66.33s/it]
Trial 14, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 14, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
0:	learn: 0.5801609	test: 0.5909252	best: 0.5909252 (0)	total: 3.17s	remaining: 2m 19s
1:	learn: 0.4936147	test: 0.5096646	best: 0.5096646 (1)	total: 6.14s	remaining: 2m 11s
2:	learn: 0.4258390	test: 0.4477475	best: 0.4477475 (2)	total: 9.18s	remaining: 2m 8s
3:	learn: 0.3703814	test: 0.4004829	best: 0.4004829 (3)	total: 12.4s	remaining: 2m 6s
4:	learn: 0.3300916	test: 0.3629523	best: 0.3629523 (4)	total: 15.4s	remaining: 2m 3s
5:	learn: 0.2984747	test: 0.3345477	best: 0.3345477 (5)	total: 18.5s	remaining: 2m
6:	learn: 0.2702072	test: 0.3118904	best: 0.3118904 (6)	total: 21.4s	remaining: 1m 56s
7:	learn: 0.2462469	test: 0.2949055	best: 0.2949055 (7)	total: 24.1s	remaining: 1m 51s
8:	learn: 0.2281451	test: 0.2814214	best: 0.2814214 (8)	total: 27.4s	remaining: 1m 49s
9:	learn: 0.2154538	test: 0.2711527	best: 0.2711527 (9)	total: 30.3s	remaining: 1m 46s
10:	learn: 0.2035282	test: 0.2635893	best: 0.2635893 (10)	total: 33.1s	remaining: 1m 42s
11:	learn: 0.1930792	test: 0.2553380	best: 0.2553380 (11)	total: 35.9s	remaining: 1m 38s
12:	learn: 0.1837754	test: 0.2489476	best: 0.2489476 (12)	total: 38.8s	remaining: 1m 35s
13:	learn: 0.1749195	test: 0.2436962	best: 0.2436962 (13)	total: 42s	remaining: 1m 32s
14:	learn: 0.1683841	test: 0.2403143	best: 0.2403143 (14)	total: 45s	remaining: 1m 30s
15:	learn: 0.1604667	test: 0.2369559	best: 0.2369559 (15)	total: 48s	remaining: 1m 26s
16:	learn: 0.1534018	test: 0.2340586	best: 0.2340586 (16)	total: 51s	remaining: 1m 24s
17:	learn: 0.1469744	test: 0.2321920	best: 0.2321920 (17)	total: 53.8s	remaining: 1m 20s
18:	learn: 0.1397973	test: 0.2299127	best: 0.2299127 (18)	total: 56.4s	remaining: 1m 17s
19:	learn: 0.1357238	test: 0.2274836	best: 0.2274836 (19)	total: 59.6s	remaining: 1m 14s
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27:	learn: 0.1066295	test: 0.2201905	best: 0.2201905 (27)	total: 1m 24s	remaining: 51.1s
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31:	learn: 0.0945755	test: 0.2185130	best: 0.2185130 (31)	total: 1m 35s	remaining: 39s
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41:	learn: 0.0706000	test: 0.2191205	best: 0.2179111 (34)	total: 2m 4s	remaining: 8.88s
42:	learn: 0.0681112	test: 0.2193127	best: 0.2179111 (34)	total: 2m 7s	remaining: 5.91s
43:	learn: 0.0656441	test: 0.2193762	best: 0.2179111 (34)	total: 2m 9s	remaining: 2.95s
44:	learn: 0.0646914	test: 0.2192511	best: 0.2179111 (34)	total: 2m 12s	remaining: 0us

bestTest = 0.2179111493
bestIteration = 34

Shrink model to first 35 iterations.
Trial 14, Fold 1: Log loss = 0.2169168552325152, Average precision = 0.9712305782721078, ROC-AUC = 0.9652470838531134, Elapsed Time = 133.0830840000017 seconds
Trial 14, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 14, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
0:	learn: 0.5799682	test: 0.5888075	best: 0.5888075 (0)	total: 2.69s	remaining: 1m 58s
1:	learn: 0.4913820	test: 0.5104179	best: 0.5104179 (1)	total: 5.55s	remaining: 1m 59s
2:	learn: 0.4222678	test: 0.4495135	best: 0.4495135 (2)	total: 8.34s	remaining: 1m 56s
3:	learn: 0.3686176	test: 0.4020057	best: 0.4020057 (3)	total: 11.1s	remaining: 1m 54s
4:	learn: 0.3272427	test: 0.3667085	best: 0.3667085 (4)	total: 14.1s	remaining: 1m 52s
5:	learn: 0.2960504	test: 0.3379471	best: 0.3379471 (5)	total: 17.2s	remaining: 1m 51s
6:	learn: 0.2708433	test: 0.3150888	best: 0.3150888 (6)	total: 19.3s	remaining: 1m 44s
7:	learn: 0.2481460	test: 0.2977073	best: 0.2977073 (7)	total: 22.4s	remaining: 1m 43s
8:	learn: 0.2295153	test: 0.2836679	best: 0.2836679 (8)	total: 25.3s	remaining: 1m 41s
9:	learn: 0.2153637	test: 0.2723696	best: 0.2723696 (9)	total: 28.2s	remaining: 1m 38s
10:	learn: 0.2033896	test: 0.2624800	best: 0.2624800 (10)	total: 31.2s	remaining: 1m 36s
11:	learn: 0.1936138	test: 0.2550728	best: 0.2550728 (11)	total: 34s	remaining: 1m 33s
12:	learn: 0.1833256	test: 0.2504981	best: 0.2504981 (12)	total: 36.9s	remaining: 1m 30s
13:	learn: 0.1736326	test: 0.2463730	best: 0.2463730 (13)	total: 40.2s	remaining: 1m 29s
14:	learn: 0.1656572	test: 0.2418261	best: 0.2418261 (14)	total: 43.1s	remaining: 1m 26s
15:	learn: 0.1592256	test: 0.2384311	best: 0.2384311 (15)	total: 46.2s	remaining: 1m 23s
16:	learn: 0.1525943	test: 0.2359437	best: 0.2359437 (16)	total: 49s	remaining: 1m 20s
17:	learn: 0.1471297	test: 0.2332217	best: 0.2332217 (17)	total: 52s	remaining: 1m 18s
18:	learn: 0.1399421	test: 0.2313575	best: 0.2313575 (18)	total: 55s	remaining: 1m 15s
19:	learn: 0.1363407	test: 0.2292368	best: 0.2292368 (19)	total: 57.9s	remaining: 1m 12s
20:	learn: 0.1334490	test: 0.2268514	best: 0.2268514 (20)	total: 1m	remaining: 1m 9s
21:	learn: 0.1299957	test: 0.2248536	best: 0.2248536 (21)	total: 1m 3s	remaining: 1m 6s
22:	learn: 0.1263746	test: 0.2227625	best: 0.2227625 (22)	total: 1m 6s	remaining: 1m 3s
23:	learn: 0.1216962	test: 0.2216588	best: 0.2216588 (23)	total: 1m 9s	remaining: 1m
24:	learn: 0.1162550	test: 0.2208000	best: 0.2208000 (24)	total: 1m 12s	remaining: 57.6s
25:	learn: 0.1128809	test: 0.2195293	best: 0.2195293 (25)	total: 1m 15s	remaining: 55s
26:	learn: 0.1103078	test: 0.2190989	best: 0.2190989 (26)	total: 1m 18s	remaining: 52.1s
27:	learn: 0.1070875	test: 0.2181046	best: 0.2181046 (27)	total: 1m 20s	remaining: 49.2s
28:	learn: 0.1040426	test: 0.2169204	best: 0.2169204 (28)	total: 1m 23s	remaining: 46.3s
29:	learn: 0.0991583	test: 0.2159539	best: 0.2159539 (29)	total: 1m 26s	remaining: 43.5s
30:	learn: 0.0964646	test: 0.2159804	best: 0.2159539 (29)	total: 1m 29s	remaining: 40.6s
31:	learn: 0.0946704	test: 0.2150655	best: 0.2150655 (31)	total: 1m 32s	remaining: 37.7s
32:	learn: 0.0914193	test: 0.2143254	best: 0.2143254 (32)	total: 1m 35s	remaining: 34.8s
33:	learn: 0.0894467	test: 0.2138953	best: 0.2138953 (33)	total: 1m 38s	remaining: 31.9s
34:	learn: 0.0872183	test: 0.2137364	best: 0.2137364 (34)	total: 1m 41s	remaining: 29s
35:	learn: 0.0859416	test: 0.2131971	best: 0.2131971 (35)	total: 1m 44s	remaining: 26.2s
36:	learn: 0.0842613	test: 0.2128493	best: 0.2128493 (36)	total: 1m 47s	remaining: 23.3s
37:	learn: 0.0815801	test: 0.2124763	best: 0.2124763 (37)	total: 1m 50s	remaining: 20.4s
38:	learn: 0.0788507	test: 0.2121357	best: 0.2121357 (38)	total: 1m 53s	remaining: 17.4s
39:	learn: 0.0754353	test: 0.2130134	best: 0.2121357 (38)	total: 1m 56s	remaining: 14.5s
40:	learn: 0.0722003	test: 0.2133775	best: 0.2121357 (38)	total: 1m 59s	remaining: 11.6s
41:	learn: 0.0698146	test: 0.2132444	best: 0.2121357 (38)	total: 2m 1s	remaining: 8.71s
42:	learn: 0.0687421	test: 0.2130809	best: 0.2121357 (38)	total: 2m 5s	remaining: 5.82s
43:	learn: 0.0668734	test: 0.2126983	best: 0.2121357 (38)	total: 2m 7s	remaining: 2.9s
44:	learn: 0.0659047	test: 0.2124649	best: 0.2121357 (38)	total: 2m 10s	remaining: 0us

bestTest = 0.2121356942
bestIteration = 38

Shrink model to first 39 iterations.
Trial 14, Fold 2: Log loss = 0.21156600761180294, Average precision = 0.9714203502763437, ROC-AUC = 0.9671939497550942, Elapsed Time = 130.8136955000009 seconds
Trial 14, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 14, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
0:	learn: 0.5906430	test: 0.6035098	best: 0.6035098 (0)	total: 3.2s	remaining: 2m 20s
1:	learn: 0.5025360	test: 0.5233086	best: 0.5233086 (1)	total: 6.5s	remaining: 2m 19s
2:	learn: 0.4334635	test: 0.4579570	best: 0.4579570 (2)	total: 10.2s	remaining: 2m 22s
3:	learn: 0.3795100	test: 0.4075997	best: 0.4075997 (3)	total: 13.5s	remaining: 2m 17s
4:	learn: 0.3351713	test: 0.3695734	best: 0.3695734 (4)	total: 17s	remaining: 2m 15s
5:	learn: 0.3001534	test: 0.3408086	best: 0.3408086 (5)	total: 20s	remaining: 2m 10s
6:	learn: 0.2733942	test: 0.3175320	best: 0.3175320 (6)	total: 22.9s	remaining: 2m 4s
7:	learn: 0.2490784	test: 0.2996460	best: 0.2996460 (7)	total: 25.3s	remaining: 1m 57s
8:	learn: 0.2312309	test: 0.2848158	best: 0.2848158 (8)	total: 28.6s	remaining: 1m 54s
9:	learn: 0.2173557	test: 0.2727211	best: 0.2727211 (9)	total: 31.7s	remaining: 1m 50s
10:	learn: 0.2043049	test: 0.2636351	best: 0.2636351 (10)	total: 34.5s	remaining: 1m 46s
11:	learn: 0.1918733	test: 0.2565412	best: 0.2565412 (11)	total: 37.6s	remaining: 1m 43s
12:	learn: 0.1824579	test: 0.2496497	best: 0.2496497 (12)	total: 40.6s	remaining: 1m 39s
13:	learn: 0.1710815	test: 0.2440102	best: 0.2440102 (13)	total: 43.6s	remaining: 1m 36s
14:	learn: 0.1618203	test: 0.2408540	best: 0.2408540 (14)	total: 46.8s	remaining: 1m 33s
15:	learn: 0.1531503	test: 0.2372968	best: 0.2372968 (15)	total: 50.1s	remaining: 1m 30s
16:	learn: 0.1460826	test: 0.2341701	best: 0.2341701 (16)	total: 52.7s	remaining: 1m 26s
17:	learn: 0.1399991	test: 0.2320540	best: 0.2320540 (17)	total: 55.8s	remaining: 1m 23s
18:	learn: 0.1345222	test: 0.2294553	best: 0.2294553 (18)	total: 58.9s	remaining: 1m 20s
19:	learn: 0.1284688	test: 0.2281663	best: 0.2281663 (19)	total: 1m 1s	remaining: 1m 16s
20:	learn: 0.1251633	test: 0.2263021	best: 0.2263021 (20)	total: 1m 4s	remaining: 1m 13s
21:	learn: 0.1222190	test: 0.2241378	best: 0.2241378 (21)	total: 1m 7s	remaining: 1m 10s
22:	learn: 0.1189313	test: 0.2225205	best: 0.2225205 (22)	total: 1m 10s	remaining: 1m 7s
23:	learn: 0.1150162	test: 0.2213763	best: 0.2213763 (23)	total: 1m 14s	remaining: 1m 4s
24:	learn: 0.1121778	test: 0.2196625	best: 0.2196625 (24)	total: 1m 17s	remaining: 1m 1s
25:	learn: 0.1070835	test: 0.2186613	best: 0.2186613 (25)	total: 1m 21s	remaining: 59.3s
26:	learn: 0.1037281	test: 0.2180018	best: 0.2180018 (26)	total: 1m 24s	remaining: 56.5s
27:	learn: 0.1016477	test: 0.2169467	best: 0.2169467 (27)	total: 1m 27s	remaining: 53.4s
28:	learn: 0.1002068	test: 0.2156831	best: 0.2156831 (28)	total: 1m 31s	remaining: 50.4s
29:	learn: 0.0974374	test: 0.2155917	best: 0.2155917 (29)	total: 1m 34s	remaining: 47.3s
30:	learn: 0.0953183	test: 0.2155103	best: 0.2155103 (30)	total: 1m 37s	remaining: 44.2s
31:	learn: 0.0934469	test: 0.2149233	best: 0.2149233 (31)	total: 1m 40s	remaining: 40.8s
32:	learn: 0.0910578	test: 0.2147090	best: 0.2147090 (32)	total: 1m 43s	remaining: 37.6s
33:	learn: 0.0880304	test: 0.2142396	best: 0.2142396 (33)	total: 1m 46s	remaining: 34.5s
34:	learn: 0.0846219	test: 0.2142565	best: 0.2142396 (33)	total: 1m 49s	remaining: 31.4s
35:	learn: 0.0813137	test: 0.2139123	best: 0.2139123 (35)	total: 1m 53s	remaining: 28.3s
36:	learn: 0.0789879	test: 0.2138254	best: 0.2138254 (36)	total: 1m 56s	remaining: 25.2s
37:	learn: 0.0771026	test: 0.2133653	best: 0.2133653 (37)	total: 1m 59s	remaining: 22.1s
38:	learn: 0.0748405	test: 0.2132859	best: 0.2132859 (38)	total: 2m 2s	remaining: 18.9s
39:	learn: 0.0734691	test: 0.2127105	best: 0.2127105 (39)	total: 2m 5s	remaining: 15.7s
40:	learn: 0.0716870	test: 0.2124159	best: 0.2124159 (40)	total: 2m 9s	remaining: 12.6s
41:	learn: 0.0695950	test: 0.2127512	best: 0.2124159 (40)	total: 2m 12s	remaining: 9.43s
42:	learn: 0.0685505	test: 0.2127837	best: 0.2124159 (40)	total: 2m 14s	remaining: 6.26s
43:	learn: 0.0673532	test: 0.2125384	best: 0.2124159 (40)	total: 2m 17s	remaining: 3.13s
44:	learn: 0.0649196	test: 0.2125478	best: 0.2124159 (40)	total: 2m 21s	remaining: 0us

bestTest = 0.2124158617
bestIteration = 40

Shrink model to first 41 iterations.
Trial 14, Fold 3: Log loss = 0.21176093735375257, Average precision = 0.9704697095516545, ROC-AUC = 0.9665134930801014, Elapsed Time = 141.51804309999716 seconds
Trial 14, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 14, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
0:	learn: 0.5913083	test: 0.6032185	best: 0.6032185 (0)	total: 3.25s	remaining: 2m 23s
1:	learn: 0.4994854	test: 0.5214923	best: 0.5214923 (1)	total: 6.39s	remaining: 2m 17s
2:	learn: 0.4284373	test: 0.4587060	best: 0.4587060 (2)	total: 9.74s	remaining: 2m 16s
3:	learn: 0.3744961	test: 0.4087844	best: 0.4087844 (3)	total: 12.9s	remaining: 2m 12s
4:	learn: 0.3323128	test: 0.3714134	best: 0.3714134 (4)	total: 15.9s	remaining: 2m 7s
5:	learn: 0.2982867	test: 0.3420492	best: 0.3420492 (5)	total: 18.8s	remaining: 2m 1s
6:	learn: 0.2726038	test: 0.3190632	best: 0.3190632 (6)	total: 22s	remaining: 1m 59s
7:	learn: 0.2514265	test: 0.3002631	best: 0.3002631 (7)	total: 25s	remaining: 1m 55s
8:	learn: 0.2322700	test: 0.2854272	best: 0.2854272 (8)	total: 27.9s	remaining: 1m 51s
9:	learn: 0.2182933	test: 0.2739475	best: 0.2739475 (9)	total: 31s	remaining: 1m 48s
10:	learn: 0.2054406	test: 0.2649456	best: 0.2649456 (10)	total: 34.4s	remaining: 1m 46s
11:	learn: 0.1944492	test: 0.2563974	best: 0.2563974 (11)	total: 37.6s	remaining: 1m 43s
12:	learn: 0.1842426	test: 0.2503772	best: 0.2503772 (12)	total: 40.6s	remaining: 1m 39s
13:	learn: 0.1744058	test: 0.2447633	best: 0.2447633 (13)	total: 44.1s	remaining: 1m 37s
14:	learn: 0.1667173	test: 0.2396696	best: 0.2396696 (14)	total: 47.1s	remaining: 1m 34s
15:	learn: 0.1602946	test: 0.2362365	best: 0.2362365 (15)	total: 50s	remaining: 1m 30s
16:	learn: 0.1527441	test: 0.2324698	best: 0.2324698 (16)	total: 53.4s	remaining: 1m 27s
17:	learn: 0.1489245	test: 0.2296899	best: 0.2296899 (17)	total: 56.4s	remaining: 1m 24s
18:	learn: 0.1439826	test: 0.2276895	best: 0.2276895 (18)	total: 59.4s	remaining: 1m 21s
19:	learn: 0.1383381	test: 0.2259605	best: 0.2259605 (19)	total: 1m 2s	remaining: 1m 17s
20:	learn: 0.1312760	test: 0.2252708	best: 0.2252708 (20)	total: 1m 5s	remaining: 1m 14s
21:	learn: 0.1274150	test: 0.2234976	best: 0.2234976 (21)	total: 1m 8s	remaining: 1m 11s
22:	learn: 0.1205500	test: 0.2229066	best: 0.2229066 (22)	total: 1m 11s	remaining: 1m 8s
23:	learn: 0.1171982	test: 0.2215082	best: 0.2215082 (23)	total: 1m 14s	remaining: 1m 5s
24:	learn: 0.1124804	test: 0.2209376	best: 0.2209376 (24)	total: 1m 17s	remaining: 1m 2s
25:	learn: 0.1080570	test: 0.2205332	best: 0.2205332 (25)	total: 1m 20s	remaining: 59.1s
26:	learn: 0.1052080	test: 0.2192549	best: 0.2192549 (26)	total: 1m 23s	remaining: 55.8s
27:	learn: 0.1032704	test: 0.2181041	best: 0.2181041 (27)	total: 1m 26s	remaining: 52.8s
28:	learn: 0.1011699	test: 0.2178589	best: 0.2178589 (28)	total: 1m 30s	remaining: 49.7s
29:	learn: 0.0981321	test: 0.2173280	best: 0.2173280 (29)	total: 1m 33s	remaining: 46.6s
30:	learn: 0.0954972	test: 0.2165293	best: 0.2165293 (30)	total: 1m 36s	remaining: 43.4s
31:	learn: 0.0935445	test: 0.2158696	best: 0.2158696 (31)	total: 1m 38s	remaining: 40s
32:	learn: 0.0920876	test: 0.2151512	best: 0.2151512 (32)	total: 1m 41s	remaining: 36.9s
33:	learn: 0.0895295	test: 0.2148285	best: 0.2148285 (33)	total: 1m 43s	remaining: 33.6s
34:	learn: 0.0877189	test: 0.2140067	best: 0.2140067 (34)	total: 1m 46s	remaining: 30.6s
35:	learn: 0.0851383	test: 0.2137625	best: 0.2137625 (35)	total: 1m 50s	remaining: 27.6s
36:	learn: 0.0818876	test: 0.2138893	best: 0.2137625 (35)	total: 1m 53s	remaining: 24.5s
37:	learn: 0.0803853	test: 0.2132556	best: 0.2132556 (37)	total: 1m 56s	remaining: 21.4s
38:	learn: 0.0781947	test: 0.2128033	best: 0.2128033 (38)	total: 1m 59s	remaining: 18.4s
39:	learn: 0.0756566	test: 0.2123925	best: 0.2123925 (39)	total: 2m 1s	remaining: 15.2s
40:	learn: 0.0744280	test: 0.2122901	best: 0.2122901 (40)	total: 2m 4s	remaining: 12.2s
41:	learn: 0.0711997	test: 0.2127214	best: 0.2122901 (40)	total: 2m 8s	remaining: 9.14s
42:	learn: 0.0695970	test: 0.2126545	best: 0.2122901 (40)	total: 2m 11s	remaining: 6.1s
43:	learn: 0.0683690	test: 0.2124755	best: 0.2122901 (40)	total: 2m 14s	remaining: 3.06s
44:	learn: 0.0668914	test: 0.2124761	best: 0.2122901 (40)	total: 2m 16s	remaining: 0us

bestTest = 0.212290099
bestIteration = 40

Shrink model to first 41 iterations.
Trial 14, Fold 4: Log loss = 0.2116095543792527, Average precision = 0.9721683375773902, ROC-AUC = 0.9668279421828319, Elapsed Time = 137.0592063999975 seconds
Trial 14, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 14, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
0:	learn: 0.5803209	test: 0.5914360	best: 0.5914360 (0)	total: 3.27s	remaining: 2m 24s
1:	learn: 0.4917459	test: 0.5130217	best: 0.5130217 (1)	total: 6.38s	remaining: 2m 17s
2:	learn: 0.4236202	test: 0.4527485	best: 0.4527485 (2)	total: 9.35s	remaining: 2m 10s
3:	learn: 0.3717660	test: 0.4063369	best: 0.4063369 (3)	total: 12.7s	remaining: 2m 9s
4:	learn: 0.3281444	test: 0.3701779	best: 0.3701779 (4)	total: 15.9s	remaining: 2m 7s
5:	learn: 0.2959626	test: 0.3413374	best: 0.3413374 (5)	total: 19.1s	remaining: 2m 4s
6:	learn: 0.2708576	test: 0.3191538	best: 0.3191538 (6)	total: 22s	remaining: 1m 59s
7:	learn: 0.2494503	test: 0.3021471	best: 0.3021471 (7)	total: 25.3s	remaining: 1m 57s
8:	learn: 0.2302248	test: 0.2890283	best: 0.2890283 (8)	total: 28.4s	remaining: 1m 53s
9:	learn: 0.2162939	test: 0.2777274	best: 0.2777274 (9)	total: 31.4s	remaining: 1m 49s
10:	learn: 0.2041671	test: 0.2684269	best: 0.2684269 (10)	total: 34.6s	remaining: 1m 46s
11:	learn: 0.1942773	test: 0.2612122	best: 0.2612122 (11)	total: 37.9s	remaining: 1m 44s
12:	learn: 0.1838066	test: 0.2548227	best: 0.2548227 (12)	total: 40.8s	remaining: 1m 40s
13:	learn: 0.1753989	test: 0.2500792	best: 0.2500792 (13)	total: 43.9s	remaining: 1m 37s
14:	learn: 0.1665754	test: 0.2459458	best: 0.2459458 (14)	total: 47.1s	remaining: 1m 34s
15:	learn: 0.1577527	test: 0.2426342	best: 0.2426342 (15)	total: 50.1s	remaining: 1m 30s
16:	learn: 0.1517693	test: 0.2404095	best: 0.2404095 (16)	total: 53.4s	remaining: 1m 27s
17:	learn: 0.1461579	test: 0.2375820	best: 0.2375820 (17)	total: 56.5s	remaining: 1m 24s
18:	learn: 0.1409462	test: 0.2358171	best: 0.2358171 (18)	total: 1m	remaining: 1m 22s
19:	learn: 0.1367763	test: 0.2334854	best: 0.2334854 (19)	total: 1m 3s	remaining: 1m 19s
20:	learn: 0.1317757	test: 0.2322473	best: 0.2322473 (20)	total: 1m 7s	remaining: 1m 16s
21:	learn: 0.1286373	test: 0.2311968	best: 0.2311968 (21)	total: 1m 10s	remaining: 1m 14s
22:	learn: 0.1226746	test: 0.2296791	best: 0.2296791 (22)	total: 1m 14s	remaining: 1m 11s
23:	learn: 0.1182230	test: 0.2284024	best: 0.2284024 (23)	total: 1m 17s	remaining: 1m 7s
24:	learn: 0.1143845	test: 0.2273456	best: 0.2273456 (24)	total: 1m 20s	remaining: 1m 4s
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34:	learn: 0.0815398	test: 0.2226362	best: 0.2226362 (34)	total: 1m 50s	remaining: 31.5s
35:	learn: 0.0790131	test: 0.2228296	best: 0.2226362 (34)	total: 1m 53s	remaining: 28.3s
36:	learn: 0.0753729	test: 0.2231285	best: 0.2226362 (34)	total: 1m 56s	remaining: 25.2s
37:	learn: 0.0734253	test: 0.2230945	best: 0.2226362 (34)	total: 1m 59s	remaining: 22s
38:	learn: 0.0702069	test: 0.2231708	best: 0.2226362 (34)	total: 2m 2s	remaining: 18.8s
39:	learn: 0.0689018	test: 0.2229141	best: 0.2226362 (34)	total: 2m 5s	remaining: 15.7s
40:	learn: 0.0677969	test: 0.2225275	best: 0.2225275 (40)	total: 2m 8s	remaining: 12.6s
41:	learn: 0.0663907	test: 0.2220228	best: 0.2220228 (41)	total: 2m 12s	remaining: 9.44s
42:	learn: 0.0649653	test: 0.2216970	best: 0.2216970 (42)	total: 2m 15s	remaining: 6.28s
43:	learn: 0.0634324	test: 0.2212573	best: 0.2212573 (43)	total: 2m 18s	remaining: 3.15s
44:	learn: 0.0621476	test: 0.2211322	best: 0.2211322 (44)	total: 2m 21s	remaining: 0us

bestTest = 0.2211322192
bestIteration = 44

Trial 14, Fold 5: Log loss = 0.22007899087043226, Average precision = 0.9700105951317912, ROC-AUC = 0.9646967281645393, Elapsed Time = 141.9878531999966 seconds
Optimization Progress:  15%|#5        | 15/100 [32:12<6:01:13, 254.98s/it]
Trial 15, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 15, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
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41:	learn: 0.3140193	test: 0.3182344	best: 0.3182344 (41)	total: 5.8s	remaining: 1.24s
42:	learn: 0.3110862	test: 0.3154983	best: 0.3154983 (42)	total: 5.95s	remaining: 1.11s
43:	learn: 0.3081155	test: 0.3126211	best: 0.3126211 (43)	total: 6.08s	remaining: 967ms
44:	learn: 0.3053485	test: 0.3099577	best: 0.3099577 (44)	total: 6.21s	remaining: 828ms
45:	learn: 0.3022643	test: 0.3070569	best: 0.3070569 (45)	total: 6.35s	remaining: 690ms
46:	learn: 0.2996876	test: 0.3046085	best: 0.3046085 (46)	total: 6.49s	remaining: 552ms
47:	learn: 0.2968019	test: 0.3018493	best: 0.3018493 (47)	total: 6.61s	remaining: 413ms
48:	learn: 0.2942260	test: 0.2994601	best: 0.2994601 (48)	total: 6.75s	remaining: 275ms
49:	learn: 0.2916104	test: 0.2969634	best: 0.2969634 (49)	total: 6.89s	remaining: 138ms
50:	learn: 0.2889257	test: 0.2943792	best: 0.2943792 (50)	total: 7.03s	remaining: 0us

bestTest = 0.294379192
bestIteration = 50

Trial 15, Fold 1: Log loss = 0.2943791920070218, Average precision = 0.9710517488769363, ROC-AUC = 0.9666720735769968, Elapsed Time = 7.14929640000264 seconds
Trial 15, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 15, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
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2:	learn: 0.6373877	test: 0.6375877	best: 0.6375877 (2)	total: 416ms	remaining: 6.66s
3:	learn: 0.6210184	test: 0.6213073	best: 0.6213073 (3)	total: 563ms	remaining: 6.61s
4:	learn: 0.6049537	test: 0.6053904	best: 0.6053904 (4)	total: 705ms	remaining: 6.48s
5:	learn: 0.5897077	test: 0.5902489	best: 0.5902489 (5)	total: 838ms	remaining: 6.29s
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50:	learn: 0.2907349	test: 0.2945330	best: 0.2945330 (50)	total: 7.01s	remaining: 0us

bestTest = 0.2945329514
bestIteration = 50

Trial 15, Fold 2: Log loss = 0.29453295141492997, Average precision = 0.9723128989222364, ROC-AUC = 0.969825939220156, Elapsed Time = 7.130247099998087 seconds
Trial 15, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 15, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
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47:	learn: 0.2980785	test: 0.2973883	best: 0.2973883 (47)	total: 6.59s	remaining: 412ms
48:	learn: 0.2950206	test: 0.2943422	best: 0.2943422 (48)	total: 6.73s	remaining: 275ms
49:	learn: 0.2926577	test: 0.2920326	best: 0.2920326 (49)	total: 6.87s	remaining: 137ms
50:	learn: 0.2907070	test: 0.2901031	best: 0.2901031 (50)	total: 7.01s	remaining: 0us

bestTest = 0.290103069
bestIteration = 50

Trial 15, Fold 3: Log loss = 0.29010306900574834, Average precision = 0.9707903406904075, ROC-AUC = 0.9698042221253768, Elapsed Time = 7.128263999999035 seconds
Trial 15, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 15, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
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40:	learn: 0.3217570	test: 0.3238747	best: 0.3238747 (40)	total: 6.13s	remaining: 1.49s
41:	learn: 0.3182879	test: 0.3205148	best: 0.3205148 (41)	total: 6.27s	remaining: 1.34s
42:	learn: 0.3143726	test: 0.3166497	best: 0.3166497 (42)	total: 6.42s	remaining: 1.19s
43:	learn: 0.3115700	test: 0.3138879	best: 0.3138879 (43)	total: 6.55s	remaining: 1.04s
44:	learn: 0.3088497	test: 0.3112254	best: 0.3112254 (44)	total: 6.68s	remaining: 891ms
45:	learn: 0.3058567	test: 0.3083014	best: 0.3083014 (45)	total: 6.82s	remaining: 742ms
46:	learn: 0.3030175	test: 0.3055288	best: 0.3055288 (46)	total: 6.96s	remaining: 593ms
47:	learn: 0.3002485	test: 0.3028262	best: 0.3028262 (47)	total: 7.11s	remaining: 444ms
48:	learn: 0.2972998	test: 0.2998738	best: 0.2998738 (48)	total: 7.26s	remaining: 296ms
49:	learn: 0.2947370	test: 0.2973342	best: 0.2973342 (49)	total: 7.4s	remaining: 148ms
50:	learn: 0.2924728	test: 0.2951406	best: 0.2951406 (50)	total: 7.54s	remaining: 0us

bestTest = 0.2951406229
bestIteration = 50

Trial 15, Fold 4: Log loss = 0.2951406228889906, Average precision = 0.9696657754605814, ROC-AUC = 0.9677570423542708, Elapsed Time = 7.65740150000056 seconds
Trial 15, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 15, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
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2:	learn: 0.6387812	test: 0.6395092	best: 0.6395092 (2)	total: 431ms	remaining: 6.89s
3:	learn: 0.6213472	test: 0.6223058	best: 0.6223058 (3)	total: 568ms	remaining: 6.67s
4:	learn: 0.6064743	test: 0.6076714	best: 0.6076714 (4)	total: 722ms	remaining: 6.64s
5:	learn: 0.5910226	test: 0.5924552	best: 0.5924552 (5)	total: 862ms	remaining: 6.47s
6:	learn: 0.5761826	test: 0.5777278	best: 0.5777278 (6)	total: 996ms	remaining: 6.26s
7:	learn: 0.5615746	test: 0.5633103	best: 0.5633103 (7)	total: 1.14s	remaining: 6.1s
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48:	learn: 0.2934179	test: 0.3017028	best: 0.3017028 (48)	total: 6.88s	remaining: 281ms
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50:	learn: 0.2886391	test: 0.2971599	best: 0.2971599 (50)	total: 7.16s	remaining: 0us

bestTest = 0.2971599153
bestIteration = 50

Trial 15, Fold 5: Log loss = 0.29715991525457464, Average precision = 0.9710148105682529, ROC-AUC = 0.9673432547423962, Elapsed Time = 7.277942400000029 seconds
Optimization Progress:  16%|#6        | 16/100 [32:56<4:27:57, 191.40s/it]
Trial 16, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 16, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
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6:	learn: 0.3745662	test: 0.3790426	best: 0.3790426 (6)	total: 7.19s	remaining: 14.4s
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12:	learn: 0.2876851	test: 0.2961616	best: 0.2961616 (12)	total: 12.6s	remaining: 7.74s
13:	learn: 0.2782803	test: 0.2875732	best: 0.2875732 (13)	total: 13.6s	remaining: 6.81s
14:	learn: 0.2668659	test: 0.2767293	best: 0.2767293 (14)	total: 14.6s	remaining: 5.86s
15:	learn: 0.2605462	test: 0.2708354	best: 0.2708354 (15)	total: 15.6s	remaining: 4.88s
16:	learn: 0.2533517	test: 0.2643266	best: 0.2643266 (16)	total: 16.6s	remaining: 3.91s
17:	learn: 0.2469424	test: 0.2579453	best: 0.2579453 (17)	total: 17.7s	remaining: 2.95s
18:	learn: 0.2388218	test: 0.2507337	best: 0.2507337 (18)	total: 18.8s	remaining: 1.98s
19:	learn: 0.2350983	test: 0.2471481	best: 0.2471481 (19)	total: 19.9s	remaining: 995ms
20:	learn: 0.2308703	test: 0.2435573	best: 0.2435573 (20)	total: 21s	remaining: 0us

bestTest = 0.2435573218
bestIteration = 20

Trial 16, Fold 1: Log loss = 0.24349434567895456, Average precision = 0.9688245044761086, ROC-AUC = 0.9641888529224798, Elapsed Time = 21.102435500000865 seconds
Trial 16, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 16, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
0:	learn: 0.6219896	test: 0.6229820	best: 0.6229820 (0)	total: 1.08s	remaining: 21.5s
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2:	learn: 0.4972675	test: 0.5001986	best: 0.5001986 (2)	total: 3.19s	remaining: 19.1s
3:	learn: 0.4566861	test: 0.4607402	best: 0.4607402 (3)	total: 4.07s	remaining: 17.3s
4:	learn: 0.4205067	test: 0.4254297	best: 0.4254297 (4)	total: 5.16s	remaining: 16.5s
5:	learn: 0.3922384	test: 0.3985706	best: 0.3985706 (5)	total: 6.28s	remaining: 15.7s
6:	learn: 0.3633713	test: 0.3695486	best: 0.3695486 (6)	total: 7.34s	remaining: 14.7s
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11:	learn: 0.2844025	test: 0.2914302	best: 0.2914302 (11)	total: 12.3s	remaining: 9.2s
12:	learn: 0.2768772	test: 0.2842791	best: 0.2842791 (12)	total: 13.3s	remaining: 8.2s
13:	learn: 0.2705951	test: 0.2783960	best: 0.2783960 (13)	total: 14.3s	remaining: 7.14s
14:	learn: 0.2637940	test: 0.2719830	best: 0.2719830 (14)	total: 15.3s	remaining: 6.1s
15:	learn: 0.2569748	test: 0.2653755	best: 0.2653755 (15)	total: 16.1s	remaining: 5.04s
16:	learn: 0.2511250	test: 0.2597700	best: 0.2597700 (16)	total: 17.1s	remaining: 4.02s
17:	learn: 0.2446844	test: 0.2544315	best: 0.2544315 (17)	total: 18.1s	remaining: 3.02s
18:	learn: 0.2393917	test: 0.2495976	best: 0.2495976 (18)	total: 19.2s	remaining: 2.02s
19:	learn: 0.2345287	test: 0.2451930	best: 0.2451930 (19)	total: 20.3s	remaining: 1.01s
20:	learn: 0.2294859	test: 0.2405264	best: 0.2405264 (20)	total: 21.4s	remaining: 0us

bestTest = 0.240526408
bestIteration = 20

Trial 16, Fold 2: Log loss = 0.24051377744740576, Average precision = 0.9685976566392376, ROC-AUC = 0.9645798665287179, Elapsed Time = 21.50512820000222 seconds
Trial 16, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 16, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
0:	learn: 0.6237145	test: 0.6246918	best: 0.6246918 (0)	total: 974ms	remaining: 19.5s
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2:	learn: 0.5194817	test: 0.5220728	best: 0.5220728 (2)	total: 3.15s	remaining: 18.9s
3:	learn: 0.4835023	test: 0.4855263	best: 0.4855263 (3)	total: 4.19s	remaining: 17.8s
4:	learn: 0.4411071	test: 0.4436720	best: 0.4436720 (4)	total: 5.08s	remaining: 16.3s
5:	learn: 0.4050742	test: 0.4075925	best: 0.4075925 (5)	total: 6.13s	remaining: 15.3s
6:	learn: 0.3778549	test: 0.3807623	best: 0.3807623 (6)	total: 7.27s	remaining: 14.5s
7:	learn: 0.3581575	test: 0.3612357	best: 0.3612357 (7)	total: 8.35s	remaining: 13.6s
8:	learn: 0.3380135	test: 0.3415768	best: 0.3415768 (8)	total: 9.18s	remaining: 12.2s
9:	learn: 0.3215406	test: 0.3254650	best: 0.3254650 (9)	total: 10.3s	remaining: 11.3s
10:	learn: 0.3076051	test: 0.3120838	best: 0.3120838 (10)	total: 11.4s	remaining: 10.3s
11:	learn: 0.2931971	test: 0.2978907	best: 0.2978907 (11)	total: 12.3s	remaining: 9.22s
12:	learn: 0.2830273	test: 0.2878913	best: 0.2878913 (12)	total: 13.2s	remaining: 8.15s
13:	learn: 0.2735831	test: 0.2790286	best: 0.2790286 (13)	total: 14.3s	remaining: 7.15s
14:	learn: 0.2663008	test: 0.2724547	best: 0.2724547 (14)	total: 15.3s	remaining: 6.14s
15:	learn: 0.2592145	test: 0.2660693	best: 0.2660693 (15)	total: 16.4s	remaining: 5.12s
16:	learn: 0.5668449	test: 0.2610921	best: 0.2610921 (16)	total: 17.4s	remaining: 4.1s
17:	learn: 0.5608359	test: 0.2554151	best: 0.2554151 (17)	total: 18.5s	remaining: 3.08s
18:	learn: 0.5569603	test: 0.2519859	best: 0.2519859 (18)	total: 19.3s	remaining: 2.03s
19:	learn: 0.5518702	test: 0.2477325	best: 0.2477325 (19)	total: 20.1s	remaining: 1s
20:	learn: 0.5469646	test: 0.2436389	best: 0.2436389 (20)	total: 21.2s	remaining: 0us

bestTest = 0.2436388868
bestIteration = 20

Trial 16, Fold 3: Log loss = 0.2438268360702275, Average precision = 0.9666008060568859, ROC-AUC = 0.9652810215638875, Elapsed Time = 21.303991299999325 seconds
Trial 16, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 16, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
0:	learn: 0.6195042	test: 0.6198789	best: 0.6198789 (0)	total: 827ms	remaining: 16.5s
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2:	learn: 0.5049490	test: 0.5062983	best: 0.5062983 (2)	total: 2.72s	remaining: 16.3s
3:	learn: 0.4586702	test: 0.4604111	best: 0.4604111 (3)	total: 3.79s	remaining: 16.1s
4:	learn: 0.4150608	test: 0.4164114	best: 0.4164114 (4)	total: 4.89s	remaining: 15.6s
5:	learn: 0.3891383	test: 0.3914193	best: 0.3914193 (5)	total: 5.95s	remaining: 14.9s
6:	learn: 0.3608860	test: 0.3637675	best: 0.3637675 (6)	total: 7.07s	remaining: 14.1s
7:	learn: 0.3393949	test: 0.3424237	best: 0.3424237 (7)	total: 8.16s	remaining: 13.3s
8:	learn: 0.3203100	test: 0.3236770	best: 0.3236770 (8)	total: 9.06s	remaining: 12.1s
9:	learn: 0.3084192	test: 0.3122982	best: 0.3122982 (9)	total: 10s	remaining: 11s
10:	learn: 0.2965423	test: 0.3009307	best: 0.3009307 (10)	total: 11s	remaining: 10s
11:	learn: 0.2855985	test: 0.2909422	best: 0.2909422 (11)	total: 12.1s	remaining: 9.07s
12:	learn: 0.2771255	test: 0.2832360	best: 0.2832360 (12)	total: 13.2s	remaining: 8.1s
13:	learn: 0.2683347	test: 0.2754139	best: 0.2754139 (13)	total: 14.1s	remaining: 7.04s
14:	learn: 0.2611082	test: 0.2690381	best: 0.2690381 (14)	total: 15.2s	remaining: 6.07s
15:	learn: 0.2545484	test: 0.2635986	best: 0.2635986 (15)	total: 16.2s	remaining: 5.08s
16:	learn: 0.2469706	test: 0.2563797	best: 0.2563797 (16)	total: 17.3s	remaining: 4.07s
17:	learn: 0.2406065	test: 0.2507442	best: 0.2507442 (17)	total: 18.4s	remaining: 3.06s
18:	learn: 0.2351302	test: 0.2460177	best: 0.2460177 (18)	total: 19.3s	remaining: 2.03s
19:	learn: 0.2307550	test: 0.2421790	best: 0.2421790 (19)	total: 20.2s	remaining: 1.01s
20:	learn: 0.2272946	test: 0.2389546	best: 0.2389546 (20)	total: 21.3s	remaining: 0us

bestTest = 0.2389546105
bestIteration = 20

Trial 16, Fold 4: Log loss = 0.23895464133929212, Average precision = 0.9696762793057822, ROC-AUC = 0.9642569523428018, Elapsed Time = 21.448489299997163 seconds
Trial 16, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 16, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
0:	learn: 0.6175041	test: 0.6192383	best: 0.6192383 (0)	total: 1.05s	remaining: 21s
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2:	learn: 0.5092998	test: 0.5142542	best: 0.5142542 (2)	total: 3.13s	remaining: 18.8s
3:	learn: 0.4611523	test: 0.4667227	best: 0.4667227 (3)	total: 4.18s	remaining: 17.8s
4:	learn: 0.4232865	test: 0.4303367	best: 0.4303367 (4)	total: 5.27s	remaining: 16.9s
5:	learn: 0.3962199	test: 0.4040572	best: 0.4040572 (5)	total: 6.35s	remaining: 15.9s
6:	learn: 0.3685334	test: 0.3775031	best: 0.3775031 (6)	total: 7.47s	remaining: 14.9s
7:	learn: 0.3440098	test: 0.3541453	best: 0.3541453 (7)	total: 8.54s	remaining: 13.9s
8:	learn: 0.3235769	test: 0.3350267	best: 0.3350267 (8)	total: 9.56s	remaining: 12.7s
9:	learn: 0.3063688	test: 0.3189450	best: 0.3189450 (9)	total: 10.5s	remaining: 11.5s
10:	learn: 0.2929550	test: 0.3064599	best: 0.3064599 (10)	total: 11.5s	remaining: 10.5s
11:	learn: 0.2825026	test: 0.2969344	best: 0.2969344 (11)	total: 12.6s	remaining: 9.45s
12:	learn: 0.2702243	test: 0.2856575	best: 0.2856575 (12)	total: 13.6s	remaining: 8.39s
13:	learn: 0.2628938	test: 0.2790292	best: 0.2790292 (13)	total: 14.7s	remaining: 7.35s
14:	learn: 0.2557582	test: 0.2727491	best: 0.2727491 (14)	total: 15.6s	remaining: 6.23s
15:	learn: 0.2495899	test: 0.2673660	best: 0.2673660 (15)	total: 16.6s	remaining: 5.19s
16:	learn: 0.2444062	test: 0.2628684	best: 0.2628684 (16)	total: 17.5s	remaining: 4.13s
17:	learn: 0.2392839	test: 0.2584712	best: 0.2584712 (17)	total: 18.5s	remaining: 3.08s
18:	learn: 0.2331003	test: 0.2525294	best: 0.2525294 (18)	total: 19.6s	remaining: 2.06s
19:	learn: 0.2307047	test: 0.2502945	best: 0.2502945 (19)	total: 20.6s	remaining: 1.03s
20:	learn: 0.2261292	test: 0.2466300	best: 0.2466300 (20)	total: 21.7s	remaining: 0us

bestTest = 0.2466300182
bestIteration = 20

Trial 16, Fold 5: Log loss = 0.2465087570707962, Average precision = 0.9651381822498915, ROC-AUC = 0.9621859056537168, Elapsed Time = 21.82507070000065 seconds
Optimization Progress:  17%|#7        | 17/100 [34:51<3:52:58, 168.42s/it]
Trial 17, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 17, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
0:	learn: 0.6524703	test: 0.6522438	best: 0.6522438 (0)	total: 51.4ms	remaining: 2.83s
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2:	learn: 0.5835970	test: 0.5833237	best: 0.5833237 (2)	total: 163ms	remaining: 2.87s
3:	learn: 0.5536792	test: 0.5533433	best: 0.5533433 (3)	total: 213ms	remaining: 2.76s
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6:	learn: 0.4807604	test: 0.4807579	best: 0.4807579 (6)	total: 364ms	remaining: 2.55s
7:	learn: 0.4607105	test: 0.4609005	best: 0.4609005 (7)	total: 413ms	remaining: 2.48s
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43:	learn: 0.2437947	test: 0.2483572	best: 0.2483572 (43)	total: 2.27s	remaining: 618ms
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46:	learn: 0.2395170	test: 0.2443485	best: 0.2443485 (46)	total: 2.42s	remaining: 463ms
47:	learn: 0.2385724	test: 0.2435913	best: 0.2435913 (47)	total: 2.47s	remaining: 412ms
48:	learn: 0.2372919	test: 0.2423855	best: 0.2423855 (48)	total: 2.53s	remaining: 361ms
49:	learn: 0.2360129	test: 0.2411318	best: 0.2411318 (49)	total: 2.58s	remaining: 310ms
50:	learn: 0.2345987	test: 0.2399265	best: 0.2399265 (50)	total: 2.63s	remaining: 258ms
51:	learn: 0.2336964	test: 0.2391719	best: 0.2391719 (51)	total: 2.69s	remaining: 207ms
52:	learn: 0.2328911	test: 0.2384003	best: 0.2384003 (52)	total: 2.74s	remaining: 155ms
53:	learn: 0.2317167	test: 0.2373192	best: 0.2373192 (53)	total: 2.8s	remaining: 104ms
54:	learn: 0.2307710	test: 0.2364900	best: 0.2364900 (54)	total: 2.85s	remaining: 51.9ms
55:	learn: 0.2300853	test: 0.2359228	best: 0.2359228 (55)	total: 2.91s	remaining: 0us

bestTest = 0.2359228495
bestIteration = 55

Trial 17, Fold 1: Log loss = 0.2359228494826574, Average precision = 0.9681689939213123, ROC-AUC = 0.9648643433083361, Elapsed Time = 3.021453900000779 seconds
Trial 17, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 17, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
0:	learn: 0.6532847	test: 0.6535746	best: 0.6535746 (0)	total: 50.7ms	remaining: 2.79s
1:	learn: 0.6167445	test: 0.6173381	best: 0.6173381 (1)	total: 104ms	remaining: 2.81s
2:	learn: 0.5829553	test: 0.5837063	best: 0.5837063 (2)	total: 158ms	remaining: 2.78s
3:	learn: 0.5528568	test: 0.5536770	best: 0.5536770 (3)	total: 211ms	remaining: 2.74s
4:	learn: 0.5272159	test: 0.5282087	best: 0.5282087 (4)	total: 262ms	remaining: 2.68s
5:	learn: 0.5023352	test: 0.5035155	best: 0.5035155 (5)	total: 317ms	remaining: 2.64s
6:	learn: 0.4796393	test: 0.4809156	best: 0.4809156 (6)	total: 372ms	remaining: 2.6s
7:	learn: 0.4595429	test: 0.4609547	best: 0.4609547 (7)	total: 425ms	remaining: 2.55s
8:	learn: 0.4408846	test: 0.4424643	best: 0.4424643 (8)	total: 480ms	remaining: 2.5s
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54:	learn: 0.2314090	test: 0.2337294	best: 0.2337294 (54)	total: 3.15s	remaining: 57.3ms
55:	learn: 0.2304081	test: 0.2327804	best: 0.2327804 (55)	total: 3.21s	remaining: 0us

bestTest = 0.2327804265
bestIteration = 55

Trial 17, Fold 2: Log loss = 0.23278042645488775, Average precision = 0.9712607334633114, ROC-AUC = 0.9678798497454127, Elapsed Time = 3.325564899998426 seconds
Trial 17, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 17, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
0:	learn: 0.6526699	test: 0.6527509	best: 0.6527509 (0)	total: 56ms	remaining: 3.08s
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2:	learn: 0.5830913	test: 0.5827618	best: 0.5827618 (2)	total: 172ms	remaining: 3.04s
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4:	learn: 0.5260483	test: 0.5252981	best: 0.5252981 (4)	total: 291ms	remaining: 2.97s
5:	learn: 0.5016584	test: 0.5006889	best: 0.5006889 (5)	total: 350ms	remaining: 2.92s
6:	learn: 0.4807880	test: 0.4796240	best: 0.4796240 (6)	total: 411ms	remaining: 2.88s
7:	learn: 0.4605764	test: 0.4593027	best: 0.4593027 (7)	total: 470ms	remaining: 2.82s
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47:	learn: 0.2416746	test: 0.2374018	best: 0.2374018 (47)	total: 2.82s	remaining: 470ms
48:	learn: 0.2404577	test: 0.2361602	best: 0.2361602 (48)	total: 2.88s	remaining: 411ms
49:	learn: 0.2389008	test: 0.2347028	best: 0.2347028 (49)	total: 2.93s	remaining: 352ms
50:	learn: 0.2379461	test: 0.2337621	best: 0.2337621 (50)	total: 2.99s	remaining: 293ms
51:	learn: 0.2366868	test: 0.2324390	best: 0.2324390 (51)	total: 3.04s	remaining: 234ms
52:	learn: 0.2354816	test: 0.2312357	best: 0.2312357 (52)	total: 3.1s	remaining: 176ms
53:	learn: 0.2343895	test: 0.2301045	best: 0.2301045 (53)	total: 3.16s	remaining: 117ms
54:	learn: 0.2333519	test: 0.2290692	best: 0.2290692 (54)	total: 3.21s	remaining: 58.4ms
55:	learn: 0.2324122	test: 0.2280821	best: 0.2280821 (55)	total: 3.27s	remaining: 0us

bestTest = 0.2280821006
bestIteration = 55

Trial 17, Fold 3: Log loss = 0.2280821005841465, Average precision = 0.9708749209136153, ROC-AUC = 0.9685720515790082, Elapsed Time = 3.3947281000000658 seconds
Trial 17, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 17, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
0:	learn: 0.6530484	test: 0.6529733	best: 0.6529733 (0)	total: 54.1ms	remaining: 2.97s
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2:	learn: 0.5830943	test: 0.5828704	best: 0.5828704 (2)	total: 169ms	remaining: 2.98s
3:	learn: 0.5531266	test: 0.5528263	best: 0.5528263 (3)	total: 226ms	remaining: 2.94s
4:	learn: 0.5275973	test: 0.5272063	best: 0.5272063 (4)	total: 280ms	remaining: 2.85s
5:	learn: 0.5028316	test: 0.5024320	best: 0.5024320 (5)	total: 334ms	remaining: 2.78s
6:	learn: 0.4814244	test: 0.4811414	best: 0.4811414 (6)	total: 389ms	remaining: 2.72s
7:	learn: 0.4620304	test: 0.4617320	best: 0.4617320 (7)	total: 443ms	remaining: 2.66s
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53:	learn: 0.2334749	test: 0.2340996	best: 0.2340996 (53)	total: 2.96s	remaining: 110ms
54:	learn: 0.2324929	test: 0.2331309	best: 0.2331309 (54)	total: 3.01s	remaining: 54.8ms
55:	learn: 0.2316293	test: 0.2324182	best: 0.2324182 (55)	total: 3.07s	remaining: 0us

bestTest = 0.2324182174
bestIteration = 55

Trial 17, Fold 4: Log loss = 0.23241821737738685, Average precision = 0.9703314041177783, ROC-AUC = 0.9657063754348566, Elapsed Time = 3.1934275000021444 seconds
Trial 17, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 17, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
0:	learn: 0.6524623	test: 0.6531228	best: 0.6531228 (0)	total: 86.9ms	remaining: 4.78s
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4:	learn: 0.5300068	test: 0.5323462	best: 0.5323462 (4)	total: 381ms	remaining: 3.88s
5:	learn: 0.5048851	test: 0.5075358	best: 0.5075358 (5)	total: 444ms	remaining: 3.7s
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55:	learn: 0.2291083	test: 0.2404637	best: 0.2404637 (55)	total: 3.37s	remaining: 0us

bestTest = 0.240463659
bestIteration = 55

Trial 17, Fold 5: Log loss = 0.2404636590437554, Average precision = 0.967340164381498, ROC-AUC = 0.9634232205562677, Elapsed Time = 3.5209907999997085 seconds
Optimization Progress:  18%|#8        | 18/100 [35:15<2:50:59, 125.12s/it]
Trial 18, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 18, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
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59:	learn: 0.2228910	test: 0.2418065	best: 0.2418065 (59)	total: 11.5s	remaining: 1.53s
60:	learn: 0.2213378	test: 0.2404923	best: 0.2404923 (60)	total: 11.7s	remaining: 1.34s
61:	learn: 0.2198124	test: 0.2391569	best: 0.2391569 (61)	total: 11.9s	remaining: 1.15s
62:	learn: 0.2182633	test: 0.2378205	best: 0.2378205 (62)	total: 12.1s	remaining: 957ms
63:	learn: 0.2168726	test: 0.2367078	best: 0.2367078 (63)	total: 12.3s	remaining: 766ms
64:	learn: 0.2155879	test: 0.2356910	best: 0.2356910 (64)	total: 12.4s	remaining: 574ms
65:	learn: 0.2142908	test: 0.2346625	best: 0.2346625 (65)	total: 12.6s	remaining: 383ms
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67:	learn: 0.2116379	test: 0.2324910	best: 0.2324910 (67)	total: 13s	remaining: 0us

bestTest = 0.232491013
bestIteration = 67

Trial 18, Fold 1: Log loss = 0.23222625511648723, Average precision = 0.9752353269523638, ROC-AUC = 0.9710207529764892, Elapsed Time = 13.157521600001928 seconds
Trial 18, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 18, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
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60:	learn: 0.2230852	test: 0.2357451	best: 0.2357451 (60)	total: 12s	remaining: 1.37s
61:	learn: 0.2215032	test: 0.2342975	best: 0.2342975 (61)	total: 12.2s	remaining: 1.18s
62:	learn: 0.2199830	test: 0.2328664	best: 0.2328664 (62)	total: 12.4s	remaining: 981ms
63:	learn: 0.2186798	test: 0.2317099	best: 0.2317099 (63)	total: 12.6s	remaining: 786ms
64:	learn: 0.2172548	test: 0.2303192	best: 0.2303192 (64)	total: 12.8s	remaining: 589ms
65:	learn: 0.2159280	test: 0.2291410	best: 0.2291410 (65)	total: 13s	remaining: 393ms
66:	learn: 0.2147298	test: 0.2280899	best: 0.2280899 (66)	total: 13.2s	remaining: 197ms
67:	learn: 0.2133562	test: 0.2269768	best: 0.2269768 (67)	total: 13.4s	remaining: 0us

bestTest = 0.2269768382
bestIteration = 67

Trial 18, Fold 2: Log loss = 0.22675746868625035, Average precision = 0.9758337479347416, ROC-AUC = 0.973149063974135, Elapsed Time = 13.523272300000826 seconds
Trial 18, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 18, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
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53:	learn: 0.2340250	test: 0.2435152	best: 0.2435152 (53)	total: 11s	remaining: 2.85s
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55:	learn: 0.2301815	test: 0.2402096	best: 0.2402096 (55)	total: 11.6s	remaining: 2.48s
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57:	learn: 0.2266239	test: 0.2370028	best: 0.2370028 (57)	total: 12.1s	remaining: 2.09s
58:	learn: 0.2248451	test: 0.2354397	best: 0.2354397 (58)	total: 12.4s	remaining: 1.89s
59:	learn: 0.2231769	test: 0.2340068	best: 0.2340068 (59)	total: 12.6s	remaining: 1.68s
60:	learn: 0.2216441	test: 0.2325612	best: 0.2325612 (60)	total: 12.9s	remaining: 1.48s
61:	learn: 0.2199736	test: 0.2310486	best: 0.2310486 (61)	total: 13.2s	remaining: 1.28s
62:	learn: 0.2185931	test: 0.2298928	best: 0.2298928 (62)	total: 13.4s	remaining: 1.06s
63:	learn: 0.2173042	test: 0.2287579	best: 0.2287579 (63)	total: 13.6s	remaining: 853ms
64:	learn: 0.2160287	test: 0.2277174	best: 0.2277174 (64)	total: 13.9s	remaining: 641ms
65:	learn: 0.2147394	test: 0.2265774	best: 0.2265774 (65)	total: 14.1s	remaining: 427ms
66:	learn: 0.2134485	test: 0.2255235	best: 0.2255235 (66)	total: 14.3s	remaining: 214ms
67:	learn: 0.2120495	test: 0.2243323	best: 0.2243323 (67)	total: 14.5s	remaining: 0us

bestTest = 0.2243322775
bestIteration = 67

Trial 18, Fold 3: Log loss = 0.2242580182581941, Average precision = 0.9748102542312739, ROC-AUC = 0.9730504828760196, Elapsed Time = 14.702690000001894 seconds
Trial 18, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 18, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
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2:	learn: 0.6207335	test: 0.6215292	best: 0.6215292 (2)	total: 638ms	remaining: 13.8s
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58:	learn: 0.2250242	test: 0.2382448	best: 0.2382448 (58)	total: 11.8s	remaining: 1.8s
59:	learn: 0.2233397	test: 0.2367420	best: 0.2367420 (59)	total: 12s	remaining: 1.6s
60:	learn: 0.2217163	test: 0.2352604	best: 0.2352604 (60)	total: 12.2s	remaining: 1.4s
61:	learn: 0.2201635	test: 0.2339396	best: 0.2339396 (61)	total: 12.4s	remaining: 1.2s
62:	learn: 0.2185645	test: 0.2326504	best: 0.2326504 (62)	total: 12.7s	remaining: 1.01s
63:	learn: 0.2172424	test: 0.2314986	best: 0.2314986 (63)	total: 12.8s	remaining: 803ms
64:	learn: 0.2158611	test: 0.2304200	best: 0.2304200 (64)	total: 13.1s	remaining: 602ms
65:	learn: 0.2144836	test: 0.2291628	best: 0.2291628 (65)	total: 13.2s	remaining: 401ms
66:	learn: 0.2131592	test: 0.2279756	best: 0.2279756 (66)	total: 13.4s	remaining: 201ms
67:	learn: 0.2118704	test: 0.2267795	best: 0.2267795 (67)	total: 13.6s	remaining: 0us

bestTest = 0.2267794595
bestIteration = 67

Trial 18, Fold 4: Log loss = 0.22656632827949785, Average precision = 0.9754526281814258, ROC-AUC = 0.9728596792730412, Elapsed Time = 13.785531000001356 seconds
Trial 18, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 18, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
0:	learn: 0.6675209	test: 0.6681154	best: 0.6681154 (0)	total: 173ms	remaining: 11.6s
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2:	learn: 0.6201287	test: 0.6219982	best: 0.6219982 (2)	total: 533ms	remaining: 11.5s
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63:	learn: 0.2159155	test: 0.2383253	best: 0.2383253 (63)	total: 12.7s	remaining: 791ms
64:	learn: 0.2145945	test: 0.2371814	best: 0.2371814 (64)	total: 12.8s	remaining: 593ms
65:	learn: 0.2133032	test: 0.2360983	best: 0.2360983 (65)	total: 13s	remaining: 395ms
66:	learn: 0.2120533	test: 0.2351231	best: 0.2351231 (66)	total: 13.3s	remaining: 198ms
67:	learn: 0.2107542	test: 0.2340904	best: 0.2340904 (67)	total: 13.5s	remaining: 0us

bestTest = 0.2340903653
bestIteration = 67

Trial 18, Fold 5: Log loss = 0.2336974523166397, Average precision = 0.9744352207003804, ROC-AUC = 0.9710939189565799, Elapsed Time = 13.6142170000021 seconds
Optimization Progress:  19%|#9        | 19/100 [36:32<2:29:23, 110.66s/it]
Trial 19, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 19, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
0:	learn: 0.6493495	test: 0.6493227	best: 0.6493227 (0)	total: 58.3ms	remaining: 233ms
1:	learn: 0.6100900	test: 0.6100392	best: 0.6100392 (1)	total: 121ms	remaining: 181ms
2:	learn: 0.5817833	test: 0.5819830	best: 0.5819830 (2)	total: 183ms	remaining: 122ms
3:	learn: 0.5564804	test: 0.5570457	best: 0.5570457 (3)	total: 240ms	remaining: 59.9ms
4:	learn: 0.5284393	test: 0.5291573	best: 0.5291573 (4)	total: 304ms	remaining: 0us

bestTest = 0.5291573093
bestIteration = 4

Trial 19, Fold 1: Log loss = 0.5294335510119113, Average precision = 0.9694654657745588, ROC-AUC = 0.9645724896402402, Elapsed Time = 0.4126544999999169 seconds
Trial 19, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 19, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
0:	learn: 0.6500462	test: 0.6501067	best: 0.6501067 (0)	total: 68.8ms	remaining: 275ms
1:	learn: 0.6176881	test: 0.6180616	best: 0.6180616 (1)	total: 131ms	remaining: 196ms
2:	learn: 0.5876388	test: 0.5884129	best: 0.5884129 (2)	total: 195ms	remaining: 130ms
3:	learn: 0.5575685	test: 0.5586698	best: 0.5586698 (3)	total: 260ms	remaining: 64.9ms
4:	learn: 0.5335288	test: 0.5349131	best: 0.5349131 (4)	total: 318ms	remaining: 0us

bestTest = 0.5349131433
bestIteration = 4

Trial 19, Fold 2: Log loss = 0.5350452831126273, Average precision = 0.9684881298278613, ROC-AUC = 0.9657373227623803, Elapsed Time = 0.4333470999990823 seconds
Trial 19, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 19, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
0:	learn: 0.6551776	test: 0.6549643	best: 0.6549643 (0)	total: 66.8ms	remaining: 267ms
1:	learn: 0.6220921	test: 0.6218435	best: 0.6218435 (1)	total: 123ms	remaining: 185ms
2:	learn: 0.5891958	test: 0.5887209	best: 0.5887209 (2)	total: 186ms	remaining: 124ms
3:	learn: 0.5634596	test: 0.5628272	best: 0.5628272 (3)	total: 249ms	remaining: 62.3ms
4:	learn: 0.5362578	test: 0.5355707	best: 0.5355707 (4)	total: 325ms	remaining: 0us

bestTest = 0.5355706597
bestIteration = 4

Trial 19, Fold 3: Log loss = 0.5358263510296953, Average precision = 0.9717330150833315, ROC-AUC = 0.968760759671967, Elapsed Time = 0.4338562000011734 seconds
Trial 19, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 19, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
0:	learn: 0.6574934	test: 0.6576269	best: 0.6576269 (0)	total: 63.8ms	remaining: 255ms
1:	learn: 0.6302521	test: 0.6307208	best: 0.6307208 (1)	total: 128ms	remaining: 192ms
2:	learn: 0.5980759	test: 0.5988533	best: 0.5988533 (2)	total: 200ms	remaining: 133ms
3:	learn: 0.5657710	test: 0.5667482	best: 0.5667482 (3)	total: 269ms	remaining: 67.2ms
4:	learn: 0.5407174	test: 0.5421242	best: 0.5421242 (4)	total: 341ms	remaining: 0us

bestTest = 0.5421241734
bestIteration = 4

Trial 19, Fold 4: Log loss = 0.5423984690305971, Average precision = 0.9679680371166938, ROC-AUC = 0.962191829939126, Elapsed Time = 0.45545209999909275 seconds
Trial 19, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 19, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
0:	learn: 0.6552983	test: 0.6563531	best: 0.6563531 (0)	total: 59.9ms	remaining: 240ms
1:	learn: 0.6182081	test: 0.6199267	best: 0.6199267 (1)	total: 123ms	remaining: 184ms
2:	learn: 0.5863516	test: 0.5885258	best: 0.5885258 (2)	total: 188ms	remaining: 125ms
3:	learn: 0.5575569	test: 0.5600240	best: 0.5600240 (3)	total: 247ms	remaining: 61.8ms
4:	learn: 0.5298845	test: 0.5328504	best: 0.5328504 (4)	total: 316ms	remaining: 0us

bestTest = 0.5328503903
bestIteration = 4

Trial 19, Fold 5: Log loss = 0.5331276533217285, Average precision = 0.9677386078861334, ROC-AUC = 0.9630175947085818, Elapsed Time = 0.42010640000080457 seconds
Optimization Progress:  20%|##        | 20/100 [36:43<1:47:31, 80.65s/it] 
Trial 20, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 20, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
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52:	learn: 0.2719497	test: 0.2735169	best: 0.2735169 (52)	total: 1.13s	remaining: 171ms
53:	learn: 0.2703102	test: 0.2718881	best: 0.2718881 (53)	total: 1.15s	remaining: 150ms
54:	learn: 0.2691377	test: 0.2708199	best: 0.2708199 (54)	total: 1.18s	remaining: 129ms
55:	learn: 0.2683711	test: 0.2700794	best: 0.2700794 (55)	total: 1.2s	remaining: 107ms
56:	learn: 0.2675773	test: 0.2694844	best: 0.2694844 (56)	total: 1.22s	remaining: 85.9ms
57:	learn: 0.2664412	test: 0.2684466	best: 0.2684466 (57)	total: 1.25s	remaining: 64.6ms
58:	learn: 0.2648318	test: 0.2668169	best: 0.2668169 (58)	total: 1.27s	remaining: 43ms
59:	learn: 0.2634645	test: 0.2654719	best: 0.2654719 (59)	total: 1.29s	remaining: 21.5ms
60:	learn: 0.2618390	test: 0.2638001	best: 0.2638001 (60)	total: 1.31s	remaining: 0us

bestTest = 0.2638000709
bestIteration = 60

Trial 20, Fold 1: Log loss = 0.2636127219803609, Average precision = 0.9632467500154142, ROC-AUC = 0.9569972887780024, Elapsed Time = 1.4266287999998895 seconds
Trial 20, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 20, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
0:	learn: 0.6613361	test: 0.6616139	best: 0.6616139 (0)	total: 21.8ms	remaining: 1.31s
1:	learn: 0.6314861	test: 0.6320707	best: 0.6320707 (1)	total: 39.2ms	remaining: 1.16s
2:	learn: 0.6088114	test: 0.6093734	best: 0.6093734 (2)	total: 62.9ms	remaining: 1.22s
3:	learn: 0.5920383	test: 0.5924745	best: 0.5924745 (3)	total: 80.7ms	remaining: 1.15s
4:	learn: 0.5719947	test: 0.5724448	best: 0.5724448 (4)	total: 103ms	remaining: 1.16s
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60:	learn: 0.2579574	test: 0.2587699	best: 0.2587699 (60)	total: 1.33s	remaining: 0us

bestTest = 0.2587698676
bestIteration = 60

Trial 20, Fold 2: Log loss = 0.2586720483515504, Average precision = 0.9648877163322858, ROC-AUC = 0.96090825298541, Elapsed Time = 1.4445759000009275 seconds
Trial 20, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 20, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
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60:	learn: 0.2554709	test: 0.2502923	best: 0.2502923 (60)	total: 1.35s	remaining: 0us

bestTest = 0.2502922561
bestIteration = 60

Trial 20, Fold 3: Log loss = 0.2502433561134991, Average precision = 0.9665725313821034, ROC-AUC = 0.9627439962535275, Elapsed Time = 1.4568521000001056 seconds
Trial 20, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 20, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
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60:	learn: 0.2598917	test: 0.2604125	best: 0.2604125 (60)	total: 1.37s	remaining: 0us

bestTest = 0.2604125491
bestIteration = 60

Trial 20, Fold 4: Log loss = 0.2602940149502716, Average precision = 0.965263836969806, ROC-AUC = 0.9590665393391846, Elapsed Time = 1.481476400000247 seconds
Trial 20, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 20, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
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60:	learn: 0.2555486	test: 0.2643443	best: 0.2643443 (60)	total: 1.37s	remaining: 0us

bestTest = 0.2643442648
bestIteration = 60

Optimization Progress:  21%|##1       | 21/100 [36:58<1:20:15, 60.95s/it]
Trial 20, Fold 5: Log loss = 0.26414094643409086, Average precision = 0.9619330790696504, ROC-AUC = 0.9570425282871634, Elapsed Time = 1.4874039000023913 seconds
Trial 21, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 21, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
0:	learn: 0.6052402	test: 0.6056813	best: 0.6056813 (0)	total: 298ms	remaining: 4.17s
1:	learn: 0.5345421	test: 0.5357213	best: 0.5357213 (1)	total: 680ms	remaining: 4.42s
2:	learn: 0.4777000	test: 0.4796167	best: 0.4796167 (2)	total: 1.08s	remaining: 4.32s
3:	learn: 0.4332827	test: 0.4362740	best: 0.4362740 (3)	total: 1.4s	remaining: 3.85s
4:	learn: 0.3946632	test: 0.3981686	best: 0.3981686 (4)	total: 1.77s	remaining: 3.54s
5:	learn: 0.3648220	test: 0.3692311	best: 0.3692311 (5)	total: 2.15s	remaining: 3.22s
6:	learn: 0.3402481	test: 0.3454375	best: 0.3454375 (6)	total: 2.5s	remaining: 2.86s
7:	learn: 0.3204276	test: 0.3265295	best: 0.3265295 (7)	total: 2.77s	remaining: 2.42s
8:	learn: 0.3028063	test: 0.3093781	best: 0.3093781 (8)	total: 3.11s	remaining: 2.08s
9:	learn: 0.2883391	test: 0.2957645	best: 0.2957645 (9)	total: 3.44s	remaining: 1.72s
10:	learn: 0.2765725	test: 0.2845841	best: 0.2845841 (10)	total: 3.81s	remaining: 1.39s
11:	learn: 0.2667665	test: 0.2754682	best: 0.2754682 (11)	total: 4.14s	remaining: 1.03s
12:	learn: 0.2592984	test: 0.2685861	best: 0.2685861 (12)	total: 4.37s	remaining: 673ms
13:	learn: 0.2516720	test: 0.2615534	best: 0.2615534 (13)	total: 4.75s	remaining: 339ms
14:	learn: 0.2450749	test: 0.2558926	best: 0.2558926 (14)	total: 5.1s	remaining: 0us

bestTest = 0.2558925518
bestIteration = 14

Trial 21, Fold 1: Log loss = 0.25565690971526495, Average precision = 0.9717095024139286, ROC-AUC = 0.9676144741495916, Elapsed Time = 5.211276900001394 seconds
Trial 21, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 21, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
0:	learn: 0.6046925	test: 0.6055960	best: 0.6055960 (0)	total: 457ms	remaining: 6.4s
1:	learn: 0.5341215	test: 0.5355399	best: 0.5355399 (1)	total: 800ms	remaining: 5.2s
2:	learn: 0.4768494	test: 0.4785904	best: 0.4785904 (2)	total: 1.11s	remaining: 4.43s
3:	learn: 0.4310689	test: 0.4333149	best: 0.4333149 (3)	total: 1.45s	remaining: 3.99s
4:	learn: 0.3931205	test: 0.3956140	best: 0.3956140 (4)	total: 1.78s	remaining: 3.57s
5:	learn: 0.3625034	test: 0.3650723	best: 0.3650723 (5)	total: 2.1s	remaining: 3.15s
6:	learn: 0.3399291	test: 0.3429229	best: 0.3429229 (6)	total: 2.4s	remaining: 2.74s
7:	learn: 0.3188097	test: 0.3223808	best: 0.3223808 (7)	total: 2.71s	remaining: 2.37s
8:	learn: 0.3018135	test: 0.3054379	best: 0.3054379 (8)	total: 3.04s	remaining: 2.03s
9:	learn: 0.2878346	test: 0.2916962	best: 0.2916962 (9)	total: 3.38s	remaining: 1.69s
10:	learn: 0.2759631	test: 0.2805473	best: 0.2805473 (10)	total: 3.64s	remaining: 1.32s
11:	learn: 0.2666824	test: 0.2718867	best: 0.2718867 (11)	total: 3.96s	remaining: 991ms
12:	learn: 0.2587605	test: 0.2646505	best: 0.2646505 (12)	total: 4.3s	remaining: 662ms
13:	learn: 0.2512243	test: 0.2571744	best: 0.2571744 (13)	total: 4.65s	remaining: 332ms
14:	learn: 0.2448386	test: 0.2512642	best: 0.2512642 (14)	total: 4.98s	remaining: 0us

bestTest = 0.2512642087
bestIteration = 14

Trial 21, Fold 2: Log loss = 0.2511007027585758, Average precision = 0.9731409802316744, ROC-AUC = 0.9695328383725232, Elapsed Time = 5.091062700001203 seconds
Trial 21, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 21, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
0:	learn: 0.6057888	test: 0.6054812	best: 0.6054812 (0)	total: 365ms	remaining: 5.11s
1:	learn: 0.5357161	test: 0.5354874	best: 0.5354874 (1)	total: 689ms	remaining: 4.48s
2:	learn: 0.4792120	test: 0.4788670	best: 0.4788670 (2)	total: 1.01s	remaining: 4.06s
3:	learn: 0.4326812	test: 0.4324274	best: 0.4324274 (3)	total: 1.35s	remaining: 3.71s
4:	learn: 0.3950740	test: 0.3952497	best: 0.3952497 (4)	total: 1.74s	remaining: 3.49s
5:	learn: 0.3654539	test: 0.3656939	best: 0.3656939 (5)	total: 2.1s	remaining: 3.15s
6:	learn: 0.3430518	test: 0.3435685	best: 0.3435685 (6)	total: 2.36s	remaining: 2.7s
7:	learn: 0.3224220	test: 0.3234336	best: 0.3234336 (7)	total: 2.69s	remaining: 2.35s
8:	learn: 0.3060553	test: 0.3072086	best: 0.3072086 (8)	total: 3.04s	remaining: 2.03s
9:	learn: 0.2936707	test: 0.2948880	best: 0.2948880 (9)	total: 3.33s	remaining: 1.67s
10:	learn: 0.2801772	test: 0.2820504	best: 0.2820504 (10)	total: 3.76s	remaining: 1.37s
11:	learn: 0.2695583	test: 0.2715100	best: 0.2715100 (11)	total: 4.09s	remaining: 1.02s
12:	learn: 0.2610255	test: 0.2633191	best: 0.2633191 (12)	total: 4.43s	remaining: 682ms
13:	learn: 0.2535457	test: 0.2563301	best: 0.2563301 (13)	total: 4.75s	remaining: 339ms
14:	learn: 0.2470052	test: 0.2500255	best: 0.2500255 (14)	total: 5.07s	remaining: 0us

bestTest = 0.250025528
bestIteration = 14

Trial 21, Fold 3: Log loss = 0.25000451183347056, Average precision = 0.9721744330022752, ROC-AUC = 0.9688436768643276, Elapsed Time = 5.176167000001442 seconds
Trial 21, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 21, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
0:	learn: 0.6044499	test: 0.6068209	best: 0.6068209 (0)	total: 340ms	remaining: 4.76s
1:	learn: 0.5336060	test: 0.5363572	best: 0.5363572 (1)	total: 682ms	remaining: 4.43s
2:	learn: 0.4781055	test: 0.4806307	best: 0.4806307 (2)	total: 978ms	remaining: 3.91s
3:	learn: 0.4325489	test: 0.4364999	best: 0.4364999 (3)	total: 1.33s	remaining: 3.65s
4:	learn: 0.3966119	test: 0.4010256	best: 0.4010256 (4)	total: 1.72s	remaining: 3.44s
5:	learn: 0.3658267	test: 0.3712067	best: 0.3712067 (5)	total: 2s	remaining: 3s
6:	learn: 0.3412521	test: 0.3476130	best: 0.3476130 (6)	total: 2.39s	remaining: 2.73s
7:	learn: 0.3209018	test: 0.3280923	best: 0.3280923 (7)	total: 2.68s	remaining: 2.35s
8:	learn: 0.3054452	test: 0.3132992	best: 0.3132992 (8)	total: 3.02s	remaining: 2.01s
9:	learn: 0.2917643	test: 0.3001822	best: 0.3001822 (9)	total: 3.37s	remaining: 1.68s
10:	learn: 0.2805816	test: 0.2893259	best: 0.2893259 (10)	total: 3.69s	remaining: 1.34s
11:	learn: 0.2697974	test: 0.2784795	best: 0.2784795 (11)	total: 4.1s	remaining: 1.02s
12:	learn: 0.2603664	test: 0.2690757	best: 0.2690757 (12)	total: 4.4s	remaining: 678ms
13:	learn: 0.2518468	test: 0.2609608	best: 0.2609608 (13)	total: 4.76s	remaining: 340ms
14:	learn: 0.2450163	test: 0.2543693	best: 0.2543693 (14)	total: 5.08s	remaining: 0us

bestTest = 0.254369291
bestIteration = 14

Trial 21, Fold 4: Log loss = 0.25420276714706525, Average precision = 0.9728859743949347, ROC-AUC = 0.9680305788631611, Elapsed Time = 5.191823100001784 seconds
Trial 21, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 21, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
0:	learn: 0.6046631	test: 0.6062912	best: 0.6062912 (0)	total: 332ms	remaining: 4.65s
1:	learn: 0.5333253	test: 0.5362119	best: 0.5362119 (1)	total: 674ms	remaining: 4.38s
2:	learn: 0.4758445	test: 0.4815178	best: 0.4815178 (2)	total: 1.13s	remaining: 4.52s
3:	learn: 0.4296908	test: 0.4372961	best: 0.4372961 (3)	total: 1.43s	remaining: 3.93s
4:	learn: 0.3952187	test: 0.4036810	best: 0.4036810 (4)	total: 1.72s	remaining: 3.44s
5:	learn: 0.3646717	test: 0.3740258	best: 0.3740258 (5)	total: 2.06s	remaining: 3.09s
6:	learn: 0.3398687	test: 0.3498655	best: 0.3498655 (6)	total: 2.4s	remaining: 2.75s
7:	learn: 0.3200923	test: 0.3311283	best: 0.3311283 (7)	total: 2.73s	remaining: 2.39s
8:	learn: 0.3022381	test: 0.3136684	best: 0.3136684 (8)	total: 3.02s	remaining: 2.01s
9:	learn: 0.2871766	test: 0.2993820	best: 0.2993820 (9)	total: 3.35s	remaining: 1.67s
10:	learn: 0.2766382	test: 0.2896269	best: 0.2896269 (10)	total: 3.63s	remaining: 1.32s
11:	learn: 0.2661843	test: 0.2797086	best: 0.2797086 (11)	total: 4s	remaining: 1s
12:	learn: 0.2575636	test: 0.2717674	best: 0.2717674 (12)	total: 4.32s	remaining: 665ms
13:	learn: 0.2495566	test: 0.2641221	best: 0.2641221 (13)	total: 4.6s	remaining: 328ms
14:	learn: 0.2433030	test: 0.2582642	best: 0.2582642 (14)	total: 4.84s	remaining: 0us

bestTest = 0.2582642295
bestIteration = 14

Trial 21, Fold 5: Log loss = 0.25793613146605265, Average precision = 0.9714345026242601, ROC-AUC = 0.9671171803875667, Elapsed Time = 4.945450200000778 seconds
Optimization Progress:  22%|##2       | 22/100 [37:31<1:08:23, 52.61s/it]
Trial 22, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 22, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
0:	learn: 0.6484288	test: 0.6485089	best: 0.6485089 (0)	total: 125ms	remaining: 873ms
1:	learn: 0.6092857	test: 0.6096999	best: 0.6096999 (1)	total: 254ms	remaining: 762ms
2:	learn: 0.5698356	test: 0.5699160	best: 0.5699160 (2)	total: 398ms	remaining: 663ms
3:	learn: 0.5376881	test: 0.5379766	best: 0.5379766 (3)	total: 537ms	remaining: 537ms
4:	learn: 0.5088473	test: 0.5095523	best: 0.5095523 (4)	total: 665ms	remaining: 399ms
5:	learn: 0.4821093	test: 0.4829636	best: 0.4829636 (5)	total: 810ms	remaining: 270ms
6:	learn: 0.4580630	test: 0.4592787	best: 0.4592787 (6)	total: 947ms	remaining: 135ms
7:	learn: 0.4363021	test: 0.4376143	best: 0.4376143 (7)	total: 1.1s	remaining: 0us

bestTest = 0.4376143381
bestIteration = 7

Trial 22, Fold 1: Log loss = 0.43821208600053174, Average precision = 0.9714780359988808, ROC-AUC = 0.9670584734947878, Elapsed Time = 1.2224325000024692 seconds
Trial 22, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 22, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
0:	learn: 0.6448862	test: 0.6451627	best: 0.6451627 (0)	total: 147ms	remaining: 1.03s
1:	learn: 0.6052244	test: 0.6058257	best: 0.6058257 (1)	total: 285ms	remaining: 854ms
2:	learn: 0.5704255	test: 0.5715395	best: 0.5715395 (2)	total: 447ms	remaining: 745ms
3:	learn: 0.5359748	test: 0.5373700	best: 0.5373700 (3)	total: 604ms	remaining: 604ms
4:	learn: 0.5030373	test: 0.5047150	best: 0.5047150 (4)	total: 764ms	remaining: 459ms
5:	learn: 0.4747069	test: 0.4762664	best: 0.4762664 (5)	total: 931ms	remaining: 310ms
6:	learn: 0.4529150	test: 0.4547509	best: 0.4547509 (6)	total: 1.08s	remaining: 154ms
7:	learn: 0.4329562	test: 0.4352767	best: 0.4352767 (7)	total: 1.23s	remaining: 0us

bestTest = 0.4352767145
bestIteration = 7

Trial 22, Fold 2: Log loss = 0.43556843681241236, Average precision = 0.9716352472754425, ROC-AUC = 0.9679856938257771, Elapsed Time = 1.3532617000018945 seconds
Trial 22, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 22, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
0:	learn: 0.6471622	test: 0.6469957	best: 0.6469957 (0)	total: 125ms	remaining: 876ms
1:	learn: 0.6071983	test: 0.6068611	best: 0.6068611 (1)	total: 244ms	remaining: 732ms
2:	learn: 0.5707154	test: 0.5699889	best: 0.5699889 (2)	total: 370ms	remaining: 617ms
3:	learn: 0.5350672	test: 0.5339401	best: 0.5339401 (3)	total: 506ms	remaining: 506ms
4:	learn: 0.5049380	test: 0.5042262	best: 0.5042262 (4)	total: 651ms	remaining: 391ms
5:	learn: 0.4792372	test: 0.4788578	best: 0.4788578 (5)	total: 778ms	remaining: 259ms
6:	learn: 0.4583073	test: 0.4579646	best: 0.4579646 (6)	total: 927ms	remaining: 132ms
7:	learn: 0.4385213	test: 0.4382335	best: 0.4382335 (7)	total: 1.06s	remaining: 0us

bestTest = 0.4382335323
bestIteration = 7

Trial 22, Fold 3: Log loss = 0.4386836080120765, Average precision = 0.9714202845719077, ROC-AUC = 0.9688746767583862, Elapsed Time = 1.1888800999986415 seconds
Trial 22, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 22, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
0:	learn: 0.6472816	test: 0.6472400	best: 0.6472400 (0)	total: 124ms	remaining: 865ms
1:	learn: 0.6081028	test: 0.6081294	best: 0.6081294 (1)	total: 243ms	remaining: 730ms
2:	learn: 0.5697098	test: 0.5698944	best: 0.5698944 (2)	total: 351ms	remaining: 585ms
3:	learn: 0.5340442	test: 0.5344432	best: 0.5344432 (3)	total: 471ms	remaining: 471ms
4:	learn: 0.5050421	test: 0.5054522	best: 0.5054522 (4)	total: 601ms	remaining: 360ms
5:	learn: 0.4789070	test: 0.4794594	best: 0.4794594 (5)	total: 721ms	remaining: 240ms
6:	learn: 0.4574948	test: 0.4583475	best: 0.4583475 (6)	total: 853ms	remaining: 122ms
7:	learn: 0.4363622	test: 0.4373440	best: 0.4373440 (7)	total: 969ms	remaining: 0us

bestTest = 0.437344016
bestIteration = 7

Trial 22, Fold 4: Log loss = 0.43779578916843814, Average precision = 0.9737375106157666, ROC-AUC = 0.9693548204420219, Elapsed Time = 1.083319900000788 seconds
Trial 22, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 22, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
0:	learn: 0.6417526	test: 0.6428893	best: 0.6428893 (0)	total: 120ms	remaining: 839ms
1:	learn: 0.5993223	test: 0.6010456	best: 0.6010456 (1)	total: 230ms	remaining: 691ms
2:	learn: 0.5615980	test: 0.5640013	best: 0.5640013 (2)	total: 356ms	remaining: 593ms
3:	learn: 0.5298356	test: 0.5327830	best: 0.5327830 (3)	total: 464ms	remaining: 464ms
4:	learn: 0.4989567	test: 0.5026511	best: 0.5026511 (4)	total: 578ms	remaining: 347ms
5:	learn: 0.4732253	test: 0.4777445	best: 0.4777445 (5)	total: 692ms	remaining: 231ms
6:	learn: 0.4529167	test: 0.4579802	best: 0.4579802 (6)	total: 807ms	remaining: 115ms
7:	learn: 0.4335662	test: 0.4388874	best: 0.4388874 (7)	total: 921ms	remaining: 0us

bestTest = 0.4388874333
bestIteration = 7

Trial 22, Fold 5: Log loss = 0.4392646548500526, Average precision = 0.9685299905654721, ROC-AUC = 0.9655006967281646, Elapsed Time = 1.0358863000001293 seconds
Optimization Progress:  23%|##3       | 23/100 [37:46<52:52, 41.20s/it]  
Trial 23, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 23, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
0:	learn: 0.5946378	test: 0.5951884	best: 0.5951884 (0)	total: 96.1ms	remaining: 5.86s
1:	learn: 0.5161184	test: 0.5179206	best: 0.5179206 (1)	total: 191ms	remaining: 5.72s
2:	learn: 0.4556386	test: 0.4575610	best: 0.4575610 (2)	total: 287ms	remaining: 5.64s
3:	learn: 0.4084679	test: 0.4113166	best: 0.4113166 (3)	total: 386ms	remaining: 5.59s
4:	learn: 0.3708326	test: 0.3740405	best: 0.3740405 (4)	total: 485ms	remaining: 5.53s
5:	learn: 0.3418623	test: 0.3456504	best: 0.3456504 (5)	total: 588ms	remaining: 5.49s
6:	learn: 0.3181901	test: 0.3226266	best: 0.3226266 (6)	total: 689ms	remaining: 5.42s
7:	learn: 0.2991157	test: 0.3043136	best: 0.3043136 (7)	total: 792ms	remaining: 5.34s
8:	learn: 0.2834271	test: 0.2892065	best: 0.2892065 (8)	total: 891ms	remaining: 5.25s
9:	learn: 0.2701944	test: 0.2765020	best: 0.2765020 (9)	total: 991ms	remaining: 5.15s
10:	learn: 0.2598416	test: 0.2668583	best: 0.2668583 (10)	total: 1.09s	remaining: 5.06s
11:	learn: 0.2511069	test: 0.2587223	best: 0.2587223 (11)	total: 1.19s	remaining: 4.96s
12:	learn: 0.2432856	test: 0.2517632	best: 0.2517632 (12)	total: 1.29s	remaining: 4.87s
13:	learn: 0.2362389	test: 0.2454121	best: 0.2454121 (13)	total: 1.39s	remaining: 4.77s
14:	learn: 0.2306967	test: 0.2407194	best: 0.2407194 (14)	total: 1.49s	remaining: 4.68s
15:	learn: 0.2259349	test: 0.2365842	best: 0.2365842 (15)	total: 1.61s	remaining: 4.62s
16:	learn: 0.2221057	test: 0.2334495	best: 0.2334495 (16)	total: 1.72s	remaining: 4.55s
17:	learn: 0.2180254	test: 0.2300064	best: 0.2300064 (17)	total: 1.84s	remaining: 4.5s
18:	learn: 0.2147617	test: 0.2272233	best: 0.2272233 (18)	total: 1.96s	remaining: 4.43s
19:	learn: 0.2119218	test: 0.2253095	best: 0.2253095 (19)	total: 2.08s	remaining: 4.36s
20:	learn: 0.2097391	test: 0.2235570	best: 0.2235570 (20)	total: 2.2s	remaining: 4.29s
21:	learn: 0.2074959	test: 0.2217911	best: 0.2217911 (21)	total: 2.32s	remaining: 4.21s
22:	learn: 0.2048109	test: 0.2196584	best: 0.2196584 (22)	total: 2.44s	remaining: 4.13s
23:	learn: 0.2026583	test: 0.2179778	best: 0.2179778 (23)	total: 2.56s	remaining: 4.05s
24:	learn: 0.2009177	test: 0.2171423	best: 0.2171423 (24)	total: 2.68s	remaining: 3.96s
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59:	learn: 0.1707844	test: 0.2022256	best: 0.2021670 (58)	total: 6.78s	remaining: 226ms
60:	learn: 0.1702490	test: 0.2021097	best: 0.2021097 (60)	total: 6.9s	remaining: 113ms
61:	learn: 0.1697146	test: 0.2023187	best: 0.2021097 (60)	total: 7.01s	remaining: 0us

bestTest = 0.2021097075
bestIteration = 60

Shrink model to first 61 iterations.
Trial 23, Fold 1: Log loss = 0.20150776947659338, Average precision = 0.9745777443313712, ROC-AUC = 0.9700378005764184, Elapsed Time = 7.127732000000833 seconds
Trial 23, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 23, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
0:	learn: 0.5944426	test: 0.5956551	best: 0.5956551 (0)	total: 113ms	remaining: 6.92s
1:	learn: 0.5168102	test: 0.5187980	best: 0.5187980 (1)	total: 222ms	remaining: 6.66s
2:	learn: 0.4557290	test: 0.4583486	best: 0.4583486 (2)	total: 332ms	remaining: 6.52s
3:	learn: 0.4091624	test: 0.4120791	best: 0.4120791 (3)	total: 439ms	remaining: 6.37s
4:	learn: 0.3715465	test: 0.3749377	best: 0.3749377 (4)	total: 555ms	remaining: 6.33s
5:	learn: 0.3415787	test: 0.3458123	best: 0.3458123 (5)	total: 691ms	remaining: 6.45s
6:	learn: 0.3185279	test: 0.3230719	best: 0.3230719 (6)	total: 845ms	remaining: 6.64s
7:	learn: 0.2999072	test: 0.3045940	best: 0.3045940 (7)	total: 1s	remaining: 6.75s
8:	learn: 0.2836892	test: 0.2890879	best: 0.2890879 (8)	total: 1.14s	remaining: 6.71s
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52:	learn: 0.1767431	test: 0.1945622	best: 0.1945622 (52)	total: 8s	remaining: 1.36s
53:	learn: 0.1759827	test: 0.1946145	best: 0.1945622 (52)	total: 8.12s	remaining: 1.2s
54:	learn: 0.1755692	test: 0.1944823	best: 0.1944823 (54)	total: 8.24s	remaining: 1.05s
55:	learn: 0.1750098	test: 0.1942576	best: 0.1942576 (55)	total: 8.37s	remaining: 897ms
56:	learn: 0.1743985	test: 0.1940159	best: 0.1940159 (56)	total: 8.49s	remaining: 745ms
57:	learn: 0.1735300	test: 0.1937917	best: 0.1937917 (57)	total: 8.62s	remaining: 594ms
58:	learn: 0.1731211	test: 0.1935804	best: 0.1935804 (58)	total: 8.74s	remaining: 444ms
59:	learn: 0.1725410	test: 0.1934648	best: 0.1934648 (59)	total: 8.87s	remaining: 296ms
60:	learn: 0.1719518	test: 0.1935744	best: 0.1934648 (59)	total: 8.99s	remaining: 147ms
61:	learn: 0.1715889	test: 0.1932486	best: 0.1932486 (61)	total: 9.11s	remaining: 0us

bestTest = 0.1932486127
bestIteration = 61

Trial 23, Fold 2: Log loss = 0.19287027784030045, Average precision = 0.9759821994962075, ROC-AUC = 0.9732471689174405, Elapsed Time = 9.256475499998487 seconds
Trial 23, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 23, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
0:	learn: 0.5951731	test: 0.5950747	best: 0.5950747 (0)	total: 115ms	remaining: 6.99s
1:	learn: 0.5170293	test: 0.5165196	best: 0.5165196 (1)	total: 235ms	remaining: 7.04s
2:	learn: 0.4567837	test: 0.4568171	best: 0.4568171 (2)	total: 356ms	remaining: 7.01s
3:	learn: 0.4099830	test: 0.4099489	best: 0.4099489 (3)	total: 474ms	remaining: 6.87s
4:	learn: 0.3732347	test: 0.3736549	best: 0.3736549 (4)	total: 600ms	remaining: 6.84s
5:	learn: 0.3437848	test: 0.3442001	best: 0.3442001 (5)	total: 722ms	remaining: 6.74s
6:	learn: 0.3199739	test: 0.3206195	best: 0.3206195 (6)	total: 848ms	remaining: 6.67s
7:	learn: 0.3008914	test: 0.3016161	best: 0.3016161 (7)	total: 971ms	remaining: 6.55s
8:	learn: 0.2858124	test: 0.2868289	best: 0.2868289 (8)	total: 1.1s	remaining: 6.46s
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53:	learn: 0.1762493	test: 0.1957325	best: 0.1957325 (53)	total: 6.57s	remaining: 973ms
54:	learn: 0.1758172	test: 0.1956071	best: 0.1956071 (54)	total: 6.69s	remaining: 851ms
55:	learn: 0.1750515	test: 0.1952493	best: 0.1952493 (55)	total: 6.81s	remaining: 730ms
56:	learn: 0.1744532	test: 0.1950777	best: 0.1950777 (56)	total: 6.93s	remaining: 608ms
57:	learn: 0.1740408	test: 0.1949342	best: 0.1949342 (57)	total: 7.05s	remaining: 486ms
58:	learn: 0.1735009	test: 0.1946910	best: 0.1946910 (58)	total: 7.16s	remaining: 364ms
59:	learn: 0.1729838	test: 0.1946532	best: 0.1946532 (59)	total: 7.28s	remaining: 243ms
60:	learn: 0.1724042	test: 0.1946504	best: 0.1946504 (60)	total: 7.4s	remaining: 121ms
61:	learn: 0.1719425	test: 0.1945666	best: 0.1945666 (61)	total: 7.57s	remaining: 0us

bestTest = 0.1945666354
bestIteration = 61

Trial 23, Fold 3: Log loss = 0.1942799515808129, Average precision = 0.9757164259236374, ROC-AUC = 0.9723673565698107, Elapsed Time = 7.702082700001483 seconds
Trial 23, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 23, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
0:	learn: 0.5957919	test: 0.5966905	best: 0.5966905 (0)	total: 108ms	remaining: 6.61s
1:	learn: 0.5176786	test: 0.5193158	best: 0.5193158 (1)	total: 216ms	remaining: 6.48s
2:	learn: 0.4572115	test: 0.4596429	best: 0.4596429 (2)	total: 320ms	remaining: 6.29s
3:	learn: 0.4093680	test: 0.4128213	best: 0.4128213 (3)	total: 425ms	remaining: 6.16s
4:	learn: 0.3721170	test: 0.3758761	best: 0.3758761 (4)	total: 529ms	remaining: 6.03s
5:	learn: 0.3426337	test: 0.3473634	best: 0.3473634 (5)	total: 635ms	remaining: 5.93s
6:	learn: 0.3194484	test: 0.3245936	best: 0.3245936 (6)	total: 757ms	remaining: 5.95s
7:	learn: 0.2991011	test: 0.3049288	best: 0.3049288 (7)	total: 897ms	remaining: 6.05s
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52:	learn: 0.1743344	test: 0.2008042	best: 0.2006403 (51)	total: 5.76s	remaining: 978ms
53:	learn: 0.1737666	test: 0.2009606	best: 0.2006403 (51)	total: 5.87s	remaining: 869ms
54:	learn: 0.1730287	test: 0.2005985	best: 0.2005985 (54)	total: 5.97s	remaining: 760ms
55:	learn: 0.1726760	test: 0.2006021	best: 0.2005985 (54)	total: 6.08s	remaining: 652ms
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58:	learn: 0.1707234	test: 0.2011379	best: 0.2005985 (54)	total: 6.41s	remaining: 326ms
59:	learn: 0.1702758	test: 0.2011670	best: 0.2005985 (54)	total: 6.51s	remaining: 217ms
60:	learn: 0.1699079	test: 0.2012349	best: 0.2005985 (54)	total: 6.62s	remaining: 108ms
61:	learn: 0.1694034	test: 0.2011856	best: 0.2005985 (54)	total: 6.72s	remaining: 0us

bestTest = 0.2005985087
bestIteration = 54

Shrink model to first 55 iterations.
Trial 23, Fold 4: Log loss = 0.20012387602368556, Average precision = 0.9744471579175493, ROC-AUC = 0.9697106936009595, Elapsed Time = 6.847606200000882 seconds
Trial 23, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 23, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
0:	learn: 0.5937950	test: 0.5956931	best: 0.5956931 (0)	total: 99.6ms	remaining: 6.07s
1:	learn: 0.5160028	test: 0.5200099	best: 0.5200099 (1)	total: 197ms	remaining: 5.91s
2:	learn: 0.4553333	test: 0.4605296	best: 0.4605296 (2)	total: 306ms	remaining: 6.01s
3:	learn: 0.4073562	test: 0.4139328	best: 0.4139328 (3)	total: 402ms	remaining: 5.83s
4:	learn: 0.3702173	test: 0.3777423	best: 0.3777423 (4)	total: 499ms	remaining: 5.69s
5:	learn: 0.3401950	test: 0.3490149	best: 0.3490149 (5)	total: 597ms	remaining: 5.57s
6:	learn: 0.3160475	test: 0.3260091	best: 0.3260091 (6)	total: 696ms	remaining: 5.47s
7:	learn: 0.2976596	test: 0.3082631	best: 0.3082631 (7)	total: 796ms	remaining: 5.37s
8:	learn: 0.2824998	test: 0.2937702	best: 0.2937702 (8)	total: 895ms	remaining: 5.27s
9:	learn: 0.2692860	test: 0.2813462	best: 0.2813462 (9)	total: 994ms	remaining: 5.17s
10:	learn: 0.2580412	test: 0.2711022	best: 0.2711022 (10)	total: 1.09s	remaining: 5.07s
11:	learn: 0.2489195	test: 0.2630277	best: 0.2630277 (11)	total: 1.19s	remaining: 4.97s
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53:	learn: 0.1726052	test: 0.2047167	best: 0.2047167 (53)	total: 5.65s	remaining: 838ms
54:	learn: 0.1723983	test: 0.2045807	best: 0.2045807 (54)	total: 5.76s	remaining: 733ms
55:	learn: 0.1716393	test: 0.2045029	best: 0.2045029 (55)	total: 5.87s	remaining: 629ms
56:	learn: 0.1708887	test: 0.2041617	best: 0.2041617 (56)	total: 5.97s	remaining: 524ms
57:	learn: 0.1705130	test: 0.2041243	best: 0.2041243 (57)	total: 6.08s	remaining: 419ms
58:	learn: 0.1699873	test: 0.2039336	best: 0.2039336 (58)	total: 6.19s	remaining: 315ms
59:	learn: 0.1695635	test: 0.2038187	best: 0.2038187 (59)	total: 6.29s	remaining: 210ms
60:	learn: 0.1687722	test: 0.2039111	best: 0.2038187 (59)	total: 6.39s	remaining: 105ms
61:	learn: 0.1681154	test: 0.2036850	best: 0.2036850 (61)	total: 6.49s	remaining: 0us

bestTest = 0.2036849841
bestIteration = 61

Trial 23, Fold 5: Log loss = 0.20301804103302334, Average precision = 0.974129227151172, ROC-AUC = 0.9709877190048865, Elapsed Time = 6.611383499999647 seconds
Optimization Progress:  24%|##4       | 24/100 [38:32<54:20, 42.90s/it]
Trial 24, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 24, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
0:	learn: 0.6438578	test: 0.6455061	best: 0.6455061 (0)	total: 260ms	remaining: 24.2s
1:	learn: 0.5998870	test: 0.6031787	best: 0.6031787 (1)	total: 523ms	remaining: 24s
2:	learn: 0.5605713	test: 0.5651558	best: 0.5651558 (2)	total: 773ms	remaining: 23.4s
3:	learn: 0.5254198	test: 0.5306228	best: 0.5306228 (3)	total: 1.06s	remaining: 23.8s
4:	learn: 0.4935897	test: 0.5001612	best: 0.5001612 (4)	total: 1.38s	remaining: 24.5s
5:	learn: 0.4653441	test: 0.4723755	best: 0.4723755 (5)	total: 1.63s	remaining: 23.9s
6:	learn: 0.4395657	test: 0.4472658	best: 0.4472658 (6)	total: 1.88s	remaining: 23.4s
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85:	learn: 0.1488026	test: 0.2014398	best: 0.2014398 (85)	total: 21.4s	remaining: 1.99s
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87:	learn: 0.1473594	test: 0.2010542	best: 0.2010542 (87)	total: 21.9s	remaining: 1.49s
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89:	learn: 0.1464939	test: 0.2008691	best: 0.2008691 (89)	total: 22.3s	remaining: 991ms
90:	learn: 0.1460058	test: 0.2007546	best: 0.2007546 (90)	total: 22.5s	remaining: 742ms
91:	learn: 0.1455250	test: 0.2006112	best: 0.2006112 (91)	total: 22.7s	remaining: 494ms
92:	learn: 0.1450330	test: 0.2005511	best: 0.2005511 (92)	total: 22.9s	remaining: 247ms
93:	learn: 0.1445806	test: 0.2004436	best: 0.2004436 (93)	total: 23.1s	remaining: 0us

bestTest = 0.2004435921
bestIteration = 93

Trial 24, Fold 1: Log loss = 0.20008381016671992, Average precision = 0.9751319074063409, ROC-AUC = 0.9714906463438533, Elapsed Time = 23.274636800000735 seconds
Trial 24, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 24, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
0:	learn: 0.6443987	test: 0.6458694	best: 0.6458694 (0)	total: 216ms	remaining: 20s
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87:	learn: 0.1493876	test: 0.1898858	best: 0.1898858 (87)	total: 19.1s	remaining: 1.3s
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93:	learn: 0.1462047	test: 0.1889960	best: 0.1889960 (93)	total: 20.4s	remaining: 0us

bestTest = 0.1889959826
bestIteration = 93

Trial 24, Fold 2: Log loss = 0.1887453295075406, Average precision = 0.9769374588088688, ROC-AUC = 0.9742078082733499, Elapsed Time = 20.600181599998905 seconds
Trial 24, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 24, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
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85:	learn: 0.1503093	test: 0.1909302	best: 0.1909302 (85)	total: 18.5s	remaining: 1.72s
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87:	learn: 0.1492057	test: 0.1906159	best: 0.1906159 (87)	total: 19s	remaining: 1.29s
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89:	learn: 0.1482241	test: 0.1904367	best: 0.1904367 (89)	total: 19.4s	remaining: 861ms
90:	learn: 0.1476080	test: 0.1902556	best: 0.1902556 (90)	total: 19.6s	remaining: 646ms
91:	learn: 0.1471805	test: 0.1901360	best: 0.1901360 (91)	total: 19.8s	remaining: 430ms
92:	learn: 0.1467775	test: 0.1900043	best: 0.1900043 (92)	total: 20s	remaining: 215ms
93:	learn: 0.1461245	test: 0.1898086	best: 0.1898086 (93)	total: 20.2s	remaining: 0us

bestTest = 0.1898085713
bestIteration = 93

Trial 24, Fold 3: Log loss = 0.1896281499984433, Average precision = 0.9762000415598325, ROC-AUC = 0.9738050895204708, Elapsed Time = 20.38147620000018 seconds
Trial 24, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 24, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
0:	learn: 0.6444883	test: 0.6459490	best: 0.6459490 (0)	total: 213ms	remaining: 19.8s
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86:	learn: 0.1505625	test: 0.1952200	best: 0.1952200 (86)	total: 19s	remaining: 1.53s
87:	learn: 0.1499320	test: 0.1951423	best: 0.1951423 (87)	total: 19.2s	remaining: 1.31s
88:	learn: 0.1492386	test: 0.1949788	best: 0.1949788 (88)	total: 19.4s	remaining: 1.09s
89:	learn: 0.1487463	test: 0.1948841	best: 0.1948841 (89)	total: 19.6s	remaining: 871ms
90:	learn: 0.1483929	test: 0.1947950	best: 0.1947950 (90)	total: 19.8s	remaining: 652ms
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92:	learn: 0.1474308	test: 0.1946732	best: 0.1946522 (91)	total: 20.2s	remaining: 217ms
93:	learn: 0.1468555	test: 0.1944609	best: 0.1944609 (93)	total: 20.4s	remaining: 0us

bestTest = 0.1944608541
bestIteration = 93

Trial 24, Fold 4: Log loss = 0.19421419745927387, Average precision = 0.9765479303642637, ROC-AUC = 0.9726961236650286, Elapsed Time = 20.537694999999076 seconds
Trial 24, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 24, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
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2:	learn: 0.5603284	test: 0.5677981	best: 0.5677981 (2)	total: 639ms	remaining: 19.4s
3:	learn: 0.5249177	test: 0.5334347	best: 0.5334347 (3)	total: 850ms	remaining: 19.1s
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83:	learn: 0.1503767	test: 0.2041618	best: 0.2041618 (83)	total: 20.1s	remaining: 2.39s
84:	learn: 0.1498510	test: 0.2041023	best: 0.2041023 (84)	total: 20.3s	remaining: 2.14s
85:	learn: 0.1493126	test: 0.2038755	best: 0.2038755 (85)	total: 20.5s	remaining: 1.9s
86:	learn: 0.1487781	test: 0.2037527	best: 0.2037527 (86)	total: 20.7s	remaining: 1.66s
87:	learn: 0.1482875	test: 0.2035227	best: 0.2035227 (87)	total: 20.9s	remaining: 1.42s
88:	learn: 0.1476303	test: 0.2033193	best: 0.2033193 (88)	total: 21.1s	remaining: 1.19s
89:	learn: 0.1471401	test: 0.2033107	best: 0.2033107 (89)	total: 21.3s	remaining: 947ms
90:	learn: 0.1466505	test: 0.2031674	best: 0.2031674 (90)	total: 21.5s	remaining: 710ms
91:	learn: 0.1460600	test: 0.2029208	best: 0.2029208 (91)	total: 21.7s	remaining: 473ms
92:	learn: 0.1454903	test: 0.2027782	best: 0.2027782 (92)	total: 22s	remaining: 236ms
93:	learn: 0.1449389	test: 0.2027605	best: 0.2027605 (93)	total: 22.2s	remaining: 0us

bestTest = 0.2027605014
bestIteration = 93

Trial 24, Fold 5: Log loss = 0.20235849248054216, Average precision = 0.9742823084834791, ROC-AUC = 0.9712950968916635, Elapsed Time = 22.32423199999903 seconds
Optimization Progress:  25%|##5       | 25/100 [40:28<1:20:43, 64.58s/it]
Trial 25, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 25, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
0:	learn: 0.6551025	test: 0.6551884	best: 0.6551884 (0)	total: 768ms	remaining: 28.4s
1:	learn: 0.6220195	test: 0.6217118	best: 0.6217118 (1)	total: 1.54s	remaining: 27.7s
2:	learn: 0.5914368	test: 0.5913060	best: 0.5913060 (2)	total: 2.34s	remaining: 27.4s
3:	learn: 0.5635964	test: 0.5635380	best: 0.5635380 (3)	total: 3.13s	remaining: 26.6s
4:	learn: 0.5393063	test: 0.5396114	best: 0.5396114 (4)	total: 3.9s	remaining: 25.7s
5:	learn: 0.5124410	test: 0.5129131	best: 0.5129131 (5)	total: 4.72s	remaining: 25.2s
6:	learn: 0.4853965	test: 0.4863230	best: 0.4863230 (6)	total: 5.51s	remaining: 24.4s
7:	learn: 0.4644841	test: 0.4658430	best: 0.4658430 (7)	total: 6.31s	remaining: 23.7s
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27:	learn: 0.2666013	test: 0.2755650	best: 0.2755650 (27)	total: 21.9s	remaining: 7.81s
28:	learn: 0.2621540	test: 0.2714702	best: 0.2714702 (28)	total: 22.6s	remaining: 7.01s
29:	learn: 0.2584063	test: 0.2681081	best: 0.2681081 (29)	total: 23.4s	remaining: 6.23s
30:	learn: 0.2539318	test: 0.2640294	best: 0.2640294 (30)	total: 24.1s	remaining: 5.45s
31:	learn: 0.2506823	test: 0.2612625	best: 0.2612625 (31)	total: 24.9s	remaining: 4.68s
32:	learn: 0.2472938	test: 0.2580703	best: 0.2580703 (32)	total: 25.7s	remaining: 3.89s
33:	learn: 0.2434965	test: 0.2547128	best: 0.2547128 (33)	total: 26.5s	remaining: 3.12s
34:	learn: 0.2409347	test: 0.2526586	best: 0.2526586 (34)	total: 27.3s	remaining: 2.33s
35:	learn: 0.2377868	test: 0.2499298	best: 0.2499298 (35)	total: 28s	remaining: 1.56s
36:	learn: 0.2348970	test: 0.2474071	best: 0.2474071 (36)	total: 28.8s	remaining: 778ms
37:	learn: 0.2317185	test: 0.2447452	best: 0.2447452 (37)	total: 29.6s	remaining: 0us

bestTest = 0.2447451662
bestIteration = 37

Trial 25, Fold 1: Log loss = 0.24474516617115755, Average precision = 0.9702032705447189, ROC-AUC = 0.9670949925438407, Elapsed Time = 29.706429499998194 seconds
Trial 25, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 25, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
0:	learn: 0.6537777	test: 0.6537922	best: 0.6537922 (0)	total: 801ms	remaining: 29.6s
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2:	learn: 0.5854789	test: 0.5863223	best: 0.5863223 (2)	total: 2.37s	remaining: 27.6s
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5:	learn: 0.5048142	test: 0.5069478	best: 0.5069478 (5)	total: 4.74s	remaining: 25.3s
6:	learn: 0.4800607	test: 0.4824727	best: 0.4824727 (6)	total: 5.52s	remaining: 24.4s
7:	learn: 0.4608285	test: 0.4636582	best: 0.4636582 (7)	total: 6.3s	remaining: 23.6s
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23:	learn: 0.2886078	test: 0.2967389	best: 0.2967389 (23)	total: 18.9s	remaining: 11s
24:	learn: 0.2829079	test: 0.2914403	best: 0.2914403 (24)	total: 19.9s	remaining: 10.4s
25:	learn: 0.2771710	test: 0.2860768	best: 0.2860768 (25)	total: 20.7s	remaining: 9.57s
26:	learn: 0.2721222	test: 0.2812859	best: 0.2812859 (26)	total: 21.6s	remaining: 8.8s
27:	learn: 0.2671184	test: 0.2763223	best: 0.2763223 (27)	total: 22.4s	remaining: 8.01s
28:	learn: 0.2629854	test: 0.2723947	best: 0.2723947 (28)	total: 23.2s	remaining: 7.21s
29:	learn: 0.2590122	test: 0.2685282	best: 0.2685282 (29)	total: 24s	remaining: 6.41s
30:	learn: 0.2550511	test: 0.2647788	best: 0.2647788 (30)	total: 24.8s	remaining: 5.6s
31:	learn: 0.2514290	test: 0.2613604	best: 0.2613604 (31)	total: 25.6s	remaining: 4.8s
32:	learn: 0.2483118	test: 0.2584439	best: 0.2584439 (32)	total: 26.5s	remaining: 4.01s
33:	learn: 0.2453928	test: 0.2558953	best: 0.2558953 (33)	total: 27.3s	remaining: 3.21s
34:	learn: 0.2425323	test: 0.2532212	best: 0.2532212 (34)	total: 28.1s	remaining: 2.41s
35:	learn: 0.2397526	test: 0.2506240	best: 0.2506240 (35)	total: 28.9s	remaining: 1.61s
36:	learn: 0.2372036	test: 0.2483513	best: 0.2483513 (36)	total: 29.7s	remaining: 803ms
37:	learn: 0.2343317	test: 0.2459917	best: 0.2459917 (37)	total: 30.5s	remaining: 0us

bestTest = 0.2459916857
bestIteration = 37

Trial 25, Fold 2: Log loss = 0.24599168572126281, Average precision = 0.9728615503006427, ROC-AUC = 0.9695893644226103, Elapsed Time = 30.65187850000075 seconds
Trial 25, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 25, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
0:	learn: 0.6534904	test: 0.6534476	best: 0.6534476 (0)	total: 878ms	remaining: 32.5s
1:	learn: 0.6202238	test: 0.6200255	best: 0.6200255 (1)	total: 1.68s	remaining: 30.2s
2:	learn: 0.5909163	test: 0.5910462	best: 0.5910462 (2)	total: 2.5s	remaining: 29.2s
3:	learn: 0.5641244	test: 0.5644968	best: 0.5644968 (3)	total: 3.31s	remaining: 28.2s
4:	learn: 0.5362643	test: 0.5366240	best: 0.5366240 (4)	total: 4.13s	remaining: 27.2s
5:	learn: 0.5123296	test: 0.5124338	best: 0.5124338 (5)	total: 4.99s	remaining: 26.6s
6:	learn: 0.4924087	test: 0.4925890	best: 0.4925890 (6)	total: 5.79s	remaining: 25.6s
7:	learn: 0.4746362	test: 0.4751843	best: 0.4751843 (7)	total: 6.57s	remaining: 24.6s
8:	learn: 0.4566507	test: 0.4569660	best: 0.4569660 (8)	total: 7.4s	remaining: 23.9s
9:	learn: 0.4397249	test: 0.4402620	best: 0.4402620 (9)	total: 8.2s	remaining: 23s
10:	learn: 0.4253159	test: 0.4260021	best: 0.4260021 (10)	total: 9s	remaining: 22.1s
11:	learn: 0.4058499	test: 0.4067009	best: 0.4067009 (11)	total: 9.83s	remaining: 21.3s
12:	learn: 0.3923014	test: 0.3933954	best: 0.3933954 (12)	total: 10.6s	remaining: 20.4s
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14:	learn: 0.3675676	test: 0.3690010	best: 0.3690010 (14)	total: 12.3s	remaining: 18.8s
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27:	learn: 0.2730162	test: 0.2772900	best: 0.2772900 (27)	total: 22.8s	remaining: 8.16s
28:	learn: 0.2679865	test: 0.2726117	best: 0.2726117 (28)	total: 23.7s	remaining: 7.34s
29:	learn: 0.2639027	test: 0.2687882	best: 0.2687882 (29)	total: 24.5s	remaining: 6.52s
30:	learn: 0.2601268	test: 0.2651994	best: 0.2651994 (30)	total: 25.3s	remaining: 5.71s
31:	learn: 0.2559390	test: 0.2612788	best: 0.2612788 (31)	total: 26.1s	remaining: 4.9s
32:	learn: 0.2527582	test: 0.2583605	best: 0.2583605 (32)	total: 27s	remaining: 4.08s
33:	learn: 0.2496792	test: 0.2554962	best: 0.2554962 (33)	total: 27.8s	remaining: 3.27s
34:	learn: 0.2465769	test: 0.2525424	best: 0.2525424 (34)	total: 28.6s	remaining: 2.45s
35:	learn: 0.2426381	test: 0.2490707	best: 0.2490707 (35)	total: 29.5s	remaining: 1.64s
36:	learn: 0.2395198	test: 0.2463333	best: 0.2463333 (36)	total: 30.3s	remaining: 820ms
37:	learn: 0.2367891	test: 0.2438643	best: 0.2438643 (37)	total: 31.2s	remaining: 0us

bestTest = 0.2438642717
bestIteration = 37

Trial 25, Fold 3: Log loss = 0.2438642716557561, Average precision = 0.9719410737754791, ROC-AUC = 0.9698646418218064, Elapsed Time = 31.299758900000597 seconds
Trial 25, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 25, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
0:	learn: 0.6579446	test: 0.6582622	best: 0.6582622 (0)	total: 808ms	remaining: 29.9s
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2:	learn: 0.5938307	test: 0.5951509	best: 0.5951509 (2)	total: 2.46s	remaining: 28.7s
3:	learn: 0.5638331	test: 0.5652799	best: 0.5652799 (3)	total: 3.28s	remaining: 27.9s
4:	learn: 0.5371041	test: 0.5387772	best: 0.5387772 (4)	total: 4.11s	remaining: 27.1s
5:	learn: 0.5142683	test: 0.5160418	best: 0.5160418 (5)	total: 4.92s	remaining: 26.2s
6:	learn: 0.4907633	test: 0.4926741	best: 0.4926741 (6)	total: 5.72s	remaining: 25.3s
7:	learn: 0.4688091	test: 0.4706404	best: 0.4706404 (7)	total: 6.53s	remaining: 24.5s
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24:	learn: 0.2882715	test: 0.2949742	best: 0.2949742 (24)	total: 21.6s	remaining: 11.2s
25:	learn: 0.2824345	test: 0.2895117	best: 0.2895117 (25)	total: 22.4s	remaining: 10.4s
26:	learn: 0.2774040	test: 0.2848424	best: 0.2848424 (26)	total: 23.3s	remaining: 9.49s
27:	learn: 0.2719488	test: 0.2799374	best: 0.2799374 (27)	total: 24.1s	remaining: 8.62s
28:	learn: 0.2667268	test: 0.2749773	best: 0.2749773 (28)	total: 25s	remaining: 7.75s
29:	learn: 0.2627683	test: 0.2712788	best: 0.2712788 (29)	total: 25.8s	remaining: 6.87s
30:	learn: 0.2593429	test: 0.2684311	best: 0.2684311 (30)	total: 26.6s	remaining: 6s
31:	learn: 0.2553482	test: 0.2646591	best: 0.2646591 (31)	total: 27.4s	remaining: 5.14s
32:	learn: 0.2516517	test: 0.2611200	best: 0.2611200 (32)	total: 28.2s	remaining: 4.28s
33:	learn: 0.2483462	test: 0.2581637	best: 0.2581637 (33)	total: 29.1s	remaining: 3.42s
34:	learn: 0.2453658	test: 0.2553378	best: 0.2553378 (34)	total: 29.9s	remaining: 2.56s
35:	learn: 0.2419220	test: 0.2522248	best: 0.2522248 (35)	total: 30.7s	remaining: 1.71s
36:	learn: 0.2390553	test: 0.2496696	best: 0.2496696 (36)	total: 31.6s	remaining: 853ms
37:	learn: 0.2360780	test: 0.2469461	best: 0.2469461 (37)	total: 32.4s	remaining: 0us

bestTest = 0.2469461168
bestIteration = 37

Trial 25, Fold 4: Log loss = 0.24694611679454234, Average precision = 0.9717403666180794, ROC-AUC = 0.9689360584752653, Elapsed Time = 32.581517300000996 seconds
Trial 25, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 25, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
0:	learn: 0.6550586	test: 0.6556575	best: 0.6556575 (0)	total: 834ms	remaining: 30.8s
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2:	learn: 0.5907224	test: 0.5926708	best: 0.5926708 (2)	total: 2.46s	remaining: 28.6s
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4:	learn: 0.5332228	test: 0.5363509	best: 0.5363509 (4)	total: 4.09s	remaining: 27s
5:	learn: 0.5087964	test: 0.5122560	best: 0.5122560 (5)	total: 4.92s	remaining: 26.2s
6:	learn: 0.4839196	test: 0.4878942	best: 0.4878942 (6)	total: 5.75s	remaining: 25.5s
7:	learn: 0.4631018	test: 0.4676830	best: 0.4676830 (7)	total: 6.6s	remaining: 24.7s
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24:	learn: 0.2813295	test: 0.2935662	best: 0.2935662 (24)	total: 20.4s	remaining: 10.6s
25:	learn: 0.2767516	test: 0.2894332	best: 0.2894332 (25)	total: 21.2s	remaining: 9.8s
26:	learn: 0.2728524	test: 0.2858342	best: 0.2858342 (26)	total: 22s	remaining: 8.97s
27:	learn: 0.2673472	test: 0.2807015	best: 0.2807015 (27)	total: 22.9s	remaining: 8.16s
28:	learn: 0.2626492	test: 0.2764948	best: 0.2764948 (28)	total: 23.6s	remaining: 7.34s
29:	learn: 0.2573908	test: 0.2717384	best: 0.2717384 (29)	total: 24.5s	remaining: 6.52s
30:	learn: 0.2541779	test: 0.2689647	best: 0.2689647 (30)	total: 25.2s	remaining: 5.7s
31:	learn: 0.2506329	test: 0.2657187	best: 0.2657187 (31)	total: 26s	remaining: 4.88s
32:	learn: 0.2477135	test: 0.2629566	best: 0.2629566 (32)	total: 26.9s	remaining: 4.07s
33:	learn: 0.2443281	test: 0.2598671	best: 0.2598671 (33)	total: 27.7s	remaining: 3.26s
34:	learn: 0.2412125	test: 0.2572139	best: 0.2572139 (34)	total: 28.5s	remaining: 2.44s
35:	learn: 0.2384432	test: 0.2547062	best: 0.2547062 (35)	total: 29.4s	remaining: 1.63s
36:	learn: 0.2353411	test: 0.2520272	best: 0.2520272 (36)	total: 30.2s	remaining: 816ms
37:	learn: 0.2328189	test: 0.2497984	best: 0.2497984 (37)	total: 31s	remaining: 0us

bestTest = 0.2497983569
bestIteration = 37

Trial 25, Fold 5: Log loss = 0.24979835686495963, Average precision = 0.970976381143047, ROC-AUC = 0.9674064247626479, Elapsed Time = 31.180757099999028 seconds
Optimization Progress:  26%|##6       | 26/100 [43:10<1:55:58, 94.04s/it]
Trial 26, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 26, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
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3:	learn: 0.4092952	test: 0.4103357	best: 0.4103357 (3)	total: 315ms	remaining: 6.45s
4:	learn: 0.3722963	test: 0.3734406	best: 0.3734406 (4)	total: 392ms	remaining: 6.34s
5:	learn: 0.3425457	test: 0.3445832	best: 0.3445832 (5)	total: 468ms	remaining: 6.24s
6:	learn: 0.3185460	test: 0.3214509	best: 0.3214509 (6)	total: 544ms	remaining: 6.13s
7:	learn: 0.3009166	test: 0.3043608	best: 0.3043608 (7)	total: 620ms	remaining: 6.04s
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70:	learn: 0.1641971	test: 0.1999070	best: 0.1999070 (70)	total: 6.05s	remaining: 1.28s
71:	learn: 0.1633571	test: 0.1997731	best: 0.1997731 (71)	total: 6.15s	remaining: 1.2s
72:	learn: 0.1627931	test: 0.1994530	best: 0.1994530 (72)	total: 6.25s	remaining: 1.11s
73:	learn: 0.1620809	test: 0.1996425	best: 0.1994530 (72)	total: 6.34s	remaining: 1.03s
74:	learn: 0.1615916	test: 0.1995293	best: 0.1994530 (72)	total: 6.44s	remaining: 944ms
75:	learn: 0.1609933	test: 0.1992014	best: 0.1992014 (75)	total: 6.53s	remaining: 860ms
76:	learn: 0.1604442	test: 0.1990088	best: 0.1990088 (76)	total: 6.63s	remaining: 775ms
77:	learn: 0.1601312	test: 0.1988077	best: 0.1988077 (77)	total: 6.72s	remaining: 690ms
78:	learn: 0.1594820	test: 0.1987086	best: 0.1987086 (78)	total: 6.82s	remaining: 604ms
79:	learn: 0.1589518	test: 0.1987760	best: 0.1987086 (78)	total: 6.92s	remaining: 519ms
80:	learn: 0.1585901	test: 0.1986273	best: 0.1986273 (80)	total: 7.02s	remaining: 434ms
81:	learn: 0.1581958	test: 0.1984428	best: 0.1984428 (81)	total: 7.12s	remaining: 347ms
82:	learn: 0.1577444	test: 0.1984267	best: 0.1984267 (82)	total: 7.22s	remaining: 261ms
83:	learn: 0.1572889	test: 0.1984218	best: 0.1984218 (83)	total: 7.32s	remaining: 174ms
84:	learn: 0.1566824	test: 0.1985619	best: 0.1984218 (83)	total: 7.42s	remaining: 87.2ms
85:	learn: 0.1561125	test: 0.1984786	best: 0.1984218 (83)	total: 7.51s	remaining: 0us

bestTest = 0.1984218466
bestIteration = 83

Shrink model to first 84 iterations.
Trial 26, Fold 1: Log loss = 0.1984218466016081, Average precision = 0.9746056884631267, ROC-AUC = 0.9702059076917928, Elapsed Time = 7.627324600001884 seconds
Trial 26, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 26, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
0:	learn: 0.5955079	test: 0.5962104	best: 0.5962104 (0)	total: 85.7ms	remaining: 7.28s
1:	learn: 0.5176591	test: 0.5193088	best: 0.5193088 (1)	total: 169ms	remaining: 7.1s
2:	learn: 0.4574553	test: 0.4596686	best: 0.4596686 (2)	total: 260ms	remaining: 7.2s
3:	learn: 0.4109490	test: 0.4135817	best: 0.4135817 (3)	total: 352ms	remaining: 7.22s
4:	learn: 0.3725170	test: 0.3752987	best: 0.3752987 (4)	total: 463ms	remaining: 7.5s
5:	learn: 0.3420745	test: 0.3452443	best: 0.3452443 (5)	total: 553ms	remaining: 7.37s
6:	learn: 0.3198002	test: 0.3232981	best: 0.3232981 (6)	total: 642ms	remaining: 7.25s
7:	learn: 0.3007652	test: 0.3042798	best: 0.3042798 (7)	total: 739ms	remaining: 7.21s
8:	learn: 0.2851011	test: 0.2891205	best: 0.2891205 (8)	total: 831ms	remaining: 7.11s
9:	learn: 0.2722871	test: 0.2763056	best: 0.2763056 (9)	total: 927ms	remaining: 7.04s
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68:	learn: 0.1671246	test: 0.1921430	best: 0.1921406 (66)	total: 6.43s	remaining: 1.58s
69:	learn: 0.1667779	test: 0.1920524	best: 0.1920524 (69)	total: 6.53s	remaining: 1.49s
70:	learn: 0.1663714	test: 0.1919754	best: 0.1919754 (70)	total: 6.62s	remaining: 1.4s
71:	learn: 0.1659194	test: 0.1918955	best: 0.1918955 (71)	total: 6.71s	remaining: 1.3s
72:	learn: 0.1652250	test: 0.1915939	best: 0.1915939 (72)	total: 6.8s	remaining: 1.21s
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74:	learn: 0.1640369	test: 0.1914843	best: 0.1914843 (74)	total: 6.99s	remaining: 1.02s
75:	learn: 0.1635718	test: 0.1915085	best: 0.1914843 (74)	total: 7.08s	remaining: 931ms
76:	learn: 0.1630772	test: 0.1912866	best: 0.1912866 (76)	total: 7.17s	remaining: 838ms
77:	learn: 0.1628294	test: 0.1913824	best: 0.1912866 (76)	total: 7.26s	remaining: 745ms
78:	learn: 0.1624014	test: 0.1913937	best: 0.1912866 (76)	total: 7.35s	remaining: 651ms
79:	learn: 0.1618613	test: 0.1914253	best: 0.1912866 (76)	total: 7.44s	remaining: 558ms
80:	learn: 0.1610970	test: 0.1912986	best: 0.1912866 (76)	total: 7.53s	remaining: 465ms
81:	learn: 0.1604630	test: 0.1911444	best: 0.1911444 (81)	total: 7.62s	remaining: 372ms
82:	learn: 0.1602410	test: 0.1911423	best: 0.1911423 (82)	total: 7.71s	remaining: 279ms
83:	learn: 0.1596833	test: 0.1909968	best: 0.1909968 (83)	total: 7.8s	remaining: 186ms
84:	learn: 0.1591767	test: 0.1906379	best: 0.1906379 (84)	total: 7.89s	remaining: 92.8ms
85:	learn: 0.1588717	test: 0.1906999	best: 0.1906379 (84)	total: 7.98s	remaining: 0us

bestTest = 0.190637882
bestIteration = 84

Shrink model to first 85 iterations.
Trial 26, Fold 2: Log loss = 0.19063788199100115, Average precision = 0.9754013776490958, ROC-AUC = 0.9728190125407404, Elapsed Time = 8.099196099999972 seconds
Trial 26, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 26, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
0:	learn: 0.5951131	test: 0.5951614	best: 0.5951614 (0)	total: 88.5ms	remaining: 7.52s
1:	learn: 0.5167258	test: 0.5167379	best: 0.5167379 (1)	total: 171ms	remaining: 7.19s
2:	learn: 0.4578782	test: 0.4575862	best: 0.4575862 (2)	total: 254ms	remaining: 7.03s
3:	learn: 0.4101744	test: 0.4098095	best: 0.4098095 (3)	total: 339ms	remaining: 6.94s
4:	learn: 0.3725409	test: 0.3719460	best: 0.3719460 (4)	total: 423ms	remaining: 6.85s
5:	learn: 0.3433236	test: 0.3427514	best: 0.3427514 (5)	total: 506ms	remaining: 6.75s
6:	learn: 0.3195739	test: 0.3194461	best: 0.3194461 (6)	total: 590ms	remaining: 6.66s
7:	learn: 0.3016137	test: 0.3014539	best: 0.3014539 (7)	total: 673ms	remaining: 6.56s
8:	learn: 0.2869274	test: 0.2868732	best: 0.2868732 (8)	total: 755ms	remaining: 6.46s
9:	learn: 0.2741532	test: 0.2739765	best: 0.2739765 (9)	total: 840ms	remaining: 6.38s
10:	learn: 0.2633825	test: 0.2635254	best: 0.2635254 (10)	total: 926ms	remaining: 6.31s
11:	learn: 0.2546346	test: 0.2551210	best: 0.2551210 (11)	total: 1.01s	remaining: 6.24s
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79:	learn: 0.1616891	test: 0.1895879	best: 0.1895879 (79)	total: 6.94s	remaining: 521ms
80:	learn: 0.1613094	test: 0.1897044	best: 0.1895879 (79)	total: 7.03s	remaining: 434ms
81:	learn: 0.1609351	test: 0.1896191	best: 0.1895879 (79)	total: 7.12s	remaining: 347ms
82:	learn: 0.1604474	test: 0.1896527	best: 0.1895879 (79)	total: 7.21s	remaining: 260ms
83:	learn: 0.1600992	test: 0.1895988	best: 0.1895879 (79)	total: 7.29s	remaining: 174ms
84:	learn: 0.1597038	test: 0.1896686	best: 0.1895879 (79)	total: 7.38s	remaining: 86.9ms
85:	learn: 0.1593001	test: 0.1897739	best: 0.1895879 (79)	total: 7.47s	remaining: 0us

bestTest = 0.189587939
bestIteration = 79

Shrink model to first 80 iterations.
Trial 26, Fold 3: Log loss = 0.18958793904807827, Average precision = 0.9765933146866443, ROC-AUC = 0.9732478438520288, Elapsed Time = 7.589770599999611 seconds
Trial 26, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 26, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
0:	learn: 0.5937701	test: 0.5940916	best: 0.5940916 (0)	total: 84.2ms	remaining: 7.15s
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2:	learn: 0.4612808	test: 0.4622554	best: 0.4622554 (2)	total: 253ms	remaining: 7s
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78:	learn: 0.1616110	test: 0.1955532	best: 0.1954306 (75)	total: 6.75s	remaining: 599ms
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80:	learn: 0.1606725	test: 0.1958271	best: 0.1954306 (75)	total: 6.93s	remaining: 428ms
81:	learn: 0.1601051	test: 0.1958182	best: 0.1954306 (75)	total: 7.02s	remaining: 342ms
82:	learn: 0.1595881	test: 0.1955278	best: 0.1954306 (75)	total: 7.1s	remaining: 257ms
83:	learn: 0.1591889	test: 0.1955023	best: 0.1954306 (75)	total: 7.19s	remaining: 171ms
84:	learn: 0.1588083	test: 0.1953440	best: 0.1953440 (84)	total: 7.28s	remaining: 85.6ms
85:	learn: 0.1583426	test: 0.1952499	best: 0.1952499 (85)	total: 7.37s	remaining: 0us

bestTest = 0.1952498939
bestIteration = 85

Trial 26, Fold 4: Log loss = 0.19524989392204334, Average precision = 0.975597634166847, ROC-AUC = 0.9713602580885141, Elapsed Time = 7.4855626999997185 seconds
Trial 26, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 26, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
0:	learn: 0.5959191	test: 0.5987927	best: 0.5987927 (0)	total: 83.5ms	remaining: 7.1s
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85:	learn: 0.1562054	test: 0.2016892	best: 0.2016892 (85)	total: 7.41s	remaining: 0us

bestTest = 0.2016892099
bestIteration = 85

Trial 26, Fold 5: Log loss = 0.2016892098712569, Average precision = 0.9737888484863096, ROC-AUC = 0.9706393177637813, Elapsed Time = 7.5332304000003205 seconds
Optimization Progress:  27%|##7       | 27/100 [43:57<1:36:54, 79.65s/it]
Trial 27, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 27, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
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78:	learn: 0.1140254	test: 0.2042638	best: 0.2042638 (78)	total: 2m 39s	remaining: 0us

bestTest = 0.2042637568
bestIteration = 78

Trial 27, Fold 1: Log loss = 0.20426375682127698, Average precision = 0.9733148873983092, ROC-AUC = 0.9677766814594952, Elapsed Time = 160.0499552000001 seconds
Trial 27, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 27, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
0:	learn: 0.6432379	test: 0.6440639	best: 0.6440639 (0)	total: 1.81s	remaining: 2m 21s
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75:	learn: 0.1128341	test: 0.1972073	best: 0.1972073 (75)	total: 2m 28s	remaining: 5.88s
76:	learn: 0.1124802	test: 0.1970767	best: 0.1970767 (76)	total: 2m 30s	remaining: 3.92s
77:	learn: 0.1119171	test: 0.1967061	best: 0.1967061 (77)	total: 2m 32s	remaining: 1.96s
78:	learn: 0.1105065	test: 0.1966736	best: 0.1966736 (78)	total: 2m 35s	remaining: 0us

bestTest = 0.1966735767
bestIteration = 78

Trial 27, Fold 2: Log loss = 0.19667357670757726, Average precision = 0.9729162475124771, ROC-AUC = 0.9692006624701323, Elapsed Time = 155.33394789999875 seconds
Trial 27, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 27, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
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65:	learn: 0.1160592	test: 0.2034049	best: 0.2034049 (65)	total: 2m 12s	remaining: 26s
66:	learn: 0.1149958	test: 0.2031103	best: 0.2031103 (66)	total: 2m 14s	remaining: 24s
67:	learn: 0.1124106	test: 0.2032830	best: 0.2031103 (66)	total: 2m 16s	remaining: 22s
68:	learn: 0.1110764	test: 0.2032203	best: 0.2031103 (66)	total: 2m 18s	remaining: 20s
69:	learn: 0.1103848	test: 0.2028467	best: 0.2028467 (69)	total: 2m 20s	remaining: 18s
70:	learn: 0.1082419	test: 0.2027802	best: 0.2027802 (70)	total: 2m 22s	remaining: 16s
71:	learn: 0.1076181	test: 0.2025474	best: 0.2025474 (71)	total: 2m 24s	remaining: 14s
72:	learn: 0.1070797	test: 0.2023765	best: 0.2023765 (72)	total: 2m 25s	remaining: 12s
73:	learn: 0.1063945	test: 0.2023573	best: 0.2023573 (73)	total: 2m 27s	remaining: 9.99s
74:	learn: 0.1055047	test: 0.2023422	best: 0.2023422 (74)	total: 2m 29s	remaining: 8s
75:	learn: 0.1039245	test: 0.2022214	best: 0.2022214 (75)	total: 2m 31s	remaining: 6s
76:	learn: 0.1028951	test: 0.2020855	best: 0.2020855 (76)	total: 2m 34s	remaining: 4s
77:	learn: 0.1015890	test: 0.2022395	best: 0.2020855 (76)	total: 2m 36s	remaining: 2s
78:	learn: 0.1008573	test: 0.2020695	best: 0.2020695 (78)	total: 2m 38s	remaining: 0us

bestTest = 0.2020695124
bestIteration = 78

Trial 27, Fold 3: Log loss = 0.20206951237268705, Average precision = 0.9720168293297896, ROC-AUC = 0.9679361775385, Elapsed Time = 158.47888109999985 seconds
Trial 27, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 27, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
0:	learn: 0.6483102	test: 0.6488308	best: 0.6488308 (0)	total: 1.8s	remaining: 2m 20s
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6:	learn: 0.4414474	test: 0.4480070	best: 0.4480070 (6)	total: 11.8s	remaining: 2m 1s
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70:	learn: 0.1169591	test: 0.2029843	best: 0.2029843 (70)	total: 2m 19s	remaining: 15.8s
71:	learn: 0.1159100	test: 0.2026803	best: 0.2026803 (71)	total: 2m 21s	remaining: 13.8s
72:	learn: 0.1148865	test: 0.2022719	best: 0.2022719 (72)	total: 2m 23s	remaining: 11.8s
73:	learn: 0.1131934	test: 0.2023443	best: 0.2022719 (72)	total: 2m 25s	remaining: 9.84s
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76:	learn: 0.1092950	test: 0.2024794	best: 0.2022719 (72)	total: 2m 31s	remaining: 3.94s
77:	learn: 0.1078511	test: 0.2024475	best: 0.2022719 (72)	total: 2m 33s	remaining: 1.97s
78:	learn: 0.1069723	test: 0.2025878	best: 0.2022719 (72)	total: 2m 35s	remaining: 0us

bestTest = 0.2022718574
bestIteration = 72

Shrink model to first 73 iterations.
Trial 27, Fold 4: Log loss = 0.20227185735420683, Average precision = 0.9719868224035556, ROC-AUC = 0.9668532765366926, Elapsed Time = 156.1056112999977 seconds
Trial 27, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 27, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
0:	learn: 0.6414369	test: 0.6433910	best: 0.6433910 (0)	total: 1.82s	remaining: 2m 21s
1:	learn: 0.5934005	test: 0.5988165	best: 0.5988165 (1)	total: 3.79s	remaining: 2m 25s
2:	learn: 0.5532644	test: 0.5601322	best: 0.5601322 (2)	total: 5.78s	remaining: 2m 26s
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78:	learn: 0.1116220	test: 0.2097965	best: 0.2094846 (71)	total: 2m 33s	remaining: 0us

bestTest = 0.2094846199
bestIteration = 71

Shrink model to first 72 iterations.
Trial 27, Fold 5: Log loss = 0.2094846198788979, Average precision = 0.9713292401149299, ROC-AUC = 0.9662394143767534, Elapsed Time = 153.3656974000005 seconds
Optimization Progress:  28%|##8       | 28/100 [57:08<5:51:48, 293.17s/it]
Trial 28, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 28, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
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88:	learn: 0.3371873	test: 0.3405192	best: 0.3405192 (88)	total: 985ms	remaining: 0us

bestTest = 0.3405191699
bestIteration = 88

Trial 28, Fold 1: Log loss = 0.34053714359201404, Average precision = 0.9315523225541416, ROC-AUC = 0.9398508110962945, Elapsed Time = 1.086109600000782 seconds
Trial 28, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 28, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
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88:	learn: 0.3409240	test: 0.3440422	best: 0.3440422 (88)	total: 1.04s	remaining: 0us

bestTest = 0.3440422231
bestIteration = 88

Trial 28, Fold 2: Log loss = 0.3440507802857193, Average precision = 0.9361610234283044, ROC-AUC = 0.9425062542849783, Elapsed Time = 1.146935000000667 seconds
Trial 28, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 28, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
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88:	learn: 0.3443524	test: 0.3398934	best: 0.3398934 (88)	total: 1s	remaining: 0us

bestTest = 0.3398934333
bestIteration = 88

Trial 28, Fold 3: Log loss = 0.3400008697202025, Average precision = 0.9431815108972704, ROC-AUC = 0.9461049536024887, Elapsed Time = 1.1075904999997874 seconds
Trial 28, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 28, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
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88:	learn: 0.3354550	test: 0.3324139	best: 0.3324139 (88)	total: 1.01s	remaining: 0us

bestTest = 0.3324139198
bestIteration = 88

Trial 28, Fold 4: Log loss = 0.3324219486263143, Average precision = 0.9391763331989472, ROC-AUC = 0.9459501247046311, Elapsed Time = 1.1102492999998503 seconds
Trial 28, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 28, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
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bestTest = 0.3419796246
bestIteration = 88

Trial 28, Fold 5: Log loss = 0.34193552435926494, Average precision = 0.9387729437504191, ROC-AUC = 0.94084387715289, Elapsed Time = 1.1335534999998345 seconds
Optimization Progress:  29%|##9       | 29/100 [57:21<4:07:29, 209.14s/it]
Trial 29, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 29, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
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7:	learn: 0.2132401	test: 0.2425724	best: 0.2425724 (7)	total: 860ms	remaining: 0us

bestTest = 0.2425723814
bestIteration = 7

Trial 29, Fold 1: Log loss = 0.24257238140409293, Average precision = 0.9737298682606091, ROC-AUC = 0.9691606712694111, Elapsed Time = 0.9674114000008558 seconds
Trial 29, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 29, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
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3:	learn: 0.3069820	test: 0.3225214	best: 0.3225214 (3)	total: 420ms	remaining: 420ms
4:	learn: 0.2681808	test: 0.2861285	best: 0.2861285 (4)	total: 524ms	remaining: 315ms
5:	learn: 0.2439793	test: 0.2642727	best: 0.2642727 (5)	total: 639ms	remaining: 213ms
6:	learn: 0.2274446	test: 0.2505672	best: 0.2505672 (6)	total: 747ms	remaining: 107ms
7:	learn: 0.2084926	test: 0.2340582	best: 0.2340582 (7)	total: 866ms	remaining: 0us

bestTest = 0.2340581928
bestIteration = 7

Trial 29, Fold 2: Log loss = 0.23405819275102638, Average precision = 0.9738304205821529, ROC-AUC = 0.9710940954633178, Elapsed Time = 0.9764156999990519 seconds
Trial 29, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 29, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
0:	learn: 0.5208002	test: 0.5252330	best: 0.5252330 (0)	total: 98.7ms	remaining: 691ms
1:	learn: 0.4041076	test: 0.4134832	best: 0.4134832 (1)	total: 223ms	remaining: 670ms
2:	learn: 0.3310082	test: 0.3424916	best: 0.3424916 (2)	total: 341ms	remaining: 569ms
3:	learn: 0.2871646	test: 0.3015236	best: 0.3015236 (3)	total: 460ms	remaining: 460ms
4:	learn: 0.2532199	test: 0.2700490	best: 0.2700490 (4)	total: 579ms	remaining: 347ms
5:	learn: 0.2283496	test: 0.2495927	best: 0.2495927 (5)	total: 702ms	remaining: 234ms
6:	learn: 0.2127983	test: 0.2353389	best: 0.2353389 (6)	total: 808ms	remaining: 115ms
7:	learn: 0.2013496	test: 0.2257840	best: 0.2257840 (7)	total: 917ms	remaining: 0us

bestTest = 0.2257839883
bestIteration = 7

Trial 29, Fold 3: Log loss = 0.22578398830624874, Average precision = 0.9739366786524106, ROC-AUC = 0.9710549775596883, Elapsed Time = 1.0257279999968887 seconds
Trial 29, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 29, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
0:	learn: 0.5262443	test: 0.5295493	best: 0.5295493 (0)	total: 107ms	remaining: 749ms
1:	learn: 0.4241625	test: 0.4325186	best: 0.4325186 (1)	total: 239ms	remaining: 716ms
2:	learn: 0.3537430	test: 0.3668450	best: 0.3668450 (2)	total: 367ms	remaining: 612ms
3:	learn: 0.3029682	test: 0.3202529	best: 0.3202529 (3)	total: 475ms	remaining: 475ms
4:	learn: 0.2639422	test: 0.2854576	best: 0.2854576 (4)	total: 592ms	remaining: 355ms
5:	learn: 0.2412212	test: 0.2651503	best: 0.2651503 (5)	total: 701ms	remaining: 234ms
6:	learn: 0.2233639	test: 0.2494902	best: 0.2494902 (6)	total: 806ms	remaining: 115ms
7:	learn: 0.2075575	test: 0.2357603	best: 0.2357603 (7)	total: 915ms	remaining: 0us

bestTest = 0.235760258
bestIteration = 7

Trial 29, Fold 4: Log loss = 0.23576025802358747, Average precision = 0.9738185492047103, ROC-AUC = 0.9691119832089863, Elapsed Time = 1.0255820999991556 seconds
Trial 29, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 29, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
0:	learn: 0.5252179	test: 0.5310030	best: 0.5310030 (0)	total: 100ms	remaining: 703ms
1:	learn: 0.4129104	test: 0.4230170	best: 0.4230170 (1)	total: 201ms	remaining: 603ms
2:	learn: 0.3500848	test: 0.3640901	best: 0.3640901 (2)	total: 318ms	remaining: 530ms
3:	learn: 0.2977744	test: 0.3164476	best: 0.3164476 (3)	total: 433ms	remaining: 433ms
4:	learn: 0.2589766	test: 0.2811380	best: 0.2811380 (4)	total: 551ms	remaining: 331ms
5:	learn: 0.2325891	test: 0.2586546	best: 0.2586546 (5)	total: 661ms	remaining: 220ms
6:	learn: 0.2162641	test: 0.2455106	best: 0.2455106 (6)	total: 764ms	remaining: 109ms
7:	learn: 0.2020155	test: 0.2336163	best: 0.2336163 (7)	total: 860ms	remaining: 0us

bestTest = 0.2336163004
bestIteration = 7

Trial 29, Fold 5: Log loss = 0.2336163004102549, Average precision = 0.9728322258126286, ROC-AUC = 0.9705157274771009, Elapsed Time = 0.9670232000025862 seconds
Optimization Progress:  30%|###       | 30/100 [57:34<2:55:18, 150.26s/it]
Trial 30, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 30, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
0:	learn: 0.6297320	test: 0.6292425	best: 0.6292425 (0)	total: 19.4ms	remaining: 194ms
1:	learn: 0.5775852	test: 0.5768352	best: 0.5768352 (1)	total: 41.6ms	remaining: 187ms
2:	learn: 0.5356362	test: 0.5347636	best: 0.5347636 (2)	total: 62.2ms	remaining: 166ms
3:	learn: 0.4875776	test: 0.4860527	best: 0.4860527 (3)	total: 82ms	remaining: 144ms
4:	learn: 0.4577605	test: 0.4563919	best: 0.4563919 (4)	total: 101ms	remaining: 122ms
5:	learn: 0.4222557	test: 0.4204229	best: 0.4204229 (5)	total: 121ms	remaining: 101ms
6:	learn: 0.3943860	test: 0.3924508	best: 0.3924508 (6)	total: 141ms	remaining: 80.4ms
7:	learn: 0.3704038	test: 0.3681564	best: 0.3681564 (7)	total: 160ms	remaining: 60.1ms
8:	learn: 0.3524545	test: 0.3499191	best: 0.3499191 (8)	total: 180ms	remaining: 40ms
9:	learn: 0.3451964	test: 0.3431423	best: 0.3431423 (9)	total: 199ms	remaining: 19.9ms
10:	learn: 0.3314616	test: 0.3295054	best: 0.3295054 (10)	total: 219ms	remaining: 0us

bestTest = 0.3295053929
bestIteration = 10

Trial 30, Fold 1: Log loss = 0.329641455503343, Average precision = 0.9564282766209009, ROC-AUC = 0.9532976626613711, Elapsed Time = 0.30890739999813377 seconds
Trial 30, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 30, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
0:	learn: 0.6304387	test: 0.6307899	best: 0.6307899 (0)	total: 15.7ms	remaining: 157ms
1:	learn: 0.5750675	test: 0.5755771	best: 0.5755771 (1)	total: 36ms	remaining: 162ms
2:	learn: 0.5212407	test: 0.5219479	best: 0.5219479 (2)	total: 56.2ms	remaining: 150ms
3:	learn: 0.4884375	test: 0.4895090	best: 0.4895090 (3)	total: 76.2ms	remaining: 133ms
4:	learn: 0.4586968	test: 0.4599780	best: 0.4599780 (4)	total: 96.4ms	remaining: 116ms
5:	learn: 0.4366844	test: 0.4379877	best: 0.4379877 (5)	total: 116ms	remaining: 96.9ms
6:	learn: 0.4110479	test: 0.4126098	best: 0.4126098 (6)	total: 136ms	remaining: 77.7ms
7:	learn: 0.3843871	test: 0.3866744	best: 0.3866744 (7)	total: 156ms	remaining: 58.6ms
8:	learn: 0.3619643	test: 0.3643699	best: 0.3643699 (8)	total: 176ms	remaining: 39.2ms
9:	learn: 0.3438019	test: 0.3463229	best: 0.3463229 (9)	total: 197ms	remaining: 19.7ms
10:	learn: 0.3345017	test: 0.3368272	best: 0.3368272 (10)	total: 218ms	remaining: 0us

bestTest = 0.3368271875
bestIteration = 10

Trial 30, Fold 2: Log loss = 0.33690868529929513, Average precision = 0.9503121000238468, ROC-AUC = 0.951458766636489, Elapsed Time = 0.31127980000019306 seconds
Trial 30, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 30, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
0:	learn: 0.6315016	test: 0.6307374	best: 0.6307374 (0)	total: 18.9ms	remaining: 189ms
1:	learn: 0.5956838	test: 0.5936444	best: 0.5936444 (1)	total: 39ms	remaining: 176ms
2:	learn: 0.5467070	test: 0.5439962	best: 0.5439962 (2)	total: 59.8ms	remaining: 159ms
3:	learn: 0.5146990	test: 0.5114295	best: 0.5114295 (3)	total: 80.9ms	remaining: 142ms
4:	learn: 0.4786422	test: 0.4753303	best: 0.4753303 (4)	total: 102ms	remaining: 122ms
5:	learn: 0.4493291	test: 0.4458540	best: 0.4458540 (5)	total: 122ms	remaining: 102ms
6:	learn: 0.4161335	test: 0.4125098	best: 0.4125098 (6)	total: 143ms	remaining: 81.9ms
7:	learn: 0.3978533	test: 0.3940485	best: 0.3940485 (7)	total: 165ms	remaining: 61.8ms
8:	learn: 0.3741194	test: 0.3700630	best: 0.3700630 (8)	total: 185ms	remaining: 41.2ms
9:	learn: 0.3588867	test: 0.3545137	best: 0.3545137 (9)	total: 205ms	remaining: 20.5ms
10:	learn: 0.3462158	test: 0.3418741	best: 0.3418741 (10)	total: 226ms	remaining: 0us

bestTest = 0.3418740508
bestIteration = 10

Trial 30, Fold 3: Log loss = 0.34216509956925406, Average precision = 0.9556468415148268, ROC-AUC = 0.955056774951604, Elapsed Time = 0.3175353000005998 seconds
Trial 30, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 30, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
0:	learn: 0.6346334	test: 0.6347239	best: 0.6347239 (0)	total: 19.6ms	remaining: 196ms
1:	learn: 0.5710477	test: 0.5711897	best: 0.5711897 (1)	total: 41.3ms	remaining: 186ms
2:	learn: 0.5389466	test: 0.5391744	best: 0.5391744 (2)	total: 62.3ms	remaining: 166ms
3:	learn: 0.4977092	test: 0.4976987	best: 0.4976987 (3)	total: 82.8ms	remaining: 145ms
4:	learn: 0.4780857	test: 0.4779143	best: 0.4779143 (4)	total: 103ms	remaining: 124ms
5:	learn: 0.4479908	test: 0.4475201	best: 0.4475201 (5)	total: 123ms	remaining: 103ms
6:	learn: 0.4216918	test: 0.4215243	best: 0.4215243 (6)	total: 144ms	remaining: 82.2ms
7:	learn: 0.4067477	test: 0.4064268	best: 0.4064268 (7)	total: 164ms	remaining: 61.6ms
8:	learn: 0.3882080	test: 0.3878213	best: 0.3878213 (8)	total: 185ms	remaining: 41ms
9:	learn: 0.3658446	test: 0.3656229	best: 0.3656229 (9)	total: 205ms	remaining: 20.5ms
10:	learn: 0.3514128	test: 0.3510706	best: 0.3510706 (10)	total: 227ms	remaining: 0us

bestTest = 0.351070582
bestIteration = 10

Trial 30, Fold 4: Log loss = 0.35116767590433345, Average precision = 0.9541383033016537, ROC-AUC = 0.9520921407469403, Elapsed Time = 0.318028099998628 seconds
Trial 30, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 30, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
0:	learn: 0.6301348	test: 0.6315408	best: 0.6315408 (0)	total: 19ms	remaining: 190ms
1:	learn: 0.5824963	test: 0.5843826	best: 0.5843826 (1)	total: 40ms	remaining: 180ms
2:	learn: 0.5452854	test: 0.5477495	best: 0.5477495 (2)	total: 60.2ms	remaining: 160ms
3:	learn: 0.5032157	test: 0.5061825	best: 0.5061825 (3)	total: 79.9ms	remaining: 140ms
4:	learn: 0.4685349	test: 0.4720053	best: 0.4720053 (4)	total: 99.6ms	remaining: 119ms
5:	learn: 0.4397417	test: 0.4433973	best: 0.4433973 (5)	total: 119ms	remaining: 99.3ms
6:	learn: 0.4214537	test: 0.4254232	best: 0.4254232 (6)	total: 140ms	remaining: 79.7ms
7:	learn: 0.4017002	test: 0.4061425	best: 0.4061425 (7)	total: 160ms	remaining: 59.9ms
8:	learn: 0.3801980	test: 0.3851741	best: 0.3851741 (8)	total: 180ms	remaining: 40ms
9:	learn: 0.3626068	test: 0.3682225	best: 0.3682225 (9)	total: 200ms	remaining: 20ms
10:	learn: 0.3428479	test: 0.3496953	best: 0.3496953 (10)	total: 223ms	remaining: 0us

bestTest = 0.3496953141
bestIteration = 10

Trial 30, Fold 5: Log loss = 0.34985093232434983, Average precision = 0.9551948787778671, ROC-AUC = 0.9522911766345243, Elapsed Time = 0.3170896999981778 seconds
Optimization Progress:  31%|###1      | 31/100 [57:43<2:04:04, 107.89s/it]
Trial 31, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 31, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
0:	learn: 0.6533743	test: 0.6532918	best: 0.6532918 (0)	total: 65.6ms	remaining: 328ms
1:	learn: 0.6194632	test: 0.6194319	best: 0.6194319 (1)	total: 141ms	remaining: 281ms
2:	learn: 0.5793730	test: 0.5792039	best: 0.5792039 (2)	total: 218ms	remaining: 218ms
3:	learn: 0.5449760	test: 0.5449069	best: 0.5449069 (3)	total: 287ms	remaining: 143ms
4:	learn: 0.5066023	test: 0.5071254	best: 0.5071254 (4)	total: 376ms	remaining: 75.3ms
5:	learn: 0.4808199	test: 0.4816899	best: 0.4816899 (5)	total: 458ms	remaining: 0us

bestTest = 0.4816899151
bestIteration = 5

Trial 31, Fold 1: Log loss = 0.48227684860666004, Average precision = 0.9616832003120429, ROC-AUC = 0.9561859535041558, Elapsed Time = 0.5688093000026129 seconds
Trial 31, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 31, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
0:	learn: 0.6578868	test: 0.6581268	best: 0.6581268 (0)	total: 52.7ms	remaining: 264ms
1:	learn: 0.6156270	test: 0.6157125	best: 0.6157125 (1)	total: 129ms	remaining: 259ms
2:	learn: 0.5762515	test: 0.5768996	best: 0.5768996 (2)	total: 210ms	remaining: 210ms
3:	learn: 0.5399041	test: 0.5419145	best: 0.5419145 (3)	total: 293ms	remaining: 146ms
4:	learn: 0.5057528	test: 0.5078313	best: 0.5078313 (4)	total: 378ms	remaining: 75.6ms
5:	learn: 0.4797256	test: 0.4821003	best: 0.4821003 (5)	total: 451ms	remaining: 0us

bestTest = 0.4821002784
bestIteration = 5

Trial 31, Fold 2: Log loss = 0.4822819670587704, Average precision = 0.9604624298469059, ROC-AUC = 0.9572299625395416, Elapsed Time = 0.5691735000000335 seconds
Trial 31, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 31, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
0:	learn: 0.6476212	test: 0.6477717	best: 0.6477717 (0)	total: 69.2ms	remaining: 346ms
1:	learn: 0.6049135	test: 0.6047355	best: 0.6047355 (1)	total: 148ms	remaining: 297ms
2:	learn: 0.5698312	test: 0.5693648	best: 0.5693648 (2)	total: 218ms	remaining: 218ms
3:	learn: 0.5357541	test: 0.5349822	best: 0.5349822 (3)	total: 308ms	remaining: 154ms
4:	learn: 0.5062721	test: 0.5053796	best: 0.5053796 (4)	total: 380ms	remaining: 76.1ms
5:	learn: 0.4794568	test: 0.4780800	best: 0.4780800 (5)	total: 473ms	remaining: 0us

bestTest = 0.4780800369
bestIteration = 5

Trial 31, Fold 3: Log loss = 0.47858746848210454, Average precision = 0.9611131940341558, ROC-AUC = 0.9581101140314551, Elapsed Time = 0.5772212000010768 seconds
Trial 31, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 31, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
0:	learn: 0.6543290	test: 0.6545023	best: 0.6545023 (0)	total: 52.1ms	remaining: 261ms
1:	learn: 0.6142547	test: 0.6148548	best: 0.6148548 (1)	total: 124ms	remaining: 249ms
2:	learn: 0.5759854	test: 0.5767401	best: 0.5767401 (2)	total: 198ms	remaining: 198ms
3:	learn: 0.5500581	test: 0.5508322	best: 0.5508322 (3)	total: 277ms	remaining: 139ms
4:	learn: 0.5153094	test: 0.5161562	best: 0.5161562 (4)	total: 361ms	remaining: 72.3ms
5:	learn: 0.4905766	test: 0.4915648	best: 0.4915648 (5)	total: 443ms	remaining: 0us

bestTest = 0.4915648485
bestIteration = 5

Trial 31, Fold 4: Log loss = 0.49187389418534044, Average precision = 0.9610626872962896, ROC-AUC = 0.9565157424694378, Elapsed Time = 0.5466653000003134 seconds
Trial 31, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 31, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
0:	learn: 0.6484769	test: 0.6489480	best: 0.6489480 (0)	total: 69.6ms	remaining: 348ms
1:	learn: 0.6056154	test: 0.6070429	best: 0.6070429 (1)	total: 151ms	remaining: 303ms
2:	learn: 0.5687554	test: 0.5708132	best: 0.5708132 (2)	total: 220ms	remaining: 220ms
3:	learn: 0.5356215	test: 0.5382773	best: 0.5382773 (3)	total: 311ms	remaining: 155ms
4:	learn: 0.5068299	test: 0.5097400	best: 0.5097400 (4)	total: 384ms	remaining: 76.8ms
5:	learn: 0.4784862	test: 0.4822374	best: 0.4822374 (5)	total: 477ms	remaining: 0us

bestTest = 0.4822374051
bestIteration = 5

Trial 31, Fold 5: Log loss = 0.4826240433436574, Average precision = 0.9592506933182632, ROC-AUC = 0.9545434479683407, Elapsed Time = 0.5817145000000892 seconds
Optimization Progress:  32%|###2      | 32/100 [57:53<1:29:08, 78.65s/it] 
Trial 32, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 32, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
0:	learn: 0.6844625	test: 0.6846968	best: 0.6846968 (0)	total: 17.5ms	remaining: 997ms
1:	learn: 0.6761904	test: 0.6766631	best: 0.6766631 (1)	total: 35.6ms	remaining: 998ms
2:	learn: 0.6683833	test: 0.6690876	best: 0.6690876 (2)	total: 57.3ms	remaining: 1.05s
3:	learn: 0.6588414	test: 0.6593342	best: 0.6593342 (3)	total: 79.3ms	remaining: 1.07s
4:	learn: 0.6516333	test: 0.6523409	best: 0.6523409 (4)	total: 100ms	remaining: 1.06s
5:	learn: 0.6449855	test: 0.6457598	best: 0.6457598 (5)	total: 119ms	remaining: 1.03s
6:	learn: 0.6361549	test: 0.6367266	best: 0.6367266 (6)	total: 140ms	remaining: 1.02s
7:	learn: 0.6278496	test: 0.6282190	best: 0.6282190 (7)	total: 163ms	remaining: 1.02s
8:	learn: 0.6200458	test: 0.6202143	best: 0.6202143 (8)	total: 184ms	remaining: 1s
9:	learn: 0.6127196	test: 0.6126888	best: 0.6126888 (9)	total: 206ms	remaining: 987ms
10:	learn: 0.6058345	test: 0.6056058	best: 0.6056058 (10)	total: 227ms	remaining: 971ms
11:	learn: 0.5996982	test: 0.5996780	best: 0.5996780 (11)	total: 249ms	remaining: 955ms
12:	learn: 0.5940028	test: 0.5941245	best: 0.5941245 (12)	total: 271ms	remaining: 937ms
13:	learn: 0.5876288	test: 0.5875588	best: 0.5875588 (13)	total: 292ms	remaining: 918ms
14:	learn: 0.5823750	test: 0.5823772	best: 0.5823772 (14)	total: 314ms	remaining: 901ms
15:	learn: 0.5774252	test: 0.5774968	best: 0.5774968 (15)	total: 337ms	remaining: 884ms
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57:	learn: 0.4322030	test: 0.4325028	best: 0.4325028 (57)	total: 1.33s	remaining: 0us

bestTest = 0.4325028339
bestIteration = 57

Trial 32, Fold 1: Log loss = 0.43268292980258755, Average precision = 0.9349689977155016, ROC-AUC = 0.941189469489492, Elapsed Time = 1.4362436999981583 seconds
Trial 32, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 32, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
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54:	learn: 0.4377596	test: 0.4398537	best: 0.4398537 (54)	total: 1.36s	remaining: 74.4ms
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56:	learn: 0.4337182	test: 0.4358178	best: 0.4358178 (56)	total: 1.42s	remaining: 24.9ms
57:	learn: 0.4310650	test: 0.4332260	best: 0.4332260 (57)	total: 1.45s	remaining: 0us

bestTest = 0.4332260341
bestIteration = 57

Trial 32, Fold 2: Log loss = 0.4333338618730923, Average precision = 0.9279569767242893, ROC-AUC = 0.9423153555708585, Elapsed Time = 1.5652832999985549 seconds
Trial 32, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 32, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
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57:	learn: 0.4324544	test: 0.4291574	best: 0.4291574 (57)	total: 1.43s	remaining: 0us

bestTest = 0.4291574479
bestIteration = 57

Trial 32, Fold 3: Log loss = 0.4294681622549487, Average precision = 0.9345971557209325, ROC-AUC = 0.9459223401488958, Elapsed Time = 1.5482555000016873 seconds
Trial 32, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 32, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
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57:	learn: 0.4316807	test: 0.4304200	best: 0.4304200 (57)	total: 1.42s	remaining: 0us

bestTest = 0.4304200261
bestIteration = 57

Trial 32, Fold 4: Log loss = 0.4305655394735344, Average precision = 0.9357193696992347, ROC-AUC = 0.9453403119046426, Elapsed Time = 1.5390923000013572 seconds
Trial 32, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 32, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
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57:	learn: 0.4308619	test: 0.4335984	best: 0.4335984 (57)	total: 1.39s	remaining: 0us

bestTest = 0.4335983829
bestIteration = 57

Trial 32, Fold 5: Log loss = 0.43367785706258694, Average precision = 0.9328699446903309, ROC-AUC = 0.9420889954108838, Elapsed Time = 1.5051772000006167 seconds
Optimization Progress:  33%|###3      | 33/100 [58:09<1:06:41, 59.73s/it]
Trial 33, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 33, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
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46:	learn: 0.3503443	test: 0.3561272	best: 0.3561272 (46)	total: 22s	remaining: 0us

bestTest = 0.3561272356
bestIteration = 46

Trial 33, Fold 1: Log loss = 0.35612723564515086, Average precision = 0.972196983245255, ROC-AUC = 0.9678050602706202, Elapsed Time = 22.13490599999932 seconds
Trial 33, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 33, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
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34:	learn: 0.3991653	test: 0.4033574	best: 0.4033574 (34)	total: 16.8s	remaining: 5.76s
35:	learn: 0.3943518	test: 0.3986350	best: 0.3986350 (35)	total: 17.3s	remaining: 5.3s
36:	learn: 0.3897634	test: 0.3941457	best: 0.3941457 (36)	total: 17.7s	remaining: 4.79s
37:	learn: 0.3852835	test: 0.3896868	best: 0.3896868 (37)	total: 18.2s	remaining: 4.3s
38:	learn: 0.3809759	test: 0.3854949	best: 0.3854949 (38)	total: 18.7s	remaining: 3.83s
39:	learn: 0.3766753	test: 0.3812858	best: 0.3812858 (39)	total: 19.2s	remaining: 3.37s
40:	learn: 0.3725566	test: 0.3772323	best: 0.3772323 (40)	total: 19.6s	remaining: 2.87s
41:	learn: 0.3686116	test: 0.3733100	best: 0.3733100 (41)	total: 20s	remaining: 2.38s
42:	learn: 0.3646371	test: 0.3694580	best: 0.3694580 (42)	total: 20.5s	remaining: 1.91s
43:	learn: 0.3607491	test: 0.3656762	best: 0.3656762 (43)	total: 21.1s	remaining: 1.44s
44:	learn: 0.3570445	test: 0.3620224	best: 0.3620224 (44)	total: 21.7s	remaining: 965ms
45:	learn: 0.3534633	test: 0.3585506	best: 0.3585506 (45)	total: 22.3s	remaining: 485ms
46:	learn: 0.3499085	test: 0.3550376	best: 0.3550376 (46)	total: 22.8s	remaining: 0us

bestTest = 0.355037595
bestIteration = 46

Trial 33, Fold 2: Log loss = 0.3550375950367974, Average precision = 0.9729696541336975, ROC-AUC = 0.9706665460777597, Elapsed Time = 22.946749499998987 seconds
Trial 33, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 33, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
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33:	learn: 0.4048313	test: 0.4061846	best: 0.4061846 (33)	total: 16.3s	remaining: 6.25s
34:	learn: 0.3998099	test: 0.4011987	best: 0.4011987 (34)	total: 16.7s	remaining: 5.74s
35:	learn: 0.3949732	test: 0.3964012	best: 0.3964012 (35)	total: 17.2s	remaining: 5.27s
36:	learn: 0.3905605	test: 0.3920238	best: 0.3920238 (36)	total: 17.7s	remaining: 4.79s
37:	learn: 0.3858692	test: 0.3873239	best: 0.3873239 (37)	total: 18.2s	remaining: 4.32s
38:	learn: 0.3816521	test: 0.3831435	best: 0.3831435 (38)	total: 18.6s	remaining: 3.82s
39:	learn: 0.3774929	test: 0.3791088	best: 0.3791088 (39)	total: 19.1s	remaining: 3.34s
40:	learn: 0.3733914	test: 0.3750757	best: 0.3750757 (40)	total: 19.5s	remaining: 2.86s
41:	learn: 0.3694223	test: 0.3711697	best: 0.3711697 (41)	total: 20s	remaining: 2.38s
42:	learn: 0.3653696	test: 0.3671026	best: 0.3671026 (42)	total: 20.4s	remaining: 1.9s
43:	learn: 0.3614132	test: 0.3631509	best: 0.3631509 (43)	total: 20.8s	remaining: 1.42s
44:	learn: 0.3576997	test: 0.3595060	best: 0.3595060 (44)	total: 21.3s	remaining: 948ms
45:	learn: 0.3541952	test: 0.3560741	best: 0.3560741 (45)	total: 21.7s	remaining: 473ms
46:	learn: 0.3507134	test: 0.3526761	best: 0.3526761 (46)	total: 22.2s	remaining: 0us

bestTest = 0.3526760709
bestIteration = 46

Trial 33, Fold 3: Log loss = 0.3526760709219909, Average precision = 0.972012422554811, ROC-AUC = 0.9703599629445927, Elapsed Time = 22.334626600000774 seconds
Trial 33, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 33, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
0:	learn: 0.6799692	test: 0.6799521	best: 0.6799521 (0)	total: 467ms	remaining: 21.5s
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2:	learn: 0.6547724	test: 0.6548321	best: 0.6548321 (2)	total: 1.41s	remaining: 20.6s
3:	learn: 0.6428845	test: 0.6429992	best: 0.6429992 (3)	total: 1.82s	remaining: 19.6s
4:	learn: 0.6310770	test: 0.6313125	best: 0.6313125 (4)	total: 2.34s	remaining: 19.7s
5:	learn: 0.6197182	test: 0.6200063	best: 0.6200063 (5)	total: 2.86s	remaining: 19.5s
6:	learn: 0.6086455	test: 0.6090045	best: 0.6090045 (6)	total: 3.31s	remaining: 18.9s
7:	learn: 0.5979433	test: 0.5983726	best: 0.5983726 (7)	total: 3.81s	remaining: 18.6s
8:	learn: 0.5874391	test: 0.5879798	best: 0.5879798 (8)	total: 4.21s	remaining: 17.8s
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34:	learn: 0.3999494	test: 0.4031748	best: 0.4031748 (34)	total: 15.9s	remaining: 5.45s
35:	learn: 0.3952151	test: 0.3985011	best: 0.3985011 (35)	total: 16.3s	remaining: 4.98s
36:	learn: 0.3904883	test: 0.3938939	best: 0.3938939 (36)	total: 16.7s	remaining: 4.52s
37:	learn: 0.3862677	test: 0.3898062	best: 0.3898062 (37)	total: 17.2s	remaining: 4.08s
38:	learn: 0.3817417	test: 0.3854080	best: 0.3854080 (38)	total: 17.7s	remaining: 3.62s
39:	learn: 0.3777289	test: 0.3814294	best: 0.3814294 (39)	total: 18s	remaining: 3.15s
40:	learn: 0.3738134	test: 0.3776291	best: 0.3776291 (40)	total: 18.4s	remaining: 2.7s
41:	learn: 0.3699027	test: 0.3738155	best: 0.3738155 (41)	total: 18.8s	remaining: 2.23s
42:	learn: 0.3660927	test: 0.3700101	best: 0.3700101 (42)	total: 19.3s	remaining: 1.79s
43:	learn: 0.3623670	test: 0.3663667	best: 0.3663667 (43)	total: 19.7s	remaining: 1.34s
44:	learn: 0.3587059	test: 0.3628643	best: 0.3628643 (44)	total: 20.1s	remaining: 892ms
45:	learn: 0.3550818	test: 0.3592529	best: 0.3592529 (45)	total: 20.5s	remaining: 445ms
46:	learn: 0.3515931	test: 0.3558125	best: 0.3558125 (46)	total: 20.9s	remaining: 0us

bestTest = 0.3558125306
bestIteration = 46

Trial 33, Fold 4: Log loss = 0.35581253057496137, Average precision = 0.9744075377306871, ROC-AUC = 0.9702797478963544, Elapsed Time = 20.999208799999906 seconds
Trial 33, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 33, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
0:	learn: 0.6798679	test: 0.6801917	best: 0.6801917 (0)	total: 508ms	remaining: 23.4s
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2:	learn: 0.6546026	test: 0.6554156	best: 0.6554156 (2)	total: 1.45s	remaining: 21.3s
3:	learn: 0.6425578	test: 0.6436337	best: 0.6436337 (3)	total: 1.89s	remaining: 20.3s
4:	learn: 0.6308166	test: 0.6321976	best: 0.6321976 (4)	total: 2.4s	remaining: 20.2s
5:	learn: 0.6193215	test: 0.6208824	best: 0.6208824 (5)	total: 2.88s	remaining: 19.7s
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37:	learn: 0.3843021	test: 0.3918855	best: 0.3918855 (37)	total: 17.6s	remaining: 4.17s
38:	learn: 0.3797245	test: 0.3875249	best: 0.3875249 (38)	total: 18.1s	remaining: 3.71s
39:	learn: 0.3754033	test: 0.3833875	best: 0.3833875 (39)	total: 18.6s	remaining: 3.25s
40:	learn: 0.3713167	test: 0.3794975	best: 0.3794975 (40)	total: 19s	remaining: 2.79s
41:	learn: 0.3671760	test: 0.3755365	best: 0.3755365 (41)	total: 19.5s	remaining: 2.33s
42:	learn: 0.3632089	test: 0.3717372	best: 0.3717372 (42)	total: 20s	remaining: 1.86s
43:	learn: 0.3592591	test: 0.3679166	best: 0.3679166 (43)	total: 20.4s	remaining: 1.39s
44:	learn: 0.3556749	test: 0.3644660	best: 0.3644660 (44)	total: 20.9s	remaining: 927ms
45:	learn: 0.3520098	test: 0.3609446	best: 0.3609446 (45)	total: 21.4s	remaining: 465ms
46:	learn: 0.3484924	test: 0.3575087	best: 0.3575087 (46)	total: 21.8s	remaining: 0us

bestTest = 0.3575087443
bestIteration = 46

Trial 33, Fold 5: Log loss = 0.35750874426837875, Average precision = 0.96991020543768, ROC-AUC = 0.9683131003474352, Elapsed Time = 21.928632199997082 seconds
Optimization Progress:  34%|###4      | 34/100 [1:00:07<1:24:54, 77.19s/it]
Trial 34, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 34, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
0:	learn: 0.5943476	test: 0.5990645	best: 0.5990645 (0)	total: 190ms	remaining: 4.38s
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2:	learn: 0.4649574	test: 0.4731094	best: 0.4731094 (2)	total: 549ms	remaining: 3.84s
3:	learn: 0.4104977	test: 0.4204379	best: 0.4204379 (3)	total: 731ms	remaining: 3.65s
4:	learn: 0.3753823	test: 0.3892092	best: 0.3892092 (4)	total: 919ms	remaining: 3.49s
5:	learn: 0.3437354	test: 0.3602125	best: 0.3602125 (5)	total: 1.14s	remaining: 3.41s
6:	learn: 0.3154581	test: 0.3346817	best: 0.3346817 (6)	total: 1.36s	remaining: 3.3s
7:	learn: 0.2891738	test: 0.3112655	best: 0.3112655 (7)	total: 1.56s	remaining: 3.12s
8:	learn: 0.2699985	test: 0.2956367	best: 0.2956367 (8)	total: 1.78s	remaining: 2.97s
9:	learn: 0.2515779	test: 0.2790994	best: 0.2790994 (9)	total: 2.02s	remaining: 2.83s
10:	learn: 0.2377430	test: 0.2673821	best: 0.2673821 (10)	total: 2.23s	remaining: 2.64s
11:	learn: 0.2264094	test: 0.2583536	best: 0.2583536 (11)	total: 2.46s	remaining: 2.46s
12:	learn: 0.2164267	test: 0.2504224	best: 0.2504224 (12)	total: 2.67s	remaining: 2.26s
13:	learn: 0.2069969	test: 0.2422374	best: 0.2422374 (13)	total: 2.89s	remaining: 2.06s
14:	learn: 0.2290826	test: 0.2352201	best: 0.2352201 (14)	total: 3.15s	remaining: 1.89s
15:	learn: 0.2556292	test: 0.2293058	best: 0.2293058 (15)	total: 3.38s	remaining: 1.69s
Training has stopped (degenerate solution on iteration 16, probably too small l2-regularization, try to increase it)
bestTest = 0.2293057997
bestIteration = 15

Shrink model to first 16 iterations.
Trial 34, Fold 1: Log loss = 0.22953166751640147, Average precision = 0.973807404451311, ROC-AUC = 0.9697118923349727, Elapsed Time = 3.7016322999988915 seconds
Trial 34, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 34, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
0:	learn: 0.6056415	test: 0.6069813	best: 0.6069813 (0)	total: 175ms	remaining: 4.01s
1:	learn: 0.5193939	test: 0.5247933	best: 0.5247933 (1)	total: 395ms	remaining: 4.35s
2:	learn: 0.4653923	test: 0.4727238	best: 0.4727238 (2)	total: 578ms	remaining: 4.05s
3:	learn: 0.4158795	test: 0.4247616	best: 0.4247616 (3)	total: 771ms	remaining: 3.85s
4:	learn: 0.3829056	test: 0.3939249	best: 0.3939249 (4)	total: 979ms	remaining: 3.72s
5:	learn: 0.3517505	test: 0.3647171	best: 0.3647171 (5)	total: 1.19s	remaining: 3.57s
6:	learn: 0.3260694	test: 0.3415961	best: 0.3415961 (6)	total: 1.39s	remaining: 3.38s
7:	learn: 0.3017450	test: 0.3195364	best: 0.3195364 (7)	total: 1.62s	remaining: 3.24s
8:	learn: 0.2784449	test: 0.2975302	best: 0.2975302 (8)	total: 1.89s	remaining: 3.14s
9:	learn: 0.2574647	test: 0.2802588	best: 0.2802588 (9)	total: 2.13s	remaining: 2.99s
10:	learn: 0.2423264	test: 0.2670958	best: 0.2670958 (10)	total: 2.36s	remaining: 2.79s
11:	learn: 0.2289134	test: 0.2566089	best: 0.2566089 (11)	total: 2.59s	remaining: 2.59s
12:	learn: 0.2170221	test: 0.2467791	best: 0.2467791 (12)	total: 2.84s	remaining: 2.4s
13:	learn: 0.2082763	test: 0.2392661	best: 0.2392661 (13)	total: 3.08s	remaining: 2.2s
14:	learn: 0.1995248	test: 0.2321519	best: 0.2321519 (14)	total: 3.33s	remaining: 2s
15:	learn: 0.1934826	test: 0.2268702	best: 0.2268702 (15)	total: 3.55s	remaining: 1.78s
16:	learn: 0.1877595	test: 0.2226930	best: 0.2226930 (16)	total: 3.79s	remaining: 1.56s
17:	learn: 0.1808638	test: 0.2176225	best: 0.2176225 (17)	total: 4.04s	remaining: 1.35s
18:	learn: 0.1756847	test: 0.2138852	best: 0.2138852 (18)	total: 4.28s	remaining: 1.13s
19:	learn: 0.1704553	test: 0.2099490	best: 0.2099490 (19)	total: 4.52s	remaining: 904ms
20:	learn: 0.1662161	test: 0.2067578	best: 0.2067578 (20)	total: 4.75s	remaining: 678ms
21:	learn: 0.1627557	test: 0.2044718	best: 0.2044718 (21)	total: 4.96s	remaining: 451ms
22:	learn: 0.1597167	test: 0.2023996	best: 0.2023996 (22)	total: 5.16s	remaining: 224ms
23:	learn: 0.1566909	test: 0.2005386	best: 0.2005386 (23)	total: 5.36s	remaining: 0us

bestTest = 0.2005385641
bestIteration = 23

Trial 34, Fold 2: Log loss = 0.2004136860628991, Average precision = 0.9754332516152835, ROC-AUC = 0.9728828224845302, Elapsed Time = 5.483135800001037 seconds
Trial 34, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 34, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
0:	learn: 0.5955809	test: 0.5964806	best: 0.5964806 (0)	total: 191ms	remaining: 4.39s
1:	learn: 0.5258734	test: 0.5281224	best: 0.5281224 (1)	total: 365ms	remaining: 4.02s
2:	learn: 0.4665994	test: 0.4704731	best: 0.4704731 (2)	total: 556ms	remaining: 3.89s
3:	learn: 0.4224107	test: 0.4268245	best: 0.4268245 (3)	total: 755ms	remaining: 3.78s
4:	learn: 0.3874139	test: 0.3932919	best: 0.3932919 (4)	total: 945ms	remaining: 3.59s
5:	learn: 0.3546507	test: 0.3670296	best: 0.3670296 (5)	total: 1.15s	remaining: 3.44s
6:	learn: 0.3300182	test: 0.3446434	best: 0.3446434 (6)	total: 1.35s	remaining: 3.27s
7:	learn: 0.3070320	test: 0.3232082	best: 0.3232082 (7)	total: 1.6s	remaining: 3.21s
8:	learn: 0.2861044	test: 0.3042426	best: 0.3042426 (8)	total: 1.86s	remaining: 3.1s
9:	learn: 0.2685478	test: 0.2884555	best: 0.2884555 (9)	total: 2.14s	remaining: 3s
10:	learn: 0.2536522	test: 0.2748480	best: 0.2748480 (10)	total: 2.41s	remaining: 2.85s
11:	learn: 0.2394473	test: 0.2635975	best: 0.2635975 (11)	total: 2.71s	remaining: 2.71s
12:	learn: 0.2279682	test: 0.2542054	best: 0.2542054 (12)	total: 3.01s	remaining: 2.55s
13:	learn: 0.2184617	test: 0.2460240	best: 0.2460240 (13)	total: 3.33s	remaining: 2.38s
14:	learn: 0.2077029	test: 0.2364710	best: 0.2364710 (14)	total: 3.62s	remaining: 2.17s
15:	learn: 0.2015176	test: 0.2310847	best: 0.2310847 (15)	total: 3.91s	remaining: 1.95s
16:	learn: 0.1949912	test: 0.2257418	best: 0.2257418 (16)	total: 4.29s	remaining: 1.76s
17:	learn: 0.1900155	test: 0.2219918	best: 0.2219918 (17)	total: 4.57s	remaining: 1.52s
18:	learn: 0.1850203	test: 0.2186533	best: 0.2186533 (18)	total: 4.84s	remaining: 1.27s
19:	learn: 0.1792839	test: 0.2148101	best: 0.2148101 (19)	total: 5.12s	remaining: 1.02s
20:	learn: 0.1743226	test: 0.2112856	best: 0.2112856 (20)	total: 5.49s	remaining: 784ms
21:	learn: 0.1695785	test: 0.2075384	best: 0.2075384 (21)	total: 5.74s	remaining: 522ms
22:	learn: 0.1663557	test: 0.2054433	best: 0.2054433 (22)	total: 5.96s	remaining: 259ms
23:	learn: 0.1809540	test: 0.2031111	best: 0.2031111 (23)	total: 6.2s	remaining: 0us

bestTest = 0.2031111368
bestIteration = 23

Trial 34, Fold 3: Log loss = 0.2032233239940844, Average precision = 0.9755286697322263, ROC-AUC = 0.9732541642187785, Elapsed Time = 6.327928200000315 seconds
Trial 34, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 34, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
0:	learn: 0.5973665	test: 0.5995453	best: 0.5995453 (0)	total: 227ms	remaining: 5.21s
1:	learn: 0.5198167	test: 0.5239807	best: 0.5239807 (1)	total: 456ms	remaining: 5.01s
2:	learn: 0.4598358	test: 0.4663113	best: 0.4663113 (2)	total: 681ms	remaining: 4.77s
3:	learn: 0.4177520	test: 0.4263924	best: 0.4263924 (3)	total: 912ms	remaining: 4.56s
4:	learn: 0.3806622	test: 0.3910781	best: 0.3910781 (4)	total: 1.15s	remaining: 4.36s
5:	learn: 0.3526744	test: 0.3655766	best: 0.3655766 (5)	total: 1.4s	remaining: 4.19s
6:	learn: 0.3301461	test: 0.3449584	best: 0.3449584 (6)	total: 1.63s	remaining: 3.96s
7:	learn: 0.3078073	test: 0.3237156	best: 0.3237156 (7)	total: 1.92s	remaining: 3.84s
8:	learn: 0.2898432	test: 0.3086806	best: 0.3086806 (8)	total: 2.18s	remaining: 3.63s
9:	learn: 0.2716349	test: 0.2934197	best: 0.2934197 (9)	total: 2.43s	remaining: 3.4s
10:	learn: 0.2590062	test: 0.2822746	best: 0.2822746 (10)	total: 2.66s	remaining: 3.14s
11:	learn: 0.2468990	test: 0.2719219	best: 0.2719219 (11)	total: 2.94s	remaining: 2.94s
12:	learn: 0.2343369	test: 0.2614777	best: 0.2614777 (12)	total: 3.18s	remaining: 2.69s
13:	learn: 0.2254935	test: 0.2550927	best: 0.2550927 (13)	total: 3.42s	remaining: 2.44s
14:	learn: 0.2161107	test: 0.2475053	best: 0.2475053 (14)	total: 3.65s	remaining: 2.19s
15:	learn: 0.2084657	test: 0.2414135	best: 0.2414135 (15)	total: 3.88s	remaining: 1.94s
16:	learn: 0.2007506	test: 0.2353869	best: 0.2353869 (16)	total: 4.13s	remaining: 1.7s
17:	learn: 0.1950521	test: 0.2311037	best: 0.2311037 (17)	total: 4.38s	remaining: 1.46s
18:	learn: 0.1881732	test: 0.2260424	best: 0.2260424 (18)	total: 4.61s	remaining: 1.21s
19:	learn: 0.1828753	test: 0.2217793	best: 0.2217793 (19)	total: 4.86s	remaining: 971ms
20:	learn: 0.1881780	test: 0.2176608	best: 0.2176608 (20)	total: 5.07s	remaining: 725ms
21:	learn: 0.4569488	test: 0.7577585	best: 0.2176608 (20)	total: 5.27s	remaining: 479ms
22:	learn: 0.7789546	test: 1.5201378	best: 0.2176608 (20)	total: 5.47s	remaining: 238ms
Training has stopped (degenerate solution on iteration 23, probably too small l2-regularization, try to increase it)
bestTest = 0.217660783
bestIteration = 20

Shrink model to first 21 iterations.
Trial 34, Fold 4: Log loss = 0.2176669441894009, Average precision = 0.9709851008514145, ROC-AUC = 0.9698864693090696, Elapsed Time = 5.810770200001571 seconds
Trial 34, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 34, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
0:	learn: 0.5944791	test: 0.5979486	best: 0.5979486 (0)	total: 208ms	remaining: 4.79s
1:	learn: 0.5221860	test: 0.5329186	best: 0.5329186 (1)	total: 382ms	remaining: 4.2s
2:	learn: 0.4600844	test: 0.4730636	best: 0.4730636 (2)	total: 568ms	remaining: 3.98s
3:	learn: 0.4157845	test: 0.4308862	best: 0.4308862 (3)	total: 757ms	remaining: 3.79s
4:	learn: 0.3803584	test: 0.3983018	best: 0.3983018 (4)	total: 990ms	remaining: 3.76s
5:	learn: 0.3412906	test: 0.3637792	best: 0.3637792 (5)	total: 1.2s	remaining: 3.6s
6:	learn: 0.3103205	test: 0.3349416	best: 0.3349416 (6)	total: 1.43s	remaining: 3.48s
7:	learn: 0.2858679	test: 0.3131531	best: 0.3131531 (7)	total: 1.64s	remaining: 3.28s
8:	learn: 0.2637704	test: 0.2933788	best: 0.2933788 (8)	total: 1.86s	remaining: 3.1s
9:	learn: 0.2473231	test: 0.2809982	best: 0.2809982 (9)	total: 2.08s	remaining: 2.92s
10:	learn: 0.2318633	test: 0.2682475	best: 0.2682475 (10)	total: 2.29s	remaining: 2.71s
11:	learn: 0.2197833	test: 0.2579069	best: 0.2579069 (11)	total: 2.53s	remaining: 2.53s
12:	learn: 0.2097791	test: 0.2491652	best: 0.2491652 (12)	total: 2.74s	remaining: 2.31s
13:	learn: 0.2012860	test: 0.2434989	best: 0.2434989 (13)	total: 2.95s	remaining: 2.11s
14:	learn: 0.1936671	test: 0.2376607	best: 0.2376607 (14)	total: 3.19s	remaining: 1.91s
15:	learn: 0.2042173	test: 0.4375058	best: 0.2376607 (14)	total: 3.42s	remaining: 1.71s
16:	learn: 0.2148160	test: 0.4328437	best: 0.2376607 (14)	total: 3.63s	remaining: 1.49s
17:	learn: 0.2098738	test: 0.4291165	best: 0.2376607 (14)	total: 3.84s	remaining: 1.28s
18:	learn: 0.1980945	test: 0.4054428	best: 0.2376607 (14)	total: 4.07s	remaining: 1.07s
19:	learn: 0.1917563	test: 0.4027966	best: 0.2376607 (14)	total: 4.26s	remaining: 853ms
20:	learn: 0.1873851	test: 0.4008291	best: 0.2376607 (14)	total: 4.5s	remaining: 643ms
21:	learn: 0.1836871	test: 0.3986377	best: 0.2376607 (14)	total: 4.73s	remaining: 430ms
22:	learn: 0.2022435	test: 0.8507112	best: 0.2376607 (14)	total: 4.95s	remaining: 215ms
23:	learn: 0.8600333	test: 1.4181642	best: 0.2376607 (14)	total: 5.17s	remaining: 0us

bestTest = 0.237660695
bestIteration = 14

Shrink model to first 15 iterations.
Trial 34, Fold 5: Log loss = 0.23766411327831838, Average precision = 0.9719393609186869, ROC-AUC = 0.9676541998773759, Elapsed Time = 5.284863400000177 seconds
Optimization Progress:  35%|###5      | 35/100 [1:00:41<1:09:45, 64.39s/it]
Trial 35, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 35, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
0:	learn: 0.5937483	test: 0.5966649	best: 0.5966649 (0)	total: 68.9ms	remaining: 2.48s
1:	learn: 0.5094030	test: 0.5140879	best: 0.5140879 (1)	total: 159ms	remaining: 2.79s
2:	learn: 0.4231109	test: 0.4316569	best: 0.4316569 (2)	total: 246ms	remaining: 2.79s
3:	learn: 0.3738528	test: 0.3845591	best: 0.3845591 (3)	total: 338ms	remaining: 2.79s
4:	learn: 0.3259108	test: 0.3385297	best: 0.3385297 (4)	total: 429ms	remaining: 2.75s
5:	learn: 0.2980231	test: 0.3122958	best: 0.3122958 (5)	total: 525ms	remaining: 2.71s
6:	learn: 0.2767767	test: 0.2931342	best: 0.2931342 (6)	total: 617ms	remaining: 2.65s
7:	learn: 0.2615518	test: 0.2782832	best: 0.2782832 (7)	total: 708ms	remaining: 2.57s
8:	learn: 0.2455048	test: 0.2639997	best: 0.2639997 (8)	total: 803ms	remaining: 2.5s
9:	learn: 0.2335749	test: 0.2541992	best: 0.2541992 (9)	total: 893ms	remaining: 2.41s
10:	learn: 0.2224435	test: 0.2445282	best: 0.2445282 (10)	total: 986ms	remaining: 2.33s
11:	learn: 0.2106635	test: 0.2357523	best: 0.2357523 (11)	total: 1.08s	remaining: 2.25s
12:	learn: 0.2048203	test: 0.2323197	best: 0.2323197 (12)	total: 1.17s	remaining: 2.16s
13:	learn: 0.2001978	test: 0.2293196	best: 0.2293196 (13)	total: 1.26s	remaining: 2.06s
14:	learn: 0.1952103	test: 0.2261645	best: 0.2261645 (14)	total: 1.35s	remaining: 1.99s
15:	learn: 0.1919123	test: 0.2241957	best: 0.2241957 (15)	total: 1.45s	remaining: 1.9s
16:	learn: 0.1864241	test: 0.2207171	best: 0.2207171 (16)	total: 1.55s	remaining: 1.82s
17:	learn: 0.1832486	test: 0.2189516	best: 0.2189516 (17)	total: 1.63s	remaining: 1.72s
18:	learn: 0.1799431	test: 0.2188193	best: 0.2188193 (18)	total: 1.73s	remaining: 1.64s
19:	learn: 0.1784047	test: 0.2179312	best: 0.2179312 (19)	total: 1.82s	remaining: 1.54s
20:	learn: 0.1746889	test: 0.2170770	best: 0.2170770 (20)	total: 1.91s	remaining: 1.46s
21:	learn: 0.1727769	test: 0.2161512	best: 0.2161512 (21)	total: 2s	remaining: 1.36s
22:	learn: 0.1700697	test: 0.2146060	best: 0.2146060 (22)	total: 2.09s	remaining: 1.27s
23:	learn: 0.1679370	test: 0.2156218	best: 0.2146060 (22)	total: 2.18s	remaining: 1.18s
24:	learn: 0.1653224	test: 0.2138320	best: 0.2138320 (24)	total: 2.27s	remaining: 1.09s
25:	learn: 0.1634993	test: 0.2136834	best: 0.2136834 (25)	total: 2.36s	remaining: 997ms
26:	learn: 0.1613185	test: 0.2129319	best: 0.2129319 (26)	total: 2.44s	remaining: 906ms
27:	learn: 0.1599076	test: 0.2121355	best: 0.2121355 (27)	total: 2.54s	remaining: 816ms
28:	learn: 0.1579754	test: 0.2122690	best: 0.2121355 (27)	total: 2.63s	remaining: 724ms
29:	learn: 0.1566730	test: 0.2118358	best: 0.2118358 (29)	total: 2.73s	remaining: 636ms
30:	learn: 0.1548380	test: 0.2115831	best: 0.2115831 (30)	total: 2.84s	remaining: 549ms
31:	learn: 0.1541379	test: 0.2110852	best: 0.2110852 (31)	total: 2.89s	remaining: 451ms
32:	learn: 0.1530239	test: 0.2109044	best: 0.2109044 (32)	total: 2.99s	remaining: 362ms
33:	learn: 0.1521723	test: 0.2104875	best: 0.2104875 (33)	total: 3.08s	remaining: 272ms
34:	learn: 0.1505012	test: 0.2108399	best: 0.2104875 (33)	total: 3.17s	remaining: 181ms
35:	learn: 0.1484048	test: 0.2110630	best: 0.2104875 (33)	total: 3.26s	remaining: 90.6ms
36:	learn: 0.1475744	test: 0.2110590	best: 0.2104875 (33)	total: 3.35s	remaining: 0us

bestTest = 0.210487509
bestIteration = 33

Shrink model to first 34 iterations.
Trial 35, Fold 1: Log loss = 0.2102661657700664, Average precision = 0.9688816097141942, ROC-AUC = 0.9656561121387227, Elapsed Time = 3.4643465999979526 seconds
Trial 35, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 35, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
0:	learn: 0.5800729	test: 0.5863184	best: 0.5863184 (0)	total: 72.7ms	remaining: 2.62s
1:	learn: 0.5114772	test: 0.5183640	best: 0.5183640 (1)	total: 161ms	remaining: 2.81s
2:	learn: 0.4561146	test: 0.4648007	best: 0.4648007 (2)	total: 252ms	remaining: 2.86s
3:	learn: 0.4086666	test: 0.4184434	best: 0.4184434 (3)	total: 336ms	remaining: 2.77s
4:	learn: 0.3618812	test: 0.3749225	best: 0.3749225 (4)	total: 431ms	remaining: 2.76s
5:	learn: 0.3233624	test: 0.3388270	best: 0.3388270 (5)	total: 529ms	remaining: 2.73s
6:	learn: 0.2952563	test: 0.3113254	best: 0.3113254 (6)	total: 614ms	remaining: 2.63s
7:	learn: 0.2737078	test: 0.2907757	best: 0.2907757 (7)	total: 702ms	remaining: 2.54s
8:	learn: 0.2556602	test: 0.2728645	best: 0.2728645 (8)	total: 788ms	remaining: 2.45s
9:	learn: 0.2382800	test: 0.2566828	best: 0.2566828 (9)	total: 883ms	remaining: 2.38s
10:	learn: 0.2253825	test: 0.2463368	best: 0.2463368 (10)	total: 966ms	remaining: 2.28s
11:	learn: 0.2170819	test: 0.2385675	best: 0.2385675 (11)	total: 1.05s	remaining: 2.19s
12:	learn: 0.2105433	test: 0.2326706	best: 0.2326706 (12)	total: 1.14s	remaining: 2.1s
13:	learn: 0.2043653	test: 0.2268359	best: 0.2268359 (13)	total: 1.23s	remaining: 2.02s
14:	learn: 0.1988833	test: 0.2223574	best: 0.2223574 (14)	total: 1.31s	remaining: 1.93s
15:	learn: 0.1952427	test: 0.2196582	best: 0.2196582 (15)	total: 1.41s	remaining: 1.85s
16:	learn: 0.1914536	test: 0.2159305	best: 0.2159305 (16)	total: 1.5s	remaining: 1.76s
17:	learn: 0.1879457	test: 0.2135012	best: 0.2135012 (17)	total: 1.59s	remaining: 1.68s
18:	learn: 0.1844419	test: 0.2119908	best: 0.2119908 (18)	total: 1.68s	remaining: 1.59s
19:	learn: 0.1798339	test: 0.2100570	best: 0.2100570 (19)	total: 1.77s	remaining: 1.5s
20:	learn: 0.1765939	test: 0.2075050	best: 0.2075050 (20)	total: 1.87s	remaining: 1.43s
21:	learn: 0.1721645	test: 0.2075740	best: 0.2075050 (20)	total: 1.95s	remaining: 1.33s
22:	learn: 0.1686517	test: 0.2072139	best: 0.2072139 (22)	total: 2.05s	remaining: 1.25s
23:	learn: 0.1666998	test: 0.2059224	best: 0.2059224 (23)	total: 2.15s	remaining: 1.16s
24:	learn: 0.1657819	test: 0.2053967	best: 0.2053967 (24)	total: 2.24s	remaining: 1.07s
25:	learn: 0.1618556	test: 0.2049278	best: 0.2049278 (25)	total: 2.33s	remaining: 984ms
26:	learn: 0.1592855	test: 0.2038046	best: 0.2038046 (26)	total: 2.43s	remaining: 900ms
27:	learn: 0.1583669	test: 0.2030972	best: 0.2030972 (27)	total: 2.53s	remaining: 813ms
28:	learn: 0.1567541	test: 0.2023227	best: 0.2023227 (28)	total: 2.62s	remaining: 723ms
29:	learn: 0.1556967	test: 0.2019420	best: 0.2019420 (29)	total: 2.71s	remaining: 632ms
30:	learn: 0.1534447	test: 0.2020709	best: 0.2019420 (29)	total: 2.8s	remaining: 542ms
31:	learn: 0.1515945	test: 0.2018600	best: 0.2018600 (31)	total: 2.9s	remaining: 452ms
32:	learn: 0.1496791	test: 0.2012439	best: 0.2012439 (32)	total: 2.99s	remaining: 362ms
33:	learn: 0.1486589	test: 0.2010294	best: 0.2010294 (33)	total: 3.08s	remaining: 272ms
34:	learn: 0.1472906	test: 0.2006491	best: 0.2006491 (34)	total: 3.17s	remaining: 181ms
35:	learn: 0.1465504	test: 0.2001808	best: 0.2001808 (35)	total: 3.26s	remaining: 90.7ms
36:	learn: 0.1453401	test: 0.2002420	best: 0.2001808 (35)	total: 3.36s	remaining: 0us

bestTest = 0.2001808059
bestIteration = 35

Shrink model to first 36 iterations.
Trial 35, Fold 2: Log loss = 0.19990868143104323, Average precision = 0.9715196457365233, ROC-AUC = 0.967846920475966, Elapsed Time = 3.4694494000032137 seconds
Trial 35, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 35, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
0:	learn: 0.5661951	test: 0.5678574	best: 0.5678574 (0)	total: 75.1ms	remaining: 2.71s
1:	learn: 0.4671416	test: 0.4730328	best: 0.4730328 (1)	total: 157ms	remaining: 2.75s
2:	learn: 0.4120820	test: 0.4162268	best: 0.4162268 (2)	total: 222ms	remaining: 2.52s
3:	learn: 0.3663406	test: 0.3728967	best: 0.3728967 (3)	total: 315ms	remaining: 2.6s
4:	learn: 0.3190951	test: 0.3286352	best: 0.3286352 (4)	total: 393ms	remaining: 2.52s
5:	learn: 0.2999258	test: 0.3117226	best: 0.3117226 (5)	total: 488ms	remaining: 2.52s
6:	learn: 0.2720919	test: 0.2859078	best: 0.2859078 (6)	total: 571ms	remaining: 2.45s
7:	learn: 0.2519738	test: 0.2679621	best: 0.2679621 (7)	total: 657ms	remaining: 2.38s
8:	learn: 0.2383162	test: 0.2557470	best: 0.2557470 (8)	total: 735ms	remaining: 2.29s
9:	learn: 0.2250496	test: 0.2458122	best: 0.2458122 (9)	total: 826ms	remaining: 2.23s
10:	learn: 0.2142764	test: 0.2370832	best: 0.2370832 (10)	total: 913ms	remaining: 2.16s
11:	learn: 0.2061246	test: 0.2310896	best: 0.2310896 (11)	total: 1s	remaining: 2.09s
12:	learn: 0.2001517	test: 0.2257756	best: 0.2257756 (12)	total: 1.09s	remaining: 2.01s
13:	learn: 0.1932860	test: 0.2208899	best: 0.2208899 (13)	total: 1.17s	remaining: 1.93s
14:	learn: 0.1886409	test: 0.2196815	best: 0.2196815 (14)	total: 1.27s	remaining: 1.86s
15:	learn: 0.1851608	test: 0.2173051	best: 0.2173051 (15)	total: 1.36s	remaining: 1.78s
16:	learn: 0.1817297	test: 0.2155324	best: 0.2155324 (16)	total: 1.45s	remaining: 1.71s
17:	learn: 0.1784238	test: 0.2137384	best: 0.2137384 (17)	total: 1.54s	remaining: 1.63s
18:	learn: 0.1751883	test: 0.2119168	best: 0.2119168 (18)	total: 1.65s	remaining: 1.56s
19:	learn: 0.1726259	test: 0.2104840	best: 0.2104840 (19)	total: 1.74s	remaining: 1.47s
20:	learn: 0.1681197	test: 0.2097635	best: 0.2097635 (20)	total: 1.82s	remaining: 1.39s
21:	learn: 0.1659280	test: 0.2087549	best: 0.2087549 (21)	total: 1.92s	remaining: 1.31s
22:	learn: 0.1635182	test: 0.2082181	best: 0.2082181 (22)	total: 2.01s	remaining: 1.22s
23:	learn: 0.1623971	test: 0.2079401	best: 0.2079401 (23)	total: 2.1s	remaining: 1.14s
24:	learn: 0.1589933	test: 0.2064739	best: 0.2064739 (24)	total: 2.19s	remaining: 1.05s
25:	learn: 0.1568628	test: 0.2067796	best: 0.2064739 (24)	total: 2.27s	remaining: 962ms
26:	learn: 0.1542814	test: 0.2063708	best: 0.2063708 (26)	total: 2.35s	remaining: 872ms
27:	learn: 0.1528129	test: 0.2064739	best: 0.2063708 (26)	total: 2.44s	remaining: 783ms
28:	learn: 0.1511428	test: 0.2061920	best: 0.2061920 (28)	total: 2.52s	remaining: 696ms
29:	learn: 0.1501552	test: 0.2058355	best: 0.2058355 (29)	total: 2.62s	remaining: 610ms
30:	learn: 0.1493821	test: 0.2053288	best: 0.2053288 (30)	total: 2.71s	remaining: 524ms
31:	learn: 0.1484321	test: 0.2049398	best: 0.2049398 (31)	total: 2.8s	remaining: 437ms
32:	learn: 0.1476440	test: 0.2043469	best: 0.2043469 (32)	total: 2.88s	remaining: 349ms
33:	learn: 0.1461820	test: 0.2040624	best: 0.2040624 (33)	total: 2.97s	remaining: 262ms
34:	learn: 0.1455454	test: 0.2041404	best: 0.2040624 (33)	total: 3.06s	remaining: 175ms
35:	learn: 0.1444435	test: 0.2042981	best: 0.2040624 (33)	total: 3.15s	remaining: 87.4ms
36:	learn: 0.1439901	test: 0.2041775	best: 0.2040624 (33)	total: 3.25s	remaining: 0us

bestTest = 0.204062404
bestIteration = 33

Shrink model to first 34 iterations.
Trial 35, Fold 3: Log loss = 0.20393327208220924, Average precision = 0.9717563195723444, ROC-AUC = 0.968178307778987, Elapsed Time = 3.354944799997611 seconds
Trial 35, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 35, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
0:	learn: 0.5845384	test: 0.5870582	best: 0.5870582 (0)	total: 60.2ms	remaining: 2.17s
1:	learn: 0.5113327	test: 0.5151457	best: 0.5151457 (1)	total: 145ms	remaining: 2.53s
2:	learn: 0.4539507	test: 0.4592735	best: 0.4592735 (2)	total: 228ms	remaining: 2.59s
3:	learn: 0.3869288	test: 0.3958203	best: 0.3958203 (3)	total: 306ms	remaining: 2.53s
4:	learn: 0.3431263	test: 0.3530457	best: 0.3530457 (4)	total: 394ms	remaining: 2.52s
5:	learn: 0.3128123	test: 0.3256252	best: 0.3256252 (5)	total: 479ms	remaining: 2.47s
6:	learn: 0.2879313	test: 0.3018420	best: 0.3018420 (6)	total: 565ms	remaining: 2.42s
7:	learn: 0.2685870	test: 0.2847382	best: 0.2847382 (7)	total: 644ms	remaining: 2.33s
8:	learn: 0.2511673	test: 0.2681880	best: 0.2681880 (8)	total: 742ms	remaining: 2.31s
9:	learn: 0.2428679	test: 0.2618678	best: 0.2618678 (9)	total: 844ms	remaining: 2.28s
10:	learn: 0.2329783	test: 0.2536927	best: 0.2536927 (10)	total: 930ms	remaining: 2.2s
11:	learn: 0.2218944	test: 0.2479151	best: 0.2479151 (11)	total: 1.02s	remaining: 2.12s
12:	learn: 0.2143622	test: 0.2450016	best: 0.2450016 (12)	total: 1.11s	remaining: 2.04s
13:	learn: 0.2069162	test: 0.2383306	best: 0.2383306 (13)	total: 1.2s	remaining: 1.97s
14:	learn: 0.2003610	test: 0.2326682	best: 0.2326682 (14)	total: 1.28s	remaining: 1.88s
15:	learn: 0.1932551	test: 0.2278519	best: 0.2278519 (15)	total: 1.37s	remaining: 1.8s
16:	learn: 0.1882226	test: 0.2245923	best: 0.2245923 (16)	total: 1.46s	remaining: 1.72s
17:	learn: 0.1830154	test: 0.2204322	best: 0.2204322 (17)	total: 1.56s	remaining: 1.65s
18:	learn: 0.1789478	test: 0.2199789	best: 0.2199789 (18)	total: 1.65s	remaining: 1.56s
19:	learn: 0.1754247	test: 0.2186780	best: 0.2186780 (19)	total: 1.73s	remaining: 1.47s
20:	learn: 0.1724616	test: 0.2180645	best: 0.2180645 (20)	total: 1.82s	remaining: 1.39s
21:	learn: 0.1691453	test: 0.2146615	best: 0.2146615 (21)	total: 1.9s	remaining: 1.3s
22:	learn: 0.1650188	test: 0.2124953	best: 0.2124953 (22)	total: 2.01s	remaining: 1.22s
23:	learn: 0.1622201	test: 0.2106509	best: 0.2106509 (23)	total: 2.1s	remaining: 1.14s
24:	learn: 0.1597533	test: 0.2107331	best: 0.2106509 (23)	total: 2.19s	remaining: 1.05s
25:	learn: 0.1568024	test: 0.2092613	best: 0.2092613 (25)	total: 2.29s	remaining: 970ms
26:	learn: 0.1542140	test: 0.2079132	best: 0.2079132 (26)	total: 2.39s	remaining: 886ms
27:	learn: 0.1529832	test: 0.2070758	best: 0.2070758 (27)	total: 2.49s	remaining: 801ms
28:	learn: 0.1509399	test: 0.2057624	best: 0.2057624 (28)	total: 2.59s	remaining: 715ms
29:	learn: 0.1497892	test: 0.2051603	best: 0.2051603 (29)	total: 2.68s	remaining: 626ms
30:	learn: 0.1486153	test: 0.2051576	best: 0.2051576 (30)	total: 2.77s	remaining: 537ms
31:	learn: 0.1475538	test: 0.2044946	best: 0.2044946 (31)	total: 2.87s	remaining: 449ms
32:	learn: 0.1457184	test: 0.2038074	best: 0.2038074 (32)	total: 2.96s	remaining: 358ms
33:	learn: 0.1450307	test: 0.2033394	best: 0.2033394 (33)	total: 3.04s	remaining: 268ms
34:	learn: 0.1435520	test: 0.2035739	best: 0.2033394 (33)	total: 3.13s	remaining: 179ms
35:	learn: 0.1426752	test: 0.2038821	best: 0.2033394 (33)	total: 3.22s	remaining: 89.4ms
36:	learn: 0.1419089	test: 0.2036470	best: 0.2033394 (33)	total: 3.31s	remaining: 0us

bestTest = 0.2033393902
bestIteration = 33

Shrink model to first 34 iterations.
Trial 35, Fold 4: Log loss = 0.20312251838546025, Average precision = 0.9728389615374551, ROC-AUC = 0.968123868895613, Elapsed Time = 3.417873400001554 seconds
Trial 35, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 35, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
0:	learn: 0.5674097	test: 0.5734460	best: 0.5734460 (0)	total: 64.7ms	remaining: 2.33s
1:	learn: 0.4792091	test: 0.4970853	best: 0.4970853 (1)	total: 140ms	remaining: 2.44s
2:	learn: 0.4150288	test: 0.4350405	best: 0.4350405 (2)	total: 223ms	remaining: 2.52s
3:	learn: 0.3630619	test: 0.3849709	best: 0.3849709 (3)	total: 309ms	remaining: 2.55s
4:	learn: 0.3148678	test: 0.3384573	best: 0.3384573 (4)	total: 394ms	remaining: 2.52s
5:	learn: 0.2937025	test: 0.3185700	best: 0.3185700 (5)	total: 481ms	remaining: 2.49s
6:	learn: 0.2655852	test: 0.2913259	best: 0.2913259 (6)	total: 569ms	remaining: 2.44s
7:	learn: 0.2483214	test: 0.2749089	best: 0.2749089 (7)	total: 656ms	remaining: 2.38s
8:	learn: 0.2314359	test: 0.2601018	best: 0.2601018 (8)	total: 738ms	remaining: 2.3s
9:	learn: 0.2193651	test: 0.2508719	best: 0.2508719 (9)	total: 826ms	remaining: 2.23s
10:	learn: 0.2135414	test: 0.2450242	best: 0.2450242 (10)	total: 863ms	remaining: 2.04s
11:	learn: 0.2060724	test: 0.2392544	best: 0.2392544 (11)	total: 954ms	remaining: 1.99s
12:	learn: 0.1985881	test: 0.2333684	best: 0.2333684 (12)	total: 1.05s	remaining: 1.94s
13:	learn: 0.1925849	test: 0.2280599	best: 0.2280599 (13)	total: 1.14s	remaining: 1.87s
14:	learn: 0.1881146	test: 0.2242419	best: 0.2242419 (14)	total: 1.22s	remaining: 1.78s
15:	learn: 0.1837271	test: 0.2215898	best: 0.2215898 (15)	total: 1.31s	remaining: 1.71s
16:	learn: 0.1806371	test: 0.2196230	best: 0.2196230 (16)	total: 1.41s	remaining: 1.66s
17:	learn: 0.1780884	test: 0.2185178	best: 0.2185178 (17)	total: 1.51s	remaining: 1.59s
18:	learn: 0.1742611	test: 0.2178843	best: 0.2178843 (18)	total: 1.59s	remaining: 1.51s
19:	learn: 0.1719116	test: 0.2169411	best: 0.2169411 (19)	total: 1.68s	remaining: 1.42s
20:	learn: 0.1691682	test: 0.2163296	best: 0.2163296 (20)	total: 1.76s	remaining: 1.34s
21:	learn: 0.1668951	test: 0.2158573	best: 0.2158573 (21)	total: 1.85s	remaining: 1.26s
22:	learn: 0.1643840	test: 0.2144327	best: 0.2144327 (22)	total: 1.94s	remaining: 1.18s
23:	learn: 0.1625394	test: 0.2139659	best: 0.2139659 (23)	total: 2.03s	remaining: 1.1s
24:	learn: 0.1604631	test: 0.2135979	best: 0.2135979 (24)	total: 2.13s	remaining: 1.02s
25:	learn: 0.1570404	test: 0.2141806	best: 0.2135979 (24)	total: 2.23s	remaining: 943ms
26:	learn: 0.1555632	test: 0.2140493	best: 0.2135979 (24)	total: 2.32s	remaining: 858ms
27:	learn: 0.1541083	test: 0.2140787	best: 0.2135979 (24)	total: 2.41s	remaining: 776ms
28:	learn: 0.1532032	test: 0.2139242	best: 0.2135979 (24)	total: 2.51s	remaining: 693ms
29:	learn: 0.1503712	test: 0.2146581	best: 0.2135979 (24)	total: 2.6s	remaining: 608ms
30:	learn: 0.1488435	test: 0.2146673	best: 0.2135979 (24)	total: 2.7s	remaining: 522ms
31:	learn: 0.1478913	test: 0.2141495	best: 0.2135979 (24)	total: 2.79s	remaining: 435ms
32:	learn: 0.1469173	test: 0.2139325	best: 0.2135979 (24)	total: 2.87s	remaining: 348ms
33:	learn: 0.1449795	test: 0.2139701	best: 0.2135979 (24)	total: 2.96s	remaining: 262ms
34:	learn: 0.1442938	test: 0.2139744	best: 0.2135979 (24)	total: 3.05s	remaining: 174ms
35:	learn: 0.1432803	test: 0.2138707	best: 0.2135979 (24)	total: 3.13s	remaining: 87.1ms
36:	learn: 0.1422826	test: 0.2138658	best: 0.2135979 (24)	total: 3.22s	remaining: 0us

bestTest = 0.2135978597
bestIteration = 24

Shrink model to first 25 iterations.
Trial 35, Fold 5: Log loss = 0.2133493554362293, Average precision = 0.9701531810737392, ROC-AUC = 0.9649087564795719, Elapsed Time = 3.324585100002878 seconds
Optimization Progress:  36%|###6      | 36/100 [1:01:06<55:57, 52.46s/it]  
Trial 36, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 36, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
0:	learn: 0.6335414	test: 0.6332409	best: 0.6332409 (0)	total: 32.2ms	remaining: 967ms
1:	learn: 0.5829026	test: 0.5825127	best: 0.5825127 (1)	total: 63.4ms	remaining: 919ms
2:	learn: 0.5379638	test: 0.5374102	best: 0.5374102 (2)	total: 96.1ms	remaining: 897ms
3:	learn: 0.4991890	test: 0.4987643	best: 0.4987643 (3)	total: 128ms	remaining: 867ms
4:	learn: 0.4664064	test: 0.4658071	best: 0.4658071 (4)	total: 161ms	remaining: 835ms
5:	learn: 0.4379235	test: 0.4373468	best: 0.4373468 (5)	total: 192ms	remaining: 801ms
6:	learn: 0.4127562	test: 0.4121545	best: 0.4121545 (6)	total: 225ms	remaining: 771ms
7:	learn: 0.3913927	test: 0.3907187	best: 0.3907187 (7)	total: 256ms	remaining: 736ms
8:	learn: 0.3734834	test: 0.3729463	best: 0.3729463 (8)	total: 292ms	remaining: 715ms
9:	learn: 0.3591529	test: 0.3586467	best: 0.3586467 (9)	total: 324ms	remaining: 680ms
10:	learn: 0.3451439	test: 0.3445689	best: 0.3445689 (10)	total: 357ms	remaining: 649ms
11:	learn: 0.3351248	test: 0.3347936	best: 0.3347936 (11)	total: 389ms	remaining: 615ms
12:	learn: 0.3239702	test: 0.3238051	best: 0.3238051 (12)	total: 422ms	remaining: 584ms
13:	learn: 0.3147448	test: 0.3146693	best: 0.3146693 (13)	total: 455ms	remaining: 552ms
14:	learn: 0.3067931	test: 0.3067517	best: 0.3067517 (14)	total: 490ms	remaining: 522ms
15:	learn: 0.2992964	test: 0.2994222	best: 0.2994222 (15)	total: 522ms	remaining: 490ms
16:	learn: 0.2931834	test: 0.2933145	best: 0.2933145 (16)	total: 556ms	remaining: 458ms
17:	learn: 0.2878462	test: 0.2884212	best: 0.2884212 (17)	total: 589ms	remaining: 425ms
18:	learn: 0.2831531	test: 0.2841201	best: 0.2841201 (18)	total: 624ms	remaining: 394ms
19:	learn: 0.2773340	test: 0.2785762	best: 0.2785762 (19)	total: 660ms	remaining: 363ms
20:	learn: 0.2726288	test: 0.2738925	best: 0.2738925 (20)	total: 693ms	remaining: 330ms
21:	learn: 0.2680302	test: 0.2695071	best: 0.2695071 (21)	total: 729ms	remaining: 298ms
22:	learn: 0.2646858	test: 0.2664582	best: 0.2664582 (22)	total: 764ms	remaining: 266ms
23:	learn: 0.2615335	test: 0.2636077	best: 0.2636077 (23)	total: 800ms	remaining: 233ms
24:	learn: 0.2582565	test: 0.2603241	best: 0.2603241 (24)	total: 837ms	remaining: 201ms
25:	learn: 0.2552632	test: 0.2575453	best: 0.2575453 (25)	total: 872ms	remaining: 168ms
26:	learn: 0.2526711	test: 0.2552128	best: 0.2552128 (26)	total: 908ms	remaining: 135ms
27:	learn: 0.2504780	test: 0.2533200	best: 0.2533200 (27)	total: 945ms	remaining: 101ms
28:	learn: 0.2479797	test: 0.2509263	best: 0.2509263 (28)	total: 984ms	remaining: 67.9ms
29:	learn: 0.2460837	test: 0.2491788	best: 0.2491788 (29)	total: 1.02s	remaining: 34ms
30:	learn: 0.2439032	test: 0.2472092	best: 0.2472092 (30)	total: 1.05s	remaining: 0us

bestTest = 0.2472092154
bestIteration = 30

Trial 36, Fold 1: Log loss = 0.24720921535051218, Average precision = 0.9651780902002346, ROC-AUC = 0.9604550228703346, Elapsed Time = 1.1556350999999268 seconds
Trial 36, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 36, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
0:	learn: 0.6321961	test: 0.6324039	best: 0.6324039 (0)	total: 32.7ms	remaining: 981ms
1:	learn: 0.5799195	test: 0.5802820	best: 0.5802820 (1)	total: 63.9ms	remaining: 927ms
2:	learn: 0.5347752	test: 0.5352168	best: 0.5352168 (2)	total: 98.2ms	remaining: 917ms
3:	learn: 0.4965712	test: 0.4971461	best: 0.4971461 (3)	total: 131ms	remaining: 883ms
4:	learn: 0.4637906	test: 0.4643334	best: 0.4643334 (4)	total: 162ms	remaining: 842ms
5:	learn: 0.4360090	test: 0.4366451	best: 0.4366451 (5)	total: 194ms	remaining: 809ms
6:	learn: 0.4117258	test: 0.4127307	best: 0.4127307 (6)	total: 228ms	remaining: 782ms
7:	learn: 0.3902043	test: 0.3911515	best: 0.3911515 (7)	total: 260ms	remaining: 746ms
8:	learn: 0.3717555	test: 0.3728308	best: 0.3728308 (8)	total: 294ms	remaining: 719ms
9:	learn: 0.3567564	test: 0.3579291	best: 0.3579291 (9)	total: 327ms	remaining: 686ms
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20:	learn: 0.2722680	test: 0.2737569	best: 0.2737569 (20)	total: 715ms	remaining: 340ms
21:	learn: 0.2685199	test: 0.2699160	best: 0.2699160 (21)	total: 751ms	remaining: 307ms
22:	learn: 0.2639899	test: 0.2652863	best: 0.2652863 (22)	total: 786ms	remaining: 273ms
23:	learn: 0.2608326	test: 0.2619790	best: 0.2619790 (23)	total: 823ms	remaining: 240ms
24:	learn: 0.2579129	test: 0.2590658	best: 0.2590658 (24)	total: 857ms	remaining: 206ms
25:	learn: 0.2551680	test: 0.2561448	best: 0.2561448 (25)	total: 893ms	remaining: 172ms
26:	learn: 0.2528416	test: 0.2537325	best: 0.2537325 (26)	total: 928ms	remaining: 137ms
27:	learn: 0.2501835	test: 0.2511464	best: 0.2511464 (27)	total: 964ms	remaining: 103ms
28:	learn: 0.2486045	test: 0.2494531	best: 0.2494531 (28)	total: 999ms	remaining: 68.9ms
29:	learn: 0.2462393	test: 0.2472098	best: 0.2472098 (29)	total: 1.03s	remaining: 34.5ms
30:	learn: 0.2445880	test: 0.2454690	best: 0.2454690 (30)	total: 1.07s	remaining: 0us

bestTest = 0.2454689512
bestIteration = 30

Trial 36, Fold 2: Log loss = 0.24546895115736625, Average precision = 0.9656584465185779, ROC-AUC = 0.9631113273348446, Elapsed Time = 1.1699856000013824 seconds
Trial 36, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 36, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
0:	learn: 0.6322704	test: 0.6316602	best: 0.6316602 (0)	total: 31.3ms	remaining: 940ms
1:	learn: 0.5800813	test: 0.5789426	best: 0.5789426 (1)	total: 63.1ms	remaining: 915ms
2:	learn: 0.5358965	test: 0.5345030	best: 0.5345030 (2)	total: 96.4ms	remaining: 899ms
3:	learn: 0.4974627	test: 0.4956794	best: 0.4956794 (3)	total: 129ms	remaining: 868ms
4:	learn: 0.4650624	test: 0.4631068	best: 0.4631068 (4)	total: 163ms	remaining: 845ms
5:	learn: 0.4372320	test: 0.4352625	best: 0.4352625 (5)	total: 196ms	remaining: 817ms
6:	learn: 0.4134574	test: 0.4112980	best: 0.4112980 (6)	total: 230ms	remaining: 789ms
7:	learn: 0.3929282	test: 0.3905459	best: 0.3905459 (7)	total: 265ms	remaining: 762ms
8:	learn: 0.3743200	test: 0.3716842	best: 0.3716842 (8)	total: 298ms	remaining: 728ms
9:	learn: 0.3583727	test: 0.3557163	best: 0.3557163 (9)	total: 333ms	remaining: 698ms
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20:	learn: 0.2725347	test: 0.2693839	best: 0.2693839 (20)	total: 736ms	remaining: 350ms
21:	learn: 0.2689243	test: 0.2660096	best: 0.2660096 (21)	total: 772ms	remaining: 316ms
22:	learn: 0.2660823	test: 0.2631206	best: 0.2631206 (22)	total: 808ms	remaining: 281ms
23:	learn: 0.2631551	test: 0.2601957	best: 0.2601957 (23)	total: 844ms	remaining: 246ms
24:	learn: 0.2595808	test: 0.2566617	best: 0.2566617 (24)	total: 881ms	remaining: 211ms
25:	learn: 0.2575431	test: 0.2546736	best: 0.2546736 (25)	total: 915ms	remaining: 176ms
26:	learn: 0.2547905	test: 0.2518268	best: 0.2518268 (26)	total: 954ms	remaining: 141ms
27:	learn: 0.2518628	test: 0.2489227	best: 0.2489227 (27)	total: 991ms	remaining: 106ms
28:	learn: 0.2497764	test: 0.2468394	best: 0.2468394 (28)	total: 1.02s	remaining: 70.7ms
29:	learn: 0.2476580	test: 0.2447387	best: 0.2447387 (29)	total: 1.07s	remaining: 35.5ms
30:	learn: 0.2456036	test: 0.2429106	best: 0.2429106 (30)	total: 1.1s	remaining: 0us

bestTest = 0.2429106425
bestIteration = 30

Trial 36, Fold 3: Log loss = 0.2429106424978244, Average precision = 0.9658629804103465, ROC-AUC = 0.9632374363147808, Elapsed Time = 1.2047749000012118 seconds
Trial 36, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 36, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
0:	learn: 0.6326380	test: 0.6324727	best: 0.6324727 (0)	total: 31.7ms	remaining: 952ms
1:	learn: 0.5799718	test: 0.5798212	best: 0.5798212 (1)	total: 63.2ms	remaining: 916ms
2:	learn: 0.5357771	test: 0.5353916	best: 0.5353916 (2)	total: 96.1ms	remaining: 897ms
3:	learn: 0.4976020	test: 0.4971809	best: 0.4971809 (3)	total: 128ms	remaining: 867ms
4:	learn: 0.4645415	test: 0.4640172	best: 0.4640172 (4)	total: 161ms	remaining: 838ms
5:	learn: 0.4366091	test: 0.4360426	best: 0.4360426 (5)	total: 193ms	remaining: 803ms
6:	learn: 0.4123956	test: 0.4117736	best: 0.4117736 (6)	total: 226ms	remaining: 776ms
7:	learn: 0.3912863	test: 0.3906664	best: 0.3906664 (7)	total: 262ms	remaining: 754ms
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18:	learn: 0.2814922	test: 0.2815347	best: 0.2815347 (18)	total: 650ms	remaining: 411ms
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20:	learn: 0.2731711	test: 0.2736776	best: 0.2736776 (20)	total: 723ms	remaining: 344ms
21:	learn: 0.2685351	test: 0.2691846	best: 0.2691846 (21)	total: 761ms	remaining: 311ms
22:	learn: 0.2652421	test: 0.2660100	best: 0.2660100 (22)	total: 796ms	remaining: 277ms
23:	learn: 0.2617761	test: 0.2625281	best: 0.2625281 (23)	total: 833ms	remaining: 243ms
24:	learn: 0.2582636	test: 0.2589763	best: 0.2589763 (24)	total: 870ms	remaining: 209ms
25:	learn: 0.2554489	test: 0.2561395	best: 0.2561395 (25)	total: 906ms	remaining: 174ms
26:	learn: 0.2531507	test: 0.2536980	best: 0.2536980 (26)	total: 942ms	remaining: 139ms
27:	learn: 0.2507715	test: 0.2512219	best: 0.2512219 (27)	total: 978ms	remaining: 105ms
28:	learn: 0.2487733	test: 0.2492508	best: 0.2492508 (28)	total: 1.01s	remaining: 69.8ms
29:	learn: 0.2467194	test: 0.2472228	best: 0.2472228 (29)	total: 1.05s	remaining: 35.1ms
30:	learn: 0.2448059	test: 0.2452990	best: 0.2452990 (30)	total: 1.09s	remaining: 0us

bestTest = 0.2452989512
bestIteration = 30

Trial 36, Fold 4: Log loss = 0.24529895123633147, Average precision = 0.966793696239375, ROC-AUC = 0.9628482132425351, Elapsed Time = 1.1857075000007171 seconds
Trial 36, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 36, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
0:	learn: 0.6321553	test: 0.6334853	best: 0.6334853 (0)	total: 32.4ms	remaining: 973ms
1:	learn: 0.5795485	test: 0.5816992	best: 0.5816992 (1)	total: 64.3ms	remaining: 932ms
2:	learn: 0.5354520	test: 0.5381245	best: 0.5381245 (2)	total: 97.8ms	remaining: 912ms
3:	learn: 0.4965649	test: 0.4999415	best: 0.4999415 (3)	total: 131ms	remaining: 881ms
4:	learn: 0.4629835	test: 0.4668330	best: 0.4668330 (4)	total: 164ms	remaining: 855ms
5:	learn: 0.4341808	test: 0.4385440	best: 0.4385440 (5)	total: 199ms	remaining: 827ms
6:	learn: 0.4098538	test: 0.4146087	best: 0.4146087 (6)	total: 231ms	remaining: 794ms
7:	learn: 0.3885036	test: 0.3937268	best: 0.3937268 (7)	total: 267ms	remaining: 769ms
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20:	learn: 0.2697440	test: 0.2787300	best: 0.2787300 (20)	total: 742ms	remaining: 353ms
21:	learn: 0.2661791	test: 0.2754238	best: 0.2754238 (21)	total: 779ms	remaining: 319ms
22:	learn: 0.2627995	test: 0.2721724	best: 0.2721724 (22)	total: 817ms	remaining: 284ms
23:	learn: 0.2585694	test: 0.2684057	best: 0.2684057 (23)	total: 853ms	remaining: 249ms
24:	learn: 0.2550688	test: 0.2652280	best: 0.2652280 (24)	total: 890ms	remaining: 214ms
25:	learn: 0.2517389	test: 0.2623029	best: 0.2623029 (25)	total: 925ms	remaining: 178ms
26:	learn: 0.2491205	test: 0.2598389	best: 0.2598389 (26)	total: 963ms	remaining: 143ms
27:	learn: 0.2469576	test: 0.2578386	best: 0.2578386 (27)	total: 998ms	remaining: 107ms
28:	learn: 0.2448775	test: 0.2558177	best: 0.2558177 (28)	total: 1.03s	remaining: 71.2ms
29:	learn: 0.2430578	test: 0.2539699	best: 0.2539699 (29)	total: 1.07s	remaining: 35.7ms
30:	learn: 0.2414040	test: 0.2523786	best: 0.2523786 (30)	total: 1.1s	remaining: 0us

bestTest = 0.252378634
bestIteration = 30

Trial 36, Fold 5: Log loss = 0.2523786340410989, Average precision = 0.9619196767010048, ROC-AUC = 0.9600014863534178, Elapsed Time = 1.2047831999989285 seconds
Optimization Progress:  37%|###7      | 37/100 [1:01:19<42:49, 40.79s/it]
Trial 37, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 37, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
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83:	learn: 0.1994663	test: 0.2197970	best: 0.2197970 (83)	total: 40.6s	remaining: 3.87s
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85:	learn: 0.1973149	test: 0.2182621	best: 0.2182621 (85)	total: 41.6s	remaining: 2.9s
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87:	learn: 0.1955205	test: 0.2169809	best: 0.2169809 (87)	total: 42.6s	remaining: 1.94s
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89:	learn: 0.1935420	test: 0.2156504	best: 0.2156504 (89)	total: 43.6s	remaining: 970ms
90:	learn: 0.1924376	test: 0.2149023	best: 0.2149023 (90)	total: 44.1s	remaining: 485ms
91:	learn: 0.1915335	test: 0.2142968	best: 0.2142968 (91)	total: 44.6s	remaining: 0us

bestTest = 0.2142968138
bestIteration = 91

Trial 37, Fold 1: Log loss = 0.21422999987221414, Average precision = 0.974891629378225, ROC-AUC = 0.9709085819914637, Elapsed Time = 44.72040569999808 seconds
Trial 37, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 37, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
0:	learn: 0.6680672	test: 0.6685114	best: 0.6685114 (0)	total: 538ms	remaining: 48.9s
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83:	learn: 0.2008667	test: 0.2171045	best: 0.2171045 (83)	total: 43.1s	remaining: 4.11s
84:	learn: 0.1998757	test: 0.2163648	best: 0.2163648 (84)	total: 43.7s	remaining: 3.6s
85:	learn: 0.1988037	test: 0.2154911	best: 0.2154911 (85)	total: 44.2s	remaining: 3.08s
86:	learn: 0.1979393	test: 0.2147038	best: 0.2147038 (86)	total: 44.7s	remaining: 2.57s
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89:	learn: 0.1953470	test: 0.2128228	best: 0.2128228 (89)	total: 46.1s	remaining: 1.02s
90:	learn: 0.1943239	test: 0.2119949	best: 0.2119949 (90)	total: 46.6s	remaining: 512ms
91:	learn: 0.1933285	test: 0.2111141	best: 0.2111141 (91)	total: 47.2s	remaining: 0us

bestTest = 0.2111141188
bestIteration = 91

Trial 37, Fold 2: Log loss = 0.2110342609411055, Average precision = 0.9765589419677143, ROC-AUC = 0.9736675102831887, Elapsed Time = 47.38509990000239 seconds
Trial 37, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 37, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
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83:	learn: 0.1984674	test: 0.2123895	best: 0.2123895 (83)	total: 41.9s	remaining: 3.99s
84:	learn: 0.1974777	test: 0.2116732	best: 0.2116732 (84)	total: 42.4s	remaining: 3.5s
85:	learn: 0.1964929	test: 0.2109526	best: 0.2109526 (85)	total: 43s	remaining: 3s
86:	learn: 0.1954628	test: 0.2101066	best: 0.2101066 (86)	total: 43.5s	remaining: 2.5s
87:	learn: 0.1945753	test: 0.2093801	best: 0.2093801 (87)	total: 44.1s	remaining: 2s
88:	learn: 0.1936345	test: 0.2086833	best: 0.2086833 (88)	total: 44.6s	remaining: 1.5s
89:	learn: 0.1925239	test: 0.2079150	best: 0.2079150 (89)	total: 45.1s	remaining: 1s
90:	learn: 0.1915430	test: 0.2070748	best: 0.2070748 (90)	total: 45.6s	remaining: 501ms
91:	learn: 0.1907836	test: 0.2065610	best: 0.2065610 (91)	total: 46s	remaining: 0us

bestTest = 0.2065610184
bestIteration = 91

Trial 37, Fold 3: Log loss = 0.20671254099511285, Average precision = 0.9754589955346551, ROC-AUC = 0.9736953860118847, Elapsed Time = 46.160713100001885 seconds
Trial 37, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 37, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
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83:	learn: 0.2005231	test: 0.2172870	best: 0.2172870 (83)	total: 41.5s	remaining: 3.95s
84:	learn: 0.1994852	test: 0.2164320	best: 0.2164320 (84)	total: 42s	remaining: 3.46s
85:	learn: 0.1985511	test: 0.2156992	best: 0.2156992 (85)	total: 42.5s	remaining: 2.96s
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89:	learn: 0.1943655	test: 0.2124868	best: 0.2124868 (89)	total: 44.6s	remaining: 991ms
90:	learn: 0.1934021	test: 0.2118300	best: 0.2118300 (90)	total: 45.1s	remaining: 496ms
91:	learn: 0.1926000	test: 0.2112235	best: 0.2112235 (91)	total: 45.5s	remaining: 0us

bestTest = 0.2112234546
bestIteration = 91

Trial 37, Fold 4: Log loss = 0.21116277335507552, Average precision = 0.9762176575154632, ROC-AUC = 0.9725466509772503, Elapsed Time = 45.66182259999914 seconds
Trial 37, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 37, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
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87:	learn: 0.1934492	test: 0.2209722	best: 0.2209722 (87)	total: 43.3s	remaining: 1.97s
88:	learn: 0.1924005	test: 0.2201586	best: 0.2201586 (88)	total: 43.8s	remaining: 1.48s
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91:	learn: 0.1894761	test: 0.2181088	best: 0.2181088 (91)	total: 45.1s	remaining: 0us

bestTest = 0.2181087983
bestIteration = 91

Trial 37, Fold 5: Log loss = 0.21790760100087853, Average precision = 0.9735336139166868, ROC-AUC = 0.9705873697118331, Elapsed Time = 45.29885459999787 seconds
Optimization Progress:  38%|###8      | 38/100 [1:05:16<1:42:49, 99.52s/it]
Trial 38, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 38, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
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61:	learn: 0.3142465	test: 0.3152509	best: 0.3152509 (61)	total: 4.16s	remaining: 603ms
62:	learn: 0.3111851	test: 0.3122019	best: 0.3122019 (62)	total: 4.23s	remaining: 537ms
63:	learn: 0.3089943	test: 0.3100030	best: 0.3100030 (63)	total: 4.3s	remaining: 470ms
64:	learn: 0.3082791	test: 0.3092742	best: 0.3092742 (64)	total: 4.37s	remaining: 404ms
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68:	learn: 0.3025167	test: 0.3034098	best: 0.3034098 (68)	total: 4.66s	remaining: 135ms
69:	learn: 0.3013735	test: 0.3022988	best: 0.3022988 (69)	total: 4.74s	remaining: 67.7ms
70:	learn: 0.2993263	test: 0.3003050	best: 0.3003050 (70)	total: 4.81s	remaining: 0us

bestTest = 0.3003050242
bestIteration = 70

Trial 38, Fold 1: Log loss = 0.30030502422053895, Average precision = 0.9597965553968761, ROC-AUC = 0.9533512089970988, Elapsed Time = 4.9189482999972824 seconds
Trial 38, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 38, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
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69:	learn: 0.3094738	test: 0.3099221	best: 0.3099221 (69)	total: 4.81s	remaining: 68.7ms
70:	learn: 0.3073763	test: 0.3078334	best: 0.3078334 (70)	total: 4.88s	remaining: 0us

bestTest = 0.3078334479
bestIteration = 70

Trial 38, Fold 2: Log loss = 0.3078334478933136, Average precision = 0.9590835624220182, ROC-AUC = 0.954372324117488, Elapsed Time = 5.006788000002416 seconds
Trial 38, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 38, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
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63:	learn: 0.3095343	test: 0.3023607	best: 0.3023607 (63)	total: 4.21s	remaining: 461ms
64:	learn: 0.3071762	test: 0.3000030	best: 0.3000030 (64)	total: 4.28s	remaining: 395ms
65:	learn: 0.3063504	test: 0.2991539	best: 0.2991539 (65)	total: 4.35s	remaining: 330ms
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68:	learn: 0.3013577	test: 0.2942633	best: 0.2942633 (68)	total: 4.55s	remaining: 132ms
69:	learn: 0.2997564	test: 0.2926013	best: 0.2926013 (69)	total: 4.61s	remaining: 65.9ms
70:	learn: 0.2979945	test: 0.2909257	best: 0.2909257 (70)	total: 4.68s	remaining: 0us

bestTest = 0.2909257083
bestIteration = 70

Trial 38, Fold 3: Log loss = 0.2909257082784127, Average precision = 0.9626860783820593, ROC-AUC = 0.958333433656615, Elapsed Time = 4.805641099999775 seconds
Trial 38, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 38, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
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70:	learn: 0.3084075	test: 0.3085451	best: 0.3085451 (70)	total: 4.57s	remaining: 0us

bestTest = 0.3085450536
bestIteration = 70

Trial 38, Fold 4: Log loss = 0.3085450535590476, Average precision = 0.959983382412791, ROC-AUC = 0.9535379872243324, Elapsed Time = 4.69804110000041 seconds
Trial 38, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 38, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
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69:	learn: 0.2978323	test: 0.3055506	best: 0.3055506 (69)	total: 4.71s	remaining: 67.3ms
70:	learn: 0.2973933	test: 0.3051493	best: 0.3051493 (70)	total: 4.78s	remaining: 0us

bestTest = 0.3051492928
bestIteration = 70

Trial 38, Fold 5: Log loss = 0.3051492927836401, Average precision = 0.9558258237773902, ROC-AUC = 0.9505639596455048, Elapsed Time = 4.897137100000691 seconds
Optimization Progress:  39%|###9      | 39/100 [1:05:48<1:20:41, 79.37s/it]
Trial 39, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 39, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
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77:	learn: 0.1434456	test: 0.1981671	best: 0.1981671 (77)	total: 18.9s	remaining: 0us

bestTest = 0.1981670521
bestIteration = 77

Trial 39, Fold 1: Log loss = 0.1981670521330898, Average precision = 0.9743004332187335, ROC-AUC = 0.970903578358976, Elapsed Time = 19.041579799999454 seconds
Trial 39, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 39, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
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1:	learn: 0.5311920	test: 0.5327690	best: 0.5327690 (1)	total: 441ms	remaining: 16.8s
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63:	learn: 0.1521930	test: 0.1891741	best: 0.1891741 (63)	total: 15.2s	remaining: 3.33s
64:	learn: 0.1514512	test: 0.1886860	best: 0.1886860 (64)	total: 15.5s	remaining: 3.1s
65:	learn: 0.1509732	test: 0.1883263	best: 0.1883263 (65)	total: 15.7s	remaining: 2.86s
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67:	learn: 0.1490176	test: 0.1878952	best: 0.1878952 (67)	total: 16.2s	remaining: 2.38s
68:	learn: 0.1484631	test: 0.1876491	best: 0.1876491 (68)	total: 16.4s	remaining: 2.14s
69:	learn: 0.1468150	test: 0.1873294	best: 0.1873294 (69)	total: 16.7s	remaining: 1.91s
70:	learn: 0.1460209	test: 0.1872629	best: 0.1872629 (70)	total: 16.9s	remaining: 1.67s
71:	learn: 0.1452779	test: 0.1868019	best: 0.1868019 (71)	total: 17.1s	remaining: 1.43s
72:	learn: 0.1444197	test: 0.1866277	best: 0.1866277 (72)	total: 17.4s	remaining: 1.19s
73:	learn: 0.1436808	test: 0.1863659	best: 0.1863659 (73)	total: 17.6s	remaining: 952ms
74:	learn: 0.1426099	test: 0.1864520	best: 0.1863659 (73)	total: 17.9s	remaining: 714ms
75:	learn: 0.1422624	test: 0.1862834	best: 0.1862834 (75)	total: 18.1s	remaining: 476ms
76:	learn: 0.1411518	test: 0.1860547	best: 0.1860547 (76)	total: 18.3s	remaining: 238ms
77:	learn: 0.1403017	test: 0.1861427	best: 0.1860547 (76)	total: 18.6s	remaining: 0us

bestTest = 0.1860546548
bestIteration = 76

Shrink model to first 77 iterations.
Trial 39, Fold 2: Log loss = 0.18605465475775707, Average precision = 0.9768771960742806, ROC-AUC = 0.9746746603353444, Elapsed Time = 18.732987100000173 seconds
Trial 39, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 39, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
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2:	learn: 0.4732816	test: 0.4748956	best: 0.4748956 (2)	total: 692ms	remaining: 17.3s
3:	learn: 0.4265924	test: 0.4284848	best: 0.4284848 (3)	total: 933ms	remaining: 17.3s
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63:	learn: 0.1483239	test: 0.1928116	best: 0.1926194 (58)	total: 16.8s	remaining: 3.68s
64:	learn: 0.1474556	test: 0.1923216	best: 0.1923216 (64)	total: 17.1s	remaining: 3.41s
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68:	learn: 0.1446627	test: 0.1914947	best: 0.1914947 (68)	total: 18s	remaining: 2.35s
69:	learn: 0.1442667	test: 0.1913570	best: 0.1913570 (69)	total: 18.2s	remaining: 2.08s
70:	learn: 0.1438342	test: 0.1913281	best: 0.1913281 (70)	total: 18.5s	remaining: 1.82s
71:	learn: 0.1430224	test: 0.1911377	best: 0.1911377 (71)	total: 18.7s	remaining: 1.56s
72:	learn: 0.1426185	test: 0.1910425	best: 0.1910425 (72)	total: 18.9s	remaining: 1.3s
73:	learn: 0.1422473	test: 0.1910114	best: 0.1910114 (73)	total: 19.2s	remaining: 1.04s
74:	learn: 0.1417248	test: 0.1908013	best: 0.1908013 (74)	total: 19.4s	remaining: 777ms
75:	learn: 0.1409100	test: 0.1909874	best: 0.1908013 (74)	total: 19.7s	remaining: 517ms
76:	learn: 0.1404153	test: 0.1908472	best: 0.1908013 (74)	total: 19.9s	remaining: 258ms
77:	learn: 0.1400520	test: 0.1908106	best: 0.1908013 (74)	total: 20.1s	remaining: 0us

bestTest = 0.1908013315
bestIteration = 74

Shrink model to first 75 iterations.
Trial 39, Fold 3: Log loss = 0.19080133152470824, Average precision = 0.9761563406455036, ROC-AUC = 0.9728910441005094, Elapsed Time = 20.284400800002913 seconds
Trial 39, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 39, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
0:	learn: 0.6021292	test: 0.6035799	best: 0.6035799 (0)	total: 220ms	remaining: 16.9s
1:	learn: 0.5295952	test: 0.5319746	best: 0.5319746 (1)	total: 441ms	remaining: 16.8s
2:	learn: 0.4715173	test: 0.4746950	best: 0.4746950 (2)	total: 666ms	remaining: 16.6s
3:	learn: 0.4242696	test: 0.4276632	best: 0.4276632 (3)	total: 877ms	remaining: 16.2s
4:	learn: 0.3859034	test: 0.3906661	best: 0.3906661 (4)	total: 1.1s	remaining: 16.1s
5:	learn: 0.3546202	test: 0.3601044	best: 0.3601044 (5)	total: 1.32s	remaining: 15.8s
6:	learn: 0.3302096	test: 0.3368171	best: 0.3368171 (6)	total: 1.55s	remaining: 15.7s
7:	learn: 0.3088577	test: 0.3162282	best: 0.3162282 (7)	total: 1.79s	remaining: 15.6s
8:	learn: 0.2911990	test: 0.2998578	best: 0.2998578 (8)	total: 2.04s	remaining: 15.6s
9:	learn: 0.2771878	test: 0.2862894	best: 0.2862894 (9)	total: 2.28s	remaining: 15.5s
10:	learn: 0.2643907	test: 0.2744334	best: 0.2744334 (10)	total: 2.54s	remaining: 15.5s
11:	learn: 0.2538555	test: 0.2644441	best: 0.2644441 (11)	total: 2.79s	remaining: 15.3s
12:	learn: 0.2453925	test: 0.2565802	best: 0.2565802 (12)	total: 3.02s	remaining: 15.1s
13:	learn: 0.2377614	test: 0.2492690	best: 0.2492690 (13)	total: 3.26s	remaining: 14.9s
14:	learn: 0.2310925	test: 0.2432068	best: 0.2432068 (14)	total: 3.51s	remaining: 14.7s
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77:	learn: 0.1393212	test: 0.1942272	best: 0.1942272 (77)	total: 18.8s	remaining: 0us

bestTest = 0.1942271736
bestIteration = 77

Trial 39, Fold 4: Log loss = 0.1942271736352091, Average precision = 0.9756894766774887, ROC-AUC = 0.9715121151860675, Elapsed Time = 18.995119199997134 seconds
Trial 39, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 39, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
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77:	learn: 0.1417955	test: 0.2028005	best: 0.2027370 (73)	total: 18.7s	remaining: 0us

bestTest = 0.2027369957
bestIteration = 73

Shrink model to first 74 iterations.
Trial 39, Fold 5: Log loss = 0.20273699574091272, Average precision = 0.9736236803525101, ROC-AUC = 0.9700538431525556, Elapsed Time = 18.862699500001327 seconds
Optimization Progress:  40%|####      | 40/100 [1:07:32<1:26:43, 86.72s/it]
Trial 40, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 40, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
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94:	learn: 0.2239324	test: 0.2290014	best: 0.2290014 (94)	total: 6.19s	remaining: 0us

bestTest = 0.2290013564
bestIteration = 94

Trial 40, Fold 1: Log loss = 0.22900135640558722, Average precision = 0.9681151514558485, ROC-AUC = 0.9633356215616831, Elapsed Time = 6.310286399999313 seconds
Trial 40, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 40, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
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92:	learn: 0.2260842	test: 0.2269171	best: 0.2269171 (92)	total: 6.32s	remaining: 136ms
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94:	learn: 0.2255642	test: 0.2263710	best: 0.2263710 (94)	total: 6.45s	remaining: 0us

bestTest = 0.2263709889
bestIteration = 94

Trial 40, Fold 2: Log loss = 0.2263709889023547, Average precision = 0.9694279016777742, ROC-AUC = 0.9650430766438609, Elapsed Time = 6.576813600000605 seconds
Trial 40, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 40, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
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bestTest = 0.221250859
bestIteration = 94

Trial 40, Fold 3: Log loss = 0.22125085901596958, Average precision = 0.970532873190614, ROC-AUC = 0.9667763149974478, Elapsed Time = 6.420953900000313 seconds
Trial 40, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 40, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
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94:	learn: 0.2258166	test: 0.2280020	best: 0.2280020 (94)	total: 6.23s	remaining: 0us

bestTest = 0.2280019958
bestIteration = 94

Trial 40, Fold 4: Log loss = 0.22800199576045335, Average precision = 0.9684660577259117, ROC-AUC = 0.9632701047471215, Elapsed Time = 6.358377000000473 seconds
Trial 40, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 40, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
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94:	learn: 0.2241443	test: 0.2361444	best: 0.2361444 (94)	total: 6.22s	remaining: 0us

bestTest = 0.2361444488
bestIteration = 94

Trial 40, Fold 5: Log loss = 0.23614444883432828, Average precision = 0.96622342609293, ROC-AUC = 0.9613915983873066, Elapsed Time = 6.3494046999985585 seconds
Optimization Progress:  41%|####1     | 41/100 [1:08:12<1:11:28, 72.69s/it]
Trial 41, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 41, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
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64:	learn: 0.3319155	test: 0.3316169	best: 0.3316169 (64)	total: 1.55s	remaining: 0us

bestTest = 0.3316168701
bestIteration = 64

Trial 41, Fold 1: Log loss = 0.33168902711184856, Average precision = 0.9583766064905149, ROC-AUC = 0.9541974950172781, Elapsed Time = 1.648871399997006 seconds
Trial 41, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 41, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
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64:	learn: 0.3293335	test: 0.3317839	best: 0.3317839 (64)	total: 1.51s	remaining: 0us

bestTest = 0.3317839282
bestIteration = 64

Trial 41, Fold 2: Log loss = 0.3318118362532073, Average precision = 0.9594057473870764, ROC-AUC = 0.9566941411090517, Elapsed Time = 1.6018395999999484 seconds
Trial 41, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 41, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
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64:	learn: 0.3353945	test: 0.3314601	best: 0.3314601 (64)	total: 1.53s	remaining: 0us

bestTest = 0.3314601398
bestIteration = 64

Trial 41, Fold 3: Log loss = 0.3316725370755788, Average precision = 0.9615542151513036, ROC-AUC = 0.9589315359815469, Elapsed Time = 1.6288577000013902 seconds
Trial 41, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 41, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
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64:	learn: 0.3327107	test: 0.3329170	best: 0.3329170 (64)	total: 1.54s	remaining: 0us

bestTest = 0.3329169814
bestIteration = 64

Trial 41, Fold 4: Log loss = 0.33297531728255003, Average precision = 0.96014019612667, ROC-AUC = 0.9559887133963294, Elapsed Time = 1.6406114000019443 seconds
Trial 41, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 41, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
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64:	learn: 0.3312880	test: 0.3384091	best: 0.3384091 (64)	total: 1.54s	remaining: 0us

bestTest = 0.3384090616
bestIteration = 64

Trial 41, Fold 5: Log loss = 0.33841465723965264, Average precision = 0.9561260913492311, ROC-AUC = 0.9514392731731787, Elapsed Time = 1.6334887000011804 seconds
Optimization Progress:  42%|####2     | 42/100 [1:08:28<53:51, 55.72s/it]  
Trial 42, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 42, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
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97:	learn: 0.2493141	test: 0.2608320	best: 0.2608320 (97)	total: 26.3s	remaining: 0us

bestTest = 0.2608320017
bestIteration = 97

Trial 42, Fold 1: Log loss = 0.2608320016904406, Average precision = 0.972556999025177, ROC-AUC = 0.968024921375757, Elapsed Time = 26.434395100000984 seconds
Trial 42, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 42, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
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97:	learn: 0.2512421	test: 0.2582550	best: 0.2582550 (97)	total: 25.4s	remaining: 0us

bestTest = 0.2582549624
bestIteration = 97

Trial 42, Fold 2: Log loss = 0.25825496239464546, Average precision = 0.9744488879065488, ROC-AUC = 0.9708409042832634, Elapsed Time = 25.533264300000155 seconds
Trial 42, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 42, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
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97:	learn: 0.2517860	test: 0.2554913	best: 0.2554913 (97)	total: 25s	remaining: 0us

bestTest = 0.2554913437
bestIteration = 97

Trial 42, Fold 3: Log loss = 0.2554913437227735, Average precision = 0.9712237322056307, ROC-AUC = 0.9705600326492088, Elapsed Time = 25.159806300001947 seconds
Trial 42, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 42, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
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97:	learn: 0.2511560	test: 0.2587722	best: 0.2587722 (97)	total: 24.7s	remaining: 0us

bestTest = 0.2587722405
bestIteration = 97

Trial 42, Fold 4: Log loss = 0.25877224052726905, Average precision = 0.9740514368891039, ROC-AUC = 0.9700750612197211, Elapsed Time = 24.857192000003124 seconds
Trial 42, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 42, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
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93:	learn: 0.2544450	test: 0.2684606	best: 0.2684606 (93)	total: 23.4s	remaining: 994ms
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95:	learn: 0.2519166	test: 0.2661495	best: 0.2661495 (95)	total: 23.8s	remaining: 497ms
96:	learn: 0.2506515	test: 0.2650242	best: 0.2650242 (96)	total: 24.1s	remaining: 248ms
97:	learn: 0.2494991	test: 0.2640062	best: 0.2640062 (97)	total: 24.3s	remaining: 0us

bestTest = 0.2640062469
bestIteration = 97

Trial 42, Fold 5: Log loss = 0.2640062469134561, Average precision = 0.9716072915204965, ROC-AUC = 0.9683154041952325, Elapsed Time = 24.468977199998335 seconds
Optimization Progress:  43%|####3     | 43/100 [1:10:42<1:15:10, 79.14s/it]
Trial 43, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 43, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
0:	learn: 0.5460660	test: 0.5450925	best: 0.5450925 (0)	total: 39.8ms	remaining: 358ms
1:	learn: 0.4601231	test: 0.4600283	best: 0.4600283 (1)	total: 76.4ms	remaining: 306ms
2:	learn: 0.3965213	test: 0.3962833	best: 0.3962833 (2)	total: 113ms	remaining: 263ms
3:	learn: 0.3598688	test: 0.3600733	best: 0.3600733 (3)	total: 148ms	remaining: 222ms
4:	learn: 0.3338137	test: 0.3344572	best: 0.3344572 (4)	total: 183ms	remaining: 183ms
5:	learn: 0.3068850	test: 0.3084256	best: 0.3084256 (5)	total: 220ms	remaining: 147ms
6:	learn: 0.2888460	test: 0.2912281	best: 0.2912281 (6)	total: 258ms	remaining: 111ms
7:	learn: 0.2724850	test: 0.2757664	best: 0.2757664 (7)	total: 295ms	remaining: 73.7ms
8:	learn: 0.2610050	test: 0.2656276	best: 0.2656276 (8)	total: 332ms	remaining: 36.9ms
9:	learn: 0.2543256	test: 0.2595717	best: 0.2595717 (9)	total: 372ms	remaining: 0us

bestTest = 0.2595717389
bestIteration = 9

Trial 43, Fold 1: Log loss = 0.25955953350980043, Average precision = 0.9663326217392855, ROC-AUC = 0.9626414609053496, Elapsed Time = 0.46879339999941294 seconds
Trial 43, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 43, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
0:	learn: 0.5551144	test: 0.5563962	best: 0.5563962 (0)	total: 38.9ms	remaining: 350ms
1:	learn: 0.4653665	test: 0.4668387	best: 0.4668387 (1)	total: 77.5ms	remaining: 310ms
2:	learn: 0.4090073	test: 0.4110196	best: 0.4110196 (2)	total: 119ms	remaining: 278ms
3:	learn: 0.3650324	test: 0.3677520	best: 0.3677520 (3)	total: 159ms	remaining: 238ms
4:	learn: 0.3333589	test: 0.3379949	best: 0.3379949 (4)	total: 198ms	remaining: 198ms
5:	learn: 0.3107982	test: 0.3150375	best: 0.3150375 (5)	total: 238ms	remaining: 159ms
6:	learn: 0.2888373	test: 0.2927803	best: 0.2927803 (6)	total: 279ms	remaining: 119ms
7:	learn: 0.2760024	test: 0.2797005	best: 0.2797005 (7)	total: 318ms	remaining: 79.5ms
8:	learn: 0.2653578	test: 0.2690237	best: 0.2690237 (8)	total: 358ms	remaining: 39.8ms
9:	learn: 0.2570825	test: 0.2604959	best: 0.2604959 (9)	total: 399ms	remaining: 0us

bestTest = 0.2604959017
bestIteration = 9

Trial 43, Fold 2: Log loss = 0.2604778162631055, Average precision = 0.9681149562319693, ROC-AUC = 0.9656772306393346, Elapsed Time = 0.4984293000015896 seconds
Trial 43, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 43, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
0:	learn: 0.5542480	test: 0.5534174	best: 0.5534174 (0)	total: 39.6ms	remaining: 356ms
1:	learn: 0.4637517	test: 0.4623683	best: 0.4623683 (1)	total: 80ms	remaining: 320ms
2:	learn: 0.4036071	test: 0.4021017	best: 0.4021017 (2)	total: 119ms	remaining: 277ms
3:	learn: 0.3647707	test: 0.3626357	best: 0.3626357 (3)	total: 155ms	remaining: 233ms
4:	learn: 0.3328382	test: 0.3303443	best: 0.3303443 (4)	total: 195ms	remaining: 195ms
5:	learn: 0.3082857	test: 0.3057334	best: 0.3057334 (5)	total: 236ms	remaining: 157ms
6:	learn: 0.2915234	test: 0.2892387	best: 0.2892387 (6)	total: 276ms	remaining: 118ms
7:	learn: 0.2738339	test: 0.2713185	best: 0.2713185 (7)	total: 317ms	remaining: 79.2ms
8:	learn: 0.2635612	test: 0.2614159	best: 0.2614159 (8)	total: 358ms	remaining: 39.7ms
9:	learn: 0.2550455	test: 0.2531532	best: 0.2531532 (9)	total: 400ms	remaining: 0us

bestTest = 0.2531531862
bestIteration = 9

Trial 43, Fold 3: Log loss = 0.25326300340831376, Average precision = 0.9703199120398129, ROC-AUC = 0.9682479070556963, Elapsed Time = 0.4978806999970402 seconds
Trial 43, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 43, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
0:	learn: 0.5592422	test: 0.5595585	best: 0.5595585 (0)	total: 41.6ms	remaining: 375ms
1:	learn: 0.4602357	test: 0.4600690	best: 0.4600690 (1)	total: 78.9ms	remaining: 316ms
2:	learn: 0.4048205	test: 0.4051879	best: 0.4051879 (2)	total: 118ms	remaining: 274ms
3:	learn: 0.3588265	test: 0.3595980	best: 0.3595980 (3)	total: 156ms	remaining: 234ms
4:	learn: 0.3289645	test: 0.3300340	best: 0.3300340 (4)	total: 195ms	remaining: 195ms
5:	learn: 0.3078039	test: 0.3094592	best: 0.3094592 (5)	total: 235ms	remaining: 156ms
6:	learn: 0.2894590	test: 0.2909770	best: 0.2909770 (6)	total: 274ms	remaining: 117ms
7:	learn: 0.2726987	test: 0.2744118	best: 0.2744118 (7)	total: 312ms	remaining: 78.1ms
8:	learn: 0.2626660	test: 0.2647081	best: 0.2647081 (8)	total: 359ms	remaining: 39.9ms
9:	learn: 0.2523929	test: 0.2547683	best: 0.2547683 (9)	total: 403ms	remaining: 0us

bestTest = 0.2547683347
bestIteration = 9

Trial 43, Fold 4: Log loss = 0.25477216931620916, Average precision = 0.9663377721735341, ROC-AUC = 0.9653137674628212, Elapsed Time = 0.5030527000017173 seconds
Trial 43, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 43, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
0:	learn: 0.5583379	test: 0.5600104	best: 0.5600104 (0)	total: 39.2ms	remaining: 353ms
1:	learn: 0.4678735	test: 0.4712954	best: 0.4712954 (1)	total: 78.6ms	remaining: 314ms
2:	learn: 0.3996488	test: 0.4041260	best: 0.4041260 (2)	total: 118ms	remaining: 274ms
3:	learn: 0.3642038	test: 0.3718420	best: 0.3718420 (3)	total: 157ms	remaining: 236ms
4:	learn: 0.3284597	test: 0.3368922	best: 0.3368922 (4)	total: 197ms	remaining: 197ms
5:	learn: 0.3080546	test: 0.3167858	best: 0.3167858 (5)	total: 236ms	remaining: 157ms
6:	learn: 0.2883535	test: 0.2975305	best: 0.2975305 (6)	total: 275ms	remaining: 118ms
7:	learn: 0.2744868	test: 0.2840836	best: 0.2840836 (7)	total: 314ms	remaining: 78.6ms
8:	learn: 0.2620734	test: 0.2720387	best: 0.2720387 (8)	total: 354ms	remaining: 39.3ms
9:	learn: 0.2496768	test: 0.2599068	best: 0.2599068 (9)	total: 394ms	remaining: 0us

bestTest = 0.2599068468
bestIteration = 9

Trial 43, Fold 5: Log loss = 0.25983842623579323, Average precision = 0.9682498962168871, ROC-AUC = 0.9643176337253591, Elapsed Time = 0.49794289999772445 seconds
Optimization Progress:  44%|####4     | 44/100 [1:10:52<54:36, 58.51s/it]  
Trial 44, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 44, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
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2:	learn: 0.4373466	test: 0.4424333	best: 0.4424333 (2)	total: 414ms	remaining: 8.82s
3:	learn: 0.3890854	test: 0.3951325	best: 0.3951325 (3)	total: 546ms	remaining: 8.6s
4:	learn: 0.3502443	test: 0.3581723	best: 0.3581723 (4)	total: 683ms	remaining: 8.46s
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54:	learn: 0.1428064	test: 0.1980129	best: 0.1980129 (54)	total: 7.99s	remaining: 1.74s
55:	learn: 0.1422153	test: 0.1980013	best: 0.1980013 (55)	total: 8.12s	remaining: 1.59s
56:	learn: 0.1416317	test: 0.1981321	best: 0.1980013 (55)	total: 8.26s	remaining: 1.45s
57:	learn: 0.1411519	test: 0.1983211	best: 0.1980013 (55)	total: 8.4s	remaining: 1.3s
58:	learn: 0.1403767	test: 0.1984673	best: 0.1980013 (55)	total: 8.55s	remaining: 1.16s
59:	learn: 0.1398065	test: 0.1983405	best: 0.1980013 (55)	total: 8.69s	remaining: 1.01s
60:	learn: 0.1393567	test: 0.1983556	best: 0.1980013 (55)	total: 8.81s	remaining: 866ms
61:	learn: 0.1388946	test: 0.1983471	best: 0.1980013 (55)	total: 8.93s	remaining: 720ms
62:	learn: 0.1384481	test: 0.1983710	best: 0.1980013 (55)	total: 9.05s	remaining: 575ms
63:	learn: 0.1374697	test: 0.1983262	best: 0.1980013 (55)	total: 9.2s	remaining: 431ms
64:	learn: 0.1366222	test: 0.1983973	best: 0.1980013 (55)	total: 9.36s	remaining: 288ms
65:	learn: 0.1361691	test: 0.1984395	best: 0.1980013 (55)	total: 9.48s	remaining: 144ms
66:	learn: 0.1358118	test: 0.1986493	best: 0.1980013 (55)	total: 9.6s	remaining: 0us

bestTest = 0.1980012852
bestIteration = 55

Shrink model to first 56 iterations.
Trial 44, Fold 1: Log loss = 0.19800128523381824, Average precision = 0.9753835438388123, ROC-AUC = 0.9718022158175726, Elapsed Time = 9.725621600002341 seconds
Trial 44, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 44, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
0:	learn: 0.5840422	test: 0.5867268	best: 0.5867268 (0)	total: 180ms	remaining: 11.9s
1:	learn: 0.5026327	test: 0.5063222	best: 0.5063222 (1)	total: 308ms	remaining: 10s
2:	learn: 0.4385963	test: 0.4438692	best: 0.4438692 (2)	total: 450ms	remaining: 9.6s
3:	learn: 0.3890092	test: 0.3949324	best: 0.3949324 (3)	total: 590ms	remaining: 9.29s
4:	learn: 0.3504626	test: 0.3571604	best: 0.3571604 (4)	total: 720ms	remaining: 8.93s
5:	learn: 0.3216285	test: 0.3297508	best: 0.3297508 (5)	total: 865ms	remaining: 8.8s
6:	learn: 0.2969872	test: 0.3066918	best: 0.3066918 (6)	total: 1.01s	remaining: 8.63s
7:	learn: 0.2795092	test: 0.2905130	best: 0.2905130 (7)	total: 1.15s	remaining: 8.48s
8:	learn: 0.2643047	test: 0.2764965	best: 0.2764965 (8)	total: 1.29s	remaining: 8.34s
9:	learn: 0.2504172	test: 0.2634512	best: 0.2634512 (9)	total: 1.46s	remaining: 8.31s
10:	learn: 0.2406903	test: 0.2541693	best: 0.2541693 (10)	total: 1.6s	remaining: 8.17s
11:	learn: 0.2312000	test: 0.2459003	best: 0.2459003 (11)	total: 1.76s	remaining: 8.08s
12:	learn: 0.2229058	test: 0.2383250	best: 0.2383250 (12)	total: 1.92s	remaining: 7.97s
13:	learn: 0.2159388	test: 0.2322990	best: 0.2322990 (13)	total: 2.09s	remaining: 7.93s
14:	learn: 0.2097941	test: 0.2266122	best: 0.2266122 (14)	total: 2.25s	remaining: 7.81s
15:	learn: 0.2049958	test: 0.2227921	best: 0.2227921 (15)	total: 2.42s	remaining: 7.71s
16:	learn: 0.2000924	test: 0.2184801	best: 0.2184801 (16)	total: 2.58s	remaining: 7.57s
17:	learn: 0.1962041	test: 0.2152319	best: 0.2152319 (17)	total: 2.72s	remaining: 7.41s
18:	learn: 0.1926165	test: 0.2128951	best: 0.2128951 (18)	total: 2.88s	remaining: 7.29s
19:	learn: 0.1896070	test: 0.2108230	best: 0.2108230 (19)	total: 3.04s	remaining: 7.14s
20:	learn: 0.1863827	test: 0.2084711	best: 0.2084711 (20)	total: 3.19s	remaining: 6.98s
21:	learn: 0.1833610	test: 0.2062647	best: 0.2062647 (21)	total: 3.34s	remaining: 6.84s
22:	learn: 0.1814086	test: 0.2047060	best: 0.2047060 (22)	total: 3.48s	remaining: 6.65s
23:	learn: 0.1793903	test: 0.2032256	best: 0.2032256 (23)	total: 3.61s	remaining: 6.47s
24:	learn: 0.1774459	test: 0.2016870	best: 0.2016870 (24)	total: 3.75s	remaining: 6.3s
25:	learn: 0.1754611	test: 0.2004702	best: 0.2004702 (25)	total: 3.9s	remaining: 6.14s
26:	learn: 0.1733517	test: 0.1995009	best: 0.1995009 (26)	total: 4.05s	remaining: 6.01s
27:	learn: 0.1720681	test: 0.1986453	best: 0.1986453 (27)	total: 4.2s	remaining: 5.84s
28:	learn: 0.1708955	test: 0.1977208	best: 0.1977208 (28)	total: 4.32s	remaining: 5.67s
29:	learn: 0.1688273	test: 0.1967883	best: 0.1967883 (29)	total: 4.48s	remaining: 5.53s
30:	learn: 0.1675519	test: 0.1960634	best: 0.1960634 (30)	total: 4.62s	remaining: 5.37s
31:	learn: 0.1665305	test: 0.1955022	best: 0.1955022 (31)	total: 4.75s	remaining: 5.2s
32:	learn: 0.1651652	test: 0.1947926	best: 0.1947926 (32)	total: 4.9s	remaining: 5.04s
33:	learn: 0.1636461	test: 0.1937048	best: 0.1937048 (33)	total: 5.04s	remaining: 4.89s
34:	learn: 0.1625192	test: 0.1930605	best: 0.1930605 (34)	total: 5.18s	remaining: 4.74s
35:	learn: 0.1618159	test: 0.1925783	best: 0.1925783 (35)	total: 5.31s	remaining: 4.57s
36:	learn: 0.1610686	test: 0.1922286	best: 0.1922286 (36)	total: 5.44s	remaining: 4.41s
37:	learn: 0.1602290	test: 0.1917865	best: 0.1917865 (37)	total: 5.57s	remaining: 4.25s
38:	learn: 0.1589877	test: 0.1912052	best: 0.1912052 (38)	total: 5.71s	remaining: 4.1s
39:	learn: 0.1578647	test: 0.1906836	best: 0.1906836 (39)	total: 5.86s	remaining: 3.95s
40:	learn: 0.1570975	test: 0.1903364	best: 0.1903364 (40)	total: 5.98s	remaining: 3.79s
41:	learn: 0.1563180	test: 0.1902962	best: 0.1902962 (41)	total: 6.13s	remaining: 3.65s
42:	learn: 0.1554624	test: 0.1899332	best: 0.1899332 (42)	total: 6.26s	remaining: 3.49s
43:	learn: 0.1544092	test: 0.1894974	best: 0.1894974 (43)	total: 6.4s	remaining: 3.35s
44:	learn: 0.1536525	test: 0.1889361	best: 0.1889361 (44)	total: 6.52s	remaining: 3.19s
45:	learn: 0.1530849	test: 0.1886167	best: 0.1886167 (45)	total: 6.65s	remaining: 3.03s
46:	learn: 0.1524084	test: 0.1882077	best: 0.1882077 (46)	total: 6.78s	remaining: 2.88s
47:	learn: 0.1513718	test: 0.1877792	best: 0.1877792 (47)	total: 6.91s	remaining: 2.73s
48:	learn: 0.1505488	test: 0.1875359	best: 0.1875359 (48)	total: 7.05s	remaining: 2.59s
49:	learn: 0.1498300	test: 0.1873960	best: 0.1873960 (49)	total: 7.18s	remaining: 2.44s
50:	learn: 0.1490776	test: 0.1873318	best: 0.1873318 (50)	total: 7.3s	remaining: 2.29s
51:	learn: 0.1482584	test: 0.1870077	best: 0.1870077 (51)	total: 7.44s	remaining: 2.15s
52:	learn: 0.1478540	test: 0.1869509	best: 0.1869509 (52)	total: 7.56s	remaining: 2s
53:	learn: 0.1476433	test: 0.1868411	best: 0.1868411 (53)	total: 7.66s	remaining: 1.84s
54:	learn: 0.1471294	test: 0.1866626	best: 0.1866626 (54)	total: 7.78s	remaining: 1.7s
55:	learn: 0.1464932	test: 0.1866297	best: 0.1866297 (55)	total: 7.91s	remaining: 1.55s
56:	learn: 0.1455631	test: 0.1862142	best: 0.1862142 (56)	total: 8.04s	remaining: 1.41s
57:	learn: 0.1451555	test: 0.1861595	best: 0.1861595 (57)	total: 8.17s	remaining: 1.27s
58:	learn: 0.1442274	test: 0.1859130	best: 0.1859130 (58)	total: 8.32s	remaining: 1.13s
59:	learn: 0.1432739	test: 0.1857194	best: 0.1857194 (59)	total: 8.47s	remaining: 988ms
60:	learn: 0.1427232	test: 0.1855276	best: 0.1855276 (60)	total: 8.6s	remaining: 846ms
61:	learn: 0.1421327	test: 0.1853745	best: 0.1853745 (61)	total: 8.73s	remaining: 704ms
62:	learn: 0.1415632	test: 0.1853328	best: 0.1853328 (62)	total: 8.85s	remaining: 562ms
63:	learn: 0.1405773	test: 0.1850808	best: 0.1850808 (63)	total: 9s	remaining: 422ms
64:	learn: 0.1399082	test: 0.1851675	best: 0.1850808 (63)	total: 9.14s	remaining: 281ms
65:	learn: 0.1396658	test: 0.1851496	best: 0.1850808 (63)	total: 9.24s	remaining: 140ms
66:	learn: 0.1390733	test: 0.1852378	best: 0.1850808 (63)	total: 9.37s	remaining: 0us

bestTest = 0.1850807937
bestIteration = 63

Shrink model to first 64 iterations.
Trial 44, Fold 2: Log loss = 0.18508079373799857, Average precision = 0.9772625433742628, ROC-AUC = 0.9745802732127828, Elapsed Time = 9.507290300000022 seconds
Trial 44, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 44, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
0:	learn: 0.5861191	test: 0.5869367	best: 0.5869367 (0)	total: 146ms	remaining: 9.67s
1:	learn: 0.5026530	test: 0.5047823	best: 0.5047823 (1)	total: 282ms	remaining: 9.16s
2:	learn: 0.4395876	test: 0.4424883	best: 0.4424883 (2)	total: 419ms	remaining: 8.93s
3:	learn: 0.3896579	test: 0.3934397	best: 0.3934397 (3)	total: 562ms	remaining: 8.85s
4:	learn: 0.3502485	test: 0.3548202	best: 0.3548202 (4)	total: 714ms	remaining: 8.86s
5:	learn: 0.3203280	test: 0.3257695	best: 0.3257695 (5)	total: 846ms	remaining: 8.6s
6:	learn: 0.2962221	test: 0.3024731	best: 0.3024731 (6)	total: 986ms	remaining: 8.45s
7:	learn: 0.2768734	test: 0.2836014	best: 0.2836014 (7)	total: 1.13s	remaining: 8.36s
8:	learn: 0.2627954	test: 0.2706321	best: 0.2706321 (8)	total: 1.27s	remaining: 8.21s
9:	learn: 0.2500447	test: 0.2588895	best: 0.2588895 (9)	total: 1.43s	remaining: 8.13s
10:	learn: 0.2401223	test: 0.2499675	best: 0.2499675 (10)	total: 1.57s	remaining: 8.01s
11:	learn: 0.2310610	test: 0.2415767	best: 0.2415767 (11)	total: 1.71s	remaining: 7.84s
12:	learn: 0.2243359	test: 0.2356182	best: 0.2356182 (12)	total: 1.84s	remaining: 7.65s
13:	learn: 0.2169950	test: 0.2291426	best: 0.2291426 (13)	total: 1.98s	remaining: 7.51s
14:	learn: 0.2106958	test: 0.2236639	best: 0.2236639 (14)	total: 2.13s	remaining: 7.39s
15:	learn: 0.2053074	test: 0.2193945	best: 0.2193945 (15)	total: 2.28s	remaining: 7.27s
16:	learn: 0.2006909	test: 0.2158890	best: 0.2158890 (16)	total: 2.43s	remaining: 7.15s
17:	learn: 0.1967982	test: 0.2127853	best: 0.2127853 (17)	total: 2.6s	remaining: 7.07s
18:	learn: 0.1932941	test: 0.2104059	best: 0.2104059 (18)	total: 2.77s	remaining: 7s
19:	learn: 0.1897495	test: 0.2080779	best: 0.2080779 (19)	total: 2.92s	remaining: 6.87s
20:	learn: 0.1870515	test: 0.2061734	best: 0.2061734 (20)	total: 3.07s	remaining: 6.72s
21:	learn: 0.1845969	test: 0.2044727	best: 0.2044727 (21)	total: 3.2s	remaining: 6.55s
22:	learn: 0.1823061	test: 0.2030811	best: 0.2030811 (22)	total: 3.34s	remaining: 6.39s
23:	learn: 0.1800785	test: 0.2020337	best: 0.2020337 (23)	total: 3.48s	remaining: 6.24s
24:	learn: 0.1774869	test: 0.2001999	best: 0.2001999 (24)	total: 3.64s	remaining: 6.11s
25:	learn: 0.1757766	test: 0.1990960	best: 0.1990960 (25)	total: 3.8s	remaining: 6s
26:	learn: 0.1742629	test: 0.1981845	best: 0.1981845 (26)	total: 3.93s	remaining: 5.82s
27:	learn: 0.1722696	test: 0.1972699	best: 0.1972699 (27)	total: 4.1s	remaining: 5.72s
28:	learn: 0.1703899	test: 0.1963275	best: 0.1963275 (28)	total: 4.25s	remaining: 5.57s
29:	learn: 0.1690729	test: 0.1958143	best: 0.1958143 (29)	total: 4.39s	remaining: 5.41s
30:	learn: 0.1680088	test: 0.1951287	best: 0.1951287 (30)	total: 4.51s	remaining: 5.24s
31:	learn: 0.1667636	test: 0.1944575	best: 0.1944575 (31)	total: 4.64s	remaining: 5.07s
32:	learn: 0.1655416	test: 0.1937138	best: 0.1937138 (32)	total: 4.77s	remaining: 4.92s
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40:	learn: 0.1573682	test: 0.1908477	best: 0.1908477 (40)	total: 5.84s	remaining: 3.7s
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48:	learn: 0.1506773	test: 0.1890883	best: 0.1890883 (48)	total: 6.89s	remaining: 2.53s
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55:	learn: 0.1467446	test: 0.1884101	best: 0.1884101 (55)	total: 7.72s	remaining: 1.51s
56:	learn: 0.1463774	test: 0.1882745	best: 0.1882745 (56)	total: 7.82s	remaining: 1.37s
57:	learn: 0.1454593	test: 0.1880341	best: 0.1880341 (57)	total: 7.96s	remaining: 1.23s
58:	learn: 0.1450016	test: 0.1879162	best: 0.1879162 (58)	total: 8.08s	remaining: 1.09s
59:	learn: 0.1446762	test: 0.1880056	best: 0.1879162 (58)	total: 8.19s	remaining: 955ms
60:	learn: 0.1439473	test: 0.1879362	best: 0.1879162 (58)	total: 8.32s	remaining: 819ms
61:	learn: 0.1434600	test: 0.1879495	best: 0.1879162 (58)	total: 8.44s	remaining: 681ms
62:	learn: 0.1427748	test: 0.1877577	best: 0.1877577 (62)	total: 8.58s	remaining: 545ms
63:	learn: 0.1424116	test: 0.1877641	best: 0.1877577 (62)	total: 8.69s	remaining: 408ms
64:	learn: 0.1419919	test: 0.1877964	best: 0.1877577 (62)	total: 8.81s	remaining: 271ms
65:	learn: 0.1412427	test: 0.1877956	best: 0.1877577 (62)	total: 8.95s	remaining: 136ms
66:	learn: 0.1404571	test: 0.1876819	best: 0.1876819 (66)	total: 9.1s	remaining: 0us

bestTest = 0.187681891
bestIteration = 66

Trial 44, Fold 3: Log loss = 0.18768189095663948, Average precision = 0.9764593874892866, ROC-AUC = 0.9739831884263852, Elapsed Time = 9.22843020000073 seconds
Trial 44, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 44, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
0:	learn: 0.5854960	test: 0.5868520	best: 0.5868520 (0)	total: 153ms	remaining: 10.1s
1:	learn: 0.5024079	test: 0.5051085	best: 0.5051085 (1)	total: 296ms	remaining: 9.61s
2:	learn: 0.4395882	test: 0.4425519	best: 0.4425519 (2)	total: 430ms	remaining: 9.18s
3:	learn: 0.3892579	test: 0.3931227	best: 0.3931227 (3)	total: 562ms	remaining: 8.85s
4:	learn: 0.3516569	test: 0.3566716	best: 0.3566716 (4)	total: 695ms	remaining: 8.62s
5:	learn: 0.3232938	test: 0.3293027	best: 0.3293027 (5)	total: 824ms	remaining: 8.38s
6:	learn: 0.2988016	test: 0.3060760	best: 0.3060760 (6)	total: 965ms	remaining: 8.27s
7:	learn: 0.2788393	test: 0.2873073	best: 0.2873073 (7)	total: 1.1s	remaining: 8.1s
8:	learn: 0.2641075	test: 0.2728782	best: 0.2728782 (8)	total: 1.23s	remaining: 7.92s
9:	learn: 0.2505797	test: 0.2602747	best: 0.2602747 (9)	total: 1.37s	remaining: 7.81s
10:	learn: 0.2407132	test: 0.2513592	best: 0.2513592 (10)	total: 1.5s	remaining: 7.62s
11:	learn: 0.2316243	test: 0.2433277	best: 0.2433277 (11)	total: 1.64s	remaining: 7.5s
12:	learn: 0.2238522	test: 0.2361248	best: 0.2361248 (12)	total: 1.77s	remaining: 7.36s
13:	learn: 0.2170028	test: 0.2304469	best: 0.2304469 (13)	total: 1.91s	remaining: 7.22s
14:	learn: 0.2118956	test: 0.2266294	best: 0.2266294 (14)	total: 2.03s	remaining: 7.04s
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16:	learn: 0.2021485	test: 0.2186928	best: 0.2186928 (16)	total: 2.31s	remaining: 6.79s
17:	learn: 0.1984789	test: 0.2158533	best: 0.2158533 (17)	total: 2.45s	remaining: 6.66s
18:	learn: 0.1954123	test: 0.2140464	best: 0.2140464 (18)	total: 2.58s	remaining: 6.51s
19:	learn: 0.1915664	test: 0.2111519	best: 0.2111519 (19)	total: 2.72s	remaining: 6.4s
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55:	learn: 0.1460729	test: 0.1918739	best: 0.1918739 (55)	total: 7.58s	remaining: 1.49s
56:	learn: 0.1455895	test: 0.1919571	best: 0.1918739 (55)	total: 7.7s	remaining: 1.35s
57:	learn: 0.1450468	test: 0.1920437	best: 0.1918739 (55)	total: 7.82s	remaining: 1.21s
58:	learn: 0.1444262	test: 0.1921577	best: 0.1918739 (55)	total: 7.94s	remaining: 1.08s
59:	learn: 0.1438357	test: 0.1922240	best: 0.1918739 (55)	total: 8.06s	remaining: 940ms
60:	learn: 0.1429416	test: 0.1923575	best: 0.1918739 (55)	total: 8.21s	remaining: 807ms
61:	learn: 0.1426589	test: 0.1923551	best: 0.1918739 (55)	total: 8.32s	remaining: 671ms
62:	learn: 0.1422864	test: 0.1922132	best: 0.1918739 (55)	total: 8.43s	remaining: 535ms
63:	learn: 0.1413716	test: 0.1923502	best: 0.1918739 (55)	total: 8.57s	remaining: 402ms
64:	learn: 0.1410270	test: 0.1922263	best: 0.1918739 (55)	total: 8.68s	remaining: 267ms
65:	learn: 0.1405457	test: 0.1923988	best: 0.1918739 (55)	total: 8.79s	remaining: 133ms
66:	learn: 0.1401072	test: 0.1924067	best: 0.1918739 (55)	total: 8.9s	remaining: 0us

bestTest = 0.1918738941
bestIteration = 55

Shrink model to first 56 iterations.
Trial 44, Fold 4: Log loss = 0.19187389408416525, Average precision = 0.9766082860624836, ROC-AUC = 0.9728536437357977, Elapsed Time = 9.03114099999948 seconds
Trial 44, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 44, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
0:	learn: 0.5838665	test: 0.5866961	best: 0.5866961 (0)	total: 152ms	remaining: 10s
1:	learn: 0.5002629	test: 0.5056570	best: 0.5056570 (1)	total: 285ms	remaining: 9.25s
2:	learn: 0.4370653	test: 0.4440596	best: 0.4440596 (2)	total: 418ms	remaining: 8.91s
3:	learn: 0.3880570	test: 0.3987871	best: 0.3987871 (3)	total: 549ms	remaining: 8.64s
4:	learn: 0.3485078	test: 0.3609780	best: 0.3609780 (4)	total: 695ms	remaining: 8.61s
5:	learn: 0.3191880	test: 0.3323440	best: 0.3323440 (5)	total: 815ms	remaining: 8.28s
6:	learn: 0.2964611	test: 0.3109227	best: 0.3109227 (6)	total: 949ms	remaining: 8.13s
7:	learn: 0.2765411	test: 0.2931271	best: 0.2931271 (7)	total: 1.1s	remaining: 8.11s
8:	learn: 0.2610990	test: 0.2789842	best: 0.2789842 (8)	total: 1.24s	remaining: 7.97s
9:	learn: 0.2487167	test: 0.2680170	best: 0.2680170 (9)	total: 1.37s	remaining: 7.82s
10:	learn: 0.2377041	test: 0.2585069	best: 0.2585069 (10)	total: 1.52s	remaining: 7.74s
11:	learn: 0.2297116	test: 0.2518608	best: 0.2518608 (11)	total: 1.66s	remaining: 7.63s
12:	learn: 0.2213496	test: 0.2449375	best: 0.2449375 (12)	total: 1.81s	remaining: 7.51s
13:	learn: 0.2146602	test: 0.2391865	best: 0.2391865 (13)	total: 1.94s	remaining: 7.33s
14:	learn: 0.2087316	test: 0.2344552	best: 0.2344552 (14)	total: 2.07s	remaining: 7.19s
15:	learn: 0.2032532	test: 0.2310314	best: 0.2310314 (15)	total: 2.23s	remaining: 7.1s
16:	learn: 0.1986062	test: 0.2278762	best: 0.2278762 (16)	total: 2.38s	remaining: 7.01s
17:	learn: 0.1946948	test: 0.2249528	best: 0.2249528 (17)	total: 2.51s	remaining: 6.84s
18:	learn: 0.1911759	test: 0.2222946	best: 0.2222946 (18)	total: 2.66s	remaining: 6.72s
19:	learn: 0.1882320	test: 0.2200526	best: 0.2200526 (19)	total: 2.79s	remaining: 6.56s
20:	learn: 0.1849461	test: 0.2174230	best: 0.2174230 (20)	total: 2.93s	remaining: 6.42s
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24:	learn: 0.1749412	test: 0.2118615	best: 0.2118615 (24)	total: 3.54s	remaining: 5.95s
25:	learn: 0.1731986	test: 0.2107829	best: 0.2107829 (25)	total: 3.69s	remaining: 5.82s
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27:	learn: 0.1693818	test: 0.2089074	best: 0.2089074 (27)	total: 3.97s	remaining: 5.53s
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57:	learn: 0.1431328	test: 0.1993341	best: 0.1993341 (57)	total: 7.8s	remaining: 1.21s
58:	learn: 0.1427390	test: 0.1992997	best: 0.1992997 (58)	total: 7.92s	remaining: 1.07s
59:	learn: 0.1422496	test: 0.1993652	best: 0.1992997 (58)	total: 8.04s	remaining: 938ms
60:	learn: 0.1417998	test: 0.1992251	best: 0.1992251 (60)	total: 8.16s	remaining: 803ms
61:	learn: 0.1412986	test: 0.1992721	best: 0.1992251 (60)	total: 8.28s	remaining: 668ms
62:	learn: 0.1407968	test: 0.1991017	best: 0.1991017 (62)	total: 8.41s	remaining: 534ms
63:	learn: 0.1403263	test: 0.1991060	best: 0.1991017 (62)	total: 8.53s	remaining: 400ms
64:	learn: 0.1398457	test: 0.1993109	best: 0.1991017 (62)	total: 8.65s	remaining: 266ms
65:	learn: 0.1394701	test: 0.1991725	best: 0.1991017 (62)	total: 8.76s	remaining: 133ms
66:	learn: 0.1388442	test: 0.1991123	best: 0.1991017 (62)	total: 8.89s	remaining: 0us

bestTest = 0.1991017119
bestIteration = 62

Shrink model to first 63 iterations.
Trial 44, Fold 5: Log loss = 0.19910171185297648, Average precision = 0.9742595684718001, ROC-AUC = 0.9715143340207718, Elapsed Time = 9.025208800001565 seconds
Optimization Progress:  45%|####5     | 45/100 [1:11:47<52:33, 57.34s/it]
Trial 45, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 45, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
0:	learn: 0.6197849	test: 0.6218831	best: 0.6218831 (0)	total: 217ms	remaining: 2.38s
1:	learn: 0.5579534	test: 0.5607020	best: 0.5607020 (1)	total: 405ms	remaining: 2.02s
2:	learn: 0.5075304	test: 0.5106510	best: 0.5106510 (2)	total: 612ms	remaining: 1.83s
3:	learn: 0.4645591	test: 0.4732353	best: 0.4732353 (3)	total: 876ms	remaining: 1.75s
4:	learn: 0.4320801	test: 0.4419082	best: 0.4419082 (4)	total: 1.11s	remaining: 1.56s
5:	learn: 0.4042108	test: 0.4144016	best: 0.4144016 (5)	total: 1.38s	remaining: 1.38s
6:	learn: 0.3821669	test: 0.3935248	best: 0.3935248 (6)	total: 1.6s	remaining: 1.14s
7:	learn: 0.3602152	test: 0.3720033	best: 0.3720033 (7)	total: 1.83s	remaining: 916ms
8:	learn: 0.3420685	test: 0.3547263	best: 0.3547263 (8)	total: 2.06s	remaining: 686ms
9:	learn: 0.3256564	test: 0.3396126	best: 0.3396126 (9)	total: 2.3s	remaining: 460ms
10:	learn: 0.3109182	test: 0.3260117	best: 0.3260117 (10)	total: 2.52s	remaining: 229ms
11:	learn: 0.2984007	test: 0.3144693	best: 0.3144693 (11)	total: 2.75s	remaining: 0us

bestTest = 0.3144693066
bestIteration = 11

Trial 45, Fold 1: Log loss = 0.31500063407156714, Average precision = 0.9716785851155554, ROC-AUC = 0.966898655979505, Elapsed Time = 2.856975099999545 seconds
Trial 45, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 45, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
0:	learn: 0.6133841	test: 0.6189109	best: 0.6189109 (0)	total: 221ms	remaining: 2.43s
1:	learn: 0.5474808	test: 0.5581981	best: 0.5581981 (1)	total: 448ms	remaining: 2.24s
2:	learn: 0.5008421	test: 0.5137028	best: 0.5137028 (2)	total: 635ms	remaining: 1.91s
3:	learn: 0.4612514	test: 0.4745482	best: 0.4745482 (3)	total: 893ms	remaining: 1.78s
4:	learn: 0.4311436	test: 0.4445738	best: 0.4445738 (4)	total: 1.12s	remaining: 1.57s
5:	learn: 0.4048848	test: 0.4191039	best: 0.4191039 (5)	total: 1.33s	remaining: 1.33s
6:	learn: 0.3774024	test: 0.3928628	best: 0.3928628 (6)	total: 1.56s	remaining: 1.12s
7:	learn: 0.3568998	test: 0.3726231	best: 0.3726231 (7)	total: 1.75s	remaining: 873ms
8:	learn: 0.3394508	test: 0.3560802	best: 0.3560802 (8)	total: 2.02s	remaining: 673ms
9:	learn: 0.3246235	test: 0.3415713	best: 0.3415713 (9)	total: 2.29s	remaining: 458ms
10:	learn: 0.3109292	test: 0.3276623	best: 0.3276623 (10)	total: 2.54s	remaining: 231ms
11:	learn: 0.2983538	test: 0.3157570	best: 0.3157570 (11)	total: 2.78s	remaining: 0us

bestTest = 0.3157569564
bestIteration = 11

Trial 45, Fold 2: Log loss = 0.31605413706255214, Average precision = 0.9714380352115993, ROC-AUC = 0.9691789625368102, Elapsed Time = 2.890101799999684 seconds
Trial 45, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 45, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
0:	learn: 0.6204854	test: 0.6216577	best: 0.6216577 (0)	total: 226ms	remaining: 2.49s
1:	learn: 0.5543362	test: 0.5566500	best: 0.5566500 (1)	total: 453ms	remaining: 2.26s
2:	learn: 0.5085264	test: 0.5113346	best: 0.5113346 (2)	total: 681ms	remaining: 2.04s
3:	learn: 0.4685437	test: 0.4721843	best: 0.4721843 (3)	total: 913ms	remaining: 1.83s
4:	learn: 0.4321419	test: 0.4368013	best: 0.4368013 (4)	total: 1.15s	remaining: 1.61s
5:	learn: 0.4067278	test: 0.4119069	best: 0.4119069 (5)	total: 1.34s	remaining: 1.34s
6:	learn: 0.3832610	test: 0.3890018	best: 0.3890018 (6)	total: 1.59s	remaining: 1.14s
7:	learn: 0.3594044	test: 0.3659051	best: 0.3659051 (7)	total: 1.83s	remaining: 916ms
8:	learn: 0.3408459	test: 0.3476536	best: 0.3476536 (8)	total: 2.02s	remaining: 675ms
9:	learn: 0.3252817	test: 0.3326134	best: 0.3326134 (9)	total: 2.27s	remaining: 454ms
10:	learn: 0.3129504	test: 0.3207333	best: 0.3207333 (10)	total: 2.49s	remaining: 227ms
11:	learn: 0.3018828	test: 0.3100266	best: 0.3100266 (11)	total: 2.72s	remaining: 0us

bestTest = 0.3100266025
bestIteration = 11

Trial 45, Fold 3: Log loss = 0.31064000572944606, Average precision = 0.9726096230830306, ROC-AUC = 0.9704358073455905, Elapsed Time = 2.8207452000024205 seconds
Trial 45, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 45, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
0:	learn: 0.6192516	test: 0.6195475	best: 0.6195475 (0)	total: 196ms	remaining: 2.15s
1:	learn: 0.5589961	test: 0.5595667	best: 0.5595667 (1)	total: 425ms	remaining: 2.12s
2:	learn: 0.5097852	test: 0.5134780	best: 0.5134780 (2)	total: 666ms	remaining: 2s
3:	learn: 0.4722750	test: 0.4764873	best: 0.4764873 (3)	total: 870ms	remaining: 1.74s
4:	learn: 0.4401100	test: 0.4448493	best: 0.4448493 (4)	total: 1.04s	remaining: 1.46s
5:	learn: 0.4118516	test: 0.4164421	best: 0.4164421 (5)	total: 1.2s	remaining: 1.2s
6:	learn: 0.3870203	test: 0.3926651	best: 0.3926651 (6)	total: 1.44s	remaining: 1.03s
7:	learn: 0.3672033	test: 0.3730975	best: 0.3730975 (7)	total: 1.63s	remaining: 816ms
8:	learn: 0.3494273	test: 0.3561897	best: 0.3561897 (8)	total: 1.89s	remaining: 629ms
9:	learn: 0.3332045	test: 0.3404028	best: 0.3404028 (9)	total: 2.07s	remaining: 415ms
10:	learn: 0.3200265	test: 0.3272365	best: 0.3272365 (10)	total: 2.27s	remaining: 206ms
11:	learn: 0.3062370	test: 0.3141036	best: 0.3141036 (11)	total: 2.48s	remaining: 0us

bestTest = 0.3141035657
bestIteration = 11

Trial 45, Fold 4: Log loss = 0.3144815270072962, Average precision = 0.9740361505912015, ROC-AUC = 0.9705727322474731, Elapsed Time = 2.58415949999835 seconds
Trial 45, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 45, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
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1:	learn: 0.5579559	test: 0.5660149	best: 0.5660149 (1)	total: 425ms	remaining: 2.13s
2:	learn: 0.5103925	test: 0.5188910	best: 0.5188910 (2)	total: 583ms	remaining: 1.75s
3:	learn: 0.4705227	test: 0.4793463	best: 0.4793463 (3)	total: 781ms	remaining: 1.56s
4:	learn: 0.4395748	test: 0.4487582	best: 0.4487582 (4)	total: 1.01s	remaining: 1.41s
5:	learn: 0.4076916	test: 0.4188010	best: 0.4188010 (5)	total: 1.23s	remaining: 1.23s
6:	learn: 0.3807648	test: 0.3930993	best: 0.3930993 (6)	total: 1.46s	remaining: 1.04s
7:	learn: 0.3610635	test: 0.3749145	best: 0.3749145 (7)	total: 1.7s	remaining: 849ms
8:	learn: 0.3423237	test: 0.3574398	best: 0.3574398 (8)	total: 1.96s	remaining: 654ms
9:	learn: 0.3269657	test: 0.3431572	best: 0.3431572 (9)	total: 2.22s	remaining: 444ms
10:	learn: 0.3115597	test: 0.3285384	best: 0.3285384 (10)	total: 2.46s	remaining: 224ms
11:	learn: 0.3000032	test: 0.3173250	best: 0.3173250 (11)	total: 2.69s	remaining: 0us

bestTest = 0.317325027
bestIteration = 11

Trial 45, Fold 5: Log loss = 0.31767776482020044, Average precision = 0.9733426175452514, ROC-AUC = 0.9698287349274474, Elapsed Time = 2.7899317999981577 seconds
Optimization Progress:  46%|####6     | 46/100 [1:12:08<41:52, 46.54s/it]
Trial 46, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 46, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
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34:	learn: 0.3280575	test: 0.3283666	best: 0.3283666 (34)	total: 1.16s	remaining: 297ms
35:	learn: 0.3234662	test: 0.3237357	best: 0.3237357 (35)	total: 1.19s	remaining: 265ms
36:	learn: 0.3199651	test: 0.3202640	best: 0.3202640 (36)	total: 1.22s	remaining: 232ms
37:	learn: 0.3166668	test: 0.3169981	best: 0.3169981 (37)	total: 1.26s	remaining: 199ms
38:	learn: 0.3135238	test: 0.3138983	best: 0.3138983 (38)	total: 1.29s	remaining: 166ms
39:	learn: 0.3097604	test: 0.3101894	best: 0.3101894 (39)	total: 1.33s	remaining: 133ms
40:	learn: 0.3066920	test: 0.3071638	best: 0.3071638 (40)	total: 1.36s	remaining: 99.6ms
41:	learn: 0.3039397	test: 0.3043985	best: 0.3043985 (41)	total: 1.4s	remaining: 66.4ms
42:	learn: 0.3012123	test: 0.3016882	best: 0.3016882 (42)	total: 1.43s	remaining: 33.2ms
43:	learn: 0.2985684	test: 0.2990954	best: 0.2990954 (43)	total: 1.46s	remaining: 0us

bestTest = 0.2990953855
bestIteration = 43

Trial 46, Fold 1: Log loss = 0.2990953855100339, Average precision = 0.9635913039585033, ROC-AUC = 0.9600948360122931, Elapsed Time = 1.564544400000159 seconds
Trial 46, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 46, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
0:	learn: 0.6726343	test: 0.6724835	best: 0.6724835 (0)	total: 32.2ms	remaining: 1.38s
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2:	learn: 0.6319513	test: 0.6327401	best: 0.6327401 (2)	total: 99.9ms	remaining: 1.36s
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33:	learn: 0.3341955	test: 0.3369217	best: 0.3369217 (33)	total: 1.15s	remaining: 339ms
34:	learn: 0.3301806	test: 0.3329138	best: 0.3329138 (34)	total: 1.19s	remaining: 305ms
35:	learn: 0.3261058	test: 0.3288463	best: 0.3288463 (35)	total: 1.22s	remaining: 271ms
36:	learn: 0.3220613	test: 0.3247919	best: 0.3247919 (36)	total: 1.26s	remaining: 238ms
37:	learn: 0.3186139	test: 0.3212669	best: 0.3212669 (37)	total: 1.29s	remaining: 204ms
38:	learn: 0.3144149	test: 0.3170407	best: 0.3170407 (38)	total: 1.32s	remaining: 170ms
39:	learn: 0.3115354	test: 0.3140940	best: 0.3140940 (39)	total: 1.36s	remaining: 136ms
40:	learn: 0.3077677	test: 0.3103022	best: 0.3103022 (40)	total: 1.39s	remaining: 102ms
41:	learn: 0.3044094	test: 0.3069964	best: 0.3069964 (41)	total: 1.43s	remaining: 68.1ms
42:	learn: 0.3011495	test: 0.3037382	best: 0.3037382 (42)	total: 1.46s	remaining: 34ms
43:	learn: 0.2978651	test: 0.3004452	best: 0.3004452 (43)	total: 1.5s	remaining: 0us

bestTest = 0.3004452474
bestIteration = 43

Trial 46, Fold 2: Log loss = 0.3004452473628985, Average precision = 0.9624983249447823, ROC-AUC = 0.9603160572526184, Elapsed Time = 1.5966767000027176 seconds
Trial 46, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 46, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
0:	learn: 0.6725103	test: 0.6724287	best: 0.6724287 (0)	total: 33.7ms	remaining: 1.45s
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2:	learn: 0.6278792	test: 0.6279205	best: 0.6279205 (2)	total: 98ms	remaining: 1.34s
3:	learn: 0.6109301	test: 0.6109110	best: 0.6109110 (3)	total: 130ms	remaining: 1.3s
4:	learn: 0.5933251	test: 0.5931071	best: 0.5931071 (4)	total: 162ms	remaining: 1.26s
5:	learn: 0.5772496	test: 0.5768387	best: 0.5768387 (5)	total: 195ms	remaining: 1.23s
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35:	learn: 0.3218946	test: 0.3198754	best: 0.3198754 (35)	total: 1.22s	remaining: 270ms
36:	learn: 0.3181483	test: 0.3161122	best: 0.3161122 (36)	total: 1.25s	remaining: 237ms
37:	learn: 0.3147775	test: 0.3126971	best: 0.3126971 (37)	total: 1.28s	remaining: 203ms
38:	learn: 0.3118251	test: 0.3097287	best: 0.3097287 (38)	total: 1.32s	remaining: 169ms
39:	learn: 0.3085471	test: 0.3064246	best: 0.3064246 (39)	total: 1.36s	remaining: 136ms
40:	learn: 0.3053220	test: 0.3032373	best: 0.3032373 (40)	total: 1.39s	remaining: 102ms
41:	learn: 0.3026113	test: 0.3004532	best: 0.3004532 (41)	total: 1.43s	remaining: 67.9ms
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43:	learn: 0.2972173	test: 0.2950397	best: 0.2950397 (43)	total: 1.5s	remaining: 0us

bestTest = 0.2950397348
bestIteration = 43

Trial 46, Fold 3: Log loss = 0.295039734799017, Average precision = 0.9602635223581758, ROC-AUC = 0.960549926081806, Elapsed Time = 1.5994404000011855 seconds
Trial 46, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 46, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
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37:	learn: 0.3169089	test: 0.3172308	best: 0.3172308 (37)	total: 1.28s	remaining: 202ms
38:	learn: 0.3135477	test: 0.3139008	best: 0.3139008 (38)	total: 1.31s	remaining: 168ms
39:	learn: 0.3108133	test: 0.3111243	best: 0.3111243 (39)	total: 1.35s	remaining: 135ms
40:	learn: 0.3082932	test: 0.3086549	best: 0.3086549 (40)	total: 1.38s	remaining: 101ms
41:	learn: 0.3053917	test: 0.3057925	best: 0.3057925 (41)	total: 1.42s	remaining: 67.4ms
42:	learn: 0.3025972	test: 0.3030643	best: 0.3030643 (42)	total: 1.45s	remaining: 33.7ms
43:	learn: 0.2999808	test: 0.3004626	best: 0.3004626 (43)	total: 1.48s	remaining: 0us

bestTest = 0.3004626167
bestIteration = 43

Trial 46, Fold 4: Log loss = 0.300462616736596, Average precision = 0.9612037416076539, ROC-AUC = 0.9587895752412426, Elapsed Time = 1.5848167000003741 seconds
Trial 46, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 46, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
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34:	learn: 0.3226307	test: 0.3303193	best: 0.3303193 (34)	total: 1.18s	remaining: 304ms
35:	learn: 0.3186624	test: 0.3264282	best: 0.3264282 (35)	total: 1.22s	remaining: 271ms
36:	learn: 0.3149332	test: 0.3227905	best: 0.3227905 (36)	total: 1.25s	remaining: 237ms
37:	learn: 0.3108251	test: 0.3187572	best: 0.3187572 (37)	total: 1.29s	remaining: 203ms
38:	learn: 0.3072792	test: 0.3152973	best: 0.3152973 (38)	total: 1.32s	remaining: 170ms
39:	learn: 0.3044311	test: 0.3125553	best: 0.3125553 (39)	total: 1.36s	remaining: 136ms
40:	learn: 0.3009517	test: 0.3091550	best: 0.3091550 (40)	total: 1.39s	remaining: 102ms
41:	learn: 0.2978727	test: 0.3061865	best: 0.3061865 (41)	total: 1.43s	remaining: 67.9ms
42:	learn: 0.2948707	test: 0.3032722	best: 0.3032722 (42)	total: 1.46s	remaining: 34ms
43:	learn: 0.2919695	test: 0.3005000	best: 0.3005000 (43)	total: 1.49s	remaining: 0us

bestTest = 0.3004999589
bestIteration = 43

Trial 46, Fold 5: Log loss = 0.3004999589374852, Average precision = 0.9605533844627435, ROC-AUC = 0.9575139252735818, Elapsed Time = 1.5927054999992833 seconds
Optimization Progress:  47%|####6     | 47/100 [1:12:24<32:52, 37.22s/it]
Trial 47, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 47, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
0:	learn: 0.5445765	test: 0.5485984	best: 0.5485984 (0)	total: 178ms	remaining: 15.8s
1:	learn: 0.4299264	test: 0.4366798	best: 0.4366798 (1)	total: 349ms	remaining: 15.3s
2:	learn: 0.3603643	test: 0.3724235	best: 0.3724235 (2)	total: 546ms	remaining: 15.8s
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6:	learn: 0.2238562	test: 0.2528266	best: 0.2528266 (6)	total: 1.36s	remaining: 16.2s
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8:	learn: 0.1927852	test: 0.2290207	best: 0.2290207 (8)	total: 1.74s	remaining: 15.7s
9:	learn: 0.1828432	test: 0.2224468	best: 0.2224468 (9)	total: 1.92s	remaining: 15.3s
10:	learn: 0.1755071	test: 0.2187629	best: 0.2187629 (10)	total: 2.09s	remaining: 15s
11:	learn: 2121.9126702	test: 2464.0379721	best: 0.2187629 (10)	total: 2.3s	remaining: 14.9s
12:	learn: 99990.2005741	test: 73489.1326533	best: 0.2187629 (10)	total: 2.48s	remaining: 14.7s
13:	learn: 223380.4554914	test: 168834.0762833	best: 0.2187629 (10)	total: 2.68s	remaining: 14.5s
14:	learn: 260895.6906733	test: 193329.0233095	best: 0.2187629 (10)	total: 2.86s	remaining: 14.3s
15:	learn: 387161.3639864	test: 281032.0530646	best: 0.2187629 (10)	total: 3.02s	remaining: 13.9s
16:	learn: 407549.0252662	test: 293471.4635057	best: 0.2187629 (10)	total: 3.2s	remaining: 13.8s
17:	learn: 508876.8258082	test: 363575.5100022	best: 0.2187629 (10)	total: 3.34s	remaining: 13.4s
18:	learn: 615532.8003254	test: 438996.4287909	best: 0.2187629 (10)	total: 3.5s	remaining: 13.1s
Training has stopped (degenerate solution on iteration 19, probably too small l2-regularization, try to increase it)
bestTest = 0.2187628502
bestIteration = 10

Shrink model to first 11 iterations.
Trial 47, Fold 1: Log loss = 0.2188074000879337, Average precision = 0.9724157484850913, ROC-AUC = 0.9689162400883268, Elapsed Time = 3.7517687999970804 seconds
Trial 47, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 47, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
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1:	learn: 0.4199534	test: 0.4324356	best: 0.4324356 (1)	total: 384ms	remaining: 16.9s
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4:	learn: 0.2637208	test: 0.2824708	best: 0.2824708 (4)	total: 973ms	remaining: 16.5s
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13:	learn: 1856.2323625	test: 0.1941526	best: 0.1941526 (13)	total: 2.77s	remaining: 15.1s
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15:	learn: 1790.5560957	test: 0.1909503	best: 0.1909503 (15)	total: 3.09s	remaining: 14.3s
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17:	learn: 1790.5424898	test: 0.1897081	best: 0.1897081 (17)	total: 3.43s	remaining: 13.7s
18:	learn: 3659.0168157	test: 3543.8937189	best: 0.1897081 (17)	total: 3.61s	remaining: 13.5s
19:	learn: 3659.0079558	test: 3543.8862557	best: 0.1897081 (17)	total: 3.73s	remaining: 13.1s
20:	learn: 4172.1539862	test: 3543.8780847	best: 0.1897081 (17)	total: 3.88s	remaining: 12.8s
21:	learn: 27389.8183839	test: 32912.1852086	best: 0.1897081 (17)	total: 4.07s	remaining: 12.6s
22:	learn: 210606.1368468	test: 314248.7028504	best: 0.1897081 (17)	total: 4.2s	remaining: 12.2s
23:	learn: 444161.2545133	test: 625601.7477575	best: 0.1897081 (17)	total: 4.33s	remaining: 11.9s
24:	learn: 655769.7163478	test: 860040.9578908	best: 0.1897081 (17)	total: 4.48s	remaining: 11.6s
25:	learn: 962552.0428314	test: 1202318.1205499	best: 0.1897081 (17)	total: 4.62s	remaining: 11.4s
26:	learn: 1189393.9362534	test: 1523284.3560800	best: 0.1897081 (17)	total: 4.74s	remaining: 11.1s

bestTest = 0.189708114
bestIteration = 17

Shrink model to first 18 iterations.
Training has stopped (degenerate solution on iteration 27, probably too small l2-regularization, try to increase it)
Trial 47, Fold 2: Log loss = 0.18946390033828614, Average precision = 0.9756765924089011, ROC-AUC = 0.9731557408766955, Elapsed Time = 5.012145599997893 seconds
Trial 47, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 47, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
0:	learn: 0.5266539	test: 0.5306859	best: 0.5306859 (0)	total: 197ms	remaining: 17.6s
1:	learn: 0.4245679	test: 0.4309326	best: 0.4309326 (1)	total: 387ms	remaining: 17s
2:	learn: 0.3616663	test: 0.3715064	best: 0.3715064 (2)	total: 590ms	remaining: 17.1s
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4:	learn: 0.2679481	test: 0.2867087	best: 0.2867087 (4)	total: 991ms	remaining: 16.8s
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8:	learn: 383.2880893	test: 0.2192734	best: 0.2192734 (8)	total: 1.86s	remaining: 16.7s
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10:	learn: 3372.1361871	test: 700.7840286	best: 0.2192734 (8)	total: 2.31s	remaining: 16.6s
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18:	learn: 9212.9823914	test: 700.7592509	best: 0.2192734 (8)	total: 4.07s	remaining: 15.2s
19:	learn: 10560.3659693	test: 6105.0388158	best: 0.2192734 (8)	total: 4.26s	remaining: 14.9s
20:	learn: 21333.8865498	test: 6105.0258843	best: 0.2192734 (8)	total: 4.48s	remaining: 14.7s
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24:	learn: 53538.5796675	test: 9160.3289761	best: 0.2192734 (8)	total: 5.24s	remaining: 13.6s
25:	learn: 53206.2414700	test: 9160.3093842	best: 0.2192734 (8)	total: 5.41s	remaining: 13.3s
26:	learn: 72000.6733033	test: 12755.4488043	best: 0.2192734 (8)	total: 5.63s	remaining: 13.1s
27:	learn: 326051.8507325	test: 183113.8079437	best: 0.2192734 (8)	total: 5.83s	remaining: 12.9s
28:	learn: 593451.4072636	test: 366527.7465668	best: 0.2192734 (8)	total: 5.98s	remaining: 12.6s
29:	learn: 817086.5939836	test: 470468.9057101	best: 0.2192734 (8)	total: 6.12s	remaining: 12.2s
30:	learn: 1094828.7866171	test: 653624.9451635	best: 0.2192734 (8)	total: 6.28s	remaining: 11.9s
31:	learn: 1255571.4823463	test: 776938.1392473	best: 0.2192734 (8)	total: 6.43s	remaining: 11.6s
32:	learn: 1248765.6190287	test: 779089.1767183	best: 0.2192734 (8)	total: 6.55s	remaining: 11.3s
33:	learn: 1255771.6931545	test: 779087.5365427	best: 0.2192734 (8)	total: 6.69s	remaining: 11s

bestTest = 0.2192733625
bestIteration = 8

Shrink model to first 9 iterations.
Training has stopped (degenerate solution on iteration 34, probably too small l2-regularization, try to increase it)
Trial 47, Fold 3: Log loss = 0.2196358003113448, Average precision = 0.9733511790412094, ROC-AUC = 0.9703974336903237, Elapsed Time = 6.92220449999877 seconds
Trial 47, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 47, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
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1:	learn: 0.4438218	test: 0.4490892	best: 0.4490892 (1)	total: 364ms	remaining: 16s
2:	learn: 0.3765651	test: 0.3852653	best: 0.3852653 (2)	total: 577ms	remaining: 16.7s
3:	learn: 0.3167976	test: 0.3304497	best: 0.3304497 (3)	total: 789ms	remaining: 17s
4:	learn: 0.2814704	test: 0.2979695	best: 0.2979695 (4)	total: 989ms	remaining: 16.8s
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17:	learn: 2834768.3175421	test: 1842874.8236286	best: 0.2314778 (8)	total: 3.67s	remaining: 14.7s
18:	learn: 3235163.9187140	test: 2481513.7720534	best: 0.2314778 (8)	total: 3.83s	remaining: 14.3s

bestTest = 0.2314777689
bestIteration = 8

Shrink model to first 9 iterations.
Training has stopped (degenerate solution on iteration 19, probably too small l2-regularization, try to increase it)
Trial 47, Fold 4: Log loss = 0.23151441709257464, Average precision = 0.9722849244760045, ROC-AUC = 0.9682106018011831, Elapsed Time = 4.109465400000772 seconds
Trial 47, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 47, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
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14:	learn: 1842301.5905066	test: 1648623.6895771	best: 0.2425345 (7)	total: 3.41s	remaining: 17s
15:	learn: 3639567.5663221	test: 3064742.6491588	best: 0.2425345 (7)	total: 3.63s	remaining: 16.8s
16:	learn: 5441478.6428118	test: 4930257.0877915	best: 0.2425345 (7)	total: 3.85s	remaining: 16.5s
Training has stopped (degenerate solution on iteration 17, probably too small l2-regularization, try to increase it)
bestTest = 0.2425344631
bestIteration = 7

Shrink model to first 8 iterations.
Trial 47, Fold 5: Log loss = 0.242535836249544, Average precision = 0.9710069158723872, ROC-AUC = 0.9665398807201383, Elapsed Time = 4.201541700000234 seconds
Optimization Progress:  48%|####8     | 48/100 [1:12:56<30:53, 35.65s/it]
Trial 48, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 48, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
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72:	learn: 0.2696523	test: 0.2844267	best: 0.2844267 (72)	total: 16.9s	remaining: 0us

bestTest = 0.2844267237
bestIteration = 72

Trial 48, Fold 1: Log loss = 0.28439988595821813, Average precision = 0.9742608270115632, ROC-AUC = 0.9698202545848209, Elapsed Time = 17.062217900001997 seconds
Trial 48, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 48, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
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66:	learn: 0.2818296	test: 0.2931224	best: 0.2931224 (66)	total: 15.3s	remaining: 1.37s
67:	learn: 0.2795916	test: 0.2910636	best: 0.2910636 (67)	total: 15.6s	remaining: 1.15s
68:	learn: 0.2775367	test: 0.2890782	best: 0.2890782 (68)	total: 15.9s	remaining: 919ms
69:	learn: 0.2755269	test: 0.2871221	best: 0.2871221 (69)	total: 16.1s	remaining: 688ms
70:	learn: 0.2734529	test: 0.2851978	best: 0.2851978 (70)	total: 16.3s	remaining: 459ms
71:	learn: 0.2714532	test: 0.2832861	best: 0.2832861 (71)	total: 16.5s	remaining: 229ms
72:	learn: 0.2695897	test: 0.2815583	best: 0.2815583 (72)	total: 16.8s	remaining: 0us

bestTest = 0.2815582753
bestIteration = 72

Trial 48, Fold 2: Log loss = 0.28151055643296685, Average precision = 0.9746979821037248, ROC-AUC = 0.9718453228752806, Elapsed Time = 16.923168199999054 seconds
Trial 48, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 48, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
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60:	learn: 0.2960474	test: 0.3026900	best: 0.3026900 (60)	total: 13.8s	remaining: 2.72s
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62:	learn: 0.2913932	test: 0.2982000	best: 0.2982000 (62)	total: 14.3s	remaining: 2.27s
63:	learn: 0.2891455	test: 0.2961048	best: 0.2961048 (63)	total: 14.5s	remaining: 2.04s
64:	learn: 0.2869643	test: 0.2940765	best: 0.2940765 (64)	total: 14.7s	remaining: 1.81s
65:	learn: 0.2846921	test: 0.2919187	best: 0.2919187 (65)	total: 14.9s	remaining: 1.58s
66:	learn: 0.2826163	test: 0.2900306	best: 0.2900306 (66)	total: 15.1s	remaining: 1.35s
67:	learn: 0.2804072	test: 0.2878951	best: 0.2878951 (67)	total: 15.4s	remaining: 1.13s
68:	learn: 0.2785007	test: 0.2860802	best: 0.2860802 (68)	total: 15.6s	remaining: 905ms
69:	learn: 0.2764114	test: 0.2841366	best: 0.2841366 (69)	total: 15.9s	remaining: 680ms
70:	learn: 0.2745131	test: 0.2824005	best: 0.2824005 (70)	total: 16.1s	remaining: 453ms
71:	learn: 0.2725973	test: 0.2805474	best: 0.2805474 (71)	total: 16.3s	remaining: 226ms
72:	learn: 0.2707414	test: 0.2787546	best: 0.2787546 (72)	total: 16.5s	remaining: 0us

bestTest = 0.278754565
bestIteration = 72

Trial 48, Fold 3: Log loss = 0.2789076748905186, Average precision = 0.9750188025336488, ROC-AUC = 0.9727258116554787, Elapsed Time = 16.703614400001243 seconds
Trial 48, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 48, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
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2:	learn: 0.6525918	test: 0.6531860	best: 0.6531860 (2)	total: 640ms	remaining: 14.9s
3:	learn: 0.6399953	test: 0.6407287	best: 0.6407287 (3)	total: 828ms	remaining: 14.3s
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63:	learn: 0.2886927	test: 0.2994848	best: 0.2994848 (63)	total: 14.3s	remaining: 2.02s
64:	learn: 0.2863284	test: 0.2973044	best: 0.2973044 (64)	total: 14.6s	remaining: 1.79s
65:	learn: 0.2842094	test: 0.2953304	best: 0.2953304 (65)	total: 14.8s	remaining: 1.57s
66:	learn: 0.2820517	test: 0.2932272	best: 0.2932272 (66)	total: 15s	remaining: 1.34s
67:	learn: 0.2800311	test: 0.2913253	best: 0.2913253 (67)	total: 15.2s	remaining: 1.12s
68:	learn: 0.2780728	test: 0.2894924	best: 0.2894924 (68)	total: 15.4s	remaining: 896ms
69:	learn: 0.2762756	test: 0.2878037	best: 0.2878037 (69)	total: 15.6s	remaining: 671ms
70:	learn: 0.2742482	test: 0.2859245	best: 0.2859245 (70)	total: 15.9s	remaining: 448ms
71:	learn: 0.2723758	test: 0.2841739	best: 0.2841739 (71)	total: 16.1s	remaining: 224ms
72:	learn: 0.2703251	test: 0.2823284	best: 0.2823284 (72)	total: 16.4s	remaining: 0us

bestTest = 0.2823284056
bestIteration = 72

Trial 48, Fold 4: Log loss = 0.28233987263072025, Average precision = 0.9758039650097482, ROC-AUC = 0.971943171765727, Elapsed Time = 16.542822800001886 seconds
Trial 48, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 48, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
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2:	learn: 0.6523629	test: 0.6532637	best: 0.6532637 (2)	total: 645ms	remaining: 15.1s
3:	learn: 0.6395157	test: 0.6407677	best: 0.6407677 (3)	total: 878ms	remaining: 15.1s
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66:	learn: 0.2807205	test: 0.2970837	best: 0.2970837 (66)	total: 15s	remaining: 1.34s
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68:	learn: 0.2765154	test: 0.2931936	best: 0.2931936 (68)	total: 15.4s	remaining: 894ms
69:	learn: 0.2745022	test: 0.2913628	best: 0.2913628 (69)	total: 15.7s	remaining: 671ms
70:	learn: 0.2725467	test: 0.2896314	best: 0.2896314 (70)	total: 15.9s	remaining: 448ms
71:	learn: 0.2706860	test: 0.2879669	best: 0.2879669 (71)	total: 16.1s	remaining: 224ms
72:	learn: 0.2688673	test: 0.2863312	best: 0.2863312 (72)	total: 16.4s	remaining: 0us

bestTest = 0.2863312174
bestIteration = 72

Trial 48, Fold 5: Log loss = 0.2862084415915731, Average precision = 0.9728769473834451, ROC-AUC = 0.9700563699533657, Elapsed Time = 16.5084491000016 seconds
Optimization Progress:  49%|####9     | 49/100 [1:14:29<44:50, 52.75s/it]
Trial 49, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 49, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
0:	learn: 0.6226962	test: 0.6264346	best: 0.6264346 (0)	total: 199ms	remaining: 8.75s
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44:	learn: 0.1250937	test: 0.2025762	best: 0.2025762 (44)	total: 10s	remaining: 0us

bestTest = 0.2025761643
bestIteration = 44

Trial 49, Fold 1: Log loss = 0.20257616434370945, Average precision = 0.9755856730081887, ROC-AUC = 0.9710382283496557, Elapsed Time = 10.18093030000091 seconds
Trial 49, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 49, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
0:	learn: 0.6169033	test: 0.6204335	best: 0.6204335 (0)	total: 299ms	remaining: 13.1s
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35:	learn: 0.1385573	test: 0.1960588	best: 0.1960588 (35)	total: 9.76s	remaining: 2.44s
36:	learn: 0.1363566	test: 0.1952296	best: 0.1952296 (36)	total: 10s	remaining: 2.17s
37:	learn: 0.1343658	test: 0.1942227	best: 0.1942227 (37)	total: 10.3s	remaining: 1.89s
38:	learn: 0.1326300	test: 0.1935040	best: 0.1935040 (38)	total: 10.5s	remaining: 1.62s
39:	learn: 0.1307258	test: 0.1929493	best: 0.1929493 (39)	total: 10.7s	remaining: 1.34s
40:	learn: 0.1294661	test: 0.1920495	best: 0.1920495 (40)	total: 10.9s	remaining: 1.06s
41:	learn: 0.1278996	test: 0.1914181	best: 0.1914181 (41)	total: 11.1s	remaining: 794ms
42:	learn: 0.1264906	test: 0.1905878	best: 0.1905878 (42)	total: 11.3s	remaining: 527ms
43:	learn: 0.1251924	test: 0.1901772	best: 0.1901772 (43)	total: 11.6s	remaining: 263ms
44:	learn: 0.1238828	test: 0.1894979	best: 0.1894979 (44)	total: 11.8s	remaining: 0us

bestTest = 0.1894978519
bestIteration = 44

Trial 49, Fold 2: Log loss = 0.18949785187423762, Average precision = 0.9774370561633179, ROC-AUC = 0.9746546296276625, Elapsed Time = 11.948648300000059 seconds
Trial 49, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 49, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
0:	learn: 0.6180050	test: 0.6208199	best: 0.6208199 (0)	total: 255ms	remaining: 11.2s
1:	learn: 0.5563472	test: 0.5610585	best: 0.5610585 (1)	total: 473ms	remaining: 10.2s
2:	learn: 0.4997161	test: 0.5067352	best: 0.5067352 (2)	total: 697ms	remaining: 9.75s
3:	learn: 0.4535401	test: 0.4624745	best: 0.4624745 (3)	total: 935ms	remaining: 9.59s
4:	learn: 0.4138956	test: 0.4252417	best: 0.4252417 (4)	total: 1.13s	remaining: 9.06s
5:	learn: 0.3828519	test: 0.3970053	best: 0.3970053 (5)	total: 1.37s	remaining: 8.88s
6:	learn: 0.3553827	test: 0.3711514	best: 0.3711514 (6)	total: 1.57s	remaining: 8.55s
7:	learn: 0.3297188	test: 0.3475921	best: 0.3475921 (7)	total: 1.81s	remaining: 8.4s
8:	learn: 0.3073202	test: 0.3273856	best: 0.3273856 (8)	total: 2.04s	remaining: 8.15s
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35:	learn: 0.1388592	test: 0.2001658	best: 0.2001658 (35)	total: 8.22s	remaining: 2.06s
36:	learn: 0.1367464	test: 0.1992638	best: 0.1992638 (36)	total: 8.49s	remaining: 1.84s
37:	learn: 0.1345366	test: 0.1984889	best: 0.1984889 (37)	total: 8.74s	remaining: 1.61s
38:	learn: 0.1329384	test: 0.1975966	best: 0.1975966 (38)	total: 8.96s	remaining: 1.38s
39:	learn: 0.1312921	test: 0.1967614	best: 0.1967614 (39)	total: 9.2s	remaining: 1.15s
40:	learn: 0.1298862	test: 0.1964088	best: 0.1964088 (40)	total: 9.43s	remaining: 920ms
41:	learn: 0.1285350	test: 0.1959249	best: 0.1959249 (41)	total: 9.64s	remaining: 689ms
42:	learn: 0.1271042	test: 0.1953395	best: 0.1953395 (42)	total: 9.86s	remaining: 459ms
43:	learn: 0.1254062	test: 0.1951089	best: 0.1951089 (43)	total: 10.1s	remaining: 230ms
44:	learn: 0.1241640	test: 0.1947249	best: 0.1947249 (44)	total: 10.4s	remaining: 0us

bestTest = 0.1947248803
bestIteration = 44

Trial 49, Fold 3: Log loss = 0.19472488028293558, Average precision = 0.974079074312862, ROC-AUC = 0.9726829234525335, Elapsed Time = 10.543397699999332 seconds
Trial 49, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 49, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
0:	learn: 0.6164632	test: 0.6194466	best: 0.6194466 (0)	total: 222ms	remaining: 9.75s
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2:	learn: 0.4947039	test: 0.5033002	best: 0.5033002 (2)	total: 708ms	remaining: 9.91s
3:	learn: 0.4478003	test: 0.4596282	best: 0.4596282 (3)	total: 928ms	remaining: 9.51s
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5:	learn: 0.3751910	test: 0.3915312	best: 0.3915312 (5)	total: 1.34s	remaining: 8.74s
6:	learn: 0.3472360	test: 0.3649324	best: 0.3649324 (6)	total: 1.51s	remaining: 8.22s
7:	learn: 0.3233482	test: 0.3431185	best: 0.3431185 (7)	total: 1.74s	remaining: 8.03s
8:	learn: 0.3031631	test: 0.3248692	best: 0.3248692 (8)	total: 1.95s	remaining: 7.8s
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30:	learn: 0.1486030	test: 0.2061454	best: 0.2061454 (30)	total: 7.14s	remaining: 3.22s
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35:	learn: 0.1380953	test: 0.2008600	best: 0.2008600 (35)	total: 8.26s	remaining: 2.06s
36:	learn: 0.1362735	test: 0.2000743	best: 0.2000743 (36)	total: 8.45s	remaining: 1.83s
37:	learn: 0.1346921	test: 0.1992842	best: 0.1992842 (37)	total: 8.67s	remaining: 1.6s
38:	learn: 0.1328996	test: 0.1984923	best: 0.1984923 (38)	total: 8.88s	remaining: 1.37s
39:	learn: 0.1314115	test: 0.1979776	best: 0.1979776 (39)	total: 9.06s	remaining: 1.13s
40:	learn: 0.1297846	test: 0.1977512	best: 0.1977512 (40)	total: 9.29s	remaining: 907ms
41:	learn: 0.1285544	test: 0.1973727	best: 0.1973727 (41)	total: 9.51s	remaining: 679ms
42:	learn: 0.1271677	test: 0.1968014	best: 0.1968014 (42)	total: 9.72s	remaining: 452ms
43:	learn: 0.1250798	test: 0.1963742	best: 0.1963742 (43)	total: 9.97s	remaining: 227ms
44:	learn: 0.1235936	test: 0.1960739	best: 0.1960739 (44)	total: 10.2s	remaining: 0us

bestTest = 0.1960738703
bestIteration = 44

Trial 49, Fold 4: Log loss = 0.19607387033436352, Average precision = 0.9766321767544495, ROC-AUC = 0.9725493334382473, Elapsed Time = 10.381020600001648 seconds
Trial 49, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 49, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
0:	learn: 0.6156308	test: 0.6196338	best: 0.6196338 (0)	total: 253ms	remaining: 11.1s
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2:	learn: 0.4945676	test: 0.5054286	best: 0.5054286 (2)	total: 675ms	remaining: 9.45s
3:	learn: 0.4482627	test: 0.4623910	best: 0.4623910 (3)	total: 867ms	remaining: 8.89s
4:	learn: 0.4086851	test: 0.4251317	best: 0.4251317 (4)	total: 1.06s	remaining: 8.48s
5:	learn: 0.3747766	test: 0.3944977	best: 0.3944977 (5)	total: 1.29s	remaining: 8.41s
6:	learn: 0.3486637	test: 0.3714950	best: 0.3714950 (6)	total: 1.55s	remaining: 8.42s
7:	learn: 0.3240233	test: 0.3489605	best: 0.3489605 (7)	total: 1.8s	remaining: 8.33s
8:	learn: 0.3033992	test: 0.3307749	best: 0.3307749 (8)	total: 2.01s	remaining: 8.03s
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35:	learn: 0.1376093	test: 0.2080114	best: 0.2080114 (35)	total: 8.24s	remaining: 2.06s
36:	learn: 0.1357996	test: 0.2077369	best: 0.2077369 (36)	total: 8.48s	remaining: 1.83s
37:	learn: 0.1340893	test: 0.2069862	best: 0.2069862 (37)	total: 8.66s	remaining: 1.6s
38:	learn: 0.1322499	test: 0.2062572	best: 0.2062572 (38)	total: 8.9s	remaining: 1.37s
39:	learn: 0.1305891	test: 0.2057270	best: 0.2057270 (39)	total: 9.12s	remaining: 1.14s
40:	learn: 0.1288804	test: 0.2050312	best: 0.2050312 (40)	total: 9.35s	remaining: 913ms
41:	learn: 0.1270981	test: 0.2044872	best: 0.2044872 (41)	total: 9.59s	remaining: 685ms
42:	learn: 0.1257052	test: 0.2040819	best: 0.2040819 (42)	total: 9.81s	remaining: 456ms
43:	learn: 0.1239099	test: 0.2035169	best: 0.2035169 (43)	total: 10.1s	remaining: 229ms
44:	learn: 0.1224298	test: 0.2032652	best: 0.2032652 (44)	total: 10.3s	remaining: 0us

bestTest = 0.2032651634
bestIteration = 44

Trial 49, Fold 5: Log loss = 0.20326516342903195, Average precision = 0.9748217651359093, ROC-AUC = 0.971378184047712, Elapsed Time = 10.45471489999909 seconds
Optimization Progress:  50%|#####     | 50/100 [1:15:30<46:09, 55.38s/it]
Trial 50, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 50, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
0:	learn: 0.6181454	test: 0.6197185	best: 0.6197185 (0)	total: 239ms	remaining: 10.8s
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2:	learn: 0.5013938	test: 0.5058207	best: 0.5058207 (2)	total: 739ms	remaining: 10.6s
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6:	learn: 0.3595621	test: 0.3692960	best: 0.3692960 (6)	total: 1.81s	remaining: 10.1s
7:	learn: 0.3369295	test: 0.3480191	best: 0.3480191 (7)	total: 2.14s	remaining: 10.1s
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36:	learn: 0.1692490	test: 0.2081049	best: 0.2081049 (36)	total: 10.8s	remaining: 2.63s
37:	learn: 0.1678820	test: 0.2073260	best: 0.2073260 (37)	total: 11.1s	remaining: 2.33s
38:	learn: 0.1663079	test: 0.2064458	best: 0.2064458 (38)	total: 11.4s	remaining: 2.04s
39:	learn: 0.1652061	test: 0.2058535	best: 0.2058535 (39)	total: 11.6s	remaining: 1.74s
40:	learn: 0.1637068	test: 0.2055528	best: 0.2055528 (40)	total: 11.9s	remaining: 1.45s
41:	learn: 0.1625866	test: 0.2049924	best: 0.2049924 (41)	total: 12.2s	remaining: 1.16s
42:	learn: 0.1612104	test: 0.2044702	best: 0.2044702 (42)	total: 12.5s	remaining: 870ms
43:	learn: 0.1598716	test: 0.2040626	best: 0.2040626 (43)	total: 12.8s	remaining: 580ms
44:	learn: 0.1586672	test: 0.2036446	best: 0.2036446 (44)	total: 13.1s	remaining: 290ms
45:	learn: 0.1575951	test: 0.2031164	best: 0.2031164 (45)	total: 13.3s	remaining: 0us

bestTest = 0.2031163996
bestIteration = 45

Trial 50, Fold 1: Log loss = 0.20281439260644013, Average precision = 0.9747480648415343, ROC-AUC = 0.9713088725904896, Elapsed Time = 13.479710599996906 seconds
Trial 50, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 50, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
0:	learn: 0.6179873	test: 0.6192485	best: 0.6192485 (0)	total: 273ms	remaining: 12.3s
1:	learn: 0.5555514	test: 0.5581638	best: 0.5581638 (1)	total: 557ms	remaining: 12.2s
2:	learn: 0.5026688	test: 0.5066382	best: 0.5066382 (2)	total: 835ms	remaining: 12s
3:	learn: 0.4576852	test: 0.4628809	best: 0.4628809 (3)	total: 1.14s	remaining: 12s
4:	learn: 0.4201382	test: 0.4265560	best: 0.4265560 (4)	total: 1.43s	remaining: 11.7s
5:	learn: 0.3880516	test: 0.3956203	best: 0.3956203 (5)	total: 1.75s	remaining: 11.7s
6:	learn: 0.3607390	test: 0.3696203	best: 0.3696203 (6)	total: 2.09s	remaining: 11.6s
7:	learn: 0.3377555	test: 0.3473652	best: 0.3473652 (7)	total: 2.37s	remaining: 11.3s
8:	learn: 0.3181891	test: 0.3288797	best: 0.3288797 (8)	total: 2.66s	remaining: 11s
9:	learn: 0.3018292	test: 0.3136081	best: 0.3136081 (9)	total: 2.97s	remaining: 10.7s
10:	learn: 0.2873064	test: 0.2997662	best: 0.2997662 (10)	total: 3.26s	remaining: 10.4s
11:	learn: 0.2752035	test: 0.2883015	best: 0.2883015 (11)	total: 3.56s	remaining: 10.1s
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19:	learn: 0.2147972	test: 0.2341731	best: 0.2341731 (19)	total: 5.9s	remaining: 7.67s
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27:	learn: 0.1889961	test: 0.2122552	best: 0.2122552 (27)	total: 8.12s	remaining: 5.22s
28:	learn: 0.1859937	test: 0.2100790	best: 0.2100790 (28)	total: 8.43s	remaining: 4.94s
29:	learn: 0.1832680	test: 0.2082171	best: 0.2082171 (29)	total: 8.72s	remaining: 4.65s
30:	learn: 0.1805123	test: 0.2064843	best: 0.2064843 (30)	total: 9.04s	remaining: 4.37s
31:	learn: 0.1785625	test: 0.2048673	best: 0.2048673 (31)	total: 9.3s	remaining: 4.07s
32:	learn: 0.1766840	test: 0.2036018	best: 0.2036018 (32)	total: 9.58s	remaining: 3.77s
33:	learn: 0.1748022	test: 0.2023486	best: 0.2023486 (33)	total: 9.9s	remaining: 3.49s
34:	learn: 0.1729100	test: 0.2015756	best: 0.2015756 (34)	total: 10.2s	remaining: 3.21s
35:	learn: 0.1712452	test: 0.2004161	best: 0.2004161 (35)	total: 10.5s	remaining: 2.91s
36:	learn: 0.1692729	test: 0.1994155	best: 0.1994155 (36)	total: 10.8s	remaining: 2.62s
37:	learn: 0.1675864	test: 0.1988043	best: 0.1988043 (37)	total: 11.1s	remaining: 2.34s
38:	learn: 0.1661034	test: 0.1978774	best: 0.1978774 (38)	total: 11.4s	remaining: 2.04s
39:	learn: 0.1647142	test: 0.1971105	best: 0.1971105 (39)	total: 11.7s	remaining: 1.75s
40:	learn: 0.1633068	test: 0.1963006	best: 0.1963006 (40)	total: 12s	remaining: 1.46s
41:	learn: 0.1619309	test: 0.1955668	best: 0.1955668 (41)	total: 12.3s	remaining: 1.17s
42:	learn: 0.1604109	test: 0.1946884	best: 0.1946884 (42)	total: 12.6s	remaining: 876ms
43:	learn: 0.1591892	test: 0.1939680	best: 0.1939680 (43)	total: 12.8s	remaining: 583ms
44:	learn: 0.1579988	test: 0.1934174	best: 0.1934174 (44)	total: 13.1s	remaining: 291ms
45:	learn: 0.1570315	test: 0.1929078	best: 0.1929078 (45)	total: 13.4s	remaining: 0us

bestTest = 0.1929078196
bestIteration = 45

Trial 50, Fold 2: Log loss = 0.1926998253958043, Average precision = 0.9767160857624644, ROC-AUC = 0.9742849720222608, Elapsed Time = 13.507884499998909 seconds
Trial 50, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 50, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
0:	learn: 0.6178544	test: 0.6190754	best: 0.6190754 (0)	total: 290ms	remaining: 13s
1:	learn: 0.5549311	test: 0.5571483	best: 0.5571483 (1)	total: 552ms	remaining: 12.1s
2:	learn: 0.5017875	test: 0.5053256	best: 0.5053256 (2)	total: 823ms	remaining: 11.8s
3:	learn: 0.4571424	test: 0.4619925	best: 0.4619925 (3)	total: 1.08s	remaining: 11.3s
4:	learn: 0.4188859	test: 0.4244106	best: 0.4244106 (4)	total: 1.35s	remaining: 11.1s
5:	learn: 0.3868612	test: 0.3935517	best: 0.3935517 (5)	total: 1.65s	remaining: 11s
6:	learn: 0.3599702	test: 0.3676090	best: 0.3676090 (6)	total: 1.95s	remaining: 10.9s
7:	learn: 0.3378926	test: 0.3464450	best: 0.3464450 (7)	total: 2.25s	remaining: 10.7s
8:	learn: 0.3184316	test: 0.3278061	best: 0.3278061 (8)	total: 2.51s	remaining: 10.3s
9:	learn: 0.3017212	test: 0.3124041	best: 0.3124041 (9)	total: 2.82s	remaining: 10.2s
10:	learn: 0.2865931	test: 0.2984180	best: 0.2984180 (10)	total: 3.15s	remaining: 10s
11:	learn: 0.2738820	test: 0.2863556	best: 0.2863556 (11)	total: 3.42s	remaining: 9.7s
12:	learn: 0.2625004	test: 0.2760126	best: 0.2760126 (12)	total: 3.74s	remaining: 9.5s
13:	learn: 0.2529927	test: 0.2674119	best: 0.2674119 (13)	total: 4.01s	remaining: 9.18s
14:	learn: 0.2442810	test: 0.2601336	best: 0.2601336 (14)	total: 4.3s	remaining: 8.89s
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16:	learn: 0.2299836	test: 0.2474844	best: 0.2474844 (16)	total: 4.9s	remaining: 8.36s
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18:	learn: 0.2183713	test: 0.2376473	best: 0.2376473 (18)	total: 5.45s	remaining: 7.74s
19:	learn: 0.2129099	test: 0.2330156	best: 0.2330156 (19)	total: 5.74s	remaining: 7.47s
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27:	learn: 0.1854910	test: 0.2116935	best: 0.2116935 (27)	total: 7.97s	remaining: 5.12s
28:	learn: 0.1827133	test: 0.2096202	best: 0.2096202 (28)	total: 8.26s	remaining: 4.84s
29:	learn: 0.1805936	test: 0.2078156	best: 0.2078156 (29)	total: 8.51s	remaining: 4.54s
30:	learn: 0.1784780	test: 0.2062052	best: 0.2062052 (30)	total: 8.8s	remaining: 4.26s
31:	learn: 0.1761777	test: 0.2048994	best: 0.2048994 (31)	total: 9.09s	remaining: 3.98s
32:	learn: 0.1745002	test: 0.2036959	best: 0.2036959 (32)	total: 9.34s	remaining: 3.68s
33:	learn: 0.1727701	test: 0.2026426	best: 0.2026426 (33)	total: 9.6s	remaining: 3.39s
34:	learn: 0.1709083	test: 0.2013716	best: 0.2013716 (34)	total: 9.89s	remaining: 3.11s
35:	learn: 0.1690686	test: 0.2003829	best: 0.2003829 (35)	total: 10.2s	remaining: 2.83s
36:	learn: 0.1676516	test: 0.1995139	best: 0.1995139 (36)	total: 10.4s	remaining: 2.54s
37:	learn: 0.1660741	test: 0.1987830	best: 0.1987830 (37)	total: 10.8s	remaining: 2.26s
38:	learn: 0.1645369	test: 0.1980025	best: 0.1980025 (38)	total: 11s	remaining: 1.98s
39:	learn: 0.1633015	test: 0.1973022	best: 0.1973022 (39)	total: 11.3s	remaining: 1.69s
40:	learn: 0.1621575	test: 0.1965038	best: 0.1965038 (40)	total: 11.5s	remaining: 1.4s
41:	learn: 0.1609752	test: 0.1957273	best: 0.1957273 (41)	total: 11.7s	remaining: 1.12s
42:	learn: 0.1597722	test: 0.1953870	best: 0.1953870 (42)	total: 12s	remaining: 838ms
43:	learn: 0.1584105	test: 0.1949930	best: 0.1949930 (43)	total: 12.3s	remaining: 558ms
44:	learn: 0.1571810	test: 0.1944654	best: 0.1944654 (44)	total: 12.5s	remaining: 278ms
45:	learn: 0.1562715	test: 0.1940207	best: 0.1940207 (45)	total: 12.8s	remaining: 0us

bestTest = 0.1940206681
bestIteration = 45

Trial 50, Fold 3: Log loss = 0.19387414893267466, Average precision = 0.9772232770454468, ROC-AUC = 0.9739894335506737, Elapsed Time = 12.906083999998373 seconds
Trial 50, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 50, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
0:	learn: 0.6183698	test: 0.6197240	best: 0.6197240 (0)	total: 266ms	remaining: 12s
1:	learn: 0.5555576	test: 0.5578863	best: 0.5578863 (1)	total: 526ms	remaining: 11.6s
2:	learn: 0.5028698	test: 0.5067444	best: 0.5067444 (2)	total: 815ms	remaining: 11.7s
3:	learn: 0.4579173	test: 0.4630229	best: 0.4630229 (3)	total: 1.08s	remaining: 11.4s
4:	learn: 0.4197925	test: 0.4259875	best: 0.4259875 (4)	total: 1.39s	remaining: 11.4s
5:	learn: 0.3884763	test: 0.3956923	best: 0.3956923 (5)	total: 1.64s	remaining: 10.9s
6:	learn: 0.3617651	test: 0.3700823	best: 0.3700823 (6)	total: 1.92s	remaining: 10.7s
7:	learn: 0.3389121	test: 0.3480724	best: 0.3480724 (7)	total: 2.18s	remaining: 10.4s
8:	learn: 0.3193144	test: 0.3292847	best: 0.3292847 (8)	total: 2.46s	remaining: 10.1s
9:	learn: 0.3028246	test: 0.3139350	best: 0.3139350 (9)	total: 2.75s	remaining: 9.91s
10:	learn: 0.2880849	test: 0.3001490	best: 0.3001490 (10)	total: 3.02s	remaining: 9.6s
11:	learn: 0.2748970	test: 0.2881450	best: 0.2881450 (11)	total: 3.3s	remaining: 9.36s
12:	learn: 0.2639638	test: 0.2783061	best: 0.2783061 (12)	total: 3.59s	remaining: 9.12s
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18:	learn: 0.2204625	test: 0.2401054	best: 0.2401054 (18)	total: 5.22s	remaining: 7.42s
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27:	learn: 0.1875146	test: 0.2141628	best: 0.2141628 (27)	total: 7.65s	remaining: 4.92s
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29:	learn: 0.1830041	test: 0.2105225	best: 0.2105225 (29)	total: 8.17s	remaining: 4.36s
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31:	learn: 0.1789685	test: 0.2078399	best: 0.2078399 (31)	total: 8.72s	remaining: 3.82s
32:	learn: 0.1771306	test: 0.2066955	best: 0.2066955 (32)	total: 8.98s	remaining: 3.54s
33:	learn: 0.1752228	test: 0.2053706	best: 0.2053706 (33)	total: 9.25s	remaining: 3.27s
34:	learn: 0.1735439	test: 0.2042049	best: 0.2042049 (34)	total: 9.51s	remaining: 2.99s
35:	learn: 0.1717815	test: 0.2031189	best: 0.2031189 (35)	total: 9.77s	remaining: 2.71s
36:	learn: 0.1700256	test: 0.2019351	best: 0.2019351 (36)	total: 10.1s	remaining: 2.44s
37:	learn: 0.1685526	test: 0.2011771	best: 0.2011771 (37)	total: 10.3s	remaining: 2.17s
38:	learn: 0.1670850	test: 0.2005026	best: 0.2005026 (38)	total: 10.6s	remaining: 1.9s
39:	learn: 0.1656754	test: 0.1999724	best: 0.1999724 (39)	total: 10.9s	remaining: 1.63s
40:	learn: 0.1644005	test: 0.1992514	best: 0.1992514 (40)	total: 11.1s	remaining: 1.36s
41:	learn: 0.1631135	test: 0.1986401	best: 0.1986401 (41)	total: 11.4s	remaining: 1.09s
42:	learn: 0.1619106	test: 0.1979694	best: 0.1979694 (42)	total: 11.7s	remaining: 815ms
43:	learn: 0.1608369	test: 0.1975967	best: 0.1975967 (43)	total: 11.9s	remaining: 543ms
44:	learn: 0.1595419	test: 0.1970484	best: 0.1970484 (44)	total: 12.2s	remaining: 271ms
45:	learn: 0.1583545	test: 0.1966812	best: 0.1966812 (45)	total: 12.5s	remaining: 0us

bestTest = 0.1966811628
bestIteration = 45

Trial 50, Fold 4: Log loss = 0.19644371453445866, Average precision = 0.976562927945438, ROC-AUC = 0.9727146028407858, Elapsed Time = 12.629328699997131 seconds
Trial 50, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 50, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
0:	learn: 0.6177552	test: 0.6202089	best: 0.6202089 (0)	total: 294ms	remaining: 13.2s
1:	learn: 0.5550832	test: 0.5592067	best: 0.5592067 (1)	total: 533ms	remaining: 11.7s
2:	learn: 0.5021079	test: 0.5082167	best: 0.5082167 (2)	total: 788ms	remaining: 11.3s
3:	learn: 0.4566007	test: 0.4651797	best: 0.4651797 (3)	total: 1.08s	remaining: 11.3s
4:	learn: 0.4188748	test: 0.4290758	best: 0.4290758 (4)	total: 1.32s	remaining: 10.8s
5:	learn: 0.3863935	test: 0.3977348	best: 0.3977348 (5)	total: 1.58s	remaining: 10.5s
6:	learn: 0.3592003	test: 0.3719025	best: 0.3719025 (6)	total: 1.82s	remaining: 10.2s
7:	learn: 0.3364689	test: 0.3507489	best: 0.3507489 (7)	total: 2.09s	remaining: 9.92s
8:	learn: 0.3173569	test: 0.3327574	best: 0.3327574 (8)	total: 2.38s	remaining: 9.77s
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10:	learn: 0.2859704	test: 0.3039735	best: 0.3039735 (10)	total: 2.96s	remaining: 9.4s
11:	learn: 0.2733486	test: 0.2926140	best: 0.2926140 (11)	total: 3.23s	remaining: 9.16s
12:	learn: 0.2623045	test: 0.2825992	best: 0.2825992 (12)	total: 3.52s	remaining: 8.94s
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18:	learn: 0.2176815	test: 0.2446697	best: 0.2446697 (18)	total: 5.21s	remaining: 7.41s
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44:	learn: 0.1567747	test: 0.2055044	best: 0.2055044 (44)	total: 12.3s	remaining: 273ms
45:	learn: 0.1556959	test: 0.2051194	best: 0.2051194 (45)	total: 12.5s	remaining: 0us

bestTest = 0.2051194058
bestIteration = 45

Trial 50, Fold 5: Log loss = 0.20477899093252747, Average precision = 0.9745866292732474, ROC-AUC = 0.9716729279304387, Elapsed Time = 12.676016099998378 seconds
Optimization Progress:  51%|#####1    | 51/100 [1:16:43<49:39, 60.80s/it]
Trial 51, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 51, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
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60:	learn: 0.2536512	test: 0.2577347	best: 0.2577347 (60)	total: 4.58s	remaining: 751ms
61:	learn: 0.2531177	test: 0.2571852	best: 0.2571852 (61)	total: 4.66s	remaining: 676ms
62:	learn: 0.2518416	test: 0.2558969	best: 0.2558969 (62)	total: 4.74s	remaining: 601ms
63:	learn: 0.2510068	test: 0.2550722	best: 0.2550722 (63)	total: 4.81s	remaining: 526ms
64:	learn: 0.2496977	test: 0.2537770	best: 0.2537770 (64)	total: 4.88s	remaining: 451ms
65:	learn: 0.2484790	test: 0.2525453	best: 0.2525453 (65)	total: 4.96s	remaining: 376ms
66:	learn: 0.2480561	test: 0.2521605	best: 0.2521605 (66)	total: 5.04s	remaining: 301ms
67:	learn: 0.2473605	test: 0.2515421	best: 0.2515421 (67)	total: 5.12s	remaining: 226ms
68:	learn: 0.2470260	test: 0.2513322	best: 0.2513322 (68)	total: 5.19s	remaining: 151ms
69:	learn: 0.2465675	test: 0.2509071	best: 0.2509071 (69)	total: 5.27s	remaining: 75.3ms
70:	learn: 0.2457878	test: 0.2501164	best: 0.2501164 (70)	total: 5.34s	remaining: 0us

bestTest = 0.2501164455
bestIteration = 70

Trial 51, Fold 1: Log loss = 0.2501164455119471, Average precision = 0.9651792434948091, ROC-AUC = 0.9593418266634166, Elapsed Time = 5.464839000000211 seconds
Trial 51, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 51, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
0:	learn: 0.6559708	test: 0.6560732	best: 0.6560732 (0)	total: 78.3ms	remaining: 5.48s
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4:	learn: 0.5562752	test: 0.5570941	best: 0.5570941 (4)	total: 364ms	remaining: 4.8s
5:	learn: 0.5339560	test: 0.5351507	best: 0.5351507 (5)	total: 436ms	remaining: 4.72s
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61:	learn: 0.2574839	test: 0.2590366	best: 0.2590366 (61)	total: 4.41s	remaining: 640ms
62:	learn: 0.2560224	test: 0.2576319	best: 0.2576319 (62)	total: 4.47s	remaining: 568ms
63:	learn: 0.2554818	test: 0.2570843	best: 0.2570843 (63)	total: 4.55s	remaining: 497ms
64:	learn: 0.2537275	test: 0.2553180	best: 0.2553180 (64)	total: 4.62s	remaining: 426ms
65:	learn: 0.2524055	test: 0.2540134	best: 0.2540134 (65)	total: 4.68s	remaining: 355ms
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68:	learn: 0.2507072	test: 0.2523578	best: 0.2523578 (68)	total: 4.9s	remaining: 142ms
69:	learn: 0.2501721	test: 0.2518411	best: 0.2518411 (69)	total: 4.97s	remaining: 71.1ms
70:	learn: 0.2497587	test: 0.2514247	best: 0.2514247 (70)	total: 5.04s	remaining: 0us

bestTest = 0.2514246839
bestIteration = 70

Trial 51, Fold 2: Log loss = 0.2514246838670997, Average precision = 0.9648018845347787, ROC-AUC = 0.9613906591954149, Elapsed Time = 5.177672900001198 seconds
Trial 51, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 51, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
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62:	learn: 0.2545499	test: 0.2486171	best: 0.2486171 (62)	total: 4.03s	remaining: 512ms
63:	learn: 0.2539498	test: 0.2479679	best: 0.2479679 (63)	total: 4.09s	remaining: 447ms
64:	learn: 0.2530525	test: 0.2471032	best: 0.2471032 (64)	total: 4.16s	remaining: 384ms
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70:	learn: 0.2483928	test: 0.2425184	best: 0.2425184 (70)	total: 4.55s	remaining: 0us

bestTest = 0.2425183739
bestIteration = 70

Trial 51, Fold 3: Log loss = 0.24251837389686698, Average precision = 0.9670952121281954, ROC-AUC = 0.9632783682137318, Elapsed Time = 4.66802319999988 seconds
Trial 51, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 51, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
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62:	learn: 0.2519337	test: 0.2524008	best: 0.2524008 (62)	total: 3.92s	remaining: 498ms
63:	learn: 0.2511284	test: 0.2516262	best: 0.2516262 (63)	total: 4s	remaining: 437ms
64:	learn: 0.2505069	test: 0.2511763	best: 0.2511763 (64)	total: 4.07s	remaining: 375ms
65:	learn: 0.2498768	test: 0.2505923	best: 0.2505923 (65)	total: 4.14s	remaining: 313ms
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68:	learn: 0.2476684	test: 0.2485791	best: 0.2485791 (68)	total: 4.34s	remaining: 126ms
69:	learn: 0.2466374	test: 0.2475473	best: 0.2475473 (69)	total: 4.41s	remaining: 62.9ms
70:	learn: 0.2457004	test: 0.2467596	best: 0.2467596 (70)	total: 4.47s	remaining: 0us

bestTest = 0.2467596263
bestIteration = 70

Trial 51, Fold 4: Log loss = 0.24675962631827011, Average precision = 0.9670576678372873, ROC-AUC = 0.9614320973745264, Elapsed Time = 4.5955150999980106 seconds
Trial 51, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 51, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
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61:	learn: 0.2554817	test: 0.2631907	best: 0.2631907 (61)	total: 3.87s	remaining: 561ms
62:	learn: 0.2547611	test: 0.2625104	best: 0.2625104 (62)	total: 3.93s	remaining: 500ms
63:	learn: 0.2531689	test: 0.2609323	best: 0.2609323 (63)	total: 4s	remaining: 438ms
64:	learn: 0.2523389	test: 0.2601910	best: 0.2601910 (64)	total: 4.08s	remaining: 376ms
65:	learn: 0.2517066	test: 0.2596108	best: 0.2596108 (65)	total: 4.14s	remaining: 314ms
66:	learn: 0.2513172	test: 0.2592946	best: 0.2592946 (66)	total: 4.21s	remaining: 251ms
67:	learn: 0.2506984	test: 0.2586273	best: 0.2586273 (67)	total: 4.27s	remaining: 189ms
68:	learn: 0.2493523	test: 0.2572411	best: 0.2572411 (68)	total: 4.34s	remaining: 126ms
69:	learn: 0.2480637	test: 0.2559594	best: 0.2559594 (69)	total: 4.41s	remaining: 63ms
70:	learn: 0.2475529	test: 0.2554910	best: 0.2554910 (70)	total: 4.47s	remaining: 0us

bestTest = 0.2554910349
bestIteration = 70

Trial 51, Fold 5: Log loss = 0.2554910349390996, Average precision = 0.9647821063377878, ROC-AUC = 0.9591003102762761, Elapsed Time = 4.5963112999997975 seconds
Optimization Progress:  52%|#####2    | 52/100 [1:17:16<41:50, 52.30s/it]
Trial 52, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 52, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
0:	learn: 0.6007363	test: 0.6117303	best: 0.6117303 (0)	total: 214ms	remaining: 16.7s
1:	learn: 0.5330657	test: 0.5516216	best: 0.5516216 (1)	total: 419ms	remaining: 16.1s
2:	learn: 0.4784990	test: 0.5018970	best: 0.5018970 (2)	total: 632ms	remaining: 16s
3:	learn: 0.4336885	test: 0.4603398	best: 0.4603398 (3)	total: 870ms	remaining: 16.3s
4:	learn: 0.3999728	test: 0.4251496	best: 0.4251496 (4)	total: 1.09s	remaining: 16.2s
5:	learn: 0.3708225	test: 0.3945938	best: 0.3945938 (5)	total: 1.31s	remaining: 15.9s
6:	learn: 0.3449674	test: 0.3676794	best: 0.3676794 (6)	total: 1.55s	remaining: 16s
7:	learn: 0.3259138	test: 0.3471731	best: 0.3471731 (7)	total: 1.84s	remaining: 16.3s
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18:	learn: 0.2064025	test: 0.2355595	best: 0.2355595 (18)	total: 4.98s	remaining: 15.7s
19:	learn: 0.2020122	test: 0.2320539	best: 0.2320539 (19)	total: 5.24s	remaining: 15.5s
20:	learn: 0.1975701	test: 0.2286192	best: 0.2286192 (20)	total: 5.51s	remaining: 15.2s
21:	learn: 0.1939508	test: 0.2260034	best: 0.2260034 (21)	total: 5.8s	remaining: 15s
22:	learn: 15.2768573	test: 0.2219228	best: 0.2219228 (22)	total: 6.06s	remaining: 14.8s
23:	learn: 12.1099148	test: 0.2191927	best: 0.2191927 (23)	total: 6.36s	remaining: 14.6s
24:	learn: 12.1067310	test: 0.2173214	best: 0.2173214 (24)	total: 6.67s	remaining: 14.4s
25:	learn: 12.1032913	test: 0.2151956	best: 0.2151956 (25)	total: 6.95s	remaining: 14.2s
26:	learn: 12.1000974	test: 0.2130473	best: 0.2130473 (26)	total: 7.22s	remaining: 13.9s
27:	learn: 33.4793361	test: 0.2108273	best: 0.2108273 (27)	total: 7.47s	remaining: 13.6s
28:	learn: 30.3142507	test: 12.3503368	best: 0.2108273 (27)	total: 7.75s	remaining: 13.4s
29:	learn: 30.3121085	test: 12.3491773	best: 0.2108273 (27)	total: 8.01s	remaining: 13.1s
30:	learn: 30.3099687	test: 12.3483104	best: 0.2108273 (27)	total: 8.27s	remaining: 12.8s
31:	learn: 54.2351964	test: 298.8604287	best: 0.2108273 (27)	total: 8.54s	remaining: 12.5s
32:	learn: 706.8792712	test: 1529.0069553	best: 0.2108273 (27)	total: 8.8s	remaining: 12.3s
Training has stopped (degenerate solution on iteration 33, probably too small l2-regularization, try to increase it)
bestTest = 0.2108272576
bestIteration = 27

Shrink model to first 28 iterations.
Trial 52, Fold 1: Log loss = 0.21069644814043154, Average precision = 0.9717256097227264, ROC-AUC = 0.9694559602672747, Elapsed Time = 9.182990499997686 seconds
Trial 52, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 52, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
0:	learn: 0.6111835	test: 0.6226610	best: 0.6226610 (0)	total: 218ms	remaining: 17s
1:	learn: 0.5394007	test: 0.5589982	best: 0.5589982 (1)	total: 442ms	remaining: 17s
2:	learn: 0.4878466	test: 0.5128214	best: 0.5128214 (2)	total: 690ms	remaining: 17.5s
3:	learn: 0.4439685	test: 0.4699998	best: 0.4699998 (3)	total: 959ms	remaining: 18s
4:	learn: 0.4043180	test: 0.4323217	best: 0.4323217 (4)	total: 1.23s	remaining: 18.1s
5:	learn: 0.3712810	test: 0.3995021	best: 0.3995021 (5)	total: 1.48s	remaining: 18s
6:	learn: 0.3457900	test: 0.3719430	best: 0.3719430 (6)	total: 1.73s	remaining: 17.8s
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8:	learn: 0.3061276	test: 0.3321531	best: 0.3321531 (8)	total: 2.3s	remaining: 17.9s
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14:	learn: 0.2352450	test: 0.2601812	best: 0.2601812 (14)	total: 3.96s	remaining: 16.9s
15:	learn: 0.2266276	test: 0.2520925	best: 0.2520925 (15)	total: 4.26s	remaining: 16.8s
16:	learn: 0.2201787	test: 0.2459260	best: 0.2459260 (16)	total: 4.53s	remaining: 16.5s
17:	learn: 0.2145855	test: 0.2408975	best: 0.2408975 (17)	total: 4.8s	remaining: 16.3s
18:	learn: 0.2090800	test: 0.2352024	best: 0.2352024 (18)	total: 5.06s	remaining: 16s
19:	learn: 0.2042282	test: 0.2307075	best: 0.2307075 (19)	total: 5.31s	remaining: 15.7s
20:	learn: 0.1994770	test: 0.2263185	best: 0.2263185 (20)	total: 5.58s	remaining: 15.4s
21:	learn: 0.1953308	test: 0.2231280	best: 0.2231280 (21)	total: 5.87s	remaining: 15.2s
22:	learn: 0.1916367	test: 0.2201898	best: 0.2201898 (22)	total: 6.13s	remaining: 14.9s
23:	learn: 0.1887684	test: 0.2178409	best: 0.2178409 (23)	total: 6.4s	remaining: 14.7s
24:	learn: 0.1855452	test: 0.2149730	best: 0.2149730 (24)	total: 6.66s	remaining: 14.4s
Training has stopped (degenerate solution on iteration 25, probably too small l2-regularization, try to increase it)
bestTest = 0.2149729902
bestIteration = 24

Shrink model to first 25 iterations.
Trial 52, Fold 2: Log loss = 0.2148990726437246, Average precision = 0.9747406236409003, ROC-AUC = 0.9717614063499165, Elapsed Time = 7.07240389999788 seconds
Trial 52, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 52, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
0:	learn: 0.6062642	test: 0.6156540	best: 0.6156540 (0)	total: 234ms	remaining: 18.2s
1:	learn: 0.5407220	test: 0.5564034	best: 0.5564034 (1)	total: 448ms	remaining: 17.3s
2:	learn: 0.4881608	test: 0.5081456	best: 0.5081456 (2)	total: 652ms	remaining: 16.5s
3:	learn: 0.4459415	test: 0.4694549	best: 0.4694549 (3)	total: 878ms	remaining: 16.5s
4:	learn: 0.4080858	test: 0.4328094	best: 0.4328094 (4)	total: 1.2s	remaining: 17.8s
5:	learn: 0.3812365	test: 0.4041893	best: 0.4041893 (5)	total: 1.56s	remaining: 19s
6:	learn: 0.3562090	test: 0.3787593	best: 0.3787593 (6)	total: 1.9s	remaining: 19.5s
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10:	learn: 0.2910198	test: 0.3107458	best: 0.3107458 (10)	total: 3.09s	remaining: 19.1s
11:	learn: 0.2771032	test: 0.2968483	best: 0.2968483 (11)	total: 3.38s	remaining: 18.8s
12:	learn: 0.2662847	test: 0.2859029	best: 0.2859029 (12)	total: 3.66s	remaining: 18.6s
13:	learn: 0.2560237	test: 0.2756404	best: 0.2756404 (13)	total: 3.95s	remaining: 18.3s
14:	learn: 0.2486171	test: 0.2681738	best: 0.2681738 (14)	total: 4.23s	remaining: 18.1s
15:	learn: 0.2398348	test: 0.2599397	best: 0.2599397 (15)	total: 4.57s	remaining: 18s
16:	learn: 12.2802762	test: 0.2528253	best: 0.2528253 (16)	total: 4.88s	remaining: 17.8s
17:	learn: 12.2724880	test: 0.2462363	best: 0.2462363 (17)	total: 5.18s	remaining: 17.6s
18:	learn: 12.2672607	test: 0.2420455	best: 0.2420455 (18)	total: 5.49s	remaining: 17.3s
19:	learn: 149.6040500	test: 191.8418295	best: 0.2420455 (18)	total: 5.76s	remaining: 17s
Training has stopped (degenerate solution on iteration 20, probably too small l2-regularization, try to increase it)
bestTest = 0.2420455077
bestIteration = 18

Shrink model to first 19 iterations.
Trial 52, Fold 3: Log loss = 0.24250937468924788, Average precision = 0.9713262805111159, ROC-AUC = 0.9697353752732806, Elapsed Time = 6.117950899999414 seconds
Trial 52, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 52, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
0:	learn: 0.6133301	test: 0.6155228	best: 0.6155228 (0)	total: 199ms	remaining: 15.5s
1:	learn: 0.5476824	test: 0.5555812	best: 0.5555812 (1)	total: 412ms	remaining: 15.9s
2:	learn: 0.4942530	test: 0.5052748	best: 0.5052748 (2)	total: 677ms	remaining: 17.2s
3:	learn: 0.4517225	test: 0.4647773	best: 0.4647773 (3)	total: 916ms	remaining: 17.2s
4:	learn: 0.4181274	test: 0.4355127	best: 0.4355127 (4)	total: 1.15s	remaining: 17s
5:	learn: 0.3860266	test: 0.4063332	best: 0.4063332 (5)	total: 1.39s	remaining: 16.9s
6:	learn: 0.3623489	test: 0.3837529	best: 0.3837529 (6)	total: 1.63s	remaining: 16.7s
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11:	learn: 0.2816033	test: 0.3054718	best: 0.3054718 (11)	total: 2.89s	remaining: 16.2s
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16:	learn: 0.2345629	test: 0.2611969	best: 0.2611969 (16)	total: 4.12s	remaining: 15s
17:	learn: 0.2285093	test: 0.2550710	best: 0.2550710 (17)	total: 4.35s	remaining: 14.7s
18:	learn: 0.2227166	test: 0.2499288	best: 0.2499288 (18)	total: 4.62s	remaining: 14.6s
19:	learn: 0.2181477	test: 0.2455096	best: 0.2455096 (19)	total: 4.87s	remaining: 14.4s
20:	learn: 0.2130815	test: 0.2414888	best: 0.2414888 (20)	total: 5.12s	remaining: 14.1s
21:	learn: 0.2078739	test: 0.2350795	best: 0.2350795 (21)	total: 5.37s	remaining: 13.9s
22:	learn: 0.2034048	test: 0.2312612	best: 0.2312612 (22)	total: 5.62s	remaining: 13.7s
23:	learn: 0.1986027	test: 0.2272488	best: 0.2272488 (23)	total: 5.89s	remaining: 13.5s
24:	learn: 24.1715773	test: 0.2234134	best: 0.2234134 (24)	total: 6.14s	remaining: 13.3s
25:	learn: 576.7273153	test: 0.2199385	best: 0.2199385 (25)	total: 6.4s	remaining: 13.1s
Training has stopped (degenerate solution on iteration 26, probably too small l2-regularization, try to increase it)
bestTest = 0.2199384653
bestIteration = 25

Shrink model to first 26 iterations.
Trial 52, Fold 4: Log loss = 0.21992247379741797, Average precision = 0.9736665888102406, ROC-AUC = 0.9709873960099286, Elapsed Time = 6.823154199999408 seconds
Trial 52, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 52, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
0:	learn: 0.5963745	test: 0.5999505	best: 0.5999505 (0)	total: 227ms	remaining: 17.7s
1:	learn: 0.5325007	test: 0.5355169	best: 0.5355169 (1)	total: 422ms	remaining: 16.3s
2:	learn: 0.4842094	test: 0.4896419	best: 0.4896419 (2)	total: 630ms	remaining: 16s
3:	learn: 0.4379176	test: 0.4479523	best: 0.4479523 (3)	total: 835ms	remaining: 15.7s
4:	learn: 0.4048628	test: 0.4157127	best: 0.4157127 (4)	total: 1.06s	remaining: 15.7s
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8:	learn: 0.3059625	test: 0.3265686	best: 0.3265686 (8)	total: 1.97s	remaining: 15.3s
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10:	learn: 0.2787450	test: 0.3021593	best: 0.3021593 (10)	total: 2.47s	remaining: 15.3s
11:	learn: 0.2662502	test: 0.2911137	best: 0.2911137 (11)	total: 2.71s	remaining: 15.1s
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34:	learn: 0.1604694	test: 0.2092465	best: 0.2092465 (34)	total: 8.53s	remaining: 10.7s
35:	learn: 626.6661337	test: 0.2086638	best: 0.2086638 (35)	total: 8.78s	remaining: 10.5s
36:	learn: 626.6631965	test: 0.2081746	best: 0.2081746 (36)	total: 9.05s	remaining: 10.3s
37:	learn: 1679.6308291	test: 289.2247117	best: 0.2081746 (36)	total: 9.31s	remaining: 10s
Training has stopped (degenerate solution on iteration 38, probably too small l2-regularization, try to increase it)
bestTest = 0.2081745998
bestIteration = 36

Shrink model to first 37 iterations.
Trial 52, Fold 5: Log loss = 0.20776672363436344, Average precision = 0.9726535945074268, ROC-AUC = 0.9700186908942274, Elapsed Time = 9.64951579999979 seconds
Optimization Progress:  53%|#####3    | 53/100 [1:18:03<39:49, 50.84s/it]
Trial 53, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 53, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
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52:	learn: 0.1074396	test: 0.2117996	best: 0.2117996 (52)	total: 2m 10s	remaining: 0us

bestTest = 0.2117996017
bestIteration = 52

Trial 53, Fold 1: Log loss = 0.21179960173124454, Average precision = 0.9733106009457305, ROC-AUC = 0.9679481492235557, Elapsed Time = 130.75538059999963 seconds
Trial 53, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 53, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
0:	learn: 0.6200862	test: 0.6262834	best: 0.6262834 (0)	total: 2.13s	remaining: 1m 51s
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52:	learn: 0.1038074	test: 0.2077162	best: 0.2077162 (52)	total: 2m 6s	remaining: 0us

bestTest = 0.2077161998
bestIteration = 52

Trial 53, Fold 2: Log loss = 0.20771619979730535, Average precision = 0.9717465272076953, ROC-AUC = 0.9676972971594939, Elapsed Time = 126.69987220000257 seconds
Trial 53, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 53, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
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47:	learn: 0.1112916	test: 0.2094349	best: 0.2094349 (47)	total: 1m 53s	remaining: 11.8s
48:	learn: 0.1100960	test: 0.2090742	best: 0.2090742 (48)	total: 1m 56s	remaining: 9.47s
49:	learn: 0.1088066	test: 0.2088259	best: 0.2088259 (49)	total: 1m 58s	remaining: 7.1s
50:	learn: 0.1069822	test: 0.2086522	best: 0.2086522 (50)	total: 2m	remaining: 4.73s
51:	learn: 0.1058120	test: 0.2087468	best: 0.2086522 (50)	total: 2m 3s	remaining: 2.37s
52:	learn: 0.1044536	test: 0.2082622	best: 0.2082622 (52)	total: 2m 5s	remaining: 0us

bestTest = 0.2082622218
bestIteration = 52

Trial 53, Fold 3: Log loss = 0.208262221827831, Average precision = 0.9721844815214287, ROC-AUC = 0.9681268419354528, Elapsed Time = 125.71645570000328 seconds
Trial 53, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 53, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
0:	learn: 0.6207817	test: 0.6262071	best: 0.6262071 (0)	total: 2.28s	remaining: 1m 58s
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7:	learn: 0.3366949	test: 0.3673127	best: 0.3673127 (7)	total: 19.2s	remaining: 1m 48s
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43:	learn: 0.1271531	test: 0.2118001	best: 0.2118001 (43)	total: 1m 46s	remaining: 21.7s
44:	learn: 0.1255582	test: 0.2113169	best: 0.2113169 (44)	total: 1m 48s	remaining: 19.3s
45:	learn: 0.1242425	test: 0.2111626	best: 0.2111626 (45)	total: 1m 50s	remaining: 16.8s
46:	learn: 0.1226406	test: 0.2106542	best: 0.2106542 (46)	total: 1m 53s	remaining: 14.4s
47:	learn: 0.1197945	test: 0.2101843	best: 0.2101843 (47)	total: 1m 55s	remaining: 12s
48:	learn: 0.1176865	test: 0.2096479	best: 0.2096479 (48)	total: 1m 58s	remaining: 9.63s
49:	learn: 0.1154716	test: 0.2096768	best: 0.2096479 (48)	total: 2m	remaining: 7.22s
50:	learn: 0.1140586	test: 0.2094562	best: 0.2094562 (50)	total: 2m 2s	remaining: 4.81s
51:	learn: 0.1130484	test: 0.2091180	best: 0.2091180 (51)	total: 2m 5s	remaining: 2.41s
52:	learn: 0.1117480	test: 0.2088547	best: 0.2088547 (52)	total: 2m 7s	remaining: 0us

bestTest = 0.2088546992
bestIteration = 52

Trial 53, Fold 4: Log loss = 0.2088546991897949, Average precision = 0.9735387786389698, ROC-AUC = 0.9687572277421308, Elapsed Time = 127.79895599999873 seconds
Trial 53, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 53, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
0:	learn: 0.6204819	test: 0.6260629	best: 0.6260629 (0)	total: 2.06s	remaining: 1m 46s
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2:	learn: 0.5044394	test: 0.5227379	best: 0.5227379 (2)	total: 6.57s	remaining: 1m 49s
3:	learn: 0.4592319	test: 0.4816138	best: 0.4816138 (3)	total: 8.86s	remaining: 1m 48s
4:	learn: 0.4210088	test: 0.4462601	best: 0.4462601 (4)	total: 11.1s	remaining: 1m 46s
5:	learn: 0.3878635	test: 0.4162088	best: 0.4162088 (5)	total: 13.5s	remaining: 1m 45s
6:	learn: 0.3591068	test: 0.3912136	best: 0.3912136 (6)	total: 15.9s	remaining: 1m 44s
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52:	learn: 0.1007672	test: 0.2175866	best: 0.2175866 (52)	total: 2m 6s	remaining: 0us

bestTest = 0.217586627
bestIteration = 52

Trial 53, Fold 5: Log loss = 0.21758662702271073, Average precision = 0.9715107242558519, ROC-AUC = 0.9662566560763987, Elapsed Time = 126.38266299999668 seconds
Optimization Progress:  54%|#####4    | 54/100 [1:28:49<2:55:41, 229.16s/it]
Trial 54, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 54, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
0:	learn: 0.6702089	test: 0.6701121	best: 0.6701121 (0)	total: 62.2ms	remaining: 4.35s
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69:	learn: 0.2577577	test: 0.2599956	best: 0.2599956 (69)	total: 4.56s	remaining: 65.2ms
70:	learn: 0.2570389	test: 0.2593645	best: 0.2593645 (70)	total: 4.64s	remaining: 0us

bestTest = 0.2593645059
bestIteration = 70

Trial 54, Fold 1: Log loss = 0.2593645058885701, Average precision = 0.9648842884960731, ROC-AUC = 0.9599296414591131, Elapsed Time = 4.751763500000379 seconds
Trial 54, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 54, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
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61:	learn: 0.2661496	test: 0.2671323	best: 0.2671323 (61)	total: 4.41s	remaining: 640ms
62:	learn: 0.2649082	test: 0.2658928	best: 0.2658928 (62)	total: 4.49s	remaining: 570ms
63:	learn: 0.2638191	test: 0.2648281	best: 0.2648281 (63)	total: 4.56s	remaining: 499ms
64:	learn: 0.2628430	test: 0.2639111	best: 0.2639111 (64)	total: 4.63s	remaining: 428ms
65:	learn: 0.2619248	test: 0.2629130	best: 0.2629130 (65)	total: 4.71s	remaining: 357ms
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67:	learn: 0.2601996	test: 0.2611431	best: 0.2611431 (67)	total: 4.86s	remaining: 214ms
68:	learn: 0.2591833	test: 0.2601208	best: 0.2601208 (68)	total: 4.93s	remaining: 143ms
69:	learn: 0.2583868	test: 0.2593355	best: 0.2593355 (69)	total: 5s	remaining: 71.5ms
70:	learn: 0.2572255	test: 0.2580699	best: 0.2580699 (70)	total: 5.08s	remaining: 0us

bestTest = 0.2580698523
bestIteration = 70

Trial 54, Fold 2: Log loss = 0.25806985231369184, Average precision = 0.9644703927296976, ROC-AUC = 0.9615634240491712, Elapsed Time = 5.203042400000413 seconds
Trial 54, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 54, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
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61:	learn: 0.2665304	test: 0.2619521	best: 0.2619521 (61)	total: 4.34s	remaining: 630ms
62:	learn: 0.2652236	test: 0.2606410	best: 0.2606410 (62)	total: 4.41s	remaining: 560ms
63:	learn: 0.2640679	test: 0.2594682	best: 0.2594682 (63)	total: 4.48s	remaining: 490ms
64:	learn: 0.2631806	test: 0.2586177	best: 0.2586177 (64)	total: 4.55s	remaining: 420ms
65:	learn: 0.2619936	test: 0.2574294	best: 0.2574294 (65)	total: 4.62s	remaining: 350ms
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67:	learn: 0.2599222	test: 0.2552712	best: 0.2552712 (67)	total: 4.77s	remaining: 210ms
68:	learn: 0.2589736	test: 0.2542752	best: 0.2542752 (68)	total: 4.84s	remaining: 140ms
69:	learn: 0.2579849	test: 0.2532525	best: 0.2532525 (69)	total: 4.91s	remaining: 70.2ms
70:	learn: 0.2571962	test: 0.2524869	best: 0.2524869 (70)	total: 4.98s	remaining: 0us

bestTest = 0.2524868978
bestIteration = 70

Trial 54, Fold 3: Log loss = 0.25248689782596256, Average precision = 0.9654262617139904, ROC-AUC = 0.9628300736292629, Elapsed Time = 5.105439099999785 seconds
Trial 54, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 54, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
0:	learn: 0.6697009	test: 0.6695279	best: 0.6695279 (0)	total: 65.2ms	remaining: 4.56s
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70:	learn: 0.2573042	test: 0.2563374	best: 0.2563374 (70)	total: 4.98s	remaining: 0us

bestTest = 0.2563374062
bestIteration = 70

Trial 54, Fold 4: Log loss = 0.25633740624047024, Average precision = 0.965722052564221, ROC-AUC = 0.96159975118684, Elapsed Time = 5.0999941999980365 seconds
Trial 54, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 54, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
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70:	learn: 0.2533542	test: 0.2626023	best: 0.2626023 (70)	total: 5.01s	remaining: 0us

bestTest = 0.2626023064
bestIteration = 70

Trial 54, Fold 5: Log loss = 0.26260230635432746, Average precision = 0.9619306863444705, ROC-AUC = 0.957235382643108, Elapsed Time = 5.132205799996882 seconds
Optimization Progress:  55%|#####5    | 55/100 [1:29:22<2:07:48, 170.40s/it]
Trial 55, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 55, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
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90:	learn: 0.1968318	test: 0.2261929	best: 0.2261929 (90)	total: 34s	remaining: 374ms
91:	learn: 0.1955144	test: 0.2251410	best: 0.2251410 (91)	total: 34.4s	remaining: 0us

bestTest = 0.2251409612
bestIteration = 91

Trial 55, Fold 1: Log loss = 0.22514096118599683, Average precision = 0.9732269855250595, ROC-AUC = 0.9696902348212195, Elapsed Time = 34.570546200000535 seconds
Trial 55, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 55, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
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86:	learn: 0.2035034	test: 0.2244757	best: 0.2244757 (86)	total: 33.1s	remaining: 1.9s
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91:	learn: 0.1977903	test: 0.2196930	best: 0.2196930 (91)	total: 35s	remaining: 0us

bestTest = 0.2196930218
bestIteration = 91

Trial 55, Fold 2: Log loss = 0.21969302183474357, Average precision = 0.9750632291154587, ROC-AUC = 0.9728057346095119, Elapsed Time = 35.137603999999556 seconds
Trial 55, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 55, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
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85:	learn: 0.2031756	test: 0.2229165	best: 0.2229165 (85)	total: 32.4s	remaining: 2.26s
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90:	learn: 0.1980359	test: 0.2193528	best: 0.2193528 (90)	total: 34.2s	remaining: 376ms
91:	learn: 0.1970997	test: 0.2186754	best: 0.2186754 (91)	total: 34.6s	remaining: 0us

bestTest = 0.218675371
bestIteration = 91

Trial 55, Fold 3: Log loss = 0.21867537101758067, Average precision = 0.973053933839598, ROC-AUC = 0.9723832327291464, Elapsed Time = 34.787642700001015 seconds
Trial 55, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 55, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
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84:	learn: 0.2044428	test: 0.2261662	best: 0.2261662 (84)	total: 32.8s	remaining: 2.7s
85:	learn: 0.2031236	test: 0.2250698	best: 0.2250698 (85)	total: 33.2s	remaining: 2.31s
86:	learn: 0.2017441	test: 0.2237796	best: 0.2237796 (86)	total: 33.6s	remaining: 1.93s
87:	learn: 0.2008668	test: 0.2231262	best: 0.2231262 (87)	total: 34s	remaining: 1.54s
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89:	learn: 0.1989752	test: 0.2216442	best: 0.2216442 (89)	total: 34.7s	remaining: 772ms
90:	learn: 0.1981556	test: 0.2210612	best: 0.2210612 (90)	total: 35.1s	remaining: 386ms
91:	learn: 0.1969564	test: 0.2201699	best: 0.2201699 (91)	total: 35.5s	remaining: 0us

bestTest = 0.2201698928
bestIteration = 91

Trial 55, Fold 4: Log loss = 0.22016989283450483, Average precision = 0.9753153670135544, ROC-AUC = 0.9728019318487998, Elapsed Time = 35.669709100002365 seconds
Trial 55, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 55, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
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91:	learn: 0.1964638	test: 0.2268257	best: 0.2268257 (91)	total: 34.6s	remaining: 0us

bestTest = 0.226825692
bestIteration = 91

Trial 55, Fold 5: Log loss = 0.22682569196956784, Average precision = 0.9742588788968436, ROC-AUC = 0.9710420452223029, Elapsed Time = 34.77915720000237 seconds
Optimization Progress:  56%|#####6    | 56/100 [1:32:26<2:07:52, 174.37s/it]
Trial 56, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 56, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
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64:	learn: 0.3100567	test: 0.3135019	best: 0.3135019 (64)	total: 10.6s	remaining: 491ms
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67:	learn: 0.3040928	test: 0.3076964	best: 0.3076964 (67)	total: 11.2s	remaining: 0us

bestTest = 0.3076964181
bestIteration = 67

Trial 56, Fold 1: Log loss = 0.3076299540603475, Average precision = 0.9646151856433388, ROC-AUC = 0.9606843684728734, Elapsed Time = 11.31044860000111 seconds
Trial 56, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 56, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
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63:	learn: 0.3166649	test: 0.3202214	best: 0.3202214 (63)	total: 10.2s	remaining: 636ms
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67:	learn: 0.3073778	test: 0.3110252	best: 0.3110252 (67)	total: 10.8s	remaining: 0us

bestTest = 0.3110251809
bestIteration = 67

Trial 56, Fold 2: Log loss = 0.3110085670418255, Average precision = 0.966155746447845, ROC-AUC = 0.9614176703012285, Elapsed Time = 11.003359300000739 seconds
Trial 56, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 56, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
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60:	learn: 0.3202992	test: 0.3196495	best: 0.3196495 (60)	total: 9.48s	remaining: 1.09s
61:	learn: 0.3182670	test: 0.3175952	best: 0.3175952 (61)	total: 9.65s	remaining: 934ms
62:	learn: 0.3160902	test: 0.3154508	best: 0.3154508 (62)	total: 9.8s	remaining: 778ms
63:	learn: 0.3142071	test: 0.3135371	best: 0.3135371 (63)	total: 9.98s	remaining: 624ms
64:	learn: 0.3122548	test: 0.3115832	best: 0.3115832 (64)	total: 10.1s	remaining: 468ms
65:	learn: 0.3101013	test: 0.3094745	best: 0.3094745 (65)	total: 10.3s	remaining: 312ms
66:	learn: 0.3083302	test: 0.3077392	best: 0.3077392 (66)	total: 10.5s	remaining: 156ms
67:	learn: 0.3065057	test: 0.3059884	best: 0.3059884 (67)	total: 10.6s	remaining: 0us

bestTest = 0.3059884176
bestIteration = 67

Trial 56, Fold 3: Log loss = 0.3060164081762603, Average precision = 0.9668191328922311, ROC-AUC = 0.9625206013859059, Elapsed Time = 10.77089270000215 seconds
Trial 56, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 56, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
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67:	learn: 0.3032752	test: 0.3056005	best: 0.3056005 (67)	total: 10.6s	remaining: 0us

bestTest = 0.3056005275
bestIteration = 67

Trial 56, Fold 4: Log loss = 0.3055728903934642, Average precision = 0.9658589619601456, ROC-AUC = 0.9609524584456988, Elapsed Time = 10.732753100000991 seconds
Trial 56, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 56, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
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64:	learn: 0.3080723	test: 0.3180555	best: 0.3180555 (64)	total: 10.1s	remaining: 467ms
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67:	learn: 0.3019237	test: 0.3121814	best: 0.3121814 (67)	total: 10.6s	remaining: 0us

bestTest = 0.3121814389
bestIteration = 67

Trial 56, Fold 5: Log loss = 0.3120946344441767, Average precision = 0.9619429643461269, ROC-AUC = 0.9570932129387066, Elapsed Time = 10.751953400002094 seconds
Optimization Progress:  57%|#####6    | 57/100 [1:33:28<1:40:56, 140.84s/it]
Trial 57, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 57, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
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85:	learn: 0.1130252	test: 0.1981144	best: 0.1979566 (74)	total: 34.7s	remaining: 3.23s
86:	learn: 0.1125481	test: 0.1981705	best: 0.1979566 (74)	total: 35s	remaining: 2.81s
87:	learn: 0.1121565	test: 0.1982503	best: 0.1979566 (74)	total: 35.3s	remaining: 2.4s
88:	learn: 0.1116439	test: 0.1981206	best: 0.1979566 (74)	total: 35.6s	remaining: 2s
89:	learn: 0.1109068	test: 0.1980684	best: 0.1979566 (74)	total: 36.1s	remaining: 1.6s
90:	learn: 0.1102356	test: 0.1980993	best: 0.1979566 (74)	total: 36.6s	remaining: 1.21s
91:	learn: 0.1100020	test: 0.1980003	best: 0.1979566 (74)	total: 36.8s	remaining: 800ms
92:	learn: 0.1093673	test: 0.1978562	best: 0.1978562 (92)	total: 37.2s	remaining: 400ms
93:	learn: 0.1088471	test: 0.1980920	best: 0.1978562 (92)	total: 37.5s	remaining: 0us

bestTest = 0.1978562267
bestIteration = 92

Shrink model to first 93 iterations.
Trial 57, Fold 1: Log loss = 0.1975462884624243, Average precision = 0.9758262951181361, ROC-AUC = 0.9721045248371354, Elapsed Time = 37.68159860000014 seconds
Trial 57, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 57, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
0:	learn: 0.5899010	test: 0.5927589	best: 0.5927589 (0)	total: 507ms	remaining: 47.1s
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84:	learn: 0.1155761	test: 0.1834683	best: 0.1833982 (80)	total: 34.6s	remaining: 3.66s
85:	learn: 0.1151151	test: 0.1833814	best: 0.1833814 (85)	total: 34.9s	remaining: 3.25s
86:	learn: 0.1146518	test: 0.1832630	best: 0.1832630 (86)	total: 35.3s	remaining: 2.84s
87:	learn: 0.1138358	test: 0.1831522	best: 0.1831522 (87)	total: 35.8s	remaining: 2.44s
88:	learn: 0.1133139	test: 0.1831560	best: 0.1831522 (87)	total: 36.2s	remaining: 2.03s
89:	learn: 0.1128933	test: 0.1830486	best: 0.1830486 (89)	total: 36.6s	remaining: 1.62s
90:	learn: 0.1124185	test: 0.1830539	best: 0.1830486 (89)	total: 36.9s	remaining: 1.22s
91:	learn: 0.1119197	test: 0.1830842	best: 0.1830486 (89)	total: 37.3s	remaining: 810ms
92:	learn: 0.1112869	test: 0.1829602	best: 0.1829602 (92)	total: 37.7s	remaining: 405ms
93:	learn: 0.1107361	test: 0.1827692	best: 0.1827692 (93)	total: 38.1s	remaining: 0us

bestTest = 0.1827692326
bestIteration = 93

Trial 57, Fold 2: Log loss = 0.18251450060014623, Average precision = 0.9773483000479155, ROC-AUC = 0.974676784804341, Elapsed Time = 38.283101299999544 seconds
Trial 57, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 57, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
0:	learn: 0.5895990	test: 0.5910811	best: 0.5910811 (0)	total: 502ms	remaining: 46.7s
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68:	learn: 0.1262620	test: 0.1885000	best: 0.1882442 (64)	total: 28.3s	remaining: 10.2s
69:	learn: 0.1254047	test: 0.1885067	best: 0.1882442 (64)	total: 28.7s	remaining: 9.84s
70:	learn: 0.1247048	test: 0.1884326	best: 0.1882442 (64)	total: 29.1s	remaining: 9.43s
71:	learn: 0.1240529	test: 0.1882513	best: 0.1882442 (64)	total: 29.5s	remaining: 9.01s
72:	learn: 0.1231799	test: 0.1882634	best: 0.1882442 (64)	total: 30s	remaining: 8.62s
73:	learn: 0.1223953	test: 0.1883437	best: 0.1882442 (64)	total: 30.4s	remaining: 8.22s
74:	learn: 0.1215914	test: 0.1882004	best: 0.1882004 (74)	total: 30.8s	remaining: 7.81s
75:	learn: 0.1210155	test: 0.1881109	best: 0.1881109 (75)	total: 31.2s	remaining: 7.38s
76:	learn: 0.1204380	test: 0.1880521	best: 0.1880521 (76)	total: 31.6s	remaining: 6.97s
77:	learn: 0.1200310	test: 0.1880305	best: 0.1880305 (77)	total: 31.9s	remaining: 6.55s
78:	learn: 0.1192955	test: 0.1878620	best: 0.1878620 (78)	total: 32.4s	remaining: 6.16s
79:	learn: 0.1187549	test: 0.1878588	best: 0.1878588 (79)	total: 32.7s	remaining: 5.73s
80:	learn: 0.1180847	test: 0.1878683	best: 0.1878588 (79)	total: 33.1s	remaining: 5.31s
81:	learn: 0.1177833	test: 0.1878580	best: 0.1878580 (81)	total: 33.3s	remaining: 4.87s
82:	learn: 0.1171217	test: 0.1878967	best: 0.1878580 (81)	total: 33.7s	remaining: 4.46s
83:	learn: 0.1169610	test: 0.1879252	best: 0.1878580 (81)	total: 33.9s	remaining: 4.03s
84:	learn: 0.1163429	test: 0.1878341	best: 0.1878341 (84)	total: 34.2s	remaining: 3.62s
85:	learn: 0.1157455	test: 0.1878266	best: 0.1878266 (85)	total: 34.7s	remaining: 3.23s
86:	learn: 0.1153676	test: 0.1877917	best: 0.1877917 (86)	total: 35s	remaining: 2.81s
87:	learn: 0.1149597	test: 0.1876916	best: 0.1876916 (87)	total: 35.3s	remaining: 2.41s
88:	learn: 0.1144851	test: 0.1876907	best: 0.1876907 (88)	total: 35.7s	remaining: 2s
89:	learn: 0.1141814	test: 0.1876340	best: 0.1876340 (89)	total: 35.9s	remaining: 1.59s
90:	learn: 0.1135851	test: 0.1875826	best: 0.1875826 (90)	total: 36.3s	remaining: 1.2s
91:	learn: 0.1128295	test: 0.1878644	best: 0.1875826 (90)	total: 36.7s	remaining: 798ms
92:	learn: 0.1123844	test: 0.1880469	best: 0.1875826 (90)	total: 37s	remaining: 398ms
93:	learn: 0.1119322	test: 0.1880237	best: 0.1875826 (90)	total: 37.3s	remaining: 0us

bestTest = 0.1875826345
bestIteration = 90

Shrink model to first 91 iterations.
Trial 57, Fold 3: Log loss = 0.18739240902453494, Average precision = 0.9752750355044638, ROC-AUC = 0.9735378282979072, Elapsed Time = 37.481389799999306 seconds
Trial 57, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 57, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
0:	learn: 0.5889277	test: 0.5908707	best: 0.5908707 (0)	total: 523ms	remaining: 48.6s
1:	learn: 0.5085563	test: 0.5136694	best: 0.5136694 (1)	total: 1.03s	remaining: 47.2s
2:	learn: 0.4449576	test: 0.4522369	best: 0.4522369 (2)	total: 1.52s	remaining: 46.1s
3:	learn: 0.3952368	test: 0.4035509	best: 0.4035509 (3)	total: 2s	remaining: 45.1s
4:	learn: 0.3566469	test: 0.3660009	best: 0.3660009 (4)	total: 2.4s	remaining: 42.8s
5:	learn: 0.3262410	test: 0.3363588	best: 0.3363588 (5)	total: 2.84s	remaining: 41.7s
6:	learn: 0.3019965	test: 0.3134657	best: 0.3134657 (6)	total: 3.43s	remaining: 42.7s
7:	learn: 0.2834502	test: 0.2960993	best: 0.2960993 (7)	total: 3.92s	remaining: 42.1s
8:	learn: 0.2668177	test: 0.2803623	best: 0.2803623 (8)	total: 4.46s	remaining: 42.1s
9:	learn: 0.2530070	test: 0.2680647	best: 0.2680647 (9)	total: 5.08s	remaining: 42.6s
10:	learn: 0.2423112	test: 0.2583277	best: 0.2583277 (10)	total: 5.62s	remaining: 42.4s
11:	learn: 0.2315711	test: 0.2489861	best: 0.2489861 (11)	total: 6.17s	remaining: 42.2s
12:	learn: 0.2230324	test: 0.2418254	best: 0.2418254 (12)	total: 6.72s	remaining: 41.9s
13:	learn: 0.2167635	test: 0.2364640	best: 0.2364640 (13)	total: 7.15s	remaining: 40.9s
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15:	learn: 0.2049591	test: 0.2271782	best: 0.2271782 (15)	total: 8.11s	remaining: 39.5s
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18:	learn: 0.1901778	test: 0.2164314	best: 0.2164314 (18)	total: 9.6s	remaining: 37.9s
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20:	learn: 0.1832753	test: 0.2118755	best: 0.2118755 (20)	total: 10.5s	remaining: 36.3s
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27:	learn: 0.1664274	test: 0.2010722	best: 0.2010722 (27)	total: 13.5s	remaining: 31.9s
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56:	learn: 0.1338217	test: 0.1900009	best: 0.1900009 (56)	total: 24.6s	remaining: 16s
57:	learn: 0.1327223	test: 0.1897877	best: 0.1897877 (57)	total: 25.2s	remaining: 15.6s
58:	learn: 0.1321515	test: 0.1896613	best: 0.1896613 (58)	total: 25.5s	remaining: 15.1s
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60:	learn: 0.1308645	test: 0.1894055	best: 0.1894055 (60)	total: 26.2s	remaining: 14.2s
61:	learn: 0.1299073	test: 0.1890797	best: 0.1890797 (61)	total: 26.6s	remaining: 13.8s
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63:	learn: 0.1283353	test: 0.1888338	best: 0.1887997 (62)	total: 27.5s	remaining: 12.9s
64:	learn: 0.1274168	test: 0.1887319	best: 0.1887319 (64)	total: 28s	remaining: 12.5s
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66:	learn: 0.1264059	test: 0.1888539	best: 0.1886889 (65)	total: 28.6s	remaining: 11.5s
67:	learn: 0.1254912	test: 0.1887942	best: 0.1886889 (65)	total: 29.1s	remaining: 11.1s
68:	learn: 0.1246803	test: 0.1887457	best: 0.1886889 (65)	total: 29.5s	remaining: 10.7s
69:	learn: 0.1236914	test: 0.1885279	best: 0.1885279 (69)	total: 30s	remaining: 10.3s
70:	learn: 0.1226526	test: 0.1883529	best: 0.1883529 (70)	total: 30.5s	remaining: 9.88s
71:	learn: 0.1224459	test: 0.1883146	best: 0.1883146 (71)	total: 30.7s	remaining: 9.37s
72:	learn: 0.1216742	test: 0.1884107	best: 0.1883146 (71)	total: 31.1s	remaining: 8.94s
73:	learn: 0.1208456	test: 0.1883510	best: 0.1883146 (71)	total: 31.6s	remaining: 8.53s
74:	learn: 0.1199646	test: 0.1881224	best: 0.1881224 (74)	total: 32.1s	remaining: 8.12s
75:	learn: 0.1192916	test: 0.1881431	best: 0.1881224 (74)	total: 32.5s	remaining: 7.69s
76:	learn: 0.1185334	test: 0.1882031	best: 0.1881224 (74)	total: 32.9s	remaining: 7.26s
77:	learn: 0.1178886	test: 0.1883596	best: 0.1881224 (74)	total: 33.3s	remaining: 6.83s
78:	learn: 0.1173348	test: 0.1883540	best: 0.1881224 (74)	total: 33.6s	remaining: 6.39s
79:	learn: 0.1169956	test: 0.1883283	best: 0.1881224 (74)	total: 33.9s	remaining: 5.94s
80:	learn: 0.1165439	test: 0.1884336	best: 0.1881224 (74)	total: 34.3s	remaining: 5.5s
81:	learn: 0.1158020	test: 0.1884126	best: 0.1881224 (74)	total: 34.7s	remaining: 5.08s
82:	learn: 0.1151602	test: 0.1882332	best: 0.1881224 (74)	total: 35.1s	remaining: 4.66s
83:	learn: 0.1144981	test: 0.1879127	best: 0.1879127 (83)	total: 35.5s	remaining: 4.23s
84:	learn: 0.1140615	test: 0.1879795	best: 0.1879127 (83)	total: 35.9s	remaining: 3.8s
85:	learn: 0.1135528	test: 0.1877893	best: 0.1877893 (85)	total: 36.2s	remaining: 3.37s
86:	learn: 0.1129208	test: 0.1878278	best: 0.1877893 (85)	total: 36.7s	remaining: 2.95s
87:	learn: 0.1126113	test: 0.1877948	best: 0.1877893 (85)	total: 37s	remaining: 2.52s
88:	learn: 0.1118528	test: 0.1878441	best: 0.1877893 (85)	total: 37.5s	remaining: 2.1s
89:	learn: 0.1111801	test: 0.1875995	best: 0.1875995 (89)	total: 37.9s	remaining: 1.68s
90:	learn: 0.1107057	test: 0.1877280	best: 0.1875995 (89)	total: 38.2s	remaining: 1.26s
91:	learn: 0.1103827	test: 0.1876648	best: 0.1875995 (89)	total: 38.5s	remaining: 837ms
92:	learn: 0.1099874	test: 0.1877476	best: 0.1875995 (89)	total: 38.8s	remaining: 417ms
93:	learn: 0.1096211	test: 0.1878518	best: 0.1875995 (89)	total: 39.1s	remaining: 0us

bestTest = 0.1875994878
bestIteration = 89

Shrink model to first 90 iterations.
Trial 57, Fold 4: Log loss = 0.18731344372084563, Average precision = 0.9773447682522916, ROC-AUC = 0.9738364676399828, Elapsed Time = 39.21282539999811 seconds
Trial 57, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 57, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
0:	learn: 0.5898857	test: 0.5925959	best: 0.5925959 (0)	total: 458ms	remaining: 42.6s
1:	learn: 0.5087441	test: 0.5175993	best: 0.5175993 (1)	total: 960ms	remaining: 44.2s
2:	learn: 0.4442358	test: 0.4561764	best: 0.4561764 (2)	total: 1.46s	remaining: 44.4s
3:	learn: 0.3950488	test: 0.4086865	best: 0.4086865 (3)	total: 1.93s	remaining: 43.5s
4:	learn: 0.3565004	test: 0.3713988	best: 0.3713988 (4)	total: 2.34s	remaining: 41.7s
5:	learn: 0.3247656	test: 0.3412669	best: 0.3412669 (5)	total: 2.81s	remaining: 41.2s
6:	learn: 0.3011869	test: 0.3212194	best: 0.3212194 (6)	total: 3.26s	remaining: 40.6s
7:	learn: 0.2811007	test: 0.3025253	best: 0.3025253 (7)	total: 3.69s	remaining: 39.7s
8:	learn: 0.2645436	test: 0.2870850	best: 0.2870850 (8)	total: 4.11s	remaining: 38.8s
9:	learn: 0.2502899	test: 0.2742906	best: 0.2742906 (9)	total: 4.59s	remaining: 38.6s
10:	learn: 0.2388811	test: 0.2648775	best: 0.2648775 (10)	total: 5s	remaining: 37.7s
11:	learn: 0.2290816	test: 0.2562717	best: 0.2562717 (11)	total: 5.43s	remaining: 37.1s
12:	learn: 0.2210691	test: 0.2495770	best: 0.2495770 (12)	total: 5.89s	remaining: 36.7s
13:	learn: 0.2137842	test: 0.2439431	best: 0.2439431 (13)	total: 6.38s	remaining: 36.5s
14:	learn: 0.2075655	test: 0.2389031	best: 0.2389031 (14)	total: 6.88s	remaining: 36.2s
15:	learn: 0.2020280	test: 0.2347723	best: 0.2347723 (15)	total: 7.32s	remaining: 35.7s
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17:	learn: 0.1926363	test: 0.2281378	best: 0.2281378 (17)	total: 8.18s	remaining: 34.5s
18:	learn: 0.1888760	test: 0.2252002	best: 0.2252002 (18)	total: 8.57s	remaining: 33.8s
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20:	learn: 0.1818603	test: 0.2204447	best: 0.2204447 (20)	total: 9.44s	remaining: 32.8s
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24:	learn: 0.1715787	test: 0.2144384	best: 0.2144384 (24)	total: 11.1s	remaining: 30.7s
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27:	learn: 0.1661004	test: 0.2113695	best: 0.2113695 (27)	total: 12.3s	remaining: 28.9s
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84:	learn: 0.1151964	test: 0.2001381	best: 0.1995030 (81)	total: 33.9s	remaining: 3.59s
85:	learn: 0.1145749	test: 0.2002601	best: 0.1995030 (81)	total: 34.2s	remaining: 3.18s
86:	learn: 0.1140095	test: 0.2002132	best: 0.1995030 (81)	total: 34.6s	remaining: 2.78s
87:	learn: 0.1134113	test: 0.2001661	best: 0.1995030 (81)	total: 34.9s	remaining: 2.38s
88:	learn: 0.1131851	test: 0.2001440	best: 0.1995030 (81)	total: 35.2s	remaining: 1.97s
89:	learn: 0.1126676	test: 0.2004825	best: 0.1995030 (81)	total: 35.5s	remaining: 1.58s
90:	learn: 0.1121592	test: 0.2005948	best: 0.1995030 (81)	total: 35.8s	remaining: 1.18s
91:	learn: 0.1115697	test: 0.2004430	best: 0.1995030 (81)	total: 36.2s	remaining: 787ms
92:	learn: 0.1112320	test: 0.2003845	best: 0.1995030 (81)	total: 36.4s	remaining: 392ms
93:	learn: 0.1106751	test: 0.2004691	best: 0.1995030 (81)	total: 36.8s	remaining: 0us

bestTest = 0.1995030142
bestIteration = 81

Shrink model to first 82 iterations.
Trial 57, Fold 5: Log loss = 0.19910033921792109, Average precision = 0.9734823221915858, ROC-AUC = 0.9713457815432064, Elapsed Time = 36.97576919999847 seconds
Optimization Progress:  58%|#####8    | 58/100 [1:36:47<1:50:41, 158.12s/it]
Trial 58, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 58, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
0:	learn: 0.6563950	test: 0.6565181	best: 0.6565181 (0)	total: 111ms	remaining: 3.88s
1:	learn: 0.6032791	test: 0.6052388	best: 0.6052388 (1)	total: 1.93s	remaining: 32.8s
2:	learn: 0.5609840	test: 0.5650305	best: 0.5650305 (2)	total: 4s	remaining: 44s
3:	learn: 0.5161422	test: 0.5234598	best: 0.5234598 (3)	total: 6.08s	remaining: 48.7s
4:	learn: 0.4861434	test: 0.4943470	best: 0.4943470 (4)	total: 8.18s	remaining: 50.7s
5:	learn: 0.4502123	test: 0.4592075	best: 0.4592075 (5)	total: 10.2s	remaining: 51s
6:	learn: 0.4186427	test: 0.4292636	best: 0.4292636 (6)	total: 12.3s	remaining: 50.9s
7:	learn: 0.3969615	test: 0.4083434	best: 0.4083434 (7)	total: 14.3s	remaining: 50.1s
8:	learn: 0.3683639	test: 0.3825851	best: 0.3825851 (8)	total: 16.4s	remaining: 49.1s
9:	learn: 0.3473279	test: 0.3626512	best: 0.3626512 (9)	total: 18.3s	remaining: 47.5s
10:	learn: 0.3335696	test: 0.3493456	best: 0.3493456 (10)	total: 20.2s	remaining: 45.9s
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12:	learn: 0.2974392	test: 0.3176397	best: 0.3176397 (12)	total: 24.3s	remaining: 42.9s
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23:	learn: 0.2054782	test: 0.2411778	best: 0.2411778 (23)	total: 45s	remaining: 22.5s
24:	learn: 0.2015411	test: 0.2389570	best: 0.2389570 (24)	total: 47.4s	remaining: 20.8s
25:	learn: 0.1978298	test: 0.2367623	best: 0.2367623 (25)	total: 50s	remaining: 19.2s
26:	learn: 0.1964178	test: 0.2354882	best: 0.2354882 (26)	total: 50.2s	remaining: 16.7s
27:	learn: 0.1924605	test: 0.2322355	best: 0.2322355 (27)	total: 52.6s	remaining: 15s
28:	learn: 0.1890591	test: 0.2298643	best: 0.2298643 (28)	total: 55.1s	remaining: 13.3s
29:	learn: 0.1868139	test: 0.2279836	best: 0.2279836 (29)	total: 57.3s	remaining: 11.5s
30:	learn: 0.1844836	test: 0.2260874	best: 0.2260874 (30)	total: 59.4s	remaining: 9.58s
31:	learn: 0.1797902	test: 0.2242731	best: 0.2242731 (31)	total: 1m 1s	remaining: 7.69s
32:	learn: 0.1773071	test: 0.2226806	best: 0.2226806 (32)	total: 1m 3s	remaining: 5.77s
33:	learn: 0.1743833	test: 0.2211380	best: 0.2211380 (33)	total: 1m 5s	remaining: 3.85s
34:	learn: 0.1699619	test: 0.2197214	best: 0.2197214 (34)	total: 1m 7s	remaining: 1.93s
35:	learn: 0.1684649	test: 0.2191012	best: 0.2191012 (35)	total: 1m 9s	remaining: 0us

bestTest = 0.2191011988
bestIteration = 35

Trial 58, Fold 1: Log loss = 0.21910119880217663, Average precision = 0.9662186733191698, ROC-AUC = 0.9648683014056771, Elapsed Time = 69.89396879999913 seconds
Trial 58, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 58, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
0:	learn: 0.6401557	test: 0.6421433	best: 0.6421433 (0)	total: 1.9s	remaining: 1m 6s
1:	learn: 0.5872724	test: 0.5915292	best: 0.5915292 (1)	total: 4.15s	remaining: 1m 10s
2:	learn: 0.5443993	test: 0.5502160	best: 0.5502160 (2)	total: 6.32s	remaining: 1m 9s
3:	learn: 0.5034012	test: 0.5116634	best: 0.5116634 (3)	total: 8.4s	remaining: 1m 7s
4:	learn: 0.4705805	test: 0.4807002	best: 0.4807002 (4)	total: 10.5s	remaining: 1m 5s
5:	learn: 0.4382018	test: 0.4503461	best: 0.4503461 (5)	total: 12.6s	remaining: 1m 3s
6:	learn: 0.4144927	test: 0.4281704	best: 0.4281704 (6)	total: 14.8s	remaining: 1m 1s
7:	learn: 0.3888419	test: 0.4042623	best: 0.4042623 (7)	total: 16.9s	remaining: 59.1s
8:	learn: 0.3674405	test: 0.3841058	best: 0.3841058 (8)	total: 19.1s	remaining: 57.2s
9:	learn: 0.3531910	test: 0.3698213	best: 0.3698213 (9)	total: 19.3s	remaining: 50.1s
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11:	learn: 0.3205432	test: 0.3393440	best: 0.3393440 (11)	total: 23.4s	remaining: 46.7s
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22:	learn: 0.2045768	test: 0.2406618	best: 0.2406618 (22)	total: 46.1s	remaining: 26s
23:	learn: 0.2012221	test: 0.2378956	best: 0.2378956 (23)	total: 48.1s	remaining: 24s
24:	learn: 0.1996418	test: 0.2362262	best: 0.2362262 (24)	total: 48.1s	remaining: 21.2s
25:	learn: 0.1941285	test: 0.2320302	best: 0.2320302 (25)	total: 50.1s	remaining: 19.3s
26:	learn: 0.1889198	test: 0.2286568	best: 0.2286568 (26)	total: 52.1s	remaining: 17.4s
27:	learn: 0.1874468	test: 0.2274730	best: 0.2274730 (27)	total: 54.3s	remaining: 15.5s
28:	learn: 0.1834615	test: 0.2244127	best: 0.2244127 (28)	total: 56.3s	remaining: 13.6s
29:	learn: 0.1802323	test: 0.2222663	best: 0.2222663 (29)	total: 58.5s	remaining: 11.7s
30:	learn: 0.1756141	test: 0.2205113	best: 0.2205113 (30)	total: 1m	remaining: 9.77s
31:	learn: 0.1725408	test: 0.2192942	best: 0.2192942 (31)	total: 1m 2s	remaining: 7.83s
32:	learn: 0.1701086	test: 0.2180738	best: 0.2180738 (32)	total: 1m 4s	remaining: 5.89s
33:	learn: 0.1681581	test: 0.2168289	best: 0.2168289 (33)	total: 1m 6s	remaining: 3.94s
34:	learn: 0.1664811	test: 0.2155451	best: 0.2155451 (34)	total: 1m 9s	remaining: 1.97s
35:	learn: 0.1644470	test: 0.2145037	best: 0.2145037 (35)	total: 1m 11s	remaining: 0us

bestTest = 0.2145036872
bestIteration = 35

Trial 58, Fold 2: Log loss = 0.21450368718369395, Average precision = 0.9710319913053642, ROC-AUC = 0.9667554745289826, Elapsed Time = 71.19416329999876 seconds
Trial 58, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 58, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
0:	learn: 0.6399583	test: 0.6406838	best: 0.6406838 (0)	total: 1.89s	remaining: 1m 6s
1:	learn: 0.5890941	test: 0.5924879	best: 0.5924879 (1)	total: 3.98s	remaining: 1m 7s
2:	learn: 0.5550763	test: 0.5593652	best: 0.5593652 (2)	total: 5.94s	remaining: 1m 5s
3:	learn: 0.5195976	test: 0.5249868	best: 0.5249868 (3)	total: 8s	remaining: 1m 4s
4:	learn: 0.4857580	test: 0.4931536	best: 0.4931536 (4)	total: 10.2s	remaining: 1m 3s
5:	learn: 0.4611532	test: 0.4681995	best: 0.4681995 (5)	total: 12.3s	remaining: 1m 1s
6:	learn: 0.4368784	test: 0.4445459	best: 0.4445459 (6)	total: 14.4s	remaining: 59.6s
7:	learn: 0.4137298	test: 0.4221526	best: 0.4221526 (7)	total: 16.5s	remaining: 57.8s
8:	learn: 0.3847982	test: 0.3949960	best: 0.3949960 (8)	total: 18.6s	remaining: 55.8s
9:	learn: 0.3652183	test: 0.3755241	best: 0.3755241 (9)	total: 20.7s	remaining: 53.8s
10:	learn: 0.3412780	test: 0.3537147	best: 0.3537147 (10)	total: 22.8s	remaining: 51.8s
11:	learn: 0.3239102	test: 0.3372665	best: 0.3372665 (11)	total: 24.9s	remaining: 49.7s
12:	learn: 0.3054150	test: 0.3199671	best: 0.3199671 (12)	total: 26.8s	remaining: 47.4s
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15:	learn: 0.2617291	test: 0.2811418	best: 0.2811418 (15)	total: 33.2s	remaining: 41.5s
16:	learn: 0.2505753	test: 0.2723612	best: 0.2723612 (16)	total: 35.3s	remaining: 39.5s
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18:	learn: 0.2318305	test: 0.2568630	best: 0.2568630 (18)	total: 39.2s	remaining: 35.1s
19:	learn: 0.2246859	test: 0.2512842	best: 0.2512842 (19)	total: 41.2s	remaining: 33s
20:	learn: 0.2186016	test: 0.2458714	best: 0.2458714 (20)	total: 43.3s	remaining: 30.9s
21:	learn: 0.2118255	test: 0.2409487	best: 0.2409487 (21)	total: 45.3s	remaining: 28.8s
22:	learn: 0.2098899	test: 0.2390141	best: 0.2390141 (22)	total: 45.4s	remaining: 25.6s
23:	learn: 18.5195175	test: 0.2362713	best: 0.2362713 (23)	total: 47.3s	remaining: 23.6s
24:	learn: 0.2003060	test: 0.2328738	best: 0.2328738 (24)	total: 49.3s	remaining: 21.7s
25:	learn: 0.1980129	test: 0.2307224	best: 0.2307224 (25)	total: 51.2s	remaining: 19.7s
26:	learn: 0.1958056	test: 0.2292250	best: 0.2292250 (26)	total: 53.1s	remaining: 17.7s
27:	learn: 0.1930674	test: 0.2272587	best: 0.2272587 (27)	total: 55.3s	remaining: 15.8s
28:	learn: 0.1918642	test: 0.2261064	best: 0.2261064 (28)	total: 55.5s	remaining: 13.4s
29:	learn: 0.1893892	test: 0.2245743	best: 0.2245743 (29)	total: 57.6s	remaining: 11.5s
30:	learn: 0.1863329	test: 0.2222948	best: 0.2222948 (30)	total: 59.6s	remaining: 9.62s
31:	learn: 0.1815548	test: 0.2201867	best: 0.2201867 (31)	total: 1m 1s	remaining: 7.71s
32:	learn: 0.1784511	test: 0.2187658	best: 0.2187658 (32)	total: 1m 3s	remaining: 5.8s
33:	learn: 0.1755985	test: 0.2172381	best: 0.2172381 (33)	total: 1m 5s	remaining: 3.88s
34:	learn: 0.1730835	test: 0.2161280	best: 0.2161280 (34)	total: 1m 7s	remaining: 1.94s
35:	learn: 0.1709884	test: 0.2155195	best: 0.2155195 (35)	total: 1m 9s	remaining: 0us

bestTest = 0.2155194589
bestIteration = 35

Trial 58, Fold 3: Log loss = 0.21551945885426307, Average precision = 0.9711899260410743, ROC-AUC = 0.9678798961774422, Elapsed Time = 69.95388779999848 seconds
Trial 58, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 58, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
0:	learn: 0.6366655	test: 0.6386189	best: 0.6386189 (0)	total: 2.09s	remaining: 1m 13s
1:	learn: 0.5921567	test: 0.5956299	best: 0.5956299 (1)	total: 4.23s	remaining: 1m 11s
2:	learn: 0.5476632	test: 0.5521359	best: 0.5521359 (2)	total: 6.42s	remaining: 1m 10s
3:	learn: 0.5077409	test: 0.5136605	best: 0.5136605 (3)	total: 8.53s	remaining: 1m 8s
4:	learn: 0.4668923	test: 0.4744174	best: 0.4744174 (4)	total: 10.7s	remaining: 1m 6s
5:	learn: 0.4388057	test: 0.4474852	best: 0.4474852 (5)	total: 12.8s	remaining: 1m 4s
6:	learn: 0.4099977	test: 0.4201464	best: 0.4201464 (6)	total: 14.9s	remaining: 1m 1s
7:	learn: 0.3862340	test: 0.3975642	best: 0.3975642 (7)	total: 17.1s	remaining: 59.8s
8:	learn: 0.3707562	test: 0.3826993	best: 0.3826993 (8)	total: 19.2s	remaining: 57.7s
9:	learn: 0.3493225	test: 0.3628015	best: 0.3628015 (9)	total: 21.2s	remaining: 55.2s
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26:	learn: 0.1938845	test: 0.2289976	best: 0.2289976 (26)	total: 53.5s	remaining: 17.8s
27:	learn: 0.1908269	test: 0.2270090	best: 0.2270090 (27)	total: 55.7s	remaining: 15.9s
28:	learn: 0.1855901	test: 0.2243107	best: 0.2243107 (28)	total: 57.8s	remaining: 14s
29:	learn: 0.1820518	test: 0.2222789	best: 0.2222789 (29)	total: 59.8s	remaining: 12s
30:	learn: 0.1794197	test: 0.2207138	best: 0.2207138 (30)	total: 1m 1s	remaining: 9.99s
31:	learn: 0.1775947	test: 0.2191747	best: 0.2191747 (31)	total: 1m 4s	remaining: 8.01s
32:	learn: 0.1753364	test: 0.2175693	best: 0.2175693 (32)	total: 1m 6s	remaining: 6.02s
33:	learn: 0.1713402	test: 0.2154056	best: 0.2154056 (33)	total: 1m 8s	remaining: 4.01s
34:	learn: 0.1671940	test: 0.2143691	best: 0.2143691 (34)	total: 1m 10s	remaining: 2.01s
35:	learn: 0.1642013	test: 0.2132699	best: 0.2132699 (35)	total: 1m 12s	remaining: 0us

bestTest = 0.2132698901
bestIteration = 35

Trial 58, Fold 4: Log loss = 0.21326989013844874, Average precision = 0.9721579843426332, ROC-AUC = 0.9667626689652378, Elapsed Time = 72.46899029999986 seconds
Trial 58, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 58, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
0:	learn: 0.6440754	test: 0.6461490	best: 0.6461490 (0)	total: 1.91s	remaining: 1m 6s
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4:	learn: 0.4858240	test: 0.4938035	best: 0.4938035 (4)	total: 9.35s	remaining: 58s
5:	learn: 0.4505198	test: 0.4613625	best: 0.4613625 (5)	total: 11.4s	remaining: 57.2s
6:	learn: 0.4237993	test: 0.4367367	best: 0.4367367 (6)	total: 13.5s	remaining: 56s
7:	learn: 0.3980840	test: 0.4140162	best: 0.4140162 (7)	total: 15.6s	remaining: 54.5s
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26:	learn: 0.1958193	test: 0.2383234	best: 0.2383234 (26)	total: 54.5s	remaining: 18.2s
27:	learn: 0.1911997	test: 0.2355670	best: 0.2355670 (27)	total: 56.5s	remaining: 16.1s
28:	learn: 0.1870194	test: 0.2324766	best: 0.2324766 (28)	total: 58.6s	remaining: 14.1s
29:	learn: 0.1826177	test: 0.2300928	best: 0.2300928 (29)	total: 1m	remaining: 12.1s
30:	learn: 0.1816922	test: 0.2294487	best: 0.2294487 (30)	total: 1m 2s	remaining: 10.1s
31:	learn: 0.1808795	test: 0.2288042	best: 0.2288042 (31)	total: 1m 2s	remaining: 7.84s
32:	learn: 0.1773129	test: 0.2268317	best: 0.2268317 (32)	total: 1m 4s	remaining: 5.9s
33:	learn: 0.1740476	test: 0.2256837	best: 0.2256837 (33)	total: 1m 7s	remaining: 3.97s
34:	learn: 0.1691995	test: 0.2240668	best: 0.2240668 (34)	total: 1m 9s	remaining: 2s
35:	learn: 0.1673609	test: 0.2231378	best: 0.2231378 (35)	total: 1m 12s	remaining: 0us

bestTest = 0.223137802
bestIteration = 35

Trial 58, Fold 5: Log loss = 0.22313780199889557, Average precision = 0.9697573158890176, ROC-AUC = 0.9646580086580087, Elapsed Time = 73.19685559999925 seconds
Optimization Progress:  59%|#####8    | 59/100 [1:42:51<2:30:23, 220.09s/it]
Trial 59, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 59, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
0:	learn: 0.6788082	test: 0.6791267	best: 0.6791267 (0)	total: 498ms	remaining: 21.9s
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5:	learn: 0.6133042	test: 0.6153863	best: 0.6153863 (5)	total: 3.12s	remaining: 20.3s
6:	learn: 0.6012732	test: 0.6036819	best: 0.6036819 (6)	total: 3.69s	remaining: 20.1s
7:	learn: 0.5897229	test: 0.5924413	best: 0.5924413 (7)	total: 4.21s	remaining: 19.5s
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36:	learn: 0.3682121	test: 0.3799916	best: 0.3799916 (36)	total: 19.1s	remaining: 4.12s
37:	learn: 0.3634498	test: 0.3755578	best: 0.3755578 (37)	total: 19.6s	remaining: 3.6s
38:	learn: 0.3588786	test: 0.3712309	best: 0.3712309 (38)	total: 20s	remaining: 3.08s
39:	learn: 0.3543924	test: 0.3670251	best: 0.3670251 (39)	total: 20.5s	remaining: 2.56s
40:	learn: 0.3499976	test: 0.3629462	best: 0.3629462 (40)	total: 20.9s	remaining: 2.04s
41:	learn: 0.3458346	test: 0.3590774	best: 0.3590774 (41)	total: 21.4s	remaining: 1.53s
42:	learn: 0.3417636	test: 0.3552783	best: 0.3552783 (42)	total: 21.9s	remaining: 1.02s
43:	learn: 0.3377368	test: 0.3515630	best: 0.3515630 (43)	total: 22.3s	remaining: 507ms
44:	learn: 0.3337020	test: 0.3478793	best: 0.3478793 (44)	total: 22.9s	remaining: 0us

bestTest = 0.3478792629
bestIteration = 44

Trial 59, Fold 1: Log loss = 0.34800591869926284, Average precision = 0.9747018812788043, ROC-AUC = 0.9704799125813129, Elapsed Time = 23.01954359999945 seconds
Trial 59, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 59, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
0:	learn: 0.6788135	test: 0.6791975	best: 0.6791975 (0)	total: 529ms	remaining: 23.3s
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2:	learn: 0.6513559	test: 0.6525846	best: 0.6525846 (2)	total: 1.53s	remaining: 21.5s
3:	learn: 0.6381727	test: 0.6398207	best: 0.6398207 (3)	total: 2.1s	remaining: 21.5s
4:	learn: 0.6254790	test: 0.6274441	best: 0.6274441 (4)	total: 2.62s	remaining: 20.9s
5:	learn: 0.6133124	test: 0.6155526	best: 0.6155526 (5)	total: 3.14s	remaining: 20.4s
6:	learn: 0.6013368	test: 0.6039661	best: 0.6039661 (6)	total: 3.7s	remaining: 20.1s
7:	learn: 0.5897090	test: 0.5925822	best: 0.5925822 (7)	total: 4.25s	remaining: 19.6s
8:	learn: 0.5784250	test: 0.5815691	best: 0.5815691 (8)	total: 4.81s	remaining: 19.2s
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35:	learn: 0.3732796	test: 0.3838681	best: 0.3838681 (35)	total: 19.4s	remaining: 4.86s
36:	learn: 0.3685155	test: 0.3792737	best: 0.3792737 (36)	total: 19.9s	remaining: 4.3s
37:	learn: 0.3637952	test: 0.3747983	best: 0.3747983 (37)	total: 20.4s	remaining: 3.76s
38:	learn: 0.3591260	test: 0.3703507	best: 0.3703507 (38)	total: 20.9s	remaining: 3.22s
39:	learn: 0.3545701	test: 0.3660154	best: 0.3660154 (39)	total: 21.4s	remaining: 2.68s
40:	learn: 0.3502678	test: 0.3619121	best: 0.3619121 (40)	total: 21.9s	remaining: 2.14s
41:	learn: 0.3460149	test: 0.3578886	best: 0.3578886 (41)	total: 22.5s	remaining: 1.6s
42:	learn: 0.3419377	test: 0.3540072	best: 0.3540072 (42)	total: 22.9s	remaining: 1.07s
43:	learn: 0.3380097	test: 0.3502696	best: 0.3502696 (43)	total: 23.4s	remaining: 532ms
44:	learn: 0.3340487	test: 0.3465553	best: 0.3465553 (44)	total: 24s	remaining: 0us

bestTest = 0.3465552565
bestIteration = 44

Trial 59, Fold 2: Log loss = 0.3466080520059207, Average precision = 0.9748791852635729, ROC-AUC = 0.9719432001969079, Elapsed Time = 24.161008299997775 seconds
Trial 59, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 59, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
0:	learn: 0.6788482	test: 0.6790861	best: 0.6790861 (0)	total: 492ms	remaining: 21.6s
1:	learn: 0.6649102	test: 0.6653491	best: 0.6653491 (1)	total: 1000ms	remaining: 21.5s
2:	learn: 0.6514657	test: 0.6521574	best: 0.6521574 (2)	total: 1.45s	remaining: 20.3s
3:	learn: 0.6382830	test: 0.6393518	best: 0.6393518 (3)	total: 2.01s	remaining: 20.6s
4:	learn: 0.6255513	test: 0.6268568	best: 0.6268568 (4)	total: 2.54s	remaining: 20.3s
5:	learn: 0.6132580	test: 0.6148541	best: 0.6148541 (5)	total: 3.09s	remaining: 20.1s
6:	learn: 0.6012789	test: 0.6031252	best: 0.6031252 (6)	total: 3.66s	remaining: 19.9s
7:	learn: 0.5895750	test: 0.5916805	best: 0.5916805 (7)	total: 4.24s	remaining: 19.6s
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35:	learn: 0.3739723	test: 0.3821415	best: 0.3821415 (35)	total: 18.6s	remaining: 4.64s
36:	learn: 0.3690357	test: 0.3774588	best: 0.3774588 (36)	total: 19.1s	remaining: 4.13s
37:	learn: 0.3642056	test: 0.3729173	best: 0.3729173 (37)	total: 19.7s	remaining: 3.63s
38:	learn: 0.3594931	test: 0.3684826	best: 0.3684826 (38)	total: 20.3s	remaining: 3.12s
39:	learn: 0.3550347	test: 0.3642827	best: 0.3642827 (39)	total: 20.8s	remaining: 2.6s
40:	learn: 0.3506965	test: 0.3601651	best: 0.3601651 (40)	total: 21.3s	remaining: 2.08s
41:	learn: 0.3463943	test: 0.3560588	best: 0.3560588 (41)	total: 21.9s	remaining: 1.56s
42:	learn: 0.3423221	test: 0.3522316	best: 0.3522316 (42)	total: 22.4s	remaining: 1.04s
43:	learn: 0.3381841	test: 0.3482952	best: 0.3482952 (43)	total: 23s	remaining: 522ms
44:	learn: 0.3342009	test: 0.3445661	best: 0.3445661 (44)	total: 23.5s	remaining: 0us

bestTest = 0.3445660829
bestIteration = 44

Trial 59, Fold 3: Log loss = 0.344823465758147, Average precision = 0.9747567286077528, ROC-AUC = 0.9717709095790275, Elapsed Time = 23.662727099999756 seconds
Trial 59, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 59, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
0:	learn: 0.6788755	test: 0.6791264	best: 0.6791264 (0)	total: 441ms	remaining: 19.4s
1:	learn: 0.6649780	test: 0.6655718	best: 0.6655718 (1)	total: 934ms	remaining: 20.1s
2:	learn: 0.6514407	test: 0.6524184	best: 0.6524184 (2)	total: 1.38s	remaining: 19.4s
3:	learn: 0.6384179	test: 0.6397497	best: 0.6397497 (3)	total: 1.86s	remaining: 19.1s
4:	learn: 0.6258656	test: 0.6274969	best: 0.6274969 (4)	total: 2.33s	remaining: 18.6s
5:	learn: 0.6136195	test: 0.6155402	best: 0.6155402 (5)	total: 2.84s	remaining: 18.5s
6:	learn: 0.6017138	test: 0.6039517	best: 0.6039517 (6)	total: 3.36s	remaining: 18.2s
7:	learn: 0.5901390	test: 0.5926049	best: 0.5926049 (7)	total: 3.89s	remaining: 18s
8:	learn: 0.5788542	test: 0.5816559	best: 0.5816559 (8)	total: 4.41s	remaining: 17.6s
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30:	learn: 0.4010527	test: 0.4091972	best: 0.4091972 (30)	total: 15s	remaining: 6.78s
31:	learn: 0.3954320	test: 0.4038219	best: 0.4038219 (31)	total: 15.4s	remaining: 6.28s
32:	learn: 0.3899786	test: 0.3985838	best: 0.3985838 (32)	total: 15.9s	remaining: 5.8s
33:	learn: 0.3847479	test: 0.3935139	best: 0.3935139 (33)	total: 16.4s	remaining: 5.29s
34:	learn: 0.3795189	test: 0.3885429	best: 0.3885429 (34)	total: 16.9s	remaining: 4.83s
35:	learn: 0.3745392	test: 0.3837331	best: 0.3837331 (35)	total: 17.4s	remaining: 4.35s
36:	learn: 0.3697033	test: 0.3791220	best: 0.3791220 (36)	total: 17.9s	remaining: 3.86s
37:	learn: 0.3650279	test: 0.3746450	best: 0.3746450 (37)	total: 18.3s	remaining: 3.37s
38:	learn: 0.3604897	test: 0.3702987	best: 0.3702987 (38)	total: 18.7s	remaining: 2.88s
39:	learn: 0.3559727	test: 0.3660789	best: 0.3660789 (39)	total: 19.2s	remaining: 2.4s
40:	learn: 0.3516797	test: 0.3620737	best: 0.3620737 (40)	total: 19.7s	remaining: 1.93s
41:	learn: 0.3472896	test: 0.3579795	best: 0.3579795 (41)	total: 20.3s	remaining: 1.45s
42:	learn: 0.3431211	test: 0.3540563	best: 0.3540563 (42)	total: 20.8s	remaining: 967ms
43:	learn: 0.3390678	test: 0.3502281	best: 0.3502281 (43)	total: 21.3s	remaining: 485ms
44:	learn: 0.3352627	test: 0.3466322	best: 0.3466322 (44)	total: 21.8s	remaining: 0us

bestTest = 0.3466321647
bestIteration = 44

Trial 59, Fold 4: Log loss = 0.34674210336313677, Average precision = 0.975791471702802, ROC-AUC = 0.9719213395137235, Elapsed Time = 21.92226539999683 seconds
Trial 59, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 59, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
0:	learn: 0.6787856	test: 0.6792613	best: 0.6792613 (0)	total: 457ms	remaining: 20.1s
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2:	learn: 0.6510763	test: 0.6526124	best: 0.6526124 (2)	total: 1.42s	remaining: 19.9s
3:	learn: 0.6379180	test: 0.6399702	best: 0.6399702 (3)	total: 1.89s	remaining: 19.4s
4:	learn: 0.6251184	test: 0.6276454	best: 0.6276454 (4)	total: 2.4s	remaining: 19.2s
5:	learn: 0.6128136	test: 0.6157750	best: 0.6157750 (5)	total: 2.88s	remaining: 18.7s
6:	learn: 0.6008636	test: 0.6042891	best: 0.6042891 (6)	total: 3.36s	remaining: 18.3s
7:	learn: 0.5890757	test: 0.5930357	best: 0.5930357 (7)	total: 3.88s	remaining: 17.9s
8:	learn: 0.5777671	test: 0.5821788	best: 0.5821788 (8)	total: 4.43s	remaining: 17.7s
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30:	learn: 0.3983200	test: 0.4114141	best: 0.4114141 (30)	total: 15.3s	remaining: 6.89s
31:	learn: 0.3926656	test: 0.4060573	best: 0.4060573 (31)	total: 15.7s	remaining: 6.39s
32:	learn: 0.3872589	test: 0.4008542	best: 0.4008542 (32)	total: 16.2s	remaining: 5.88s
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34:	learn: 0.3767399	test: 0.3910294	best: 0.3910294 (34)	total: 17.1s	remaining: 4.89s
35:	learn: 0.3717397	test: 0.3863409	best: 0.3863409 (35)	total: 17.6s	remaining: 4.4s
36:	learn: 0.3668721	test: 0.3817965	best: 0.3817965 (36)	total: 18s	remaining: 3.9s
37:	learn: 0.3621842	test: 0.3773573	best: 0.3773573 (37)	total: 18.5s	remaining: 3.41s
38:	learn: 0.3575882	test: 0.3730834	best: 0.3730834 (38)	total: 19s	remaining: 2.92s
39:	learn: 0.3530361	test: 0.3688405	best: 0.3688405 (39)	total: 19.5s	remaining: 2.44s
40:	learn: 0.3485890	test: 0.3647311	best: 0.3647311 (40)	total: 20.1s	remaining: 1.96s
41:	learn: 0.3444678	test: 0.3608375	best: 0.3608375 (41)	total: 20.6s	remaining: 1.47s
42:	learn: 0.3404278	test: 0.3570630	best: 0.3570630 (42)	total: 21.1s	remaining: 981ms
43:	learn: 0.3363753	test: 0.3532952	best: 0.3532952 (43)	total: 21.6s	remaining: 491ms
44:	learn: 0.3325530	test: 0.3496843	best: 0.3496843 (44)	total: 22s	remaining: 0us

bestTest = 0.3496842704
bestIteration = 44

Trial 59, Fold 5: Log loss = 0.34972687949873343, Average precision = 0.973546495446978, ROC-AUC = 0.9693380896642698, Elapsed Time = 22.15098339999895 seconds
Optimization Progress:  60%|######    | 60/100 [1:44:56<2:07:40, 191.52s/it]
Trial 60, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 60, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
0:	learn: 0.6413976	test: 0.6414130	best: 0.6414130 (0)	total: 47.6ms	remaining: 1.71s
1:	learn: 0.6014448	test: 0.6007142	best: 0.6007142 (1)	total: 79.1ms	remaining: 1.38s
2:	learn: 0.5637975	test: 0.5633736	best: 0.5633736 (2)	total: 177ms	remaining: 2.01s
3:	learn: 0.5271508	test: 0.5279555	best: 0.5279555 (3)	total: 1.7s	remaining: 14s
4:	learn: 0.5123892	test: 0.5128429	best: 0.5128429 (4)	total: 1.73s	remaining: 11.1s
5:	learn: 0.4948159	test: 0.4951438	best: 0.4951438 (5)	total: 1.76s	remaining: 9.11s
6:	learn: 0.4831600	test: 0.4835002	best: 0.4835002 (6)	total: 1.8s	remaining: 7.73s
7:	learn: 0.4710140	test: 0.4714156	best: 0.4714156 (7)	total: 1.84s	remaining: 6.68s
8:	learn: 0.4501674	test: 0.4513507	best: 0.4513507 (8)	total: 3.5s	remaining: 10.9s
9:	learn: 0.4394774	test: 0.4409881	best: 0.4409881 (9)	total: 3.54s	remaining: 9.56s
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25:	learn: 0.2761297	test: 0.2871051	best: 0.2871051 (25)	total: 14.3s	remaining: 6.07s
26:	learn: 0.2702937	test: 0.2816563	best: 0.2816563 (26)	total: 14.6s	remaining: 5.41s
27:	learn: 0.2642921	test: 0.2766935	best: 0.2766935 (27)	total: 16.2s	remaining: 5.22s
28:	learn: 0.2607330	test: 0.2734164	best: 0.2734164 (28)	total: 16.3s	remaining: 4.51s
29:	learn: 0.2572977	test: 0.2709045	best: 0.2709045 (29)	total: 17.9s	remaining: 4.18s
30:	learn: 0.2561531	test: 0.2699409	best: 0.2699409 (30)	total: 17.9s	remaining: 3.47s
31:	learn: 0.2523842	test: 0.2672599	best: 0.2672599 (31)	total: 19.6s	remaining: 3.06s
32:	learn: 0.2503917	test: 0.2658524	best: 0.2658524 (32)	total: 19.9s	remaining: 2.41s
33:	learn: 0.2447839	test: 0.2611254	best: 0.2611254 (33)	total: 21.4s	remaining: 1.89s
34:	learn: 0.2422343	test: 0.2584929	best: 0.2584929 (34)	total: 21.5s	remaining: 1.23s
35:	learn: 0.2412699	test: 0.2575430	best: 0.2575430 (35)	total: 21.5s	remaining: 598ms
36:	learn: 0.2394385	test: 0.2557080	best: 0.2557080 (36)	total: 21.6s	remaining: 0us

bestTest = 0.2557080153
bestIteration = 36

Trial 60, Fold 1: Log loss = 0.2557080153293043, Average precision = 0.9666998600671489, ROC-AUC = 0.9599315831672426, Elapsed Time = 21.708156699998653 seconds
Trial 60, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 60, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
0:	learn: 0.6448603	test: 0.6452140	best: 0.6452140 (0)	total: 96.6ms	remaining: 3.48s
1:	learn: 0.6182217	test: 0.6183731	best: 0.6183731 (1)	total: 128ms	remaining: 2.24s
2:	learn: 0.5947500	test: 0.5949152	best: 0.5949152 (2)	total: 164ms	remaining: 1.86s
3:	learn: 0.5455926	test: 0.5467219	best: 0.5467219 (3)	total: 614ms	remaining: 5.07s
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23:	learn: 0.2765862	test: 0.2860704	best: 0.2860704 (23)	total: 10.8s	remaining: 5.83s
24:	learn: 0.2718900	test: 0.2812744	best: 0.2812744 (24)	total: 10.8s	remaining: 5.18s
25:	learn: 0.2669548	test: 0.2763522	best: 0.2763522 (25)	total: 11s	remaining: 4.63s
26:	learn: 0.2643858	test: 0.2737162	best: 0.2737162 (26)	total: 11s	remaining: 4.08s
27:	learn: 0.2619637	test: 0.2712143	best: 0.2712143 (27)	total: 11s	remaining: 3.55s
28:	learn: 0.2559296	test: 0.2662291	best: 0.2662291 (28)	total: 12.7s	remaining: 3.49s
29:	learn: 0.2530744	test: 0.2633230	best: 0.2633230 (29)	total: 12.7s	remaining: 2.97s
30:	learn: 0.2505544	test: 0.2608503	best: 0.2608503 (30)	total: 12.8s	remaining: 2.48s
31:	learn: 0.2447663	test: 0.2558049	best: 0.2558049 (31)	total: 14.2s	remaining: 2.22s
32:	learn: 0.2437215	test: 0.2547681	best: 0.2547681 (32)	total: 14.2s	remaining: 1.73s
33:	learn: 0.2418779	test: 0.2529739	best: 0.2529739 (33)	total: 14.3s	remaining: 1.26s
34:	learn: 0.2379906	test: 0.2494713	best: 0.2494713 (34)	total: 15.8s	remaining: 904ms
35:	learn: 0.2344856	test: 0.2468180	best: 0.2468180 (35)	total: 17.2s	remaining: 479ms
36:	learn: 0.2317095	test: 0.2450267	best: 0.2450267 (36)	total: 18.7s	remaining: 0us

bestTest = 0.2450266556
bestIteration = 36

Trial 60, Fold 2: Log loss = 0.24502665558572578, Average precision = 0.967378661128013, ROC-AUC = 0.9627475120191834, Elapsed Time = 18.81169299999965 seconds
Trial 60, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 60, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
0:	learn: 0.6588095	test: 0.6584035	best: 0.6584035 (0)	total: 49.8ms	remaining: 1.79s
1:	learn: 0.6323251	test: 0.6311241	best: 0.6311241 (1)	total: 77.8ms	remaining: 1.36s
2:	learn: 0.5959688	test: 0.5942062	best: 0.5942062 (2)	total: 129ms	remaining: 1.46s
3:	learn: 0.5634785	test: 0.5616200	best: 0.5616200 (3)	total: 268ms	remaining: 2.21s
4:	learn: 0.5409569	test: 0.5389323	best: 0.5389323 (4)	total: 315ms	remaining: 2.02s
5:	learn: 0.5087824	test: 0.5068007	best: 0.5068007 (5)	total: 594ms	remaining: 3.07s
6:	learn: 0.4842817	test: 0.4827371	best: 0.4827371 (6)	total: 1.9s	remaining: 8.16s
7:	learn: 0.4734201	test: 0.4715804	best: 0.4715804 (7)	total: 1.94s	remaining: 7.03s
8:	learn: 0.4540761	test: 0.4521849	best: 0.4521849 (8)	total: 2.04s	remaining: 6.35s
9:	learn: 0.4345103	test: 0.4327809	best: 0.4327809 (9)	total: 2.2s	remaining: 5.93s
10:	learn: 0.4191090	test: 0.4171904	best: 0.4171904 (10)	total: 2.27s	remaining: 5.36s
11:	learn: 0.4077537	test: 0.4060432	best: 0.4060432 (11)	total: 2.35s	remaining: 4.89s
12:	learn: 0.3875249	test: 0.3864551	best: 0.3864551 (12)	total: 3.97s	remaining: 7.33s
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15:	learn: 0.3432861	test: 0.3429983	best: 0.3429983 (15)	total: 5.94s	remaining: 7.79s
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18:	learn: 0.3248798	test: 0.3245554	best: 0.3245554 (18)	total: 6.05s	remaining: 5.73s
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20:	learn: 0.3069301	test: 0.3068898	best: 0.3068898 (20)	total: 6.46s	remaining: 4.92s
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22:	learn: 0.2901937	test: 0.2909450	best: 0.2909450 (22)	total: 6.76s	remaining: 4.11s
23:	learn: 0.2824676	test: 0.2839734	best: 0.2839734 (23)	total: 8.49s	remaining: 4.6s
24:	learn: 0.2745594	test: 0.2771194	best: 0.2771194 (24)	total: 8.96s	remaining: 4.3s
25:	learn: 0.2696678	test: 0.2730696	best: 0.2730696 (25)	total: 10s	remaining: 4.24s
26:	learn: 0.2656806	test: 0.2693603	best: 0.2693603 (26)	total: 10.1s	remaining: 3.73s
27:	learn: 0.2602056	test: 0.2645170	best: 0.2645170 (27)	total: 11.5s	remaining: 3.71s
28:	learn: 0.2595660	test: 0.2638414	best: 0.2638414 (28)	total: 11.6s	remaining: 3.19s
29:	learn: 0.2553395	test: 0.2595942	best: 0.2595942 (29)	total: 11.6s	remaining: 2.71s
30:	learn: 0.2535263	test: 0.2578080	best: 0.2578080 (30)	total: 11.7s	remaining: 2.26s
31:	learn: 0.2497116	test: 0.2542769	best: 0.2542769 (31)	total: 11.9s	remaining: 1.86s
32:	learn: 0.2462814	test: 0.2517107	best: 0.2517107 (32)	total: 13s	remaining: 1.57s
33:	learn: 0.2458571	test: 0.2512928	best: 0.2512928 (33)	total: 13s	remaining: 1.15s
34:	learn: 0.2407878	test: 0.2480634	best: 0.2480634 (34)	total: 14.3s	remaining: 816ms
35:	learn: 0.2398007	test: 0.2471224	best: 0.2471224 (35)	total: 14.3s	remaining: 398ms
36:	learn: 0.2345810	test: 0.2441826	best: 0.2441826 (36)	total: 15.7s	remaining: 0us

bestTest = 0.2441825604
bestIteration = 36

Trial 60, Fold 3: Log loss = 0.24418256040899053, Average precision = 0.9683457883783508, ROC-AUC = 0.9630323253652571, Elapsed Time = 15.805479599999671 seconds
Trial 60, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 60, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
0:	learn: 0.6574676	test: 0.6572983	best: 0.6572983 (0)	total: 33.7ms	remaining: 1.21s
1:	learn: 0.6182762	test: 0.6181347	best: 0.6181347 (1)	total: 68.5ms	remaining: 1.2s
2:	learn: 0.5845651	test: 0.5844408	best: 0.5844408 (2)	total: 106ms	remaining: 1.2s
3:	learn: 0.5611499	test: 0.5609818	best: 0.5609818 (3)	total: 155ms	remaining: 1.28s
4:	learn: 0.5371533	test: 0.5372228	best: 0.5372228 (4)	total: 428ms	remaining: 2.74s
5:	learn: 0.5072072	test: 0.5072931	best: 0.5072931 (5)	total: 537ms	remaining: 2.77s
6:	learn: 0.4864527	test: 0.4863553	best: 0.4863553 (6)	total: 604ms	remaining: 2.59s
7:	learn: 0.4518773	test: 0.4528886	best: 0.4528886 (7)	total: 2s	remaining: 7.23s
8:	learn: 0.4343471	test: 0.4354716	best: 0.4354716 (8)	total: 2.03s	remaining: 6.32s
9:	learn: 0.4169959	test: 0.4182098	best: 0.4182098 (9)	total: 2.07s	remaining: 5.6s
10:	learn: 0.3976837	test: 0.3990857	best: 0.3990857 (10)	total: 2.12s	remaining: 5.02s
11:	learn: 0.3845413	test: 0.3858483	best: 0.3858483 (11)	total: 2.17s	remaining: 4.53s
12:	learn: 0.3662193	test: 0.3676189	best: 0.3676189 (12)	total: 2.25s	remaining: 4.15s
13:	learn: 0.3512467	test: 0.3526482	best: 0.3526482 (13)	total: 2.51s	remaining: 4.13s
14:	learn: 0.3399024	test: 0.3417811	best: 0.3417811 (14)	total: 2.75s	remaining: 4.03s
15:	learn: 0.3251858	test: 0.3280744	best: 0.3280744 (15)	total: 4.49s	remaining: 5.9s
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18:	learn: 0.2905798	test: 0.2968210	best: 0.2968210 (18)	total: 7.82s	remaining: 7.41s
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22:	learn: 0.2644558	test: 0.2732310	best: 0.2732310 (22)	total: 10.1s	remaining: 6.17s
23:	learn: 0.2618922	test: 0.2708126	best: 0.2708126 (23)	total: 10.2s	remaining: 5.55s
24:	learn: 0.2603178	test: 0.2692047	best: 0.2692047 (24)	total: 10.3s	remaining: 4.93s
25:	learn: 0.2539343	test: 0.2648315	best: 0.2648315 (25)	total: 11.9s	remaining: 5.03s
26:	learn: 0.2499462	test: 0.2609698	best: 0.2609698 (26)	total: 12s	remaining: 4.44s
27:	learn: 0.2482950	test: 0.2592730	best: 0.2592730 (27)	total: 12s	remaining: 3.87s
28:	learn: 0.2446846	test: 0.2557489	best: 0.2557489 (28)	total: 12.2s	remaining: 3.36s
29:	learn: 0.2428156	test: 0.2537874	best: 0.2537874 (29)	total: 12.2s	remaining: 2.85s
30:	learn: 0.2389014	test: 0.2506735	best: 0.2506735 (30)	total: 13.9s	remaining: 2.69s
31:	learn: 0.2375219	test: 0.2492950	best: 0.2492950 (31)	total: 14s	remaining: 2.18s
32:	learn: 0.2345016	test: 0.2466344	best: 0.2466344 (32)	total: 15.4s	remaining: 1.87s
33:	learn: 0.2313273	test: 0.2444394	best: 0.2444394 (33)	total: 15.7s	remaining: 1.39s
34:	learn: 0.2290705	test: 0.2422995	best: 0.2422995 (34)	total: 15.9s	remaining: 910ms
35:	learn: 0.2283370	test: 0.2415466	best: 0.2415466 (35)	total: 16s	remaining: 444ms
36:	learn: 0.2272679	test: 0.2405275	best: 0.2405275 (36)	total: 16s	remaining: 0us

bestTest = 0.2405275154
bestIteration = 36

Trial 60, Fold 4: Log loss = 0.2405275153543953, Average precision = 0.9692593498615643, ROC-AUC = 0.9630999920122273, Elapsed Time = 16.162527300002694 seconds
Trial 60, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 60, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
0:	learn: 0.6576798	test: 0.6585479	best: 0.6585479 (0)	total: 31.5ms	remaining: 1.13s
1:	learn: 0.6151640	test: 0.6176017	best: 0.6176017 (1)	total: 1.21s	remaining: 21.1s
2:	learn: 0.5614830	test: 0.5655334	best: 0.5655334 (2)	total: 2.25s	remaining: 25.5s
3:	learn: 0.5202086	test: 0.5251524	best: 0.5251524 (3)	total: 3.34s	remaining: 27.6s
4:	learn: 0.4829995	test: 0.4896788	best: 0.4896788 (4)	total: 4.92s	remaining: 31.5s
5:	learn: 0.4566853	test: 0.4636357	best: 0.4636357 (5)	total: 4.97s	remaining: 25.7s
6:	learn: 0.4388278	test: 0.4464226	best: 0.4464226 (6)	total: 5.02s	remaining: 21.5s
7:	learn: 0.4152344	test: 0.4234051	best: 0.4234051 (7)	total: 5.16s	remaining: 18.7s
8:	learn: 0.3971550	test: 0.4057560	best: 0.4057560 (8)	total: 5.21s	remaining: 16.2s
9:	learn: 0.3747583	test: 0.3851649	best: 0.3851649 (9)	total: 5.71s	remaining: 15.4s
10:	learn: 0.3642525	test: 0.3751092	best: 0.3751092 (10)	total: 5.75s	remaining: 13.6s
11:	learn: 0.3536658	test: 0.3647984	best: 0.3647984 (11)	total: 5.83s	remaining: 12.1s
12:	learn: 0.3493382	test: 0.3608830	best: 0.3608830 (12)	total: 5.87s	remaining: 10.8s
13:	learn: 0.3402022	test: 0.3520801	best: 0.3520801 (13)	total: 5.91s	remaining: 9.71s
14:	learn: 0.3362336	test: 0.3485150	best: 0.3485150 (14)	total: 5.95s	remaining: 8.72s
15:	learn: 0.3194443	test: 0.3335837	best: 0.3335837 (15)	total: 7.5s	remaining: 9.84s
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18:	learn: 0.2961694	test: 0.3110668	best: 0.3110668 (18)	total: 7.86s	remaining: 7.44s
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23:	learn: 0.2685592	test: 0.2859805	best: 0.2859805 (23)	total: 11s	remaining: 5.97s
24:	learn: 0.2666814	test: 0.2840956	best: 0.2840956 (24)	total: 11.1s	remaining: 5.31s
25:	learn: 0.2624431	test: 0.2805231	best: 0.2805231 (25)	total: 12.7s	remaining: 5.37s
26:	learn: 0.2574608	test: 0.2760920	best: 0.2760920 (26)	total: 13s	remaining: 4.8s
27:	learn: 0.2543037	test: 0.2729270	best: 0.2729270 (27)	total: 13s	remaining: 4.19s
28:	learn: 0.2483865	test: 0.2696507	best: 0.2696507 (28)	total: 14.7s	remaining: 4.06s
29:	learn: 0.2470795	test: 0.2684145	best: 0.2684145 (29)	total: 14.8s	remaining: 3.44s
30:	learn: 0.2434520	test: 0.2652954	best: 0.2652954 (30)	total: 16.4s	remaining: 3.17s
31:	learn: 0.2390298	test: 0.2610622	best: 0.2610622 (31)	total: 16.5s	remaining: 2.58s
32:	learn: 0.2366013	test: 0.2588209	best: 0.2588209 (32)	total: 17.5s	remaining: 2.13s
33:	learn: 0.2343291	test: 0.2566679	best: 0.2566679 (33)	total: 17.7s	remaining: 1.56s
34:	learn: 0.2315951	test: 0.2541974	best: 0.2541974 (34)	total: 18.2s	remaining: 1.04s
35:	learn: 0.2291459	test: 0.2517521	best: 0.2517521 (35)	total: 18.2s	remaining: 506ms
36:	learn: 0.2280693	test: 0.2507450	best: 0.2507450 (36)	total: 18.3s	remaining: 0us

bestTest = 0.2507450171
bestIteration = 36

Trial 60, Fold 5: Log loss = 0.2507450170550699, Average precision = 0.9648030042260477, ROC-AUC = 0.9588524608438771, Elapsed Time = 18.41302600000199 seconds
Optimization Progress:  61%|######1   | 61/100 [1:46:35<1:46:27, 163.78s/it]
Trial 61, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 61, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
0:	learn: 0.5311544	test: 0.5345780	best: 0.5345780 (0)	total: 114ms	remaining: 2.27s
1:	learn: 0.4264863	test: 0.4314730	best: 0.4314730 (1)	total: 222ms	remaining: 2.11s
2:	learn: 0.3606027	test: 0.3680394	best: 0.3680394 (2)	total: 339ms	remaining: 2.03s
3:	learn: 0.3150024	test: 0.3260176	best: 0.3260176 (3)	total: 458ms	remaining: 1.95s
4:	learn: 0.2793900	test: 0.2935812	best: 0.2935812 (4)	total: 577ms	remaining: 1.85s
5:	learn: 0.2583997	test: 0.2745268	best: 0.2745268 (5)	total: 691ms	remaining: 1.73s
6:	learn: 0.2392554	test: 0.2576366	best: 0.2576366 (6)	total: 807ms	remaining: 1.61s
7:	learn: 0.2234033	test: 0.2437379	best: 0.2437379 (7)	total: 923ms	remaining: 1.5s
8:	learn: 0.2127209	test: 0.2355047	best: 0.2355047 (8)	total: 1.06s	remaining: 1.41s
9:	learn: 0.2019645	test: 0.2275382	best: 0.2275382 (9)	total: 1.19s	remaining: 1.31s
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11:	learn: 0.1875968	test: 0.2160301	best: 0.2160301 (11)	total: 1.42s	remaining: 1.07s
12:	learn: 0.1827931	test: 0.2132209	best: 0.2132209 (12)	total: 1.54s	remaining: 946ms
13:	learn: 0.1786750	test: 0.2109046	best: 0.2109046 (13)	total: 1.65s	remaining: 823ms
14:	learn: 0.1975012	test: 0.2928730	best: 0.2109046 (13)	total: 1.76s	remaining: 706ms
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18:	learn: 0.2812899	test: 0.4908095	best: 0.2109046 (13)	total: 2.23s	remaining: 234ms
19:	learn: 0.2784393	test: 0.4891935	best: 0.2109046 (13)	total: 2.35s	remaining: 117ms
20:	learn: 0.2784471	test: 0.4883762	best: 0.2109046 (13)	total: 2.46s	remaining: 0us

bestTest = 0.2109046388
bestIteration = 13

Shrink model to first 14 iterations.
Trial 61, Fold 1: Log loss = 0.21090464423234495, Average precision = 0.9735671156286745, ROC-AUC = 0.9698844803152618, Elapsed Time = 2.5722095999990415 seconds
Trial 61, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 61, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
0:	learn: 0.5170240	test: 0.5224300	best: 0.5224300 (0)	total: 140ms	remaining: 2.8s
1:	learn: 0.4223446	test: 0.4304005	best: 0.4304005 (1)	total: 275ms	remaining: 2.61s
2:	learn: 0.3653666	test: 0.3755443	best: 0.3755443 (2)	total: 394ms	remaining: 2.36s
3:	learn: 0.3160177	test: 0.3281427	best: 0.3281427 (3)	total: 515ms	remaining: 2.19s
4:	learn: 0.2816628	test: 0.2955258	best: 0.2955258 (4)	total: 637ms	remaining: 2.04s
5:	learn: 0.2600185	test: 0.2754751	best: 0.2754751 (5)	total: 758ms	remaining: 1.89s
6:	learn: 0.2428474	test: 0.2596154	best: 0.2596154 (6)	total: 897ms	remaining: 1.79s
7:	learn: 0.2268989	test: 0.2448804	best: 0.2448804 (7)	total: 1.04s	remaining: 1.69s
8:	learn: 0.2170804	test: 0.2366342	best: 0.2366342 (8)	total: 1.17s	remaining: 1.55s
9:	learn: 0.2085159	test: 0.2283819	best: 0.2283819 (9)	total: 1.28s	remaining: 1.41s
10:	learn: 0.2051550	test: 0.2503914	best: 0.2283819 (9)	total: 1.4s	remaining: 1.27s
11:	learn: 0.2109780	test: 0.2599792	best: 0.2283819 (9)	total: 1.52s	remaining: 1.14s
12:	learn: 0.2255988	test: 0.3123151	best: 0.2283819 (9)	total: 1.63s	remaining: 1s
13:	learn: 0.2201515	test: 0.3074553	best: 0.2283819 (9)	total: 1.73s	remaining: 866ms
14:	learn: 0.2141763	test: 0.3017693	best: 0.2283819 (9)	total: 1.84s	remaining: 737ms
15:	learn: 0.2084611	test: 0.2973281	best: 0.2283819 (9)	total: 1.97s	remaining: 617ms
16:	learn: 0.2040906	test: 0.2940727	best: 0.2283819 (9)	total: 2.1s	remaining: 495ms
17:	learn: 0.2008244	test: 0.2912497	best: 0.2283819 (9)	total: 2.23s	remaining: 372ms
18:	learn: 0.1973140	test: 0.2885535	best: 0.2283819 (9)	total: 2.36s	remaining: 248ms
19:	learn: 0.1937425	test: 0.2856299	best: 0.2283819 (9)	total: 2.48s	remaining: 124ms
20:	learn: 0.1902070	test: 0.2838196	best: 0.2283819 (9)	total: 2.62s	remaining: 0us

bestTest = 0.2283818946
bestIteration = 9

Shrink model to first 10 iterations.
Trial 61, Fold 2: Log loss = 0.22838190879653822, Average precision = 0.9730120451891991, ROC-AUC = 0.9716699783091715, Elapsed Time = 2.731778999997914 seconds
Trial 61, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 61, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
0:	learn: 0.5265614	test: 0.5283679	best: 0.5283679 (0)	total: 118ms	remaining: 2.35s
1:	learn: 0.4182210	test: 0.4219841	best: 0.4219841 (1)	total: 233ms	remaining: 2.22s
2:	learn: 0.3597721	test: 0.3647261	best: 0.3647261 (2)	total: 343ms	remaining: 2.06s
3:	learn: 0.3188557	test: 0.3255736	best: 0.3255736 (3)	total: 454ms	remaining: 1.93s
4:	learn: 0.2902434	test: 0.2987632	best: 0.2987632 (4)	total: 577ms	remaining: 1.85s
5:	learn: 0.2615270	test: 0.2724171	best: 0.2724171 (5)	total: 718ms	remaining: 1.79s
6:	learn: 0.2432002	test: 0.2555061	best: 0.2555061 (6)	total: 838ms	remaining: 1.68s
7:	learn: 0.2284978	test: 0.2426520	best: 0.2426520 (7)	total: 964ms	remaining: 1.56s
8:	learn: 0.2158200	test: 0.2314811	best: 0.2314811 (8)	total: 1.09s	remaining: 1.46s
9:	learn: 0.2061480	test: 0.2230007	best: 0.2230007 (9)	total: 1.21s	remaining: 1.33s
10:	learn: 0.1982370	test: 0.2161273	best: 0.2161273 (10)	total: 1.33s	remaining: 1.21s
11:	learn: 0.1919049	test: 0.2118250	best: 0.2118250 (11)	total: 1.45s	remaining: 1.09s
12:	learn: 0.1860883	test: 0.2079150	best: 0.2079150 (12)	total: 1.58s	remaining: 970ms
13:	learn: 0.1810199	test: 0.2039577	best: 0.2039577 (13)	total: 1.7s	remaining: 849ms
14:	learn: 0.1907710	test: 0.2001616	best: 0.2001616 (14)	total: 1.81s	remaining: 726ms
15:	learn: 0.1872608	test: 0.1983682	best: 0.1983682 (15)	total: 1.93s	remaining: 603ms
16:	learn: 0.1863851	test: 0.1972791	best: 0.1972791 (16)	total: 2.05s	remaining: 482ms
17:	learn: 0.1832934	test: 0.1956441	best: 0.1956441 (17)	total: 2.17s	remaining: 362ms
18:	learn: 0.1797063	test: 0.1944977	best: 0.1944977 (18)	total: 2.28s	remaining: 241ms
19:	learn: 0.1988476	test: 0.3231839	best: 0.1944977 (18)	total: 2.39s	remaining: 120ms
20:	learn: 0.1965443	test: 0.3218225	best: 0.1944977 (18)	total: 2.5s	remaining: 0us

bestTest = 0.1944977159
bestIteration = 18

Shrink model to first 19 iterations.
Trial 61, Fold 3: Log loss = 0.19449771790342277, Average precision = 0.9749471046359938, ROC-AUC = 0.9730193324970384, Elapsed Time = 2.6221650000006775 seconds
Trial 61, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 61, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
0:	learn: 0.5196888	test: 0.5218917	best: 0.5218917 (0)	total: 122ms	remaining: 2.44s
1:	learn: 0.4250635	test: 0.4292221	best: 0.4292221 (1)	total: 226ms	remaining: 2.15s
2:	learn: 0.3680109	test: 0.3742214	best: 0.3742214 (2)	total: 340ms	remaining: 2.04s
3:	learn: 0.3264765	test: 0.3347420	best: 0.3347420 (3)	total: 465ms	remaining: 1.98s
4:	learn: 0.2955975	test: 0.3053098	best: 0.3053098 (4)	total: 575ms	remaining: 1.84s
5:	learn: 0.2705389	test: 0.2819182	best: 0.2819182 (5)	total: 685ms	remaining: 1.71s
6:	learn: 0.2480537	test: 0.2606801	best: 0.2606801 (6)	total: 795ms	remaining: 1.59s
7:	learn: 0.2339199	test: 0.2477329	best: 0.2477329 (7)	total: 909ms	remaining: 1.48s
8:	learn: 0.2204580	test: 0.2362331	best: 0.2362331 (8)	total: 1.03s	remaining: 1.37s
9:	learn: 0.2056297	test: 0.2238851	best: 0.2238851 (9)	total: 1.16s	remaining: 1.27s
10:	learn: 0.1987083	test: 0.2185771	best: 0.2185771 (10)	total: 1.27s	remaining: 1.15s
11:	learn: 0.1931468	test: 0.2145469	best: 0.2145469 (11)	total: 1.39s	remaining: 1.04s
12:	learn: 0.1866961	test: 0.2095464	best: 0.2095464 (12)	total: 1.51s	remaining: 932ms
13:	learn: 0.1833701	test: 0.2072871	best: 0.2072871 (13)	total: 1.62s	remaining: 812ms
14:	learn: 0.1824937	test: 0.2047178	best: 0.2047178 (14)	total: 1.75s	remaining: 701ms
15:	learn: 0.2097261	test: 0.2023134	best: 0.2023134 (15)	total: 1.88s	remaining: 587ms
16:	learn: 0.2058819	test: 0.1988227	best: 0.1988227 (16)	total: 1.99s	remaining: 469ms
17:	learn: 0.2034074	test: 0.1964292	best: 0.1964292 (17)	total: 2.11s	remaining: 351ms
18:	learn: 0.2208410	test: 0.2565879	best: 0.1964292 (17)	total: 2.23s	remaining: 234ms
19:	learn: 0.2189360	test: 0.2545922	best: 0.1964292 (17)	total: 2.35s	remaining: 117ms
20:	learn: 0.2163566	test: 0.2538703	best: 0.1964292 (17)	total: 2.48s	remaining: 0us

bestTest = 0.1964291975
bestIteration = 17

Shrink model to first 18 iterations.
Trial 61, Fold 4: Log loss = 0.1964291992932503, Average precision = 0.9765616380414603, ROC-AUC = 0.9729477534091098, Elapsed Time = 2.6020305000020016 seconds
Trial 61, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 61, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
0:	learn: 0.5170266	test: 0.5220017	best: 0.5220017 (0)	total: 121ms	remaining: 2.41s
1:	learn: 0.4239195	test: 0.4318673	best: 0.4318673 (1)	total: 239ms	remaining: 2.27s
2:	learn: 0.3648167	test: 0.3747425	best: 0.3747425 (2)	total: 351ms	remaining: 2.1s
3:	learn: 0.3195297	test: 0.3314538	best: 0.3314538 (3)	total: 461ms	remaining: 1.96s
4:	learn: 0.2824791	test: 0.2977562	best: 0.2977562 (4)	total: 580ms	remaining: 1.86s
5:	learn: 0.2604306	test: 0.2776919	best: 0.2776919 (5)	total: 702ms	remaining: 1.75s
6:	learn: 0.2388056	test: 0.2579652	best: 0.2579652 (6)	total: 822ms	remaining: 1.64s
7:	learn: 0.2221904	test: 0.2441964	best: 0.2441964 (7)	total: 943ms	remaining: 1.53s
8:	learn: 0.2115120	test: 0.2357255	best: 0.2357255 (8)	total: 1.06s	remaining: 1.41s
9:	learn: 0.2019193	test: 0.2283650	best: 0.2283650 (9)	total: 1.19s	remaining: 1.31s
10:	learn: 0.1947171	test: 0.2237741	best: 0.2237741 (10)	total: 1.32s	remaining: 1.2s
11:	learn: 0.1883263	test: 0.2201550	best: 0.2201550 (11)	total: 1.44s	remaining: 1.08s
12:	learn: 0.1817500	test: 0.2151684	best: 0.2151684 (12)	total: 1.56s	remaining: 960ms
13:	learn: 0.1771992	test: 0.2127784	best: 0.2127784 (13)	total: 1.67s	remaining: 836ms
14:	learn: 0.1731531	test: 0.2097602	best: 0.2097602 (14)	total: 1.79s	remaining: 716ms
15:	learn: 0.1692000	test: 0.2073409	best: 0.2073409 (15)	total: 1.9s	remaining: 595ms
16:	learn: 0.1747308	test: 0.2578248	best: 0.2073409 (15)	total: 2.02s	remaining: 476ms
17:	learn: 0.1722062	test: 0.2563285	best: 0.2073409 (15)	total: 2.14s	remaining: 357ms
18:	learn: 0.1697991	test: 0.2553895	best: 0.2073409 (15)	total: 2.25s	remaining: 237ms
19:	learn: 0.1671298	test: 0.2541773	best: 0.2073409 (15)	total: 2.38s	remaining: 119ms
20:	learn: 0.1655066	test: 0.2539007	best: 0.2073409 (15)	total: 2.49s	remaining: 0us

bestTest = 0.207340894
bestIteration = 15

Shrink model to first 16 iterations.
Trial 61, Fold 5: Log loss = 0.2073408952745976, Average precision = 0.9740838197006582, ROC-AUC = 0.9712975493748025, Elapsed Time = 2.611271099998703 seconds
Optimization Progress:  62%|######2   | 62/100 [1:46:56<1:16:38, 121.02s/it]
Trial 62, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 62, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
0:	learn: 0.6844508	test: 0.6845223	best: 0.6845223 (0)	total: 17.5ms	remaining: 978ms
1:	learn: 0.6753745	test: 0.6755910	best: 0.6755910 (1)	total: 39ms	remaining: 1.07s
2:	learn: 0.6647018	test: 0.6648454	best: 0.6648454 (2)	total: 65.2ms	remaining: 1.17s
3:	learn: 0.6552653	test: 0.6552997	best: 0.6552997 (3)	total: 88.9ms	remaining: 1.18s
4:	learn: 0.6479941	test: 0.6478817	best: 0.6478817 (4)	total: 106ms	remaining: 1.1s
5:	learn: 0.6414312	test: 0.6413197	best: 0.6413197 (5)	total: 126ms	remaining: 1.07s
6:	learn: 0.6344055	test: 0.6341630	best: 0.6341630 (6)	total: 142ms	remaining: 1.02s
7:	learn: 0.6272578	test: 0.6269855	best: 0.6269855 (7)	total: 164ms	remaining: 1s
8:	learn: 0.6197120	test: 0.6192801	best: 0.6192801 (8)	total: 191ms	remaining: 1.02s
9:	learn: 0.6128665	test: 0.6123594	best: 0.6123594 (9)	total: 217ms	remaining: 1.02s
10:	learn: 0.6043475	test: 0.6037690	best: 0.6037690 (10)	total: 243ms	remaining: 1.01s
11:	learn: 0.5963485	test: 0.5956907	best: 0.5956907 (11)	total: 269ms	remaining: 1.01s
12:	learn: 0.5900110	test: 0.5892001	best: 0.5892001 (12)	total: 295ms	remaining: 998ms
13:	learn: 0.5837312	test: 0.5828971	best: 0.5828971 (13)	total: 320ms	remaining: 983ms
14:	learn: 0.5779650	test: 0.5769839	best: 0.5769839 (14)	total: 341ms	remaining: 955ms
15:	learn: 0.5725532	test: 0.5715547	best: 0.5715547 (15)	total: 366ms	remaining: 939ms
16:	learn: 0.5674115	test: 0.5664241	best: 0.5664241 (16)	total: 392ms	remaining: 921ms
17:	learn: 0.5619958	test: 0.5609627	best: 0.5609627 (17)	total: 417ms	remaining: 905ms
18:	learn: 0.5570120	test: 0.5559777	best: 0.5559777 (18)	total: 446ms	remaining: 892ms
19:	learn: 0.5513930	test: 0.5503800	best: 0.5503800 (19)	total: 471ms	remaining: 871ms
20:	learn: 0.5451141	test: 0.5441129	best: 0.5441129 (20)	total: 496ms	remaining: 851ms
21:	learn: 0.5398541	test: 0.5388667	best: 0.5388667 (21)	total: 522ms	remaining: 831ms
22:	learn: 0.5352422	test: 0.5343396	best: 0.5343396 (22)	total: 551ms	remaining: 815ms
23:	learn: 0.5307334	test: 0.5297916	best: 0.5297916 (23)	total: 585ms	remaining: 804ms
24:	learn: 0.5250148	test: 0.5240294	best: 0.5240294 (24)	total: 621ms	remaining: 795ms
25:	learn: 0.5195180	test: 0.5185688	best: 0.5185688 (25)	total: 650ms	remaining: 775ms
26:	learn: 0.5157740	test: 0.5148946	best: 0.5148946 (26)	total: 677ms	remaining: 752ms
27:	learn: 0.5109339	test: 0.5100797	best: 0.5100797 (27)	total: 701ms	remaining: 726ms
28:	learn: 0.5069566	test: 0.5061592	best: 0.5061592 (28)	total: 729ms	remaining: 704ms
29:	learn: 0.5011404	test: 0.5003144	best: 0.5003144 (29)	total: 753ms	remaining: 677ms
30:	learn: 0.4934397	test: 0.4925084	best: 0.4925084 (30)	total: 777ms	remaining: 652ms
31:	learn: 0.4901011	test: 0.4891083	best: 0.4891083 (31)	total: 799ms	remaining: 624ms
32:	learn: 0.4842487	test: 0.4831369	best: 0.4831369 (32)	total: 826ms	remaining: 600ms
33:	learn: 0.4785746	test: 0.4774927	best: 0.4774927 (33)	total: 854ms	remaining: 578ms
34:	learn: 0.4752922	test: 0.4742171	best: 0.4742171 (34)	total: 879ms	remaining: 552ms
35:	learn: 0.4726217	test: 0.4715927	best: 0.4715927 (35)	total: 915ms	remaining: 534ms
36:	learn: 0.4672490	test: 0.4661072	best: 0.4661072 (36)	total: 950ms	remaining: 514ms
37:	learn: 0.4634060	test: 0.4623897	best: 0.4623897 (37)	total: 982ms	remaining: 491ms
38:	learn: 0.4598609	test: 0.4588186	best: 0.4588186 (38)	total: 1.01s	remaining: 469ms
39:	learn: 0.4569103	test: 0.4559239	best: 0.4559239 (39)	total: 1.05s	remaining: 445ms
40:	learn: 0.4530464	test: 0.4521406	best: 0.4521406 (40)	total: 1.07s	remaining: 419ms
41:	learn: 0.4507702	test: 0.4499974	best: 0.4499974 (41)	total: 1.1s	remaining: 393ms
42:	learn: 0.4484451	test: 0.4477748	best: 0.4477748 (42)	total: 1.13s	remaining: 368ms
43:	learn: 0.4437996	test: 0.4430414	best: 0.4430414 (43)	total: 1.16s	remaining: 342ms
44:	learn: 0.4392108	test: 0.4385112	best: 0.4385112 (44)	total: 1.19s	remaining: 316ms
45:	learn: 0.4370629	test: 0.4364344	best: 0.4364344 (45)	total: 1.21s	remaining: 290ms
46:	learn: 0.4323968	test: 0.4318368	best: 0.4318368 (46)	total: 1.24s	remaining: 264ms
47:	learn: 0.4303447	test: 0.4297893	best: 0.4297893 (47)	total: 1.27s	remaining: 238ms
48:	learn: 0.4284930	test: 0.4279803	best: 0.4279803 (48)	total: 1.29s	remaining: 210ms
49:	learn: 0.4244805	test: 0.4239394	best: 0.4239394 (49)	total: 1.32s	remaining: 184ms
50:	learn: 0.4203528	test: 0.4197308	best: 0.4197308 (50)	total: 1.34s	remaining: 158ms
51:	learn: 0.4164418	test: 0.4157287	best: 0.4157287 (51)	total: 1.37s	remaining: 132ms
52:	learn: 0.4131444	test: 0.4124318	best: 0.4124318 (52)	total: 1.4s	remaining: 106ms
53:	learn: 0.4105543	test: 0.4098521	best: 0.4098521 (53)	total: 1.43s	remaining: 79.4ms
54:	learn: 0.4087138	test: 0.4079893	best: 0.4079893 (54)	total: 1.46s	remaining: 52.9ms
55:	learn: 0.4062619	test: 0.4055346	best: 0.4055346 (55)	total: 1.48s	remaining: 26.5ms
56:	learn: 0.4023852	test: 0.4016481	best: 0.4016481 (56)	total: 1.51s	remaining: 0us

bestTest = 0.4016481397
bestIteration = 56

Trial 62, Fold 1: Log loss = 0.4017622730911705, Average precision = 0.9567265941778511, ROC-AUC = 0.95159209611276, Elapsed Time = 1.622585799999797 seconds
Trial 62, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 62, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
0:	learn: 0.6826367	test: 0.6827674	best: 0.6827674 (0)	total: 21.2ms	remaining: 1.19s
1:	learn: 0.6745823	test: 0.6747142	best: 0.6747142 (1)	total: 42.4ms	remaining: 1.17s
2:	learn: 0.6649258	test: 0.6650794	best: 0.6650794 (2)	total: 58.9ms	remaining: 1.06s
3:	learn: 0.6573809	test: 0.6574891	best: 0.6574891 (3)	total: 75.7ms	remaining: 1s
4:	learn: 0.6496580	test: 0.6498265	best: 0.6498265 (4)	total: 97.2ms	remaining: 1.01s
5:	learn: 0.6415860	test: 0.6417270	best: 0.6417270 (5)	total: 121ms	remaining: 1.03s
6:	learn: 0.6328362	test: 0.6329736	best: 0.6329736 (6)	total: 139ms	remaining: 994ms
7:	learn: 0.6239147	test: 0.6241192	best: 0.6241192 (7)	total: 165ms	remaining: 1.01s
8:	learn: 0.6168021	test: 0.6170908	best: 0.6170908 (8)	total: 192ms	remaining: 1.02s
9:	learn: 0.6094188	test: 0.6096694	best: 0.6096694 (9)	total: 210ms	remaining: 987ms
10:	learn: 0.6033625	test: 0.6035996	best: 0.6035996 (10)	total: 235ms	remaining: 982ms
11:	learn: 0.5975308	test: 0.5978020	best: 0.5978020 (11)	total: 261ms	remaining: 980ms
12:	learn: 0.5904572	test: 0.5908286	best: 0.5908286 (12)	total: 286ms	remaining: 967ms
13:	learn: 0.5826039	test: 0.5831017	best: 0.5831017 (13)	total: 309ms	remaining: 950ms
14:	learn: 0.5750201	test: 0.5756510	best: 0.5756510 (14)	total: 332ms	remaining: 930ms
15:	learn: 0.5695985	test: 0.5702878	best: 0.5702878 (15)	total: 359ms	remaining: 921ms
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48:	learn: 0.4383607	test: 0.4399227	best: 0.4399227 (48)	total: 1.25s	remaining: 203ms
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53:	learn: 0.4257941	test: 0.4273153	best: 0.4273153 (53)	total: 1.38s	remaining: 76.9ms
54:	learn: 0.4228094	test: 0.4243174	best: 0.4243174 (54)	total: 1.42s	remaining: 51.5ms
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56:	learn: 0.4186743	test: 0.4202287	best: 0.4202287 (56)	total: 1.47s	remaining: 0us

bestTest = 0.4202286886
bestIteration = 56

Trial 62, Fold 2: Log loss = 0.42030386935676656, Average precision = 0.950718282227725, ROC-AUC = 0.9486870933349034, Elapsed Time = 1.580957899997884 seconds
Trial 62, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 62, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
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48:	learn: 0.4354107	test: 0.4305587	best: 0.4305587 (48)	total: 1.25s	remaining: 204ms
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51:	learn: 0.4296197	test: 0.4245141	best: 0.4245141 (51)	total: 1.33s	remaining: 128ms
52:	learn: 0.4275533	test: 0.4223441	best: 0.4223441 (52)	total: 1.35s	remaining: 102ms
53:	learn: 0.4231141	test: 0.4179516	best: 0.4179516 (53)	total: 1.38s	remaining: 76.6ms
54:	learn: 0.4204749	test: 0.4152823	best: 0.4152823 (54)	total: 1.41s	remaining: 51.1ms
55:	learn: 0.4168541	test: 0.4117985	best: 0.4117985 (55)	total: 1.43s	remaining: 25.6ms
56:	learn: 0.4136381	test: 0.4084437	best: 0.4084437 (56)	total: 1.46s	remaining: 0us

bestTest = 0.4084436948
bestIteration = 56

Trial 62, Fold 3: Log loss = 0.4086846132751064, Average precision = 0.9557043540753563, ROC-AUC = 0.9529976897554681, Elapsed Time = 1.5760173000016948 seconds
Trial 62, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 62, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
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56:	learn: 0.3987116	test: 0.3977191	best: 0.3977191 (56)	total: 1.45s	remaining: 0us

bestTest = 0.3977190798
bestIteration = 56

Trial 62, Fold 4: Log loss = 0.39783009426448285, Average precision = 0.9591092684337645, ROC-AUC = 0.9544344507869745, Elapsed Time = 1.5617993999985629 seconds
Trial 62, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 62, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
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56:	learn: 0.4264440	test: 0.4341871	best: 0.4341871 (56)	total: 1.43s	remaining: 0us

bestTest = 0.4341871003
bestIteration = 56

Trial 62, Fold 5: Log loss = 0.4341949787366909, Average precision = 0.9490793838424075, ROC-AUC = 0.9438020177247646, Elapsed Time = 1.5355920000001788 seconds
Optimization Progress:  63%|######3   | 63/100 [1:47:14<55:23, 89.83s/it]   
Trial 63, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 63, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
0:	learn: 0.5820811	test: 0.5824604	best: 0.5824604 (0)	total: 88.5ms	remaining: 7.52s
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85:	learn: 0.1834004	test: 0.2041771	best: 0.2041637 (84)	total: 8.68s	remaining: 0us

bestTest = 0.2041637214
bestIteration = 84

Shrink model to first 85 iterations.
Trial 63, Fold 1: Log loss = 0.20358020977818503, Average precision = 0.9731543004024821, ROC-AUC = 0.9684258094234381, Elapsed Time = 8.812033399997745 seconds
Trial 63, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 63, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
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76:	learn: 0.1891665	test: 0.1980625	best: 0.1980625 (76)	total: 7.57s	remaining: 885ms
77:	learn: 0.1885349	test: 0.1977478	best: 0.1977478 (77)	total: 7.69s	remaining: 789ms
78:	learn: 0.1882743	test: 0.1977397	best: 0.1977397 (78)	total: 7.81s	remaining: 692ms
79:	learn: 0.1882003	test: 0.1976872	best: 0.1976872 (79)	total: 7.92s	remaining: 594ms
80:	learn: 0.1873944	test: 0.1972069	best: 0.1972069 (80)	total: 8.04s	remaining: 496ms
81:	learn: 0.1868225	test: 0.1968147	best: 0.1968147 (81)	total: 8.14s	remaining: 397ms
82:	learn: 0.1868200	test: 0.1968178	best: 0.1968147 (81)	total: 8.19s	remaining: 296ms
83:	learn: 0.1867856	test: 0.1967854	best: 0.1967854 (83)	total: 8.29s	remaining: 197ms
84:	learn: 0.1865873	test: 0.1965710	best: 0.1965710 (84)	total: 8.39s	remaining: 98.7ms
85:	learn: 0.1863535	test: 0.1965029	best: 0.1965029 (85)	total: 8.49s	remaining: 0us

bestTest = 0.1965028818
bestIteration = 85

Trial 63, Fold 2: Log loss = 0.19611343832119293, Average precision = 0.9738883789691233, ROC-AUC = 0.9704723089123599, Elapsed Time = 8.637516799997684 seconds
Trial 63, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 63, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
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83:	learn: 0.1844655	test: 0.1962964	best: 0.1962964 (83)	total: 10.3s	remaining: 245ms
84:	learn: 0.1840193	test: 0.1961947	best: 0.1961947 (84)	total: 10.4s	remaining: 122ms
85:	learn: 0.1835652	test: 0.1961769	best: 0.1961769 (85)	total: 10.5s	remaining: 0us

bestTest = 0.1961768734
bestIteration = 85

Trial 63, Fold 3: Log loss = 0.19586252378887894, Average precision = 0.9733974224892061, ROC-AUC = 0.9702815602999104, Elapsed Time = 10.650340999996843 seconds
Trial 63, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 63, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
0:	learn: 0.6192880	test: 0.6196967	best: 0.6196967 (0)	total: 95.2ms	remaining: 8.09s
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76:	learn: 0.1875746	test: 0.2017044	best: 0.2017044 (76)	total: 7.79s	remaining: 911ms
77:	learn: 0.1872923	test: 0.2015372	best: 0.2015372 (77)	total: 7.9s	remaining: 810ms
78:	learn: 0.1870680	test: 0.2014064	best: 0.2014064 (78)	total: 8s	remaining: 709ms
79:	learn: 0.1869238	test: 0.2013210	best: 0.2013210 (79)	total: 8.11s	remaining: 608ms
80:	learn: 0.1863689	test: 0.2011748	best: 0.2011748 (80)	total: 8.21s	remaining: 507ms
81:	learn: 0.1857803	test: 0.2009863	best: 0.2009863 (81)	total: 8.32s	remaining: 406ms
82:	learn: 0.1855220	test: 0.2011513	best: 0.2009863 (81)	total: 8.42s	remaining: 304ms
83:	learn: 0.1855220	test: 0.2011513	best: 0.2009863 (81)	total: 8.45s	remaining: 201ms
84:	learn: 0.1853702	test: 0.2012014	best: 0.2009863 (81)	total: 8.54s	remaining: 101ms
85:	learn: 0.1851354	test: 0.2010169	best: 0.2009863 (81)	total: 8.64s	remaining: 0us

bestTest = 0.2009862839
bestIteration = 81

Shrink model to first 82 iterations.
Trial 63, Fold 4: Log loss = 0.20050170080730612, Average precision = 0.9738741510547041, ROC-AUC = 0.9689064023786872, Elapsed Time = 8.791355500001373 seconds
Trial 63, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 63, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
0:	learn: 0.5979033	test: 0.6005158	best: 0.6005158 (0)	total: 91.6ms	remaining: 7.79s
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2:	learn: 0.4921501	test: 0.4965468	best: 0.4965468 (2)	total: 194ms	remaining: 5.37s
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76:	learn: 0.1864556	test: 0.2086884	best: 0.2086884 (76)	total: 7.46s	remaining: 872ms
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78:	learn: 0.1856220	test: 0.2084091	best: 0.2084091 (78)	total: 7.66s	remaining: 679ms
79:	learn: 0.1854471	test: 0.2082673	best: 0.2082673 (79)	total: 7.77s	remaining: 583ms
80:	learn: 0.1850585	test: 0.2082012	best: 0.2082012 (80)	total: 7.87s	remaining: 486ms
81:	learn: 0.1846068	test: 0.2077918	best: 0.2077918 (81)	total: 7.98s	remaining: 389ms
82:	learn: 0.1841956	test: 0.2075242	best: 0.2075242 (82)	total: 8.08s	remaining: 292ms
83:	learn: 0.1841956	test: 0.2075241	best: 0.2075241 (83)	total: 8.12s	remaining: 193ms
84:	learn: 0.1839335	test: 0.2073266	best: 0.2073266 (84)	total: 8.21s	remaining: 96.6ms
85:	learn: 0.1838526	test: 0.2072912	best: 0.2072912 (85)	total: 8.32s	remaining: 0us

bestTest = 0.2072912112
bestIteration = 85

Trial 63, Fold 5: Log loss = 0.20660221002593426, Average precision = 0.971973721771268, ROC-AUC = 0.9688050833286885, Elapsed Time = 8.45616439999867 seconds
Optimization Progress:  64%|######4   | 64/100 [1:48:07<47:23, 78.97s/it]
Trial 64, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 64, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
0:	learn: 0.6270816	test: 0.6272108	best: 0.6272108 (0)	total: 63.6ms	remaining: 3.31s
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2:	learn: 0.5232252	test: 0.5237121	best: 0.5237121 (2)	total: 191ms	remaining: 3.19s
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49:	learn: 0.1968132	test: 0.2132059	best: 0.2132059 (49)	total: 3.34s	remaining: 201ms
50:	learn: 0.1959770	test: 0.2125990	best: 0.2125990 (50)	total: 3.41s	remaining: 134ms
51:	learn: 0.1954323	test: 0.2123275	best: 0.2123275 (51)	total: 3.47s	remaining: 66.8ms
52:	learn: 0.1947887	test: 0.2119892	best: 0.2119892 (52)	total: 3.54s	remaining: 0us

bestTest = 0.2119891636
bestIteration = 52

Trial 64, Fold 1: Log loss = 0.21198916364723694, Average precision = 0.973417810367677, ROC-AUC = 0.9688073550709053, Elapsed Time = 3.6468724999976985 seconds
Trial 64, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 64, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
0:	learn: 0.6272498	test: 0.6275179	best: 0.6275179 (0)	total: 63.3ms	remaining: 3.29s
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6:	learn: 0.3904693	test: 0.3935761	best: 0.3935761 (6)	total: 448ms	remaining: 2.94s
7:	learn: 0.3687386	test: 0.3719287	best: 0.3719287 (7)	total: 512ms	remaining: 2.88s
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42:	learn: 0.2029579	test: 0.2113977	best: 0.2113977 (42)	total: 2.92s	remaining: 680ms
43:	learn: 0.2021849	test: 0.2108946	best: 0.2108946 (43)	total: 2.99s	remaining: 612ms
44:	learn: 0.2015106	test: 0.2106167	best: 0.2106167 (44)	total: 3.06s	remaining: 544ms
45:	learn: 0.2006209	test: 0.2096512	best: 0.2096512 (45)	total: 3.13s	remaining: 476ms
46:	learn: 0.1999761	test: 0.2092365	best: 0.2092365 (46)	total: 3.19s	remaining: 408ms
47:	learn: 0.1993171	test: 0.2087032	best: 0.2087032 (47)	total: 3.26s	remaining: 340ms
48:	learn: 0.1985722	test: 0.2085370	best: 0.2085370 (48)	total: 3.33s	remaining: 272ms
49:	learn: 0.1979320	test: 0.2080002	best: 0.2080002 (49)	total: 3.4s	remaining: 204ms
50:	learn: 0.1975199	test: 0.2079426	best: 0.2079426 (50)	total: 3.46s	remaining: 136ms
51:	learn: 0.1968898	test: 0.2074155	best: 0.2074155 (51)	total: 3.53s	remaining: 67.9ms
52:	learn: 0.1964597	test: 0.2072443	best: 0.2072443 (52)	total: 3.6s	remaining: 0us

bestTest = 0.2072443174
bestIteration = 52

Trial 64, Fold 2: Log loss = 0.20724431744109836, Average precision = 0.9740108750986209, ROC-AUC = 0.9711155677749617, Elapsed Time = 3.70022660000177 seconds
Trial 64, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 64, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
0:	learn: 0.6272106	test: 0.6269623	best: 0.6269623 (0)	total: 70.7ms	remaining: 3.68s
1:	learn: 0.5711270	test: 0.5708883	best: 0.5708883 (1)	total: 139ms	remaining: 3.55s
2:	learn: 0.5236185	test: 0.5233514	best: 0.5233514 (2)	total: 209ms	remaining: 3.49s
3:	learn: 0.4824140	test: 0.4816175	best: 0.4816175 (3)	total: 279ms	remaining: 3.42s
4:	learn: 0.4474830	test: 0.4466406	best: 0.4466406 (4)	total: 349ms	remaining: 3.35s
5:	learn: 0.4176684	test: 0.4172260	best: 0.4172260 (5)	total: 419ms	remaining: 3.28s
6:	learn: 0.3917373	test: 0.3914493	best: 0.3914493 (6)	total: 490ms	remaining: 3.22s
7:	learn: 0.3693899	test: 0.3691466	best: 0.3691466 (7)	total: 562ms	remaining: 3.16s
8:	learn: 0.3502680	test: 0.3504018	best: 0.3504018 (8)	total: 633ms	remaining: 3.09s
9:	learn: 0.3339829	test: 0.3339490	best: 0.3339490 (9)	total: 704ms	remaining: 3.03s
10:	learn: 0.3198958	test: 0.3202395	best: 0.3202395 (10)	total: 778ms	remaining: 2.97s
11:	learn: 0.3075153	test: 0.3079284	best: 0.3079284 (11)	total: 849ms	remaining: 2.9s
12:	learn: 0.2968222	test: 0.2974408	best: 0.2974408 (12)	total: 921ms	remaining: 2.83s
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46:	learn: 0.1994922	test: 0.2085245	best: 0.2085245 (46)	total: 3.44s	remaining: 440ms
47:	learn: 0.1985125	test: 0.2085387	best: 0.2085245 (46)	total: 3.52s	remaining: 366ms
48:	learn: 0.1979046	test: 0.2080937	best: 0.2080937 (48)	total: 3.59s	remaining: 293ms
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50:	learn: 0.1964600	test: 0.2073387	best: 0.2073387 (50)	total: 3.72s	remaining: 146ms
51:	learn: 0.1956467	test: 0.2068461	best: 0.2068461 (51)	total: 3.79s	remaining: 72.9ms
52:	learn: 0.1951465	test: 0.2065540	best: 0.2065540 (52)	total: 3.86s	remaining: 0us

bestTest = 0.2065540451
bestIteration = 52

Trial 64, Fold 3: Log loss = 0.20655404506131142, Average precision = 0.9726031615999413, ROC-AUC = 0.9698857097109728, Elapsed Time = 3.9769863999972586 seconds
Trial 64, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 64, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
0:	learn: 0.6277257	test: 0.6278012	best: 0.6278012 (0)	total: 65.2ms	remaining: 3.39s
1:	learn: 0.5708531	test: 0.5715551	best: 0.5715551 (1)	total: 131ms	remaining: 3.33s
2:	learn: 0.5224588	test: 0.5234789	best: 0.5234789 (2)	total: 198ms	remaining: 3.3s
3:	learn: 0.4818117	test: 0.4830826	best: 0.4830826 (3)	total: 265ms	remaining: 3.24s
4:	learn: 0.4461144	test: 0.4475577	best: 0.4475577 (4)	total: 331ms	remaining: 3.18s
5:	learn: 0.4162141	test: 0.4177487	best: 0.4177487 (5)	total: 398ms	remaining: 3.12s
6:	learn: 0.3904792	test: 0.3924021	best: 0.3924021 (6)	total: 466ms	remaining: 3.06s
7:	learn: 0.3683291	test: 0.3703427	best: 0.3703427 (7)	total: 532ms	remaining: 2.99s
8:	learn: 0.3502889	test: 0.3524590	best: 0.3524590 (8)	total: 600ms	remaining: 2.93s
9:	learn: 0.3334856	test: 0.3358245	best: 0.3358245 (9)	total: 668ms	remaining: 2.87s
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44:	learn: 0.1998084	test: 0.2128348	best: 0.2128348 (44)	total: 3.04s	remaining: 541ms
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48:	learn: 0.1969545	test: 0.2109298	best: 0.2109298 (48)	total: 3.31s	remaining: 270ms
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51:	learn: 0.1952197	test: 0.2102183	best: 0.2102183 (51)	total: 3.51s	remaining: 67.5ms
52:	learn: 0.1946508	test: 0.2101184	best: 0.2101184 (52)	total: 3.58s	remaining: 0us

bestTest = 0.2101184371
bestIteration = 52

Trial 64, Fold 4: Log loss = 0.21011843705512998, Average precision = 0.9732822428368338, ROC-AUC = 0.968786883838709, Elapsed Time = 3.68862259999878 seconds
Trial 64, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 64, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
0:	learn: 0.6276437	test: 0.6290933	best: 0.6290933 (0)	total: 64ms	remaining: 3.33s
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52:	learn: 0.1943478	test: 0.2176515	best: 0.2176515 (52)	total: 3.55s	remaining: 0us

bestTest = 0.2176514974
bestIteration = 52

Trial 64, Fold 5: Log loss = 0.21765149735541112, Average precision = 0.970327625197403, ROC-AUC = 0.9678532969176746, Elapsed Time = 3.6505628999984765 seconds
Optimization Progress:  65%|######5   | 65/100 [1:48:34<36:53, 63.23s/it]
Trial 65, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 65, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
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93:	learn: 0.2091610	test: 0.2284485	best: 0.2284485 (93)	total: 27.6s	remaining: 0us

bestTest = 0.2284485328
bestIteration = 93

Trial 65, Fold 1: Log loss = 0.22844853284198552, Average precision = 0.9717435611306037, ROC-AUC = 0.9682847368597144, Elapsed Time = 27.705972499999916 seconds
Trial 65, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 65, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
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93:	learn: 0.2149731	test: 0.2289427	best: 0.2289427 (93)	total: 26.4s	remaining: 0us

bestTest = 0.2289426739
bestIteration = 93

Trial 65, Fold 2: Log loss = 0.22894267389669753, Average precision = 0.9732539860836594, ROC-AUC = 0.970380577376044, Elapsed Time = 26.59802010000203 seconds
Trial 65, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 65, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
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91:	learn: 0.2154492	test: 0.2269436	best: 0.2269436 (91)	total: 25.3s	remaining: 549ms
92:	learn: 0.2146970	test: 0.2262395	best: 0.2262395 (92)	total: 25.6s	remaining: 275ms
93:	learn: 0.2139478	test: 0.2254583	best: 0.2254583 (93)	total: 25.8s	remaining: 0us

bestTest = 0.2254582995
bestIteration = 93

Trial 65, Fold 3: Log loss = 0.2254582994719636, Average precision = 0.9740730539862928, ROC-AUC = 0.9704280573720757, Elapsed Time = 25.97395140000299 seconds
Trial 65, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 65, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
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bestTest = 0.2227093909
bestIteration = 93

Trial 65, Fold 4: Log loss = 0.22270939086504088, Average precision = 0.9741357559715035, ROC-AUC = 0.9701410795653697, Elapsed Time = 25.479954099999304 seconds
Trial 65, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 65, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
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89:	learn: 0.2111193	test: 0.2335366	best: 0.2335366 (89)	total: 24s	remaining: 1.07s
90:	learn: 0.2104673	test: 0.2329916	best: 0.2329916 (90)	total: 24.3s	remaining: 801ms
91:	learn: 0.2097130	test: 0.2323336	best: 0.2323336 (91)	total: 24.6s	remaining: 534ms
92:	learn: 0.2090172	test: 0.2317873	best: 0.2317873 (92)	total: 24.8s	remaining: 267ms
93:	learn: 0.2083880	test: 0.2311678	best: 0.2311678 (93)	total: 25s	remaining: 0us

bestTest = 0.2311678229
bestIteration = 93

Trial 65, Fold 5: Log loss = 0.23116782291661211, Average precision = 0.9720569451347603, ROC-AUC = 0.9682291213793359, Elapsed Time = 25.182768200000282 seconds
Optimization Progress:  66%|######6   | 66/100 [1:50:52<48:36, 85.79s/it]
Trial 66, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 66, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
0:	learn: 0.6038481	test: 0.6125825	best: 0.6125825 (0)	total: 6.31s	remaining: 44.2s
1:	learn: 0.5188425	test: 0.5425638	best: 0.5425638 (1)	total: 14.1s	remaining: 42.2s
2:	learn: 0.4562161	test: 0.4901837	best: 0.4901837 (2)	total: 20.4s	remaining: 34s
3:	learn: 0.4043148	test: 0.4462300	best: 0.4462300 (3)	total: 25.8s	remaining: 25.8s
4:	learn: 0.3525741	test: 0.4048929	best: 0.4048929 (4)	total: 31.7s	remaining: 19s
5:	learn: 0.3168511	test: 0.3752779	best: 0.3752779 (5)	total: 37s	remaining: 12.3s
6:	learn: 0.2783105	test: 0.3446947	best: 0.3446947 (6)	total: 42.6s	remaining: 6.09s
7:	learn: 0.2486222	test: 0.3212030	best: 0.3212030 (7)	total: 48.1s	remaining: 0us

bestTest = 0.3212030184
bestIteration = 7

Trial 66, Fold 1: Log loss = 0.3212030184293957, Average precision = 0.9676886982497143, ROC-AUC = 0.9611996679380375, Elapsed Time = 48.305870099997264 seconds
Trial 66, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 66, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
0:	learn: 0.6081135	test: 0.6189193	best: 0.6189193 (0)	total: 5.91s	remaining: 41.4s
1:	learn: 0.5252401	test: 0.5494736	best: 0.5494736 (1)	total: 11.8s	remaining: 35.5s
2:	learn: 0.4587531	test: 0.4973216	best: 0.4973216 (2)	total: 17.8s	remaining: 29.7s
3:	learn: 0.4069466	test: 0.4514136	best: 0.4514136 (3)	total: 23.3s	remaining: 23.3s
4:	learn: 0.3518388	test: 0.4090534	best: 0.4090534 (4)	total: 28.8s	remaining: 17.3s
5:	learn: 0.3089903	test: 0.3736304	best: 0.3736304 (5)	total: 34.2s	remaining: 11.4s
6:	learn: 0.2696581	test: 0.3435978	best: 0.3435978 (6)	total: 39.7s	remaining: 5.67s
7:	learn: 0.2446864	test: 0.3223327	best: 0.3223327 (7)	total: 45.2s	remaining: 0us

bestTest = 0.3223327159
bestIteration = 7

Trial 66, Fold 2: Log loss = 0.32233271594206875, Average precision = 0.967443880222844, ROC-AUC = 0.9625607104952715, Elapsed Time = 45.320567999999184 seconds
Trial 66, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 66, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
0:	learn: 0.5913281	test: 0.6115472	best: 0.6115472 (0)	total: 5.82s	remaining: 40.8s
1:	learn: 0.5068758	test: 0.5396878	best: 0.5396878 (1)	total: 11.6s	remaining: 34.9s
2:	learn: 0.4450620	test: 0.4883430	best: 0.4883430 (2)	total: 17.5s	remaining: 29.2s
3:	learn: 0.3917756	test: 0.4448439	best: 0.4448439 (3)	total: 23.1s	remaining: 23.1s
4:	learn: 0.3472829	test: 0.4040824	best: 0.4040824 (4)	total: 28.9s	remaining: 17.4s
5:	learn: 0.3168064	test: 0.3764005	best: 0.3764005 (5)	total: 34.6s	remaining: 11.5s
6:	learn: 0.2789554	test: 0.3460343	best: 0.3460343 (6)	total: 40.4s	remaining: 5.77s
7:	learn: 0.2502393	test: 0.3222995	best: 0.3222995 (7)	total: 46.1s	remaining: 0us

bestTest = 0.3222995379
bestIteration = 7

Trial 66, Fold 3: Log loss = 0.32229953792725474, Average precision = 0.9654392335880897, ROC-AUC = 0.9622990123373559, Elapsed Time = 46.269129299998895 seconds
Trial 66, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 66, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
0:	learn: 0.6001260	test: 0.6116289	best: 0.6116289 (0)	total: 6.26s	remaining: 43.8s
1:	learn: 0.5196074	test: 0.5403403	best: 0.5403403 (1)	total: 12.5s	remaining: 37.4s
2:	learn: 0.4534521	test: 0.4869906	best: 0.4869906 (2)	total: 18.3s	remaining: 30.5s
3:	learn: 0.4071649	test: 0.4453260	best: 0.4453260 (3)	total: 24s	remaining: 24s
4:	learn: 0.3583900	test: 0.4032130	best: 0.4032130 (4)	total: 29.7s	remaining: 17.8s
5:	learn: 0.3197381	test: 0.3706860	best: 0.3706860 (5)	total: 35.6s	remaining: 11.9s
6:	learn: 0.2809273	test: 0.3411497	best: 0.3411497 (6)	total: 41.4s	remaining: 5.91s
7:	learn: 0.2509235	test: 0.3179334	best: 0.3179334 (7)	total: 47.1s	remaining: 0us

bestTest = 0.317933405
bestIteration = 7

Trial 66, Fold 4: Log loss = 0.3179334050373481, Average precision = 0.9689730529116045, ROC-AUC = 0.9634770268079191, Elapsed Time = 47.31287929999962 seconds
Trial 66, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 66, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
0:	learn: 0.5946099	test: 0.6130502	best: 0.6130502 (0)	total: 5.94s	remaining: 41.6s
1:	learn: 0.5153158	test: 0.5479888	best: 0.5479888 (1)	total: 11.9s	remaining: 35.6s
2:	learn: 0.4490802	test: 0.4936994	best: 0.4936994 (2)	total: 17.8s	remaining: 29.6s
3:	learn: 0.4003560	test: 0.4501497	best: 0.4501497 (3)	total: 23.4s	remaining: 23.4s
4:	learn: 0.3501528	test: 0.4120923	best: 0.4120923 (4)	total: 29.3s	remaining: 17.6s
5:	learn: 0.3103541	test: 0.3784655	best: 0.3784655 (5)	total: 35.1s	remaining: 11.7s
6:	learn: 0.2710949	test: 0.3481161	best: 0.3481161 (6)	total: 40.7s	remaining: 5.81s
7:	learn: 0.2405829	test: 0.3245025	best: 0.3245025 (7)	total: 46.6s	remaining: 0us

bestTest = 0.3245024617
bestIteration = 7

Trial 66, Fold 5: Log loss = 0.32450246170250396, Average precision = 0.964914102627159, ROC-AUC = 0.9582429073072849, Elapsed Time = 46.748971100001654 seconds
Optimization Progress:  67%|######7   | 67/100 [1:54:54<1:12:56, 132.61s/it]
Trial 67, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 67, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
0:	learn: 0.5308367	test: 0.5334931	best: 0.5334931 (0)	total: 61.5ms	remaining: 1.04s
1:	learn: 0.4119444	test: 0.4171130	best: 0.4171130 (1)	total: 125ms	remaining: 999ms
2:	learn: 0.3379642	test: 0.3449680	best: 0.3449680 (2)	total: 188ms	remaining: 942ms
3:	learn: 0.3011151	test: 0.3083902	best: 0.3083902 (3)	total: 247ms	remaining: 864ms
4:	learn: 0.2696773	test: 0.2773132	best: 0.2773132 (4)	total: 308ms	remaining: 802ms
5:	learn: 0.2498357	test: 0.2592433	best: 0.2592433 (5)	total: 372ms	remaining: 743ms
6:	learn: 0.2347770	test: 0.2458484	best: 0.2458484 (6)	total: 431ms	remaining: 677ms
7:	learn: 0.2255207	test: 0.2379487	best: 0.2379487 (7)	total: 492ms	remaining: 615ms
8:	learn: 0.2177878	test: 0.2318626	best: 0.2318626 (8)	total: 554ms	remaining: 554ms
9:	learn: 0.2123682	test: 0.2282185	best: 0.2282185 (9)	total: 617ms	remaining: 494ms
10:	learn: 0.2073813	test: 0.2236595	best: 0.2236595 (10)	total: 677ms	remaining: 431ms
11:	learn: 0.2024308	test: 0.2201602	best: 0.2201602 (11)	total: 740ms	remaining: 370ms
12:	learn: 0.1981273	test: 0.2180637	best: 0.2180637 (12)	total: 805ms	remaining: 309ms
13:	learn: 0.1947860	test: 0.2167048	best: 0.2167048 (13)	total: 868ms	remaining: 248ms
14:	learn: 0.1929895	test: 0.2152865	best: 0.2152865 (14)	total: 929ms	remaining: 186ms
15:	learn: 0.1890878	test: 0.2125549	best: 0.2125549 (15)	total: 991ms	remaining: 124ms
16:	learn: 0.1873403	test: 0.2117731	best: 0.2117731 (16)	total: 1.05s	remaining: 62.1ms
17:	learn: 0.1852215	test: 0.2109326	best: 0.2109326 (17)	total: 1.12s	remaining: 0us

bestTest = 0.2109326431
bestIteration = 17

Trial 67, Fold 1: Log loss = 0.2109326430998132, Average precision = 0.9693742843982487, ROC-AUC = 0.9667552683022421, Elapsed Time = 1.2243921000008413 seconds
Trial 67, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 67, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
0:	learn: 0.5116023	test: 0.5141740	best: 0.5141740 (0)	total: 63ms	remaining: 1.07s
1:	learn: 0.4257167	test: 0.4316891	best: 0.4316891 (1)	total: 125ms	remaining: 1s
2:	learn: 0.3543765	test: 0.3612416	best: 0.3612416 (2)	total: 188ms	remaining: 939ms
3:	learn: 0.3109329	test: 0.3187222	best: 0.3187222 (3)	total: 250ms	remaining: 875ms
4:	learn: 0.2777554	test: 0.2878122	best: 0.2878122 (4)	total: 312ms	remaining: 812ms
5:	learn: 0.2500602	test: 0.2604702	best: 0.2604702 (5)	total: 375ms	remaining: 750ms
6:	learn: 0.2360304	test: 0.2476421	best: 0.2476421 (6)	total: 441ms	remaining: 693ms
7:	learn: 0.2246791	test: 0.2380085	best: 0.2380085 (7)	total: 498ms	remaining: 622ms
8:	learn: 0.2178107	test: 0.2314467	best: 0.2314467 (8)	total: 560ms	remaining: 560ms
9:	learn: 0.2129245	test: 0.2271637	best: 0.2271637 (9)	total: 622ms	remaining: 497ms
10:	learn: 0.2076415	test: 0.2229373	best: 0.2229373 (10)	total: 684ms	remaining: 435ms
11:	learn: 0.2017919	test: 0.2188999	best: 0.2188999 (11)	total: 751ms	remaining: 376ms
12:	learn: 0.1961952	test: 0.2138627	best: 0.2138627 (12)	total: 813ms	remaining: 313ms
13:	learn: 0.1942325	test: 0.2129220	best: 0.2129220 (13)	total: 876ms	remaining: 250ms
14:	learn: 0.1920418	test: 0.2108790	best: 0.2108790 (14)	total: 937ms	remaining: 187ms
15:	learn: 0.1895377	test: 0.2089532	best: 0.2089532 (15)	total: 1s	remaining: 125ms
16:	learn: 0.2047257	test: 0.2422178	best: 0.2089532 (15)	total: 1.06s	remaining: 62.6ms
17:	learn: 0.2034148	test: 0.2417271	best: 0.2089532 (15)	total: 1.13s	remaining: 0us

bestTest = 0.2089532283
bestIteration = 15

Shrink model to first 16 iterations.
Trial 67, Fold 2: Log loss = 0.208953228258031, Average precision = 0.9717722839040971, ROC-AUC = 0.9683297060554348, Elapsed Time = 1.232151399999566 seconds
Trial 67, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 67, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
0:	learn: 0.5269672	test: 0.5302358	best: 0.5302358 (0)	total: 65.5ms	remaining: 1.11s
1:	learn: 0.4347368	test: 0.4376431	best: 0.4376431 (1)	total: 129ms	remaining: 1.03s
2:	learn: 0.3579215	test: 0.3620770	best: 0.3620770 (2)	total: 192ms	remaining: 960ms
3:	learn: 0.3063125	test: 0.3113898	best: 0.3113898 (3)	total: 257ms	remaining: 900ms
4:	learn: 0.2754623	test: 0.2818396	best: 0.2818396 (4)	total: 321ms	remaining: 835ms
5:	learn: 0.2533066	test: 0.2618419	best: 0.2618419 (5)	total: 386ms	remaining: 771ms
6:	learn: 0.2379659	test: 0.2474018	best: 0.2474018 (6)	total: 448ms	remaining: 704ms
7:	learn: 0.2280851	test: 0.2383508	best: 0.2383508 (7)	total: 510ms	remaining: 638ms
8:	learn: 0.2178364	test: 0.2294978	best: 0.2294978 (8)	total: 574ms	remaining: 574ms
9:	learn: 0.2332119	test: 0.2543952	best: 0.2294978 (8)	total: 638ms	remaining: 511ms
10:	learn: 0.2292176	test: 0.2520130	best: 0.2294978 (8)	total: 700ms	remaining: 445ms
11:	learn: 0.2203169	test: 0.2459908	best: 0.2294978 (8)	total: 762ms	remaining: 381ms
12:	learn: 0.2174781	test: 0.2450912	best: 0.2294978 (8)	total: 826ms	remaining: 318ms
13:	learn: 0.2140852	test: 0.2421165	best: 0.2294978 (8)	total: 888ms	remaining: 254ms
14:	learn: 0.2113313	test: 0.2398027	best: 0.2294978 (8)	total: 953ms	remaining: 191ms
15:	learn: 0.2104487	test: 0.2380092	best: 0.2294978 (8)	total: 1.02s	remaining: 127ms
16:	learn: 0.2082114	test: 0.2379327	best: 0.2294978 (8)	total: 1.08s	remaining: 63.5ms
17:	learn: 0.2064527	test: 0.2373509	best: 0.2294978 (8)	total: 1.14s	remaining: 0us

bestTest = 0.2294977727
bestIteration = 8

Shrink model to first 9 iterations.
Trial 67, Fold 3: Log loss = 0.22949777268239238, Average precision = 0.9704896064872417, ROC-AUC = 0.9673667425913264, Elapsed Time = 1.2557278999993287 seconds
Trial 67, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 67, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
0:	learn: 0.5399962	test: 0.5420976	best: 0.5420976 (0)	total: 59ms	remaining: 1s
1:	learn: 0.4366313	test: 0.4396874	best: 0.4396874 (1)	total: 122ms	remaining: 976ms
2:	learn: 0.3659843	test: 0.3710791	best: 0.3710791 (2)	total: 186ms	remaining: 929ms
3:	learn: 0.3138812	test: 0.3212191	best: 0.3212191 (3)	total: 247ms	remaining: 865ms
4:	learn: 0.2790154	test: 0.2882059	best: 0.2882059 (4)	total: 304ms	remaining: 789ms
5:	learn: 0.2567535	test: 0.2676397	best: 0.2676397 (5)	total: 367ms	remaining: 733ms
6:	learn: 0.2411789	test: 0.2527669	best: 0.2527669 (6)	total: 430ms	remaining: 676ms
7:	learn: 0.2295620	test: 0.2410108	best: 0.2410108 (7)	total: 490ms	remaining: 613ms
8:	learn: 0.2211462	test: 0.2335786	best: 0.2335786 (8)	total: 555ms	remaining: 555ms
9:	learn: 0.2110079	test: 0.2249983	best: 0.2249983 (9)	total: 621ms	remaining: 497ms
10:	learn: 0.2058577	test: 0.2209926	best: 0.2209926 (10)	total: 686ms	remaining: 436ms
11:	learn: 0.2022723	test: 0.2184358	best: 0.2184358 (11)	total: 752ms	remaining: 376ms
12:	learn: 0.1994626	test: 0.2176441	best: 0.2176441 (12)	total: 816ms	remaining: 314ms
13:	learn: 0.2040053	test: 0.2308095	best: 0.2176441 (12)	total: 883ms	remaining: 252ms
14:	learn: 0.2005297	test: 0.2304924	best: 0.2176441 (12)	total: 949ms	remaining: 190ms
15:	learn: 0.1983725	test: 0.2300883	best: 0.2176441 (12)	total: 1.01s	remaining: 127ms
16:	learn: 0.1965882	test: 0.2300897	best: 0.2176441 (12)	total: 1.08s	remaining: 63.6ms
17:	learn: 0.1911552	test: 0.2295691	best: 0.2176441 (12)	total: 1.15s	remaining: 0us

bestTest = 0.2176441442
bestIteration = 12

Shrink model to first 13 iterations.
Trial 67, Fold 4: Log loss = 0.21764414422675507, Average precision = 0.9714140523055634, ROC-AUC = 0.9661470441664223, Elapsed Time = 1.2504069999995409 seconds
Trial 67, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 67, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
0:	learn: 0.5564492	test: 0.5592982	best: 0.5592982 (0)	total: 58.2ms	remaining: 989ms
1:	learn: 0.4272732	test: 0.4346879	best: 0.4346879 (1)	total: 119ms	remaining: 954ms
2:	learn: 0.3465059	test: 0.3563917	best: 0.3563917 (2)	total: 186ms	remaining: 930ms
3:	learn: 0.2970443	test: 0.3087665	best: 0.3087665 (3)	total: 251ms	remaining: 877ms
4:	learn: 0.2668272	test: 0.2797645	best: 0.2797645 (4)	total: 319ms	remaining: 830ms
5:	learn: 0.2480203	test: 0.2624281	best: 0.2624281 (5)	total: 382ms	remaining: 765ms
6:	learn: 0.2355684	test: 0.2519021	best: 0.2519021 (6)	total: 446ms	remaining: 701ms
7:	learn: 0.2255662	test: 0.2436928	best: 0.2436928 (7)	total: 511ms	remaining: 639ms
8:	learn: 0.2186008	test: 0.2373635	best: 0.2373635 (8)	total: 574ms	remaining: 574ms
9:	learn: 0.2099999	test: 0.2307744	best: 0.2307744 (9)	total: 639ms	remaining: 511ms
10:	learn: 0.2046994	test: 0.2272234	best: 0.2272234 (10)	total: 704ms	remaining: 448ms
11:	learn: 0.2006855	test: 0.2250941	best: 0.2250941 (11)	total: 770ms	remaining: 385ms
12:	learn: 0.1966524	test: 0.2231804	best: 0.2231804 (12)	total: 834ms	remaining: 321ms
13:	learn: 0.2250210	test: 0.2228473	best: 0.2228473 (13)	total: 900ms	remaining: 257ms
14:	learn: 0.2206247	test: 0.2210096	best: 0.2210096 (14)	total: 966ms	remaining: 193ms
15:	learn: 0.2130708	test: 0.2192445	best: 0.2192445 (15)	total: 1.03s	remaining: 129ms
16:	learn: 0.2108473	test: 0.2178783	best: 0.2178783 (16)	total: 1.09s	remaining: 64.5ms
17:	learn: 0.2045721	test: 0.2187451	best: 0.2178783 (16)	total: 1.17s	remaining: 0us

bestTest = 0.2178783235
bestIteration = 16

Shrink model to first 17 iterations.
Trial 67, Fold 5: Log loss = 0.21787832346472952, Average precision = 0.9695044188372833, ROC-AUC = 0.9658037641900303, Elapsed Time = 1.2686074999983248 seconds
Optimization Progress:  68%|######8   | 68/100 [1:55:08<51:47, 97.12s/it]   
Trial 68, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 68, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
0:	learn: 0.6715229	test: 0.6713295	best: 0.6713295 (0)	total: 38.4ms	remaining: 1.65s
1:	learn: 0.6507826	test: 0.6513380	best: 0.6513380 (1)	total: 77ms	remaining: 1.62s
2:	learn: 0.6307988	test: 0.6319539	best: 0.6319539 (2)	total: 116ms	remaining: 1.59s
3:	learn: 0.6125123	test: 0.6135329	best: 0.6135329 (3)	total: 155ms	remaining: 1.54s
4:	learn: 0.5954194	test: 0.5963300	best: 0.5963300 (4)	total: 196ms	remaining: 1.53s
5:	learn: 0.5802584	test: 0.5811088	best: 0.5811088 (5)	total: 234ms	remaining: 1.48s
6:	learn: 0.5642124	test: 0.5650419	best: 0.5650419 (6)	total: 280ms	remaining: 1.48s
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34:	learn: 0.3267493	test: 0.3269202	best: 0.3269202 (34)	total: 1.43s	remaining: 368ms
35:	learn: 0.3230301	test: 0.3232363	best: 0.3232363 (35)	total: 1.47s	remaining: 326ms
36:	learn: 0.3193846	test: 0.3195837	best: 0.3195837 (36)	total: 1.51s	remaining: 285ms
37:	learn: 0.3158514	test: 0.3160780	best: 0.3160780 (37)	total: 1.55s	remaining: 244ms
38:	learn: 0.3120899	test: 0.3123455	best: 0.3123455 (38)	total: 1.59s	remaining: 203ms
39:	learn: 0.3079103	test: 0.3081418	best: 0.3081418 (39)	total: 1.63s	remaining: 163ms
40:	learn: 0.3042969	test: 0.3045559	best: 0.3045559 (40)	total: 1.67s	remaining: 122ms
41:	learn: 0.3012648	test: 0.3015400	best: 0.3015400 (41)	total: 1.7s	remaining: 81.2ms
42:	learn: 0.2986541	test: 0.2989321	best: 0.2989321 (42)	total: 1.74s	remaining: 40.6ms
43:	learn: 0.2956651	test: 0.2959083	best: 0.2959083 (43)	total: 1.78s	remaining: 0us

bestTest = 0.2959083192
bestIteration = 43

Trial 68, Fold 1: Log loss = 0.29608877817783463, Average precision = 0.9585629280319068, ROC-AUC = 0.9581787434937841, Elapsed Time = 1.891680599997926 seconds
Trial 68, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 68, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
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34:	learn: 0.3258825	test: 0.3286914	best: 0.3286914 (34)	total: 1.35s	remaining: 347ms
35:	learn: 0.3217549	test: 0.3245288	best: 0.3245288 (35)	total: 1.39s	remaining: 308ms
36:	learn: 0.3181378	test: 0.3209042	best: 0.3209042 (36)	total: 1.42s	remaining: 270ms
37:	learn: 0.3144700	test: 0.3172310	best: 0.3172310 (37)	total: 1.46s	remaining: 231ms
38:	learn: 0.3109824	test: 0.3137513	best: 0.3137513 (38)	total: 1.5s	remaining: 193ms
39:	learn: 0.3080440	test: 0.3107391	best: 0.3107391 (39)	total: 1.54s	remaining: 154ms
40:	learn: 0.3042367	test: 0.3069854	best: 0.3069854 (40)	total: 1.58s	remaining: 116ms
41:	learn: 0.3014078	test: 0.3041849	best: 0.3041849 (41)	total: 1.62s	remaining: 77ms
42:	learn: 0.2985788	test: 0.3013575	best: 0.3013575 (42)	total: 1.65s	remaining: 38.5ms
43:	learn: 0.2960224	test: 0.2987755	best: 0.2987755 (43)	total: 1.69s	remaining: 0us

bestTest = 0.2987755422
bestIteration = 43

Trial 68, Fold 2: Log loss = 0.2988582516607877, Average precision = 0.9620673966498999, ROC-AUC = 0.9586472868558192, Elapsed Time = 1.8046482000027027 seconds
Trial 68, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 68, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
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4:	learn: 0.5909711	test: 0.5916551	best: 0.5916551 (4)	total: 197ms	remaining: 1.54s
5:	learn: 0.5743030	test: 0.5748195	best: 0.5748195 (5)	total: 234ms	remaining: 1.48s
6:	learn: 0.5589338	test: 0.5592601	best: 0.5592601 (6)	total: 271ms	remaining: 1.43s
7:	learn: 0.5443850	test: 0.5446039	best: 0.5446039 (7)	total: 309ms	remaining: 1.39s
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34:	learn: 0.3252100	test: 0.3238709	best: 0.3238709 (34)	total: 1.34s	remaining: 344ms
35:	learn: 0.3217640	test: 0.3203806	best: 0.3203806 (35)	total: 1.38s	remaining: 306ms
36:	learn: 0.3182303	test: 0.3169225	best: 0.3169225 (36)	total: 1.41s	remaining: 267ms
37:	learn: 0.3143921	test: 0.3130544	best: 0.3130544 (37)	total: 1.45s	remaining: 229ms
38:	learn: 0.3103072	test: 0.3088740	best: 0.3088740 (38)	total: 1.49s	remaining: 191ms
39:	learn: 0.3073250	test: 0.3058891	best: 0.3058891 (39)	total: 1.52s	remaining: 153ms
40:	learn: 0.3038696	test: 0.3023889	best: 0.3023889 (40)	total: 1.56s	remaining: 114ms
41:	learn: 0.3011901	test: 0.2996574	best: 0.2996574 (41)	total: 1.6s	remaining: 76.3ms
42:	learn: 0.2982444	test: 0.2966805	best: 0.2966805 (42)	total: 1.64s	remaining: 38.1ms
43:	learn: 0.2950725	test: 0.2934390	best: 0.2934390 (43)	total: 1.68s	remaining: 0us

bestTest = 0.2934390283
bestIteration = 43

Trial 68, Fold 3: Log loss = 0.2936613666425326, Average precision = 0.9607125775671368, ROC-AUC = 0.9599722897063497, Elapsed Time = 1.778061400000297 seconds
Trial 68, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 68, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
0:	learn: 0.6712144	test: 0.6721568	best: 0.6721568 (0)	total: 37.9ms	remaining: 1.63s
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2:	learn: 0.6298424	test: 0.6327392	best: 0.6327392 (2)	total: 111ms	remaining: 1.51s
3:	learn: 0.6115376	test: 0.6144401	best: 0.6144401 (3)	total: 146ms	remaining: 1.46s
4:	learn: 0.5952882	test: 0.5981323	best: 0.5981323 (4)	total: 180ms	remaining: 1.41s
5:	learn: 0.5800681	test: 0.5829000	best: 0.5829000 (5)	total: 216ms	remaining: 1.36s
6:	learn: 0.5645040	test: 0.5675465	best: 0.5675465 (6)	total: 253ms	remaining: 1.34s
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34:	learn: 0.3269603	test: 0.3286256	best: 0.3286256 (34)	total: 1.29s	remaining: 331ms
35:	learn: 0.3228780	test: 0.3245392	best: 0.3245392 (35)	total: 1.32s	remaining: 294ms
36:	learn: 0.3190155	test: 0.3209712	best: 0.3209712 (36)	total: 1.36s	remaining: 257ms
37:	learn: 0.3156400	test: 0.3176353	best: 0.3176353 (37)	total: 1.4s	remaining: 221ms
38:	learn: 0.3125416	test: 0.3145118	best: 0.3145118 (38)	total: 1.43s	remaining: 184ms
39:	learn: 0.3092820	test: 0.3111987	best: 0.3111987 (39)	total: 1.47s	remaining: 147ms
40:	learn: 0.3062303	test: 0.3081425	best: 0.3081425 (40)	total: 1.51s	remaining: 110ms
41:	learn: 0.3030187	test: 0.3049246	best: 0.3049246 (41)	total: 1.54s	remaining: 73.5ms
42:	learn: 0.2996441	test: 0.3014703	best: 0.3014703 (42)	total: 1.58s	remaining: 36.8ms
43:	learn: 0.2965647	test: 0.2983066	best: 0.2983066 (43)	total: 1.62s	remaining: 0us

bestTest = 0.2983065604
bestIteration = 43

Trial 68, Fold 4: Log loss = 0.29843035982676436, Average precision = 0.9635127854350897, ROC-AUC = 0.9594627984386884, Elapsed Time = 1.7159068999972078 seconds
Trial 68, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 68, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
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37:	learn: 0.3120157	test: 0.3198850	best: 0.3198850 (37)	total: 1.4s	remaining: 220ms
38:	learn: 0.3083454	test: 0.3163205	best: 0.3163205 (38)	total: 1.43s	remaining: 184ms
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42:	learn: 0.2953463	test: 0.3037274	best: 0.3037274 (42)	total: 1.58s	remaining: 36.8ms
43:	learn: 0.2928051	test: 0.3012900	best: 0.3012900 (43)	total: 1.62s	remaining: 0us

bestTest = 0.3012899958
bestIteration = 43

Trial 68, Fold 5: Log loss = 0.3013720648789694, Average precision = 0.9613763111211567, ROC-AUC = 0.9575561377106443, Elapsed Time = 1.71973749999961 seconds
Optimization Progress:  69%|######9   | 69/100 [1:55:26<37:49, 73.22s/it]
Trial 69, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 69, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
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60:	learn: 0.2985729	test: 0.3086691	best: 0.3086691 (60)	total: 14s	remaining: 1.6s
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65:	learn: 0.2866527	test: 0.2977326	best: 0.2977326 (65)	total: 15.1s	remaining: 458ms
66:	learn: 0.2845009	test: 0.2958421	best: 0.2958421 (66)	total: 15.4s	remaining: 230ms
67:	learn: 0.2822558	test: 0.2937921	best: 0.2937921 (67)	total: 15.6s	remaining: 0us

bestTest = 0.2937921196
bestIteration = 67

Trial 69, Fold 1: Log loss = 0.29378810395775945, Average precision = 0.9742611456593842, ROC-AUC = 0.9695210074896163, Elapsed Time = 15.787811100002727 seconds
Trial 69, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 69, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
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67:	learn: 0.2839300	test: 0.2929573	best: 0.2929573 (67)	total: 15.7s	remaining: 0us

bestTest = 0.2929572957
bestIteration = 67

Trial 69, Fold 2: Log loss = 0.2929318457145807, Average precision = 0.9736490952490129, ROC-AUC = 0.9713565432582858, Elapsed Time = 15.902804399996967 seconds
Trial 69, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 69, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
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60:	learn: 0.2998095	test: 0.3050152	best: 0.3050152 (60)	total: 13.9s	remaining: 1.6s
61:	learn: 0.2974216	test: 0.3027739	best: 0.3027739 (61)	total: 14.2s	remaining: 1.37s
62:	learn: 0.2952444	test: 0.3006627	best: 0.3006627 (62)	total: 14.4s	remaining: 1.14s
63:	learn: 0.2928217	test: 0.2983377	best: 0.2983377 (63)	total: 14.6s	remaining: 914ms
64:	learn: 0.2906497	test: 0.2962776	best: 0.2962776 (64)	total: 14.9s	remaining: 686ms
65:	learn: 0.2884194	test: 0.2941583	best: 0.2941583 (65)	total: 15.1s	remaining: 458ms
66:	learn: 0.2861784	test: 0.2920314	best: 0.2920314 (66)	total: 15.4s	remaining: 230ms
67:	learn: 0.2840460	test: 0.2900096	best: 0.2900096 (67)	total: 15.6s	remaining: 0us

bestTest = 0.2900096478
bestIteration = 67

Trial 69, Fold 3: Log loss = 0.2901548698436933, Average precision = 0.9735075759623979, ROC-AUC = 0.9721246243896331, Elapsed Time = 15.7711761999999 seconds
Trial 69, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 69, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
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60:	learn: 0.3000835	test: 0.3078608	best: 0.3078608 (60)	total: 13.6s	remaining: 1.56s
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63:	learn: 0.2929571	test: 0.3011256	best: 0.3011256 (63)	total: 14.3s	remaining: 895ms
64:	learn: 0.2905817	test: 0.2988896	best: 0.2988896 (64)	total: 14.6s	remaining: 672ms
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67:	learn: 0.2841460	test: 0.2929045	best: 0.2929045 (67)	total: 15.2s	remaining: 0us

bestTest = 0.2929044525
bestIteration = 67

Trial 69, Fold 4: Log loss = 0.2929207009664367, Average precision = 0.9752771384417679, ROC-AUC = 0.971326205736413, Elapsed Time = 15.37222980000297 seconds
Trial 69, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 69, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
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67:	learn: 0.2819347	test: 0.2966230	best: 0.2966230 (67)	total: 15.7s	remaining: 0us

bestTest = 0.2966229601
bestIteration = 67

Trial 69, Fold 5: Log loss = 0.29650773010314624, Average precision = 0.9724871864318616, ROC-AUC = 0.9693863961503445, Elapsed Time = 15.81532199999856 seconds
Optimization Progress:  70%|#######   | 70/100 [1:56:52<38:36, 77.23s/it]
Trial 70, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 70, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
0:	learn: 0.5905772	test: 0.5924819	best: 0.5924819 (0)	total: 494ms	remaining: 48s
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96:	learn: 0.1059280	test: 0.2008478	best: 0.1992568 (57)	total: 46.3s	remaining: 477ms
97:	learn: 0.1053433	test: 0.2010897	best: 0.1992568 (57)	total: 46.8s	remaining: 0us

bestTest = 0.1992568471
bestIteration = 57

Shrink model to first 58 iterations.
Trial 70, Fold 1: Log loss = 0.19856434204522747, Average precision = 0.9738502437692736, ROC-AUC = 0.971148158902224, Elapsed Time = 46.91665510000166 seconds
Trial 70, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 70, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
0:	learn: 0.5903495	test: 0.5937671	best: 0.5937671 (0)	total: 733ms	remaining: 1m 11s
1:	learn: 0.5109283	test: 0.5157377	best: 0.5157377 (1)	total: 1.26s	remaining: 1m
2:	learn: 0.4486895	test: 0.4564853	best: 0.4564853 (2)	total: 2.08s	remaining: 1m 5s
3:	learn: 0.3991440	test: 0.4083987	best: 0.4083987 (3)	total: 2.67s	remaining: 1m 2s
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84:	learn: 0.1134625	test: 0.1824195	best: 0.1821638 (78)	total: 41.1s	remaining: 6.28s
85:	learn: 0.1130208	test: 0.1823941	best: 0.1821638 (78)	total: 41.5s	remaining: 5.79s
86:	learn: 0.1123604	test: 0.1823931	best: 0.1821638 (78)	total: 42s	remaining: 5.3s
87:	learn: 0.1120570	test: 0.1824323	best: 0.1821638 (78)	total: 42.2s	remaining: 4.8s
88:	learn: 0.1116114	test: 0.1824444	best: 0.1821638 (78)	total: 42.7s	remaining: 4.31s
89:	learn: 0.1113400	test: 0.1823912	best: 0.1821638 (78)	total: 43s	remaining: 3.82s
90:	learn: 0.1109395	test: 0.1822926	best: 0.1821638 (78)	total: 43.4s	remaining: 3.34s
91:	learn: 0.1106336	test: 0.1823708	best: 0.1821638 (78)	total: 43.6s	remaining: 2.85s
92:	learn: 0.1099393	test: 0.1824049	best: 0.1821638 (78)	total: 44.1s	remaining: 2.37s
93:	learn: 0.1096727	test: 0.1824819	best: 0.1821638 (78)	total: 44.4s	remaining: 1.89s
94:	learn: 0.1089879	test: 0.1824099	best: 0.1821638 (78)	total: 44.9s	remaining: 1.42s
95:	learn: 0.1081779	test: 0.1823502	best: 0.1821638 (78)	total: 45.5s	remaining: 948ms
96:	learn: 0.1076583	test: 0.1820390	best: 0.1820390 (96)	total: 45.9s	remaining: 474ms
97:	learn: 0.1070059	test: 0.1822019	best: 0.1820390 (96)	total: 46.4s	remaining: 0us

bestTest = 0.1820390205
bestIteration = 96

Shrink model to first 97 iterations.
Trial 70, Fold 2: Log loss = 0.18155097510167162, Average precision = 0.9771623947613914, ROC-AUC = 0.9744726840328856, Elapsed Time = 46.57567019999988 seconds
Trial 70, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 70, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
0:	learn: 0.5896952	test: 0.5909205	best: 0.5909205 (0)	total: 742ms	remaining: 1m 11s
1:	learn: 0.5099365	test: 0.5122185	best: 0.5122185 (1)	total: 1.41s	remaining: 1m 7s
2:	learn: 0.4474920	test: 0.4514671	best: 0.4514671 (2)	total: 2.14s	remaining: 1m 7s
3:	learn: 0.3974730	test: 0.4033041	best: 0.4033041 (3)	total: 2.71s	remaining: 1m 3s
4:	learn: 0.3587799	test: 0.3665277	best: 0.3665277 (4)	total: 3.3s	remaining: 1m 1s
5:	learn: 0.3282815	test: 0.3365930	best: 0.3365930 (5)	total: 3.85s	remaining: 59.1s
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69:	learn: 0.1234649	test: 0.1868858	best: 0.1868858 (69)	total: 34.4s	remaining: 13.8s
70:	learn: 0.1228557	test: 0.1868086	best: 0.1868086 (70)	total: 34.8s	remaining: 13.2s
71:	learn: 0.1222460	test: 0.1866279	best: 0.1866279 (71)	total: 35.1s	remaining: 12.7s
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83:	learn: 0.1147294	test: 0.1862893	best: 0.1862893 (83)	total: 40.3s	remaining: 6.72s
84:	learn: 0.1140109	test: 0.1863474	best: 0.1862893 (83)	total: 40.9s	remaining: 6.25s
85:	learn: 0.1134467	test: 0.1863306	best: 0.1862893 (83)	total: 41.3s	remaining: 5.76s
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87:	learn: 0.1126590	test: 0.1863500	best: 0.1862893 (83)	total: 41.8s	remaining: 4.75s
88:	learn: 0.1121741	test: 0.1862952	best: 0.1862893 (83)	total: 42.3s	remaining: 4.27s
89:	learn: 0.1118659	test: 0.1864118	best: 0.1862893 (83)	total: 42.5s	remaining: 3.78s
90:	learn: 0.1115306	test: 0.1863569	best: 0.1862893 (83)	total: 42.8s	remaining: 3.29s
91:	learn: 0.1111340	test: 0.1863664	best: 0.1862893 (83)	total: 43.1s	remaining: 2.81s
92:	learn: 0.1103868	test: 0.1861989	best: 0.1861989 (92)	total: 43.6s	remaining: 2.35s
93:	learn: 0.1097452	test: 0.1864650	best: 0.1861989 (92)	total: 44.2s	remaining: 1.88s
94:	learn: 0.1094328	test: 0.1862831	best: 0.1861989 (92)	total: 44.5s	remaining: 1.4s
95:	learn: 0.1089817	test: 0.1863722	best: 0.1861989 (92)	total: 44.8s	remaining: 933ms
96:	learn: 0.1083339	test: 0.1862068	best: 0.1861989 (92)	total: 45.3s	remaining: 467ms
97:	learn: 0.1077885	test: 0.1861192	best: 0.1861192 (97)	total: 45.8s	remaining: 0us

bestTest = 0.186119214
bestIteration = 97

Trial 70, Fold 3: Log loss = 0.18577943179712123, Average precision = 0.9753117041345816, ROC-AUC = 0.9742210298465777, Elapsed Time = 45.941452199997 seconds
Trial 70, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 70, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
0:	learn: 0.5903866	test: 0.5926740	best: 0.5926740 (0)	total: 647ms	remaining: 1m 2s
1:	learn: 0.5116286	test: 0.5162784	best: 0.5162784 (1)	total: 1.18s	remaining: 56.9s
2:	learn: 0.4494189	test: 0.4555256	best: 0.4555256 (2)	total: 1.74s	remaining: 55.2s
3:	learn: 0.3985661	test: 0.4068757	best: 0.4068757 (3)	total: 2.43s	remaining: 57s
4:	learn: 0.3599436	test: 0.3705242	best: 0.3705242 (4)	total: 2.95s	remaining: 55s
5:	learn: 0.3291947	test: 0.3413378	best: 0.3413378 (5)	total: 3.54s	remaining: 54.2s
6:	learn: 0.3056764	test: 0.3193571	best: 0.3193571 (6)	total: 4.03s	remaining: 52.3s
7:	learn: 0.2848018	test: 0.2993082	best: 0.2993082 (7)	total: 4.47s	remaining: 50.3s
8:	learn: 0.2683200	test: 0.2836972	best: 0.2836972 (8)	total: 5.03s	remaining: 49.8s
9:	learn: 0.2544236	test: 0.2708741	best: 0.2708741 (9)	total: 5.55s	remaining: 48.9s
10:	learn: 0.2421673	test: 0.2598218	best: 0.2598218 (10)	total: 6.08s	remaining: 48.1s
11:	learn: 0.2321446	test: 0.2512839	best: 0.2512839 (11)	total: 6.51s	remaining: 46.7s
12:	learn: 0.2247804	test: 0.2449503	best: 0.2449503 (12)	total: 6.97s	remaining: 45.6s
13:	learn: 0.2167446	test: 0.2385240	best: 0.2385240 (13)	total: 7.59s	remaining: 45.6s
14:	learn: 0.2104622	test: 0.2331092	best: 0.2331092 (14)	total: 8.09s	remaining: 44.8s
15:	learn: 0.2049084	test: 0.2288381	best: 0.2288381 (15)	total: 8.7s	remaining: 44.6s
16:	learn: 0.1996204	test: 0.2248549	best: 0.2248549 (16)	total: 9.22s	remaining: 43.9s
17:	learn: 0.1944856	test: 0.2213899	best: 0.2213899 (17)	total: 9.94s	remaining: 44.2s
18:	learn: 0.1904442	test: 0.2183846	best: 0.2183846 (18)	total: 10.5s	remaining: 43.6s
19:	learn: 0.1869269	test: 0.2154487	best: 0.2154487 (19)	total: 10.9s	remaining: 42.6s
20:	learn: 0.1841956	test: 0.2135939	best: 0.2135939 (20)	total: 11.3s	remaining: 41.4s
21:	learn: 0.1809192	test: 0.2116390	best: 0.2116390 (21)	total: 11.7s	remaining: 40.6s
22:	learn: 0.1780880	test: 0.2101559	best: 0.2101559 (22)	total: 12.2s	remaining: 39.8s
23:	learn: 0.1752879	test: 0.2084411	best: 0.2084411 (23)	total: 12.6s	remaining: 38.8s
24:	learn: 0.1722349	test: 0.2067880	best: 0.2067880 (24)	total: 13.3s	remaining: 38.7s
25:	learn: 0.1697879	test: 0.2052590	best: 0.2052590 (25)	total: 13.8s	remaining: 38.1s
26:	learn: 0.1680131	test: 0.2040930	best: 0.2040930 (26)	total: 14.2s	remaining: 37.3s
27:	learn: 0.1661250	test: 0.2029495	best: 0.2029495 (27)	total: 14.6s	remaining: 36.6s
28:	learn: 0.1643004	test: 0.2020483	best: 0.2020483 (28)	total: 15.2s	remaining: 36.2s
29:	learn: 0.1622007	test: 0.2021418	best: 0.2020483 (28)	total: 16s	remaining: 36.3s
30:	learn: 0.1605851	test: 0.2014845	best: 0.2014845 (30)	total: 16.7s	remaining: 36.2s
31:	learn: 0.1593385	test: 0.2009049	best: 0.2009049 (31)	total: 17.3s	remaining: 35.6s
32:	learn: 0.1573883	test: 0.2003655	best: 0.2003655 (32)	total: 18s	remaining: 35.4s
33:	learn: 0.1561716	test: 0.2000978	best: 0.2000978 (33)	total: 18.4s	remaining: 34.5s
34:	learn: 0.1547655	test: 0.1996476	best: 0.1996476 (34)	total: 18.9s	remaining: 34s
35:	learn: 0.1537214	test: 0.1989982	best: 0.1989982 (35)	total: 19.2s	remaining: 33.1s
36:	learn: 0.1524592	test: 0.1986614	best: 0.1986614 (36)	total: 19.7s	remaining: 32.4s
37:	learn: 0.1512639	test: 0.1982159	best: 0.1982159 (37)	total: 20.2s	remaining: 31.9s
38:	learn: 0.1500103	test: 0.1978623	best: 0.1978623 (38)	total: 20.7s	remaining: 31.4s
39:	learn: 0.1489568	test: 0.1976017	best: 0.1976017 (39)	total: 21.2s	remaining: 30.7s
40:	learn: 0.1482261	test: 0.1970594	best: 0.1970594 (40)	total: 21.4s	remaining: 29.8s
41:	learn: 0.1472388	test: 0.1969252	best: 0.1969252 (41)	total: 21.8s	remaining: 29.1s
42:	learn: 0.1459125	test: 0.1965207	best: 0.1965207 (42)	total: 22.3s	remaining: 28.5s
43:	learn: 0.1450426	test: 0.1960739	best: 0.1960739 (43)	total: 22.6s	remaining: 27.8s
44:	learn: 0.1440874	test: 0.1959868	best: 0.1959868 (44)	total: 23s	remaining: 27.1s
45:	learn: 0.1434531	test: 0.1960275	best: 0.1959868 (44)	total: 23.3s	remaining: 26.3s
46:	learn: 0.1426864	test: 0.1959422	best: 0.1959422 (46)	total: 23.6s	remaining: 25.7s
47:	learn: 0.1417727	test: 0.1955950	best: 0.1955950 (47)	total: 24.1s	remaining: 25.1s
48:	learn: 0.1406708	test: 0.1952917	best: 0.1952917 (48)	total: 24.6s	remaining: 24.6s
49:	learn: 0.1398507	test: 0.1952145	best: 0.1952145 (49)	total: 25s	remaining: 24s
50:	learn: 0.1389719	test: 0.1949487	best: 0.1949487 (50)	total: 25.5s	remaining: 23.5s
51:	learn: 0.1380472	test: 0.1949459	best: 0.1949459 (51)	total: 25.9s	remaining: 22.9s
52:	learn: 0.1372903	test: 0.1949670	best: 0.1949459 (51)	total: 26.3s	remaining: 22.3s
53:	learn: 0.1366290	test: 0.1948453	best: 0.1948453 (53)	total: 26.6s	remaining: 21.7s
54:	learn: 0.1356674	test: 0.1947245	best: 0.1947245 (54)	total: 27.1s	remaining: 21.2s
55:	learn: 0.1343231	test: 0.1943676	best: 0.1943676 (55)	total: 27.8s	remaining: 20.8s
56:	learn: 0.1334436	test: 0.1941313	best: 0.1941313 (56)	total: 28.2s	remaining: 20.3s
57:	learn: 0.1326704	test: 0.1939875	best: 0.1939875 (57)	total: 28.6s	remaining: 19.8s
58:	learn: 0.1322473	test: 0.1937925	best: 0.1937925 (58)	total: 28.9s	remaining: 19.1s
59:	learn: 0.1316569	test: 0.1938481	best: 0.1937925 (58)	total: 29.2s	remaining: 18.5s
60:	learn: 0.1308835	test: 0.1937013	best: 0.1937013 (60)	total: 29.7s	remaining: 18s
61:	learn: 0.1301896	test: 0.1936174	best: 0.1936174 (61)	total: 30.1s	remaining: 17.5s
62:	learn: 0.1294747	test: 0.1936500	best: 0.1936174 (61)	total: 30.5s	remaining: 16.9s
63:	learn: 0.1291463	test: 0.1937705	best: 0.1936174 (61)	total: 30.7s	remaining: 16.3s
64:	learn: 0.1282698	test: 0.1938988	best: 0.1936174 (61)	total: 31.2s	remaining: 15.8s
65:	learn: 0.1275179	test: 0.1939173	best: 0.1936174 (61)	total: 31.6s	remaining: 15.3s
66:	learn: 0.1264292	test: 0.1938085	best: 0.1936174 (61)	total: 32.3s	remaining: 15s
67:	learn: 0.1255920	test: 0.1936310	best: 0.1936174 (61)	total: 32.8s	remaining: 14.5s
68:	learn: 0.1247783	test: 0.1933971	best: 0.1933971 (68)	total: 33.3s	remaining: 14s
69:	learn: 0.1239627	test: 0.1934268	best: 0.1933971 (68)	total: 33.9s	remaining: 13.5s
70:	learn: 0.1229439	test: 0.1931467	best: 0.1931467 (70)	total: 34.6s	remaining: 13.2s
71:	learn: 0.1225462	test: 0.1931909	best: 0.1931467 (70)	total: 35s	remaining: 12.6s
72:	learn: 0.1217475	test: 0.1929360	best: 0.1929360 (72)	total: 35.5s	remaining: 12.1s
73:	learn: 0.1212338	test: 0.1928165	best: 0.1928165 (73)	total: 35.8s	remaining: 11.6s
74:	learn: 0.1204093	test: 0.1929877	best: 0.1928165 (73)	total: 36.4s	remaining: 11.1s
75:	learn: 0.1199375	test: 0.1929339	best: 0.1928165 (73)	total: 36.7s	remaining: 10.6s
76:	learn: 0.1192027	test: 0.1928159	best: 0.1928159 (76)	total: 37.2s	remaining: 10.1s
77:	learn: 0.1183243	test: 0.1926144	best: 0.1926144 (77)	total: 37.8s	remaining: 9.7s
78:	learn: 0.1178015	test: 0.1926250	best: 0.1926144 (77)	total: 38.2s	remaining: 9.19s
79:	learn: 0.1172633	test: 0.1926208	best: 0.1926144 (77)	total: 38.6s	remaining: 8.69s
80:	learn: 0.1168945	test: 0.1925953	best: 0.1925953 (80)	total: 39s	remaining: 8.17s
81:	learn: 0.1160975	test: 0.1925826	best: 0.1925826 (81)	total: 39.5s	remaining: 7.7s
82:	learn: 0.1155785	test: 0.1927122	best: 0.1925826 (81)	total: 39.9s	remaining: 7.2s
83:	learn: 0.1147983	test: 0.1927527	best: 0.1925826 (81)	total: 40.4s	remaining: 6.73s
84:	learn: 0.1143240	test: 0.1927585	best: 0.1925826 (81)	total: 40.8s	remaining: 6.24s
85:	learn: 0.1133622	test: 0.1930191	best: 0.1925826 (81)	total: 41.4s	remaining: 5.78s
86:	learn: 0.1125493	test: 0.1929498	best: 0.1925826 (81)	total: 41.9s	remaining: 5.3s
87:	learn: 0.1118920	test: 0.1926952	best: 0.1925826 (81)	total: 42.4s	remaining: 4.82s
88:	learn: 0.1114217	test: 0.1926927	best: 0.1925826 (81)	total: 42.8s	remaining: 4.33s
89:	learn: 0.1110389	test: 0.1926702	best: 0.1925826 (81)	total: 43.1s	remaining: 3.83s
90:	learn: 0.1106194	test: 0.1926314	best: 0.1925826 (81)	total: 43.4s	remaining: 3.34s
91:	learn: 0.1101499	test: 0.1924486	best: 0.1924486 (91)	total: 43.8s	remaining: 2.86s
92:	learn: 0.1095291	test: 0.1924287	best: 0.1924287 (92)	total: 44.4s	remaining: 2.38s
93:	learn: 0.1090287	test: 0.1925581	best: 0.1924287 (92)	total: 44.8s	remaining: 1.91s
94:	learn: 0.1084822	test: 0.1925460	best: 0.1924287 (92)	total: 45.3s	remaining: 1.43s
95:	learn: 0.1079710	test: 0.1927072	best: 0.1924287 (92)	total: 45.9s	remaining: 956ms
96:	learn: 0.1073525	test: 0.1929110	best: 0.1924287 (92)	total: 46.3s	remaining: 478ms
97:	learn: 0.1069757	test: 0.1929770	best: 0.1924287 (92)	total: 46.7s	remaining: 0us

bestTest = 0.1924287435
bestIteration = 92

Shrink model to first 93 iterations.
Trial 70, Fold 4: Log loss = 0.19186932567833512, Average precision = 0.9759671294837424, ROC-AUC = 0.9719536780712985, Elapsed Time = 46.824653199997556 seconds
Trial 70, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 70, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
0:	learn: 0.5904299	test: 0.5958298	best: 0.5958298 (0)	total: 538ms	remaining: 52.2s
1:	learn: 0.5092095	test: 0.5168225	best: 0.5168225 (1)	total: 1.3s	remaining: 1m 2s
2:	learn: 0.4463328	test: 0.4558214	best: 0.4558214 (2)	total: 1.78s	remaining: 56.5s
3:	learn: 0.3967623	test: 0.4094800	best: 0.4094800 (3)	total: 2.36s	remaining: 55.4s
4:	learn: 0.3571436	test: 0.3725866	best: 0.3725866 (4)	total: 2.84s	remaining: 52.9s
5:	learn: 0.3270604	test: 0.3443047	best: 0.3443047 (5)	total: 3.43s	remaining: 52.6s
6:	learn: 0.3015068	test: 0.3206474	best: 0.3206474 (6)	total: 4s	remaining: 52s
7:	learn: 0.2815891	test: 0.3025004	best: 0.3025004 (7)	total: 4.6s	remaining: 51.8s
8:	learn: 0.2648195	test: 0.2866234	best: 0.2866234 (8)	total: 5.05s	remaining: 49.9s
9:	learn: 0.2514650	test: 0.2746255	best: 0.2746255 (9)	total: 5.54s	remaining: 48.7s
10:	learn: 0.2398131	test: 0.2643554	best: 0.2643554 (10)	total: 6.14s	remaining: 48.6s
11:	learn: 0.2298553	test: 0.2563370	best: 0.2563370 (11)	total: 6.72s	remaining: 48.2s
12:	learn: 0.2210606	test: 0.2487205	best: 0.2487205 (12)	total: 7.21s	remaining: 47.2s
13:	learn: 0.2142005	test: 0.2431083	best: 0.2431083 (13)	total: 7.75s	remaining: 46.5s
14:	learn: 0.2085150	test: 0.2382231	best: 0.2382231 (14)	total: 8.17s	remaining: 45.2s
15:	learn: 0.2028113	test: 0.2338603	best: 0.2338603 (15)	total: 8.71s	remaining: 44.6s
16:	learn: 0.1976013	test: 0.2301886	best: 0.2301886 (16)	total: 9.31s	remaining: 44.3s
17:	learn: 0.1929889	test: 0.2266481	best: 0.2266481 (17)	total: 9.93s	remaining: 44.1s
18:	learn: 0.1889342	test: 0.2239191	best: 0.2239191 (18)	total: 10.5s	remaining: 43.6s
19:	learn: 0.1852748	test: 0.2213895	best: 0.2213895 (19)	total: 11s	remaining: 42.9s
20:	learn: 0.1822415	test: 0.2192372	best: 0.2192372 (20)	total: 11.4s	remaining: 42s
21:	learn: 0.1788892	test: 0.2169057	best: 0.2169057 (21)	total: 11.9s	remaining: 41.1s
22:	learn: 0.1760892	test: 0.2154420	best: 0.2154420 (22)	total: 12.5s	remaining: 40.6s
23:	learn: 0.1733774	test: 0.2141357	best: 0.2141357 (23)	total: 13.1s	remaining: 40.3s
24:	learn: 0.1706547	test: 0.2128073	best: 0.2128073 (24)	total: 13.6s	remaining: 39.8s
25:	learn: 0.1681768	test: 0.2117522	best: 0.2117522 (25)	total: 14.1s	remaining: 39s
26:	learn: 0.1659039	test: 0.2110429	best: 0.2110429 (26)	total: 14.6s	remaining: 38.4s
27:	learn: 0.1638167	test: 0.2097277	best: 0.2097277 (27)	total: 15.2s	remaining: 37.9s
28:	learn: 0.1618886	test: 0.2091572	best: 0.2091572 (28)	total: 15.6s	remaining: 37s
29:	learn: 0.1598940	test: 0.2084769	best: 0.2084769 (29)	total: 16.1s	remaining: 36.4s
30:	learn: 0.1581990	test: 0.2078567	best: 0.2078567 (30)	total: 16.7s	remaining: 36s
31:	learn: 0.1564426	test: 0.2072040	best: 0.2072040 (31)	total: 17.1s	remaining: 35.4s
32:	learn: 0.1548277	test: 0.2060797	best: 0.2060797 (32)	total: 17.6s	remaining: 34.7s
33:	learn: 0.1533100	test: 0.2055582	best: 0.2055582 (33)	total: 18.2s	remaining: 34.3s
34:	learn: 0.1516797	test: 0.2047594	best: 0.2047594 (34)	total: 18.7s	remaining: 33.7s
35:	learn: 0.1502815	test: 0.2043170	best: 0.2043170 (35)	total: 19.2s	remaining: 33.1s
36:	learn: 0.1491845	test: 0.2037814	best: 0.2037814 (36)	total: 19.6s	remaining: 32.3s
37:	learn: 0.1480542	test: 0.2037177	best: 0.2037177 (37)	total: 20.1s	remaining: 31.8s
38:	learn: 0.1471103	test: 0.2035313	best: 0.2035313 (38)	total: 20.6s	remaining: 31.1s
39:	learn: 0.1462939	test: 0.2031819	best: 0.2031819 (39)	total: 20.9s	remaining: 30.3s
40:	learn: 0.1449860	test: 0.2028769	best: 0.2028769 (40)	total: 21.4s	remaining: 29.8s
41:	learn: 0.1440613	test: 0.2027071	best: 0.2027071 (41)	total: 21.9s	remaining: 29.1s
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55:	learn: 0.1314993	test: 0.1993890	best: 0.1993890 (55)	total: 27.9s	remaining: 20.9s
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57:	learn: 0.1298450	test: 0.1992431	best: 0.1992163 (56)	total: 28.8s	remaining: 19.8s
58:	learn: 0.1287671	test: 0.1990168	best: 0.1990168 (58)	total: 29.3s	remaining: 19.4s
59:	learn: 0.1280426	test: 0.1987992	best: 0.1987992 (59)	total: 29.8s	remaining: 18.9s
60:	learn: 0.1272466	test: 0.1989128	best: 0.1987992 (59)	total: 30.3s	remaining: 18.4s
61:	learn: 0.1262062	test: 0.1987534	best: 0.1987534 (61)	total: 30.8s	remaining: 17.9s
62:	learn: 0.1255443	test: 0.1986378	best: 0.1986378 (62)	total: 31.2s	remaining: 17.4s
63:	learn: 0.1246252	test: 0.1982837	best: 0.1982837 (63)	total: 31.7s	remaining: 16.9s
64:	learn: 0.1238588	test: 0.1980848	best: 0.1980848 (64)	total: 32.2s	remaining: 16.4s
65:	learn: 0.1234637	test: 0.1981026	best: 0.1980848 (64)	total: 32.5s	remaining: 15.7s
66:	learn: 0.1230882	test: 0.1980267	best: 0.1980267 (66)	total: 32.7s	remaining: 15.2s
67:	learn: 0.1222745	test: 0.1982945	best: 0.1980267 (66)	total: 33.2s	remaining: 14.7s
68:	learn: 0.1217571	test: 0.1984015	best: 0.1980267 (66)	total: 33.5s	remaining: 14.1s
69:	learn: 0.1211085	test: 0.1981569	best: 0.1980267 (66)	total: 34s	remaining: 13.6s
70:	learn: 0.1203771	test: 0.1980290	best: 0.1980267 (66)	total: 34.5s	remaining: 13.1s
71:	learn: 0.1198889	test: 0.1979246	best: 0.1979246 (71)	total: 34.8s	remaining: 12.6s
72:	learn: 0.1192985	test: 0.1977843	best: 0.1977843 (72)	total: 35.2s	remaining: 12s
73:	learn: 0.1183789	test: 0.1976746	best: 0.1976746 (73)	total: 35.7s	remaining: 11.6s
74:	learn: 0.1176186	test: 0.1976629	best: 0.1976629 (74)	total: 36.1s	remaining: 11.1s
75:	learn: 0.1170817	test: 0.1976510	best: 0.1976510 (75)	total: 36.5s	remaining: 10.6s
76:	learn: 0.1165258	test: 0.1975140	best: 0.1975140 (76)	total: 36.9s	remaining: 10.1s
77:	learn: 0.1159144	test: 0.1976282	best: 0.1975140 (76)	total: 37.3s	remaining: 9.57s
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96:	learn: 0.1068337	test: 0.1979027	best: 0.1971900 (80)	total: 44.5s	remaining: 459ms
97:	learn: 0.1066497	test: 0.1978261	best: 0.1971900 (80)	total: 44.8s	remaining: 0us

bestTest = 0.1971900379
bestIteration = 80

Shrink model to first 81 iterations.
Trial 70, Fold 5: Log loss = 0.1964205843343422, Average precision = 0.9743607278685225, ROC-AUC = 0.972080931943593, Elapsed Time = 44.91034370000125 seconds
Optimization Progress:  71%|#######1  | 71/100 [2:00:51<1:00:42, 125.62s/it]
Trial 71, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 71, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
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2:	learn: 0.4561093	test: 0.4571813	best: 0.4571813 (2)	total: 275ms	remaining: 5.41s
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52:	learn: 0.1692670	test: 0.2000185	best: 0.1999850 (50)	total: 5.59s	remaining: 950ms
53:	learn: 0.1683271	test: 0.1998274	best: 0.1998274 (53)	total: 5.7s	remaining: 845ms
54:	learn: 0.1675133	test: 0.1993993	best: 0.1993993 (54)	total: 5.81s	remaining: 740ms
55:	learn: 0.1669431	test: 0.1991634	best: 0.1991634 (55)	total: 5.92s	remaining: 635ms
56:	learn: 0.1665231	test: 0.1990390	best: 0.1990390 (56)	total: 6.03s	remaining: 529ms
57:	learn: 0.1660398	test: 0.1987856	best: 0.1987856 (57)	total: 6.14s	remaining: 423ms
58:	learn: 0.1654167	test: 0.1988480	best: 0.1987856 (57)	total: 6.25s	remaining: 318ms
59:	learn: 0.1646436	test: 0.1985806	best: 0.1985806 (59)	total: 6.35s	remaining: 212ms
60:	learn: 0.1639878	test: 0.1984373	best: 0.1984373 (60)	total: 6.46s	remaining: 106ms
61:	learn: 0.1634405	test: 0.1985401	best: 0.1984373 (60)	total: 6.57s	remaining: 0us

bestTest = 0.1984373284
bestIteration = 60

Shrink model to first 61 iterations.
Trial 71, Fold 1: Log loss = 0.19781661846645085, Average precision = 0.9755612533028836, ROC-AUC = 0.9712075303623406, Elapsed Time = 6.687996600001497 seconds
Trial 71, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 71, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
0:	learn: 0.5940360	test: 0.5951529	best: 0.5951529 (0)	total: 91ms	remaining: 5.55s
1:	learn: 0.5165636	test: 0.5183048	best: 0.5183048 (1)	total: 192ms	remaining: 5.77s
2:	learn: 0.4554413	test: 0.4580697	best: 0.4580697 (2)	total: 293ms	remaining: 5.75s
3:	learn: 0.4080998	test: 0.4115470	best: 0.4115470 (3)	total: 392ms	remaining: 5.69s
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55:	learn: 0.1683431	test: 0.1898963	best: 0.1898963 (55)	total: 5.97s	remaining: 640ms
56:	learn: 0.1677596	test: 0.1897196	best: 0.1897196 (56)	total: 6.09s	remaining: 534ms
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59:	learn: 0.1658443	test: 0.1882827	best: 0.1882827 (59)	total: 6.42s	remaining: 214ms
60:	learn: 0.1650057	test: 0.1881357	best: 0.1881357 (60)	total: 6.53s	remaining: 107ms
61:	learn: 0.1644020	test: 0.1879339	best: 0.1879339 (61)	total: 6.66s	remaining: 0us

bestTest = 0.1879338785
bestIteration = 61

Trial 71, Fold 2: Log loss = 0.18752264196186716, Average precision = 0.9766229820117249, ROC-AUC = 0.9739342828900428, Elapsed Time = 6.798018499997852 seconds
Trial 71, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 71, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
0:	learn: 0.5942032	test: 0.5938701	best: 0.5938701 (0)	total: 91ms	remaining: 5.55s
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2:	learn: 0.4562968	test: 0.4560423	best: 0.4560423 (2)	total: 293ms	remaining: 5.76s
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59:	learn: 0.1660785	test: 0.1900649	best: 0.1898491 (56)	total: 6.24s	remaining: 208ms
60:	learn: 0.1655087	test: 0.1902696	best: 0.1898491 (56)	total: 6.34s	remaining: 104ms
61:	learn: 0.1646410	test: 0.1905068	best: 0.1898491 (56)	total: 6.44s	remaining: 0us

bestTest = 0.1898490596
bestIteration = 56

Shrink model to first 57 iterations.
Trial 71, Fold 3: Log loss = 0.18957033622173933, Average precision = 0.976479376844621, ROC-AUC = 0.9735502433040228, Elapsed Time = 6.571405700000469 seconds
Trial 71, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 71, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
0:	learn: 0.5932970	test: 0.5940946	best: 0.5940946 (0)	total: 92.5ms	remaining: 5.64s
1:	learn: 0.5163668	test: 0.5173917	best: 0.5173917 (1)	total: 192ms	remaining: 5.76s
2:	learn: 0.4554311	test: 0.4568227	best: 0.4568227 (2)	total: 293ms	remaining: 5.77s
3:	learn: 0.4079927	test: 0.4098404	best: 0.4098404 (3)	total: 394ms	remaining: 5.71s
4:	learn: 0.3704868	test: 0.3728434	best: 0.3728434 (4)	total: 493ms	remaining: 5.62s
5:	learn: 0.3410873	test: 0.3435445	best: 0.3435445 (5)	total: 594ms	remaining: 5.55s
6:	learn: 0.3174559	test: 0.3202128	best: 0.3202128 (6)	total: 692ms	remaining: 5.44s
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53:	learn: 0.1687940	test: 0.1948157	best: 0.1948157 (53)	total: 6.53s	remaining: 967ms
54:	learn: 0.1683268	test: 0.1946654	best: 0.1946654 (54)	total: 6.64s	remaining: 846ms
55:	learn: 0.1676812	test: 0.1944610	best: 0.1944610 (55)	total: 6.75s	remaining: 724ms
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58:	learn: 0.1659377	test: 0.1940179	best: 0.1940179 (58)	total: 7.1s	remaining: 361ms
59:	learn: 0.1653240	test: 0.1937910	best: 0.1937910 (59)	total: 7.22s	remaining: 241ms
60:	learn: 0.1647261	test: 0.1935352	best: 0.1935352 (60)	total: 7.33s	remaining: 120ms
61:	learn: 0.1642977	test: 0.1934569	best: 0.1934569 (61)	total: 7.45s	remaining: 0us

bestTest = 0.1934568578
bestIteration = 61

Trial 71, Fold 4: Log loss = 0.19298445961855104, Average precision = 0.9759976408213453, ROC-AUC = 0.9718607606028742, Elapsed Time = 7.578010999997787 seconds
Trial 71, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 71, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
0:	learn: 0.5929156	test: 0.5955466	best: 0.5955466 (0)	total: 101ms	remaining: 6.15s
1:	learn: 0.5151365	test: 0.5193759	best: 0.5193759 (1)	total: 213ms	remaining: 6.4s
2:	learn: 0.4535585	test: 0.4595309	best: 0.4595309 (2)	total: 326ms	remaining: 6.41s
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4:	learn: 0.3683643	test: 0.3768900	best: 0.3768900 (4)	total: 536ms	remaining: 6.11s
5:	learn: 0.3380560	test: 0.3476656	best: 0.3476656 (5)	total: 644ms	remaining: 6.01s
6:	learn: 0.3138138	test: 0.3244874	best: 0.3244874 (6)	total: 754ms	remaining: 5.93s
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58:	learn: 0.1644788	test: 0.2019069	best: 0.2019069 (58)	total: 6.86s	remaining: 349ms
59:	learn: 0.1639615	test: 0.2018521	best: 0.2018521 (59)	total: 6.97s	remaining: 232ms
60:	learn: 0.1633990	test: 0.2015790	best: 0.2015790 (60)	total: 7.09s	remaining: 116ms
61:	learn: 0.1629623	test: 0.2011565	best: 0.2011565 (61)	total: 7.2s	remaining: 0us

bestTest = 0.2011565328
bestIteration = 61

Trial 71, Fold 5: Log loss = 0.20047779827987935, Average precision = 0.9737315361605978, ROC-AUC = 0.9710568344388087, Elapsed Time = 7.334567800000514 seconds
Optimization Progress:  72%|#######2  | 72/100 [2:01:34<47:01, 100.78s/it]  
Trial 72, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 72, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
0:	learn: 0.6149957	test: 0.6187336	best: 0.6187336 (0)	total: 548ms	remaining: 24.1s
1:	learn: 0.5483818	test: 0.5553574	best: 0.5553574 (1)	total: 1.17s	remaining: 25.2s
2:	learn: 0.4915612	test: 0.5016770	best: 0.5016770 (2)	total: 1.82s	remaining: 25.6s
3:	learn: 0.4441540	test: 0.4571943	best: 0.4571943 (3)	total: 2.62s	remaining: 26.9s
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5:	learn: 0.3701658	test: 0.3896019	best: 0.3896019 (5)	total: 4.1s	remaining: 26.7s
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31:	learn: 0.1440529	test: 0.2121062	best: 0.2121062 (31)	total: 22.7s	remaining: 9.21s
32:	learn: 0.1413557	test: 0.2111486	best: 0.2111486 (32)	total: 23.5s	remaining: 8.55s
33:	learn: 0.1390516	test: 0.2105831	best: 0.2105831 (33)	total: 24.2s	remaining: 7.84s
34:	learn: 0.1367291	test: 0.2097764	best: 0.2097764 (34)	total: 25s	remaining: 7.15s
35:	learn: 0.1346859	test: 0.2089525	best: 0.2089525 (35)	total: 25.8s	remaining: 6.44s
36:	learn: 0.1328724	test: 0.2080605	best: 0.2080605 (36)	total: 26.5s	remaining: 5.74s
37:	learn: 0.1304706	test: 0.2074053	best: 0.2074053 (37)	total: 27.5s	remaining: 5.06s
38:	learn: 0.1285974	test: 0.2067868	best: 0.2067868 (38)	total: 28.4s	remaining: 4.36s
39:	learn: 0.1265553	test: 0.2063475	best: 0.2063475 (39)	total: 29.2s	remaining: 3.65s
40:	learn: 0.1243125	test: 0.2059421	best: 0.2059421 (40)	total: 30s	remaining: 2.92s
41:	learn: 0.1223950	test: 0.2055957	best: 0.2055957 (41)	total: 30.9s	remaining: 2.21s
42:	learn: 0.1205241	test: 0.2052764	best: 0.2052764 (42)	total: 31.8s	remaining: 1.48s
43:	learn: 0.1193109	test: 0.2048634	best: 0.2048634 (43)	total: 32.5s	remaining: 738ms
44:	learn: 0.1179902	test: 0.2045313	best: 0.2045313 (44)	total: 33.2s	remaining: 0us

bestTest = 0.2045313427
bestIteration = 44

Trial 72, Fold 1: Log loss = 0.20453134266117223, Average precision = 0.975197454205744, ROC-AUC = 0.9709214271375518, Elapsed Time = 33.35583069999848 seconds
Trial 72, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 72, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
0:	learn: 0.6143779	test: 0.6175117	best: 0.6175117 (0)	total: 669ms	remaining: 29.4s
1:	learn: 0.5479272	test: 0.5541530	best: 0.5541530 (1)	total: 1.31s	remaining: 28.2s
2:	learn: 0.4922476	test: 0.5009081	best: 0.5009081 (2)	total: 1.96s	remaining: 27.5s
3:	learn: 0.4457773	test: 0.4568396	best: 0.4568396 (3)	total: 2.59s	remaining: 26.5s
4:	learn: 0.4064229	test: 0.4198544	best: 0.4198544 (4)	total: 3.34s	remaining: 26.7s
5:	learn: 0.3730805	test: 0.3887238	best: 0.3887238 (5)	total: 4.09s	remaining: 26.6s
6:	learn: 0.3442587	test: 0.3625739	best: 0.3625739 (6)	total: 4.86s	remaining: 26.4s
7:	learn: 0.3202496	test: 0.3397839	best: 0.3397839 (7)	total: 5.6s	remaining: 25.9s
8:	learn: 0.2988347	test: 0.3206673	best: 0.3206673 (8)	total: 6.44s	remaining: 25.8s
9:	learn: 0.2798775	test: 0.3043069	best: 0.3043069 (9)	total: 7.32s	remaining: 25.6s
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29:	learn: 0.1487618	test: 0.2027375	best: 0.2027375 (29)	total: 22.2s	remaining: 11.1s
30:	learn: 0.1461726	test: 0.2015406	best: 0.2015406 (30)	total: 22.9s	remaining: 10.4s
31:	learn: 0.1435980	test: 0.2001510	best: 0.2001510 (31)	total: 23.8s	remaining: 9.68s
32:	learn: 0.1416305	test: 0.1988856	best: 0.1988856 (32)	total: 24.5s	remaining: 8.9s
33:	learn: 0.1396139	test: 0.1978820	best: 0.1978820 (33)	total: 25.2s	remaining: 8.15s
34:	learn: 0.1373274	test: 0.1963720	best: 0.1963720 (34)	total: 26s	remaining: 7.43s
35:	learn: 0.1354210	test: 0.1954332	best: 0.1954332 (35)	total: 26.8s	remaining: 6.69s
36:	learn: 0.1328897	test: 0.1947397	best: 0.1947397 (36)	total: 27.8s	remaining: 6s
37:	learn: 0.1310737	test: 0.1939943	best: 0.1939943 (37)	total: 28.5s	remaining: 5.25s
38:	learn: 0.1292011	test: 0.1932524	best: 0.1932524 (38)	total: 29.3s	remaining: 4.51s
39:	learn: 0.1277136	test: 0.1926010	best: 0.1926010 (39)	total: 30s	remaining: 3.75s
40:	learn: 0.1261487	test: 0.1918664	best: 0.1918664 (40)	total: 30.8s	remaining: 3.01s
41:	learn: 0.1245997	test: 0.1912512	best: 0.1912512 (41)	total: 31.8s	remaining: 2.27s
42:	learn: 0.1231387	test: 0.1907170	best: 0.1907170 (42)	total: 32.9s	remaining: 1.53s
43:	learn: 0.1214810	test: 0.1901939	best: 0.1901939 (43)	total: 33.8s	remaining: 768ms
44:	learn: 0.1203049	test: 0.1895541	best: 0.1895541 (44)	total: 34.5s	remaining: 0us

bestTest = 0.1895541267
bestIteration = 44

Trial 72, Fold 2: Log loss = 0.18955412666489446, Average precision = 0.9770493220330668, ROC-AUC = 0.9740655447244732, Elapsed Time = 34.72651369999949 seconds
Trial 72, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 72, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
0:	learn: 0.6146411	test: 0.6170722	best: 0.6170722 (0)	total: 602ms	remaining: 26.5s
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2:	learn: 0.4925319	test: 0.4999656	best: 0.4999656 (2)	total: 1.95s	remaining: 27.3s
3:	learn: 0.4461457	test: 0.4560761	best: 0.4560761 (3)	total: 2.73s	remaining: 27.9s
4:	learn: 0.4051635	test: 0.4182496	best: 0.4182496 (4)	total: 3.56s	remaining: 28.5s
5:	learn: 0.3721658	test: 0.3874574	best: 0.3874574 (5)	total: 4.21s	remaining: 27.4s
6:	learn: 0.3439259	test: 0.3605786	best: 0.3605786 (6)	total: 4.87s	remaining: 26.4s
7:	learn: 0.3194314	test: 0.3380282	best: 0.3380282 (7)	total: 5.66s	remaining: 26.2s
8:	learn: 0.2981969	test: 0.3192649	best: 0.3192649 (8)	total: 6.35s	remaining: 25.4s
9:	learn: 0.2805961	test: 0.3033386	best: 0.3033386 (9)	total: 6.95s	remaining: 24.3s
10:	learn: 0.2652540	test: 0.2899312	best: 0.2899312 (10)	total: 7.66s	remaining: 23.7s
11:	learn: 0.2514556	test: 0.2780060	best: 0.2780060 (11)	total: 8.39s	remaining: 23.1s
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31:	learn: 0.1456100	test: 0.2014230	best: 0.2014230 (31)	total: 23.3s	remaining: 9.46s
32:	learn: 0.1430670	test: 0.2002027	best: 0.2002027 (32)	total: 24.1s	remaining: 8.76s
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34:	learn: 0.1385312	test: 0.1983223	best: 0.1983223 (34)	total: 25.6s	remaining: 7.32s
35:	learn: 0.1362730	test: 0.1975713	best: 0.1975713 (35)	total: 26.5s	remaining: 6.62s
36:	learn: 0.1343658	test: 0.1968252	best: 0.1968252 (36)	total: 27.2s	remaining: 5.88s
37:	learn: 0.1323774	test: 0.1965569	best: 0.1965569 (37)	total: 28s	remaining: 5.17s
38:	learn: 0.1307742	test: 0.1959996	best: 0.1959996 (38)	total: 28.8s	remaining: 4.42s
39:	learn: 0.1288817	test: 0.1953884	best: 0.1953884 (39)	total: 29.6s	remaining: 3.71s
40:	learn: 0.1273027	test: 0.1947979	best: 0.1947979 (40)	total: 30.4s	remaining: 2.96s
41:	learn: 0.1257281	test: 0.1943178	best: 0.1943178 (41)	total: 31.1s	remaining: 2.22s
42:	learn: 0.1239085	test: 0.1937616	best: 0.1937616 (42)	total: 31.9s	remaining: 1.49s
43:	learn: 0.1223016	test: 0.1931401	best: 0.1931401 (43)	total: 32.8s	remaining: 745ms
44:	learn: 0.1212052	test: 0.1928733	best: 0.1928733 (44)	total: 33.4s	remaining: 0us

bestTest = 0.1928733353
bestIteration = 44

Trial 72, Fold 3: Log loss = 0.19287333528003864, Average precision = 0.975518451660432, ROC-AUC = 0.9732680088316591, Elapsed Time = 33.61581719999958 seconds
Trial 72, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 72, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
0:	learn: 0.6139814	test: 0.6174364	best: 0.6174364 (0)	total: 695ms	remaining: 30.6s
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2:	learn: 0.4915874	test: 0.5011974	best: 0.5011974 (2)	total: 2.3s	remaining: 32.2s
3:	learn: 0.4450791	test: 0.4570694	best: 0.4570694 (3)	total: 2.97s	remaining: 30.4s
4:	learn: 0.4053955	test: 0.4198277	best: 0.4198277 (4)	total: 3.83s	remaining: 30.6s
5:	learn: 0.3714320	test: 0.3884709	best: 0.3884709 (5)	total: 4.75s	remaining: 30.9s
6:	learn: 0.3435730	test: 0.3627896	best: 0.3627896 (6)	total: 5.46s	remaining: 29.6s
7:	learn: 0.3195855	test: 0.3406056	best: 0.3406056 (7)	total: 6.12s	remaining: 28.3s
8:	learn: 0.2991483	test: 0.3215932	best: 0.3215932 (8)	total: 6.83s	remaining: 27.3s
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44:	learn: 0.1226573	test: 0.1936647	best: 0.1936647 (44)	total: 33.1s	remaining: 0us

bestTest = 0.1936646567
bestIteration = 44

Trial 72, Fold 4: Log loss = 0.1936646567102457, Average precision = 0.9771454589891937, ROC-AUC = 0.9734168115195605, Elapsed Time = 33.27767400000084 seconds
Trial 72, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 72, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
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44:	learn: 0.1176498	test: 0.2016388	best: 0.2016388 (44)	total: 34.1s	remaining: 0us

bestTest = 0.2016387522
bestIteration = 44

Trial 72, Fold 5: Log loss = 0.20163875215161584, Average precision = 0.974648495045539, ROC-AUC = 0.97209832227858, Elapsed Time = 34.260632699999405 seconds
Optimization Progress:  73%|#######3  | 73/100 [2:04:32<55:48, 124.02s/it]
Trial 73, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 73, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
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64:	learn: 0.2886881	test: 0.2916989	best: 0.2916989 (64)	total: 12.4s	remaining: 383ms
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66:	learn: 0.2851654	test: 0.2883727	best: 0.2883727 (66)	total: 12.8s	remaining: 0us

bestTest = 0.2883726847
bestIteration = 66

Trial 73, Fold 1: Log loss = 0.2883726847206898, Average precision = 0.9673291485710853, ROC-AUC = 0.9624328766435812, Elapsed Time = 12.948463799999445 seconds
Trial 73, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 73, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
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66:	learn: 0.2854805	test: 0.2881788	best: 0.2881788 (66)	total: 12.9s	remaining: 0us

bestTest = 0.2881788354
bestIteration = 66

Trial 73, Fold 2: Log loss = 0.28817883537160904, Average precision = 0.9696547592949077, ROC-AUC = 0.9662350554987176, Elapsed Time = 12.999011100000644 seconds
Trial 73, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 73, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
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57:	learn: 0.3020755	test: 0.3004631	best: 0.3004631 (57)	total: 11.1s	remaining: 1.73s
58:	learn: 0.2996399	test: 0.2980974	best: 0.2980974 (58)	total: 11.3s	remaining: 1.53s
59:	learn: 0.2974485	test: 0.2958928	best: 0.2958928 (59)	total: 11.5s	remaining: 1.34s
60:	learn: 0.2952686	test: 0.2936857	best: 0.2936857 (60)	total: 11.7s	remaining: 1.15s
61:	learn: 0.2930483	test: 0.2915285	best: 0.2915285 (61)	total: 11.8s	remaining: 954ms
62:	learn: 0.2913060	test: 0.2897843	best: 0.2897843 (62)	total: 12s	remaining: 762ms
63:	learn: 0.2894612	test: 0.2879684	best: 0.2879684 (63)	total: 12.2s	remaining: 573ms
64:	learn: 0.2872966	test: 0.2858261	best: 0.2858261 (64)	total: 12.4s	remaining: 383ms
65:	learn: 0.2852603	test: 0.2838687	best: 0.2838687 (65)	total: 12.7s	remaining: 192ms
66:	learn: 0.2833924	test: 0.2820576	best: 0.2820576 (66)	total: 12.8s	remaining: 0us

bestTest = 0.2820576015
bestIteration = 66

Trial 73, Fold 3: Log loss = 0.2820576015164774, Average precision = 0.9683603365207796, ROC-AUC = 0.9661705379414626, Elapsed Time = 12.962134000001242 seconds
Trial 73, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 73, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
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58:	learn: 0.3007139	test: 0.3018401	best: 0.3018401 (58)	total: 11.2s	remaining: 1.52s
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60:	learn: 0.2960961	test: 0.2972640	best: 0.2972640 (60)	total: 11.7s	remaining: 1.15s
61:	learn: 0.2942306	test: 0.2954276	best: 0.2954276 (61)	total: 11.8s	remaining: 952ms
62:	learn: 0.2923092	test: 0.2935401	best: 0.2935401 (62)	total: 12s	remaining: 763ms
63:	learn: 0.2908237	test: 0.2920348	best: 0.2920348 (63)	total: 12.2s	remaining: 571ms
64:	learn: 0.2886555	test: 0.2899157	best: 0.2899157 (64)	total: 12.4s	remaining: 382ms
65:	learn: 0.2871719	test: 0.2884501	best: 0.2884501 (65)	total: 12.6s	remaining: 191ms
66:	learn: 0.2854511	test: 0.2867326	best: 0.2867326 (66)	total: 12.8s	remaining: 0us

bestTest = 0.286732647
bestIteration = 66

Trial 73, Fold 4: Log loss = 0.2867326470217839, Average precision = 0.9672012912634944, ROC-AUC = 0.9637673286980407, Elapsed Time = 12.946753800002625 seconds
Trial 73, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 73, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
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61:	learn: 0.2930767	test: 0.3006777	best: 0.3006777 (61)	total: 12.1s	remaining: 975ms
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63:	learn: 0.2892684	test: 0.2970477	best: 0.2970477 (63)	total: 12.4s	remaining: 581ms
64:	learn: 0.2878738	test: 0.2957441	best: 0.2957441 (64)	total: 12.6s	remaining: 386ms
65:	learn: 0.2863658	test: 0.2943141	best: 0.2943141 (65)	total: 12.8s	remaining: 194ms
66:	learn: 0.2843684	test: 0.2924208	best: 0.2924208 (66)	total: 13s	remaining: 0us

bestTest = 0.2924208183
bestIteration = 66

Trial 73, Fold 5: Log loss = 0.292420818332159, Average precision = 0.9663694513644887, ROC-AUC = 0.9618022035189417, Elapsed Time = 13.122513300000719 seconds
Optimization Progress:  74%|#######4  | 74/100 [2:05:45<47:07, 108.74s/it]
Trial 74, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 74, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
0:	learn: 0.6232529	test: 0.6255123	best: 0.6255123 (0)	total: 1.72s	remaining: 2m 46s
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89:	learn: 0.0785158	test: 0.2050455	best: 0.2049317 (75)	total: 2m 40s	remaining: 14.3s
90:	learn: 0.0781232	test: 0.2048764	best: 0.2048764 (90)	total: 2m 42s	remaining: 12.5s
91:	learn: 0.0776499	test: 0.2047474	best: 0.2047474 (91)	total: 2m 43s	remaining: 10.7s
92:	learn: 0.0760567	test: 0.2048019	best: 0.2047474 (91)	total: 2m 45s	remaining: 8.91s
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95:	learn: 0.0733102	test: 0.2054166	best: 0.2047474 (91)	total: 2m 50s	remaining: 3.56s
96:	learn: 0.0722445	test: 0.2052965	best: 0.2047474 (91)	total: 2m 52s	remaining: 1.78s
97:	learn: 0.0719465	test: 0.2053182	best: 0.2047474 (91)	total: 2m 54s	remaining: 0us

bestTest = 0.2047473662
bestIteration = 91

Shrink model to first 92 iterations.
Trial 74, Fold 1: Log loss = 0.20372707646786092, Average precision = 0.9743246850650773, ROC-AUC = 0.9691592523288549, Elapsed Time = 174.74297730000035 seconds
Trial 74, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 74, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
0:	learn: 0.6305059	test: 0.6346441	best: 0.6346441 (0)	total: 1.63s	remaining: 2m 38s
1:	learn: 0.5705205	test: 0.5765615	best: 0.5765615 (1)	total: 2.3s	remaining: 1m 50s
2:	learn: 0.5189749	test: 0.5278152	best: 0.5278152 (2)	total: 3.97s	remaining: 2m 5s
3:	learn: 0.4737566	test: 0.4850789	best: 0.4850789 (3)	total: 5.66s	remaining: 2m 13s
4:	learn: 0.4361327	test: 0.4492156	best: 0.4492156 (4)	total: 7.41s	remaining: 2m 17s
5:	learn: 0.4035306	test: 0.4187688	best: 0.4187688 (5)	total: 9.16s	remaining: 2m 20s
6:	learn: 0.3741846	test: 0.3926622	best: 0.3926622 (6)	total: 10.9s	remaining: 2m 22s
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bestTest = 0.1977060533
bestIteration = 97

Trial 74, Fold 2: Log loss = 0.1970840858190826, Average precision = 0.9740080719974162, ROC-AUC = 0.9705176815002151, Elapsed Time = 168.68866730000082 seconds
Trial 74, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 74, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
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94:	learn: 0.0737671	test: 0.1970393	best: 0.1970393 (94)	total: 2m 41s	remaining: 5.09s
95:	learn: 0.0730550	test: 0.1970078	best: 0.1970078 (95)	total: 2m 42s	remaining: 3.39s
96:	learn: 0.0721950	test: 0.1971381	best: 0.1970078 (95)	total: 2m 44s	remaining: 1.7s
97:	learn: 0.0712071	test: 0.1972132	best: 0.1970078 (95)	total: 2m 46s	remaining: 0us

bestTest = 0.1970077741
bestIteration = 95

Shrink model to first 96 iterations.
Trial 74, Fold 3: Log loss = 0.19643855032112573, Average precision = 0.9725789577283187, ROC-AUC = 0.9709949340755651, Elapsed Time = 166.7922209999997 seconds
Trial 74, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 74, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
0:	learn: 0.6233121	test: 0.6261686	best: 0.6261686 (0)	total: 1.58s	remaining: 2m 32s
1:	learn: 0.5620001	test: 0.5685956	best: 0.5685956 (1)	total: 3.13s	remaining: 2m 30s
2:	learn: 0.5108070	test: 0.5196249	best: 0.5196249 (2)	total: 4.7s	remaining: 2m 28s
3:	learn: 0.4676212	test: 0.4785555	best: 0.4785555 (3)	total: 6.33s	remaining: 2m 28s
4:	learn: 0.4305388	test: 0.4437370	best: 0.4437370 (4)	total: 7.96s	remaining: 2m 27s
5:	learn: 0.3983257	test: 0.4134129	best: 0.4134129 (5)	total: 9.64s	remaining: 2m 27s
6:	learn: 0.3700675	test: 0.3881180	best: 0.3881180 (6)	total: 11.4s	remaining: 2m 28s
7:	learn: 0.3463396	test: 0.3664051	best: 0.3664051 (7)	total: 13.2s	remaining: 2m 28s
8:	learn: 0.3253771	test: 0.3472186	best: 0.3472186 (8)	total: 14.9s	remaining: 2m 27s
9:	learn: 0.3070548	test: 0.3305121	best: 0.3305121 (9)	total: 16.8s	remaining: 2m 27s
10:	learn: 0.2911812	test: 0.3164166	best: 0.3164166 (10)	total: 18.5s	remaining: 2m 26s
11:	learn: 0.2771547	test: 0.3041283	best: 0.3041283 (11)	total: 20.2s	remaining: 2m 24s
12:	learn: 0.2644678	test: 0.2923060	best: 0.2923060 (12)	total: 22s	remaining: 2m 23s
13:	learn: 0.2528891	test: 0.2834818	best: 0.2834818 (13)	total: 23.8s	remaining: 2m 22s
14:	learn: 0.2437088	test: 0.2758140	best: 0.2758140 (14)	total: 25.5s	remaining: 2m 21s
15:	learn: 0.2351982	test: 0.2679816	best: 0.2679816 (15)	total: 27.2s	remaining: 2m 19s
16:	learn: 0.2269520	test: 0.2616733	best: 0.2616733 (16)	total: 28.8s	remaining: 2m 17s
17:	learn: 0.2189886	test: 0.2563810	best: 0.2563810 (17)	total: 30.4s	remaining: 2m 15s
18:	learn: 0.2121767	test: 0.2511327	best: 0.2511327 (18)	total: 32.1s	remaining: 2m 13s
19:	learn: 0.2059364	test: 0.2468459	best: 0.2468459 (19)	total: 33.8s	remaining: 2m 11s
20:	learn: 0.2005769	test: 0.2431924	best: 0.2431924 (20)	total: 35.5s	remaining: 2m 10s
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59:	learn: 0.1056580	test: 0.2038225	best: 0.2038225 (59)	total: 1m 42s	remaining: 1m 4s
60:	learn: 0.1046071	test: 0.2038012	best: 0.2038012 (60)	total: 1m 43s	remaining: 1m 2s
61:	learn: 0.1039441	test: 0.2034323	best: 0.2034323 (61)	total: 1m 45s	remaining: 1m 1s
62:	learn: 0.1031614	test: 0.2031178	best: 0.2031178 (62)	total: 1m 47s	remaining: 59.6s
63:	learn: 0.1020375	test: 0.2027943	best: 0.2027943 (63)	total: 1m 49s	remaining: 58s
64:	learn: 0.1006474	test: 0.2026049	best: 0.2026049 (64)	total: 1m 50s	remaining: 56.2s
65:	learn: 0.0995097	test: 0.2024234	best: 0.2024234 (65)	total: 1m 52s	remaining: 54.6s
66:	learn: 0.0990005	test: 0.2022181	best: 0.2022181 (66)	total: 1m 54s	remaining: 52.9s
67:	learn: 0.0978009	test: 0.2020653	best: 0.2020653 (67)	total: 1m 56s	remaining: 51.2s
68:	learn: 0.0964343	test: 0.2020447	best: 0.2020447 (68)	total: 1m 57s	remaining: 49.5s
69:	learn: 0.0951296	test: 0.2020527	best: 0.2020447 (68)	total: 1m 59s	remaining: 47.8s
70:	learn: 0.0941058	test: 0.2020281	best: 0.2020281 (70)	total: 2m 1s	remaining: 46.1s
71:	learn: 0.0931910	test: 0.2018797	best: 0.2018797 (71)	total: 2m 2s	remaining: 44.4s
72:	learn: 0.0923266	test: 0.2018316	best: 0.2018316 (72)	total: 2m 4s	remaining: 42.7s
73:	learn: 0.0905134	test: 0.2017651	best: 0.2017651 (73)	total: 2m 6s	remaining: 41s
74:	learn: 0.0898224	test: 0.2014375	best: 0.2014375 (74)	total: 2m 8s	remaining: 39.3s
75:	learn: 0.0892318	test: 0.2010123	best: 0.2010123 (75)	total: 2m 10s	remaining: 37.7s
76:	learn: 0.0887047	test: 0.2008404	best: 0.2008404 (76)	total: 2m 12s	remaining: 36s
77:	learn: 0.0880062	test: 0.2006235	best: 0.2006235 (77)	total: 2m 13s	remaining: 34.3s
78:	learn: 0.0875249	test: 0.2004301	best: 0.2004301 (78)	total: 2m 15s	remaining: 32.6s
79:	learn: 0.0867684	test: 0.2004069	best: 0.2004069 (79)	total: 2m 17s	remaining: 30.9s
80:	learn: 0.0857148	test: 0.2004665	best: 0.2004069 (79)	total: 2m 19s	remaining: 29.2s
81:	learn: 0.0852709	test: 0.2003482	best: 0.2003482 (81)	total: 2m 20s	remaining: 27.5s
82:	learn: 0.0849478	test: 0.2002940	best: 0.2002940 (82)	total: 2m 22s	remaining: 25.8s
83:	learn: 0.0846921	test: 0.2001292	best: 0.2001292 (83)	total: 2m 24s	remaining: 24s
84:	learn: 0.0836338	test: 0.1999401	best: 0.1999401 (84)	total: 2m 25s	remaining: 22.3s
85:	learn: 0.0820915	test: 0.1999892	best: 0.1999401 (84)	total: 2m 27s	remaining: 20.6s
86:	learn: 0.0803187	test: 0.2000436	best: 0.1999401 (84)	total: 2m 29s	remaining: 18.9s
87:	learn: 0.0796612	test: 0.2001032	best: 0.1999401 (84)	total: 2m 31s	remaining: 17.2s
88:	learn: 0.0789598	test: 0.2000365	best: 0.1999401 (84)	total: 2m 32s	remaining: 15.4s
89:	learn: 0.0773750	test: 0.1998569	best: 0.1998569 (89)	total: 2m 34s	remaining: 13.7s
90:	learn: 0.0768555	test: 0.1997263	best: 0.1997263 (90)	total: 2m 36s	remaining: 12s
91:	learn: 0.0765776	test: 0.1996282	best: 0.1996282 (91)	total: 2m 37s	remaining: 10.3s
92:	learn: 0.0755715	test: 0.1997974	best: 0.1996282 (91)	total: 2m 39s	remaining: 8.57s
93:	learn: 0.0746660	test: 0.1998339	best: 0.1996282 (91)	total: 2m 41s	remaining: 6.85s
94:	learn: 0.0734693	test: 0.2000120	best: 0.1996282 (91)	total: 2m 42s	remaining: 5.14s
95:	learn: 0.0723868	test: 0.2001724	best: 0.1996282 (91)	total: 2m 44s	remaining: 3.42s
96:	learn: 0.0719337	test: 0.2001340	best: 0.1996282 (91)	total: 2m 46s	remaining: 1.71s
97:	learn: 0.0715309	test: 0.2000712	best: 0.1996282 (91)	total: 2m 47s	remaining: 0us

bestTest = 0.1996282366
bestIteration = 91

Shrink model to first 92 iterations.
Trial 74, Fold 4: Log loss = 0.19890881742148034, Average precision = 0.9744471072122258, ROC-AUC = 0.9696595778164052, Elapsed Time = 168.1239002000002 seconds
Trial 74, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 74, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
0:	learn: 0.6308905	test: 0.6354902	best: 0.6354902 (0)	total: 1.69s	remaining: 2m 44s
1:	learn: 0.5708977	test: 0.5783548	best: 0.5783548 (1)	total: 2.1s	remaining: 1m 40s
2:	learn: 0.5168869	test: 0.5299628	best: 0.5299628 (2)	total: 3.8s	remaining: 2m
3:	learn: 0.4721512	test: 0.4877199	best: 0.4877199 (3)	total: 5.55s	remaining: 2m 10s
4:	learn: 0.4344857	test: 0.4520350	best: 0.4520350 (4)	total: 7.31s	remaining: 2m 16s
5:	learn: 0.4012680	test: 0.4209405	best: 0.4209405 (5)	total: 8.98s	remaining: 2m 17s
6:	learn: 0.3768902	test: 0.3967692	best: 0.3967692 (6)	total: 9.06s	remaining: 1m 57s
7:	learn: 0.3509952	test: 0.3739018	best: 0.3739018 (7)	total: 10.8s	remaining: 2m 2s
8:	learn: 0.3302289	test: 0.3544784	best: 0.3544784 (8)	total: 12.5s	remaining: 2m 3s
9:	learn: 0.3110280	test: 0.3377808	best: 0.3377808 (9)	total: 14.2s	remaining: 2m 5s
10:	learn: 0.2952648	test: 0.3226714	best: 0.3226714 (10)	total: 16.1s	remaining: 2m 7s
11:	learn: 0.2806385	test: 0.3105674	best: 0.3105674 (11)	total: 17.8s	remaining: 2m 7s
12:	learn: 0.2676585	test: 0.2998964	best: 0.2998964 (12)	total: 19.6s	remaining: 2m 7s
13:	learn: 0.2563801	test: 0.2900135	best: 0.2900135 (13)	total: 21.3s	remaining: 2m 7s
14:	learn: 0.2467377	test: 0.2818983	best: 0.2818983 (14)	total: 23s	remaining: 2m 7s
15:	learn: 0.2380399	test: 0.2743534	best: 0.2743534 (15)	total: 24.8s	remaining: 2m 7s
16:	learn: 0.2284140	test: 0.2681902	best: 0.2681902 (16)	total: 26.6s	remaining: 2m 6s
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18:	learn: 0.2153416	test: 0.2574413	best: 0.2574413 (18)	total: 30s	remaining: 2m 4s
19:	learn: 0.2092178	test: 0.2530583	best: 0.2530583 (19)	total: 31.9s	remaining: 2m 4s
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27:	learn: 0.1734745	test: 0.2309293	best: 0.2309293 (27)	total: 46s	remaining: 1m 54s
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81:	learn: 0.0857624	test: 0.2091354	best: 0.2091354 (81)	total: 2m 20s	remaining: 27.3s
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89:	learn: 0.0795743	test: 0.2086171	best: 0.2085573 (88)	total: 2m 33s	remaining: 13.7s
90:	learn: 0.0779795	test: 0.2086772	best: 0.2085573 (88)	total: 2m 35s	remaining: 12s
91:	learn: 0.0772961	test: 0.2089322	best: 0.2085573 (88)	total: 2m 37s	remaining: 10.3s
92:	learn: 0.0768116	test: 0.2087248	best: 0.2085573 (88)	total: 2m 39s	remaining: 8.56s
93:	learn: 0.0764007	test: 0.2084266	best: 0.2084266 (93)	total: 2m 41s	remaining: 6.85s
94:	learn: 0.0756760	test: 0.2082492	best: 0.2082492 (94)	total: 2m 42s	remaining: 5.14s
95:	learn: 0.0750344	test: 0.2082277	best: 0.2082277 (95)	total: 2m 44s	remaining: 3.43s
96:	learn: 0.0729086	test: 0.2082732	best: 0.2082277 (95)	total: 2m 46s	remaining: 1.71s
97:	learn: 0.0724173	test: 0.2083103	best: 0.2082277 (95)	total: 2m 47s	remaining: 0us

bestTest = 0.208227748
bestIteration = 95

Shrink model to first 96 iterations.
Trial 74, Fold 5: Log loss = 0.20718779155537703, Average precision = 0.9731871268430515, ROC-AUC = 0.9684625531835833, Elapsed Time = 168.26519910000206 seconds
Optimization Progress:  75%|#######5  | 75/100 [2:19:59<2:18:31, 332.46s/it]
Trial 75, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 75, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
0:	learn: 0.5097813	test: 0.5126082	best: 0.5126082 (0)	total: 201ms	remaining: 1.41s
1:	learn: 0.4078349	test: 0.4143247	best: 0.4143247 (1)	total: 402ms	remaining: 1.21s
2:	learn: 0.3377477	test: 0.3489414	best: 0.3489414 (2)	total: 628ms	remaining: 1.05s
3:	learn: 0.2877183	test: 0.3029563	best: 0.3029563 (3)	total: 848ms	remaining: 848ms
4:	learn: 0.2565791	test: 0.2753284	best: 0.2753284 (4)	total: 1.07s	remaining: 643ms
5:	learn: 0.2344505	test: 0.2559626	best: 0.2559626 (5)	total: 1.27s	remaining: 424ms
6:	learn: 0.2171929	test: 0.2410710	best: 0.2410710 (6)	total: 1.46s	remaining: 209ms
7:	learn: 0.2030235	test: 0.2301766	best: 0.2301766 (7)	total: 1.69s	remaining: 0us

bestTest = 0.2301766315
bestIteration = 7

Trial 75, Fold 1: Log loss = 0.23017663145427283, Average precision = 0.9720650521748062, ROC-AUC = 0.9672628009348876, Elapsed Time = 1.8035525000013877 seconds
Trial 75, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 75, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
0:	learn: 0.4925732	test: 0.4968635	best: 0.4968635 (0)	total: 205ms	remaining: 1.44s
1:	learn: 0.3834791	test: 0.3908931	best: 0.3908931 (1)	total: 428ms	remaining: 1.28s
2:	learn: 0.3238229	test: 0.3341868	best: 0.3341868 (2)	total: 638ms	remaining: 1.06s
3:	learn: 0.2730064	test: 0.2873088	best: 0.2873088 (3)	total: 874ms	remaining: 874ms
4:	learn: 0.2397216	test: 0.2581664	best: 0.2581664 (4)	total: 1.11s	remaining: 668ms
5:	learn: 0.2213316	test: 0.2417468	best: 0.2417468 (5)	total: 1.33s	remaining: 443ms
6:	learn: 0.2066362	test: 0.2291984	best: 0.2291984 (6)	total: 1.54s	remaining: 221ms
7:	learn: 0.1935625	test: 0.2184730	best: 0.2184730 (7)	total: 1.77s	remaining: 0us

bestTest = 0.2184730204
bestIteration = 7

Trial 75, Fold 2: Log loss = 0.2184730203782967, Average precision = 0.9736359492697437, ROC-AUC = 0.9703997734709058, Elapsed Time = 1.880307499999617 seconds
Trial 75, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 75, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
0:	learn: 0.5126189	test: 0.5148431	best: 0.5148431 (0)	total: 232ms	remaining: 1.62s
1:	learn: 0.3974469	test: 0.4027819	best: 0.4027819 (1)	total: 453ms	remaining: 1.36s
2:	learn: 0.3328133	test: 0.3412621	best: 0.3412621 (2)	total: 669ms	remaining: 1.11s
3:	learn: 0.2847019	test: 0.2961406	best: 0.2961406 (3)	total: 909ms	remaining: 909ms
4:	learn: 0.2500534	test: 0.2646369	best: 0.2646369 (4)	total: 1.16s	remaining: 695ms
5:	learn: 0.2224107	test: 0.2413757	best: 0.2413757 (5)	total: 1.45s	remaining: 484ms
6:	learn: 0.2078418	test: 0.2297494	best: 0.2297494 (6)	total: 1.73s	remaining: 248ms
7:	learn: 0.1956612	test: 0.2204956	best: 0.2204956 (7)	total: 2.08s	remaining: 0us

bestTest = 0.2204955656
bestIteration = 7

Trial 75, Fold 3: Log loss = 0.22049556556625982, Average precision = 0.9723505203681976, ROC-AUC = 0.9691137220579595, Elapsed Time = 2.214204299998528 seconds
Trial 75, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 75, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
0:	learn: 0.5167488	test: 0.5195956	best: 0.5195956 (0)	total: 222ms	remaining: 1.55s
1:	learn: 0.4153409	test: 0.4227675	best: 0.4227675 (1)	total: 428ms	remaining: 1.28s
2:	learn: 0.3489447	test: 0.3594281	best: 0.3594281 (2)	total: 649ms	remaining: 1.08s
3:	learn: 0.2945964	test: 0.3079072	best: 0.3079072 (3)	total: 870ms	remaining: 870ms
4:	learn: 0.2609621	test: 0.2782619	best: 0.2782619 (4)	total: 1.11s	remaining: 669ms
5:	learn: 0.2289680	test: 0.2484209	best: 0.2484209 (5)	total: 1.36s	remaining: 453ms
6:	learn: 0.2136895	test: 0.2349619	best: 0.2349619 (6)	total: 1.55s	remaining: 222ms
7:	learn: 0.2001226	test: 0.2243627	best: 0.2243627 (7)	total: 1.76s	remaining: 0us

bestTest = 0.2243626571
bestIteration = 7

Trial 75, Fold 4: Log loss = 0.22436265714959572, Average precision = 0.9714429749314285, ROC-AUC = 0.9686229556666692, Elapsed Time = 1.9285004000012123 seconds
Trial 75, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 75, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
0:	learn: 0.5078668	test: 0.5128209	best: 0.5128209 (0)	total: 189ms	remaining: 1.32s
1:	learn: 0.4043368	test: 0.4130910	best: 0.4130910 (1)	total: 381ms	remaining: 1.14s
2:	learn: 0.3373254	test: 0.3509105	best: 0.3509105 (2)	total: 592ms	remaining: 987ms
3:	learn: 0.2912234	test: 0.3089344	best: 0.3089344 (3)	total: 806ms	remaining: 806ms
4:	learn: 0.2563167	test: 0.2779319	best: 0.2779319 (4)	total: 1.03s	remaining: 619ms
5:	learn: 0.2314248	test: 0.2571290	best: 0.2571290 (5)	total: 1.24s	remaining: 413ms
6:	learn: 0.2137800	test: 0.2425057	best: 0.2425057 (6)	total: 1.44s	remaining: 205ms
7:	learn: 0.2004658	test: 0.2324861	best: 0.2324861 (7)	total: 1.65s	remaining: 0us

bestTest = 0.2324860652
bestIteration = 7

Trial 75, Fold 5: Log loss = 0.23248606523157098, Average precision = 0.9710552546928959, ROC-AUC = 0.9676153317355034, Elapsed Time = 1.773934900000313 seconds
Optimization Progress:  76%|#######6  | 76/100 [2:20:17<1:35:14, 238.09s/it]
Trial 76, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 76, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
0:	learn: 0.6531104	test: 0.6532013	best: 0.6532013 (0)	total: 83.6ms	remaining: 4.59s
1:	learn: 0.6171570	test: 0.6171406	best: 0.6171406 (1)	total: 163ms	remaining: 4.4s
2:	learn: 0.5840155	test: 0.5838715	best: 0.5838715 (2)	total: 244ms	remaining: 4.3s
3:	learn: 0.5586446	test: 0.5587452	best: 0.5587452 (3)	total: 324ms	remaining: 4.21s
4:	learn: 0.5307507	test: 0.5306415	best: 0.5306415 (4)	total: 401ms	remaining: 4.09s
5:	learn: 0.5056393	test: 0.5054170	best: 0.5054170 (5)	total: 481ms	remaining: 4.01s
6:	learn: 0.4830397	test: 0.4828917	best: 0.4828917 (6)	total: 571ms	remaining: 4s
7:	learn: 0.4621968	test: 0.4619300	best: 0.4619300 (7)	total: 651ms	remaining: 3.9s
8:	learn: 0.4451438	test: 0.4448856	best: 0.4448856 (8)	total: 732ms	remaining: 3.82s
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46:	learn: 0.2437376	test: 0.2481237	best: 0.2481237 (46)	total: 4.2s	remaining: 805ms
47:	learn: 0.2425706	test: 0.2469646	best: 0.2469646 (47)	total: 4.3s	remaining: 716ms
48:	learn: 0.2412710	test: 0.2457741	best: 0.2457741 (48)	total: 4.39s	remaining: 627ms
49:	learn: 0.2404419	test: 0.2450671	best: 0.2450671 (49)	total: 4.48s	remaining: 538ms
50:	learn: 0.2396209	test: 0.2443544	best: 0.2443544 (50)	total: 4.57s	remaining: 449ms
51:	learn: 0.2384248	test: 0.2432264	best: 0.2432264 (51)	total: 4.67s	remaining: 359ms
52:	learn: 0.2371256	test: 0.2419730	best: 0.2419730 (52)	total: 4.75s	remaining: 269ms
53:	learn: 0.2363813	test: 0.2412341	best: 0.2412341 (53)	total: 4.84s	remaining: 179ms
54:	learn: 0.2353438	test: 0.2402713	best: 0.2402713 (54)	total: 4.94s	remaining: 89.8ms
55:	learn: 0.2343987	test: 0.2393465	best: 0.2393465 (55)	total: 5.04s	remaining: 0us

bestTest = 0.2393465114
bestIteration = 55

Trial 76, Fold 1: Log loss = 0.23934651142423916, Average precision = 0.969694644871701, ROC-AUC = 0.9642598746313742, Elapsed Time = 5.158317500001431 seconds
Trial 76, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 76, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
0:	learn: 0.6527278	test: 0.6527310	best: 0.6527310 (0)	total: 90ms	remaining: 4.95s
1:	learn: 0.6165312	test: 0.6169178	best: 0.6169178 (1)	total: 180ms	remaining: 4.85s
2:	learn: 0.5838936	test: 0.5842661	best: 0.5842661 (2)	total: 265ms	remaining: 4.69s
3:	learn: 0.5592647	test: 0.5598867	best: 0.5598867 (3)	total: 354ms	remaining: 4.59s
4:	learn: 0.5314428	test: 0.5323539	best: 0.5323539 (4)	total: 438ms	remaining: 4.47s
5:	learn: 0.5069410	test: 0.5079333	best: 0.5079333 (5)	total: 525ms	remaining: 4.37s
6:	learn: 0.4849835	test: 0.4861545	best: 0.4861545 (6)	total: 614ms	remaining: 4.29s
7:	learn: 0.4689742	test: 0.4701814	best: 0.4701814 (7)	total: 699ms	remaining: 4.19s
8:	learn: 0.4525387	test: 0.4535573	best: 0.4535573 (8)	total: 781ms	remaining: 4.08s
9:	learn: 0.4347356	test: 0.4359207	best: 0.4359207 (9)	total: 867ms	remaining: 3.99s
10:	learn: 0.4191025	test: 0.4202875	best: 0.4202875 (10)	total: 950ms	remaining: 3.89s
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47:	learn: 0.2441586	test: 0.2461674	best: 0.2461674 (47)	total: 4.4s	remaining: 733ms
48:	learn: 0.2425827	test: 0.2445981	best: 0.2445981 (48)	total: 4.5s	remaining: 642ms
49:	learn: 0.2412957	test: 0.2433997	best: 0.2433997 (49)	total: 4.59s	remaining: 551ms
50:	learn: 0.2402813	test: 0.2424389	best: 0.2424389 (50)	total: 4.68s	remaining: 459ms
51:	learn: 0.2396413	test: 0.2417546	best: 0.2417546 (51)	total: 4.77s	remaining: 367ms
52:	learn: 0.2383801	test: 0.2404653	best: 0.2404653 (52)	total: 4.86s	remaining: 275ms
53:	learn: 0.2379039	test: 0.2400035	best: 0.2400035 (53)	total: 4.95s	remaining: 183ms
54:	learn: 0.2368026	test: 0.2388992	best: 0.2388992 (54)	total: 5.03s	remaining: 91.5ms
55:	learn: 0.2357659	test: 0.2378747	best: 0.2378747 (55)	total: 5.12s	remaining: 0us

bestTest = 0.2378747225
bestIteration = 55

Trial 76, Fold 2: Log loss = 0.23787472252935388, Average precision = 0.9701072622343252, ROC-AUC = 0.9667991778911973, Elapsed Time = 5.23895039999843 seconds
Trial 76, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 76, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
0:	learn: 0.6534712	test: 0.6533331	best: 0.6533331 (0)	total: 104ms	remaining: 5.7s
1:	learn: 0.6229017	test: 0.6225270	best: 0.6225270 (1)	total: 200ms	remaining: 5.39s
2:	learn: 0.5954637	test: 0.5947158	best: 0.5947158 (2)	total: 295ms	remaining: 5.2s
3:	learn: 0.5662584	test: 0.5652448	best: 0.5652448 (3)	total: 388ms	remaining: 5.04s
4:	learn: 0.5434886	test: 0.5422109	best: 0.5422109 (4)	total: 475ms	remaining: 4.84s
5:	learn: 0.5172492	test: 0.5157733	best: 0.5157733 (5)	total: 565ms	remaining: 4.71s
6:	learn: 0.4994856	test: 0.4978205	best: 0.4978205 (6)	total: 656ms	remaining: 4.59s
7:	learn: 0.4824454	test: 0.4805643	best: 0.4805643 (7)	total: 744ms	remaining: 4.46s
8:	learn: 0.4673186	test: 0.4652066	best: 0.4652066 (8)	total: 832ms	remaining: 4.34s
9:	learn: 0.4496785	test: 0.4474887	best: 0.4474887 (9)	total: 922ms	remaining: 4.24s
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46:	learn: 0.2468392	test: 0.2425216	best: 0.2425216 (46)	total: 4.86s	remaining: 931ms
47:	learn: 0.2457948	test: 0.2414838	best: 0.2414838 (47)	total: 4.95s	remaining: 826ms
48:	learn: 0.2446968	test: 0.2403951	best: 0.2403951 (48)	total: 5.04s	remaining: 720ms
49:	learn: 0.2432976	test: 0.2389691	best: 0.2389691 (49)	total: 5.13s	remaining: 616ms
50:	learn: 0.2418680	test: 0.2375337	best: 0.2375337 (50)	total: 5.22s	remaining: 512ms
51:	learn: 0.2407199	test: 0.2365008	best: 0.2365008 (51)	total: 5.32s	remaining: 409ms
52:	learn: 0.2394790	test: 0.2353198	best: 0.2353198 (52)	total: 5.41s	remaining: 306ms
53:	learn: 0.2381995	test: 0.2340263	best: 0.2340263 (53)	total: 5.49s	remaining: 203ms
54:	learn: 0.2373588	test: 0.2331685	best: 0.2331685 (54)	total: 5.58s	remaining: 102ms
55:	learn: 0.2363980	test: 0.2322473	best: 0.2322473 (55)	total: 5.67s	remaining: 0us

bestTest = 0.232247298
bestIteration = 55

Trial 76, Fold 3: Log loss = 0.2322472979786853, Average precision = 0.9708223233526677, ROC-AUC = 0.9666556260895108, Elapsed Time = 5.81767519999994 seconds
Trial 76, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 76, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
0:	learn: 0.6530051	test: 0.6529084	best: 0.6529084 (0)	total: 89.5ms	remaining: 4.92s
1:	learn: 0.6162401	test: 0.6160806	best: 0.6160806 (1)	total: 177ms	remaining: 4.79s
2:	learn: 0.5838820	test: 0.5837486	best: 0.5837486 (2)	total: 265ms	remaining: 4.68s
3:	learn: 0.5559254	test: 0.5557755	best: 0.5557755 (3)	total: 353ms	remaining: 4.59s
4:	learn: 0.5288952	test: 0.5287341	best: 0.5287341 (4)	total: 438ms	remaining: 4.47s
5:	learn: 0.5045107	test: 0.5043156	best: 0.5043156 (5)	total: 523ms	remaining: 4.36s
6:	learn: 0.4831589	test: 0.4829664	best: 0.4829664 (6)	total: 612ms	remaining: 4.28s
7:	learn: 0.4622842	test: 0.4621265	best: 0.4621265 (7)	total: 698ms	remaining: 4.19s
8:	learn: 0.4454401	test: 0.4452076	best: 0.4452076 (8)	total: 783ms	remaining: 4.09s
9:	learn: 0.4313235	test: 0.4312412	best: 0.4312412 (9)	total: 873ms	remaining: 4.02s
10:	learn: 0.4182152	test: 0.4182931	best: 0.4182931 (10)	total: 963ms	remaining: 3.94s
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48:	learn: 0.2417522	test: 0.2417570	best: 0.2417570 (48)	total: 4.43s	remaining: 633ms
49:	learn: 0.2410197	test: 0.2410956	best: 0.2410956 (49)	total: 4.53s	remaining: 543ms
50:	learn: 0.2395860	test: 0.2396216	best: 0.2396216 (50)	total: 4.62s	remaining: 453ms
51:	learn: 0.2384784	test: 0.2384867	best: 0.2384867 (51)	total: 4.72s	remaining: 363ms
52:	learn: 0.2372873	test: 0.2373924	best: 0.2373924 (52)	total: 4.8s	remaining: 272ms
53:	learn: 0.2362577	test: 0.2363555	best: 0.2363555 (53)	total: 4.89s	remaining: 181ms
54:	learn: 0.2352613	test: 0.2354361	best: 0.2354361 (54)	total: 4.98s	remaining: 90.6ms
55:	learn: 0.2341704	test: 0.2344225	best: 0.2344225 (55)	total: 5.07s	remaining: 0us

bestTest = 0.2344224768
bestIteration = 55

Trial 76, Fold 4: Log loss = 0.2344224767786823, Average precision = 0.9702264133128352, ROC-AUC = 0.9650462664911742, Elapsed Time = 5.200046199999633 seconds
Trial 76, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 76, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
0:	learn: 0.6605860	test: 0.6610183	best: 0.6610183 (0)	total: 89.4ms	remaining: 4.92s
1:	learn: 0.6231751	test: 0.6238760	best: 0.6238760 (1)	total: 175ms	remaining: 4.72s
2:	learn: 0.5952613	test: 0.5966417	best: 0.5966417 (2)	total: 262ms	remaining: 4.63s
3:	learn: 0.5699543	test: 0.5719818	best: 0.5719818 (3)	total: 352ms	remaining: 4.58s
4:	learn: 0.5428247	test: 0.5450093	best: 0.5450093 (4)	total: 440ms	remaining: 4.48s
5:	learn: 0.5164566	test: 0.5188019	best: 0.5188019 (5)	total: 528ms	remaining: 4.4s
6:	learn: 0.4925379	test: 0.4952004	best: 0.4952004 (6)	total: 617ms	remaining: 4.32s
7:	learn: 0.4730110	test: 0.4759275	best: 0.4759275 (7)	total: 702ms	remaining: 4.21s
8:	learn: 0.4545458	test: 0.4576443	best: 0.4576443 (8)	total: 789ms	remaining: 4.12s
9:	learn: 0.4382148	test: 0.4415603	best: 0.4415603 (9)	total: 878ms	remaining: 4.04s
10:	learn: 0.4255626	test: 0.4291900	best: 0.4291900 (10)	total: 967ms	remaining: 3.96s
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55:	learn: 0.2331999	test: 0.2425203	best: 0.2425203 (55)	total: 5.13s	remaining: 0us

bestTest = 0.2425203036
bestIteration = 55

Trial 76, Fold 5: Log loss = 0.24252030364660943, Average precision = 0.9669052919651657, ROC-AUC = 0.9631877821749066, Elapsed Time = 5.259091599997191 seconds
Optimization Progress:  77%|#######7  | 77/100 [2:20:52<1:07:53, 177.11s/it]
Trial 77, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 77, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
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bestTest = 0.2398939532
bestIteration = 10

Shrink model to first 11 iterations.
Training has stopped (degenerate solution on iteration 51, probably too small l2-regularization, try to increase it)
Trial 77, Fold 1: Log loss = 0.23989395324427604, Average precision = 0.9677350413395026, ROC-AUC = 0.9640070791691162, Elapsed Time = 7.274260000001959 seconds
Trial 77, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 77, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
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Training has stopped (degenerate solution on iteration 36, probably too small l2-regularization, try to increase it)
bestTest = 0.2429889915
bestIteration = 10

Shrink model to first 11 iterations.
Trial 77, Fold 2: Log loss = 0.2429889914899703, Average precision = 0.9712504661585221, ROC-AUC = 0.967077786825318, Elapsed Time = 5.351572499999747 seconds
Trial 77, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 77, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
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89:	learn: 9721.6915942	test: 4899.6588979	best: 0.2775164 (7)	total: 12s	remaining: 0us

bestTest = 0.2775164099
bestIteration = 7

Shrink model to first 8 iterations.
Trial 77, Fold 3: Log loss = 0.2775164098647098, Average precision = 0.9677509471789273, ROC-AUC = 0.9655515182122874, Elapsed Time = 12.092096499996842 seconds
Trial 77, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 77, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
0:	learn: 0.5673725	test: 0.5685589	best: 0.5685589 (0)	total: 158ms	remaining: 14.1s
1:	learn: 0.4843944	test: 0.4858121	best: 0.4858121 (1)	total: 303ms	remaining: 13.3s
2:	learn: 0.4281729	test: 0.4298057	best: 0.4298057 (2)	total: 452ms	remaining: 13.1s
3:	learn: 0.3709796	test: 0.3733062	best: 0.3733062 (3)	total: 615ms	remaining: 13.2s
4:	learn: 0.3319446	test: 0.3349015	best: 0.3349015 (4)	total: 755ms	remaining: 12.8s
5:	learn: 0.3045139	test: 0.3079489	best: 0.3079489 (5)	total: 902ms	remaining: 12.6s
6:	learn: 0.2780954	test: 0.2823795	best: 0.2823795 (6)	total: 1.07s	remaining: 12.7s
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41:	learn: 1505747.1478393	test: 1550635.5817794	best: 0.2342114 (10)	total: 5.86s	remaining: 6.7s
Training has stopped (degenerate solution on iteration 42, probably too small l2-regularization, try to increase it)
bestTest = 0.234211359
bestIteration = 10

Shrink model to first 11 iterations.
Trial 77, Fold 4: Log loss = 0.23421135897576462, Average precision = 0.9724300880487378, ROC-AUC = 0.9673531829486088, Elapsed Time = 6.118050200002472 seconds
Trial 77, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 77, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
0:	learn: 0.5648622	test: 0.5668813	best: 0.5668813 (0)	total: 190ms	remaining: 16.9s
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21:	learn: 3932.6627594	test: 6910.6720447	best: 0.2202242 (16)	total: 3.34s	remaining: 10.3s
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28:	learn: 7738.8760008	test: 13359.5700871	best: 0.2202242 (16)	total: 4.41s	remaining: 9.28s

bestTest = 0.2202242128
bestIteration = 16

Shrink model to first 17 iterations.
Training has stopped (degenerate solution on iteration 29, probably too small l2-regularization, try to increase it)
Trial 77, Fold 5: Log loss = 0.22022421278444518, Average precision = 0.970420295649105, ROC-AUC = 0.9660487895509355, Elapsed Time = 4.705272299997887 seconds
Optimization Progress:  78%|#######8  | 78/100 [2:21:36<50:16, 137.12s/it]  
Trial 78, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 78, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
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97:	learn: 0.2372579	test: 0.2521268	best: 0.2521268 (97)	total: 27.1s	remaining: 0us

bestTest = 0.2521268089
bestIteration = 97

Trial 78, Fold 1: Log loss = 0.25212680890439687, Average precision = 0.9735816065453561, ROC-AUC = 0.9701956763835715, Elapsed Time = 27.27744139999777 seconds
Trial 78, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 78, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
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97:	learn: 0.2388192	test: 0.2492520	best: 0.2492520 (97)	total: 25.9s	remaining: 0us

bestTest = 0.2492519866
bestIteration = 97

Trial 78, Fold 2: Log loss = 0.24925198663235323, Average precision = 0.9749105582168802, ROC-AUC = 0.9726450337047008, Elapsed Time = 26.056784600001265 seconds
Trial 78, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 78, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
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bestTest = 0.2459940375
bestIteration = 97

Trial 78, Fold 3: Log loss = 0.24599403752822974, Average precision = 0.9733445425441831, ROC-AUC = 0.9720903138272771, Elapsed Time = 26.411656200001744 seconds
Trial 78, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 78, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
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97:	learn: 0.2385434	test: 0.2496040	best: 0.2496040 (97)	total: 25.7s	remaining: 0us

bestTest = 0.2496040322
bestIteration = 97

Trial 78, Fold 4: Log loss = 0.2496040321948194, Average precision = 0.9748202675363168, ROC-AUC = 0.9709528965809948, Elapsed Time = 25.881493100001535 seconds
Trial 78, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 78, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
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97:	learn: 0.2363540	test: 0.2542366	best: 0.2542366 (97)	total: 28.3s	remaining: 0us

bestTest = 0.2542366469
bestIteration = 97

Trial 78, Fold 5: Log loss = 0.25423664690806513, Average precision = 0.9730233495599783, ROC-AUC = 0.9702950039945749, Elapsed Time = 28.444352600003185 seconds
Optimization Progress:  79%|#######9  | 79/100 [2:24:00<48:46, 139.34s/it]
Trial 79, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 79, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
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23:	learn: 0.4828183	test: 0.4831544	best: 0.4831544 (23)	total: 897ms	remaining: 0us

bestTest = 0.4831543544
bestIteration = 23

Trial 79, Fold 1: Log loss = 0.4831543544418097, Average precision = 0.9595032524219984, ROC-AUC = 0.9549524460742846, Elapsed Time = 1.003319499999634 seconds
Trial 79, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 79, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
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16:	learn: 0.5286465	test: 0.5296278	best: 0.5296278 (16)	total: 679ms	remaining: 280ms
17:	learn: 0.5210325	test: 0.5220354	best: 0.5220354 (17)	total: 719ms	remaining: 240ms
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23:	learn: 0.4810645	test: 0.4821145	best: 0.4821145 (23)	total: 963ms	remaining: 0us

bestTest = 0.4821144775
bestIteration = 23

Trial 79, Fold 2: Log loss = 0.48211447753772685, Average precision = 0.9586106387625188, ROC-AUC = 0.9574851264408072, Elapsed Time = 1.0759388000005856 seconds
Trial 79, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 79, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
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15:	learn: 0.5392196	test: 0.5383264	best: 0.5383264 (15)	total: 627ms	remaining: 314ms
16:	learn: 0.5314669	test: 0.5305302	best: 0.5305302 (16)	total: 666ms	remaining: 274ms
17:	learn: 0.5238884	test: 0.5229044	best: 0.5229044 (17)	total: 706ms	remaining: 235ms
18:	learn: 0.5181006	test: 0.5170288	best: 0.5170288 (18)	total: 746ms	remaining: 196ms
19:	learn: 0.5109003	test: 0.5097603	best: 0.5097603 (19)	total: 786ms	remaining: 157ms
20:	learn: 0.5038761	test: 0.5026678	best: 0.5026678 (20)	total: 824ms	remaining: 118ms
21:	learn: 0.4972045	test: 0.4958972	best: 0.4958972 (21)	total: 863ms	remaining: 78.4ms
22:	learn: 0.4905627	test: 0.4892067	best: 0.4892067 (22)	total: 902ms	remaining: 39.2ms
23:	learn: 0.4840477	test: 0.4826243	best: 0.4826243 (23)	total: 941ms	remaining: 0us

bestTest = 0.4826243037
bestIteration = 23

Trial 79, Fold 3: Log loss = 0.48262430371767545, Average precision = 0.9563391169999513, ROC-AUC = 0.9576537685036262, Elapsed Time = 1.0467338000016753 seconds
Trial 79, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 79, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
0:	learn: 0.6808044	test: 0.6806958	best: 0.6806958 (0)	total: 36.2ms	remaining: 832ms
1:	learn: 0.6689185	test: 0.6688384	best: 0.6688384 (1)	total: 74.2ms	remaining: 816ms
2:	learn: 0.6592496	test: 0.6591650	best: 0.6591650 (2)	total: 115ms	remaining: 808ms
3:	learn: 0.6479851	test: 0.6479253	best: 0.6479253 (3)	total: 154ms	remaining: 769ms
4:	learn: 0.6370696	test: 0.6369886	best: 0.6369886 (4)	total: 192ms	remaining: 731ms
5:	learn: 0.6285412	test: 0.6284022	best: 0.6284022 (5)	total: 231ms	remaining: 692ms
6:	learn: 0.6182118	test: 0.6180816	best: 0.6180816 (6)	total: 268ms	remaining: 650ms
7:	learn: 0.6082648	test: 0.6081088	best: 0.6081088 (7)	total: 306ms	remaining: 613ms
8:	learn: 0.6004576	test: 0.6002422	best: 0.6002422 (8)	total: 344ms	remaining: 573ms
9:	learn: 0.5914173	test: 0.5911966	best: 0.5911966 (9)	total: 382ms	remaining: 534ms
10:	learn: 0.5821085	test: 0.5818981	best: 0.5818981 (10)	total: 419ms	remaining: 495ms
11:	learn: 0.5739695	test: 0.5737452	best: 0.5737452 (11)	total: 456ms	remaining: 456ms
12:	learn: 0.5650316	test: 0.5647915	best: 0.5647915 (12)	total: 496ms	remaining: 420ms
13:	learn: 0.5564704	test: 0.5562435	best: 0.5562435 (13)	total: 536ms	remaining: 383ms
14:	learn: 0.5485246	test: 0.5483240	best: 0.5483240 (14)	total: 575ms	remaining: 345ms
15:	learn: 0.5404097	test: 0.5402036	best: 0.5402036 (15)	total: 614ms	remaining: 307ms
16:	learn: 0.5342874	test: 0.5340219	best: 0.5340219 (16)	total: 653ms	remaining: 269ms
17:	learn: 0.5265623	test: 0.5262619	best: 0.5262619 (17)	total: 692ms	remaining: 231ms
18:	learn: 0.5195440	test: 0.5192442	best: 0.5192442 (18)	total: 731ms	remaining: 192ms
19:	learn: 0.5122576	test: 0.5119674	best: 0.5119674 (19)	total: 770ms	remaining: 154ms
20:	learn: 0.5052272	test: 0.5049095	best: 0.5049095 (20)	total: 808ms	remaining: 115ms
21:	learn: 0.4985568	test: 0.4983171	best: 0.4983171 (21)	total: 846ms	remaining: 76.9ms
22:	learn: 0.4933021	test: 0.4930400	best: 0.4930400 (22)	total: 884ms	remaining: 38.4ms
23:	learn: 0.4869741	test: 0.4867494	best: 0.4867494 (23)	total: 921ms	remaining: 0us

bestTest = 0.4867493757
bestIteration = 23

Trial 79, Fold 4: Log loss = 0.48674937572540555, Average precision = 0.9639261039203338, ROC-AUC = 0.9609027584044484, Elapsed Time = 1.0273558999979286 seconds
Trial 79, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 79, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
0:	learn: 0.6806743	test: 0.6809337	best: 0.6809337 (0)	total: 37.4ms	remaining: 861ms
1:	learn: 0.6688381	test: 0.6692686	best: 0.6692686 (1)	total: 74.6ms	remaining: 820ms
2:	learn: 0.6592730	test: 0.6599742	best: 0.6599742 (2)	total: 112ms	remaining: 785ms
3:	learn: 0.6478407	test: 0.6486789	best: 0.6486789 (3)	total: 151ms	remaining: 754ms
4:	learn: 0.6374438	test: 0.6383525	best: 0.6383525 (4)	total: 192ms	remaining: 728ms
5:	learn: 0.6267919	test: 0.6278068	best: 0.6278068 (5)	total: 233ms	remaining: 700ms
6:	learn: 0.6163181	test: 0.6175326	best: 0.6175326 (6)	total: 278ms	remaining: 676ms
7:	learn: 0.6061708	test: 0.6074680	best: 0.6074680 (7)	total: 319ms	remaining: 638ms
8:	learn: 0.5969601	test: 0.5983699	best: 0.5983699 (8)	total: 357ms	remaining: 596ms
9:	learn: 0.5874465	test: 0.5889329	best: 0.5889329 (9)	total: 397ms	remaining: 555ms
10:	learn: 0.5786995	test: 0.5803007	best: 0.5803007 (10)	total: 436ms	remaining: 515ms
11:	learn: 0.5696905	test: 0.5713550	best: 0.5713550 (11)	total: 476ms	remaining: 476ms
12:	learn: 0.5609250	test: 0.5626557	best: 0.5626557 (12)	total: 515ms	remaining: 436ms
13:	learn: 0.5529244	test: 0.5546776	best: 0.5546776 (13)	total: 557ms	remaining: 398ms
14:	learn: 0.5448550	test: 0.5466922	best: 0.5466922 (14)	total: 596ms	remaining: 358ms
15:	learn: 0.5368933	test: 0.5387851	best: 0.5387851 (15)	total: 635ms	remaining: 317ms
16:	learn: 0.5290210	test: 0.5310254	best: 0.5310254 (16)	total: 674ms	remaining: 278ms
17:	learn: 0.5221144	test: 0.5241632	best: 0.5241632 (17)	total: 713ms	remaining: 238ms
18:	learn: 0.5147030	test: 0.5168626	best: 0.5168626 (18)	total: 752ms	remaining: 198ms
19:	learn: 0.5074022	test: 0.5096849	best: 0.5096849 (19)	total: 792ms	remaining: 158ms
20:	learn: 0.5003742	test: 0.5027194	best: 0.5027194 (20)	total: 830ms	remaining: 119ms
21:	learn: 0.4941771	test: 0.4966091	best: 0.4966091 (21)	total: 869ms	remaining: 79ms
22:	learn: 0.4875385	test: 0.4900336	best: 0.4900336 (22)	total: 908ms	remaining: 39.5ms
23:	learn: 0.4810223	test: 0.4836139	best: 0.4836139 (23)	total: 947ms	remaining: 0us

bestTest = 0.4836138542
bestIteration = 23

Trial 79, Fold 5: Log loss = 0.483613854201858, Average precision = 0.9560993198853893, ROC-AUC = 0.9541198372443007, Elapsed Time = 1.0542547999939416 seconds
Optimization Progress:  80%|########  | 80/100 [2:24:14<33:49, 101.49s/it]
Trial 80, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 80, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
0:	learn: 0.5529500	test: 0.5528939	best: 0.5528939 (0)	total: 101ms	remaining: 8.97s
1:	learn: 0.4472210	test: 0.4489717	best: 0.4489717 (1)	total: 206ms	remaining: 9.07s
2:	learn: 0.3876643	test: 0.3908444	best: 0.3908444 (2)	total: 309ms	remaining: 8.96s
3:	learn: 0.3348323	test: 0.3406929	best: 0.3406929 (3)	total: 410ms	remaining: 8.81s
4:	learn: 0.3059041	test: 0.3125768	best: 0.3125768 (4)	total: 500ms	remaining: 8.5s
5:	learn: 0.2787688	test: 0.2871093	best: 0.2871093 (5)	total: 602ms	remaining: 8.42s
6:	learn: 0.2621827	test: 0.2721093	best: 0.2721093 (6)	total: 700ms	remaining: 8.3s
7:	learn: 0.2448374	test: 0.2557445	best: 0.2557445 (7)	total: 804ms	remaining: 8.24s
8:	learn: 4.7553184	test: 0.2443686	best: 0.2443686 (8)	total: 908ms	remaining: 8.17s
9:	learn: 4.7454163	test: 0.2357062	best: 0.2357062 (9)	total: 1.01s	remaining: 8.05s
10:	learn: 4.7375817	test: 0.2295680	best: 0.2295680 (10)	total: 1.11s	remaining: 7.96s
11:	learn: 4.7302209	test: 0.2236569	best: 0.2236569 (11)	total: 1.21s	remaining: 7.88s
12:	learn: 4.7242175	test: 0.2196267	best: 0.2196267 (12)	total: 1.31s	remaining: 7.78s
13:	learn: 6.0765476	test: 2.0228969	best: 0.2196267 (12)	total: 1.41s	remaining: 7.67s
14:	learn: 6.0712761	test: 2.0186821	best: 0.2196267 (12)	total: 1.52s	remaining: 7.61s
15:	learn: 6.0681860	test: 2.0164181	best: 0.2196267 (12)	total: 1.61s	remaining: 7.46s
16:	learn: 8.4483231	test: 5.8221667	best: 0.2196267 (12)	total: 1.71s	remaining: 7.35s
Training has stopped (degenerate solution on iteration 17, probably too small l2-regularization, try to increase it)
bestTest = 0.2196266537
bestIteration = 12

Shrink model to first 13 iterations.
Trial 80, Fold 1: Log loss = 0.21962665373815726, Average precision = 0.9736935458727383, ROC-AUC = 0.969040882814031, Elapsed Time = 1.926980200005346 seconds
Trial 80, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 80, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
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1:	learn: 0.4394560	test: 0.4437305	best: 0.4437305 (1)	total: 208ms	remaining: 9.17s
2:	learn: 0.3768750	test: 0.3826198	best: 0.3826198 (2)	total: 319ms	remaining: 9.25s
3:	learn: 0.3276336	test: 0.3341281	best: 0.3341281 (3)	total: 422ms	remaining: 9.07s
4:	learn: 0.2946404	test: 0.3024942	best: 0.3024942 (4)	total: 527ms	remaining: 8.95s
5:	learn: 0.2700044	test: 0.2786865	best: 0.2786865 (5)	total: 635ms	remaining: 8.89s
6:	learn: 0.2475294	test: 0.2572929	best: 0.2572929 (6)	total: 744ms	remaining: 8.82s
7:	learn: 0.2348978	test: 0.2453161	best: 0.2453161 (7)	total: 843ms	remaining: 8.64s
8:	learn: 0.2217459	test: 0.2332528	best: 0.2332528 (8)	total: 943ms	remaining: 8.49s
9:	learn: 0.2123231	test: 0.2244717	best: 0.2244717 (9)	total: 1.06s	remaining: 8.47s
10:	learn: 0.2043987	test: 0.2172099	best: 0.2172099 (10)	total: 1.16s	remaining: 8.32s
11:	learn: 0.1990532	test: 0.2125029	best: 0.2125029 (11)	total: 1.26s	remaining: 8.2s
Training has stopped (degenerate solution on iteration 12, probably too small l2-regularization, try to increase it)
bestTest = 0.2125029044
bestIteration = 11

Shrink model to first 12 iterations.
Trial 80, Fold 2: Log loss = 0.2125029043547971, Average precision = 0.9746115179581751, ROC-AUC = 0.9721019283805082, Elapsed Time = 1.4735628999987966 seconds
Trial 80, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 80, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
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2:	learn: 0.3722028	test: 0.3757026	best: 0.3757026 (2)	total: 318ms	remaining: 9.24s
3:	learn: 0.3290222	test: 0.3326867	best: 0.3326867 (3)	total: 418ms	remaining: 8.99s
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89:	learn: 56.4240339	test: 0.1881168	best: 0.1878119 (69)	total: 7.99s	remaining: 0us

bestTest = 0.1878119485
bestIteration = 69

Shrink model to first 70 iterations.
Trial 80, Fold 3: Log loss = 0.18781194846724483, Average precision = 0.9755745016444857, ROC-AUC = 0.9733321154086929, Elapsed Time = 8.1260306999975 seconds
Trial 80, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 80, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
0:	learn: 0.5538821	test: 0.5551782	best: 0.5551782 (0)	total: 109ms	remaining: 9.73s
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2:	learn: 0.3811483	test: 0.3857977	best: 0.3857977 (2)	total: 319ms	remaining: 9.25s
3:	learn: 0.3317547	test: 0.3376816	best: 0.3376816 (3)	total: 420ms	remaining: 9.03s
4:	learn: 0.2919761	test: 0.2983985	best: 0.2983985 (4)	total: 523ms	remaining: 8.88s
5:	learn: 0.2702518	test: 0.2784221	best: 0.2784221 (5)	total: 637ms	remaining: 8.92s
6:	learn: 0.2520465	test: 0.2612995	best: 0.2612995 (6)	total: 745ms	remaining: 8.83s
7:	learn: 0.2364687	test: 0.2466696	best: 0.2466696 (7)	total: 849ms	remaining: 8.7s
8:	learn: 0.2263920	test: 0.2373249	best: 0.2373249 (8)	total: 946ms	remaining: 8.51s
9:	learn: 0.2144470	test: 0.2267600	best: 0.2267600 (9)	total: 1.05s	remaining: 8.39s
10:	learn: 0.2065679	test: 0.2206621	best: 0.2206621 (10)	total: 1.16s	remaining: 8.32s
11:	learn: 0.1992731	test: 0.2138082	best: 0.2138082 (11)	total: 1.26s	remaining: 8.18s
12:	learn: 0.1942910	test: 0.2098400	best: 0.2098400 (12)	total: 1.36s	remaining: 8.08s
13:	learn: 11.0677119	test: 0.2062640	best: 0.2062640 (13)	total: 1.46s	remaining: 7.95s
Training has stopped (degenerate solution on iteration 14, probably too small l2-regularization, try to increase it)
bestTest = 0.2062639631
bestIteration = 13

Shrink model to first 14 iterations.
Trial 80, Fold 4: Log loss = 0.20626396314467765, Average precision = 0.97516142641915, ROC-AUC = 0.971032848821267, Elapsed Time = 1.6831536000026972 seconds
Trial 80, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 80, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
0:	learn: 0.5423717	test: 0.5460791	best: 0.5460791 (0)	total: 111ms	remaining: 9.88s
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2:	learn: 0.3800993	test: 0.3881039	best: 0.3881039 (2)	total: 325ms	remaining: 9.43s
3:	learn: 0.3312859	test: 0.3409895	best: 0.3409895 (3)	total: 427ms	remaining: 9.19s
4:	learn: 0.2960988	test: 0.3069963	best: 0.3069963 (4)	total: 530ms	remaining: 9s
5:	learn: 0.2716820	test: 0.2848152	best: 0.2848152 (5)	total: 639ms	remaining: 8.95s
6:	learn: 0.2515691	test: 0.2668433	best: 0.2668433 (6)	total: 744ms	remaining: 8.82s
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89:	learn: 63.7875184	test: 67.1367441	best: 0.2069018 (19)	total: 9.25s	remaining: 0us

bestTest = 0.2069018419
bestIteration = 19

Shrink model to first 20 iterations.
Trial 80, Fold 5: Log loss = 0.20690184187035557, Average precision = 0.9726087518814819, ROC-AUC = 0.9694474852758115, Elapsed Time = 9.387293200001295 seconds
Optimization Progress:  81%|########1 | 81/100 [2:24:44<25:24, 80.22s/it] 
Trial 81, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 81, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
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12:	learn: 0.3442676	test: 0.3695106	best: 0.3695106 (12)	total: 24.6s	remaining: 3.78s
13:	learn: 0.3304700	test: 0.3579138	best: 0.3579138 (13)	total: 26.6s	remaining: 1.9s
14:	learn: 0.3181491	test: 0.3464089	best: 0.3464089 (14)	total: 28.6s	remaining: 0us

bestTest = 0.3464088985
bestIteration = 14

Trial 81, Fold 1: Log loss = 0.34652195021508936, Average precision = 0.9723340245495337, ROC-AUC = 0.9666713267661777, Elapsed Time = 28.784268800001882 seconds
Trial 81, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 81, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
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7:	learn: 0.4355824	test: 0.4488519	best: 0.4488519 (7)	total: 15.8s	remaining: 13.8s
8:	learn: 0.4149601	test: 0.4296197	best: 0.4296197 (8)	total: 17.6s	remaining: 11.8s
9:	learn: 0.3961978	test: 0.4122041	best: 0.4122041 (9)	total: 19.4s	remaining: 9.7s
10:	learn: 0.3789618	test: 0.3964055	best: 0.3964055 (10)	total: 21.1s	remaining: 7.69s
11:	learn: 0.3623884	test: 0.3818805	best: 0.3818805 (11)	total: 23.2s	remaining: 5.79s
12:	learn: 0.3480172	test: 0.3683754	best: 0.3683754 (12)	total: 25.2s	remaining: 3.88s
13:	learn: 0.3343903	test: 0.3562661	best: 0.3562661 (13)	total: 27.5s	remaining: 1.97s
14:	learn: 0.3221214	test: 0.3452412	best: 0.3452412 (14)	total: 30.1s	remaining: 0us

bestTest = 0.3452411566
bestIteration = 14

Trial 81, Fold 2: Log loss = 0.34531134344144665, Average precision = 0.9727726788725815, ROC-AUC = 0.9692984639178668, Elapsed Time = 30.26727309999842 seconds
Trial 81, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 81, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
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13:	learn: 0.3356987	test: 0.3571446	best: 0.3571446 (13)	total: 24.2s	remaining: 1.73s
14:	learn: 0.3230783	test: 0.3459297	best: 0.3459297 (14)	total: 25.8s	remaining: 0us

bestTest = 0.3459297189
bestIteration = 14

Trial 81, Fold 3: Log loss = 0.3461829771105294, Average precision = 0.9726650811982974, ROC-AUC = 0.9686962016401652, Elapsed Time = 26.005644000004395 seconds
Trial 81, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 81, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
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6:	learn: 0.4581449	test: 0.4711481	best: 0.4711481 (6)	total: 9.55s	remaining: 10.9s
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8:	learn: 0.4142076	test: 0.4303301	best: 0.4303301 (8)	total: 12.8s	remaining: 8.53s
9:	learn: 0.3954101	test: 0.4122559	best: 0.4122559 (9)	total: 14.5s	remaining: 7.25s
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11:	learn: 0.3621336	test: 0.3815036	best: 0.3815036 (11)	total: 17.9s	remaining: 4.47s
12:	learn: 0.3467396	test: 0.3683085	best: 0.3683085 (12)	total: 19.6s	remaining: 3.01s
13:	learn: 0.3335605	test: 0.3560212	best: 0.3560212 (13)	total: 21.2s	remaining: 1.51s
14:	learn: 0.3212953	test: 0.3451804	best: 0.3451804 (14)	total: 22.9s	remaining: 0us

bestTest = 0.3451803561
bestIteration = 14

Trial 81, Fold 4: Log loss = 0.34528470439268066, Average precision = 0.9725478250183603, ROC-AUC = 0.9676028753597479, Elapsed Time = 23.048615799998515 seconds
Trial 81, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 81, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
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4:	learn: 0.5077914	test: 0.5219324	best: 0.5219324 (4)	total: 7.96s	remaining: 15.9s
5:	learn: 0.4806945	test: 0.4965350	best: 0.4965350 (5)	total: 9.59s	remaining: 14.4s
6:	learn: 0.4556132	test: 0.4732812	best: 0.4732812 (6)	total: 11.3s	remaining: 12.9s
7:	learn: 0.4330831	test: 0.4528873	best: 0.4528873 (7)	total: 12.9s	remaining: 11.3s
8:	learn: 0.4123145	test: 0.4338871	best: 0.4338871 (8)	total: 14.5s	remaining: 9.68s
9:	learn: 0.3931433	test: 0.4160274	best: 0.4160274 (9)	total: 16.2s	remaining: 8.11s
10:	learn: 0.3758558	test: 0.4001885	best: 0.4001885 (10)	total: 18s	remaining: 6.53s
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12:	learn: 0.3454401	test: 0.3728685	best: 0.3728685 (12)	total: 21.4s	remaining: 3.29s
13:	learn: 0.3317022	test: 0.3614192	best: 0.3614192 (13)	total: 23s	remaining: 1.65s
14:	learn: 0.3195137	test: 0.3502241	best: 0.3502241 (14)	total: 24.7s	remaining: 0us

bestTest = 0.3502241195
bestIteration = 14

Trial 81, Fold 5: Log loss = 0.35025012848988823, Average precision = 0.9698996793264779, ROC-AUC = 0.9654267506456349, Elapsed Time = 24.826989799999865 seconds
Optimization Progress:  82%|########2 | 82/100 [2:27:05<29:33, 98.51s/it]
Trial 82, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 82, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
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64:	learn: 0.2854325	test: 0.3005119	best: 0.3005119 (64)	total: 11.7s	remaining: 1.43s
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72:	learn: 0.2694383	test: 0.2860770	best: 0.2860770 (72)	total: 13s	remaining: 0us

bestTest = 0.2860769624
bestIteration = 72

Trial 82, Fold 1: Log loss = 0.28607187175267457, Average precision = 0.9733830444439191, ROC-AUC = 0.9695839636416645, Elapsed Time = 13.178475799999433 seconds
Trial 82, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 82, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
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63:	learn: 0.2883738	test: 0.2991267	best: 0.2991267 (63)	total: 11s	remaining: 1.55s
64:	learn: 0.2861251	test: 0.2970165	best: 0.2970165 (64)	total: 11.2s	remaining: 1.38s
65:	learn: 0.2837496	test: 0.2947313	best: 0.2947313 (65)	total: 11.4s	remaining: 1.21s
66:	learn: 0.2816101	test: 0.2926883	best: 0.2926883 (66)	total: 11.5s	remaining: 1.03s
67:	learn: 0.2795636	test: 0.2907648	best: 0.2907648 (67)	total: 11.7s	remaining: 861ms
68:	learn: 0.2776174	test: 0.2889710	best: 0.2889710 (68)	total: 11.9s	remaining: 690ms
69:	learn: 0.2757547	test: 0.2871858	best: 0.2871858 (69)	total: 12.1s	remaining: 517ms
70:	learn: 0.2738764	test: 0.2854262	best: 0.2854262 (70)	total: 12.3s	remaining: 345ms
71:	learn: 0.2720170	test: 0.2836972	best: 0.2836972 (71)	total: 12.4s	remaining: 173ms
72:	learn: 0.2702694	test: 0.2820841	best: 0.2820841 (72)	total: 12.6s	remaining: 0us

bestTest = 0.2820841335
bestIteration = 72

Trial 82, Fold 2: Log loss = 0.28204453817430003, Average precision = 0.9761361480874985, ROC-AUC = 0.9731854834426472, Elapsed Time = 12.764068100004806 seconds
Trial 82, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 82, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
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60:	learn: 0.2954674	test: 0.3033861	best: 0.3033861 (60)	total: 10.3s	remaining: 2.03s
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62:	learn: 0.2908309	test: 0.2990322	best: 0.2990322 (62)	total: 10.7s	remaining: 1.69s
63:	learn: 0.2886761	test: 0.2969864	best: 0.2969864 (63)	total: 10.8s	remaining: 1.52s
64:	learn: 0.2864962	test: 0.2948855	best: 0.2948855 (64)	total: 11s	remaining: 1.36s
65:	learn: 0.2843459	test: 0.2928905	best: 0.2928905 (65)	total: 11.2s	remaining: 1.19s
66:	learn: 0.2823076	test: 0.2909830	best: 0.2909830 (66)	total: 11.5s	remaining: 1.02s
67:	learn: 0.2802192	test: 0.2889893	best: 0.2889893 (67)	total: 11.7s	remaining: 858ms
68:	learn: 0.2783028	test: 0.2871605	best: 0.2871605 (68)	total: 11.9s	remaining: 688ms
69:	learn: 0.2763354	test: 0.2853150	best: 0.2853150 (69)	total: 12.2s	remaining: 524ms
70:	learn: 0.2743284	test: 0.2834205	best: 0.2834205 (70)	total: 12.4s	remaining: 351ms
71:	learn: 0.2722057	test: 0.2814113	best: 0.2814113 (71)	total: 12.7s	remaining: 176ms
72:	learn: 0.2702409	test: 0.2795353	best: 0.2795353 (72)	total: 12.9s	remaining: 0us

bestTest = 0.279535281
bestIteration = 72

Trial 82, Fold 3: Log loss = 0.2796964981801753, Average precision = 0.9760290100970991, ROC-AUC = 0.97292445175333, Elapsed Time = 13.040741799995885 seconds
Trial 82, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 82, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
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2:	learn: 0.6525691	test: 0.6531326	best: 0.6531326 (2)	total: 632ms	remaining: 14.7s
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63:	learn: 0.2876371	test: 0.2986937	best: 0.2986937 (63)	total: 11.9s	remaining: 1.68s
64:	learn: 0.2853937	test: 0.2965709	best: 0.2965709 (64)	total: 12.1s	remaining: 1.49s
65:	learn: 0.2832035	test: 0.2946093	best: 0.2946093 (65)	total: 12.3s	remaining: 1.3s
66:	learn: 0.2809842	test: 0.2924394	best: 0.2924394 (66)	total: 12.4s	remaining: 1.11s
67:	learn: 0.2789306	test: 0.2905178	best: 0.2905178 (67)	total: 12.6s	remaining: 928ms
68:	learn: 0.2768288	test: 0.2884988	best: 0.2884988 (68)	total: 12.8s	remaining: 740ms
69:	learn: 0.2749537	test: 0.2867382	best: 0.2867382 (69)	total: 12.9s	remaining: 554ms
70:	learn: 0.2730189	test: 0.2848890	best: 0.2848890 (70)	total: 13.1s	remaining: 369ms
71:	learn: 0.2712314	test: 0.2832119	best: 0.2832119 (71)	total: 13.3s	remaining: 185ms
72:	learn: 0.2693663	test: 0.2815881	best: 0.2815881 (72)	total: 13.5s	remaining: 0us

bestTest = 0.2815881418
bestIteration = 72

Trial 82, Fold 4: Log loss = 0.28160085343236496, Average precision = 0.9745131789422415, ROC-AUC = 0.9722249046832191, Elapsed Time = 13.665348000002268 seconds
Trial 82, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 82, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
0:	learn: 0.6793029	test: 0.6796546	best: 0.6796546 (0)	total: 150ms	remaining: 10.8s
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2:	learn: 0.6524875	test: 0.6535071	best: 0.6535071 (2)	total: 451ms	remaining: 10.5s
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72:	learn: 0.2675926	test: 0.2861980	best: 0.2861980 (72)	total: 12s	remaining: 0us

bestTest = 0.2861979562
bestIteration = 72

Trial 82, Fold 5: Log loss = 0.2860766131001255, Average precision = 0.973495634018563, ROC-AUC = 0.9706371625513256, Elapsed Time = 12.147943600000872 seconds
Optimization Progress:  83%|########2 | 83/100 [2:28:18<25:44, 90.87s/it]
Trial 83, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 83, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
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4:	learn: 0.6080865	test: 0.6083350	best: 0.6083350 (4)	total: 743ms	remaining: 0us

bestTest = 0.6083349893
bestIteration = 4

Trial 83, Fold 1: Log loss = 0.6085548097717014, Average precision = 0.9677374077073312, ROC-AUC = 0.9623888148052555, Elapsed Time = 0.855243199999677 seconds
Trial 83, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 83, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
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4:	learn: 0.6060927	test: 0.6068532	best: 0.6068532 (4)	total: 639ms	remaining: 0us

bestTest = 0.6068531554
bestIteration = 4

Trial 83, Fold 2: Log loss = 0.6069581930502699, Average precision = 0.9667052353253812, ROC-AUC = 0.9635686951602471, Elapsed Time = 0.7514337999964482 seconds
Trial 83, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 83, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
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3:	learn: 0.6248566	test: 0.6245883	best: 0.6245883 (3)	total: 545ms	remaining: 136ms
4:	learn: 0.6089148	test: 0.6084059	best: 0.6084059 (4)	total: 684ms	remaining: 0us

bestTest = 0.6084059102
bestIteration = 4

Trial 83, Fold 3: Log loss = 0.6085853531793376, Average precision = 0.9679774228780744, ROC-AUC = 0.9647212928942221, Elapsed Time = 0.795822699998098 seconds
Trial 83, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 83, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
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3:	learn: 0.6192175	test: 0.6193365	best: 0.6193365 (3)	total: 589ms	remaining: 147ms
4:	learn: 0.6042852	test: 0.6046300	best: 0.6046300 (4)	total: 744ms	remaining: 0us

bestTest = 0.6046299578
bestIteration = 4

Trial 83, Fold 4: Log loss = 0.6048031026637479, Average precision = 0.9675249858750178, ROC-AUC = 0.9630636297631566, Elapsed Time = 0.8545415999979014 seconds
Trial 83, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 83, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
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3:	learn: 0.6280163	test: 0.6291749	best: 0.6291749 (3)	total: 521ms	remaining: 130ms
4:	learn: 0.6107263	test: 0.6120034	best: 0.6120034 (4)	total: 692ms	remaining: 0us

bestTest = 0.6120033708
bestIteration = 4

Trial 83, Fold 5: Log loss = 0.6121398935774273, Average precision = 0.9638235104347864, ROC-AUC = 0.9585350500715307, Elapsed Time = 0.8031102000022656 seconds
Optimization Progress:  84%|########4 | 84/100 [2:28:30<17:55, 67.19s/it]
Trial 84, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 84, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
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64:	learn: 0.0996886	test: 0.2045019	best: 0.2045019 (64)	total: 2m 13s	remaining: 28.7s
65:	learn: 0.0987240	test: 0.2039679	best: 0.2039679 (65)	total: 2m 15s	remaining: 26.7s
66:	learn: 0.0978308	test: 0.2038873	best: 0.2038873 (66)	total: 2m 17s	remaining: 24.6s
67:	learn: 0.0960536	test: 0.2039998	best: 0.2038873 (66)	total: 2m 19s	remaining: 22.6s
68:	learn: 0.0945890	test: 0.2040756	best: 0.2038873 (66)	total: 2m 21s	remaining: 20.6s
69:	learn: 0.0937330	test: 0.2038856	best: 0.2038856 (69)	total: 2m 23s	remaining: 18.5s
70:	learn: 0.0929279	test: 0.2040413	best: 0.2038856 (69)	total: 2m 25s	remaining: 16.4s
71:	learn: 0.0920982	test: 0.2041623	best: 0.2038856 (69)	total: 2m 28s	remaining: 14.4s
72:	learn: 0.0913867	test: 0.2036681	best: 0.2036681 (72)	total: 2m 30s	remaining: 12.3s
73:	learn: 0.0899584	test: 0.2037983	best: 0.2036681 (72)	total: 2m 32s	remaining: 10.3s
74:	learn: 0.0892696	test: 0.2038845	best: 0.2036681 (72)	total: 2m 34s	remaining: 8.22s
75:	learn: 0.0879513	test: 0.2040471	best: 0.2036681 (72)	total: 2m 36s	remaining: 6.17s
76:	learn: 0.0860195	test: 0.2041985	best: 0.2036681 (72)	total: 2m 38s	remaining: 4.11s
77:	learn: 0.0848201	test: 0.2043522	best: 0.2036681 (72)	total: 2m 40s	remaining: 2.06s
78:	learn: 0.0837079	test: 0.2041704	best: 0.2036681 (72)	total: 2m 42s	remaining: 0us

bestTest = 0.2036680954
bestIteration = 72

Shrink model to first 73 iterations.
Trial 84, Fold 1: Log loss = 0.2036680954383175, Average precision = 0.9731174438057646, ROC-AUC = 0.9678373224980046, Elapsed Time = 162.88111989999743 seconds
Trial 84, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 84, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
0:	learn: 0.6335797	test: 0.6368632	best: 0.6368632 (0)	total: 1.83s	remaining: 2m 22s
1:	learn: 0.5809681	test: 0.5869622	best: 0.5869622 (1)	total: 3.82s	remaining: 2m 27s
2:	learn: 0.5343481	test: 0.5429518	best: 0.5429518 (2)	total: 5.88s	remaining: 2m 29s
3:	learn: 0.4938466	test: 0.5050078	best: 0.5050078 (3)	total: 7.82s	remaining: 2m 26s
4:	learn: 0.4540233	test: 0.4696324	best: 0.4696324 (4)	total: 9.85s	remaining: 2m 25s
5:	learn: 0.4234748	test: 0.4412902	best: 0.4412902 (5)	total: 12s	remaining: 2m 25s
6:	learn: 0.3938232	test: 0.4130453	best: 0.4130453 (6)	total: 14.1s	remaining: 2m 24s
7:	learn: 0.3697086	test: 0.3903493	best: 0.3903493 (7)	total: 16.1s	remaining: 2m 23s
8:	learn: 0.3498581	test: 0.3720554	best: 0.3720554 (8)	total: 18.2s	remaining: 2m 21s
9:	learn: 0.3288462	test: 0.3538594	best: 0.3538594 (9)	total: 20.3s	remaining: 2m 20s
10:	learn: 0.3102425	test: 0.3374088	best: 0.3374088 (10)	total: 22.4s	remaining: 2m 18s
11:	learn: 0.2945745	test: 0.3230983	best: 0.3230983 (11)	total: 24.4s	remaining: 2m 16s
12:	learn: 0.2800103	test: 0.3099994	best: 0.3099994 (12)	total: 26.3s	remaining: 2m 13s
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60:	learn: 0.0999223	test: 0.2024500	best: 0.2024500 (60)	total: 2m 5s	remaining: 37s
61:	learn: 0.0989392	test: 0.2024379	best: 0.2024379 (61)	total: 2m 7s	remaining: 34.9s
62:	learn: 0.0972842	test: 0.2023262	best: 0.2023262 (62)	total: 2m 9s	remaining: 32.8s
63:	learn: 0.0964389	test: 0.2022893	best: 0.2022893 (63)	total: 2m 11s	remaining: 30.8s
64:	learn: 0.0950705	test: 0.2025118	best: 0.2022893 (63)	total: 2m 13s	remaining: 28.7s
65:	learn: 0.0936718	test: 0.2023874	best: 0.2022893 (63)	total: 2m 15s	remaining: 26.7s
66:	learn: 0.0932789	test: 0.2021917	best: 0.2021917 (66)	total: 2m 17s	remaining: 24.6s
67:	learn: 0.0923367	test: 0.2014782	best: 0.2014782 (67)	total: 2m 19s	remaining: 22.6s
68:	learn: 0.0908682	test: 0.2009574	best: 0.2009574 (68)	total: 2m 21s	remaining: 20.5s
69:	learn: 0.0904143	test: 0.2008391	best: 0.2008391 (69)	total: 2m 23s	remaining: 18.5s
70:	learn: 0.0900280	test: 0.2007522	best: 0.2007522 (70)	total: 2m 25s	remaining: 16.4s
71:	learn: 0.0889042	test: 0.2006465	best: 0.2006465 (71)	total: 2m 27s	remaining: 14.3s
72:	learn: 0.0883537	test: 0.2001193	best: 0.2001193 (72)	total: 2m 29s	remaining: 12.3s
73:	learn: 0.0878648	test: 0.1999491	best: 0.1999491 (73)	total: 2m 31s	remaining: 10.2s
74:	learn: 0.0867566	test: 0.1999300	best: 0.1999300 (74)	total: 2m 33s	remaining: 8.21s
75:	learn: 0.0858405	test: 0.1998709	best: 0.1998709 (75)	total: 2m 35s	remaining: 6.16s
76:	learn: 0.0852366	test: 0.1997926	best: 0.1997926 (76)	total: 2m 38s	remaining: 4.11s
77:	learn: 0.0843234	test: 0.1997731	best: 0.1997731 (77)	total: 2m 40s	remaining: 2.05s
78:	learn: 0.0829058	test: 0.1999155	best: 0.1997731 (77)	total: 2m 42s	remaining: 0us

bestTest = 0.1997731316
bestIteration = 77

Shrink model to first 78 iterations.
Trial 84, Fold 2: Log loss = 0.19977313163179264, Average precision = 0.9717598719261711, ROC-AUC = 0.9678683169137171, Elapsed Time = 162.48672610000358 seconds
Trial 84, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 84, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
0:	learn: 0.6411888	test: 0.6435786	best: 0.6435786 (0)	total: 1.87s	remaining: 2m 25s
1:	learn: 0.5862412	test: 0.5921817	best: 0.5921817 (1)	total: 3.84s	remaining: 2m 27s
2:	learn: 0.5412774	test: 0.5499072	best: 0.5499072 (2)	total: 5.81s	remaining: 2m 27s
3:	learn: 0.4991514	test: 0.5096556	best: 0.5096556 (3)	total: 7.86s	remaining: 2m 27s
4:	learn: 0.4614866	test: 0.4741943	best: 0.4741943 (4)	total: 9.98s	remaining: 2m 27s
5:	learn: 0.4284875	test: 0.4425605	best: 0.4425605 (5)	total: 12s	remaining: 2m 26s
6:	learn: 0.3990706	test: 0.4159774	best: 0.4159774 (6)	total: 14.1s	remaining: 2m 24s
7:	learn: 0.3747137	test: 0.3933945	best: 0.3933945 (7)	total: 16.2s	remaining: 2m 23s
8:	learn: 0.3524902	test: 0.3730264	best: 0.3730264 (8)	total: 18.3s	remaining: 2m 22s
9:	learn: 0.3333216	test: 0.3550407	best: 0.3550407 (9)	total: 20.4s	remaining: 2m 20s
10:	learn: 0.3156683	test: 0.3387291	best: 0.3387291 (10)	total: 22.4s	remaining: 2m 18s
11:	learn: 0.2989038	test: 0.3238481	best: 0.3238481 (11)	total: 24.4s	remaining: 2m 16s
12:	learn: 0.2831910	test: 0.3101632	best: 0.3101632 (12)	total: 26.3s	remaining: 2m 13s
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73:	learn: 0.0886667	test: 0.2025830	best: 0.2025830 (73)	total: 2m 30s	remaining: 10.1s
74:	learn: 0.0878814	test: 0.2022322	best: 0.2022322 (74)	total: 2m 32s	remaining: 8.12s
75:	learn: 0.0867054	test: 0.2022369	best: 0.2022322 (74)	total: 2m 34s	remaining: 6.09s
76:	learn: 0.0855802	test: 0.2021956	best: 0.2021956 (76)	total: 2m 36s	remaining: 4.06s
77:	learn: 0.0845170	test: 0.2023166	best: 0.2021956 (76)	total: 2m 38s	remaining: 2.03s
78:	learn: 0.0838546	test: 0.2023017	best: 0.2021956 (76)	total: 2m 40s	remaining: 0us

bestTest = 0.2021955532
bestIteration = 76

Shrink model to first 77 iterations.
Trial 84, Fold 3: Log loss = 0.2021955531654966, Average precision = 0.9718344579736193, ROC-AUC = 0.9678893767275669, Elapsed Time = 160.59512720000203 seconds
Trial 84, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 84, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
0:	learn: 0.6368613	test: 0.6388689	best: 0.6388689 (0)	total: 1.85s	remaining: 2m 24s
1:	learn: 0.5813839	test: 0.5881084	best: 0.5881084 (1)	total: 3.88s	remaining: 2m 29s
2:	learn: 0.5379309	test: 0.5464319	best: 0.5464319 (2)	total: 5.88s	remaining: 2m 28s
3:	learn: 0.4972070	test: 0.5075352	best: 0.5075352 (3)	total: 7.88s	remaining: 2m 27s
4:	learn: 0.4598051	test: 0.4736560	best: 0.4736560 (4)	total: 9.88s	remaining: 2m 26s
5:	learn: 0.4276172	test: 0.4435499	best: 0.4435499 (5)	total: 11.9s	remaining: 2m 24s
6:	learn: 0.4004253	test: 0.4178298	best: 0.4178298 (6)	total: 13.9s	remaining: 2m 23s
7:	learn: 0.3781427	test: 0.3973535	best: 0.3973535 (7)	total: 16s	remaining: 2m 21s
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55:	learn: 0.1122888	test: 0.2055289	best: 0.2054371 (54)	total: 1m 54s	remaining: 47s
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59:	learn: 0.1067137	test: 0.2048913	best: 0.2048913 (59)	total: 2m 2s	remaining: 38.8s
60:	learn: 0.1058648	test: 0.2048184	best: 0.2048184 (60)	total: 2m 4s	remaining: 36.8s
61:	learn: 0.1040737	test: 0.2047293	best: 0.2047293 (61)	total: 2m 6s	remaining: 34.8s
62:	learn: 0.1020093	test: 0.2046767	best: 0.2046767 (62)	total: 2m 8s	remaining: 32.8s
63:	learn: 0.1008272	test: 0.2043146	best: 0.2043146 (63)	total: 2m 11s	remaining: 30.7s
64:	learn: 0.0997973	test: 0.2038967	best: 0.2038967 (64)	total: 2m 13s	remaining: 28.7s
65:	learn: 0.0992689	test: 0.2036688	best: 0.2036688 (65)	total: 2m 15s	remaining: 26.6s
66:	learn: 0.0981360	test: 0.2036178	best: 0.2036178 (66)	total: 2m 17s	remaining: 24.6s
67:	learn: 0.0973920	test: 0.2036369	best: 0.2036178 (66)	total: 2m 19s	remaining: 22.5s
68:	learn: 0.0966626	test: 0.2035521	best: 0.2035521 (68)	total: 2m 21s	remaining: 20.5s
69:	learn: 0.0948123	test: 0.2034362	best: 0.2034362 (69)	total: 2m 23s	remaining: 18.5s
70:	learn: 0.0939253	test: 0.2033088	best: 0.2033088 (70)	total: 2m 25s	remaining: 16.4s
71:	learn: 0.0933529	test: 0.2032825	best: 0.2032825 (71)	total: 2m 27s	remaining: 14.3s
72:	learn: 0.0914871	test: 0.2033811	best: 0.2032825 (71)	total: 2m 29s	remaining: 12.3s
73:	learn: 0.0906449	test: 0.2032462	best: 0.2032462 (73)	total: 2m 31s	remaining: 10.3s
74:	learn: 0.0895147	test: 0.2035366	best: 0.2032462 (73)	total: 2m 33s	remaining: 8.21s
75:	learn: 0.0882197	test: 0.2036146	best: 0.2032462 (73)	total: 2m 35s	remaining: 6.15s
76:	learn: 0.0870073	test: 0.2033073	best: 0.2032462 (73)	total: 2m 38s	remaining: 4.1s
77:	learn: 0.0865690	test: 0.2033617	best: 0.2032462 (73)	total: 2m 40s	remaining: 2.05s
78:	learn: 0.0855324	test: 0.2034026	best: 0.2032462 (73)	total: 2m 42s	remaining: 0us

bestTest = 0.2032461992
bestIteration = 73

Shrink model to first 74 iterations.
Trial 84, Fold 4: Log loss = 0.20324619917569225, Average precision = 0.9722407572545252, ROC-AUC = 0.9675492261398075, Elapsed Time = 162.48230349999358 seconds
Trial 84, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 84, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
0:	learn: 0.6386066	test: 0.6451767	best: 0.6451767 (0)	total: 2.01s	remaining: 2m 37s
1:	learn: 0.5901165	test: 0.6007657	best: 0.6007657 (1)	total: 4.04s	remaining: 2m 35s
2:	learn: 0.5434056	test: 0.5564862	best: 0.5564862 (2)	total: 6.1s	remaining: 2m 34s
3:	learn: 0.5040828	test: 0.5198070	best: 0.5198070 (3)	total: 8.17s	remaining: 2m 33s
4:	learn: 0.4653934	test: 0.4840638	best: 0.4840638 (4)	total: 10.3s	remaining: 2m 33s
5:	learn: 0.4318003	test: 0.4529650	best: 0.4529650 (5)	total: 12.4s	remaining: 2m 31s
6:	learn: 0.4023466	test: 0.4258236	best: 0.4258236 (6)	total: 14.4s	remaining: 2m 28s
7:	learn: 0.3758371	test: 0.4021800	best: 0.4021800 (7)	total: 16.5s	remaining: 2m 26s
8:	learn: 0.3517399	test: 0.3804228	best: 0.3804228 (8)	total: 18.6s	remaining: 2m 24s
9:	learn: 0.3310841	test: 0.3617111	best: 0.3617111 (9)	total: 20.6s	remaining: 2m 21s
10:	learn: 0.3113875	test: 0.3447409	best: 0.3447409 (10)	total: 22.6s	remaining: 2m 19s
11:	learn: 0.2948346	test: 0.3299564	best: 0.3299564 (11)	total: 24.5s	remaining: 2m 16s
12:	learn: 0.2798435	test: 0.3167105	best: 0.3167105 (12)	total: 26.5s	remaining: 2m 14s
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15:	learn: 0.2439816	test: 0.2858273	best: 0.2858273 (15)	total: 32.8s	remaining: 2m 9s
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63:	learn: 0.1056288	test: 0.2126309	best: 0.2126309 (63)	total: 2m 11s	remaining: 30.8s
64:	learn: 0.1038828	test: 0.2128675	best: 0.2126309 (63)	total: 2m 13s	remaining: 28.8s
65:	learn: 0.1025286	test: 0.2130771	best: 0.2126309 (63)	total: 2m 15s	remaining: 26.7s
66:	learn: 0.1007979	test: 0.2130048	best: 0.2126309 (63)	total: 2m 17s	remaining: 24.6s
67:	learn: 0.0994120	test: 0.2129897	best: 0.2126309 (63)	total: 2m 19s	remaining: 22.6s
68:	learn: 0.0979883	test: 0.2130735	best: 0.2126309 (63)	total: 2m 21s	remaining: 20.5s
69:	learn: 0.0963644	test: 0.2131812	best: 0.2126309 (63)	total: 2m 23s	remaining: 18.5s
70:	learn: 0.0953885	test: 0.2133328	best: 0.2126309 (63)	total: 2m 25s	remaining: 16.4s
71:	learn: 0.0948946	test: 0.2133831	best: 0.2126309 (63)	total: 2m 27s	remaining: 14.4s
72:	learn: 0.0938279	test: 0.2134766	best: 0.2126309 (63)	total: 2m 30s	remaining: 12.3s
73:	learn: 0.0930973	test: 0.2130488	best: 0.2126309 (63)	total: 2m 32s	remaining: 10.3s
74:	learn: 0.0922761	test: 0.2127744	best: 0.2126309 (63)	total: 2m 34s	remaining: 8.21s
75:	learn: 0.0897579	test: 0.2128126	best: 0.2126309 (63)	total: 2m 36s	remaining: 6.16s
76:	learn: 0.0891907	test: 0.2126399	best: 0.2126309 (63)	total: 2m 38s	remaining: 4.1s
77:	learn: 0.0875503	test: 0.2127995	best: 0.2126309 (63)	total: 2m 40s	remaining: 2.05s
78:	learn: 0.0864590	test: 0.2126447	best: 0.2126309 (63)	total: 2m 42s	remaining: 0us

bestTest = 0.2126308641
bestIteration = 63

Shrink model to first 64 iterations.
Trial 84, Fold 5: Log loss = 0.21263086407727635, Average precision = 0.9705225371020497, ROC-AUC = 0.9647914088772458, Elapsed Time = 162.46784879999905 seconds
Optimization Progress:  85%|########5 | 85/100 [2:42:09<1:13:10, 292.69s/it]
Trial 85, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 85, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
0:	learn: 0.5770920	test: 0.5797050	best: 0.5797050 (0)	total: 319ms	remaining: 14.3s
1:	learn: 0.4824920	test: 0.4881338	best: 0.4881338 (1)	total: 719ms	remaining: 15.8s
2:	learn: 0.4203312	test: 0.4290134	best: 0.4290134 (2)	total: 1.06s	remaining: 15.1s
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36:	learn: 0.1351698	test: 0.1998407	best: 0.1998407 (36)	total: 15.4s	remaining: 3.75s
37:	learn: 0.1341826	test: 0.1993334	best: 0.1993334 (37)	total: 15.7s	remaining: 3.31s
38:	learn: 0.1333215	test: 0.1995160	best: 0.1993334 (37)	total: 16.1s	remaining: 2.88s
39:	learn: 0.1324425	test: 0.1993451	best: 0.1993334 (37)	total: 16.4s	remaining: 2.46s
40:	learn: 0.1314108	test: 0.1994328	best: 0.1993334 (37)	total: 16.7s	remaining: 2.04s
41:	learn: 0.1306209	test: 0.1994252	best: 0.1993334 (37)	total: 17s	remaining: 1.62s
42:	learn: 0.1295533	test: 0.1991343	best: 0.1991343 (42)	total: 17.4s	remaining: 1.22s
43:	learn: 0.1285126	test: 0.1991541	best: 0.1991343 (42)	total: 17.8s	remaining: 809ms
44:	learn: 0.1274700	test: 0.1992658	best: 0.1991343 (42)	total: 18.2s	remaining: 404ms
45:	learn: 0.1264146	test: 0.1991209	best: 0.1991209 (45)	total: 18.6s	remaining: 0us

bestTest = 0.1991208792
bestIteration = 45

Trial 85, Fold 1: Log loss = 0.19912087921877314, Average precision = 0.9740483458617527, ROC-AUC = 0.970261843822142, Elapsed Time = 18.72625550000521 seconds
Trial 85, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 85, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
0:	learn: 0.5577841	test: 0.5601287	best: 0.5601287 (0)	total: 365ms	remaining: 16.4s
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4:	learn: 0.3177924	test: 0.3293174	best: 0.3293174 (4)	total: 2.12s	remaining: 17.3s
5:	learn: 0.2887423	test: 0.3026863	best: 0.3026863 (5)	total: 2.67s	remaining: 17.8s
6:	learn: 0.2673697	test: 0.2842895	best: 0.2842895 (6)	total: 3.2s	remaining: 17.9s
7:	learn: 0.2505867	test: 0.2691317	best: 0.2691317 (7)	total: 3.72s	remaining: 17.7s
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36:	learn: 0.1356870	test: 0.1836680	best: 0.1836680 (36)	total: 15.3s	remaining: 3.71s
37:	learn: 0.1345229	test: 0.1833390	best: 0.1833390 (37)	total: 15.6s	remaining: 3.29s
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40:	learn: 0.1319301	test: 0.1827026	best: 0.1827026 (40)	total: 16.5s	remaining: 2.02s
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44:	learn: 0.1286581	test: 0.1816566	best: 0.1816566 (44)	total: 17.7s	remaining: 394ms
45:	learn: 0.1277946	test: 0.1815602	best: 0.1815602 (45)	total: 18s	remaining: 0us

bestTest = 0.1815602377
bestIteration = 45

Trial 85, Fold 2: Log loss = 0.1815602376785173, Average precision = 0.9763477449441452, ROC-AUC = 0.9742676727747173, Elapsed Time = 18.20334570000705 seconds
Trial 85, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 85, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
0:	learn: 0.5538765	test: 0.5587728	best: 0.5587728 (0)	total: 376ms	remaining: 16.9s
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3:	learn: 0.3432441	test: 0.3554483	best: 0.3554483 (3)	total: 1.75s	remaining: 18.4s
4:	learn: 0.3080315	test: 0.3226303	best: 0.3226303 (4)	total: 2.29s	remaining: 18.8s
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6:	learn: 0.2600449	test: 0.2788260	best: 0.2788260 (6)	total: 3.37s	remaining: 18.8s
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37:	learn: 0.1331639	test: 0.1861729	best: 0.1861729 (37)	total: 16.7s	remaining: 3.51s
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40:	learn: 0.1302905	test: 0.1859531	best: 0.1859531 (40)	total: 17.7s	remaining: 2.16s
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43:	learn: 0.1279580	test: 0.1856586	best: 0.1856586 (43)	total: 18.7s	remaining: 849ms
44:	learn: 0.1275637	test: 0.1856695	best: 0.1856586 (43)	total: 18.9s	remaining: 420ms
45:	learn: 0.1269717	test: 0.1855773	best: 0.1855773 (45)	total: 19.2s	remaining: 0us

bestTest = 0.185577321
bestIteration = 45

Trial 85, Fold 3: Log loss = 0.18557732102419647, Average precision = 0.9761387659789403, ROC-AUC = 0.9733354260769905, Elapsed Time = 19.36717679999856 seconds
Trial 85, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 85, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
0:	learn: 0.5580235	test: 0.5612987	best: 0.5612987 (0)	total: 373ms	remaining: 16.8s
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2:	learn: 0.4092826	test: 0.4164132	best: 0.4164132 (2)	total: 1.18s	remaining: 16.9s
3:	learn: 0.3611395	test: 0.3701735	best: 0.3701735 (3)	total: 1.55s	remaining: 16.3s
4:	learn: 0.3265729	test: 0.3386638	best: 0.3386638 (4)	total: 2.02s	remaining: 16.6s
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33:	learn: 0.1387834	test: 0.1902692	best: 0.1902692 (33)	total: 14.8s	remaining: 5.22s
34:	learn: 0.1372407	test: 0.1903692	best: 0.1902692 (33)	total: 15.2s	remaining: 4.77s
35:	learn: 0.1363554	test: 0.1901581	best: 0.1901581 (35)	total: 15.5s	remaining: 4.31s
36:	learn: 0.1348952	test: 0.1898436	best: 0.1898436 (36)	total: 16s	remaining: 3.88s
37:	learn: 0.1341444	test: 0.1898435	best: 0.1898435 (37)	total: 16.2s	remaining: 3.42s
38:	learn: 0.1330026	test: 0.1898139	best: 0.1898139 (38)	total: 16.6s	remaining: 2.98s
39:	learn: 0.1321643	test: 0.1895867	best: 0.1895867 (39)	total: 17s	remaining: 2.55s
40:	learn: 0.1313591	test: 0.1897707	best: 0.1895867 (39)	total: 17.3s	remaining: 2.11s
41:	learn: 0.1304489	test: 0.1897396	best: 0.1895867 (39)	total: 17.6s	remaining: 1.68s
42:	learn: 0.1292855	test: 0.1897507	best: 0.1895867 (39)	total: 18s	remaining: 1.25s
43:	learn: 0.1285475	test: 0.1898824	best: 0.1895867 (39)	total: 18.3s	remaining: 832ms
44:	learn: 0.1281189	test: 0.1899241	best: 0.1895867 (39)	total: 18.6s	remaining: 413ms
45:	learn: 0.1275156	test: 0.1898012	best: 0.1895867 (39)	total: 18.9s	remaining: 0us

bestTest = 0.1895867295
bestIteration = 39

Shrink model to first 40 iterations.
Trial 85, Fold 4: Log loss = 0.18958672972794569, Average precision = 0.974982758563082, ROC-AUC = 0.9725237010331647, Elapsed Time = 19.01251290000073 seconds
Trial 85, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 85, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
0:	learn: 0.5518133	test: 0.5576849	best: 0.5576849 (0)	total: 417ms	remaining: 18.8s
1:	learn: 0.4551307	test: 0.4642812	best: 0.4642812 (1)	total: 795ms	remaining: 17.5s
2:	learn: 0.3921763	test: 0.4041626	best: 0.4041626 (2)	total: 1.22s	remaining: 17.4s
3:	learn: 0.3478186	test: 0.3624957	best: 0.3624957 (3)	total: 1.66s	remaining: 17.4s
4:	learn: 0.3120564	test: 0.3308596	best: 0.3308596 (4)	total: 2.13s	remaining: 17.5s
5:	learn: 0.2858774	test: 0.3078343	best: 0.3078343 (5)	total: 2.6s	remaining: 17.3s
6:	learn: 0.2627277	test: 0.2873952	best: 0.2873952 (6)	total: 3.12s	remaining: 17.4s
7:	learn: 0.2439212	test: 0.2708964	best: 0.2708964 (7)	total: 3.65s	remaining: 17.4s
8:	learn: 0.2288328	test: 0.2588669	best: 0.2588669 (8)	total: 4.17s	remaining: 17.2s
9:	learn: 0.2155693	test: 0.2479589	best: 0.2479589 (9)	total: 4.7s	remaining: 16.9s
10:	learn: 0.2041771	test: 0.2388870	best: 0.2388870 (10)	total: 5.16s	remaining: 16.4s
11:	learn: 0.1963735	test: 0.2331124	best: 0.2331124 (11)	total: 5.57s	remaining: 15.8s
12:	learn: 0.1878242	test: 0.2266824	best: 0.2266824 (12)	total: 6.07s	remaining: 15.4s
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18:	learn: 0.1604465	test: 0.2091596	best: 0.2091596 (18)	total: 8.71s	remaining: 12.4s
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27:	learn: 0.1403767	test: 0.2013008	best: 0.2013008 (27)	total: 12.5s	remaining: 8.02s
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30:	learn: 0.1364902	test: 0.2000528	best: 0.2000528 (30)	total: 13.6s	remaining: 6.58s
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34:	learn: 0.1323059	test: 0.1994286	best: 0.1994286 (34)	total: 14.9s	remaining: 4.7s
35:	learn: 0.1316215	test: 0.1994192	best: 0.1994192 (35)	total: 15.2s	remaining: 4.22s
36:	learn: 0.1307832	test: 0.1993241	best: 0.1993241 (36)	total: 15.5s	remaining: 3.77s
37:	learn: 0.1299739	test: 0.1991084	best: 0.1991084 (37)	total: 15.8s	remaining: 3.32s
38:	learn: 0.1290823	test: 0.1989465	best: 0.1989465 (38)	total: 16.1s	remaining: 2.89s
39:	learn: 0.1281790	test: 0.1990339	best: 0.1989465 (38)	total: 16.4s	remaining: 2.46s
40:	learn: 0.1273732	test: 0.1990427	best: 0.1989465 (38)	total: 16.7s	remaining: 2.04s
41:	learn: 0.1268070	test: 0.1991239	best: 0.1989465 (38)	total: 17s	remaining: 1.62s
42:	learn: 0.1261087	test: 0.1990270	best: 0.1989465 (38)	total: 17.3s	remaining: 1.21s
43:	learn: 0.1249252	test: 0.1986211	best: 0.1986211 (43)	total: 17.6s	remaining: 802ms
44:	learn: 0.1243504	test: 0.1987310	best: 0.1986211 (43)	total: 18s	remaining: 399ms
45:	learn: 0.1235734	test: 0.1987615	best: 0.1986211 (43)	total: 18.3s	remaining: 0us

bestTest = 0.1986211047
bestIteration = 43

Shrink model to first 44 iterations.
Trial 85, Fold 5: Log loss = 0.19862110415844503, Average precision = 0.9733036329049334, ROC-AUC = 0.970154915184958, Elapsed Time = 18.505486000001838 seconds
Optimization Progress:  86%|########6 | 86/100 [2:43:51<54:56, 235.45s/it]  
Trial 86, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 86, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
0:	learn: 0.6607903	test: 0.6614257	best: 0.6614257 (0)	total: 129ms	remaining: 11.8s
1:	learn: 0.6267838	test: 0.6288625	best: 0.6288625 (1)	total: 285ms	remaining: 12.8s
2:	learn: 0.6018658	test: 0.6049775	best: 0.6049775 (2)	total: 436ms	remaining: 12.9s
3:	learn: 0.5705498	test: 0.5743843	best: 0.5743843 (3)	total: 599ms	remaining: 13.2s
4:	learn: 0.5501953	test: 0.5547343	best: 0.5547343 (4)	total: 728ms	remaining: 12.7s
5:	learn: 0.5277518	test: 0.5332887	best: 0.5332887 (5)	total: 880ms	remaining: 12.6s
6:	learn: 0.5043359	test: 0.5112466	best: 0.5112466 (6)	total: 1.06s	remaining: 12.9s
7:	learn: 0.4836345	test: 0.4913260	best: 0.4913260 (7)	total: 1.24s	remaining: 13s
8:	learn: 0.4647843	test: 0.4730046	best: 0.4730046 (8)	total: 1.4s	remaining: 12.9s
9:	learn: 0.4451632	test: 0.4546503	best: 0.4546503 (9)	total: 1.56s	remaining: 12.8s
10:	learn: 0.4258987	test: 0.4365593	best: 0.4365593 (10)	total: 1.77s	remaining: 13s
11:	learn: 0.4100786	test: 0.4220490	best: 0.4220490 (11)	total: 1.95s	remaining: 13s
12:	learn: 0.3923375	test: 0.4047609	best: 0.4047609 (12)	total: 2.13s	remaining: 13s
13:	learn: 0.3773307	test: 0.3901331	best: 0.3901331 (13)	total: 2.29s	remaining: 12.8s
14:	learn: 0.3615284	test: 0.3758961	best: 0.3758961 (14)	total: 2.46s	remaining: 12.6s
15:	learn: 0.3525954	test: 0.3677233	best: 0.3677233 (15)	total: 2.61s	remaining: 12.4s
16:	learn: 0.3441058	test: 0.3597350	best: 0.3597350 (16)	total: 2.79s	remaining: 12.3s
17:	learn: 0.3332464	test: 0.3495469	best: 0.3495469 (17)	total: 3.02s	remaining: 12.4s
18:	learn: 0.3267047	test: 0.3434973	best: 0.3434973 (18)	total: 3.26s	remaining: 12.5s
19:	learn: 0.3171536	test: 0.3345593	best: 0.3345593 (19)	total: 3.52s	remaining: 12.7s
20:	learn: 0.3070936	test: 0.3246610	best: 0.3246610 (20)	total: 3.76s	remaining: 12.7s
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25:	learn: 0.2765557	test: 0.2978265	best: 0.2978265 (25)	total: 4.79s	remaining: 12.2s
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27:	learn: 0.2661093	test: 0.2889147	best: 0.2889147 (27)	total: 5.14s	remaining: 11.7s
28:	learn: 0.2613386	test: 0.2846978	best: 0.2846978 (28)	total: 5.31s	remaining: 11.5s
29:	learn: 0.2565320	test: 0.2806673	best: 0.2806673 (29)	total: 5.5s	remaining: 11.4s
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82:	learn: 0.1565658	test: 0.2136573	best: 0.2136573 (82)	total: 14.2s	remaining: 1.54s
83:	learn: 0.1559498	test: 0.2133525	best: 0.2133525 (83)	total: 14.4s	remaining: 1.37s
84:	learn: 0.1555804	test: 0.2130978	best: 0.2130978 (84)	total: 14.5s	remaining: 1.2s
85:	learn: 0.1545227	test: 0.2125955	best: 0.2125955 (85)	total: 14.7s	remaining: 1.02s
86:	learn: 0.1539988	test: 0.2124978	best: 0.2124978 (86)	total: 14.8s	remaining: 851ms
87:	learn: 0.1531883	test: 0.2122427	best: 0.2122427 (87)	total: 15s	remaining: 680ms
88:	learn: 0.1526658	test: 0.2119708	best: 0.2119708 (88)	total: 15.1s	remaining: 509ms
89:	learn: 0.1520332	test: 0.2117644	best: 0.2117644 (89)	total: 15.2s	remaining: 339ms
90:	learn: 0.1516098	test: 0.2116089	best: 0.2116089 (90)	total: 15.4s	remaining: 169ms
91:	learn: 0.1512332	test: 0.2114271	best: 0.2114271 (91)	total: 15.5s	remaining: 0us

bestTest = 0.2114270719
bestIteration = 91

Trial 86, Fold 1: Log loss = 0.21126570723582958, Average precision = 0.9710056253674182, ROC-AUC = 0.9667329386587515, Elapsed Time = 15.706904600003327 seconds
Trial 86, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 86, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
0:	learn: 0.6570273	test: 0.6580254	best: 0.6580254 (0)	total: 142ms	remaining: 12.9s
1:	learn: 0.6247366	test: 0.6266219	best: 0.6266219 (1)	total: 269ms	remaining: 12.1s
2:	learn: 0.5953530	test: 0.5980538	best: 0.5980538 (2)	total: 426ms	remaining: 12.6s
3:	learn: 0.5667545	test: 0.5701429	best: 0.5701429 (3)	total: 575ms	remaining: 12.6s
4:	learn: 0.5420990	test: 0.5467014	best: 0.5467014 (4)	total: 734ms	remaining: 12.8s
5:	learn: 0.5204643	test: 0.5261911	best: 0.5261911 (5)	total: 885ms	remaining: 12.7s
6:	learn: 0.5000966	test: 0.5073233	best: 0.5073233 (6)	total: 1.05s	remaining: 12.8s
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85:	learn: 0.1471445	test: 0.2077911	best: 0.2077911 (85)	total: 13.5s	remaining: 942ms
86:	learn: 0.1467216	test: 0.2076149	best: 0.2076149 (86)	total: 13.7s	remaining: 785ms
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88:	learn: 0.1451481	test: 0.2070441	best: 0.2070327 (87)	total: 14s	remaining: 470ms
89:	learn: 0.1443096	test: 0.2067678	best: 0.2067678 (89)	total: 14.1s	remaining: 313ms
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91:	learn: 0.1416097	test: 0.2060932	best: 0.2060932 (91)	total: 14.4s	remaining: 0us

bestTest = 0.206093221
bestIteration = 91

Trial 86, Fold 2: Log loss = 0.20596878467745464, Average precision = 0.9718932341074019, ROC-AUC = 0.968086530229221, Elapsed Time = 14.600737399996433 seconds
Trial 86, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 86, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
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83:	learn: 0.1558776	test: 0.2073277	best: 0.2073277 (83)	total: 13s	remaining: 1.24s
84:	learn: 0.1552766	test: 0.2071503	best: 0.2071503 (84)	total: 13.2s	remaining: 1.08s
85:	learn: 0.1546103	test: 0.2069356	best: 0.2069356 (85)	total: 13.3s	remaining: 928ms
86:	learn: 0.1540670	test: 0.2067228	best: 0.2067228 (86)	total: 13.5s	remaining: 773ms
87:	learn: 0.1535814	test: 0.2064586	best: 0.2064586 (87)	total: 13.6s	remaining: 619ms
88:	learn: 0.1529628	test: 0.2061218	best: 0.2061218 (88)	total: 13.7s	remaining: 463ms
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90:	learn: 0.1497265	test: 0.2060038	best: 0.2060038 (90)	total: 14.1s	remaining: 154ms
91:	learn: 0.1492452	test: 0.2058521	best: 0.2058521 (91)	total: 14.2s	remaining: 0us

bestTest = 0.2058520868
bestIteration = 91

Trial 86, Fold 3: Log loss = 0.2059723953464966, Average precision = 0.9721881416040364, ROC-AUC = 0.9689537565852202, Elapsed Time = 14.365914299996803 seconds
Trial 86, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 86, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
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bestTest = 0.20726021
bestIteration = 91

Trial 86, Fold 4: Log loss = 0.20703913607806468, Average precision = 0.9713014618504603, ROC-AUC = 0.9657980261855883, Elapsed Time = 14.118714299998828 seconds
Trial 86, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 86, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
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84:	learn: 0.1524632	test: 0.2179127	best: 0.2179127 (84)	total: 12.8s	remaining: 1.05s
85:	learn: 0.1515704	test: 0.2177434	best: 0.2177434 (85)	total: 13s	remaining: 905ms
86:	learn: 0.1506836	test: 0.2174917	best: 0.2174917 (86)	total: 13.1s	remaining: 754ms
87:	learn: 0.1498233	test: 0.2174006	best: 0.2174006 (87)	total: 13.3s	remaining: 603ms
88:	learn: 0.1487814	test: 0.2174194	best: 0.2174006 (87)	total: 13.4s	remaining: 453ms
89:	learn: 0.1477605	test: 0.2170277	best: 0.2170277 (89)	total: 13.6s	remaining: 302ms
90:	learn: 0.1466891	test: 0.2167914	best: 0.2167914 (90)	total: 13.7s	remaining: 151ms
91:	learn: 0.1460294	test: 0.2167179	best: 0.2167179 (91)	total: 13.9s	remaining: 0us

bestTest = 0.2167178935
bestIteration = 91

Trial 86, Fold 5: Log loss = 0.21634899604568314, Average precision = 0.9692303375647867, ROC-AUC = 0.9646564479869202, Elapsed Time = 14.080775299997185 seconds
Optimization Progress:  87%|########7 | 87/100 [2:45:12<40:57, 189.07s/it]
Trial 87, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 87, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
0:	learn: 0.5979648	test: 0.5986029	best: 0.5986029 (0)	total: 483ms	remaining: 15.5s
1:	learn: 0.5336807	test: 0.5342894	best: 0.5342894 (1)	total: 925ms	remaining: 14.3s
2:	learn: 0.4708385	test: 0.4721676	best: 0.4721676 (2)	total: 1.43s	remaining: 14.3s
3:	learn: 0.4233674	test: 0.4254571	best: 0.4254571 (3)	total: 1.91s	remaining: 13.8s
4:	learn: 0.3843473	test: 0.3874524	best: 0.3874524 (4)	total: 2.35s	remaining: 13.2s
5:	learn: 0.3544349	test: 0.3583425	best: 0.3583425 (5)	total: 2.83s	remaining: 12.7s
6:	learn: 0.3290461	test: 0.3333950	best: 0.3333950 (6)	total: 3.28s	remaining: 12.2s
7:	learn: 0.3097138	test: 0.3152287	best: 0.3152287 (7)	total: 3.75s	remaining: 11.7s
8:	learn: 0.2925720	test: 0.2991148	best: 0.2991148 (8)	total: 4.26s	remaining: 11.3s
9:	learn: 0.2811674	test: 0.2883987	best: 0.2883987 (9)	total: 4.7s	remaining: 10.8s
10:	learn: 0.2696040	test: 0.2778146	best: 0.2778146 (10)	total: 5.15s	remaining: 10.3s
11:	learn: 0.2577412	test: 0.2669309	best: 0.2669309 (11)	total: 5.61s	remaining: 9.82s
12:	learn: 0.2480571	test: 0.2588033	best: 0.2588033 (12)	total: 6.16s	remaining: 9.48s
13:	learn: 0.2400766	test: 0.2520185	best: 0.2520185 (13)	total: 6.63s	remaining: 8.99s
14:	learn: 0.2328620	test: 0.2460226	best: 0.2460226 (14)	total: 7.08s	remaining: 8.5s
15:	learn: 0.2270929	test: 0.2415492	best: 0.2415492 (15)	total: 7.58s	remaining: 8.06s
16:	learn: 0.2229506	test: 0.2379446	best: 0.2379446 (16)	total: 8.04s	remaining: 7.56s
17:	learn: 0.2186440	test: 0.2348978	best: 0.2348978 (17)	total: 8.51s	remaining: 7.09s
18:	learn: 0.2141708	test: 0.2309676	best: 0.2309676 (18)	total: 8.96s	remaining: 6.6s
19:	learn: 0.2101795	test: 0.2278651	best: 0.2278651 (19)	total: 9.41s	remaining: 6.12s
20:	learn: 0.2069499	test: 0.2258239	best: 0.2258239 (20)	total: 9.87s	remaining: 5.64s
21:	learn: 0.2033169	test: 0.2235803	best: 0.2235803 (21)	total: 10.4s	remaining: 5.18s
22:	learn: 0.2006582	test: 0.2217062	best: 0.2217062 (22)	total: 10.8s	remaining: 4.71s
23:	learn: 0.1980467	test: 0.2201499	best: 0.2201499 (23)	total: 11.3s	remaining: 4.22s
24:	learn: 0.1950203	test: 0.2183745	best: 0.2183745 (24)	total: 11.7s	remaining: 3.75s
25:	learn: 0.1928271	test: 0.2171644	best: 0.2171644 (25)	total: 12.2s	remaining: 3.27s
26:	learn: 0.1904452	test: 0.2159134	best: 0.2159134 (26)	total: 12.6s	remaining: 2.8s
27:	learn: 0.1878669	test: 0.2142171	best: 0.2142171 (27)	total: 13.1s	remaining: 2.33s
28:	learn: 0.1865281	test: 0.2135737	best: 0.2135737 (28)	total: 13.5s	remaining: 1.86s
29:	learn: 0.1845983	test: 0.2123142	best: 0.2123142 (29)	total: 14s	remaining: 1.4s
30:	learn: 0.1825122	test: 0.2113888	best: 0.2113888 (30)	total: 14.4s	remaining: 932ms
31:	learn: 0.1805114	test: 0.2103413	best: 0.2103413 (31)	total: 14.9s	remaining: 466ms
32:	learn: 0.1789055	test: 0.2095260	best: 0.2095260 (32)	total: 15.4s	remaining: 0us

bestTest = 0.2095260472
bestIteration = 32

Trial 87, Fold 1: Log loss = 0.2095260472118056, Average precision = 0.9732601739127758, ROC-AUC = 0.969033564068004, Elapsed Time = 15.50528530000156 seconds
Trial 87, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 87, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
0:	learn: 0.5993474	test: 0.6000179	best: 0.6000179 (0)	total: 464ms	remaining: 14.8s
1:	learn: 0.5245379	test: 0.5256546	best: 0.5256546 (1)	total: 936ms	remaining: 14.5s
2:	learn: 0.4652941	test: 0.4674935	best: 0.4674935 (2)	total: 1.41s	remaining: 14.1s
3:	learn: 0.4192603	test: 0.4223822	best: 0.4223822 (3)	total: 1.88s	remaining: 13.6s
4:	learn: 0.3819867	test: 0.3850986	best: 0.3850986 (4)	total: 2.35s	remaining: 13.2s
5:	learn: 0.3527381	test: 0.3564098	best: 0.3564098 (5)	total: 2.84s	remaining: 12.8s
6:	learn: 0.3265573	test: 0.3306972	best: 0.3306972 (6)	total: 3.31s	remaining: 12.3s
7:	learn: 0.3067856	test: 0.3114599	best: 0.3114599 (7)	total: 3.74s	remaining: 11.7s
8:	learn: 0.2926375	test: 0.2976059	best: 0.2976059 (8)	total: 4.2s	remaining: 11.2s
9:	learn: 0.2781556	test: 0.2838650	best: 0.2838650 (9)	total: 4.64s	remaining: 10.7s
10:	learn: 0.2671514	test: 0.2733344	best: 0.2733344 (10)	total: 5.09s	remaining: 10.2s
11:	learn: 0.2569171	test: 0.2640093	best: 0.2640093 (11)	total: 5.52s	remaining: 9.65s
12:	learn: 0.2484177	test: 0.2559890	best: 0.2559890 (12)	total: 5.97s	remaining: 9.18s
13:	learn: 0.2422310	test: 0.2497373	best: 0.2497373 (13)	total: 6.41s	remaining: 8.69s
14:	learn: 0.2362693	test: 0.2447288	best: 0.2447288 (14)	total: 6.86s	remaining: 8.23s
15:	learn: 0.2312685	test: 0.2404111	best: 0.2404111 (15)	total: 7.31s	remaining: 7.77s
16:	learn: 0.2257480	test: 0.2355749	best: 0.2355749 (16)	total: 7.78s	remaining: 7.32s
17:	learn: 0.2205232	test: 0.2311074	best: 0.2311074 (17)	total: 8.24s	remaining: 6.87s
18:	learn: 0.2170305	test: 0.2278502	best: 0.2278502 (18)	total: 8.69s	remaining: 6.4s
19:	learn: 0.2127663	test: 0.2240463	best: 0.2240463 (19)	total: 9.16s	remaining: 5.95s
20:	learn: 0.2091103	test: 0.2212702	best: 0.2212702 (20)	total: 9.61s	remaining: 5.49s
21:	learn: 0.2060539	test: 0.2187406	best: 0.2187406 (21)	total: 10s	remaining: 5.02s
22:	learn: 0.2032762	test: 0.2166570	best: 0.2166570 (22)	total: 10.5s	remaining: 4.57s
23:	learn: 0.2011693	test: 0.2151968	best: 0.2151968 (23)	total: 11s	remaining: 4.11s
24:	learn: 0.1983193	test: 0.2132012	best: 0.2132012 (24)	total: 11.4s	remaining: 3.66s
25:	learn: 0.1959187	test: 0.2114574	best: 0.2114574 (25)	total: 11.9s	remaining: 3.2s
26:	learn: 0.1940094	test: 0.2099624	best: 0.2099624 (26)	total: 12.3s	remaining: 2.74s
27:	learn: 0.1918848	test: 0.2081180	best: 0.2081180 (27)	total: 12.8s	remaining: 2.28s
28:	learn: 0.1900260	test: 0.2067508	best: 0.2067508 (28)	total: 13.2s	remaining: 1.82s
29:	learn: 0.1878730	test: 0.2052367	best: 0.2052367 (29)	total: 13.7s	remaining: 1.37s
30:	learn: 0.1863779	test: 0.2043670	best: 0.2043670 (30)	total: 14.1s	remaining: 912ms
31:	learn: 0.1846111	test: 0.2033344	best: 0.2033344 (31)	total: 14.6s	remaining: 457ms
32:	learn: 0.1830895	test: 0.2023268	best: 0.2023268 (32)	total: 15.1s	remaining: 0us

bestTest = 0.2023267731
bestIteration = 32

Trial 87, Fold 2: Log loss = 0.20232677307511587, Average precision = 0.9745892673875997, ROC-AUC = 0.9726937447438361, Elapsed Time = 15.20940100000007 seconds
Trial 87, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 87, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
0:	learn: 0.5962464	test: 0.5961243	best: 0.5961243 (0)	total: 440ms	remaining: 14.1s
1:	learn: 0.5214249	test: 0.5208244	best: 0.5208244 (1)	total: 869ms	remaining: 13.5s
2:	learn: 0.4619152	test: 0.4615166	best: 0.4615166 (2)	total: 1.33s	remaining: 13.3s
3:	learn: 0.4193659	test: 0.4191105	best: 0.4191105 (3)	total: 1.81s	remaining: 13.1s
4:	learn: 0.3838926	test: 0.3839896	best: 0.3839896 (4)	total: 2.26s	remaining: 12.7s
5:	learn: 0.3536035	test: 0.3542439	best: 0.3542439 (5)	total: 2.74s	remaining: 12.4s
6:	learn: 0.3291970	test: 0.3304250	best: 0.3304250 (6)	total: 3.23s	remaining: 12s
7:	learn: 0.3074703	test: 0.3087674	best: 0.3087674 (7)	total: 3.68s	remaining: 11.5s
8:	learn: 0.2910127	test: 0.2931548	best: 0.2931548 (8)	total: 4.16s	remaining: 11.1s
9:	learn: 0.2788115	test: 0.2812167	best: 0.2812167 (9)	total: 4.61s	remaining: 10.6s
10:	learn: 0.2674923	test: 0.2705730	best: 0.2705730 (10)	total: 5.07s	remaining: 10.1s
11:	learn: 0.2574784	test: 0.2617937	best: 0.2617937 (11)	total: 5.54s	remaining: 9.69s
12:	learn: 0.2477850	test: 0.2528985	best: 0.2528985 (12)	total: 6s	remaining: 9.22s
13:	learn: 0.2413339	test: 0.2474711	best: 0.2474711 (13)	total: 6.45s	remaining: 8.75s
14:	learn: 0.2345226	test: 0.2418859	best: 0.2418859 (14)	total: 6.91s	remaining: 8.3s
15:	learn: 0.2280313	test: 0.2360501	best: 0.2360501 (15)	total: 7.38s	remaining: 7.84s
16:	learn: 0.2224162	test: 0.2312713	best: 0.2312713 (16)	total: 7.84s	remaining: 7.38s
17:	learn: 0.2183848	test: 0.2281921	best: 0.2281921 (17)	total: 8.3s	remaining: 6.91s
18:	learn: 0.2140639	test: 0.2252703	best: 0.2252703 (18)	total: 8.76s	remaining: 6.46s
19:	learn: 0.2100347	test: 0.2215298	best: 0.2215298 (19)	total: 9.21s	remaining: 5.99s
20:	learn: 0.2067034	test: 0.2191462	best: 0.2191462 (20)	total: 9.66s	remaining: 5.52s
21:	learn: 0.2042045	test: 0.2170261	best: 0.2170261 (21)	total: 10.1s	remaining: 5.05s
22:	learn: 0.2011403	test: 0.2151073	best: 0.2151073 (22)	total: 10.6s	remaining: 4.59s
23:	learn: 0.1984376	test: 0.2131087	best: 0.2131087 (23)	total: 11s	remaining: 4.13s
24:	learn: 0.1958274	test: 0.2109619	best: 0.2109619 (24)	total: 11.5s	remaining: 3.67s
25:	learn: 0.1937077	test: 0.2098184	best: 0.2098184 (25)	total: 11.9s	remaining: 3.21s
26:	learn: 0.1915538	test: 0.2086524	best: 0.2086524 (26)	total: 12.4s	remaining: 2.76s
27:	learn: 0.1894374	test: 0.2075572	best: 0.2075572 (27)	total: 12.9s	remaining: 2.31s
28:	learn: 0.1877358	test: 0.2062409	best: 0.2062409 (28)	total: 13.4s	remaining: 1.85s
29:	learn: 0.1861840	test: 0.2049505	best: 0.2049505 (29)	total: 13.9s	remaining: 1.39s
30:	learn: 0.1842443	test: 0.2032357	best: 0.2032357 (30)	total: 14.5s	remaining: 934ms
31:	learn: 0.1828503	test: 0.2024304	best: 0.2024304 (31)	total: 15s	remaining: 468ms
32:	learn: 0.1816063	test: 0.2017099	best: 0.2017099 (32)	total: 15.5s	remaining: 0us

bestTest = 0.2017099228
bestIteration = 32

Trial 87, Fold 3: Log loss = 0.2017099228008445, Average precision = 0.9733782745427833, ROC-AUC = 0.9717642882424323, Elapsed Time = 15.610752100001264 seconds
Trial 87, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 87, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
0:	learn: 0.6034239	test: 0.6031853	best: 0.6031853 (0)	total: 501ms	remaining: 16s
1:	learn: 0.5278947	test: 0.5279966	best: 0.5279966 (1)	total: 1.01s	remaining: 15.7s
2:	learn: 0.4699870	test: 0.4705527	best: 0.4705527 (2)	total: 1.49s	remaining: 14.9s
3:	learn: 0.4215622	test: 0.4227468	best: 0.4227468 (3)	total: 1.96s	remaining: 14.2s
4:	learn: 0.3862521	test: 0.3873475	best: 0.3873475 (4)	total: 2.46s	remaining: 13.8s
5:	learn: 0.3543504	test: 0.3561496	best: 0.3561496 (5)	total: 2.94s	remaining: 13.2s
6:	learn: 0.3304117	test: 0.3326436	best: 0.3326436 (6)	total: 3.37s	remaining: 12.5s
7:	learn: 0.3131375	test: 0.3160112	best: 0.3160112 (7)	total: 3.87s	remaining: 12.1s
8:	learn: 0.2963663	test: 0.2998239	best: 0.2998239 (8)	total: 4.37s	remaining: 11.6s
9:	learn: 0.2821970	test: 0.2864286	best: 0.2864286 (9)	total: 4.87s	remaining: 11.2s
10:	learn: 0.2686774	test: 0.2735334	best: 0.2735334 (10)	total: 5.38s	remaining: 10.8s
11:	learn: 0.2583447	test: 0.2641925	best: 0.2641925 (11)	total: 5.86s	remaining: 10.3s
12:	learn: 0.2507479	test: 0.2573097	best: 0.2573097 (12)	total: 6.32s	remaining: 9.73s
13:	learn: 0.2432010	test: 0.2505628	best: 0.2505628 (13)	total: 6.77s	remaining: 9.19s
14:	learn: 0.2352277	test: 0.2434837	best: 0.2434837 (14)	total: 7.23s	remaining: 8.68s
15:	learn: 0.2297929	test: 0.2390057	best: 0.2390057 (15)	total: 7.7s	remaining: 8.18s
16:	learn: 0.2242159	test: 0.2344314	best: 0.2344314 (16)	total: 8.21s	remaining: 7.73s
17:	learn: 0.2205332	test: 0.2311335	best: 0.2311335 (17)	total: 8.65s	remaining: 7.21s
18:	learn: 0.2169182	test: 0.2279640	best: 0.2279640 (18)	total: 9.11s	remaining: 6.71s
19:	learn: 0.2133690	test: 0.2252672	best: 0.2252672 (19)	total: 9.56s	remaining: 6.21s
20:	learn: 0.2098273	test: 0.2225547	best: 0.2225547 (20)	total: 10s	remaining: 5.72s
21:	learn: 0.2067150	test: 0.2202535	best: 0.2202535 (21)	total: 10.5s	remaining: 5.24s
22:	learn: 0.2039577	test: 0.2184848	best: 0.2184848 (22)	total: 10.9s	remaining: 4.76s
23:	learn: 0.2016740	test: 0.2171626	best: 0.2171626 (23)	total: 11.4s	remaining: 4.28s
24:	learn: 0.1993031	test: 0.2154478	best: 0.2154478 (24)	total: 11.8s	remaining: 3.79s
25:	learn: 0.1967950	test: 0.2137650	best: 0.2137650 (25)	total: 12.3s	remaining: 3.31s
26:	learn: 0.1945628	test: 0.2124074	best: 0.2124074 (26)	total: 12.7s	remaining: 2.83s
27:	learn: 0.1921014	test: 0.2105601	best: 0.2105601 (27)	total: 13.2s	remaining: 2.35s
28:	learn: 0.1900517	test: 0.2092146	best: 0.2092146 (28)	total: 13.7s	remaining: 1.88s
29:	learn: 0.1880741	test: 0.2082274	best: 0.2082274 (29)	total: 14.1s	remaining: 1.41s
30:	learn: 0.1865056	test: 0.2075731	best: 0.2075731 (30)	total: 14.6s	remaining: 942ms
31:	learn: 0.1843959	test: 0.2065516	best: 0.2065516 (31)	total: 15.1s	remaining: 471ms
32:	learn: 0.1827090	test: 0.2055915	best: 0.2055915 (32)	total: 15.5s	remaining: 0us

bestTest = 0.2055914631
bestIteration = 32

Trial 87, Fold 4: Log loss = 0.2055914630869302, Average precision = 0.9750701798218468, ROC-AUC = 0.9708940314646713, Elapsed Time = 15.679794000003312 seconds
Trial 87, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 87, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
0:	learn: 0.5994466	test: 0.6013817	best: 0.6013817 (0)	total: 483ms	remaining: 15.5s
1:	learn: 0.5268747	test: 0.5300558	best: 0.5300558 (1)	total: 928ms	remaining: 14.4s
2:	learn: 0.4727546	test: 0.4772197	best: 0.4772197 (2)	total: 1.37s	remaining: 13.7s
3:	learn: 0.4220637	test: 0.4278634	best: 0.4278634 (3)	total: 1.83s	remaining: 13.3s
4:	learn: 0.3829023	test: 0.3900753	best: 0.3900753 (4)	total: 2.3s	remaining: 12.9s
5:	learn: 0.3508385	test: 0.3587660	best: 0.3587660 (5)	total: 2.75s	remaining: 12.4s
6:	learn: 0.3251154	test: 0.3345719	best: 0.3345719 (6)	total: 3.23s	remaining: 12s
7:	learn: 0.3074022	test: 0.3181526	best: 0.3181526 (7)	total: 3.68s	remaining: 11.5s
8:	learn: 0.2911988	test: 0.3023950	best: 0.3023950 (8)	total: 4.13s	remaining: 11s
9:	learn: 0.2784838	test: 0.2905837	best: 0.2905837 (9)	total: 4.57s	remaining: 10.5s
10:	learn: 0.2651303	test: 0.2779505	best: 0.2779505 (10)	total: 5.05s	remaining: 10.1s
11:	learn: 0.2544452	test: 0.2683732	best: 0.2683732 (11)	total: 5.48s	remaining: 9.59s
12:	learn: 0.2465667	test: 0.2607981	best: 0.2607981 (12)	total: 5.94s	remaining: 9.14s
13:	learn: 0.2394794	test: 0.2541920	best: 0.2541920 (13)	total: 6.4s	remaining: 8.69s
14:	learn: 0.2325538	test: 0.2483284	best: 0.2483284 (14)	total: 6.85s	remaining: 8.22s
15:	learn: 0.2266476	test: 0.2437346	best: 0.2437346 (15)	total: 7.3s	remaining: 7.75s
16:	learn: 0.2216562	test: 0.2397115	best: 0.2397115 (16)	total: 7.77s	remaining: 7.32s
17:	learn: 0.2174181	test: 0.2362816	best: 0.2362816 (17)	total: 8.22s	remaining: 6.85s
18:	learn: 0.2137981	test: 0.2338264	best: 0.2338264 (18)	total: 8.67s	remaining: 6.39s
19:	learn: 0.2104137	test: 0.2313832	best: 0.2313832 (19)	total: 9.14s	remaining: 5.94s
20:	learn: 0.2066001	test: 0.2287038	best: 0.2287038 (20)	total: 9.62s	remaining: 5.5s
21:	learn: 0.2028381	test: 0.2261015	best: 0.2261015 (21)	total: 10.1s	remaining: 5.04s
22:	learn: 0.2000682	test: 0.2239463	best: 0.2239463 (22)	total: 10.5s	remaining: 4.58s
23:	learn: 0.1976455	test: 0.2224265	best: 0.2224265 (23)	total: 11s	remaining: 4.12s
24:	learn: 0.1945067	test: 0.2201386	best: 0.2201386 (24)	total: 11.4s	remaining: 3.66s
25:	learn: 0.1923430	test: 0.2187905	best: 0.2187905 (25)	total: 11.9s	remaining: 3.2s
26:	learn: 0.1900933	test: 0.2175582	best: 0.2175582 (26)	total: 12.3s	remaining: 2.74s
27:	learn: 0.1882242	test: 0.2165777	best: 0.2165777 (27)	total: 12.8s	remaining: 2.28s
28:	learn: 0.1859723	test: 0.2150782	best: 0.2150782 (28)	total: 13.2s	remaining: 1.83s
29:	learn: 0.1841249	test: 0.2135696	best: 0.2135696 (29)	total: 13.7s	remaining: 1.37s
30:	learn: 0.1827077	test: 0.2131972	best: 0.2131972 (30)	total: 14.1s	remaining: 913ms
31:	learn: 0.1809727	test: 0.2119271	best: 0.2119271 (31)	total: 14.6s	remaining: 456ms
32:	learn: 0.1793632	test: 0.2113421	best: 0.2113421 (32)	total: 15s	remaining: 0us

bestTest = 0.211342051
bestIteration = 32

Trial 87, Fold 5: Log loss = 0.21134205104358925, Average precision = 0.9732365419463417, ROC-AUC = 0.9697751518867398, Elapsed Time = 15.177862399999867 seconds
Optimization Progress:  88%|########8 | 88/100 [2:46:37<31:34, 157.84s/it]
Trial 88, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 88, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
0:	learn: 0.6224519	test: 0.6231479	best: 0.6231479 (0)	total: 130ms	remaining: 1.17s
1:	learn: 0.5622996	test: 0.5634602	best: 0.5634602 (1)	total: 242ms	remaining: 969ms
2:	learn: 0.5123254	test: 0.5138418	best: 0.5138418 (2)	total: 353ms	remaining: 824ms
3:	learn: 0.4704919	test: 0.4728426	best: 0.4728426 (3)	total: 472ms	remaining: 708ms
4:	learn: 0.4351923	test: 0.4385608	best: 0.4385608 (4)	total: 616ms	remaining: 616ms
5:	learn: 0.4034795	test: 0.4079117	best: 0.4079117 (5)	total: 752ms	remaining: 501ms
6:	learn: 0.3764601	test: 0.3815135	best: 0.3815135 (6)	total: 871ms	remaining: 373ms
7:	learn: 0.3539116	test: 0.3596568	best: 0.3596568 (7)	total: 992ms	remaining: 248ms
8:	learn: 0.3355914	test: 0.3419586	best: 0.3419586 (8)	total: 1.11s	remaining: 124ms
9:	learn: 0.3192551	test: 0.3265294	best: 0.3265294 (9)	total: 1.24s	remaining: 0us

bestTest = 0.326529436
bestIteration = 9

Trial 88, Fold 1: Log loss = 0.3265525342698958, Average precision = 0.9740318048854449, ROC-AUC = 0.9688813640230758, Elapsed Time = 1.3460980000018026 seconds
Trial 88, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 88, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
0:	learn: 0.6234151	test: 0.6239920	best: 0.6239920 (0)	total: 131ms	remaining: 1.18s
1:	learn: 0.5645143	test: 0.5655781	best: 0.5655781 (1)	total: 271ms	remaining: 1.08s
2:	learn: 0.5136015	test: 0.5158391	best: 0.5158391 (2)	total: 409ms	remaining: 955ms
3:	learn: 0.4725953	test: 0.4753741	best: 0.4753741 (3)	total: 545ms	remaining: 818ms
4:	learn: 0.4368350	test: 0.4400977	best: 0.4400977 (4)	total: 676ms	remaining: 676ms
5:	learn: 0.4067551	test: 0.4106645	best: 0.4106645 (5)	total: 805ms	remaining: 537ms
6:	learn: 0.3795999	test: 0.3838291	best: 0.3838291 (6)	total: 948ms	remaining: 406ms
7:	learn: 0.3576553	test: 0.3627150	best: 0.3627150 (7)	total: 1.07s	remaining: 269ms
8:	learn: 0.3388352	test: 0.3442753	best: 0.3442753 (8)	total: 1.21s	remaining: 134ms
9:	learn: 0.3213956	test: 0.3272290	best: 0.3272290 (9)	total: 1.34s	remaining: 0us

bestTest = 0.3272289582
bestIteration = 9

Trial 88, Fold 2: Log loss = 0.32723467518616073, Average precision = 0.9724646298231266, ROC-AUC = 0.9693479336959295, Elapsed Time = 1.4484083999996074 seconds
Trial 88, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 88, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
0:	learn: 0.6237516	test: 0.6241148	best: 0.6241148 (0)	total: 142ms	remaining: 1.27s
1:	learn: 0.5638580	test: 0.5642638	best: 0.5642638 (1)	total: 275ms	remaining: 1.1s
2:	learn: 0.5136600	test: 0.5140643	best: 0.5140643 (2)	total: 403ms	remaining: 941ms
3:	learn: 0.4709290	test: 0.4712186	best: 0.4712186 (3)	total: 541ms	remaining: 811ms
4:	learn: 0.4360744	test: 0.4368341	best: 0.4368341 (4)	total: 700ms	remaining: 700ms
5:	learn: 0.4057766	test: 0.4066058	best: 0.4066058 (5)	total: 830ms	remaining: 553ms
6:	learn: 0.3801435	test: 0.3812701	best: 0.3812701 (6)	total: 967ms	remaining: 415ms
7:	learn: 0.3584478	test: 0.3599220	best: 0.3599220 (7)	total: 1.1s	remaining: 274ms
8:	learn: 0.3387494	test: 0.3405054	best: 0.3405054 (8)	total: 1.22s	remaining: 136ms
9:	learn: 0.3227168	test: 0.3248497	best: 0.3248497 (9)	total: 1.36s	remaining: 0us

bestTest = 0.3248496803
bestIteration = 9

Trial 88, Fold 3: Log loss = 0.32492671269582485, Average precision = 0.9734236674287723, ROC-AUC = 0.9705342997274417, Elapsed Time = 1.4675580999974045 seconds
Trial 88, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 88, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
0:	learn: 0.6229557	test: 0.6236307	best: 0.6236307 (0)	total: 131ms	remaining: 1.18s
1:	learn: 0.5630670	test: 0.5646025	best: 0.5646025 (1)	total: 280ms	remaining: 1.12s
2:	learn: 0.5133630	test: 0.5154427	best: 0.5154427 (2)	total: 408ms	remaining: 951ms
3:	learn: 0.4712449	test: 0.4737168	best: 0.4737168 (3)	total: 548ms	remaining: 823ms
4:	learn: 0.4348760	test: 0.4378488	best: 0.4378488 (4)	total: 686ms	remaining: 686ms
5:	learn: 0.4037121	test: 0.4071164	best: 0.4071164 (5)	total: 812ms	remaining: 542ms
6:	learn: 0.3797202	test: 0.3836552	best: 0.3836552 (6)	total: 925ms	remaining: 397ms
7:	learn: 0.3575981	test: 0.3623004	best: 0.3623004 (7)	total: 1.07s	remaining: 268ms
8:	learn: 0.3384951	test: 0.3435950	best: 0.3435950 (8)	total: 1.19s	remaining: 132ms
9:	learn: 0.3210183	test: 0.3262889	best: 0.3262889 (9)	total: 1.31s	remaining: 0us

bestTest = 0.3262888851
bestIteration = 9

Trial 88, Fold 4: Log loss = 0.32631804141618903, Average precision = 0.9741333940399586, ROC-AUC = 0.9694683034447569, Elapsed Time = 1.4225041000026977 seconds
Trial 88, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 88, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
0:	learn: 0.6234003	test: 0.6246371	best: 0.6246371 (0)	total: 140ms	remaining: 1.26s
1:	learn: 0.5644428	test: 0.5672313	best: 0.5672313 (1)	total: 255ms	remaining: 1.02s
2:	learn: 0.5146575	test: 0.5189076	best: 0.5189076 (2)	total: 388ms	remaining: 905ms
3:	learn: 0.4722280	test: 0.4773995	best: 0.4773995 (3)	total: 519ms	remaining: 778ms
4:	learn: 0.4359995	test: 0.4423026	best: 0.4423026 (4)	total: 649ms	remaining: 649ms
5:	learn: 0.4052814	test: 0.4128553	best: 0.4128553 (5)	total: 801ms	remaining: 534ms
6:	learn: 0.3792006	test: 0.3876542	best: 0.3876542 (6)	total: 928ms	remaining: 398ms
7:	learn: 0.3574206	test: 0.3669476	best: 0.3669476 (7)	total: 1.05s	remaining: 263ms
8:	learn: 0.3379189	test: 0.3481084	best: 0.3481084 (8)	total: 1.19s	remaining: 132ms
9:	learn: 0.3209056	test: 0.3319787	best: 0.3319787 (9)	total: 1.32s	remaining: 0us

bestTest = 0.3319786655
bestIteration = 9

Trial 88, Fold 5: Log loss = 0.3319333786595974, Average precision = 0.9699001520709711, ROC-AUC = 0.9654956431265445, Elapsed Time = 1.4293959999995423 seconds
Optimization Progress:  89%|########9 | 89/100 [2:46:52<21:05, 115.01s/it]
Trial 89, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 89, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
0:	learn: 0.6013315	test: 0.6025500	best: 0.6025500 (0)	total: 318ms	remaining: 31.2s
1:	learn: 0.5257102	test: 0.5271772	best: 0.5271772 (1)	total: 614ms	remaining: 29.8s
2:	learn: 0.4700853	test: 0.4715170	best: 0.4715170 (2)	total: 898ms	remaining: 28.7s
3:	learn: 0.4220272	test: 0.4240443	best: 0.4240443 (3)	total: 1.19s	remaining: 28.3s
4:	learn: 0.3909089	test: 0.3933190	best: 0.3933190 (4)	total: 1.46s	remaining: 27.5s
5:	learn: 0.3625561	test: 0.3653810	best: 0.3653810 (5)	total: 1.71s	remaining: 26.6s
6:	learn: 0.3396757	test: 0.3434287	best: 0.3434287 (6)	total: 2.01s	remaining: 26.5s
7:	learn: 0.3211635	test: 0.3256694	best: 0.3256694 (7)	total: 2.24s	remaining: 25.4s
8:	learn: 0.3057485	test: 0.3127153	best: 0.3127153 (8)	total: 2.49s	remaining: 24.9s
9:	learn: 0.2904888	test: 0.2977730	best: 0.2977730 (9)	total: 2.77s	remaining: 24.7s
10:	learn: 0.2927099	test: 0.2870272	best: 0.2870272 (10)	total: 3.02s	remaining: 24.2s
11:	learn: 0.2764124	test: 0.2751959	best: 0.2751959 (11)	total: 3.33s	remaining: 24.1s
12:	learn: 0.2687353	test: 0.2681612	best: 0.2681612 (12)	total: 3.62s	remaining: 23.9s
13:	learn: 0.2624698	test: 0.2623015	best: 0.2623015 (13)	total: 3.85s	remaining: 23.4s
14:	learn: 0.2504236	test: 0.2547461	best: 0.2547461 (14)	total: 4.16s	remaining: 23.3s
15:	learn: 0.2543011	test: 0.2488625	best: 0.2488625 (15)	total: 4.46s	remaining: 23.1s
16:	learn: 0.2702687	test: 0.2443205	best: 0.2443205 (16)	total: 4.76s	remaining: 23s
17:	learn: 0.2657432	test: 0.2400647	best: 0.2400647 (17)	total: 5.03s	remaining: 22.6s
18:	learn: 0.2610073	test: 0.2355997	best: 0.2355997 (18)	total: 5.33s	remaining: 22.4s
19:	learn: 0.2568091	test: 0.2322644	best: 0.2322644 (19)	total: 5.64s	remaining: 22.3s
20:	learn: 0.2526542	test: 0.2289424	best: 0.2289424 (20)	total: 5.95s	remaining: 22.1s
21:	learn: 0.2497173	test: 0.2265896	best: 0.2265896 (21)	total: 6.25s	remaining: 21.9s
22:	learn: 0.2465668	test: 0.2241723	best: 0.2241723 (22)	total: 6.54s	remaining: 21.6s
Training has stopped (degenerate solution on iteration 23, probably too small l2-regularization, try to increase it)
bestTest = 0.2241722929
bestIteration = 22

Shrink model to first 23 iterations.
Trial 89, Fold 1: Log loss = 0.22403616605955526, Average precision = 0.9728023961458518, ROC-AUC = 0.9680574823274688, Elapsed Time = 6.91963570000371 seconds
Trial 89, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 89, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
0:	learn: 0.5902305	test: 0.5911349	best: 0.5911349 (0)	total: 302ms	remaining: 29.6s
1:	learn: 0.5217683	test: 0.5238795	best: 0.5238795 (1)	total: 606ms	remaining: 29.4s
2:	learn: 0.4680902	test: 0.4711911	best: 0.4711911 (2)	total: 908ms	remaining: 29.1s
3:	learn: 0.4225324	test: 0.4260958	best: 0.4260958 (3)	total: 1.21s	remaining: 28.8s
4:	learn: 0.3860159	test: 0.3899937	best: 0.3899937 (4)	total: 1.48s	remaining: 27.8s
5:	learn: 0.3608215	test: 0.3655095	best: 0.3655095 (5)	total: 1.77s	remaining: 27.4s
6:	learn: 0.3414423	test: 0.3466158	best: 0.3466158 (6)	total: 2.08s	remaining: 27.3s
7:	learn: 0.3207897	test: 0.3268568	best: 0.3268568 (7)	total: 2.38s	remaining: 27s
8:	learn: 0.3036343	test: 0.3101614	best: 0.3101614 (8)	total: 2.68s	remaining: 26.8s
9:	learn: 0.2883941	test: 0.2951290	best: 0.2951290 (9)	total: 2.95s	remaining: 26.3s
10:	learn: 0.2775877	test: 0.2841583	best: 0.2841583 (10)	total: 3.23s	remaining: 25.8s
11:	learn: 0.2673975	test: 0.2739177	best: 0.2739177 (11)	total: 3.53s	remaining: 25.6s
12:	learn: 0.2582042	test: 0.2649657	best: 0.2649657 (12)	total: 3.84s	remaining: 25.4s
13:	learn: 0.2506904	test: 0.2574492	best: 0.2574492 (13)	total: 4.13s	remaining: 25.1s
14:	learn: 0.2408881	test: 0.2484660	best: 0.2484660 (14)	total: 4.44s	remaining: 24.9s
15:	learn: 0.2356407	test: 0.2432841	best: 0.2432841 (15)	total: 4.72s	remaining: 24.5s
16:	learn: 0.2297170	test: 0.2375761	best: 0.2375761 (16)	total: 5.01s	remaining: 24.2s
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22:	learn: 0.2074410	test: 0.2166215	best: 0.2166215 (22)	total: 6.86s	remaining: 22.7s
23:	learn: 0.2047785	test: 0.2143016	best: 0.2143016 (23)	total: 7.17s	remaining: 22.4s
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26:	learn: 0.1981367	test: 0.2083979	best: 0.2083979 (26)	total: 8.07s	remaining: 21.5s
27:	learn: 0.1963336	test: 0.2069289	best: 0.2069289 (27)	total: 8.38s	remaining: 21.2s
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30:	learn: 0.2091986	test: 0.2031639	best: 0.2031639 (30)	total: 9.17s	remaining: 20.1s
31:	learn: 0.2320290	test: 0.3974868	best: 0.2031639 (30)	total: 9.42s	remaining: 19.7s
32:	learn: 0.2304974	test: 0.3964309	best: 0.2031639 (30)	total: 9.66s	remaining: 19.3s
33:	learn: 0.2291906	test: 0.3954546	best: 0.2031639 (30)	total: 9.92s	remaining: 19s
34:	learn: 0.2286059	test: 0.3949846	best: 0.2031639 (30)	total: 10.1s	remaining: 18.4s
35:	learn: 0.2276136	test: 0.3942723	best: 0.2031639 (30)	total: 10.3s	remaining: 18.1s
36:	learn: 0.2262040	test: 0.3934373	best: 0.2031639 (30)	total: 10.6s	remaining: 17.8s
37:	learn: 0.2256230	test: 0.3931389	best: 0.2031639 (30)	total: 10.8s	remaining: 17.3s
38:	learn: 0.2253682	test: 0.3929809	best: 0.2031639 (30)	total: 10.9s	remaining: 16.8s
39:	learn: 0.2501523	test: 0.3927480	best: 0.2031639 (30)	total: 11.1s	remaining: 16.4s
40:	learn: 0.3226237	test: 0.4872740	best: 0.2031639 (30)	total: 11.2s	remaining: 15.9s

bestTest = 0.2031638743
bestIteration = 30

Shrink model to first 31 iterations.
Training has stopped (degenerate solution on iteration 41, probably too small l2-regularization, try to increase it)
Trial 89, Fold 2: Log loss = 0.20292609529155894, Average precision = 0.97405188344116, ROC-AUC = 0.971974004997358, Elapsed Time = 11.54586019999988 seconds
Trial 89, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 89, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
0:	learn: 0.6065515	test: 0.6056032	best: 0.6056032 (0)	total: 264ms	remaining: 25.9s
1:	learn: 0.5322196	test: 0.5316723	best: 0.5316723 (1)	total: 545ms	remaining: 26.4s
2:	learn: 0.4724222	test: 0.4716816	best: 0.4716816 (2)	total: 844ms	remaining: 27s
3:	learn: 0.4244193	test: 0.4237725	best: 0.4237725 (3)	total: 1.1s	remaining: 26.2s
4:	learn: 0.3880140	test: 0.3879097	best: 0.3879097 (4)	total: 1.41s	remaining: 26.5s
5:	learn: 0.3576776	test: 0.3575856	best: 0.3575856 (5)	total: 1.72s	remaining: 26.7s
6:	learn: 0.3363683	test: 0.3364709	best: 0.3364709 (6)	total: 2.03s	remaining: 26.7s
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11:	learn: 0.2617315	test: 0.2659180	best: 0.2659180 (11)	total: 3.41s	remaining: 24.8s
12:	learn: 0.2524261	test: 0.2569010	best: 0.2569010 (12)	total: 3.72s	remaining: 24.6s
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14:	learn: 0.2389509	test: 0.2442122	best: 0.2442122 (14)	total: 4.34s	remaining: 24.3s
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38:	learn: 0.3066941	test: 0.2578095	best: 0.1989359 (35)	total: 10.6s	remaining: 16.2s
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40:	learn: 0.3051972	test: 0.2568941	best: 0.1989359 (35)	total: 11s	remaining: 15.5s
41:	learn: 0.3045701	test: 0.2565725	best: 0.1989359 (35)	total: 11.2s	remaining: 15.2s
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50:	learn: 0.3453169	test: 0.3071520	best: 0.1989359 (35)	total: 12.8s	remaining: 12s
51:	learn: 0.7488106	test: 0.3887059	best: 0.1989359 (35)	total: 13.1s	remaining: 11.8s
Training has stopped (degenerate solution on iteration 52, probably too small l2-regularization, try to increase it)
bestTest = 0.1989359254
bestIteration = 35

Shrink model to first 36 iterations.
Trial 89, Fold 3: Log loss = 0.1987894436384647, Average precision = 0.9754225044156319, ROC-AUC = 0.9718194409665708, Elapsed Time = 13.423010099999374 seconds
Trial 89, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 89, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
0:	learn: 0.5946326	test: 0.5953119	best: 0.5953119 (0)	total: 279ms	remaining: 27.3s
1:	learn: 0.5271274	test: 0.5279984	best: 0.5279984 (1)	total: 578ms	remaining: 28.1s
2:	learn: 0.4764557	test: 0.4774341	best: 0.4774341 (2)	total: 851ms	remaining: 27.2s
3:	learn: 0.4314397	test: 0.4328545	best: 0.4328545 (3)	total: 1.14s	remaining: 27.1s
4:	learn: 0.3915860	test: 0.3936177	best: 0.3936177 (4)	total: 1.45s	remaining: 27.2s
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89:	learn: 0.4923887	test: 0.2240072	best: 0.1956138 (54)	total: 19.3s	remaining: 1.93s
90:	learn: 0.4921137	test: 0.2239805	best: 0.1956138 (54)	total: 19.5s	remaining: 1.71s
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94:	learn: 0.4912507	test: 0.2238714	best: 0.1956138 (54)	total: 20.1s	remaining: 845ms
95:	learn: 0.4911323	test: 0.2239271	best: 0.1956138 (54)	total: 20.2s	remaining: 630ms
96:	learn: 0.4910053	test: 0.2238548	best: 0.1956138 (54)	total: 20.3s	remaining: 419ms
97:	learn: 0.4908478	test: 0.2238116	best: 0.1956138 (54)	total: 20.5s	remaining: 209ms
98:	learn: 0.4906750	test: 0.2239164	best: 0.1956138 (54)	total: 20.7s	remaining: 0us

bestTest = 0.1956138421
bestIteration = 54

Shrink model to first 55 iterations.
Trial 89, Fold 4: Log loss = 0.195184653196275, Average precision = 0.975645862695531, ROC-AUC = 0.9715126367757057, Elapsed Time = 20.797833399999945 seconds
Trial 89, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 89, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
0:	learn: 0.5997204	test: 0.6015978	best: 0.6015978 (0)	total: 296ms	remaining: 29s
1:	learn: 0.5331859	test: 0.5368385	best: 0.5368385 (1)	total: 607ms	remaining: 29.4s
2:	learn: 0.4750935	test: 0.4799975	best: 0.4799975 (2)	total: 926ms	remaining: 29.6s
3:	learn: 0.4279704	test: 0.4343782	best: 0.4343782 (3)	total: 1.21s	remaining: 28.6s
4:	learn: 0.3876051	test: 0.3946569	best: 0.3946569 (4)	total: 1.47s	remaining: 27.6s
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98:	learn: 0.1801199	test: 0.2351566	best: 0.2036864 (66)	total: 21.7s	remaining: 0us

bestTest = 0.2036863672
bestIteration = 66

Shrink model to first 67 iterations.
Optimization Progress:  90%|######### | 90/100 [2:48:14<17:31, 105.14s/it]
Trial 89, Fold 5: Log loss = 0.20299273275865473, Average precision = 0.9739603927737867, ROC-AUC = 0.9704795347713803, Elapsed Time = 21.790719999997236 seconds
Trial 90, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 90, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
0:	learn: 0.6247489	test: 0.6244435	best: 0.6244435 (0)	total: 17.3ms	remaining: 1.54s
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89:	learn: 0.2262096	test: 0.2310983	best: 0.2310983 (89)	total: 1.94s	remaining: 0us

bestTest = 0.2310983104
bestIteration = 89

Trial 90, Fold 1: Log loss = 0.23051339982515368, Average precision = 0.9680075702390486, ROC-AUC = 0.9627265226576427, Elapsed Time = 2.0497087000039755 seconds
Trial 90, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 90, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
0:	learn: 0.6384403	test: 0.6390188	best: 0.6390188 (0)	total: 18.8ms	remaining: 1.67s
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2:	learn: 0.5564713	test: 0.5574446	best: 0.5574446 (2)	total: 62.4ms	remaining: 1.81s
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85:	learn: 0.2253614	test: 0.2256235	best: 0.2256235 (85)	total: 1.94s	remaining: 90.1ms
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89:	learn: 0.2247448	test: 0.2249847	best: 0.2249847 (89)	total: 2.03s	remaining: 0us

bestTest = 0.2249846571
bestIteration = 89

Trial 90, Fold 2: Log loss = 0.22462144683921156, Average precision = 0.9684789005706683, ROC-AUC = 0.9639012504321018, Elapsed Time = 2.1495166000022436 seconds
Trial 90, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 90, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
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85:	learn: 0.2291966	test: 0.2254915	best: 0.2254915 (85)	total: 1.94s	remaining: 90.4ms
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89:	learn: 0.2279549	test: 0.2244594	best: 0.2244594 (89)	total: 2.04s	remaining: 0us

bestTest = 0.2244593587
bestIteration = 89

Trial 90, Fold 3: Log loss = 0.22415890753308929, Average precision = 0.9699822864344301, ROC-AUC = 0.9653471596873766, Elapsed Time = 2.163629700000456 seconds
Trial 90, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 90, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
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89:	learn: 0.2261967	test: 0.2274735	best: 0.2274735 (89)	total: 1.92s	remaining: 0us

bestTest = 0.2274734577
bestIteration = 89

Trial 90, Fold 4: Log loss = 0.2270119405669334, Average precision = 0.9691139041471833, ROC-AUC = 0.9630855365279657, Elapsed Time = 2.0362184999976307 seconds
Trial 90, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 90, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
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89:	learn: 0.2215599	test: 0.2316752	best: 0.2316752 (89)	total: 1.99s	remaining: 0us

bestTest = 0.2316751919
bestIteration = 89

Trial 90, Fold 5: Log loss = 0.23093247191285196, Average precision = 0.9647195552947815, ROC-AUC = 0.9601831187410588, Elapsed Time = 2.099057699997502 seconds
Optimization Progress:  91%|#########1| 91/100 [2:48:33<11:52, 79.17s/it] 
Trial 91, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 91, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
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87:	learn: 0.1282061	test: 0.1965027	best: 0.1965027 (87)	total: 25s	remaining: 284ms
88:	learn: 0.1278069	test: 0.1964489	best: 0.1964489 (88)	total: 25.3s	remaining: 0us

bestTest = 0.1964488876
bestIteration = 88

Trial 91, Fold 1: Log loss = 0.1961050109510587, Average precision = 0.975616059910034, ROC-AUC = 0.9717072214813859, Elapsed Time = 25.408131299998786 seconds
Trial 91, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 91, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
0:	learn: 0.5890081	test: 0.5904561	best: 0.5904561 (0)	total: 330ms	remaining: 29s
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85:	learn: 0.1307397	test: 0.1802306	best: 0.1801853 (81)	total: 24s	remaining: 838ms
86:	learn: 0.1301798	test: 0.1802534	best: 0.1801853 (81)	total: 24.3s	remaining: 558ms
87:	learn: 0.1297075	test: 0.1799366	best: 0.1799366 (87)	total: 24.6s	remaining: 279ms
88:	learn: 0.1292356	test: 0.1796815	best: 0.1796815 (88)	total: 24.8s	remaining: 0us

bestTest = 0.1796815343
bestIteration = 88

Trial 91, Fold 2: Log loss = 0.1794548550293013, Average precision = 0.977897147942451, ROC-AUC = 0.9755842365614438, Elapsed Time = 24.99169129999791 seconds
Trial 91, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 91, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
0:	learn: 0.5892725	test: 0.5900181	best: 0.5900181 (0)	total: 322ms	remaining: 28.4s
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2:	learn: 0.4450656	test: 0.4468568	best: 0.4468568 (2)	total: 950ms	remaining: 27.2s
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80:	learn: 0.1313023	test: 0.1861968	best: 0.1857865 (71)	total: 22.7s	remaining: 2.25s
81:	learn: 0.1309181	test: 0.1860843	best: 0.1857865 (71)	total: 23s	remaining: 1.96s
82:	learn: 0.1301590	test: 0.1862413	best: 0.1857865 (71)	total: 23.3s	remaining: 1.68s
83:	learn: 0.1297592	test: 0.1861068	best: 0.1857865 (71)	total: 23.5s	remaining: 1.4s
84:	learn: 0.1293246	test: 0.1860841	best: 0.1857865 (71)	total: 23.8s	remaining: 1.12s
85:	learn: 0.1289072	test: 0.1860847	best: 0.1857865 (71)	total: 24.1s	remaining: 839ms
86:	learn: 0.1284405	test: 0.1858796	best: 0.1857865 (71)	total: 24.3s	remaining: 559ms
87:	learn: 0.1279580	test: 0.1859814	best: 0.1857865 (71)	total: 24.6s	remaining: 279ms
88:	learn: 0.1275617	test: 0.1861427	best: 0.1857865 (71)	total: 24.8s	remaining: 0us

bestTest = 0.1857864827
bestIteration = 71

Shrink model to first 72 iterations.
Trial 91, Fold 3: Log loss = 0.18561336490114735, Average precision = 0.9770555590377952, ROC-AUC = 0.9741588043310765, Elapsed Time = 24.98324109999521 seconds
Trial 91, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 91, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
0:	learn: 0.5890804	test: 0.5904647	best: 0.5904647 (0)	total: 300ms	remaining: 26.4s
1:	learn: 0.5080049	test: 0.5104645	best: 0.5104645 (1)	total: 616ms	remaining: 26.8s
2:	learn: 0.4445021	test: 0.4477090	best: 0.4477090 (2)	total: 917ms	remaining: 26.3s
3:	learn: 0.3952452	test: 0.3998256	best: 0.3998256 (3)	total: 1.23s	remaining: 26.2s
4:	learn: 0.3562410	test: 0.3620580	best: 0.3620580 (4)	total: 1.54s	remaining: 25.8s
5:	learn: 0.3263296	test: 0.3333527	best: 0.3333527 (5)	total: 1.85s	remaining: 25.7s
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78:	learn: 0.1327115	test: 0.1906436	best: 0.1902049 (66)	total: 22.8s	remaining: 2.89s
79:	learn: 0.1321652	test: 0.1907227	best: 0.1902049 (66)	total: 23.1s	remaining: 2.6s
80:	learn: 0.1316142	test: 0.1905422	best: 0.1902049 (66)	total: 23.4s	remaining: 2.31s
81:	learn: 0.1310750	test: 0.1905031	best: 0.1902049 (66)	total: 23.6s	remaining: 2.02s
82:	learn: 0.1304846	test: 0.1904162	best: 0.1902049 (66)	total: 23.9s	remaining: 1.73s
83:	learn: 0.1298653	test: 0.1904046	best: 0.1902049 (66)	total: 24.1s	remaining: 1.44s
84:	learn: 0.1295551	test: 0.1902365	best: 0.1902049 (66)	total: 24.4s	remaining: 1.15s
85:	learn: 0.1291643	test: 0.1901823	best: 0.1901823 (85)	total: 24.7s	remaining: 862ms
86:	learn: 0.1286904	test: 0.1902194	best: 0.1901823 (85)	total: 25s	remaining: 574ms
87:	learn: 0.1283029	test: 0.1903262	best: 0.1901823 (85)	total: 25.3s	remaining: 287ms
88:	learn: 0.1277280	test: 0.1903904	best: 0.1901823 (85)	total: 25.5s	remaining: 0us

bestTest = 0.1901823167
bestIteration = 85

Shrink model to first 86 iterations.
Trial 91, Fold 4: Log loss = 0.18991550218277667, Average precision = 0.976480947163987, ROC-AUC = 0.9723979979303323, Elapsed Time = 25.658579900002223 seconds
Trial 91, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 91, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
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88:	learn: 0.1266924	test: 0.1943095	best: 0.1943095 (88)	total: 24.7s	remaining: 0us

bestTest = 0.1943094949
bestIteration = 88

Trial 91, Fold 5: Log loss = 0.19393170180505884, Average precision = 0.9757090510226494, ROC-AUC = 0.972612154655073, Elapsed Time = 24.828430500005197 seconds
Optimization Progress:  92%|#########2| 92/100 [2:50:46<12:43, 95.48s/it]
Trial 92, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 92, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
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bestTest = 0.1961268191
bestIteration = 55

Trial 92, Fold 1: Log loss = 0.19556131489577427, Average precision = 0.9764224469448858, ROC-AUC = 0.9721163244480769, Elapsed Time = 6.934846899996046 seconds
Trial 92, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 92, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
0:	learn: 0.5951823	test: 0.5955069	best: 0.5955069 (0)	total: 94.6ms	remaining: 5.21s
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49:	learn: 0.1782838	test: 0.1911751	best: 0.1911751 (49)	total: 5.96s	remaining: 715ms
50:	learn: 0.1778684	test: 0.1910411	best: 0.1910411 (50)	total: 6.08s	remaining: 596ms
51:	learn: 0.1772020	test: 0.1904617	best: 0.1904617 (51)	total: 6.19s	remaining: 476ms
52:	learn: 0.1765163	test: 0.1899463	best: 0.1899463 (52)	total: 6.3s	remaining: 357ms
53:	learn: 0.1758807	test: 0.1896040	best: 0.1896040 (53)	total: 6.42s	remaining: 238ms
54:	learn: 0.1753024	test: 0.1894535	best: 0.1894535 (54)	total: 6.54s	remaining: 119ms
55:	learn: 0.1748359	test: 0.1890643	best: 0.1890643 (55)	total: 6.66s	remaining: 0us

bestTest = 0.1890642783
bestIteration = 55

Trial 92, Fold 2: Log loss = 0.18868938554398013, Average precision = 0.9770155439451153, ROC-AUC = 0.9744132747748746, Elapsed Time = 6.808789899994736 seconds
Trial 92, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 92, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
0:	learn: 0.5959971	test: 0.5957627	best: 0.5957627 (0)	total: 94.6ms	remaining: 5.21s
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2:	learn: 0.4586978	test: 0.4579242	best: 0.4579242 (2)	total: 308ms	remaining: 5.43s
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4:	learn: 0.3744221	test: 0.3734777	best: 0.3734777 (4)	total: 649ms	remaining: 6.62s
5:	learn: 0.3456696	test: 0.3446873	best: 0.3446873 (5)	total: 770ms	remaining: 6.42s
6:	learn: 0.3217520	test: 0.3208486	best: 0.3208486 (6)	total: 885ms	remaining: 6.19s
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47:	learn: 0.1797342	test: 0.1923955	best: 0.1923955 (47)	total: 5.85s	remaining: 975ms
48:	learn: 0.1791832	test: 0.1923709	best: 0.1923709 (48)	total: 5.97s	remaining: 853ms
49:	learn: 0.1786737	test: 0.1918943	best: 0.1918943 (49)	total: 6.08s	remaining: 729ms
50:	learn: 0.1781390	test: 0.1915384	best: 0.1915384 (50)	total: 6.19s	remaining: 607ms
51:	learn: 0.1775685	test: 0.1914385	best: 0.1914385 (51)	total: 6.31s	remaining: 485ms
52:	learn: 0.1769999	test: 0.1911639	best: 0.1911639 (52)	total: 6.43s	remaining: 364ms
53:	learn: 0.1764002	test: 0.1910262	best: 0.1910262 (53)	total: 6.55s	remaining: 243ms
54:	learn: 0.1758641	test: 0.1907972	best: 0.1907972 (54)	total: 6.66s	remaining: 121ms
55:	learn: 0.1753564	test: 0.1904493	best: 0.1904493 (55)	total: 6.79s	remaining: 0us

bestTest = 0.1904492758
bestIteration = 55

Trial 92, Fold 3: Log loss = 0.19015322927809597, Average precision = 0.9758680395724453, ROC-AUC = 0.9732675573768913, Elapsed Time = 6.929672999998729 seconds
Trial 92, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 92, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
0:	learn: 0.5956446	test: 0.5956901	best: 0.5956901 (0)	total: 93.3ms	remaining: 5.13s
1:	learn: 0.5195863	test: 0.5196381	best: 0.5196381 (1)	total: 201ms	remaining: 5.42s
2:	learn: 0.4595773	test: 0.4594265	best: 0.4594265 (2)	total: 313ms	remaining: 5.53s
3:	learn: 0.4117250	test: 0.4113480	best: 0.4113480 (3)	total: 423ms	remaining: 5.5s
4:	learn: 0.3749985	test: 0.3747277	best: 0.3747277 (4)	total: 527ms	remaining: 5.37s
5:	learn: 0.3453747	test: 0.3454005	best: 0.3454005 (5)	total: 637ms	remaining: 5.3s
6:	learn: 0.3221247	test: 0.3224230	best: 0.3224230 (6)	total: 746ms	remaining: 5.22s
7:	learn: 0.3027358	test: 0.3035132	best: 0.3035132 (7)	total: 858ms	remaining: 5.14s
8:	learn: 0.2864028	test: 0.2878888	best: 0.2878888 (8)	total: 969ms	remaining: 5.06s
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55:	learn: 0.1739218	test: 0.1933340	best: 0.1933340 (55)	total: 6.56s	remaining: 0us

bestTest = 0.1933339555
bestIteration = 55

Trial 92, Fold 4: Log loss = 0.1928947959492357, Average precision = 0.9764457522976637, ROC-AUC = 0.9724084297230986, Elapsed Time = 6.705496800001129 seconds
Trial 92, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 92, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
0:	learn: 0.5952907	test: 0.5970351	best: 0.5970351 (0)	total: 94ms	remaining: 5.17s
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2:	learn: 0.4567117	test: 0.4612822	best: 0.4612822 (2)	total: 326ms	remaining: 5.75s
3:	learn: 0.4090908	test: 0.4149374	best: 0.4149374 (3)	total: 431ms	remaining: 5.61s
4:	learn: 0.3717366	test: 0.3785830	best: 0.3785830 (4)	total: 538ms	remaining: 5.49s
5:	learn: 0.3414930	test: 0.3495358	best: 0.3495358 (5)	total: 646ms	remaining: 5.38s
6:	learn: 0.3182762	test: 0.3271378	best: 0.3271378 (6)	total: 754ms	remaining: 5.28s
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55:	learn: 0.1721205	test: 0.2017962	best: 0.2017962 (55)	total: 6.63s	remaining: 0us

bestTest = 0.2017961921
bestIteration = 55

Trial 92, Fold 5: Log loss = 0.20111419071654832, Average precision = 0.9742303516876331, ROC-AUC = 0.9709538301469631, Elapsed Time = 6.771970400004648 seconds
Optimization Progress:  93%|#########3| 93/100 [2:51:28<09:16, 79.48s/it]
Trial 93, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 93, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
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85:	learn: 0.1952230	test: 0.2069677	best: 0.2069677 (85)	total: 6.11s	remaining: 0us

bestTest = 0.2069677297
bestIteration = 85

Trial 93, Fold 1: Log loss = 0.20636956049698438, Average precision = 0.9728329234990032, ROC-AUC = 0.9695648452846961, Elapsed Time = 6.222817199995916 seconds
Trial 93, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 93, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
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79:	learn: 0.1984442	test: 0.2022186	best: 0.2022186 (79)	total: 5.69s	remaining: 427ms
80:	learn: 0.1983635	test: 0.2021398	best: 0.2021398 (80)	total: 5.75s	remaining: 355ms
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83:	learn: 0.1977752	test: 0.2016664	best: 0.2016664 (83)	total: 5.95s	remaining: 142ms
84:	learn: 0.1976139	test: 0.2016491	best: 0.2016491 (84)	total: 6.02s	remaining: 70.8ms
85:	learn: 0.1975491	test: 0.2016201	best: 0.2016201 (85)	total: 6.08s	remaining: 0us

bestTest = 0.2016200922
bestIteration = 85

Trial 93, Fold 2: Log loss = 0.201206877637942, Average precision = 0.9739785718672523, ROC-AUC = 0.9707472758996292, Elapsed Time = 6.19267260000197 seconds
Trial 93, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 93, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
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83:	learn: 0.1999737	test: 0.2022101	best: 0.2022101 (83)	total: 5.97s	remaining: 142ms
84:	learn: 0.1996405	test: 0.2018349	best: 0.2018349 (84)	total: 6.04s	remaining: 71.1ms
85:	learn: 0.1991676	test: 0.2014304	best: 0.2014304 (85)	total: 6.11s	remaining: 0us

bestTest = 0.201430364
bestIteration = 85

Trial 93, Fold 3: Log loss = 0.20110905395662873, Average precision = 0.9740746789266266, ROC-AUC = 0.9705698141691788, Elapsed Time = 6.227032500006317 seconds
Trial 93, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 93, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
0:	learn: 0.5999119	test: 0.6007997	best: 0.6007997 (0)	total: 74.3ms	remaining: 6.32s
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83:	learn: 0.1999360	test: 0.2075237	best: 0.2075237 (83)	total: 6s	remaining: 143ms
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85:	learn: 0.1996900	test: 0.2073762	best: 0.2073762 (85)	total: 6.14s	remaining: 0us

bestTest = 0.207376201
bestIteration = 85

Trial 93, Fold 4: Log loss = 0.20686190202264296, Average precision = 0.9735082620764466, ROC-AUC = 0.9684091784277677, Elapsed Time = 6.252805199997965 seconds
Trial 93, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 93, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
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80:	learn: 0.1934145	test: 0.2122430	best: 0.2122430 (80)	total: 5.9s	remaining: 364ms
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84:	learn: 0.1927560	test: 0.2118282	best: 0.2118282 (84)	total: 6.19s	remaining: 72.8ms
85:	learn: 0.1922354	test: 0.2113304	best: 0.2113304 (85)	total: 6.25s	remaining: 0us

bestTest = 0.2113303999
bestIteration = 85

Trial 93, Fold 5: Log loss = 0.2105990331303193, Average precision = 0.9715499741051221, ROC-AUC = 0.9682608550248036, Elapsed Time = 6.37430100000347 seconds
Optimization Progress:  94%|#########3| 94/100 [2:52:08<06:44, 67.39s/it]
Trial 94, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 94, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
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45:	learn: 0.1713381	test: 0.2127849	best: 0.2127849 (45)	total: 14.5s	remaining: 0us

bestTest = 0.212784861
bestIteration = 45

Trial 94, Fold 1: Log loss = 0.21252819744697407, Average precision = 0.974217960654209, ROC-AUC = 0.9709315090836094, Elapsed Time = 14.636794699996244 seconds
Trial 94, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 94, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
0:	learn: 0.6374757	test: 0.6390619	best: 0.6390619 (0)	total: 303ms	remaining: 13.6s
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29:	learn: 0.2051835	test: 0.2284691	best: 0.2284691 (29)	total: 8.79s	remaining: 4.69s
30:	learn: 0.2022807	test: 0.2260844	best: 0.2260844 (30)	total: 9.1s	remaining: 4.4s
31:	learn: 0.1996252	test: 0.2238293	best: 0.2238293 (31)	total: 9.35s	remaining: 4.09s
32:	learn: 0.1970528	test: 0.2218816	best: 0.2218816 (32)	total: 9.64s	remaining: 3.8s
33:	learn: 0.1944145	test: 0.2199244	best: 0.2199244 (33)	total: 9.94s	remaining: 3.51s
34:	learn: 0.1917403	test: 0.2179858	best: 0.2179858 (34)	total: 10.2s	remaining: 3.22s
35:	learn: 0.1899630	test: 0.2164320	best: 0.2164320 (35)	total: 10.5s	remaining: 2.91s
36:	learn: 0.1877038	test: 0.2147595	best: 0.2147595 (36)	total: 10.8s	remaining: 2.62s
37:	learn: 0.1856771	test: 0.2130536	best: 0.2130536 (37)	total: 11s	remaining: 2.32s
38:	learn: 0.1837598	test: 0.2119098	best: 0.2119098 (38)	total: 11.3s	remaining: 2.04s
39:	learn: 0.1817612	test: 0.2102657	best: 0.2102657 (39)	total: 11.6s	remaining: 1.74s
40:	learn: 0.1803498	test: 0.2090995	best: 0.2090995 (40)	total: 11.9s	remaining: 1.45s
41:	learn: 0.1787670	test: 0.2080725	best: 0.2080725 (41)	total: 12.1s	remaining: 1.16s
42:	learn: 0.1769770	test: 0.2068264	best: 0.2068264 (42)	total: 12.4s	remaining: 867ms
43:	learn: 0.1754227	test: 0.2057044	best: 0.2057044 (43)	total: 12.7s	remaining: 577ms
44:	learn: 0.1736131	test: 0.2046041	best: 0.2046041 (44)	total: 13s	remaining: 289ms
45:	learn: 0.1722434	test: 0.2035451	best: 0.2035451 (45)	total: 13.3s	remaining: 0us

bestTest = 0.2035451156
bestIteration = 45

Trial 94, Fold 2: Log loss = 0.20336771407503848, Average precision = 0.9753278846963165, ROC-AUC = 0.9732267588402947, Elapsed Time = 13.444962800000212 seconds
Trial 94, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 94, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
0:	learn: 0.6368930	test: 0.6383617	best: 0.6383617 (0)	total: 295ms	remaining: 13.3s
1:	learn: 0.5882204	test: 0.5906543	best: 0.5906543 (1)	total: 548ms	remaining: 12.1s
2:	learn: 0.5448378	test: 0.5478843	best: 0.5478843 (2)	total: 815ms	remaining: 11.7s
3:	learn: 0.5067284	test: 0.5104835	best: 0.5104835 (3)	total: 1.07s	remaining: 11.3s
4:	learn: 0.4730618	test: 0.4775106	best: 0.4775106 (4)	total: 1.36s	remaining: 11.1s
5:	learn: 0.4430664	test: 0.4480938	best: 0.4480938 (5)	total: 1.61s	remaining: 10.7s
6:	learn: 0.4170043	test: 0.4225212	best: 0.4225212 (6)	total: 1.88s	remaining: 10.5s
7:	learn: 0.3944895	test: 0.4008806	best: 0.4008806 (7)	total: 2.17s	remaining: 10.3s
8:	learn: 0.3740515	test: 0.3810249	best: 0.3810249 (8)	total: 2.44s	remaining: 10s
9:	learn: 0.3557720	test: 0.3636558	best: 0.3636558 (9)	total: 2.71s	remaining: 9.77s
10:	learn: 0.3391468	test: 0.3477249	best: 0.3477249 (10)	total: 3.01s	remaining: 9.58s
11:	learn: 0.3241903	test: 0.3333545	best: 0.3333545 (11)	total: 3.29s	remaining: 9.31s
12:	learn: 0.3112677	test: 0.3209282	best: 0.3209282 (12)	total: 3.55s	remaining: 9.01s
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27:	learn: 0.2111208	test: 0.2323461	best: 0.2323461 (27)	total: 7.94s	remaining: 5.1s
28:	learn: 0.2082995	test: 0.2300834	best: 0.2300834 (28)	total: 8.22s	remaining: 4.82s
29:	learn: 0.2049655	test: 0.2274763	best: 0.2274763 (29)	total: 8.53s	remaining: 4.55s
30:	learn: 0.2017700	test: 0.2250271	best: 0.2250271 (30)	total: 8.84s	remaining: 4.28s
31:	learn: 0.1993558	test: 0.2231235	best: 0.2231235 (31)	total: 9.11s	remaining: 3.98s
32:	learn: 0.1966483	test: 0.2210074	best: 0.2210074 (32)	total: 9.41s	remaining: 3.71s
33:	learn: 0.1938348	test: 0.2189417	best: 0.2189417 (33)	total: 9.72s	remaining: 3.43s
34:	learn: 0.1915713	test: 0.2170042	best: 0.2170042 (34)	total: 9.98s	remaining: 3.14s
35:	learn: 0.1895156	test: 0.2154928	best: 0.2154928 (35)	total: 10.2s	remaining: 2.85s
36:	learn: 0.1877999	test: 0.2141650	best: 0.2141650 (36)	total: 10.5s	remaining: 2.55s
37:	learn: 0.1856674	test: 0.2125714	best: 0.2125714 (37)	total: 10.8s	remaining: 2.27s
38:	learn: 0.1836816	test: 0.2111144	best: 0.2111144 (38)	total: 11.1s	remaining: 1.99s
39:	learn: 0.1819892	test: 0.2099267	best: 0.2099267 (39)	total: 11.4s	remaining: 1.71s
40:	learn: 0.1804165	test: 0.2088309	best: 0.2088309 (40)	total: 11.6s	remaining: 1.42s
41:	learn: 0.1787422	test: 0.2076416	best: 0.2076416 (41)	total: 12s	remaining: 1.14s
42:	learn: 0.1770495	test: 0.2066743	best: 0.2066743 (42)	total: 12.3s	remaining: 861ms
43:	learn: 0.1756790	test: 0.2057967	best: 0.2057967 (43)	total: 12.6s	remaining: 573ms
44:	learn: 0.1743221	test: 0.2048611	best: 0.2048611 (44)	total: 12.9s	remaining: 286ms
45:	learn: 0.1728689	test: 0.2040439	best: 0.2040439 (45)	total: 13.2s	remaining: 0us

bestTest = 0.2040438513
bestIteration = 45

Trial 94, Fold 3: Log loss = 0.2039125345162296, Average precision = 0.9768937951634301, ROC-AUC = 0.9732710185301114, Elapsed Time = 13.332860399998026 seconds
Trial 94, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 94, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
0:	learn: 0.6377034	test: 0.6387764	best: 0.6387764 (0)	total: 266ms	remaining: 12s
1:	learn: 0.5888450	test: 0.5907178	best: 0.5907178 (1)	total: 511ms	remaining: 11.2s
2:	learn: 0.5455360	test: 0.5481811	best: 0.5481811 (2)	total: 774ms	remaining: 11.1s
3:	learn: 0.5075927	test: 0.5112164	best: 0.5112164 (3)	total: 1.06s	remaining: 11.1s
4:	learn: 0.4734769	test: 0.4777740	best: 0.4777740 (4)	total: 1.33s	remaining: 10.9s
5:	learn: 0.4435133	test: 0.4489226	best: 0.4489226 (5)	total: 1.64s	remaining: 10.9s
6:	learn: 0.4172800	test: 0.4234896	best: 0.4234896 (6)	total: 1.9s	remaining: 10.6s
7:	learn: 0.3938127	test: 0.4011187	best: 0.4011187 (7)	total: 2.22s	remaining: 10.6s
8:	learn: 0.3729450	test: 0.3810529	best: 0.3810529 (8)	total: 2.5s	remaining: 10.3s
9:	learn: 0.3541240	test: 0.3628072	best: 0.3628072 (9)	total: 2.77s	remaining: 9.96s
10:	learn: 0.3388183	test: 0.3481408	best: 0.3481408 (10)	total: 3.07s	remaining: 9.77s
11:	learn: 0.3239743	test: 0.3341090	best: 0.3341090 (11)	total: 3.35s	remaining: 9.49s
12:	learn: 0.3116891	test: 0.3222758	best: 0.3222758 (12)	total: 3.6s	remaining: 9.15s
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27:	learn: 0.2112076	test: 0.2328174	best: 0.2328174 (27)	total: 7.95s	remaining: 5.11s
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31:	learn: 0.1983594	test: 0.2227115	best: 0.2227115 (31)	total: 9.13s	remaining: 3.99s
32:	learn: 0.1961655	test: 0.2208738	best: 0.2208738 (32)	total: 9.37s	remaining: 3.69s
33:	learn: 0.1941645	test: 0.2191744	best: 0.2191744 (33)	total: 9.62s	remaining: 3.4s
34:	learn: 0.1918496	test: 0.2174560	best: 0.2174560 (34)	total: 9.89s	remaining: 3.11s
35:	learn: 0.1896796	test: 0.2159683	best: 0.2159683 (35)	total: 10.2s	remaining: 2.83s
36:	learn: 0.1875833	test: 0.2143209	best: 0.2143209 (36)	total: 10.5s	remaining: 2.54s
37:	learn: 0.1855823	test: 0.2127931	best: 0.2127931 (37)	total: 10.7s	remaining: 2.25s
38:	learn: 0.1838957	test: 0.2117682	best: 0.2117682 (38)	total: 11s	remaining: 1.97s
39:	learn: 0.1820986	test: 0.2104551	best: 0.2104551 (39)	total: 11.3s	remaining: 1.69s
40:	learn: 0.1806989	test: 0.2094100	best: 0.2094100 (40)	total: 11.5s	remaining: 1.4s
41:	learn: 0.1791737	test: 0.2082330	best: 0.2082330 (41)	total: 11.8s	remaining: 1.12s
42:	learn: 0.1775020	test: 0.2069686	best: 0.2069686 (42)	total: 12s	remaining: 838ms
43:	learn: 0.1760642	test: 0.2058805	best: 0.2058805 (43)	total: 12.3s	remaining: 557ms
44:	learn: 0.1744906	test: 0.2047745	best: 0.2047745 (44)	total: 12.5s	remaining: 279ms
45:	learn: 0.1730480	test: 0.2038025	best: 0.2038025 (45)	total: 12.8s	remaining: 0us

bestTest = 0.2038025047
bestIteration = 45

Trial 94, Fold 4: Log loss = 0.20360199853009206, Average precision = 0.9772784588469647, ROC-AUC = 0.9738545742517126, Elapsed Time = 12.94422840000334 seconds
Trial 94, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 94, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
0:	learn: 0.6377750	test: 0.6396367	best: 0.6396367 (0)	total: 296ms	remaining: 13.3s
1:	learn: 0.5886315	test: 0.5921352	best: 0.5921352 (1)	total: 552ms	remaining: 12.1s
2:	learn: 0.5448371	test: 0.5499350	best: 0.5499350 (2)	total: 815ms	remaining: 11.7s
3:	learn: 0.5063041	test: 0.5128082	best: 0.5128082 (3)	total: 1.07s	remaining: 11.2s
4:	learn: 0.4721258	test: 0.4802964	best: 0.4802964 (4)	total: 1.34s	remaining: 11s
5:	learn: 0.4421518	test: 0.4517627	best: 0.4517627 (5)	total: 1.65s	remaining: 11s
6:	learn: 0.4156068	test: 0.4265917	best: 0.4265917 (6)	total: 1.92s	remaining: 10.7s
7:	learn: 0.3919919	test: 0.4041110	best: 0.4041110 (7)	total: 2.18s	remaining: 10.3s
8:	learn: 0.3711357	test: 0.3841629	best: 0.3841629 (8)	total: 2.45s	remaining: 10.1s
9:	learn: 0.3522460	test: 0.3665733	best: 0.3665733 (9)	total: 2.76s	remaining: 9.93s
10:	learn: 0.3354763	test: 0.3511934	best: 0.3511934 (10)	total: 3.04s	remaining: 9.69s
11:	learn: 0.3203715	test: 0.3372535	best: 0.3372535 (11)	total: 3.33s	remaining: 9.43s
12:	learn: 0.3072184	test: 0.3249341	best: 0.3249341 (12)	total: 3.61s	remaining: 9.16s
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18:	learn: 0.2520932	test: 0.2755090	best: 0.2755090 (18)	total: 5.38s	remaining: 7.65s
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45:	learn: 0.1689392	test: 0.2119678	best: 0.2119678 (45)	total: 13.1s	remaining: 0us

bestTest = 0.2119678358
bestIteration = 45

Trial 94, Fold 5: Log loss = 0.21166354035556453, Average precision = 0.9743657194657328, ROC-AUC = 0.9717300782193486, Elapsed Time = 13.27798199999961 seconds
Optimization Progress:  95%|#########5| 95/100 [2:53:24<05:50, 70.11s/it]
Trial 95, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 95, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
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58:	learn: 0.3198230	test: 0.3239961	best: 0.3239961 (58)	total: 2.68s	remaining: 409ms
59:	learn: 0.3172879	test: 0.3214283	best: 0.3214283 (59)	total: 2.72s	remaining: 363ms
60:	learn: 0.3154897	test: 0.3198259	best: 0.3198259 (60)	total: 2.77s	remaining: 318ms
61:	learn: 0.3127970	test: 0.3171170	best: 0.3171170 (61)	total: 2.82s	remaining: 273ms
62:	learn: 0.3104596	test: 0.3147810	best: 0.3147810 (62)	total: 2.87s	remaining: 228ms
63:	learn: 0.3085754	test: 0.3129700	best: 0.3129700 (63)	total: 2.91s	remaining: 182ms
64:	learn: 0.3064386	test: 0.3108300	best: 0.3108300 (64)	total: 2.96s	remaining: 137ms
65:	learn: 0.3042285	test: 0.3086596	best: 0.3086596 (65)	total: 3.01s	remaining: 91.2ms
66:	learn: 0.3022735	test: 0.3066470	best: 0.3066470 (66)	total: 3.06s	remaining: 45.7ms
67:	learn: 0.3005506	test: 0.3049620	best: 0.3049620 (67)	total: 3.1s	remaining: 0us

bestTest = 0.3049619817
bestIteration = 67

Trial 95, Fold 1: Log loss = 0.3048867323429682, Average precision = 0.9689622510859973, ROC-AUC = 0.9619546936761255, Elapsed Time = 3.2133815999986837 seconds
Trial 95, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 95, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
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59:	learn: 0.3185491	test: 0.3232094	best: 0.3232094 (59)	total: 2.86s	remaining: 381ms
60:	learn: 0.3168306	test: 0.3216199	best: 0.3216199 (60)	total: 2.91s	remaining: 334ms
61:	learn: 0.3147377	test: 0.3196414	best: 0.3196414 (61)	total: 2.96s	remaining: 287ms
62:	learn: 0.3124491	test: 0.3173254	best: 0.3173254 (62)	total: 3.01s	remaining: 239ms
63:	learn: 0.3108643	test: 0.3158627	best: 0.3158627 (63)	total: 3.06s	remaining: 191ms
64:	learn: 0.3087950	test: 0.3136918	best: 0.3136918 (64)	total: 3.1s	remaining: 143ms
65:	learn: 0.3069371	test: 0.3118866	best: 0.3118866 (65)	total: 3.15s	remaining: 95.5ms
66:	learn: 0.3055162	test: 0.3105978	best: 0.3105978 (66)	total: 3.2s	remaining: 47.8ms
67:	learn: 0.3035205	test: 0.3085440	best: 0.3085440 (67)	total: 3.25s	remaining: 0us

bestTest = 0.3085439697
bestIteration = 67

Trial 95, Fold 2: Log loss = 0.3085093171522456, Average precision = 0.9674176039455818, ROC-AUC = 0.9631101892264533, Elapsed Time = 3.363808900001459 seconds
Trial 95, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 95, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
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59:	learn: 0.3165687	test: 0.3164985	best: 0.3164985 (59)	total: 2.82s	remaining: 376ms
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61:	learn: 0.3122219	test: 0.3121100	best: 0.3121100 (61)	total: 2.91s	remaining: 282ms
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63:	learn: 0.3091181	test: 0.3090444	best: 0.3090444 (63)	total: 3s	remaining: 187ms
64:	learn: 0.3070386	test: 0.3069553	best: 0.3069553 (64)	total: 3.04s	remaining: 140ms
65:	learn: 0.3049912	test: 0.3049199	best: 0.3049199 (65)	total: 3.09s	remaining: 93.7ms
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67:	learn: 0.3007319	test: 0.3006518	best: 0.3006518 (67)	total: 3.17s	remaining: 0us

bestTest = 0.300651798
bestIteration = 67

Trial 95, Fold 3: Log loss = 0.3007512603381208, Average precision = 0.9702529092382228, ROC-AUC = 0.9648927704635417, Elapsed Time = 3.283542600001965 seconds
Trial 95, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 95, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
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59:	learn: 0.3204712	test: 0.3255720	best: 0.3255720 (59)	total: 2.84s	remaining: 378ms
60:	learn: 0.3182302	test: 0.3233158	best: 0.3233158 (60)	total: 2.88s	remaining: 331ms
61:	learn: 0.3162891	test: 0.3214306	best: 0.3214306 (61)	total: 2.94s	remaining: 285ms
62:	learn: 0.3144732	test: 0.3198163	best: 0.3198163 (62)	total: 2.98s	remaining: 237ms
63:	learn: 0.3125707	test: 0.3179617	best: 0.3179617 (63)	total: 3.03s	remaining: 189ms
64:	learn: 0.3109220	test: 0.3164184	best: 0.3164184 (64)	total: 3.08s	remaining: 142ms
65:	learn: 0.3086698	test: 0.3140866	best: 0.3140866 (65)	total: 3.13s	remaining: 94.7ms
66:	learn: 0.3069303	test: 0.3124961	best: 0.3124961 (66)	total: 3.17s	remaining: 47.4ms
67:	learn: 0.3048198	test: 0.3103390	best: 0.3103390 (67)	total: 3.22s	remaining: 0us

bestTest = 0.3103390211
bestIteration = 67

Trial 95, Fold 4: Log loss = 0.3102611696645102, Average precision = 0.9689651838747172, ROC-AUC = 0.9625386870486012, Elapsed Time = 3.3399668999991263 seconds
Trial 95, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 95, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
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67:	learn: 0.3024380	test: 0.3136068	best: 0.3136068 (67)	total: 3.17s	remaining: 0us

bestTest = 0.3136068054
bestIteration = 67

Trial 95, Fold 5: Log loss = 0.31342355489084844, Average precision = 0.9655505776422212, ROC-AUC = 0.9594478197053304, Elapsed Time = 3.287348599995312 seconds
Optimization Progress:  96%|#########6| 96/100 [2:53:48<03:45, 56.39s/it]
Trial 96, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 96, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
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85:	learn: 0.1835394	test: 0.2038675	best: 0.2038675 (85)	total: 6.3s	remaining: 0us

bestTest = 0.2038675462
bestIteration = 85

Trial 96, Fold 1: Log loss = 0.2038675462430337, Average precision = 0.9733697266440057, ROC-AUC = 0.9685632226141485, Elapsed Time = 6.410741300001973 seconds
Trial 96, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 96, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
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79:	learn: 0.1868492	test: 0.1980385	best: 0.1980385 (79)	total: 5.75s	remaining: 431ms
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83:	learn: 0.1848067	test: 0.1969554	best: 0.1969554 (83)	total: 6.06s	remaining: 144ms
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85:	learn: 0.1838869	test: 0.1963117	best: 0.1963117 (85)	total: 6.21s	remaining: 0us

bestTest = 0.1963117067
bestIteration = 85

Trial 96, Fold 2: Log loss = 0.19631170670130296, Average precision = 0.9740709703615107, ROC-AUC = 0.9711709557166577, Elapsed Time = 6.3368633999998565 seconds
Trial 96, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 96, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
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83:	learn: 0.1859901	test: 0.1964380	best: 0.1964380 (83)	total: 6.04s	remaining: 144ms
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85:	learn: 0.1851646	test: 0.1961880	best: 0.1961880 (85)	total: 6.18s	remaining: 0us

bestTest = 0.1961880349
bestIteration = 85

Trial 96, Fold 3: Log loss = 0.19618803491667963, Average precision = 0.9748705927722668, ROC-AUC = 0.9713855176922113, Elapsed Time = 6.298042399997939 seconds
Trial 96, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 96, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
0:	learn: 0.6185156	test: 0.6185551	best: 0.6185551 (0)	total: 43.1ms	remaining: 3.67s
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85:	learn: 0.1830048	test: 0.2000830	best: 0.2000830 (85)	total: 7.21s	remaining: 0us

bestTest = 0.2000829843
bestIteration = 85

Trial 96, Fold 4: Log loss = 0.2000829843369374, Average precision = 0.9743039508856786, ROC-AUC = 0.9693250153198327, Elapsed Time = 7.345657800004119 seconds
Trial 96, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 96, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
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85:	learn: 0.1827516	test: 0.2081256	best: 0.2081256 (85)	total: 6.01s	remaining: 0us

bestTest = 0.2081256289
bestIteration = 85

Trial 96, Fold 5: Log loss = 0.208125628933512, Average precision = 0.9717449732708364, ROC-AUC = 0.9684660461141148, Elapsed Time = 6.138437400004477 seconds
Optimization Progress:  97%|#########7| 97/100 [2:54:29<02:35, 51.70s/it]
Trial 97, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 97, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
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76:	learn: 0.1787905	test: 0.2126844	best: 0.2126368 (75)	total: 18.1s	remaining: 2.82s
77:	learn: 0.1781388	test: 0.2122521	best: 0.2122521 (77)	total: 18.4s	remaining: 2.59s
78:	learn: 0.1778257	test: 0.2121034	best: 0.2121034 (78)	total: 18.6s	remaining: 2.35s
79:	learn: 0.1778250	test: 0.2120998	best: 0.2120998 (79)	total: 18.6s	remaining: 2.1s
80:	learn: 0.1773806	test: 0.2120601	best: 0.2120601 (80)	total: 18.9s	remaining: 1.87s
81:	learn: 0.1767622	test: 0.2116690	best: 0.2116690 (81)	total: 19.2s	remaining: 1.64s
82:	learn: 0.1765371	test: 0.2115220	best: 0.2115220 (82)	total: 19.4s	remaining: 1.4s
83:	learn: 0.1761051	test: 0.2113049	best: 0.2113049 (83)	total: 19.7s	remaining: 1.17s
84:	learn: 0.1756087	test: 0.2112098	best: 0.2112098 (84)	total: 19.9s	remaining: 938ms
85:	learn: 0.1755840	test: 0.2111842	best: 0.2111842 (85)	total: 20s	remaining: 697ms
86:	learn: 0.1751622	test: 0.2112076	best: 0.2111842 (85)	total: 20.2s	remaining: 465ms
87:	learn: 0.1750480	test: 0.2111571	best: 0.2111571 (87)	total: 20.5s	remaining: 233ms
88:	learn: 0.1749526	test: 0.2111600	best: 0.2111571 (87)	total: 20.7s	remaining: 0us

bestTest = 0.2111571232
bestIteration = 87

Shrink model to first 88 iterations.
Trial 97, Fold 1: Log loss = 0.21082753340035829, Average precision = 0.9728964449793732, ROC-AUC = 0.9681904893343465, Elapsed Time = 20.86236479999934 seconds
Trial 97, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 97, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
0:	learn: 0.6236358	test: 0.6249362	best: 0.6249362 (0)	total: 238ms	remaining: 21s
1:	learn: 0.5647823	test: 0.5672655	best: 0.5672655 (1)	total: 463ms	remaining: 20.1s
2:	learn: 0.5158725	test: 0.5192086	best: 0.5192086 (2)	total: 690ms	remaining: 19.8s
3:	learn: 0.4734664	test: 0.4772955	best: 0.4772955 (3)	total: 924ms	remaining: 19.6s
4:	learn: 0.4370236	test: 0.4412982	best: 0.4412982 (4)	total: 1.16s	remaining: 19.5s
5:	learn: 0.4062735	test: 0.4113503	best: 0.4113503 (5)	total: 1.43s	remaining: 19.8s
6:	learn: 0.3800769	test: 0.3859503	best: 0.3859503 (6)	total: 1.7s	remaining: 19.9s
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74:	learn: 0.1824044	test: 0.2073265	best: 0.2073265 (74)	total: 18.3s	remaining: 3.41s
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76:	learn: 0.1812162	test: 0.2066588	best: 0.2066588 (76)	total: 18.8s	remaining: 2.93s
77:	learn: 0.1809103	test: 0.2064620	best: 0.2064620 (77)	total: 19s	remaining: 2.68s
78:	learn: 0.1804555	test: 0.2061097	best: 0.2061097 (78)	total: 19.3s	remaining: 2.44s
79:	learn: 0.1799295	test: 0.2058797	best: 0.2058797 (79)	total: 19.6s	remaining: 2.2s
80:	learn: 0.1798065	test: 0.2057419	best: 0.2057419 (80)	total: 19.8s	remaining: 1.96s
81:	learn: 0.1793505	test: 0.2055453	best: 0.2055453 (81)	total: 20.1s	remaining: 1.71s
82:	learn: 0.1786427	test: 0.2050845	best: 0.2050845 (82)	total: 20.3s	remaining: 1.47s
83:	learn: 0.1786370	test: 0.2050919	best: 0.2050845 (82)	total: 20.4s	remaining: 1.21s
84:	learn: 0.1783328	test: 0.2048034	best: 0.2048034 (84)	total: 20.6s	remaining: 970ms
85:	learn: 0.1778130	test: 0.2045678	best: 0.2045678 (85)	total: 20.9s	remaining: 728ms
86:	learn: 0.1769276	test: 0.2042207	best: 0.2042207 (86)	total: 21.1s	remaining: 485ms
87:	learn: 0.1768412	test: 0.2041974	best: 0.2041974 (87)	total: 21.3s	remaining: 242ms
88:	learn: 0.1767174	test: 0.2040588	best: 0.2040588 (88)	total: 21.3s	remaining: 0us

bestTest = 0.2040587624
bestIteration = 88

Trial 97, Fold 2: Log loss = 0.20386114733480118, Average precision = 0.9730014843998919, ROC-AUC = 0.9699471098268527, Elapsed Time = 21.46011730000464 seconds
Trial 97, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 97, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
0:	learn: 0.6241282	test: 0.6247628	best: 0.6247628 (0)	total: 230ms	remaining: 20.2s
1:	learn: 0.5656643	test: 0.5668731	best: 0.5668731 (1)	total: 466ms	remaining: 20.3s
2:	learn: 0.5171349	test: 0.5187945	best: 0.5187945 (2)	total: 695ms	remaining: 19.9s
3:	learn: 0.4738572	test: 0.4760360	best: 0.4760360 (3)	total: 930ms	remaining: 19.8s
4:	learn: 0.4372940	test: 0.4399121	best: 0.4399121 (4)	total: 1.18s	remaining: 19.8s
5:	learn: 0.4062023	test: 0.4092518	best: 0.4092518 (5)	total: 1.42s	remaining: 19.7s
6:	learn: 0.3794880	test: 0.3831727	best: 0.3831727 (6)	total: 1.68s	remaining: 19.7s
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80:	learn: 0.1747512	test: 0.2068748	best: 0.2068748 (80)	total: 20.3s	remaining: 2.01s
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82:	learn: 0.1743342	test: 0.2066502	best: 0.2066502 (82)	total: 20.8s	remaining: 1.51s
83:	learn: 0.1739140	test: 0.2064403	best: 0.2064403 (83)	total: 21.1s	remaining: 1.25s
84:	learn: 0.1730627	test: 0.2058265	best: 0.2058265 (84)	total: 21.4s	remaining: 1s
85:	learn: 0.1725394	test: 0.2058478	best: 0.2058265 (84)	total: 21.6s	remaining: 754ms
86:	learn: 0.1717642	test: 0.2054969	best: 0.2054969 (86)	total: 21.9s	remaining: 503ms
87:	learn: 0.1711538	test: 0.2051542	best: 0.2051542 (87)	total: 22.1s	remaining: 252ms
88:	learn: 0.1708165	test: 0.2050370	best: 0.2050370 (88)	total: 22.4s	remaining: 0us

bestTest = 0.2050370016
bestIteration = 88

Trial 97, Fold 3: Log loss = 0.20484532578439052, Average precision = 0.9731240040616084, ROC-AUC = 0.9694139394785759, Elapsed Time = 22.550649700002396 seconds
Trial 97, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 97, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
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80:	learn: 0.1747356	test: 0.2069967	best: 0.2069967 (80)	total: 19.4s	remaining: 1.91s
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82:	learn: 0.1742584	test: 0.2068169	best: 0.2068169 (82)	total: 19.9s	remaining: 1.44s
83:	learn: 0.1737316	test: 0.2064098	best: 0.2064098 (83)	total: 20.1s	remaining: 1.2s
84:	learn: 0.1731003	test: 0.2061261	best: 0.2061261 (84)	total: 20.4s	remaining: 960ms
85:	learn: 0.1727715	test: 0.2060280	best: 0.2060280 (85)	total: 20.6s	remaining: 720ms
86:	learn: 0.1726464	test: 0.2059896	best: 0.2059896 (86)	total: 20.9s	remaining: 480ms
87:	learn: 0.1720495	test: 0.2059620	best: 0.2059620 (87)	total: 21.2s	remaining: 240ms
88:	learn: 0.1713322	test: 0.2058243	best: 0.2058243 (88)	total: 21.4s	remaining: 0us

bestTest = 0.2058242623
bestIteration = 88

Trial 97, Fold 4: Log loss = 0.20557429699161273, Average precision = 0.9736747205772149, ROC-AUC = 0.968906551404298, Elapsed Time = 21.568858400001773 seconds
Trial 97, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 97, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
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79:	learn: 0.1753735	test: 0.2142868	best: 0.2142868 (79)	total: 21.2s	remaining: 2.39s
80:	learn: 0.1753735	test: 0.2142844	best: 0.2142844 (80)	total: 21.3s	remaining: 2.1s
81:	learn: 0.1746820	test: 0.2142283	best: 0.2142283 (81)	total: 21.6s	remaining: 1.84s
82:	learn: 0.1741871	test: 0.2141666	best: 0.2141666 (82)	total: 21.9s	remaining: 1.58s
83:	learn: 0.1739976	test: 0.2140269	best: 0.2140269 (83)	total: 22.1s	remaining: 1.32s
84:	learn: 0.1738205	test: 0.2139671	best: 0.2139671 (84)	total: 22.4s	remaining: 1.05s
85:	learn: 0.1734329	test: 0.2138714	best: 0.2138714 (85)	total: 22.7s	remaining: 791ms
86:	learn: 0.1730285	test: 0.2136505	best: 0.2136505 (86)	total: 23s	remaining: 528ms
87:	learn: 0.1730282	test: 0.2136485	best: 0.2136485 (87)	total: 23s	remaining: 262ms
88:	learn: 0.1725256	test: 0.2135325	best: 0.2135325 (88)	total: 23.3s	remaining: 0us

bestTest = 0.2135325497
bestIteration = 88

Trial 97, Fold 5: Log loss = 0.2131723402127683, Average precision = 0.9711635361806826, ROC-AUC = 0.9676526392062873, Elapsed Time = 23.49738530000468 seconds
Optimization Progress:  98%|#########8| 98/100 [2:56:27<02:23, 71.62s/it]
Trial 98, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 98, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
0:	learn: 0.5968750	test: 0.5991725	best: 0.5991725 (0)	total: 138ms	remaining: 10.5s
1:	learn: 0.5167462	test: 0.5208641	best: 0.5208641 (1)	total: 281ms	remaining: 10.5s
2:	learn: 0.4547995	test: 0.4584184	best: 0.4584184 (2)	total: 410ms	remaining: 10.1s
3:	learn: 0.4095910	test: 0.4147788	best: 0.4147788 (3)	total: 557ms	remaining: 10.2s
4:	learn: 0.3766586	test: 0.3820032	best: 0.3820032 (4)	total: 697ms	remaining: 10s
5:	learn: 0.3478912	test: 0.3546664	best: 0.3546664 (5)	total: 831ms	remaining: 9.83s
6:	learn: 0.3195902	test: 0.3280797	best: 0.3280797 (6)	total: 978ms	remaining: 9.78s
7:	learn: 0.3019855	test: 0.3116274	best: 0.3116274 (7)	total: 1.11s	remaining: 9.58s
8:	learn: 0.2838560	test: 0.2944675	best: 0.2944675 (8)	total: 1.26s	remaining: 9.53s
9:	learn: 0.2688377	test: 0.2817223	best: 0.2817223 (9)	total: 1.42s	remaining: 9.5s
10:	learn: 0.2563287	test: 0.2701333	best: 0.2701333 (10)	total: 1.56s	remaining: 9.36s
11:	learn: 0.2436324	test: 0.2586319	best: 0.2586319 (11)	total: 1.69s	remaining: 9.14s
12:	learn: 1.4499444	test: 0.2474971	best: 0.2474971 (12)	total: 1.85s	remaining: 9.09s
13:	learn: 1.4402849	test: 0.2388634	best: 0.2388634 (13)	total: 1.99s	remaining: 8.95s
14:	learn: 1.4323262	test: 0.2329519	best: 0.2329519 (14)	total: 2.15s	remaining: 8.88s
15:	learn: 2.6433769	test: 0.2269289	best: 0.2269289 (15)	total: 2.29s	remaining: 8.75s
16:	learn: 94.7961396	test: 108.9798878	best: 0.2269289 (15)	total: 2.43s	remaining: 8.59s

bestTest = 0.2269289229
bestIteration = 15

Shrink model to first 16 iterations.
Training has stopped (degenerate solution on iteration 17, probably too small l2-regularization, try to increase it)
Trial 98, Fold 1: Log loss = 0.22696060308168534, Average precision = 0.9719228885972324, ROC-AUC = 0.9674070847851337, Elapsed Time = 2.6826937000005273 seconds
Trial 98, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 98, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
0:	learn: 0.6016898	test: 0.6029375	best: 0.6029375 (0)	total: 144ms	remaining: 11s
1:	learn: 0.5279998	test: 0.5304055	best: 0.5304055 (1)	total: 282ms	remaining: 10.6s
2:	learn: 0.4590162	test: 0.4637861	best: 0.4637861 (2)	total: 444ms	remaining: 11s
3:	learn: 0.4147109	test: 0.4213086	best: 0.4213086 (3)	total: 579ms	remaining: 10.6s
4:	learn: 0.3727901	test: 0.3797932	best: 0.3797932 (4)	total: 730ms	remaining: 10.5s
5:	learn: 0.3403563	test: 0.3487030	best: 0.3487030 (5)	total: 864ms	remaining: 10.2s
6:	learn: 0.3153129	test: 0.3238323	best: 0.3238323 (6)	total: 1.01s	remaining: 10.1s
7:	learn: 0.2940122	test: 0.3036071	best: 0.3036071 (7)	total: 1.16s	remaining: 9.97s
8:	learn: 0.2760001	test: 0.2871191	best: 0.2871191 (8)	total: 1.29s	remaining: 9.79s
9:	learn: 0.2583909	test: 0.2719383	best: 0.2719383 (9)	total: 1.46s	remaining: 9.76s
10:	learn: 0.2440667	test: 0.2580860	best: 0.2580860 (10)	total: 1.62s	remaining: 9.7s
11:	learn: 0.2336067	test: 0.2478549	best: 0.2478549 (11)	total: 1.76s	remaining: 9.55s
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17:	learn: 7.9096676	test: 44.4203935	best: 0.2322450 (13)	total: 2.69s	remaining: 8.82s
18:	learn: 7.9056335	test: 44.4169357	best: 0.2322450 (13)	total: 2.84s	remaining: 8.66s
19:	learn: 7.9001740	test: 44.4115922	best: 0.2322450 (13)	total: 2.99s	remaining: 8.53s
20:	learn: 7.8612052	test: 44.4059114	best: 0.2322450 (13)	total: 3.13s	remaining: 8.36s
21:	learn: 7.8582231	test: 44.4032758	best: 0.2322450 (13)	total: 3.29s	remaining: 8.22s
22:	learn: 7.8562915	test: 44.4016636	best: 0.2322450 (13)	total: 3.42s	remaining: 8.03s
23:	learn: 7.6473516	test: 44.3998431	best: 0.2322450 (13)	total: 3.57s	remaining: 7.89s
24:	learn: 7.6446830	test: 44.3986193	best: 0.2322450 (13)	total: 3.73s	remaining: 7.75s
25:	learn: 9.4715406	test: 49.9575426	best: 0.2322450 (13)	total: 3.88s	remaining: 7.62s
26:	learn: 69.9872737	test: 96.3613415	best: 0.2322450 (13)	total: 4.01s	remaining: 7.42s
27:	learn: 817.0419979	test: 936.4704886	best: 0.2322450 (13)	total: 4.14s	remaining: 7.25s
28:	learn: 629.5373794	test: 925.6394512	best: 0.2322450 (13)	total: 4.34s	remaining: 7.19s
29:	learn: 2047.5605626	test: 2621.8375430	best: 0.2322450 (13)	total: 4.49s	remaining: 7.03s
30:	learn: 3056.8632855	test: 3784.3916738	best: 0.2322450 (13)	total: 4.64s	remaining: 6.89s
Training has stopped (degenerate solution on iteration 31, probably too small l2-regularization, try to increase it)
bestTest = 0.2322450297
bestIteration = 13

Shrink model to first 14 iterations.
Trial 98, Fold 2: Log loss = 0.23230951948803025, Average precision = 0.9733879690908728, ROC-AUC = 0.9699329972828041, Elapsed Time = 4.96705149999616 seconds
Trial 98, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 98, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
0:	learn: 0.5942542	test: 0.5969691	best: 0.5969691 (0)	total: 271ms	remaining: 20.6s
1:	learn: 0.5055235	test: 0.5084790	best: 0.5084790 (1)	total: 541ms	remaining: 20.3s
2:	learn: 0.4493551	test: 0.4527923	best: 0.4527923 (2)	total: 740ms	remaining: 18.3s
3:	learn: 0.4061785	test: 0.4111673	best: 0.4111673 (3)	total: 947ms	remaining: 17.3s
4:	learn: 0.3703365	test: 0.3757627	best: 0.3757627 (4)	total: 1.14s	remaining: 16.4s
5:	learn: 0.3398797	test: 0.3456551	best: 0.3456551 (5)	total: 1.32s	remaining: 15.7s
6:	learn: 0.3170382	test: 0.3236042	best: 0.3236042 (6)	total: 1.5s	remaining: 15s
7:	learn: 0.2968404	test: 0.3044536	best: 0.3044536 (7)	total: 1.68s	remaining: 14.5s
8:	learn: 0.2805288	test: 0.2885937	best: 0.2885937 (8)	total: 1.89s	remaining: 14.3s
9:	learn: 0.2664177	test: 0.2746376	best: 0.2746376 (9)	total: 2.07s	remaining: 13.9s
10:	learn: 0.2529465	test: 0.2620822	best: 0.2620822 (10)	total: 2.25s	remaining: 13.5s
11:	learn: 0.2416425	test: 0.2527405	best: 0.2527405 (11)	total: 2.42s	remaining: 13.1s
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15:	learn: 1.0069789	test: 0.2256273	best: 0.2256273 (15)	total: 3.1s	remaining: 11.8s
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19:	learn: 3.0078039	test: 0.2089574	best: 0.2089574 (19)	total: 3.78s	remaining: 10.8s
20:	learn: 36.4268463	test: 20.4843156	best: 0.2089574 (19)	total: 3.94s	remaining: 10.5s
21:	learn: 35.7446589	test: 20.0914923	best: 0.2089574 (19)	total: 4.09s	remaining: 10.2s
22:	learn: 37.6268162	test: 18.0897885	best: 0.2089574 (19)	total: 4.24s	remaining: 9.96s
23:	learn: 106.1007271	test: 103.7938452	best: 0.2089574 (19)	total: 4.39s	remaining: 9.71s
24:	learn: 1979.0993002	test: 2245.7357907	best: 0.2089574 (19)	total: 4.55s	remaining: 9.47s
25:	learn: 1173.5200037	test: 647.4769983	best: 0.2089574 (19)	total: 4.69s	remaining: 9.21s
26:	learn: 1530.6013296	test: 960.4686493	best: 0.2089574 (19)	total: 4.86s	remaining: 9s
27:	learn: 2081.5896091	test: 1496.1813577	best: 0.2089574 (19)	total: 5s	remaining: 8.75s
28:	learn: 3208.8884857	test: 1926.0695910	best: 0.2089574 (19)	total: 5.15s	remaining: 8.53s
29:	learn: 7742.9858344	test: 7342.1096130	best: 0.2089574 (19)	total: 5.29s	remaining: 8.29s
Training has stopped (degenerate solution on iteration 30, probably too small l2-regularization, try to increase it)
bestTest = 0.208957374
bestIteration = 19

Shrink model to first 20 iterations.
Trial 98, Fold 3: Log loss = 0.20909505132557127, Average precision = 0.9720119165738083, ROC-AUC = 0.970647765359093, Elapsed Time = 5.5795787000024575 seconds
Trial 98, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 98, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
0:	learn: 0.5982989	test: 0.5996583	best: 0.5996583 (0)	total: 154ms	remaining: 11.7s
1:	learn: 0.5398699	test: 0.5431375	best: 0.5431375 (1)	total: 283ms	remaining: 10.6s
2:	learn: 0.4729225	test: 0.4762672	best: 0.4762672 (2)	total: 445ms	remaining: 11s
3:	learn: 0.4153591	test: 0.4215758	best: 0.4215758 (3)	total: 607ms	remaining: 11.1s
4:	learn: 0.3798469	test: 0.3864927	best: 0.3864927 (4)	total: 782ms	remaining: 11.3s
5:	learn: 0.3502816	test: 0.3574635	best: 0.3574635 (5)	total: 916ms	remaining: 10.8s
6:	learn: 0.3246987	test: 0.3320685	best: 0.3320685 (6)	total: 1.07s	remaining: 10.7s
7:	learn: 0.3064033	test: 0.3155896	best: 0.3155896 (7)	total: 1.25s	remaining: 10.8s
8:	learn: 0.2896150	test: 0.2994228	best: 0.2994228 (8)	total: 1.41s	remaining: 10.7s
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18:	learn: 1.2165518	test: 0.2153574	best: 0.2153574 (18)	total: 3s	remaining: 9.15s
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23:	learn: 194.8643802	test: 263.9515660	best: 0.2123962 (19)	total: 3.72s	remaining: 8.21s
24:	learn: 681.4881060	test: 891.8477545	best: 0.2123962 (19)	total: 3.86s	remaining: 8.02s
25:	learn: 1312.8094138	test: 1760.5341177	best: 0.2123962 (19)	total: 4.01s	remaining: 7.87s
26:	learn: 2655.9003149	test: 3285.5872254	best: 0.2123962 (19)	total: 4.16s	remaining: 7.71s
27:	learn: 3489.7889882	test: 4301.8698840	best: 0.2123962 (19)	total: 4.31s	remaining: 7.54s
28:	learn: 4245.1091760	test: 5288.8966329	best: 0.2123962 (19)	total: 4.44s	remaining: 7.34s
29:	learn: 5059.6217207	test: 6357.6695161	best: 0.2123962 (19)	total: 4.57s	remaining: 7.16s
30:	learn: 5924.8992738	test: 7406.9601978	best: 0.2123962 (19)	total: 4.71s	remaining: 6.98s
31:	learn: 6257.6974106	test: 7789.3104141	best: 0.2123962 (19)	total: 4.84s	remaining: 6.8s
32:	learn: 7963.6232091	test: 9291.5857935	best: 0.2123962 (19)	total: 4.97s	remaining: 6.62s
33:	learn: 11206.3073787	test: 12192.1746278	best: 0.2123962 (19)	total: 5.09s	remaining: 6.44s
Training has stopped (degenerate solution on iteration 34, probably too small l2-regularization, try to increase it)
bestTest = 0.2123961827
bestIteration = 19

Shrink model to first 20 iterations.
Trial 98, Fold 4: Log loss = 0.21227717522735867, Average precision = 0.9746143472649325, ROC-AUC = 0.9699343810429886, Elapsed Time = 5.330942900000082 seconds
Trial 98, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 98, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
0:	learn: 0.5897678	test: 0.5942515	best: 0.5942515 (0)	total: 154ms	remaining: 11.7s
1:	learn: 0.5068883	test: 0.5158148	best: 0.5158148 (1)	total: 301ms	remaining: 11.3s
2:	learn: 0.4397798	test: 0.4490221	best: 0.4490221 (2)	total: 455ms	remaining: 11.2s
3:	learn: 0.3985913	test: 0.4078524	best: 0.4078524 (3)	total: 600ms	remaining: 11s
4:	learn: 0.3634282	test: 0.3738948	best: 0.3738948 (4)	total: 740ms	remaining: 10.7s
5:	learn: 0.3379700	test: 0.3495524	best: 0.3495524 (5)	total: 886ms	remaining: 10.5s
6:	learn: 0.3144078	test: 0.3275431	best: 0.3275431 (6)	total: 1.02s	remaining: 10.2s
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19:	learn: 1.8148490	test: 6.7084264	best: 0.2158849 (18)	total: 2.86s	remaining: 8.16s
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26:	learn: 7.7090136	test: 13.1870298	best: 0.2158849 (18)	total: 3.84s	remaining: 7.11s
27:	learn: 9.2326318	test: 14.7048453	best: 0.2158849 (18)	total: 3.97s	remaining: 6.95s
28:	learn: 14.1997910	test: 18.6640683	best: 0.2158849 (18)	total: 4.1s	remaining: 6.78s
29:	learn: 14.9950483	test: 18.6635939	best: 0.2158849 (18)	total: 4.24s	remaining: 6.64s
30:	learn: 14.5698483	test: 18.6633189	best: 0.2158849 (18)	total: 4.38s	remaining: 6.5s
31:	learn: 14.5680957	test: 18.6625665	best: 0.2158849 (18)	total: 4.5s	remaining: 6.32s
Training has stopped (degenerate solution on iteration 32, probably too small l2-regularization, try to increase it)
bestTest = 0.2158848539
bestIteration = 18

Shrink model to first 19 iterations.
Trial 98, Fold 5: Log loss = 0.21565013467815503, Average precision = 0.9731286815912488, ROC-AUC = 0.9690805046169852, Elapsed Time = 4.74505119999958 seconds
Optimization Progress:  99%|#########9| 99/100 [2:56:59<00:59, 59.68s/it]
Trial 99, Fold 1: Train size = 20663 where 0 = 10533, 1 = 10130, 0/1 = 1.0397828232971371
Trial 99, Fold 1: Validation size = 5175 where  0 = 2592, 1 = 2583, 0/1 = 1.0034843205574913
0:	learn: 0.6744634	test: 0.6743991	best: 0.6743991 (0)	total: 641ms	remaining: 28.2s
1:	learn: 0.6566847	test: 0.6565880	best: 0.6565880 (1)	total: 1.33s	remaining: 28.6s
2:	learn: 0.6393980	test: 0.6392001	best: 0.6392001 (2)	total: 1.94s	remaining: 27.2s
3:	learn: 0.6228420	test: 0.6227753	best: 0.6227753 (3)	total: 2.59s	remaining: 26.6s
4:	learn: 0.6072265	test: 0.6070361	best: 0.6070361 (4)	total: 3.23s	remaining: 25.9s
5:	learn: 0.5931676	test: 0.5931443	best: 0.5931443 (5)	total: 3.87s	remaining: 25.2s
6:	learn: 0.5796245	test: 0.5797473	best: 0.5797473 (6)	total: 4.5s	remaining: 24.5s
7:	learn: 0.5662836	test: 0.5664043	best: 0.5664043 (7)	total: 5.16s	remaining: 23.9s
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36:	learn: 0.3401328	test: 0.3432058	best: 0.3432058 (36)	total: 24s	remaining: 5.18s
37:	learn: 0.3362188	test: 0.3394144	best: 0.3394144 (37)	total: 24.6s	remaining: 4.54s
38:	learn: 0.3326719	test: 0.3360860	best: 0.3360860 (38)	total: 25.3s	remaining: 3.89s
39:	learn: 0.3287436	test: 0.3323041	best: 0.3323041 (39)	total: 25.9s	remaining: 3.24s
40:	learn: 0.3250274	test: 0.3287641	best: 0.3287641 (40)	total: 26.6s	remaining: 2.59s
41:	learn: 0.3214353	test: 0.3251875	best: 0.3251875 (41)	total: 27.2s	remaining: 1.94s
42:	learn: 0.3181001	test: 0.3220649	best: 0.3220649 (42)	total: 27.9s	remaining: 1.3s
43:	learn: 0.3150407	test: 0.3192379	best: 0.3192379 (43)	total: 28.5s	remaining: 648ms
44:	learn: 0.3118676	test: 0.3162341	best: 0.3162341 (44)	total: 29.2s	remaining: 0us

bestTest = 0.3162340849
bestIteration = 44

Trial 99, Fold 1: Log loss = 0.31628329592305343, Average precision = 0.9689417572860706, ROC-AUC = 0.964567112602343, Elapsed Time = 29.312696900000446 seconds
Trial 99, Fold 2: Train size = 20701 where 0 = 10471, 1 = 10230, 0/1 = 1.0235581622678396
Trial 99, Fold 2: Validation size = 5137 where  0 = 2654, 1 = 2483, 0/1 = 1.0688683044703986
0:	learn: 0.6744150	test: 0.6745858	best: 0.6745858 (0)	total: 638ms	remaining: 28.1s
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2:	learn: 0.6397327	test: 0.6401877	best: 0.6401877 (2)	total: 1.92s	remaining: 26.9s
3:	learn: 0.6237044	test: 0.6243550	best: 0.6243550 (3)	total: 2.55s	remaining: 26.1s
4:	learn: 0.6081255	test: 0.6090012	best: 0.6090012 (4)	total: 3.19s	remaining: 25.5s
5:	learn: 0.5928721	test: 0.5938639	best: 0.5938639 (5)	total: 3.84s	remaining: 24.9s
6:	learn: 0.5786552	test: 0.5797750	best: 0.5797750 (6)	total: 4.47s	remaining: 24.3s
7:	learn: 0.5646416	test: 0.5658531	best: 0.5658531 (7)	total: 5.09s	remaining: 23.6s
8:	learn: 0.5514865	test: 0.5527920	best: 0.5527920 (8)	total: 5.73s	remaining: 22.9s
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32:	learn: 0.3597500	test: 0.3629276	best: 0.3629276 (32)	total: 21.4s	remaining: 7.76s
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34:	learn: 0.3505584	test: 0.3540098	best: 0.3540098 (34)	total: 22.6s	remaining: 6.47s
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36:	learn: 0.3423468	test: 0.3459850	best: 0.3459850 (36)	total: 23.9s	remaining: 5.17s
37:	learn: 0.3379922	test: 0.3417053	best: 0.3417053 (37)	total: 24.6s	remaining: 4.53s
38:	learn: 0.3338099	test: 0.3376184	best: 0.3376184 (38)	total: 25.2s	remaining: 3.88s
39:	learn: 0.3297058	test: 0.3335881	best: 0.3335881 (39)	total: 25.9s	remaining: 3.24s
40:	learn: 0.3262325	test: 0.3301156	best: 0.3301156 (40)	total: 26.6s	remaining: 2.59s
41:	learn: 0.3223386	test: 0.3263185	best: 0.3263185 (41)	total: 27.2s	remaining: 1.95s
42:	learn: 0.3190346	test: 0.3230853	best: 0.3230853 (42)	total: 27.9s	remaining: 1.3s
43:	learn: 0.3158184	test: 0.3199045	best: 0.3199045 (43)	total: 28.6s	remaining: 649ms
44:	learn: 0.3126849	test: 0.3168083	best: 0.3168083 (44)	total: 29.2s	remaining: 0us

bestTest = 0.3168082874
bestIteration = 44

Trial 99, Fold 2: Log loss = 0.31685294828349275, Average precision = 0.9704712253162898, ROC-AUC = 0.9675230148278833, Elapsed Time = 29.368694899996626 seconds
Trial 99, Fold 3: Train size = 20682 where 0 = 10517, 1 = 10165, 0/1 = 1.034628627643876
Trial 99, Fold 3: Validation size = 5156 where  0 = 2608, 1 = 2548, 0/1 = 1.0235478806907379
0:	learn: 0.6746909	test: 0.6747288	best: 0.6747288 (0)	total: 637ms	remaining: 28s
1:	learn: 0.6570252	test: 0.6570360	best: 0.6570360 (1)	total: 1.25s	remaining: 26.9s
2:	learn: 0.6406150	test: 0.6405484	best: 0.6405484 (2)	total: 1.89s	remaining: 26.5s
3:	learn: 0.6250812	test: 0.6250113	best: 0.6250113 (3)	total: 2.56s	remaining: 26.2s
4:	learn: 0.6094696	test: 0.6093519	best: 0.6093519 (4)	total: 3.19s	remaining: 25.6s
5:	learn: 0.5950482	test: 0.5949568	best: 0.5949568 (5)	total: 3.88s	remaining: 25.2s
6:	learn: 0.5807817	test: 0.5805808	best: 0.5805808 (6)	total: 4.55s	remaining: 24.7s
7:	learn: 0.5668144	test: 0.5666183	best: 0.5666183 (7)	total: 5.2s	remaining: 24.1s
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36:	learn: 0.3403512	test: 0.3406765	best: 0.3406765 (36)	total: 24.6s	remaining: 5.32s
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38:	learn: 0.3326999	test: 0.3331160	best: 0.3331160 (38)	total: 25.9s	remaining: 3.99s
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40:	learn: 0.3258598	test: 0.3263424	best: 0.3263424 (40)	total: 27.2s	remaining: 2.65s
41:	learn: 0.3221307	test: 0.3226926	best: 0.3226926 (41)	total: 27.8s	remaining: 1.99s
42:	learn: 0.3191234	test: 0.3196907	best: 0.3196907 (42)	total: 28.5s	remaining: 1.32s
43:	learn: 0.3159448	test: 0.3166050	best: 0.3166050 (43)	total: 29.2s	remaining: 664ms
44:	learn: 0.3130781	test: 0.3137362	best: 0.3137362 (44)	total: 29.9s	remaining: 0us

bestTest = 0.313736158
bestIteration = 44

Trial 99, Fold 3: Log loss = 0.3139282110978027, Average precision = 0.9694548904810062, ROC-AUC = 0.9677273044659108, Elapsed Time = 30.014548099999956 seconds
Trial 99, Fold 4: Train size = 20656 where 0 = 10479, 1 = 10177, 0/1 = 1.0296747568045592
Trial 99, Fold 4: Validation size = 5182 where  0 = 2646, 1 = 2536, 0/1 = 1.0433753943217665
0:	learn: 0.6747939	test: 0.6747461	best: 0.6747461 (0)	total: 634ms	remaining: 27.9s
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bestTest = 0.3150991339
bestIteration = 44

Trial 99, Fold 4: Log loss = 0.3151704117016752, Average precision = 0.9708887439177136, ROC-AUC = 0.966269468705814, Elapsed Time = 29.271147599996766 seconds
Trial 99, Fold 5: Train size = 20650 where 0 = 10500, 1 = 10150, 0/1 = 1.0344827586206897
Trial 99, Fold 5: Validation size = 5188 where  0 = 2625, 1 = 2563, 0/1 = 1.0241904018728054
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bestTest = 0.3197955685
bestIteration = 44

Trial 99, Fold 5: Log loss = 0.3197794008180273, Average precision = 0.9691484953841378, ROC-AUC = 0.9645229734500121, Elapsed Time = 29.368412000003445 seconds
Optimization Progress: 100%|##########| 100/100 [2:59:34<00:00, 88.22s/it]
Optuna Optimization Elapsed Time: 10774.411937399997 seconds
Optimization Progress: 100%|##########| 100/100 [2:59:34<00:00, 107.75s/it]

Training with Best Trial 91

{'bootstrap_type': 'Bernoulli', 'subsample': 0.8903206126906871, 'grow_policy': 'Lossguide', 'posterior_sampling': False, 'model_shrink_mode': 'Constant', 'iterations': 89, 'learning_rate': 0.08799821484684099, 'l2_leaf_reg': 0.26486908504555773, 'random_strength': 0.8218257282282394, 'depth': 11, 'min_data_in_leaf': 46, 'has_time': False, 'rsm': 0.8824980018648074, 'leaf_estimation_method': 'Gradient', 'leaf_estimation_backtracking': 'No', 'fold_len_multiplier': 3.6584072693303096, 'auto_class_weights': 'SqrtBalanced', 'boost_from_average': False, 'allow_const_label': True, 'score_function': 'L2', 'border_count': 165, 'max_leaves': 48}
Full_params: {'objective': 'Logloss', 'eval_metric': 'Logloss', 'custom_metric': ['AUC', 'PRAUC'], 'random_seed': 42, 'thread_count': -1, 'verbose': True, 'allow_writing_files': False, 'bootstrap_type': 'Bernoulli', 'subsample': 0.8903206126906871, 'grow_policy': 'Lossguide', 'posterior_sampling': False, 'model_shrink_mode': 'Constant', 'iterations': 89, 'learning_rate': 0.08799821484684099, 'l2_leaf_reg': 0.26486908504555773, 'random_strength': 0.8218257282282394, 'depth': 11, 'min_data_in_leaf': 46, 'has_time': False, 'rsm': 0.8824980018648074, 'leaf_estimation_method': 'Gradient', 'leaf_estimation_backtracking': 'No', 'fold_len_multiplier': 3.6584072693303096, 'auto_class_weights': 'SqrtBalanced', 'boost_from_average': False, 'allow_const_label': True, 'score_function': 'L2', 'border_count': 165, 'max_leaves': 48}
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Training Elapsed Time: 27.940681599997333 seconds
Log loss: (Train) 0.18900408697386856 vs (Test) 0.19319317338089453
PR-AUC:   (Train) 0.9765517530153833 vs (Test) 0.9757429181240718
ROC-AUC:  (Train) 0.9732920829918623 vs (Test) 0.9722518803451436
In [40]:
save_results(clf_name = "CatBoostClassifier",
             best_trials = best_trials_cbc,
             exec_time = exec_time_cbc,
             lloss_auc_train = lloss_auc_train_cbc,
             lloss_auc_test = lloss_auc_test_cbc,
             df_metrics = df_metrics_cbc,
             cm_final = cm_final_cbc,
             cm_all = cm_cbc_all,
             cm_labels = cm_labels_cbc_all)

Comparison (best trials)¶

  • Parameters
  • Mutli-objectives
  • Classification report
  • Confusion matrices
  • Elapsed times
In [41]:
# List of classifiers for downstream processing
clfs = ["LGBM", "HistGradientBoostingClassifier", "CatBoostClassifier", "XGBClassifier"]

Parameters¶

In [42]:
# List of JSON files and classifier names
json_files = [{"file": "best_trials_LGBM.json", "clf": "LGBMClassifier"},
              {"file": "best_trials_HistGradientBoostingClassifier.json", "clf": "HistGradientBoostingClassifier"},
              {"file": "best_trials_CatBoostClassifier.json", "clf": "CatBoostClassifier"},
              {"file": "best_trials_XGBClassifier.json", "clf": "XGBClassifier"}
             ]

# Dictionary to store the combined data
all_records = {}

# Read and process JSON files
for item in json_files:
    file_path = os.path.join(temp_dir, item["file"])
    clf_name = item["clf"]
    
    with open(file_path, "r") as f:
        data = json.load(f)
    
    # Store parameters for each model and trial in the dictionary
    for trial_num, params in data.items():
        all_records[(clf_name, trial_num)] = params

# Show all rows without truncation
pd.set_option("display.max_rows", None)  

display(pd.DataFrame(all_records))

# Default setting
pd.reset_option("display.max_rows")
LGBMClassifier HistGradientBoostingClassifier CatBoostClassifier XGBClassifier
68 69 91 65 76
objective binary NaN Logloss binary:logistic binary:logistic
metric [binary_logloss, auc, average_precision] NaN NaN NaN NaN
device_type cpu NaN NaN NaN NaN
verbosity 2 NaN NaN 1 1
random_state 42 42 NaN NaN NaN
deterministic True NaN NaN NaN NaN
bagging_seed 42 NaN NaN NaN NaN
feature_fraction_seed 42 NaN NaN NaN NaN
drop_seed 42 NaN NaN NaN NaN
force_col_wise True NaN NaN NaN NaN
num_threads -1 NaN NaN NaN NaN
max_depth -1 None NaN 0 0
boosting_type gbdt NaN NaN NaN NaN
lambda_l1 0.001799 NaN NaN NaN NaN
lambda_l2 0.0 NaN NaN NaN NaN
num_leaves 81 NaN NaN NaN NaN
feature_fraction 0.852907 NaN NaN NaN NaN
bagging_fraction 0.723172 NaN NaN NaN NaN
bagging_freq 6 NaN NaN NaN NaN
min_child_samples 28 NaN NaN NaN NaN
learning_rate 0.074913 0.096154 0.087998 NaN NaN
is_unbalance True NaN NaN NaN NaN
max_bin 184 NaN NaN 47 92
min_sum_hessian_in_leaf 0.0052 NaN NaN NaN NaN
max_delta_step 40.700039 NaN NaN 65.541538 37.673488
feature_fraction_bynode 0.952448 NaN NaN NaN NaN
num_boost_round 88 NaN NaN NaN NaN
loss NaN log_loss NaN NaN NaN
verbose NaN 2 True NaN NaN
categorical_features NaN from_dtype NaN NaN NaN
max_iter NaN 64 NaN NaN NaN
max_leaf_nodes NaN 81 NaN NaN NaN
min_samples_leaf NaN 47 NaN NaN NaN
l2_regularization NaN 0.000012 NaN NaN NaN
class_weight NaN None NaN NaN NaN
max_features NaN 0.77363 NaN NaN NaN
max_bins NaN 217 NaN NaN NaN
interaction_cst NaN pairwise NaN NaN NaN
warm_start NaN False NaN NaN NaN
eval_metric NaN NaN Logloss [logloss, auc, aucpr] [logloss, auc, aucpr]
custom_metric NaN NaN [AUC, PRAUC] NaN NaN
random_seed NaN NaN 42 NaN NaN
thread_count NaN NaN -1 NaN NaN
allow_writing_files NaN NaN False NaN NaN
bootstrap_type NaN NaN Bernoulli NaN NaN
subsample NaN NaN 0.890321 0.56143 0.894273
grow_policy NaN NaN Lossguide depthwise depthwise
posterior_sampling NaN NaN False NaN NaN
model_shrink_mode NaN NaN Constant NaN NaN
iterations NaN NaN 89 NaN NaN
l2_leaf_reg NaN NaN 0.264869 NaN NaN
random_strength NaN NaN 0.821826 NaN NaN
depth NaN NaN 11 NaN NaN
min_data_in_leaf NaN NaN 46 NaN NaN
has_time NaN NaN False NaN NaN
rsm NaN NaN 0.882498 NaN NaN
leaf_estimation_method NaN NaN Gradient NaN NaN
leaf_estimation_backtracking NaN NaN No NaN NaN
fold_len_multiplier NaN NaN 3.658407 NaN NaN
auto_class_weights NaN NaN SqrtBalanced NaN NaN
boost_from_average NaN NaN False NaN NaN
allow_const_label NaN NaN True NaN NaN
score_function NaN NaN L2 NaN NaN
border_count NaN NaN 165 NaN NaN
max_leaves NaN NaN 48 128 196
device NaN NaN NaN cpu cpu
validate_parameters NaN NaN NaN True True
seed NaN NaN NaN 42 42
sampling_method NaN NaN NaN uniform uniform
num_parallel_tree NaN NaN NaN 1 1
booster NaN NaN NaN gbtree gbtree
eta NaN NaN NaN 0.087594 0.064891
gamma NaN NaN NaN 0.000005 1.733278
min_child_weight NaN NaN NaN 0.000026 0.001577
colsample_bytree NaN NaN NaN 0.812988 0.570634
colsample_bylevel NaN NaN NaN 0.918444 0.83765
colsample_bynode NaN NaN NaN 0.625508 0.916695
lambda NaN NaN NaN 7.025645 0.005622
alpha NaN NaN NaN 0.037279 0.0
tree_method NaN NaN NaN auto hist
scale_pos_weight NaN NaN NaN 1 1

Multi-objectives¶

  • log-loss, PR-AUC, ROC-AUC
In [43]:
# Combine training/test performance metrics CSVs
lloss_auc_train_df = pd.concat([pd.read_csv(f'{temp_dir}/lloss_auc_train_{clf}.csv') for clf in clfs], ignore_index = True)
lloss_auc_test_df = pd.concat([pd.read_csv(f'{temp_dir}/lloss_auc_test_{clf}.csv') for clf in clfs], ignore_index = True)
lloss_auc_df = pd.concat([lloss_auc_train_df, lloss_auc_test_df], ignore_index = True).sort_values(by = ["Classifier", "Best Trial", "Set"], 
                                                                                                   ascending=[False, True, False]).reset_index(drop = True)

# Reshape to long format
df_long = lloss_auc_df.melt(id_vars = ["Classifier", "Best Trial", "Set"],
                            value_vars = ["Log loss", "PR-AUC", "ROC-AUC"],
                            var_name = "Metric",
                            value_name = "Score")

# Combine Classifier+Trial label for x-axis
df_long["Classifier_Trial"] = df_long["Classifier"] + " (Trial " + df_long["Best Trial"].astype(str) + ")"

fig = px.bar(df_long, x = "Classifier_Trial", y = "Score",
             color = "Set", barmode = "group", facet_col = "Metric",
             text = "Score",              
             color_discrete_map = {"Training": "#56B4E9", "Test": "#009E73"},
             facet_col_spacing = 0.04 # Increase facet spacing
            )

# Format numbers on bars
fig.update_traces(texttemplate = "%{text:.3f}", textposition = "outside")

# Independent y-axes for each facet
fig.update_yaxes(matches = None, showticklabels = True, showgrid = True)

# Remove "Classifier_Trial" from x-aixs title
fig.update_xaxes(title_text = None)

# Remove all y-axis titles, then add only for the first facet
fig.update_yaxes(title_text = None)
fig.layout["yaxis"].title.text = "Score"

# Title and layout tweaks
fig.update_layout(height = 600, width = 1600, title = dict(text = "Classifier Performance (Train vs Test Across Metrics and Best Trials)", 
                                                           x = 0.5, xanchor = "center", yanchor = "top",
                                                           font = dict(size = 20, family = "Arial", color = "black", weight = "bold")),
                  legend_title = dict(text = "Set", side = "top"),
                  legend = dict(orientation = "v", x = 1.04, xanchor = "center", y = 0.9, yanchor = "bottom"),
                  margin = dict(t = 80, b = 40)
                 )

# Remove "Metric=" from facet titles
fig.for_each_annotation(lambda a: a.update(text = a.text.split("=")[-1]))

# Custom intervals for y-axis
fig.layout["yaxis1"].range = [0.18, 0.23]   # PR-AUC facet
fig.layout["yaxis2"].range = [0.96, 0.98]   # PR-AUC facet
fig.layout["yaxis3"].range = [0.96, 0.98]   # ROC-AUC facet

fig.show();

display(lloss_auc_df)

del lloss_auc_train_df, lloss_auc_test_df, lloss_auc_df, df_long, fig;
Classifier Best Trial Set Log loss PR-AUC ROC-AUC
0 XGBClassifier 65 Training 0.187296 0.976407 0.973166
1 XGBClassifier 65 Test 0.192752 0.975963 0.972528
2 XGBClassifier 76 Training 0.188652 0.976516 0.973094
3 XGBClassifier 76 Test 0.194125 0.976004 0.972184
4 LGBM 68 Training 0.186913 0.976984 0.973562
5 LGBM 68 Test 0.193374 0.975140 0.971766
6 HistGradientBoostingClassifier 69 Training 0.211290 0.971433 0.966883
7 HistGradientBoostingClassifier 69 Test 0.221352 0.967821 0.962714
8 CatBoostClassifier 91 Training 0.189004 0.976552 0.973292
9 CatBoostClassifier 91 Test 0.193193 0.975743 0.972252

The grouped bar chart above compares the training and test performance of 4 tree-based classifiers (two XGBoost variants, LightGBM, HistGradientBoostingClassifier and CatBoostClassifier) based on the best trial selected from the multi-objective 5-fold, 100-trial Optuna optimization. The metrics reported include log-loss as a calibration measure, and PR-AUC and ROC-AUC as discriminative measures.

A lower log-loss indicates that the model’s predicted probabilities align more closely with the actual outcomes, reflecting both well-calibrated probability estimates and improved predictive accuracy. In contrast, PR-AUC and ROC-AUC assess the model’s ability to distinguish between safe and risky loans across decision thresholds, with higher values indicating stronger discriminative power. A smaller train–test gap across these metrics suggests better generalization to unseen data and reduced risk of overfitting.

Looking first at the two XGBoost models i.e. Trial 65 and Trial 76, both perform at nearly identical levels, with very strong discriminative power (PR-AUC $\approx 0.976$, ROC-AUC $\approx 0.972$) and competitive calibration (test log-loss $\approx 0.193 - 0.194$). Their train–test gaps are exceptionally small i.e. $<0.001$ for both PR-AUC and ROC-AUC, and $\approx 0.005 - 0.006$ in log-loss. Between the two, XGBoost Trial 65 has a slight edge, with marginally lower test log-loss (0.1928 vs. 0.1941) and higher ROC-AUC (0.9725 vs. 0.9722), making it the more robust choice.

CatBoostClassifier and LightGBM also achieve strong results, clearly outperforming HistGradientBoostingClassifier. CatBoostClassifier, in particular, records one of the lowest test log-loss values ($\approx 0.1932$) and maintains excellent AUCs (PR-AUC $\approx 0.9757$, ROC-AUC $\approx 0.9723$), though with slightly larger train–test gaps than XGBoost Trial 65. LightGBM is similarly competitive, with strong calibration (test log-loss $\approx 0.1934$) and high discriminative power (PR-AUC $\approx 0.9751$, ROC-AUC $\approx 0.9718$), though its train–test gaps are larger still.

In contrast, the HistGradientBoostingClassifier performs noticeably worse. Its test log-loss ($\approx 0.2214$) is substantially higher, and both PR-AUC ($\approx 0.9678$) and ROC-AUC ($\approx 0.9627$) are lower than the other models. This highlights its weaker calibration and discrimination.

Taken together, XGBoost Trial 65 demonstrates the best overall balance of calibration, discriminative performance and generalization, with CatBoostClassifier and LightGBM as strong alternatives that also clearly outperform HistGradientBoostingClassifier.

So when these numbers are put in context for XGBoost Trial 65, the test log-loss of $\approx 0.193$ means that when the model assigns a loan a $\approx 19.3\%$ probability of being risky, the actual proportion of loans that the model scores at $\approx 19.3\%$ tends to be close to 19.3% in reality. The test PR-AUC of $\approx 0.976$ shows the model is performing at $\approx 97.6\%$ of the best possible performance. In other words, it’s almost perfect at finding risky loans while making very few mistakes in wrongly flagging safe loans as risky, and it balances this well across different probability thresholds. Finally, the test ROC-AUC of $\approx 0.973$ means that if we randomly select one safe and one risky loan, the model will correctly give the risky loan a higher risk score $\approx 97.3\%$ of the time, regardless of where the decision threshold is set.

Classification reports¶

In [44]:
# Combine classification report CSVs
metrics_df = pd.concat([pd.read_csv(f'{temp_dir}/df_metrics_{clf}.csv') for clf in clfs], ignore_index = True)

# Custom order of metrics
ordered_metrics = ["Accuracy",
                   "Precision (Safe)", "Precision (Risky)", "Precision (Macro avg)", "Precision (Weighted avg)",
                   "Recall (Safe)", "Recall (Risky)", "Recall (Macro avg)", "Recall (Weighted avg)",
                   "F1-score (Safe)", "F1-score (Risky)", "F1-score (Macro avg)", "F1-score (Weighted avg)"]

metrics_df["Classifier_Trial"] = metrics_df["Classifier"] + " (Trial " + metrics_df["Trial"].astype(str) + ")"

df_long = metrics_df.melt(id_vars=["Classifier_Trial"], value_vars = ordered_metrics,
                          var_name = "Metric", value_name = "Score")

fig = px.bar(df_long, x = "Metric", y = "Score",
             color = "Classifier_Trial", barmode = "group",
             category_orders = {"Metric": ordered_metrics},
            )

fig.update_layout(height = 600, width = 1600, 
                  title = dict(text = "Classifier Comparison Across Metrics (Best Trials)", 
                               x = 0.5, xanchor = "center", yanchor = "top",
                               font = dict(size = 20, family = "Arial", color = "black", weight = "bold")),
                  legend_title = None,
                  xaxis_tickangle = -45, template = "plotly_white",
                  yaxis = dict(range=[0.84, 0.98])
                 )

fig.show();

display(metrics_df.drop(columns = "Classifier_Trial"))

del metrics_df, ordered_metrics, df_long, fig;
Classifier Trial Accuracy Precision (Safe) Recall (Safe) F1-score (Safe) Precision (Risky) Recall (Risky) F1-score (Risky) Precision (Macro avg) Recall (Macro avg) F1-score (Macro avg) Precision (Weighted avg) Recall (Weighted avg) F1-score (Weighted avg)
0 LGBM 68 0.919215 0.879867 0.973122 0.924148 0.969155 0.864062 0.913597 0.924511 0.918592 0.918872 0.924001 0.919215 0.918933
1 HistGradientBoostingClassifier 69 0.913346 0.869317 0.975260 0.919246 0.971082 0.850000 0.906516 0.920199 0.912630 0.912881 0.919618 0.913346 0.912953
2 CatBoostClassifier 91 0.921841 0.880848 0.977703 0.926752 0.974296 0.864688 0.916225 0.927572 0.921195 0.921488 0.927038 0.921841 0.921549
3 XGBClassifier 65 0.919370 0.878855 0.974954 0.924414 0.971147 0.862500 0.913605 0.925001 0.918727 0.919009 0.924473 0.919370 0.919071
4 XGBClassifier 76 0.918134 0.878378 0.972816 0.923188 0.968750 0.862187 0.912368 0.923564 0.917502 0.917778 0.923048 0.918134 0.917840

At first glance, CatBoostClassifier stands out on the grouped bar chart because it shows the strongest classification report metrics overall i.e. accuracy, precision, recall, F1-score and both macro and weighted averages.

However, the most important question here is how well a model can catch risky loans, because missing them means real financial loss. On this point, CatBoostClassifier reports a recall on risky loans of $\approx 0.8647$, which is very slightly lower than LightGBM's $\approx 0.8641$, but the difference is only in the fourth decimal place, essentially negligible in practice. In other words, both models correctly flag about 86 out of every 100 risky loans when using the standard 0.5 decision threshold.

It's worth noting that both CatBoostClassifier and LightGBM used class reweighting. LightGBM was tuned with is_unbalance = True, which applies a relatively strong weighting scheme by directly up-weighting risky loans. This often improves recall on the minority class but can also slightly reduce precision because the model becomes more aggressive in labeling loans as risky. CatBoostClassifier, in contrast, was tuned with auto_class_weights = SqrtBalanced, which applies a softer adjustment (inverse square-root of class frequencies). This's less aggressive than LightGBM’s scheme and tends to yield a more balanced trade-off between precision and recall.

Importantly, the choice of these weighting parameters reflects Optuna’s multi-objective optimization rather than true dataset imbalance. Even though the folds were nearly 1:1 balanced, Optuna favoured configurations with weighting because they slightly improved log-loss through better probability calibration and PR-AUC by enhancing sensitivity to risky loans. In this setting, class-weighting acted less as an imbalance correction and more as a fine-tuning mechanism that interacted with other hyperparameters to achieve marginal but meaningful gains in the combined objectives.

Despite this, CatBoostClassifier still edges out LightGBM overall, nearly matching it on risky recall ($\approx 0.8647$ vs $\approx 0.8641$) while clearly dominating on other key metrics like accuracy, precision, F1-score, both macro and weighted averages. This suggests that CatBoostClassifier benefits from weighting but does so in a way that preserves stability and avoids overcompensating. Its macro and weighted recall scores are also strong at $\approx 0.9212$ and $\approx 0.9218$, showing that it treats both safe and risky loans fairly well. Moreover, the model also shows the same level of consistency between training and test sets on the optimization metrics in log-loss, PR-AUC and ROC-AUC, suggesting its predictions are better calibrated and more likely to generalize well in practice.

Breaking down the numbers:

  • Accuracy ($\approx 92.2\%$): Out of every 100 loans, CatBoostClassifier correctly flags $\approx 92$ as risky or safe.
  • Safe loans:
    • Precision ($\approx 88.1\%$): When CatBoostClassifier says a loan is safe, it’s right ($\approx 88$) times out of 100.
    • Recall ($\approx 97.8\%$): Out of 100 genuinely safe loans, CatBoostClassifier correctly classifies ($\approx 98$) and misclassifies ($\approx 2$).
    • F1-score ($\approx 92.7\%$): Strong balance, showing it captures the majority of safe loans while avoiding unnecessary misclassifications.
  • Risky loans:
    • Precision ($\approx 97.4\%$): When the model classifies a loan as risky, ($\approx 97$) in 100 are truly risky. Very few safe loans are mislabeled as risky.
    • Recall ($\approx 86.5\%$): Out of 100 truly risky loans, it catches about ($\approx 87$), with ($\approx 13$) slipping through incorrectly as safe.
    • F1-score ($\approx 91.6\%$): Shows a solid balance between catching risky loans and maintaining precision.

Looking at the averages:

  • Macro precision ($\approx 92.8\%$) | Macro recall ($\approx 92.1\%$): Strong fairness, treating safe and risky loans equally well.
  • Weighted precision ($\approx 92.7\%$) | Weighted recall ($\approx 92.2\%$): In real-world distributions, where datasets usually have far more safe loans, CatBoostClassifier still achieves the same levels of performance.

Summary:

  • Both CatBoostClassifier and LightGBM use class weighting, but CatBoostClassifier applies a softer scheme (SqrtBalanced) that promotes balance rather than aggressiveness.
  • CatBoostClassifier correctly classifies most safe loans and most risky loans, with particularly strong reliability when labeling risky loans (precision $\approx 97.4\%$).
  • Its recall for risky loans ($\approx 86.5\%$) means it can identify the majority of high-risk loans, reducing the chance that risky loans are misclassified as safe.
  • Its macro and weighted averages ($\approx 92\% - 93\%$) highlight that it treats both loan classes fairly.
  • Its optimization consistency (log-loss, PR-AUC, ROC-AUC) suggests well-calibrated predictions and better generalization in practice.
  • Optuna’s selection of weighting parameters, despite near-balanced folds, shows that subtle reweighting can still improve calibration and discrimination, reinforcing why CatBoostClassifier is the strongest overall choice.

Confusion matrices¶

In [45]:
# Load confusion matrices and their labels 
grouped_matrices = []
grouped_labels = []

for clf in clfs:
    with open(f'{temp_dir}/cm_{clf}_all.pkl', "rb") as f:
        cm_all = pickle.load(f)
    with open(f'{temp_dir}/cm_labels_{clf}_all.pkl', "rb") as f:
        cm_labels = pickle.load(f)

    grouped_matrices.append(cm_all)
    grouped_labels.append(cm_labels)

# Plot confusion matrices
# Determine grid size: one row per classifier, columns = maximum number of matrices among classifiers
num_classifiers = len(grouped_matrices)
max_cols = max(len(cm_list) for cm_list in grouped_matrices)

# Subplots grid (rows = classifiers, cols = matrices) with size scaled for readability
fig, axes = plt.subplots(num_classifiers, max_cols, figsize = (4 * max_cols, 2 * num_classifiers))

# Ensure axes is a 2D array
if num_classifiers == 1:
    axes = [axes]
if max_cols == 1:
    axes = [[ax] for ax in axes]

# For each classifier group, plot all its confusion matrices along one row
for row_idx, (cm_list, label_list, row_axes) in enumerate(zip(grouped_matrices, grouped_labels, axes)):
    for col_idx in range(max_cols):
        if col_idx < len(label_list) and col_idx < len(cm_list):
            ax = row_axes[col_idx]
            sns.heatmap(cm_list[col_idx], annot = True, fmt = "d", cmap = "Blues",
                        ax = ax, cbar = False, annot_kws = {"size": 10})
            # Update title and axis labels (introducing newline for clarity)
            ax.set_title(label_list[col_idx].replace("for Best Trial", "\nfor Best Trial"), fontsize = 10)
            ax.set_xlabel("Predicted Labels", fontsize = 10)
            ax.set_ylabel("True Labels", fontsize = 10)
            ax.set_xticklabels(["Safe", "Risky"], fontsize = 10)
            ax.set_yticklabels(["Safe", "Risky"], fontsize = 10)
        else:
            row_axes[col_idx].axis('off')  # Hide empty subplot

plt.tight_layout()
plt.show()

del grouped_matrices, grouped_labels, num_classifiers, max_cols, fig, axes;
No description has been provided for this image

When looking at the confusion matrices of the best trials, it’s clear that in loan risk models, false negatives are especially costly. They represent risky loans wrongly classified as safe, which can directly translate into financial losses. Using the standard decision threshold of 0.5, CatBoostClassifier reports the lowest number of false negatives (433), slightly better than LightGBM (435) and ahead of both XGBoost (440–441) and HistGradientBoostingClassifier (480).

At the same time, CatBoostClassifier also produces the fewest false positives (73), meaning it avoids unnecessarily misclassifying safe loans as risky. By contrast, LightGBM makes 88 false positives, XGBoost 82–89 and HistGradientBoostingClassifier 81. This combination of strong recall on risky loans with minimal false alarms gives CatBoostClassifier the clearest edge among the models.

HistGradientBoostingClassifier performs notably worse at this threshold, allowing nearly 480 risky loans through incorrectly. XGBoost is stronger, but it still lags behind CatBoostClassifier by permitting more risky loans to slip through and by flagging more safe loans incorrectly.

Overall, CatBoostClassifier provides the best trade-off at the conventional 0.5 threshold. It captures the majority of risky loans while keeping false positives very low, which both protects the bank from defaults and preserves customer relationships by not unfairly labeling safe loans as risky. Importantly, Optuna’s optimization tuned CatBoostClassifier with auto_class_weights = SqrtBalanced and LightGBM with is_unbalance = True. While both involve class reweighting, CatBoostClassifier’s softer square-root scheme achieves a more balanced outcome, suggesting its results are not only competitive but also more stable and better calibrated in practice.

Elapsed times¶

In [46]:
# Combine execution time CSVs
exec_time_df = pd.concat([pd.read_csv(f'{temp_dir}/exec_time_{clf}.csv') for clf in clfs], ignore_index = True)

display(exec_time_df);

del exec_time_df;
Classifier Best Trial Optimization Elapsed Time (s) Training Elapsed Time (s)
0 LGBM 68 1416.186557 3.886080
1 HistGradientBoostingClassifier 69 1254.363137 3.126119
2 CatBoostClassifier 91 10774.411937 27.940682
3 XGBClassifier 65 14523.852434 2.708413
4 XGBClassifier 76 14523.852434 3.324781

In my setup, Optuna optimization and model training were run only on the CPU of my personal laptop. With these limited resources, models such as LightGBM and HistGradientBoosting were very efficient, completing training in just a few seconds and finishing optimization in $\approx 20$ minutes ($\approx 1,250 – 1,400$ seconds). CatBoostClassifier, on the other hand, required noticeably more time per training run, close to half a minute, and its full optimization stretched into several hours ($> 10,000$ seconds). XGBoost’s individual training runs were as fast as LightGBM and HistGradientBoostingClassifier, but the overall optimization process was by far the slowest, taking $> 4$ hours ($>14,000$ seconds). This slowdown was mainly due to Optuna exploring a larger and more complex hyperparameter space, which caused the optimization process to balloon far beyond the actual training time. On this kind of hardware, LightGBM appeared to be the most practical choice for speed.

These results, however, reflect the limits of personal hardware more than the true capabilities of the algorithms. Large organizations, especially banks, rely on advanced IT infrastructure with powerful servers, far more memory, and distributed frameworks that handle demanding machine learning workloads efficiently. In such environments, runtime differences between LightGBM, CatBoostClassifier and XGBoost become much smaller because the infrastructure is built for scale and speed.

That’s why I believe the focus should be on predictive performance rather than raw training time. In financial risk modeling, accuracy is what prevents losses. CatBoostClassifier has consistently shown stronger and more balanced predictive performance without the need for artificial weighting adjustments. While it ran more slowly on my laptop, in an enterprise IT setting the runtime disadvantage would no longer be a major concern. What ultimately matters is CatBoostClassifier’s ability to classify safe and risky loans accurately, which makes it the better choice.

Overall Review¶

Context and Model Choice¶

When I tested models on my personal laptop, some models such as LightGBM and HistGradientBoostingClassifier ran very fast while others like CatBoostClassifier and XGBoost felt slower. This difference was really more about the limits of my laptop than the models themselves.

In a bank or corporation setting, models are normally trained on much more powerful machines with far greater processing power. On that type of setup, the slower models also run fast enough to be practical.

During optimization, LightGBM produced repeated warnings such as "No further splits with positive gain" until the later trials. This's normal behavior when the algorithm cannot find additional useful splits. The warnings diminished toward the end and did not affect overall performance as shown by the small train–test gap in log-loss, PR-AUC and ROC-AUC.

For the modelling phase, the binary target is the loan’s current status, derived from a nominal feature and encoded so one class represents safe loans and the other represents risky loans. The important point here is that in this dataset the ratio of safe to risky loans is roughly equal, about one to one. Because of this balance, accuracy is a meaningful metric when comparing models. At the same time recall for risky loans is still especially important from a business perspective because missing those loans has greater financial consequences.

So for me, the real question is not about speed but about which model provides the most reliable results while also recognizing that banks and regulators may require other measures for compliance and specific best practices, which go beyond my current knowledge. A good model should catch risky loans, treat safe loans fairly, produce risk scores that can be trusted, and be easy to explain when someone asks why a particular decision was made. Across these points, CatBoostClassifier stands out as the best overall choice.

Strengths and Limitations of CatBoostClassifier¶

Strengths

  • Finds risky loans effectively without being overly harsh on safe ones
  • Handles different kinds of data naturally such as numbers, categories, and yes or no answers
  • Produces probabilities that are usually well-aligned with real outcomes
  • Includes tools such as feature importance and SHAP to help explain what drives predictions
  • Runs efficiently once used on enterprise‑level systems

Limitations

  • Appears slower when run only on a small personal machine
  • Requires subject matter expert (SME) input to make sure the data used makes sense in a business and compliance setting
  • Needs periodic updates to stay reliable, such as tuning thresholds or retraining
  • Can become too complex if too many engineered features are added without proper discipline
  • Like any model, accuracy will decline over time if data drift occurs, for example when borrower behavior, inputs or their relationships change

Data and Feature Engineering¶

Adding richer and more varied data can improve the model’s ability to detect risk. Examples include loan usage patterns, broader economic conditions, ratios such as debt to income or balance to limit, and inputs from other trusted external sources.

It's important to work with SMEs so that chosen features are not only predictive but also reasonable, explainable and compliant with business rules and regulatory expectations. Proper tracking and clear documentation of why each feature and data source included, make the model easier to explain and ensure traceability for audits.

Adjusting the Decision Threshold¶

By default models apply a 50% cutoff where anything above is labeled risky and anything below is labeled safe. In lending though, missing a risky loan which is a false negative is generally much more costly than incorrectly flagging a safe loan as risky which is a false positive.

So keeping the threshold fixed at 50% may not be the best option. For example lowering it to 40% would capture more risky loans but would incorrectly flag more safe loans. Raising it would cut down on false positives but would cause more risky loans to slip through.

The best threshold is about finding the right balance for the business and aligning it with its appetite for financial risk. This should be reviewed regularly.

Monitoring and Data Drift¶

Over time borrower behavior, financial status, loan types and market conditions change. Because of this, models do not stay accurate forever. This is known as data drift.

To handle this we should track changes in input data and monitor how predictions behave. Alerts should be set up if recall or accuracy drops below an agreed level. The model should be retrained with recent data every few months, or faster if the market shifts quickly, such as during a downturn. Documentation on how drift is tracked and addressed should always be maintained.

Fairness and Bias¶

The model must treat different borrower groups fairly, across loan types, incomes or regions. This can be checked by comparing performance across these groups and ensuring no group is consistently disadvantaged.

Borrowers should also receive a clear, plain-language explanation when their loans are no longer in good standing. Fairness is therefore not just a technical matter, but also about how decisions are communicated back to customers.

Interpretability and Business Narratives¶

It's not enough to simply say the model makes a prediction. The reasoning needs to be explained in plain words.

For example, instead of giving a math‑heavy explanation, one could say "Debt to income ratio was a key factor because it reflects how comfortably someone can repay".

This kind of everyday explanation makes the model easier to defend with both internal teams and external reviewers.

Operational Integration¶

The model should work smoothly in the loan lifecycle. Sometimes a quick decision is required, while in other cases checks can be run overnight in batches. What matters most is that the system works reliably, runs efficiently and always uses correct data so that decisions are not delayed.

Before going live, the model should also be reviewed by an independent validation team to ensure checks are not only done by the same people who built.

Data Quality and Governance¶

Even the most advanced model will fail if the inputs are messy. Data quality checks for missing values, odd values, incorrect fields or format changes are necessary. Changes should be logged and a record kept so the data can be reviewed later if questions arise. This provides a clear audit trail for transparency and regulatory trust.

Stress Testing and Segment Stability¶

The model should also be tested under tough conditions such as higher unemployment or interest rate hikes. Stress testing shows if the model still provides useful guidance when the economy is unstable.

It's also important to check how well the model performs across different loan products such as mortgages and credit cards, and across borrower groups. A reliable model should perform consistently and not favour one group over another.

Human in the Loop¶

The model shouldn’t decide everything on its own. Loan officers need to review predictions and override them when needed. If overrides happen often, it may signal weaknesses in the model.

Retraining Policy¶

A decision should be made in advance about how often the model will be retrained. One option is retraining every quarter which is simpler to manage. Another option is continuous updates with new data which is more responsive but harder to govern. Whatever the approach, it should be clearly documented and approved.

Privacy and Security¶

When using new external data sources, it's important to ensure the data is handled safely and kept private. Data should only be used in ways that customers and regulators would view as fair and responsible.

Recommendations¶

  • Adopt CatBoostClassifier as the main model
  • Use LightGBM as a backup or in an ensemble
  • Benchmark CatBoostClassifier against other algorithms to validate that it remains the best option
  • Work with SMEs to confirm features are meaningful and compliant
  • Adjust decision thresholds regularly to balance business needs and risk appetite
  • Put in place monitoring for drift and fairness
  • Carry out stress testing and stability checks across products and groups
  • Define override rules for credit officers
  • Create a clear retraining policy and data handling guidelines
  • Keep documentation transparent for both internal and external review

Next Steps¶

  • Work with SMEs to fine-tune which data the model uses
  • Test how well the model’s thresholds work and adjust if needed
  • Set up a monitoring system to track fairness, data drift and how the model performs in tough situations
  • Decide how often the model should be updated, every few months or more often if needed
  • Agree on whether the model will make predictions instantly (real-time) or in batches (like overnight runs)
  • Pilot the model on a smaller group before deploying it widely
  • Explore and benchmark alternative algorithms to confirm CatBoostClassifier remains the best choice
  • Use benchmarking results to validate CatBoostClassifier’s performance and meet regulatory expectations

Closing Message¶

In this case, CatBoostClassifier is a strong choice for telling apart safe loans from risky ones. But the algorithm by itself isn’t enough. The real value comes from managing the model carefully throughout its entire life.

That means working with SMEs to choose the right data, fine-tuning thresholds, keeping an eye on changes over time, checking that the model is fair, testing how it performs under pressure, retraining it when needed, making sure it fits smoothly into everyday processess and letting people step in to review tricky cases.

Handled this way, the bank ends up with a model that is not only accurate but also fair, easy to explain, reliable and built to last. This protects both the business and its customers over the long run.

Neverthelss, the finer details on regulations and governance would need SME and legal guidance to ensure full compliance with regulatory, authorities, business needs and maintain customer trust.

Session Information¶

Log the full session environment including OS, CPU, Python version and loaded modules to support reproducibility and assist in debugging environment-specific issues.

In [47]:
import importlib
importlib.metadata.version("markupsafe")
Out[47]:
'3.0.2'
In [48]:
display(Markdown(f"<span style = 'font-size: 18px; font-weight: bold;'> Session Information </span>"))

# https://pypi.org/project/session-info/
session_info.show(na = True, os = True, cpu = True, jupyter = True, dependencies = True,
                  std_lib = True, private = True, write_req_file = False, req_file_name = None, html = None
                 )

Session Information

C:\Users\grace\AppData\Local\Programs\Python\Python311\Lib\site-packages\session_info\main.py:213: UserWarning:

The '__version__' attribute is deprecated and will be removed in MarkupSafe 3.1. Use feature detection, or `importlib.metadata.version("markupsafe")`, instead.

Out[48]:
Click to view session information
-----
__main__            NA
catboost            1.2.7
collections         NA
gc                  NA
importlib           NA
io                  NA
json                2.0.9
lightgbm            4.6.0
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session_info        v1.0.1
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time                NA
tqdm                4.67.1
xgboost             2.0.3
-----
Click to view modules imported as dependencies
PIL                         10.4.0
__future__                  NA
__mp_main__                 NA
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-----
IPython             8.31.0
jupyter_client      8.6.3
jupyter_core        5.7.2
jupyterlab          4.3.4
notebook            7.3.2
-----
Python 3.11.2 (tags/v3.11.2:878ead1, Feb  7 2023, 16:38:35) [MSC v.1934 64 bit (AMD64)]
Windows-10-10.0.22631-SP0
8 logical CPU cores, Intel64 Family 6 Model 140 Stepping 1, GenuineIntel
-----
Session information updated at 2025-09-24 01:01